AbstractBackgroundThe malaria test positivity rate (TPR) is increasingly used as an indicator of malaria morbidity because TPR is based on laboratory-confirmed cases and is simple to incorporate into existing surveillance systems. However, temporal trends in TPR may reflect changes in factors associated with malaria rather than true changes in malaria morbidity. This study examines the effects of age, area of residence and diagnostic test on TPR at two health facilities in regions of Uganda with differing malaria endemicity.MethodsThe analysis included data from diagnostic blood smears performed at health facilities in Walukuba and Aduku between January 2009 and December 2010. The associations between age and time and between age and TPR were evaluated independently to determine the potential for age to confound temporal trends in TPR. Subsequently, differences between observed TPR and TPR adjusted for age were compared to determine if confounding was present. A similar analysis was performed for area of residence. Temporal trends in observed TPR were compared to trends in TPR expected using rapid diagnostic tests, which were modelled based upon sensitivity and specificity in prior studies.ResultsAge was independently associated with both TPR and time at both sites. At Aduku, age-adjusted TPR increased relative to observed TPR due to the association between younger age and TPR and the gradual increase in age distribution. At Walukuba, there were no clear differences between observed and age-adjusted TPR. Area of residence was independently associated with both TPR and time at both sites, though there were no clear differences in temporal trends in area of residence-adjusted TPR and observed TPR at either site. Expected TPR with pLDH- and HRP-2-based rapid diagnostic tests (RDTs) was higher than observed TPR at all time points at both sites.ConclusionsAdjusting for potential confounders such as age and area of residence can ensure that temporal trends in TPR due to confounding are not mistakenly ascribed to true changes in malaria morbidity. The potentially large effect of diagnostic test on TPR can be accounted for by calculating and adjusting for the sensitivity and specificity of the test used.

Background

Recently the use of indoor residual spraying of insecticide (IRS) has greatly increased in Africa; however, limited data exist on the quantitative impacts of IRS on health outcomes in highly malaria endemic areas.

Methodology/principal findings

Routine data were collected on more than 90,000 patient visits at a single health facility over a 56 month period covering five rounds of IRS using three different insecticides. Temporal associations between the timing of IRS and the probability of a patient referred for microscopy having laboratory confirmed malaria were estimated controlling for seasonality and age. Considering patients less than five years of age there was a modest decrease in the odds of malaria following the 1(st) round of IRS using DDT (OR = 0.76, p<0.001) and the 2(nd) round using alpha-cypermethrin (OR = 0.83, p = 0.002). Following rounds 3-5 using bendiocarb there was a much greater decrease in the odds of malaria (ORs 0.34, 0.16, 0.17 respectively, p<0.001 for all comparisons). Overall, the impact of IRS was less pronounced among patients 5 years or older.

Conclusions/significance

IRS was associated with a reduction in malaria morbidity in an area of high transmission intensity in Uganda and the benefits appeared to be greatest after switching to a carbamate class of insecticide.

Background

Heath facility-based sentinel site surveillance has been proposed as a means of monitoring trends in malaria morbidity but may also provide an opportunity to improve malaria case management. Here we described the impact of a sentinel site malaria surveillance system on promoting laboratory testing and rational antimalarial drug use.

Methodology/principal findings

Sentinel site malaria surveillance was established at six health facilities in Uganda between September 2006 and January 2007. Data were collected from all patients presenting to the outpatient departments including demographics, laboratory results, diagnoses, and treatments prescribed. Between the start of surveillance and March 2010, a total 424,701 patients were seen of which 229,375 (54%) were suspected of having malaria. Comparing the first three months with the last three months of surveillance, the proportion of patients with suspected malaria who underwent diagnostic testing increased from 39% to 97% (p<0.001). The proportion of patients with an appropriate decision to prescribe antimalarial therapy (positive test result prescribed, negative test result not prescribed) increased from 64% to 95% (p<0.001). The proportion of patients appropriately prescribed antimalarial therapy who were prescribed the recommended first-line regimen artemether-lumefantrine increased from 48% to 69% (p<0.001).

Conclusions/significance

The establishment of a sentinel site malaria surveillance system in Uganda achieved almost universal utilization of diagnostic testing in patients with suspected malaria and appropriate decisions to prescribed antimalarial based on test results. Less success was achieved in promoting prescribing practice for the recommended first-line therapy. This system could provide a model for improving malaria case management in other health facilities in Africa.

The primary source of malaria surveillance data in Uganda is the Health Management Information System (HMIS), which does not require laboratory confirmation of reported malaria cases. To improve data quality, an enhanced inpatient malaria surveillance system (EIMSS) was implemented with emphasis on malaria testing of all children admitted in select hospitals. Data were compared between the HMIS and the EIMSS at four hospitals over a period of 12 months. After the implementation of the EIMSS, over 96% of admitted children under 5 years of age underwent laboratory testing for malaria. The HMIS significantly overreported the proportion of children under 5 years of age admitted with malaria (average absolute difference = 19%, range = 8-27% across the four hospitals) compared with the EIMSS. To improve the quality of the HMIS data for malaria surveillance, the National Malaria Control Program should, in addition to increasing malaria testing rates, focus on linking laboratory test results to reported malaria cases.