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Background Reliable surveillance systems are essential for identifying disease outbreaks and allocating resources to ensure universal access to diagnostics and treatment for endemic diseases. Yet, most countries with high disease burdens rely entirely on facility-based passive surveillance systems, which miss the vast majority of cases in rural settings with low access to health care. This is especially true for malaria, for which the World Health Organization estimates that routine surveillance detects only 14% of global cases. The goal of this study was to develop a novel method to obtain accurate estimates of disease spatio-temporal incidence at very local scales from routine passive surveillance, less biased by populations' financial and geographic access to care. Methods We use a geographically explicit dataset with residences of the 73,022 malaria cases confirmed at health centers in the Ifanadiana District in Madagascar from 2014 to 2017. Malaria incidence was adjusted to account for underreporting due to stock-outs of rapid diagnostic tests and variable access to healthcare. A benchmark multiplier was combined with a health care utilization index obtained from statistical models of non-malaria patients. Variations to the multiplier and several strategies for pooling neighboring communities together were explored to allow for fine-tuning of the final estimates. Separate analyses were carried out for individuals of all ages and for children under five. Cross-validation criteria were developed based on overall incidence, trends in financial and geographical access to health care, and consistency with geographic distribution in a district-representative cohort. The most plausible sets of estimates were then identified based on these criteria. Results Passive surveillance was estimated to have missed about 4 in every 5 malaria cases among all individuals and 2 out of every 3 cases among children under five. Adjusted malaria estimates were less biased by differences in populations’ financial and geographic access to care. Average adjusted monthly malaria incidence was nearly four times higher during the high transmission season than during the low transmission season. By gathering patient-level data and removing systematic biases in the dataset, the spatial resolution of passive malaria surveillance was improved over ten-fold. Geographic distribution in the adjusted dataset revealed high transmission clusters in low elevation areas in the northeast and southeast of the district that were stable across seasons and transmission years. Conclusions Understanding local disease dynamics from routine passive surveillance data can be a key step towards achieving universal access to diagnostics and treatment. Methods presented here could be scaled-up thanks to the increasing availability of e-health disease surveillance platforms for malaria and other diseases across the developing world.

BackgroundAlthough it is accepted that long-lasting insecticidal net (LLIN) use is an effective means to prevent malaria, children aged 5 to 15 years do not appear to be sufficiently protected in Madagascar; the malaria prevalence is highest in this age group. The purpose of this research is to summarize recent qualitative studies describing LLIN use among the Malagasy people with a focus on children aged 5–15 years.MethodsQualitative data from three studies on malaria conducted between 2012 and 2016 in 10 districts of Madagascar were analysed. These studies cover all malaria epidemiological profiles and 10 of the 18 existing ethnic groups in Madagascar. A thematic analysis was conducted on the collected data from semi-structured interviews, direct observation data, and informal interviews.ResultsA total of 192 semi-structured interviews were conducted. LLINs are generally perceived positively because they protect the health and well-being of users. However, regional representations of mosquito nets may contribute to LLIN lower use by children over 5 years of age including the association between married status and LLIN use, which leads to the refusal of unmarried young men to sleep under LLINs; the custom of covering the dead with a mosquito net, which leads to fear of LLIN use; and taboos governing sleeping spaces for siblings of opposite sexes, which leads to LLIN shortages in households. Children under 5 years of age are known to be the most vulnerable age group for acquiring malaria and, therefore, are prioritized for LLIN use when there are limited supplies in households. In contrast, children over 5 years of age, who are perceived to be at less risk for malaria, often sleep without LLINs.ConclusionsPerceptions, social practices and regional beliefs regarding LLINs and vulnerability to malaria contribute to the nonuse of LLINs among children over 5 years of age in Madagascar. Modifying LLIN policies to account for these factors may increase LLIN use in this age group and reduce disease burden.

Background: Although it is accepted that long lasting insecticidal net (LLIN) use is an effective means to prevent malaria, children aged 5 to 15 years do not appear to be sufficiently protected in Madagascar; the malaria prevalence is highest in this age group. The purpose of this article is to summarize recent qualitative studies describing LLIN use among children aged 5-15 years and explore options to increase their use in this age group.Methods: Qualitative data from three studies on malaria conducted between 2012 and 2016 in 10 districts of Madagascar were analyzed. These studies cover all malaria epidemiological profiles and 10 of the 18 existing ethnic groups in Madagascar. A thematic analysis was conducted on the collected data from semi-structured interviews, direct observation data, and informal interviews.Results: A total of 192 semi-structured interviews were conducted. LLINs are generally perceived positively because they protect the health and well-being of users. However, regional representations of mosquito nets may contribute to LLIN nonuse by children over 5 years of age including the association between married status and LLIN use, which leads to the refusal of unmarried young men to sleep under LLINs; the custom of covering the dead with a mosquito net, which leads to fear of LLIN use; and taboos governing sleeping spaces for siblings of opposite sexes, which leads to LLIN shortages in households. Children under 5 years of age are known to be the most vulnerable age group for acquiring malaria and therefore are prioritized for LLIN use when there are limited supplies in households. In contrast, children over 5 years of age, who are perceived to be at less risk for malaria, often sleep without LLINs.Conclusions: Perceptions, social practices and regional beliefs regarding LLINs and vulnerability to malaria contribute to the nonuse of LLINs among children over 5 years of age in Madagascar. Modifying LLIN policies to account for these factors may increase LLIN use in this age group and reduce disease burden.Trial registration: Not applicable

