Malaria is a major global health problem, including the Brazilian Amazon region. The theory of frontier malaria—linking agricultural expansion, migration, and road networks to altered land cover and disease transmission—is increasingly inadequate for contemporary Amazonian frontiers. Instead, we propose the concept of frontier malaria mosaics, reflecting diverse occupational profiles, mobility patterns, rural-urban connectivity, and land uses. As malaria declines in the Amazon, transmission becomes highly focal, clustering in localized areas with distinct characteristics. Using locality-level data from Brazil’s Malaria Surveillance System and a novel method to identify nearly all case locations, we analyze spatiotemporal evolution via parasite species, infection origin, land use, settlement patterns, and sociodemographics. Annualized indicators enable cross-period comparisons. Through cluster regionalization, we reveal distinct low- and high-transmission mosaics, each shaped by successive government policies. This approach overcomes key data limitations and demonstrates how local spatial configurations scale into regional patterns over time.

Human mobility drives disease transmission in border regions, yet cross-border movement in the Amazon remains poorly characterized. We describe mobility patterns and social network structure among Indigenous and non-Indigenous communities along the Ecuador-Peru border. 76 Ecuadorian and 61 Peruvian communities were enrolled using respondent-driven sampling between November 2022 and May 2023. Key informants reported communities to and from which residents traveled, travel volume, travel reasons, and seasonality. We constructed a directed, weighted mobility network, calculated network- and node-level metrics, and used the Leiden algorithm to identify community mobility clusters. The network comprised 312 communities connected by 1,760 weighted edges. It was sparse (density 1.8%), with moderate clustering (global transitivity 36.8%) and reciprocity (59.8%). The most common travel reasons were social activities (26.4%), visiting family (20%), buying and selling goods (16.3%), and healthcare (11.1%). Social and family travel generated highly clustered and reciprocal subnetworks, whereas healthcare and commercial travel were concentrated around regional hubs. Although 94.6% of mobility connections occurred within Ecuador or Peru, 15 mobility clusters were identified, including four spanning the international border. Several cross-border clusters and bridging communities occupied key positions linking otherwise distinct regions of the network. Mobility occurred year-round for 98.8% of reported connections. Mobility along the Peru-Ecuador border is organized through functionally cohesive networks shaped by social relationships, healthcare access, trade, and Indigenous territorial connections. While most travel occurs within national territories, mobility extends through cross-border clusters and bridging communities that represent strategic locations for coordinating binational health, development, or other interventions in the Amazon.

Globally, most farms are smaller than 10 hectares. Land use by these smallholder farmers in agricultural frontiers is crucial to conservation, food security, and exposure to infectious diseases. However, solely survey-based land use studies miss the fine scale movements that collectively form an individual’s land use and thus environmental exposures, particularly in fragmented landscapes. We applied movement ecology-based approaches with GPS and survey data to investigate how sociodemographic variables corresponded with land use among farmers living adjacent to Marojejy National Park in northeastern Madagascar. Our data on 902 individuals spanning 3 years revealed striking differences in land use by gender and socioeconomic status. Men traversed 65% (95% CI [51%, 82%]) more area and spent 30% less time (95% CI [20, 41%]) in the village than women. Socioeconomic status, educational attainment, and having young children covaried with time spent in secondary forests where vanilla is grown, swidden areas where slash-and-burn farming methods are used, and the national park. By revealing the extent to which social and demographic variables predict individual movement patterns, our work identifies targets for addressing emerging diseases and health disparities. The GPS and analytical approaches developed here can be applied elsewhere to provide policy insights and to understand variation across human-ecological systems.

Blastocystis is a globally transmitted gastrointestinal protozoa that commonly infects people living in low- and middle-income countries. Transmission is thought to occur via consumption of contaminated water or food and through contact with infected animals, although the specific factors that influence infection in low-resource, rural settings remain unclear. We applied a One Health Disparities framework that considers the interconnectedness of human, nonhuman animal, and environmental health to investigate disparities in Blastocystis spp. infection in rural northeastern Madagascar. We focused on a suite of predictors including wealth, animal contact, hand hygiene, and demographic factors. Overall, 76.5% of 783 participants were infected with at least one of three subtypes of Blastocystis, and 19% of people were co-infected with two or more subtypes (ST1, 2, and 3). We found that men had lower risk of infection than women, while individuals who reported washing their hands without soap had higher odds of infection across all subtypes. Within a single subtype, soap-use remained significant for both ST1 and ST2, while for ST3, the effect of gender remained significant. Wealth and animal interactions had no significant associations with infection. Our study sheds light on gender disparities and the importance of hand hygiene in explaining variation in Blastocystis infection in rural Madagascar, while failing to support hypotheses based on socioeconomic status and exposure to domesticated animal reservoirs of disease. The findings also underscore the importance of gastrointestinal infections in vulnerable rural populations in Madagascar and highlight ways to address health equity and environmental justice in rural, low-resource settings.

