Research article

Information for decision making from imperfect national data: tracking major changes in health care use in Kenya using geostatistics

Peter W Gething¹ , Abdisalan M Noor² , Catherine A Goodman³ , Priscilla W Gikandi² , Simon I Hay^2,4 , Shahnaaz K Sharif⁵ , Peter M Atkinson¹ and Robert W Snow^2,6

¹School of Geography, University of Southampton, Highfield, Southampton, SO17 1BJ, UK

²Malaria Public Health & Epidemiology Group, Centre for Geographic Medicine, Kenya Medical Research Institute/Wellcome Trust Collaborative Programme, PO Box 43640, 00100 GPO, Nairobi, Kenya

³Consortium for Research on Equitable Health Systems (CREHS), Kenya Medical Research Institute/Wellcome Trust Collaborative Programme and London School of Hygiene and Tropical Medicine, PO Box 43640, 00100 GPO, Nairobi, Kenya

⁴Spatial Ecology and Epidemiology Group, Tinbergen Building, Department of Zoology, University of Oxford, South Parks Road, Oxford, OX1 3PS, UK

⁵Ministry of Health, P.O. Box 30016, Afya House, Cathedral Road, Nairobi, Kenya

⁶Centre for Tropical Medicine, University of Oxford, John Radcliffe Hospital, Headington, Oxford, OX3 9DU, UK

author email corresponding author email

BMC Medicine 2007, 5:37doi:10.1186/1741-7015-5-37

Published:	11 December 2007

Abstract

Background

Most Ministries of Health across Africa invest substantial resources in some form of health management information system (HMIS) to coordinate the routine acquisition and compilation of monthly treatment and attendance records from health facilities nationwide. Despite the expense of these systems, poor data coverage means they are rarely, if ever, used to generate reliable evidence for decision makers. One critical weakness across Africa is the current lack of capacity to effectively monitor patterns of service use through time so that the impacts of changes in policy or service delivery can be evaluated. Here, we present a new approach that, for the first time, allows national changes in health service use during a time of major health policy change to be tracked reliably using imperfect data from a national HMIS.

Methods

Monthly attendance records were obtained from the Kenyan HMIS for 1 271 government-run and 402 faith-based outpatient facilities nationwide between 1996 and 2004. A space-time geostatistical model was used to compensate for the large proportion of missing records caused by non-reporting health facilities, allowing robust estimation of monthly and annual use of services by outpatients during this period.

Results

We were able to reconstruct robust time series of mean levels of outpatient utilisation of health facilities at the national level and for all six major provinces in Kenya. These plots revealed reliably for the first time a period of steady nationwide decline in the use of health facilities in Kenya between 1996 and 2002, followed by a dramatic increase from 2003. This pattern was consistent across different causes of attendance and was observed independently in each province.

Conclusion

The methodological approach presented can compensate for missing records in health information systems to provide robust estimates of national patterns of outpatient service use. This represents the first such use of HMIS data and contributes to the resurrection of these hugely expensive but underused systems as national monitoring tools. Applying this approach to Kenya has yielded output with immediate potential to enhance the capacity of decision makers in monitoring nationwide patterns of service use and assessing the impact of changes in health policy and service delivery.