Gething, Peter W., Noor, Abdisalan M., Gikandi, Priscilla W., Ogara, Esther A. A., Hay, Simon I., Nixon, Mark S., Snow, Robert S. and Atkinson, Peter M.
Improving imperfect Health Management Information System data in Africa using space-time geostatistics
PLoS Medicine, 3, (6), . (doi:10.1371/journal.pmed.0030271).
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Available under License Other.
Reliable 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 Findings
This 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.
The 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.
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