Rationale: Data on risk factors for chronic hypoxemia in low- and middle-income countries are lacking. Objectives: We aimed to quantify the association between potential risk factors and chronic hypoxemia among adults hospitalized in Kenya. Methods: A hospital-based, case-control study was conducted at Moi Teaching and Referral Hospital in Eldoret, Kenya. Adult inpatients were screened on admission and enrolled in a 1:2 case-to-control ratio. Cases were patients with chronic hypoxemia, defined as resting oxygen saturation as measured by pulse oximetry (SpO2) ⩽ 88% on admission and either 1-month postdischarge SpO2 ⩽ 88% or, if they died before follow-up, documented SpO2 ⩽ 88% in the 6 months before enrollment. Control subjects were randomly selected, stratified by sex, among nonhypoxemic inpatients. Data were collected using questionnaires and structured chart review. Regression was used to assess the associations between chronic hypoxemia and age, sex, smoking status, biomass fuel use, elevation, and self-reported history of tuberculosis and human immunodeficiency virus diagnosis. Odds ratios (ORs) and 95% confidence intervals (CIs) are reported. Results: We enrolled 108 chronically hypoxemic cases and 240 nonhypoxemic control subjects into our Chronic Hypoxemia among Kenyan Adults (CHAKA) cohort. In multivariable analysis, compared with control subjects, chronically hypoxemic cases had significantly higher odds of older age (OR, 1.2 per 5-year increase [95% CI, 1.1-1.3]), female sex (OR, 3.6 [95% CI, 1.8-7.2]), current or former tobacco use (OR, 4.7 [95% CI, 2.3-9.6]), and prior tuberculosis (OR, 11.8 [95% CI, 4.7-29.6]) but no increase in the odds of human immunodeficiency virus diagnosis and biomass fuel use. Conclusions: These findings highlight the potential impact of prior tuberculosis on chronic lung disease in Kenya and the need for further studies on posttuberculosis lung disease.

Malaria transmission is influenced by climate and land use/land cover change (LULC). This study examines the impact of climate and LULC on malaria risk in the Ecuadorian Amazon. Weekly malaria surveillance data between 2008 and 2019 from Ecuador’s Ministry of Public Health were combined with hydrometeorological and LULC data. Cross-correlation analyses identified time lags. Bayesian spatiotemporal models estimated annual LULC rates of change (ARC) by census area and assessed the effects on Plasmodium vivax and Plasmodium falciparum incidence. ARC for the five land cover classes (forest, agriculture, urban, shrub vegetation, water) ranged from -1 to 4% with agriculture increasing across areas. Forest and shrub vegetation ARC were significantly associated with both Plasmodium vivax and Plasmodium falciparum. Temperature and terrestrial water content showed consistent negative relationships with both species. Precipitation had varying effects on Plasmodium vivax (null) and Plasmodium falciparum (increase) incidence. Shrubs and forest expansion, increased temperature, and terrestrial water content reduced malaria incidence, while increased precipitation had varying effects. Relationships between malaria, LULC, and climate are complex, influencing risk profiles. These findings aid decision-making and guide further research in the region.

