Data Use to Inform HIV Programs and Policies. Usma Khan, MS Hilary Spindler, MPH Prevention and Public Health Group Global Health Sciences

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1 Data Use to Inform HIV Programs and Policies Usma Khan, MS Hilary Spindler, MPH Prevention and Public Health Group Global Health Sciences

2 Who We Are Prevention and Public Health Group of Global Health Sciences (formerly Institute for Global Health) Recently joined Global Health Sciences Primary Activities Research Education Cochrane HIV/AIDS Evidence base Technical Assistance and Capacity Building

3 Background University Technical Assistance Project (UTAP) Provide Technical Assistance, Capacity Building, and Education to CDC s Global AIDS Program 3 main service areas Surveillance Monitoring and Evaluation Research

4 Surveillance HIV/AIDS Surveillance Modules Tailored specifically to Caribbean, African, Middle Eastern, and South/ South East Asian contexts Overview of HIV/AIDS epidemic w/ Intro to Public Health Surveillance HIV Clinical Staging and Case Reporting HIV Sero-Surveillance Surveillance for Sexually Transmitted Infections Surveillance of HIV Risk Behaviors Surveillance of Populations at High Risk for HIV Transmission

5 Monitoring and Evaluation Literature Digest Biweekly review of HIV literature, specifically focusing on publications pertinent to resource-limited settings PEPFAR Country Data Analyses Database management and analysis of country-level program indicators in Treatment, PMTCT, CT, and OVC Database management and analysis of multi-country ART data to inform CDC/HRSA of program scale-up and quality ART Outcomes Assess the clinical success of ART roll out in a nationally representative sample of ART patients in PEPFAR countries (implemented in Kenya) Evaluating Electronic Patient Monitoring Systems (EPMS) Develop evaluation methodology to assess effectiveness of EPMS Triangulation Identify, compile, analyze existing data sources to better inform program planning

6 Research Technical Support Kenya Male circumcision and positive prevention research project Uganda Facility-based vs. home-based ART for adults and children Botswana Mortality validation study School-based behavioral surveillance survey Qualitative research to understand the decreasing HIV prevalence among youth Surveillance of sexual behavior and HIV prevalence among those who frequent drinking places

7 Data Use Technical Assistance PPHG helps countries with the following: Gain basic understanding of analytic methods Develop databases, manage and analyze data Learn and utilize Geographic Information Systems Conduct training workshops (data use, data analysis, writing, triangulation) Assist with data cleaning and analysis for manuscript preparation Model used: twinning -PPHG staff paired with in-country staff

8 Data Use: Triangulation Synthesis of data from multiple sources for program decision-making Country-driven/collaborative process Addresses wide-range of key questions as determined by country stakeholders Uses multi-disciplinary team and approach Focus on: Program improvement Capacity building (one-on-one twinning and stakeholder development)

9 How is triangulation different from conventional data analysis? Conventional analysis 1. Emphasis on data of highest scientific rigor 2. Focus on statistics as basis for conclusions 3. Focus on internal validity Did A cause B in our study? 4. Based on independent samples 5. Mathematical modeling Triangulation analysis 1. Emphasis on best possible existing data 2. Focus on plausibility as basis for conclusions (with or without statistics) 3. Focus on external validity Can observed effects be generalized to the larger population? 4. Based on inter-connected pieces of the same picture 5. Model at the level of interpretation

10 When to use triangulation When data are scant When data are plentiful When the best data aren t available - The best are often very expensive (e.g., randomized controlled trial) or impossible to obtain (unethical to do certain trials) - All data are potentially biased When a rapid response is needed - With the AIDS epidemic, it always is - Can t wait for new data; use existing data

11 Types of Data Used in Triangulation Sources and Examples: Research: institutional (NGO / University) studies; behavioral surveillance surveys (BSS); demographic and health surveys (DHS) Surveillance: sentinel sites; ANCs Programmatic: ART registries; VCT, hospital / clinic records; STI treatment; condom distribution; Institutional (NGO / University) programs; other prevention and treatment activities Census Other: Grey literature (unpublished), conference presentations and reports, other published and unpublished studies from data base studies, and from digital libraries and print repositories of relevant institutions

12 Triangulation: 12-step process Which part of the process? Planning for triangulation Conducting triangulation Communicating results What steps are involved? Step 1: Identify key questions Step 2: Ensure question is answerable/ actionable Step 3: Identify sources& gather data Step 4: Refine research question Step 5: Gather data Step 6: Make observations from each data set Step 7: Note trend across data sets & hypothesize Step 8: Refine hypotheses Step 9: IF NECESSARY, identify & gather additional data and go back to Step 5 Step 10: Summarize findings and draw conclusions Step 11: Communicate hypotheses, limitations & recommendations Step 12: Outline next steps

13 Triangulation Part 1: Planning Which part of the process? Planning for Triangulation Conducting Triangulation Communicating Triangulation What steps are involved? Step 1: Identify key questions Step 2: Ensure question is answerable/ actionable Step 3: Identify sources& gather data Step 4: Refine research question Step 5: Gather data Stakeholder Involvement Step 6: Make observations from each data set Step 7: Note trend across data sets & hypothesize Step 8: Refine hypotheses Step 9: IF NECESSARY Identify & gather add l data go back to Step 5 Step 10: Summarize findings and draw conclusions Step 11: Communicate hypotheses, limitations & recommendations Step 12: Outline next steps Interest from country Meet with stakeholders Develop In-country Working Team Question Identification Identify key questions of interest Identify data sources Refine research question(s)