BackgroundAlthough it is accepted that long lasting insecticide bed net (LLIN) use is an effective means to prevent malaria, children aged 5 to 15 years do not appear to be sufficiently protected in Madagascar; malaria prevalence is highest in this age group. The purpose of this article is to summarize recent qualitative studies describing LLIN use among children aged 5–15 years and explore options to increase their use in this age group.MethodsQualitative data from three anthropological studies on malaria conducted between 2012 and 2016 in 10 districts of Madagascar were analyzed. These studies cover all malaria epidemiological profiles and 10 of the 18 existing ethnic groups in Madagascar. A thematic analysis was conducted on the collected data from semi-structured interviews, direct observation data, and informal interviews.ResultsA total of 192 semi-structured interviews were conducted. LLINs are generally perceived positively because they protect the health and well-being of users. However, regional representations of mosquito nets may contribute to LLIN nonuse by children over 5 years of age including the association between married status and LLIN use, which leads to the refusal of unmarried young men to sleep under LLINs; the custom of covering the dead with a mosquito net, which leads to fear of LLIN use; and taboos governing sleeping spaces for siblings of opposite sexes, which leads to LLIN shortages in households. Children under 5 years of age are known to be the most vulnerable age group for acquiring malaria and therefore are prioritized for LLIN use when there are limited supplies in households. In contrast, children over 5 years of age, who are perceived to be at less risk for malaria, often sleep without LLINs.ConclusionsPerceptions, social practices and regional beliefs regarding LLINs s and vulnerability to malaria contribute to the nonuse of LLINs among children over 5 years of age in Madagascar. Modifying LLIN policies to account for these factors may increase LLIN use in this age group and reduce disease burden.Trial registrationNot applicable

Background Madagascar’s Malaria National Strategic Plan 2018–2022 calls for progressive malaria elimination beginning in low-incidence districts ( Methods A mixed methods survey was performed in September 2018 in five districts of Madagascar. Randomly selected HFs were assessed for availability of malaria commodities and frequency of training and supervision conducted. Health providers (HPs) and community health volunteers (CHVs) were interviewed, and outpatient consultations at HFs were observed. To evaluate elimination readiness, a composite score ranging from 0 to 100 was designed from all study tools and addressed four domains: (1) resource availability, (2) case management (CM), (3) data management and use, and (4) training, supervision, and technical assistance; scores were calculated for each HF catchment area and district based on survey responses. Stakeholder interviews on malaria elimination planning were conducted at national, regional and district levels. Results A quarter of the 35 HFs surveyed had no rapid diagnostic tests (RDTs). Of 129 patients with reported or recorded fever among 300 consultations observed, HPs tested 56 (43%) for malaria. Three-quarters of the 35 HF managers reviewed data for trends. Only 68% of 41 HPs reported receiving malaria-specific training. Of 34 CHVs surveyed, 24% reported that treating fever was no longer among their responsibilities. Among treating CHVs, 13 (50%) reported having RDTs, and 11 (42%) had anti-malarials available. The average district elimination readiness score was 52 out of 100, ranging from 48 to 57 across districts. Stakeholders identified several challenges to commodity management, malaria CM, and epidemic response related to lack of training and funding disruptions. Conclusion This evaluation highlighted gaps in malaria CM and elimination readiness in Madagascar to address during elimination planning. Strategies are needed that include training, commodity provision, supervision, and support for CHVs. The MERA can be repeated to assess progress in filling identified gaps and is a feasible tool that could be used to assess elimination targets in other countries.

Malaria transmission in Madagascar is highly heterogeneous, exhibiting spatial, seasonal and long-term trends. Previous efforts to map malaria risk in Madagascar used prevalence data from Malaria Indicator Surveys. These cross-sectional surveys, conducted during the high transmission season most recently in 2013 and 2016, provide nationally representative prevalence data but cover relatively short time frames. Conversely, monthly case data are collected at health facilities but suffer from biases, including incomplete reporting and low rates of treatment seeking. We combined survey and case data to make monthly maps of prevalence between 2013 and 2016. Health facility catchment populations were estimated to produce incidence rates from the case data. Smoothed incidence surfaces, environmental and socioeconomic covariates, and survey data informed a Bayesian prevalence model, in which a flexible incidence-to-prevalence relationship was learned. Modelled spatial trends were consistent over time, with highest prevalence in the coastal regions and low prevalence in the highlands and desert south. Prevalence was lowest in 2014 and peaked in 2015 and seasonality was widely observed, including in some lower transmission regions. These trends highlight the utility of monthly prevalence estimates over the four year period. By combining survey and case data using this two-step modelling approach, we were able to take advantage of the relative strengths of each metric while accounting for potential bias in the case data. Similar modelling approaches combining large datasets of different malaria metrics may be applicable across sub-Saharan Africa.

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.

Many malaria-endemic areas experience seasonal fluctuations in case incidence as Anopheles mosquito and Plasmodium parasite life cycles respond to changing environmental conditions. While most existing maps of malaria seasonality use fixed thresholds of rainfall, temperature, and/or vegetation indices to identify suitable transmission months, we develop a statistical modelling framework for characterising the seasonal patterns derived directly from case data. The procedure involves a spatiotemporal regression model for estimating the monthly proportions of total annual cases and an algorithm to identify operationally relevant characteristics such as the transmission start and peak months. A seasonality index combines the monthly proportion estimates and existing estimates of annual case incidence to provide a summary of "how seasonal" locations are relative to their surroundings. An advancement upon past seasonality mapping endeavours is the presentation of the uncertainty associated with each map, which will enable policymakers to make more statistically sound decisions. The methodology is illustrated using health facility data from Madagascar.