Estimating the size of populations at risk for malaria: a case study in cattle herders and agricultural workers in Northern Namibia.

Cattle herders and agricultural workers have been identified has key high-risk populations for malaria in northern Namibia. Population size estimates for these groups are lacking but are important for planning, monitoring and evaluating the effectiveness of targeted strategies towards malaria elimination in the region. In this analysis, we extend population size estimation methods routinely used in HIV research, specifically social mapping and multiple source capture-recapture, to the context of malaria to estimate how many cattle herders and agricultural workers lived in two regions of northern Namibia over the course of the 2019-2020 malaria season. Both methods estimated two to three times more agricultural workers than cattle herders but size estimates based on the multiple source capture-recapture method were two to three times greater than the mapping-based, highlighting important methodological considerations to apply such methods to these highly mobile populations. In particular, we compared open versus closed populations assumptions for the capture-recapture method and assessed the impact of sensitivity analyses on the procedure to link records across multiple data sources on population size estimates. Our results are important for national control programs to target their resources and consider integrating routine population size estimation of high risk populations in their surveillance activities.

WASH interventions and child diarrhea at the interface of climate and socioeconomic position in Bangladesh.

Many diarrhea-causing pathogens are climate-sensitive, and populations with the lowest socioeconomic position (SEP) are often most vulnerable to climate-related transmission. Household Water, Sanitation, and Handwashing (WASH) interventions constitute one potential effective strategy to reduce child diarrhea, especially among low-income households. Capitalizing on a cluster randomized trial population (360 clusters, 4941 children with 8440 measurements) in rural Bangladesh, one of the world's most climate-sensitive regions, we show that improved WASH substantially reduces diarrhea risk with largest benefits among children with lowest SEP and during the monsoon season. We extrapolated trial results to rural Bangladesh regions using high-resolution geospatial layers to identify areas most likely to benefit. Scaling up a similar intervention could prevent an estimated 734 (95% CI 385, 1085) cases per 1000 children per month during the seasonal monsoon, with marked regional heterogeneities. Here, we show how to extend large-scale trials to inform WASH strategies among climate-sensitive and low-income populations.

Geographic pair matching in large-scale cluster randomized trials.

Cluster randomized trials are often used to study large-scale public health interventions. In large trials, even small improvements in statistical efficiency can have profound impacts on the required sample size and cost. Location integrates many socio-demographic and environmental characteristics into a single, readily available feature. Here we show that pair matching by geographic location leads to substantial gains in statistical efficiency for 14 child health outcomes that span growth, development, and infectious disease through a re-analysis of two large-scale trials of nutritional and environmental interventions in Bangladesh and Kenya. Relative efficiencies from pair matching are ≥1.1 for all outcomes and regularly exceed 2.0, meaning an unmatched trial would need to enroll at least twice as many clusters to achieve the same level of precision as the geographically pair matched design. We also show that geographically pair matched designs enable estimation of fine-scale, spatially varying effect heterogeneity under minimal assumptions. Our results demonstrate broad, substantial benefits of geographic pair matching in large-scale, cluster randomized trials.

Excess risk in infant mortality among populations living in flood-prone areas in Bangladesh: A cluster-matched cohort study over three decades, 1988 to 2017.

The Ganges-Brahmaputra-Meghna river basin, running through Tibet, Nepal, Bhutan, Bangladesh, and northern India, is home to more than 618 million people. Annual monsoons bring extensive flooding to the basin, with floods predicted to be more frequent and extreme due to climate change. Yet, evidence regarding the long-term impacts of floods on children's health is lacking. In this analysis, we used high-resolution maps of recent large floods in Bangladesh to identify flood-prone areas over the country. We then used propensity score techniques to identify, among 58,945 mothers interviewed in six demographic population-based surveys throughout Bangladesh, matched cohorts of exposed and unexposed mothers and leverage data on 150,081 births to estimate that living in flood-prone areas was associated with an excess risk in infant mortality of 5.3 (95% CI 2.2 to 8.4) additional deaths per 1,000 births compared to living in non-flood-prone areas over the 30-y period between 1988 and 2017, with higher risk for children born during rainy (7.9, 95% CI: 3.3 to 12.5) vs. dry months (3.1, 95% CI: -1.1 to 7.2). Finally, drawing on national-scale, high-resolution estimates of flood risk and population distribution, we estimated an excess of 152,753 (64,120 to 241,386) infant deaths were attributable to living in flood-prone areas in Bangladesh over the past 30 y, with marked heterogeneity in attributable burden by subdistrict. Our approach demonstrates the importance of measuring longer-term health impacts from floods and provides a generalizable example for how to study climate-related exposures and long-term health effects.

