Recent Work Linking Population-based Survey Data with Facility Data

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Presented by Martha Skiles

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  • Sampling:Substantial misclassification for clusters (linked/not-linked) which led to a substantial underestimation of the adequacy of health service environment – absolute link;Substantial misclassification error when considering the relative service environment;Differential error introduced – biases estimates and direction of bias is unpredictable.Cluster Displacement:1.No additional error introduced when linking to all facilities within an administrative boundary BUT non-trivial misclassification introduced at smaller geographic areas particularly if linking to the closest facility;
  • Facilities:930 = public and privateDROPPED: 234 facilities missing unique ID (mostly privates); 125 facilities missing GPSDepo:483 = depo delivery points per supply data infoDROPPED: 60 with missing GPSMerged 571 mapped facilities with 423 Depo service delivery sites
  • Use Master Facility lists:model the systematic misclassification error due to facility sampling and include in regressions to improve estimatesCalibrate facility sample data and use for small area estimationLook at other services, outcomes, diseases – Malaria (bednets, disease)

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  • 1. Recent Work Linking Population-Based Survey Data with Facility Data Martha Skiles September 11, 2013
  • 2. Outline I. Geographically linking population and facility surveys: methodological considerations Authors: Skiles MP, Burgert CR, Curtis SL, Spencer J  Methods – data sets, linking techniques, analysis  Findings – take home messages II. Relating Depo-Provera Access with Use in Malawi Partners: Deliver/JSI – Inglis A, Cunningham M. MEASURE Evaluation – Skiles MP, Wilkes B, Bardon-O’Fallon J, Spencer J  Application of methods  Findings – take home messages III. Discussion: where do we go from here?
  • 3. Data sets Rwanda SPA: Census S4 S2 S3 S5 S1 Rwanda DHS Undisplaced D4 D2 D3 D5 D1 “True” Case 5 Census - Displaced 5 Sample - Undisplac ed 25 Samples - Displaced
  • 4. What is Kernel Density Estimation (KDE)? Health Center: Depo Delivery Site Health Center KDE is a technique employed to distribute a value associated with a discrete point across a plane or continuous surface.
  • 5. Operationalizing the links  Health service variables associated with a facility merged with DHS cluster data if “linked”  Absolute measures – type of facility, FP method avail.  Relative measures – FP Readiness Score, VCT Readiness Score Analysis  Descriptive: percent disagreement between distribution of key variables in master data and the sample/displaced data  Associations: basic logistic regression to assess association between relative health service measures and use of modern contraception
  • 6. Effects of…  Facility Sampling:  Substantial misclassification for clusters (linked/not-linked)  Substantial misclassification when considering the relative service environment;  Differential error introduced – direction and magnitude of bias is unpredictable.  Cluster Displacement:  Non-trivial misclassification introduced at geographic areas smaller than admin units, particularly if linking to the closest facility;  Descriptive analyses with DHS data at cluster-level is possible, BUT the measurement error introduced from displacement will bias relationships between service environment and individual level health outcomes  Linking Methods:  Admin link was least affected by sampling and displacement, but may be a less relevant link;  Linking to the closest facility performed poorly across all methods;  KDE did not appear to perform better than less sophisticated methods.
  • 7. Take Home Messages  Linking independently sampled DHS clusters and a sample of facilities is NOT recommended at low levels of geographic disaggregation.  Linking to the closest facility is inappropriate if your analysis conceptually depends on linking an individual with the facility presumably used; better to link to a service environment.  Definition of service environment will influence the amount of error introduced in linking.
  • 8. Application with PRH & DELIVER/JSI: Relating Depo-Provera Access with Use in Malawi Research Question  Is availability of Depo-Provera associated with use of or demand for injectable contraceptives among married women of reproductive age? Data Available  Facility data – Master public health facility list  Contraceptive supply data - DELIVER LMIS 2010  Population data - MEASURE DHS 2010 Malawi
  • 9. Defining Supply Reliability 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 Operationalizing Access Algorithm for “proportion of months with Depo stock”:  Monthly closing balance  Dispensing in prior months  Volume dispensed was NOT considered
  • 10. Kernel Density Estimation: Access KDE Surface: 10Km radius around all Depo Service Delivery Sites Health Center: Depo Delivery Site Health Center Health Center: Depo Delivery Site Health Center KDE Surface: 10Km radius around Depo sites using weighted variable representing Depo-Provera supply
  • 11. National KDE: Access KDE Surface: Distance Only KDE Surface: Distance + Supply
  • 12. Multivariate Model: Injectable Use and Access KDE: Distance KDE: Distance + Supply -0.04 -0.02 0 0.02 0.04 0.06 0.08 0.1 Less Access Most Access -0.04 -0.02 0 0.02 0.04 0.06 0.08 0.1 Less Access Most Access
  • 13. Conclusions  Access to Depo-Provera (Reliability + Distance) is:  + associated with a married woman’s use of an injectable  not associated with an increase in family planning demand  In the Central Region, Depo-Provera supply is either unreliable or suffers from incomplete reporting Next Steps  Clarify and include role of CHW in depo provision  Consider comparative analysis of DELIVER vs country LMIS system
  • 14. Where do we go from here?
  • 15. MEASURE Evaluation is funded by the U.S. Agency for International Development (USAID) through Cooperative Agreement GPO-A-00-03-00003-00 and is implemented by the Carolina Population Center at the University of North Carolina in partnership with Futures Group, John Snow, Inc., ORC Macro, and Tulane University. Visit us online at http://www.cpc.unc.edu/measure.