IMS RWE AccessPoint Issue 9 vol.5

VOLUME 5, ISSUE 9 • NOVEMBER 2014

News, views and insights from leading international experts in RWE and HEOR

Putting RWE at the heart of decision making Diabetes special focus Propelling stakeholder engagement and collaboration Optimizing resource allocation in primary care Harnessing transformational methodologies

1 5

2 6 4

IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR

3

RWE x 6

= $1bn

Six ways to release untapped RWE potential

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“WeInsIghts have observed several important trends that could shape the way companies create or use RWE, which will be of importance to our industry moving forward.”

Jon Resnick Vice President and General Manager Real-World Evidence Solutions, IMS Health Jresnick@imshealth.com

Headline Headline Welcome Welcome to our latest AccessPoint as we continue to explore the dynamics shaping the HEOR and real-world evidence (RWE) landscape.

In our last edition, we highlighted our evolving understanding of oncology innovations and outcomes and the role of RWE in these areas.

This time, we expand that lens to another important disease, diabetes, where stakeholders are seeking much deeper knowledge of treatment outcomes in patient subgroups. RWE is transforming a broad understanding with real insights into differential cohort responses, based on powerful clinical and even genomic data to evaluate benefits and risks. We also take a broader look at trends in RWE and spotlight ongoing advances in real-world data (RWD), methodologies and RWE applications.

We have focused this edition around these three topics • RWE research reveals new insights into more effective ways of researching diabetes, assessing outcomes and understanding the implications for broader care provision. Although the quantity of diabetes-related patient data is significant, gaps in the completeness of datasets have impeded researchers. Now, new mixed methods approaches such as we describe in Germany, and analytic innovations including the IMS CORE Diabetes Model, make research for this critical condition easier to conduct with increased confidence and scientific rigor. A UK analysis of utility values provides a basis for improving diabetes modeling and a recent study in Canada shows how RWE analysis can pinpoint the resource drivers requiring policy and clinical practice changes. This is a hopeful time in diabetes.

"RWE is transforming a broad understanding in diabetes with real insights into differential patient cohort responses, based on powerful clinical and even genomic data."

AccessPoint is published twice yearly by the IMS Health Real-World Evidence (RWE) Solutions and Health Economics & Outcomes Research (HEOR) team.

VOLUME 5, ISSUE 9. PUbLISHEd NOVEMbER 2014.

IMS HEALTH 210 Pentonville Road, London N1 9JY, UK Tel: +44 (0) 20 3075 4800 • www.imshealth.com/rwe RWEinfo@imshealth.com

©2014 IMS Health Incorporated and its affiliates.

All rights reserved.

Trademarks are registered in the United States and in various other countries.

• We have observed several important trends that could shape the way companies create or use RWE, which will be of importance to our industry moving forward. New ways of thinking about RWD strategies are emerging, leading us to propose a disease-centric framework to help guide those efforts.

We also comment on how involving commercial colleagues in RWE is driving substantial value for companies that enable this approach. And we look forward to seeing continued collaborations with external stakeholders, namely payers, and in new geographies, specifically Asia Pacific.

• Advancements continue to derive more value from RWE, including improved data sourcing, methodologies and stakeholder engagement. Predictive modeling is increasing RWE accuracy with demonstrated benefits in risk stratification. We are seeing leaders leverage the richness of Scandinavian data to enable new disease-level insights. RWE also continues to support value demonstration, such as showing the impact of adherence on mortality, readmission risk and costs in ACS. And it is helping companies move ‘beyond the pill’ by creating even more value through enabling care management services.

At IMS Health, we are committed to providing insights to help advance health and improve patient outcomes across all care settings globally.

We hope you find this edition particularly useful in your RWE journey.

IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR

VOLUME 5, ISSUE 9 • NOVEMBER 2014

RWE driving deeper insights in diabetes 5 15 40 45 50

Major validation upholds relevance of IMS CORE diabetes Model diabetes complexities drive resource consumption in Canada Identifying reference utility values for economic models in diabetes A collaborative foundation for new diabetes insights in Germany demonstrating external validity of the IMS CORE diabetes Model

Perspectives and trends in RWE 6 10 20 36

Enabling disease-specific RWE through fit-for-purpose RWd A roadmap for increasing RWE use in payer decisions Finding the true potential of RWE through scientific-commercial collaboration Preparing for RWE in Asia Pacific

Advances in RWD, methodology and RWE applications Improving outcomes through predictive modeling Holistic real-world data brings a new view of patients and diseases Evaluating disease burden, unmet need and QoL in a chronic inflammatory disorder demonstrating the impact of non-adherence to antiplatelet therapy in ACS Modeling disease management above the brand with RWE

26 32 56 60 63

PROJECt FOCUs

4 FORUMS ACCELERATE RWE USE

56 CHRONIC INFLAMMATORY DISORDER Evaluating patient-reported outcomes 60 ACUTE CORONARY SYNDROME Demonstrating the impact of non-adherence 63 RWE-BASED DISEASE MANAGEMENT Informing the value of treatments

5 IMS CDM CONFIRMS CONTEMPORARY RELEVANCE

IMs RWEs & hEOR OVERVIEW

nEWs 2 PARTNERSHIP ENRICHES SCANDINAVIAN DATASETS 3 RESEARCH INFORMS POLICY PRIORITIES

66 ENABLING YOUR REAL-WORLD SUCCESS Solutions, locations and expertise

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nEWs SCANDINAVIAN RWE COLLABORATION

Partnership linkage of unique, Norwegian biobank data opens up groundbreaking research potential with global impact

IMS Health/Lifandis AS elevate real-world insights with enriched Scandinavian datasets Further expanding IMS Health’s distinctive and growing real-world evidence capabilities in Northern Europe, the company has announced a collaboration with Lifandis AS, an independent company that works closely with the HUNT Research Centre in Norway. The agreement combines IMS Health’s Pygargus extraction methodology with access to the HUNT biobank and databank, as well as other Norwegian biobanks and health registries, enabling the creation of significantly enhanced real-world datasets.

Underscoring the rising importance of Scandinavia as a rich hub for RWE, this linkage affords one of the most holistic patient-level views imaginable with potential for unprecedented insights of both local and global relevance.

RICH SETTING FOR REAL-WORLD DATA Scandinavia is unrivalled in opportunities to generate RWE given its well-structured public healthcare, long established high-quality electronic medical records (EMR) and mature regulatory research framework. In a first-of-its kind RWE approach, IMS Health brings the most complete, integrated view of patient-level care through anonymous EMR data along with national and disease-specific registers.

The new collaboration with Lifandis in Norway extends application of the IMS Health Pygargus patented extraction methodology, first launched in Sweden, to the HUNT biobank and databank, recognized by international researchers for its value in personalized medicine (biomarker Id and validation, disease etiology, patient subgroup stratification), epidemiology (RWE, post-marketing studies, burden of disease, comparison of treatment outcomes), drug discovery (target identification, target validation) and clinical trial optimization. Containing unique patient data from 125,000 anonymous individuals, with more than 25 years of follow-up, and covering 6,000 distinct variables, the Nord-Trøndelag Health (HUNT) Study is one of the largest population-based health studies ever performed.1

UNIQUE FOUNDATION FOR TAILORED RESEARCH Lifandis was founded to drive partnership between Norwegian biobanks, academia and industry, and the company has also established a strong foothold within register-based epidemiology. Its heritage includes recruitment of at least 1.4 million Norwegians, around 30% of the population, into consent-based research biobanks based on populationbased studies, with an additional 25-30 million samples in clinical biobanks. Legislation, broad consent and the existence of a personal identification number opens up the opportunity to build high-quality and comprehensive datasets with access to more than 40 healthcare and disease-specific registries, hospital and primary care EMRs and separate endpoint registries with validated outcomes (Figure 1).

Importantly, while affording direct insights from Scandinavia, the data can also inform scientific research to support global decisions across a range of disease areas.

1

The strategic collaboration with IMS Health allows researchers to look at a broader set of data in Norway as well as Sweden and other Scandinavian markets through IMS Health’s existing real-world solutions assets. Clients will now be able to benefit from the Lifandis integrated partnership in addition to IMS Health’s other information assets, scientific capabilities and involvement in research projects.

ESTABLISHED EXCELLENCE WITH GLOBAL IMPACT This development enriches an already distinctive offering that allows healthcare researchers to develop globally and locally relevant insights into populations, diseases and treatment experience.

The ability of the IMS Health and Lifandis team to create holistic views across settings of care over time enables Scandinavian-based affiliates and global headquarters to answer meaningful and challenging research questions, based on Long-term study reviews for anonymous patients across settings of care Difficult-to-get patient attributes for more meaningful treatment journeys Information to determine the economic value of different outcomes measures Analytics to support research from epidemiology to comparative effectiveness

• • • •

TOWARDS A REAL-TIME UNDERSTANDING The extension of IMS Health’s RWE capabilities in Northern Europe marks another important step in helping healthcare decision makers identify, link and interpret real-world outcomes in near real time.

For further information on the IMS Health/Lifandis AS approach to RWE and the exciting opportunities for integration of complex datasets in the Scandinavian region, please email Patrik Sobocki at Psobocki@se.imshealth.com or Christian Jonasson at cj@lifandis.com

FIGURE 1: LEGISLATION, CONSENT ANd A PERSONAL Id CREATE POTENTIAL FOR HIGH QUALITY, COMPREHENSIVE dATASETS Healthcar Healthcare e R Registries egistries

Elec Electronic tronic Medical Medical R Records ecords

HUNT Biobank

HUNT Da Databank tabank

Endpoin Endpointt Registries Registries Personal ersonal ID A rchival issue Archival samples

Krokstad S, et al. Cohort Profile:The HUNT Study, Norway. Int. J Epidemiol. 2013 Aug; 42(4): 968-77

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IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR

nEWs EMERGING HEALTHCARE TRENDS

Research from IMS Health informs opportunities for harnessing trends to achieve the triple aim of US health reform

Study reveals ten dynamics for policy prioritization in US managed care At a time of tremendous flux in the US healthcare system, a new report, underpinned by IMS Health research, has identified potential for strategies to achieve the triple aim of health reform (improved care, improved health and reduced cost) leveraging the top emerging healthcare trends. The findings provide real-world insights into key policy priorities for healthcare stakeholders.

The report, “Ahead of the Curve: Top 10 Emerging Health Care Trends – Implications for Patients, Providers, Payers and Pharmaceuticals” was developed under the direction of the American Managed Care Pharmacy (AMCP) Foundation, in collaboration with Pfizer, Inc. The Foundation is a research, education and philanthropic organization established in 1990 with the goal of advancing collective knowledge and insights into major issues associated with the practice of pharmacy in managed healthcare settings.

In seeking to help stakeholders proactively prepare for the impact of changes in the US healthcare marketplace, the collaborative project was designed to systematically identify and assess current and emerging trends impacting healthcare delivery and MCP practices.

Reflecting a strong focus on partnering with stakeholders to improve patient outcomes and advance healthcare globally, the research was conducted by IMS Health on behalf of the Foundation, along with development of the report itself. The company has established excellence in generating scientifically credible real-world evidence that drives powerful insights for more efficient decision making. The process employed was designed to add scientific rigor by drawing on secondary research evidence in addition to key opinion leaders’ insights. It was systematic and replicable and drew upon the cross-functional expertise and knowledge base of team members from multiple practice areas.

The six-month program of research followed a two-part methodology in which distilled information from a targeted literature review was analyzed by an advisory panel of healthcare thought leaders from academia, industry, managed care, government and patient advocacy.

The panel was engaged to validate, identify and prioritize trends and provide insight into implications across healthcare stakeholders. This process included participation in a full-day, facilitated discussion and trends assessment.

TOP TEN TRENDS DRIVING POLICY PRIORITIES The top ten trends identified for their impact over the next five years are 1. Migration from fee-for-service to new provider payment models that better align incentives for cost control and high-quality patient care 2. Consolidation of healthcare stakeholders, fueling standardization of decisions and opportunities to evolve patient care practices 3. Widespread use of data and analytics in patient care, providing novel opportunities for improving care effectiveness and efficiency 4. Increased utilization and spending for specialty medicines, burdening payers and manufacturers to develop novel approaches to formulary design and pricing practices that ensure patient access 5. Medicaid expansion, shifting a larger portion of economic risk to payers and providers and driving creation of new models for care delivery and tactics to improve efficiency

ACCESSPOINT • VOLUME 5 ISSUE 9

6. Migration to a value-oriented healthcare marketplace, reflecting new approaches to balancing care quality and cost 7. Growth and performance of accountable care organizations, with long-term success requiring investments in data structure and analytics and willingness to evolve new models of care 8. Greater patient engagement through technology, which will empower patients and providers to enhance practices for managing and coordinating healthcare 9. Increasing patient cost-sharing, to curtail costs and incentivize patient involvement 10. Healthcare everywhere through new tools and mobile applications, with new avenues for patient engagement and new healthcare delivery roles as wellbeing becomes a community-wide effort

A NEED FOR NOVEL SOLUTIONS Overall, the report suggests an advance towards a system of patientcentric holistic care over the next five years, with shared accountability across stakeholders and value being the core currency of the healthcare marketplace – changes that are expected to translate into improved patient outcomes. In preparation, stakeholders will need to move beyond conventional practices and generate novel solutions that improve patient metrics and tracking, enhance patient engagement and find the balance between driving accountability, curtailing costs and incentivizing. Specifically, this will involve

•

Providers becoming increasingly accountable for driving care efficiency. This may require a fundamental shift from conventional care approaches. To support the transition, providers can leverage healthcare technologies and the expansion of patient data to drive quality in patient care and improve care processes.

Payers designing and implementing new payment models that share risk and drive accountability across stakeholders and populations with varying needs and requirements. They should increasingly leverage technology tools, patient data and health care analytics to better engage patients and track provider performance.

Pharmaceutical companies experiencing increased demand for proof of value and real-world effectiveness data beyond trial-based safety and efficacy, and being asked to share the risk for supporting improved patient outcomes. They can prepare by investing in evidence-generation capabilities that move beyond clinical trials to leverage real-world data from provider and payer organizations.

The report concludes that while the path forward will vary by stakeholder, all players in the US healthcare system will need to place the patient center stage and consider their role in supporting long-term improvements in patient health in a more holistic manner.

• •

For further information, the report is available to download from the Foundation’s website at www.amcpfoundation.org

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nEWs RWE DEBATE

Experts gather with IMS Health to accelerate the application of real-world evidence for maximum utility in healthcare decision making

Stakeholders unite to improve collaboration in realizing RWE potential Alongside greater demand for real-world evidence and increasing recognition of its value across the healthcare spectrum, there are clear signs that many stakeholders still struggle to act on its potential. Its appropriate use can deliver benefits to all, but more open dialogue and enhanced collaboration between relevant stakeholders is needed. Together with other partners, IMS Health works to help all constituent groups achieve the common goal of advancing healthcare.

As part of the company's commitment to accelerating the application of RWE in pricing and market access decisions, two recent initiatives in the US and UK have broken new ground in connecting perspectives and broadening thinking about key issues for the current use of RWE and solutions for realizing its true value.

US: REAL-WORLD EVIDENCE LEADERSHIP SYMPOSIUM A first-of-its-kind event, the Real-World Evidence Leadership Symposium was held on 4 November 2014.

Co-sponsored through a thought leadership partnership between IMS Health and Johns Hopkins Center for drug Safety & Effectiveness in baltimore, Md, “Realizing the full potential of real-world evidence to support pricing and reimbursement decisions”, offered a forum for invited payers, pharmaceutical executives and academicians to engage in frank and constructive discussion on how payers and life sciences companies were using RWE and to look for pragmatic opportunities to maximize its utility in pricing and reimbursement decisions. A key focus was to explore potential collaborations between pharma and payers in RWE generation.

Under the Chairmanship of dr. Lou Garrison, Professor and Associate director in the Pharmaceutical Outcomes Research and Policy Program, department of Pharmacy, at the University of Washington in Seattle, the debate was structured into three sessions 1. Review of illustrative use cases showing effective and ineffective use of RWE, to demonstrate opportunities and limitations facing its broader application 2. Facilitated payer panel to discuss payer views on the role of RWE in decision making and requirements for further use 3. Discussion and proposed solutions as a starting point for action to identify potential for united efforts to increase the value of RWE shaping the RWE opportunity Reactions to the symposium from both speakers and participants underscored its value in highlighting opportunities for making RWE more core to pricing and market access decisions, whilst also capturing a need for life sciences companies to hear directly from payers that their RWE can have impact in order to increase their confidence in its use.

The key discussion points and actionable outputs from the symposium are being taken forward for further exploration in post-forum research, the findings of which will form the basis of an authoritative white paper to further the discussion and serve as a catalyst for more collaborative generation and use of RWE in the future.

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UK: DECISION MAKING USING REAL-WORLD DATA Pushing forward the RWE conversation in the UK, the first IMS Health Decision Making Using Real-World Data Conference, “Understanding the changing landscape of patient data: Informed decision making in the UK healthcare market”, was held on 30 September, 2014. The event was organized in response to a request from IMS Health clients to learn more about RWE best practice in the UK and its use by other players in the healthcare arena. bringing together life sciences industry leaders with a variety of healthcare stakeholders, the conference afforded a unique opportunity to explore, through open debate, the ways that realworld data should be utilized for healthcare decision making in the UK. The event and panel discussion were chaired by Professor Sir Alasdair breckenridge, former Chairman of the UK Medicines and Healthcare Products Regulatory Agency (MHRA) who brought a deep understanding of pharmaceutical regulators, their goals and requirements.

Broadening thinking on optimizing use of RWE The presentations offered a variety of perspectives and cross-sectional view of decision making. Speakers included dr Sarah Gardner, Associate director of R&d at the National Institute for Health and Care Excellence (NICE); Kevin V. blake, Scientific Administrator, best Evidence development Office, at the European Medicines Agency (EMA); Skip Olson, Global Head of HEOR Excellence at Novartis; and Professor Liam Smeeth, Professor of Clinical Epidemiology and Head of the department of Non-communicable disease Epidemiology at the London School of Hygiene and Tropical Medicine. IMS Health was represented by dr. Patrik Sobocki who shared the company’s view of RWE and vision for its use.

Among the topics covered by the panel of guest speakers were Real-world data and the changing policy landscape EMA use of best evidence in regulatory decision making Leadership in RWE: An industry perspective Leveraging patient-centric data and generating evidence across the product lifecycle Confounding, its impact and how it can be managed to maximize the benefit of RWE

• • • • •

The speakers discussed how effectively RWE is used in their sectors currently, how they believe it should be used to help decision making and how they see the landscape changing in the future.

Feedback from both speakers and attendees was extremely positive and there are plans to develop and expand the "Decision Making Using RealWorld Data" conference for 2015.

IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR

nEWs IMS CORE DIABETES MODEL VALIDATION

IMS CORE diabetes Model demonstrates continued credibility as the leading tool for policy and reimbursement strategy in diabetes

Major validation upholds relevance of IMS CORE diabetes Model The IMS CORE diabetes Model (CdM) is a well-published and validated simulation model that predicts long-term health outcomes and costs in type 1 and type 2 diabetes.

For those developing policy and implementing decisions informed by CdM analyses, confirmation that the model remains contemporary and validated is essential. Findings from a new validation to recent diabetes outcome studies1 reaffirm the model’s suitability to support policy decisions for improving diabetes management. disease simulation models are increasingly being applied to inform a wide range of issues in healthcare decision making. Their ability to project long-term outcomes and costs on the basis of short-term study data is particularly relevant in a chronic condition like diabetes, given its progressive course, associated complications and high and growing economic burden.

The market-leading CdM is designed to assess the lifetime health outcomes and economic consequences of interventions in diabetes, and comprises 17 interdependent sub-models that simulate the major complications of the disease. It allows estimation of direct and indirect costs; adjusts for quality of life; and enables users to perform both costeffectiveness and cost utility analyses. It is routinely used to inform reimbursement decisions, public health issues, clinical trial design and optimal patient management strategies.

ROBUST VALIDATION PEDIGREE Validation to external studies has been an intrinsic part of the CdM’s development process. In a major evaluation in 2004, its operational predictive validity was demonstrated against 66 clinical endpoints from 11 epidemiological and clinical studies. Evolution of the model also reflects its strong links with the Mount Hood Challenge, a recognized biennial forum for comparing the structure and performance of diabetes health economic models with data from clinical trials (see Insights on page 50).

RECENT ENHANCEMENTS An ongoing commitment to ensuring that the CdM remains the best available tool for economic evaluations in diabetes has seen the model undergo a series of significant updates in recent years. These include

• • • • •

Ability to model individual anonymous patient-level data Incorporation of treat-to-target efficacy data for HbA1c Inclusion of a detailed hypoglycemia sub-model Expansion of variables for probabilistic sensitivity analysis Addition of UKPdS 68 and 82 risk equations

ENSURING CONTEMPORARY RELEVANCE To ensure the CdM’s continued relevance and accuracy following these enhancements, the aim of the latest validation study, published in 2014, was to examine the validity of the updated model to results from recent major long-term and short-term diabetes outcome studies. Particular emphasis was placed on cardiovascular (CV) risk.

ACCESSPOINT • VOLUME 5 ISSUE 9

Independent researchers with unrestricted access to the CdM and its source code worked with IMS Health to verify (ensure the model is coded as intended and free from errors) and externally validate (quantify how well outcomes observed in the real world are predicted) the model. In total 121 validation simulations were performed, stratified by study followup duration, study endpoints, year of publications and diabetes type. goodness of fit A number of statistical measures of goodness-of-fit were used, including

• • • •

Testing of null hypothesis of no difference between the annualized event rates (observed vs. predicted) and relative risk reduction across all validation endpoints Assessment of whether the confidence intervals for the number of events predicted by the model and those reported in the validation studies overlapped Evaluation of goodness-of-fit between simulated and observed endpoints for trials, endpoints, treatment arm, and date of study using the mean absolute percentage error (MAPE) and the root mean square percentage error (RMSPE) Scatterplots of observed vs. predicted endpoints along with the coefficient of determination (R2)

Impact of choice of CV risk equations The CdM currently uses, amongst others, CV risk equations derived from the United Kingdom Prospective diabetes Study Outcomes Model (UKPdS68) but, given the increasing choice of equations that is emerging, assessing the continued relevance of UKPdS68 is essential.

As part of the validation exercise, the absolute level of risk and relative risk reduction was compared for 12 CV disease risk equations developed specifically for T2dM patients.

RESULTS At conventional levels of statistical significance, the study found that the CdM fitted the contemporary validation data well, supporting the model as a credible tool for predicting the absolute number of clinical events in dCCT- and UKPdS-like populations.

Underscoring the significance of these results, Professor Phil McEwan of Swansea University, the lead researcher of the study, emphasized that "Organizations developing policy and implementing decisions informed by CDM require the reassurance that the model and its results are current and validated. This study helps to demonstrate that the model is a validated tool for predicting major diabetes outcomes and consequently is potentially suitable for supporting policy decisions relating to disease management in diabetes."

A copy of the full validation study is available to download online at: http://www.valueinhealthjournal.com/article/S1098-3015(14)01928-7/pdf For further information on the IMS CORE diabetes Model, please email Mark Lamotte at Mlamotte@be.imshealth.com 1

McEwan P, Foos V, Palmer JL, Lamotte MD, Lloyd A, Grant D. Validation of the IMS CORE Diabetes Model. Value in Health, 2014; 17: 714-724

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InsIghts DISEASE-SPECIFIC RWE

Enabling disease-specific RWE through fit-for-purpose RWD Increased stakeholder demand and the greater supply of electronic real-world data are expanding the application of real-world evidence across the product lifecycle. The most successful organizations are developing RWE platforms, capabilities and analytical methodologies focused on therapeutic areas. Increasingly, understanding how the characteristics of a particular disease area can influence the availability and use of real-world data for evidence generation is important in setting strategies that create dierentiation.

The author

Rob Kotchie, M.CHEM, MSC is Vice President, RWE Solutions, IMS Health Rkotchie@imshealth.com

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IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR

A framework for reference in key disease areas Globally, intensified pressure to obtain better value for healthcare spending has elevated the importance of real-world evidence (RWE) as an enabler of improved healthcare decision making. Increased stakeholder demand and the greater supply of electronic real-world data (RWD) are expanding its application across the product lifecycle as companies become attuned to the insights it can deliver.

Leading life sciences organizations are now using RWE to support clinical development, improve launch performance and drive better commercial results. The most successful are moving beyond a product-specific, study-based approach to develop RWE platforms, capabilities and analytical methodologies focused on a single or set of therapy areas to drive sustained value across their franchises.

As these trends continue, the ability to compare and understand how the characteristics of a particular disease area can influence the availability and use of RWD is an important step in setting focused and relevant RWE strategies that create differentiation and drive achievement of commercial goals. This article offers a framework for assessing RWD availability by therapy area to guide internal decision making.

NUANCED CHALLENGES FOR RWE RESEARCH By 2017, IMS Health estimates that the largest therapeutic classes in the developed markets will include a combination of both traditional primary care and specialized areas, led by oncology, diabetes, anti-TNFs, pain and asthma/COPD (Figure 1). Each of these disease areas presents markedly different patient populations, unmet medical need, standards of care and disease outcomes, leading to a nuanced set of challenges for RWE research.

DISEASE-DRIVEN DETERMINANTS OF RWE In seeking to inform the ease and extent of RWE development in a particular therapeutic class, IMS Health has identified five key characteristics of a disease area that have influenced the evolution of RWD development to date 1.

2.

3.

4.

5.

Routine capture of clinical measures Nature of the critical endpoint Number of treatment settings Length of follow-up Available sample size

By assessing each disease area against these five characteristics it is possible to identify the specific factors limiting an expansion of RWD use and the levers that can be engaged to accelerate future adoption. This point is illustrated in Figure 2 and discussed below for the projected top five therapy areas in 2017.

Oncology: Complex patient subgroups For oncology, a disease area that is often more amenable to RWE research due to the nature of the critical endpoint and frequent short length of required patient follow-up, analysis can be often limited by the complexity of patient subgroups and the need to capture detailed information on disease staging, therapy sequencing, role of surgery and patient biomarker status.

These challenges are now being overcome to a degree by healthcare stakeholders working together to link important rich clinical information with genomic and proteomic data, increasing the value and uses of RWD in this area.

For example, RWD is increasingly being leveraged in oncology to facilitate pricing and reimbursement of therapies by use, enabling a mechanism for greater alignment between manufacturers and healthcare payers and providers on the value and costs of treatment in a specific indication or patient population.

TOP

continued on next page

20 =

ACCESSPOINT • VOLUME 5 ISSUE 9

71%

market value by

2017 PAGE 7

InsIghts DISEASE-SPECIFIC RWE

Diabetes: Extended timeframe and multiple care settings In diabetes the generation and application of RWE, either by researchers to support burden of disease, comparative effectiveness or safety research or by commercial functions for forecasting or sales and marketing purposes, is often hindered by the need to track patients over long periods of time and across multiple settings of care. In other words, in order to infer the effects of a diabetes intervention on delaying the worsening of a secondary condition (eg, renal disease) or a reduction in a related complication (eg, microvascular or macrovascular events) patients must be followed over several years. This includes tracking their admissions and discharge to and from hospital, and across multiple treatment centers.

Hence, to fully assess the comparative effectiveness of a diabetes intervention in the real-world setting requires linking one or more datasets across both ambulatory and specialist treatment settings, and/or combining a closed database of medical and pharmacy claims with EMR data to provide meaningful clinical data on outcomes and confounding factors such as Body Mass Index and HbA1c.

Despite the proliferation of data in a primary care disease

like diabetes, the challenge is in bringing it together in a meaningful way that will increase the usability of diabetes RWD. Anti-tnFs/Pain: Patient-reported endpoints In the case of anti-TNFs or therapies to treat pain, RWE research is often limited by the lack of routine capture of patient-reported endpoints in clinical practice. While disease-specific instruments that are used to assess a patient’s response to therapy are systematically applied in clinical trials, they are typically either not routinely recorded in clinical practice or the data is stored in unstructured clinical notes making it challenging and time consuming to extract, analyze and interpret.

Asthma/COPD: Routine tests and acute events Similarly, in other chronic disease areas such as asthma/COPD, research can be restricted by the lack of routine capturing of test results used to assess the longterm deterioration of the disease (eg, spirometry measures such as FEV1) or detailed descriptions of acute episodic events, such as admission to hospital for a major COPD exacerbation, or the documentation of rescue medication use for a mild to moderate exacerbation.

