SDGs and UHC regional monitoring framework: applications, analysis and technical information by Alexander Pascual

SUSTAINABLE DEVELOPMENT GOALS AND UNIVERSAL HEALTH COVERAGE

REGIONAL MONITORING FRAMEWORK APPLICATIONS, ANALYSIS AND TECHNICAL INFORMATION

SUSTAINABLE DEVELOPMENT GOALS AND UNIVERSAL HEALTH COVERAGE

REGIONAL MONITORING FRAMEWORK APPLICATIONS, ANALYSIS AND TECHNICAL INFORMATION

© World Health Organization 2017 ISBN 978 92 9061 837 9 Some rights reserved. This work is available under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO licence (CC BY-NC-SA 3.0 IGO; https://creativecommons.org/licenses/by-nc-sa/3.0/igo). Under the terms of this licence, you may copy, redistribute and adapt the work for non-commercial purposes, provided the work is appropriately cited, as indicated below. In any use of this work, there should be no suggestion that WHO endorses any specific organization, products or services. The use of the WHO logo is not permitted. If you adapt the work, then you must license your work under the same or equivalent Creative Commons licence. If you create a translation of this work, you should add the following disclaimer along with the suggested citation: “This translation was not created by the World Health Organization (WHO). WHO is not responsible for the content or accuracy of this translation. The original English edition shall be the binding and authentic edition”. Any mediation relating to disputes arising under the licence shall be conducted in accordance with the mediation rules of the World Intellectual Property Organization (http://www.wipo.int/amc/en/mediation/rules). Suggested citation. Sustainable development goals and universal health coverage regional monitoring framework: applications, analysis and technical information. Manila. World Health Organization Regional Office for the Western Pacific. 2017. Licence: CC BYNC-SA 3.0 IGO. Cataloguing-in-Publication (CIP) data. 1. Conservation of natural resources. 2. Health services. 3. Universal coverage. I. World Health Organization Regional Office for the Western Pacific. (NLM Classification: WT31) Sales, rights and licensing. To purchase WHO publications, see http://apps.who.int/bookorders. To submit requests for commercial use and queries on rights and licensing, see http://www.who.int/about/licensing. For WHO Western Pacific Regional Publications, request for permission to reproduce should be addressed to Publications Office, World Health Organization, Regional Office for the Western Pacific, P.O. Box 2932, 1000, Manila, Philippines, Fax. No. (632) 521-1036, email: wpropuballstaff@who.int Third-party materials. If you wish to reuse material from this work that is attributed to a third party, such as tables, figures or images, it is your responsibility to determine whether permission is needed for that reuse and to obtain permission from the copyright holder. The risk of claims resulting from infringement of any third-party-owned component in the work rests solely with the user. General disclaimers. The designations employed and the presentation of the material in this publication do not imply the expression of any opinion whatsoever on the part of WHO concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Dotted and dashed lines on maps represent approximate border lines for which there may not yet be full agreement. The mention of specific companies or of certain manufacturers’ products does not imply that they are endorsed or recommended by WHO in preference to others of a similar nature that are not mentioned. Errors and omissions excepted, the names of proprietary products are distinguished by initial capital letters. All reasonable precautions have been taken by WHO to verify the information contained in this publication. However, the published material is being distributed without warranty of any kind, either expressed or implied. The responsibility for the interpretation and use of the material lies with the reader. In no event shall WHO be liable for damages arising from its use.

CONTENTS FOREWORD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v ACKNOWLEDGEMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi ABBREVIATIONS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii EXECUTIVE SUMMARY. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii 1. INTRODUCTION. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

STRUCTURE OF THE REPORT. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

2. BACKGROUND. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

MDGS AND SDGS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

3. THE SDG AND UHC REGIONAL MONITORING FRAMEWORK. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

3.1 METHODS AND FRAMEWORK DEVELOPMENT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Indicator selection process. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

3.2 MONITORING DOMAINS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

3.3 MONITORING INDICATORS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

4. COUNTRY APPLICATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 4.1 HOW CAN COUNTRIES ADAPT THE SDG AND UHC REGIONAL MONITORING FRAMEWORK TO THEIR OWN CONTEXT? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Overview. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

From data to policy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Methods and analysis to support SDG and UHC monitoring. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

4.2 HOW CAN COUNTRIES USE LOGIC MODELS TO SUPPORT SDG AND UHC MONITORING? . . . . . . . 15

4.3 HOW CAN COUNTRIES CONDUCT EQUITY ANALYSIS AND MONITORING?. . . . . . . . . . . . . . . . . . . . . . 19

Gender analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Human rights-based analysis (or HRBA analysis). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

A focus on the social determinants of health. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

4.4 HOW CAN COUNTRIES USE MONITORING DATA EFFECTIVELY IN POLICY AND DECISION-MAKING?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

4.5 WHAT MISTAKES SHOULD COUNTRIES AVOID WHEN MONITORING SDG AND UHC?. . . . . . . . . . . . 24

5. INDICATORS, DATA AND DATA SOURCES. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

5.1 SOURCES OF HEALTH INFORMATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Population-based data sources. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Institution-based data sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Surveillance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

5.2 METADATA AND TRACER INDICATORS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

Metadata. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 Tracer Indicators. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

5.3 DATA AVAILABILITY AND EXISTING METHODOLOGIES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

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6. MOVING FORWARD. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

6.1 CHALLENGES. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

6.2 SHORT-TERM OPPORTUNITIES AND SOLUTIONS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Strengthening HIS and health information capacities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Subnational geographic analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

Electronic health records and data linkages. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

Development of new indicators. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

6.3 OPPORTUNITIES IN THE MEDIUM AND LONG TERM. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

APPENDICES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

APPENDIX 1. MONITORING FRAMEWORK FOR SDGS AND UHC IN THE WESTERN PACIFIC. . . . . . . . . . . 48

APPENDIX 2. WHO WESTERN PACIFIC REGION SDG AND UHC INDICATOR LIST . . . . . . . . . . . . . . . . . . . . . 49

Table A. SDG 3 Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Table B. Other health-related SDG Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

Table C. Additional Indicators to monitor UHC. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

APPENDIX 3. WHO WESTERN PACIFIC REGION MAPPING OF SDG AND UHC INDICATORS . . . . . . . . . . . 52

Table A. Health indicators in SDG 3 mapped to the SDG and UHC Regional Monitoring Framework. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 Table B. Health indicators in other SDG mapped to the SDG and UHC Regional Monitoring Framework. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 Table C. Additional indicators to monitor UHC mapped to the SDG and UHC Regional Monitoring Framework. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

APPENDIX 4. REFERENCE LIST: 88 SDG AND UHC HEALTH INDICATORS LISTED ACCORDING TO THE HEALTH SYSTEM RESULTS CHAIN (LOGIC MODEL). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

APPENDIX 5. EXAMPLES OF METADATA FOR 16 TRACER INDICATORS (UHC SERVICE COVERAGE INDEX). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

Reproductive, maternal, newborn and child health. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

Communicable diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 Noncommunicable diseases. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

Service capacity and access. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

APPENDIX 6. ADDITIONAL FOCUS AREAS FOR INDICATOR DEVELOPMENT (MAPPED TO THE SDG AND UHC REGIONAL MONITORING FRAMEWORK) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

APPENDIX 7. LONG-TERM DATA OPPORTUNITIES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

FOREWORD I am pleased to release this technical report on the Sustainable Development Goals and Universal Health Coverage Regional Monitoring Framework, which includes applications, analysis and technical information. The report aims to support countries to guide, monitor and review progress towards the health and health-related Sustainable Development Goal (SDG) targets and the achievement of universal health coverage (UHC). This report has three objectives. The first is to present and describe the SDG and UHC Regional Monitoring Framework for the Western Pacific, including the Regionâ&#x20AC;&#x2122;s core reference list of 88 indicators. This objective will enable Member States to adopt the globally agreed definitions for standardized collection and analysis of data for both within- and cross-country comparison. The second objective is to describe the adaptation of the Regional Monitoring Framework to a countryâ&#x20AC;&#x2122;s context and to support policy- and decision-making. The final objective is to provide technical information on indicator development and analysis, and current and future monitoring activities. The SDG and UHC Regional Monitoring Framework provides the basis to identify priority areas and needs and to select monitoring indicators. Each country will need to build a monitoring framework that meets its own priorities for policy- and decision-making. This will allow policy-makers to assess where they are now and set a trajectory for where they want to go. The countryâ&#x20AC;&#x2122;s framework will help to show if efforts are focused in the right areas and whether they are making a difference. The monitoring will also help foster dialogue on progress and encourage knowledge-sharing within countries and also between countries in the Region. This report is a technical resource for Member States. It aims to present the critical aspects of monitoring in a user-friendly manner, to provide an appreciation of the complexities of the process, and to share practical knowledge and techniques for systematic monitoring of the SDGs and UHC. We hope countries will use this report as a guide to support monitoring efforts and activities, and the formulation of evidence-informed policies, programmes and practices for health system development.

Shin Young-soo, MD, Ph.D. WHO Regional Director for the Western Pacific

ACKNOWLEDGEMENTS This report was produced under the guidance of Dr Vivian Lin, Director of the Division of Health Systems, with the technical assistance of Dr Guillermo A. Sandoval, WHO Consultant and Assistant Professor at the University of Toronto Institute of Health Policy, Management and Evaluation, and of Ms Navreet Bhattal, WHO Consultant. The work also benefited from the contributions made by Dr Stephen John Duckett from the Grattan Institute in Australia. Valuable contributions, comments and feedback were provided by the Health Intelligence and Innovation Unit at the WHO Regional Office for the Western Pacific, by technical officers from the Regional Office and WHO country offices, and by representatives from Member States.

ABBREVIATIONS AMI

acute myocardial infarction

CRVS

civil registration and vital statistics

CSDH

Commission on Social Determinants of Health

DHIS

District Health Information Software

DHS

Demographic Health Survey

DTP3

diphtheria, tetanus and pertussis (three doses)

EHRs

electronic health records

GIS

geographic information systems

HIS

health information system

HRBA

human rights-based analysis

IAEG-SDG

United Nations Statistical Commissionâ&#x20AC;&#x2122;s Inter-Agency and Expert Group on SDG Indicators

ICD-10

International Statistical Classification of Diseases and Related Health Problems, 10th Revision

IHR (2005)

International Health Regulations (2005)

information technology

LHIs

leading health indicators

MDGs

Millennium Development Goals

MICS

Multiple Indicator Cluster Survey

NCDs

noncommunicable diseases

NHA

national health accounts

NHID

National Health Information Database

NHIS

National Health Insurance Service

SDGs

Sustainable Development Goals

UHC

universal health coverage

WHO

World Health Organization

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EXECUTIVE SUMMARY Monitoring progress towards the Sustainable Development Goals (SDGs) and universal health coverage (UHC) is a priority in the Western Pacific Region. It is a complex process that includes a wide range of activities, from data collection and infrastructure to data transformation and analysis to inform and drive policy change. This technical report seeks to present a user-friendly overview of the critical aspects of this monitoring to guide Member States as they improve and use their own monitoring frameworks to review and evaluate progress towards the SDGs and UHC. The SDG and UHC Regional Monitoring Framework described in this technical report was endorsed at the sixty-seventh session of the Regional Committee for the Western Pacific in October 2016 (see Appendix 1). The Framework sets out the priority areas to guide action over the next 14 years, to 2030, and provides the basis for indicator selection by each Member State. The Framework is made up of four overarching monitoring domains, within which are 17 indicator domains, currently comprising a total of 88 indicators. Of these, 27 indicators fall under SDG 3, which is the health-focused goal, 20 are from other SDGs and 41 are additional indicators to monitor progress towards UHC. The use of monitoring and indicator domains is flexible. Each Member State can select those domains that best suit their priorities and needs and use them to build their own monitoring frameworks or models, or overlap them with existing frameworks or models. The process of deciding what to measure will require clear directions from each country and supportive analysis where indicators are linked to help guide decisions on cost-effective policies and interventions. To measure and report progress towards the SDGs and UHC, and to ensure that policies and actions are evidence-based, each country will need robust monitoring and review processes to make timely and high-quality data available to planners and practitioners. This technical report addresses five questions to guide SDG and UHC monitoring in the Western Pacific Region: 1. 2. 3. 4. 5.

How can countries adapt the SDG and UHC Regional Monitoring Framework to their own context? How can countries use logic models to support SDG and UHC monitoring? How can countries conduct equity analysis and monitoring? How can countries use monitoring data effectively in policy- and decision-making? What mistakes should countries avoid when monitoring SDGs and UHC?

Question 1 summarizes the process or steps to build a monitoring framework that is integrated with a countryâ&#x20AC;&#x2122;s priorities and needs. Questions 2 and 3 describe models to respond to analytical needs in policy- and decision-making. Question 4 deals with the communication of monitoring data to different audiences, and Question 5 describes some common scenarios of risk to the effective monitoring of SDGs and UHC. Effective monitoring of SDG and UHC progress will require a strong national health information system (HIS). A well-managed HIS supports decision-making, accountability and the coordination of health investment from all stakeholders, including government and donors. Country leadership will

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be essential to building a strong HIS. Some key guiding components of a well-functioning national HIS, for which best practices should be pursued, include: yy yy yy yy yy

governance information use and transparency infrastructure human capital development system and data interoperability.

These components can assist countries to shift focus and attention to more strategic areas by providing a foundation for discussion and a framework for action. An example of a best practice under governance would include an active national coordinating body (such as the National Statistics Office) with strong oversight and control, working with multiple agencies and sectors, and with strong human, technical and financial capacities to meet the countryâ&#x20AC;&#x2122;s needs for data collection, disaggregated data and analysis. Under good governance, the Ministry of Health would provide continuous leadership and coordination and both broad and specific incentives for datasharing and use, and would lead coordinated government-led direct and indirect incentive-based investments in basic information infrastructure and tools. The availability of reliable, good-quality data and information will enable priority-setting and informed decision-making, and will promote accountability of various stakeholders. Effective monitoring of SDG and UHC progress will also require addressing some commonly identified regional challenges, such as those involved with data collection and gaps, and those related to the strengthening of health information systems and capacities. Specifically, these challenges include: yy yy yy yy yy yy yy

limited data availability insufficient disaggregated data poor data quality and reliability silos/fragmented information systems limited use of information standards and exchange mechanisms poor information infrastructure and tools limited capacity to generate knowledge for decision-making.

Meeting these challenges will require improvements in governance, commitment and leadership; short- and long-term investment in health information infrastructure and human resources; and innovative approaches to the use of existing data sources. The range of diversity of countries in the Western Pacific Region â&#x20AC;&#x201C; in terms of current monitoring-related readiness and activities â&#x20AC;&#x201C; means that countries will have different pathways, timelines and priorities for building up their monitoring capability and processes.

1. INTRODUCTION The World Health Organization (WHO) Regional Office for the Western Pacific has prepared this technical report to support Member States to guide, monitor and review their progress towards the health-related Sustainable Development Goals (SDGs) and the achievement of universal health coverage (UHC). The document describes the SDG and UHC Regional Monitoring Framework and addresses a series of questions to guide Member States as they work to build and use their own monitoring framework to inform policy- and decision-making. The aim of monitoring SDGs and UHC by individual countries is to ensure that progress reflects each countryâ&#x20AC;&#x2122;s epidemiological and demographic profile, health system and level of economic development, as well as the demands and needs of its population.1 These country-specific dimensions are critical for deciding what should be monitored. For example, emerging economies may focus on expanding essential services to remote areas. Countries with limited financial and human resources may develop a monitoring framework that focuses on high-burden health issues and Millennium Development Goals (MDGs) that have not yet been achieved. Developed economies, on the other hand, may focus on modifying service delivery to improve coordination of care for the growing older population, ensuring people-centred services, with more emphasis on health promotion and prevention and removing inequities in service delivery. At the regional level, measuring and analysing progress towards the SDGs and UHC will help identify the challenges faced by the Region and their potential root causes, and provide evidence to develop policies and programmes for equitable health improvement in the populations of all Member States.

Structure of the report This report consists of six chapters. Following the introduction in Chapter 1, Chapter 2 summarizes the official global and regional work completed to date on SDGs and UHC, and the relationship between the MDGs and the SDGs. Chapter 3 presents the SDG and UHC Regional Monitoring Framework, with a description of the indicator selection process and the core reference list of 88 proposed indicators.2 Chapter 4 describes a guiding process that includes actions each country can take to adapt the Regional Framework to its own policy priorities and decision-making. Chapter 5 summarizes technical information on the collection, measurement, analysis and reporting of data. Chapter 6 sets out the main challenges to be addressed for effective monitoring of SDG and UHC progress, and outlines short- and long-term solutions and opportunities. The appendices provide additional information, including the complete list of monitoring indicators and their mapping to the monitoring framework, metadata for proposed tracer indicators for coverage of the essential health services index, and additional details on the opportunities outlined in Chapter 6. 1

Towards a monitoring framework with targets and indicators for the health goals of the post-2015 Sustainable Development Goals. Geneva: World Health Organization; 2015 (http://www.who.int/healthinfo/indicators/hsi_indicators_sdg_targetindicators_draft.pdf; accessed 21 August 2017).

The core list will evolve and may be modified in the future.

2. BACKGROUND In September 2015, the General Assembly of the United Nations (UN) adopted the 2030 Agenda for Sustainable Development.3 The Agenda includes 17 SDGs, with 169 associated targets, which are intended to ensure that all human beings can fulfil their potential in dignity and equality and in a healthy environment. While SDG 3 aims to â&#x20AC;&#x153;ensure healthy lives and promote well-being for all at all agesâ&#x20AC;?, core health targets are also embedded in other SDGs. In May 2016, the World Health Assembly reaffirmed the 2030 Agenda and urged Member States to scale up comprehensive action at the national, regional and global levels to achieve the goals and targets of the 2030 Agenda for Sustainable Development relating to health by 2030.4 In June 2016, a regional consultation on achieving the SDGs in the Western Pacific was held in Manila. The consultation and follow-up work focused on the place of health in the SDGs and the monitoring of the SDGs. A Regional Action Agenda on Achieving the Sustainable Development Goals in the Western Pacific was endorsed at the sixty-seventh session of the Regional Committee for the Western Pacific in October 2016.5 This Agenda provides guidance on actions to accelerate achievement of the SDGs, building on the regional action framework Universal Health Coverage: Moving Towards Better Health endorsed at the sixty-sixth session of the WHO Regional Committee for the Western Pacific in October 2015.6

MDGs and SDGs The MDGs, adopted at the United Nations Millennium Summit in 2000, guided global development planning and action from 2000 to 2015, with remarkable progress achieved. Health was central to the MDGs as the explicit focus of three of the eight goals and linked with, or influenced by, the other five goals. Progress towards the MDGs was realized largely through programmatic approaches targeting specific disease and health issues. Globally, the HIV, tuberculosis and malaria epidemics were, to varying degrees, contained; and child and maternal mortality decreased, despite falling short of the MDG targets. However, this progress towards the MDGs did not benefit all groups in society equally, and many programmes neglected to build on links between the different goals. Overall, the most progress was made among the groups that were easiest to reach or whose situations were easiest to improve, leaving behind many of the poorest and most disadvantaged. It became clear that for development to benefit everyone, more integrated and inclusive strategies were needed. Thus, the SDGs address a more complex agenda than did the MDGs, building on the interconnections of different

Resolution adopted by the General Assembly on 25 September 2015 (A/Res/70/1). New York: United Nations; 2015 (http://www.un.org/en/ga/ search/view_doc.asp?symbol=A/RES/70/1; accessed 21 August 2017).

Health in the 2030 Agenda for Sustainable Development. Geneva: World Health Organization; 2016 (http://apps.who.int/gb/ebwha/pdf_files/ WHA69/A69_R11-en.pdf; accessed 21 August 2017).

Regional action agenda on achieving the Sustainable Development Goals in the Western Pacific. Manila: WHO Regional Office for the Western Pacific; 2016 (http://www.wpro.who.int/about/regional_committee/67/documents/wpr_rc67_8_sdgs.pdf; accessed 21 August 2017).

WHO Regional Committee for the Western Pacific Resolution WPR/RC66/6 on universal health coverage. Manila: WHO Regional Office for the Western Pacific; 2015 (http://www.wpro.who.int/about/regional_committee/66/documents/wpr_rc66_06_uhc_7sep.pdf?ua=1; accessed 21 August 2017).

BACKGROUND

development challenges. The SDG Agenda has an explicit focus on equity and serving the hardestto-reach populations to ensure that “no one is left behind”, with UHC acting as a unifying platform.7 SDG 3 in particular builds on and extends the MDG health agenda. SDG 3 has nine targets or subgoals. Three of these relate to the MDGs, three are concerned with noncommunicable disease (NCD) and injury, and three are cross-cutting or systems-focused, including UHC. Thus, UHC is a specific target within SDG 3, as well as the platform that links programmes and actions for health and development. Four additional targets – referred to as means-of-implementation targets – have also been identified (for example, strengthen implementation of the WHO Framework Convention on Tobacco Control).8 Crucially, core health issues are also included in other goals beyond SDG 3. This means that the health SDG is complementary to and a beneficiary of several other goals, including the implementation of social protection schemes (SDG 1); food security and nutrition (SDG 2); gender equality and reducing all forms of violence against women and girls (SDG 5); safe drinking water and adequate sanitation and hygiene (SDG 6); making cities inclusive, safe, resilient and sustainable (SDG 11); and promoting peaceful and inclusive societies and reducing all forms of violence and related deaths (SDG 16). SDGs 13, 14 and 15, which focus on ecosystems and environmental well-being, also indirectly complement SDG 3. The SDG Agenda emphasizes the linkages between the goals, the use of integrated, collaborative and participatory approaches to sustainable development and “leaving no one behind”. This emphasis is especially relevant in the Western Pacific Region, which has significant and growing differences in health and well-being, both among and within countries and areas. Thus, the SDG Agenda has greater focus on identifying and addressing disparities across population groups – not only as a matter of fairness and social justice but also as a critical factor in sustainability.

3. THE SDG AND UHC REGIONAL MONITORING FRAMEWORK This chapter presents the SDG and UHC Regional Monitoring Framework. It describes the indicator selection process and lists the 88 core reference indicators proposed to date. The Framework sets out the priority areas to guide action over the next 14 years, to 2030. Each country is expected to use these indicators as a reference to undertake its own regular monitoring and review, guided by its own national health policies, priorities and strategies, and making best use of its monitoring capacity.

3.1 Methods and framework development The SDG and UHC Regional Monitoring Framework builds on extensive work conducted over the last decade at global, regional and national levels. This work includes the UHC Action Framework for the Western Pacific Region,9 the WHOâ&#x20AC;&#x201C;World Bank global framework for monitoring UHC,10 the WHO frameworks for health systems performance11 and for the social determinants of health,12 and the framework for health care quality of the Organisation for Economic Co-operation and Development (OECD).13 The Regional Monitoring Framework is presented in Figure 1. It is organized into four common, standardized monitoring domains (for example, health system resources and capacity) and 17 indicator domains (for example, quality and safety). Some countries, including Canada and Australia, have made extensive use of this type of information framework to monitor performance and the achievement of the strategic objectives of their health systems.14 The advantage of this type of framework is that each country can identify the monitoring and indicator domains that are relevant to its own national context, and can use them to build up its own monitoring framework or models. The following chapter (Chapter 4) discusses how to adapt the SDG and UHC Regional Monitoring Framework to a countryâ&#x20AC;&#x2122;s context to support policy- and decision-making. The Framework is expected to evolve further over time.

