Needs Assessment of the HOYA Clinic Patient Population Georgetown University
Margaret Burke, Nikola Lekic, Anna McEvoy, Christine Oh, Stephen Sarmiento, Jenny Van Kirk, Emily Wang, Erwin Wang Georgetown University School of Medicine, Washington, D.C. Abstract
One of the great areas of health inequality in Washington, D.C. is lack of physician access for uninsured and underinsured patients. The Georgetown University School of Medicine-organized HOYA Clinic was developed in response to this gap in services, in order to provide free healthcare to those in need. In order to improve quality care and ensure patients are receiving the services they are most in need of, patient data was examined from HOYA Clinic visits for 2013. This needs assessment focused on examining the reasons patients visit the HOYA Clinic, their insurance status, and where else they would access healthcare services if the HOYA Clinic did not exist. The goal is that by better understanding patient utilization of the healthcare system, the HOYA Clinic can adapt to fulfill their needs and continue to provide meaningful service to the community. The data shows that insured patients would be more likely to visit the ER for the same reason they came to HOYA Clinic, while uninsured patients are most likely to not seek care anywhere if HOYA Clinic was not available. Given the mostly ambulatory diagnoses these patients present with, it points to the further need for the HOYA Clinic to improve primary care services. Ideally, this project can be used to advocate for the continued and evolving need for the HOYA Clinic to provide the healthcare services that will be most beneficial to improving overall health outcomes in its population.
Results A
B
Introduction Over the past decade, there has been a significant focus on improving access to quality healthcare nationally as well as within the District of Columbia. The nation’s capital has a lower rate of uninsured than the United States as a whole-- only an estimated 42,000 people, or 8% of the District’s population, compared to the national average of 15%.1 There is also a high number of physicians-per-capita. Even so, D.C. continues to have a higher rate of death attributable to heart disease (222.4/100,000) and diabetes (24.9/100,000) compared to the rest of the country (179.1/100,000 and 20.8/100,000 respe2ctively).1 There is a shift in healthcare towards an emphasis on preventive medicine, with heart disease and diabetes as its forefront, so one may wonder why such paradoxical statistics exist in Washington D.C. With a majority of the citizens insured and a plethora of physicians, the concern has less to do with how many doctors there are than where they are. According to the D.C. Board of Medicine Physician and Physician Assistant Workforce Capacity Report published September 2013, of the more than 8000 physicians licensed to work in the nation’s capital, only 453 of them are primary-care physicians who spend more than 20 hours a week seeing patients.2 Most of the actively practicing primary-care doctors are clustered in wards 1, 2, 3 and 5 near the city’s hospitals while many of the District’s uninsured population live in wards 4 and 7.2 Given such a need for ambulatory care within the underserved wards, the response by the Georgetown University School of Medicine is the HOYA Clinic, a student-driven free clinic partnered with the MedStar Georgetown University Hospital. The clinic first opened its doors in 2007 at D.C. General Emergency Family Shelter in ward 6. It has since grown to serving a total of 767 patients in 2013. The clinic aims to serve the homeless and uninsured population. The HOYA Clinic has provided services to bridge the gap that exists between those without any insurance as well as those with insurance but inadequate physician access. This research project aims to assess the insurance status of the HOYA Clinic’s patients as well as its role in providing ambulatory services within a community the lacks access to actively practicing primary care physicians.
Fig. 1 (A-B). A breakdown of where patients would have gone if the HOYA clinic were not available. A) Divided by insurance status. B) Divided by diagnosis type.
Of the 767 patient encounters at the HOYA Clinic in 2013, 486 encounters (63.4%) listed both full details about insurance status and preferred alternative care venues to HOYA. A similar proportion of uninsured (39.7%) and insured (31.0%) would have next sought care at another free clinic. Many more patients with insurance (28.5%) would go to the ER or urgent care while only 12.5% of uninsured would do so. A much higher proportion of uninsured patients (44.4%) would choose to do nothing than insured patients (18.4%) when no other choices are available. A noticeably higher number of insured patients (20.9%) would have seen their primary care doctor when compared to uninsured patients (3.4%). (p=0.66). Depending on individual diagnosis type, although more insured patients were more likely to see a primary care physician, this difference was not significant (p=0.17 to p=0.86). Among top diagnoses seen at the HOYA Clinic in 2013, there was no noticeable difference between what alternative care venues were considered by insured and uninsured patients.
Discussion and Conclusion
Methods Data is gathered from intake information received at HOYA Clinic that reports basic patient and visit information. This is collected by the current coordinator staff for the publically available annual report. The information aims to determine health markers in this patient population. Each patient that is seen at the clinic has one of these information forms filled out for them. Data collection through this method will continue indefinitely, the goal is to constantly evaluate the health of and identify any risks in this population over time. These findings will assist in improving the future quality and effectiveness of care offered to patients. The data stems from information including insurance status, reason for visit, if they are a resident of the shelter, how they heard about HOYA, what they would have done if they did not come to HOYA, vital signs, as well as the types of prescriptions or vaccines administered during the visit. The focus of this analysis is looking at healthcare habits of patients who are insured versus those who are uninsured. Co-investigators were provided with a de-identified database containing no HIPAA identifiers, unique codes, or keys. IRB exemption was granted for the project. Statistical analysis was conducted using pivot table analysis and t-tests for all patients seen in the 2013 calendar year.