BACKGROUND:Cost-effectiveness analyses have been conducted for many interventions for HIV/AIDS, malaria, syphilis, and tuberculosis, but they have not been conducted for all interventions that are currently recommended in all countries. To support national decision makers in the effective allocation of resources, we conducted a meta-regression analysis of published incremental cost-effectiveness ratios (ICERs) for interventions for these causes, and predicted ICERs for 14 recommended interventions for Global Fund-eligible countries. METHODS:In the meta-regression analysis, we used data from the Tufts University Center for the Evaluation of Value and Risk in Health (Boston, MA, USA) Cost-Effectiveness Registries (the CEA Registry beginning in 1976 and the Global Health CEA registry beginning in 1995) up to Jan 1, 2018. To create analysis files, we standardised and mapped the data, extracted additional data from published articles, and added variables from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD). Then we selected ratios for interventions with a minimum of two published articles and three published ICERs that mapped to one of five GBD causes (HIV/AIDS, malaria, syphilis, drug-susceptible tuberculosis, or multi-drug resistant tuberculosis), and to a GBD country; reported a currency year during or after 1990; and for which the comparator intervention was defined as no intervention, standard of care, or placebo. Our meta-regression analysis used all available data on 25 eligible interventions, and quantified the association between ICERs and factors at country level and intervention level. We used a five-stage statistical model that was developed to synthesise evidence on cost-effectiveness analyses, and we adapted it for smaller sample sizes by grouping interventions by cause and type (ie, prevention, diagnostics, and treatment). Using the meta-regression parameters we predicted country-specific median ICERs, IQRs, and 95% uncertainty intervals in 2019 US$ per disability-adjusted life-year (DALY) for 14 currently recommended interventions. We report ICERs in league tables with gross domestic product (GDP) per capita and country-specific thresholds. FINDINGS:The sample for the analysis was 1273 ratios from 144 articles, of which we included 612 ICERs from 106 articles in our meta-regression analysis. We predicted ICERs for antiretroviral therapy for prevention for two age groups and pregnant women, pre-exposure prophylaxis against HIV for two risk groups, four malaria prevention interventions, antenatal syphilis screening, two tuberculosis prevention interventions, the Xpert tuberculosis test, and chemotherapy for drug-sensitive tuberculosis. At the country level, ranking of interventions and number of interventions with a predicted median ICER below the country-specific threshold varied greatly. For instance, median ICERs for six of 14 interventions were below the country-specific threshold in Sudan, whereas 12 of 14 were below the country-specific threshold in Peru. Antenatal syphilis screening had the lowest median ICER among all 14 interventions in 81 (63%) of 128 countries, ranging from $3 (IQR 2-4) per DALY averted in Equatorial Guinea to $3473 (2244-5222) in Ukraine. Pre-exposure prophylaxis for HIV/AIDS for men who have sex with men had the highest median ICER among all interventions in 116 (91%) countries, ranging from $2326 (1077-4567) per DALY averted in Lesotho to $53 559 (23 841-108 534) in Maldives. INTERPRETATION:Country-specific league tables highlight the interventions that offer better value per DALY averted, and can support decision making at a country level that is more tailored to available resources than GDP per capita and country-specific thresholds. Meta-regression is a promising method to synthesise cost-effectiveness analysis results and transfer them across settings. FUNDING:Bill & Melinda Gates Foundation.

Increasing sulfadoxine-pyrimethamine (SP) resistance in the Democratic Republic of the Congo (DRC) has threatened its use for prevention of malaria in one of the most malarious countries in the world. Using geographic information on mining operations in the DRC and genetic data on SP drug resistance markers from the 2013-2014 Demographic and Health Surveys, we evaluated associations between close residence to mining and the presence of mutations conferring resistance to sulfadoxine. Close residential proximity to mining was associated with increased prevalence odds ratio (POR) of the dhps540E mutation (POR: 2.11, 95% uncertainty interval: 1.15-3.96) with adjustments for confounding variables and space. Our findings indicate that exposure to mining is associated with increased presence of an antimalarial drug resistance haplotype that threatens effective use of SP for vulnerable populations. Areas actively engaged in mining could be considered for interventions to reduce the spread of emerging drug resistance in the DRC.

