2014.ISDS.Abstracts.Final.pdf


ISDS Annual Conference Proceedings 2014. This is an Open Access article distributed under the terms of the Creative Commons Attribution-
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ISDS 2014 Conference Abstracts

A Novel Method for Defining Health Facility Catchment 
Areas in a Low Income Country
Kate Zinszer*2, Ruth Kigozi3, Katia Charland1, Dr. Grant Dorsey4, Dr. Moses Kamya5 and 
David Buckeridge1

1McGill University, Montreal, QC, Canada; 2Harvard Medical School, Boston, MA, USA; 3Uganda Malaria Surveillance Project, 
Kampala, Uganda; 4University of California San Francisco, San Francisco, CA, USA; 5Makerere University, Kampala, Uganda

Objective
We propose a simple statistical method, the cumulative case ratio, 

for defining a catchment area using surveillance data.

Introduction
The catchment area of a health-care facility is used to assess 

health service utilization and calculate population-based rates of 
disease. Current approaches for catchment definition have significant 
limitations such as being based solely on distance from the facility or 
using an arbitrary threshold for inclusion.

Methods
The catchment areas of six health-care facilities in Uganda were 

determined using the cumulative case ratio: the ratio of the observed 
to expected utilization of a facility for a particular condition by 
patients from small administrative areas. The cumulative case ratio 
for malaria-related visits to these facilities was determined using data 
from the Uganda Malaria Surveillance Project. Catchment areas were 
also derived other methods such as the straight line and road network 
distances from the facility. Subsequently, the 1-year cumulative 
malaria case rate (the total number of cases during one year) was 
calculated for each catchment area, with at-risk populations estimate 
from catchment areas determined using the cumulative case ratio, the 
straight-line distance, and the road network distance.

Results
The 1-year cumulative malaria case rate varied considerably with 

the method used to define the catchment areas. With the cumulative 
case ratio approach, the catchment area could include noncontiguous 
areas. With the distance approaches, the denominator increased 
substantially with distance, whereas the numerator increased only 
slightly. The largest cumulative case rate per 1000 population was 
for the Kamwezi facility: 234.9 (95% confidence interval, CI: 226.2–
243.8) for a straight-line distance of 5 km, 193.1 (95% CI: 186.8–
199.6) for the cumulative case ratio approach and 156.1 (95% CI: 
150.9–161.4) for a road network distance of 5 km.

Conclusions
The variation in estimates of the cumulative rate of confirmed 

malaria cases was attributable to the differential change in the 
numerator and denominator of the case rate calculation as a function 
of the distance used to define the catchment area. An erroneous view 
of the catchment area can lead to inefficient and inadequate services, 
misspecification of the catchment population and potentially flawed 
decision-making on other facilities, such as deciding where to locate 
a new facility. Our approach is simple, reproducible, and is based on 
a statistical measure to decide which administrative units should be 
included in catchment areas.

Keywords
catchment; surveillance; Uganda

*Kate Zinszer
E-mail: kate.zinszer@mail.mcgill.ca    

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * (1):e98, 201