Universal health coverage: moving towards better health. Action framework for the Western Pacific Region. Manila: WHO Regional Office for the Western Pacific; 2016 (http://iris.wpro.who.int/bitstream/handle/10665.1/13371/9789290617563_eng.pdf?sequence=1; accessed 21 August 2017).

Monitoring progress towards universal health coverage at country and global levels: framework, measures and targets. Geneva: WHO and World Bank; 2014 (http://apps.who.int/iris/bitstream/10665/112824/1/WHO_HIS_HIA_14.1_eng.pdf?ua=1; accessed 21 August 2017); Boerma T, AbouZahr C, Evans D, Evans T. Monitoring intervention coverage in the context of universal health coverage. PLoS Med. 2014:11(9):e1001728.

Monitoring, evaluation and review of national health strategies: a country-led platform for information and accountability. Geneva: World Health Organization; 2011 (http://www.who.int/healthinfo/country_monitoring_evaluation/1085_IER_131011_web.pdf; accessed 21 August 2017).

Solar O, Irwin A. A conceptual framework for action on the social determinants of health. Social Determinants of Health Discussion Paper 2. Geneva: World Health Organization; 2010 (http://apps.who.int/iris/bitstream/10665/44489/1/9789241500852_eng.pdf?ua=1; accessed 21 August 2017).

Kelley E, Hurst J. Health care quality indicators project: conceptual framework paper. OECD Health Working Papers No. 23. Paris: Organisation for Economic Co-operation and Development; 2006 (http://www.oecd.org/els/health-systems/36262363.pdf; accessed 21 August 2017).

Hurst J, Jee-Hughes M. Performance measurement and performance management in OECD health systems. OECD Labour Market and Social Policy Occasional Papers, No. 47. Paris: Organisation for Economic Co-operation and Development; 2001.

THE SDG AND UHC REGIONAL MONITORING FRAMEWORK

Indicator selection process The indicators used in this Framework were identified from the SDG indicators, from existing global collections of health indicators (including those used in the Pacific islands) and from various other programmes. Existing data were leveraged to the extent possible so as to minimize the need for extra reporting from Member States. The selection of indicators aims to provide adequate coverage of all population groups and to show progress on UHC and the potential drivers of progress by including inputs, structures and processes, as well as outcome indicators. Figure 1. SDG and UHC Regional Monitoring Framework

HEALTH IMPACT THROUGH THE LIFE COURSE

DETERMINANTS OF HEALTH

INDIVIDUAL HEALTH

POPULATION HEALTH

Are these factors contributing to good health?

Indicator Domain 1. Physical environment factors 2. Individual characteristics and behaviours 3. Socioeconomic factors 4. Social environment factors

UNIVERSAL HEALTH COVERAGE

Are all people accessing needed services without suffering financial hardship?

Indicator Domain 1. Financial protection 2. Health service coverage 3. Accessibility and use

HEALTH SYSTEM RESOURCES AND CAPACITY Indicator Domain 1. Effectiveness 2. Quality and safety 3. Responsiveness and peoplecentredness

Does the system deliver value for money and is it sustainable? 4. 5. 6. 7.

Resources and infrastructure Availability and readiness Health financing Efficiency and sustainability

MULTIPLE POPULATION GROUPS (EQUITY-FOCUSED MONITORING)

Indicator Domain 1. Mortality 2. Morbidity 3. Life expectancy and wellbeing

How healthy are people in the Western Pacific?

Source: Adapted from the Framework agreed to at the sixty-seventh session of the Regional Committee of the Western Pacific in October 2016.

SDG AND UHC REGIONAL MONITORING FRAMEWORK

The selection process began with a review of the 230 indicators identified for the United Nations Statistical Commission’s global indicator framework for monitoring SDGs.15 Those deemed to be health or health-related were compiled, including the 27 health indicators from SDG 3 and 20 healthrelated indicators from other SDGs. Additional indicators were selected after review of the WHO Global Reference List of 100 Core Health Indicators, Universal Health Coverage: Moving Towards Better Health framework adopted by Member States in the Western Pacific Region in October 2015, and the Healthy Islands Monitoring Framework endorsed by the Pacific heads of health in April 2016.16 During this review, indicators that were duplicated or had been replaced by improved ones were removed. This was followed by technical consultations and a prioritization exercise within the WHO Regional Office for the Western Pacific. Informed by reports of similar exercises,17 the following criteria were used to determine a list of “fit for purpose” indicators: 1. Focus on common health issues and indicators across the Western Pacific Region to allow withincountry and cross-country comparisons, mutual learning and sharing of experience. 2. Align the regional-level indicators with existing global collections where possible to encourage information/data exchange between Member States in the Region. 3. Ensure that, in addition to tracking progress in SDGs and UHC, the indicators can be used to review progress and to support policy and programme development at multiple levels (national, subnational, local) and for different population groups. 4. Ensure where possible that information to track progress towards SDGs and UHC is disaggregated by sex, age, socioeconomic status, education, ethnicity and place of residence. 5. Ensure the indicators are theoretically sound, commonly understood and technically accurate. 6. Ensure that the indicators reflect a balance in selection of targets, not overemphasizing one health condition, but capturing characteristics that reflect the health profile of country populations. Using this indicator selection process, the SDG and UHC Regional Monitoring Framework currently includes a total of 88 indicators in three groups. Of these, 27 indicators fall under SDG 3 (health), 20 are from other SDGs, and 41 are additional indicators of progress towards UHC. Appendix 2 summarizes the complete list of 88 indicators in these three categories.

3.2 Monitoring domains The SDG and UHC Regional Monitoring Framework sets out priority areas to guide action over the next 14 years. These areas are grouped into four monitoring domains made up of 17 indicator domains. The monitoring domains are broad conceptual categories that define SDG and UHC progress. They allow the grouping of the indicator domains in ways that are meaningful and conceptually related,

United Nations Statistical Commission. Report of the Inter-Agency and Expert Group of Sustainable Development Goal Indicators. New York: United Nations Economic and Social Council; 2016 (http://unstats.un.org/unsd/statcom/47th-session/documents/2016-2-SDGs-Rev1-E.pdf; accessed 21 August 2017).

Global reference list of 100 core health indicators, 2015. Geneva: World Health Organization; 2015 (http://www.who.int/healthinfo/indicators/2015/ en/; accessed 21 August 2017); Universal health coverage: moving towards better health. Action framework for the Western Pacific Region. Manila: WHO Regional Office for theWestern Pacific; 2016 (http://iris.wpro.who.int/bitstream/handle/10665.1/13371/9789290617563_eng.pdf?sequence=1; accessed 21 August 2017); Soakai S, Park K. Healthy island monitoring framework. Manila: WHO Regional Office for the Western Pacific; n.d. (http://nebula.wsimg.com/e7c7ae233b713a901255052f7895d57e?AccessKeyId=3BF845C13E3CC727DFDB&disposition=0&alloworigin=1; accessed 21 August 2017).

Criteria for leading health indicators. Washington, DC: Institute of Medicine; 1998 (https://www.ncbi.nlm.nih.gov/books/NBK230723/; accessed 21 August 2017).

THE SDG AND UHC REGIONAL MONITORING FRAMEWORK

so that when indicators are reported, they all measure an important dimension of the SDGs and UHC. For example, mortality, morbidity and life expectancy and well-being are all related indicator domains to measure how healthy people are in the Western Pacific Region. The use of these monitoring and indicator domains is flexible. Each Member State can select the domains that best suit their priorities and use them to build their own monitoring frameworks or models, or overlap them with existing frameworks or models. Figure 1 outlines the main components of the Framework, which is organized under the following four monitoring domains: 1. 2. 3. 4.

Health impact through the life course Determinants of health Universal health coverage Health system resources and capacity.

The first monitoring domain – health impact through the life course – captures the level of health of people in the Western Pacific Region at different stages of life, from the prenatal period, through adolescence and youth, to the adult years and into older age. The focus of this domain is on health status changes for both individuals and populations, measured through mortality, morbidity, and life expectancy and well-being. As an outcome-oriented domain, this monitoring domain is less actionable, but it reflects the combined progress made in the other three domains – determinants of health, UHC, and health system resources and capacity. The domain determinants of health contains the personal, social, economic and environmental factors that influence health status. These determinants often contribute to health inequity, which is the unfair and avoidable difference in health status seen within and between populations. They are classified into three groups: physical environment factors; individual characteristics and behaviours; and social environment factors. Some fundamental determinants of health are not included in the present Regional Framework, such as poverty, education and employment. Individual countries may consider including these in their country-specific frameworks. Examples of these indicators include the proportion of the population living in poverty, and the proportion of youth and adults who have achieved literacy and numeracy. The indicator domain “socioeconomic factors” reflects the importance of these determinants and the interlinkages between health and other SDGs, which also provide the basis for equity analysis. The third monitoring domain, universal health coverage (UHC), is the vision that all people are able to obtain quality health services without suffering financial hardship. UHC is the foundation for SDG 3, while also contributing to other SDGs as a pathway to more equitable and sustainable health outcomes and more resilient health systems. This monitoring domain incorporates three indicator domains: financial protection; health service coverage; and accessibility and use. As a platform for SDG 3, UHC has more actionable domains. For example, evidence showing significant variation in skilled birth attendance between urban and rural populations may help countries to adjust health policies and interventions on health worker density and distribution. Similarly, data on impoverishing health expenditure by certain disadvantaged groups – persons with disabilities, ethnic minorities, older persons, working poor – may provide the basis to develop a policy or action plan for improved financial protection. The fourth monitoring domain, health system resources and capacity, aims to assess and monitor whether the system is delivering value for money and whether it is sustainable. This domain also

SDG AND UHC REGIONAL MONITORING FRAMEWORK

seeks to monitor quality of care for a range of patient care activities. It contains seven indicator domains, with a substantial number of actionable indicators. For example, within the indicator domain “resources and infrastructure”, birth registration capability is relevant to measuring progress in several SDGs, as well as to support direct health actions, strategies and interventions. Equity-focused monitoring is integral to each of the four domains, in line with the aim of the SDGs to ensure that no one is left behind. Creation of an equity-oriented health sector requires first the systematic identification of inequities, and then monitoring of any change in these inequities over time.18 The monitoring process and activities outlined in the Framework should be country-led and countryowned. Member States are expected to develop their monitoring mechanisms based on their own SDG and UHC priorities, adjusting for the relative importance that the country attaches to each SDG target. These country-focused priorities will be conditioned by factors outside the health system that may influence the expected level of goal attainment, including a country’s income, education levels and political factors.

3.3 Monitoring indicators This section summarizes the core reference list of 88 indicators selected by the Region for monitoring progress towards the SDGs and UHC over time. Progress in these indicators is essential to ensure that the development goals are met across various population groups. The use of these common indicators, with globally agreed definitions, allows comparison, both within and across countries, of trends, successes, challenges and opportunities, including locality-, equity- and gender-based analyses across population groups. Countries should use these indicators as a reference for their own regular monitoring and review, guided by their national health policies, priorities, strategies and capacity to implement monitoring activities. The 88 indicators of the core reference list have been mapped to the monitoring and indicator domains of the Framework, as outlined in Figure 1. Appendix 2 presents the complete list of indicators and Appendix 3 shows the complete mapping of indicators. The definitions of the indicators in the Framework provide a level of specificity to help countries assess focus areas. For example, harmful use of alcohol is in the indicator domain “individual characteristics and behaviours”, within the monitoring domain “determinants of health”. Setting goals for this indicator, based on progress expected from the baseline value, provides a guide to develop policies and interventions to modify this behaviour. Monitoring this indicator (that is, “harmful use of alcohol”) will help measure progress towards SDG 3.5, which aims to improve prevention and treatment of substance abuse, including narcotic drug abuse and harmful use of alcohol. The complete framework in Appendix 3 provides the basis for indicator selection by each Member State. The process of deciding what to measure will require clear directions from each country on priorities and needs, and supportive analysis where indicators are linked, to help guide decisions on cost-effective policies and interventions. The next chapter provides examples of analyses and models to illustrate how countries can apply this framework within their own national monitoring activities. 18

Handbook on health inequality monitoring: with a special focus on low- and middle-income countries. Geneva: World Health Organization; 2013 (http://apps.who.int/iris/bitstream/10665/85345/1/9789241548632_eng.pdf; accessed 21 August 2017).

4. COUNTRY APPLICATIONS Based on the SDG and UHC Regional Monitoring Framework, Member States themselves need to identify those targets and indicators of highest priority, taking into account the country realities, characteristics, challenges and capacities. To measure and report on the SDGs and UHC, and to ensure that policies and actions are informed by evidence, each country needs to have robust monitoring and review processes, with timely and high-quality data and information available to planners and practitioners. This chapter offers a guide to Member States as they prepare to build or adapt their own monitoring framework. The chapter addresses five guiding questions in five sections (Figure 2). Figure 2. Guiding questions to support countries in SDG and UHC monitoring

2. How can countries use logic models to support SDG and UHC monitoring?

1. How can countries adapt the SDG and UHC Regional Monitoring Framework to their own context?

3. How can countries conduct equity analysis and monitoring?

SDG and UHC Monitoring

4. How can countries use monitoring data effectively in policy- and decision-making?

5. What mistakes should countries avoid when monitoring SDGs and UHC?

Question 1 summarizes a guiding process to build a monitoring framework that is integrated with a countryâ&#x20AC;&#x2122;s priorities and needs. This includes a wide range of activities, stages and aspects involved in monitoring. Questions 2 and 3 describe models to respond to analytical needs in policy- and decision-making. Question 4 discusses the importance of communicating monitoring data to different audiences, and Question 5 describes some common scenarios that may limit the effective monitoring of SDGs and UHC.

SDG AND UHC REGIONAL MONITORING FRAMEWORK

4.1 How can countries adapt the SDG and UHC Regional Monitoring Framework to their own context? Overview To monitor progress towards SDGs and UHC, each country needs to have a monitoring framework that responds to their own priorities and needs, and that supports policy- and decision-making. The SDG and UHC Regional Monitoring Framework provides the basis to identify priority areas and needs, and to select suitable monitoring indicators. The formulation of a countryâ&#x20AC;&#x2122;s monitoring framework should integrate with the policy development cycle. This is an organized process to define and target focus areas for policy changes and interventions (Figure 3), and typically involves four main stages: 1. 2. 3. 4.

problem definition or redefinition agenda setting policy formulation policy implementation and evaluation.

Figure 3. Opportunities to incorporate monitoring data into policy- and decision-making Equity analysis and monitoring

SDG AND UHC Regional Monitoring Framework

1 INFORM

Country Monitoring Framework

2 Policy Development Cycle

1. Problem definition 2. Agenda setting 3. Policy formulation 4. Policy implementation and evaluation

The country monitoring framework has the potential to inform every stage and level of policy- and decision-making, and should evolve following new developments and directions arising from the policy development process. For example, the evaluation of a policy or intervention may lead to changes in policy and programme priorities. The country monitoring framework will need to be adjusted to reflect these changes. Health policies within a country are typically at different stages of development. This means countries may not necessarily have to go through the complete policy development process, but rather find opportunities within already established processes to incorporate the use of monitoring data to support policy- and decision-making. As mentioned, equity analysis is an important element of policy- and decision-making. Equity analysis involves the use of disaggregated data and other equity-oriented approaches to systematically identify and monitor excluded and disadvantaged population groups. Equity analysis can be especially valuable during the processes of problem definition and policy evaluation.

COUNTRY APPLICATIONS

From data to policy Monitoring progress towards the SDGs and UHC is a complex and demanding process. It includes a wide range of activities, including data collection, provision of information technology (IT) infrastructure and then data processing and analysis to inform and drive policy improvement. The aim of country-specific monitoring of SDGs and UHC is to be able to ensure that progress reflects each countryâ&#x20AC;&#x2122;s own epidemiological and demographic profile, health system, and level of economic development, as well as the demands and needs of the population. Figure 4 depicts the monitoring process as a data-to-policy continuum, where the ultimate goal is to make progress towards the SDGs and UHC. The range of diversity of countries in the Western Pacific Region in terms of current monitoring systems and activities means that countries will have different pathways, timelines and priorities throughout this process. The data-to-policy continuum recognizes both the role of other sectors in data linkages and engagement, and also the multiple options and restrictions countries will face throughout the monitoring process. Figure 4 highlights a two-part process. The first stage describes the key elements and actions that are needed for a country to select indicators. Key supporting elements in this process include data and IT infrastructure, a data governance and information plan, and a country-specific monitoring framework. Key actions include data transformation, comparative analysis and indicator selection. During this first stage, WHO can support countries with development and provision of metadata, assessment of data and indicator maturity, data transformation processes, comparative data analysis, and the development of a data and information plan, and a country-specific monitoring framework. For example, WHO can support assessment of current data and indicator availability so that an action plan can be developed to strengthen data governance and IT infrastructure. The second part of the process presents a series of actions for which the use of the selected indicators is the main focus. The main actions involve target-setting, policy and action, and progress monitoring. During this second stage of monitoring, WHO can support countries in the use of logic models and indicator sequencing to set targets and make policy changes, and by producing regular regional reports to compare baseline and progress towards the SDGs and UHC across countries. The data-to-policy continuum recognizes two types of indicators. The first type groups indicators for which the cause and effect of related interventions are relatively clear. For example, child immunization is important for overall childrenâ&#x20AC;&#x2122;s health and likely a significant predictor of under-5 mortality. Targeting child immunization as a priority action will probably generate little debate about its effectiveness, target-setting and policy options to improve child immunization. The second type groups indicators for which the cause and effect of related interventions are still debatable. For example, in the case of harmful use of alcohol, there may be significant debate about what interventions may be most effective to change this behaviour. The selection of indicators should be based on each countryâ&#x20AC;&#x2122;s priorities and needs so that progress can be measured in relevant areas where policy changes and interventions are being considered. When it comes to individual metrics, however, countries will have to use certain criteria to support their decision-making process. In general, a good indicator should be relevant to what is to be measured and should be clear, practical, doable and appropriate.19 Indicators need to contain certain basic information and should pass tests of reliability, feasibility and utility in decision-

Indicator module section 3.5. Washington, DC: Search for Common Ground and UK Aid; n.d. DME for Peace [website] (http://dmeforpeace.org/ sites/default/files/3.9%20Indicators.pdf; accessed 21 August 2017).

SDG AND UHC REGIONAL MONITORING FRAMEWORK

Figure 4. Monitoring process (as a data-to-policy continuum)

SUSTAINABLE DEVELOPMENT GOALS (SDGs) UNIVERSAL HEALTH COVERAGE (UHC) Progress Monitoring • Common syntax supply -- Analysis, visualization • Ideas sharing, PHIN, mutual support, exchange • Capacitybuilding, knowledge transfer • Advice on advisers

Action / Policy Help with sequencing, priority setting

Target Setting

Indicators for which causeand-effect is relatively clear

• Data analysis and display • Enhancing value add • Analysis, linkages, summary measures, tracer indicators • Transparency choices (to whom)

Help with logic model, sequencing

Indicators for which causeand-effect is debatable Comparative data

Data Transformation

Indicator Selection

• What metrics, methods • What to highlight • How to disaggregate

Data and Information Plan Country-Specific Monitoring Framework CRVS, Infections, RMNCH Hospital, NCDs

Maturity assessment

Comparative Data, Benchmarks and Trend Data • Politics (private sectors, other sectors, civil society) • Priority setting for new data collections • Avoiding duplication • Working on rationalizing external demands (partners, donors, WHO requests)

Data Governance

Metadata (global, local) Ideas sharing e.g. automation

Audit, data flow to enhance meaning

Country diversity: Direction, path, timelines, priorities

Country options: Need, interest, receptivity, resources from WHO/other donors

Data + IT Infrastructure

CRVS = civil registration and vital statistics PHIN = Pacific Health Information Network RMNCH = reproductive, maternal, newborn and child health

Monitoring Framework Baseline Report

Need for ongoing linkage and engagement with other sectors/ civil society

COUNTRY APPLICATIONS

making.20 Some commonly used standards to assess an indicator are SMART and SPICED, acronyms created using the first letter of their respective indicators (see Table 1). For example, the hospital readmission rate for acute myocardial infarction (AMI) is a specific, measurable and result-oriented indicator. However, it may be costly to collect the relevant data without a reliable, country-wide, hospital-based data source. For some countries, this means that the indicator hospital readmission rate may not be favourably assessed under the criteria achievable/attainable and time-bound. Table 1. Criteria for SMART vs. SPICED Indicators SMART Indicators

SPICED Indicators

Specific: measures as closely as possible the result it is intended to measure; disaggregated data (where appropriate)

Subjective: using informants for their insights

Measurable: quantitative (where possible); no ambiguity on what is being measured

Participatory: involving a project’s ultimate beneficiaries, involving local staff and other stakeholders

Achievable/Attainable: it is technically possible to obtain data at a reasonable cost

Interpreted and communicable: explaining locally defined indicators to other stakeholders

Result-oriented: reliable; general agreement Cross-checked and compared: comparing different indicators and progress, over interpretation of the results and using different informants, methods and researchers Time-bound: data can be collected frequently Empowering: allowing groups and individuals to reflect critically on their enough to inform the progress and influence changing situation the decisions Diverse and disaggregated: seeking out different indicators from a range of groups, especially men and women, to assess their differences over time Source: Design, Monitoring and Evaluation for Peacebuilding (DME for Peace) website: http://dmeforpeace.org/.

Methods and analysis to support SDG and UHC monitoring A number of methods and analyses can be used – alone or in combination – to assist the monitoring process. Table 2 outlines some common methods and analyses and the main ways they can be used to inform decision-making. Table 2. Methods and analysis to support SDG and UHC monitoring Method/analysis

Context and Uses

Grouping and tracer indicators • Monitoring: cluster indicators in common themes and/or use fewer indicators Exploratory relationships

• Initial policy dialogue and discussions • Guiding additional analyses

Regression analysis, including • Policy-making: better understanding of relationships to inform policy dialogue and evaluation longitudinal data • Indicator selection Qualitative analysis

• Monitoring, including equity-focused monitoring • Logic models/relationships • Policy-making

• Combining data and information from multiple sources Province-/district-level analysis • Building/understanding relationships • Assessing health inequities Qualitative/quantitative approaches (expert panel/ consensus – Delphi method) 20

• Defining country monitoring framework • Logic-models/relationships • Indicator selection • Policy-making

ibid.

SDG AND UHC REGIONAL MONITORING FRAMEWORK

The first approach attempts to simplify the monitoring burden. Indicators may be grouped using factor analysis â&#x20AC;&#x201C; a statistical technique that clusters indicators under a common theme (for example, NCDs). In this situation, policy interventions targeting these common themes may be more effective as the multiple indicators are statistically related. Similarly, tracer indicators are one or a group of indicators that provide a good picture of some or several aspects of the health-care system. For instance, hypertension (high blood pressure) can be treated in primary and community care, and admission to hospital for hypertension may indicate problems with access to and quality of primary care. Hospital admission rates for hypertension and other ambulatory care-sensitive conditions can then be used to trace/track/monitor access to and quality of primary care. Exploratory relationships and regression analysis can help to inform and guide policy dialogue and discussion by showing how multiple factors relate to each other, and by identifying factors that are likely to have the greatest impact on health outcomes. These statistical techniques may help select different strategies for different programmes. For example, child immunization is an effective strategy that contributes to minimizing child mortality; therefore, regression analysis is likely to find stronger relationships in this area. On the other hand, the factors that relate to good nutritional outcomes are likely to be too complex to be uncovered through regression analysis. Qualitative techniques are most valuable for understanding health relationships that cannot be measured through quantitative approaches. For example, gathering information from communities and families can help us understand why access to primary care is problematic in certain areas. The same information can be used to adjust policy and resource allocation and, if regularly collected, can be used to monitor progress in access to primary care. Provincial- or district-level analysis may be used, combining data from multiple sources so that relationships between factors can be shown within an individual country. For example, health worker density, socioeconomic status and education can be measured at the province or district level and used in a regression model to understand how they relate to the proportion of births attended by skilled personnel. Indicators at this level can also be used to assess health inequities. The last method in Table 2 relates to the use of mixed quantitative/qualitative approaches such as the Delphi method. The Delphi method is a systematic, structured technique used to arrive at an expert consensus; it may have a number of applications during the monitoring process. Expert consensus can be used to prepare a countryâ&#x20AC;&#x2122;s monitoring framework, including the selection of indicators, to clarify logic models and relationships, and to identify policy changes needed to improve progress towards the SDGs and UHC.

