Abstract
BackgroundAccurate measures of malaria incidence are essential to track progress and target high-risk populations. While health management information system (HMIS) data provide counts of malaria cases, quantifying the denominator for incidence using these data is challenging because catchment areas and care-seeking behaviours are not well defined. This study’s aim was to estimate malaria incidence using HMIS data by adjusting the population denominator accounting for travel time to the health facility.MethodsOutpatient data from two public health facilities in Uganda (Kihihi and Nagongera) over a 3-year period (2011–2014) were used to model the relationship between travel time from patient village of residence (available for each individual) to the facility and the relative probability of attendance using Poisson generalized additive models. Outputs from the model were used to generate a weighted population denominator for each health facility and estimate malaria incidence. Among children aged 6 months to 11 years, monthly HMIS-derived incidence estimates, with and without population denominators weighted by probability of attendance, were compared with gold standard measures of malaria incidence measured in prospective cohorts.ResultsA total of 48,898 outpatient visits were recorded across the two sites over the study period. HMIS incidence correlated with cohort incidence over time at both study sites (correlation in Kihihi = 0.64, p < 0.001; correlation in Nagongera = 0.34, p = 0.045). HMIS incidence measures with denominators unweighted by probability of attendance underestimated cohort incidence aggregated over the 3 years in Kihihi (0.5 cases per person-year (PPY) vs 1.7 cases PPY) and Nagongera (0.3 cases PPY vs 3.0 cases PPY). HMIS incidence measures with denominators weighted by probability of attendance were closer to cohort incidence, but remained underestimates (1.1 cases PPY in Kihihi and 1.4 cases PPY in Nagongera).ConclusionsAlthough malaria incidence measured using HMIS underestimated incidence measured in cohorts, even when adjusting for probability of attendance, HMIS surveillance data are a promising and scalable source for tracking relative changes in malaria incidence over time, particularly when the population denominator can be estimated by incorporating information on village of residence.
Highlights
Accurate measures of malaria incidence are essential to track progress and target high-risk populations
Weighted estimates, which leveraged information on village of residence and travel time to the health facility to account for differences in careseeking behaviour, fell much closer to the gold standard incidence than unweighted estimates that incorrectly assumed all individuals in the assigned catchment areas had a uniform probability of attendance
Even using weighted estimates, health management information system (HMIS) data produced estimates of malaria incidence that were consistently lower than estimates from cohort studies, suggesting that not all episodes of malaria were being captured through the HMIS system
Summary
Accurate measures of malaria incidence are essential to track progress and target high-risk populations. While health management information system (HMIS) data provide counts of malaria cases, quantifying the denomi‐ nator for incidence using these data is challenging because catchment areas and care-seeking behaviours are not well defined. There are several strengths in conducting HMIS-based surveillance: data provide direct measures of morbidity, are collected continuously over time, and cover a broad geographic range [2] These data are often hindered by reporting delays and gaps, poor data quality, health-seeking behaviour, and lack of laboratory-confirmed diagnostics [3, 4]. The major challenge of translating HMIS data into accurate measures of malaria incidence is quantifying the denominator, because catchment areas around health facilities are not well defined. Previous efforts to quantify this denominator have relied on representative cross-sectional surveys with information on household care-seeking [13, 14], an additional source of information that is costly to collect and requires population-level representativeness
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