FIGURE 1: LEAdING THERAPEUTIC CLASSES IN 2017 WILL INCLUdE PRIMARY CARE ANd SPECIALIST AREAS

Developed Markets

Sales in 2017 (LC$)

Oncology

$74-84Bn

Diabetes

$34-39Bn

Anti-TNFs

$32-37Bn

Pain

$31-36Bn

Asthma/COPD

$31-36Bn

Other CNS Drugs

$26-31Bn

Hypertension

$23-26Bn

Immunostimulants

$22-25Bn

HIV Antivirals

$22-25Bn

Dermatology

$22-25Bn

Antibiotics

$18-21Bn

Cholesterol

$16-19Bn

Anti-Epileptics

$15-18Bn

Immunosuppressants

$15-18Bn

Antipsychotics

$13-16Bn

Antiulcerants

$12-14Bn

Antidepressants

$10-12Bn

Antivirals excluding HIV

Others 29%

Top 20 Classes 71%

$8-10Bn

ADHD

$7-9Bn

Interferons

$6-8Bn

Source: Rickwood S, Kleinrock M, Nunez-Gaviria M. The global use of medicines: Outlook to 2017.

IMS Institute for Healthcare Informatics, 2013 Nov.

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IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR

FIGURE 2: FRAMEWORK FOR dETERMINING CHALLENGES OF RWE GENERATION bY dISEASE

Levers Routine capture Abundant of clinical measures

Infrequent

Supplementation

Nature of the critical endpoint

Hard

Soft

Supplementation NLP

Number of treatment settings

Single

Multi

Linkage

Length of follow up

Short

Long

Linkage retention modeling

Available sample size

Large

Small

Pooling

Oncology

Diabetes

Anti-TNF

LEVERAGING PROGRESS TO REALIZE VALUE Growing need and rapidly expanding applications of RWE are driving the development of innovative techniques to link, supplement and pool data sources for deeper and more meaningful research in this area.

The deployment of data encryption engines and greater collaboration between key players is enabling ever increasing scope to link anonymous information across datasets and settings of care, while preserving patient confidentiality and appropriate use.

Innovative techniques are now available to supplement secondary data from the electronic health record through novel primary data collection from physician and/or patients at the point of care (‘over the top’ data collection),

“

Pain

Asthma/COPD

and deploy Natural Language Processing (NLP) to extract additional rich information from clinical notes in a HIPAAcompliant manner.

These developments are providing life science researchers with unprecedented access to comprehensive disease area real-world datasets spanning multiple sources and settings of care - with sufficient sample size and patient follow-up to power an expanded set of RWE applications.

As companies look to maximize the value of RWE in their organization, a focus on understanding the specific needs and challenges for evidence generation presented by disease areas of interest will be a key step to leveraging the progress being made and realizing its full potential across their franchises.

Understanding how the characteristics of a disease area can influence availability and use of real-world data for evidence generation is increasingly important.

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”

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InsIghts

RWE ROADMAP

A roadmap for increasing RWE use in payer decisions Real-world evidence has been part of healthcare for more than 30 years. Despite this, its application to really improve the efficiency of healthcare delivery remains uneven and siloed.

Some of the greatest opportunities lie within the realms of collaborative and partnership initiatives between stakeholders, especially payers.

The authors

Marla Kessler, MBA is Vice President, IMS Consulting Group Mkessler@imscg.com

Ragnar Linder, MSC is Principal, RWE Solutions & HEOR, IMS Health Rlinder@se.imshealth.com

PAGE 10

IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR

Bridging the gap between promise and reality Real-world evidence has been part of healthcare for over 30 years, applied at varying levels by regulators, clinicians, payers and manufacturers to inform decisions, build programs and improve health. IMS Health has documented more than 100 case studies where RWE has actively influenced product labeling, price, access and use.1 Despite this, the application of RWE to really improve the efficiency of healthcare delivery remains uneven and siloed. Does this suggest a lack of comprehensive, quality data? Are healthcare professionals, policy makers and other key stakeholders waiting for better tools? Are the skills sets to link and analyze data not widely accessible?

In fact the evidence suggests that the ability to produce RWE is expanding, and rather quickly. However, the gap between the exponential increase in RWE sources and the capacity to harness these effectively is also growing.

Our research suggests that this widening gap between the promise and reality is due to three critical – but manageable – barriers.

" "

"

"

GROWING VOLUME BUT UNREALIZED POTENTIAL The quantity and importance of RWE has expanded tremendously in recent years (Figure 1). RWE is generated and applied throughout the lifecycle of pharmaceuticals and other medical interventions to demonstrate effectiveness, safety and value. It can be used for population health management, for example in identifying significant health factors by geography or demographics for the design and evaluation of interventions to improve health. It can enable better understanding and characterization of disease epidemiology, treatment paradigm and associated resource utilization. It can inform quality of care assessment, point of care decision guides and translational research projects. And it can also serve to assess a drug’s performance outside the randomized controlled trial (RCT) setting and describe any shifts in practice once the drug is approved and used.

FIGURE 1: THERE HAS bEEN AN EXPLOSION OF REAL-WORLd dATA FOR ANALYSIS

44% ACCESSPOINT • VOLUME 5 ISSUE 9

continued on next page

"

of payer respondents had no confidence in the economic evidence provided by pharma

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InsIghts RWE ROADMAP

FIGURE 2: CASE STUdY bREAdTH ANd VOLUME dEMONSTRATE EXISTING RWE dEMANd

25

22

21

Number of case studies

20

16 15

11

10

10

9

5

4

3

3

France

Germany

2

0 Italy

USA

UK

Label

Sweden

Canada

Launch access

Spain

Ongoing access

Netherlands

Price

Denmark

Use

Source: Hughes B, Kessler M. RWE market impact on medicines: A lens for pharma. IMS Health AccessPoint, 2013; 3(6): 12-17

While RCT data is still regarded as being top of the evidence hierarchy, there has been an increased use of approaches that assess patient outcomes and follow all the care and interventions they receive. Real-world data (RWD) is now being used to complement RCT information, providing valuable evidence of the way pharmaceuticals are being used in practice and in many populations, which cannot be gained from RCTs. The breadth and volume of demand for RWE by payers across markets is shown in Figure 2, based on research conducted in 2013.1 In addition, payers are involved in a plethora of RWE activities, building RWD for commercial purposes (eg, Humana, Lifandis), collaborating more broadly with other payers (eg, Health Care Cost Institute), or simply using their own data for internal assessments.

Clearly, payers have not ‘opted-out’ of RWE. And yet examples of them accepting industry-generated RWE or working collaboratively with pharma to generate RWE are few. These two key players may often be on opposite sides of a negotiating table but opportunities exist for partnerships that could potentially improve the entire healthcare system. While current examples do provide hope for a more collaborative future, they also force a more fundamental question: what are the barriers to greater use of RWE by payers and their willingness to work with pharma and other stakeholders to broaden its application in pricing, reimbursement and access decision?

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SOME IDENTIFIED BARRIERS In reviewing this issue with many payers and pharma executives and in published literature, conferences and other forums, barriers emerge in three key areas: data and technology; science; and collaboration. While not exhaustive or quantified, the challenges discussed below within these areas provide a view of the roadblocks being encountered.

Data and technology barriers • Data infrastructure While fully adjudicated claims data is structured with fewer and more consistently defined variables, the volume of it is expanding even as it is increasingly linked with laboratory records, medical records, patient social media and now genomic data, stretching the bounds of healthcare informatics. All players in the healthcare system seek more clinical and patient outcomes information but now appear to be drowning in vast amounts of data without it being sufficiently complete for effective decision making. A study from the Health Research Institute (HRI) in the US2 notes that payers themselves believe they lack an adequate data infrastructure to apply RWE in areas such as outcomesbased contracting. And although the related technology is growing and scalable, it is too expensive and time consuming for most stakeholders to realize its full potential at this time.

IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR

• Data extraction and linkage Many payers have built distinctive capabilities in understanding claims-related data but clinical data requires a different set of expertise. The magnitude of the challenge is just as great for pharma although its nature is different. Companies may have acquired substantial data and even technology integration solutions but the data sits in functional and geographic silos using new and old technologies, making it challenging to link let alone analyze.

Even in a country like Sweden, where almost all patient data can be tied to a consistent national social security number, linkage is possible but not immediate.

• Data programming and processing Speed is critical. However, a well-constructed research study involving intensive SAS programming can take months to conduct, extended by delays in gaining answers to questions, with knock-on implications for the timeliness of the insights delivered.

scientific barriers • Lack of consistent RWD methodologies The insights to be gained from RWD are substantial, but the growing availability of data highlights important methodological challenges. Even at a basic level, questions can arise. For example, what defines a diabetic patient? Is it based on medications taken, a recorded diagnosis code, or an actual laboratory or series of laboratory results?

Not every patient record contains all that information or even some of it. This quickly leads to more complex challenges: when should data matching be deterministic versus probabilistic? When is it acceptable to impute missing values? How will these decisions bias the results? How can advanced analytics, including predictive analytics, improve the quality of and confidence in RWE? The expertise to deal with this exists, but not always in-house. Furthermore, payers can be skeptical of data because there is no easy way of ensuring that the deployed methodologies are sufficiently robust.

• Absence of standardized measures The current lack of consensus around many key measures means that even issues such as how long a patient needs to demonstrate an outcome before a treatment is deemed cost-effective, are not universally agreed.

“

The variation in approaches can significantly impact study results. Exploring methods used to score physician spending patterns (cost profiling), a measure frequently assessed by payers, a Rand Health research study showed that even slight changes in attribution rules can dramatically change the characterization of physician performance. For example, “Between 17 and 61 percent of physicians would be assigned to a different cost category if an attribution rule other than the most common rule were used.”3

Collaboration barriers • Lack of trust This is perhaps the elephant in the room that everyone is willing to talk about. While payers and pharma should be aligned around patient outcomes, economic incentives are more complex. The previously referenced HRI study found that 44% of payer respondents had no confidence in the economic evidence provided by pharma.2 Fewer than 1 in 10 were very confident in using pharma-generated information to evaluate a drug’s comparative effectiveness.

For data holders, the need to protect patient privacy and the integrity of the data being used has created many hurdles to access. Even straightforward protocols can take months to approve if each proposal is evaluated individually.

• Lack of imperative While some payers see their data as entirely adequate to support comparative effectiveness and other analysis, others are not even sure the analysis is required to achieve their goals. If the main objectives are managing unit costs of treatments, payers have other mechanisms such as rebates, formulary design and traditional analysis of claims data, which they may find easier to use.

In parallel, many pharma companies can be risk averse to generating RWE with a payer without fully understanding what will be said and how it will be used. continued on next page

Some of the greatest opportunities for achieving the goal of improved efficiency in healthcare lie within the realms of collaborative and partnership initiatives between stakeholders, to ensure implementation.

ACCESSPOINT • VOLUME 5 ISSUE 9

” PAGE 13

InsIghts RWE ROADMAP

SOME POTENTIAL SOLUTIONS None of the barriers referenced are insurmountable.

Indeed, interesting examples are already emerging of innovative solutions on the path towards greater use of RWE in pricing and reimbursement decisions.

• Evolution of methodologies and technologyenabled analytics This edition of AccessPoint alone spotlights the area of predictive modeling where novel methodologies are driving a new generation of applications in RWE (see article on page 26). In these areas, researchers are taking advantage of improved data and computing power to run analytics that otherwise would have been too time-consuming, if not impossible, to conduct.

• Richer data sources Not every research question must rely on locallysourced data. In countries such as Scandinavia, more than two decades of rich patient-level data exists electronically. Technologies such as the IMS Pygargus Customized eXtraction Program facilitate linkage between the various sources by extracting the desired data from an electronic medical record (EMR) to build databases of EMR and register data. A 2014 retrospective cohort study linked national Swedish mandatory registries to EMR data from outpatient urology clinics to study prostate cancer (PC) patients.

The use of this approach provided a unique understanding of the clinical course of PC that can inform treatment and research across developed markets – not only in Sweden.4

• third-party involvement The involvement of independent, objective third parties can increase confidence in the underlying data as well as the resulting analysis. It can also be an important enabler of packaged analytics where data can be used for a variety of applications within a spectrum of pre-approved uses. A trusted third party can deliver that protection. In addition, for data providers interested in commercializing their data, a third party can enable the full value potential of that data to be captured across a range of research goals involving many different types of organizations.

FULFILLING THE PROMISE The importance of RWE is continuing to grow along with its ability to inform critical decisions for payers, pharma companies and other healthcare stakeholders. However, the full impact of its potential has yet to be realized. This article has considered some of the barriers to wider use of RWE and proposed some solutions to address them.

Some of the greatest opportunities for achieving the goal of improved efficiency in healthcare lie within the realms of collaborative and partnership initiatives between stakeholders, to ensure implementation.

Only then can we provide the best care for patients and improve outcomes.

• Collaborations Organizations such as the Healthcare Cost Institute (HCCI) have been established with the goal of pooling data (in this case, from US payers) and increasing its quality. In reality, the value of cooperation between stakeholders in different parts of the system – payers, providers and pharma – will be critical, not only in improving data sources but also in increasing buy-in to and application of the insights from them. This checkand-balance will enable stakeholders to put the patient at the center of RWE and provide care that actually improves outcomes.

In addition, it can enable a movement away from different parties running analytics to stakeholders working together to solve problems. For example, RWE can support efforts to improve decision making, adherence and efficient care delivery, where the focus goes beyond analytics and ultimately to better patient care.

1

Hughes B, Kessler M. RWE market impact on medicines: A lens for pharma. IMS Health AccessPoint, 2013; 3(6): 12-17 Health Research Institute/PWC. Unleashing value: The changing payment landscape for the US pharmaceutical industry. May, 2012 3 Mehrotra D, Adams JL, Thomas WJ, McGlynn EA. Is physician cost profiling ready for prime time? Research Brief, Rand Health, 2010 4 Banefelt J, Liede A, Mesterton J, Stålhammar J, Hernandez RK, Sobocki P, Persson BE. Survival and clinical metastases among prostate cancer patients treated with androgen deprivation therapy in Sweden. Cancer Epidemiology, 2014, Aug; 38(4): 442-7. doi: 10.1016/j.canep.2014.04.007. Epub 2014 May 27.

2

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IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR

InsIghts

PRIMARY CARE UTILIZATION IN CANADA

Diabetes complexities drive resource consumption in Canada According to the OECD, Canada currently ranks 27 out of 34 member countries in the number of physicians per 1,000 persons.1 Around 15% of Canadians report either being unable to access a primary care doctor or choosing not to do so.2 A new IMS Health analysis of EMR data reveals diabetes as the main consumer of GP resource among chronic conditions in Canada, with key insights for improvement initiatives.

The authors

Richard Borrelli, B. COMM, MBA is Principal, CES, IMS Brogan Rborrelli@ca.imshealth.com

ACCESSPOINT • VOLUME 5 ISSUE 9

Sergey Mokin, MSC, MBA is Consultant, CES, IMS Brogan SMokin@ca.imsbrogan.com

Michael Sung, MSC, MBA is Consultant, CES, IMS Brogan Msung@ca.imsbrogan.com

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InsIghts PRIMARY CARE UTILIZATION IN CANADA

A case study of EMR data in diabetes LEVERAGING REAL-WORLD EVIDENCE

STUDY FINDINGS

Findings from the 2013 National Physician Survey in Canada indicate that 64% of family physicians and 59% of specialists now utilize electronic medical records (EMR) in their practices.3 The improved availability of EMR data makes it a powerful source of real-world evidence to better understand demands on the healthcare system. In seeking to evaluate primary care utilization in the country, a study was conducted using Canadian data from the IMS Evidence 360 EMR database. This provided access to a panel of around 500 general practitioners (GPs) and specialists covering more than 500,000 anonymous patients as a sample of the Canadian population in major chronic indications.

Primary care system utilization overview In the study period, a total of 122,296 unique patients recorded visits to physicians in the EMR database. The concentration of visits showed that 10% of patients were responsible for nearly 40% of primary care visits (Figure 1).

FIGURE 1: 10% OF PATIENTS ACCOUNTEd FOR 40% OF PRIMARY CARE VISITS

Frequency of visits Vs. Number of patients concentration curve 100 80 % Visits

Objectives The cross-sectional EMR study had three key objectives 1. Identify medical conditions that are the highest consumers of physicians’ time in Canada, measured in visits per patient per year

60 40 20 0

2. Describe the contributing factors for the medical condition associated with the most frequent visits per patient per year

0

10

20

30

40 50 60 % Patients

70

80

90

100

Among the patients with chronic conditions, those with diabetes made more repeat visits to a physician, as indicated by the significantly higher average number of visits per patient (2.6 per year) compared to other chronic diseases (Table 1A). Among the acute conditions (which were not studied further), patients with diseases of the respiratory system had the highest average number of visits per year (1.6 per patient) over the study period (Table 1B). The further analysis focused on diabetes given its chronic status and the significantly larger portion of year-to-year healthcare spending on this condition.

3. Propose areas of high potential impact for further investigation and intervention Methodology A cohort of all patients with at least one physician visit recorded during the study period of June 2013–May 2014 was extracted from the EMR dataset. The overall concentration of patient visits and average visits per patient was then determined across different diagnosed conditions. These conditions were prioritized based on the average visits per patient, and statistical significance calculated to identify the top consumer of physicians’ time for both the acute and chronic conditions.

TAbLE 1A: CHRONIC CONdITIONS

Medical Condition Diabetes mellitus Mental health disorders Hypertension & other heart diseases Chronic musculoskeletal system & connective tissue disorders Chronic diseases of the respiratory system

Patients

Visits

Visits per patient

p-value*

2765 5901 4764 9263 3970

7205 11425 8270 13906 5319

2.61 1.94 1.74 1.50 1.34

<0.001 <0.001 0.066 <0.001

Patients

Visits

Visits per patient

p-value*

15706 5155 3820 4702 1970 2205

25083 6609 4844 5627 2354 2522

1.60 1.28 1.27 1.20 1.19 1.14

<0.001 0.92 <0.001 0.31 <0.001

TAbLE 1b: ACUTE CONdITIONS

Medical Condition Acute diseases of the respiratory system Diseases of the urinary system (cystitis) Family planning, contraceptive advice, advice on sterilization or abortion Immunization (all types) Acute musculoskeletal system & connective tissue disorders Diarrhea, gastroenteritis, viral gastroenteritis

*p-value for the Wilcoxon rank sum test measures the significance of the difference in visits/patient between each medical condition and the next highest medical condition

Note: ICD-9 Code 078 containing other diseases due to virus was excluded due to potential for multiple viral infections to be captured under this single code

PAGE 16

IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR

Resource use contributors in diabetes To determine potential contributors to the high level of resource use in diabetes, data on its associated demographics, co-morbidities/concomitances and lab tests was extracted and analyzed. All diabetic patients were identified in the cohort on the basis of having at least one ICD-9 diagnosis code 250 or at least one prescription for an anti-diabetic described by the ATC code A10.

Body Mass Index (BMI), HbA1c and fasting glucose levels were analyzed for the diabetic cohorts based on the latest available result within the study period. Patients with fasting glucose >6.9 mmol/L or HbA1c >7% were further segmented as ‘out of control’. Those treated with a metformin product alone for the entire study period and those who received metformin plus another anti-diabetic class in the study period were also segmented. Statistical tests were conducted to determine if observed differences between patient segments were statistically significant.

Patients A total of 4,390 diabetic patients recorded physician visits in the EMR dataset over the study period. More males (55%) than females (45%) were observed among these patients, which is representative of the Canadian diabetic population (54% males vs. 46% females).4 The majority (73%) were over 50 years of age (Figure 2). Of the 1,697 patients with measurable BMI, more than 50% were classified as obese (BMI >30.00) and another 30% as overweight (BMI 25.00–29.99) (Figure 3).

FIGURE 2: AGE dISTRIbUTION OF dIAbETIC PATIENTS (N=4390)

More than 70% of patients were treated with metformin.

However, multiple classes of anti-diabetic medications were used to manage the disease, with DPP-IV inhibitors and sulphonylureas being the next two most frequently prescribed (Table 2). Diabetic patients were also likely to be taking medications for cholesterol and triglyceride control as well as for hypertension or other cardiovascular conditions (Table 3). The type and prevalence of concomitances were consistent with an older and mostly overweight patient population.

Of patients whose med lab test results were available and who had been treated with an anti-diabetic, distribution analysis of their most recent HbA1c and fasting glucose levels (Figure 4) showed that 51% did not meet the HbA1c control threshold and 60% were out of control based on the fasting glucose threshold.

Patients on metformin alone were compared with those who had metformin plus at least one other anti-diabetic in the study period. There was a statistically significant relationship between the medication regimen (metformin vs. metformin plus other) and achieved control state (in control vs. out of control) within the study period (Table 4).

Fasting glucose and HbA1c levels were significantly higher for patients treated with metformin and another anti-diabetic in the study period. These patients also had a significantly higher number of GP visits (Table 5). However, further studies are required to determine the link between the medications prescribed and control of diabetes.

FIGURE 3: bMI dISTRIbUTION OF dIAbETIC PATIENTS (N=1697)

30.0

51.0%

23.4%

20.0 15.3%

15.0

50.0 16.1% 8.2%

10.0 4.1%

5.0

6.6%

0.7% 0.0 0.1% 0-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 Age Range

% Patients

% Patients

60.0

25.5%

25.0

40.0 30.8%

30.0 20.0

17.7%

10.0 0.0

0.4% <18.50

18.50-24.99 BMI

25.00-29.99

>30.00

continued on next page

“

The findings of the study utilizing EMR data identify diabetes as the primary consumer of GP resource among chronic conditions in Canada.

ACCESSPOINT • VOLUME 5 ISSUE 9

” PAGE 17

InsIghts PRIMARY CARE UTILIZATION IN CANADA

TAbLE 2: dIAbETES TREATMENT LANdSCAPE

Type

Class

Anti-diabetic

Metformin DPP-IV Inhibitor Sulphonylurea Human insulins and analogues Other anti-diabetics Total treated patients

TAbLE 3: TOP dIAbETES CONCOMITANCES

No. of Patients

% Patients

1514 624 619 212 135 2094

72.3% 29.8% 29.6% 10.1% 6.4% 100.0%

Note: Patients treated with multiple product classes would be counted multiple times, once within each row corresponding to each product class prescribed

Indication

Treatment type

No. of Patients % Patients

Anti-hyperlipidemia

Cholesterol & triglyceride regulating preparations

1500

34.1%

Cardiovascular Gastrointestinal Cardiovascular Cardiovascular Cardiovascular Cardiovascular

Ace inhibitors Antiulcerants Calcium antagonists Angiotensin II antagonists Beta blocker agents Diuretics

743 525 478 459 446 413

16.9% 11.9% 10.9% 10.4% 10.1% 9.4%

45% 40% 35%

Control level HbA1c: >= 7% --> Out of control (51%) Fasting glucose: >6.9 mmol/L --> Out of control (60%)

30% 25% 20% 15% 10% 5%

20+

19-<20

18-<19

17-<18

16-<17

15-<16

14-<15

13-<14

12-<13

11-<12

10-<11

9-<10

8-<9

7-<8

6-<7

5-<6

4-<5

3-<4

0% 2-<3

Patient Distribution Between Test Levels (%)

FIGURE 4: dISTRIbUTION OF dIAbETIC PATIENTS bY HbA1C ANd FASTING GLUCOSE LEVEL

HbA1c (%) & Fasting glucose (mmol/L) HbA1c

Fasting glucose

TAbLE 4: PEARSON CHI-SQUAREd TESTS FOR INdEPENdENCE bETWEEN TREATMENT TYPE ANd CLINICAL OUTCOMES bY FASTING GLUCOSE ANd HbA1C TEST RESULTS

Fasting glucose level In control Out of control Total p-value

Metformin

Metformin plus other*

Total

213 148 361 <0.001

89 204 293

302 352 654

Metformin

Metformin plus other*

Total

289 134 423 <0.001

120 238 358

409 372 781

HbA1c In control Out of control Total p-value

TAbLE 5: NON-PARAMETRIC TESTS FOR SIGNIFICANT dIFFERENCE IN OUTCOMES (MEASUREd bY FASTING GLUCOSE ANd HbA1C TEST RESULTS) ANd VISITS TO A PHYSICIAN

Fasting glucose (mmol/L) HbA1c (%) Visits

PAGE 18

Metformin

Metformin plus other*

p-value

7.08 6.88 2.46

8.59 7.96 3.42

<0.001 <0.001 <0.001

*Refers to a treatment with metformin in combination with any other anti-diabetic in the study period

IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR

IMPLICATIONS FOR FUTURE INTERVENTIONS It has been estimated that by 2020 around 10.8% of the Canadian population will be diagnosed with diabetes, a 57% increase over a 10-year period. In addition, 22.6% of the population will be classified as pre-diabetic and at risk of developing diabetes in the future.5 This could significantly increase the financial burden to Canadian healthcare; direct medical costs are projected to reach CN$3.8 billion by 2020 (37% growth since 2010), with about 5% attributed to GP and specialist visits.5 The findings of the study utilizing EMR data identify diabetes as the primary consumer of GP resource among chronic conditions in Canada. With 80% of diabetic patients classified as being either overweight or obese there is a clear need for weight management programs and lifestyle counseling.

Many diabetics are also often treated for co-morbidities with antihypertensive, gastrointestinal or hyperlipidemia medications. This is indicative of a more complex patient, leading to greater demands on a primary care physician in managing these interrelated conditions.

Despite the availability of multiple treatment choices, more than half of the diabetic patients in the study cohort failed to achieve control of their most recent HbA1c levels. Although the study was not designed to evaluate the drivers of diabetes control, further investigation into

“

the real-world effectiveness of various therapies is encouraged. The results could potentially inform treatment choices, resulting in a more efficient allocation of resources.

A further observation from the study is that treatment complexity, as indicated by a drug regimen including metformin plus other, is associated with poorer HbA1c/glucose-level control and an increased demand for physician time. Thus, patients who were unable to achieve target control and required more complex treatment regimens consumed a higher number of primary care visits. This implies that maintaining better control of patients during earlier treatment phases can reduce the additional resource required for more advanced diabetes care.

Finally, the study findings point to four key areas with high potential impact for intervention to improve the real-world management of diabetes in primary care 1. Controlling weight 2. Efficiently managing the challenges of treating a patient for multiple conditions 3. Evaluating and identifying the most appropriate and effective medications per patient 4. Achieving and maintaining effective early control of diabetes.

The study findings point to four key areas with high potential impact to improve the management of diabetes in primary care.

”

1

OECD Health Statistics 2014 : How does Canada compare? Available at: http://www.oecd.org/els/health-systems/Briefing-Note-CANADA-2014.pdf.

Accessed 6 October, 2014 2 Statistics Canada, Community Health Survey 2012. Available at http://www.statcan.gc.ca/pub/82-625-x/2013001/article/11832-eng.htm.

Accessed 6 October, 2014 3 2013 National Physician Survey. The College of Family Physicians of Canada, Canadian Medical Association, The Royal College of Physicians and Surgeons of Canada. Available at: http://nationalphysiciansurvey.ca/wp-content/uploads/2013/10/2013-National-ENr.pdf. Accessed 6 October, 2014 4 Statistics Canada. Data for 2013. Available at: http://www.statcan.gc.ca/tables-tableaux/sum-som/l01/cst01/health53a-eng.htm.

Accessed 6 October 2014 5 Canadian Diabetes Association, Diabetes Québec, 2011. Diabetes: Canada at the tipping point. Charting a new path. Available at: http://www.diabetes.ca/CDA/media/documents/publications-and-newsletters/advocacy-reports/canada-at-the-tipping-point-english.pdf.

Accessed 6 October 2014

ACCESSPOINT • VOLUME 5 ISSUE 9

PAGE 19

InsIghts

SCIENTIFIC-COMMERCIAL RWE SUPPORT

Finding the true potential of RWE through scientificcommercial collaboration A recent report from IMS Health demonstrates the value that real-world evidence delivers throughout the pharmaceutical lifecycle and proposes the more active engagement of commercial teams in RWE â&#x20AC;&#x201C; both in terms of leadership and consumption. This article summarizes key highlights of that research and presents a framework for increasing scientific-commercial collaboration in support of RWE.

The authors

Ben Hughes, PHD, MBA, MRES, MSC

Marla Kessler, MBA

Amanda McDonell, MSC

is Vice President, RWE Solutions, IMS Health Bhughes@uk.imshealth.com

is Vice President, IMS Consulting Group Mkessler@imscg.com

is Senior Consultant, RWE Solutions & HEOR, IMS Health Amcdonell@uk.imshealth.com

PAGE 20

IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR

Realizing a US$1 billion opportunity through scientific-commercial collaboration The IMS Health report1 shows how a few leading companies pursue RWE as a capability, implementing RWE platforms that move beyond narrow, study-based approaches to create sustained value across the product lifecycle and disease franchises. By following this approach, a top-10 pharmaco could derive US$1 billion in value from RWE. For commercial teams the expanding applications of RWE come at just the right time, when their stakeholders are demanding ever more support of a product’s value proposition just as they and others are producing evidence of its performance in real-life settings.