Acknowledgments We would like to thank the HOYA Clinic’s medical directors Dr. Eileen Moore and Dr. Matthew Levy, Georgetown University School of Medicine, MedStar Georgetown University Hospital, the 2013 HOYA Clinic coordinator staff, especially Leslie Andriani, the HOYA Clinic volunteers, and our patients for their continued support of the HOYA Clinic.
References 1 “District of Columbia State Health Facts.” Kaiser State Health Facts. 2014. Web. 2 Mar 2014. http://kff.org/statedata/?state=dc 2 Physician and Physician Assistant Workforce Capacity Report 2.0. Government of the District of Columbia Department of Health Board of Medicine. September 2013. www.doh.dc/gov/bomed 3 Darnell, JS. Free Clinics in the United States: A Nationwide Survey. Arch Intern Med. 2010;170(11):946-953. 4 Zucker, J, et al. Measuring and Assessing Preventive Medicine Services in a Student-Run Free Clinic. Journal of Health Care for the Poor and Underserved. 2013;24(1):344-358. 5 Dubey V, Glazier R. Preventive care checklist form Evidence-based tool to improve preventive health care during complete health assessment of adults. Can Fam Physician. 2006 Jan;52:48–55. 6 http://www.commonwealthfund.org/Performance-Snapshots/Overuse-of-Health-Care-Services/Hospitalizations-for-Ambulatory-Care--8211-SensitiveConditions.aspx. Poster produced by Faculty & Curriculum Support, Georgetown University School of Medicine Poster produced by Faculty & Curriculum Support (FACS), Georgetown University Medical Center
PPACA and Closing the Healthcare Access Gap in DC Christopher Angus, Nicholas Rizik, Eileen Moore MD Georgetown University School of Medicine, Washington, D.C. Introduction With the passage of the Patient Protection and Affordable Care Act, insurance should not be a barrier to obtaining healthcare. Many will be covered by the Medicaid expansion to include individuals below 138% of the federal poverty level (FPL). Ideally, this expansion should decrease the usage of the Emergency Department for non-emergent reasons. 1 Half of patients below 133% FPL will be rejected from a doctor’s office based on insurance and 41% will not be able to find an affordable doctor. 1 Previous explorations into wait times found disparities between public and private insured patient experiences.2,3 One study used a secret shopper survey to look at wait times for pediatricians and pediatric subspecialists. They discovered that Medicaid patients waited longer for appointments with subspecialists than privately insured patients.2 Another study looked specifically at gastroenterologists performing screening colonoscopies and noted that choices were dramatically fewer for patients on Medicaid than privately insured.3 Our objective was to determine if Medicaid patients would experience longer wait times than patients with BlueCross insurance when scheduling a new patient appointment in a primary care specialty. The hypothesis, given the previous data, was that Medicaid patients would face longer wait times for new patient appointments than BlueCross patients.
Methods Given the paucity of data on primary care specialties available in the literature on the subject of wait times experienced by Medicaid patients, we surveyed practices in the District of Columbia. Inclusion: listed on Medicaid website, address in DC, specialty is Internal Medicine, Family Medicine, General Surgery, Pediatrics, Obstetrics, or Psychiatry Exclusion: phone number incorrect, non-physician providers, non-unique phone numbers for physicians in group practice, and inability to reach an office supervisor Patient scenarios were drafted for each specialty chosen detailing a specific problem that had to be taken care of by that specialty. Researchers called the physician’s office posing as a Medicaid and BlueCross patient trying to schedule an appointment. The date of the phone call and the date of the appointment were recorded. The appointment was cancelled before the end of the phone call. The principal investigator spoke to an office supervisor to debrief the office on the use of deception to obtain data from their office and request permission to use said data in the study. After the debriefing session, the office’s identifying data was deleted from the data spreadsheet.
Family Medicine Script Researcher: Hello, my name is Eric Brody/Brian Ericson. I’d like to make an appointment. Family Medicine: I’m new to the area and I want a primary care provider. I have diabetes. My medication has run out and the last time I saw a doctor was 6 months ago. I feel like I need to see a doctor sooner rather than later. Male age 53 DOB: 6/3/1960 Info1: I was on this medication that started with an M but I can’t remember the whole name. Before I moved, I was checking my sugars daily and they were pretty good. Since I got here I haven’t been able to find which box I put my extra testing strips in. MENTION INSURANCE I have Medicaid insurance. When can I be seen by the doctor? IF YOU ARE ASKED: Do you have your Medicaid number? Info2: I don’t have it with me. What medications are you on? Info3: I take a little tiny baby aspirin and that other one that starts with an M. I ran out of the M one about 2 months ago. When was the last time you saw a foot doctor/podiatrist? Info4: never seen one Do you have high blood pressure/hypertension? Info5: My old doc said I had pressures and I needed to take another med for it but I moved before I could start taking it. When did you last see an eye doctor/ophthalmologist? Info6: I got glasses about a year ago, so I guess it must have been a year ago. IF APPOINTMENT SCHEDULED:RECORD DATE I’ll have to check my work schedule and call you back before I can confirm the appointment
Georgetown University
Results Ultimately, six practices were included in the study. From these six, the Medicaid patients faired as good as or better than their private insurances cohorts.
