Mapping the endemicity and seasonality of clinical malaria for intervention targeting in Haiti using routine case data

Fine-scale maps of malaria incidence to inform risk stratification in Laos

BACKGROUND

Malaria risk maps are crucial for controlling and eliminating malaria by identifying areas of varying transmission risk. In the Greater Mekong Subregion, these maps guide interventions and resource allocation. This article focuses on analysing changes in malaria transmission and developing fine-scale risk maps using five years of routine surveillance data in Laos (2017-2021). The study employed data from 1160 geolocated health facilities in Laos, along with high-resolution environmental data.

METHODS

A Bayesian geostatistical framework incorporating population data and treatment-seeking propensity was developed. The models incorporated static and dynamic factors and accounted for spatial heterogeneity.

RESULTS

Results showed a significant decline in malaria cases in Laos over the five-year period and a shift in transmission patterns. While the north became malaria-free, the south experienced ongoing transmission with sporadic outbreaks.

CONCLUSION

The risk maps provided insights into changing transmission patterns and supported risk stratification. These risk maps are valuable tools for malaria control in Laos, aiding resource allocation, identifying intervention gaps, and raising public awareness. The study enhances understanding of malaria transmission dynamics and facilitates evidence-based decision-making for targeted interventions in high-risk areas.

Geospatial analysis of Plasmodium falciparum serological indicators: school versus community sampling in a low-transmission malaria setting

BACKGROUND

Due to low numbers of active infections and persons presenting to health facilities for malaria treatment, case-based surveillance is inefficient for understanding the remaining disease burden in low malaria transmission settings. Serological data through the detection of IgG antibodies from previous malaria parasite exposure can fill this gap by providing a nuanced picture of where sustained transmission remains. Study enrollment at sites of gathering provides a potential approach to spatially estimate malaria exposure and could preclude the need for more intensive community-based sampling.

METHODS

This study compared spatial estimates of malaria exposure from cross-sectional school- and community-based sampling in Haiti. A total of 52,405 blood samples were collected from 2012 to 2017. Multiplex bead assays (MBAs) tested IgG against P. falciparum liver stage antigen-1 (LSA-1), apical membrane antigen 1 (AMA1), and merozoite surface protein 1 (MSP1). Predictive geospatial models of seropositivity adjusted for environmental covariates, and results were compared using correlations by coordinate points and communes across Haiti.

RESULTS

Consistent directional associations were observed between seroprevalence and environmental covariates for elevation (negative), air temperature (negative), and travel time to urban centers (positive). Spearman's rank correlation for predicted seroprevalence at coordinate points was lowest for LSA-1 (ρ = 0.10, 95% CI: 0.09-0.11), but improved for AMA1 (ρ = 0.36, 95% CI: 0.35-0.37) and MSP1 (ρ = 0.48, 95% CI: 0.47-0.49).

CONCLUSIONS

In settings approaching P. falciparum elimination, case-based prevalence data does not provide a resolution of ongoing malaria transmission in the population. Immunogenic antigen targets (e.g., AMA1, MSP1) that give higher population rates of seropositivity provide moderate correlation to gold standard community sampling designs and are a feasible approach to discern foci of residual P. falciparum transmission in an area.

Malaria risk stratification in Lao PDR guides program planning in an elimination setting

Malaria in Lao People's Democratic Republic (Lao PDR) has declined rapidly over the last two decades, from 279,903 to 3926 (99%) cases between 2001 and 2021. Elimination of human malaria is an achievable goal and limited resources need to be targeted at remaining hotspots of transmission. In 2022, the Center of Malariology, Parasitology and Entomology (CMPE) conducted an epidemiological stratification exercise to assign districts and health facility catchment areas (HFCAs) in Lao PDR based on malaria risk. The stratification used reported malaria case numbers from 2019 to 2021, risk maps derived from predictive modelling, and feedback from malaria staff nationwide. Of 148 districts, 14 were deemed as burden reduction (high risk) districts and the remaining 134 as elimination (low risk) districts. Out of 1235 HFCAs, 88 (7%) were classified as highest risk, an improvement from 187 (15%) in the last stratification in 2019. Using the HFCA-level stratification, the updated stratification resulted in the at-risk population (total population in Strata 2, 3 and 4 HFCAs) declining from 3,210,191 to 2,366,068, a 26% decrease. CMPE are using the stratification results to strengthen targeting of resources. Updating national stratifications is a necessary exercise to assess progress in malaria control, reassign interventions to the highest risk populations in the country and ensure greatest impact of limited resources.