14 Triangulation Part 2: Conducting Which part of the process? Planning for Triangulation Conducting Triangulation Communicating Triangulation What steps are involved? Step 1: Identify key questions Step 2: Ensure question is answerable/ actionable Data Collection Step 3: Identify sources& gather data Step 4: Refine research question Step 5: Gather data Point person to work in-country to gather data Collect all data possible Step 6: Make observations from each data set Hypothesis Development Step 7: Note trend across data sets & hypothesize Make observations within data sets and across data sets Step 8: Refine hypotheses Note trends Step 9: IF NECESSARY Identify & gather add l data go back to Step 5 Develop hypotheses to answer key questions Step 10: Summarize findings and draw conclusions Hypothesis Checking Note evidence that supports or refutes Step 11: Communicate hypotheses, limitations & recommendations Identify and gather more data, as needed Step 12: Outline next steps Choose promising hypotheses

15 Triangulation Part 3: Communicating Which part of the process? Planning for Triangulation Conducting Triangulation Communicating Triangulation What steps are involved? Step 1: Identify key questions Step 2: Ensure question is answerable/ actionable Step 3: Identify sources& gather data Step 4: Refine research question Summation of Findings Step 5: Gather data Step 6: Make observations from each data set Step 7: Note trend across data sets & hypothesize Step 8: Refine hypotheses Step 9: IF NECESSARY Identify & gather add l data go back to Step 5 Step 10: Summarize findings and draw conclusions Step 11: Communicate hypotheses, limitations & recommendations Step 12: Outline next steps Develop report of methods and findings Next Steps Identify any existing data to fill gaps Identify research needed to fill gaps Application Recommend policy &/or program changes

16 Triangulation is iterative Gather data from multiple sources Refine hypothesis (corroborate, refute or modify) Examine data, make observations When does it end?...

17 Drawing conclusions At some point, conclusions must be reached, decisions must be made Favor the hypothesis that is supported by the most data Favor the hypothesis that is supported by the best data Favor the hypothesis that comes closest to consensus Agreement of experts, policy makers, owners, and users of the data Favor the hypothesis that is most actionable Articulate the conclusions putting your best case forward But also note dissenting opinions and leading alternative interpretations And, note the limitations of the data and of triangulation

18 Limitations of triangulation Data quality: Should be assessed throughout Generalizability: Population- or community-based vs. facility-based Causality: Difficult to establish in triangulation compared to trials or observational studies (e.g., Bradford-Hill criteria, cause before effect, consistency, strength, etc) Data dredging

19 Malawi: Triangulation questions Question #1: Is HIV prevalence (incidence) increasing, decreasing or level in Malawi? Question #2: What is the reach and intensity of HIV prevention efforts?

20 Data gathering More than 100 sources of data identified! Surveillance biologic and behavioural (examples) ANC Sentinel Surveillance, DHS 1996, 2000, 2004 BSS 2004 AIDS, TB, STI case reporting Demographic Census National Statistics Office Integrated household Survey, 1998 The Welfare Monitoring Survey Programmatic VCT PMTCT Blood donation Condom distribution Health education activity reporting Research Local academic research International academic partners Published research studies (qualitative and qualitative)

21 Answer to Question #1: A decline in HIV prevalence in Malawi is indicated Percent Median HIV prevalence women at ANC sites

22 Malawi National HIV Indicators, No multiple partners, male Condom access, male No unprotected sex with CSW No unprotected sex with NCP (%) Condom access, female Teen abstinence, female Teen abstinence, male Condoms per man 4 3 Number ANC HIV prevalence all ages ANC prevalence, Ever tested, men HIV tests per 100 Ever tested, women

23 Rural HIV prevalence may be rising as urban is falling 30 Percent Semiurban Urban Rural

24 Drivers of the Epidemic 1. Decreasing HIV prevalence in urban North driven by high intensity of prevention, education, and good infrastructures 2. Rising HIV prevalence on Kaporo border driven by behaviour of mobile men with money 3. Rising HIV prevalence in rural Karonga driven by male-female power imbalances 4. Decreasing HIV prevalence in Lilongwe driven by improvement in some behaviors, but slowing due to worsening of others 5. HIV prevalence remains low in Kasungu by all risk behaviors low or improving and high quality prevention programs with monitoring 6. Rising HIV prevalence in Dedza driven by low reach, intensity, and quality of prevention programs 7. Rising HIV prevalence in the rural South is driven by complex interactions between risk behavior and socio-cultural practices 8. Persistently high HIV prevalence in the South is driven by areas of economic activity

25 Recommendations for Prevention Programs 1. Ramp-up and monitor HIV prevention program reach, intensity, and quality in underserved areas. Replicate and sustain programs that have proven success 2. Target prevention programs to mobile male populations with disposable income, for example, a Popular Opinion Leader model. Target multiple partnering and condom use with noncohabitating partners. Involve working men in HIV prevention. 3. Enhance women s economic opportunities, social support, and human rights while combining such programs with access to reproductive health and HIV prevention options. 4. Work with and target prevention activities to local custodians of culture. 5. Renew and enhance government prevention programs with commercial sex workers, particularly around estates, borders, and urban economic zones. 6. Provide stigma-free HIV/AIDS prevention services. 7. Continue HIV surveillance vigilance for any changes in epidemic patterns drawing on multiple sources of data. 8. Conduct incident HIV infection surveillance to directly understand transmission rates. 9. Improve surveillance for STI through case reporting, epidemiological surveys, and sentinel surveillance to better track current trends in sexual risk behavior and potential HIV transmission. 10. Avoid stagnation and complacency in the reach, intensity, and quality of programs.

26 The End! Contact information: Usma Khan: Hilary Spindler: