Admissions by month for the period January 1999 to March 2007 at three sites on the Kenyan coast

<p><b>Copyright information:</b></p><p>Taken from "The decline in paediatric malaria admissions on the coast of Kenya"</p><p>http://www.malariajournal.com/content/6/1/151</p><p>Malaria Journal 2007;6():151-151.</p><p>Published online 15 Nov 2007</p><p>PMCID:PMC2194691.</p><p></p> The top row is Kilifi, the middle row Kwale and the bottom Malindi. The graphs show malaria admissions (left column) and non-malaria admissions (right column) as dashed lines. The yellow solid line is a 13-point moving average applied to filter seasonal variation and highlight the long-term movements in the data. The two solid tone lines illustrate the change in admissions adjusted for seasonality (light grey) and seasonality, rainfall and non-malaria admissions (black). The intercept was chosen (from the potential 11) based on the maximal correlation with the 13-point m.a

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

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-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-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