Background Early identification of newborns with congenital cytomegalovirus (CMV) is necessary to provide antiviral therapy and other interventions that can improve outcomes. Prior research demonstrates that universal newborn CMV screening would be the most cost-effective approach to identifying newborns who are infected. CMV is not uniformly prevalent, and it is uncertain whether universal screening would remain cost-effective in lower-prevalence neighborhoods. Our aim was to identify geographic heterogeneity in the cost-effectiveness of universal newborn CMV screening by combining a geospatial analysis with a preexisting cost-effectiveness analysis.Methods This study used the CMV testing results and zip code location data of 96 785 newborns in 7 metropolitan areas who had been tested for CMV as part of the CMV and Hearing Multicenter Screening study. A hierarchical bayesian generalized additive model was constructed to evaluate geographic variability in the odds of CMV. The zip code-level odds of CMV were then used to weight the results of a previously published model evaluating universal CMV screening vs symptom-targeted screening.Results The odds of CMV were heterogeneous over large geographic scales, with the highest odds in the southeastern United States. Universal screening was more cost-effective and afforded more averted cases of severe hearing loss than targeted testing. Universal screening remained the most cost-effective option even in areas with the lowest CMV prevalence.Conclusions Universal newborn CMV screening is cost-effective regardless of underlying CMV prevalence and is the preferred strategy to reduce morbidity from congenital CMV. A geographically weighted cost-effectiveness analysis demonstrates that universal newborn cytomegalovirus screening remains the most cost-effective strategy to identify newborn cytomegalovirus regardless of local prevalence.

Many recent studies have examined the impact of predicted changes in temperature and precipitation patterns on infectious diseases under different greenhouse gas emissions scenarios. But these emissions scenarios symbolize more than altered temperature and precipitation regimes; they also represent differing levels of change in energy, transportation, and food production at a global scale to reduce the effects of climate change. The ways humans respond to climate change, either through adaptation or mitigation, have underappreciated, yet hugely impactful effects on infectious disease transmission, often in complex and sometimes nonintuitive ways. Thus, in addition to investigating the direct effects of climate changes on infectious diseases, it is critical to consider how human preventative measures and adaptations to climate change will alter the environments and hosts that support pathogens. Here, we consider the ways that human responses to climate change will likely impact disease risk in both positive and negative ways. We evaluate the evidence for these impacts based on the available data, and identify research directions needed to address climate change while minimizing externalities associated with infectious disease, especially for vulnerable communities. We identify several different human adaptations to climate change that are likely to affect infectious disease risk independently of the effects of climate change itself. We categorize these changes into adaptation strategies to secure access to water, food, and shelter, and mitigation strategies to decrease greenhouse gas emissions. We recognize that adaptation strategies are more likely to have infectious disease consequences for under-resourced communities, and call attention to the need for socio-ecological studies to connect human behavioral responses to climate change and their impacts on infectious disease. Understanding these effects is crucial as climate change intensifies and the global community builds momentum to slow these changes and reduce their impacts on human health, economic productivity, and political stability.

OBJECTIVES:Understanding disease transmission is a fundamental challenge in ecology. We used transmission potential networks to investigate whether a gastrointestinal protozoan (Blastocystis spp.) is spread through social, environmental, and/or zoonotic pathways in rural northeast Madagascar. MATERIALS AND METHODS:We obtained survey data, household GPS coordinates, and fecal samples from 804 participants. Surveys inquired about social contacts, agricultural activity, and sociodemographic characteristics. Fecal samples were screened for Blastocystis using DNA metabarcoding. We also tested 133 domesticated animals for Blastocystis. We used network autocorrelation models and permutation tests (network k-test) to determine whether networks reflecting different transmission pathways predicted infection. RESULTS:We identified six distinct Blastocystis subtypes among study participants and their domesticated animals. Among the 804 human participants, 74% (n = 598) were positive for at least one Blastocystis subtype. Close proximity to infected households was the most informative predictor of infection with any subtype (model averaged OR [95% CI]: 1.56 [1.33-1.82]), and spending free time with infected participants was not an informative predictor of infection (model averaged OR [95% CI]: 0.95 [0.82-1.10]). No human participant was infected with the same subtype as the domesticated animals they owned. DISCUSSION:Our findings suggest that Blastocystis is most likely spread through environmental pathways within villages, rather than through social or animal contact. The most likely mechanisms involve fecal contamination of the environment by infected individuals or shared food and water sources. These findings shed new light on human-pathogen ecology and mechanisms for reducing disease transmission in rural, low-income settings.

