Understanding the dynamics of the HIV epidemic in Rwanda Dr. Anita Asiimwe, CNLS Andrew Koleros, MEASURE Evaluation Dr. Je...
<ul><li>&quot;Essentially, all models are wrong, but some are useful.&quot; </li></ul><ul><ul><ul><li> –  George E. P. Box...
Introduction to the Modes of Transmission Model <ul><li>UNAIDS/WHO Incidence Model </li></ul><ul><li>Models the expected d...
 
Methods <ul><li>Every individual, aged 15-49 is allocated to an independent risk group </li></ul><ul><li>Detailed demograp...
Composition of risk groups by category Category 1: General heterosexual population Category 2: Other risk groups No risk (...
Sensitivity Analyses <ul><li>Further analysis looking at conditions under which risk groups would be major contributors of...
Independent scenario analysis models for risk groups High population size Low HIV prevalence High population size Medium H...
<ul><li>Scenario Analysis: Distribution of projected new infections by size estimation scenarios in medium prevalence scen...
Choosing scenarios <ul><li>Focus on medium prevalence estimates </li></ul><ul><ul><li>Incidence estimate within range of S...
Results <ul><li>Heterosexual transmission – 85% of new infections </li></ul><ul><ul><li>1 sex partner (last 12 months): 27...
Interpretation: individuals reporting 1 sex partner (last 12 months) <ul><li>Largest risk group: more than 1.5 million ind...
Interpretation: FSW <ul><li>Difficult to distinguish the relative magnitude of commercial sex versus transactional sex and...
Major Limitations <ul><li>Crude groupings of population according to main exposure to HIV </li></ul><ul><ul><li>Model does...
Recommendations <ul><li>Research and Surveillance </li></ul><ul><ul><li>Carry out size estimations of FSW (commercial and ...
<ul><li>MEASURE Evaluation is funded by the U.S. Agency for  </li></ul><ul><li>International Development and is implemente...
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Understanding the dynamics of the HIV epidemic in Rwanda

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Understanding the dynamics of the HIV epidemic in Rwanda