Geographic pair-matching in large-scale cluster randomized trials.

Custer randomized trials are often used to study large-scale public health interventions. In large trials, even small improvements in statistical efficiency can have profound impacts on the required sample size and cost. Pair matched randomization is one strategy with potential to increase trial efficiency, but to our knowledge there have been no empirical evaluations of pair-matching in large-scale, epidemiologic field trials. Location integrates many socio-demographic and environmental characteristics into a single feature. Here, we show that geographic pair-matching leads to substantial gains in statistical efficiency for 14 child health outcomes that span growth, development, and infectious disease through a re-analysis of two large-scale trials of nutritional and environmental interventions in Bangladesh and Kenya. We estimate relative efficiencies ≥1.1 for all outcomes assessed and relative efficiencies regularly exceed 2.0, meaning an unmatched trial would have needed to enroll at least twice as many clusters to achieve the same level of precision as the geographically pair-matched design. We also show that geographically pair-matched designs enable estimation of fine-scale, spatially varying effect heterogeneity under minimal assumptions. Our results demonstrate broad, substantial benefits of geographic pair-matching in large-scale, cluster randomized trials.

Characterizing mobility patterns of forest goers in southern Lao PDR using GPS loggers.

BACKGROUND: In the Greater Mekong Subregion (GMS), forest-going populations are considered high-risk populations for malaria and are increasingly targeted by national control programmes' elimination efforts. A better understanding of forest-going populations' mobility patterns and risk associated with specific types of forest-going trips is necessary for countries in the GMS to achieve their objective of eliminating malaria by 2030. METHODS: Between March and November 2018, as part of a focal test and treat intervention (FTAT), 2,904 forest-goers were recruited in southern Lao PDR. A subset of forest-goers carried an "i-Got-U" GPS logger for roughly 2 months, configured to collect GPS coordinates every 15 to 30 min. The utilization distribution (UD) surface around each GPS trajectory was used to extract trips to the forest and forest-fringes. Trips with shared mobility characteristics in terms of duration, timing and forest penetration were identified by a hierarchical clustering algorithm. Then, clusters of trips with increased exposure to dominant malaria vectors in the region were further classified as high-risk. Finally, gradient boosting trees were used to assess which of the forest-goers' socio-demographic and behavioural characteristics best predicted their likelihood to engage in such high-risk trips. RESULTS: A total of 122 forest-goers accepted carrying a GPS logger resulting in the collection of 803 trips to the forest or forest-fringes. Six clusters of trips emerged, helping to classify 385 (48%) trips with increased exposure to malaria vectors based on high forest penetration and whether the trip happened overnight. Age, outdoor sleeping structures and number of children were the best predictors of forest-goers' probability of engaging in high-risk trips. The probability of engaging in high-risk trips was high (~ 33%) in all strata of the forest-going population. CONCLUSION: This study characterized the heterogeneity within the mobility patterns of forest-goers and attempted to further segment their role in malaria transmission in southern Lao People's Democratic Republic (PDR). National control programmes across the region can leverage these results to tailor their interventions and messaging to high-risk populations and accelerate malaria elimination.

Characterizing the spatial distribution of multiple malaria diagnostic endpoints in a low-transmission setting in Lao PDR.

