Search

Metabolic syndrome is a cluster of risk factors for cardiovascular disease afflicting more than 1 billion people worldwide and is increasingly being identified in younger age groups and in socioeconomically disadvantaged settings in the global south. Enteropathogen exposure and environmental enteropathy in infancy may contribute to metabolic syndrome by disrupting the metabolic profile in a way that is detectable in cardiometabolic markers later in childhood. A total of 217 subjects previously enrolled in a birth cohort in Amazonian Peru were monitored annually from ages 2 to 5 years. A total of 197 blood samples collected in later childhood were analyzed for 37 cardiometabolic biomarkers, including adipokines, apolipoproteins, cytokines, which were matched to extant early-life markers of enteropathy ascertained between birth and 2 years. Multivariate and multivariable regression models were fitted to test for associations, adjusting for confounders. Fecal and urinary markers of intestinal permeability and inflammation (myeloperoxidase, lactulose, and mannitol) measured in infancy were associated with later serum concentrations of soluble CD40-ligand, a proinflammatory cytokine correlated with adverse metabolic outcomes. Fecal myeloperoxidase was also associated with later levels of omentin-1. Enteric protozoa exposure showed stronger associations with later cardiometabolic markers than viruses, bacteria, and overall diarrheal episodes. Early-life enteropathy markers were associated with altered adipokine, apolipoprotein, and cytokine profiles later in childhood consistent with an adverse cardiometabolic disease risk profile in this cohort. Markers of intestinal permeability and inflammation measured in urine (lactulose, mannitol) and stool (myeloperoxidase, protozoal infections) during infancy may predict metabolic syndrome in adulthood.

Background: Diarrhea remains a leading cause of childhood illness throughout the world and is caused by various species of ecologically sensitive pathogens. The emerging Planetary Health movement emphasizes the interdependence of human health with natural systems, and much of its focus has been on infectious diseases and their interactions with environmental and human processes. Meanwhile, the era of big data has engendered a public appetite for interactive web-based dashboards for infectious diseases. However, enteric infectious diseases have been largely overlooked by these developments. Methods: The Planetary Child Health and Enterics Observatory (Plan-EO) is a new initiative that builds on existing partnerships between epidemiologists, climatologists, bioinformaticians, and hydrologists as well as investigators in numerous low- and middle-income countries. Its objective is to provide the research and stakeholder community with an evidence base for the geographical targeting of enteropathogen-specific child health interventions such as novel vaccines. The initiative will produce, curate, and disseminate spatial data products relating to the distribution of enteric pathogens and their environmental and sociodemographic determinants. Discussion: As climate change accelerates there is an urgent need for etiology-specific estimates of diarrheal disease burden at high spatiotemporal resolution. Plan-EO aims to address key challenges and knowledge gaps by making rigorously obtained, generalizable disease burden estimates freely available and accessible to the research and stakeholder communities. Pre-processed environmental and EO-derived spatial data products will be housed, continually updated, and made publicly available to the research and stakeholder communities both within the webpage itself and for download. These inputs can then be used to identify and target priority populations living in transmission hotspots and for decision-making, scenario-planning, and disease burden projection. Study registration: PROSPERO protocol #CRD42023384709

