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Abstract Background In the near future, the incidence of mosquito-borne diseases may expand to new sites due to changes in temperature and rainfall patterns caused by climate change. Therefore, there is a need to use recent technological advances to improve vector surveillance methodologies. Unoccupied Aerial Vehicles (UAVs), often called drones, have been used to collect high-resolution imagery to map detailed information on mosquito habitats and direct control measures to specific areas. Supervised classification approaches have been largely used to automatically detect vector habitats. However, manual data labelling for model training limits their use for rapid responses. Open-source foundation models such as the Meta AI Segment Anything Model (SAM) can facilitate the manual digitalization of high-resolution images. This pre-trained model can assist in extracting features of interest in a diverse range of images. Here, we evaluated the performance of SAM through the Samgeo package, a Python-based wrapper for geospatial data, as it has not been applied to analyse remote sensing images for epidemiological studies. Results We tested the identification of two land cover classes of interest: water bodies and human settlements, using different UAV acquired imagery across five malaria-endemic areas in Africa, South America, and Southeast Asia. We employed manually placed point prompts and text prompts associated with specific classes of interest to guide the image segmentation and assessed the performance in the different geographic contexts. An average Dice coefficient value of 0.67 was obtained for buildings segmentation and 0.73 for water bodies using point prompts. Regarding the use of text prompts, the highest Dice coefficient value reached 0.72 for buildings and 0.70 for water bodies. Nevertheless, the performance was closely dependent on each object, landscape characteristics and selected words, resulting in varying performance. Conclusions Recent models such as SAM can potentially assist manual digitalization of imagery by vector control programs, quickly identifying key features when surveying an area of interest. However, accurate segmentation still requires user-provided manual prompts and corrections to obtain precise segmentation. Further evaluations are necessary, especially for applications in rural areas.

Abstract Despite progress towards malaria reduction in Peru, measuring exposure in low transmission areas is crucial for achieving elimination. This study focuses on two very low transmission areas in Loreto (Peruvian Amazon) and aims to determine the relationship between malaria exposure and proximity to health facilities. Individual data was collected from 38 villages in Indiana and Belen, including geo-referenced households and blood samples for microscopy, PCR and serological analysis. A segmented linear regression model identified significant changes in seropositivity trends among different age groups. Local Getis-Ord Gi* statistic revealed clusters of households with high (hotspots) or low (coldspots) seropositivity rates. Findings from 4000 individuals showed a seropositivity level of 2.5% (95%CI: 2.0%-3.0%) for P. falciparum and 7.8% (95%CI: 7.0%-8.7%) for P. vivax, indicating recent or historical exposure. The segmented regression showed exposure reductions in the 40–50 age group (β1 = 0.043, p = 0.003) for P. vivax and the 50–60 age group (β1 = 0.005, p = 0.010) for P. falciparum. Long and extreme distance villages from Regional Hospital of Loreto exhibited higher malaria exposure compared to proximate and medium distance villages (p

ObjectivesThis study aims to estimate the short-term preventable mortality and associated economic costs of complying with the World Health Organization (WHO) air quality guidelines (AQGs) limit values for PM10 and PM2.5 in nine major Latin American cities.MethodsWe estimated city-specific PM-mortality associations using time-series regression models and calculated the attributable mortality fraction. Next, we used the value of statistical life to calculate the economic benefits of complying with the WHO AQGs limit values.ResultsIn most cities, PM concentrations exceeded the WHO AQGs limit values more than 90% of the days. PM10 was found to be associated with an average excess mortality of 1.88% with concentrations above WHO AQGs limit values, while for PM2.5 it was 1.05%. The associated annual economic costs varied widely, between US$ 19.5 million to 3,386.9 million for PM10, and US$ 196.3 million to 2,209.6 million for PM2.5.ConclusionOur findings suggest that there is an urgent need for policymakers to develop interventions to achieve sustainable air quality improvements in Latin America. Complying with the WHO AQGs limit values for PM10 and PM2.5 in Latin American cities would substantially benefits for urban populations.

Background: Temperature variability (TV) is associated with increased mortality risk. However, it is still unknown whether intra-day or inter-day TV has different effects. Objectives: We aimed to assess the association of intra-day TV and inter-day TV with all-cause, cardiovascular, and respiratory mortality. Methods: We collected data on total, cardiovascular, and respiratory mortality and meteorology from 758 locations in 47 countries or regions from 1972 to 2020. We defined inter-day TV as the standard deviation (SD) of daily mean temperatures across the lag interval, and intra-day TV as the average SD of minimum and maximum temperatures on each day. In the first stage, inter-day and intra-day TVs were modelled simultaneously in the quasi-Poisson time-series model for each location. In the second stage, a multi-level analysis was used to pool the location-specific estimates. Results: Overall, the mortality risk due to each interquartile range [IQR] increase was higher for intra-day TV than for inter-day TV. The risk increased by 0.59% (95% confidence interval [CI]: 0.53, 0.65) for all-cause mortality, 0.64% (95% CI: 0.56, 0.73) for cardiovascular mortality, and 0.65% (95% CI: 0.49, 0.80) for respiratory mortality per IQR increase in intra-day TV0–7 (0.9 °C). An IQR increase in inter-day TV0–7 (1.6 °C) was associated with 0.22% (95% CI: 0.18, 0.26) increase in all-cause mortality, 0.44% (95% CI: 0.37, 0.50) increase in cardiovascular mortality, and 0.31% (95% CI: 0.21, 0.41) increase in respiratory mortality. The proportion of all-cause deaths attributable to intra-day TV0–7 and inter-day TV0–7 was 1.45% and 0.35%, respectively. The mortality risks varied by lag interval, climate area, season, and climate type. Conclusions: Our results indicated that intra-day TV may explain the main part of the mortality risk related to TV and suggested that comprehensive evaluations should be proposed in more countries to help protect human health.

Assessing the association between temperature frequency and mortality can provide insights into human adaptation to local ambient temperatures. We collected daily time-series data on mortality and temperature from 757 locations in 47 countries/regions during 1979–2020. We used a two-stage time series design to assess the association between temperature frequency and all-cause mortality. The results were pooled at the national, regional, and global levels. We observed a consistent decrease in the risk of mortality as the normalized frequency of temperature increases across the globe. The average increase in mortality risk comparing the 10th to 100th percentile of normalized frequency was 13.03% (95% CI: 12.17–13.91), with substantial regional differences (from 4.56% in Australia and New Zealand to 33.06% in South Europe). The highest increase in mortality was observed for high-income countries (13.58%, 95% CI: 12.56–14.61), followed by lower-middle-income countries (12.34%, 95% CI: 9.27–15.51). This study observed a declining risk of mortality associated with higher temperature frequency. Our findings suggest that populations can adapt to their local climate with frequent exposure, with the adapting ability varying geographically due to differences in climatic and socioeconomic characteristics.

IntroductionThe use of drones in environment and health research is a relatively new phenomenon. A principal research activity drones are used for is environmental monitoring, which can raise concerns in local communities. Existing ethical guidance for researchers is often not specific to drone technology and practices vary between research settings. Therefore, this scoping review aims to gather the evidence available on ethical considerations surrounding drone use as perceived by local communities, ethical considerations reported on by researchers implementing drone research, and published ethical guidance related to drone deployment.Methods and analysisThis scoping review will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) and the Joanna Briggs Institute (JBI) guidelines. The literature search will be conducted using academic databases and grey literature sources. After pilot testing the inclusion criteria and data extraction tool, two researchers will double-screen and then chart available evidence independently. A content analysis will be carried out to identify patterns of categories or terms used to describe ethical considerations related to drone usage for environmental monitoring in the literature using the R Package RQDA. Discrepancies in any phase of the project will be solved through consensus between the two reviewers. If consensus cannot be reached, a third arbitrator will be consulted.Ethics and disseminationEthical approval is not required; only secondary data will be used. This protocol is registered on the Open Science Framework (osf.io/a78et). The results will be disseminated through publication in a scientific journal and will be used to inform drone field campaigns in the Wellcome Trust funded HARMONIZE project. HARMONIZE aims to develop cost-effective and reproducible digital infrastructure for stakeholders in climate change hotspots in Latin America & the Caribbean and will use drone technology to collect data on fine scale landscape changes.