COUNTRY APPLICATIONS

4.2 How can countries use logic models to support SDG and UHC monitoring? Logic models provide a rationale for identifying areas for policy intervention and help ensure that policies and actions are based on evidence. Logic models focus on a sequence of relationships in order to understand the process by which resources and inputs may contribute to expected changes or results (Boxâ&#x20AC;&#x201E; 1). Through this process, a country can assess whether an initiative is being implemented as planned, if it is leading to improvements, and whether it is necessary to adjust interventions that are not effective.

Box 1

What is a logic model?

A logic model is also known as theory of change and is typically used to evaluate the effectiveness of a programme. The figure below presents the key elements of a logic model. Input indicators measure the human, physical and financial resources involved in a programme or system. Process indicators measure ways in which programme (or system) services and goods are provided.a From a health system perspective, inputs and processes capture the foundational elements (for example, health workforce, essential medicines) as they form the basis for a proper functioning of the system, delivering a range of quality health services. Output indicators measure the quantity of goods and services produced and, when compared with the inputs, indicate the efficiency of production.a Outcome and impact indicators capture all the effects of health care on patients and populations, including changes to health status, knowledge and behaviour, as well as patient satisfaction and health-related quality of life.b Appendix 4 categorizes all 88 indicators from the Regional Monitoring Framework according to the logic model.

Inputs/Processes (Resources)

Outputs

Planned activities and interventions a

Outcomes

Impacts

Expected results

Horsch K. Indicators: definition and use in a results-based accountability system. Cambridge: Harvard Family Research Project; 1997 (http:// www.hfrp.org/publications-resources/browse-our-publications/indicators-definition-and-use-in-a-results-based-accountabilitysystem; accessed 22 August 2017. Donabedian A. An introduction to quality assurance in health care. New York: Oxford University Press; 2002.

Source: Adapted from Designing a results framework for achieving results: a how-to guide. The World Bank Group. Washington DC, 2012. (https://siteresources. worldbank.org/EXTEVACAPDEV/Resources/designing_results_framework.pdf)

The use of logic models in SDG and UHC monitoring should improve critical thinking in policy development. The logic model helps to identify problems and their causes, to define targeted solutions/interventions, and to formulate, implement and evaluate policy changes. Although typically presented as a linear process, in practice progress through any sequence of relationships may not necessarily be linear. Countries need to be aware that this situation may affect policymaking as well as evaluation of the effect of policy change. Countries are also encouraged to use additional techniques, such as qualitative analysis, to understand aspects of progress not captured through the logic model. The use of a linear logic model should help organize complex interactions and guide initial policy discussion. In 2016, WHO Regional Office for the Western Pacific developed an action framework for UHC21 in which the use of a logic model approach from input to impact was highlighted (Figure 5). 21

SDG AND UHC REGIONAL MONITORING FRAMEWORK

The Western Pacific Region logic model captures crucial health sector inputs and interventions, as well as health-related initiatives from other sectors that improve coverage of health services and financial risk protection, in order to attain the highest possible level of well-being and health for populations. The model is linked to the regional UHC action domains through the five essential health systems attributes: quality, efficiency, equity, accountability, and sustainability and resilience. The regional logic model in Figure 5 provides a template to guide countries in their own analytical process. Targets and indicators from a country’s monitoring framework can be organized according to this model. Countries may choose to take a broad perspective to choose interventions for multiple changes in the health system or a narrower perspective to target particular populations, patient groups or conditions. Some examples are given below. Figure 5. Western Pacific Region logic model

INPUTS AND PROCESSES

OUTPUTS

HEALTH SECTOR Governance Health financing Health workforce Service delivery

• Medicines and technologies • Health information • Surveillance systems

OTHER SECTORS

HEAL TH

• Governance and policies • Financing • Infrastructure and technologies

HOUSEHOLD AND SOCIAL IMPACT

POPULATION HEALTH

HEALTH SYSTEM

• • • • •

IMPACTS

INDIVIDUAL AND FAMILIES

• Poverty impact • Health security • Social inclusion

Well-being Life expectancy Mortality Morbidity Disability

TOR C E S MUNIT I ES C OM

Quality Efficiency Equity Accountability Resilience

HEALTH SERVICE DELIVERY

HEALTH FINANCING

• • • • •

• Out-of-pocket spending • Government investment in health

Availability and readiness Effectiveness and safety Accessibility Efficiency People-centredness

HEALTH-RELATED INTERVENTIONS AND SOCIAL DETERMINANTS • Education • Food and nutrition • Infrastructural and environmental interventions

IAL N ET W OR SE R E OTH

• Peoplecentredness • Housing • Employment

HOUSEHOLD HEALTH-RELATED EXPENDITURE

KS CT O RS

• • • •

• Catastrophic expenditure

HEALTH SERVICE COVERAGE

LIFESTYLE FACTORS AND PRACTICES

• • • • •

Promotive Preventive Curative Rehabilitative Palliative

Health literacy Substance use Nutrition Physical activity Safe practices

OUTCOMES

Source: Universal health coverage: moving towards better health. Action framework for the Western Pacific Region. Manila: WHO Regional Office for the Western Pacific; 2016.

Example 1 A country targets the input element health information system as a strategic priority and decides to focus on strengthening coverage of birth and death registration. This would improve data and therefore decision-making in many areas of the health system, including assessment of children’s needs, access to and outputs of care, and outcomes for individual health conditions, for groups of patients and for the overall population (for example, for malaria and hepatitis mortality rates). The data would help with setting policy priorities and planned actions in the five UHC action domains. This sequence of potential improvement follows the overall logic approach of the model, contributing to advances in individual and population health.

COUNTRY APPLICATIONS

Example 2 A country targets equity-focused access to essential medicines and technologies and improved distribution and composition of the health workforce as strategic priorities. Improvements in these system inputs may contribute to a sequence of positive changes, including increased coverage of and access to health services, and better quality and improved efficiency of the services provided. These improvements would also be expected to benefit equity in health outputs and outcomes. Example 3 A logic model can guide monitoring activities in particular focus areas. Figure 6 presents an example to support health policies and interventions in children’s health. The example uses indicators from the core SDG and UHC reference list to show how countries can apply both the SDG and UHC Regional Monitoring Framework and a logic model. Additional indicators may be included to reflect the full pathway towards children’s health. Figure 6. Example of the Western Pacific Region logic model applied to children’s health

INPUTS AND PROCESSES

OUTPUTS Equity-focused service delivery:

TOR C E S MUNIT I ES C OM INDIVIDUAL AND FAMILIES

• Under-5 mortality • Neonatal mortality

KS CT O RS

Health worker density and distribution Availability of essential medicines Health facilities with basic water supply Health facilities with basic sanitation Birth and death registration coverage

HEAL TH

• • • • •

IAL N ET W OR SE R E OTH

• • • •

Skilled birth attendance Institutional deliveries Newborn receiving essential newborn care People-centredness

Disaggregated by age, sex, place of resi-dence, socioeconomic status

• • • • • • • •

Children <5 stunted Children <5 wasted Children <5 overweight Low birth weight Care-seeking for symptoms of pneumonia Exclusive breast-feeding Full immunization Measles immunization

Disaggregated by age, sex, place of residence, socioeconomic status

IMPACTS

OUTCOMES

In this example, improvements in health worker density and distribution, availability of essential medicines and increased number of health facilities with functioning water and sanitation will all contribute to higher rates of skilled birth attendance, institutional deliveries and essential newborn care. These service outputs in turn all contribute to improved outcomes. The determinants of some child health outcomes are not currently included in the Region’s core reference list of indicators. Where countries identify these gaps, they can propose additional indicators to help define their own pathways. The overall logic of the model assumes that changes in most of the indicators listed in the example will reflect changes in under-5 and neonatal mortality rates. For policy development, each country should undertake additional analyses and discussions, including equity analysis and cost-effectiveness analysis of interventions. For example, if health worker density and distribution are highly uneven across the country, strategies or mechanisms to

SDG AND UHC REGIONAL MONITORING FRAMEWORK

incentivize health workers to relocate to underserviced areas may be investigated and developed. After the agreed policy has been formulated and implemented, indicators will allow monitoring and evaluation to assess whether the intervention improved the density and distribution of health workers and if there was consequent improvement in outputs and outcomes. At this point, the country can reassess the problem areas initially identified and, if necessary, modify their policy and interventions for children’s health. Example 4 The same logic approach can be applied to specific diseases. For example, for malaria in poor, rural areas, the indicators in Figure 7 could be used to monitor progress towards malaria-related outputs and outcomes. In this example, health policies and interventions should target those areas with evidence showing high incidence of malaria or high risk of widespread occurrence of the infection. Actions might include financial and non-financial incentives for health workers to relocate to these areas, and policies to improve procurement, distribution and management of essential medicines to ensure they are available when needed. Measures of quality and efficiency (in the output box) would reflect improvements in availability of essential medicines and in the density and distribution of health workers. They might also reflect other actions directly targeting hospitals and the health-care system, as described in the Western Pacific UHC action framework.22 In this example, better quality and more efficient care should lead to reduction in the incidence of malaria and in malaria-associated mortality in the targeted areas. Figure 7. Example of the Western Pacific Region logic model applied to malaria in poor rural areas

INPUTS AND PROCESSES

OUTPUTS Equity-focused service delivery:

TOR C E S MUNIT I ES C OM INDIVIDUAL AND FAMILIES

• Malaria incidence rate • Malaria mortality rate

• • • •

Service utilization (outpatient visits) Hospital admission and readmission rates Hospital mortality rate Hospital average length of stay For malaria patients Disaggregated by age, sex, place of residence, socio-economic status

KS CT O RS

Health policies and Interventions target malaria in poor rural areas

HEAL TH

• Availability of essential medicines • Health worker density and distribution

IAL N ET W OR E S R OT HE

Disaggregated by age, sex, place of residence, socioeconomic status

IMPACTS

OUTCOMES

COUNTRY APPLICATIONS

Logic models can support critical thinking throughout the policy development process. The examples presented in this section show a starting point for this line of analytical thinking. However, they would be complemented by additional techniques and tools, as described in Table 2, to better assess the extent of the health issues.

4.3 How can countries conduct equity analysis and monitoring? In the SDG agenda, equity focuses on the poor and disadvantaged, with the aim of leaving no one behind. This means ensuring that all people can obtain the health services they need without facing financial hardship or other barriers â&#x20AC;&#x201C; usually related to the social determinants of health. Equity analysis aims to identify who is and is not being reached and why certain populations are being left behind. A necessary prerequisite for an equity-oriented health sector is to identify clearly where inequities exist and then monitor how these inequities change over time.23 One approach to equity analysis is to disaggregate data using common attributes as stratifiers. These may include the following: yy yy yy yy yy

Demographic: age, sex Socioeconomic status: wealth, education Place of residence: rural, urban Geography: province, district, municipality Other characteristics: disability, race, migratory status, ethnic minorities.

Monitoring such disaggregated data over time helps to assess progress towards meeting the health needs of those at risk of being left behind. Double disaggregation can also be conducted, meaning the filtering of data using two criteria, such as wealth and place of residence. Double disaggregation can help identify disadvantaged populations, such as those with low coverage of essential services. Within the Region, six countries can already provide data on selected aspects of health and wellbeing disaggregated by sex, education, place of residence, subnational region and income level (quintile/decile). These are Cambodia, the Lao Peopleâ&#x20AC;&#x2122;s Democratic Republic, Mongolia, the Philippines, Vanuatu and Viet Nam. The data come from household-based surveys such as the Demographic Health Survey (DHS), the Multiple Indicator Cluster Survey (MICS) and the Living Standards Measurement Study. However, for a number of indicators and countries, there are limited data for equity analysis. Expanding the use of household surveys and/or conducting other regular studies may be the only option for many countries to be able to address this analytical need. The use of routinely collected administrative data can also support equity analysis. Data from health facilities and from vital registration, for example, may show geographic differences in mortality and morbidity by using district and/or subdistrict. Figure 8 presents an example of equity analysis using routinely collected data for stratification. This example, building on that used in Section 4.2 on childrenâ&#x20AC;&#x2122;s health, uses double stratification to identify four population groups, including urban poor and non-poor, and rural poor and non-poor. Figure 8 shows clear inequities in inputs, outputs and outcomes given the variation in values of the indicators across all four population groups in 2017. When comparing urban non-poor with rural poor, there are significant differences in values for all indicators, with a clear gradient when the other 23

SDG AND UHC REGIONAL MONITORING FRAMEWORK

two population groups are included. In urban non-poor areas, close to 90% of health facilities have functioning water supply and sanitation, while in rural poor areas, this percentage is much lower at 40%. There are similar gaps for health worker density and distribution, skilled birth attendance and under-5 mortality. This information provides evidence to initiate policy dialogue, to discuss policy options and to consider which cost-effective interventions should be introduced or strengthened in the poor rural areas. This example also shows how values for these indicators may change over time. Improvements in health facilities with functioning water supply and sanitation and adequate health worker density are greater in rural areas, likely reflecting efforts to target these essential attributes of a health system in disadvantaged populations. As a consequence, skilled birth attendance and under-5 mortality also show greater improvement in rural areas. This type of equity analysis provides a valuable layer of information to inform the policy development process, both for identifying problem situations and for evaluating the effect of policy changes Figure 8. Hypothetical example of equity-focused analysis Inputs: • Health-care facilities with basic water supply • Health-worker density and distribution and sanitation (%) (per 10 000 population) 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%

60 50 40 30 20

Rural poor Urban poor

2030

2029

2028

2027

2026

2025

2024

2023

2022

2021

2020

2019

2017

Rural non-poor Urban non-poor

Outputs and Impacts: • Skilled birth attendance (%) •

Rural non-poor Urban non-poor

Under-5 mortality rate (deaths per 1 000 live births)

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%

60 50 40 30 20 10

Source: WHO

Rural non-poor Urban non-poor

Rural poor Urban poor

Rural non-poor Urban non-poor

2030

2029

2028

2027

2026

2025

2024

2023

2022

2021

2020

2019

2018

2017

2030

2029

2028

2027

2026

2025

2024

2023

2022

2021

2020

2019

2018

2017

Rural poor Urban poor

2018

2030

2029

2028

2027

2025

Rural poor Urban poor

2026

2023

2024

2021

2022

2019

2020

2018

2017

COUNTRY APPLICATIONS

on the targeted disadvantaged groups. Countries are expected to incorporate this type of equity analysis when monitoring and reviewing progress towards the SDGs and UHC. Member States may wish to complement equity analysis with other equity-oriented approaches, including gender- and human rights-based analyses. In combination, gender, equity and human rights-based analyses provide complementary and mutually reinforcing lenses for analysis. All three types of analysis draw on both quantitative and qualitative data.

Gender analysis24 Gender analysis in health examines how biological and sociocultural factors interact to influence the health of women and men, boys and girls. It can help to unpack women’s and men’s different roles and activities, the norms that define their behaviours, relations between men and women, the resources they have access to and control over, and the constraints they face. Gender analysis identifies, assesses and informs actions to address inequality that comes from: (1) different gender norms, roles and relations; (2) unequal power relations between and among groups of men and women; and (3) the interaction of gender with such other determinants as ethnicity, education or income. WHO has developed a number of tools to support gender analysis in health, including the gender analysis matrix and gender analysis questions.

Human rights-based analysis (or HRBA analysis)25 The UN Statement of Common Understanding on Human Rights-Based Approaches to Development Cooperation and Programming was adopted by the United Nations Development Group in 2003. It stresses that human rights standards and principles – such as nondiscrimination, participation and accountability – should guide all development cooperation and programming in all sectors and in all phases of the project cycle. There are several different approaches and tools for conducting HRBA. Common methods or steps include causality analysis (unpacking the immediate, underlying and root causes of an issue), role/pattern analysis (identifying rights-holder/duty-bearer roles and relationships) and capacity gap analysis (understanding gaps in capacity, including responsibility/motivation/commitment/ leadership, authority and access to and control over resources).

A focus on the social determinants of health The greater focus of the SDGs on disadvantaged populations will require Member States to strengthen their emphasis on the connections between health and the broader social determinants of health. Health either underpins or is affected by all of these factors through multiple, complex and interrelated mechanisms. The SDG Agenda highlights the influence of the social precursors of good health and hence the broader front on which health improvements must be tackled.

Gender mainstreaming for health managers: a practical approach. Geneva: World Health Organization; 2011 (http://www.who.int/genderequity-rights/knowledge/health_managers_guide/en/; accessed 22 August 2017).

The human rights based approach to development cooperation towards a common understanding among UN agencies. New York: United Nations Development Group; 2003 (https://undg.org/document/the-human-rights-based-approach-to-development-cooperation-towardsa-common-understanding-among-un-agencies/; accessed 22 August 2017); UN Common Learning Package on HRBA (http://hrbaportal.org/ common-learning-package-on-hrba). In: UN Practitioners’ Portal on HRBA website]. New York: United Nations Development Group; 2017 (http://hrbaportal.org/; accessed 22 August 2017).

SDG AND UHC REGIONAL MONITORING FRAMEWORK

For example, gender inequality typically results in lower school enrolment rates for girls than for boys. In turn, poor education results in poorer health outcomes for girls and women themselves and for their children and families. Similarly, disability, marginalization or ethnicity can compound gender-based disadvantage and further limit access to health and other social services. The complexities of these relationships and therefore of population health are summarized and synthesized in the Commission on Social Determinants of Health (CSDH) conceptual framework for action (Box 2). The CSDH framework can provide an extra layer of guidance in formulating a countryâ&#x20AC;&#x2122;s monitoring framework, for example, by overlapping the CSDH framework with existing logic models. Improving health from the perspective of the broader social determinants of health will require countries to take concerted and coordinated actions, and for the health sector to form partnerships with various sectors and levels of government to identify critical entry points for effective actions.

Box 2

Commission on Social Determinants of Health (CSDH) conceptual framework

SOCIOECONOMIC AND POLITICAL CONTEXT Governance

Socioeconomic Position

(Living and Working, Conditions, Food, Availability, etc.)

Macroeconomic Policies Social Policies Labour Market, Housing, Land

Public Policies

Social Class Gender Ethnicity (racism) Education

Education, Health, Social Protection

Occupation

Culture and Societal Values

Income

STRUCTURAL DETERMINANTS SOCIAL DETERMINANTS OF HEALTH INEQUITIES

Material Circumstances

Behaviours and Biological Factors

IMPACT ON EQUITY IN HEALTH AND WELL-BEING

Psychological Factors Social Cohesion and Social Capital

Health System

INTERMEDIARY DETERMINANTS SOCIAL DETERMINANTS OF HEALTH

Source: Solar O, Irwin A. A conceptual framework for action on the social determinants of health. Social Determinants of Health Discussion Paper 2. Geneva: World Health Organization; 2010.

4.4 How can countries use monitoring data effectively in policy- and decision-making? Effective use and communication of monitoring data are fundamental to driving change at different levels of the health system. Each country will need to ensure that the content (including level of detail) of their action plan and the formats for reporting health information are tailored to the needs of different audiences from across the community and the health system.26 The action plan should 26

Review of Australiaâ&#x20AC;&#x2122;s health system performance information and reporting frameworks. Australian Government Department of Health [website]. Canberra: Government of Australia; 2017 (https://consultations.health.gov.au/research-data-and-evaluation-division/review-ofaustralia-s-health-system-performance-in/; accessed 22 August 2017).

COUNTRY APPLICATIONS

make use of analytical and interactive tools to communicate the monitoring results to different audiences. Likely audiences will include the general public, patients and their families, care providers, community and other advocates, the media, public and private insurers, local and international donors, health-care purchasers, professional health provider associations, policy-makers at all levels of government, researchers, medical and nursing schools, and regulatory bodies, associations and societies. Overall, the general public will require the least detail; the purpose of reporting to the public is to inform consumers, promote transparency and support research. Audiences at points of care may require the most detail, as the aim is to show service improvements and to discuss and guide further progress. The purpose of reporting to policy-makers at different levels is to indicate strategic direction and the level and distribution of funds and other resources, and to inform policy changes to improve the health system.27 For example, policy-makers may be told how cervical cancer screening rates vary across population groups and regions to show that action is needed so that no one is left behind. Patients, families and community advocates may be less interested in the proportion of women screened but more in whether screening is available where they live and whether the service is free. Similarly, regional variation of foundational aspects of health system development may only be relevant for policymakers and, depending on the specific issue, for health-care purchasers and insurers. From the SDG and UHC Regional Monitoring Framework, indicators for these foundational aspects include those capturing health worker density and distribution, birth and death registration, and the number and proportion of health facilities with functioning sanitation and water. An important audience is those who influence policy- and decision-making. Within government, this may include the minister and/or deputy minister of health, internal or external committees and organizations advising the ministry on policy, and legislative assemblies or parliamentary committees that approve government funding or changes in regulations, acts or laws. Depending on the degree of decentralization of health services and financing in a country, this group may also include provincial or district ministers of health, city mayors, and/or directors of public health units. Within this decision-making group, the type of information to be provided may also vary. Up to the level of minister, associate minister or deputy minister, the evidence should clearly show the nature of the problem, main options to address the problem, including costs and benefits, and a recommendation for the best option. For example, if hospital access is a priority, the ministerâ&#x20AC;&#x2122;s briefing should incorporate an equity analysis and a logic model assessing not only the problem but also its impact on the health system and on the health status of key population groups. Although several options for policy intervention can be presented, the minister may expect a recommendation on the intervention likely to have the greatest impact and to reach the most disadvantaged groups. In some cases, policy development may involve legislative action. In this situation, the evidence and information should only point to the problem, the effect on the population, and the option recommended. If care providers are directly involved in a particular policy priority, detailed data and information will be very helpful when engaging in dialogue with them. For example, if a policy aims to target high rates of postoperative sepsis, then detailed stratification and a logic model-based analysis

Nous Group. Public consultation summary report. For the Australian Ministersâ&#x20AC;&#x2122; Advisory Council (AHMAC). Australian Government Department of Health [website]. Canberra: Government of Australia; 2017 (https://static1.squarespace.com/static/584f45a59de4bbb88e87f673/t/58dc4ae50 3596edd8bf1209a/1490832104406/Public_Consultation_Summary_Report.pdf; accessed 22 August 2017).

SDG AND UHC REGIONAL MONITORING FRAMEWORK

should help to clarify the root causes of the problem and to define solutions, in collaboration with medical and nursing staff, professional associations and hospital managers. Monitoring data should be available to all audiences. This involves posting clear, concise information on various publicly accessible platforms, including the reporting of SDG progress, in order to increase transparency and community participation, and to promote the accountability of government, donors, and development and nongovernmental partners.