In parallel, commercial teams appreciate the shortcomings of traditional approaches to gaining market insights but feel they lack ready alternatives. Primary market research is inherently limited in sample size and depth of insight, as well as being time intensive. It can also be inaccurate and thus an inconsistent indicator of actual behavior. There is a growing need for more timeefficient, fact-based research.

FOUR GOLDEN PRINCIPLES FOR TRANSFORMATION Leading companies have recognized these challenges and taken steps to address them. Their experiences suggest Four Golden Principles of using RWE to transform performance, with direct implications for commercial teams.

1. RWE capabilities converge in a platform Leaders approach platform investments in information, technology and analytics tools with a plan to support a range of uses – both scientific and commercial. In these companies, commercial teams can respond rapidly to queries about product use and evolving treatment paradigms rather than having to wait a year to answer the most fundamental questions.

$1bn ACCESSPOINT • VOLUME 5 ISSUE 9

INCLUDING

Leaders think carefully about the platform capabilities they should buy versus build, and how best to balance the benefits of centralization (economies of skill) with the benefits of embedding capabilities within the business unit (responsiveness to business needs) (Figure 1).

FIGURE 1: CAPAbILITIES LAYER IN AN RWE PLATFORM

Channels for dissemination & engagement

RWE capabilities stack

STEPPING UP TO UNTAPPED RWE POTENTIAL

CoEs for scientific & commercial analytics

Technology-enabled tools & analytics

Information, networks & data linkage Business specific setup/build Partially consolidated capabilities/build Consolidated capabilities/buy

The necessary layers of capabilities are • Information, networks and data linkage Increasingly, technology is enabling managed access to new information with consent. Leaders develop relationships with healthcare stakeholders to access specific data sources relevant to their research needs.

They are able to link datasets, comply with privacy laws, use technologies that anonymize data at source, or integrate routine databases with traditional prospective data. The result is a rich end-to-end view of patient journeys.

• technology-enabled tools and analytics Leaders provide users with direct access to data insights through user-friendly interfaces. Pre-defined, validated queries under scientific leadership facilitate simple requests. This flexibility, coupled with highperformance architecture, reduces time to insight. It does not replace experienced scientific and statistical staff, but rather ensures their focus on value-added instead of routine tasks. continued on next page

5% brand growth via RWE-enabled marketing 20% launch improvement via patient pool segmentation 3-month acceleration of market access submissions 25-90% cost savings versus primary research

PAGE 21

InsIghts SCIENTIFIC-COMMERCIAL RWE SUPPORT

FIGURE 2: PLATFORM dEPLOYMENT TO FUNCTIONS

R&D

HEOR

Medical & Safety

Market Access

Commercial

t Drug pathways

t HEOR productivity (speed & quality)

t Drug utilization/ monitoring

t Speed to market (dossier, CED1)

t RWE-enabled marketing (eg, undertreated)

t Target population/ product profile

t Local burden of illness/disease/costs

t Risk management

t New pricing mechanisms

t AE/signal detection

t Formulary simulation

t Rapid FDA/EMA responses

t Ongoing value differentiation

t Launch/promotion planning via physician-patient segmentation

t Translational research

t Trial simulation/ recruitment t Pragmatic clinical trials (pRCTs)

t Forecasting

1 CED: Coverage with

Evidence Development

Analytics CoE

Analytics CoE

Analytics CoE

t Engagement services (eg, adherence)

Analytics CoE

Data discovery & interrogation tools

Insights & reporting tools

Technology-enabled tools & analytics Information, networks & data linkage RWE-enabled insights also have potential to accelerate drug development (eg, by improving target selection) which has not been accounted for in this assessment.

• Channels for dissemination and engagement Leaders formalize the use of RWE across global and local channels to engage stakeholders. This ranges from global branding programs promoting the overall credibility of RWE platforms to locally deployed initiatives for improving RWE capabilities within medical and pricing & market access teams.

Internally, on-demand RWE insights are being embedded into operational processes across functions.

FIGURE 3: AdVANCEd PLATFORM STRATEGIES bY THERAPEUTIC AREA ANd GEOGRAPHICAL SCOPE

Thus, the broader organization – including scientific and commercial functions - can benefit from RWE-enabled insights tailored to their research interests or operational needs, as illustrated in Figure 2.

Multi TA

B

A

2. narrow precedes broad

A

C

C D

J

Single TA

Therapy area (TA) scope

• Centers of Excellence (CoEs) for scientific and commercial analytics Leaders standardize analytics across markets and data sources, pooling analysts in a flexible and scalable service capacity. The continued tendency to manage scientific and commercial CoEs separately allows economies of skill where possible but also the development of deep analytical methods specific to a therapeutic area (TA) or function.

D

H

US

G

Multi-market

Market coverage

X

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Target platform scope (ongoing build)

Current platform scope

Company

Evolution

Leaders focus on select TAs and markets to ensure their investments generate differential value. Commercial teams are often responsible for the overall franchise performance, best positioning them to understand evidence needs and priorities.

Companies need to funnel their investment into a ‘must-win’ TA. In our experience, they can only be distinctive in areas of internal expertise and products/treatments that give them credibility and real-world experience with stakeholders. Many emerging leaders have elected to use RWE in one or two TAs where there is a strong pipeline and in-market portfolio, and within mission-critical markets (to include the US and up to three to five additional markets worldwide).

Even today, no one has full RWE-platform capabilities across multiple TAs and geographies. However, companies have had successes in single TAs or with single market

IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR

approaches that they have expanded over time, as shown by the migration of individual platforms in Figure 3.

Many will debate this view, given the desire to drive distinctive capabilities simultaneously in all key TAs, markets and functions. In reality, it takes several years to develop the necessary capabilities and deliver value, which is easier to do when those involved are aligned by common data and/or challenges, often defined by TA. Companies outlining a transformation agenda must set the right expectations. There is no silver bullet; success requires a multi-year effort of continuous improvement.

3. Commercial leads the charge HEOR and other scientific colleagues are sometimes critical of commercial-driven RWE, as the speed to insight is contrary to their experience of time-intensive study design and implementation. Yet platform-based RWE capabilities will help them deliver more and better research publications with greater scientific and market impact. Commercial teams must champion the overall platform to broaden RWE’s application and value for many reasons – including their unique ability to secure resources – while HEOR continues to lead the development and implementation of scientifically rigorous studies.

The need for commercial to take the lead in this traditionally scientific domain is not immediately obvious.

However, leaders realize that scientific can be the data custodian and user of RWE for protocol-driven studies while commercial can be given appropriate access to drive strategic decisions. Strong governance, allowing nominated individuals outside scientific access to data insights, enables scale in RWE investments.

The largest immediate financial value of RWE is in supporting about-to-launch and launched products, areas where commercial drives decision making. Many decisions related to labeling and identifying target patients, contracting and pricing strategies, and launch planning are transformed by RWE, requiring commercial to be close to RWE strategy. Ultimately, only franchise leaders can really champion the longer-term investment in their patients and key markets.

How can commercial initiate its leadership role in a pragmatic way? More product teams are now sharing their priorities across functions and mapping their current and pending evidence plans against them. One company reoriented several expensive prospective studies to build a platform capability linking key information sets for required insights. Thus, longer-term evidence planning and commercial’s ability to remove organizational barriers is an emerging vehicle for RWE leadership.

4. speed is a goal Leaders seek speed to insight and can perform end-toend scientific studies in weeks. In their vision of ondemand insights, quality and speed are harmonious, not trade-offs. With better, timelier information, commercial teams can become more nimble and work more effectively with their customers.

Platform-based RWE capabilities challenge the paradigm that robust, scientific-led insights require significant time.

With existing data agreements in place and pre-defined analytics established, analyses can start almost immediately.

In companies where RWE delivery teams have a customer service mindset (at least three to our knowledge), full scientific studies using platform-enabled analytics have been completed in less than a month, rather than up to a year.

FIGURE 4: VALUE CAPTURE FROM RWE ACROSS LIFECYCLE FOR A TOP-10 PHARMACO

Development

Launch

In-market

Clinical development*

Initial pricing & market access*

Safety & value demonstration

US$100m

US$200-600m

Launch planning & tracking

Commercial spend effectiveness

US$150m

US$200-300m

US$100-200m

Productivity & cost savings

US$100m *Selected operational opportunities only; excludes increased R&D pipeline throughput and better pricing

continued on next page ACCESSPOINT • VOLUME 5 ISSUE 9

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InsIghts SCIENTIFIC-COMMERCIAL RWE SUPPORT

Insights from RWE can provide commercial teams with feedback on market changes and the impact of their actions within weeks. Leaders realize such speed only matters if there is willingness to act on these insights promptly. This could mean changing sales call plans, reprioritizing physician targets, altering or dropping promotional plans and even engaging with payers more frequently or differently. RWE leaders make this more realtime information available, adopt more dynamic marketing plans, and empower key account managers and others to leverage the new knowledge.

SOURCES OF THE US$1 BILLION RWE OPPORTUNITY The experience of companies living the Four Golden Principles demonstrates the significant value RWE can deliver at different stages of the pharmaceutical lifecycle.

Our research identified six main areas of value capture: clinical development; initial pricing & market access; launch planning & tracking; safety & value demonstration; commercial spend effectiveness; and overall productivity & cost savings. As shown in Figure 4, most of the value is likely to come after product launch.

FIGURE 5: CASE STUdIES OF RWE IMPACT ACROSS OPPORTUNITY AREAS

Examples of impact

Commercial spend effectiveness

US$200-300m

Safety & value demonstration US$100m (upside)

US$100-500m (downside avoidance)

Launch planning & tracking

t 5% brand growth via RWE-enabled marketing t 20-50% improved promotion via physicianâ&#x20AC;&#x201C;patient segments t Better forecasting via disease progression models

t Formulary improvement from Tier-3 to -2 t Avoidance of label changes t 2-week responses to FDA/3rd party journal publications

t 20% launch improvement via patient pool segmentation t Rapid adjustment of messaging/resource allocation at launch

US$150m

Initial pricing & market access* US$100m

Productivity & cost savings

t 3-month acceleration of market access submissions t Payment by use/indication, more effective price negotiations (not quantified) t Conditional access via coverage with evidence development

t 25-90% cost saving versus primary market research t Doubling of impact factor of publications1

US$100m

Clinical development* US$100-200m

Traditional focus

t 30% improvement in trial enrolment t Reduction in strategic trial design flaws t Better product profile design (not quantified)

Leadersâ&#x20AC;&#x2122; additional focus

1 Hruby GW, et al. J Am Med Inform Assoc, 2013; 20: 563-567 * Selected operational opportunities only; excludes increased R&D pipeline throughput and better pricing

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IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR

“

The opportunity for RWE to add value is substantial but commercial needs to step up and take accountability for implementing RWE capabilities.

In companies without RWE platform capabilities, the roles of scientific and commercial are compartmentalized: scientific teams are asked for studies to support specific ad hoc arguments without long-term strategic input, while commercial teams face increasing scrutiny of their products but are often unarmed with the evidence to defend them. Leaders have built RWE capabilities that span both functions, enabling immediate and strategic evidence generation.

Diving deeper into the buckets of RWE value, the research sought to provide more information about the value drivers and financial magnitude. Case studies enabled a richer understanding. While RWE can help increase revenues, it can also avoid downside risk as well as unnecessary costs.

Of particular interest were areas where leaders think beyond traditional RWE applications (Figure 5).

IMPLICATIONS FOR SCIENTIFIC AND COMMERCIAL COLLABORATION The involvement of commercial does not diminish the role of HEOR and other scientific and medical teams.

Rather, it should be complementary, serving to focus on removing roadblocks to broader commitment for RWE and increasing its overall application to demonstrate the value of a franchise.

At the same time, scientific teams should champion the treatment of RWE as a capability instead of a series of studies to increase their overall effectiveness and productivity. With the right RWE information and tools, these teams can focus on the highest-value analytics rather than lower value activities such as ad hoc data sourcing and protocol development. Just as commercial teams will need to generate, analyze and apply insights more frequently, scientific colleagues will have to integrate more seamlessly into the faster pace of decision making enabled by systematic application of RWE.

1

”

Best practice example A leading company provides an intriguing lens into best practice. It began its RWE journey by creating an integrated evidence platform in response to value and safety demonstration challenges. When the FDA questioned the appropriate use of its blockbuster oncology product, up to US$500m of revenue was placed at risk due to potential label changes. By developing the broadest RWE platform at the time, the company enabled a variety of insights to inform discussions with a multitude of stakeholders, successfully responding to the FDA challenge.

Having experienced the power of RWE insights, the company continued to invest beyond value and safety demonstration. Commercial leaders acquainted with RWE capabilities started to systematically lever detailed patient pathways to understand product use, identify patterns of under-diagnosis and under-treatment, and shape highly targeted marketing campaigns. These campaigns nearly doubled sales growth. Over time, RWE became the company’s currency and competitive advantage for engaging health systems, with granular forecasting and disease progression models levered by a series of medical center partners for their own service planning. For the first time in the industry it effectively developed a closedloop system, using insights to engage and improve patient pathways.

SIGNIFICANT ADDED VALUE The opportunity for RWE to add value is thus substantial, especially for in-market products. As the principal organizational owners of these products, commercial needs to step up and take accountability for implementing RWE capabilities. Working collaboratively and crossfunctionally with scientific will ensure that investment in RWE spans the interests of both respective functions.

Hughes B, Kessler M, McDonell A. Breaking New Ground with RWE: How Some Pharmacos are Poised to Realize a $1 Billion Opportunity.

A White Paper from IMS Health. August 2014.

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InsIghts

PREDICTIVE MODELING

Improving outcomes through predictive modeling Predictive modeling involves assigning values to new or unseen data. With growing promise across a wide range of fields, it is increasingly being applied in various healthcare settings both to reduce costs and drive quality improvements. However, while its potential contribution is substantial, even exciting, applications involving its use are not widespread and demonstrable evidence on eďŹ&#x20AC;ectiveness is limited.

The author

John Rigg, PHD is director Predictive Analytics, RWE Solutions, IMS Health John.rigg@uk.imshealth.com

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IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR

Potential and challenges for developing successful models Referencing real-world cases studies that have emerged, this article discusses ways in which predictive modeling is currently being used, considers the potential for innovations from machine learning to extend its value and accuracy, and highlights the challenges to developing a successful predictive modeling application.

DIVERSE APPLICATIONS IN PRIMARY CARE The scope of predictive modeling applications is wide ranging, with models used to stratify risk both at a population and patient level. At the population level, risk stratification is routinely employed by payers/ commissioners to understand resource need and help shape service delivery. Typically, this involves estimates of disease prevalence, including age-demographic adjustments. These models will likely become increasingly advanced, helping to quantify the depth of clinical need and define the type and scope of service.

At patient level, the applications principally focus on identifying patients at high risk of particular events such as unplanned hospital (re)admission, or the onset of a chronic disease such as diabetes. High-risk patients are then targeted with an intervention aimed at mitigating the event.

1. Reducing hospitalizations Identifying patients at greatest risk of unplanned hospital readmission is currently by far the most widespread use of predictive modeling in primary care.1 Readmissions within thirty days of discharge are common, costly and hazardous. Moreover, many readmissions are considered avoidable.2 Reducing them is thus a major focus in virtually all healthcare systems.3,4,5 It has certainly captivated policymakers as a goal that can both improve quality and reduce healthcare costs, seen in the US, for example, with powerful incentives in the Patient Protection and Affordable Care Act penalizing hospitals that have higher-than-expected readmission rates.5

Heart failure has been a particular target, being one of the most common reasons for hospitalization in the developed world and accounting for the highest thirtyday readmission rates.3 Parkland Health & Hospital System: Informing CHF and expanded disease areas One example of a successful program is Parkland Health & Hospital System in Dallas, Texas. In 2009, Parkland began analyzing electronic medical records (EMR) with the aim of using predictive modeling to identify patients at high risk of hospital readmission. The initial focus was on congestive heart failure (CHF). Today, case managers and other frontline providers receive details of high-risk patients on a near real-time basis, information that is used to prioritize workflow and allocate scarce resources to support those most in need. Interventions are both hospital- and community-based.6 Evaluation of the program identified a reduction in thirtyday all-cause readmission rates from 26.2% to 21.2%.7 As observed in an editorial by McAlister, “This effect size was achieved even though the programme was only offered to approximately a quarter of discharged patients, was only deployed on weekdays (weekend discharges actually exhibit the highest rate of readmissions) and despite the fact that only a minority of readmissions may be truly preventable.”3 Given the observed fall in readmissions and costs for CHF patients at Parkland, the program has been expanded to patients with diabetes, acute myocardial infarction and pneumonia. Preliminary data suggests similar success with readmission rates in these conditions.6 NorthShore University HealthSystem: Supporting hospital and primary care Positive results have also been achieved through the use of an effective predictive model at NorthShore University HealthSystem in Chicago. Reports stratifying inpatients by high, medium or low risk of readmission in 30 days are provided to health system hospitalists on a daily basis and scores noted as a value in every inpatient EMR. continued on next page

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21%

reduction in re-admission rates

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InsIghts PREDICTIVE MODELING

These have proved so useful that reports are also now sent to the system’s primary care physicians listing their patients with a high risk of readmission. The program has seen a reduction in readmissions from 35% to 28% among high-risk patients.8 Despite these successes, recent reviews reveal little systematic evidence on what works in terms of communitybased alternatives to hospital admissions.4,5,9 However, there is evidence to suggest some impact of particular initiatives in targeted populations, such as education with selfmanagement in asthma, and specialist heart failure interventions. Moreover, certain types of interventions, such as post-discharge telephone calls, have also been identified as effective.5 Beyond that, most other interventions appear to have no effect in reducing emergency admissions in a wide range of patients. There is a clear need to better understand what works and for whom.

Interventions to reduce emergency admissions take place within a complex environment where the nature and structure of existing care services, individual professional attitudes, patient and family preferences, and general attitudes to risk management can affect their implementation. While some interventions fail to reduce admissions, they may have other beneficial effects, such as reducing length of stay or improving the experience of care.4 2. Mitigating risk NorthShore University HealthSystem: Predictive modeling in hypertension NorthShore is a pioneer in the use of various risk stratification applications. One success story involves predictive modeling to identify undiagnosed patients with hypertension (HTN).10 Although many patients with HTN are actively managed, the condition is often overlooked. The risk stratification is based on three screening algorithms, developed using established HTN diagnosis guidelines, to identify patients with consistently elevated blood pressure readings and exclude those with only intermittent elevations. Patients are considered at risk for undiagnosed HTN if they meet the criteria of any of the three algorithms. The screening tool was built using outpatient data from the NorthShore data warehouse and the model has an accuracy rate (Predictive Positive Value) of approximately 50%.

Veterans Health Administration (VHA): Population-wide risk scores The VHA has also invested heavily in risk stratification applications, covering its entire primary care population.11 This includes models that output a patient’s percentile scores associated with risk of hospitalization and mortality. Updated weekly to reflect changes in individual clinical status, the models rely on six data domains pulled from the VHA’s extensive data platform: demographics;

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diagnoses (inpatient and outpatient); vital signs; medications; laboratory results; and prior use of health services. Risk scores can be accessed on-line by each care team, alongside other information such as active diagnoses, recent visits to primary care and enrollment in care management programs. They can also be rendered as high-resolution geospatial maps to assist managers with program planning and determining where new sites for service delivery might be located.

While it is too early to determine whether the risk scores help improve outcomes, the VHA suggests that based on the frequency of access, healthcare providers are finding them worthwhile. In addition, testimonials from clinicians and care managers indicate that the scores are more useful than clinical reminders, since each score takes into account the patient’s unique needs and allows staff members to focus on what is most likely to improve future outcomes on an individual basis.

The VHA has also implemented a system for early detection and management of chronic kidney disease, including risk-based clinical EMR reminders which play an important part in the effectiveness of the program.12

DEVELOPING AND APPLYING A PREDICTIVE MODEL An outline of the main stages associated with developing, validating and operationalizing a typical predicting modeling application is shown in Figure 1 (page 30) and described below.

1. Cohort creation from raw input data In the initial stage, patient cohorts are created from the input data. There are generally two: one cohort for model development, the other for validation. A common practice is to randomly split the data approximately twothirds and one-third between development and validation cohorts respectively.

2. Algorithm development In the second stage, the predictive model is estimated on the development sample using an appropriate statistical method such as regression analysis. The model is then used to identify at-risk patient profiles and key predictors/ characteristics are described and clinically verified.

3. Algorithm validation It is important that model development and validation are carried out on separate data. This enables independent assessment of its performance, ensuring it is not ‘overfitting’ (where a model may accurately describe data upon which it is estimated but poorly describe new or unseen data). Thus, the third stage involves detailed evaluation of model performance using a variety of metrics. In the case of hospital readmission modeling, for example, the metrics may include the number of

IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR

readmitted patients correctly predicted to be readmitted, the number of readmitted patients incorrectly predicted not to be readmitted, and the Area Under Curve (AUC; a summary measure of model accuracy).

4. Model operationalization Finally, the model is operationalized to identify high-risk patients. This can be done in a number of ways, from paper-based forms used by clinicians to a host of technological solutions. One option is a real-time clinical alert/screening tool that generates a pop-up on the EMR system each time a high-risk patient presents.

THE PROMISE OF MACHINE LEARNING To-date, risk models in primary care have almost exclusively employed standard statistical methods, such as regression analysis. State-of-the-art techniques from the field of machine learning13 are particularly effective at producing robust predictions in complex settings.

Machine learning, sometimes referred to as predictive analytics, encompasses the fields of artificial intelligence and data mining and is designed to identify complex, often subtle, patterns in data – detecting ‘signal’ from ‘noise’.

Examples in healthcare include identifying genotypephenotype associations predictive of disease risk14 and the automatic extraction of accurate diagnostic information from medical images, such as MRI scans and X-rays.15 Next generation predictive modeling applications Innovative methods from machine learning are likely to play an important part in some of the next generation of predictive modeling applications in primary care in several ways 1. First, they can help improve the accuracy of current applications, such as readmission modeling. The benefit is likely to be most notable where many potential risk factors are modeled simultaneously (eg, where data is combined from multiple care settings).

2. Second, risk models almost always simply identify high-risk patients for the event in question. Where patients identified are eligible for a range of interventions, models need to go beyond current practice and determine which patients are most likely to benefit from which intervention. Given the considerable heterogeneity in patient response to different interventions, predictive modeling applications potentially could be far more effective by unpicking this heterogeneity.

In statistical modeling and machine learning, this is an area known as uplift modeling which relates to the expected ‘uplift’ (improvement in outcome) if a patient

were to receive an alternative intervention compared to a benchmark. While standard statistical approaches (such as regression analysis) are not designed to optimize this problem, recent innovations in machine learning have demonstrated remarkable effectiveness in this area16,17,18 and can thus help ensure that the patients likely to benefit most from an intervention will receive it.

3. Third, the journey towards precision medicine will be facilitated by physician decision support tools designed to show expected/predicted outcomes associated with different treatment choices, based on detailed patientlevel demographic, treatment history, biomarker and other information. Machine learning can help provide robust evidence for complex disease states. The EuResist project19 is a key case in point, involving management of treatment for a complex disease (HIV) and highly-effective machine learning algorithms.

Uplift modeling is important here, too, since personalized medicine means the right patient receiving the right treatment which in turn involves analysis of expected outcomes for different treatments.

4. Finally, machine learning can help identify patients with conditions that often slip beneath the radar in primary care practice, such as those with rare or early stage diseases. Machine learning comes into its own if there are multiple idiosyncratic factors that determine whether the condition is present; these are circumstances calling for specialist, purpose-built methods for detecting ‘signal from noise’.

Clinical Vigilance for Sepsis: Sophisticated early detection Sophisticated screening tools are more common in secondary rather than primary care. Clinical Vigilance for Sepsis is a sophisticated early detection system for sepsis,20 a disease that often goes undetected until too late and where early detection depends on multiple patient-specific factors. There are several applications to detect sepsis, many with a high rate of false-positive signals, but a particularly effective solution uses machine learning techniques, including natural language processing of unstructured data.

The system has more than one hundred variables derived from real-time telemetry data from bedside machines, structured data (such as medical codes and other numeric values) and unstructured data (such as doctor’s notes and operative reports). When it detects that a patient is heading towards sepsis, a text is sent to the doctor or nurse. Hospitals using the tool are able to spot sepsis earlier, which translates into quicker administration of antibiotics and a shorter stay in the hospital. For a 300bed hospital, an average of US$2 million in direct savings is estimated as measured by length of stay. continued on next page

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InsIghts PREDICTIVE MODELING

FIGURE 1: dEVELOPING ANd APPLYING A PREdICTIVE MOdEL

1

Development cohort 5XP UIJSET PG EBUB

Raw input data $SFBUF UXP DPIPSUT

2

Validation cohort 0OF UIJSE PG EBUB

3 Algorithm development 6TJOH EFWFMPQNFOU EBUB

4 Algorithm validation 6TJOH WBMJEBUJPO EBUB

t &TUJNBUF NPEFMT FH MPHJTUJD SFHSFTTJPO

t &WBMVBUF BMHPSJUIN VTJOH TFQBSBUF WBMJEBUJPO DPIPSU

t *EFOUJGZ BU SJTL QBUJFOU QSPĂśMFT

t $PNQVUF BDDVSBDZ PG BMHPSJUIN FH

t %FTDSJCF BOE DMJOJDBMMZ WFSJGZ LFZ QSFEJDUPST DIBSBDUFSJTUJDT

Algorithm operationalization t 0QUJPOT JODMVEF SFBM UJNF DMJOJDBM BMFSUT TDSFFOJOH UPPM t 1PQ VQT BQQFBS XIFO BU SJTL QBUJFOU QSFTFOUT

t Number of actual diagnosed patients correctly predicted to have the diagnosis t Number of actual diagnosed patients incorrectly predicted not to have the diagnosis t AUC (summary measure of model accuracy)

essential to ensure the solution is both accurate and fit for purpose. Accuracy is far more likely to be achieved if the solution is developed with all relevant clinical knowledge, captures the specific circumstances of the local setting and is constructed according to sound methodological principles.

End-users, whether clinicians or health executives, must buy in to the solution, so it is imperative that their requirements are integrated to ensure it is fit for purpose. All this may seem nothing more than common sense, but so often applications fail due to lack of adherence to these basic principles.

OVERCOMING CHALLENGES TO SUCCESSFUL APPLICATION Developing and implementing a successful predictive modeling application is not without its challenges. Some of the key barriers, with guiding principles, are outlined below.

â&#x20AC;˘

â&#x20AC;˘

Understand effectiveness of proposed intervention Often, the purpose of the modeling is to identify patients who may be â&#x20AC;&#x2DC;best-placedâ&#x20AC;&#x2122; to receive a given intervention, such as a visit from a community nurse, to help avoid hospital readmission. While the models themselves may be adept at stratifying patients by risk, the program as a whole will not be effective unless the interventions work â&#x20AC;&#x201C; a more difficult challenge than many may have imagined in areas such as reducing readmissions. Thus, the success of predictive modeling will be judged as part of the broader success of risk stratification initiatives and there is a long way to go to fully understand the determinants of effective interventions in many settings.

Ensure sound methodology and full buy-in A predictive modeling application must be built and validated with input from all relevant stakeholders, including end-users, clinicians and statisticians. This is

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â&#x20AC;˘

Maintain ongoing relevance A challenge that has not yet come to the fore is how to ensure solutions remain contemporary and relevant.

Circumstances change, from treatment regimens to data feeds, and applications must capture these developments. Too frequently they are launched with insufficient consideration to post-launch evaluation and maintenance, and even less to post-launch model optimization. Data generated post-launch can, and should, be used to recalibrate models. This can make a huge difference, especially if a key data component was sparsely populated pre-launch (eg, details on a new and/or infrequently discharged treatment).

IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR

HIGH TRANSFORMATIONAL POTENTIAL With the knowledge to overcome the challenges it presents, predictive modeling has a part to play in improving the quality, efficiency and effectiveness of primary care. Successful applications, albeit currently confined to isolated pockets of best practice, illustrate the potential for transformational innovation. Important applications include physician decision support tools, screening tools and early warning systems.

“

Standard statistical methods are perfectly adequate for the models underpinning many of these applications. However, innovations from the world of machine learning are likely to be pivotal to many of the next generation of predictive modeling applications, especially where personalized medicine involves complex disease states and conditions (such as many rare diseases) are difficult to detect.

Predictive modeling has a part to play in improving the quality, efficiency and effectiveness of primary care with the potential for transformational innovation.