Background The study of the etiology of acute febrile illness (AFI) has historically been designed as a prevalence of pathogens detected from a case series. This strategy has an inherent unrealistic assumption that all pathogen detection allows for causal attribution, despite known asymptomatic carriage of the principal causes of acute febrile illness in most low- and middle-income countries (LMICs). We designed a semi-quantitative PCR in a modular format to detect bloodborne agents of acute febrile illness that encompassed common etiologies of AFI in the region, etiologies of recent epidemics, etiologies that require an immediate public health response and additional pathogens of unknown endemicity. We then designed a study that would delineate background levels of transmission in the community in the absence of symptoms to provide corrected estimates of attribution for the principal determinants of AFI. Methods A case-control study of acute febrile illness in patients ten years or older seeking health care in Iquitos, Loreto, Peru, was planned. Upon enrollment, we will obtain blood, saliva, and mid-turbinate nasal swabs at enrollment with a follow-up visit on day 21–28 following enrollment to attain vital status and convalescent saliva and blood samples, as well as a questionnaire including clinical, socio-demographic, occupational, travel, and animal contact information for each participant. Whole blood samples are to be simultaneously tested for 32 pathogens using TaqMan array cards. Mid-turbinate samples will be tested for SARS-CoV-2, Influenza A and Influenza B. Conditional logistic regression models will be fitted treating case/control status as the outcome and with pathogen-specific sample positivity as predictors to attain estimates of attributable pathogen fractions for AFI. Discussion The modular PCR platforms will allow for reporting of all primary results of respiratory samples within 72 h and blood samples within one week, allowing for results to influence local medical practice and enable timely public health responses. The inclusion of controls will allow for a more accurate estimate of the importance of specific prevalent pathogens as a cause of acute illness. Study Registration Project 1791, Registro de Proyectos de Investigación en Salud Pública (PRISA), Instituto Nacional de Salud, Perú.

Background: Diarrhea remains a leading cause of childhood illness throughout the world and is caused by various species of ecologically sensitive pathogens. The emerging Planetary Health movement emphasizes the interdependence of human health with natural systems, and much of its focus has been on infectious diseases and their interactions with environmental and human processes. Meanwhile, the era of big data has engendered a public appetite for interactive web-based dashboards for infectious diseases. However, enteric infectious diseases have been largely overlooked by these developments. Methods: The Planetary Child Health and Enterics Observatory (Plan-EO) is a new initiative that builds on existing partnerships between epidemiologists, climatologists, bioinformaticians, and hydrologists as well as investigators in numerous low- and middle-income countries. Its objective is to provide the research and stakeholder community with an evidence base for the geographical targeting of enteropathogen-specific child health interventions such as novel vaccines. The initiative will produce, curate, and disseminate spatial data products relating to the distribution of enteric pathogens and their environmental and sociodemographic determinants. Discussion: To date Plan-EO has compiled data from 23 studies comprising almost 80,000 stool samples from 35,000 children aged 0 – 59 months at 80 sites in 24 countries and georeferenced to over 9,000 unique locations, with DUAs for two further studies under negotiation. An initial analysis of Shigella has been published and has yielded detailed prediction maps.Results like these can be used to identify and target priority populations living in transmission hotspots and to provide an urgently needed evidence base for decision-making, scenario-planning. Study registration: PROSPERO protocol #CRD42023384709

BACKGROUND: Diarrhoeal disease is a leading cause of childhood illness and death globally, and Shigella is a major aetiological contributor for which a vaccine might soon be available. The primary objective of this study was to model the spatiotemporal variation in paediatric Shigella infection and map its predicted prevalence across low-income and middle-income countries (LMICs). METHODS: Individual participant data for Shigella positivity in stool samples were sourced from multiple LMIC-based studies of children aged 59 months or younger. Covariates included household-level and participant-level factors ascertained by study investigators and environmental and hydrometeorological variables extracted from various data products at georeferenced child locations. Multivariate models were fitted and prevalence predictions obtained by syndrome and age stratum. FINDINGS: 20 studies from 23 countries (including locations in Central America and South America, sub-Saharan Africa, and south and southeast Asia) contributed 66 563 sample results. Age, symptom status, and study design contributed most to model performance followed by temperature, wind speed, relative humidity, and soil moisture. Probability of Shigella infection exceeded 20% when both precipitation and soil moisture were above average and had a 43% peak in uncomplicated diarrhoea cases at 33°C temperatures, above which it decreased. Compared with unimproved sanitation, improved sanitation decreased the odds of Shigella infection by 19% (odds ratio [OR]=0·81 [95% CI 0·76-0·86]) and open defecation decreased them by 18% (OR=0·82 [0·76-0·88]). INTERPRETATION: The distribution of Shigella is more sensitive to climatological factors, such as temperature, than previously recognised. Conditions in much of sub-Saharan Africa are particularly propitious for Shigella transmission, although hotspots also occur in South America and Central America, the Ganges-Brahmaputra Delta, and the island of New Guinea. These findings can inform prioritisation of populations for future vaccine trials and campaigns. FUNDING: NASA, National Institutes of Health-The National Institute of Allergy and Infectious Diseases, and Bill & Melinda Gates Foundation. publishedVersion

