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Using routinely
collected PMTCT data
to identify HIV
epidemic hotspots at
sub-national levels;
lessons learned from
the HPP
Anita Datar
June 2015
Agenda
Background
 Overview of HPP’s scope of work
 Approach to analysis
Opportunities and challenges of using routinely collected
PMTCT data for geographic targeting
 Why use routinely collected PMTCT data?
 How does routinely collected PMTCT improve our understanding
of variations in HIV burden at sub-national levels?
Way Forward
 What investments are needed to ensure sustainability at sub-
national levels?
Global Fund 2013
 strategic resource allocation
and programming at the
country level
PEPFAR 2014
 Right Thing, Right Place,
Right Time
A Call for Action: Taking a
Geographic Approach
Source: PEPFAR 3.0, http://www.pepfar.gov/documents/organization/234744.pdf
Source: Joint United Nations Programme on HIV/AIDS (UNAIDS). 2014. Local Epidemics Issues Brief. Geneva: UNAIDS. Available
at http://www.unaids.org/sites/default/files/media_asset/JC2559_local-epidemics_en.pdf.
HPP: Scope of work
Source: Dr. Frank Tanser/University of KwaZulu-Natal
Approach
Estimate HIV positivity using PMTCT testing
data at each health facility
Use HIV positivity to estimate HIV
prevalence at each facility
Interpolate to estimate HIV
prevalence for all areas between
health facilities
Calculate estimated numbers of people
living with HIV (PLHIV) at various
boundary levels
Determine estimated treatment coverage
using numbers of PLHIV and numbers on
antiretroviral therapy (ART)
Low cost, readily available
Provides comparable estimates with population
based surveys
More representative at sub-national levels
Why use routinely collected
PMTCT data?
PhotoSource:HealthPolicyProject,Ghana
Facility-based health
management information
systems
DHIS 2.0 rolled out in more
than 47 countries
Data use improves data quality
Low-cost, Readily
Available
Routinely Collected PMTCT Data
Representative at Granular Levels
1 ANC SS site versus >150 PMTCT sites, Homa Bay County, Kenya
Using routinely collected PMTCT data, we can:
 Monitor trends in HIV testing at PMTCT clinics over time
 Estimate # PLHIV
 Estimate HIV prevalence and create interpolated surface
 Estimate HIV prevalence and compare to facilities
 Examine # of patients on ART and compare with estimated
PLHIV in need of treatment
Improved understanding of HIV
variation at subnational levels
Trends in HIV Positivity Over Time:
KwaZulu-Natal Province
23.8
21.3
20.5
19
20
21
22
23
24
25
Jan-Dec 2012 Jan-Dec 2013 Jan-Dec 2014
HIVPositivity(%)
90-90 - 90
Estimated PLHIV in KwaZulu-Natal,
South Africa, by District, 2014
Estimated HIV Prevalence in
Kwa Zulu-Natal, South Africa, 2014
Estimated HIV Prevalence in
Eastern Cape, South Africa 2014
Supply and distribution
of health clinics meeting
demand?
Patients on ART and Estimated
PLHIV Not on ART, Dec. 2014
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
On ART
Estimated PLHIV not on ART
90-90-90
Data quality, availability, ownership variable across
and within countries
Lack of availability of disaggregated data by age
and gender
Duplication of HIV testing
Limited to those that visit PMTCT clinics
Extrapolating PMTCT data to describe general
population
Challenges and Limitations
“Donors and international
technical agencies should
prioritize efforts to strengthen
routine reporting, data
collection and analytic
capacity at the national and
sub-national levels.”
Way Forward
Source: amfAR, The Foundation for AIDS Research; and AVAC, Global
Advocacy for HIV Prevention. 2014. Data Watch: Closing a Persistent Gap
in the AIDS Response. Washington, DC and New York: amfAR and AVAC.
 USAID
 OGAC
 The Global Fund to Fight AIDS, Tuberculosis and Malaria
 South Africa National AIDS Council
 University of KwaZulu-Natal
 HPP Team
Acknowledgments
Photo by Eftekharul Alam Kingshuk, National AIDS/STD Programme, Bangladesh
Questions?
www.healthpolicyproject.com
Thank You!
The Health Policy Project is a five-year cooperative agreement funded by the U.S. Agency for International
Development under Agreement No. AID-OAA-A-10-00067, beginning September 30, 2010. The project’s HIV
activities are supported by the U.S. President’s Emergency Plan for AIDS Relief (PEPFAR). It is implemented by
Futures Group, in collaboration with Plan International USA, Avenir Health (formerly Futures Institute), Partners in
Population and Development, Africa Regional Office (PPD ARO), Population Reference Bureau (PRB), RTI
International, and the White Ribbon Alliance for Safe Motherhood (WRA).