The epidemiology of malaria changes as prevalence falls in low-transmission settings, with remaining infections becoming more difficult to detect and diagnose. At this stage active surveillance is critical to detect residual hotspots of transmission. However, diagnostic tools used in active surveillance generally only detect concurrent infections, and surveys may benefit from sensitive tools such as serological assays. Serology can be used to interrogate and characterize individuals' previous exposure to malaria over longer durations, providing information essential to the detection of remaining foci of infection. We ran blood samples collected from a 2016 population-based survey in the low-transmission setting of northern Lao PDR on a multiplexed bead assay to characterize historic and recent exposures to Plasmodium falciparum and vivax. Using geostatistical methods and remote-sensing data we assessed the environmental and spatial associations with exposure, and created predictive maps of exposure within the study sites. We additionally linked the active surveillance PCR and serology data with passively collected surveillance data from health facility records. We aimed to highlight the added information which can be gained from serology as a tool in active surveillance surveys in low-transmission settings, and to identify priority areas for national surveillance programmes where malaria risk is higher. We also discuss the issues faced when linking malaria data from multiple sources using multiple diagnostic endpoints.

Population size estimation of seasonal forest-going populations in southern Lao PDR.

Forest-going populations are key to malaria transmission in the Greater Mekong Sub-region (GMS) and are therefore targeted for elimination efforts. Estimating the size of this population is essential for programs to assess, track and achieve their elimination goals. Leveraging data from three cross-sectional household surveys and one survey among forest-goers, the size of this high-risk population in a southern province of Lao PDR between December 2017 and November 2018 was estimated by two methods: population-based household surveys and capture-recapture. During the first month of the dry season, the first month of the rainy season, and the last month of the rainy season, respectively, 16.2% [14.7; 17.7], 9.3% [7.2; 11.3], and 5.3% [4.4; 6.1] of the adult population were estimated to have engaged in forest-going activities. The capture-recapture method estimated a total population size of 18,426 [16,529; 20,669] forest-goers, meaning 61.0% [54.2; 67.9] of the adult population had engaged in forest-going activities over the 12-month study period. This study demonstrates two methods for population size estimation to inform malaria research and programming. The seasonality and turnover within this forest-going population provide unique opportunities and challenges for control programs across the GMS as they work towards malaria elimination.

Spatio-temporal associations between deforestation and malaria incidence in Lao PDR.

As countries in the Greater Mekong Sub-region (GMS) increasingly focus their malaria control and elimination efforts on reducing forest-related transmission, greater understanding of the relationship between deforestation and malaria incidence will be essential for programs to assess and meet their 2030 elimination goals. Leveraging village-level health facility surveillance data and forest cover data in a spatio-temporal modeling framework, we found evidence that deforestation is associated with short-term increases, but long-term decreases confirmed malaria case incidence in Lao People's Democratic Republic (Lao PDR). We identified strong associations with deforestation measured within 30 km of villages but not with deforestation in the near (10 km) and immediate (1 km) vicinity. Results appear driven by deforestation in densely forested areas and were more pronounced for infections with Plasmodium falciparum (P. falciparum) than for Plasmodium vivax (P. vivax). These findings highlight the influence of forest activities on malaria transmission in the GMS.

Biting mosquitos spread the malaria parasite to humans. Along the Mekong River in Southeast Asia, spending time in the surrounding forest increases a person's risk of malaria. This has led to a debate about whether deforestation in this area, which is called the Greater Mekong Sub-region (GMS), will increase or decrease malaria transmission. The answer to the debate is not clear because some malaria-transmitting mosquitos thrive in heavily forested areas, in particular in the GMS, while others prefer less forested areas. Scientists studying malaria in the Amazon in South America suspect that malaria transmission increases shortly after deforestation but decreases six to eight years later. Some studies have tested this 'frontier malaria' theory but the results have been conflicting. Fewer studies have tested this theory in Southeast Asia. But deforestation has been blamed for recent malaria outbreaks in the GMS. Using data on malaria testing and forest cover in the GMS, Rerolle et al. show that deforestation around villages increases malaria transmission in the first two years and decreases malaria rates later. This trend was driven mostly by a type of malaria called Plasmodium falciparum and was less strong for Plasmodium vivax. The location of deforested areas also mattered. Deforestation within one to 10 kilometer of villages did not affect malaria rates. Deforestation further away in about a 30 kilometer radius did affect malaria transmission. Rerolle et al. suggest this may be because villagers have to spend longer times trekking through forests to hunt or harvest wood when the wider area is deforested. Currently, National Malaria Control Programs in the GMS focus their efforts on reducing forest-related transmission. This study strengthens the evidence supporting this approach. The results also suggest that different malaria elimination strategies may be necessary for different types of malaria parasite. Using this new information could help malaria control programs better target resources or educate villagers on how to protect themselves. The innovative methods used by Rerolle et al. reveal a more complex role of deforestation in malaria transmission and may inspire other scientists to think more carefully about environmental drivers of malaria.

