Depo-Provera Access and Family Planning Use in Malawi
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Presented by Marc Cunningham, MEASURE Evaluation, at the MEASURE Evaluation Geographic Information Systems (GIS) Working Group Meeting.
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Depo-Provera Access and Family Planning Use in Malawi
- 1. Relating Depo-Provera Access with Use in Malawi 2 Jun 2015
- 2. Outline • Activity Goal • Analysis Goal • Methodology • Findings • Limitations • Conclusions
- 3. Activity Goal We seek an improved understanding of the relationship between product availability, population demand, and use of modern family planning methods Population Demand Product Availability CPR
- 4. Analysis Goal We seek an improved understanding of the relationship between • Depo-Provera Availability – Household Distance from SDP – Stock Reliability • Household Demand and Use of Injectable Contraceptives Distance and Stock Reliability Access and Product Availability Demand and Use
- 5. Research Focus • Is availability of Depo-Provera associated with either demand for or use of injectable contraceptives among married women of reproductive age? Methods • Data being used – MEASURE DHS 2010 Malawi Survey – DELIVER PROJECT LMIS data • Linking the data • Regression and Assumptions
- 6. MEASURE DHS Malawi CPR 0 10 20 30 40 50 1992 2000 2004 2010 Male sterilization IUD Implants Condom Pill Female sterilization Injections
- 7. MEASURE DHS Malawi CPR & Injectable Contraceptive Use in 2010 0 10 20 30 40 50 1992 2000 2004 2010 Male sterilization IUD Implants Condom Pill Female sterilization Injections
- 8. Available LMIS Data The DELIVER Project provided data on Depo-Provera supplies for virtually all the public facilities in Malawi providing family planning care. • Health facility level issues data: 2008-2013 • Health facility stockout data: 2008-2013 • Created Reliability Measure to quantify product availability
- 9. Access = Distance + Supply Reliability Distance Reliability Product Access
- 10. Distance Reliability Product Availability Availability = Distance + Supply Reliability • Depo SDPs—any facility with any record in 2010 of ever providing Depo or ever having Depo stock on hand. • Reliability Score for each facility based on stock on hand
- 11. Merging Data: DELIVER Supply data with Facility Master 696 Health Facilities 483 Depo service delivery sites 930 Health Facilities Unique facility identifier 571 Health Facilities Valid GPS (dropped 234) (dropped 125) Final Mapped Facility Dataset with Supply Data • N=571 Total Facilities • 423 counted as Depo-Provera Service Delivery sites 423 Depo service delivery sites
- 12. Population-Based Data: Malawi DHS 2010 827 DHS Clusters: 22,480 Women 849 DHS Clusters: 23,020 Women Valid GPS (dropped 22 clusters) (dropped 7391 women) Final Mapped DHS data • 827 Clusters • N=15,089 married women 15-49 years old (unwgtd) Married Women 827 DHS Clusters: 15,089 Women
- 14. Geographic Linking Methods Access High Low Health Facility DHS Survey Cluster Admin Unit Boundary 5 Km Cluster Buffer Euclidean Buffer Link Administrative Link KDE Link
- 15. What is Kernel Density Estimation (KDE)? KDE is a technique employed to distribute a value associated with a discrete point across a plane or continuous surface.