”

1

Kansagara D, Englander H, Salanitro A, Kagen D, Theobald C, Freeman M, Kripalani S. Risk prediction models for hospital readmission: A systematic review. JAMA, 2011; 306(15):1688-1698. doi:10.1001/jama.2011.1515 2 Van Walraven C, Bennett C, Jennings A, Austin PC, Forster AJ. Proportion of hospital readmissions deemed avoidable: A systematic review. CMAJ, 2011; 183: E391-402 3 McAlister FA. Decreasing readmissions: It can be done but one size does not fit all. BMJ Qual Saf, 2013; 22: 975-976. Epub 4 September 2013 doi:10.1136/bmjqs-2013-002407 4 Purdy S. Avoiding hospital admissions. What does the research evidence say? The King’s Fund, 2010, December. ISBN: 978 1 85717 607 0 5 Hansen LO, Young RS, Hinami K, Leung A, Williams MV. Interventions to reduce 30-day rehospitalization: A systematic review, 2011, Oct 18; 155(8): 520-8. doi: 10.7326/0003-4819-155-8-201110180-00008.

6 AHRQ. Hospital uses data analytics and predictive modeling to identify and allocate scarce resources to high-risk patients, leading to fewer readmissions. Available at: https://innovations.ahrq.gov/profiles/hospital-uses-data-analytics-and-predictive-modeling-identify-and-allocate-scarceresources. Accessed 21 October, 2014.

7 Amarasingham R, Patel PC, Toto K, Nelson LL, Swanson TS, Moore BJ, et al. Allocating scarce resources in real-time to reduce heart failure admissions: A prospective, controlled study. BMJ Quality Saf, 2013; 22(12): 1-8 8 Van Dyke M. Predictive analytics: Pinpointing how best to allocate patient resources. Healthcare Financial Management Association, 1 March, 2013.

Available at http://www.hfma.org/Content.aspx?id=16069. Accessed 27 October, 2014 9 Bardsley M, Steventon A, Smith J, Dixon J. Evaluating integrated and community-based care. How do we know what works? Nuffield Trust, 2013, June. Available at: http://www.nuffieldtrust.org.uk/sites/files/nuffield/publication/evaluation_summary_final.pdf. Accessed 21 October, 2014 10 Rakotz MK, Ewigman BG, Sarav M, Ross RE, Robicsek A, Konchak CW, et al. A technology-based quality innovation to identify undiagnosed hypertension among active primary care patients. Ann Fam Med, 2014; 12(4):352-358. doi: 10.1370/afm.1665.

11 Fihn D, Francis J, Clancy C, Nielson C, Nelson K, Rumsfeld J, Cullen T, Bates J, Graham GL. Insights from advanced analytics at the Veterans Health Administration. Health Affairs, 2014; 33(7): 1203-1211 12 Patel TG, Pogach LM, Barth RH. CKD screening and management in the Veterans Health Administration: The impact of system organization and an innovative electronic record. Am J Kidney Dis, 2009; 53(3 Suppl 3): S78-S85 13 Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: Data mining, inference, and prediction. Springer Series in Statistics 2009 (2nd Edition), New York: Springer 14 Gondro C, van der Werf J, Hayes B. Genome-wide association studies and genomic prediction. Methods in Molecular Biology. New York: Humana Press. 2013; 1019. Springer Protocols.

15 Criminisi A, Shotton J. Decision forests for computer vision and medical image analysis. 2013. London: Springer-Verlag 16 Kuusisto F, Santos Costa V, Nassif H, Burnside E, Page D, Shavlik J. Support vector machines for differential prediction. Computer Science, 2014; 8725: 50-65 17 Sołtys M, Jaroszewicz S, Rzepakowski P. Ensemble methods for uplift modeling. Data Min Knowl Disc, 2014 September. DOI 10.1007/s10618-014-0383-9.

18 Radcliffe NJ, Surry PD. Real-world uplift modelling with significance-based uplift trees. Portrait Technical Report. 2011 TR-2011-1, Stochastic solutions 19 EUResist. http://www.euresist.org 20 Woodie A. Fighting sepsis with real-time analytics. Datanami. 2014, March. Available at: http://www.datanami.com/2014/03/04/fighting_sepsis_with_real-time_analytics/ Accessed 21 October, 2014

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SCANDINAVIAN REAL-WORLD DATA

Holistic real-world data brings a new view of patients and diseases A promise of increasingly rich, anonymous clinical data is the ability to understand more about the way in which modern healthcare affects the patient experience in the real world, based on all the relevant evidence to make informed and confident decisions. In reality, this goal remains a vision of the future in many cases. We are, however, seeing breakthrough research opportunities using unparalleled Scandinavian healthcare data and linking critical variables for the first time ever across the entire patient journey.

The authors

Ragnar Linder, MSC is Principal, RWE Solutions & HEOR, IMS Health Rlinder@se.imshealth.com

Mats Rosenlund, PHD, MPH is Principal, RWE Solutions & HEOR, IMS Health Mrosenlund@se.imshealth.com

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IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR

Gateway to breakthrough outcomes research globally WHAT MAKES SCANDINAVIA SO UNIQUE? Scandinavia has a long history of electronic medical records (EMRs) and the collection of healthcare and socioeconomic data in registries. These EMRs provide broad coverage of the population but are uniquely rich in clinical depth for many diseases and patient populations.

According to a recent IMS Health study of real-world evidence (RWE) impact1 and an assessment of progress towards value-based healthcare by Soderland, et al,2 the Nordic countries, Sweden in particular, have reached the highest maturity for the supply and use of RWE. This reflects their well-structured public healthcare system, legal and ethical frameworks, and universal implementation of EMRs since the 1990s.

Today, both primary and hospital care in Scandinavia have 100 percent EMR coverage. In many cases, national health and medical care or social services registries have been collecting data since the 1950s. Further, there are more than 100 national disease-specific registries

available, including in Sweden the National Diabetes Register, the National Cataract Register, the Stroke Registry and the National Rheumatology Register.

Available clinical data includes co-morbidity (ICD-10 diagnoses), demographic information, hospitalizations, laboratory results, risk factors and measures, prescriptions and mortality. The inclusion of socioeconomic factors (eg, educational level, long-term sick leave, early retirement) further enrich the dataset, with decades-long longitudinal data enabling insights around disease progression and long-term outcomes (Figure 1).

Finally, there is the possibility to link this rich data through technology advances such as the Pygargus methodology (Customized eXtraction Program) and a mature legal and ethical framework. Linkage is also possible due to the longstanding use of unique personal Social Security numbers, which eliminates many of the patient-matching issues that afflict linked datasets in other areas. continued on next page

FIGURE 1: SCANdINAVIAN REAL-WORLd dATA IS UNPARALLELEd IN dEPTH ANd LONGITUdINAL AVAILAbILITY

Longitudinal history of EMR data for 20+ years Mortality data Date of mortality

Patient info Age, gender, vitals Date of diagnosis Co-morbid conditions Socioeconomic data

•

Rx data Medication files (type, strength, days supply) Treatment switch Patient co-pay Total cost

• • • •

Risk factors, measures BMI, smoking status Blood pressure Tumor staging Biomarkers Performance status

• • • •

hOLIstIC PAtIEnt VIEW

hCP contact Healthcare visit Type of HCP Referrals

• • •

• • • • •

Lab results Tests performed (HbA1c, lipids, etc) Test results & interpretation

• •

100%

100+

20+

EMR coverage

disease-specific registries

years of data

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InsIghts SCANDINAVIAN REAL-WORLD DATA

The common set-up of healthcare, health information and classification systems in Scandinavia ensures the quality and consistency of data variables. The legal and ethical frameworks for research purposes enable linkage of EMR with public health registers within any given therapeutic area. And the strength of academic hubs throughout the region provides potential for extensive scientific collaboration.

SCANDINAVIAN DATA SUPPORTS GLOBAL RESEARCH WITH REAL-WORLD INSIGHT Opportunities to apply these insights are significant in Scandinavian markets, where RWE is already in formal use for HTA processes. However, the real potential for transformation in healthcare will come from applying this information to answer questions that affect healthcare decisions globally.

In collaboration with research specialists and academics, companies are experiencing firsthand the unique nature of outcomes research based on Scandinavian real-world data. They are constructing entire patient journeys through the healthcare system, analyzing resource utilization and assessing the outcomes of interventions in a wide range of leading disease areas.

Robust, comprehensive databases built on data from Scandinavian EMR registries have supported global research with real-world insight addressing diverse questions around comparative effectiveness, pharmacovigilance, epidemiology and clinical outcome.

DRIVING LANDMARK RESEARCH IN PRIMARY CARE A practical example of the process and impact of combining primary care EMR and national register data is a study in chronic obstructive pulmonary disease (COPD).

This observational retrospective epidemiological analysis, conducted in Sweden, generated data from patients with COPD during the first decade of the 21st century. COPD is the fourth leading cause of mortality, affects 1 in 4 adults aged over 35 years, and is increasing in prevalence worldwide. The implications of the findings for managing the disease were thus considerable.

The study was made possible by identifying COPD patients in primary care EMRs and linking them to register data from some of Sweden’s national healthcare registers.

“

This enabled the creation of a unique and detailed database of more than 20,000 patients. The aim was to describe the COPD population, the disease characteristics and approaches to management, based on more than 11 years of longitudinal data. Emphasis was placed on prevalence, incidence, exacerbations, co-morbidities and mortality. The study also compared the outcomes and safety of modern COPD treatments.

Published in leading clinical journals and presented at major international conferences, this landmark study brought a new understanding to recent COPD epidemiology. Mapping out the longitudinal management of patients with the disease, it revealed changes in care over the last decade, the importance of ‘specialist’ competence in primary care, and the impact of therapy on exacerbations and pneumonia-related mortality.

CHARTING DISEASE PROGRESSION IN SECONDARY CARE In secondary care, real-world data can be used to study disease progression, from early symptoms and the patient’s first contact with healthcare through to their death. Unique population-based studies can be performed by linking hospital EMR data with diseasespecific registers and national healthcare and socioeconomic registers.

An example published recently by Banefelt, et al, in Cancer Epidemiology3 studied the incidence of metastases and clinical course of prostate cancer patients who were without confirmed metastasis when initiated on androgen deprivation therapy (ADT). In this study, EMR data from outpatient urology clinics was linked with national mandatory registers to capture medical and demographic data. The unique ability to examine PSA values recorded over time provided important insights into risk determinants of metastasis and death, for advancing optimal management of the disease.

INFORMING OUTCOMES IN SMALL POPULATIONS The large datasets that exist in Scandinavia also make it possible to stratify populations and study different cohorts, which is especially valuable in rare diseases and small patient groups. A recent study of T-cell lymphomas

by Ellin, et al, illustrates this point.4

We can track the entire patient journey and make really deep dives.

By following social security numbers we can study the connection between early diagnosis and improved therapy and care. We can also understand how care develops and see which factors drive improvements in treatment results.

Bo Lidman, Co-founder of Pygargus, and Principal, IMS RWE Solutions & HEOR

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IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR

“

The use of unique personal identification numbers in combination with a legal framework that allows linking of registers and access to longitudinal data make Scandinavia unique for RWE research. It enables a deeper understanding than it is possible to get in any other region.

Lars-Åke Levin, Professor of Health Economics, Linköping University, Sweden

Lymphoma is the most common form of blood cancer, principally comprising Hodgkin lymphoma and nonHodgkin lymphoma. In turn, these are subdivided into about 50 different histological subtypes. In Sweden, peripheral T-cell lymphomas have an annual incidence of only slightly more than one hundred cases per year.

Typically aggressive, they have a poor outcome with the treatments that are currently available.

Due to the very low incidence of peripheral T-cell lymphomas, population-based studies of these conditions are scarce or evaluate small cohorts. In their study, Ellin and colleagues assessed a cohort of 755 patients with peripheral T-cell lymphomas based on the entire population of Sweden over a period of 10 years.

This represented the largest population-based material reported for this type of disease. The analysis included frequency, factors of prognostic impact, therapeutic response to radiation, effects of chemotherapy with or without addition of etoposide, and autologous stem cell transplantation.

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PROGRESSING VALUE-BASED HEALTHCARE GLOBALLY The use of EMRs and registries has proved valuable for quality improvements in healthcare by enhancing health outcomes and reducing costs. Going forward, there is potential to maximize the value of existing resources and ensure more effective, efficient and insightful outcomes research to improve patient care. Based on the available data and experience, the Scandinavian countries can lead the transformation to value-based healthcare globally.

The data obtained in this study provides important information on outcome in an unselected cohort of patients, bringing the potential to aid development of new treatment regimens and also serve as a baseline for comparison of novel therapies and their outcomes.

1

Hughes B, Kessler M. RWE market impact on medicines: A lens for pharma. IMS Health AccessPoint, 2013; 3(6): 12-17 Soderland N, Kent J, Lawyer P, Larsson S. Progress towards value-based health care, Boston Consulting Group, BCG, June 2012 3 Banefelt J, Liede A, Mesterton J, Stålhammar J, Hernandez RK, Sobocki P, Persson B-E. Survival and clinical metastases among prostate cancer patients treated with androgen deprivation therapy in Sweden. Cancer Epidemiology, 2014 Aug; 38(4): 442-7. doi: 10.1016/j.canep.2014.04.007. Epub 2014 May 27.

4 Ellin F, Landström J, Jerkeman M, Relander T. Real world data on prognostic factors and treatment in peripheral T-cell lymphomas: A study from the Swedish Lymphoma Registry. Blood, 2014; 124(10): 1570-7 2

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InsIghts

IMS HEALTH SYMPOSIUM

Preparing for real-world evidence in Asia Pacific Real-world evidence is increasingly informing healthcare decisions in Western markets. Technology advances have increased the quantity of electronic data, enabled linking of disparate datasets, and powered more sophisticated analytic applications of the data. This has opened the window onto the full patient journey with visibility across each care setting where valuable clinical information is collected. However, the situation is very diďŹ&#x20AC;erent in Asia Pacific.

The authors

Jovan Willford, MBA is Senior Principal, RWE Solutions, IMS Health Jovan.Willford@us.imshealth.com

Joe Caputo, BSC is Regional Principal, RWE Solutions & HEOR, IMS Health Jcaputo@sg.imshealth.com

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IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR

Challenges and novel approaches use within the region, highlighting the need for tailored responses according to market conditions.

The dynamics that have significantly advanced the use of RWE in North America and the EU are still relatively nascent in Asia Pacific, meaning that RWE has not developed to the same extent. These dynamics include

• • • •

As an example, a high degree of formalized evidence is incorporated into reimbursement and access Cum decisions in E Korea, whereas in China no such formal requirement exists to date. However, when considering fundamentals such as overall market, prescribing and disease prevalence, it is clear that the sheer size of China relative to other markets means that it cannot be ignored completely from a RWE-development perspective.

Regulatory and reimbursement requirements The level of scrutiny afforded by formalized HTA and regulatory processes is historically lower in Asia Pacific than in Western markets although this is rapidly changing Data accessibility The majority of healthcare data is owned by government with limited or no access to private organizations

One of the key consequences of this diversity is that local healthcare stakeholders have not yet fully embraced RWE as an evidence source even though they have a growing need for more evidence to allocate scarce resources. This becomes especially critical given the stated aims of many governments across the region to provide universal coverage – particularly within the context of improving access to general medicine services in response to growing needs for primary healthcare in these markets.

Data quality There are issues over data integrity/reliability and resulting complexities for research methodology Infrastructure Data sources are fragmented due to lack of data standardization and integration, partly driven by historical lower demand/need for sophisticated RWE solutions

Not only is Asia Pacific very different from North America and EU, but also there is clear diversity among the region’s markets themselves with respect to the application of RWE. Figure 1 illustrates some of the key drivers of RWE

Today, two key drivers of change offer light at the end of the tunnel. Firstly, is the range of data sources that are emerging across Asia Pacific and China. Although fragmented, the ability to access government-owned claims data in several markets and hospital-based continued on next page

FIGURE 1: HETEROGENEITY OF RWE ACROSS THE ASIA-PACIFIC REGION: ILLUSTRATIVE dRIVERS ANd MARKET COMPARISON Formalized evidence in access

Low

High

Value with commercial decision makers

Low

High

Accessibility of sufficient data

Low

High

Standards, methods, legal structures in place

Low

High

Compelling market fundamentals

Low

High

India

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China

Japan

Taiwan

Australia

South Korea

Industry needs to push for greater accessibility to government-owned data sources, particularly in markets where such data exists and is being used for reimbursement and access decision making.

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InsIghts IMS HEALTH SYMPOSIUM

information systems and private electronic medical records (EMRs) in others brings promise for applications of RWE. Secondly, market leaders on the global RWE stage have been increasingly looking to Asia as their RWE capabilities mature in developed markets. Consequently, innovative pricing schemes, responsiveness to growing demands for HTA, and exploration of novel mechanisms to generate relevant data are accelerating in the region.

NEW PATIENT-CENTRIC APPROACHES As the industry seeks to overcome the barriers and challenges presented within Asia Pacific and China, there is a need to adopt new and innovative approaches by leveraging opportunities for RWE generation through patient-centric programs. By their very nature large scale, such programs generate significant amounts of locallyrelevant RWE. Traditionally, they have been run in isolation with data locked down and results consigned to archives once the original study question has been analyzed and reported. Yet they have much greater potential to yield data that could modify clinical practice when analyzed beyond the scope of their original intent, particularly if the data could be supplemented with preexisting or concurrent datasets such as EMR or health insurance claims.

Through this type of innovation and integration – expanding purpose-collected patient-level data by linking and applying it more broadly – the full potential can be unlocked through multiple concurrent uses. These might include outcomes research, clinical practice audit feedback, treatment guideline validation, risk-share agreement design and implementation, or even as a physician tool in informing individual patient treatment management.

EMERGING APPLICATIONS Certainly the use of patient-centric programs represents one way of overcoming lack of data across Asia Pacific and China. Further evidence generation methods that are growing in the region include retrospective analysis of existing databases via protocol-ized research, partnerships and sponsorship of patient registries, and primary observational research. In Asia, in particular, the use of registries has doubled every two years since 2004.

One such example is China Cardiometabolic Registries, an evidence-based research program to improve understanding of unmet medical need and clinical outcomes in cardiovascular (CV) and metabolic diseases in the country. It is currently following over 25,000 patients with type-2 diabetes and CV risk factors. 1

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Aside from traditional applications of RWE, is the increasing application of RWE across the product lifecycle.

A very strong trend emerging from the US and EU is the convergence of clinical and commercial evidence requirements, and the need to leverage RWE for a wide variety of internal stakeholders at various points in the product development cycle.

As RWE infrastructure grows in the region so, too, will the potential applications, in particular the opportunity for more scientifically-robust health economics and outcomes research studies. Local examples include Avastin (bevacizumab, Chugai Seiyaku) in Japan, which gained a 5% ‘premium’ during re-pricing negotiations through post-marketing evidence of prolonged overall survival in lung cancer in 2012, and Bayer’s use of Japan Medical Data Center (JMDC) data in Japan to support removal of a label warning for Adalat (nifedipine).

IMPERATIVES FOR PROGRESS Looking ahead to the future of RWE in Asia Pacific, there are clear pointers for the industry as a whole, as well as individual companies and other stakeholders, in moving the RWE agenda forward. Key amongst these is the need to leverage existing data, including linking complementary datasets and investing in emergent data sources. Industry needs to push for greater accessibility to government-owned data sources, particularly in markets where such data exists and is being used for reimbursement and access decision making.

The linkage of datasets is emerging as a useful and valid research technique that can expand the application of individual databases, as demonstrated in a recent retrospective observational cohort study in Japan.2 Innovative data sourcing requires integration across fragmented sources, which in itself presents an opportunity for industry, manifested through collaboration with IT systems providers, use of social media or app-driven data, or other innovative initiatives that can help to fulfill the requirements of multiple stakeholders.

Regardless of data considerations, there is the need to develop innovative approaches to research objectives, to move away from singular research studies towards broader value studies meeting a variety of needs across stakeholder groups. Last, but by no means least, is the need to embed RWE and especially HEOR capabilities, which are critical to leverage existing and evolving data sources to conduct high-quality outcomes research specific to local settings.

The APAC nations are already making rapid progress in learning from the triumphs and failures of other regions as well as from each other – and that must continue.

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IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR

THE EVOLVING LANDSCAPE FOR RWE IN ASIA PACIFIC: AN INDUSTRY PERSPECTIVE IntERVIEW WIth AnDREW EggLEstOn, B. PhARM, M.MED.sC Andrew Eggleston is Regional (JAPAC) Market Access Director at AbbVie. A trained pharmacist and clinical epidemiologist, he has worked in the pharmaceutical industry for 26 years including roles at Pharmacia Upjohn and at Johnson & Johnson in Health Economics, Outcomes Research and Policy in Australia, Canada, Belgium and Asia.

He also worked in the medical device industry for a number of years as Senior Director Health Economics and Reimbursement, Asia Pacific at Medtronic and was most recently Head of Market Access Asia at Sanofi. Here he shares his perspective on the changing landscape for RWE in Asia Pacific.

Q: What do you see as the key differences between RWE in APAC versus the Us & EU? AE: The differences that exist are due to a shorter evolution time in APAC. The drivers for the use of RWE in the region have not been in forceful existence for as long as they have in North America and EU. Specifically, I would say data hungry reimbursement and regulatory systems as well as ex-government health insurers. These drive lower demand, although this is changing. Hence, the key differences are in the smaller number of RWE sources and the less elaborate infrastructures supporting this data.

Q: People often talk about the diversity of the Asian markets. Is this truly a barrier or can it be also an opportunity for pharma?

AE: Diversity in culture, political form, health system and economic development all suggest that a simple adoption strategy from North America and EU for RWE will fail badly. There is a need for locallygenerated evidence that can be used for local decision support. This is an opportunity.

Q: the limitations of physical data are clear, but how limiting is the lack of scientific and commercial expertise in the region?

AE: There is no real lack of scientific or commercial expertise per se in APAC. I sense it is more an underdevelopment of RWE and health system-specific experience and education. The APAC nations are already making rapid progress in learning from the triumphs and failures of other regions as well as from each other – and that must continue. In addition, they should actively seek intellectual investment and skills transfer from RWE-sophisticated jurisdictions.

1 2

Q: Patient-centric programs offer significant potential for RWE generation. Why do you think they have been under-used so far?

AE: I see three principal reasons: first is a lack of awareness of the potential, possibly deriving from a reluctance to think and work outside of comfortable boundaries of existing practice; second is technology – real-time use of very large and structurally complex datasets in a user-friendly way demands a lot of processing power and we haven’t had this until relatively recently; thirdly, historically RWE has not been highly regarded for various reasons, usually because of the inability to eliminate or account for systematic bias in datasets. This in turn was partly driven by a lack of computing power to drive corrective algorithms – but that has changed recently, too. So the ‘status’ if you like of RWE has improved as a basis of decision-informing evidence in recent times.

Q: What could be the potential benefits of an integrated approach to RWE? AE: Efficiency mostly. Generating and accessing patient data is an expensive exercise so we need to maximize the opportunities this data offers. An integrated approach helps with that. As to investment, this covers finance, expertise development, infrastructure building and, to a degree, decision-making process realignments. If done well the impact on patients would be faster improvements in standards of care and health outcomes, physicians should experience an improvement in their ability to make good therapeutic decisions. The payoff for industry is a better, earlier understanding of product strengths and weaknesses, the best places for therapies in the treatment pathway, and a fast, valid way of clarifying the value of interventions in specific situations.

This article draws on presentations from the IMS Health Symposium, “Real-world evidence in Asia-Pacific: Are we ready? Challenges and novel approaches to generating evidence in the absence of real-world data”, held during the ISPOR 6th Asia-Pacific Meeting in Beijing, China, in September 2014. Chair: Jovan Willford, MBA, Senior Principal, RWE Solutions, IMS Health. Speakers: Andrew Eggleston, B. Pharm, M. Med Sc, Regional (JAPAC) Market Access Director, AbbVie, and Joe Caputo, BSc, Regional Principal, RWE Solutions & HEOR, IMS Health.

China Metabolic Registers. Available at: http://www.ccmregistry.org/ Accessed 17 October, 2014 Shimizu E, Kawahara K. Assessment of medical information databases to estimate patient numbers. Jpn J Pharmacepidemiol, 2014; 19(1): 1-11

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UTILITY VALUES IN DIABETES MODELING

Identifying reference utility values for economic models in diabetes Diabetes is a long-term condition associated with excess mortality and substantial morbidity due to its range of fatal and non-fatal complications. Health-related quality of life is severely impacted in diabetes patients, who report significantly lower health state utility scores compared to the non-diabetic population and experience reduced quality-adjusted life expectancy. This article identifies a preferred set of values for assessing utility consistent with the NICE reference case.

The authors

Adam Lloyd, MPHIL is Senior Principal, RWE Solutions & HEOR, IMS Health Alloyd@uk.imshealth.com

AmĂŠlie Beaudet, MSC is Senior Consultant, RWE Solutions & HEOR, IMS Health Abeaudet@ch.imshealth.com

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IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR

Challenges of systematic utility data selection Globally, there are around 382 million people with diabetes; by 2035 this number is set to reach 592 million.1 In the top five European countries,i approximately 6-12% of all healthcare spending is attributable to the disease.2 There is thus an imperative public health and economic need to ensure efficient allocation of resources for more effective diabetes management.

The National Institute for Health and Care Excellence (NICE) in England and Wales is a highly respected health technology assessment authority. Consistent with the NHS goal of maximizing health gain from limited resources, NICE has defined appropriate methods for assessing the effectiveness and cost-effectiveness of medical technologies. This ‘reference case’ recommends the use of quality-adjusted life-years and measurement of health states through the EuroQol five-dimensional (EQ5D) valuation questionnaire.3,4,5,6 The EQ-5D comprises a descriptive system and the EuroQol visual analogue scale (EQ-VAS). The former is a selfadministered questionnaire addressing five key areas: mobility; self-care; usual activities; pain/discomfort; and anxiety/depression. The EQ-VAS is a vertical visual analogue scale on which respondents rate their current health state from ‘best imaginable’ (100) to ‘worst imaginable’ (0).7 The descriptive system allows EQ-5D questionnaire index values to be generated using scores from a set of preference weights measured on a sample from the general population. The index value can thus be seen as a societal valuation of the patient’s health state rather than the patient’s own assessment and is therefore preferable from an economic perspective.

DEFINING UTILITY VALUES FOR ECONOMIC MODELING A key element of economic analysis in type 2 diabetes mellitus (T2DM) is the need to evaluate the impact of multiple, wide-ranging complications. The choice of methodology for assessing utility can significantly affect predicted values and thus the outcome of economic evaluation.8,9 A study was therefore carried out to identify a set of utility values consistent with the NICE reference case, based on extensive review of the published literature.

Publications for five computer models simulating long-term outcomes in T2DM were appraised to identify diabetic complications impacting patient utility 1. IMS CORE Diabetes Model10 2. UK Prospective Diabetes Study (UKPDS) Outcomes Model11 3. Cardiff Diabetes Model12 4. Sheffield Diabetes Model13 5. CDC and Prevention/Research Triangle Institute Type 2 Diabetes Model13 Health states used in these models were considered relevant if they described microvascular or macrovascular complications of T2DM or direct consequences of treatment (such as hypoglycemia), or were related to excess body weight (Table 1). Where available, data was also extracted on the utility value for patients without specific complications.

TAbLE 1: COMPLICATIONS OF TYPE 2 dIAbETES IdENTIFIEd dURING MOdEL REVIEW

T2DM Complications Angina Cataract Diabetic retinopathy Excess weight* Foot ulcer Heart failure Hypoglycemia Macular edema

Microalbuminuria/protenuria Myocardial infarction Neuropathy Peripheral vascular disease Renal dialysis Renal transplant Stroke Vision loss

*Defined as presence vs. absence of obesity or increased BMI

Articles reporting utility values for diabetic complications were identified in MEDLINE; Medline In-Process; Embase; EconLIT; and the National Health Service Economic Evaluation Database, from the earliest available date to May, 2012. Only those containing original data, reporting a study performed in adults, and written in English were considered. The shortlist was then reviewed to identify papers meeting the NICE reference case criteria. As NICE recommends utilities estimated from a sample of the general population, those using the EQ-5D were considered more appropriate than the EQ-VAS.8,14 continued on next page

France, Germany, Italy, Spain and UK

DIABETES

i

Approach

382m 2014

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592m 2035

6-12% of EU5 healthcare spend

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InsIghts UTILITY VALUES IN DIABETES MODELING

Studies consistent with the reference case were considered for the preferred input set and the following attributes extracted

• • • • • •

Country in which each study took place

Patient demographic characteristics

For measures of utility in the preferred set, the 95% confidence intervals were extracted if available; otherwise they were estimated around each point value using reported mean and sample size values, assuming a normal distribution.

Statistical methods

Results

Measures of precision reported

A total of 16,574 records were identified from which 339 full-text articles were retrieved. Of these, 61 included utility values assessed against NICE reference criteria (Figure 1). Among the 21 studies considered for the preferred set, 19 met the NICE criteria, reporting utility values for all complications apart from renal transplant; in this case, two studies using alternative methodology were considered for inclusion.