4.5 What mistakes should countries avoid when monitoring SDG and UHC? The following scenarios are reminders to avoid the common barriers to implementation of monitoring for SDGs and UHC.

1. (Do not) treat monitoring as an isolated activity Each country should build a monitoring framework that suits its own priorities and needs and can support policy- and decision-making. The SDG and UHC monitoring framework, including equity analysis and monitoring, should be developed in coordination with the policy development process. This will ensure that the indicators will be selected according to policy priorities and needs and that they will measure progress in relevant areas and especially where policy changes and interventions are being considered. SDG and UHC monitoring will be adapted to meet new developments and directions arising from the policy development process.

2. (Do not) collect excessive, unusable data and information Data should be collected only for those indicators selected for SDG and UHC monitoring, linked to clear strategic and policy directions. Try to avoid selecting indicators where there are no data, as significant resources may be needed to build them. A country’s SDG and UHC monitoring framework should consolidate all existing information frameworks and become the overarching framework for health system performance information and reporting. The aim of a consolidated framework is to direct actions towards tracking indicators for health and its determinants – including equity – using data from a range of sources across health and other sectors. These sources include facility data, population-based surveys, surveillance data, civil registration and vital statistics, and relevant data from stakeholders beyond government. An overarching monitoring framework would create a single database on a country’s health system performance and allow for information gaps to be identified and addressed in a coordinated manner. It would reduce the cost and administrative burden by streamlining processes and roles in health system reporting.28 The Healthy Islands Monitoring Framework is a good example of streamlining efforts to lead and unify monitoring of priority health issues (Box 3), as is Australia’s process of consolidating multiple health performance information and reporting frameworks (Box 4).

Ibid.

COUNTRY APPLICATIONS

Box 3

Healthy Islands monitoring framework and indicators

In 1995, the Pacific health ministers declared their vision of Healthy Islands in the Yanuca Island Declaration. The Declaration’s comprehensive and integrated approach has served to protect and promote health in the Pacific by addressing priority health issues, including both NCDs and lingering and emerging and re-emerging infectious diseases. The recently developed Healthy Islands Monitoring Framework comprises 52 mandatory indicators (36 core and 16 complementary) and 26 optional indicators from which countries can make their selections based on national priorities and reporting capacity. The indicators were identified based on specific criteria, including the availability of subregional and national information to assess 20-year trends, as well as of agreed collection methodologies or secondary data sources, and the ability to chart progress on the Healthy Islands descriptors and the five elements of the Healthy Islands vision. The Framework provides institutional mechanisms and opportunities to better measure, report and share data, including on indicators for the five Healthy Islands elements and priority actions. It enables the use of regional health governance mechanisms to report progress regularly and share success stories to help build national capacities. Source: Regional Action Agenda on Achieving the Sustainable Development Goals in the Western Pacific. Manila: WHO Regional Office for the Western Pacific; 2016.

Box 4

Australia’s health system performance information and reporting framework

In August 2016, the Australia Health Ministers’ Advisory Council commissioned a review of Australia’s multiple health system performance information and reporting frameworks. The review proposed a combined whole-of-system performance framework comprehensive enough to serve as a single source of Australia’s health system performance. The benefits of a single framework include better identification of information gaps that can be addressed in a coordinated manner, and reduction in resource costs and the administrative burden by streamlining processes and roles in health system reporting. The review emphasizes the need for a national model for data collection, supply and use. The benefits of this approach include improved consistency in and efficiency of data collection; a reduction in duplication of data collection, analysis and reporting; more sophisticated and targeted analyses; and more timely, meaningful and tailored reporting of data and findings at all levels of the system as well as to consumers and the general public. A tiered reporting framework was proposed to ensure health data are effectively used. The audiences include the public, jurisdictions, regions and points of care, with more detailed data for audiences at points of care. The figure below presents a simplified version of the framework.

Purpose of the Framework Determinants of Health • HEALTH BEHAVIOUR • COMMUNITY AND SOCIOECONOMIC FACTOR • ENVIRONMENTAL FACTORS • BIOMEDICAL FACTORS

Health System Performance • EFFECTIVENESS • SAFETY • RESPONSIVENESS AND CONSUMER SATISFACTION • CONTINUITY OF CARE • ACCESSIBILITY • EFFICIENCY AND SUSTAINABILITY

Health Status • • • •

HEALTH CONDITIONS HUMAN FUNCTION WELL-BEING DEATHS

Transparency Equity Source: Nous Group. Public Consultation Summary Report. For the Australian Ministers’ Advisory Council (AHMAC).

SDG AND UHC REGIONAL MONITORING FRAMEWORK

3. (Do not) immediately select all the indicators of the SDG and UHC Regional Monitoring Framework Attempting to immediately collect data and information on all indicators from the SDG and UHC Regional Monitoring Framework may divert effort from those requiring immediate attention as a priority. Each country is expected to identify high-priority targets and indicators, taking into consideration the country realities, characteristics, challenges and capacities. For some indicators, there are no data currently available and focusing on them unnecessarily may hinder progress in monitoring areas with known, real health needs. Indicators should be selected in line with each country’s strategic plan for health system development. This will require analyses similar to those presented in this report, including equity analysis, policy dialogue and discussions, and careful consideration of the country’s capacity to engage in all aspects of indicator development. One way to address the challenge of limiting the number of indicators is by using tracer indicators. Tracer indicators are typically a smaller, methodologically identified set of indicators that provide a good picture of more than one aspect of the health system. For example, hospital readmission rates may be used to trace both the overall quality of the care provided in hospitals and also access to and quality of primary care.

4. (Do not) select SDG and UHC indicators to follow donor funding exclusively While indicators should reflect the development interest of donors, this should not be the only focus of a country’s monitoring framework. Ideally, the framework and indicators are based on the country’s priorities and needs. Indicators that are important to donors should be incorporated into the framework to the extent possible, without diverting attention to specific donor-linked aspects of a country’s strategic health plan. Typically, donor funding is linked to a country’s health priorities.

5. (Do not) forget to systematically identify where health inequities exist One important step to support equity-focused data collection and analysis is to disaggregate indicators according to commonly used stratifiers, as outlined in Table 3. Some stratifiers are important for a large number of indicators (for example, sex, age, wealth quintile), while others may be relevant only for a few indicators (such as provider type). The systematic identification of areas and community groups that are either disadvantaged or vulnerable becomes the basis for refining policies and targeting programmes to improve equity in health services and outcomes. Table 3. Commonly used stratifiers available for disaggregation of SDG and UHC data Stratifiers

Age, mother’s age Disability status Education, maternal education Ethnic group, race, indigenous groups Facility type (e.g. public/private), provider type, health subsector Key populations (e.g. HIV status, transgender, prisoners) Marital status Place of residence, urban/rural, subnational district, geographic location Sex, sex of household head Socioeconomic status, wealth quintile, employment status

5. INDICATORS, DATA AND DATA SOURCES This chapter presents an overview of technical information relevant to the development, analysis and interpretation of indicators, including the collection, measurement and reporting of data.

5.1 Sources of health information There are many sources of health information, each with strengths and weaknesses. The scope and availability of each source may vary across countries. Effective SDG and UHC monitoring requires each country to make the best use of all data sources available. There are two broad categories of data sources: (a) population-based; and (b) institution-based (Figure 9). Surveillance systems, which often combine population-based and institution-based data, are sometimes classified as a third category.29 Figure 9. Examples of type of data sources for indicator development

• • • •

Population-based sources

Institution-based sources (administrative data)

Censuses Vital registration systems Household surveys Setting-based surveys

• Resource and service records • Individual records • Payment and/or insurance-based records

Surveillance Examples: • Outbreak and disease surveillance • Sentinel surveillance • Risk factor surveillance • Demographic surveillance

Source: Handbook on health inequality monitoring: with a special focus on low- and middle-income countries. Geneva: World Health Organization; 2013.

Qualitative information Participatory and qualitative methods of data collection are not extensively used to build indicators. However, Member States are encouraged to use them together with quantitative approaches to enhance understanding of progress towards SDGs and UHC and improve policy and accountability. Qualitative information can help show the complexity of the agenda and offer insights into why progress is or is not being made. For example, people, families and communities themselves are an important source of information as well as recipients of information, especially from the perspective of responsiveness and accountability. Through focus groups, they can provide information on demand-side issues that more disadvantaged subpopulations disproportionately face – often linked to adverse social and environmental determinants as well as gender norms, roles and relations.30

World health statistics 2017: monitoring health for the SDGs. Geneva: World Health Organization; 2017 (http://apps.who.int/iris/bitstre am/10665/255336/1/9789241565486-eng.pdf; accessed 22 August 2017).

SDG AND UHC REGIONAL MONITORING FRAMEWORK

Population-based data sources Population-based data sources provide either information on every individual in a population or on a representative sample of the population.31 The three best-known sources of data within this category are censuses, vital registration systems and household surveys. Censuses In most countries, national population and household censuses are conducted every 10 years. The census should be carried out at a well-defined point in time and at regular intervals, in order to make comparable information available in a fixed sequence.32 Census data provide information on the size, composition and spatial (geographic) distribution of the population, as well as key demographic and socioeconomic characteristics, including age, sex, socioeconomic status and race or ethnicity. These characteristics can all be used as stratifiers for monitoring the degree and distribution of inequity.33 A population census provides data either for the whole population or for a large representative sample of the population, which allows the estimation of results for relatively small geographic areas and population subgroups. However, the general census typically includes little direct healthrelated information. Despite this limitation, census data can support health equity analysis through linkages with other sources of health information. This use of census data to improve health-related analysis is a common practice in high-income countries. In low- and middle-income countries, the census may be strengthened by including small-area identifiers, such as postcodes. This allows for equity analysis by linking census data with healthrelated sources, including vital statistics, surveillance data and administrative health facility data. While individual-level identifiers are ideal for data linkages, such small-area identifiers – if standardized across different data sources – offer an alternative, more easily implemented option. Vital registration systems In general, the civil registration and vital statistics (CRVS) system records the births, deaths, marriages and divorces in a population. Countries with a strong CRVS system can reliably determine and track mortality rates, life expectancy and cause-of-death data at the population level.34 These data play a fundamental role in planning and monitoring of public health activities and outcomes. For example, an effective CRVS system can help ensure enrolment of every child into immunization programmes, and indicators can be tracked to monitor the prevention of avoidable diseases. CRVS data are also very valuable for equity analysis. The CRVS system is a multisectoral undertaking usually managed by the government’s Statistics Office or Bureau of Statistics. Those involved in the recording, notification and registration of vital events include citizens, doctors and other health workers, police, clerics, population registries and ministries of health. The system is used by many groups, including health, tax, planning and other government authorities and the policy-makers of many sectors. The involvement of multiple

Suharto S. Complementary sources of demographic and social statistics. Reported in: Symposium on Global Review of 2000 Round of Population and Housing Censuses: Mid-Decade Assessment and Future Prospects; New York, 7–10 August 2001. New York: United Nations (http://unstats.un.org/unsd/demog/docs/symposium_03.htm#_Toc7427911; accessed 22 August 2017).

Civil registration and vital statistics 2013: challenges, best practice and design principles for modern systems. Geneva: World Health Organization; 2013 (http://www.who.int/healthinfo/civil_registration/crvs_report_2013.pdf?ua=1; accessed 22 August 2017).

INDICATORS, DATA AND DATA SOURCES

institutions and actors makes coordination and governance a key challenge in building effective CRVS systems.35 Until recently, CRVS systems in low- and middle-income countries have been largely paper-based and manually managed, but efforts are now being made to strengthen the systems with computerand mobile-based technologies.36 For example, health institutions in many countries can now transmit name-based birth and death records electronically to the national civil registry office. CRVS systems in many low- and middle-income countries do not yet provide satisfactory coverage nor adequate quality in the methods for aggregating, using and sharing data. Frequency of data availability, time delays and under-reporting remain significant problems in some countries.37 There is, however, some global momentum to improve CRVS in countries where national coverage is relatively weak.38 Household surveys Household surveys are usually conducted to assess the status of a specific topic or topics at the national level. They are typically administered by country governments or national research bodies with the assistance of international aid agencies or nongovernmental organizations. Many countries have instituted a programme of periodic surveys, which may include annual or quarterly labour force surveys or annual surveys of cost of living, including socioeconomic, income and expenditure information. Ad hoc household surveys may also be conducted to meet specific statistical data needs. These ad hoc surveys may satisfy immediate needs, but do not provide a framework for a continuing database and/or time series. Continuing periodic surveys, on the other hand, are used to investigate a highly important phenomenon that needs to be monitored frequently. All household survey programmes should be part of the country’s overall integrated statistical data collection system, including censuses and administrative records, so that the overall needs for statistical data can be adequately met.39 Usually, household surveys cover a large number of indicators on a specific theme, such as reproductive, maternal and child health, or nutrition. Household surveys can provide specific information on health topics of interest in low- and middle-income countries, in conjunction with socioeconomic, demographic and geographical information at both the individual and household levels. Household surveys are designed to draw information from a sample of the population rather than from every individual within the population. While the surveys generally have a sample size large enough to draw precise conclusions about the overall population, they may not be sufficient for

Ibid.

Kariyawasam N, Weerasekera V, Dayaratne M, Hewapathirana R, Karunapema R, Bandara I. eIMMR: the future of health statistics in Sri Lanka. Sri Lanka J Bio-Med Inform. 2011;1(Suppl 14):1.

An overarching health indicator for the Post-2015 Development Agenda. Brief summary of some proposed candidate indicators. Background paper for Expert Consultation 11–12 December 2014. Geneva: World Health Organization; 2014 (http://www.who.int/healthinfo/indicators/ hsi_indicators_SDG_TechnicalMeeting_December2015_BackgroundPaper.pdf; accessed 22 August 2017).

Strengthening civil registration and vital statistics for births, deaths and causes of death. Resource kit. Geneva: World Health Organization; 2013 (http://apps.who.int/iris/bitstream/10665/78917/1/9789241504591_eng.pdf; accessed 22 August 2017); Phillips DE, AbouZahr C, Lopez AD, Mikkelson L, Savigny D, Lozano R, et al. Are well functioning civil registration and vital statistics systems associated with better health outcomes? Lancet. 2015;286(10001):1386–94. doi: 10.1016/S0140-6736(15)60172-6; Ooman N, Mehl G, Berg M, Silverman R. Modernising vital registration systems: why now? Lancet. 2013;381(9875):1336–7. doi: 10.1016/S0140-6736(13)60847-8.

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valid disaggregation of information for all population subgroups. In such a situation, the survey design may compensate by extra sampling in certain minority subgroups or regions. Household surveys are often the prime instrument to collect data on equity and may be a rich source of disaggregated data on coverage of health services and financial protection. However, they do present some challenges.40 The data for service coverage and financial protection often come from different surveys, including DHS, MICS and others, such as household budget surveys. The comparability of results may also be affected by differences in the survey design, questions or implementation. For example, health expenditures reported in surveys focusing on health tend to be higher than those reported in surveys where health is only one of many items under consideration. In the Western Pacific Region, China collects health expenditure data from its national health service surveys, whereas Cambodia, the Lao Peopleâ&#x20AC;&#x2122;s Democratic Republic, Mongolia, the Philippines and Viet Nam use socioeconomic surveys or income and expenditure surveys. Additional challenges include the recall periods and survey years, which may vary across countries. Despite these challenges, household health surveys should be introduced in countries where they are not currently conducted, with resources to ensure they occur on a regular basis. Household health surveys can potentially be expanded to cover topics where data are often unavailable, such as NCDs and injury.41 Setting-based survey Setting-based surveys should also be considered a population-based source if they collect information from a representative sample of the population. For example, school-based surveys can identify specific health issues affecting the school-going population at a particular point in time, covering a random sample of schools. The benefit of setting-based surveys lies in the convenience of having the target population in one place, which allows the use of different strategies to maximize the response rate.

Institution-based data sources Institutions produce administrative data in the course of government and health system activities, and these data can provide many useful statistics, for example, derived from reports based on hospital records. The three main sources of data in this category are: resource and service records, individual records, and payment or insurance-based records. Resource and service records Resource and service records include internally kept records describing the activities of the institution. The data are typically reported at the institution level and can be aggregated to higher reporting levels from the community level up to the national level. The institutions may include primary care centres and other clinics for ambulatory patients, public health units or centres, hospitals and other health-care institutions, laboratories, and local, provincial or federal health authorities and governments.

Hosseinpoor AR, Bergen N. Area-based units of analysis for strengthening health inequality monitoring. Bull World Health Organ. 2016;94(1):856â&#x20AC;&#x201C;8. DOI: 10.2471/BLT.15.165266.

Hosseinpoor AR, Bergen N, Schlotheuber A. Promoting health equity: WHO health inequalityÂ monitoring at global and national levels. Glob Health Action. 2015; 8(1):29034. doi: 10.3402/gha.v8.29034.

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For example, statistics from 10â&#x20AC;&#x201C;15 primary care centres within a community can be aggregated to the community level. Then statistics from multiple communities can be aggregated to the provincial or national level. For some service types, higher-level reports may also be disaggregated. For programmes run directly by a local government, statistics may be produced at lower levels (such as primary care centres or hospitals). Examples of indicators that can be produced from these data sources include: the number and type of vaccine doses given at a primary care centre; the number of bed nets distributed in a community; the number of nurses working in hospitals or public health units; the total budget of an institution and breakdown by spending categories; the total and average number of patients served in a day, month or year; and the number of surgical procedures performed in a hospital. Several of the input, process and output indicators proposed in the monitoring framework may be obtained from resource and service records, including health worker density and distribution, hospital bed density, service utilization and bed occupancy rate. Individual records Individual records include medical and non-medical information for those who have had contact with, or are waiting for, health-care services. Typically recorded in medical charts, this information may become part of the patient-level database of an institution. In many high-income countries, data on hospital inpatient and emergency care are categorized according to the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) and the International Classification of Health Interventions. These databases allow summary and analysis of reasons for admission or consultation and of the diagnostic and therapeutic interventions carried out. High-priority programmes such as those targeting malaria, HIV and tuberculosis are often delivered through health facilities, so programmeand patient-level data for these conditions are also available in institutional databases. Some institutions retain the historical medical information of individual patients, along with demographic characteristics such as age (date of birth), sex/gender, marital status, race/ethnicity, education, occupation and place of residence. Several indicators in the SDG and UHC Regional Monitoring Framework can be derived from individual health records. Examples include hospital admission and readmission rates, 30-day inhospital mortality rate for acute myocardial infarction, postoperative sepsis, average length of stay, treatment of confirmed malaria cases, HIV viral load suppression, and institutional mortality rates for selected medical conditions. Payment and insurance-based records Payment and insurance-based records may contain detailed information on patients and the services they have received. In a typical fee-for-service scheme, for example, providers send a record for every service rendered to the corresponding payer. Payers may include governmental and nongovernmental organizations, and private health insurance organizations. In a fee-for-service scheme, where payment is made on the basis of complete and accurate records, there may be some information not available at other sites. For example, insurance organizations may keep detailed records of diagnostic tests and procedures performed for individual patients. This information can be used for some of the indicators in the monitoring framework, such as those for cervical cancer screening and duplicate medical tests. Drug payment plans keep detailed

SDG AND UHC REGIONAL MONITORING FRAMEWORK

information on medications prescribed. If a medication is prescribed for specific medical conditions, this information can be used to support estimates of the prevalence of these conditions. Payment and insurance-based records can help to validate data on indicators coming from other institution-based sources. For example, data on the number of immunizations delivered in a community, obtained from service or individual records, may be compared with data from payment records if the programme is delivered under a fee-for-service format. One limiting characteristic of institution-based data is that by definition it only covers people who have interacted with the institution or service. In monitoring inequity, populations with limited institutional access are often of high interest. Therefore, institution-based data may not be helpful in monitoring equity, unless the social and economic characteristics of the service users can be compared with those of the general catchment population. This means that estimates of the denominator â&#x20AC;&#x201C; all people who need services â&#x20AC;&#x201C; must be derived from other sources. It is important to continuously monitor and improve institution- or facility-based systems of recording, reporting and compiling statistics, as they are valuable data sources complementary to population-based census and survey data.Â Administrators at different levels of the system need clear guidance and support on the requirements for maintenance of the systems (that is, concepts, definitions, classifications, timeliness, etc.). Countries should make the effort to reduce fragmentation and improve standardization of administrative data at the country level. Despite some problems, administrative data may be essential for monitoring inequity at lower administrative levels such as districts or municipalities. The detail available in such data is not usually found in other data sources. In fact, at the local level, administrative data may be the only source of health information.42

Surveillance Surveillance is the continuous analysis, interpretation and feedback of systematically collected data, generally using methods selected for their practicality, uniformity and rapidity rather than for detailed accuracy or completeness.43 By observing trends over time, related to place and persons, changes can be observed or anticipated and appropriate action can be taken, including investigative or control measures. Sources of data may relate directly to disease conditions or to factors influencing disease. Thus, they may include:44 a. mortality and morbidity reports based on death certificates, hospital records, general practice sentinels, or notifications; b. laboratory diagnoses; c. outbreak reports; d. vaccine utilization-uptake and side effects; e. sickness absence records; f. disease determinants such as biological changes in agents, vectors, or reservoirs; and g. susceptibility to disease, as by skin testing or serological surveillance (for example, serum banks).

Last JM, editor. A dictionary of epidemiology. Third edition. New York: Oxford University Press; 1995.

Ibid.

INDICATORS, DATA AND DATA SOURCES

There are multiple types of surveillance systems. Examples include outbreak and disease surveillance, sentinel surveillance, risk factor surveillance and demographic surveillance.45 Outbreak and disease surveillance Outbreak and disease surveillance systems are designed to track cases of epidemic-prone diseases as well as their risk factors. Frequent reporting by health facilities, including laboratories, is the main source of data, but other sources are also used, such as media reports. Sentinel surveillance In sentinel surveillance systems, a sample of clinics is used for intensive monitoring of specific disease-control programmes, such as those for HIV and malaria. These selected sites (such as a few primary care centres) provide timely information representative of the broader clinic system dealing with that condition. Risk factor surveillance Risk factor surveillance is the gathering and monitoring of data for factors linked to the propensity for NCDs in a community. These data are most often obtained through regular surveys either of particular groups or of the general population. Demographic surveillance There are demographic surveillance sites in many low- and middle-income countries. They maintain a longitudinal birth and death registration system for a defined local population and often collect data on maternal and child health, cause of death, and other health-related issues relevant to the community. Data from surveillance systems can provide detailed information on a single condition or from selected sites. The data can help to correct over- or under-reporting of diseases or conditions from other sources. However, surveillance data may not be representative of the whole population and reporting practices vary from country to country. This challenges comparability in global disease surveillance, as information on the same disease is collected in a somewhat different way in different countries. Some of these challenges can be addressed by integrating surveillance data into larger health information systems. For example, many countries use District Health Information Software (DHIS), an open-source health management information platform, to monitor health interventions, improve surveillance and speed up data access. For health organizations and governments, DHIS can help manage operations, monitor health processes and services, and improve communication.

