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New frontiers: Linking
family planning users to
    health facilities
Why Focus on Urban
            Reproductive Health?
 More than half of the world’s population lives in
  urban areas
 Most population growth is occurring in towns and
  cities in developing countries
  – Urban populations of Africa, Asia and Latin America
    will increase 1.9 Billion by 2030
 One in three urban residents live in slums
 Little attention devoted to urban reproductive
  health
Data source: MLE Baseline Nigeria 2010
Background on the
   Urban Reproductive Health Initiative
 Integrate family planning services with maternal and
  newborn health services and HIV/AIDS services
 Improve quality of family planning services
 Increase family planning access through public-
  private partnerships
 Create sustained demand for family planning
  services among the urban poor
 Countries: India (UP), Kenya, Nigeria, Senegal
 Supported by Bill & Melinda Gates Foundation
Measurement, Learning & Evaluation
            Project Objectives
1. To monitor and evaluate the impact of the Urban RH
   Initiative within and across target countries using rigorous
   study designs and multiple data collection approaches.
2. To build country and regional capacity to undertake
   rigorous measurement and evaluation of population, family
   planning, and integrated reproductive health activities with
   a focus on urban and peri-urban poor and vulnerable
   populations.
3. To facilitate knowledge sharing, document and
   disseminate best practices across CC, in the region, and
   within the global CoP.
MLE Partners

 Carolina Population Center (CPC) at the
  University of North Carolina at Chapel Hill

 International Center for Research on Women
  (ICRW) – Asia Regional Office

 African Population and Health Research Center
  (APHRC)
Evaluation Design
 Large Longitudinal Sample
  – To measure causal impact of the program
 Smaller Cross Sectional Survey
  – To measure change in key indicators between baseline
    and endline
  – Men’s cross section at baseline, midline and endline
 Facility Surveys
  –   All facilities mentioned in the individual survey
  –   Random sample of additional facilities
  –   Census of high volume facilities
  –   Public and private facilities
  –   Longitudinal
Surveys to Date
 2010 Baseline surveys: Women, men, households, facilities
 2012 Midline surveys: Women, households
   – Men’s surveys in Nigeria, Kenya
   – Facility surveys in India

 India (Uttar Pradesh)
   – Agra, Aligarh, Allahabad, Gorakhpur, Varanasi, Moradabad
 Kenya
   – Nairobi, Mombassa, Kisumu, Machakos, Kakamega
 Nigeria
   – Abuja, Ilorin, Ibadan, Kaduna, Benin City, Zaria
 Senegal
   – Dakar, Mbour, Kaolak
Geographic Data Collection

 PSU/cluster latitude and longitude coordinates
   – Longitudinal sample: new household locations
 Health facility locations
   – Longitudinal sample
   – New facilities added at midterm (India)
Use of Spatial Data for Sampling in India
 Slums were delineated by the Remote Sensing
  Applications Center, India
  – Polygon data, approximately 500-800 slum areas in
    each city
 Slum areas were overlaid on QuickBird imagery
  at CPC Spatial Analysis Unit
  – Slum polygons were merged/divided to contain
    approximately 100 households in each PSU
 Slum polygons were clipped out of city ward data
 Final sample for slums and non-slums was selected
Example of Slum Polygon Merging
Linking Family Planning Users
            to Health Facilities
 Where did you obtain your current FP method?
 Where did you last go for ANC, CH, MH services
 Where did you last go for HIV testing (African
  countries)
 What pharmacy do you usually go to for
  medicine?
 Preferred providers
  – The most popular provider in the cluster
Unique Datasets for Analysis

 Women’s preferences (the last place they went
  for services)
 Facility surveys in the preferred facilities, and all
  high volume facilities in the cities
   – Facility audit, exit interviews
 Census of facilities in Senegal cities
   – Facility audit, exit interviews
 Location and type of all facilities in Kenya,
  Nigeria
India

 Preliminary results
  – FP users were more likely to use modern
    contraceptive methods if their preferred facility offered
    integrated services and well-trained service providers
 Next steps
  – Explore individual level preferred providers, quality,
    and use
Nigeria

 Is quality of care associated with an increased
  probability of current contraceptive use?
 Are women who identify high-quality facilities
  more likely to use a long-acting permanent
  method?
 Does distance vary according to quality of care
  and/or type of method?
Innovations in Evaluation
 Urban women can chose from many facilities to
  get their family planning counseling and
  contraceptive methods
 Urban women may not even consider sources
  close to their residence
 Choice of facility is intricately linked to the
  choice of contraception
 Program evaluation methods that simply link
  individuals to nearby facilities may include a
  completely incorrect choice set
Conclusion

 MLE data provides new datasets which will allow
  for the exploration of the influences of distance
  and quality on choice of facilities, and use of FP
 Combining MLE data with existing health facility
  datasets will add value to the MLE analysis
THANK YOU


 The Measurement, Learning & Evaluation (MLE) Project for
the Urban Reproductive Health Initiative is funded by the Bill
      & Melinda Gates Foundation. The MLE project is
    implemented by the Carolina Population Center at the
University of North Carolina at Chapel Hill, in partnership with
African Population and Health Research Center, International
         Center for Research on Women, and K4H.
 The views expressed in this presentation do not necessarily
            reflect those of the Gates Foundation.