Objectives We aim to explore spatial variations in socioeconomic inequalities in HIV testing uptake in sub-Saharan Africa (SSA) at different geographical scales to identify potential geographical hotspots of inequalities. Additionally, to evaluate the potential benefits of HIV testing programmes, we assess whether local levels of HIV testing match the local levels of HIV prevalence.Design A multi-country analysis of population-based cross-sectional surveys in SSA.Setting We analysed data from 25 SSA countries with Demographic and Health Surveys between 2011 and 2019.Participants Country-level analysis included 473 775 participants (312 104 women and 161 671 men) and cluster-level analysis included 328 283 individuals (241 084 women and 87 199 men). Women aged 15–49 years and men aged 15–54/59 years in selected households who were tested for HIV in the last 12 months were eligible. We quantified inequalities in self-reported recent HIV testing with the Slope Index of Inequality (SII) and the Relative Index of Inequality (RII) across geographical scales to capture sex-specific within-country spatial variations. We also conducted local Getis-Ord Gi* statistics to consider the autocorrelation in fine-scale SII and RII across countries. To assess the efficiency of HIV testing programmes, we measured the correlation between recent HIV testing and HIV prevalence through Spearman correlation across geographical scales.Results We observed varying inequalities in recent HIV testing in magnitude and spatial distribution on both absolute and relative scales in many countries for both sexes at national and subnational levels. Hotspots of absolute and relative inequalities were mostly observed in Western and Central Africa with a few regions in Eastern and Southern Africa. Despite significant sex-specific correlations between testing and prevalence in all countries when assessed at the national level, we report an absence of such a correlation at fine scale in 17 of 50 sex-country combinations.Conclusions We highlight the importance of investigating the spatial variability of various HIV indicators and related inequalities across different geographical levels. Results may help inform an equitable distribution of HIV testing services.

Background: The epidemiological evidence on the interaction between heat and ambient air pollution on mortality is still inconsistent. Objectives: To investigate the interaction between heat and ambient air pollution on daily mortality in a large dataset of 620 cities from 36 countries. Methods: We used daily data on all-cause mortality, air temperature, particulate matter ≤ 10 μm (PM10), PM ≤ 2.5 μm (PM2.5), nitrogen dioxide (NO2), and ozone (O3) from 620 cities in 36 countries in the period 1995–2020. We restricted the analysis to the six consecutive warmest months in each city. City-specific data were analysed with over-dispersed Poisson regression models, followed by a multilevel random-effects meta-analysis. The joint association between air temperature and air pollutants was modelled with product terms between non-linear functions for air temperature and linear functions for air pollutants. Results: We analyzed 22,630,598 deaths. An increase in mean temperature from the 75th to the 99th percentile of city-specific distributions was associated with an average 8.9 % (95 % confidence interval: 7.1 %, 10.7 %) mortality increment, ranging between 5.3 % (3.8 %, 6.9 %) and 12.8 % (8.7 %, 17.0 %), when daily PM10 was equal to 10 or 90 μg/m3, respectively. Corresponding estimates when daily O3 concentrations were 40 or 160 μg/m3 were 2.9 % (1.1 %, 4.7 %) and 12.5 % (6.9 %, 18.5 %), respectively. Similarly, a 10 μg/m3 increment in PM10 was associated with a 0.54 % (0.10 %, 0.98 %) and 1.21 % (0.69 %, 1.72 %) increase in mortality when daily air temperature was set to the 1st and 99th city-specific percentiles, respectively. Corresponding mortality estimate for O3 across these temperature percentiles were 0.00 % (-0.44 %, 0.44 %) and 0.53 % (0.38 %, 0.68 %). Similar effect modification results, although slightly weaker, were found for PM2.5 and NO2. Conclusions: Suggestive evidence of effect modification between air temperature and air pollutants on mortality during the warm period was found in a global dataset of 620 cities.

Abstract In recent years, global health security has been threatened by the geographical expansion of vector-borne infectious diseases such as malaria, dengue, yellow fever, Zika and chikungunya. For a range of these vector-borne diseases, an increase in residual (exophagic) transmission together with ecological heterogeneity in everything from weather to local human migration and housing to mosquito species’ behaviours presents many challenges to effective mosquito control. The novel use of drones (or uncrewed aerial vehicles) may play a major role in the success of mosquito surveillance and control programmes in the coming decades since the global landscape of mosquito-borne diseases and disease dynamics fluctuates frequently and there could be serious public health consequences if the issues of insecticide resistance and outdoor transmission are not adequately addressed. For controlling both aquatic and adult stages, for several years now remote sensing data have been used together with predictive modelling for risk, incidence and detection of transmission hot spots and landscape profiles in relation to mosquito-borne pathogens. The field of drone-based remote sensing is under continuous change due to new technology development, operation regulations and innovative applications. In this review we outline the opportunities and challenges for integrating drones into vector surveillance (i.e. identification of breeding sites or mapping micro-environmental composition) and control strategies (i.e. applying larval source management activities or deploying genetically modified agents) across the mosquito life-cycle. We present a five-step systematic environmental mapping strategy that we recommend be undertaken in locations where a drone is expected to be used, outline the key considerations for incorporating drone or other Earth Observation data into vector surveillance and provide two case studies of the advantages of using drones equipped with multispectral cameras. In conclusion, recent developments mean that drones can be effective for accurately conducting surveillance, assessing habitat suitability for larval and/or adult mosquitoes and implementing interventions. In addition, we briefly discuss the need to consider permissions, costs, safety/privacy perceptions and community acceptance for deploying drone activities. Graphical Abstract

Abstract In the Amazon Region of Peru, occupational activities are important drivers of human mobility and may increase the individual risk of being infected while contributing to increasing malaria community-level transmission. Even though out-of-village working activities and other mobility patterns have been identified as determinants of malaria transmission, no studies have quantified the effect of out-of-village working activities on recent malaria exposure and proposed plausible intervention scenarios. Using two population-based cross-sectional studies in the Loreto Department in Peru, and the parametric g-formula method, we simulated various hypothetical scenarios intervening in out-of-village working activities to reflect their potential health benefits. This study estimated that the standardized mean outcome (malaria seroprevalence) in the unexposed population (no out-of-village workers) was 44.6% (95% CI: 41.7%–47.5%) and 66.7% (95% CI: 61.6%–71.8%) in the exposed population resulting in a risk difference of 22.1% (95% CI: 16.3%–27.9%). However, heterogeneous patterns in the effects of interest were observed between peri-urban and rural areas (Cochran’s Q test = 15.5, p

BackgroundGlobally, access to life-saving vaccines has improved considerably in the past 5 decades. However, progress has started to slow down and even reverse in recent years. Understanding subnational heterogeneities in essential child immunization will be critical for closing the global vaccination gap.Methods and findingsWe use vaccination information for over 220,000 children across 1,366 administrative regions in 43 low- and middle-income countries (LMICs) from the most recent Demographic and Health Surveys. We estimate essential immunization coverage at the national and subnational levels and quantify socioeconomic inequalities in such coverage using adjusted concentration indices. Within- and between-country variations are summarized via the Theil index. We use local indicator of spatial association (LISA) statistics to identify clusters of administrative regions with high or low values. Finally, we estimate the number of missed vaccinations among children aged 15 to 35 months across all 43 countries and the types of vaccines most often missed. We show that national-level vaccination rates can conceal wide subnational heterogeneities. Large gaps in child immunization are found across West and Central Africa and in South Asia, particularly in regions of Angola, Chad, Nigeria, Guinea, and Afghanistan, where less than 10% of children are fully immunized. Furthermore, children living in these countries consistently lack all 4 basic vaccines included in the WHO's recommended schedule for young children. Across most countries, children from poorer households are less likely to be fully immunized. The main limitations include subnational estimates based on large administrative divisions for some countries and different periods of survey data collection.ConclusionsThe identified heterogeneities in essential childhood immunization, especially given that some regions consistently are underserved for all basic vaccines, can be used to inform the design and implementation of localized intervention programs aimed at eliminating child suffering and deaths from existing and novel vaccine-preventable diseases.

