1. Data Triangulation in HIV Prof Madhulekha Bhattacharya HOD ,Deptt of Community Health Administration National Institute of Health & Family Welfare Munirka,New Delhi--67

2. Data Triangulation Is an analytical approach that integrates multiple data sources to improve understanding of a public health problem and to guide programmatic decision-making to address these problems Involves the synthesis and integration of data from multiple sources through collection, examination, comparison and interpretation By collecting and comparing multiple data sets with each other, triangulation helps to overcome biases inherent in each data source bhattacrya-NIHFW

3. Gather data from multiple sources Refine hypothesis (corroborate, refute or modify) Examine data Planning Triangulation Conducting Triangulation Communicating Results (for Action) A visual representation of the triangulation process

4. Difference from --- Meta Analysis Data triangulation Meta analysis combines rigourous scientific data of similar quality and design to conduct statistical analysis Uses data from diverse sources Lists judgements and limitations of each To be used by programme managers policy makers , and also researchers bhattacrya-NIHFW

5. Steps in Data Triangulation Specify the question Identify data sources, organize the data and identify data gaps Conduct data quality and validation checks Decide on data outlier and/or missing data Refine/revisit the questions chosen for data triangulation Analyze data from different sources for each question Data triangulation Summarize findings and draw conclusions Outline next steps based on findings bhattacrya-NIHFW

6. Questions What are the levels, differentials and trends in HIV/STI in general population, high-risk groups, and the bridge population? What are the drivers of the epidemic? What are the gaps in HIV/AIDS response at district level? What are the data gaps? bhattacrya-NIHFW

8. Important, answerable

9. Actionable, appropriate

10. Method appropriate

11. Feasiblebhattacrya-NIHFW

12. Data sources 1. Data from HIV Sentinel Surveillance for different population groups 2. ICTC/PPTCT data on HIV prevalence 3. Mapping of HRGs - urban (under TI program) & rural (under Link Worker Program) 4. ART registration data 5. Behavioural Sentinel Survey (BSS) 6. Integrated Biological & Behavioural Assessment (IBBA) 7. Blood Bank data, STD Clinic data 8. Census of India, NFHS-3, DLHS-3 9.Any special studies bhattacrya-NIHFW NATIONAL INSTITUTE OF HEALTH & FAMILY WELFARE MUNIRKA, NEW DELHI – 110067.

14. Integration and triangulation using data from different sources

16. SURAT PHC – 75, CHC – 17 ICTC – 50, DIC – 3 Blood Bank – 8 STD Clinics – 31 ART Centre – 2 CCC - 2

18. On ART – 2603

19. MSM/1000 Adult Population -1.21

21. HIV Positivity Talukawise – for General Clients (ICTC) (2008-09) Gujarat HIV Positivity at ICTC – General (2008-09) Gujarat – 5.5 (14645/267840) Mehsana – 11.9 (435/3656) Rapar – 21.8 (12/55) Vadodara - 10.8 (864/7992) Bhachau – 11.7 (40/343) Rajkot – 17.6 (1278/7261) Rajula – 13.3 (8/60) Keshod – 10.1 (57/567) To 5 Amreli –11.6 (113/972) Surat City – 12.4 (2814/22609)

22. HIV prevalence amongst General Clients from ICTC by various characteristics in Gujarat, 2008

23. Differentials of HIV Tested & %Positivity in ICTC Attendees(CMIS), Jamnagar

24. Trends What has been the trend in HIV/STI prevalence in the general population, among FSWs, MSM-T, IDUs, and clients of FSWs in the district? bhattacrya-NIHFW

25. Comparative trend of HIV Infection among pregnant women from different sources in Gandhinagar

26. Prevalence of HIV in Low Risk Group, SURAT (SAPCU)

27. Prevalence of HIV among High Risk Group, SURAT(SAPCU)

28. Trends among High Risk Group, SURAT(SAPCU)

29. Drivers of the Epidemic Size estimation Size and distribution of HRGs and bridge population Underlying vulnerabilities Migration Risk behaviours Risk profile of HRGs and bridge population including condom use behaviour Profile of PLHIV Characteristics and geographic distribution bhattacrya-NIHFW