SDG AND UHC REGIONAL MONITORING FRAMEWORK

5.2 Metadata and tracer indicators Metadata Metadata refer to the detailed information that is necessary to understand every aspect of indicator development, analysis and interpretation. Metadata are typically presented as a template with standardized attributes (for example, indicator numerator and denominator, data sources and method of estimation). Indicator metadata are available from the WHO Global Reference List of 100 Core Health Indicators,46 and from the UN Statistical Commission’s Inter-Agency and Expert Group on SDG Indicators (IAEG-SDG).47 Volume 2 of this report presents the metadata for all 88 indicators that have so far been included in the SDG and UHC Regional Monitoring Framework. Key indicator metadata comprise: the indicator definition, including numerator and denominator; and disaggregation, including equity stratifiers, data sources, method of estimation and other associated attributes of nationally reported health-related statistics and global estimates. Metadata give users a better understanding of available data, promote the proper interpretation and use of information for exploring health situations and trends, and facilitate standardization and harmonization of indicators collected by different agencies in various countries. To illustrate the information available in metadata, Table 4 presents some data elements and attributes available for the indicator “births attended by skilled health personnel”. For countries, the use of metadata has the following benefits: Table 4. Key metadata for SDG 3 Indicator 3.1.2 proportion of births attended by skilled health personnel Field

Information

Indicator name

Births attended by skilled health personnel (%).

Definition

Percentage of live births attended by skilled health personnel during a specified time period.

Numerator

Number of births attended by skilled health personnel (doctors, nurses or midwives) trained in providing life-saving obstetric care, including giving the necessary supervision, care and advice to women during pregnancy, childbirth and the postpartum period, to conduct deliveries on their own, and to care for newborns.

Denominator

The total number of live births in the same period.

Disaggregation

Age, parity, place of residence, socioeconomic status, type of provider.

Method of estimation

Data for global monitoring are reported by the United Nations Children’s Fund (UNICEF) and WHO. These agencies obtain the data – both survey and registry data – from national sources. Before data can be included in the global databases, UNICEF and WHO undertake a process of data verification that includes correspondence with field offices to clarify any questions. In terms of survey data, some survey reports may present a total percentage of births attended by a type of provider that does not conform to the MDG definition (e.g. total includes providers who are not considered skilled, such as community health workers). In this case, the percentage delivered by a physician, nurse or midwife are totaled and entered into the global database as the MDG estimate. Predominant type of statistics: adjusted.

Preferred data sources

Household surveys

Source: Inter-agency and Expert Group on SDG Indicators (IAEG-SDG). Compilation of metadata for the proposed global indicators for the review of the 2030 Agenda for Sustainable Development. New York: United Nations; 2016. 46

Global Reference List of 100 core health indicators, 2015. Geneva: World Health Organization; 2015 (http://www.who.int/healthinfo/ indicators/2015/en/; accessed 21 August 2017).

Inter-agency and Expert Group on SDG Indicators (IAEG-SDG). Compilation of metadata for the proposed global indicators for the review of the 2030 Agenda for Sustainable Development. New York: United Nations; 2016 (http://unstats.un.org/sdgs/iaeg-sdgs/metadata-compilation/; accessed 22 August 2017).

INDICATORS, DATA AND DATA SOURCES

For countries, the use of metadata has the following benefits:

reduces excessive and duplicative reporting requirements serves as a general reference and guidance for standard indicators and definitions enhances efficiency of investment in data collection enhances availability and quality of data on results improves transparency and accountability.

Tracer Indicators A tracer indicator is a methodologically identified indicator that can provide a good picture of several aspects of the health-care system. For example, the hospital readmission rate may be used to trace or monitor the overall quality of care provided in hospitals, and also reflect to some extent access to and quality of primary care. The United States publication Healthy People 2020 offers a good example of the use of tracer indicators. It provides a comprehensive set of 10-year national goals and objectives for improving the health of all Americans.48 The document contains 42 topic areas that together reflect more than 1200 objectives. A small set of indicators, however, called leading health indicators (LHIs), is used to assess the status of high-priority health issues and actions that can be taken to address them. For example, two LHIs are used to measure progress towards access to health services: the percentage of persons with medical insurance and the percentage of persons with a usual primary care provider. For SDG 3, Indicator 3.8.1 specifically targets coverage of essential health services. This indicator is defined as the average coverage of essential services based on tracer interventions for reproductive, maternal, newborn and child health; infectious disease; NCD; and service capacity and access, among the general and also the most disadvantaged population. Following the SDG Indicator 3.8.1 definition, WHO has identified 16 tracer indicators and proposed a methodology to combine them into one index.49 The index provides an overall picture of essential health service coverage in a country. The methodology proposed by WHO is expected to evolve over time to capture inequality in service coverage, to improve the relevance of the index to higher-income countries and to incorporate feedback from various stakeholders. Additional technical information, including index calculation and data availability, can be found in the technical note Developing an Index for the Coverage of Essential Health Services.50 All 16 current tracer indicators are presented in Box 5, and their metadata are available in Appendix 5 of this report. All 16 tracer indicators are combined into a single index for coverage of essential health services as defined in the SDG Indicator 3.8.1. The indicators were identified under the principle of effective coverage, that is, they should capture the extent to which people in need of health services receive quality (effective) care. The definition includes four categories of tracer interventions: reproductive, maternal, newborn and child health; infectious disease; NCD; and service capacity and access.

Leading health indicators. In: HealthyPeople.gov [website]. Washington, DC: Office of Disease Prevention and Health Promotion; 2017 (https:// www.healthypeople.gov/2020/Leading-Health-Indicators; accessed 22 August 2017).

Hogan D, Hosseinpoor AR, Boerma T. (2016). Technical note. Developing an index for the coverage of essential health services. Geneva: World Health Organization; 2016 (http://www.who.int/healthinfo/universal_health_coverage/UHC_WHS2016_TechnicalNote_May2016.pdf?ua=1; accessed 22 August 2017).

Ibid.

SDG AND UHC REGIONAL MONITORING FRAMEWORK

For each tracer category, four tracer indicators are combined into a single index (see Box 5). Figure 10 presents the tracer indicators and interventions along with the construction of the UHC service coverage index.

Box 5

Tracer indicators for monitoring progress on health service coverage (estimated for the national population and, when available, for disadvantaged populations)

REPRODUCTIVE, MATERNAL, NEWBORN AND CHILD HEALTH • Demand for family planning satisfied with a modern method among women aged 15–49 (%) • Antenatal care – 4 or more visits (%) • 1-year-old children who have received three doses of a vaccine containing diphtheria, tetanus and pertussis (%) • Care-seeking behaviour for children with suspected pneumonia (%) INFECTIOUS DISEASES • Tuberculosis cases detected and treated (%) • People living with HIV receiving antiretroviral treatment (%) • Population at risk sleeping under insecticide-treated bed nets (%) • Households with access to improved sanitation (%)

NONCOMMUNICABLE DISEASES • Prevalence of non-raised blood pressure (%) • Mean fasting plasma glucose (mmol/L) • Cervical cancer screening among women aged 30–49 (%) • Adults aged ≥15 years not smoking tobacco in last 30 days (%) SERVICE CAPACITY (AND ACCESS) • Hospital beds per capita (in relation to a minimum threshold) • Health professionals per capita (in relation to a minimum threshold): physicians, psychiatrists and surgeons • Proportion of health facilities with basket of essential medicines available • International Health Regulations (2005) core capacity index

Source: World health statistics 2017: monitoring health for the SDGs. Geneva: World Health Organization; 2017.

In Figure 10, the tracer indicator “immunization coverage” is based on children 1 year of age having received three doses of a vaccine containing diphtheria, tetanus and pertussis (DTP3). This vaccine often includes antigens against other conditions (such as hepatitis B and Haemophilus influenzae type B); this means monitoring DTP3 coverage may be a reasonable proxy for assessing full child immunization in a country. When combined with the other three tracer indicators, a tracer index can be built and used to trace the coverage of essential health services for reproductive, maternal, newborn and child health. The four tracer indices in Figure 10 can then be combined to reflect the overall national coverage of essential health services. This composite index will help measure progress toward UHC (SDG Target 3.8).

INDICATORS, DATA AND DATA SOURCES

Figure 10. Calculation of UHC service coverage index

Tracer Intervention 1: Reproductive, maternal, newborn and child health Tracer indicators

• • • •

Family planning Pregnancy and delivery care Child immunization Child treatment

Tracer Index 1

Tracer Intervention 2: Infectious diseases • Tracer indicators…

Tracer Index 2

UHC Service Coverage Index

Tracer Intervention 3: Noncommunicable disease • Tracer indicators…

Tracer Index 3

Tracer Intervention 4: Service capacity and access • Tracer indicators…

It traces national coverage of essential health services

Tracer Index 4 Equity-adjusted UHC coverage index

Source: Hogan D, Hosseinpoor AR, Boerma T. (2016). Technical note. Developing an index for the coverage of essential health services. Geneva: World Health Organization; 2016 (http://www.who.int/healthinfo/universal_health_coverage/UHC_WHS2016_TechnicalNote_May2016.pdf?ua=1; accessed 22 August 2017).

5.3 Data availability and existing methodologies Availability of timely, quality data is fundamental to measurement of progress towards SDGs and UHC and for guiding resource allocation. However, data may be lacking for many indicators, especially in low- and middle-income countries.51 Statistical modelling is frequently used to produce health statistics that are comparable across countries.52 However, efforts are still needed to improve estimates, address data gaps, set common standards for documentation, share data and methods, and ensure regular interaction and collaboration among all groups involved.53 Although improvement is needed in the comparability of data, there are established methodologies and standards available for approximately 87% of the health and health-related SDG indicators. Table 5 classifies these indicators into tiers, agreed by the IAEG-SDG.54 Twenty-two of the 47 SDG indicators are classified as Tier I, which means data are regularly produced by countries. For 19 indicators (Tier II), data are not regularly produced and for the remaining six indicators (Tier III) data

Boerma T, Mathers CD. The World Health Organization and global health estimates: improving collaboration and capacity. BMC Medicine. 2015;13:50. doi: 10.1186/s12916-015-0286-7.

Ibid.

Inter-agency and Expert Group on SDG Indicators (IAEG-SDG). Tier classification for global SDG indicators. New York: United Nations; 2017 (https://unstats.un.org/sdgs/iaeg-sdgs/tier-classification/; accessed 22 August 2017).

SDG AND UHC REGIONAL MONITORING FRAMEWORK

Table 5. Classification of health and health-related SDG indicators (as of 20 April 2017) Tier

Definition

Tier I

Indicator conceptually clear Established methodology and standards available Data regularly produced by countries Indicator conceptually clear Established methodology and standards available Data are not regularly produced by countries Indicator for which there is no established methodology Standards or methodology are being developed/tested

Tier II Tier III Total

# of health SDG indicators

# of health-related SDG indicators

Total

Source: WHO Source: Inter-agency and Expert Group on SDG Indicators (IAEG-SDG). Tier classification for global SDG indicators. New York: United Nations; 2017 (https:// unstats.un.org/sdgs/iaeg-sdgs/tier-classification/ accessed 22 August 2017).

are not available. Data are regularly collected for about 50% of the additional indicators to monitor UHC, as listed in Appendix 2. The SDG health indicators in Tier III (not available) include coverage of treatment interventions for substance use disorders (3.5.1), coverage of essential health services (3.8.1), proportion of the target population immunized with all vaccines included in the national programme (3.b.1), and the proportion of health facilities that have a core set of relevant essential medicines available and affordable on a sustainable basis (3.b.3). Data for the health and health-related SDG indicators and for the additional indicators to monitor UHC may be collected from routine administrative systems and programme collection on an annual or biennial basis, or through regular survey. The latter may include census or special household surveys, which may only be updated every three to five years, depending on survey cycles. For the irregularly produced indicators, data are currently collected intermittently through specificpurpose survey collection, observatory data, the criminal justice system, and/or public health and civil registration. A systematic strategy is needed for data collection for these indicators as data for most of the health-related SDG indicators are held outside the health sector. A few of these will also require methodological work to ensure comparability.

6. MOVING FORWARD This chapter examines some of the challenges to be addressed to ensure effective monitoring of progress towards SDGs and UHC, including those involved with data collection and gaps and those related to the strengthening of HIS and health information capacities. The chapter also discusses short- and long-term solutions and opportunities to address these challenges.

6.1 Challenges Common regional challenges to data collection for SDG and UHC monitoring include the limited availability of data, insufficient disaggregation of data, and poor data quality and reliability. Factors limiting the strengthening of HIS include the use of fragmented and independent information systems, limited use of information standards and exchange mechanisms, poor information infrastructure and tools, and limited capacity to generate knowledge for decision-making. Meeting these challenges will require improvements in governance, commitment and leadership; short- and long-term investment in health information infrastructure and human resources; and innovative approaches to the use of existing data sources. Table 6 classifies these challenges into seven categories and maps the potential solutions and opportunities that can help address them. The check marks indicate the solutions and opportunities with the greatest potential impact. Countries are encouraged to use this information to guide policy dialogue on interventions that might address their own information-related challenges and be included in action plans. The most common gaps in data sources for monitoring SDGs and UHC relate to health service coverage and financial protection, disaggregation to expose coverage inequities, and the effectiveness of coverage. This last includes information on whether people receive the services they need, as well as the quality of services provided and the ultimate impact on health.55 For some SDG indicators, there are no data or the data are not collected regularly, and for others there is no established methodology or the methodologies are still being developed. Given the current uncertain comparability of country data and the importance of improving cross-country comparability, countries are expected to use the established indicator metadata to support data collection and reporting. In general, a large amount of data used to inform health indicators is drawn from global estimates. Although these estimates may enhance cross-country comparisons, they may differ from official statistics prepared and endorsed by the countries. Global estimates have large confidence intervals, especially for countries with weak HIS and where the quality of underlying empirical data is limited. Disaggregated data are not available for all indicators and stratifiers. Currently, the best disaggregated data are for reproductive, maternal and child health, which are drawn from surveys. For other health 55

Tracking universal health coverage: first global monitoring report. Joint WHO/World Bank group report. Geneva: World Health Organization and World Bank; 2015 (http://www.who.int/healthinfo/universal_health_coverage/report/2015/en/; accessed 22 August 2017); Murray CJL. The Data for Health initiative: improving availability and quality of health data. In: Healthdata.org [website]. Seattle: Institute for Health Metrics and Evaluation; 2017 (http://www.healthdata.org/acting-data/data-health-initiative-improving-availability-and-quality-health-data; accessed 22 August 2017).

SDG AND UHC REGIONAL MONITORING FRAMEWORK

targets, disaggregation varies significantly. Disaggregation within countries can reveal important health differences across population groups. Without disaggregation, national-level reporting will mask these differences. Table 6. Challenges and opportunities to effectively monitor progress towards achieving the SDG and UHC Challenges to SDG and UHC monitoring

Solution/opportunity

Limited use of Poor Limited Silos / Insufficient Poor data information information data fragmented disaggregated quality and standards infrastrucavailainformation data reliability and exchange ture and bility systems mechanisms tools

Limited capacity to generate knowledge for decisionmaking

Short-term solution: Strengthen capacity of statistical offices at all levels Promote the use of metadata and international standardized indicators Develop multisectoral, coordinated, and standardized data collection and reporting strategies Catalogue data sources available at national, provincial and district levels Harmonize data collection through surveys and health facility reporting systems Promote the use of national health accounts (NHAs) Promote the use of international and national information classifications and standards (e.g. postcodes) Adopt electronic data collection and storage methods Strengthen health information literacy of health-care providers and managers Adopt electronic health records (EHRs) Qualitative monitoring

3 3 3

3 3

Medium- and long-term opportunity: Big data Geospatial data and technologies Sources of information from other sectors

For most indicators, effective population-based surveys may be the key to improving the picture of health service and financial protection coverage. For example, two main indicators on catastrophic and impoverishing health expenditure depend on household expenditure data, typically obtained through household surveys. However, household surveys do present some shortcomings, including lack of standardization across countries regarding the recall period used and the survey years. China uses national health service surveys, whereas Cambodia, the Lao Peopleâ&#x20AC;&#x2122;s Democratic Republic, Mongolia, the Philippines and Viet Nam use socioeconomic surveys or income and expenditure surveys. Efforts are needed to standardize survey instruments and methods of implementation.

MOVING FORWARD

Many countries face difficulties in measuring health service needs, especially in settings where a large proportion of the population may not seek services and whose health issues therefore remain invisible. Most of these indicators require data from population-based surveys. Data blind spots on key public health concerns such as NCDs have also been identified, particularly in low- and middleincome countries. Registration is often weak, disease registries are suboptimal, and risk factor surveys are sporadic.56 Other challenges include fragmented information systems, limited use of standards and poor information infrastructure. These should be addressed through preparing an action plan to prioritize the implementation of interventions in a sequentially coordinated manner. For example, countries should ensure that good infrastructure and information tools are in place before other challenges such as limited data availability and limited use of information standards are tackled. The major challenge of fragmented information systems is faced by many countries. This fragmentation leads to ineffective and inefficient use of health data and information. Many health systems produce large, and sometimes unnecessary, volumes of health data that may be duplicated, and may be difficult to combine in ways to support progress reviews and decision-making. Efforts to produce unfragmented and coordinated health and health-related information systems at all levels are paramount, including linkages with social information systems. Generally, the Region has limited capacity to generate knowledge for decision-making, particularly in low- and middle-income countries. Challenges in the health workforce include limited ability to use information to fill knowledge gaps and inform decision-making, identify relevant knowledge gaps and commission needed research, to link research, policy and action, and to make use of foresight methodologies and trend analysis to strengthen planning. A strong national HIS requires health-care providers, managers, decision-makers and policy-makers with strong health information literacy.

6.2 Short-term opportunities and solutions Some short-term opportunities and solutions to support effective monitoring of SDGs and UHC are outlined in Table 7. This section describes them through four main categories or applications: strengthening HIS and health information capacities, subnational geographical analysis, electronic health records and data linkages, and development of new indicators.

Strengthening HIS and health information capacities Strong national HIS are critical to effective monitoring of SDG and UHC progress. Well-managed HIS supports decision-making, accountability and the coordination of health investments made by governments and other stakeholders, including donors. Country leadership is essential to building a strong HIS. Table 7 outlines some key guiding components of a well-functioning national HIS; these can assist countries to shift focus and attention to more strategic areas by providing a foundation for discussion and a framework for action. An active national coordinating body (for example, the National Statistics Office) with strong oversight and control, working with multiple agencies and sectors, is central to developing a strong HIS. The human, technical and financial capacities of this body should be strengthened to meet the 56

Noncommunicable diseases in the Western Pacific Region: a profile. Manila: WHO Regional Office for the Western Pacific; 2012 (http://www. wpro.who.int/noncommunicable_diseases/documents/ncd_in_the_wpr.pdf?ua=1; accessed 22 August 2017).

SDG AND UHC REGIONAL MONITORING FRAMEWORK

Table 7. Key components of a strong national health information system (HIS) Component

Best practice

Governance

An active national coordinating body (e.g. National Statistics Office) provides oversight, control and support for the multiple agencies and sectors involved. Ministry of Health provides leadership, coordination and both broad and specific incentives for data sharing and use through institutionalized arrangements.

Information use and transparency

Reliable and good-quality data and information are compiled and increasingly shared and used in policy- and decision-making. Different levels of data and data disaggregation are available.

Infrastructure

There is coordinated, government-led direct and indirect incentive-based investment in fundamental information infrastructure and tools. Coordinated health and health-related data and information systems are available at all levels. Fragmented data collection is gradually eliminated.

Human capital development

There is strong health information literacy among health-care providers, managers, decision-makers and policy-makers.

System and data interoperability

Strong and harmonized data collection systems have clear information flows and data processes. International and national information classifications and standards are adopted.

Source: Adapted from the HIS Country Ownership and Leadership Continuum developed by WHO, USAID and ITU (http://www.who.int/ehealth/resources/his_ continuum.pdf?ua=1; accessed 21 August 2017).

countryâ&#x20AC;&#x2122;s needs for data collection, disaggregated data and analysis. This body could, for example, streamline and centralize the management of human resources in statistics within line ministries. The Ministry of Health would provide continuous leadership and coordination and both broad and specific incentives for data-sharing and use. The Ministry of Health may also lead coordinated government-led direct and indirect incentive-based investments in basic information infrastructure and tools. The availability of reliable, good-quality data and information will enable priority-setting and informed decision-making, and will promote accountability of various stakeholders.57 To ensure indicators are consistent, valid and comparable, each country must use globally agreed indicator definitions and global measurement methods, and promote the use of internationally and nationally standardized information classification systems and standards (such as the ICD-10). Given the nature of SDGs, indicators that are cross-cutting and multisectoral also require coordinated strategies to support standardized data collection and reporting. It may be useful for each country to catalogue and describe the data sources available at national, province, district or other administrative levels determine which sources can be used for SDG, UHC and equity monitoring. Mapping of these data sources against the indicators in the monitoring framework would also help identify important gaps where additional information system capacity is required to support reporting of health indicators and related equity stratifiers. For many countries, household surveys are a key source of information as they can provide accurate population statistics for a number of indicators. These statistics can be further disaggregated by socioeconomic status, place of residence, sex and other relevant stratifiers. Health facility data, including stratifying data, are a valuable data source for several indicators in some countries. 57

Kindornay S, Bhattacharya D, Higgins K. Implementing Agenda 2030: unpacking the data revolution at country level. Dhaka: Centre for Policy Dialogue; 2016 (http://www.post2015datatest.com/wp-content/uploads/2016/07/Implementing-Agenda-2030-Unpacking-the-DataRevolution-at-Country-Level.pdf; accessed 22 August 2017).

MOVING FORWARD

Strengthening and harmonizing currently fragmented data collections from surveys and health facility reporting systems will be critical for the effective monitoring of the SDGs and UHC. The use of national health accounts (NHAs) should also be encouraged. Overall, governments have limited information on financial flows and the generation of human and material resources. NHAs can provide essential information to monitor the ratio of capital to recurrent expenditure, or of any one input to the total, and to observe trends. NHAs capture foreign as well as domestic, public and private inputs, and usefully assemble data on physical quantities – such as the numbers of nurses, items of medical equipment, district hospitals – as well as their costs. There are now NHAs in some form for most countries, but they are often rudimentary and not yet widely used as tools of stewardship. NHA data may help the Ministry of Health to think critically about input purchases by all fund-holders in the health system. It may prove useful to commission specific studies to build monitoring capacity in support of best practices on information use. For example, the Philippines is conducting a 15-year longitudinal cohort study on adolescents to track the changes they go through, the opportunities and challenges they face, and the kinds of choices they make. Information on their profiles, characteristics, vulnerabilities and needs will be collected through household and community surveys, focus group discussions and case studies. The purpose of this study is to put a human face to the 2030 Agenda for Sustainable Development and to inform policy-making and programming on health, education and other key areas for today’s and tomorrow’s young people.

Subnational geographic analysis Geographic analysis is key to understanding how health service coverage develops, from simply plotting health facilities on a map to show availability to simultaneous analysis of multiple layers of data to demonstrate UHC effectiveness. It is an essential part of gathering disaggregated data to expose hidden gaps in service provision and to promote UHC. Stratifying data at the level of subnational geographic areas, such as provinces, states or districts, can complement the analysis of data at the individual or household level. For within-country monitoring, dimensions unavailable in one data source may be captured from other sources. Subnational areas are often aligned with administrative districts, which facilitates the use of administrativelevel data.58 For example, the distribution of health system inputs and outputs such as service delivery can be compared to the determinants of health – poverty, education and/or employment. Since interventions to reduce inequities are likely to be implemented at the administrative level, subnational monitoring of health inequities may be useful for benchmarking, with implications for resource allocation, planning and evaluation.59 Actions to increase the quality and availability of area-based data include standardizing data collection by health facilities, and adoption of electronic data collection and storage methods. Common systems of small-area coding can be applied across data sources, such as censuses, CRVS, surveys and facility data; this permits linkages among different sources. By increasing the use of area-based units for analysis, including greater integration of data from other reliable sources (vital registration, censuses and administrative data), the monitoring of health inequities may be strengthened and expanded across a range of health topics. 58

Hosseinpoor AR, Bergen N. Area-based units of analysis for strengthening health inequality monitoring. Bull World Health Organ. 2016;94(1):856–8. DOI: 10.2471/BLT.15.165266.