A Randomized Study to Assess the Effect of Including the Graduate Record Examinations Results on Reviewer Scores for Underrepresented Minorities.

Whether requiring Graduate Record Examinations (GRE) results for doctoral applicants affects the diversity of admitted cohorts remains uncertain. This study randomized applications to 2 population-health doctoral programs at the University of California San Francisco to assess whether masking reviewers to applicant GRE results differentially affects reviewers' scores for underrepresented minority (URM) applicants from 2018-2020. Applications with GRE results and those without were randomly assigned to reviewers to designate scores for each copy (1-10, 1 being best). URM was defined as self-identification as African American/Black, Filipino, Hmong, Vietnamese, Hispanic/Latinx, Native American/Alaska Native, or Native Hawaiian/Other Pacific Islander. We used linear mixed models with random effects for the applicant and fixed effects for each reviewer to evaluate the effect of masking the GRE results on the overall application score and whether this effect differed by URM status. Reviewer scores did not significantly differ for unmasked versus masked applications among non-URM applicants (ß = 0.15; 95% CI: -0.03, 0.33) or URM applicants (ß = 0.02, 95% CI: -0.49, 0.54). We did not find evidence that removing GREs differentially affected URM compared with non-URM students (ß for interaction = -0.13, 95% CI: -0.55, 0.29). Within these doctoral programs, results indicate that GRE scores neither harm nor help URM applicants.

Finding hotspots: development of an adaptive spatial sampling approach.

The identification of disease hotspots is an increasingly important public health problem. While geospatial modeling offers an opportunity to predict the locations of hotspots using suitable environmental and climatological data, little attention has been paid to optimizing the design of surveys used to inform such models. Here we introduce an adaptive sampling scheme optimized to identify hotspot locations where prevalence exceeds a relevant threshold. Our approach incorporates ideas from Bayesian optimization theory to adaptively select sample batches. We present an experimental simulation study based on survey data of schistosomiasis and lymphatic filariasis across four countries. Results across all scenarios explored show that adaptive sampling produces superior results and suggest that similar performance to random sampling can be achieved with a fraction of the sample size.

Overall, anti-malarial, and non-malarial effect of intermittent preventive treatment during pregnancy with sulfadoxine-pyrimethamine on birthweight: a mediation analysis.

BACKGROUND: Trials of intermittent preventive treatment (IPTp) of malaria in pregnant women that compared dihydroartemisinin-piperaquine with the standard of care, sulfadoxine-pyrimethamine, showed dihydroartemisinin-piperaquine was superior at preventing malaria infection, but not at improving birthweight. We aimed to assess whether sulfadoxine-pyrimethamine shows greater non-malarial benefits for birth outcomes than does dihydroartemisinin-piperaquine, and whether dihydroartemisinin-piperaquine shows greater antimalarial benefits for birth outcomes than does sulfadoxine-pyrimethamine. METHODS: We defined treatment as random assignment to sulfadoxine-pyrimethamine or dihydroartemisinin-piperaquine before pooling individual participant-level data from 1617 HIV-uninfected pregnant women in Kenya (one trial; n=806) and Uganda (two trials; n=811). We quantified the relative effect of treatment on birthweight (primary outcome) attributed to preventing placental malaria infection (mediator). We estimated antimalarial (indirect) and non-malarial (direct) effects of IPTp on birth outcomes using causal mediation analyses, accounting for confounders. We used two-stage individual participant data meta-analyses to calculate pooled-effect sizes. FINDINGS: Overall, birthweight was higher among neonates of women randomly assigned to sulfadoxine-pyrimethamine compared with women assigned to dihydroartemisinin-piperaquine (mean difference 69 g, 95% CI 26 to 112), despite placental malaria infection being lower in the dihydroartemisinin-piperaquine group (relative risk [RR] 0·64, 95% CI 0·39 to 1·04). Mediation analyses showed sulfadoxine-pyrimethamine conferred a greater non-malarial effect than did dihydroartemisinin-piperaquine (mean difference 87 g, 95% CI 43 to 131), whereas dihydroartemisinin-piperaquine conferred a slightly larger antimalarial effect than did sulfadoxine-pyrimethamine (8 g, -9 to 26), although more frequent dosing increased the antimalarial effect (31 g, 3 to 60). INTERPRETATION: IPTp with sulfadoxine-pyrimethamine appears to have potent non-malarial effects on birthweight. Further research is needed to evaluate monthly dihydroartemisinin-piperaquine with sulfadoxine-pyrimethamine (or another compound with non-malarial effects) to achieve greater protection against malarial and non-malarial causes of low birthweight. FUNDING: Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bill & Melinda Gates Foundation, and Worldwide Antimalarial Resistance Network.

