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 …

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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  • 1. Jakarta, May 11, 2010
  • 2. Data used for estimation
    • Mapping data (Dinkes, KPA, NGO, other local institution)
    • 2007-2009 Sero-surveillance data (23 provinces)
    • 2007 IBBS data (11 provinces)
    • 2009 IBBS data (8 provinces)
    • 2008 Village Potential Statistics (PODES)
      • Percentage of urban villages
      • Percentage of village with
        • sexual transaction site
        • pub/discotheque/karaoke
        • theatre
        • drug abuse
        • illegal drug trading
  • 3. Mapping data availability
  • 4. Estimation for FSW, Waria, MSM, and IDU
    • Based on mapping data
    • Using Poisson regression to estimate the district without mapping data (positive count outcome variable)
    • 2008 Potential village statistics data as predictors
    • Model specific for direct FSW, indirect FSW, waria, MSM, and IDU
    • Stratified by province
  • 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)     
  • 7. Estimation for other subpopulations
    • Where t, k, and p were from BSS data
  • 8.  
  • 9. The distribution of IDU
  • 10. The distribution of sex partner of IDU
  • 11. The distribution of direct FSW
  • 12. The distribution of indirect FSW
  • 13. The distribution of waria
  • 14. The distribution of client of SW
  • 15. The distribution of sex partner of FSW’s client
  • 16. The distribution of MSM
  • 17. The distribution of MARP
  • 18. The distribution of PLWHA
  • 19. PLWHA by Subpopulation Subpopulation Median Low High Injecting drug user (IDU)
        • 52,565
      • 37,225
      • 96,453
    Female sex worker (FSW)
      • 13,106
      • 11,151
      • 15,411
      • Direct FSW
      • 8,836
      • 7,634
      • 10,231
      • Indirect FSW
      • 4,270
      • 3,517
      • 5,180
    Waria
      • 6,078
      • 4,212
      • 9,562
    Men sex with men (MSM)
      • 24,138
      • 15,530
      • 44,142
    Client of sex worker
      • 39,207
      • 31,519
      • 46,186
    Sex partner of IDU
      • 9,635
      • 4,611
      • 14,421
    Sex partner of FSW's client
      • 11,442
      • 9,744
      • 12,718
    Prisoner
      • 5,106
      • 3,233
      • 6,978
  • 20.  
  • 21. Limitations
    • 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
    • The same thing happened when we estimate the PLWHA, the HIV prevalence data were also very limited
    • Assuming mapping data between sources are independent
  • 22. Strengths
    • Less subjective bias when we estimate the population size of FSW, waria, MSM, IDU
    • Have more mapping data compared to 2006 estimation
  • 23.