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Monitoring the impact of decentralised chronic care services on patient travel time in rural Africa - methods and results in Northern Malawi

Rein MGJ Houben12*, Thomas P Van Boeckel34, Venance Mwinuka1, Peter Mzumara5, Keith Branson2, Catherine Linard34, Frank Chimbwandira6, Neil French7, Judith R Glynn2 and Amelia C Crampin12

Author Affiliations

1 Karonga Prevention Study, Chilumba, Malawi

2 Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom

3 Biological Control and Spatial Ecology, Université Libre de Bruxelles, CP 160/12, Avenue FD Roosevelt 50, Brussels, B, 1050, Belgium

4 Fonds National de la Recherche Scientifique (F.R.S.-FNRS), Rue d’Egmont 5, Brussels, B, 1000, Belgium

5 Karonga District Hospital, Karonga, Malawi

6 Ministry of Health – HIV/AIDS unit, Lilongwe, Malawi

7 Institute of Infection & Global Health, University of Liverpool, Liverpool, United Kingdom

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International Journal of Health Geographics 2012, 11:49  doi:10.1186/1476-072X-11-49

Published: 15 November 2012



Decentralised health services form a key part of chronic care strategies in resource-limited settings by reducing the distance between patient and clinic and thereby the time and costs involved in travelling. However, few tools exist to evaluate the impact of decentralisation on patient travel time or what proportion of patients attend their nearest clinic. Here we develop methods to monitor changes in travel time, using data from the antiretroviral therapy (ART) roll-out in a rural district in North Malawi.


Clinic position was combined with GPS information on the home village of patients accessing ART services in Karonga District (North Malawi) between July 2005 and July 2009. Potential travel time was estimated as the travel time for an individual attending their nearest clinic, and estimated actual travel time as the time to the clinic attended. This allowed us to calculate changes in potential and actual travel time as new clinics opened and track the proportion and origin of patients not accessing their nearest clinic.


The model showed how the opening of further ART clinics in Karonga District reduced median potential travel time from 83 to 43 minutes, and median actual travel time fell from 83 to 47 minutes. The proportion of patients not attending their nearest clinic increased from 6% when two clinics were open, to 12% with four open.


Integrating GPS information with patient data shows the impact of decentralisation on travel time and clinic choice to inform policy and research questions. In our case study, travel time decreased, accompanied by an increased uptake of services. However, the model also identified an increasing proportion of ART patients did not attend their nearest clinic.