Discussion Medicaid patients face great challenges in trying to access care. Finding a physician with the specialty for the problem the patient has who also accepts Medicaid can be very difficult. This was exhibited excellently in our study. The number of offices that had to be excluded from the sample set was quite large (96% of family medicine practices). A Medicaid patient might be discouraged after a similar experience in trying to find a provider who accepts Medicaid. Limitations of this study stem from the study design with regard to using the Medicaid website. The information therein was frequently out of date, incorrectly classified, and difficult to navigate. Additional challenges faced were that our surgical complaint (hernia) was not a problem upon which all surgeons operated. When internal medicine was used as a filter, all internal medicine subspecialties were listed and our protocol detailed a preventative medicine problem. Reducing the call volume could be achieved by not using deception and identifying researchers truthfully. Using local chapters of professional societies to obtain current contact information would greatly reduce dropouts.
Conclusions
3083 physicians with DC addresses
Medicaid patients have many obstacles to accessing healthcare including finding a physician in the correct specialty who accepts Medicaid. The resources currently available are inadequate to accessing providers.
927 excluded, not primary specialty
2156 physicians in the 6 chosen specialties
References 174 family medicine physicians
1.
168 excluded for various reasons including wrong specialty, wrong number listed, non-unique number, and inability to reach supervisor 6 offices included
2. 3.
Collins SR, Robertson R, Garber T, Doty MM. The income divide in health care: how the Affordable Care Act will help restore fairness to the U.S. health system. Issue Brief (Commonw Fund). 2012 Feb;3:1-24. PubMed PMID: 22351972. Bisgaier J, Rhodes KV. Auditing access to specialty care for children with public insurance. N Engl J Med. 2011 Jun 16;364(24):2324-33. doi: 10.1056/NEJMsa1013285. PubMed PMID: 21675891. Patel VB, Nahar R, Murray B, Salner AL. Exploring implications of Medicaid participation and wait times for colorectal screening on early detection efforts in Connecticut--a secret-shopper survey. Conn Med. 2013 Apr;77(4):197-203. PubMed PMID: 23691732.
Acknowledgments I would like to thank Nicholas Rizik and Daniela Radu for all their help. Poster produced by Faculty & Curriculum Support (FACS), Georgetown University Medical Center
Health Outcomes Associated with the Use of Enabling Services at a Medical Home for the Underserved in Washington, D.C. Catherine Spaulding, Adam Knudson, Matthew Burke M.D., Ling Cai Ph.D. Georgetown University School of Medicine, Washington, D.C.
Georgetown University
Abstract
Abstract
Discussion
Background: Bread for the City is a clinic for the underserved which also provides additional enabling services including a food pantry, legal aid and case management services. We aimed to determine if patients who received enabling services have better health outcomes (as measured by A1C, Total Cholesterol, HDL, and LDL) than underserved patients who did not use any of the additional enabling services. Methods: We performed a retrospective analysis of BFTC’s patient database for data from 2009-2011. Results: Our analysis did not demonstrate any statistically significant difference in health outcomes among the patient groups. Conclusion: While our analysis did not yield any significant results, there were limitations to our study which may have masked potential relationships between enabling services and health outcomes. Further studies are warranted.
In the first analysis, of all the health outcomes measures, only HDL in 2011 was found to have a p value <0.05. All others were non-significant. Table 1 shows the individual health outcomes by year. In the second analysis, the data did not provide strong evidence on difference between groups (p = 0.96 for HDL, p = 0.24 for LDL, p = 0.23 for Cholesterol, and p = 0.08 for HgB A1C). Figure 1 (A-D) shows the estimated mean values for each combination of group and year.
BFTC is a medical home that provides medical care as well as additional enabling services to underserved patients in Washington D.C. Using their EMR, we pooled data from 2009 to 2011 to determine if patients seen at BFTC’s medical clinic who additionally utilized their “enabling services” had better health outcomes compared to BFTC patients who only used medical services. While we expected more significant results, only the mean HDL level in 2011 in the first analysis was found to be significant (p=0.025). Despite this p-value, if we use a Bonferroni correction to account for the multiple testing performed in the first analysis, a p-value <0.00416 would be required to demonstrate statistical significance. Taking this into account, none of our results were statistically significant.
Introduction Bread for the City (BFTC) is a community center in Washington, D.C. that provides a variety of social and medical services to underserved and underinsured populations in a single, centralized facility.1 In doing so, BFTC addresses not only the physical and mental well-being of their patients, but seeks to aid patients with a well-rounded and holistic approach. Previously, no study had determined whether patient use of additional “enabling services” offered by BFTC including a food bank, legal aid, and case management services significantly impacted health outcomes. Thus, this study was performed in order to determine if BFTC patients who also utilized the “enabling services” have better health outcomes compared to those patients who only used the medical services offered at BFTC.
Table 1: Health outcomes variables (mean HbG A1C, mean total cholesterol, mean HDL, mean LDL) by calendar year and frequency of use of enabling services and associated p-values.
