GIS Data Linking to
Enhance Multi-sectoral Decision Making for
Family Planning and Reproductive Health:
A Case Study in Rwanda
James Stewart
MEASURE Evaluation PRH
May 16, 2013

Organization of the Webinar
 Speaker Information
 Acknowledgements
 Introduction
 GIS data linking considerations for multi-sectoral and/or
multi-program data
 Examples of GIS linking, visualization, and analysis
based on data for Rwanda
 Lessons learned
 Question and answer session
2

Speaker Information
 James Stewart
 Geographer / Senior
Spatial Analyst with
MEASURE Evaluation
 15 years of experience
as a GIS professional
 j.stewart@unc.edu
3

Acknowledgements
 Based on their significant contributions to the
development of the case study, special thanks are
extended to the following individuals:
 Dr. Fidel Ngabo, Director of Maternal and Child Health
(MCH), Rwanda Ministry of Health (MOH).
 Dr. Charles Ntare, Head of Integrated Health
Management Information Systems/HMIS, Rwanda
MOH.
4

Acknowledgements (continued)
 Mr. Randy Wilson, Senior Advisor, Health Information
Systems and Data Use, Management Sciences for
Health.
 Mr. Norbert-Aimé Péhé, Country Director, USAID |
DELIVER PROJECT, John Snow, Inc.
 Mr. Max Kabalisa, Mr. Jovith Ndahinyuka, and Mr.
Charles Nzumatuma, also of the USAID | DELIVER
PROJECT in Rwanda.
5

Acknowledgements (continued)
 MEASURE Evaluation PRH also extends sincere
appreciation to everyone in Rwanda who participated
in or facilitated stakeholder interviews conducted in
September 2011.
 Organizations represented:
 MCH and HMIS units at the MOH
 USAID Monitoring & Evaluation Management Services
(MEMS) Project
 MEASURE Evaluation
6

INTRODUCTION
7

Value of Linking Multi-sectoral
Data using a GIS
Family planning and
reproductive health
(FP/RH) services help
provide the foundation
for a healthy, stable,
and economically
viable society.
Kigali, Rwanda, Sep. 2011
8

Value of Linking Multi-sectoral
Data using a GIS
 Past global strategies have often led to the
implementation of FP/RH programs that operate in
isolation, despite the value of integrated approaches.
 The effectiveness of FP/RH decision making can be
undermined by a lack of information from other
sectors (e.g., education or food security), or from
other health areas (e.g., MCH or HIV/AIDS).
OVC FP/RH HIVEDU AGRICULTURE
PTMCT TB
FOOD
SECURITY
TRANSPORT POVERTY
9

Value of Linking Multi-sectoral
Data using a GIS
OVC FP/RH HIVEDU AGRICULTURE
PTMCT TB
FOOD
SECURITY
TRANSPORT POVERTY
Global Health Initiative (GHI) principle number five
emphasizes the need for strategic coordination and
integration to increase the impact of health programs.
“The integration of health sector activities and the
integration of health sector activities with activities in
other sectors – such as water and sanitation, education,
food security, agriculture, economic growth, microfinance,
and democracy and governance – can potentially achieve
high-yield gains for health.”
Source: www.ghi.gov, May 2013.
10

Value of Linking Multi-sectoral
Data using a GIS
 Multi-sector or multi-program integration can be
facilitated by linking data sources.
 Linking multi-sectoral data sources is often deterred
by information systems that are developed and
maintained independently of one another, leading to
datasets that are unconnected or ‘stovepiped.’
OVC FP HIVEDU AGRICULTURE
PTMCT TB
FOOD
SECURITY
TRANSPORT POVERTY
11

Value of Linking Multi-sectoral
Data using a GIS
Through its ability to
link data using
common geographic
identifiers, a
geographic
information system
(GIS) can help
overcome this
‘stovepiping’ of data.
12

Value of Linking Multi-sectoral
Data using a GIS
 After multi-sectoral links
have been established, a
GIS can
 Enhance visualization
and analysis of FP/RH
program data.
 Make program data
much easier to
understand and to use
for evidence-based
decision making.
13

Value of Linking Multi-sectoral
Data using a GIS
Many benefits:
 Provides maps, which are highly visual tools.
 Establishes a more comprehensive foundation for
decision making.
 Increases data demand and use.
 Helps identify data quality issues.
 Supplies shared knowledge base for stakeholder
cooperation.
 Facilitates better targeting of interventions.
14