Abstract Background In Brazil, malaria is concentrated in the Amazon Basin, where more than 99% of the annual cases are reported. The main goal of this study was to investigate the population structure and genetic association of the biting behavior of Nyssorhynchus (also known as Anopheles) darlingi, the major malaria vector in the Amazon region of Brazil, using low-coverage genomic sequencing data. Methods Samples were collected in the municipality of Mâncio Lima, Acre state, Brazil between 2016 and 2017. Different approaches using genotype imputation and no gene imputation for data treatment and low-coverage sequencing genotyping were performed. After the samples were genotyped, population stratification analysis was performed. Results Weak but statistically significant stratification signatures were identified between subpopulations separated by distances of approximately 2–3 km. Genome-wide association studies (GWAS) were performed to compare indoor/outdoor biting behavior and blood-seeking at dusk/dawn. A statistically significant association was observed between biting behavior and single nucleotide polymorphism (SNP) markers adjacent to the gene associated with cytochrome P450 (CYP) 4H14, which is associated with insecticide resistance. A statistically significant association between blood-seeking periodicity and SNP markers adjacent to genes associated with the circadian cycle was also observed. Conclusion The data presented here suggest that low-coverage whole-genome sequencing with adequate processing is a powerful tool to genetically characterize vector populations at a microgeographic scale in malaria transmission areas, as well as for use in GWAS. Female mosquitoes entering houses to take a blood meal may be related to a specific CYP4H14 allele, and female timing of blood-seeking is related to circadian rhythm genes. Graphical Abstract

Summary: Background: Missed Opportunities for Vaccination (MOV) represent a major risk in the re-emergence of immunopreventable diseases. However, in the region, there are few published studies on MOVs using national databases such as demographic and health surveys (DHS). This study aims to describe the frequency and trends of MOVs for the first dose of vaccines against the leading causes of infant morbidity and mortality, their complete vaccination coverage, and trends in socioeconomic inequalities at the national and departmental levels for an 11-years period. Methods: Using DHS data from an 11-year period (2010–2020), we calculated frequencies and trends in MOVs of vaccines for the leading causes of child morbidity and mortality, estimated inequalities in MOVs using the Slope Inequality Index (SII) and conducted a spatial autocorrelation test to identify clusters of higher or lower inequality in MOVs at the national level. Findings: We found that, at the national level, greater inequality was concentrated in the wealthiest categories of each socioeconomic variable. We identified that departments with higher poverty rates concentrated higher levels of inequality in the MOVs in the lowest strata of the socioeconomic variables. In addition, we found that some departments with similar geographic and socioeconomic characteristics had spatially correlated levels of inequality on MOVs. Interpretation: These findings can help to identify the heterogeneity that exists in the distribution of MOVs among departments and socioeconomic strata, which would help to prioritize specific areas and subpopulations for national immunization strategies. Funding: No additional funding source was required for this study.

Abstract Despite reductions in malaria incidence and mortality across Sub-Saharan (SSA) countries, malaria control and elimination efforts are currently facing multiple global challenges such as climate and land use change, invasive vectors, and disruptions in healthcare delivery. Although relationships between malaria risks and socioeconomic factors have been widely demonstrated, the strengths and variability of these associations have not been quantified across SSA. In this study, we used data from population-based malaria indicator surveys in SSA countries to assess spatial trends in relative and absolute socioeconomic inequalities, analyzed as social (mothers’ highest educational level—MHEL) and economic (wealth index—WI) inequalities in malaria prevalence. To capture spatial variations in socioeconomic (represented by both WI and MHEL) inequalities in malaria, we calculated both the Slope Index of Inequality (SII) and Relative Index of Inequality (RII) in each administrative region. We also conducted cluster analyses based on Local Indicator of Spatial Association (LISA) to consider the spatial auto-correlation in SII and RII across regions and countries. A total of 47,404 participants in 1874 Primary Sampling Units (PSU) were analyzed across the 13 SSA countries. Our multi-country assessment provides estimations of strong socioeconomic inequalities between and within SSA countries. Such within- and between- countries inequalities varied greatly according to the socioeconomic metric and the scale used. Countries located in Eastern Africa showed a higher median Slope Index of Inequality (SII) and Relative Index of Inequality (RII) in malaria prevalence relative to WI in comparison to countries in other locations across SSA. Pockets of high SII in malaria prevalence in relation to WI and MHEL were observed in the East part of Africa. This study was able to map this wide range of malaria inequality metrics at a very local scale and highlighted the spatial clustering patterns of pockets of high and low malaria inequality values.

The impact of human population movement (HPM) on the epidemiology of vector-borne diseases, such as malaria, has been described. However, there are limited data on the use of new technologies for the study of HPM in endemic areas with difficult access such as the Amazon. In this study conducted in rural Peruvian Amazon, we used self-reported travel surveys and GPS trackers coupled with a Bayesian spatial model to quantify the role of HPM on malaria risk. By using a densely sampled population cohort, this study highlighted the elevated malaria transmission in a riverine community of the Peruvian Amazon. We also found that the high connectivity between Amazon communities for reasons such as work, trading or family plausibly sustains such transmission levels. Finally, by using multiple human mobility metrics including GPS trackers, and adapted causal inference methods we identified for the first time the effect of human mobility patterns on malaria risk in rural Peruvian Amazon. This study provides evidence of the causal effect of HPM on malaria that may help to adapt current malaria control programmes in the Amazon.

Disease control programs are needed to identify the breeding sites of mosquitoes, which transmit malaria and other diseases, in order to target interventions and identify environmental risk factors. The increasing availability of very-high-resolution drone data provides new opportunities to find and characterize these vector breeding sites. Within this study, drone images from two malaria-endemic regions in Burkina Faso and Côte d’Ivoire were assembled and labeled using open-source tools. We developed and applied a workflow using region-of-interest-based and deep learning methods to identify land cover types associated with vector breeding sites from very-high-resolution natural color imagery. Analysis methods were assessed using cross-validation and achieved maximum Dice coefficients of 0.68 and 0.75 for vegetated and non-vegetated water bodies, respectively. This classifier consistently identified the presence of other land cover types associated with the breeding sites, obtaining Dice coefficients of 0.88 for tillage and crops, 0.87 for buildings and 0.71 for roads. This study establishes a framework for developing deep learning approaches to identify vector breeding sites and highlights the need to evaluate how results will be used by control programs.

BackgroundThe landscape of malaria transmission in the Peruvian Amazon is temporally and spatially heterogeneous, presenting different micro-geographies with particular epidemiologies. Most cases are asymptomatic and escape routine malaria surveillance based on light microscopy (LM). Following the implementation of control programs in this region, new approaches to stratify transmission and direct efforts at an individual and community level are needed. Antibody responses to serological exposure markers (SEM) to Plasmodium vivax have proven diagnostic performance to identify people exposed in the previous 9 months.MethodologyWe measured antibody responses against 8 SEM to identify recently exposed people and determine the transmission dynamics of P. vivax in peri-urban (Iquitos) and riverine (Mazán) communities of Loreto, communities that have seen significant recent reductions in malaria transmission. Socio-demographic, geo-reference, LM and qPCR diagnosis data were collected from two cross-sectional surveys. Spatial and multilevel analyses were implemented to describe the distribution of seropositive cases and the risk factors associated with exposure to P. vivax.Principal findingsLow local transmission was detected by qPCR in both Iquitos (5.3%) and Mazán (2.7%); however, seroprevalence indicated a higher level of (past) exposure to P. vivax in Mazán (56.5%) than Iquitos (38.2%). Age and being male were factors associated with high odds of being seropositive in both sites. Higher antibody levels were found in individuals >15 years old. The persistence of long-lived antibodies in these individuals could overestimate the detection of recent exposure. Antibody levels in younger populations (ConclusionsThe large number of current and past infections detected by SEMs allows for detailed local epidemiological analyses, in contrast to data from qPCR prevalence surveys which did not produce statistically significant associations. Serological surveillance will be increasingly important in the Peruvian Amazon as malaria transmission is reduced by continued control and elimination efforts.