There were multiple challenges with our study design, which led to two separate approaches for analysis. Our goal was to maintain the same patient allotment over the three years of data. However, there were only 44 patients who met the criteria of using enabling services at least once in each of the three calendar years. We hypothesized that using enabling services once per year would not be enough to have a significant impact on health outcomes. Our analysis confirmed that hypothesis. However, increasing the frequency of enabling services requirement in the experimental group would have led to the study being underpowered. In order to examine the impact of the frequency of enabling services, we analyzed each calendar year of the data individually. As discussed above, this also did not yield any statistically significant results. However given the retrospective nature of the study, we have no means of knowing whether individuals in our control groups were receiving any “enabling services” at outside locations, which could impact our results. A future study that also tracked the use of other enabling services would likely yield more reliable results.
Figures A
B
Methods A retrospective analysis was performed with de-indentified patient data logged by the EMR used at BFTC for 2009, 2010 and 2011 calendar years. IRB exemption was obtained. Only patients with a known diagnosis of type 2 diabetes or hyperlipidemia were included. Hemoglobin A1C, total cholesterol, HDL, and LDL were used as surrogate markers for health outcomes. Two approaches were used for data analysis. First, the frequency of use of enabling services per calendar year was grouped as 0, 1-3 and ≥4 uses. A linear mixed effect model was fit using the year and the number of times the enabling services as covariates. For each year, we examined whether the mean health outcome measures (HgB A1C, HDL, LDL, and Total Cholesterol) differed between the three categories. Secondly, patients were allotted into two groups, which was maintained over the three years of data. Patients who utilized enabling services at least once in each of the three calendar years were assigned to the experimental group. All others were assigned to the control group. Using the linear mixed effect model, we examined whether the trend over the three years of these outcome variables was the same between the experimental and control groups.
While we did not do formal analysis, there appeared to be a relationship between the frequency of using enabling services and the frequency of provider visits. It’s impossible to say at this point if there is a causal relationship between the two. However, this correlation demonstrates a purpose for an organizational model of health care like Bread for the City’s.
Conclusions C
D
• Retrospective analysis of Bread for the City’s patient database did not show any significant difference in health outcomes between patients who utilized enabling services at the organization and those who did not. • There were multiple limitations to our study, some of which may have masked potentially significant results. • Further studies to examine the relationship between enabling services and health outcomes are warranted.
References
1.
Fig. 1 (A-D). A) Mean LDL value by year and group B) Mean HDL value by year and group C) Mean total cholesterol value by year and group D) Mean HgB A1C level by year and group. The trends in the above graphs are not statistically significant.
Bread for the City, Washington D.C. http://www.breadforthecity.org
Acknowledgments We would like to acknowledge the following individuals for their contributions and support: Dr. Randi Abramson and Andrew Lomax at Bread for the City, and Dr. Matthew Burke, and Dr. Ling Cai at Georgetown University. Poster produced by&Faculty & Curriculum GeorgetownUniversity UniversityMedical School Center of Medicine Poster produced by Faculty Curriculum Support Support, (FACS), Georgetown
Establishing a medical student-run opt-out HIV testing initiative in an urban emergency department Jeffrey Bien, BS1; Christopher A. Thomas, BS1; Andrea Huth, BA1; Tulsi Patel, BS, MSc1; Michael Plankey, PhD2; Brendan Furlong, MD2; Joseph Timpone, MD3; Princy Kumar, MD3 Georgetown University School of Medicine, Washington, DC; Georgetown University Medical Center, Washington, DC 3 Department of Medicine, Georgetown University Hospital, Washington, DC
1
2
•
Since July 2012, an initiative by preclinical medical students at Georgetown University School of Medicine has trained student volunteers to perform OraQuick® rapid HIV-1/2 Ab tests on an opt-out basis in the emergency department (ED).
•
This program is unique among current opt-out ED rapid HIV
• The first goal was to offer HIV testing to emergency department patients and provide immediate linkage to care for those patients diagnosed with HIV
•
The first major hurdle was the elimination of a longstanding GUMC policy that required a written consent for all HIV tests conducted within the hospital
• The second goal was to provide a unique clinical learning opportunity for preclinical medical students to perform clinical tasks independently in a busy emergency department. The experience of interacting with patients, physicians, and other hospital staff is subjectively valuable for preclinical medical students.
•
The responsibility for ordering a confirmatory Western Blot was difficult to assign, but ultimately decided that the ED attending physician would assume this responsibility instead of an ID attending with the caveat of ID social work coordinating all follow-up and further patient interaction
Discussion
•
Obtaining the permission to train medical students to perform a CLIA-waived test in the hospital was challenging. LM worked with the student supervisors and an OraSure representative to ensure that the training was appropriate and mandated 1) its own written test to ensure competency, 2) maintenance of a separate patient log, and 3) random “spot checks” with control samples.
testing programs in the United States: all program aspects are coordinated and operated by preclinical medical students. •
This poster will address key considerations in the conception, development, and implementation of the pilot project as well and notable barriers that were overcome.
