Abstract
BackgroundDue to challenges in laboratory confirmation, reporting completeness, timeliness, and health access, routine incidence data from health management information systems (HMIS) have rarely been used for the rigorous evaluation of malaria control program scale-up in Africa.MethodsWe used data from the Zambia HMIS for 2009–2011, a period of rapid diagnostic and reporting scale-up, to evaluate the association between insecticide-treated net (ITN) program intensity and district-level monthly confirmed outpatient malaria incidence using a dose–response national platform approach with district-time units as the unit of analysis. A Bayesian geostatistical model was employed to estimate longitudinal district-level ITN coverage from household survey and programmatic data, and a conditional autoregressive model (CAR) was used to impute missing HMIS data. The association between confirmed malaria case incidence and ITN program intensity was modeled while controlling for known confounding factors, including climate variability, reporting, testing, treatment-seeking, and access to health care, and additionally accounting for spatial and temporal autocorrelation.ResultsAn increase in district level ITN coverage of one ITN per household was associated with an estimated 27% reduction in confirmed case incidence overall (incidence rate ratio (IRR): 0 · 73, 95% Bayesian Credible Interval (BCI): 0 · 65–0 · 81), and a 41% reduction in areas of lower malaria burden.ConclusionsWhen improved through comprehensive parasitologically confirmed case reporting, HMIS data can become a valuable tool for evaluating malaria program scale-up. Using this approach we provide further evidence that increased ITN coverage is associated with decreased malaria morbidity and use of health services for malaria illness in Zambia. These methods and results are broadly relevant for malaria program evaluations currently ongoing in sub-Saharan Africa, especially as routine confirmed case data improve.Electronic supplementary materialThe online version of this article (doi:10.1186/s12963-014-0030-0) contains supplementary material, which is available to authorized users.
Highlights
As countries in sub-Saharan Africa (SSA) continue to scale up malaria control interventions with many moving toward elimination, rigorous evaluations are needed to ensure national programs are achieving desired impacts on malaria burden
We present a novel framework for rigorously evaluating full-coverage malaria programs, as well as child survival programs in general, that rely on imperfect health management information systems (HMIS) data, by controlling for variability in diagnostic procedures, completeness of reporting, access and demand for health services, and climate, while accounting for the inherent correlation of these types of data across time and space
The proportion of households with at least one insecticide-treated net (ITN) increased from 38% in 2006 to 62% in 2008 and 64% in 2010; the proportion of households receiving indoor residual spraying (IRS) in the past 12 months increased from 10% in 2006 to 15% in 2008 and 23% in 2010; [9] rapid diagnostic tests (RDTs) scale-up has allowed for confirmed diagnosis at the majority of facilities nationally since 2009 [11], and the HMIS reporting system was overhauled in 2008, which has greatly strengthened routine reporting
Summary
As countries in sub-Saharan Africa (SSA) continue to scale up malaria control interventions with many moving toward elimination, rigorous evaluations are needed to ensure national programs are achieving desired impacts on malaria burden. Because of the known biases of routine malaria incidence data measured through health management information systems (HMIS) [2], these data have rarely been used to provide rigorous evidence of program effectiveness for decision-making in Africa [3]. In rare cases have such studies directly controlled for important confounding factors, including changing diagnostic confirmation practices, access and use of health services, HMIS completeness, and rainfall and temperature, all of which likely lead to biased findings of program effectiveness [2,6]. Due to challenges in laboratory confirmation, reporting completeness, timeliness, and health access, routine incidence data from health management information systems (HMIS) have rarely been used for the rigorous evaluation of malaria control program scale-up in Africa
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