Value of Linking Multi-sectoral
Data using a GIS
Facilitates answers to geography-based questions:
 Do areas with a higher modern contraceptive prevalence
rate (MCPR) exhibit lower HIV prevalence among women
of reproductive age in union?
 Is unmet need for FP different in urban and rural areas?
15

 To explore these benefits, MEASURE Evaluation
PRH sponsored a case study in Rwanda (fall 2011).
 Rwanda was selected as a case study for two
primary reasons:
1. Designated by the USAID Office of PRH as a priority
country for the support of FP/RH programming.
2. Possesses a national spatial data infrastructure
(NSDI) that is mature enough to facilitate GIS data
linking and analysis.
Case Study in Rwanda
16

Case Study
in Rwanda
 Goal was to explore data
linking opportunities using
free and open source GIS
solutions.
 Available in the
publications section of the
MEASURE Evaluation
site.
www.measureevaluation.org/
publications/sr-12-74
17

Goals of the Webinar
Based on the Rwanda experience:
 Highlight the value of common geographic identifiers in
key data sources.
 Identify free and open source software (FOSS) solutions
for GIS data linking, visualization, and analysis.
 Show how these GIS solutions can be used with multi-
sectoral and/or multi-program data to enhance evidence-
based decision making.
 Provide lessons learned to help accelerate the effective
use of multi-sectoral GIS data linking.
18

GIS DATA LINKING
CONSIDERATIONS
19

Key Data Sources
 Field visit in 2011 focused on exploring data linking
opportunities to provide useful examples.
 Some key data sources could not be accessed for
GIS linking during the field visit because of their
confidential or sensitive nature (e.g., PBF, TRACnet).
 Others could not be accessed because of timing of
visit (e.g., HMIS, SISCom).
20

 In this context, focused on data sources that had a
higher likelihood of being available in other countries.
 Primary data sources and sectors represented:
 Rwanda Demographic and Health Survey 2010:
FP/RH, HIV, education, and nutrition.
 USAID | DELIVER PROJECT: FP (commodities).
 National Agricultural Survey, 2008: food security.
 Poverty Household Surveys for 2000 to 2011: poverty.
Key Data Sources
21

Common Geographic Identifiers
for Rwanda
 Primary consideration
for data linking.
 Linked key data
sources using
crosswalk.
22

GIS Options Explored
Focused on free and open source software (FOSS)
solutions to complement existing tools:
 Excel to Google Earth (E2G)
 Single indicator maps using Excel.
 Quantum GIS (QGIS)
 Multi-indicator and publication-quality maps as well as
advanced GIS analysis to extend functionality of DHIS 2.
 OpenGeoDa
 Simple but effective exploratory spatial data analysis (ESDA)
using data in shapefile format.
23

Excel to Google Earth (E2G)
 Quick and simple program
from MEASURE Evaluation.
 Color-shaded (choropleth)
map of a single variable
without a GIS.
 Displayed on Google Earth’s
rich base map.
 Useful for data quality
checks and illustrating
reports.
 Good option for non-GIS
specialists working in Excel.
24
www.measureevaluation.org/e2g

Quantum GIS (QGIS)
 Fully functional GIS.
 Excellent for multi-
sectoral GIS data linking,
visualization, and
analysis.
 Publication-quality maps.
 Perform advanced GIS
analysis.
25
www.qgis.org

OpenGeoDa
Percent Married Women Age 15–49
using Any Method of Contraception
Data Source: Rwanda DHS 2010, Table D.32.
26
geodacenter.asu.edu

EXAMPLES OF GIS LINKING
AND ANALYSIS FOR RWANDA
27

28

29

Comparing the Two
No discernible correlation between general use of
contraception, which includes both traditional and modern
methods, and HIV prevalence.
 No spatial overlap between districts with highest % of women using
contraception and districts in Kigali with highest HIV prevalence.
 Districts with lowest contraception use do not appear to coincide
with either a lower or higher HIV prevalence.
30

QGIS: Contraception Use vs. HIV
Prevalence
31

QGIS: Contraception Use vs. HIV
Prevalence
32

QGIS: Contraception Use vs. HIV
Prevalence
33

QGIS: Linking FP, Education, and
Poverty Data
34

QGIS: Linking FP, Nutrition, and
Food Security Data
35

Linking FP/RH Program Data with
FP Commodities Data
Example: Women using Any Modern Method of
Contraception (MCPR) versus Couple Years of
Protection (CYP)
 Integrating FP commodities data from USAID | DELIVER
PROJECT represents significant data linking opportunity
for many FP/RH programs.
 Relies on same data linking principles used in previous
sections.
 This example can be used as a model for integrating
USAID | DELIVER PROJECT data into an existing HMIS.
36