Year of publication Sample size and recruitment method

When the diabetic complication had no index values that met the NICE reference case, an alternative source was sought.

The preferred input set was created on the basis of several criteria

• •

• •

If these criteria were insufficient to identify a preferred measure, the study most precisely matching the definition of the preferred complication was selected.

If only one measure meeting NICE criteria was available, this was accepted When more than one estimate was identified, studies reporting the marginal impact of T2DM complications relative to a baseline ‘no complication’ state were preferred over those reporting disutilities alone.

Consideration was given to synthesizing health utility values if identified studies were sufficiently homogeneous.4 When an article provided utility values using multiple statistical models, the best-fitting model or the one preferred by the authors was selected Where possible, disutility estimates calculated from statistical models were selected, otherwise the difference between patients with and without the specific complication was presented.

Index value estimates for T2DM without complications ranged from 0.711 to 0.940.15,16 The utility decrement associated with complications ranged from 0.014 (minor hypoglycemia)15 to 0.28 (amputation).17 Discussion The preferred set of values for modeling T2DM complications is shown in Table 2, alongside 95% confidence intervals. Uncertainty around the point estimate was important. In the case of every complication, the interval overlapped with that of the ‘T2DM without complication health state’, reflecting the expectation of amputation.

Most values for the preferred reference set were extracted from Clarke, et al, due to its large sample size, T2DMspecific nature, methodological quality and use of EQ-5D in a UK population.17

FIGURE 1: FLOW OF STUdY SELECTION FOR PREFERREd SET OF UTILITY VALUES Records identified through database searching (n=19,195) Records after duplicates removed (n=16,574) Additional records identified through other sources (n=0)

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Abstracts screened (n=16,574 )

Full-text articles assessed for eligibility (n=339)

Studies assessed against NICE criteria (n=61)

Abstracts excluded (n=16,235)

Full-text articles excluded (n=278) Reasons for exclusion - Not presenting utility values - Presenting only utility values associated with specific intervention - Not presenting utility values associated with diabetes complications

Full-text articles excluded (n=40) - Health states not valuated by patents - Not using societal valuation algorithm - Not using EQ-5D

Studies considered for preferred set (n=21); - Meeting NICE criteria (n=19) - Reporting values where no available studies met NICE criteria (n=2)

IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR

Sourcing predominantly from a single study was beneficial for retaining internal consistency although in some cases the sample size was relatively small.

When values were not reported by Clarke, et al, utilities presented in Bagust and Beale18 were selected. While not including UK patients, the large sample size, use of EQ-5D, and robust methodology made it a suitable alternative.

Values for health states not selected from these articles were sourced from the references shown in Table 2.

DIFFICULTIES IN DERIVING THE PROPOSED REFERENCE SET Deriving a set of utilities from multiple heterogeneous studies highlighted a number of challenges and uncertainties including 1. Variability in baseline utility value The choice of baseline utility value (BUV) influences predicted cost-effectiveness considerably. In this case, the 0.785 value obtained by Clarke, et al, was considered most appropriate, being in the range of identified values for T2DM without complications.

Ideally, BUV should be obtained through a metaanalysis if there is sufficient data meeting the NICE recommendation, and sensitivity analyses performed using the limits of the confidence interval.

2. Uncertainty around disutility associated with each complication Nearly all studies report measures of uncertainty.

However, assumptions were required to estimate confidence intervals which may not reflect the full range of uncertainty in the underlying studies. Most of the selected utility values were adjusted for age, sex, and presence of multiple complications. However, values from Wasserfallen, et al,19 and Kiberd and Jindal20 were not.

Unadjusted disutility values should be interpreted with caution and sensitivity analyses performed to test the uncertainty around these values, especially for the most frequent diabetes complications.

3. heterogeneity across identified studies Variations in study country, study size and type, and inclusion of diabetes type provided limited justification for pooling disutility estimates.

4. Use of different value sets All index values in this analysis were based on a UK value set, with the exception of cataracts. None of the articles reported the values with two different value sets.

However, while there may be a difference in mean calculated index values, a study by Sakthong, et al,23 comparing EQ-5D index scores in a Thai T2DM sample, found that UK, US and Japanese value sets showed equivalent psychometric properties.

TAbLE 2: PREFERREd UTILITY VALUES FOR MOdELING T2dM COMPLICATIONS

Parameter T2DM without complication Myocardial infarction Ischemic heart disease Heart failure Stroke Severe vision loss Amputation event Peripheral vascular disease Proteinuria Neuropathy Active ulcer Excess BMI (each unit above 25 kg/m2) Hemodialysis Peritoneal dialysis Renal transplant Cataract Moderate nonproliferative background diabetic retinopathy Moderate macular edema Vision-threatening diabetic retinopathy Major hypoglycemia event Minor hypoglycemia event

Proposed reference

Proposed utility value

Clarke, et al.17 Clarke, et al.17 Clarke, et al.17 Clarke, et al.17 Clarke, et al.17 Clarke, et al.17 Clarke, et al.17 Bagust and Beale18 Bagust and Beale18 Bagust and Beale18 Bagust and Beale18 Bagust and Beale18 Wasserfallen, et al.19 Wasserfallen, et al.19 Kiberd and Jindal20 Lee, et al.21 Fenwick, et al.22 Fenwick, et al.22 Fenwick, et al.22 Currie, et al.15 Currie, et al.15

0.785 –0.055 –0.090 –0.108 –0.164 –0.074 –0.280 –0.061 –0.048 –0.084 –0.170 –0.006 –0.164 –0.204 0.762 –0.016 –0.040 –0.040 –0.070 –0.047 –0.014

95% CI 0.681–0.889 –0.067 to –0.042 –0.126 to –0.054 –0.169 to –0.048 –0.222 to –0.105 –0.124 to –0.025 –0.389 to –0.170 –0.090 to –0.032* –0.091 to –0.005* –0.111 to –0.057* –0.207 to –0.133* –0.008 to –0.004* –0.274 to –0.054* –0.342 to –0.066* 0.658–0.866 –0.031 to –0.001* –0.066 to –0.014† –0.066 to –0.014† –0.099 to –0.041† –0.012‡ –0.004‡

Range of candidate values 0.690–0.940 –0.059 to –0.007 –0.090 to –0.027 –0.108 to –0.051 –0.164 to –0.070 –0.070 to –0.012 –0.280 to –0.063 –0.186 to –0.061 One reference identified –0.247 to –0.050 –0.206 to –0.016 –0.006 to –0.002 One reference identified One reference identified 0.762–0.820 One reference identified One reference identified One reference identified –0.070 to –0.012 –0.020–0.005‡ –0.031 to –0.001‡

CI, confidence interval; T2DM, type 2 diabetes mellitus. * Estimated from the standard error values provided. † Estimated from the interquartile range values provided. ‡ Disutilities converted into annual values.

continued on next page

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InsIghts UTILITY VALUES IN DIABETES MODELING

Furthermore, the variation in estimated EQ-5D questionnaire index values was found to have a marginal impact on the projected incremental cost utilization ratio.

Nevertheless, when creating a utility value set for modeling, it would be preferable to select values using the same EQ-5D questionnaire value set or convert the original data using the appropriate EQ-5D questionnaire value set.

5. Variation in utility over time Utility can vary over time, both among different subpopulations as well as in line with evolving medical practice. The two main studies contributing to the proposed reference set, while still the best sources identified, reported on data collected some years ago.

However, unless there has been recent progress in treating a particular complication, it is preferable to select publications on the basis of methodological strength rather than on whether they are contemporary.

6. Changing impact of clinical events on hRQoL over course of disease To allow for this uncertainty, it may be appropriate to vary the disutility values associated with a complication depending on time since onset. In the present case, Clarke, et al, reported the relationship between health state utilities and clinical events occurring in the previous year or prior to the previous year. The authors did not find a greater disutility during the year of the event versus the disutility for events that occurred during the previous years.

COMMON INPUT FOR ECONOMIC EVALUATION Despite inherent challenges and uncertainties, the preferred reference set was derived in a transparent way and may serve as a common input for evaluating different technologies for diabetes in line with the NICE reference case. Although the specific characteristics of novel interventions may justify additional or different values from those presented, the dataset may provide future economic evaluations with a starting point for such considerations. The relevance of the identified utility value set should be considered before it is applied to a modeling study, and appropriate sensitivity analyses conducted.

The learnings from this methodological approach may also serve to inform similar economic evaluations in other countries.

Consistency in the use of statistical models and reporting would improve comparability of utility-related research. In addition, further research surrounding appropriate estimation of utility values for patients experiencing several complications would improve the current evidence base. This is likely to be of increasing importance for T2DM patients with advanced disease given the typical development of additional complications over time.

Further details of the review and analysis can be found in the original study publication on which this article is based.

Beaudet A, Clegg J, Thuresson PO, Lloyd A, McEwan P. Review of Utility Values for Economic Modeling in Type 2 Diabetes.

Value in Health, 2014; 17: 462-70. Available at www.sciencedirect.com

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1

IDF Diabetes Atlas, 6th Edition. Available at www.idf.org/diabetesatlas/introduction. Accessed 16 October, 2014 2 Zhang P, Zhang X, Brown J, Vistisen D, Sicree R, Shaw J, et al. Global healthcare expenditure on diabetes for 2010 and 2030. Diabetes Res Clin Pract. 2010 Mar;87(3):293-301 3 NICE Guide to the methods of technology appraisal, 2013. Available at: http://www.nice.org.uk/article/PMG9/chapter/Foreword. Accessed 16 October, 2014 4 Papaioannou D, Brazier J, Paisley S. TSD 9: The identification, review and synthesis of health state utility values from the literature. Decision Support Unit, School of Health and Related Research. Sheffield, UK: University of Sheffield, 2010 5 Brazier J, Longworth L. TSD 8: An introduction to the measurement and valuation of health for NICE submissions. Sheffield, UK: Decision Support Unit, ScHARR, University of Sheffield, 2011 6 National Institute for Health and Care Excellence. Single technology appraisal (STA), Specification for manufacturer/sponsor submission of evidence. Rotterdam, The Netherlands: NICE, 2012.

7 Rabin R, Oemar M, Oppe M. EQ-5D-3L User Guide (Version 4.0).

Rotterdam, The Netherlands: EuroQol Group, 2011 8 Conner-Spady B, Suarez-Almazor ME. Variation in the estimation of quality-adjusted life-years by different preference-based instruments.

Med Care 2003; 41: 791-801 9 Kopec JA, Willison KD. A comparative review of four preference weighted measures of health-related quality of life. J Clin Epidemiol 2003; 56: 317-25 10 Palmer AJ, Roze S, Valentine WJ, et al. The CORE Diabetes Model: projecting long-term clinical outcomes, costs and cost-effectiveness of interventions in diabetes mellitus (types 1 and 2) to support clinical and reimbursement decision-making. Curr Med Res Opin, 2004; 20 (Suppl. 1): S5-26 11 Clarke PM, Gray AM, Briggs A, et al. A model to estimate the lifetime health outcomes of patients with type 2 diabetes: the United Kingdom Prospective Diabetes Study (UKPDS) Outcomes Model (UKPDS no. 68).

Diabetologia, 2004; 47: 1747-59 12 McEwan P, Peters JR, Bergenheim K, Currie CJ. Evaluation of the costs and outcomes from changes in risk factors in type 2 diabetes using the Cardiff stochastic simulation cost-utility model (DiabForecaster). Curr Med Res Opin, 2006; 22: 121-9 13 Mount Hood 4 Modeling Group. Computer modeling of diabetes and its complications: A report on the Fourth Mount Hood Challenge Meeting. Diabetes Care, 2007; 30: 1638-46 14 Lung TW, Hayes AJ, Hayen A, et al. A meta-analysis of health state valuations for people with diabetes: explaining the variation across methods and implications for economic evaluation. Qual Life Res, 2011; 20: 1669-78 15 Currie CJ, Morgan CL, Poole CD, et al. Multivariate models of health related utility and the fear of hypoglycaemia in people with diabetes.

Curr Med Res Opin, 2006; 22: 1523-34 16 Smith DH, Johnson ES, Russell A, et al. Lower visual acuity predicts worse utility values among patients with type 2 diabetes. Qual Life Res, 2008; 17: 1277-84 17 Clarke P, Gray A, Holman R. Estimating utility values for health states of type 2 diabetic patients using the EQ-5D (UKPDS 62). Med Decis Making, 2002; 22: 340-9 18 Bagust A, Beale S. Modelling EuroQol health-related utility values for diabetic complications from CODE-2 data. Health Econ, 2005; 14: 217-30 19 Wasserfallen JB, Halabi G, Saudan P, et al. Quality of life on chronic dialysis: Comparison between haemodialysis and peritoneal dialysis.

Nephrol Dial Transplant, 2004 ;19: 1594-9 20 Kiberd BA, Jindal KK. Screening to prevent renal failure in insulin dependent diabetic patients: An economic evaluation. BMJ, 1995; 311: 1595-9 21 Lee WJ, Song KH, Noh JH, et al. Health-related quality of life using the EuroQol 5D questionnaire in Korean patients with type 2 diabetes. J Korean Med Sci, 2012; 27: 255-60 22 Fenwick EK, Xie J, Ratcliffe J, et al. The impact of diabetic retinopathy and diabetic macular edema on health-related quality of life in type 1 and type 2 diabetes. Invest Ophthalmol Vis Sci, 2012; 53: 677-84 23 Sakthong P, Charoenvisuthiwongs R, Shabunthom R. A comparison of EQ-5D index scores using the UK, US, and Japan preference weights in a Thai sample with type 2 diabetes. Health Qual Life Outcomes, 2008; 6: 1

IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR

InsIghts

MIXED METHODS REGISTRY CREATION

A collaborative foundation for new diabetes insights in Germany Researchers conducting analytics and epidemiological studies using electronic medical record databases frequently find themselves short of critical variables. The value from data collected through a mixed methods registry like DIAREG spans scientific and commercial applications and creates new potential for exploring relationships between perspectives, actions and outcomes.

The authors

Joshua Hiller, MBA is Senior Principal, RWE Solutions, IMS Health jhiller@imshealth.com

Laura Garcia Alvarez, PHD is Senior Consultant, RWE Solutions, IMS Health LGarciaAlvarez@uk.imshealth.com

ACCESSPOINT â&#x20AC;˘ VOLUME 5 ISSUE 9

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InsIghts MIXED METHODS REGISTRY CREATION

Enhanced insights from a mixed methods approach Researchers conducting analytics and epidemiological studies using electronic medical record (EMR) databases frequently find themselves short of critical variables, potentially limiting the breadth of research they can perform. Although widely available EMR databases such as The Health Improvement Network (THIN), IMS® Disease Analyzer, and the Clinical Practice Research Datalink (CPRD) contain a great deal of longitudinal primary care data, it is often the case that certain types of information are missing – either because an EMR field has not been completed or because a particular field does not exist within the database.

In particular, behavioral detail such as reasons for changing therapy or the physician’s perspective of important clinical characteristics are rarely part of a structured health record and thus are not contained in mainstream EMR databases.

Typically, researchers must then decide whether to sacrifice the breadth of variables captured, and hence limit the study scope, or use a purely prospective design and sacrifice time and cost to implement an extended prospective observational study.

LEVERAGING MIXED METHODS FOR A COMPREHENSIVE RESOURCE To address these challenges, IMS Health, in partnership with AstraZeneca, has developed an innovative registry (DIAREG) of patients with type 2 diabetes mellitus (T2DM). AstraZeneca is committed to demonstrating the efficacy and benefit of its medicines in a real-world setting, especially in terms of patient-relevant outcomes.

The registry is based on the complementary methods of retrospective and prospective data collection, thereby overcoming the individual limitations of each, enabling the creation of a rich data resource for observational research in this area.

IDENTIFYING REQUIREMENTS

DIAREG

Work on DIAREG began in 2012. Understanding the key requirements for a comprehensive prospective disease registry, IMS® Disease Analyzer in Germany was selected as the core data backbone, being representative with input from physicians in general practice as well as

PAGE 46

diabetologists,i and validated with a documented history of application in published scientific studies.

Initial analysis of data variables confirmed that Disease Analyzer contained rich information on population characteristics (eg, demographics, medical history) and treatment patterns (eg, diagnosis, prescriptions, comedications, co-morbid conditions) in diabetes patients.

However, while some data existed for certain diabetesrelevant clinical parameters, such as HbA1c and body mass index (BMI), this was often recorded less frequently or sometimes not at all. Furthermore, other clinical outcomes (eg, cardiovascular events, hypoglycemic episodes, hospitalizations), physician behavior (eg, drivers of therapy decision, reasons for dose or treatment modification) and patient-reported outcomes (PRO) (eg, general quality of life, disease-specific quality of life or treatment satisfaction), were not captured as structured data within the patient record at all.

As a result of this initial analysis, a set of 27 variables were identified for their potential research value if collected, to enhance the available EMR resource.

DIAREG IS BORN The identified need for an ‘enhanced’ EMR registry took the next stage of development down two separate paths – technical and ethical – to achieve an optimal solution. technical implementation To facilitate technical implementation of the registry, IMS Health worked closely with the EMR software vendor responsible for collecting the data underpinning Disease Analyzer. Together, they designed and created the capability for a retrieve form data capture window (or ‘pop up’) to be triggered in the physician office during the patient visit, based on a set of criteria available within the patient EMR (eg, diagnosis code, existence of prior antidiabetic treatment, etc). Every time an eligible patient was identified through the trigger, the physician completed an electronic case report form (eCRF) in the ‘pop-up’ window to provide the required additional clinical data. i

Becher H, Kostev K, Schröder-Bernhardi D. Validity and representativeness of the Disease Analyzer patient database for use in pharmacoepidemiological and pharmacoeconomic studies. Int J Clin Pharmacol Ther, 2009; 47: 617-626

1,071

22%

patients

changed therapy

IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR

FIGURE 1: CUSTOMIZEd EMR ANd REGISTRY dATA COHORT

eCRF pop-up

IMS Disease Analyzer

Enhanced Disease Cohort PRO

Double hash algorithm applied for data anonymization Patient characteristics of interest programmed into EMR database to trigger eCRF

Since patient EMR was used as the basis for including or excluding a patient from the registry, the potential impact of subjective selection was reduced. Consecutive new patients continued to be triggered for inclusion in the registry until the physician reached a pre-defined cap, thus providing a framework for random sample selection. Data collected from the retrieve form data capture window eCRF is currently being linked back to the EMR using a hash de-id process that removes protected health information (PHI) prior to extraction to the IMS Health database.

In addition to the enhanced clinical data collection, a second phase of the registry build involved the introduction of PROs to provide a further layer of information. These are collected via paper-based questionnaires handed to patients at the physician site where they are filled in and returned for entry into an electronic database. An additional hash algorithm has been deployed for one-way linkage of the PRO data to the EMR and eCRF (Figure 1).

Ethical implementation From an ethical perspective, it was essential to ensure that the registry was developed in accordance with sound observational research practices. To that end, a Scientific Advisory Board was created to provide guidance on the methods for site identification, eCRF review, inclusion of PROs, use of patient informed consent, and submissions for ethics approval. The Committee is made up of six independent academic researchers and physicians who have no affiliation with either AstraZeneca or IMS Health.

Patients participating in the registry have given informed consent for inclusion of their information from EMR, as well as the eCRF and PRO questionnaire. The registry protocol was reviewed and approved by the Ethics Committee, Nordrhein, Germany (Ethikkommission der Ă&#x201E;rztekammer Nordrhein) under the name of DIAREG.

EMR and PRO linked to disease-specific data at patient level creating enhanced patient record

UNIQUELY GRANULAR OBSERVATIONAL RESEARCH As of September 2014, DIAREG has been collecting data for more than 18 months. The registry currently contains eCRF questionnaires, with comprehensive, longitudinal data variables, for 1,071 diabetes patients, enabling granular observational research. A subset analysis of these patients (n=824) shows that 77% were enrolled by GPs, the remainder being recruited by diabetologists.

Based on data from half of the cohort, average length of time with T2DM is 12.3 years (median 11 years). Twentytwo percent of patients (n=181/824) in the registry have experienced a change to their anti-diabetes therapy at least once within the last year, mostly by the GP (57%) but also by diabetologists, who were responsible for 35% of therapy changes. For 152 patients (84% of the therapy modification population), this took the form of a dose adjustment to their existing therapy, mainly due to insufficient control of HbA1c (Figure 2). A change of drug was recorded for 60 patients (33%) for the same reason.

Overall, doctors have reported high expectations of HbA1c reduction when deciding on a new treatment regimen.

A total of 475 patients (58%) self-monitored their blood glucose levels, with 30% checking their blood sugar more than twice a day. Visits to other specialists were recorded for 43% of 824 patients, the most frequently visited being ophthalmologists (57%) for diagnosis of retinopathies.

Of the 824 patients in the subset, 43 experienced at least one hypoglycemic event, four of whom required hospitalization (Figure 3).

ENABLING EVIDENCE-BASED CONNECTIONS The data captured in DIAREG enables researchers to identify and explore associations across measures that have not been collected before in a sustainable and integrated manner. continued on next page

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InsIghts MIXED METHODS REGISTRY CREATION

FIGURE 2: MOST THERAPY AdJUSTMENTS ARE dUE TO POOR HbA1C CONTROL Number of patients with at least one dose adjustment

Reason for therapy adjustment

39

Other

9

Co-medication

13

Weight gain

84% (n=152)

10

Patient request

16% (n=29)

22

Hypoglycemic events

16

Microvascular complications

Yes

No

Macrovascular complications

2

Change of substance combination

23 112

Insufficient HbA1c reduction 0

20

40

60

80

100

120

Source: Disease registries including Patient Reported Outcomes - IMS速 DIAREG

By allowing comparison of clinical parameters at a patient level, it provides evidence of associations from a real-world setting that previously could only be identified anecdotally or through market research.

As an example, the capture of BMI and HbA1c

measurements without DIAREG was recorded in 61.9% and 42.3% of the population respectively. With DIAREG, the capture of these critical lab measurements increases to 83.3% and 77.6% respectively (Figure 4).

FIGURE 3: PATIENTS EXPERIENCING A HYPOGLYCEMIC EVENT Type of hypoglycemic event Number of patients having a hypoglycemic event

1

Hypoglycemia requiring hospitalization

3 2 1

Hypoglycemia requiring assistance

2 6 3 7

Blood sugar <70 mg/dl measured by patient

12 15 4 9

Hypoglycemia with glucose consumption

13 17 0

2

4

Number of events per patient

6

8

4 or more

10

3

12

2

14

16

1

N= 824 Patients, of which 43 had at least 1 hypoglycemic event as reported in DIAREG Source: Disease registries including Patient Reported Outcomes - IMS速 DIAREG

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IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR

FIGURE 4: dIAREG ENAbLES INCREASEd CAPTURE OF CRITICAL MEASUREMENTS

42.3%

HbA1c

41.0%

61.9%

Height/Weight

57.0%

Blood pressure

% 0

15.7%

10

20

30

40

Already in DA

50

22.4%

29.0% 60

Update in DIAREG

70

16.7%

80

14.0% 90

100

Missing

DIAREG: n=407 patients Source: Disease registries including Patient Reported Outcomes - IMS® DIAREG

FIGURE 5: CATEGORIES OF dATA ENHANCEd THROUGH A MIXEd METHOdS APPROACH

Information in IMS® Disease Analyzer

Information in IMS® DIAREG

Documented type of diabetes

Confirmation of type 2 diabetes diagnosis

Therapy duration at the treating physician

Start/duration of type 2 diabetes

Disease-relevant parameters (eg, HbA1c, blood glucose, weight/BMI, blood pressure)

Complete documentation of all disease-relevant parameters

–

Frequency and severity of hypoglycemias

–

Treatment goals (related to symptoms, laboratory parameters and complications)

–

Reasons for change of therapy and treatment goals associated with the change

Diabetes-related complications

Complete documentation of all diabetes-related complications

Referral to hospital

All stays in hospital with reasons for hospitalization, diagnosis at discharge and hospital days

Referral to specialists

All specialist consultations with diagnosis

Referral to rehabilitation

All rehabilitation measures with diagnosis

Patient education

All educational activities

–

Frequency of blood glucose self monitoring

–

Physician's estimate of the patient's therapy adherence

Prior to implementation of DIAREG, real-world information on the proportion of patients checking blood sugar, the reason for modifying treatment, the number and type of hypoglycemic events, diagnosis for specialist visits or quantity of lab measurements captured was nonexistent. Figure 5 outlines categories of data enhanced through the mixed methods approach.

comprehensive patient record allows retrospective analysis using measures that are not available in other datasets. For brand teams, the behavioral information from physicians and patients, such as reasons for switch and quality of life, creates new potential for exploring relationships between perspectives, actions and outcomes.

EXTENDED VALUE WITH MULTIPLE APPLICATIONS

The IMS® DIAREG registry is open to other collaborations.

The value from data collected through a mixed methods registry like DIAREG spans scientific and commercial applications. For researchers, the depth of detail from the

For further information please email Jhiller@imshealth.com

ACCESSPOINT • VOLUME 5 ISSUE 9

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InsIghts

IMS CORE DIABETES MODEL AT MOUNT HOOD

Demonstrating external validity of the IMS CORE Diabetes Model The Mount Hood Challenges provide an opportunity to understand the performance of the IMS CORE Diabetes Model against a series of external validation tests and inform its continued development.

A comparison of the modelâ&#x20AC;&#x2122;s results with published studies across each of the areas covered at the 2014 meeting demonstrates how well it is equipped to address those issues.

The authors

Mark Lamotte, MD is Senior Principal, RWE Solutions & HEOR, IMS Health Mlamotte@be.imshealth.com

Volker Foos, MSC is Senior Consultant, RWE Solutions & HEOR, IMS Health Vfoos@ch.imsheath.com

PAGE 50

IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR

The 2014 Mount Hood Challenge In June 2014, members of the IMS CORE Diabetes Model (CDM) team participated in the 7th Mount Hood Challenge at Stanford University in Palo Alto, CA. Focused on economic aspects of diabetes and its complications, this biennial event is an important forum for comparing different health economic diabetes simulation models (DSM), particularly in terms of structure and performance.

The goal is to advance the science of economic modeling in diabetes and increase its validity and relevance to realworld decision making. Attendees include leading diabetes modeling groups, academics, industry representatives and payers with an interest in the economics of diabetes.

A particular theme of the 2014 Challenge was how to generalize DSM for different populations and over time, specifically exploring the ability of existing models to adjust for risk differentials arising from ethnic and socioeconomic variability and any secular improvements in diabetes care. Participants were tasked with three ‘challenges’ to compare model projections to real-world or clinical trial outcomes and explain and discuss observed differences 1. Replication of key endpoints from the Action for Health Diabetes (Look AHEAD) study 2. Prediction of mortality following first myocardial infarction (MI) or stroke 3. Variation in event rates due to ethnicity

TAKING THE CHALLENGE The CDM is a simulation model that predicts the longterm clinical and economic outcomes of diabetes mellitus type 1 (T1DM) and type 2 (T2DM). It is the leading tool for policy analysis and reimbursement strategy in the disease and is regularly updated and revalidated (see News section, page 5). The Mount Hood Challenges provide an opportunity to understand its performance against a series of external validation tests and inform the continued development and enhancement of its functionalities. Although findings from the challenges themselves cannot be reported here, comparing CDM results with published studies across each of the areas they covered demonstrates how well the model is equipped to address these issues.

“

Challenge 1: Look AhEAD validation The LOOK AHEAD study1 included 5,145 overweight or obese T2DM patients to participate in an intensive lifestyle intervention (ILI) promoting weight loss through decreased caloric intake and increased physical activity (intervention group) or to receive diabetes support and education (DSE) (control group). Patients in the ILI group experienced greater weight loss (8.6% vs. 0.7%) and reductions in HbA1c (0.6% points vs. 0.1% points) at one year. The study concluded that weight loss did not reduce the rate of cardiovascular (CV) events in overweight or obese adults with T2DM. External validation of DSM against studies that primarily focused on weight reduction is of interest particularly following a recent review criticizing the degree by which models translate weight improvements into benefits.2 The authors assert that their fundamental assumptions relating to weight effects are too strong and unsupported by the literature.

Like many models targeted in the review and participating in the Mount Hood Challenge, the CDM applies UKPDS risk equations3,4 that incorporate BMI as a risk factor for heart failure. In fact, no other micro- or macrovascular complication risk is affected by weight. The overall magnitude of weight-changes on clinical outcomes in DSM is therefore modest when compared to the impact of other risk factors such as HbA1c, lipids or blood pressure.

For example, a CDM lifetime analysis utilizing UKPDS 68 equations3 in a ACCORD-like population to explore the benefits of reducing BMI from 32 to 31 kg/m2 translates into 0.008 life years gained and 0.004 QALYs gained with no substantial improvements projected for any endpoint except a 4.5% relative reduction of heart failure (HF) incidence (16.1% vs. 16.8%).