Introduction Understanding human mobility’s role in malaria transmission is critical to successful control and elimination. However, common approaches to measuring mobility are ill-equipped for remote regions such as the Amazon. This study develops a network survey to quantify the effect of community connectivity and mobility on malaria transmission.Methods We measure community connectivity across the study area using a respondent driven sampling design among key informants who are at least 18 years of age. 45 initial communities will be selected: 10 in Brazil, 10 in Ecuador and 25 in Peru. Participants will be recruited in each initial node and administered a survey to obtain data on each community’s mobility patterns. Survey responses will be ranked and the 2–3 most connected communities will then be selected and surveyed. This process will be repeated for a third round of data collection. Community network matrices will be linked with each country’s malaria surveillance system to test the effects of mobility on disease risk.Ethics and dissemination This study protocol has been approved by the institutional review boards of Duke University (USA), Universidad San Francisco de Quito (Ecuador), Universidad Peruana Cayetano Heredia (Peru) and Universidade Federal Minas Gerais (Brazil). Results will be disseminated in communities by the end of the study.

Lyme disease is a spatially heterogeneous tick-borne infection, with approximately 85% of US cases concentrated in the mid-Atlantic and northeastern states. Surveillance for Lyme disease and its causative agent, including public health case reporting and entomologic surveillance, is necessary to understand its endemic range, but currently used case detection methods have limitations. To evaluate an alternative approach to Lyme disease surveillance, we have performed a geospatial analysis of Lyme disease cases from the Johns Hopkins Health System in Maryland. We used two sources of cases: a) individuals with both a positive test for Lyme disease and a contemporaneous diagnostic code consistent with a Lyme disease-related syndrome; and b) individuals referred for a Lyme disease evaluation who were adjudicated to have Lyme disease. Controls were individuals from the referral cohort judged not to have Lyme disease. Residential address data were available for all cases and controls. We used a hierarchical Bayesian model with a smoothing function for a coordinate location to evaluate the probability of Lyme disease within 100 km of Johns Hopkins Hospital. We found that the probability of Lyme disease was greatest in the north and west of Baltimore, and the local probability that a subject would have Lyme disease varied by as much as 30-fold. Adjustment for demographic and ecological variables partially attenuated the spatial gradient. Our study supports the suitability of electronic medical record data for the retrospective surveillance of Lyme disease.

Background: The overarching aim of this study was to evaluate the effectiveness over time of government interventions and policy restrictions and the impact of determinants on spread and mortality during the first-wave of the COVID-19 pandemic, globally, regionally and by country-income level, up to 18 May 2020. Methods: We created a global database merging World Health Organization daily case reports (from 218 countries/territories) with other socio-demographic and population health measures from 21 January to 18 May 2020. A four-level government policy interventions score (low to very high) was created based on the Oxford Stringency Index. Results: Our results support the use of very high government interventions to suppress both COVID-19 spread and mortality effectively during wave one globally compared to other policy levels of control. Similar trends in virus propagation and mortality were observed in all country-income levels and specific regions. Conclusions: Rapid implementation of government interventions was needed to contain the first wave of the COVID-19 outbreak and to reduce COVID-19-related mortality.