Summary: Background: Increased mortality risk is associated with short-term temperature variability. However, to our knowledge, there has been no comprehensive assessment of the temperature variability-related mortality burden worldwide. In this study, using data from the MCC Collaborative Research Network, we first explored the association between temperature variability and mortality across 43 countries or regions. Then, to provide a more comprehensive picture of the global burden of mortality associated with temperature variability, global gridded temperature data with a resolution of 0·5° × 0·5° were used to assess the temperature variability-related mortality burden at the global, regional, and national levels. Furthermore, temporal trends in temperature variability-related mortality burden were also explored from 2000–19. Methods: In this modelling study, we applied a three-stage meta-analytical approach to assess the global temperature variability-related mortality burden at a spatial resolution of 0·5° × 0·5° from 2000–19. Temperature variability was calculated as the SD of the average of the same and previous days’ minimum and maximum temperatures. We first obtained location-specific temperature variability related-mortality associations based on a daily time series of 750 locations from the Multi-country Multi-city Collaborative Research Network. We subsequently constructed a multivariable meta-regression model with five predictors to estimate grid-specific temperature variability related-mortality associations across the globe. Finally, percentage excess in mortality and excess mortality rate were calculated to quantify the temperature variability-related mortality burden and to further explore its temporal trend over two decades. Findings: An increasing trend in temperature variability was identified at the global level from 2000 to 2019. Globally, 1 753 392 deaths (95% CI 1 159 901–2 357 718) were associated with temperature variability per year, accounting for 3·4% (2·2–4·6) of all deaths. Most of Asia, Australia, and New Zealand were observed to have a higher percentage excess in mortality than the global mean. Globally, the percentage excess in mortality increased by about 4·6% (3·7–5·3) per decade. The largest increase occurred in Australia and New Zealand (7·3%, 95% CI 4·3–10·4), followed by Europe (4·4%, 2·2–5·6) and Africa (3·3, 1·9–4·6). Interpretation: Globally, a substantial mortality burden was associated with temperature variability, showing geographical heterogeneity and a slightly increasing temporal trend. Our findings could assist in raising public awareness and improving the understanding of the health impacts of temperature variability. Funding: Australian Research Council, Australian National Health & Medical Research Council.

Climate change constitutes an unprecedented challenge for public health and one of its main direct effects are extreme temperatures. It varies between intra-urban areas and this difference is called surface urban heat island (SUHI) effect. We aimed to assess SUHI distribution among socioeconomic levels in Lima, Peru by conducting a cross-sectional study at the block-level. The mean land surface temperature (LST) from 2017 to 2021 were estimated using the TIRS sensor (Landsat-8 satellite [0.5 km scale]) and extracted to block level. SUHI was calculated based on the difference on mean LST values (2017–2021) per block and the lowest LST registered in a block. Socioeconomic data were obtained from the 2017 Peruvian census. A principal component analysis was performed to construct a socioeconomic index and a mixture analysis based on quantile g-computation was conducted to estimate the joint and specific effects of socioeconomic variables on SUHI. A total of 69 618 blocks were included in the analysis. In the Metropolitan Lima area, the mean SUHI estimation per block was 6.44 (SD = 1.44) Celsius degrees. We found that blocks with high socioeconomic status (SES) showed a decreased exposure to SUHI, compared to those blocks where the low SES were predominant ( p -value < 0.001) and that there is a significant SUHI exposure variation ( p -value < 0.001) between predominant ethnicities per block (Non-White, Afro-American, and White ethnicities). The mixture analysis showed that the overall mixture effect estimates on SUHI was −1.01 (effect on SUHI of increasing simultaneously every socioeconomic variable by one quantile). Our study highlighted that populations with low SES are more likely to be exposed to higher levels of SUHI compared to those who have a higher SES and illustrates the importance to consider SES inequalities when designing urban adaptation strategies aiming at reducing exposure to SUHI.

Abstract Growing evidence suggests pollution and other environmental factors have a role in the development of tuberculosis (TB), however, such studies have never been conducted in Peru. Considering the association between air pollution and specific geographic areas, our objective was to determine the spatial distribution and clustering of TB incident cases in Lima and their co-occurrence with clusters of fine particulate matter (PM2.5) and poverty. We found co-occurrences of clusters of elevated concentrations of air pollutants such as PM2.5, high poverty indexes, and high TB incidence in Lima. These findings suggest an interplay of socio-economic and environmental in driving TB incidence.

Summary: Background: The emergence of fentanyl around 2013 represented a new, deadly stage of the opioid epidemic in the USA. We aimed to develop a statistical regression approach to identify counties at the highest risk of high overdose mortality in the subsequent years by predicting annual county-level overdose death rates across the contiguous USA and to validate our approach against observed overdose mortality data collected between 2013 and 2018. Methods: We fit mixed-effects negative binomial regression models to predict overdose death rates in the subsequent year for 2013–18 for all contiguous state counties in the USA (ie, excluding Alaska and Hawaii). We used publicly available county-level data related to health-care access, drug markets, socio-demographics, and the geographical spread of opioid overdose as model predictors. The crude number of county-level overdose deaths was extracted from restricted US Centers for Disease Control and Prevention mortality records. To predict county-level overdose rates for the year 201X: (1) a model was trained on county-level predictor data for the years 2010–201(X–2) paired with county-level overdose deaths for the year 2011–201(X–1); (2) county-level predictor data for the year 201(X–1) was fed into the model to predict the 201X county-level crude number of overdose deaths; and (3) the latter were converted to a population-adjusted rate. For comparison, we generated a benchmark set of predictions by applying the observed slope of change in overdose death rates in the previous year to 201(X–1) rates. To assess the predictive performance of the model, we compared predicted values (of both the model and benchmark) to observed values by (1) calculating the mean average error, root mean squared error, and Spearman's correlation coefficient and (2) assessing the proportion of counties in the top decile (10%) of overdose death rates that were correctly predicted as such. Finally, in a post-hoc analysis, we sought to identify variables with greatest predictive utility. Findings: Between 2013 and 2018, among the 3106 US counties included, our modelling approach outperformed the benchmark strategy across all metrics. The observed average county-level overdose death rate rose from 11·8 per 100 000 people in 2013 to 15·4 in 2017 before falling to 14·6 in 2018. Our negative binomal modelling approach similarly identified an increasing trend, predicting an average 11·8 deaths per 100 000 in 2013, up to 15·1 in 2017, and increasing further to 16·4 in 2018. The benchmark model over-predicted average death rates each year, ranging from 13·0 per 100 000 in 2013 to 18·3 in 2018. Our modelling approach successfully ranked counties by overdose death rate identifying between 42% and 57% of counties in the top decile of overdose mortality (compared with 29% and 43% using the benchmark) each year and identified 194 of the 808 counties with emergent overdose outbreaks (ie, newly entered the top decile) across the study period, versus 31 using the benchmark. In the post-hoc analysis, we identified geospatial proximity of overdose in nearby counties, opioid prescription rate, presence of an urgent care facility, and several economic indicators as the variables with the greatest predictive utility. Interpretation: Our model shows that a regression approach can effectively predict county-level overdose death rates and serve as a risk assessment tool to identify future high mortality counties throughout an emerging drug use epidemic. Funding: National Institute on Drug Abuse.

Successful malaria control interventions, mostly based on the training of health workers, distribution of insecticide-treated nets, and spraying, decrease malaria incidence; however, when these interventions are interrupted, a resurgence may occur. In the Peruvian Amazon, after discontinuing the control activities implemented by the PAMAFRO project (2006–2010)-a Global Fund-sponsored project for the strengthening of malaria control and surveillance in multiple countries in Latin America– malaria cases re-emerged dramatically. In parallel, meteorological factors determine the conditions suitable for the development, reproduction, and survival of mosquito vectors and parasites. This study hypothesized that interruption of malaria interventions may have modified the meteorological-malaria relationships over time (i.e., temporal changes in the dose-response between meteorological variables and malaria incidence). In this panel data analysis, we assessed the extent that relationships between meteorological variables and malaria changed temporally using data of monthly malaria incidence due to Plasmodium vivax or P. falciparum in Loreto, Peru (2000–2017). Generalized additive models were used to explore how the effects of meteorological variables changed in magnitude before, during, and after the PAMAFRO intervention. We found that once the PAMAFRO intervention had been interrupted, the estimated effects (dose-response) of meteorological variables on incidence rates decreased for both malaria parasite species. However, these fitted effect estimates did not reach their baseline levels (before the PAMAFRO period); variations of time-varying slopes between 0.45 and 2.07 times were observed after the PAMAFRO intervention. We also reported significant heterogeneity in the geographical distributions of malaria, parasite species, and meteorological variables. High malaria transmission occurred consistently in the northwestern provinces of Loreto Department. Since the end of the PAMAFRO period, a higher effect of precipitation and actual evapotranspiration was described on P. falciparum compared to P. vivax. The effect of temperature on malaria was greater over a shorter time (1-month lag or less), compared with precipitation and actual evapotranspiration (12-month lag). These findings demonstrate the importance of sustained malaria control efforts since interruption may enhance the links between meteorological factors and malaria. Our results also emphasize the importance of considering the time-varying effect of meteorological factors on malaria incidence to tailor control interventions, especially to better manage the current and future climate change crisis.