The external validation of the LOOK AHEAD trial across several DSM was relevant to further inform this research question and help eliminate doubts that models overestimate the clinical effects associated with weight changes.

continued on next page

The goal of the Mount Hood Challenge is to advance the science of economic modeling in diabetes and increase its validity and relevance to real-world decision making.

ACCESSPOINT • VOLUME 5 ISSUE 9

”

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InsIghts IMS CORE DIABETES MODEL AT MOUNT HOOD

Challenge 2: Mortality validation It is important to understand how DSM are capable of predicting mortality across populations that were not used for the construction of their inherent risk equations (external validation). This challenge examined their ability to predict mortality vs. trends observed in 20,836 people with T2DM from the Swedish National Diabetes Register.

The CDM utilizes risk equations from the UKPDS3,4 to predict mortality following major diabetes-related complications. It has recently undergone a single ‘allcause mortality’ (ACM) validation against various contemporary outcome studies (UKPDS, ACCORD, ADVANCE, VADT, ASPEN)5,6,7,8,9 which has shown a below average fit with an R2-statistic of 0.651. This compares to an overall R2-statistic of 0.90 as obtained in the 2014 CDM revalidation exercise including 112 microvascular, macrovascular and mortality validation endpoints.10 Lack of fit in the ACM validation was associated with a model over-estimation of ACM when compared to contemporary outcome studies (ACCORD, ADVANCE, and VADT). It is generally understood that these studies reported low mortality incidence, likely because patients were managed under controlled clinical trial (RCT) conditions. CDM mortality predictions were therefore contrasted against several external datasets that were more eligible to represent real-world conditions • Charlson Co-morbidity Index (CCI) The CCI11 is widely utilized to measure burden of disease and predict mortality in various disease subgroups, including cancer, renal disease, liver disease and diabetes. Its ability to predict mortality risk has been validated extensively. The CCI was applied to predict 10-year mortality risk for diabetes patients aged 50, 60, 70 and 80 years. Risk scores were generated for four different co-morbidity levels: no complications (NC); MI; MI and stroke (MI+S); MI+S and CHF (MI+S+CHF); and MI+S+CHF and end-stage renal disease (MI+S+CHF+ESRD).

CCI mortality scores were compared to corresponding 10-year ACM predictions from the CDM. • UK general Practice Research Database (gPRD) The CDM was validated to data from a retrospective cohort study12 from the UK General Practice Research Database (GPRD). The study compared ACM across five glucose-lowering regimens: metformin monotherapy; sulfonylurea monotherapy; insulin monotherapy; metformin plus sulfonylurea combination therapy; and insulin plus metformin combination therapy.

• Administrative dataset from Western Australia (WA) Predictions from an online life expectancy calculator that applies mortality risk equations derived from 13,884 WA hospital and mortality records13 were compared to respective predictions from the CDM. The coefficient of determination (R2) goodness-of-fit measure was evaluated separately for the individual exercises. R2 scores of 0.76, 0.82 and 0.99 were obtained when the CDM was compared to predictions from the CCI, GPRD and WA life expectancy calculator. Overall R2, including all mortality and life expectancy validation

PAGE 52

outcomes, amounted to 0.88 which supports the CDM as a credible tool for predicting mortality in real-world settings. Individual outcomes for the four validation settings are shown in Figure 1.

This validation exercise outlines the observed discrepancy when the CDM is compared to data from RCTs or to data from non-controlled, real-world observations. As the overall intention of DSM is to predict the implications of new technologies in clinical practice, this emphasizes the need to compare against evidence from real-world settings when assessing the validity of DSM. Challenge 3: Ethnicity differences Economic evaluations of health interventions need to consider differences in healthcare expenditures across subgroups.14 Accounting for the heterogeneity of the treated population to identify subgroups is important to expose variation in incremental cost-effectiveness ratios (ICERs); a given intervention may be cost-effective for some subgroups but not for others.

Ethnicity can considerably influence the incidence of diabetes-related complications which may be caused by genetic, socioeconomic and socio-cultural factors.15 It is thus important to understand the extent to which DSM consider ethnic differences in the modeling. Several studies have reported on ethnic differences in rates of diabetes-related complications.16,17,18,19 The aim of the third challenge was to understand the degree to which these differences are incorporated into the various DSM and affect predicted outcomes.

The goal for the modeling groups was to estimate survival, costs and diabetes-related outcomes for an average T2DM profile and all ethnic groups considered in the respective models. The CDM considers ethnicity adjustments for a number of micro- and macrovascular complications including renal disease, eye disease, neuropathy, and coronary heart disease. Details of the ethnicity adjustments applied in the CDM, together with respective literature sources, are reported in Table 1.

In this respect, a comparison of ethnic adjustments in the CDM to a systematic review20 documenting a similar trend of ethnicity-related risk associations for renal disease, eye disease, CV disease and mortality, is revealing.

Consolidating the evidence of 51 articles reporting data on ethnic differences in mortality and diabetes-related complications, the review quantified the number of identified studies (see Table 2) that reported higher (red), lower (blue) or equal (green) risk for several ethnicity groups versus a white reference population.

Studies were classified in different groups to distinguish those that reported significant risk associations with ethnicity after adjustment of confounders (eg, age, duration of diabetes and sex) and those that further adjusted for additional risk factors (eg socioeconomic status, smoking). Although the CDM applies data from different sources, comparability to the review demonstrates that its ethnicity adjustments are contemporary and qualified to differentiate ethnicityrelated differences in the natural history of diabetes.

IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR

Number of deaths in study population during trial follow-up

FIGURE 1: ALL CAUSE MORTALITY VALIdATION WITH THE CdM AGAINST EXTERNAL SOURCES

CDM vs. outcome studies

1200 1000 800 600 400 200

80

VADT

70

ASPEN SI

ADVANCE

60

ASPEN PP

ACCORD GL

UKPDS 80 - MET

UKPDS 80 - SU

UKPDS 33

ACCORD BP

Study

VADT

ASPEN SI

ASPEN PP

ADVANCE

ACCORD GL

ACCORD BP

UKPDS 80 - MET

UKPDS 80 - SU

UKPDS 33

0

CDM

Number of deaths in 10 years per 1000 patients

CDM vs. prediction from CCI 1000 800 600 400 200 0

40

50

60

70

80

50

60

No Complication

70

Study

50

60

70

80

50

MI + Stroke

MI + Stroke + CHF

50

60

70

80

MI + Stroke + CHF + ESRD

CDM

CDM vs. retrospective cohort study from GPRD

3500

Number of deaths in study population during trial follow-up

80

MI

3000 2500 2000 1500 1000 500 0 GPRD MET

GPRD SU

GPRD MET + SU

Life Expectancy (years)

Study

12

GPRD Insulin

GPRD Insulin + MET

CDM

CDM validation against predictions from WA life expectancy calculator

10 8 6 4 2 0

Female Female Female <65 65-84 85+

Male <65

Male 65-84

Male 85+

Female <65

Post MI

Female Female 65-84 85+

Male <65

Male 65-84

Male 85+

Post Stroke

Study

CDM

Source: IMS CORE Diabetes Model analysis

continued on next page ACCESSPOINT â&#x20AC;˘ VOLUME 5 ISSUE 9

PAGE 53

InsIghts IMS CORE DIABETES MODEL AT MOUNT HOOD

TAbLE 1: SUMMARY OF OddS RATIOS APPLIEd TO ETHNIC AdJUSTMENTS IN THE CdM

Microvascular

Type

Eye disease BDR onset BDR to PDR PDR to SVL ME to SVL Renal disease MAU onset MAU to GRP GRP to ESRD ESRD death post HD post PD post RT Neuropathy Macrovascular MI

Stroke IHD CHF Mortality Event fatality Long-term diabetes related mortality Non diabetes-specific mortality

White

Black

Hispanic

Am. Indian Asian/Pacific Islander

Indian

T2DM T2DM T2DM T2DM

x x x x

1.0321 3.7721 122 2.323

1.0321 3.6321 122 2.323

1.0321 11 122 123

x x x x

x x x x

T2DM T1DM & T2DM T1DM & T2DM T1DM & T2DM T1DM & T2DM T1DM & T2DM T1DM & T2DM

x x x x 112 112 112 x

1.38211 4.425 4.425

124 2.525 2.525

1.04824 1.525 1.525

x x x

x x x

0.7226 0.5626 1.0026 0.5327

x x x 128

x x x 0.2927

x x x x

x x x x

T1DM & T2DM

x

x

x

x

UK 82 RE4

x x x

x x x

x x x

x x x

x x WHO30

x x WHO30

x x WHO30,19

UK 82 RE x x

T1DM & T2DM T1DM & T2DM T1DM & T2DM

x x x

UK 68 RE3 UK 82 RE4 UK 56 RE29 x x x

T1DM & T2DM T1DM & T2DM T1DM & T2DM

x x WHO30

x x WHO30

WHO = Ethnicity specific life table data from World health organization (http://apps.who.int/gho /data/view.main.61780) adjusted (reduced) for all diabetes-related causes of death that are tracked in the CDM; UK 68 RE=UKPDS 68 risk equations; UK 82 RE=UKPDS 82 risk equations; UK 56 RE=UKPDS 56 risk engine; T1DM= Type 1 diabetes; T2DM= Type 2 diabetes; x = no race adjustment

TAbLE 2: SUMMARY FINdINGS ON NUMbER OF STUdIES REPORTING EITHER LOWER, EQUAL OR HIGHER RISK ASSOCIATEd WITH ETHNICITIES VERSUS WHITE POPULATION IN US & UK

Higher risk (average) Lower risk (average) Equal risk (average) Retinopathy Black Hispanic Asian Nephropathy & ESRD Black Hispanic Asian CV complications Black Hispanic Asian Neuropathy Black Hispanic Asian Mortality Black Hispanic Asian

PAGE 54

US

UK

Adjusted for confounders Adjusted for confounders Adjusted for confounders Adjusted for confounders (age, duration, sex) & additional risk factors (age, duration, sex) & additional risk factors

higher lower equal higher lower equal higher lower equal higher lower equal 1 1

2 1

0 0

0

0 0

1 1

1 3

2 1 1

3 0

0

0

1

0

0

0

0

0

0

0

1

0

1

1

1

1 1

1 2

1

1 1

1 1

1

0

1 3 3

2 2

4 1

1 1

1

1 3

2

1 1

IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR

1

The Look AHEAD Research Group. Cardiovascular effects of intensive lifestyle intervention in type 2 diabetes. N Engl J Med, 2013; 369: 145-54 Asche CV, Hippler SE, Eurich DT. Review of models used in economic analyses of new oral treatments for type 2 diabetes mellitus.

Pharmacoeconomics, 2014, Jan; 32(1): 15-27 3 Clarke PM, Gray AM, Briggs A, Farmer AJ, Fenn P, Stevens RJ, et al. A model to estimate the lifetime health outcomes of patients with type 2 diabetes: The United Kingdom Prospective Diabetes Study (UKPDS) Outcomes Model (UKPDS no. 68). Diabetologia, 2004, Oct; 47(10): 1747-59.

Epub 2004 Oct 27.

4 Hayes AJ, Leal J, Gray AM, Holman RR, Clarke PM. UKPDS outcomes model 2: A new version of a model to simulate lifetime health outcomes of patients with type 2 diabetes mellitus using data from the 30 year United Kingdom Prospective Diabetes Study: UKPDS 82. Diabetologia, 2013, Sep; 56(9): 1925-33. Epub 2013 Jun 22.

5 Holman RR, Paul SK, Bethel MA, Matthews DR, Neil HA. 10-year follow-up of intensive glucose control in type 2 diabetes. N Engl J Med, 2008; 359(15): 1577-89 6 The Action to Control Cardiovascular Risk in Diabetes Study Group. Effects of intensive glucose lowering in type 2 diabetes. N Engl J Med, 2008; 358: 2545-255 7 ADVANCE Collaborative Group. Intensive blood glucose control and vascular outcomes in patients with type 2 diabetes. N Engl J Med, 2008; 358 (24): 2560-72 8 Duckworth W, Abraira C, Moritz T, Reda D, Emanuele N, Reaven PD, et al; VADT Investigators. Glucose control and vascular complications in veterans with type 2 diabetes. N Engl J Med, 2009; 360(2): 129-39 9 Knopp RH, d'Emden M, Smilde JG, Pocock SJ. Efficacy and safety of atorvastatin in the prevention of cardiovascular end points in subjects with type 2 diabetes: The Atorvastatin Study for Prevention of Coronary Heart Disease Endpoints in non-insulin-dependent diabetes mellitus (ASPEN). Diabetes Care, 2006 Jul; 29(7): 1478-85 10 McEwan P, Foos V, Palmer JL, Lamotte M, Lloyd A, Grant D. Validation of the IMS CORE Diabetes Model. Value in Health, 2014; 17: 714-724 11 Quan H, Li B, Couris CM, Fushimi K, Graham P, Hider P, Januel JM, Sundararajan V. Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. Am J Epidemiol, 2011; 173(6): 676-82 12 Currie C J, Poole CD, Evans M, Peters JR, Morgan CL. Mortality and other important diabetes-related outcomes with insulin vs other antihyperglycemic therapies in type 2 diabetes. J Clin Endocrinol Metab, February 2013; 98(2): 668-677 13 Hayes A, Leal J, Kelman C, Clarke P. Risk equations to predict life expectancy of people with type 2 diabetes mellitus following major complications: A study from Western Australia. Diabet Med, 2011, Apr; 28(4): 428-35. Calculator available at: http://sydney.edu.au/medicine/publichealth/heconomics/resources/supplementary.php Accessed 19 October, 2014 14 Sculpher M. Sculper subgroups and heterogeneity in cost-effectiveness analysis. Pharmacoeconomics, 2008; 26 (9): 799-806 15 Uniken Venema HP, Garretsen HF, van der Maas PJ. Health of migrants and migrant health policy: The Netherlands as an example. Soc Sci Med, 1995; 41:809-818 16 Skyler JS, Oddo C. Diabetes trends in the USA. Diabetes Metab Res Rev, 2002; 18 (Suppl 3): S21-S26 17 Samanta A, Burden AC, Fent B. Comparative prevalence of non-insulin-dependent diabetes mellitus in Asian and white Caucasian adults. Diabetes Res Clin Pract, 1987; 4:1-6 18 Samanta A, Burden AC, Jones GR, Woollands IG, Clarke M, Swift PG, Hearnshaw JR. Prevalence of insulin-dependent diabetes mellitus in Asian children. Diabet Med, 1987; 4: 65-67 19 Davis TM, Coleman RL, Holman RR; UKPDS Group. Ethnicity and long-term vascular outcomes in type 2 diabetes: A prospective observational study (UKPDS 83). Diabet Med, 2014, Feb; 31(2): 200-7 20 Lanting LC, Joung IM, Mackenbach JP, Lamberts SW, Bootsma AH. Ethnic differences in mortality, end-stage complications, and quality of care among diabetic patients. Diabetes Care, 2005, Sep; 28(9): 2280-8 21 Zhang X, Saaddine JB, Chou C-F, Cotch MF, Cheng YJ, Geiss LS , et al. Prevalence of diabetic retinopathy in the United States, 2005-2008. JAMA, 2010, Aug 11; 304(6): 649-56. doi: 10.1001/jama.2010.1111 22 Sivaprasad S, Gupta B, Gulliford MC, Dodhia H, Mann S, Nagi D, Evans J. Ethnic variation in the prevalence of visual impairment in people attending diabetic retinopathy screening in the United Kingdom (DRIVE UK). PLoS One, 2012; 7(6): e39608. doi: 10.1371/journal.pone.0039608.

Epub 2012 Jun 27 23 Emanuele N, Moritz T, Klein R, Davis MD, Glander K, Khanna A, et al. Ethnicity, race and clinically significant macular edema in the Veterans Affairs Diabetes Trial (VADT). Diabetes Res Clin Pract, 2009, Nov; 86(2): 104-10 doi: 10.1016/j.diabres.2009.08.001. Epub 2009 Aug 31 24 U.S. Renal Data System, USRDS 2013 Annual Data Report: Atlas of Chronic Kidney Disease and End-Stage Renal Disease in the United States, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, 2013. Volume 1, table 2.6 http://www.usrds.org/atlas.aspx [07.05.2014] 25 DiPiro J, Talbert RL, Yee GC, Matzke GR, Wells BG, Posey LM. Pharmacotherapy: A pathophysiologic approach 9th Ed, McGraw Hill, 2014 based on U.S. Renal Data System, USRDS 2012 Annual Data Report: Atlas of Chronic Kidney Disease and End-Stage Renal Disease in the United States, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, 2012 26 U.S. Renal Data System, USRDS 2010 Annual Data Report: Atlas of Chronic Kidney Disease and End-Stage Renal Disease in the United States, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, 2010 27 Abbott CA, Garrow AP, Carrington AL, Morris J, Van Ross ER, Boulton AJ. Foot ulcer risk is lower in South-Asian and African-Caribbean compared with European diabetic patients in the U.K.: The North-West diabetes foot care study. Diabetes Care, 2005, Aug; 28(8): 1869-75 28 Franklin GM, Shetterly SM, Cohe JA, Baxter J, Hamman RF. Risk factors for distal symmetric neuropathy in NIDDM. The San Luis Valley Diabetes Study. Diabetes Care, 1994; 17: 1172-1177 29 Stevens RJ, Kothari V, Adler AI, Stratton IM. The UKPDS risk engine: A model for the risk of coronary heart disease in type II diabetes (UKPDS 56). Clin Sci (Lond), 2001, Dec; 101(6): 671-9 30 World Health Organization. Global Health Observatory Data Repository, Life tables by country (http://apps.who.int/gho/data/view.main.61780) 2

A manuscript reporting on the 7th Mount Hood Meeting held at Stanford University in Palo Alto, CA, 17-19 June, 2014, including results of the challenge for all participating models, is being prepared for publication by the Mount Hood Modeling Group.

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PROJECt FOCUs CHRONIC INFLAMMATORY DISORDER

Evaluating disease burden, unmet need and quality of life in a chronic inflammatory disorder Primary data collection, with a focus on patient-reported outcomes, provides an evidence base for improved disease awareness and more efficient treatment on a pan-European scale Chronic inflammatory disorders (CIDs) place a high and growing demand on healthcare resources with significant socioeconomic implications, both in terms of cost and lost productivity.

Encompassing a wide range of diverse long-term conditions, including Crohn’s disease, COPD, rheumatoid arthritis, multiple sclerosis and ulcerative colitis, they affect millions of people worldwide, often causing a devastating impact on health-related quality of life (HRQoL).

When measuring HRQoL, selecting the right patientreported outcome (PRO) tool is key to capturing the expected outcome and understanding disparities among different patient types. The decision-making process and tool administration benefit greatly from the involvement of an expert team to both define the optimal administration time sequence as well as ensure correct study analysis.

The authors núria Lara, MD, MsC is Senior Principal, RWE Solutions & HEOR, IMS Health Nlara@es.imshealth.com Mark Lynam, MsC is Senior Consultant, RWE Solutions & HEOR, IMS Health Mlynam@es.imshealth.com

required clinical data. Eleven countries were selected for inclusion in the study, which was designed as shown in Figure 1.

FIGURE 1: STUdY dESIGN screening and enrolment

DETERMINING HRQOL IN A DEBILITATING CID With in-house PRO experts in markets around the world, IMS Health has extensive experience in selecting and generating PROs to answer specific research questions, using validated and ad-hoc questionnaires, and in analyzing and interpreting patient responses in a meaningful way.

Recognizing the value of these capabilities, a company developing a new drug for a debilitating CID approached IMS Health for help in designing and executing a survey examining disease impact on patient HRQoL, and determining unmet clinical need and patient satisfaction with current treatments. A particular complexity of the study was the number of participant countries and sites, drawing on IMS Health’s demonstrated strengths in the top-level and local project management of these types of projects. study design Following a series of internal and external discussions, IMS Health experts in the CID area from four representative European countries identified a retrospective medical chart review as the best and most efficient way to collect the

PAGE 56

Patient informed consent Index date (t0) 12 months

12 months

2-year limited chart review (T-24)

1-year mandatory chart review (T-12)

Limited chart review completed by physician if available

Chart review completed by physician

Patient characteristics completed by physician survey completed by patients

IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR

FIGURE 2: THE STUdY FOLLOWEd A PHASEd ANd STRUCTUREd APPROACH

Project kick-off

study protocol & CRF

site screening & selection

Ethics Committees & contracting

Data collection & site managment

Analysis & reporting

study implementation Combining the knowledge of both global and local IMS Health experts in each of the participant countries, the study followed a phased and structured approach (Figure 2), commencing with clarification of the objectives, alignment with stakeholder expectations, and definition of rules and communication pathways. Regular, scheduled meetings with the company and local country teams were a key part of the overall process.

The licensing of proprietary PROs was handled by IMS Health, along with translation of the validated and ad-hoc questionnaires to the relevant language of each study country.

The PROs were translated in accordance with the authors’ instructions following pilot testing with a small sample of patients. The master versions of the protocol and CRF were prepared in English and translated to local languages or adapted in English as required for Ethics Committee (EC) presentations or study execution.

study protocol and CRF development IMS Health began by preparing a study protocol and CRF in conjunction with the company and CID experts, ensuring clear case definition in a set of unambiguous inclusion/exclusion criteria. The most appropriate validated PROs were identified and administered (following translation into local language) via short ad-hoc questionnaires to study patients. These consisted of

site screening and selection Site selection is a key part of any clinical or observational study given the role of successful patient recruitment in its overall success. In this case, the company was keen to maintain a clear distance from the sites included in order to avoid creating bias and therefore requested IMS Health to select all participant sites. Investigators from 11 different countries were chosen and invited to take part, using the IMS Health internal database of physicians known to have an interest in contributing to observational research.

• • • • • •

EQ-5D generic HRQoL questionnaire to allow comparison of results to other disease populations as well as the general population Disease-specific hRQoL questionnaire to enable small differences between patients in the study to be distinguished tsQM generic questionnaire to evaluate treatment satisfaction related to side effects, effectiveness, convenience and global satisfaction Disease-specific work productivity and activity questionnaire to measure work- and activity-related impact of CID on patients Ad-hoc questions on patient knowledge of disease to determine patients’ understanding of their disease, including possible progression, complications and available treatments Ad-hoc questions on disease activity to understand patients’ assessment of current disease activity and collect information on treatment preferences.

Potential sites were screened using a questionnaire reflective of the study protocol to confirm availability of evaluable patients, level of interest in the study and ability to use an online electronic CRF (eCRF). Site selection was conducted in the local language by IMS Health local teams in the interests of efficiency and to maximize the rate of acceptance.

Ethics Committees and contracting The EC process can be time consuming and its efficient management by local experts who understand the culture, language and procedure, is key for an expedited and successful outcome.

In this study, IMS Health local regulatory experts presented the study to national and/or local ECs in accordance with legislation. The same experts also managed contracts with the study centers – important for ensuring conformity with local requirements and sensitivities.

continued on next page

ACCESSPOINT • VOLUME 5 ISSUE 9

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PROJECt FOCUs CHRONIC INFLAMMATORY DISORDER

Data collection Optimal collection of data was enabled through the use of a user-friendly eCRF developed by IMS Health for all study countries (Figure 3). This was intuitive and allowed physicians and study monitors to easily navigate from screen to screen.

Customized tracking tools and downloadable Excel reports were built into the eCRF (Figure 4).

FIGURE 3: SAMPLE SCREENS FROM THE eCRF

FIGURE 4: TRACKING TOOL ANd EXCEL dOWNLOAdS FROM THE eCRF

PAGE 58

IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR

site management When planning site management and monitoring, it is important to be aware that the needs of observational studies are very different from those of randomized clinical trials (RCTs). All too often there is a tendency to over complicate this element in observational research as though it was a RCT, resulting in an unnecessarily protracted and expensive process. In this case, once each site had received EC approval and a signed contract was in place, the local CRAs were able to train investigators and their teams.

Based on previous successful experiences, and in agreement with the company, IMS Health conducted telephonic training with each site. This not only allowed the sites to be trained at the most convenient time for them, even at very short notice, but also provided study teams with the chance to ask more questions than they would be able (or want) to in a group training call.

REAL-WORLD EVIDENCE THAT IMPACTS PATIENTS’ TREATMENT The findings of the study provided detailed insights into the impact of the CID on patients’ HRQoL and evidenced the unmet clinical need. They also served to drive awareness of the societal impact of the CID and support the company’s efforts to demonstrate the benefits of an early treatment intervention.

The observational study was successfully executed in 11 European countries. The results have already formed the basis of several poster presentations at international conferences and a further publication is being prepared for presentation in the most important international conference for the CID in question before the end of 2014.

Scientifically important conclusions have been drawn from the study with the potential for the evidence generated to influence clinical practice in the future.

During the patient recruitment period, regular follow-up calls were made to the sites by IMS Health to motivate patient inclusion, resolve queries and check for unreported serious adverse events. Queries were managed on an ongoing basis to maximize data quality and avoid unnecessary delays in study closure, facilitated by the integration of a query management system in the eCRF. A number of on-site close-out visits (SCVs) were made for source data verification. As an important cost driver of primary data collection, limiting SCVs to a randomly selected percentage of sites is recommended.

Analysis and reporting Once all the data had been entered into the eCRF, it was cleaned and the database locked for analysis. Paper PROs completed by patients were collected on an ongoing basis and entered into a database designed by IMS Health.

Reported diseases and co-morbidities were also coded.

As a final study step, IMS Health ran the study analysis and report in accordance with the approved Statistical Analysis Plan, and documented the results in a statistical report.

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PROJECt FOCUs ACUTE CORONARY SYNDROME

demonstrating the impact of non-adherence to antiplatelet therapy in acute coronary syndrome The linkage of real-world data on medication adherence, resource use and mortality from multiple sources can reveal untapped potential for lower costs and improved patient outcomes Adherence to medical therapies (the extent to which patients take their medicines as prescribed) is a primary determinant of treatment success,1 with significant health and cost implications.

Importantly, at a time of increased emphasis on improving quality and efficiency in healthcare, it is also a key modifier of health system effectiveness.1 Non-adherence to medication has been found to reduce the potential benefit shown in clinical trials by as much as 50%.2 It is a particular problem in cardiovascular disease, reportedly occurring in more than 60% of patients.3 According to a Cochrane review of adherence-improving programs, identifying effective ways of helping people follow medical treatments could have a far greater impact on health than any treatment itself.4 Understanding the prevalence and clinical and economic impact of non-adherence is essential to achieving this goal.

ADHERENCE IN ACUTE CORONARY SYNDROME Acute coronary syndrome (ACS) refers to a range of emergency cardiac conditions that are triggered by a sudden reduction of blood flow to the heart. Current practice guidelines for ACS recommend the administration of antiplatelet therapy (AT) both during and after hospital discharge.5,6 Non-adherence to AT has been associated with higher risk of thrombosis, myocardial infarction and mortality.7 To date, real-world evidence on the relationship between adherence with AT post-discharge and patient outcomes has been restricted to data from self-reported surveys on medication adherence, and administrative data from two integrated healthcare systems.8,9,10 In addition to their lack of generalizability, these studies have been limited by lack of access to data on inpatient prescriptions for AT, preventing determination of long-term patient persistence from the point of initial treatment within the hospital. In addition, no study has reported on the relationship between AT adherence and healthcare costs.

PAGE 60

The author Jerrold hill, PhD, Ms is Director, RWE Solutions & HEOR, IMS Health J.hill@us.imshealth.com

DOCUMENTING ANTIPLATELET TREATMENT ADHERENCE POST-DISCHARGE Against this background, and to better understand the mortality, readmission risk and cost implications of failure to adhere to AT therapy, IMS Health undertook a study to document AT adherence following discharge from hospitalization for ACS. The study also sought to estimate the relationship between adherence post-discharge and the following outcomes

• • •

Rate of hospital readmission within 30 days and one year Mortality within one year post-discharge (and time to death) Healthcare costs at one year post-discharge

study data Patients hospitalized for ACS between July 2009 and July 2012 were identified from IMS Comprehensive Disease Records (CDRs) for ACS. CDRs links anonymous patient-level data from four separate IMS Health databases 1. PharMetrics Plus™ health plan claims 2. Hospital Charge Data Master (CDM) 3. Ambulatory Electronic Medical Record (EMR) data 4. Mortality data derived from the Social Security Death Index (SSDI) The linkage allows a comprehensive set of measures not available in a single data source. It is made possible via a unique patient ID, created through the IMS Health patented and HIPAA-compliant encryption methodology. This enables deterministic matching of patients across databases.

Three sources of data from the ACS CDRs were used for the study: more than 500,000 patients from PharMetrics Plus and 410,000 from IMS Hospital Charge Master were linked to 130 million individuals with mortality data derived from the SSDI.

IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR

To be included in the study, patients were required to

• • • • •

Have an ACS diagnosis and hospitalization in PharMetrics Plus and CDM Be enrolled in PharMetrics Plus six months prior to index hospitalization for ACS with 12 months of follow-up data post-index or until death within one year post-discharge Have data on death status in IMS mortality data Be prescribed AT at index ACS hospitalization Be alive more than one month post-discharge

The prerequisite for patients to survive at least one month was necessary because the date of death in the database is recorded as calendar month and year – not calendar day. As a result, the relationship between period of non-adherence and date of death in the first month was unknown. Mortality risk was therefore evaluated among patients for whom there was at least one month of adherence data prior to data on death in the subsequent month.

Methodology Patients with no diagnosis of ACS 180 days prior to hospital admission and with confirmed inpatient AT therapy were followed until the earlier of 360 days post-discharge or date of death. Adherence was measured by

•

• •

time to first fill post-discharge Four dichotomous variables measured whether the patient filled a prescription within 30 days, 60 days, 90 days, or within one year post-discharge. Indicators of early fill dates after discharge are valuable since risk of mortality and readmission is highest in the months immediately after discharge Proportion of days covered (PDC) from days’ supply on Rx claims This is measured as the percent of days one year post-discharge or up to the time of the event of interest (death or readmission) or censure from sample.

PDC at each month post-discharge for use as the adherence measure in Extended Cox models with time varying adherence

Unadjusted estimates of study outcomes covered four categories of adherence: 0%; 1-39%; 40-79%; ≥80%.

Adjusted estimates controlled for patient demographics, hospital characteristics, ACS hospital interventions and cardiovascular risk factors, using logistic regression (mortality, readmission), Cox Proportional Hazards and extended Cox (mortality, readmission) and a Generalized Linear Model (costs).

FIGURE 1: TIME TO FIRST ANTIPLATELET FILL

Only 50% of patients filled an AT prescription at 30 days post-discharge Filled Rx for AT within 1 year

67.0%

Filled Rx for AT within 90 days

62.8%

Filled Rx for AT within 60 days

59.9%

Filled Rx for AT within 30 days

50.1%

PRINCIPAL STUDY RESULTS The analysis showed that of the 2,994 patients selected for analysis, only 50% filled an AT prescription at 30 days post-discharge and only 67% by the end of year one (Figure 1). Patients who filled for AT within 30 days had a lower readmission rate (7.4%) compared to those with no fill (14.2%). The greater benefits associated with first AT fill reflected the higher risk of death and readmission in the months immediately following index discharge.

After adjusting for patient characteristics, high adherence to AT (>80%) after hospital discharge for ACS was linked to significantly lower mortalityE (70%), with death increasing markedly as adherence fell below 80% (Figure 2). continued on next page

FIGURE 2: AdHERENCE ANd MORTALITY ONE YEAR POST-dISCHARGE

Mortality increases markedly as adherence falls below 80% (unadjusted)

Adjusted results show that the impact of adherence on mortality is markedly higher when over 80% COXPH Results

4.1%

0%

4.5%

1% - <40%

3.1%

40% - <80%

≥80%

0.7%

1% - <40%

(CI: 0.583 - 1.674 )

40% - <80%

(CI: 0.512 - 1.418 )

≥80%

(CI: 0.166 - 0.503)

0.988

0.852

0.289

Extended COX Results Time varying adherence

(CI: 0.187 - 0.544)

Mortality rate

0.319

Hazards ratio

Reference group for Cox models is patients not filling a script for AT post-discharge. P<0.001 for all hazards ratios.

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PROJECt FOCUs ACUTE CORONARY SYNDROME

NEW INSIGHTS FOR IMPROVED OUTCOMES AND REDUCED COSTS This study provides new and important evidence-based insights into the impact of AT adherence on mortality, readmission risk and costs in ACS. The results reveal significant opportunities to reduce healthcare costs as well as improve clinical outcomes in ACS through efforts to increase AT adherence. In addition, the study demonstrates the value of linking data across large administrative databases. Specifically, the study showed

• •

FIGURE 3: ADHERENCE AND HOSPITAL READMISSION ONE YEAR POST-DISCHARGE (UNADJUSTED)

Adherence - PDC

Adherence of ≥80% was also associated with substantially lower readmission rates (Figure 3) and lower total healthcare costs at one-year post-hospital discharge.

44.8%

0%

39.2%

1%- <40%

40.8%

40% - <80% 16.9%

≥80%

Readmission rate P<0.0001 for 30 day and 1-year readmissions

The potential to link adherence, healthcare cost/use and mortality data from multiple healthcare administrative data sources to enhance research capabilities, using data sources such as IMS Comprehensive Disease Records for ACS. Valuable applications from linking data across multiple administrative data sources including potential preclusion of costly primary data collection; addition of mortality data similar to this study; and linkage of large patient populations to extend geographic and provider coverage, thus increasing confidence to generalize study results.

Acknowledgement The outstanding scientific merit of this study was recognized with an ISPOR Research Presentation Podium Award following oral presentation of the findings at the ISPOR 19th Annual International Meeting in Montreal, May 31–June 4, 2014.* Dr Hill gratefully acknowledges the contribution of his co-authors. *Hill J, Pokras S, Makin C, Schabert VF, Nelson M, Foody J. Non-adherence to antiplatelet therapy after hospitalization for acute coronary syndrome (ACS) increases readmissions, mortality, healthcare use and costs. Value in Health, 2014; 17: A8

1

World Health Organization. Adherence to long-term therapies: Evidence for action. Geneva: WHO, 2003 Cherry SB, Benner JS, Hussein MA, Tang SS, Nichol MB. The clinical and economic burden of nonadherence with antihypertensive and lipid-lowering therapy in hypertensive patients. Value in Health, 2009, Jun; 12(4): 489-97 3 Kravitz RL, Hays RD, Sherbourne CD, DiMatteo MR, Rogers WH, Ordway L, Greenfield S. Recall of recommendations and adherence to advice among patients with chronic medical conditions. Arch Intern Med, 1993, Aug 23; 153(16): 1869-78 4 Haynes RB, Ackloo E, Sahota N, McDonald HP, Yao X. Interventions for enhancing medication adherence. Cochrane Database Syst Rev 2008; (CD000011) 5 Jneid H, Anderson JL, Scott Wright R, Adams CD, Bridges CR, Casey DE, et al . 2012 ACCF/AHA Focused update of the guidelines for management of patients with unstable angina/non-ST elevation myocardial infarction. (Updating the 2007 Guideline and Replacing the 2011 Focused Update): A Report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. Circulation, 2012; 176: 875-910 6 O'Gara PT, Kushner FG, Ascheim DD, Casey DE, Jr, Chung MK, de Lemos JA, et al. 2013 ACCF/AHA Guideline for the management of ST elevation myocardial infarction. A report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines.

Circulation, 2013; 127: 529-555 7 Jarvie JL, Foody JM. Predictors of early discontinuation of dual-antiplatelet therapy: Room for improvement. Circulation, 2010; 22: 946-948 8 Valgimigli M, Campo G, Arcozzi C, Malagutti P, Carletti R, Ferrari F, et al. Two-year clinical follow-up after sirolimus-eluting versus bare-metal stent implantation assisted by systematic glycoprotein IIb/IIIa Inhibitor Infusion in patients with myocardial infarction: Results from the STRATEGY study.

J Am Coll Cardiol, 2007; 50: 138-145 9 Ho PM, Peterson ED, Wang L, Magid DJ, Fihn SD, Larsen GC, et al. Incidence of death and acute myocardial infarction associated with stopping clopidogrel after acute coronary syndrome. JAMA, 2008; 299: 532-539 10 Ho PM, Tsai TT, Wang TY, Shetterly SM, Clarke CL, Go AS, et al. Adverse events after stopping clopidogrel in post–acute coronary syndrome patients: Insights from a large integrated healthcare delivery system. Circ Cardiovasc Qual Outcomes, 2010; 3: 303-308 2

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IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR

PROJECt FOCUs RWE-BASED DISEASE MANAGEMENT

Modeling disease management above the brand with real-world evidence Insights from RWE can illustrate the value of treatments alongside the full suite of care activities to inform and enable comprehensive disease management To operate in a value-oriented healthcare marketplace, decision makers require new approaches to disease management that reduce costs while supporting high-quality patient outcomes. However, with advances in medical science and the rising number of novel alternative therapeutic interventions, disease management is becoming more complex. New data and tools are needed to understand how individual treatment decisions fit into the overall management of a disease to help drive care efficiency in a comprehensive manner.

The tremendous growth of real-world evidence is creating opportunities to transform the decision-making landscape. Data-driven insights are increasingly required to inform treatment choices and resource allocation for patient care at a broader level, based on a complete picture of disease management options. Manufacturers can play a role in supporting patient health at a holistic level by expanding their above-the-brand offerings and aligning with external stakeholders seeking to improve patient outcomes.

RWE-based disease management tools can assist healthcare payers and providers in their efforts to understand the value of new treatments alongside the full suite of patient care activities. For one leading pharmaceutical organization, the development of such a tool in support of its portfolio has paved the way for evidence-based discussions with decision makers on the potential of different disease management programs to promote efficiency improvements and advance patient health.

BUILDING A VALUE PROPOSITION ABOVE THE BRAND The company was seeking to position itself as a partner with several external customers who bear financial risk of disease management decisions. To help meet this objective, IMS Health was asked to design and develop a tool for the

The authors Julie Munakata, Ms is Senior Principal, RWE Solutions & HEOR IMS Health jmunakata@us.imshealth.com stacey Kowal, Ms is Director, RWE Solutions & HEOR IMS Health skowal@us.imshealth.com Cheryl Ferrufino is Senior Consultant, RWE Solutions & HEOR IMS Health cferrufino@us.imshealth.com Beth Wehler, MPh is Consultant, RWE Solutions & HEO IMS Health bwehler@us.imshealth.com

company’s customer-facing field team to address questions surrounding pharmaceutical and healthcare cost, utilization patterns and the impact of disease management alternatives (Figure 1).

MODELING DISEASE MANAGEMENT LEVERS AND TOTAL HEALTHCARE COST IMS Health began by collecting cost and utilization data for patients in three different disease areas from a custom RWE platform that integrates the IMS National Prescription Audit (NPA™) database with the IMS PharMetrics Plus database. For each disease, fully-adjudicated pharmacy and medical claims for patients meeting key inclusion and exclusion criteria were combined to create a longitudinal cohort with a pre-index of 180 days and a follow-up of 360 days. Results of cohort analyses represented total direct medical costs and can be interpreted as the cost of care to a fiscal risk-bearing stakeholder in the US, including healthcare payers and providers. continued on next page

ACCESSPOINT • VOLUME 5 ISSUE 9

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PROJECt FOCUs RWE-BASED DISEASE MANAGEMENT

FIGURE 1: THE TOTAL HEALTHCARE COST TOOL AddRESSES KEY PAYER ANd PROVIdER QUESTIONS

• How much am I

• What are the national/ Questions answered by RWE-based insights from the total healthcare cost tool

regional benchmarks for the total cost of care across diseases?

• Where are the opportunities to make a meaningful impact on total cost patterns?

Actionable options with the highest impact on fiscal savings

spending to provide healthcare and how do my costs compare to others?

• How do I best allocate limited resources to meet population needs?

• How can new treatment

• How do treatment

Key questions among payers & providers facing a high fiscal burden

alternatives or treatment approaches change cost trends?

options help to shift the cost curve?

Using the output on health service utilization and direct medical costs from the custom RWE platform, IMS Health developed a total healthcare cost tool to support the company’s discussions on direct medical cost trends and the potential of concepts proven useful in disease management to change these trends. The concepts were derived from the medical literature to identify ’levers’ that could shift total costs, including patient and physician behaviors, treatments/interventions and healthcare quality initiatives.

The model compares the current, RWE-based costs of disease to the projected costs after applying disease management levers, with savings estimated by cohort analyses in the RWE platform. All costs and disease trends are based on a one-year time horizon. The levers focus on

broad disease concepts as well as above-the-brand treatment concepts that dovetail with brand-level propositions for the company’s product portfolio.

Developed in MS Excel, the tool includes three distinct disease modules with nationally- and regionallyrepresentative benchmark data presented in a user-friendly format. The model enables ad hoc explorations of current cost trends and the impact of potential actions (levers) using benchmark or custom data inputs. For each disease, model inputs and results can be dynamically viewed across changes in patient age, geographies (national, regional), cost types (allowable medical costs, patient out-of-pocket payments), care settings (inpatient, outpatient, pharmacy) and pre-defined patient sub-groups (all diagnosed patients, newly diagnosed patients, treated or untreated patients).

FIGURE 2A: HYPOTHETICAL IMPACT OF ALTERNATIVE dISEASE MANAGEMENT ACTIVITIES IN HEART dISEASE - CHANGE IN TOTAL HEALTHCARE COSTS

Relative savings from change in ‘lever’

Current costs

Reducing the risk of major bleeds Reducing the risk of ischemic bleeds Closing the treatment gap Improved management of diabetes co-morbidity Reducing the likelihood of readminssion -20.00%

-15.00%

-10.00%

-5.00%

0.00%

Change in total healthcare costs

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IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR

FIGURE 2b: HYPOTHETICAL IMPACT OF ALTERNATIVE dISEASE MANAGEMENT ACTIVITIES IN HEART dISEASE - CURRENT ANd FUTURE HEALTHCARE COSTS $500m Absolute savings from change in ‘lever’

$450m $400m $350m $300m $250m $200m $150m $100m $50m $0m Improved management of diabetes co-morbidity

Closing the treatment gap

Reducing the likelihood of readmission

Current healthcare costs

FIELD-READY TOOL WITH REAL-TIME UPDATES The field-ready tool presents real-time updates of projected impact based on external customer inputs for costs, population characteristics and potential changes in disease management activities. For example, a payer could compare the likely impact of several new disease management efforts in heart disease to help inform future decisions on resource allocation. Figures 2A and 2B present a scenario evaluating the impact of a hypothetical 25% improvement from baseline lever values, based on national benchmark data for one million covered lives.

Examples of levers modeled for heart disease are shown in Table 1.

NOVEL DYNAMIC SUPPORT FOR INTERACTIVE CUSTOMER DISCUSSIONS Through working with IMS Health, the company has gained a novel and dynamic tool that leverages RWE to move beyond the currently published evidence, presenting nationally representative benchmark data with the ability to quickly view differences across age groups, geographies, cost types and care settings. The tool facilitates and informs interactive engagement with external customers on several levels. Firstly, by quantifying the magnitude of costs across diseases and highlighting the value of addressing avoidable outcomes or improving symptom management, it supports the foundation of the value proposition for the company’s portfolio of products in the disease area. Secondly, by identifying challenges in disease management at a more macro level, it helps the field team transition to discussions around the benefits of the company’s abovethe-brand offerings, thereby also serving as a pathway to its suite of resources for improving population health.

ACCESSPOINT • VOLUME 5 ISSUE 9

Reducing the risk of ischemic stroke

Reducing the risk of major bleeds

Future healthcare costs

TAbLE 1: SELECTEd LEVERS MOdELEd FOR HEART dISEASE MANAGEMENT

Lever

Benefits

Closing the treatment gap

Closing the anticoagulant treatment gap in treatment-eligible patients (CHADS2 score of >2) who are not contraindicated for treatment can improve disease management and reduce avoidable costs

Changing the likelihood of readmission

Improving inpatient care and posthospitalization treatment can reduce the likelihood of readmission and associated costs

Reducing the risk of ischemic stroke

Effective treatment and disease management could potentially reduce the risk of ischemic stroke and stroke-related healthcare costs

Improving the management of diabetes co-morbidity

Care activities and education efforts can help reduce the risk of contracting diabetes and reducing any avoidable, excess diabetesrelated costs (effective treatment) in patients with heart disease

Reducing the risk of major bleeds

Effective disease management with treatments with a low (or reduced) risk of avoidable treatment-related bleeding events can reduce healthcare costs

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IMs hEALth RWE sOLUtIOns & hEOR OVERVIEW

Enabling your real-world success IMS Health has a globally unique and powerful RWE approach to understand patient outcomes and support successful market access. It is built off a long history in HEOR, scientific methodologies, real-world data and cross-stakeholder collaborations.

Our approach is designed to enable your success

• •

Largest global team with HEOR, epidemiology, drug safety and RWE expertise based across 18 countries strong scientific voice reflected in over 2,600 publications

• Established success in generating and communicating RWE to advance stakeholder engagement, including guideline development, compliance program management and managed entry agreements

•

Market leadership in developing and adapting robust economic models

• Most advanced RWE capabilities and technology including data sourcing, warehousing, integration, curation and protection, driving powerful scientific and commercial insights • Broadest and deepest collection of scientifically validated, anonymous patient-level data assets, enabling therapy area, market-specific and global insights

Leadership and innovation across the RWE and HEOR spectrum

IMS LifeLink – the broadest and deepest collection of scientifically validated, anonymous patient-level data assets • Health plan claims • PharMetrics Plus • Longitudinal Rx • Electronic medical records • Hospital disease • Custom data sourcing TM

Real-W orl d

TM

Services and Engagement Generating scientifically and commercially relevant insights using novel patient-centric metrics

Strategic Support • Corporate, franchise and product RWE strategy development • Evidence plans aligned with commercial priorities • dynamic marketing plans and outcomes-based commercialization • Stakeholder engagement • RWE training/organizational readiness

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ta Da

IMS HEAL HEALTH LTH TH REAL-WORLD REAL-WORLD EVIDENCE EVIDENCE SOLUTIONS SOLUTIONS & HEOR

abled -En gy ics lo alyt n

Tec hn o A

Real-World Data

Technology-Enabled Analytics Analytic tools that leverage powerful technologies to deliver scientific and commercial insights efficiently • data integration & linking • Evidence platform development • data warehouse/data marts • User interface & sophisticated analytics library • IMS Evidence 360™

a n d S e r v i ce s e nt Engage m

Outcomes Research, Epidemiology & drug Safety

Health Economic Modeling & Market Access

• • • • • • •

• Health economic evaluations • Global models & local adaptations • budget impact • Indirect comparisons • Value dossiers • HTA readiness • Value communication

Evidence generation PRO, QoL studies Late-phase studies Mixed methods database studies, CER drug utilization studies Comparative safety & outcomes studies

Market-Level Engagement • Patient journeys, market landscapes and forecasts • Incentive compensation approaches • Performance management approaches • Targeted media and patient (where allowed) engagement models

IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR

IMs hEALth RWE sOLUtIOns & hEOR LOCATIONS

Global scope, local expertise IMS RWE Solutions & HEOR experts are located in 18 countries worldwide and they have published on projects completed in more than 50 countries on all continents.

Your primary contacts Jon Resnick, Vice President and General Manager One IMS drive, Plymouth Meeting, PA 19462, USA • Tel: +1 610 834 0800 • Jresnick@imshealth.com Dr. Jacco Keja, Senior Principal 210 Pentonville Road, London N1 9JY, UK • Tel: +31 (0) 631 693 939 • Jkeja@nl.imshealth.com Dr. Patrik Sobocki, Senior Principal Sveavägen 155, SE-113 46 Stockholm, Sweden • Tel: +46 (0) 8 508 999 95 • Psobocki@se.imshealth.com

IMS RWE Solutions & HEOR key office locations ASIA PACIFIC REGIONAL HEADQUARTERS 8 Cross Street #21-01/02/03 Singapore 048424 Tel: +65 6412 7365 EUROPE REGIONAL HEADQUARTERS 210 Pentonville Road London N1 9JY United Kingdom Tel: +44 (0) 20 3075 4800 JAPAN Toranomon Towers 4-1-28 Toranomon Minato-ku Tokyo 105-0001 Japan Tel: +81 3 5425 9541 LATIN AMERICA REGIONAL HEADQUARTERS Insurgentes Sur # 2375 5th Floor, Col. Tizapan México City d.F. - C.P. 01090 México Tel: +52 55 5089 5205 NORTH AMERICA REGIONAL HEADQUARTERS 11 Waterview boulevard Parsippany, NJ 07054 USA Tel: +1 973 316 4000

AUSTRALIA Level 5, Charter Grove 29-57 Christie Street St Leonards, NSW 2065 Australia Telephone: +61 2 9805 6800 BELGIUM Medialaan 38 1800 Vilvoorde belgium Tel: +32 2 627 3211 CANADA 16720 Route Transcanadienne Kirkland, Québec H9H 5M3 Canada Tel: +1 514 428 6000 CHINA 7/F Central Tower China Overseas Plaza Jianguomenwai Avenue, Chaoyang district beijing 100001 China Tel: +86 10 8567 4414 FRANCE 29ème Etage Tour Ariane 5-7 Place de la Pyramide 92088 La défense Cedex France Tel: +33 1 41 35 1000

GERMANY Erika-Mann-Str. 5 80636 München Germany Tel: +49 89 457912 6400 ITALY Viale Certosa 2 20155 Milano Italy Tel: +39 02 69 78 6721 SOUTH KOREA 9F Handok building 735 Yeoksam1-dong Kangnam-ku Seoul 135-755 S. Korea Tel: +82 2 3459 7307 SPAIN dr Ferran, 25-27 08034 barcelona Spain Tel: +34 93 749 63 00 SWEDEN Sveavägen 155/Plan9 11346 Stockholm Sweden Tel: +46 8 508 842 00

SWITZERLAND Theaterstr. 4 4051 basle Switzerland Tel: +41 61 204 5071 Tel: +44 (0) 20 3075 4800 TAIWAN 18/F 216 Tun Hwa South Road Sec 2 Taipei 10669 Taiwan ROC Tel: +886 2 2376 1836 UNITED KINGDOM 210 Pentonville Road London N1 9JY United Kingdom Tel: +44 (0) 20 3075 4800 UNITED STATES 8280 Willow Oaks Corporate drive, Suite 775 Fairfax, Virginia 22031 USA Tel: +1 (703) 992 1025 One IMS drive Plymouth Meeting PA 19462 USA Tel: +1 610 834 0800

For further information, email RWEinfo@imshealth.com or visit www.imshealth.com/rwe

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IMs hEALth RWE sOLUtIOns & hEOR EXPERTISE

Expertise in depth The IMS Health RWE Solutions & HEOR team brings unrivalled experience and specialist knowledge from industry, consulting, government and academia globally, and includes leading scientists in epidemiology, drug safety and risk management. With proven expertise in all key therapy areas, we have a track record of helping clients meet the growing demands of an increasingly complex pharmaceutical landscape.

Our senior team Jean-Marc Aubert, M.Eng, MsC • Jean-Marc Aubert is a Senior Principal, supporting healthcare providers, health authorities and payers.

• Jean-Marc has extensive pharmaceutical experience ranging from real-world effectiveness and the regulatory process to sales force, marketing effectiveness and brand performance. His background includes roles as a partner heading business development in the healthcare sector at Jalma, as deputy director at CNAMTS (French National Health Insurance Fund for Salaried Workers) and as Chief of Staff of the State Secretary for Health Insurance.

• An expert in the French healthcare system, market access, commercial effectiveness, RWE and HEOR, Jean-Marc holds a Master’s degree in Engineering and a Master of Science degree, both from École Polytechnique, France; a Specialist Postgraduate diploma in Statistics and Economics from École Nationale de la Statistique et de l'Administration Économique (ENSAE); and a Specialist Postgraduate diploma in Economics (dEA) from École des Hautes Études en Sciences Sociales (EHESS), France.

Yumiko Asukai, MsC • Yumi Asukai is a Principal, specializing in the development of economic models across the product lifecycle and the interpretation of model outputs for strategic market access and value demonstration. Her expertise in this field spans from early strategic modeling through to global core cost-utility models.

• Yumi’s background includes roles at Fourth Hurdle Consulting and in healthcare and business consulting in San Francisco and Tokyo, where she focused on comparative studies of health policies between Japan and the US complemented by analyses of primary data. Yumi has worked extensively in the cardiovascular, oncology and respiratory disease areas and she is part of a global modeling taskforce for COPd composed of academic and industry members.

• Yumi holds a Master's degree in Health Policy, Planning and Financing from the London School of Hygiene & Tropical Medicine and the London School of Economics, and a bachelor's degree in Political Science from Stanford University.

Karin Berger, MBA • Karin berger is a Principal, with a focus on RWE, PROs and cost-effectiveness evaluation analyses at a national and international level.

• Formerly Managing director of MERG (Medical Economics Research Group), an independent German organization providing health economics services to the pharmaceutical industry, university hospitals and European Commission, Karin has more than 15 years experience in the health economics arena. She lectures at several universities, has published extensively in peer-reviewed journals, and regularly presents at economic and medical conferences around the world.

• Karin graduated as diplom-Kaufmann (German MbA equivalent) from the bayreuth University, Germany, with a special focus on health economics.

Christopher M. Blanchette, PhD, MsC, MBA, MA • Dr. Christopher Blanchette is a Principal, experienced in leading clinical and health services research programs and teams in the pharmaceutical industry and consultancy. He is skilled in the use of healthcare databases to assess clinical and economic outcomes, with a particular focus on chronic diseases.

• Prior to joining IMS Health, Chris was Associate Dean for Research & Public Engagement, Director of Data Sciences & Business Analytics and an Associate Professor of Public Health Sciences, College of Health & Human Services, University of North Carolina. He was also a Research Health Scientist at the W.G. (Bill) Heffner VA Medical Center, Salisbury, NC. • Chris is a peer reviewer for the AHRQ and PCORI, editor-in-chief for Drugs in Context, and editorial board member for Journal of Medical Economics and Current Medical Research and Opinion. He holds a PhD in Pharmaceutical Health Services Research with a concentration in Pharmacoepidemiology and a Master of Science degree in Epidemiology from the University of Maryland; an MBA in Pharmaceutical & Healthcare Marketing from Saint Joseph’s University; and a Master of Arts degree in Medical Sociology from the University of North Carolina.

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IMs hEALth RWE sOLUtIOns & hEOR EXPERTISE Ian Bonzani, PhD, BsC • dr. Ian bonzani is a Principal, leveraging his scientific background and consulting experience to help clients create and implement franchise strategies in the pricing and market access and RWE space. He manages large-scale RWE engagements across stakeholders, functions and geographies.

• With a background that includes roles in the IMS Consulting Group in Europe, Ian has expertise in RWE strategy, franchise evidence generation strategy, real-world data sourcing, and data marts/technology platforms. He has been involved in a wide range of global projects, including RWE planning, large-scale data mart creation and implementation, collaborative engagement models, and disruptive evidence generation.

• Ian holds a Phd in Regenerative Medicine from Imperial College London (Marshall Scholar) and a bachelor of Science degree in biomedical Engineering from Worcester Polytechnic Institute, Worcester, Massachusetts.

Richard Borrelli, MBA • Richard borrelli is a Principal, leading a team in Canada supporting evidence-based solutions for healthcare stakeholders.

He is recognized for his expertise in leveraging longitudinal patient-level data to better understand and quantify realworld treatment pathways, and heads development of innovative research protocols involving EMR supplemented with patient and physician feedback.

• Richard has extensive experience leveraging Canadian pharmacy and claims data to evaluate patient utilization of medicines. These insights have informed decision making for market access, health economic, sales and marketing divisions of pharmaceutical companies, as well as payer organizations in the country. Richard also utilizes Canadian EMR to describe indirect and direct burden of illness while evaluating patient real-world outcomes.

• Richard holds an MbA (with distinction) from deGroote School of business, McMaster University, and a bachelor of Commerce degree from the University of Toronto. nevzeta Bosnic, BA • Nevzeta bosnic is a Principal, focused on managing projects to meet the broad spectrum of client needs in the Canadian pharmaceutical market.

• Formerly director of Economic Consulting at brogan Inc, Nev has led many strategic consulting, policy and data analyses for pharmaceutical clients, government bodies and academic institutions in Canada. She has extensive knowledge of public and private drug plans across the country and in-depth expertise and experience on the drug reimbursement process.

• Nev holds a bachelor’s degree in business Economics from the School of Economics and business at the University of Sarajevo, bosnia-Herzegovina.

Chakkarin Burudpakdee, PhARM. D • dr. Chakkarin burudpakdee is a Principal, with extensive experience in HEOR and strategic consulting, including product value development and communication, market entry strategies and lifecycle management plans. He has led teams in observational research, economic modeling, patient and provider surveys, systematic reviews and meta-analyses.

• Prior to joining IMS Health, Chakkarin was VP, Evidence development at MKTXS, where he built and oversaw scientific direction of the HEOR department and developed relationships with academic institutions around the world that provided access to patient-level data for observational research. He began his career as a clinical analyst at ValueMedics Research LLC. • Chakkarin holds a Pharm.d from Philadelphia College of Pharmacy and Science, now University of the Sciences in Philadelphia, and is a Research Assistant Professor in the College of Health and Human Services, University of North Carolina at Charlotte.

