Background Trials of intermittent preventive treatment (IPTp) of malaria in pregnant women that compared dihydroartemisinin-piperaquine with the standard of care, sulfadoxine-pyrimethamine, showed dihydroartemisininpiperaquine 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. MethodsWe defined treatment as random assignment to sulfadoxine-pyrimethamine or dihydroartemisininpiperaquine 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 dihydroartemisininpiperaquine 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.

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)

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 in 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.

An alternative approach is to use less costly survey methods to sample a higher proportion of locations than would otherwise be possible. Techniques such as lot quality assurance sampling, a method designed to minimize sampling effort in order to categorize outcomes over a given population, is one such approach and has been used to identify hotspot communities for schistosomiasis 8,9. Similarly, school-based questionnaires relating to blood in urine and eye worm occurrence, have been used to map urinary schistosomiasis 10-12 and loiasis 13,14 respectively. These methods are inherently noisy as they only allow measurement of proxies of infection and can suffer from issues of recall. Another approach to mapping hotspots, which reduces the need to sample a large fraction of the population, is using geospatial modeling. Climatological, environmental and ecological layers can help predict the spatial distribution of many infectious diseases. Furthermore, above and beyond patterns that can be explained by these layers alone, disease outcomes often display some spatial structure, with neighbouring values being correlated due to shared characteristics and transmission. This spatial structure means that information from one site provides information about neighbouring sites. Over the past decade, the ability to predict pathogen infection prevalence across entire regions based on survey data and relationships using geospatial modeling has improved considerably 15-17. These advances in geospatial modeling have opened the door to more targeted approaches, potentially allowing decisions about treatment to be made with higher precision and granularity. Despite these advances, surprisingly little attention has been paid to optimizing the survey design for risk mapping efforts. Evidence from other fields has shown that random sampling is suboptimal for spatial prediction 18-21. For lymphatic filariasis, a grid sampling approach has been proposed as a mechanism to allow for more efficient spatial interpolation 22,23. Diggle and Lophaven 24 propose the use of grid sampling supplemented with clusters of close pairs of points which is useful when estimates of Kriging (covariance) parameters are required 24. Simulation studies also suggest that this design provides a cost-effective approach to mapping schistosomiasis 3. Similarly, Fronterre et al. 25 show that spatially regulated surveys, in combination with spatial modeling, can reduce the sample size required to estimate IU level prevalence. Recent studies by Chipeta et al. 26 and Kabaghe et al. 27 propose the use of spatially adaptive designs that leverage information from prior data to inform the locations of future sampling sites to minimize prediction error. Using malaria as an example, results from simulations and field studies show that adaptive spatial designs can be used to produce more precise predictions of infection prevalence using geostatistical modeling 26. Building on the adaptive spatial sampling approach, we incorporate ideas from Bayesian optimization theory 28,29...