Study protocol for a cluster-randomized split-plot design trial to assess the effectiveness of targeted active malaria case detection among high-risk populations in Southern Lao PDR (the AcME-Lao study).

Introduction: Novel interventions are needed to accelerate malaria elimination, especially in areas where asymptomatic parasitemia is common, and where transmission generally occurs outside of village-based settings. Testing of community members linked to a person with clinical illness (reactive case detection, RACD) has not shown effectiveness in prior studies due to the limited sensitivity of current point-of-care tests. This study aims to assess the effectiveness of active case finding in village-based and forested-based settings using novel high-sensitivity rapid diagnostic tests in Lao People's Democratic Republic (Lao PDR). Methods and analysis: This study is a cluster-randomized split-plot design trial. The interventions include village-based mass test and treat (MTAT), focal test and treat in high-risk populations (FTAT), and the combination of these approaches, using high-sensitivity rapid diagnostic tests (HS-RDTs) to asses P. falciparum infection status. Within four districts in Champasak province, Lao PDR fourteen health center-catchment areas will be randomized to either FTAT or control; and within these HCCAs, 56 villages will be randomized to either MTAT or control. In intervention areas, FTAT will be conducted by community-based peer navigators on a routine basis, and three separate rounds of MTAT are planned. The primary study outcome will be PCR-based Plasmodium falciparum prevalence after one year of implementation. Secondary outcomes include malaria incidence; interventional coverage; operational feasibility and acceptability; and cost and cost- effectiveness. Ethics and dissemination: Findings will be reported on clinicaltrials.gov, in peer-reviewed publications and through stakeholder meetings with Ministry of Health and community leaders in Lao PDR and throughout the Greater Mekong Subregion. Trial registration: clinicaltrials.gov NCT03783299 (21/12/2018).

Systematic Motorcycle Management and Health Care Delivery: A Field Trial.

OBJECTIVES: We investigated whether managed transportation improves outreach-based health service delivery to rural village populations. METHODS: We examined systematic transportation management in a small-cluster interrupted time series field trial. In 8 districts in Southern Zambia, we followed health workers at 116 health facilities from September 2011 to March 2014. The primary outcome was the average number of outreach trips per health worker per week. Secondary outcomes were health worker productivity, motorcycle performance, and geographical coverage. RESULTS: Systematic fleet management resulted in an increase of 0.9 (SD = 1.0) trips to rural villages per health worker per week (P < .001), village-level health worker productivity by 20.5 (SD = 5.9) patient visits, 10.2 (SD = 1.5) measles immunizations, and 5.2 (SD = 5.4) child growth assessments per health worker per week. Motorcycle uptime increased by 3.5 days per week (P < .001), use by 1.5 days per week (P < .001), and mean distance by 9.3 kilometers per trip (P < .001). Geographical coverage of health outreach increased in experimental (P < .001) but not control districts. CONCLUSIONS: Systematic motorcycle management improves basic health care delivery to rural villages in resource-poor environments through increased health worker productivity and greater geographical coverage.