Acceptability, Feasibility, Drug Safety, and Effectiveness of a Pilot Mass Drug Administration with a Single Round of Sulfadoxine-Pyrimethamine Plus Primaquine and Indoor Residual Spraying in Communities with Malaria Transmission in Haiti, 2018

For a malaria elimination strategy, Haiti's National Malaria Control Program piloted a mass drug administration (MDA) with indoor residual spraying (IRS) in 12 high-transmission areas across five communes after implementing community case management and strengthened surveillance. The MDA distributed sulfadoxine-pyrimethamine and single low-dose primaquine to eligible residents during house visits. The IRS campaign applied pirimiphos-methyl insecticide on walls of eligible houses. Pre- and post-campaign cross-sectional surveys were conducted to assess acceptability, feasibility, drug safety, and effectiveness of the combined interventions. Stated acceptability for MDA before the campaign was 99.2%; MDA coverage estimated at 10 weeks post-campaign was 89.6%. Similarly, stated acceptability of IRS at baseline was 99.9%; however, household IRS coverage was 48.9% because of the high number of ineligible houses. Effectiveness measured by Plasmodium falciparum prevalence at baseline and 10 weeks post-campaign were similar: 1.31% versus 1.43%, respectively. Prevalence of serological markers were similar at 10 weeks post-campaign compared with baseline, and increased at 6 months. No severe adverse events associated with the MDA were identified in the pilot; there were severe adverse events in a separate, subsequent campaign. Both MDA and IRS are acceptable and feasible interventions in Haiti. Although a significant impact of a single round of MDA/IRS on malaria transmission was not found using a standard pre- and post-intervention comparison, it is possible there was blunting of the peak transmission. Seasonal malaria transmission patterns, suboptimal IRS coverage, and low baseline parasitemia may have limited the effectiveness or the ability to measure effectiveness.

Using antenatal care as a platform for malaria surveillance data collection: study protocol

BACKGROUND

While many malaria-endemic countries have health management information systems that can measure and report malaria trends in a timely manner, these routine systems have limitations. Periodic community cross-sectional household surveys are used to estimate malaria prevalence and intervention coverage but lack geographic granularity and are resource intensive. Incorporating malaria testing for all women at their first antenatal care (ANC) visit (i.e., ANC1) could provide a more timely and granular source of data for monitoring trends in malaria burden and intervention coverage. This article describes a protocol designed to assess if ANC-based surveillance could be a pragmatic tool to monitor malaria.

METHODS

This is an observational, cross-sectional study conducted in Benin, Burkina Faso, Mozambique, Nigeria, Tanzania, and Zambia. Pregnant women attending ANC1 in selected health facilities will be tested for malaria infection by rapid diagnostic test and administered a brief questionnaire to capture key indicators of malaria control intervention coverage and care-seeking behaviour. In each location, contemporaneous cross-sectional household surveys will be leveraged to assess correlations between estimates obtained using each method, and the use of ANC data as a tool to track trends in malaria burden and intervention coverage will be validated.

RESULTS

This study will assess malaria prevalence at ANC1 aggregated at health facility and district levels, and by gravidity relative to current pregnancy (i.e., gravida 1, gravida 2, and gravida 3 +). ANC1 malaria prevalence will be presented as monthly trends. Additionally, correlation between ANC1 and household survey-derived estimates of malaria prevalence, bed net ownership and use, and care-seeking will be assessed.

CONCLUSION

ANC1-based surveillance has the potential to provide a cost-effective, localized measure of malaria prevalence that is representative of the general population and useful for tracking monthly changes in parasite prevalence, as well as providing population-representative estimates of intervention coverage and care-seeking behavior. This study will evaluate the representativeness of these measures and collect information on operational feasibility, usefulness for programmatic decision-making, and potential for scale-up of malaria ANC1 surveillance.

Spatial, environmental, and individual associations with Anopheles albimanus salivary antigen IgG in Haitian children

