Mapping malaria seasonality in Madagascar using health facility data

Abstract

Background: Many malaria-endemic areas experience seasonal fluctuations in case incidence as Anopheles mosquito and Plasmodium parasite life cycles respond to changing environmental conditions. Identifying location-specific seasonality characteristics is useful for planning interventions. While most existing maps of malaria seasonality use fixed thresholds of rainfall, temperature, and/or vegetation indices to identify suitable transmission months, we construct a statistical modelling framework for characterising the seasonal patterns derived directly from monthly health facility data. Methods: With data from 2669 of the 3247 health facilities in Madagascar, a spatiotemporal regression model was used to estimate seasonal patterns across the island. In the absence of catchment population estimates or the ability to aggregate to the district level, this focused on the monthly proportions of total annual cases by health facility level. The model was informed by dynamic environmental covariates known to directly influence seasonal malaria trends. To identify operationally relevant characteristics such as the transmission start months and associated uncertainty measures, an algorithm was developed and applied to model realisations. A seasonality index was used to incorporate burden information from household prevalence surveys and summarise 'how seasonal' locations are relative to their surroundings. Results: Positive associations were detected between monthly case proportions and temporally lagged covariates of rainfall and temperature suitability. Consistent with the existing literature, model estimates indicate that while most parts of Madagascar experience peaks in malaria transmission near March-April, the eastern coast experiences an earlier peak around February. Transmission was estimated to start in southeast districts before southwest districts, suggesting that indoor residual spraying should be completed in the same order. In regions where the data suggested conflicting seasonal signals or two transmission seasons, estimates of seasonal features had larger deviations and therefore less certainty. Conclusions: Monthly health facility data can be used to establish seasonal patterns in malaria burden and augment the information provided by household prevalence surveys. The proposed modelling framework allows for evidence-based and cohesive inferences on location-specific seasonal characteristics. As health surveillance systems continue to improve, it is hoped that more of such data will be available to improve our understanding and planning of intervention strategies.

Beyond national indicators: adapting the Demographic and Health Surveys’ sampling strategies and questions to better inform subnational malaria intervention policy

“…Monthly reports from health facilities provide routine surveillance data that can be aggregated up to admin-2 units. Routine data is used to identify malaria trends and needs at the local level [ 7 , 8 ], as recommended by the GTS.…”

Malaria micro-stratification using routine surveillance data in Western Kenya

“…The analysis presented here was limited to 2-year time-series data and could potentially be improved by the inclusion of longer space–time data sets to extract long-term trends [ 48 , 49 ]. However, the data before 2018 were influenced by nationwide medical staff strikes [ 50 ]. For the period considered (2018–2019), data from the lower-tier facilities were less likely to be complete compared to hospitals and suggests that the quality of data from these facilities remains inadequate [ 9 ], but can be improved by increased training of health workers and health records officers.…”

Malaria micro-stratification using routine surveillance data in Western Kenya

“…However, the immediate application of such a hybrid approach is dependent upon a better understanding of the relationship between TPR to PR at varying endemicity, which is not always linear [41,42]. The analysis presented here was limited to a two-year time-series data and could potentially be improved by the inclusion of longer space-time data sets to extract long term trends [50,51]. However, the data before 2018 were influenced by nationwide medical staff strikes [52].…”