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Using PMTCT data to identify HIV epidemic hotspots at sub-national levels; lessons learned from the Health Policy Project

  • 1. Using routinely collected PMTCT data to identify HIV epidemic hotspots at sub-national levels; lessons learned from the HPP Anita Datar June 2015
  • 2. Agenda Background  Overview of HPP’s scope of work  Approach to analysis Opportunities and challenges of using routinely collected PMTCT data for geographic targeting  Why use routinely collected PMTCT data?  How does routinely collected PMTCT improve our understanding of variations in HIV burden at sub-national levels? Way Forward  What investments are needed to ensure sustainability at sub- national levels?
  • 3. Global Fund 2013  strategic resource allocation and programming at the country level PEPFAR 2014  Right Thing, Right Place, Right Time A Call for Action: Taking a Geographic Approach Source: PEPFAR 3.0, http://www.pepfar.gov/documents/organization/234744.pdf Source: Joint United Nations Programme on HIV/AIDS (UNAIDS). 2014. Local Epidemics Issues Brief. Geneva: UNAIDS. Available at http://www.unaids.org/sites/default/files/media_asset/JC2559_local-epidemics_en.pdf.
  • 5. Source: Dr. Frank Tanser/University of KwaZulu-Natal Approach Estimate HIV positivity using PMTCT testing data at each health facility Use HIV positivity to estimate HIV prevalence at each facility Interpolate to estimate HIV prevalence for all areas between health facilities Calculate estimated numbers of people living with HIV (PLHIV) at various boundary levels Determine estimated treatment coverage using numbers of PLHIV and numbers on antiretroviral therapy (ART)
  • 6. Low cost, readily available Provides comparable estimates with population based surveys More representative at sub-national levels Why use routinely collected PMTCT data?
  • 7. PhotoSource:HealthPolicyProject,Ghana Facility-based health management information systems DHIS 2.0 rolled out in more than 47 countries Data use improves data quality Low-cost, Readily Available
  • 8.
  • 9. Routinely Collected PMTCT Data Representative at Granular Levels 1 ANC SS site versus >150 PMTCT sites, Homa Bay County, Kenya
  • 10. Using routinely collected PMTCT data, we can:  Monitor trends in HIV testing at PMTCT clinics over time  Estimate # PLHIV  Estimate HIV prevalence and create interpolated surface  Estimate HIV prevalence and compare to facilities  Examine # of patients on ART and compare with estimated PLHIV in need of treatment Improved understanding of HIV variation at subnational levels
  • 11. Trends in HIV Positivity Over Time: KwaZulu-Natal Province 23.8 21.3 20.5 19 20 21 22 23 24 25 Jan-Dec 2012 Jan-Dec 2013 Jan-Dec 2014 HIVPositivity(%) 90-90 - 90
  • 12. Estimated PLHIV in KwaZulu-Natal, South Africa, by District, 2014
  • 13. Estimated HIV Prevalence in Kwa Zulu-Natal, South Africa, 2014
  • 14. Estimated HIV Prevalence in Eastern Cape, South Africa 2014 Supply and distribution of health clinics meeting demand?
  • 15. Patients on ART and Estimated PLHIV Not on ART, Dec. 2014 0 100,000 200,000 300,000 400,000 500,000 600,000 700,000 On ART Estimated PLHIV not on ART 90-90-90
  • 16. Data quality, availability, ownership variable across and within countries Lack of availability of disaggregated data by age and gender Duplication of HIV testing Limited to those that visit PMTCT clinics Extrapolating PMTCT data to describe general population Challenges and Limitations
  • 17. “Donors and international technical agencies should prioritize efforts to strengthen routine reporting, data collection and analytic capacity at the national and sub-national levels.” Way Forward Source: amfAR, The Foundation for AIDS Research; and AVAC, Global Advocacy for HIV Prevention. 2014. Data Watch: Closing a Persistent Gap in the AIDS Response. Washington, DC and New York: amfAR and AVAC.
  • 18.  USAID  OGAC  The Global Fund to Fight AIDS, Tuberculosis and Malaria  South Africa National AIDS Council  University of KwaZulu-Natal  HPP Team Acknowledgments
  • 19. Photo by Eftekharul Alam Kingshuk, National AIDS/STD Programme, Bangladesh Questions?
  • 20. www.healthpolicyproject.com Thank You! The Health Policy Project is a five-year cooperative agreement funded by the U.S. Agency for International Development under Agreement No. AID-OAA-A-10-00067, beginning September 30, 2010. The project’s HIV activities are supported by the U.S. President’s Emergency Plan for AIDS Relief (PEPFAR). It is implemented by Futures Group, in collaboration with Plan International USA, Avenir Health (formerly Futures Institute), Partners in Population and Development, Africa Regional Office (PPD ARO), Population Reference Bureau (PRB), RTI International, and the White Ribbon Alliance for Safe Motherhood (WRA).

Editor's Notes

  1. So having looked at the opportunities and challenges, what investments are needed to scale up and ensure sustainability of surveillance efforts? During the Bangkok meeting, we heard from Global fund: that 3% of funds are allocated for M&E. But is that really enough? Given that using routinely collected data is low-cost and that with continued use, countries will improve their data quality, we recommend that donors and development partners really invest in strengthening routine reporting, data collection, and analytic capacity at national and sub-national levels—there is a demand within countries and this really aligns with the 2014 amFAR report that includes a number of recommendations for countries, donors, and development partners. Under the HPP and this activity, we developed a roadmap for GIS strengthening to support strategic planning and also developed a costing workbook. While we weren’t able to use these tools in countries, we’re working to make them available to others in the hopes that they will be adapted to address GIS strengthening, but also strengthening of routine data collection systems, helping countries to work towards the 90-90-90 targets using the data they already have and to allow for real-time monitoring, management, and mid-course corrections.