Existing acute febrile illness (AFI) surveillance systems can be leveraged to identify and characterize emerging pathogens, such as SARS-CoV-2, which causes COVID-19. The US Centers for Disease Control and Prevention collaborated with ministries of health and implementing partners in Belize, Ethiopia, Kenya, Liberia, and Peru to adapt AFI surveillance systems to generate COVID-19 response information. Staff at sentinel sites collected epidemiologic data from persons meeting AFI criteria and specimens for SARS-CoV-2 testing. A total of 5,501 patients with AFI were enrolled during March 2020-October 2021;69% underwent SARS-CoV-2 testing. Percentage positivity for SARS-CoV-2 ranged from 4% (87/2,151, Kenya) to 19% (22/115, Ethiopia). We show SARS-CoV-2 testing was successfully integrated into AFI surveillance in 5 low- to middle-income countries to detect COVID-19 within AFI care-seeking populations. AFI surveillance systems can be used to build capacity to detect and respond to both emerging and endemic infectious disease threats.

BackgroundDiarrheal disease remains a leading cause of childhood illness and mortality and Shigella is a major etiological contributor for which a vaccine may soon be available. This study aimed to model the spatiotemporal variation in pediatric Shigella infection and map its predicted prevalence across low- and middle-income countries (LMICs).MethodsIndependent participant data on Shigella positivity in stool samples collected from children aged ≤59 months were sourced from multiple LMIC-based studies. Covariates included household- and subject-level factors ascertained by study investigators and environmental and hydrometeorological variables extracted from various data products at georeferenced child locations. Multivariate models were fitted, and prevalence predictions obtained by syndrome and age stratum.Findings20 studies from 23 countries contributed 66,563 sample results. Age, symptom status, and study design contributed most to model performance followed by temperature, wind speed, relative humidity, and soil moisture. Shigella probability exceeded 20% when both precipitation and soil moisture were above average and had a 43% peak in uncomplicated diarrhea cases at 33°C temperatures, above which it decreased. Improved sanitation and open defecation decreased Shigella odds by 19% and 18% respectively compared to unimproved sanitation.InterpretationThe distribution of Shigella is more sensitive to climatological factors like temperature than previously recognized. Conditions in much of sub-Saharan Africa are particularly propitious for Shigella transmission, though hotspots also occur in South and Central America, the Ganges–Brahmaputra Delta, and New Guinea. These findings can inform prioritization of populations for future vaccine trials and campaigns.FundingNASA 16-GEO16-0047; NIH-NIAID 1R03AI151564-01; BMGF OPP1066146.

BackgroundThe COVID-19 pandemic has caused societal disruption globally and South America has been hit harder than other lower-income regions. This study modeled effects of 6 weather variables on district-level SARS-CoV-2 reproduction numbers (Rt) in three contiguous countries of Tropical Andean South America (Colombia, Ecuador, and Peru), adjusting for environmental, policy, healthcare infrastructural and other factors.MethodsDaily time-series data on SARS-CoV-2 infections were sourced from health authorities of the three countries at the smallest available administrative level. Rt values were calculated and merged by date and unit ID with variables from a Unified COVID-19 dataset and other publicly available sources for May – December 2020. Generalized additive mixed effects models were fitted.FindingsRelative humidity and solar radiation were inversely associated with SARS-CoV-2 Rt. Days with radiation above 1,000 KJ/m2 saw a 1.3%, and those with humidity above 50%, a 1.0% reduction in Rt. Transmission was highest in densely populated districts, and lowest in districts with poor healthcare access and on days with least population mobility. Temperature, region, aggregate government policy response and population age structure had little impact. The fully adjusted model explained 3.9% of Rt variance.InterpretationDry atmospheric conditions of low humidity increase, and higher solar radiation decrease district-level SARS-CoV-2 reproduction numbers, effects that are comparable in magnitude to population factors like lockdown compliance. Weather monitoring could be incorporated into disease surveillance and early warning systems in conjunction with more established risk indicators and surveillance measures.FundingNASA’s Group on Earth Observations Work Programme (16-GEO16-0047).