IgG serology can be utilized to estimate exposure to Anopheline malaria vectors and the Plasmodium species they transmit. A multiplex bead-based assay simultaneously detected IgG to Anopheles albimanus salivary gland extract (SGE) and four Plasmodium falciparum antigens (CSP, LSA-1, PfAMA1, and PfMSP1) in 11,541 children enrolled at 350 schools across Haiti in 2016. Logistic regression estimated odds of an above-median anti-SGE IgG response adjusting for individual- and environmental-level covariates. Spatial analysis detected statistically significant clusters of schools with students having high anti-SGE IgG levels, and spatial interpolation estimated anti-SGE IgG levels in unsampled locations. Boys had 11% (95% CI: 0.81, 0.98) lower odds of high anti-SGE IgG compared to girls, and children seropositive for PfMSP1 had 53% (95% CI: 1.17, 2.00) higher odds compared to PfMSP1 seronegatives. Compared to the lowest elevation, quartiles 2-4 of higher elevation were associated with successively lower odds (0.81, 0.43, and 0.34, respectively) of high anti-SGE IgG. Seven significant clusters of schools were detected in Haiti, while spatially interpolated results provided a comprehensive picture of anti-SGE IgG levels in the study area. Exposure to malaria vectors by IgG serology with SGE is a proxy to approximate vector biting in children and identify risk factors for vector exposure.

Combining school-catchment area models with geostatistical models for analysing school survey data from low-resource settings: Inferential benefits and limitations

School-based sampling has been used to inform targeted responses for malaria and neglected tropical diseases. Standard geostatistical methods for mapping disease prevalence use the school location to model spatial correlation, which is questionable since exposure to the disease is more likely to occur in the residential location. In this paper, we propose to overcome the limitations of standard geostatistical methods by introducing a modelling framework that accounts for the uncertainty in the location of the residence of the students. By using cost distance and cost allocation models to define spatial accessibility and in absence of any information on the travel mode of students to school, we consider three school catchment area models that assume walking only, walking and bicycling and, walking and motorized transport. We illustrate the use of this approach using two case studies of malaria in Kenya and compare it with the standard approach that uses the school locations to build geostatistical models. We argue that the proposed modelling framework presents several inferential benefits, such as the ability to combine data from multiple surveys some of which may also record the residence location, and to deal with ecological bias when estimating the effects of malaria risk factors. However, our results show that invalid assumptions on the modes of travel to school can worsen the predictive performance of geostatistical models. Future research in this area should focus on collecting information on the modes of transportation to school which can then be used to better parametrize the catchment area models.

Spatiotemporal mapping of malaria incidence in Sudan using routine surveillance data

Malaria is a serious threat to global health, with over \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$95\%$$\end{document}95% of the cases reported in 2020 by the World Health Organization in African countries, including Sudan. Sudan is a low-income country with a limited healthcare system and a substantial burden of malaria. The epidemiology of malaria in Sudan is rapidly changing due to factors including the rapidly developing resistance to drugs and insecticides among the parasites and vectors, respectively; the growing population living in humanitarian settings due to political instability; and the recent emergence of Anopheles stephensi in the country. These factors contribute to changes in the distribution of the parasites species as well as malaria vectors in Sudan, and the shifting patterns of malaria epidemiology underscore the need for investment in improved situational awareness, early preparedness, and a national prevention and control strategy that is updated, evidence based, and proactive. A key component of this strategy is accurate, high-resolution endemicity maps of species-specific malaria. Here, we present a spatiotemporal Bayesian model, developed in collaboration with the Sudanese Ministry of Health, that predicts a fine-scale (1 km \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times $$\end{document}× 1 km) clinical incidence and seasonality profiles for Plasmodium falciparum and Plasmodium vivax across the country. We use monthly malaria case counts for both species collected via routine surveillance between January 2017 and December 2019, as well as a suite of high-resolution environmental covariates to inform our predictions. These epidemiological maps provide a useful resource for strategic planning and cost-effective implementation of malaria interventions, thus informing policymakers in Sudan to achieve success in malaria control and elimination.

Improving access to care and community health in Haiti with optimized community health worker placement

The national deployment of polyvalent community health workers (CHWs) is a constitutive part of the strategy initiated by the Ministry of Health to accelerate efforts towards universal health coverage in Haiti. Its implementation requires the planning of future recruitment and deployment activities for which mathematical modelling tools can provide useful support by exploring optimised placement scenarios based on access to care and population distribution. We combined existing gridded estimates of population and travel times with optimisation methods to derive theoretical CHW geographical placement scenarios including constraints on walking time and the number of people served per CHW. Four national-scale scenarios that align with total numbers of existing CHWs and that ensure that the walking time for each CHW does not exceed a predefined threshold are compared. The first scenario accounts for population distribution in rural and urban areas only, while the other three also incorporate in different ways the proximity of existing health centres. Comparing these scenarios to the current distribution, insufficient number of CHWs is systematically identified in several departments and gaps in access to health care are identified within all departments. These results highlight current suboptimal distribution of CHWs and emphasize the need to consider an optimal (re-)allocation.