Summary: Background: Exposure to cold or hot temperatures is associated with premature deaths. We aimed to evaluate the global, regional, and national mortality burden associated with non-optimal ambient temperatures. Methods: In this modelling study, we collected time-series data on mortality and ambient temperatures from 750 locations in 43 countries and five meta-predictors at a grid size of 0·5° × 0·5° across the globe. A three-stage analysis strategy was used. First, the temperature–mortality association was fitted for each location by use of a time-series regression. Second, a multivariate meta-regression model was built between location-specific estimates and meta-predictors. Finally, the grid-specific temperature–mortality association between 2000 and 2019 was predicted by use of the fitted meta-regression and the grid-specific meta-predictors. Excess deaths due to non-optimal temperatures, the ratio between annual excess deaths and all deaths of a year (the excess death ratio), and the death rate per 100 000 residents were then calculated for each grid across the world. Grids were divided according to regional groupings of the UN Statistics Division. Findings: Globally, 5 083 173 deaths (95% empirical CI [eCI] 4 087 967–5 965 520) were associated with non-optimal temperatures per year, accounting for 9·43% (95% eCI 7·58–11·07) of all deaths (8·52% [6·19–10·47] were cold-related and 0·91% [0·56–1·36] were heat-related). There were 74 temperature-related excess deaths per 100 000 residents (95% eCI 60–87). The mortality burden varied geographically. Of all excess deaths, 2 617 322 (51·49%) occurred in Asia. Eastern Europe had the highest heat-related excess death rate and Sub-Saharan Africa had the highest cold-related excess death rate. From 2000–03 to 2016–19, the global cold-related excess death ratio changed by −0·51 percentage points (95% eCI −0·61 to −0·42) and the global heat-related excess death ratio increased by 0·21 percentage points (0·13–0·31), leading to a net reduction in the overall ratio. The largest decline in overall excess death ratio occurred in South-eastern Asia, whereas excess death ratio fluctuated in Southern Asia and Europe. Interpretation: Non-optimal temperatures are associated with a substantial mortality burden, which varies spatiotemporally. Our findings will benefit international, national, and local communities in developing preparedness and prevention strategies to reduce weather-related impacts immediately and under climate change scenarios. Funding: Australian Research Council and the Australian National Health and Medical Research Council.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Summary: Background: Temperature and rainfall patterns are known to influence seasonal patterns of dengue transmission. However, the effect of severe drought and extremely wet conditions on the timing and intensity of dengue epidemics is poorly understood. In this study, we aimed to quantify the non-linear and delayed effects of extreme hydrometeorological hazards on dengue risk by level of urbanisation in Brazil using a spatiotemporal model. Methods: We combined distributed lag non-linear models with a spatiotemporal Bayesian hierarchical model framework to determine the exposure-lag-response association between the relative risk (RR) of dengue and a drought severity index. We fit the model to monthly dengue case data for the 558 microregions of Brazil between January, 2001, and January, 2019, accounting for unobserved confounding factors, spatial autocorrelation, seasonality, and interannual variability. We assessed the variation in RR by level of urbanisation through an interaction between the drought severity index and urbanisation. We also assessed the effect of hydrometeorological hazards on dengue risk in areas with a high frequency of water supply shortages. Findings: The dataset included 12 895 293 dengue cases reported between 2001 and 2019 in Brazil. Overall, the risk of dengue increased between 0–3 months after extremely wet conditions (maximum RR at 1 month lag 1·56 [95% CI 1·41–1·73]) and 3–5 months after drought conditions (maximum RR at 4 months lag 1·43 [1·22–1·67]). Including a linear interaction between the drought severity index and level of urbanisation improved the model fit and showed the risk of dengue was higher in more rural areas than highly urbanised areas during extremely wet conditions (maximum RR 1·77 [1·32–2·37] at 0 months lag vs maximum RR 1·58 [1·39–1·81] at 2 months lag), but higher in highly urbanised areas than rural areas after extreme drought (maximum RR 1·60 [1·33–1·92] vs 1·15 [1·08–1·22], both at 4 months lag). We also found the dengue risk following extreme drought was higher in areas that had a higher frequency of water supply shortages. Interpretation: Wet conditions and extreme drought can increase the risk of dengue with different delays. The risk associated with extremely wet conditions was higher in more rural areas and the risk associated with extreme drought was exacerbated in highly urbanised areas, which have water shortages and intermittent water supply during droughts. These findings have implications for targeting mosquito control activities in poorly serviced urban areas, not only during the wet and warm season, but also during drought periods. Funding: Royal Society, Medical Research Council, Wellcome Trust, National Institutes of Health, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, and Conselho Nacional de Desenvolvimento Científico e Tecnológico. Translation: For the Portuguese translation of the abstract see Supplementary Materials section.

Abstract Background Malaria remains an important public health problem in Peru where incidence has been increasing since 2011. Of over 55,000 cases reported in 2017, Plasmodium vivax was the predominant species (76%), with P. falciparum responsible for the remaining 24%. Nyssorhynchus darlingi (previously Anopheles darlingi) is the main vector in Amazonian Peru, where hyperendemic Plasmodium transmission pockets have been found. Mazán district has pronounced spatial heterogeneity of P. vivax malaria. However, little is known about behavior, ecology or seasonal dynamics of Ny. darlingi in Mazán. This study aimed to gather baseline information about bionomics of malaria vectors and transmission risk factors in a hyperendemic malaria area of Amazonian Peru. Methods To assess vector biology metrics, five surveys (two in the dry and three in the rainy season), including collection of sociodemographic information, were conducted in four communities in 2016–2017 on the Napo (Urco Miraño, URC; Salvador, SAL) and Mazán Rivers (Visto Bueno, VIB; Libertad, LIB). Human-biting rate (HBR), entomological inoculation rate (EIR) and human blood index (HBI) were measured to test the hypothesis of differences in entomological indices of Ny. darlingi between watersheds. A generalized linear mixed effect model (GLMM) was constructed to model the relationship between household risk factors and the EIR. Results Nyssorhynchus darlingi comprised 95% of 7117 Anophelinae collected and its abundance was significantly higher along the Mazán River. The highest EIRs (3.03–4.54) were detected in March and June in URC, LIB and VIB, and significantly more Ny. darlingi were infected outdoors than indoors. Multivariate analysis indicated that the EIR was >12 times higher in URC compared with SAL. The HBI ranged from 0.42–0.75; humans were the most common blood source, followed by Galliformes and cows. There were dramatic differences in peak biting time and malaria incidence with similar bednet coverage in the villages. Conclusions Nyssorhynchus darlingi is the predominant contributor to malaria transmission in the Mazán District, Peru. Malaria risk in these villages is higher in the peridomestic area, with pronounced heterogeneities between and within villages on the Mazán and the Napo Rivers. Spatiotemporal identification and quantification of the prevailing malaria transmission would provide new evidence to orient specific control measures for vulnerable or at high risk populations.

Vale do Rio Juruá in western Acre, Brazil, is a persistent malaria transmission hotspot partly due to fish farming development that was encouraged to improve local standards of living. Fish ponds can be productive breeding sites for Amazonian malaria vector species, including Nyssorhynchus darlingi, which, combined with high human density and mobility, add to the local malaria burden.This study reports entomological profile of immature and adult Ny. darlingi at three sites in Mâncio Lima, Acre, during the rainy and dry season (February to September, 2017). From 63 fishponds, 10,859 larvae were collected, including 5,512 first-instar Anophelinae larvae and 4,927 second, third and fourth-instars, of which 8.5% (n = 420) were Ny. darlingi. This species was most abundant in not-abandoned fishponds and in the presence of emerging aquatic vegetation. Seasonal analysis of immatures in urban landscapes found no significant difference in the numbers of Ny. darlingi, corresponding to equivalent population density during the rainy to dry transition period. However, in the rural landscape, significantly higher numbers of Ny. darlingi larvae were collected in August (IRR = 5.80, p = 0.037) and September (IRR = 6.62, p = 0.023) (dry season), compared to February (rainy season), suggesting important role of fishponds for vector population maintenance during the seasonal transition in this landscape type. Adult sampling detected mainly Ny. darlingi (~93%), with similar outdoor feeding behavior, but different abundance according to landscape profile: urban site 1 showed higher peaks of human biting rate in May (46 bites/person/hour), than February (4) and September (15), while rural site 3 shows similar HBR during the same sampling period (22, 24 and 21, respectively). This study contributes to a better understanding of the larvae biology of the main malaria vector in the Vale do Rio Juruá region and, ultimately will support vector control efforts.