Bauze AE, Tran LN, Nguyen KH, Firth S, Jimenez-Soto E, Dwyer-Lindgren L, et al. Equity and geography: the case of child mortality in Papua New Guinea. PloS One. 2012;7(5):e37861. doi: 10.1371/journal.pone.0037861.

SDG AND UHC REGIONAL MONITORING FRAMEWORK

Electronic health records and data linkages Countries in the Region are increasingly adopting digitization of data and electronic health records (EHRs), in response to mandatory requirements and efforts to improve the quality of health care and reduce costs. EHR data are input by providers in the process of providing care. Health-care statistics are derived from EHR data warehouses. However, the data requirements for health statistics differ from those for patient care. Using data designed for patient care to track indicators for development and policymaking requires that countries develop strong privacy and confidentiality frameworks. An important challenge may be interoperability. EHRs store data in a variety of standardized domains (such as the ICD-10), but the frequent use of local enhancements to the code sets prevents full interoperability among EHR systems. Efficient linking and anonymizing of EHRs will be crucial to exploitation of these data for the benefit of patients and public health. It will also be critical to develop a framework and methods for data linkages.60

Development of new indicators There are some areas in the framework where indicators have not been proposed. This may be because data are not currently collected or because further work is needed to develop or select an appropriate indicator. Over the next two years, additional indicators may be explored using existing data sources. The focus areas for future indicator development are outlined in Table 8. They have been mapped to the framework and indicator domains of the SDG and UHC Regional Monitoring Framework as presented in this report. Additional details can be found in Appendix 6. Table 8. Focus areas for future indicator development mapped to the SDG and UHC Regional Monitoring Framework Monitoring domain / indicator domain

Focus area

Health Impact Through the Life Course: Life expectancy and well-being Mortality

• Healthy life expectancy • Subjective well-being • Mental health care • Palliative care

Universal Health Coverage: Heath service coverage

• Hepatitis treatment care • Mental health care

Health System Resources and Capacity: Effectiveness Quality and safety Efficiency and sustainability Resources and infrastructure

• Disability-specific and community-based rehabilitation • Disability-specific and community-based rehabilitation • Health system performance • Quality in long-term care • Health system performance • Information and communication technologies • Mental health care • Violence and injury prevention

OECD. Strengthening health information infrastructure for health care quality governance: good practices, new opportunities and data privacy protection challenges. Paris: Organisation for Economic Co-operation and Development; 2013 (http://www.oecd.org/publications/ strengthening-health-information-infrastructure-for-health-care-quality-governance-9789264193505-en.htm; accessed 22 August 2017).

MOVING FORWARD

For example, mental health care requires the development of indicators in the areas of mortality, health service coverage, and resources and infrastructure. Country HIS do not routinely collect data on a core set of mental health indicators and cannot provide reliable information on service coverage. While the need for surveillance of specific disorders may vary from country to country, basic data gathering is needed in all countries.61 The complete recording of suicide deaths in deathregistration systems requires good linkages with coronial and police systems as the process is often disrupted by stigma and delays in determining the cause of death.

6.3 Opportunities in the medium and long term A number of innovative approaches have the potential to enhance current information use and the production of official statistics and indicators. These approaches involve big data, geospatial data and technologies, and data from sources beyond the health sector. The potential value of big data in health care lies in combining traditional and new forms of data at both the individual and population levels.62 Big data approaches may be able to manage the increased quantity of data that need to be generated to support SDG monitoring, including equity analyses. Geospatial analysis is the application of statistical and other analytic techniques to geographic data. This information can describe where people are and their spatial relationship to each other; this in turn can help governments plan service coverage improvements and measure and monitor outputs and outcomes. Information from other sectors and from some commercial datasets can complement data already collected within the health sector. Appendix 7 describes in more detail these innovative approaches, including their relationships with SDG and UHC monitoring. Box 6 presents examples of countries using geographic information systems (GIS) to support health service coverage, and Box 7 outlines the benefits of using big data in the Korean National Health Insurance System.

Box 6

Country examples of GIS use to support health service coverage

Philippines: In the context of prenatal, delivery and postpartum services, GIS has been used for design of household surveys to ensure a geographically based sample of health facilities and households. Cambodia and Lao People’s Democratic Republic: These two countries have used GIS in the context of emergency obstetric care services for spatial analysis and modelling to measure physical accessibility and geographic coverage at the subnational level and to provide scaling-up scenarios towards UHC. China, Republic of Korea, Lao People’s Democratic Republic, Solomon Islands and Vanuatu: In the context of malaria control and elimination, these countries have used GIS for spatial analysis, for the modelling of the spatial distribution of malaria risk and to support decision-making. Source: Roth S, Landry M, Ebener S, Marcelo A, Kijsanayotin B, Parry J. The geography of universal health coverage. Why geographic information systems are needed to ensure equitable access to quality health care. ADB Briefs No. 55. Manila: Asian Development Bank and World Health Organization; 2016.

Regional agenda for implementing the Mental Health Action Plan 2013–2020 in the Western Pacific. Towards a social movement for action on mental health and well-being. Manila: WHO Regional Office for the Western Pacific; 2015 (http://iris.wpro.who.int/bitstream/ handle/10665.1/10893/9789290617020_eng.pdf; accessed 22 August 2017).

Wyber R, Vaillancourt S, Perry W, Mannava P, Folarnmi T, Celi LA. Big data in global health: improving health in low- and middle-income countries. Bull World Health Organ. 2015;93(3):203–8. doi: 10.2471/BLT.14.139022.

SDG AND UHC REGIONAL MONITORING FRAMEWORK

Box 7

Big Data and the Korean National Health Insurance System

The Republic of Korea’s National Health Insurance Service (NHIS) is the nonprofit single insurer for everyone in the country. In providing health and long-term insurance, reimbursing fees to institutions and conducting health screening for all, a large body of computerized longitudinal data has become available in the NHIS on various health dimensions, including pathophysiological status, health behaviour, health conditions, and service use and fees. The data are representative of everyone in the country. The NHIS has set up the National Health Information Database (NHID), which integrates these data through individual identification linkages. Analyses of these big data, such as the following, have produced evidence to inform public health policy: • • •

Identifying causality and predicting risk by linking health-screening data with medical history and socioeconomic status. Creating an evidence base on health risk and disease by region and workplace to develop customized services in communities and workplaces. Developing an accurate health–disease index and surveillance system to target chronic diseases, based on information on use of services by chronic disease patients.

The data are being used to address pressing health issues, including the low birth rate, an ageing population and the chronic diseases burden. Future plans include integrating the NHID with other public health data (for example, electronic medical records) and climate, pollution and spatial network data. Source: Regional Action Agenda on Achieving the Sustainable Development Goals in the Western Pacific. WHO Regional Office for the Western Pacific Region; 2017.

APPENDICES

SDG AND UHC REGIONAL MONITORING FRAMEWORK

Appendix 1. Monitoring framework for SDGs and UHC in the Western Pacific Health impact through the life course – How healthy are people in the Western Pacific? Is it the same for everyone at all stages of life?

INDIVIDUAL HEALTH

• Low birth weight • Neonatal mortality • Under-5 mortality • Malnutrition among children <5 • Stunting among children <5

• Adolescent births • Maternal mortality • Intimate partner violence

• Hepatitis B incidence • NCD mortality

• HIV incidence • TB incidence • Malaria incidence

POPULATION HEALTH • • • • •

Life expectancy at birth Intentional homicide deaths Unintentional poisoning mortality Conflict-related deaths Use of assistive devices among people

• Mortality attributed to household and ambient air pollution • Mortality attributed to unsafe water, unsafe sanitation and lack of hygiene

• People requiring interventions (preventive chemotherapy) against neglected tropical diseases • Need for family planning satisfied with modern methods

Determinants of health – Are these factors contributing to good health? Where and for whom are these factors changing? Is it the same for everyone? ENVIRONMENTAL FACTORS

• Annual mean levels of fine particulate matter in cities • Clean household energy*

• % of population using safely managed sanitation services • % of population using safely HEALTH BEHAVIOURS managed drinking-water • Harmful use of alcohol services • Current tobacco use

SOCIOECONOMIC FACTORS • Unemployment rate* • Proportion of population living in poverty* • Proportion of youth and adults, both men and

women, who have achieved literacy and numeracy*

PERSON-RELATED FACTORS • Overweight and obesity

Universal health coverage – Are all people accessing needed services without suffering financial hardship? FINANCIAL PROTECTION

HEALTH SERVICE COVERAGE AND ACCESSIBILITY

• % of population protected against catastrophic/impoverishing out-of-pocket expenditure • % of population covered by social protection floors/systems

• Coverage of essential health services • Access to affordable medicines and vaccines on sustainable basis

Health system resources and capacity – Does the system deliver value for money and is it sustainable? What is the level of quality of care across the range of patient care needs? EFFECTIVENESS • Immunization coverage for measles • Births attended by skilled health personnel • Cervical cancer screening

QUALITY AND SAFETY • 30-day hospital case fatality rate acute myocardial infarction

• Postoperative sepsis as % of all surgeries

RESPONSIVENESS AND PEOPLECENTREDNESS • Patient experience • Laws and regulations that guarantee women access to sexual and reproductive health

RESOURCES AND INFRASTRUCTURE • Health worker density and distribution • Health facilities with functioning water services

AVAILABILITY (AND READINESS) • International Health Regulations (2005) capacity and health emergency

Note: Framework agreed to at the sixty-seventh session of the Regional Committee of the Western Pacific in October 2016. * Not part of the proposed WHO collections, but included to illustrate the breadth of the monitoring framework.

preparedness

HEALTH FINANCING • Total current expenditure on health as % of GDP

EFFICIENCY AND SUSTAINABILITY • Average length of stay

APPENDICES

Appendix 2. WHO Western Pacific Region SDG and UHC indicator list Table A. SDG 3 Indicators Target

SDG 3 (Ensure healthy lives and promote well-being for all at all ages) health indicators

3.1.1

Maternal mortality ratio

3.1.2

Proportion of births attended by skilled health personnel

3.2.1

Under-5 mortality rate

3.2.2

Neonatal mortality rate

3.3.1

Number of new HIV infections per 1000 uninfected population, by sex, age and key populations

3.3.2

Tuberculosis incidence per 100 000 population

3.3.3

Malaria incidence per 1000 population

3.3.4

Hepatitis B incidence per 100 000 population

3.3.5

Number of people requiring interventions against neglected tropical diseases

3.4.1

Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease

3.4.2

Suicide mortality rate

3.5.1

Coverage of treatment interventions (pharmacological, psychosocial, and rehabilitation and aftercare services) for substance use disorders

3.5.2

Harmful use of alcohol, defined according to the national context as alcohol per capita consumption (aged 15 years and older) within a calendar year in litres of pure alcohol

3.6.1

Death rate due to road traffic injuries

3.7.1

Proportion of women of reproductive age (aged 15–49 years) who have their need for family planning satisfied with modern methods

3.7.2

Adolescent birth rate (aged 10–14 years; aged 15–19 years) per 1000 women in that age group

3.8.1

Coverage of essential health services (defined as the average coverage of essential services based on tracer interventions that include reproductive, maternal, newborn and child health; infectious disease; noncommunicable diseases; and service capacity and access, among the general and the most disadvantaged population)*

3.8.2

Proportion of population with large household expenditures on heath as a share of total household expenditure or income

3.9.1

Mortality rate attributed to household and ambient air pollution

3.9.2

Mortality rate attributed to unsafe water, unsafe sanitation, and lack of hygiene (exposure to unsafe water, sanitation and hygiene for all [WASH] services)

3.9.3

Mortality rate attributed to unintentional poisoning

3.a.1

Age-standardized prevalence of current tobacco use among persons aged 15 years and older

3.b.1

Proportion of the target population covered by all vaccines included in their national programme

3.b.2

Total net official development assistance to the medical research and basic health sectors

3.b.3

Proportion of health facilities that have a core set of relevant essential medicines available and affordable on a sustainable basis

3.c.1

Health worker density and distribution

3.d.1

International Health Regulations (IHR) capacity and health emergency preparedness

* WHO has proposed an index with 16 tracer indicators for coverage of essential health services (Appendix 5). Twelve of these 16 indicators are in the core reference list of 88 indicators in the monitoring framework (Appendix 2, Tables A, B and C). The remaining four are: (1) antenatal care – four or more visits (%); (2) tuberculosis cases detected and treated (%); (3) population at risk sleeping under insecticide-treated bed nets (%); and (4) hospital beds per capita (in relation to a minimum threshold).

SDG AND UHC REGIONAL MONITORING FRAMEWORK

Table B. Other health-related SDG Indicators Target

Health indicators from other SDGs

1.a.2

Proportion of total government spending on essential services (education, health and social protection)

1.3.1

Proportion of population covered by social protection floors/systems, by sex, distinguishing children, unemployed persons, older persons, persons with disabilities, pregnant women, newborns, work injury victims, and the poor and the vulnerable

1.5.1 11.5.1 13.1.1

Number of deaths, missing persons and directly affected persons attributed to disasters per 100 000 population

2.2.1

Prevalence of stunting (height for age < –2 standard deviation from the median of WHO Child Growth Standards) among children under 5 years of age

2.2.2

Prevalence of malnutrition (weight for height > +2 or < –2 standard deviation from median of the WHO Child Growth Standards) among children under 5 years of age, by type (wasting and overweight)

5.2.1

Proportion of ever-partnered women and girls aged 15 years and older subjected to physical, sexual or psychological violence by a current or former intimate partner in previous 12 months, by form of violence and by age

5.2.2

Proportion of women and girls aged 15 years and older subjected to sexual violence by persons other than an intimate partner in the previous 12 months, by age and place of occurrence

5.6.2

Number of countries with laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education

6.1.1

Proportion of population using safely managed drinking-water services

6.2.1

Proportion of population using safely managed sanitation services, including a handwashing facility with soap and water

7.1.2

Proportion of population with primary reliance on clean fuels and technology

11.6.2

Annual mean levels of fine particulate matter (e.g. PM2.5 and PM10) in cities (population weighted)

16.1.1

Number of victims of intentional homicide per 100 000 population, by sex and age

16.1.2

Conflict-related deaths per 100 000 population, by sex, age and cause

16.1.3

Proportion of the population subjected to physical, psychological or sexual violence in the previous 12 months

16.2.1

Proportion of children aged 1–17 years who experienced any physical punishment and/or psychological aggression by caregivers in the past month

16.2.2

Number of victims of human trafficking per 100 000 population, by sex, age and form of exploitation

16.2.3

Proportion of young women and men aged 18–29 years who experienced sexual violence by age 18

16.9.1

Proportion of children under 5 years of age whose births have been registered with a civil authority, by age

17.19.2(b)

Proportion of countries that have achieved 100% birth registration and 80% death registration

APPENDICES

Table C. Additional Indicators to monitor UHC Additional indicators to monitor UHC

Life expectancy at birth Total current expenditure on health as percentage of gross domestic product Seat belt-wearing rate Motorcycle helmet-wearing rate Bed occupancy rate Immunization coverage rate for DTP3 (diphtheria-tetanus-pertussis) Immunization coverage rate for measles Stillbirth rate (per 1000 total births) Case rate of congenital syphilis (per 100 000 live births) Exclusive breastfeeding rate in infants 0–5 months of age Incidence of low birth weight among newborns Prevalence of anaemia in children aged 6–59 months Anaemia prevalence in women of reproductive age (aged 15–49 years) Age-standardized prevalence of raised blood glucose level among adults 18+ years Age-standardized prevalence of overweight and obesity in persons aged 18+ years Age-standardized prevalence of raised blood pressure among persons aged 18+ years Age-standardized prevalence of insufficiently physically active persons aged 18+ years Percentage of children under 5 years of age with suspected pneumonia who were taken to a health facility Antiretroviral therapy (ART) coverage Second-line treatment coverage among multidrug-resistant tuberculosis (MDR-TB) cases Cervical cancer screening (rate) Coverage of services for severe mental health disorders Current expenditure on health by general government and compulsory schemes as percentage of total current expenditure on health Rate of use of assistive devices among people with disabilities Proportion of newborns receiving essential newborn care 30-day hospital case fatality rate – acute myocardial infarction Patient experience (to be defined) Proportion of health-care facilities with basic water supply Proportion of health-care facilities with basic sanitation Hospital average length of stay Dengue mortality rate Mortality rate attributable to hepatitis B virus (HBV) and hepatitis C virus (HCV) infections Proportion of deliveries in health facilities Age-standardized prevalence of current tobacco use among persons aged 13–15 years Outpatient service utilization rate Cataract surgical rate and coverage Postoperative sepsis rate Hospital readmission rate Proportion of the population utilizing the rehabilitation services they require HIV testing coverage among people living with HIV Viral suppression rate among people on ART

SDG AND UHC REGIONAL MONITORING FRAMEWORK

Appendix 3. WHO Western Pacific Region mapping of SDG and UHC indicators Table A. Health indicators in SDG 3 mapped to the SDG and UHC Regional Monitoring Framework Health indicators in SDG 3 mapped to the SDG and UHC Regional Monitoring Framework Monitoring Domain

Health impact through the life course

Indicator domain

Mortality

Morbidity

Determinants of health

Universal health coverage

Life expectancy and well-being Individual characteristics and behaviours Health service coverage

Financial protection Health system resources and capacity

Resources and infrastructure Availability and readiness

Indicator

3.1.1 Maternal mortality ratio 3.2.1 Under-5 mortality rate 3.2.2 Neonatal mortality rate 3.4.1 Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease 3.4.2 Suicide mortality rate 3.6.1 Death rate due to road traffic injuries 3.9.1 Mortality rate attributed to household and ambient air pollution 3.9.2 Mortality rate attributed to unsafe water, unsafe sanitation, and lack of hygiene (exposure to unsafe WASH services) 3.9.3 Mortality rate attributed to unintentional poisoning 3.3.1 Number of new HIV infections per 1000 uninfected population, by sex, age and key populations 3.3.2 Tuberculosis incidence per 100 000 population 3.3.3 Malaria incidence per 1000 population 3.3.4 Hepatitis B incidence per 100 000 population 3.7.2 Adolescent birth rate (aged 10–14 years; aged 15–19 years) per 1000 women in that age group 3.5.2 Harmful use of alcohol, defined according to the national context as alcohol per capita consumption (aged 15 years and older) within a calendar year in litres of pure alcohol 3.a.1 Age-standardized prevalence of current tobacco use among persons aged 15 years and older 3.3.5 Number of people requiring interventions against neglected tropical diseases 3.5.1 Coverage of treatment interventions (pharmacological, psychosocial, and rehabilitation and aftercare services) for substance use disorders 3.7.1 Proportion of women of reproductive age (aged 15–49 years) who have their need for family planning satisfied with modern methods 3.8.1 Coverage of essential health services (defined as the average coverage of essential services based on tracer interventions that include reproductive, maternal, newborn and child health; infectious disease; noncommunicable diseases; and service capacity and access, among the general and the most disadvantaged population) 3.b.1 Proportion of the target population covered by all vaccines included in their national programme 3.1.2 Proportion of births attended by skilled health personnel 3.8.2 Proportion of population with large household expenditures on health as a share of total household expenditure or income 3.b.2 Total net official development assistance to the medical research and basic health sectors 3.c.1 Health worker density and distribution 3.b.3 Proportion of health facilities that have a core set of relevant essential medicines available and affordable on a sustainable basis 3.d.1 International Health Regulations (2005) capacity and health emergency preparedness

APPENDICES

Table B. Health indicators in other SDG mapped to the SDG and UHC Regional Monitoring Framework Health indicators in SDG 3 mapped to the SDG and UHC Regional Monitoring Framework Monitoring Domain

Health impact through the life course

Indicator domain

Mortality

Morbidity

Determinants of health

Social environment factors

Physical environment factors

Universal health coverage

Financial protection

Health system resources and capacity

Resources and infrastructure Responsiveness and People-centredness Health financing

Indicator

1.5.1 11.5.1 13.1.1 Number of deaths, missing persons and directly affected persons attributed to disasters per 100 000 population 16.1.1 Number of victims of intentional homicide per 100 000 population, by sex and age 16.1.2 Conflict-related deaths per 100 000 population, by sex, age and cause 2.2.1 Prevalence of stunting (height for age < –2 standard deviation from the median of WHO Child Growth Standards) among children under 5 years of age 2.2.2 Prevalence of malnutrition (weight for height > +2 or < –2 standard deviation from the median of the WHO Child Growth Standards) among children under 5 years of age, by type (wasting and overweight) 5.2.1 Proportion of ever-partnered women and girls aged 15 years and older subjected to physical, sexual or psychological violence by a current or former intimate partner in the previous 12 months, by form of violence and by age 5.2.2 Proportion of women and girls aged 15 years and older subjected to sexual violence by persons other than an intimate partner in the previous 12 months, by age and place of occurrence 16.1.3 Proportion of the population subjected to physical, psychological or sexual violence in the previous 12 months 16.2.1 Proportion of children aged 1–17 years who experienced any physical punishment and/or psychological aggression by caregivers in the past month 16.2.2 Number of victims of human trafficking per 100 000 population, by sex, age and form of exploitation 16.2.3 Proportion of young women and men aged 18–29 years who experienced sexual violence by age 18 6.1.1 Proportion of population using safely managed drinking-water services 6.2.1 Proportion of population using safely managed sanitation services, including a handwashing facility with soap and water 7.1.2 Proportion of population with primary reliance on clean fuels and technology 11.6.2 Annual mean levels of fine particulate matter (e.g. PM2.5 and PM10) in cities (population weighted) 1.3.1 Proportion of population covered by social protection floors/systems, by sex, distinguishing children, unemployed persons, older persons, persons with disabilities, pregnant women, newborns, work injury victims and the poor and the vulnerable 16.9.1 Proportion of children under 5 years of age whose births have been registered with a civil authority, by age 17.19.2(b) Proportion of countries that have achieved 100% birth registration and 80% death registration 5.6.2 Number of countries with laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education 1.a.2 Proportion of total government spending on essential services (education, health and social protection)

SDG AND UHC REGIONAL MONITORING FRAMEWORK

Table C. Additional indicators to monitor UHC mapped to the SDG and UHC Regional Monitoring Framework Additional indicators to monitor UHC mapped to the SDG and UHC Regional Monitoring Framework Monitoring Indicator domain Indicator Domain Health impact Mortality Dengue mortality rate through the life Mortality rate attributable to HBV and HCV infections course Stillbirth rate (per 1000 total births) Morbidity Incidence of low birth weight among newborns Anaemia prevalence in women of reproductive age (aged 15–49 years) Prevalence of anaemia in children aged 6–59 months Case rate of congenital syphilis (per 100 000 live births) Life expectancy and Life expectancy at birth well-being Determinants of Individual Age-standardized prevalence of raised blood glucose level among adults 18+ years health characteristics and Age-standardized prevalence of raised blood pressure among persons aged 18+ years behaviours Age-standardized prevalence of overweight and obesity in persons aged 18+ years Age-standardized prevalence of insufficiently physically active persons aged 18+ years Seat belt–wearing rate Motorcycle helmet–wearing rate Age-standardized prevalence of current tobacco use among persons aged 13–15 years Percentage of children under 5 years of age with suspected pneumonia who were taken to a health facility Exclusive breastfeeding rate in infants 0–5 months of age Universal health coverage

