New frontiers: Linkingfamily planning users to    health facilities
Why Focus on Urban            Reproductive Health? More than half of the world’s population lives in  urban areas Most p...
Data source: MLE Baseline Nigeria 2010
Background on the   Urban Reproductive Health Initiative Integrate family planning services with maternal and  newborn he...
Measurement, Learning & Evaluation            Project Objectives1. To monitor and evaluate the impact of the Urban RH   In...
MLE Partners Carolina Population Center (CPC) at the  University of North Carolina at Chapel Hill International Center f...
Evaluation Design Large Longitudinal Sample  – To measure causal impact of the program Smaller Cross Sectional Survey  –...
Surveys to Date 2010 Baseline surveys: Women, men, households, facilities 2012 Midline surveys: Women, households   – Me...
Geographic Data Collection PSU/cluster latitude and longitude coordinates   – Longitudinal sample: new household location...
Use of Spatial Data for Sampling in India Slums were delineated by the Remote Sensing  Applications Center, India  – Poly...
Example of Slum Polygon Merging
Linking Family Planning Users            to Health Facilities Where did you obtain your current FP method? Where did you...
Unique Datasets for Analysis Women’s preferences (the last place they went  for services) Facility surveys in the prefer...
India Preliminary results  – FP users were more likely to use modern    contraceptive methods if their preferred facility...
Nigeria Is quality of care associated with an increased  probability of current contraceptive use? Are women who identif...
Innovations in Evaluation Urban women can chose from many facilities to  get their family planning counseling and  contra...
Conclusion MLE data provides new datasets which will allow  for the exploration of the influences of distance  and qualit...
THANK YOU The Measurement, Learning & Evaluation (MLE) Project forthe Urban Reproductive Health Initiative is funded by th...
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New frontiers: Linking family planning users to health facilities

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Presented by Livia Montana, Measurement, Learning & Evaluation Project Carolina Population Center (UNC-CH), at the June 2012 MEASURE GIS Working Group meeting.

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  • Married women, modern contraceptive use
  • 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.
  • New frontiers: Linking family planning users to health facilities

    1. 1. New frontiers: Linkingfamily planning users to health facilities
    2. 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. 3. Data source: MLE Baseline Nigeria 2010
    4. 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. 5. Measurement, Learning & Evaluation Project Objectives1. 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. 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. 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. 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. 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. 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. 11. Example of Slum Polygon Merging
    12. 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. 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. 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. 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. 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. 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. 18. THANK YOU The Measurement, Learning & Evaluation (MLE) Project forthe Urban Reproductive Health Initiative is funded by the Bill & Melinda Gates Foundation. The MLE project is implemented by the Carolina Population Center at theUniversity of North Carolina at Chapel Hill, in partnership withAfrican 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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