Joe Caputo, BsC • Joe Caputo is Regional Principal, leveraging more than 20 years experience in the pharmaceutical sector to help clients address the challenges of global reimbursement and market access throughout the drug development program in the Asia-Pacific region. He has led numerous projects involving payer research, value dossiers, local market access models and HTA submissions.

• Joe's background includes industry roles in drug development, sales and marketing, and UK and global health outcomes, as well as consulting in health economics. He has wide-ranging knowledge of the drug development process at both local and international level and a unique understanding of evidence gaps in light of reimbursement and market access requirements.

• Joe holds a bachelor's degree in Applied Statistics and Operational Research from Sheffield Hallam University, UK. Adam Collier, MsC • Adam Collier is a Senior Principal, with responsibility for consulting and data related to IMS Health patient-level data assets in the UK. He has 18 years commercial experience in the UK and European healthcare industry.

• Adam’s background spans pharmaceuticals, consulting and healthcare provision, allowing an unusually broad view of the challenges inherent across the healthcare arena. He spent nine years at GlaxoSmithKline in roles within customer and trading strategy, commercial analysis and European marketing, and two years at Accenture, where he also completed a secondment to the Medicines & Healthcare Products Regulatory Agency (MHRA) to work on their patient data asset GPRd (now CPRd). Prior to joining IMS Health, he spent several years with a private healthcare provider.

• Adam holds a Master’s degree in Chemistry from the University of Oxford. continued on next page ACCESSPOINT • VOLUME 5 ISSUE 9

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IMs hEALth RWE sOLUtIOns & hEOR EXPERTISE neil Corner • Neil Corner is a Leader, RWE Solutions, supporting government, academics and the pharmaceutical industry in understanding and delivering health outcomes data, with a focus on mHealth, integrated patient data and EMRs, including the creation of interactive electronic patient registries.

• Neil has 27 years experience in the pharmaceutical industry in the UK, US, EMEA and Canada, 16 of which were spent at Janssen Pharmaceuticals, including the post of Global Commercial Leader. Prior to joining IMS Health, where his roles have included international franchise lead for patient and medical data, Neil led Helix Healthcare, a division of Quintiles.

• Neil is the author of several publications on EMR data validation, RWE in the Canadian market and Customer Relationship Management. His research and development activities currently focus on the innovative design and construction of integrated health data ecosystems to create outcomes in the world of big data.

Bruce Crawford, MA, MPh, BsC • bruce Crawford is a Senior Principal, with over 20 years consulting experience and expertise in prospective study design, patient-reported outcome evaluations, cost-effectiveness analyses and reimbursement.

• Over the past 13 years, bruce has worked on projects throughout Asia and Japan. He was previously Managing director Asia and Senior VP at Adelphi, prior to which he was Operations director at Mapi Values. He has worked in managed care and for a major CRO as a health economist, and been involved in research and training with the US FdA, the Japanese PMdA, and the Thailand FdA and National List of Essential drugs committee.

• bruce has written and lectured on pharmacoeconomic and outcomes research methodologies and impacts on study validity, and recently held appointments as Adjunct Project Professor of HTA and Public Policy at Tokyo University Graduate School of Public Policy and as Adjunct Instructor at Kyoto University, School of Medicine and Public Health, dept. of Pharmacoepidemiology. He holds a Master of Arts degree in Economics and a bachelor of Science degree in Mathematics and Economics from the University of New Hampshire, and an MPH, specializing in Epidemiology and biostatistics, from Tufts University School of Medicine.

Mitch DeKoven, MhsA • Mitch deKoven is a Principal, leading teams in a variety of projects, including value development plans, retrospective database studies and observational surveys.

• Prior to joining IMS Health, Mitch was an Associate director of Reimbursement and Market Access at ValueMedics Research LLC. His previous roles include Manager of Reimbursement Services at United bioSource Corporation’s Center for Pricing & Reimbursement, Consultant with CHPS Consulting, and Program Manager of the Center for Cancer and blood disorders Children’s National Medical Center in Washington, dC, a position he held after completing an administrative fellowship with the Johns Hopkins Health System.

• A past president of the board of directors of the Lupus Foundation of America Greater Washington Chapter, Mitch serves on six editorial advisory boards and is a peer reviewer for a number of international healthcare journals. He has also authored several articles. Mitch holds an MHSA from the University of Michigan School of Public Health and a bachelor’s degree in Spanish from Washington University in St. Louis.

Frank-Ulrich Fricke, PhD, MsC • dr. Frank-Ulrich Fricke is a Principal at IMS Health and Professor for Health Economics, Georg-Simon-Ohm University of Applied Sciences, Nuremberg in Germany, with a focus on health economic evaluations, market access strategies and health policy.

• Formerly a Managing director of Fricke & Pirk GmbH, and previously Head of Health Economics at Novartis Pharmaceuticals, Frank-Ulrich has conducted health economic evaluations across a wide range of therapeutic areas, developing a wealth of experience in pricing, health affairs and health policy. As a co-founder of the NIG 21 association, he has forged strong relationships with health economists, physicians and related researchers working in the German healthcare system.

• Frank-Ulrich holds a Phd in Economics from the bayreuth University, and an MbA equivalent from the Christian-AlbrechtsUniversity, Kiel.

Joshua hiller, MBA • Joshua Hiller is a Senior Principal, supporting the strategic planning and development of IMS Health capabilities for data sourcing, integration, analytics and studies. He is also currently serving as Alliance director in the company’s collaboration with AstraZeneca for the advancement of RWE. • during a career that includes roles in market analytics, government and healthcare consulting in both the US and UK, Joshua has led a wide range of projects for clients in the pharmaceutical and biotech sector as well as industry associations. He has extensive experience in pharmaceutical pricing, contracting, market landscape development, supply management, cross border trade, lifecycle management, competitive defense, generics market drivers and account management, with expertise across US and European markets.

• Joshua holds an MbA (beta Gamma Sigma) from Columbia business School, New York, and a bachelor of Science degree in Mathematics from James Madison University, Virginia.

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IMs hEALth RWE sOLUtIOns & hEOR EXPERTISE Benjamin hughes, PhD, MBA, MREs, MsC • dr. ben Hughes is a Vice President, leading development of the company’s RWE strategy and offering. He has helped many clients in the pharmaceutical industry to articulate and implement their RWE strategies through definition of RWE vision, business cases for RWE investments, capability roadmaps, partnerships, brand evidence reviews, HEOR function design, RWE training programs and related clinical IT strategies.

• Previously head of the European RWE service line at McKinsey & Co, ben has extensive experience advising healthcare stakeholders on health informatics and RWE-related topics. This includes work on France’s electronic health record strategy, EMR adoption strategy for governments across Europe and Asia, data releases to support the UK’s transparency agenda, and the development of payer health analytics and RWE capabilities across countries in Europe.

• A widely published author on health informatics, ben holds a Phd in Medical Informatics from ESAdE barcelona, an MbA from HEC Paris, and Masters’ degrees in Research from ESAdE barcelona and in Physics from University College, London.

Kjel A. Johnson, PhARM.D, BCPs, FCCP, FAMCP • dr. Kjel Johnson is a Vice President, focused on developing the company’s oncology data, analytics and informatics business across key geographies.

• Kjel was previously Senior Vice President of Strategy and business development at Magellan Pharmacy Services/ICORE Healthcare, developing comprehensive specialty management strategies and services for payers. A co-founder of ICORE, he has significant expertise in outsourcing and turn-around strategies, outcomes measures and cost reduction strategies gained during a career that includes senior roles at deloitte Consulting, UPMC HealthPlan and Coventry Healthcare.

• Principle investigator on more than 40 clinical trials, Kjel has authored over 50 papers and founded Managed Care Oncology. He lectures at the University of North Carolina and is a Fellow of the Academy of Managed Care Pharmacists and the American College of Clinical Pharmacy. Kjel holds degrees from the University of Minnesota and he completed a post-doctoral fellowship at St. Paul Ramsey Medical Center, Minnesota. He is board Certified in Pharmacotherapy.

Jacco Keja, PhD • dr. Jacco Keja is a Senior Principal, drawing on deep expertise in global market access, operational and strategic pricing, and health economics and outcomes research.

• Jacco’s background includes four years as global head of pricing, reimbursement, health outcomes and market access consulting services at a large clinical research organization and more than 13 years experience in the pharmaceutical industry, including senior-level international and global roles in strategic marketing, pricing and reimbursement and health economics.

• Jacco holds a Phd in biology (Neurophysiology) from Vrije Universiteit in Amsterdam, a Master's degree in Medical biology, and an undergraduate degree in biology, both from Utrecht. He is also visiting Professor at the Institute of Health Policy & Management at Erasmus University, Rotterdam. tim Kelly, MsC, Bs • Tim Kelly is a Vice President, with responsibility for the company’s RWE data assets and data architecture backbone, and for overseeing platform delivery infrastructure and engagements to ensure at-scale, high-quality data mart deployment. He also leads the client services team supporting data and technology applications.

• Tim’s background includes two decades of life-science experience managing large-scale data warehousing, technology, and analytic applications and engagements. He has worked with many clients in the pharmaceutical and biotech sectors, leveraging deep expertise in information management and modeling, commercial operations and analytics, advanced analytics, business intelligence, data warehousing and longitudinal analytics.

• Tim holds a Master’s degree in Management Science from Temple University, Philadelphia and a bachelor’s degree in Quantitative business Analysis from Penn State University.

Marla Kessler, MBA • Marla Kessler is a Vice President, heads overall marketing efforts for IMS Health RWE Solutions and is an active leader of global RWE projects. She helps clients develop commercial strategies for products and portfolios, define evidence plans to support them, and coordinate implementation to ensure successful execution.

• Marla has 15 years strategic and business line experience gained through previous leadership roles at McKinsey & Company and Pfizer. during her career at IMS Health she has designed and led RWE boot camps to help clients build capabilities in this area across the broader organization, and also developed thought leadership in RWE. This includes coauthoring a major IMS Health benchmarking study exploring variations in RWE supply and demand across the pharmaceutical industry’s top markets.

• Marla holds an MbA from duke University’s Fuqua School of business in durham, North Carolina.

Joseph Kim, PhD, MPh • dr. Joe Kim is a Senior Principal, providing scientific direction in the design and analysis of observational studies across a wide range of projects.

• A trained epidemiologist and statistician, Joe has over 20 years experience in population-based research in the US and Europe.

He was previously Senior director in benefit-Risk Management at Quintiles assisting in the development of pharmacovigilance systems, risk management plans and benefit-risk evaluation reports, and in the design of post-authorization safety studies.

Prior to this, worked in epidemiology at Roche and Amgen.

• For the last 10 years, Joe has taught pharmacoepidemiology and pharmacovigilance at the London School of Hygiene & Tropical Medicine, and more recently on the MPH program at the French School of Public Health in Paris. He holds a Phd in Epidemiology from the University of Minnesota, and an MPH from the Graduate School of Public Health, San diego. continued on next page ACCESSPOINT • VOLUME 5 ISSUE 9

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IMs hEALth RWE sOLUtIOns & hEOR EXPERTISE Rob Kotchie, M.ChEM, MsC • Rob Kotchie is a Vice President, with a focus on bringing innovative solutions to clients through strategic alliances, collaborations and the deployment of novel technology.

• Previously with ZS Associates, Rob has more than 10 years consulting experience, specializing in the synthesis and application of RWE to facilitate market access, drug uptake and the responsible use of medicines. In his former role as Chief of Staff at IMS Health, he supported all operational and management activities related to execution of the company’s strategy, and played an integral role in its 2013 dividend recapitalizations and initial public offering in 2014.

• Rob has particular expertise in the areas of oncology, respiratory, cardiovascular and CNS and has published more than 30 peer-reviewed journal articles and poster presentations. He holds a first class honors degree in Chemistry from the University of Oxford and an MSC in International Health Policy from the London School of Economics.

Mark Lamotte, MD • dr. Mark Lamotte is a Senior Principal, responsible for project management and quality assurance within his team, and for leadership of health economic modeling.

• A medical doctor specialized in cardiology, Mark spent six years in clinical practice before joining Rhône-Poulenc Rorer as Cardiovascular Medical Advisor and later becoming Project Manager and Scientific director at the belgian research organization, HEdM. He has worked on over 300 cardiovascular, pulmonary, diabetes, urology and oncology projects, incorporating expert interviews, patient record review, modeling and report writing. Many of these projects have resulted in peer-reviewed publications.

• Mark holds an Md from the Free University of brussels (Vrije Univeristeit brussel, belgium) and is fluent in dutch, French, English and Spanish.

Claude Le Pen, PhD • dr. Claude Le Pen is a member of the strategic committee of IMS Health and Professor of Health Economics at Paris-dauphine University, providing expert economic advisory services to the consulting practice.

• A renowned economist, leading academic and respected public commentator, Claude has served as an appointed senior member of several state commissions in the French Ministry of Health and is an expert for a number of parliamentary bodies, bringing a unique perspective and unparalleled insights into the economic evaluation of pharmaceutical technologies at the highest level.

• Claude studied business Administration in HEC business School in Paris and holds a Phd in Economics from Panthéon-Sorbonne University.

Bo Lidman, MsC • bo Lidman is a Principal, with more than 30 years experience in the life sciences industry.

• bo’s background spans roles in marketing, sales and business development in both start-up companies and pharmaceutical organizations, including Upjohn Ab and Merck Sharpe & dohme Ab. He served as the General Manager of Profdoc Ab, CPC Ab and Peritide Ab before co-founding the Nordic-based consultancy and research organization, Pygargus Ab, specializing in realworld evidence. He was most recently responsible for developing the company’s IT platform and EMR extraction methodology.

• bo holds a Pol. Mag. (Master’s equivalent) and bachelor’s degree in business and Economics from the Institute of Philosophy in Marketing and Economics, Uppsala University, Sweden.

Ragnar Linder, MsC • Ragnar Linder is a Principal, with more than 25 years experience in pharmaceutical marketing, sales and business development.

• Co-founder of the Nordic-based consultancy and research organization, Pygargus Ab, Ragnar has worked in various senior level industry roles. These include General Manager of Amgen Nordic Ab, director of International Marketing at Aventis/Hoechst Marion Roussel, and Head of Sales & Marketing at Hoechst Pharmaceuticals Ab. He has also served on the board of directors for several CRO and biotech companies.

• Ragnar holds a Master of Science degree in Chemical Engineering from the Royal Institute of Technology in Sweden.

Adam Lloyd, M.PhIL, BA • Adam Lloyd is a Senior Principal, with a focus on economic modeling and the global application of economic tools to support the needs of local markets.

• A co-founder and former director of Fourth Hurdle, and previously Senior Manager of Global Health Outcomes at GlaxoWellcome, Adam has extensive experience leading economic evaluations of pre-launched and marketed products, developing submissions to NICE and the SMC, decision-analytic and Markov modeling, and in the use of health economics in reimbursement and marketing in continental Europe.

• Adam holds a Master's degree in Economics and a bachelor's degree (Hons) in Philosophy, Politics and Economics from the University of Oxford.

Frédérique Maurel, Ms, MPh • Frédérique Maurel is a Principal, with a focus on observational research and health economics studies.

• A skilled consultant and project manager, Frédérique has extensive experience in the economic evaluation of medical technologies gained in roles at ANdEM, Medicoeconomie, and AREMIS Consultants.

• Frédérique holds a Master’s degree in Economics – equivalent to an MS – and completed a post-graduate degree equivalent to an MPH with a specialization in Health Economics at the University of Paris-dauphine (Paris IX) as well as a degree in Industrial Strategies at the Pantheon-Sorbonne University (Paris I).

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IMs hEALth RWE sOLUtIOns & hEOR EXPERTISE Joan McCormick, MBA • Joan McCormick is a Principal, leading a team providing strategic advice to companies with new products coming to market and ongoing consultation on the rules for existing drugs post launch.

• Formerly Head of Price Regulation Consulting at brogan Inc, Joan has supported many major pharmaceutical companies with the preparation of pricing submissions to the Patented Medicine Prices Review board (PMPRb), gaining extensive insights into the operation of the Canadian pharmaceutical market.

• Joan holds an MbA from the University of Ottawa, Canada and a bachelor’s degree in Life Sciences from Queen’s University in Kingston, Canada.

Lisa stockwell Morris, PhD, RPh • dr. Lisa Morris is a Vice President, with global responsibility for LifeLink, the company’s suite of patient-centered insights, and for developing patient-centered information capabilities within the US, EMEA, and Asia Pacific.

• Lisa has many years experience in applying market research tools to answer a wide range of business questions, delivering customized solutions using LRx, medical and pharmacy claims data, EMRs and other clinically-rich secondary information sources. Previously Associate director for Health Outcomes Assessment at Wyeth-Ayerst Research, where she incorporated health outcomes and economic information into drug development plans, Lisa has also held roles as a senior manager in the Outcomes Research group at diversified Pharmaceutical Services (dPS) and United Healthcare Corporation (UHC), managing all aspects of customized health services research projects.

• A registered pharmacist, Lisa holds a doctorate in Pharmacoeconomics with an emphasis on Marketing from the University of South Carolina, where she also received a bachelor’s degree in Pharmacy.

Julie Munakata, Ms • Julie Munakata is a Senior Principal, with a focus on global economic modeling, value development planning, and survey data analysis.

• An accomplished researcher and author of more than 25 original articles, Julie has extensive experience in managing clinical trials, health economic studies and decision analytic modeling work, gained in senior roles at ValueMedics Research LLC, the VA Health Economics Resource Center and Stanford Center for Primary Care & Outcomes Research, and Wyeth Pharmaceuticals.

• Julie holds an a Master's degree in Health Policy and Management from the Harvard School of Public Health and a bachelor’s degree in Psychobiology from the University of California, Los Angeles. stefan Plantör, PhD, MBA, MsC • dr. Stefan Plantör is a Principal, with a focus on AMNOG-related projects, including benefit dossiers, as well as reference price management, health economic evaluations and health policy analyses.

• Stefan’s background includes roles as a researcher and five years experience in the pharmaceutical industry. He has also served as a board member of ProGenerika, the German pharmaceutical association. Over the course of his career, Stefan has broadened his expertise to include data analyses and decision analytic modeling, authored a number of publications in international journals and presented his research at major congresses.

• Stefan holds a Phd in biology from the University of Tübingen, an MbA in International Marketing from the European business School, Reutlingen and an a Master's degree in Microbiology from the Eberhardt-Karls-University (Tübingen).

Antonella Porta, MsC • Antonella Porta is a Principal, with a focus on the RWE Solutions Quality Management Program. She brings 15 years of management experience in quality assurance, compliance and risk management in the pharmaceutical industry and highly regulated fast-moving consumer goods (FMCG) sector.

• during the course of her career, Antonella has held leadership roles in operational quality, quality systems, remediation programs, auditing and compliance. She was most recently Quality & Compliance director at Shire, heading the global Local Operating Companies’ quality team. Antonella began her management career at Procter & Gamble as Regional Head of External Operation Quality, progressing with roles of increasing responsibility to become latterly Global Head of the Microbiological Risk Management Program.

• Antonella holds a Master’s degree in Industrial Chemistry from Federico II University in Naples and is currently studying for an MbA at Warwick University, UK.

Emile schokker, MBA, MsC • Emile Schokker is a Senior Principal, with nearly 20 years of international pharmaceutical and consulting experience including expertise in launch, brand and portfolio strategy, commercial model redesign and post-merger integration.

• Prior to joining IMS Health, Emile was a global senior expert at McKinsey’s global benchmarking service line in belgium, where he previously served as an associate principal responsible for leading strategic engagements at board and senior management level. He has also worked in leadership roles at Unaxis/Oerlikon in Switzerland, Arthur d. Little in the Netherlands, and Unilever in various international locations.

• Emile holds an MbA from IMd in Lausanne and a Master of Science degree in Applied Physics from the delft University of Technology, the Netherlands. continued on next page ACCESSPOINT • VOLUME 5 ISSUE 9

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IMs hEALth RWE sOLUtIOns & hEOR EXPERTISE Jon Resnick, MBA • Jon Resnick is a Vice President and General Manager, leading the company’s global RWE & HEOR business, including the development of RWE strategy, offerings, collaborations and foundational technologies to meet the RWE needs of healthcare stakeholders.

• A former Legislative Research Assistant in Washington dC and member of the Professional Health and Social Security staff for the US Senate Committee on Finance, Jon has 10 years consulting experience at IMS. He was most recently responsible for leading the European management consulting team and global HEOR business teams of 300 colleagues, advising clients on a wide range of strategic, pricing and market access issues.

• Jon holds an MbA from the Kellogg School of Management, Northwestern University, with majors in Management and Strategy, Finance, Health Industry Management, and biotechnology.

Mats Rosenlund, PhD, MPh • dr. Mats Rosenlund is a Principal, with long experience in epidemiology, outcomes research and health economics from academia, the pharmaceutical industry and consultancy.

• Prior to joining IMS Health, Mats was director of Health Economics at OptumInsight and director of Epidemiology and Health Outcomes at GSK. He was previously a researcher at the Karolinska Institute, a Public Health Official at the Karolinska Hospital, and completed two post-doctoral periods in Italy and Sweden. An affiliated researcher at the Center for Pharmacoepidemiology, Clinical Epidemiology Unit, Karolinska Institutet, he has authored more than 20 peerreviewed articles.

• Mats holds a Phd in Epidemiology, a Master’s degree in Public Health and a bachelor’s degree in Environmental Health from Karolinska Institutet. He has also completed university training in health economics in belgium and the UK. Daniel simpson, M.BIOChEM • daniel Simpson is a Senior Principal, with responsibility for diabetes portfolios and involvement in the UK CObIC initiative, focused on moving healthcare commissioning towards patient-based outcome measures. He also takes a leadership position on commercial analytics.

• daniel has more than 18 years experience in healthcare and pharmaceutical markets. Over the course of his career he has worked for all the top 10 pharmaceutical companies and healthcare systems in major markets, delivering insights from patient-level data to support improved decision making on resource allocation. He previously worked in the healthcare/pharmaceutical strategy divisions for both Accenture and the Monitor Group.

• Published in a series of conference posters and papers, dan holds a Master’s degree in biochemistry from St Anne’s College, University of Oxford.

Patrik sobocki, PhD, MsC • dr. Patrik Sobocki is a Senior Principal, with more than 14 years experience in RWE, HEOR and market access.

• Patrik’s background spans academia, consulting and the life-science industry within RWE and HEOR, including international management responsibilities in various senior roles. He was most recently a partner at the Nordic-based consultancy and research organization, Pygargus Ab, where he worked with the company’s unique methodology for generating population-based RWE based on anonymous patient-level data from EMR and health registers.

• Patrik has conducted numerous health economics projects, outcomes research and epidemiology studies and published more than 40 articles in international peer-reviewed journals. He holds a Phd in Health Economics from the Karolinska Institutet, a Master’s degree in Economics and business Administration from the Stockholm School of Economics, and an Associate Professorship at the Karolinska Institutet. núria Lara surinach, MD, MsC • dr. Núria Lara is a Senior Principal, with a focus on the design and coordination of local and international observational and patient-reported outcomes studies.

• A former practicing GP and clinical researcher, Núria’s experience spans roles in outcomes research at the Institute of Public Health in barcelona and in Catalan Health Authorities, and consulting positions within the pharmaceutical and medical device industries focusing on medical regulatory and pricing affairs, pharmacoeconomics and market access strategies.

• Núria holds an Md (specializing in Family and Community Medicine in barcelona), and a Master’s degree in Public Health from the London School of Hygiene and Tropical Medicine and London School of Economics.

Patrick svarvar, PhD • dr. Patrick Svarvar is a Principal, with more than 15 years pharmaceutical industry experience in HEOR and related functional areas.

• Patrick’s background includes Swedish/Nordic affiliate roles in HEOR and pricing at Pfizer and Schering-Plough as well as global HEOR roles at AstraZeneca, Pfizer and Merck. Most recently, he was Executive director and Franchise Leader, Global Health Outcomes at Merck. Prior to moving into the industry, he worked for a number of years in health economics and health services research at the Swedish Institute for Health Economics (IHE).

• Patrick has been involved in a wide range of HEOR study types across multiple therapeutic categories. He is also experienced in market access, health technology assessment, clinical development strategies & processes, strategic pricing & reimbursement, marketing, and payer market research. He holds a Phd in business Administration/Economics from Lund University.

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IMs hEALth RWE sOLUtIOns & hEOR EXPERTISE Marc tapies, MBA • Marc Tapies is a Principal, with a focus on market access, health economics and outcomes research.

• Prior to his current role at IMS Health, Marc worked within the company’s wholesaler data business in Spain where he was responsible for wholesaler panel management, design and quality control of data production and the development of new offerings based on wholesaler data. As Engagement Manager in the IMS Consulting team in Spain, he led projects in the areas of commercial effectiveness, market analysis and portfolio management in the pharmaceutical industry. Marc previously worked for 5 years in the areas of supply chain management and information technologies.

• Marc holds an MbA from IESE business School and a degree in Industrial Engineering from Universitat Politècnica de Catalunya, Spain.

Massoud toussi, MD, PhD, MsC, MBA • dr. Massoud Toussi is a Principal and Medical director, applying his expertise to assure the quality of outcomes research and pharmacovigilance. He is also the representative of IMS Health in ENCePP. • Previously head of Global Clinical Research Operations at Cegedim, Massoud has also worked with the French High Authority for Health (HAS) and various CROs as Project Lead, Scientific Manager and Operations director. His experience includes drug safety reporting, natural language processing, database linkage and drug utilization studies.

• Massoud holds an Md from Mashad University in Iran, a Master's degree in Medical Informatics and Communication Technology from Paris VI, a Phd in Medical Informatics from Paris XIII University, and an executive MbA from a joint program of Universities of Paris-dauphine and Quebec à Montreal.

Arnaud troubat, PhARM.D, MBA, MhEM • dr. Arnaud Troubat is a Principal, with extensive consulting experience and special expertise in the development of registration dossiers and market access strategies across a large number of therapeutic areas.

• A pharmacist by training, Arnaud began his career at the French pharmaceutical industry association (LEEM). He then spent a number of years in the pharmaceutical affairs department at ICI, leading regulatory work on registration submissions and reimbursement strategies, before subsequently moving into consulting. Most recently he was director at Carré-Castan Consultants, managing a research team.

• Arnaud holds a doctor of Pharmacy degree and an MbA from IAE Paris and a Master’s degree in Health Economics and Management from Paris-dauphine University.

Rolin Wade, RPh, Ms • Ron Wade is a Principal and a recognized expert in the applications and limitations of using large retrospective datasets and late-phase datasets for health economics and outcomes research.

• Prior to joining IMS Health, Ron served as a Healthcare Executive and Principal Investigator with Cerner Research and as a Research director at HealthCore. He also has experience generating evidence to support value messages to managed care, government payers and public health associations, gained in leadership roles within the pharmaceutical industry.

• A widely published author with expertise in many therapy areas, Ron lectures at colleges of pharmacy and he has had leadership roles with the American College of Clinical Pharmacy and the Academy of Managed Care Pharmacy. He is a licensed pharmacist and holds a Master's degree in Pharmaceutical Sciences from the University of the Pacific, California and a bachelor of Science degree in Pharmacy.

Jovan Willford, MBA • Jovan Willford is a Senior Principal, supporting growth strategy, offering development and commercialization of RWE solutions in the Asia-Pacific region.

• Jovan’s background includes more than 10 years strategic advisory experience across payers, providers, life science organizations and technology companies, including several cross-industry collaborations to advance quality and value of care delivery.

• Jovan holds an MbA from the Kellogg School of Management, Northwestern University, with majors in Management and Strategy, Managerial Economics and International business, and an undergraduate degree from the University of Notre dame with majors in Marketing and Philosophy.

Ashley Woolmore, D.CLIn PsYCh, MBA • dr. Ashley Woolmore is a Senior Principal, with a focus on developing innovative approaches to help clients reinforce differentiation through the integration of real-world data into strategic decision making. He has 20 years experience in the life sciences and healthcare sector.

• Ashley leverages a uniquely diverse background in clinical, healthcare system management and life sciences strategy consulting in senior advisory roles to support clients across developed and emerging markets on a wide set of healthcare system issues. His expertise includes strategy development, healthcare analytics, RWE for strategic insight, population health management applications, and differentiated market access approaches.

• A thought leader with a particular interest in opportunities arising from convergence between the life sciences industry and broader healthcare system, Ashley holds a doctorate in Clinical Psychology from the University of Oxford, an MbA in Strategy from HEC in Paris, and a bachelor of Science (Hons) degree in Natural Sciences and Psychology.

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IMs hEALth REAL-WORLD EVIDEnCE sOLUtIOns AnD hEALth ECOnOMICs & OUtCOMEs REsEARCh is based in 18 countries worldwide with regional headquarters in THE AMERICAS 8280 Willow Oaks Corporate Drive Suite 775 Fairfax, Virginia 22031 USA Tel: +1 (703) 992 1025

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