Use/Accessibility Health service coverage

Health system resources and capacity

Effectiveness

Quality and safety Responsiveness and people-centredness Resources and infrastructure Efficiency and sustainability Health financing

Rate of use of assistive devices among people with disabilities Proportion of the population utilizing the rehabilitation services they require Outpatient service utilization rate Coverage of services for severe mental health disorders HIV testing coverage among people living with HIV Cervical cancer screening (rate) ART coverage Second-line treatment coverage among MDR-TB cases Proportion of deliveries in health facilities Immunization coverage rate for DTP3 (diphtheria-tetanus-pertussis) Immunization coverage rate for measles Viral suppression rate among people on ART Proportion of newborns receiving essential newborn care Cataract surgical rate and coverage 30-day hospital case fatality rate – acute myocardial infarction Post-operative sepsis rate Hospital readmission rate Patient experience (to be defined) Proportion of health-care facilities with basic water supply Proportion of health-care facilities with basic sanitation Bed occupancy rate Hospital average length of stay Total current expenditure on health as percentage of gross domestic product Current expenditure on health by general government and compulsory schemes as a percentage of total current expenditure on health

APPENDICES

Appendix 4. Reference list: 88 SDG and UHC health indicators listed according to the health system results chain (logic model) Inputs and Processes

Outputs

Outcomes

Impact

• Total net official development assistance to the medical research and basic health sectors • Health worker density and distribution • Proportion of children under 5 years of age whose births have been registered with a civil authority • Proportion of countries that have achieved 100% birth registration and 80% death registration • Total current expenditure on health as percentage of gross domestic product • Current expenditure on health by general government and compulsory schemes as percentage of total current expenditure on health • Proportion of health-care facilities with basic water supply • Proportion of health-care facilities with basic sanitation • Proportion of total government spending on essential services (education, health and social protection)

• International Health Regulations (2005) capacity and health emergency preparedness • Outpatient service utilization rate • Postoperative sepsis rate • Bed occupancy rate • 30-day hospital case fatality rate – acute myocardial infarction • Patient experience (to be defined) • Hospital average length of stay • Hospital readmission rate

• Proportion of births attended by skilled health personnel • Number of people requiring interventions against neglected tropical diseases • Coverage of treatment for substance use disorders • Harmful use of alcohol • Proportion of women reproductive age (aged 15–49 years) who have their need for family planning satisfied with modern methods • Adolescent birth rate • Coverage of essential health services • Proportion of population with large household expenditure on health as a share of total household expenditure or income • Age-standardized prevalence of current tobacco use among persons aged 15 years and older • Proportion of the target population covered by all vaccines included in their national programme • Proportion of population covered by social protection floors/systems • Prevalence of stunting among children under 5 years of age • Prevalence of malnutrition among children under 5 years of age, by type (wasting and overweight) • Proportion of population using safely managed drinkingwater services • Proportion of population using safely managed sanitation services, including a handwashing facility with soap and water • Annual mean levels of fine particulate matter (e.g. PM2.5 and PM10) in cities (population weighted) • Proportion of population with primary reliance on clean fuels and technology • Proportion of ever-partnered women and girls aged 15 years and older subjected to physical, sexual or psychological violence by a current or former intimate partner in previous 12 months • Seat belt-wearing rate • Motorcycle helmet-wearing rate • Immunization coverage for DTP3 (diphtheria-tetanuspertussis) • Immunization coverage rate for measles • Exclusive breastfeeding rate in infants 0–5 months of age • Incidence of low birth weight among newborns • Prevalence of anaemia in children aged 6–59 months • Anaemia prevalence in women of reproductive age (aged 15–49 years) • Age-standardized prevalence of raised blood glucose level among adults 18+ years • Age-standardized prevalence of overweight and obesity in persons aged 18+ years • Age-standardized prevalence of raised blood pressure among persons aged 18+ years • Age-standardized prevalence of insufficiently physically active persons aged 18+ years • Percentage of children under 5 years of age with suspected pneumonia who were taken to a health facility • Antiretroviral therapy (ART) coverage • Second-line treatment coverage among multidrugresistant tuberculosis (MDR-TB) cases • Cervical cancer screening (rate) • Coverage of services for severe mental health disorders • Rate of use of assistive devices among people with disabilities • Proportion of newborns receiving essential newborn care • Proportion of deliveries in health facilities • Age-standardized prevalence of current tobacco use among persons aged 13–15 years • Cataract surgical rate and coverage • Proportion of population utilizing the rehabilitation services they require • HIV testing coverage among people living with HIV • Viral suppression rate among people on ART

• Maternal mortality ratio • Under-5 mortality rate • Neonatal mortality rate • Number of new HIV infections per 1000 uninfected population • Malaria incidence per 1000 population • Hepatitis B incidence per 100 000 population • Suicide mortality rate • Death rate due to road traffic injuries • Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease • Tuberculosis incidence per 100 000 population • Mortality rate attributed to household and ambient air pollution • Mortality rate attributed to unsafe water, unsafe sanitation, and lack of hygiene • Mortality rate attributed to unintentional poisoning • Proportion of health facilities that have a core set of relevant essential medicines available and affordable on a sustainable basis • Number of deaths, missing persons and directly affected persons attributed to disasters per 100 000 population • Proportion of women and girls aged 15 years and older subjected to sexual violence by persons other than an intimate partner in the previous 12 months • Number of countries with laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education • Number of victims of intentional homicide per 100 000 population • Conflict-related deaths per 100 000 population • Proportion of population subjected to physical, psychological or sexual violence in the previous 12 months • Proportion of children aged 1–17 years who experienced any physical punishment and/or psychological aggression by caregivers in the past month • Number of victims of human trafficking per 100 000 population • Proportion of young women and men aged 18–29 years who experienced sexual violence by age 18 • Life expectancy at birth • Stillbirth rate (per 1000 total births) • Case rate of congenital syphilis (per 100 000 live births) • Mortality rate attributable to HBV and HCV infections • Dengue mortality rate

SDG AND UHC REGIONAL MONITORING FRAMEWORK

Appendix 5. Examples of Metadata for 16 tracer indicators (UHC service coverage index)63 Reproductive, maternal, newborn and child health Tracer area

Indicator definition Numerator Denominator Main data sources Method of measurement

Method of estimation

UHC-related notes Tracer area Indicator definition

Numerator Denominator Main data sources Method of measurement

Method of estimation UHC-related notes

As of 28 February 2017.

Family planning

Percentage of women of reproductive age (15−49 years) who are married or in-union who have their need for family planning satisfied with modern methods Number of women aged 15–49 who are married or in-union who use modern methods Total number of women aged 15–49 who are married or in-union in need of family planning Population-based health surveys Household surveys include a series of questions to measure modern contraceptive prevalence rate and demand for family planning. Total demand for family planning is defined as the sum of the number of women of reproductive age (15–49 years) who are married or in a union and who are currently using – or whose sexual partner is currently using – at least one contraceptive method, and the unmet need for family planning. Unmet need for family planning is the proportion of women of reproductive age (15–49 years) either married or in a consensual union, who are fecund and sexually active but who are not using any method of contraception (modern or traditional), and report not wanting any more children or wanting to delay the birth of their next child for at least two years. Included are: 1. all pregnant women (married or in a consensual union) whose pregnancies were unwanted or mistimed at the time of conception; 2. all postpartum amenorrhoeic women (married or in consensual union) who are not using family planning and whose last birth was unwanted or mistimed; and 3. all fecund women (married or in consensual union) who are neither pregnant nor postpartum amenorrhoeic, and who either do not want any more children (want to limit family size), or who wish to postpone the birth of a child for at least two years or do not know when or if they want another child (want to space births), but are not using any contraceptive method. The United Nations Population Division produces a systematic and comprehensive series of annual estimates and projections of the percentage of demand for family planning that is satisfied among married or in-union women. A Bayesian hierarchical model combined with country-specific data are used to generate the estimates, projections and uncertainty assessments from survey data. The model accounts for differences by data source, sample population, and contraceptive methods. See here for details: http://www.un.org/en/development/desa/population/theme/family-planning/cp_model.shtml Pregnancy and delivery care Percentage of women aged 15–49 years with a live birth in a given time period who received antenatal care four or more times Number of women aged 15−49 years with a live birth in a given time period who received antenatal care four or more times Total number of women aged 15−49 years with a live birth in the same period Household surveys and routine facility information systems Data on four or more antenatal care visits is based on questions that ask if and how many times the health of the woman was checked during pregnancy. Household surveys that can generate this indicator include demographic health surveys, multiple indicator cluster surveys, reproductive health surveys and other surveys based on similar methodologies. Service/facility reporting systems can be used where the coverage is high, usually in higher-income countries. WHO maintains a data base on coverage of antenatal care: http://www.who.int/gho/maternal_health/ who_rhr_anc4_detailed_2017.xls Ideally this indicator would be replaced with a more comprehensive measure of pregnancy and delivery care, for example, the proportion of women who receive both 4+ antenatal care visits and have a skilled provider attend the birth or have an institutional delivery. A challenge in measuring skilled attendance at birth is determining which providers are “skilled”. WHO and UNICEF are currently leading a process to come to agreement across countries about the definition of a skilled provider, after which a more comprehensive indicator of pregnancy and delivery care could be incorporated into the index. Once comparable values are available across countries, SDG 3.1.2 could be used.

APPENDICES

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Full child immunization

Indicator definition

Percentage of infants receiving three doses of diphtheria-tetanus-pertussis-containing vaccine (DTP3).

Numerator

Children 1 year of age who have received three doses of DTP3.

Denominator

All children 1 year of age.

Main data sources

Household surveys and facility information systems.

Method of measurement

For survey data, the vaccination status of children aged 12–23 months is collected from child health cards or, if there is no card, from recall by the caretaker. For administrative data, the total number of doses administered to the target population is extracted.

Method of estimation

Together, WHO and UNICEF derive estimates of DTP3 coverage based on data officially reported to WHO and UNICEF by Member States, as well as data reported in the published and grey literature. They also consult with local experts – primarily national Expanded Programme on Immunization (EPI) managers and WHO Regional Office staff – for additional information regarding the performance of specific local immunization services. Based on the available data, consideration of potential biases, and contributions from local experts, WHO/ UNICEF determine the most likely true level of immunization coverage. For details, see here: http://www.who.int/bulletin/volumes/87/7/08-053819/en/ http://www.who.int/immunization/monitoring_surveillance/routine/coverage/en/index4.html

UHC-related notes

There is variability in national vaccine schedules across countries. Given this, one option for monitoring full child immunization is to monitor the fraction of children receiving vaccines included in their country’s national schedule. A second option, which may be more comparable across countries and time, is to monitor DTP3 coverage as a proxy for full child immunization. Diphtheria-tetanus-pertussis-containing vaccine often includes other vaccines, e.g. against hepatitis B and Haemophilus influenza type B, and is a reasonable measure of the extent to which there is a robust vaccine delivery platform within a country. The vaccine coverage indicator for SDG target 3.b is still under development, but once available could be adopted in lieu of DTP3 coverage as part of the UHC service coverage index in future years.

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Child treatment (care seeking for symptoms of pneumonia)

Indicator definition

Percentage of children under 5 years of age with suspected pneumonia (cough and difficult breathing not due to a problem in the chest and a blocked nose) in the two weeks preceding the survey taken to an appropriate health facility or provider.

Numerator

Number of children with suspected pneumonia in the two weeks preceding the survey taken to an appropriate health provider.

Denominator

Number of children with suspected pneumonia in the two weeks preceding the survey.

Main data sources

Household surveys

Method of measurement

During the UNICEF/WHO Meeting on Child Survival Survey-based Indicators held in New York, 17–18 June 2004, it was recommended that acute respiratory infections (ARI) be described as “presumed pneumonia” to better reflect probable cause and the recommended interventions. The definition of presumed pneumonia used in the DHS and MICS was chosen by the group and is based on mothers’ perceptions of a child who has a cough, is breathing faster than usual with short, quick breaths, or is having difficulty breathing, excluding children that had only a blocked nose. The definition of “appropriate” care provider varies between countries. WHO maintains a data base of country-level observations from household surveys that can be accessed here: http://www.who.int/gho/child_health/prevention/pneumonia/en/

Method of estimation

There are currently no internationally comparable estimates for this indicator.

UHC-related notes

This indicator is not typically measured in higher-income countries with well-established health systems. For countries without observed data, coverage was estimated from a regression that predicts coverage of care seeking for symptoms of pneumonia (on the logit scale), obtained from the WHO database described above, as a function of the log of the estimated under-five pneumonia mortality rate, which can be found here: http:// www.who.int/healthinfo/global_burden_disease/

SDG AND UHC REGIONAL MONITORING FRAMEWORK

Communicable diseases Tracer area

Tuberculosis detection and treatment

Indicator definition

Percentage of incidence TB cases that are detected and successfully treated in a given year

Numerator

Number of new and relapse cases detected in a given year and successfully treated

Denominator

Number of new and relapse cases in the same year

Main data sources

Facility information systems, surveillance systems, population-based health surveys with TB diagnostic testing, TB register and related quarterly reporting system (or electronic TB registers)

Method of measurement

This indicator requires three main inputs: 1. The number of new and relapse TB cases diagnosed and treated in national TB control programmes and reported to WHO in a given year. 2. The number of incident TB cases for the same year, typically estimated by WHO. 3. Percentage of TB cases successfully treated (cured plus treatment completed) among TB cases reported to the national health authorities. The final indicator = (1)/(2) x (3)

Method of estimation

Estimates of TB incidence are produced through a consultative and analytical process led by WHO and are published annually. These estimates are based on annual case notifications, assessments of the quality and coverage of TB notification data, national surveys of the prevalence of TB disease and information from death (vital) registration systems. Estimates of incidence for each country are derived, using one or more of the following approaches depending on available data: 1. incidence = case notifications/estimated proportion of cases detected; 2. incidence = prevalence/duration of condition; 3. incidence = deaths/proportion of incident cases that die. These estimates of TB incidence are combined with country-reported data on the number of cases detected and treated, and the percentage of cases successfully treated, as described above.

UHC-related notes

To compute the indicator using WHO estimates, one can access necessary files here: http://www.who.int/tb/country/data/download/en/, and compute the indicator as = c_cdr x c_new_tsr

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HIV treatment

Indicator definition

Percentage of people living with HIV currently receiving antiretroviral therapy (ART)

Numerator

Number of adults and children who are currently receiving ART at the end of the reporting period

Denominator

Number of adults and children living with HIV during the same period

Main data sources

Facility-reporting systems, sentinel surveillance sites, population-based surveys

Method of measurement

Numerator: The numerator can be generated by counting the number of adults and children who received antiretroviral combination therapy at the end of the reporting period. Data can be collected from facility-based ART registers or drug supply management systems. These are then tallied and transferred to cross-sectional monthly or quarterly reports that can then be aggregated for national totals. Patients receiving ART in the private sector and public sector should be included in the numerator. Denominator: Data on the number of people with HIV infection may come from population-based surveys or, as is common in sub-Saharan Africa, surveillance systems based on antenatal care clinics.

Method of estimation

Estimates of ART treatment coverage among people living with HIV in 2015 are derived as part of the 2016 UNAIDS’ estimation round or, in some limited instances, taken from data submitted to UNAIDS through the Global AIDS Response Progress Reporting tool. To estimate the number of people living with HIV across time in high-burden countries, UNAIDS in collaboration with countries uses an epidemic model (Spectrum) that combines surveillance data on prevalence with the current number of patients receiving ART and assumptions about the natural history of HIV disease progression. Since ART is now recommended for all individuals living with HIV, monitoring ART coverage is less complicated than before, when only those with a certain level of disease severity were eligible to receive ART. Estimates of ART coverage can be found here: http://aidsinfo.unaids.org/

UHC-related notes

Comparable estimates of ART coverage in high-income countries, in particular time trends, are not always available.

APPENDICES

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Insecticide-treated bed nets (ITN) coverage for malaria prevention

Indicator definition

Percentage of population in malaria-endemic areas who slept under an ITN the previous night.

Numerator

Number of people in malaria-endemic areas who slept under an ITN.

Denominator

Total number of people in malaria-endemic areas.

Main data sources

Data on household access and use of ITNs come from nationally representative household surveys such as DHS, MICS, and Malaria Indicator Surveys. Data on the number of ITNs delivered by manufacturers to countries are compiled by Milliner Global Associates, and data on the number of ITNs distributed within countries are reported by National Malaria Control Programs.

Method of measurement

Many recent national surveys report the number of ITNs observed in each respondent household. Ownership rates can be converted to the proportion of people sleeping under an ITN using a linear relationship between access and use that has been derived from 62 surveys that collect information on both indicators.

Method of estimation

Mathematical models can be used to combine data from household surveys on access and use with information on ITN deliveries from manufacturers and ITN distribution by national malaria programmes to produce annual estimates of ITN coverage. WHO uses this approach in collaboration with the Malaria Atlas Project. Methodological details can be found in the Annex of the World Malaria Report 2015: http://www.who.int/malaria/publications/world-malaria-report-2015/report/en/.

UHC-related notes

WHO produces comparable ITN coverage estimates for 40 high-burden countries. For other countries, ITN coverage is not included in the UHC service coverage index due to data limitations. However, future research will focus on estimating ITN coverage among those at risk in countries outside of Africa with (potentially localized) malaria burden.

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Improved water and adequate sanitation source

Indicator definition

Percentage of households using improved sanitation facilities

Numerator

Population living in a household with: flush or pour-flush to piped sewer system, septic tank or pit latrine, ventilated improved pit latrine, pit latrine with slab, or composting toilet.

Denominator

Total population

Main data sources

Population-based household surveys and censuses

Method of measurement

Household-level responses, weighted by household size, are used to compute population coverage.

Method of estimation

The WHO/UNICEF Joint Monitoring Programme for Water Supply and Sanitation (JMP) has produced regular estimates of coverage of improved sanitation for MDG monitoring. After compiling a database of available data sources for each country, simple linear regressions are fitted to the countryâ&#x20AC;&#x2122;s data series to obtain an in-sample estimate, as well as to produce a two-year extrapolation beyond the last available data point, after which coverage is held constant for four years and then assumed missing. This is done separately for urban and rural regions, and then combined to obtain national coverage estimates. Details of the methodology and most recent estimates can be found here: http://www. wssinfo.org/.

UHC-related notes

The SDG indicator for sanitation (SDG 6.2.1) is an expanded version of the MDG indicator, incorporating the quality of sanitation facilities. Once country data and estimates are available for this new indicator, it could be used for UHC monitoring in lieu of the MDG indicator definition described above. A joint indicator that identifies the proportion of households with access to both safe water and sanitation could also be considered.

SDG AND UHC REGIONAL MONITORING FRAMEWORK

Noncommunicable diseases Tracer area

Treatment of cardiovascular disease

Indicator definition

Age-standardized prevalence of non-raised blood pressure among adults aged 18+

Numerator

Number of adults aged 18 or older without systolic blood pressure (SBP) ≥140 mmHg and diastolic blood pressure (DBP) ≥90 mmHg

Denominator

Number of adults aged 18 or older

Main data sources

Population-based surveys and surveillance systems

Method of measurement

Data sources recording measured blood pressure are used (self-reported data are excluded). If multiple blood pressure readings are taken per participant, the first reading is dropped and the remaining readings are averaged.

Method of estimation

For producing comparable national estimates, data observations of prevalence defined in terms of alternate SBP and/or DBP cutoffs are converted into prevalence of SBP ≥140 mmHg or DBP ≥90 mmHg using regression equations. A Bayesian hierarchical model is then fitted to these data to calculate age-sex-year-country-specific prevalences, which accounts for national versus subnational data sources, urban versus rural data sources, and allows for variation in prevalence across age and sex. Age-standardized estimates are then produced by applying the crude estimates to the WHO Standard Population. Details on the statistical methods are here: http://www.thelancet.com/journals/lancet/article/PIIS0140-6736(16)31919-5/fulltext. WHO and the NCD Risk Factor Collaboration (NCD-RisC) has produced comparable estimates for this indicator up through year 2015, which are available here: http://apps.who.int/gho/data/node.main. A875STANDARD?lang=en.

UHC-related notes

Prevalence estimates are converted to the prevalence of non-raised blood pressure for incorporation into the UHC index, so that a value of 100% is the optimal target. This is computed as: non-raised blood pressure prevalence = 1 – raised blood pressure prevalence. As more data become available, this indicator will likely be replaced by the fraction of population with hypertension receiving effective treatment. For now, prevalence of non-raised blood pressure is used as a proxy measure of the effective coverage of prevention and treatment of hypertension. The above estimates are done separately for men and women; for the UHC tracer indicator a simple average of values for men and women is computed.

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Management of diabetes

Indicator definition

Age-standardized mean fasting plasma glucose (FPG) for adults aged 25 years and older

Main data sources

Population-based surveys and surveillance systems

Method of measurement

FPG levels are determined by taking a blood sample from participants who have fasted for at least 8 hours. Other related biomarkers, such as haemoglobin A1c (HbA1c), were used to help calculate estimates (see below).

Method of estimation

For producing comparable national estimates, data observations based on mean FPG, oral glucose tolerance test (OGTT), HbA1c, or combinations therein, are all converted to mean FPG. A Bayesian hierarchical model is then fitted to these data to calculate age-sex-year-country-specific prevalences, which accounts for national versus subnational data sources, urban versus rural data sources, and allows for variation in prevalence across age and sex. Age-standardized estimates are then produced by applying the crude estimates to the WHO Standard Population. Methodological details can be found here: http://www.thelancet.com/journals/lancet/article/PIIS0140-6736(11)60679-X/abstract

UHC-related notes

As more data become available, this indicator will likely be replaced by the fraction of population with diabetes under treatment. For now, mean FPG is used as a proxy measure of the effective coverage of prevention and treatment of diabetes. The above estimates are done separately for men and women; for the UHC tracer indicator a simple average of values for men and women is computed.

APPENDICES

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Cervical cancer screening

Indicator definition

Percentage of women aged 30−49 years who report ever having been screened for cervical cancer

Numerator

Number of women aged 30−49 years who report ever having had a screening test for cervical cancer using any of these methods: visual inspection with acetic acid (VIA), pap smear and human papillomavirus (HPV) test.

Denominator

All women aged 30–49 years

Main data sources

Population-based surveys

Method of measurement

Self-reported data on respondents’ cervical cancer screening history are collected through surveys.

Method of estimation

There are currently no comparable estimates of cervical cancer screening coverage.

UHC-related notes

There are currently few countries with recent data for this indicator and it is therefore excluded from the UHC service coverage index calculations. An additional challenge for international comparability is that data sources may use different time periods (ever screened versus screened in past 5 years) and different age groups.

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Tobacco control

Indicator definition

Age-standardized prevalence of adults ≥15 years old not smoking tobacco in last 30 days

Numerator

Adults 15 years and older who have not smoked tobacco in the last 30 days

Denominator

Adults 15 years and older

Main data sources

Household surveys

Method of measurement

“Current tobacco smoking” includes cigarettes, cigars, pipes or any other smoked tobacco products used in the past 30 days. Data are collected via self-report in surveys.