  1. 1. Understanding the dynamics of the HIV epidemic in Rwanda Dr. Anita Asiimwe, CNLS Andrew Koleros, MEASURE Evaluation Dr. Jenifer Chapman, MEASURE Evaluation Pierre Dongier, CNLS
  2. 2. <ul><li>&quot;Essentially, all models are wrong, but some are useful.&quot; </li></ul><ul><ul><ul><li> – George E. P. Box </li></ul></ul></ul>
  3. 3. Introduction to the Modes of Transmission Model <ul><li>UNAIDS/WHO Incidence Model </li></ul><ul><li>Models the expected distribution of new HIV infections by exposure group </li></ul><ul><li>Results may have important policy / resource allocation implications </li></ul><ul><ul><li>But…should be interpreted with caution </li></ul></ul>
  4. 5. Methods <ul><li>Every individual, aged 15-49 is allocated to an independent risk group </li></ul><ul><li>Detailed demographic, epidemiological and behavioral data is collected for each risk group </li></ul><ul><ul><li>100 primary data sources collected </li></ul></ul><ul><ul><li>Rwanda-specific data and regional data </li></ul></ul>
  5. 6. Composition of risk groups by category Category 1: General heterosexual population Category 2: Other risk groups No risk (individuals reporting no sexual intercourse in last 12 months) Female sex workers (commercial and transactional) Low Risk (individuals reporting one sexual partner in last 12 months) Clients of female sex workers (commercial and transactional) High Risk (individuals reporting >1 sexual partner in the last 12 months) Men who have sex with men (MSM) Partners of those reporting high risk sexual intercourse Prisoners Medical injections Blood transfusions
  6. 7. Sensitivity Analyses <ul><li>Further analysis looking at conditions under which risk groups would be major contributors of new infections </li></ul><ul><ul><li>HIV prevalence </li></ul></ul><ul><ul><li>Population size </li></ul></ul>
  7. 8. Independent scenario analysis models for risk groups High population size Low HIV prevalence High population size Medium HIV prevalence High population size High HIV prevalence Medium population Low HIV prevalence Medium population size Medium HIV prevalence Med. population size High HIV prevalence Low population size Low HIV prevalence Low population size Medium HIV prevalence Low population size High HIV prevalence
  8. 9. <ul><li>Scenario Analysis: Distribution of projected new infections by size estimation scenarios in medium prevalence scenarios </li></ul>
  9. 10. Choosing scenarios <ul><li>Focus on medium prevalence estimates </li></ul><ul><ul><li>Incidence estimate within range of SPECTRUM estimates </li></ul></ul><ul><ul><li>Regional HIV prevalence data more reliable than size estimates </li></ul></ul>
  10. 11. Results <ul><li>Heterosexual transmission – 85% of new infections </li></ul><ul><ul><li>1 sex partner (last 12 months): 27-53% </li></ul></ul><ul><ul><li>FSW (commercial & transactional): 9-46% </li></ul></ul><ul><ul><li>Clients of FSW: 9-11% </li></ul></ul><ul><ul><li>>1 sex partner (last 12 months): 1-4% </li></ul></ul><ul><ul><li>Partners of individuals with >1 sex partner (including partners of clients): 1-4% </li></ul></ul><ul><li>Homosexual transmission – 15% of new infections </li></ul>
  11. 12. Interpretation: individuals reporting 1 sex partner (last 12 months) <ul><li>Largest risk group: more than 1.5 million individuals </li></ul><ul><li>Steady sero-discordant cohabitating couples </li></ul><ul><ul><li>90% of women & 70% of men were married (DHS, 05) </li></ul></ul><ul><ul><li>Low condom use </li></ul></ul><ul><li>Corroborates other findings </li></ul><ul><ul><li>Dunkle KL, Stephenson R, Karita E, et al. New heterosexually transmitted HIV infections in married or cohabitating couples in urban Zambia and Rwanda: an analysis of survey and clinical data. Lancet 2008; 371:2183-91 </li></ul></ul><ul><ul><li>Rwandan HIV/AIDS Data Synthesis Project: Final Report. Treatment and Research AIDS Center, Ministry of Health, Government of Rwanda, 2008. </li></ul></ul>
  12. 13. Interpretation: FSW <ul><li>Difficult to distinguish the relative magnitude of commercial sex versus transactional sex and how they both contribute to new infections </li></ul><ul><li>Major challenge is population size estimations </li></ul><ul><ul><li>If regional data are a good proxy for the situation in Rwanda, then FSW will be a major contributor of new infections </li></ul></ul>
  13. 14. Major Limitations <ul><li>Crude groupings of population according to main exposure to HIV </li></ul><ul><ul><li>Model does not take into account the distribution of behaviors within risk groups </li></ul></ul><ul><li>Model is only as good as data: GIGO </li></ul><ul><ul><li>R-DHS 2005 is most recent population-based data </li></ul></ul><ul><ul><li>Very limited data for risk groups – need to use regional data </li></ul></ul><ul><ul><li>Bias in self-reported behaviors </li></ul></ul>
  14. 15. Recommendations <ul><li>Research and Surveillance </li></ul><ul><ul><li>Carry out size estimations of FSW (commercial and transactional) and MSM </li></ul></ul><ul><ul><li>Explore dynamics of commercial versus transactional sex, including frequency of sexual partnerships </li></ul></ul><ul><li>Using MOT modeling where data availability is low </li></ul><ul><ul><li>Evaluate validity and added value </li></ul></ul><ul><ul><li>Interpret results with caution </li></ul></ul>
  15. 16. <ul><li>MEASURE Evaluation is funded by the U.S. Agency for </li></ul><ul><li>International Development and is implemented by the </li></ul><ul><li>Carolina Population Center at the University of North </li></ul><ul><li>Carolina at Chapel Hill in partnership with Futures Group </li></ul><ul><li>International, ICF Macro, John Snow, Inc., Management </li></ul><ul><li>Sciences for Health, and Tulane University. The views </li></ul><ul><li>expressed in this presentation do not necessarily reflect </li></ul><ul><li>the views of USAID or the United States Government. </li></ul>

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