To better estimate the travel time to the most proximate health care facility (HCF) and determine differences across heterogeneous land coverage types, this study explored the use of a novel cloud-based geospatial modeling approach. Geospatial data of 145,134 cities and villages and 8,067 HCF were gathered with land coverage types, roads and river networks, and digital elevation data to produce high-resolution (30 m) estimates of travel time to HCFs across Peru. This study estimated important variations in travel time to HCFs between urban and rural settings and major land coverage types in Peru. The median travel time to primary, secondary, and tertiary HCFs was 1.9-, 2.3-, and 2.2-fold higher in rural than urban settings, respectively. This study provides a new methodology to estimate the travel time to HCFs as a tool to enhance the understanding and characterization of the profiles of accessibility to HCFs in low- and middle-income countries.

Human movement affects malaria epidemiology at multiple geographical levels; however, few studies measure the role of human movement in the Amazon Region due to the challenging conditions and cost of movement tracking technologies. We developed an open-source low-cost 3D printable GPS-tracker and used this technology in a cohort study to characterize the role of human population movement in malaria epidemiology in a rural riverine village in the Peruvian Amazon. In this pilot study of 20 participants (mean age = 40 years old), 45,980 GPS coordinates were recorded over 1 month. Characteristic movement patterns were observed relative to the infection status and occupation of the participants. Applying two analytical animal movement ecology methods, utilization distributions (UDs) and integrated step selection functions (iSSF), we showed contrasting environmental selection and space use patterns according to infection status. These data suggested an important role of human movement in the epidemiology of malaria in the Peruvian Amazon due to high connectivity between villages of the same riverine network, suggesting limitations of current community-based control strategies. We additionally demonstrate the utility of this low-cost technology with movement ecology analysis to characterize human movement in resource-poor environments.

Malaria is a vector-borne disease of significant public health concern. Despite widespread success of many elimination initiatives, elimination efforts in some regions of the world have stalled. Barriers to malaria elimination include climate and land use changes, such as warming temperatures and urbanization, which can alter mosquito habitats. Socioeconomic factors, such as political instability and regional migration, also threaten elimination goals. This is particularly relevant in areas where local elimination has been achieved and consequently surveillance and control efforts are dwindling and are no longer a priority. Understanding how environmental change, impacts malaria elimination has important practical implications for vector control and disease surveillance strategies. It is important to consider climate change when monitoring the threat of malaria resurgence due to socioeconomic influences. However, there is limited assessment of how the combination of climate variation, interventions and socioeconomic pressures influence long-term trends in malaria transmission and elimination efforts. In this study, we used Bayesian hierarchical mixed models and malaria case data for a 29-year period to disentangle the impacts of climate variation and malaria control efforts on malaria risk in the Ecuadorian province of El Oro, which achieved local elimination in 2011. We found shifting patterns of malaria between rural and urban areas, with a relative increase of Plasmodium vivax in urbanized areas. Minimum temperature was an important driver of malaria seasonality and the association between warmer minimum temperatures and malaria incidence was greater for Plasmodium falciparum compared to P. vivax malaria. There was considerable heterogeneity in the impact of three chemical vector control measures on both P. falciparum and P. vivax malaria. We found statistically significant associations between two of the three measures [indoor residual spraying (IRS) and space spraying] and a reduction in malaria incidence, which varied between malaria type. We also found environmental suitability for malaria transmission is increasing in El Oro, which could limit future elimination efforts if malaria is allowed to re-establish. Our findings have important implications for understanding environmental obstacles to malaria elimination and highlights the importance of designing and sustaining elimination efforts in areas that remain vulnerable to resurgence.

The Amazon is Brazil's greatest natural resource and invaluable to the rest of the world as a buffer against climate change. The recent election of Brazil's president brought disputes over development plans for the region back into the spotlight. Historically, the development model for the Amazon has focused on exploitation of natural resources, resulting in environmental degradation, particularly deforestation. Although considerable attention has focused on the long-term global cost of "losing the Amazon," too little attention has focused on the emergence and reemergence of vector-borne diseases that directly impact the local population, with spillover effects to other neighboring areas. We discuss the impact of Amazon development models on human health, with a focus on vector-borne disease risk. We outline policy actions that could mitigate these negative impacts while creating opportunities for environmentally sensitive economic activities.

Despite efforts made over decades by the Peruvian government to eliminate malaria, Plasmodium vivax remains a challenge for public health decision-makers in the country. The uneven distribution of its incidence, plus its complex pattern of dispersion, has made ineffective control measures based on global information that lack the necessary detail to understand transmission fully. In this sense, population genetic tools can complement current surveillance. This study describes the genetic diversity and population structure from September 2012 to March 2015 in three geographically distant settlements, Cahuide (CAH), Lupuna (LUP) and Santa Emilia (STE), located in the Peruvian Amazon. A total 777 P. vivax mono-infections, out of 3264, were genotyped. Among study areas, LUP showed 19.7% of polyclonal infections, and its genetic diversity (Hexp) was 0.544. Temporal analysis showed a significant increment of polyclonal infections and Hexp, and the introduction and persistence of a new parasite population since March 2013. In STE, 40.1% of infections were polyclonal, with Hexp = 0.596. The presence of four genetic clusters without signals of clonal expansion and infections with lower parasite densities compared against the other two areas were also found. At least four parasite populations were present in CAH in 2012, where, after June 2014, malaria cases decreased from 213 to 61, concomitant with a decrease in polyclonal infections (from 0.286 to 0.18), and expectedly variable Hexp. Strong signals of gene flow were present in the study areas and wide geographic distribution of highly diverse parasite populations were found. This study suggests that movement of malaria parasites by human reservoirs connects geographically distant malaria transmission areas in the Peruvian Amazon. The maintenance of high levels of parasite genetic diversity through human mobility is a critical barrier to malaria elimination in this region.

Abstract Background The incidence of malaria due both to Plasmodium falciparum and Plasmodium vivax in the Peruvian Amazon has risen in the past 5 years. This study tested the hypothesis that the maintenance and emergence of malaria in hypoendemic regions such as Amazonia is determined by submicroscopic and asymptomatic Plasmodium parasitaemia carriers. The present study aimed to precisely quantify the rate of very-low parasitaemia carriers in two sites of the Peruvian Amazon in relation to transmission patterns of P. vivax and P. falciparum in this area. Methods This study was carried out within the Amazonian-ICEMR longitudinal cohort. Blood samples were collected for light microscopy diagnosis and packed red blood cell (PRBC) samples were analysed by qPCR. Plasma samples were tested for total IgG reactivity against recombinant PvMSP-10 and PfMSP-10 antigens by ELISA. Occupation and age 10 years and greater were considered surrogates of occupation-related mobility. Risk factors for P. falciparum and P. vivax infections detected by PRBC-qPCR were assessed by multilevel logistic regression models. Results Among 450 subjects, the prevalence of P. vivax by PRBC-PCR (25.1%) was sixfold higher than that determined by microscopy (3.6%). The prevalence of P. falciparum infection was 4.9% by PRBC-PCR and 0.2% by microscopy. More than 40% of infections had parasitaemia under 5 parasites/μL. Multivariate analysis for infections detected by PRBC-PCR showed that participants with recent settlement in the study area (AOR 2.1; 95% CI 1.03:4.2), age ≥ 30 years (AOR 3.3; 95% CI 1.6:6.9) and seropositivity to P. vivax (AOR 1.8; 95% CI 1.0:3.2) had significantly higher likelihood of P. vivax infection, while the odds of P. falciparum infection was higher for participants between 10 and 29 years (AOR 10.7; 95% CI 1.3:91.1) and with a previous P. falciparum infection (AOR 10.4; 95% CI 1.5:71.1). Conclusions This study confirms the contrasting transmission patterns of P. vivax and P. falciparum in the Peruvian Amazon, with stable local transmission for P. vivax and the source of P. falciparum to the study villages dominated by very low parasitaemia carriers, age 10 years and older, who had travelled away from home for work and brought P. falciparum infection with them.