Background There is a need to evaluate antibiotic use, duration of therapy, and stewardship in low- and middle-income countries to guide the development of appropriate stewardship programs that are global in scope and effectively decrease unnecessary antibiotic use. Methods We prospectively collected information on illness occurrence and antibiotic use from a cohort of 303 children. We evaluated the incidence, duration of therapy, and appropriateness of antibiotic prescriptions by 5 main antibiotic prescribers (physicians and nurses, pharmacists, nursing assistants, self-prescriptions, and neighbors or family members). Results Ninety percent of children received an antibiotic during follow-up, and on average, by the end of follow-up a child had spent 4.3% of their first 5 years of life on antibiotics. The most frequent prescribers were physicians/nurses (79.4%), followed by pharmacists (8.1%), self-prescriptions (6.8%), nursing assistants (3.7%), and family or neighbors (1.9%). Of the 3702 courses of antibiotics prescribed, 30.9% were done so for the occurrence of fever, 25.3% for diarrhea, 2.8% for acute lower respiratory disease, 2.7% for dysentery, and 38.2% for an undetermined illness. Courses exceeding the recommended duration were common for the principal diseases for which treatment was initiated, with 27.3% of courses exceeding the recommended length duration, representing a potential reduction in 13.2% of days on which this cohort spent on antibiotics. Conclusions Stewardship programs should target medical personnel for a primary care stewardship program even in a context in which antibiotics are available to the public with little or no restrictions and appropriate duration should be emphasized in this training.

Bifidobacterium longum subspecies detected in infant stool have been associated with numerous subsequent health outcomes and are potential early markers of deviation from healthy developmental trajectories. This analysis derived indicators of carriage and early colonization with B. infantis and B. longum and quantified their associations with a panel of early-life exposures and outcomes. In a sub-study nested within a multi-site birth cohort, extant stool samples from infants in Bangladesh, Pakistan and Tanzania were tested for presence and quantity of two Bifidobacterium longum subspecies. The results were matched to indicators of nutritional status, enteropathogen infection, histo-blood group antigens, vaccine response and feeding status and regression models were fitted to test for associations while adjusting for covariates. B. infantis was associated with lower quantity of and decreased odds of colonization with B. longum, and vice versa. Length at birth was associated with a 0.36 increase in log10B. infantis and a 0.28 decrease in B. longum quantity at 1 month of age. B. infantis colonization was associated with fewer viral infections and small reductions in the risk of rotavirus and sapovirus infections, but not reduced overall diarrheal disease risk. No associations with vaccine responses, HBGAs or later nutritional status were identified. Suboptimal intrauterine growth and a shorter duration of exclusive breastfeeding may predispose infants to early intestinal colonization with the B. longum subspecies at the expense of B. infantis, thus denying them potential benefits of reduced enteric virus episodes.