Background •
The current estimated HIV prevalence of 2.4% in the District of Columbia is an epidemic (Figure 1, Figure 2). Furthermore, the DC Department of Health (DOH) estimates that up to 20% of HIV positive DC residents are unaware of their status
•
In 2006, the CDC released HIV testing guidelines recommending routine opt-out HIV testing in all healthcare settings (including EDs) for all those aged 13-65
•
Results of CLIA-waived point-of-care HIV rapid tests are available within 20 minutes
•
Identified barriers preventing more widespread uptake of ED HIV testing include (1) the lack of personnel for testing and counseling; (2) lack of sustainability, (3) lack of reliable linkage to care, and (4) high program costs
• •
GUMC had a long standing requirement for written consent for HIV testing, which made opt-out testing impractical. Various stakeholders with interest in rapid HIV testing include: Dept. of Infectious Disease (ID), Emergency Department (ED) and Laboratory Medicine (LM)
Obstacles and Facilitators
Project Aims
Abstract
Table 1 (A-B). A) Total number of ED students trained by student-organized training sessions with an Orasure ® representative on site and representatives from the DC DOH. B) Total person-hours accumulated in the ED from July 2012 to April 2014. Summer/Fall 2012 Spring 2013 Sumer 2013 Fall 2013 Spring 2014 Total
A) # Students Trained 15 36 11 26 planned for 6/14 88
Figure 2. HIV Demographics in the District of Columbia. MedStar GUH is located in Ward 3, with an overall HIV burden similar to the city as a whole.
B) ED Person-hours 220 168 180 161 112 841
Figure from DC Department of Health 2012 Annual Epidemiology and Surveillance Report
Conclusions
Advantages of a medical student centered testing model •Use of paid staff time is minimized, allowing for the testing program to run in parallel to
•
normal ED activity instead of interrupting it. •Projected costs for the maintenance of the program are further minimized as students can set up their own training sessions and teach each other how to run the program. Medical students can be attracted by nonfinancial incentives such as course or elective credit or community service hours.
•
•The nature of a student group allows for more sustainability. The leadership of a student group can evolve within a predictable cycle within the academic year.
•
Disadvantages of a medical student centered testing model •Although 88 students attended training sessions, only approximately 30 ever performed ED testing, suggesting a minority shouldered the majority of the work Division of Responsibility •Medical students interact with the patients, conduct the tests, log and report epidemiological data as mandated by the DC Department of Health, and manage inventory of testing kits. They also organize training sessions yearly and coordinate communication between the medical students, ED, ID, and LM departments
• 1. 2. 3. 4.
•The ED provides faculty oversight and confirmatory testing for any positive test
5.
•The division of ID provides on-call social work staff during each shift in the event of a reactive test to coordinate delivery of test results and options for linkage to care
6.
•LM ensures all testing documentation was in accordance with hospital procedure
Figure 1 from DC Department of Health 2012 Annual Epidemiology and Surveillance Report
Georgetown University
•The DC Department of Health (DCDOH) donates OraQuick ® rapid HIV test kits with the stipulation that the students submit monthly reports, including confidential (not anonymous) demographic data about patients who receive an HIV test.
7. 8.
A student-run ED testing program can effectively and sustainably broaden the scope of opt-out HIV testing at minimal cost for academic medical centers This model also benefits medical students who seek nonfinancial rewards and clinical experience This model is sustainable, and can be attempted by a program with minimal risk and financial investment Tight coordination between varied departments and faculty champions are key to circumvent obstacles References
Branson BM, Handsfield HH, Lampe MA, et al. Revised recommendations for HIV testing of adults, adolescents, and pregnant women in healthcare settings. MMWR Recomm Rep. 2006;55(RR-14):1–17; quiz CE1–4. Brown J, Shesser R, Simon G. Establishing an ED HIV screening program: lessons from the front lines. Acad Emerg Med. 2007;14(7):658–661. doi:10.1197 j.aem.2007.02.033. Haukoos JS, Hopkins E, Hull A, et al. HIV testing in emergency departments in the United States: a national survey. Ann Emerg Med. 2011;58(1 Suppl 1):S10–16.e1–8. doi:10.1016/j.annemergmed.2011.03.033. Haukoos JS, White DAE, Lyons MS, et al. Operational methods of HIV testing in emergency departments: a systematic review. Ann Emerg Med. 2011;58(1 Suppl 1):S96–103. doi:10.1016/j.annemergmed.2011.03.017. Mumma BE, Suffoletto BP. Less encouraging lessons from the front lines: barriers to implementation of an emergency department-based HIV screening program. Ann Emerg Med. 2011;58(1 Suppl 1):S44–48. doi:10.1016/j.annemergmed.2011.03.022. Qaseem A, Snow V, Shekelle P, Hopkins J, Robert, Owens DK. Screening for HIV in Health Care Settings: A Guidance Statement From the American College of Physicians and HIV Medicine Association. Ann Intern Med. 2009;150(2):125–131. doi:10.7326/0003-4819-150-2200901200-00300. Rothman RE, Hsieh Y-H, Harvey L, et al. 2009 US emergency department HIV testing practices. Ann Emerg Med. 2011;58(1 Suppl 1):S3–9.e1– 4. doi:10.1016/j.annemergmed.2011.03.016. DC Department of Health Annual Epidemiology & Surveillance Report. 2012.