Abstract Malaria has steadily increased in the Peruvian Amazon over the last five years. This study aimed to determine the parasite prevalence and micro-geographical heterogeneity of Plasmodium vivax parasitaemia in communities of the Peruvian Amazon. Four cross-sectional active case detection surveys were conducted between May and July 2015 in four riverine communities in Mazan district. Analysis of 2785 samples of 820 individuals nested within 154 households for Plasmodium parasitaemia was carried out using light microscopy and qPCR. The spatio-temporal distribution of Plasmodium parasitaemia, dominated by P. vivax, was shown to cluster at both household and community levels. Of enrolled individuals, 47% had at least one P. vivax parasitaemia and 10% P. falciparum, by qPCR, both of which were predominantly sub-microscopic and asymptomatic. Spatial analysis detected significant clustering in three communities. Our findings showed that communities at small-to-moderate spatial scales differed in P. vivax parasite prevalence, and multilevel Poisson regression models showed that such differences were influenced by factors such as age, education, and location of households within high-risk clusters, as well as factors linked to a local micro-geographic context, such as travel and occupation. Complex transmission patterns were found to be related to human mobility among communities in the same micro-basin.

Abstract Background Understanding the dynamics of malaria transmission in diverse endemic settings is key for designing and implementing locally adapted and sustainable control and elimination strategies. A parasitological and epidemiological survey was conducted in September–October 2012, as a baseline underlying a 3-year population-based longitudinal cohort study. The aim was to characterize malaria transmission patterns in two contrasting ecological rural sites in the Peruvian Amazon, Lupuna (LUP), a riverine environment, and Cahuide (CAH), associated with road-linked deforestation. Methods After a full population census, 1941 individuals 3 years and older (829 in LUP, 1112 in CAH) were interviewed, clinically examined and had a blood sample taken for the detection of malaria parasites by microscopy and PCR. Species-specific parasite prevalence was estimated overall and by site. Multivariate logistic regression models assessed risk factors for parasite infection by PCR, while SaTScan detected spatial clusters of PCR-positive individuals within each site. In addition, data from routine malaria surveillance in the period 2009–2012 were obtained. Results Parasite prevalence by PCR was higher in CAH than in LUP for Plasmodium vivax (6.2% vs. 3.9%) and for Plasmodium falciparum (2.6% vs. 1.2%). Among PCR-confirmed infections, asymptomatic (Asy) parasite carriers were always more common than symptomatic (Sy) infections for P. vivax (Asy/Sy ratio: 2/1 in LUP and 3.7/1 in CAH) and for P. falciparum (Asy/Sy ratio: 1.3/1 in LUP and 4/1 in CAH). Sub-patent (Spat) infections also predominated over patent (Pat) infections for both species: P. vivax (Spat/Pat ratio: 2.8/1 in LUP and 3.7/1 in CAH) and P. falciparum malaria (Spat/Pat ratio: 1.9/1 in LUP and 26/0 in CAH). For CAH, age, gender and living in a household without electricity were significantly associated with P. vivax infection, while only age and living in a household with electricity was associated with P. falciparum infection. For LUP, only household overcrowding was associated with P. falciparum infection. The spatial analysis only identified well-defined clusters of P. vivax and P. falciparum infected individuals in CAH. Reported malaria incidence indicated that malaria transmission has long occurred in LUP with primarily seasonal patterns, and confirmed a malaria outbreak in CAH since May 2012. Conclusions This parasitological and epidemiological baseline assessment demonstrates that malaria transmission and parasite prevalence is heterogeneous in the Peruvian Amazon, and influenced by local socio-demographics and ecological contexts. Riverine and road construction/deforestation contexts must be taken into account in order to carry out effective anti-malaria control and elimination efforts.

In Amazonian Peru, the primary malaria vector, Nyssorhynchus darlingi (formerly Anopheles darlingi), is difficult to target using standard vector control methods because it mainly feeds and rests outdoors. Larval source management could be a useful supplementary intervention, but to determine its feasibility, more detailed studies on the larval ecology of Ny. darlingi are essential. We conducted a multi-level study of the larval ecology of Anophelinae mosquitoes in the peri-Iquitos region of Amazonian Peru, examining the environmental characteristics of the larval habitats of four species, comparing the larval microbiota among species and habitats, and placing Ny. darlingi larval habitats in the context of spatial heterogeneity in human malaria transmission. We collected Ny. darlingi, Nyssorhynchus rangeli (formerly Anopheles rangeli), Nyssorhynchus triannulatus s.l. (formerly Anopheles triannulatus s.l.), and Nyssorhynchus sp. nr. konderi (formerly Anopheles sp. nr. konderi) from natural and artificial water bodies throughout the rainy and dry seasons. We found that, consistent with previous studies in this region and in Brazil, the presence of Ny. darlingi was significantly associated with water bodies in landscapes with more recent deforestation and lower light intensity. Nyssorhynchus darlingi presence was also significantly associated with a lower vegetation index, other Anophelinae species, and emergent vegetation. Though they were collected in the same water bodies, the microbial communities of Ny. darlingi larvae were distinct from those of Ny. rangeli and Ny. triannulatus s.l., providing evidence either for a species-specific larval microbiome or for segregation of these species in distinct microhabitats within each water body. We demonstrated that houses with more reported malaria cases were located closer to Ny. darlingi larval habitats; thus, targeted control of these sites could help ameliorate malaria risk. The co-occurrence of Ny. darlingi larvae in water bodies with other putative malaria vectors increases the potential impact of larval source management in this region.

Infectious disease dynamics are affected by human mobility more powerfully than previously thought, and thus reliable traceability data are essential. In rural riverine settings, lack of infrastructure and dense tree coverage deter the implementation of cutting-edge technology to collect human mobility data. To overcome this challenge, this study proposed the use of a novel open mobile mapping tool, GeoODK. This study consists of a purposive sampling of 33 participants in six villages with contrasting patterns of malaria transmission that demonstrates a feasible approach to map human mobility. The self-reported traceability data allowed the construction of the first human mobility framework in rural riverine villages in the Peruvian Amazon. The mobility spectrum in these areas resulted in travel profiles ranging from 2 hours to 19 days; and distances between 10 to 167 km. Most Importantly, occupational-related mobility profiles with the highest displacements (in terms of time and distance) were observed in commercial, logging, and hunting activities. These data are consistent with malaria transmission studies in the area that show villages in watersheds with higher human movement are concurrently those with greater malaria risk. The approach we describe represents a potential tool to gather critical information that can facilitate malaria control activities.

Interest in larval source management (LSM) as an adjunct intervention to control and eliminate malaria transmission has recently increased mainly because long-lasting insecticidal nets (LLINs) and indoor residual spray (IRS) are ineffective against exophagic and exophilic mosquitoes. In Amazonian Peru, the identification of the most productive, positive water bodies would increase the impact of targeted mosquito control on aquatic life stages. The present study explores the use of unmanned aerial vehicles (drones) for identifying Nyssorhynchus darlingi (formerly Anopheles darlingi) breeding sites with high-resolution imagery (~0.02m/pixel) and their multispectral profile in Amazonian Peru. Our results show that high-resolution multispectral imagery can discriminate a profile of water bodies where Ny. darlingi is most likely to breed (overall accuracy 86.73%- 96.98%) with a moderate differentiation of spectral bands. This work provides proof-of-concept of the use of high-resolution images to detect malaria vector breeding sites in Amazonian Peru and such innovative methodology could be crucial for LSM malaria integrated interventions.