Method of estimation

WHO estimates prevalence of current tobacco (non) smoking with a negative binomial meta-regression model, which generates comparable estimates by adjusting for differences in age groups and indicator definition across national surveys included in the analysis. These estimates are done separately for men and women. Methodological details can be found here: http://www.thelancet.com/journals/lancet/article/PIIS01406736(15)60264-1/supplemental. WHO estimates of the prevalence of tobacco smoking can be accessed here (see “current smoking of any tobacco product”): http://apps.who.int/gho/data/node.main.1250?lang=en.

UHC-related notes

Prevalence of not smoking tobacco is computed as 1 minus the prevalence of tobacco smoking.

SDG AND UHC REGIONAL MONITORING FRAMEWORK

Service capacity and access Tracer area

Hospital access

Indicator definition

Hospital beds per capita, relative to a maximum threshold of 18 per 10 000 population

Numerator

Number of hospital beds (should exclude labour and delivery beds)

Denominator

Total population

Main data sources

Administrative systems/health facility reporting system

Method of measurement

Country administrative systems are used to total the number of hospital beds, which are divided by the total estimated population, and multiplied by 10 000. WHO regional offices and other groups collect information on national hospital bed density, including the following online resources: WHO Regional Office for the Eastern Mediterranean Regional Health Observatory: https://rho.emro.who.int/ rhodata/node.main.A36 WHO Regional Office for Africa Regional Health Observatory: http://www.aho.afro.who.int/en/datastatistics/hospital-beds-10-000-population WHO Regional Office for South-East Asia Health Information Platform (HIP): http://hip.searo.who.int/dhis/dhis-web-commons/security/login.action WHO European Health Information Gateway: http://gateway.euro.who.int/en/data-sources/european-database-on-human-andtechnical-resources-for-health/ OECD: http://www.oecd-ilibrary.org/social-issues-migration-health/health-at-a-glance-asia-pacific-2014_health_ glance_ap-2014-en

Method of estimation

Using available data, the indicator is computed relative to a threshold value of 18 hospital beds per 10 000 population. This threshold is below the observed OECD high-income country minimum (since year 2000) of 20 per 10 000 and tends to correspond to an inpatient hospital admission rate of around 5 per 100 per year. This indicator is designed to capture low levels of hospital capacity; the maximum threshold is used because very high hospital bed densities are not necessarily an efficient use of resources. The indicator is computed as follows, using country data on hospital bed density (x), which results in values ranging from 0 to 100: • Country with a hospital bed density x < 18 per 10 000 per year, the indicator = x /18*100. • Country with a hospital bed density x ≥ 18 per 10 000 per year, the indicator = 100.

UHC-related notes

An alternative indicator could be hospital inpatient admission rate, relative to a maximum threshold. However, that indicator is currently not reported widely across regions, in particular in the African Region.

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Access to essential medicines

Indicator definition

Percentage of health facilities with essential medicines

Numerator

Number of facilities with essential medicines in stock

Denominator

Total number of health facilities

Main data sources

Special facility surveys or, potentially, routine facility information systems

Method of measurement

Data on the availability of a specific list of medicines are collected from a survey of a sample of facilities. Availability is reported as the percentage of medicine outlets where a particular medicine was found on the day of the survey. If routine facility reporting on stocks is accurate and complete, it may also be possible to use data from the routine system. Regular independent verification is required.

Method of estimation

This indicator is still under development, both in terms of the core list of medicines to be monitored and data collection strategies. The Service Availability and Readiness Assessment (SARA) surveys have collected data for a limited number of countries; see here: http://www.who.int/healthinfo/systems/sara_methods/en/.

UHC-related notes

There are currently about 30 countries with recent data on access to essential medicines, and it is therefore excluded from the UHC service coverage index calculations. Importantly, the SDG-IAEG has recently recommended that, under target 3.b, there be separate indicators for vaccines and access to essential medicines. If adopted by the UN Statistical Commission, a definition and metadata for an SDG indicator on access to essential medicines will be completed. Once reporting on this indicator begins, it can be used in the UHC index.

APPENDICES

Tracer area

Health workforce

Indicator definition

Health professionals (physicians, psychiatrists and surgeons) per capita, relative to maximum thresholds for each cadre

Numerator

Number of physicians, psychiatrists and surgeons

Denominator

Total population

Main data sources

National database or registry of health workers, ideally coupled with regular assessment of completeness using census data, professional association registers, or facility censuses.

Method of measurement

The classification of health workers is based on criteria for vocational education and training, regulation of health professions, and activities and tasks of jobs, i.e. a framework for categorizing key workforce variables according to shared characteristics. The WHO framework largely draws on the latest revisions to the internationally standardized classification systems of the International Labour Organization (International Standard Classification of Occupations); United Nations Educational, Scientific and Cultural Organization (International Standard Classification of Education); and the United Nations Statistics Division (International Standard Industrial Classification of All Economic Activities). Methodological details and data can be found here: http://www.who.int/hrh/statistics/hwfstats/en/

Method of estimation

Using available data, the indicator is computed by first rescaling, separately, health worker density ratios for each of the three cadres (physicians, psychiatrists and surgeons) relative to the minimum observed values across OECD countries since 2000, which are as follows: physicians = 0.9 per 1000, psychiatrists = 1 per 100 000, and surgeons = 14 per 100 000. This rescaling is done in the same way as that for the hospital bed density indicator described above, resulting in indicator values that range from 0 to 100 for each of the three cadres. For example, using country data on physicians per 1000 population (x), the cadre-specific indicator would be computed as: • Country with x < 0.9 per 1000 per year, the cadre-specific indicator = x /0.9*100 • Country with x ≥ 0.9 per 1000 per year, the cadre-specific indicator = 100. As a final step, the geometric mean of the three cadre-specific indicator values is computed to obtain the final indicator of health workforce density.

UHC-related notes

The “physicians” category would ideally be expanded to include all “core health professionals”, such as nurses and midwives. However, no internationally comparable database exists that uses consistent definitions of nonphysician core health professionals to allow for fully accurate cross-country comparisons. Work on measuring SDG indicator 3.c.1 could resolve this issue and allow for a more comprehensive indicator.

Tracer area Indicator definition

Health security International Health Regulations (2005) core capacity index, which is the average percentage of attributes of 13 core capacities that have been attained at a specific point in time. The 13 core capacities are: (1) National legislation, policy and financing; (2) Coordination and National Focal Point communications; (3) Surveillance; (4) Response; (5) Preparedness; (6) Risk communication; (7) Human resources; (8) Laboratory; (9) Points of entry; (10) Zoonotic events; (11) Food safety; (12) Chemical events; (13) Radionuclear emergencies. Number of attributes attained Total number of attributes Key informant survey Key informants report on attainment of a set of attributes for each of 13 core capacities using a standard WHO instrument, as described here: http://apps.who.int/iris/bitstream/10665/84933/1/WHO_HSE_GCR_2013.2_eng. pdf Capacity-level indicator values can be found here: http://www.who.int/gho/ihr/monitoring/legislation/en/index1.html The indicator is computed by averaging, across the 13 core capacities, the percentage of attributes for each capacity that have been attained.

Numerator Denominator Main data sources Method of measurement

Method of estimation UHC-related notes

SDG AND UHC REGIONAL MONITORING FRAMEWORK

Appendix 6. Additional focus areas for indicator development (mapped to the SDG and UHC Regional Monitoring Framework) Focus area

Indicator domain

Details

Disability-specific and community-based rehabilitation

• Effectiveness • Quality and safety

To the extent possible, within the limits of data availability and quality, process and output indicators will be used to assess the quality, effectiveness and equity of community-based rehabilitation interventions, especially at the country level.64 We need to be able to count people with disabilities to quantify service and support needs, to study the life course of people with specific disabilities, and to accurately target prevention strategies. While starting with measurement of disability prevalence, we need to move towards data sources that can inform functionality.

Healthy life expectancy

• Life expectancy and well-being

If it could be measured reliably, healthy life expectancy would be a useful indicator, showing both mortality and years of life lived in less than good health, i.e. disability, which is affected by all health and health-related programmes. Healthy life expectancy can be measured accurately, based on complete death registration systems, with an equity dimension. It is well understood and widely used.

Health system performance

• Quality and safety • Efficiency and sustainability

This area takes account of the five UHC attributes and enables systemic standardized measurement of performance, public reporting of data and the use of performance data to catalyze policy change. The results may be presented in the form of league tables or report cards. • Quality and safety indicators. Some countries in the region, including Australia, Japan, the Republic of Korea and Singapore, have developed or are developing indicators to monitor service quality and patient safety in hospitals. Indicators currently captured in the monitoring framework include 30-day hospital case fatality rate for acute myocardial infarction, postoperative sepsis as a percentage of all surgical procedures, and the hospital readmission rate. Potentially preventable hospitalizations and a composite index for quality may also be considered. Other safety measurements may include hand hygiene, compliance and safe surgery. Insurance data, hospital performance assessment and pay for performance data may be better linked with service quality assessments conducted by health insurance agencies. Data may be strengthened in the area of primary health care. OECD is also working to develop minimum common indicators to assess and compare quality of care across different countries. The existence of a national adverse event reporting system for traditional medicine products and services may also be considered as part of qualitative indicators in traditional medicine. • Efficiency indicators. The current monitoring framework includes indicators for bed occupancy rate and for average length of stay (ALOS) of inpatients (all hospitals). Waiting times and a composite index for efficiency may also be considered. Health-care expenditures, for example, dollars spent as the principal input, may be prioritized. They could then be correlated with health system output measures, such as potential years of life lost. The iterative process of developing health system efficiency measures and demonstrating their potential uses should not be deferred or slowed down while waiting for the “perfect data” to be collected.

Hepatitis treatment and care

• Health service coverage

The proportion of persons with hepatatis B virus (HBV) infection who are currently receiving treatment and forecasts of the number of people that require treatment, and the cost of treatment. The number of persons with chronic HBV infection may be obtained from programme records, but the information may be incomplete or difficult to obtain due to varying health system capacities, for example, lack of a centralized treatment programme. Additional data on treatment courses offered in the country by pharmaceutical companies and pharmacies would help in validating data on treatment coverage.65

Regional framework for action on community-based rehabilitation: 2010–2020. Manila: WHO Regional Office for the Western Pacific; 2010 (http://iris.wpro.who.int/bitstream/handle/10665.1/6761/9789290614821_eng.pdf?sequence=1; accessed 22 August 2017).

Monitoring and evaluation for viral hepatitis B and C: recommended indicators and framework. Technical report. Geneva: World Health Organization; 2016 (http://apps.who.int/iris/bitstream/10665/204790/1/9789241510288_eng.pdf; accessed 22 August 2017).

APPENDICES

Focus area

Indicator domain

Details

Information and communication technologies

• Resources and infrastructure

Measures of information communication technology (ICT) to facilitate cross-country data collection, comparisons and learning on the availability and use of health ICT.66 This is one measure of health system inputs.

Mental health care

• Mortality • Health service coverage • Resources and infrastructure

Most information systems do not include mental health conditions and their impact on overall mental health outcomes. The need for surveillance for specific disorders varies from country to country, but basic data gathering is needed in all countries.67 The complete recording of suicide deaths in death-registration systems requires good linkages with coronial and police systems, but can be impeded by stigma, and by delay in determining the cause of death. Country health information systems do not routinely collect data on a core set of mental health indicators, and cannot provide reliable information on service coverage. Global indicators are currently being developed. The introduction of Mental Health Law in countries such as the Lao People’s Democratic Republic may help incentivize improved data collection.

Palliative care

• Mortality

The WHO Western Pacific, European and South-East Asia regions contain almost three fourths of adults in need of end-of-life palliative care. Currently, mortality data from diseases requiring palliative care are adjusted by the estimated pain prevalence for each disease category.68

Patient experience and people-centred health care

• Responsiveness and peoplecentredness

The WHO Regional Offices for the Western Pacific and South-East Asia have jointly developed a policy framework for people-centred health care. The framework includes indicators for performance within and across the four domains of policy action identified in the framework: individuals, families and communities; health practitioners; health-care organizations; and health systems.

Quality in long-term care

• Quality and safety

With ageing populations and growing costs, ensuring the quality of long-term care services has become an important policy priority for many countries in the Region.69 Measurement may be aligned with models of service delivery and integrated care.

Subjective well-being

• Life expectancy and well-being

Subjective well-being is defined as a good mental state, taking account of all the various evaluations, positive and negative, that people make of their lives, and the affective reactions of people to their experience. Like all self-reported measures, survey-based measures of subjective well-being are sensitive to measurement methodology. For reporting, a consistent measurement approach will need to be adopted across all survey instruments and countries where possible, to limit the additional variance potentially introduced by differing approaches.70

Violence and injury prevention

• Resources and infrastructure

Access to complete and quality epidemiologic data is a major driver for national action for violence and injury prevention and a major responsibility for ministries of health. Despite this, information systems in many Member States remain underdeveloped. Where available, data may not currently be routinely disseminated or used for scaling up preventive action.71

OECD. Draft OECD guide to measuring ICT in the health sector. Paris: Organisation for Economic Co-operation and Development; 2013 (http:// www.oecd.org/health/health-systems/Draft-oecd-guide-to-measuring-icts-in-the-health-sector.pdf; accessed 22 August 2017).

Regional Agenda for Implementing the Mental Health Action Plan 2013–2020 in the Western Pacific. Towards a social movement for action on mental health and well-being. Manila: WHO Regional Office for the Western Pacific; 2015 (http://iris.wpro.who.int/bitstream/ handle/10665.1/10893/9789290617020_eng.pdf; accessed 22 August 2017).

Worldwide Palliative Care Alliance. Global atlas of palliative care at the end of life. Geneva: World Health Organization and WPCA; 2014 (http:// www.who.int/nmh/Global_Atlas_of_Palliative_Care.pdf; accessed 22 August 2017).

OECD. A good life in old age? Monitoring and improving quality in long-term care. Organisation for Economic Co-operation and Development; 2013 (http://www.oecd.org/health/health-systems/a-good-life-in-old-age-9789264194564-en.htm; accessed 22 August 2017).

OECD. Guidelines on measuring subjective well-being. Paris: Organisation for Economic Co-operation and Development; 2013 (http://www. oecd.org/statistics/oecd-guidelines-on-measuring-subjective-well-being-9789264191655-en.htm; accessed 22 August 2017).

WHO Regional Committee on the Western Pacific Resolution WPR/RC66/7 on Violence and Injury Prevention. Manila: WHO Regional Office for the Western Pacific Region; 2015 (http://www.wpro.who.int/about/regional_committee/66/documents/wpr_rc66_07_violence_and_injury_ prevention.pdf; accessed 22 August 2017).

SDG AND UHC REGIONAL MONITORING FRAMEWORK

Appendix 7. Long-term data opportunities Approach

Details

Big data

“Big data” refers to large volumes of complex and variable data that require advanced techniques and technologies for capture, storage, distribution, management and analysis.72 Big data include clinical data from clinical decision support systems (physicians’ written notes and prescriptions, medical imaging, laboratory, pharmacy, insurance, and other administrative data); patient data in electronic health records (EHRs), machine- or sensor-generated data, such as from monitoring vital signs; social media posts, including Twitter feeds, status updates on Facebook and other platforms, and web pages; and less patient-specific information, including emergency care data, news feeds, and articles in medical journals. Big data can provide new and consistent sources and methodologies to integrate multiple “location-based” variables in order to support and inform official statistics and the indicators for the SDGs. Geography and location data enable a richer picture of countries and their spatial subunits, and what is happening in and across them. At a population level, traditional health data include information from vital statistics registries and hospital admission statistics. The benefit of big data in health care lies in combining traditional and new forms of data, at both the individual and population levels.73 Big data approaches may be able to manage the increasing quantity of data needed to support SDG monitoring, including equity analysis. Although the use of big data in low- and middle-income countries is challenging, it offers great potential rewards. Most countries have separate vertical programmes for control of HIV, tuberculosis, malaria and other infections. Each programme requires detailed information to be collected by community health workers. There is often a mismatch between the information needs of the programme and the capacity of the associated field personnel to collect data of sufficient quality for reporting, tracking and learning. The advent of electronic tools is now circumventing some of the logistic and quality issues in data collection.74 Community health workers can use mobile phones, tablets and computers for patient care and programme statistics. Optimizing the application of big data involves more than confidentiality safeguards and minimum standards. A broad effort to establish interoperability standards is imperative to maximize the benefits of big data. Currently, some subgroups may not be included in data collection and analysis due to the multivariate nature of the data.

Geospatial data and technologies

Geospatial analysis is the application of statistical and other analytic techniques to geographical data. The need for geospatial information systems (GIS) in sustainable development processes is evident. This information can describe where people are and their spatial relationships, which will help governments to develop goals and plans, and to measure and monitor outcomes. The literature suggests that geospatial information can assist in overcoming the aforementioned data challenges and in strengthening the capability of governments and agencies to analyse and report on the SDGs. In fact, the final 2015 MDG Report notes the need for geospatial information and suggests that geospatial data can help monitor many aspects of development.75 In the Future We Want report, the UN emphasized the importance of using geospatial technology to drive sustainable development and to support evidence-based policy-making.76 GIS is an essential tool to ensure that disease control programmes effectively target the relevant population groups. For communicable disease control, GIS can strengthen surveillance and the outbreak response. For example, to achieve malaria elimination, staff in national malaria programmes must know the location of malaria cases and whether they have adequate access to testing, treatment and follow-up services. In an emergency, GIS can provide a common operational picture to all stakeholders involved in the response, for a more coordinated, targeted intervention. Visualization on a map of the services and populations affected helps decision-makers.

Raghupathi W, Raghupathi V. Big data analytics in healthcare: promise and potential. Health Inf Sci Syst. 2014;2:3. doi: 10.1186/2047-2501-2-3.

Avilés W, Ortega O, Kuan G, Coloma J, Harris E. Quantitative assessment of the benefits of specific information technologies applied to clinical studies in developing countries. Am J Trop Med Hyg. 2008;78(2):311–5. PMID: 18256435.

The Millennium Development Goals Report 2015. New York: United Nations; 2015 (http://www.un.org/millenniumgoals/2015_MDG_Report/ pdf/MDG%202015%20rev%20(July%201).pdf; accessed 22 August 2017).

UN System Task Team on the Post-2015 UN Development Agenda. Realizing the Future We Want for All: Report to the Secretary-General. New York: United Nations; 2012 (http://www.un.org/millenniumgoals/pdf/Post_2015_UNTTreport.pdf; accessed 22 August 2017).

APPENDICES

Approach

Details

Geospatial data and technologies

There are numerous examples of the use of these capabilities in Asia and the Pacific, including assessing barriers to access for maternity services in the Philippines;77 mapping the distribution of women of childbearing age, pregnancies and births in Bangladesh;78 and generating scale-up scenarios in Cambodia and the Lao People’s Democratic Republic.79 GIS has been used in several countries, including the People’s Republic of China, the Lao People’s Democratic Republic, Solomon Islands and Vanuatu, to show the distribution of malaria risk, to guide development of elimination strategies, and to monitor interventions.80 Geospatial information is especially valuable in measurement, monitoring and tracking processes for the SDGs. Countries are encouraged to review their indicators and metadata through a “geographic location” lens and to identify geospatial data gaps, as well as methodological issues (e.g. provide granularity and disaggregation of indicators where appropriate). However, despite the importance of geospatial information and technologies in SDG measurement and monitoring, there are few studies that specify the types of geospatial information and technologies needed or how they should be provided. At the Asia eHealth Information Network (AeHIN) fourth General Meeting in October 2015 an agreement was made with Esri, a leading GIS software company, to establish an AeHIN GIS laboratory. Under the agreement, AeHIN members and ministries of health will have free access to Esri’s ArcGIS technology for one year. The AeHIN GIS laboratory’s remit is to enable countries to: (i) learn to embed GIS technology into good data management practices; and (ii) receive support to geo-enable their health information system.81

Sources of information from other sectors

As recommended by the IEAG-SDG on the data revolution, we should harness the richness of both traditional and new data, and work with “think-tanks, academics and NGOs as well as the whole UN family in analyzing, producing, verifying and auditing data, providing a place for experimentation with methods for integrating different data sources, including qualitative data, perceptions data and citizen-generated data, and eventually produce a “people’s baseline” for new goals.”82 Information sources from other sectors, such as commercial datasets on alcohol and tobacco use, and pharmaceutical data on HBV vaccines, should be considered along with existing health data sources (e.g. civil and vital registries, health insurance data, medical records and immunization registers). A high-level panel to advise the global development agenda has emphasized the need to improve the quality of information available to citizens and has called for a data revolution for sustainable development.83 In any country, measuring progress will depend on the availability of and access to national fundamental data themes and to spatial data infrastructure that reliably collects, integrates, analyzes, models, fuses and aggregates data for dissemination and decision-making.

Dayrit MM, Ramirez CMO, Asuncion W, Ebener S, Bernardo C, Liwanag HJ, et al. Assessing Access to Prenatal, Delivery and Postpartum Services in the Eastern Visayas Region, Philippines. Final technical report to WHO Philippines. Manila: Regional Office for the Western Pacific; 2015.

Tatem AJ, Campbell J, Guerra-Arias M, de Bernis L, Moran A, Matthews Z. Mapping for maternal and newborn health: the distributions of women of childbearing age, pregnancies and births. International Journal of Health Geographics 2014;13:2 (https://ij-healthgeographics. biomedcentral.com/articles/10.1186/1476-072X-13-2).

Investing the Marginal Dollar for Maternal and Newborn Health (MNH): Geographic Accessibility Analysis for Cambodia and Investing the Marginal Dollar for Maternal and Newborn Health (MNH): Geographic Accessibility Analysis in Lao People’s Democratic Republic. Geneva: World Health Organization; 2015. (WHO/HIS/HGF/GIS/2016.2; http://apps.who.int/iris/bitstream/10665/250271/2/WHO-HIS-HGF-GIS-2016.2eng.pdf , accessed 28 August 2017. WHO/HIS/HGF/GIS/2016.4; http://apps.who.int/iris/bitstream/10665/250273/1/WHO-HIS-HGF-GIS-2016.4eng.pdf, accessed 28 August 2017.

Daash A, Srivastava A, Nagpal BN, Saxena R, Gupta SK. Geographic information system (GIS) in decision support to control malaria—a case study of Korapu district in Orissa, India. J. Vector Borne Dis. 2009;46(1):72–4. PMID: 19326711; Kelly GC, Hale E, Donald W, Batarii W, Bugoro H, Nausien J, et al. A high-resolution geospatial surveillance-response system for malaria elimination in Solomon Islands and Vanuatu. Malar J. 2013;12:108. doi: 10.1186/1475-2875-12-108; Shirayama Y, Phompida S, Shibuya K. 2009. Geographic information system (GIS) maps and malaria control monitoring: intervention coverage and health outcome in distal villages of Khammouane province, Laos. Malar J. 8:217. doi: 10.1186/1475-2875-8-217.

Roth S, Landry M, Ebener S, Marcelo A, Kijsanayotin B, Parry J. The geography of universal health coverage. Why geographic information systems are needed to ensure equitable access to quality health care. ADB Briefs No. 55. Manila: Asian Development Bank and World Health Organization; 2016 (https://www.adb.org/sites/default/files/publication/183422/geography-uhc.pdf; accessed 22 August 2017).

UN Secretary-General’s Independent Expert Advisory Group on the Data Revolution for Sustainable Development. A world that counts: mobilising the data revolution for sustainable development. New York: United Nations; 2014 (http://www.undatarevolution.org/report/; accessed 22 August 2017).

Ibid.