Acknowledgments
Special thanks to Drs. Kumar and Furlong, Dr. Timpone and Dr. Plankey, Meghan Fredette and Christina Moynihan for their support and guidance. DC Dept. of Health and Orasure Inc. were also instrumental in establishing the program. Finally, our fellow student volunteers and the ED staff for their time and interest in our project. Poster produced by Faculty & Curriculum Support (FACS), Georgetown University Medical Center
Health Care for the Homeless: Policy Advocacy from All Angles Alice Lee Georgetown University School of Medicine, Washington, D.C.
Georgetown University
Data and Facts
Background
Policy Solutions
Homelessness affects about 1.5 million Americans in a given year. With studies highlighting the importance of social determinants of health (SDH), homelessness is increasingly recognized as a public health issue. 1 The U.S. ranks #1 for health care spending among the countries in the Organization for Economic Cooperation and Development, but 25th on social services spending. 2 Our country spends too little on SDH such as healthy food, education, employment and safe housing, which ultimately leads to high health care costs. Only 10% of one’s health is thought to depend on medical care, while the remaining 90% depends on behavioral,
social
and
environmental
factors.3
Homelessness
contributes
negatively to all these factors, resulting in disproportionately ill, vulnerable population. Addressing the issues of homelessness as a society has the recognizable health benefits as well as the overall financial benefit. Because of its interconnected nature, the health advocacy for the homeless population must target not only medical, but also social factors.
Introduction Homelessness is considered a health diagnosis, with an ICD9 code of V60.0: “Lack of Housing.” It has been shown to complicate continuity of care, exacerbate illnesses and cause early death.4 The homeless individuals have barriers to selfcare and poor access to medical care, and tend to be high users of costly emergency room care.5 Poverty is at the center of this “disease,” with the average monthly income of a homeless person being $348.6 Due to its interconnected nature, homelessness requires advocacy at the policy level. On February 27 th, 2014, providers, patients and community organizations gathered in Annapolis, MD for the Homeless Persons’ Lobby Day (Fig. 1). They came together to advocate for policy changes that would improve the health of nearly 10,000 homeless Marylanders by addressing SDH: increasing minimum wage, supporting low wage workers, giving second chances to ex-offenders and protecting their housing rights.
Conclusions
Fig. 1
Social issues of fair workplace benefits and wages, and safe housing are intimately linked to clinical care of patients. They cause constant exacerbation of health without opportunities for recovery. The health care providers thus have strong reasons to advocate for them and to speak about them at the policy level. To improve the health of persons experiencing homelessness and poverty, the policy makers first need to address current laws hindering them from securing affordable housing, practicing self-care and protecting their future.
References 1. Doran KM, Misa EJ, Shah NR. Housing as health care--New York's boundary- crossing experiment. N Engl J Med. 2013 Dec 19;369(25):2374-7. doi: 10.1056/ NEJMp1310121. 2. Bradley EH, Elkins BR, Herrin J, Elbel B. Health and social services expenditures: associations with health outcomes. BMJ Qual Saf 2011;20:826-831. 3. Asch DA, Volpp KG. What business are we in? The emergence of health as the business of health care. N Engl J Med 2012;367:888-889. 4. Institute of Medicine. Homelessness, Health, and Human Needs. Washington, DC: National Academy Press; 1988. 5. Zlotnick C. Health Care for the Homeless: What We Have Learned in the Past 30 Years and What's Next. American Journal Of Public Health. December 2, 2013;103(S2):S199-S205. 6. National Law Center on Homelessness and Poverty. 7. Out of Reach 2013: Maryland state report. National Low Income Housing Coalition. (2013). Available at http://nlihc.org/oor/2013/MD 8. Abay Asfaw, Regina Pana-Cryan, and Roger Rosa. Paid sick leave and nonfatal occupational injuries. American Journal of Public Health. 2012 102:9, e59-e64. 9. Rossman, Shelli B. and Caterina Gouvis Roman. 2003. "Case-Managed Reentry and Employment: Lessons from the Opportunity to Succeed Program." Justice Research and Policy, 5(2). 10. The Dismantling of Baltimore’s Public Housing. Abell Foundation. (2007). 11. Housing Choice Voucher Program Users. The Council of Large Public Housing Authorities. (2009). 12. Understanding ‘Source of Income Non-Discrimination Legislation.” Baltimore Neighborhoods, Inc. (2011). 13 Ludwig J, Sanbonmatsu L, Gennetian L, et al. Neighborhoods, obesity, and diabetes—a randomized social experiment. N Engl J Med. 2011;365(16):1509–19. 14. The Section 8 Housing Choice Voucher Program: Making Housing Markets Work for Low-Income Families.(2002). Council of Large Public Housing Authorities, et al Poster produced by Faculty & Curriculum Support, Georgetown University School of Medicine
Lingira Project: Transforming Education in rural Uganda Jonathan Miller, BS, & Dr. Eileen Moore, MD Georgetown University School of Medicine, Washington, D.C.
Georgetown University
Introduction
By the Numbers: USA & Uganda
I spent the summer of 2006 living in Uganda, Africa, on a small island in Lake Victoria called Lingira Island. There I saw extreme poverty and disease firsthand. Children played in bare feet wearing tattered clothing malnourished with swollen bellies while their mothers, many suffering with HIV, dried fish on the rocks for dinner to eat in their one-room clay huts, which often housed eight or more. More than poverty secondary to geographic isolation, I saw people living with authentic joy and contagious energy. The simple living conditions and overall lack of basic resources such as clean water and electricity did not limit the beauty of land and people that I met. The Ugandan people have a rich history and deep national pride with a culture full of its own sport, dance, food, language, and tradition.
