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Jakarta, May 11, 2010
Data used for estimation <ul><li>Mapping data (Dinkes, KPA, NGO, other local institution) </li></ul><ul><li>2007-2009 Sero...
Mapping data availability
Estimation for FSW, Waria, MSM, and IDU <ul><li>Based on mapping data </li></ul><ul><li>Using Poisson regression to estima...
 
Poisson regression or Predictors D-FSW I-FSW Waria MSM IDU Percentage of urban-villages      Percentage of male who l...
Estimation for other subpopulations <ul><li>Where  t, k,  and  p  were from BSS data </li></ul>
 
The distribution of IDU
The distribution of sex partner of IDU
The distribution of direct FSW
The distribution of indirect FSW
The distribution of waria
The distribution of client of SW
The distribution of sex partner of FSW’s client
The distribution of MSM
The distribution of MARP
The distribution of PLWHA
PLWHA by Subpopulation Subpopulation Median Low High Injecting drug user (IDU) <ul><ul><ul><li>52,565 </li></ul></ul></ul>...
 
Limitations <ul><li>For estimating the population size that use behavioral parameters, since the IBBS data were very limit...
Strengths <ul><li>Less subjective bias when we estimate the population size of FSW, waria, MSM, IDU </li></ul><ul><li>Have...
 
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Estimasi Populasi Dewasa Rawan Terinfeksi HIV 2009

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Dalam program pengendalian HIV dan AIDS, estimasi populasi rawan terinfeksi HIV dan Orang dengan HIV dan AIDS (ODHA) yang akurat merupakan kebutuhan yang mendesak. Estimasi disusun setidaknya 3 tahun sekali, estimasi terakhir disusun tahun 2006, oleh karena itu pada tahun 2009 melalui kegiatan Surveilans HIV disusun kembali estimasi populasi rawan terinfeksi HIV dan Orang dengan HIV dan AIDS (ODHA). Kebutuhan akan data tersebut dipicu oleh adanya keinginan untuk mengetahui seberapa besar masalah epidemi HIV dan AIDS dan sebarannya di Indonesia sampai tingkat kabupaten/kota.
Penyelenggaraan estimasi ini menunjukkan suatu upaya yang terintegrasi dari program pengendalian HIV dan AIDS karena melibatkan banyak pihak dalam penyusunannya. Hasil estimasi diharapkan dapat menjadi milik kita bersama dan bermanfaat untuk melakukan advokasi pada pemangku kepentingan. Selain itu, kita juga dapat mengembangkan program pengendalian HIV dan AIDS sampai tingkat kabupaten/kota.

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Transcript of "Estimasi Populasi Dewasa Rawan Terinfeksi HIV 2009"

  1. 1. Jakarta, May 11, 2010
  2. 2. Data used for estimation <ul><li>Mapping data (Dinkes, KPA, NGO, other local institution) </li></ul><ul><li>2007-2009 Sero-surveillance data (23 provinces) </li></ul><ul><li>2007 IBBS data (11 provinces) </li></ul><ul><li>2009 IBBS data (8 provinces) </li></ul><ul><li>2008 Village Potential Statistics (PODES) </li></ul><ul><ul><li>Percentage of urban villages </li></ul></ul><ul><ul><li>Percentage of village with </li></ul></ul><ul><ul><ul><li>sexual transaction site </li></ul></ul></ul><ul><ul><ul><li>pub/discotheque/karaoke </li></ul></ul></ul><ul><ul><ul><li>theatre </li></ul></ul></ul><ul><ul><ul><li>drug abuse </li></ul></ul></ul><ul><ul><ul><li>illegal drug trading </li></ul></ul></ul>
  3. 3. Mapping data availability
  4. 4. Estimation for FSW, Waria, MSM, and IDU <ul><li>Based on mapping data </li></ul><ul><li>Using Poisson regression to estimate the district without mapping data (positive count outcome variable) </li></ul><ul><li>2008 Potential village statistics data as predictors </li></ul><ul><li>Model specific for direct FSW, indirect FSW, waria, MSM, and IDU </li></ul><ul><li>Stratified by province </li></ul>
  5. 6. Poisson regression or Predictors D-FSW I-FSW Waria MSM IDU Percentage of urban-villages      Percentage of male who live in the urban-villages   Percentage of female who live in the urban-villages  Percentage of villages with sexual transaction   Percentage of villages with pub/discotheque/karaoke    Percentage of villages with theatre  Percentage of villages with drug abuse  Percentage of villages with illegal drug trading  Population size (15-49 years)      District rank on specific group (dummy)     Province (dummy)     
  6. 7. Estimation for other subpopulations <ul><li>Where t, k, and p were from BSS data </li></ul>
  7. 9. The distribution of IDU
  8. 10. The distribution of sex partner of IDU
  9. 11. The distribution of direct FSW
  10. 12. The distribution of indirect FSW
  11. 13. The distribution of waria
  12. 14. The distribution of client of SW
  13. 15. The distribution of sex partner of FSW’s client
  14. 16. The distribution of MSM
  15. 17. The distribution of MARP
  16. 18. The distribution of PLWHA
  17. 19. PLWHA by Subpopulation Subpopulation Median Low High Injecting drug user (IDU) <ul><ul><ul><li>52,565 </li></ul></ul></ul><ul><ul><li>37,225 </li></ul></ul><ul><ul><li>96,453 </li></ul></ul>Female sex worker (FSW) <ul><ul><li>13,106 </li></ul></ul><ul><ul><li>11,151 </li></ul></ul><ul><ul><li>15,411 </li></ul></ul><ul><ul><li>Direct FSW </li></ul></ul><ul><ul><li>8,836 </li></ul></ul><ul><ul><li>7,634 </li></ul></ul><ul><ul><li>10,231 </li></ul></ul><ul><ul><li>Indirect FSW </li></ul></ul><ul><ul><li>4,270 </li></ul></ul><ul><ul><li>3,517 </li></ul></ul><ul><ul><li>5,180 </li></ul></ul>Waria <ul><ul><li>6,078 </li></ul></ul><ul><ul><li>4,212 </li></ul></ul><ul><ul><li>9,562 </li></ul></ul>Men sex with men (MSM) <ul><ul><li>24,138 </li></ul></ul><ul><ul><li>15,530 </li></ul></ul><ul><ul><li>44,142 </li></ul></ul>Client of sex worker <ul><ul><li>39,207 </li></ul></ul><ul><ul><li>31,519 </li></ul></ul><ul><ul><li>46,186 </li></ul></ul>Sex partner of IDU <ul><ul><li>9,635 </li></ul></ul><ul><ul><li>4,611 </li></ul></ul><ul><ul><li>14,421 </li></ul></ul>Sex partner of FSW's client <ul><ul><li>11,442 </li></ul></ul><ul><ul><li>9,744 </li></ul></ul><ul><ul><li>12,718 </li></ul></ul>Prisoner <ul><ul><li>5,106 </li></ul></ul><ul><ul><li>3,233 </li></ul></ul><ul><ul><li>6,978 </li></ul></ul>
  18. 21. Limitations <ul><li>For estimating the population size that use behavioral parameters, since the IBBS data were very limited so we made a lot of assumption for the district with no IBSS data </li></ul><ul><li>The same thing happened when we estimate the PLWHA, the HIV prevalence data were also very limited </li></ul><ul><li>Assuming mapping data between sources are independent </li></ul>
  19. 22. Strengths <ul><li>Less subjective bias when we estimate the population size of FSW, waria, MSM, IDU </li></ul><ul><li>Have more mapping data compared to 2006 estimation </li></ul>
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