Malaria transmission requires that Anopheles mosquitoes ingest Plasmodium gametocyte stages circulating in the human bloodstream. In the context of malaria elimination, understanding the epidemiology of gametocytes relative to all Plasmodium infections and the contribution of asymptomatic and sub-microscopic parasite carriers to the gametocyte reservoir is necessary, especially in low endemic settings with predominance of P.vivax. A 13-month longitudinal study was conducted in two communities (n = 1935 individuals) of Loreto Department, Peru, with five active screenings for Plasmodium infections and gametocyte stages by quantitative real-time PCR (qPCR) and reverse transcription (RT)-qPCR, respectively. Parasite prevalence by qPCR was 7.2% for P.vivax (n = 520/7235; range by survey 6.0%-8.1%) and 3.2% for P.falciparum (n = 235/7235; range by survey 0.4%-7.7%). Sub-microscopic infections accounted for 73.5% of P.vivax (range by survey 60%-89%) and almost the totality of P.falciparum cases. Gametocytes were found in 28.4% P.vivax infections (range by survey 18.7%-34.1%), with a peak of 61.5% in one community at the start of the transmission season. About 59.8% of all P.vivax gametocyte carriers were asymptomatic and 31.9% were sub-microscopic. Age patterns for gametocyte prevalence paralleled asexual stage infections and peaked among >15-25 year old individuals. Asexual parasite density was found to be the strongest predictor for P.vivax gametocyte presence in longitudinal multivariate analysis (odds ratio 2.33 [95% confidence interval 1.96, 2.78]; P

Loop-mediated isothermal DNA amplification (LAMP) methodology offers an opportunity for point-of-care (POC) molecular detection of asymptomatic malaria infections. However, there is still little evidence on the feasibility of implementing this technique for population screenings in isolated field settings.Overall, we recruited 1167 individuals from terrestrial ('road') and hydric ('riverine') communities of the Peruvian Amazon for a cross-sectional survey to detect asymptomatic malaria infections. The technical performance of LAMP was evaluated in a subgroup of 503 samples, using real-time Polymerase Chain Reaction (qPCR) as reference standard. The operational feasibility of introducing LAMP testing in the mobile screening campaigns was assessed based on field-suitability parameters, along with a pilot POC-LAMP assay in a riverine community without laboratory infrastructure.LAMP had a sensitivity of 91.8% (87.7-94.9) and specificity of 91.9% (87.8-95.0), and the overall accuracy was significantly better among samples collected during road screenings than riverine communities (p≤0.004). LAMP-based diagnostic strategy was successfully implemented within the field-team logistics and the POC-LAMP pilot in the riverine community allowed for a reduction in the turnaround time for case management, from 12-24 hours to less than 5 hours. Specimens with haemolytic appearance were regularly observed in riverine screenings and could help explaining the hindered performance/interpretation of the LAMP reaction in these communities.LAMP-based molecular malaria diagnosis can be deployed outside of reference laboratories, providing similar performance as qPCR. However, scale-up in remote field settings such as riverine communities needs to consider a number of logistical challenges (e.g. environmental conditions, labour-intensiveness in large population screenings) that can influence its optimal implementation.

With low and markedly seasonal malaria transmission, increasingly sensitive tools for better stratifying the risk of infection and targeting control interventions are needed. A cross-sectional survey to characterize the current malaria transmission patterns, identify hotspots, and detect recent changes using parasitological and serological measures was conducted in three sites of the Peruvian Amazon.After full census of the study population, 651 participants were interviewed, clinically examined and had a blood sample taken for the detection of malaria parasites (microscopy and PCR) and antibodies against P. vivax (PvMSP119, PvAMA1) and P. falciparum (PfGLURP, PfAMA1) antigens by ELISA. Risk factors for malaria infection (positive PCR) and malaria exposure (seropositivity) were assessed by multivariate survey logistic regression models. Age-specific seroprevalence was analyzed using a reversible catalytic conversion model based on maximum likelihood for generating seroconversion rates (SCR, λ). SaTScan was used to detect spatial clusters of serology-positive individuals within each site.The overall parasite prevalence by PCR was low, i.e. 3.9% for P. vivax and 6.7% for P. falciparum, while the seroprevalence was substantially higher, 33.6% for P. vivax and 22.0% for P. falciparum, with major differences between study sites. Age and location (site) were significantly associated with P. vivax exposure; while location, age and outdoor occupation were associated with P. falciparum exposure. P. falciparum seroprevalence curves showed a stable transmission throughout time, while for P. vivax transmission was better described by a model with two SCRs. The spatial analysis identified well-defined clusters of P. falciparum seropositive individuals in two sites, while it detected only a very small cluster of P. vivax exposure.The use of a single parasitological and serological malaria survey has proven to be an efficient and accurate method to characterize the species specific heterogeneity in malaria transmission at micro-geographical level as well as to identify recent changes in transmission.

The aim of this study was to determine whether the abundance of regulatory T cells (Tregs) (CD4+CD25high) affects the de novo development of anti-HLA donor-specific antibodies (DSAs) in kidney transplant recipients (KTRs). Methods. Unsensitized (PRA ≤ 10%, no DSA) adult primary KTRs who received a living (83%) or deceased (17%) KT in our Institution during 2004/2005 were included. DSA testing was performed monthly, and Tregs were quantified by flow cytometry every 3 months, during the 1st year after KT. All patients received triple drug immunosuppressive therapy (CNI + MMF or AZA + PDN); 83% received anti-CD25. Results. 53 KTRs were included; 32% developed DSA during the 1st year after KT. Significantly lower 7-year graft survival was observed in those who developed DSA. No difference was observed in Treg numbers up to 9 months after KT, between DSA positive and negative. However, at 12 months after KT, DSA-negative patients had significantly higher numbers of Treg. Conclusions. Early development of DSA was not associated to variations in Treg abundance. The differences in Treg numbers observed at the late time point may reflect better immune acceptance of the graft and may be associated to long-term effects. Additional inhibitory mechanisms participating earlier in DSA development after KT deserve to be sought.

IntroductionExposure to air pollution disproportionately affects racial/ethnic minorities that could contribute to health inequalities including metabolic disorders. However, most existing studies used a static assessment of air pollution exposure (mostly using the residential address) and do not account for activity space when modelling exposure to air pollution. The aim of this study is to understand how exposure to air pollution impacts metabolic disorders biomarkers, how this effect differs according to ethnicity, and for the first time compare these findings with two methods of exposure assessment: dynamic and static measures.MethodsAmong the Community of Mine study, a cross-sectional study conducted in San Diego County, insulin resistance, diabetes, hypertension, obesity, dyslipidemia, and metabolic syndrome (MetS) were assessed. Exposure to air pollution (PM2.5, NO2, traffic) was calculated using static measures around the home, and dynamic measures of mobility derived from Global Positioning Systems (GPS) traces using kernel density estimators to account for exposure variability across space and time. Associations of air pollution with metabolic disorders were quantified using generalized estimating equation models to account for the clustered nature of the data.ResultsAmong 552 participants (mean age 58.7 years, 42% Hispanic/Latino), Hispanics/Latinos had a higher exposure to PM2.5 compared to non-Hispanics using static measures. In contrast, Hispanics/Latinos had less exposure to PM2.5 using dynamic measures. For all participants, higher dynamic exposure to PM2.5 and NO2 was associated with increased insulin resistance and cholesterol levels, and increased risk of obesity, dyslipidemia and MetS (RR 1.17, 95% CI: 1.07-1.28; RR 1.21, 95% CI: 1.12-1.30, respectively). The association between dynamic PM2.5 exposure and MetS differed by Hispanic/Latino ethnicity.ConclusionThese results highlight the importance of considering people's daily mobility in assessing the impact of air pollution on health.

Objectives: This study aims to estimate the short-term preventable mortality and associated economic costs of complying with the World Health Organization (WHO) air quality guidelines (AQGs) limit values for PM10 and PM2.5 in nine major Latin American cities. Methods: We estimated city-specific PM-mortality associations using time-series regression models and calculated the attributable mortality fraction. Next, we used the value of statistical life to calculate the economic benefits of complying with the WHO AQGs limit values. Results: In most cities, PM concentrations exceeded the WHO AQGs limit values more than 90% of the days. PM10 was found to be associated with an average excess mortality of 1.88%with concentrations above WHO AQGs limit values, while for PM2.5 it was 1.05%. The associated annual economic costs varied widely, between US$ 19.5 million to 3,386.9 million for PM10, and US$ 196.3 million to 2,209.6 million for PM2.5. Conclusion: Our findings suggest that there is an urgent need for policymakers to develop interventions to achieve sustainable air quality improvements in Latin America. Complying with the WHO AQGs limit values for PM10 and PM2.5 in Latin American cities would substantially benefits for urban populations. [ABSTRACT FROM AUTHOR]