Linking FP/RH Program Data with
FP Commodities Data
 Used district-level geographic identifiers for linking.
 Summarized CYP by district using Supply Chain Manager
(SCM) data.
 CYP calculated relatively easily using routinely collected
data and CYP conversion factors from USAID.
 CYP data need to be adjusted for unreported data and inventory
balance errors.
37

Linking FP/RH Program Data with
FP Commodities Data
 CYP is simple indicator of volume of FP commodities
distribution for a given geographic area.
 As simple sum of estimated contraceptive method
durations:
 Does not take into account differences in sizes of reported
areas or underlying populations.
 Unsuitable for choropleth mapping.
 First necessary to normalize calculations based on
proportion of district populations corresponding to women
of reproductive age.
38

QGIS: Linking FP/RH Data with the
USAID | DELIVER PROJECT
39

Map of MCPR vs. CYP
 Highlights ability of multi-program data linking to
uncover unexpected patterns and relationships.
 Shows how linking indicators in a single map can
help target geographic areas for potential
interventions.
 Illustrates the usefulness of GIS data linking for
conducting cross-database comparisons.
40

LESSONS LEARNED
41

Lessons Learned
Three categories:
 Key data sources
 Common geographic identifiers
 Software
42

Lessons Learned:
Key Data Sources
 Some datasets are easily accessed and are at an
appropriate geographic scale for analysis (e.g.,
DHS).
 Others may be difficult to access and use for a
variety of potential reasons, such as
 Data confidentiality concerns;
 Organizational barriers to data sharing;
 Geographic scale issues; and
 Timing of data access request.
43

Lessons Learned:
Key Data Sources
To overcome data access limitations, recommend
setting up local stakeholder meetings to
 Discuss data linking benefits.
 Identify opportunities to collaborate.
 Develop an action plan.
 Establish long-term working relationships.
44

Lessons Learned:
Key Data Sources
Supply Chain Manager (SCM) database from the
USAID | DELIVER PROJECT:
 Need to work closely with USAID | DELIVER PROJECT
staff to obtain data adjusted to 100% reporting rate.
 Highly important to have accurate population data for
normalizing CYP.
 USAID | DELIVER PROJECT a key partner for GIS data
linking.
45

Lessons Learned:
Key Data Sources
HIV/AIDS Data Management System:
 FP/RH programs could benefit from linking HMIS to non-
identifiable, aggregated data from HIV/AIDS system.
 Example based on stakeholder interviews:
 Could facilitate a more rapid response to such question as,
“Is there an uptake of FP and HIV testing referrals
associated with integration of FP/RH and HIV services?”
46

Lessons Learned:
Key Data Sources
Performance-Based Financing (PBF) System:
 Linkage of HMIS and PBF data could provide a cross-
check of common indicators.
 Such a national-level data display and feedback
mechanism could provide strong incentive for health
centers to perform.
47

Lessons Learned:
Common Geographic Identifiers
Excellent availability of geographic data and identifiers
for Rwanda:
 Could be downloaded from the NISR or MOH sites.
 Provides a good model for other countries to follow.
48

Lessons Learned: Software
 Free and open source GIS software options have
advanced in recent years:
 E2G and Google Earth are accessible to non-GIS
specialists.
 QGIS offers high degree of functionality.
 GeoDa provides point-and-click geographic data
visualization and analysis.
 These solutions can complement existing systems.
49

Summary and Conclusions
 Multi-sectoral/multi-program integration offers many
benefits and is a GHI priority.
 Multi-sectoral/multi-program integration can be facilitated
by GIS data linking, which requires common geographic
identifiers.
 Free and open source GIS solutions can meet many data
linking needs of FP/RH programs.
 The Rwanda case study can serve as a reference for how
to apply multi-sectoral GIS data linking to enhance FP/RH
decision making.
50

Thank you.
Questions?
www.measureevaluation.org/prh
51
MEASURE Evaluation Population
and Reproductive Health (PRH) is
funded by the U.S. Agency for
International Development (USAID)
through cooperative agreement
associate award number GPO-A-00-
09-00003-00 and is implemented by
the Carolina Population Center at
the University of North Carolina at
Chapel Hill, in partnership with
Futures Group, Management
Sciences for Health, and Tulane
University. The opinions expressed
are those of the authors and do not
necessarily reflect the views of
USAID or the U.S. government.