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Sources of data: estimates of the size of key population groups – mortality data

Sources of data: estimates of the size of key population groups – mortality data. Peter Ghys, UNAIDS Txema Calleja, WHO Paloma Cuchi , UNITAID John Stover, Futures Institute. HIV and AIDS case & mortality reporting. Size Estimation of Risk Groups. STI Surveillance. S econd

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Sources of data: estimates of the size of key population groups – mortality data

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  1. Sources of data: estimates of the size of key population groups – mortality data Peter Ghys, UNAIDS Txema Calleja, WHO Paloma Cuchi, UNITAID John Stover, Futures Institute

  2. HIV and AIDS case & mortality reporting Size Estimation of Risk Groups STI Surveillance Second Generation Surveillance Behavioural or Bio-Behavioural Surveys Sentinel Surveillance Second Generation Surveillance framework 1

  3. Size matters Contribution of a subpopulation to the HIV epidemic is determined by HIV prevalence + risk behaviors + size of subpopulation: • Small population + high HIV incidence +efficient bridge/interactions = important role to the epidemic • Big population + low prevalence = main contributor HIV epidemic Use of the SE data: • National estimates: policy, response planning, resource allocation, advocacy, Understanding HIV surveillance • Local estimates: program planning and management (assessing commodity, coverage, HIV program evaluation)

  4. Size estimation issues Few countries with good size estimation of sub-pops at risk • No regular, scientific SE studies & trends • Subpopulation "hidden" and poorly characterized • Not triangulated & validated w/multiple sources • Ad hoc assumptions often made in projection • Point estimates instead of time-varying trends (size change over time) • Pressure to use “official” estimates & politics • Low accuracy, large uncertainty of SE estimates and HIV Estimates

  5. Methods Methods based on data collected from at-risk population: • Census/Enumeration, Capture-recapture, Multiplier Methods based on data collected from general population: • Population survey • Network scale-up Limitations: • Stigmatized populations need to disclose behaviors (e.g. illegal) • Geographically limited (1 city, 1 neighborhood) = not nationally representative • Collect data on 1 population at a time = multiple studies for a full picture

  6. Network Scale Up • Ideal but not feasible: ask respondents directly about their behaviors (national survey) • Challenges: stigma, embarrassment, fear • Ask respondents about acquaintances: national survey, behaviors of others • Individual’s behaviors are not disclosed • Each respondent’s personal network contributes to sample 8 countries: Moldova, Ukraine, Kazakhstan, Japan, China, Brazil, Rwanda Conclusions • On the radar (stigmatized situations) • Feasible in diverse circumstances & survey methods • Not for every occasions, needs to be used appropriately and have data available • Pending issues

  7. Data sources for the size of populations Often multiple data sources are available: Sizes of at-risk populations Studies from any of the methods (i.e. Capture-recapture) Mappings of higher risk sites Estimates from NGOs (service statistics) Police arrest records Security office estimates 2007 en

  8. Regardless of the method • National ownership • Build consensus and agreed on a single estimate • Use the results • No harm • Determine use of SE • Know what you know • Use multiple methods to get a better estimate • Deal with conflicting results • Repeat study every 2-3 years

  9. HIV and AIDS case & mortality reporting STI Surveillance Size Estimation of Risk Groups Second Generation Surveillance Behavioural or Bio-Behavioural Surveys Sentinel Surveillance Second Generation Surveillance framework 2

  10. HIV epidemiology 1. Incidence of HIV Infection 3. Mortality from AIDS 2. Prevalence of HIV Infection Underreporting, delays and misclassification to other causes of death in death registration systems

  11. Analyses of the overall mortality can gauge the level of HIV mortality Analyses for miscoding of AIDS deaths in vital registration data for S Africa, R Fed, Belarus, Ukraine and Thailand (Source and slides “HIV deaths in vital registration data” from Doris Ma Fat, Mortality and Burden of Disease Unit, Department of Health Statistics and Informatics, Dec 2010)

  12. South Africa 2004: Further analyses of trends and patterns are necessary to identify potentially misclassified HIV deaths Acute lower resp. inf -male Meningitis - male Other infectious dis - male Diarrhoea - female Endocrine disorders - female Ill-defined injuries - male

  13. AIDS-related mortality • HIV has a significant impact on mortality • Measuring HIV mortality to evaluate the impact of NAP’s efforts • One of the clearest indicators of success is a decrease in HIV mortality • Two of the 10 MDG require mortality data • Provide evidence of equity in distribution of health services In many cases this information is not available

  14. Data sources HIV-related mortality • Civil registration systems - gold standard • Verbal autopsy – most common • Nationally representative sample vital registration with verbal autopsy (SAVVY) • Facility-based mortality surveillance (e.g., HIV treatment and care facilities, hospitals, prisons, drug treatment facilities, morgues)

  15. Data sources HIV-related mortality • Burial systems with verbal autopsy (cadaver autopsy,) • Surveys & research • Population-based surveys with verbal autopsy (VA) (e.g., DHS and post-census mortality surveys) - retrospective • Prospective Demographic Surveillance Systems (DSS) with verbal autopsy (ALPHA Analyzing Longitudinal Population-based HIV/AIDS data on Africa) linkages between DSS participants and HIV prevention, treatment and care services

  16. 5 data considerations • Data identification: multiple data sources need to identify all. Organization, creativity, ongoing • Data quality and completeness: evaluate allpotential sources (strengths, weaknesses). Underestimation, quality (cause, date, sex, age…. ) • Data management: different sources & ways to collect data, duplications, use • Data Analysis: limitations in the analysis of mortality data

  17. 5 data considerations 5. Future data issues: strengthening current systems, data collection, sharing systems and collaboration • Short-term goals: obtaining measures of HIV mortality • Longer-term goals: identify opportunities and advocacy strategies for health systems strengthening and creating strong civil registration systems

  18. GUIDELINES Guidelines available on UNAIDS and WHO website WWW.UNAIDS.ORG WWW.WHO.INT

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