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

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.

Figure 1

Government sector outpatient cases (all diagnoses) by province. Map of Kenya shows provincial boundaries and the distribution of 1 271 government health facilities included in this study (red dots). Plots are annual time series showing mean number of all-cause outpatient cases per facility per month at government health facilities in six provinces during 1996–2004. Unadjusted means were calculated directly from incomplete HMIS data. Adjusted means were based on a geostatistically-completed version of this dataset. Vertical bars on the adjusted mean plots show 95% confidence intervals.

Figure 2

Government sector outpatient cases (all diagnoses). Plots show adjusted and unadjusted annual and monthly time series of the mean number of all-cause outpatient cases per facility per month at government health facilities in Kenya during 1996–2004. Unadjusted means were calculated directly from incomplete HMIS data. Adjusted means were based on a geostatistically-completed version of this dataset. Vertical bars on the adjusted annual mean plot show 95% confidence intervals. The provenance and sensitivity of the HMIS data were affirmed by the observations of two marked aberrations in the monthly data: December 1997, a month of industrial action nationwide by nurses, and July 2004 when large publicity surrounded the reduction of user fees at government clinics.

Figure 3

Government sector non-malaria (A) and malaria outpatient diagnoses (B), and faith-based sector all-cause outpatient cases (C). Annual and monthly time series show mean values per facility per month at health facilities in Kenya during 1996–2004. Unadjusted means were calculated directly from incomplete HMIS data. Adjusted means were based on a geostatistically-completed version of this dataset. Vertical bars on the adjusted annual mean plots show 95% confidence intervals.