- 25: On average, Ugandans die 25 years younger than Americans! - $1500 per year: An annual income of $50,000 in the US would equal an income of less than $1500 in Uganda! - 10: Ugandan children have a 10-fold higher chance of dying in infancy compared to American children! - 9: Ugandans on average are 9 times more likely to suffer from HIV/AIDS than an average American! - 99.5%: Ugandans have 99.5% less electricity than Americans. That would be equal to only having electricity 2 days per year!
The reason for my travel was to help launch a prenatal clinic for expectant mothers in a country where pregnancy rates are more than six children per woman, providing basic medical supplies and a fetal Doppler that allowed mothers to hear the heartbeat of their child three months before they feel the baby kick. I also traveled the 52-island cluster participating in the childhood vaccination program. I’m returning to Uganda in April, nearly eight years after my original trip, as part of an Advocacy elective to assess the current state of the islands and their greatest needs as they relate to childhood education. Following the elective, I plan to develop long-term relationships in the form of a non-profit organization known as Lingira Project.
Vision & Mission Lingira Project is a U.S. based non-profit organization helping children and youth on the Lingira Island off the shores of Lake Victoria in Uganda. Our primary mission is to serve the children of Lingira by promoting and improving their education. Specific initiatives will cater to the particular needs of the community, ranging from tuition assistance for primary, secondary, and college educational level, support for travel or clothing, school health clinic opportunities, supporting the further development of vocational-technical tracks in the secondary school, and providing support for other education-related expenses or programs as needed.
Fig. 1. Soccer Fun. For many in Uganda, the words ‘sport’ and ‘soccer’ are synonymous. Pictured here on Lingira Island playing “football” with the local teenage boys.
Fig. 2. Immunizations. Mothers participated in the Childhood Immunization Program, which traveled all over the larger 52-island cluster immunizing infants and toddlers.
Community Partners Shepherd’s Heart International Ministry (SHIM) is a multi-national ministry started by Ugandan Pastors, Christian Leaders and Karina Smith, a local missionary. SHIM Uganda is headquartered on Lingira Island and contains six major program areas: Evangelism and Discipleship, The Lake Victoria Pure Water Project, Economic Development, Educational Development, Agricultural Development and Family Ministries. Peace Corps Global Health Service Partnership (GHSP) led by Laura Foradori, sends trained health professionals to serve as adjunct faculty in medical, nursing and clinical officer training schools of partnering countries. Launched in March 2012, the first cohort of medical and nursing professionals left for Tanzania, Malawi, and Uganda in July 2013. Mission Santa Maria (MSM) led by founder Jim Campbell is a sister partner and U.S. based non-profit organization helping children and youth in the Ecuador. Programs provide food, clothing, and scholarships for children to attend school. Established in 2007, MSM provides a great example of what a grass roots effort can develop into with a small group of committed people. Fundraising totals for 2013 reached nearly $90,000, sponsoring 128 children. Holy Cross Lake View Senior Secondary School (Lake View) is the cornerstone of the Congregation of Holy Cross' educational mission in Jinja, Uganda. Founded in 1993 in an abandoned one-room school house, the need to educate the children of this impoverished area was great and so was the village's response. Today, staff of 80 Holy Cross religious and lay collaborators educate the hearts and minds of nearly 800 students and is ranked among Uganda's top 50 schools. Ocer Campion Jesuit College (Ocer) fills a desperate need for secondary education in the war-ravaged northern Uganda around the city of Gulu. Launched in 2008 on 98.5 acres of donated land, the school continues to actively recruit students and financial aid. The primary constituent group for students are bright, but disadvantaged, youth.
Source: CIA World Facebook
Fig. 3. Lingira Island. This picture is from the hilltop at the center of the island overlooking Lingira village below in the distance. Residents lived in mud hats with metal roofs and often owned livestock and fished for their food.
Fig. 4. Lingira Secondary School. The Living Hope Lingira Secondary Schoolhouse had just been built and was beginning to accept students for classes in 2006. It will be exciting to witness the developments made over the past eight years in person.
The Mission Ahead In April 2014, I will return to Lingira Island to perform a comprehensive needs assessment of the community as it relates to educational quality, access,, and development in order to enter long-term partnerships as a non-profit known as Lingira Project. Poster produced by Faculty & Curriculum Support (FACS), Georgetown University Medical Center
The use of text messaging and Geographic Information Systems (GIS) to address the Malaria burden in the rural Mutasa District, Zimbabwe Mutsa Nyakabau M.P.H ., Clive Shiff Ph.D. 1
2
Health Justice Scholars Week, Georgetown University School \of Medicine, Washington, D.C. Results
Abstract Introduction: The Mutasa district is a rural locale in eastern Zimbabwe that incurs a high malaria burden. It is here that P. falciparum is propagated by An. funestus. This study was designed to implement GIS and the growing use of mobile
A
Table 1. Malaria incidence rates calculated from RDT positive cases with 95% confidence intervals for 16 participating rural health centers
Fig. 2. Monthly Malaria incidence rates for Mutasa, Zimbabwe (A) Overall, (B) by elevation B
technology in the characterization of the malaria epidemiology for targeted intervention. Methods: Monthly incidence rates with 95% confidence intervals were calculated for each RHC for comparison by season and geographic location. GIS software was also implemented for visualization of malaria trends. Results: Significantly higher incidence rates were reported for lowveld compared to middleveld and highveld areas for all seasons in the study Discussion/Conclusion: Growing An. funestus resistance to ITN infused with pyrethroids and adoption of new compounds for IRs to which An. funestus is susceptible warrants continued monitoring. Focusing of malarial interventions on lower altitude areas and dry season reservoirs could decrease incidence rates.
