5 research outputs found
Assessing the feasibility of eHealth and mHealth: a systematic review and analysis of initiatives implemented in Kenya
Improving imperfect data from health management information systems in Africa using space-time geostatistics
BackgroundReliable and timely information on disease-specific treatment burdens within a health system is critical for the planning and monitoring of service provision. Health management information systems (HMIS) exist to address this need at national scales across Africa but are failing to deliver adequate data because of widespread underreporting by health facilities. Faced with this inadequacy, vital public health decisions often rely on crudely adjusted regional and national estimates of treatment burdens.Methods and FindingsThis study has taken the example of presumed malaria in outpatients within the largely incomplete Kenyan HMIS database and has defined a geostatistical modelling framework that can predict values for all data that are missing through space and time. The resulting complete set can then be used to define treatment burdens for presumed malaria at any level of spatial and temporal aggregation. Validation of the model has shown that these burdens are quantified to an acceptable level of accuracy at the district, provincial, and national scale.ConclusionsThe modelling framework presented here provides, to our knowledge for the first time, reliable information from imperfect HMIS data to support evidence-based decision-making at national and sub-national levels
Schematic Diagram of the Modelling Framework
<p>Four stages were used to predict the count of outpatients treated for malaria (MC) for each facility-month with missing data: (1) MMTC was estimated for each facility using both existing and predicted values of TC; (2) existing MC data at each facility were standardised by the corresponding MMTC value to create SMC values; (3) STK was used to predict all missing values of SMC; and (4) MMTC values were used to back-transform the predicted SMC values in order to obtain final predictions of MC.</p
Number of Outpatients Treated for Malaria at Government Facilities
<p>Predicted mean annual totals for each district for the period 1996ā2002. Values represent the combined sum of existing and predicted values.</p
Percentage of Government Health Facilities in Each Kenyan District (Fourth Level Administrative Unit) Submitting a Monthly Outpatient Morbidity Report to the HMIS
<p>The 2 mo shown are (A) the most complete (February 1996) and (B) the least complete (December 1997) during the 84-mo study period January 1996āDecember 2002.</p