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New frontiers: Linking family planning users to health facilities

  • 1. New frontiers: Linking family planning users to health facilities
  • 2. Why Focus on Urban Reproductive Health?  More than half of the world’s population lives in urban areas  Most population growth is occurring in towns and cities in developing countries – Urban populations of Africa, Asia and Latin America will increase 1.9 Billion by 2030  One in three urban residents live in slums  Little attention devoted to urban reproductive health
  • 3. Data source: MLE Baseline Nigeria 2010
  • 4. Background on the Urban Reproductive Health Initiative  Integrate family planning services with maternal and newborn health services and HIV/AIDS services  Improve quality of family planning services  Increase family planning access through public- private partnerships  Create sustained demand for family planning services among the urban poor  Countries: India (UP), Kenya, Nigeria, Senegal  Supported by Bill & Melinda Gates Foundation
  • 5. Measurement, Learning & Evaluation Project Objectives 1. To monitor and evaluate the impact of the Urban RH Initiative within and across target countries using rigorous study designs and multiple data collection approaches. 2. To build country and regional capacity to undertake rigorous measurement and evaluation of population, family planning, and integrated reproductive health activities with a focus on urban and peri-urban poor and vulnerable populations. 3. To facilitate knowledge sharing, document and disseminate best practices across CC, in the region, and within the global CoP.
  • 6. MLE Partners  Carolina Population Center (CPC) at the University of North Carolina at Chapel Hill  International Center for Research on Women (ICRW) – Asia Regional Office  African Population and Health Research Center (APHRC)
  • 7. Evaluation Design  Large Longitudinal Sample – To measure causal impact of the program  Smaller Cross Sectional Survey – To measure change in key indicators between baseline and endline – Men’s cross section at baseline, midline and endline  Facility Surveys – All facilities mentioned in the individual survey – Random sample of additional facilities – Census of high volume facilities – Public and private facilities – Longitudinal
  • 8. Surveys to Date  2010 Baseline surveys: Women, men, households, facilities  2012 Midline surveys: Women, households – Men’s surveys in Nigeria, Kenya – Facility surveys in India  India (Uttar Pradesh) – Agra, Aligarh, Allahabad, Gorakhpur, Varanasi, Moradabad  Kenya – Nairobi, Mombassa, Kisumu, Machakos, Kakamega  Nigeria – Abuja, Ilorin, Ibadan, Kaduna, Benin City, Zaria  Senegal – Dakar, Mbour, Kaolak
  • 9. Geographic Data Collection  PSU/cluster latitude and longitude coordinates – Longitudinal sample: new household locations  Health facility locations – Longitudinal sample – New facilities added at midterm (India)
  • 10. Use of Spatial Data for Sampling in India  Slums were delineated by the Remote Sensing Applications Center, India – Polygon data, approximately 500-800 slum areas in each city  Slum areas were overlaid on QuickBird imagery at CPC Spatial Analysis Unit – Slum polygons were merged/divided to contain approximately 100 households in each PSU  Slum polygons were clipped out of city ward data  Final sample for slums and non-slums was selected
  • 11. Example of Slum Polygon Merging
  • 12. Linking Family Planning Users to Health Facilities  Where did you obtain your current FP method?  Where did you last go for ANC, CH, MH services  Where did you last go for HIV testing (African countries)  What pharmacy do you usually go to for medicine?  Preferred providers – The most popular provider in the cluster
  • 13. Unique Datasets for Analysis  Women’s preferences (the last place they went for services)  Facility surveys in the preferred facilities, and all high volume facilities in the cities – Facility audit, exit interviews  Census of facilities in Senegal cities – Facility audit, exit interviews  Location and type of all facilities in Kenya, Nigeria
  • 14. India  Preliminary results – FP users were more likely to use modern contraceptive methods if their preferred facility offered integrated services and well-trained service providers  Next steps – Explore individual level preferred providers, quality, and use
  • 15. Nigeria  Is quality of care associated with an increased probability of current contraceptive use?  Are women who identify high-quality facilities more likely to use a long-acting permanent method?  Does distance vary according to quality of care and/or type of method?
  • 16. Innovations in Evaluation  Urban women can chose from many facilities to get their family planning counseling and contraceptive methods  Urban women may not even consider sources close to their residence  Choice of facility is intricately linked to the choice of contraception  Program evaluation methods that simply link individuals to nearby facilities may include a completely incorrect choice set
  • 17. Conclusion  MLE data provides new datasets which will allow for the exploration of the influences of distance and quality on choice of facilities, and use of FP  Combining MLE data with existing health facility datasets will add value to the MLE analysis
  • 18. THANK YOU The Measurement, Learning & Evaluation (MLE) Project for the Urban Reproductive Health Initiative is funded by the Bill & Melinda Gates Foundation. The MLE project is implemented by the Carolina Population Center at the University of North Carolina at Chapel Hill, in partnership with African Population and Health Research Center, International Center for Research on Women, and K4H. The views expressed in this presentation do not necessarily reflect those of the Gates Foundation.

Editor's Notes

  1. Married women, modern contraceptive use
  2. This demonstrates how at times, two slums (the figure on the left) were joined to make one slum PSU. Other times, big slum PSUs were divided to make smaller PSUs.