Introduction
Zimbabwe is a landlocked southern African nation with regions possessing high malaria endemicity. 50% of the population lives in high malaria transmission zones in which P. falciparum is propagated by An. funestus, gambiae and arabiensis1. Malaria control coverage is 40-60% and incorporates indoor residual spraying, insecticide treated nets and intermitted preventative therapy1. The Mutasa district located in eastern Zimbabwe consists of subsistence farmland, surrounding villages and Fig. 1. Location of the Mutasa District, tea estates. Surface topography ranges between Zimbabwe 600-1600m above sea level. Rural health centers employ mobile technology in malaria surveillance with weekly reporting of positive cases by rapid diagnostic testing (RDT)3-5. Trends in RDT positive case detection follow a seasonal pattern in which higher case detection rates are reported with the onset of rain (November to April)2 while lower figures are reported in the cool (May-July) and dry (AugustOctober) seasons. The purpose of this study was to characterize the seasonal and geographical difference in malaria incidence rates for 16 RHCs in the Mutasa District.
Fig. 3. Elevation maps of the Mutasa District, Zimbabwe illustrating malaria incidence rates (A) Nov. 2012-Oct. 2013 (B) Nov. 2013-Oct 2014
Discussion
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• 2 years of historic data now permit the calculation of epidemic thresholds to aid in surveillance.
Conclusions B
Implementation of the increased use in mobile technology coupled with mapping via GIS software has permitted the construction of an epidemiologic profile in which lowveld areas are observed to experience the greatest malaria burden throughout the duration of the year. It is also possible that these areas nestle vector reservoirs that contribute to the significantly higher malaria burden. Targeted interventions in these areas are likely to result in significant reduction in the malaria burden in this rural community.
Methods •
Weekly RDT positive cases were collected for 16 RHCs in the Mutasa district from Nov. 2012 –Oct. 2014 via text message to a reporting center.
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Latitude, longitude and altitude data was retrieved for the aforementioned RHCs via GPS
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Population estimates for RHC catchment area were obtained from each facility
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Monthly incidence rates were calculated for each RHC using the summation of weekly RDT positive cases in a given month and catchment area estimates as denominators
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Plots of malaria incidence rate vs. month were constructed for the aggregated data of all RHCs in Mutasa and by geographic classification
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QGIS software was used to map trends in incidence rates on an annual basis
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Malaria incidence rate data was tabulated with 95% confidence intervals to assess significance of findings.
During study period initial malaria control was provided through pyrethroid infused insecticide treated nets (ITNs), which were met with increasing An. funestus resistance. Indoor residual spray (IRS) with new compounds to which An. funestus is susceptible was introduced. Continued follow-up of malaria epidemiology following this intervention is thus necessary. Malaria incidence rates were observed to be highest in lowveld followed by middleveld and highveld areas during all seasons of the year. Healthcare resources, proposed interventions and personnel ought to be distributed with this stratification in mind. High malaria incidence rates during the dry seasons in lowveld areas could indicate presence of reservoirs that promote vector persistence at times that should be inhospitable.
References 1. 2. 3. 4. 5.
World Health Organization, World Malaria Report 2013 (World Health Organization, 2013). Mharakurwa, S. et al. Changes in the burden of malaria following scale up of malaria control interventions in Mutasa District, Zimbabwe. Malaria journal 12, 223 (2013). Kamanga, A., Moono, P., Stresman, G., Mharakurwa, S. & Shiff, C. Rural health centres, communities and malaria case detection in Zambia using mobile telephones: a means to detect potential reservoirs of infection in unstable transmission conditions. Malaria journal 9, 96 (2010). Batwala, V., Magnussen, P., Hansen, K. S. & Nuwaha, F. Cost-effectiveness of malaria microscopy and rapid diagnostic tests versus presumptive diagnosis: implications for malaria control in Uganda. Malar J 10, 1475-2875 (2011). Shiff, C. J., Stoyanov, C., Choobwe, C., Kamanga, A. & Mukonka, V. M. Measuring malaria by passive case detection: a new perspective based on Zambian experience. Malaria journal 12, 1-8 (2013).
Acknowledgments Special thanks to Dr. Clive Shiff and all affiliated research personnel at the Internationa Center for Excellence in Malaria Research, Zimbabwe Poster produced by&Faculty & Curriculum GeorgetownUniversity UniversityMedical School Center of Medicine Poster produced by Faculty Curriculum Support Support, (FACS), Georgetown