- 16. Operationalizing “Access” Used KDE to create variables measuring access Access 1 = distance from DHS cluster to a Depo- Provera service delivery point Access 2 = distance from DHS cluster to a Depo- Provera service delivery point + reliability of Depo-Provera supply For each variable, created relative measure - quintiles
- 17. KDE Surface: 10Km radius around all Depo Service Delivery Sites KDE Surface: 10Km radius around Depo sites using weighted variable representing Depo-Provera supply Kernel Density Estimation: Access Health Center: Depo Delivery Site Health Center Health Center: Depo Delivery Site Health Center
- 18. National KDE: Access KDE Surface: Distance Only KDE Surface: Distance + Supply
- 19. KDE Surface: 10Km radius Depo Service Delivery Sites, plus 5km buffer around DHS clusters KDE Surface: 10Km radius around Depo Sites with supply weight and 5km buffer around clusters KDE Access & DHS Clusters Health Center offering family planning Health Center DHS Cluster with 5km buffer Health Center offering family planning Health Center DHS Cluster with 5km buffer
- 20. Research Questions 1. Is access to Depo-Provera supply positively associated with use of injectable contraceptives? Injectable Use Yes 2. Is access to Depo-Provera supply positively associated with desire to space and/or limit children? Demand Yes Access to Depo = quintiled variable, ~ 20% per group Key Outcomes Key Independent Variable
- 21. Multivariate Model Q1: Injectable Use and Access KDE: Distance KDE: Distance + Supply
- 22. Multivariate Model Q2: Demand and Access KDE: Distance KDE: Distance + Supply
- 23. Limitations • Incomplete data – supply data, GPS data, respondents • DHS cluster displacement • Alternative measures of product availability • Single time point • Cannot determine causality • Importance of distance in developing the KDE in terms of how far people can travel—used a “flat” surface
- 24. Conclusions • Access to Depo-Provera is positively associated with a married woman’s use of an injectable family planning method in rural areas. • Access to Depo-Provera (Reliability + Distance) is associated with an increase in family planning demand in urban areas. • In the Central Region, Depo-Provera supply is either unreliable or suffers from incomplete reporting • Application and validation of method for linking survey data with facility data applicable
- 25. MEASURE Evaluation is a MEASURE project funded by the U.S. Agency for International Development and implemented by the Carolina Population Center at the University of North Carolina at Chapel Hill in partnership with Futures Group International, ICF Macro, John Snow, Inc., Management Sciences for Health, and Tulane University. The USAID | DELIVER PROJECT is funded through contract no. GPO-I- 00-06-00007-00 and is implemented by John Snow, Inc.. Views expressed in this presentation do not necessarily reflect the views of USAID or the U.S. Government.
- 26. Graphic on KDE
- 27. Stock Reliability Detailed Calculations • Reliability Score for each facility – Months with stock on hand at the end of the month have a value of 1 – Month with no record of stock on hand but which dispense continuously for 3 months have a value of 1 – A value of 0.5 was assigned to months which were missing a LMIS record but had more than twice the Average Monthly Consumption in on-hand in the previous month – Annual reliability score = average of monthly values
Editor's Notes
- Andrew Presents first followed by Martha
- This is a core funded activity which seeks to strengthen the statement “No Product No Program” by using data to demonstrate the relationship between an effective supply chain and program outcomes.
- Globally, A 1% increase in Average Quarterly Consumption of injectibles per 100 Women of Reproductive Age ( AQC 100WRA ) is associated with a .661% increase in CPR.
- 1. Differentiate the other Sources
- 1. Differentiate the other Sources
- Months with stock on hand at the end of the month have a value of 1 Month with no record of stock on hand but which dispense continuously for 3 months have a value of 1 A value of 0.5 was assigned to months which were missing a LMIS record but had more than twice the Average Monthly Consumption in on-hand in the previous month Annual reliability score = average of monthly values
- 930 = public and private 696 = primarily public, facilities providing patient care 483 = depo delivery points per supply data info
- What we learned from previous linking experience: 1. Critical to have a full list of Facilities and their geographic location 2. Link to a service environment rather than the nearest facility. 3. Use of kernel density estimation (KDE) to map this service environment. Maximum reach of 10 km around each facility Average KDE value for each cluster as a proxy measure for “access” Potential error from displacement of DHS clusters
- SAND analogy
- Use of kernel density estimation (KDE) to map this service environment. Maximum reach of 10 km around each facility Average KDE value for each cluster as a proxy measure for “access” Potential error from displacement of DHS clusters
- Nationally this mapping may highlight areas with less reliable depo supply – Central region L=if supply was reliable everywhere then we’d have this distribution R=what happens to access when supply is unreliable
- Distance: a married women with most access to a facility offering depo services is ~ 5% more likely to use an injectable compared to a woman with least access. Distance + Supply: a married women with most access to a facility offering a reliable supply of depo is ~ 5% more likely to use an injectable compared to a woman with least access.
- Distance: a married women with better access to a facility offering depo services is ~ 3-6% more likely to use an injectable compared to a woman with poor access, and holding all other factors constant. Distance + Supply: a married women with better access to a facility offering a reliable supply of depo is ~ 4-5% more likely to use an injectable compared to a woman with poor access, and holding all other factors constant. Note: When model is run for married women in rural areas ONLY, the dip for those with “Most Access” disappears. Why? Marc?? Note – when this model is run for Rural, it becomes more apparent, and dissipates for rural.
- Now switching to our second outcome – demand or our assumption of demand based on a woman’s desire to space or limit children. Demand: Yes = want to limit, want > 2yrs No = want within 2 yrs, undecided, steril, infecund Note – while there appears to be no pattern here, a pattern emerges for the urban centers
- Incomplete data = potential measurement error DHS cluster displacement = potential spatial error introduced. Effort to minimize error by averaging the KDE value across a 5km buffer around the cluster GPS point.