Abstract
BackgroundMalaria surveillance is critical for monitoring changes in malaria morbidity over time. National Malaria Control Programmes often rely on surrogate measures of malaria incidence, including the test positivity rate (TPR) and total laboratory confirmed cases of malaria (TCM), to monitor trends in malaria morbidity. However, there are limited data on the accuracy of TPR and TCM for predicting temporal changes in malaria incidence, especially in high burden settings.MethodsThis study leveraged data from 5 malaria reference centres (MRCs) located in high burden settings over a 15-month period from November 2018 through January 2020 as part of an enhanced health facility-based surveillance system established in Uganda. Individual level data were collected from all outpatients including demographics, laboratory test results, and village of residence. Estimates of malaria incidence were derived from catchment areas around the MRCs. Temporal relationships between monthly aggregate measures of TPR and TCM relative to estimates of malaria incidence were examined using linear and exponential regression models.ResultsA total of 149,739 outpatient visits to the 5 MRCs were recorded. Overall, malaria was suspected in 73.4% of visits, 99.1% of patients with suspected malaria received a diagnostic test, and 69.7% of those tested for malaria were positive. Temporal correlations between monthly measures of TPR and malaria incidence using linear and exponential regression models were relatively poor, with small changes in TPR frequently associated with large changes in malaria incidence. Linear regression models of temporal changes in TCM provided the most parsimonious and accurate predictor of changes in malaria incidence, with adjusted R2 values ranging from 0.81 to 0.98 across the 5 MRCs. However, the slope of the regression lines indicating the change in malaria incidence per unit change in TCM varied from 0.57 to 2.13 across the 5 MRCs, and when combining data across all 5 sites, the R2 value reduced to 0.38.ConclusionsIn high malaria burden areas of Uganda, site-specific temporal changes in TCM had a strong linear relationship with malaria incidence and were a more useful metric than TPR. However, caution should be taken when comparing changes in TCM across sites.
Highlights
Malaria surveillance is critical for monitoring changes in malaria morbidity over time
The monitoring of temporal and geographic trends in malaria morbidity using health management information systems (HMIS) data typically relies on surrogate measures of malaria incidence such as the test positivity rate (TPR) or total laboratory confirmed cases of malaria (TCM)
69.7% of those tested for malaria were positive, with TPRs ranging from 59.8 to 77.3% across the five malaria reference centres (MRCs) (Table 1)
Summary
Malaria surveillance is critical for monitoring changes in malaria morbidity over time. National Malaria Control Programmes often rely on surrogate measures of malaria incidence, including the test positivity rate (TPR) and total laboratory confirmed cases of malaria (TCM), to monitor trends in malaria morbidity. The quality of HMIS data has improved over the last decade in most countries in sub-Saharan Africa due to expanded diagnostics and a reliance on laboratory confirmed cases of malaria, it is not possible to routinely estimate malaria incidence because of lack of information on where patients reside and undefined catchment populations around the health facilities. The monitoring of temporal and geographic trends in malaria morbidity using HMIS data typically relies on surrogate measures of malaria incidence such as the test positivity rate (TPR) or total laboratory confirmed cases of malaria (TCM)
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