Background Antimicrobial resistance (AMR) is an urgent global health challenge and a critical threat to modern health care. Quantifying its burden in the WHO Region of the Americas has been elusive-despite the region's long history of resistance surveillance. This study provides comprehensive estimates of AMR burden in the Americas to assess this growing health threat.Methods Weestimated deaths and disability-adjusted life-years (DALYs) attributable to and associated with AMR for 23 bacterial pathogens and 88 pathogen-drug combinations for countries in the WHO Region of the Americas in 2019. We obtained data from mortality registries, surveillance systems, hospital systems, systematic literature reviews, and other sources, and applied predictive statistical modelling to produce estimates of AMR burden for all countries in the Americas. Five broad components were the backbone of our approach: the number of deaths where infection had a role, the proportion of infectious deaths attributable to a given infectious syndrome, the proportion of infectious syndrome deaths attributable to a given pathogen, the percentage of pathogens resistant to an antibiotic class, and the excess risk of mortality (or duration of an infection) associated with this resistance. We then used these components to estimate the disease burden by applying two counterfactual scenarios: deaths attributable to AMR (compared to an alternative scenario where resistant infections are replaced with susceptible ones), and deaths associated with AMR (compared to an alternative scenario where resistant infections would not occur at all). We generated 95% uncertainty intervals (UIs) for final estimates as the 25th and 975th ordered values across 1000 posterior draws, and models were cross-validated for out-of-sample predictive validity. Findings We estimated 569,000 deaths (95% UI 406,000-771,000) associated with bacterial AMR and 141,000 deaths (99,900-196,000) attributable to bacterial AMR among the 35 countries in the WHO Region of the Americas in 2019. Lower respiratory and thorax infections, as a syndrome, were responsible for the largest fatal burden of AMR in the region, with 189,000 deaths (149,000-241,000) associated with resistance, followed by bloodstream infections (169,000 deaths [94,200-278,000]) and peritoneal/intra-abdominal infections (118,000 deaths [78,600-168,000]). The six leading pathogens (by order of number of deaths associated with resistance) were Staphylococcus aureus , Escherichia coli , Klebsiella pneumoniae , Streptococcus pneumoniae , Pseudomonas aeruginosa , and Acinetobacter baumannii. Together, these pathogens were responsible for 452,000 deaths (326,000-608,000) associated with AMR. Methicillin-resistant S. aureus predominated as the leading pathogen-drug combination in 34 countries for deaths attributable to AMR, while aminopenicillin-resistant E. coli was the leading pathogen-drug combination in 15 countries for deaths associated with AMR. Interpretation Given the burden across different countries, infectious syndromes, and pathogen-drug combinations, AMR represents a substantial health threat in the Americas. Countries with low access to antibiotics and basic health-care services often face the largest age-standardised mortality rates associated with and attributable to AMR in the region, implicating specific policy interventions. Evidence from this study can guide mitigation efforts that are tailored to the needs of each country in the region while informing decisions regarding funding and resource allocation. Multisectoral and joint cooperative efforts among countries will be a key to success in tackling AMR in the Americas. Funding Bill & Melinda Gates Foundation, Wellcome Trust, and Department of Health and Social Care using UK aid funding managed by the Fleming Fund.Copyright (c) 2023 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).

Background Since 2004, malaria transmission on Bioko Island has declined significantly as a result of the scaling-up of control interventions. The aim of eliminating malaria from the Island remains elusive, however, underscoring the need to adapt control to the local context. Understanding the factors driving the risk of malaria infection is critical to inform optimal suits of interventions in this adaptive approach. Methods This study used individual and household-level data from the 2015 and 2018 annual malaria indicator surveys on Bioko Island, as well as remotely-sensed environmental data in multilevel logistic regression models to quantify the odds of malaria infection. The analyses were stratified by urban and rural settings and by survey year. Results Malaria prevalence was higher in 10–14-year-old children and similar between female and male individuals. After adjusting for demographic factors and other covariates, many of the variables investigated showed no significant association with malaria infection. The factor most strongly associated was history of travel to mainland Equatorial Guinea (mEG), which increased the odds significantly both in urban and rural settings (people who travelled had 4 times the odds of infection). Sleeping under a long-lasting insecticidal net decreased significantly the odds of malaria across urban and rural settings and survey years (net users had around 30% less odds of infection), highlighting their contribution to malaria control on the Island. Improved housing conditions indicated some protection, though this was not consistent across settings and survey year. Conclusions Malaria risk on Bioko Island is heterogeneous and determined by a combination of factors interacting with local mosquito ecology. These interactions grant further investigation in order to better adapt control according to need. The single most important risk factor identified was travel to mEG, in line with previous investigations, and represents a great challenge for the success of malaria control on the Island.

