Presented by Martha Skiles

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

5. 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.

6. 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

7. 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.

8. 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.

9. 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

10. 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

11. 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

12. National
KDE: Access
KDE Surface: Distance Only
KDE Surface: Distance + Supply

13. 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

14. 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

15. Where do we go
from here?

16. 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.