30. Map of HIV prevalence district wise of PPTCT and number of High Risk Groups at Gujarat.

31. Dual Drivers of STD Epidemics: Populations and Pathogens bhattacrya-NIHFW Pathogen Population Demography Sexual Structure Infectiousness Virulence Duration ß, efficiency of transmission c, contact rate between infected and susceptible D, duration of infectiousness

32. HIV Positivity Talukawise – for General Clients (ICTC) (2008-09) Gujarat HIV Positivity at ICTC – General (2008-09) Gujarat – 5.5 (14645/267840) Mehsana – 11.9 (435/3656) Rapar – 21.8 (12/55) Vadodara - 10.8 (864/7992) Bhachau – 11.7 (40/343) Rajkot – 17.6 (1278/7261) Rajula – 13.3 (8/60) Keshod – 10.1 (57/567) To 5 Amreli –11.6 (113/972) Surat City – 12.4 (2814/22609)

33. HIV prevalence amongst General Clients from ICTC by various characteristics in Gujarat, 2008

34. Drivers of the Epidemic Size estimation Size and distribution of HRGs and bridge population Underlying vulnerabilities Migration Risk behaviours Risk profile of HRGs and bridge population including condom use behaviour Profile of PLHIV Characteristics and geographic distribution

35. HIV Prevalence amongst MSM in consistent sites of Gujarat, 2005-2007

36. Occupation of MSM

37. HIV Prevalence amongst FSW in consistent sites of Gujarat, 2005-2007

38. Risk Profile Of High Risk Group(MSM),Surat-SAPCU

39. Response Gaps What are the gaps in HIV prevention programs HRGs yet to be covered in each taluka of the district HRGs currently being contacted on a regular basis Condom supply against requirement among HRGs HRG’s access to services What are the gaps in HIV/AIDS care, support and treatment programs? Size estimation of PLHA in the district Detection of HIV against the estimate ART registration against the diagnosed Started on ART against the registered Currently on ART against those ever started Differences in the profile of the registered, currently on ART and those who have dropped out of ART bhattacrya-NIHFW

40. Question /Response Gaps What are the gaps in HIV prevention programs HRGs yet to be covered in each taluka of the district HRGs currently being contacted on a regular basis Condom supply against requirement among HRGs HRG’s access to services What are the gaps in HIV/AIDS care, support and treatment programs? Size estimation of PLHA in the district Detection of HIV against the estimate ART registration against the diagnosed Started on ART against the registered Currently on ART against those ever started Differences in the profile of the registered, currently on ART and those who have dropped out of ART

41. Route of Transmission of HIV positive cases in VCTC (Direct walk-in) Gujarat, 2007

42. PPTCT coverage in Gujarat

43. Yearly Performance Trend for NVP Coverage of MBP (ICTC- PPTCT)

44. Yearly Performance HIV-TB (2006-2010)

45. Status of ART/Link ART & CCC Centre in Gujarat 2008

46. District & Residence Wise Patients on ART per 1 Lac Population

47. District wise PLHIV by sex in Gujarat, 2008

48. IEC Programs Implemented (GUJARAT) IEC Programs Implemented (GUJARAT) I J R C D D C J I R C J D C J C J Category of District J I I R C C D J J C J J C C D J C D R C D C R J I C D U U D R C R J C D 16 DIC -12 J C C J U J 20 JEEVAN DEEP - 20 I R C J C D D J 5 UJAAS - 5 U U J U I IRHAP/LINK WORKER - 8 I D J C I R 8 RED RIBBON CLUB - 8 IDC CAMPAIGN - 22 J C C

49. SURAT

50. Outline for presenting the triangulation process and findings…2 4. Discuss data interpretation findings (secondary findings). i. Summarize other secondary results identified through the triangulation analysis. 5. Note limitations (be honest). 6. Summarize findings. 7. Translate findings into: i. need for additional data; ii. programmatic recommendations; iii. policy recommendations. bhattacrya-NIHFW

51. Information flow in the monitoring and evaluation system within the context of strategic information: an overview Health information systems HMIS, vital statistics Programme monitoring Programme evaluation Qualitative studies Behavioural surveys DLHS 1,2,3 Operations research HIV surveillance; Pop based eg NFHS Data management Data analysis and synthesis Use of data for action Communication to the media Advocacy material Estimates (e.g. HIV, ART) Resource allocation Programme planning Reports Policy Formulation bhattacrya-NIHFW

52. 4545 THANKS