Background Although malaria control investments worldwide have resulted in dramatic declines in transmission since 2000, progress has stalled. In the Amazon, malaria resurgence has followed withdrawal of Global Fund support of the Project for Malaria Control in Andean Border Areas (PAMAFRO). We estimate intervention-specific and spatially-explicit effects of the PAMAFRO program on malaria incidence across the Loreto region of Peru, and consider the influence of the environmental risk factors in the presence of interventions. Methods We conducted a retrospective, observational, spatial interrupted time series analysis of malaria incidence rates among people reporting to health posts across Loreto, Peru between the first epidemiological week of January 2001 and the last epidemiological week of December 2016. Model inference is at the smallest administrative unit (district), where the weekly number of diagnosed cases of Plasmodium vivax and Plasmodium falciparum were determined by microscopy. Census data provided population at risk. We include as covariates weekly estimates of minimum temperature and cumulative precipitation in each district, as well as spatially-and temporally-lagged malaria incidence rates. Environmental data were derived from a hydrometeorological model designed for the Amazon. We used Bayesian spatiotemporal modeling techniques to estimate the impact of the PAMAFRO program, variability in environmental effects, and the role of climate anomalies on transmission after PAMAFRO withdrawal. Findings During the PAMAFRO program, incidence of P. vivax declined from 42.8 to 10.1 cases/1000 people/year. Incidence for P. falciparum declined from 14.3 to 2.5 cases/1000 people/year over this same period. The effects of PAMAFRO-supported interventions varied both by geography and species of malaria. Interventions were only effective in districts where interventions were also deployed in surrounding districts. Further, interventions diminished the effects of other prevailing demographic and environmental risk factors. Withdrawal of the program led to a resurgence in transmission. Increasing minimum temperatures and variability and intensity of rainfall events from 2011 onward and accompanying population displacements contributed to this resurgence. Interpretation Malaria control programs must consider the climate and environmental scope of interventions to maximize effectiveness. They must also ensure financial sustainability to maintain local progress and commitment to malaria prevention and elimination efforts, as well as to offset the effects of environmental change that increase transmission risk. 2023;20: 2023 1016/j.lana.2023. 100477

Background Transmission of malaria in sub-Saharan Africa has become increasingly stratified following decades of malaria control interventions. The extent to which environmental and land cover risk factors for malaria may differ across distinct strata of transmission intensity is not well known and could provide actionable targets to maximize the success of malaria control efforts. Methods This study used cross-sectional malaria survey data from a nationally representative cohort of school-aged children in Tanzania, and satellite-derived measures for environmental features and land cover. Hierarchical logistic regression models were applied to evaluate associations between land cover and malaria prevalence within three distinct strata of transmission intensity: low and unstable, moderate and seasonal, and high and perennial. Results In areas with low malaria transmission, each 10-percentage point increase in cropland cover was associated with an increase in malaria prevalence odds of 2.44 (95% UI: 1.27, 5.11). However, at moderate and higher levels of transmission intensity, no association between cropland cover and malaria prevalence was detected. Small associations were observed between greater grassland cover and greater malaria prevalence in high intensity settings (prevalence odds ratio (POR): 1.10, 95% UI: 1.00, 1.21), and between greater forest cover and reduced malaria prevalence in low transmission areas (POR: 0.74, 95% UI: 0.51, 1.03), however the uncertainty intervals of both estimates included the null. Conclusions The intensity of malaria transmission appears to modify relationships between land cover and malaria prevalence among school-aged children in Tanzania. In particular, greater cropland cover was positively associated with increased malaria prevalence in areas with low transmission intensity and presents an actionable target for environmental vector control interventions to complement current malaria control activities. As areas are nearing malaria elimination, it is important to re-evaluate environmental risk factors and employ appropriate interventions to effectively address low-level malaria transmission.

The epidemiological profile of rabies virus within Mongolia remains poorly characterized despite 21,302 domestic animal cases being reported between 1970-2005. This lack of knowledge is particularly concerning given that roughly 26% of the population lives a pastoral herding lifestyle and livestock production contributes up to 18% of Mongolia's total gross domestic product (GDP). The gaps in knowledge of the rabies disease ecology within Mongolia combined with the lack of routine vaccination of domestic animals and wildlife poses a significant threat to the more than 60 million heads of livestock within Mongolia. Animal rabies case data from the General Authority for Veterinary Services and National Center for Zoonotic Diseases were used in this study. Each data point included year of report, an animal descriptor, geographic coordinates and the aimag (province) of origin. A total of 2,359 animal rabies cases were reported between 2012-2018. Cattle were the most commonly reported animal overall (861 cases), followed by goats (268), sheep (251) and dogs (221) within the domestic animal category. Red foxes were responsible for most reported wildlife cases (317) followed by wolves (151). Most rabid animals were reported in the Khuvsgul, Uvurkhangai and Govi-Altai aimags, and a positive correlation was found between livestock numbers per soum and the number of rabies cases reported. Rabies poses a significant threat to the Mongolian economy and the health of human and animal populations within Mongolia. The close association of the nomadic pastoralists with both domestic animals and wildlife represents a significant threat for disease emergence and necessitates studies that describe the ecology of rabies, which may threaten these populations.