Information for decision making from imperfect national data: tracking major changes in health care use in Kenya using geostatistics-2

<p><b>Copyright information:</b></p><p>Taken from "Information for decision making from imperfect national data: tracking major changes in health care use in Kenya using geostatistics"</p><p>http://www.biomedcentral.com/1741-7015/5/37</p><p>BMC Medicine 2007;5():37-37.</p><p>Published online 11 Dec 2007</p><p>PMCID:PMC2225405.</p><p></p>eans 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

Information for decision making from imperfect national data: tracking major changes in health care use in Kenya using geostatistics-0

<p><b>Copyright information:</b></p><p>Taken from "Information for decision making from imperfect national data: tracking major changes in health care use in Kenya using geostatistics"</p><p>http://www.biomedcentral.com/1741-7015/5/37</p><p>BMC Medicine 2007;5():37-37.</p><p>Published online 11 Dec 2007</p><p>PMCID:PMC2225405.</p><p></p>. 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

Information for decision making from imperfect national data: tracking major changes in health care use in Kenya using geostatistics-3

<p><b>Copyright information:</b></p><p>Taken from "Information for decision making from imperfect national data: tracking major changes in health care use in Kenya using geostatistics"</p><p>http://www.biomedcentral.com/1741-7015/5/37</p><p>BMC Medicine 2007;5():37-37.</p><p>Published online 11 Dec 2007</p><p>PMCID:PMC2225405.</p><p></p>. 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

Number of Outpatients Treated for Malaria at Government Facilities

<p>Predicted mean annual totals for each district for the period 1996–2002. Values represent the combined sum of existing and predicted values.</p

Information for decision making from imperfect national data: tracking major changes in health care use in Kenya using geostatistics-1

<p><b>Copyright information:</b></p><p>Taken from "Information for decision making from imperfect national data: tracking major changes in health care use in Kenya using geostatistics"</p><p>http://www.biomedcentral.com/1741-7015/5/37</p><p>BMC Medicine 2007;5():37-37.</p><p>Published online 11 Dec 2007</p><p>PMCID:PMC2225405.</p><p></p>onth 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

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

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 1996–December 2002.</p