Summary Background Climate change threatens to undermine recent progress in reducing global deaths from diarrhoeal disease in children. However, the scarcity of evidence about how individual environmental factors affect transmission of specific pathogens makes prediction of trends under different climate scenarios challenging. We aimed to model associations between daily estimates of a suite of hydrometeorological variables and rotavirus infection status ascertained through community-based surveillance. Methods For this analysis of multisite cohort data, rotavirus infection status was ascertained through community-based surveillance of infants in the eight-site MAL-ED cohort study, and matched by date with earth observation estimates of nine hydrometeorological variables from the Global Land Data Assimilation System: daily total precipitation volume (mm), daily total surface runoff (mm), surface pressure (mbar), wind speed (m/s), relative humidity (%), soil moisture (%), solar radiation (W/m 2 ), specific humidity (kg/kg), and average daily temperatures (°C). Lag relationships, independent effects, and interactions were characterised by use of modified Poisson models and compared with and without adjustment for seasonality and between-site variation. Final models were created with stepwise selection of main effects and interactions and their validity assessed by excluding each site in turn and calculating Tjur's Coefficients of Determination. Findings All nine hydrometeorological variables were significantly associated with rotavirus infection after adjusting for seasonality and between-site variation over multiple consecutive or non-consecutive lags, showing complex, often non-linear associations that differed by symptom status and showed considerable mutual interaction. The final models explained 5·9% to 6·2% of the variability in rotavirus infection in the pooled data and their predictions explained between 0·0% and 14·1% of the variability at individual study sites. Interpretation These results suggest that the effect of climate on rotavirus transmission was mediated by four independent mechanisms: waterborne dispersal, airborne dispersal, virus survival on soil and surfaces, and host factors. Earth observation data products available at a global scale and at subdaily resolution can be combined with longitudinal surveillance data to test hypotheses about routes and drivers of transmission but showed little potential for making predictions in this setting. Funding Bill & Melinda Gates Foundation; Foundation for the National Institutes of Health, National Institutes of Health, Fogarty International Center; Sherrilyn and Ken Fisher Center for Environmental Infectious Diseases, Johns Hopkins School of Medicine; and NASA's Group on Earth Observations Work Programme.

An impressive number of COVID-19 data catalogs exist. None, however, are optimized for data science applications, e.g., inconsistent naming and data conventions, uneven quality control, and lack of alignment between disease data and potential predictors pose barriers to robust modeling and analysis. To address this gap, we generated a unified dataset that integrates and implements quality checks of the data from numerous leading sources of COVID-19 epidemiological and environmental data. We use a globally consistent hierarchy of administrative units to facilitate analysis within and across countries. The dataset applies this unified hierarchy to align COVID-19 case data with a number of other data types relevant to understanding and predicting COVID-19 risk, including hydrometeorological data, air quality, information on COVID-19 control policies, and key demographic characteristics.

BackgroundMetabolic syndrome is a cluster of risk factors for cardiovascular disease thought to afflict over a billion people worldwide and is increasingly being identified in younger age groups and socio-economically disadvantaged settings in the global south. Enteropathogen exposure and environmental enteropathy in infancy may lead to metabolic syndrome by disrupting the metabolic profile in a way that is detectable in cardiometabolic markers later in childhood.Methods217 subjects previously enrolled in a birth cohort in Amazonian Peru were followed up annually from ages 2 to 5 years. Blood samples collected in later childhood were analyzed for a panel of 37 cardiometabolic biomarkers, including adipokines, apolipoproteins, cytokines, and other analytes. These were matched to extant early-life markers of enteropathy ascertained between birth and 2 years of age. Multivariate and multivariable regression models were fitted to test for associations adjusting for confounders.ResultsFecal and urinary markers of intestinal permeability and inflammation (myeloperoxidase, lactulose and mannitol) measured from birth to 2 years of age were independently associated with later serum concentrations of soluble CD40-ligand, a proinflammatory cytokine correlated with adverse metabolic outcomes. Fecal myeloperoxidase was also strongly, directly associated with later levels of the anti-inflammatory adipocytokine omentin-1. Cumulative enteric protozoa exposure before 2 years of age showed stronger associations with later cardiometabolic markers than enteric viruses and bacteria and overall diarrheal episodes.ConclusionEarly-life markers of enteric infection and enteropathy were associated with numerous changes in adipokine, apolipoprotein and cytokine profiles later in childhood consistent with those of an adverse cardiometabolic disease risk profile in this Peruvian birth cohort. Markers of intestinal permeability and inflammation measured in urine (lactulose, mannitol) and stool (myeloperoxidase, protozoal infections) during infancy, may predict disruptions to cytokine and adipocytokine production in later childhood that are precursors to metabolic syndrome in adulthood. Chronic enteric infections, such as by protozoan pathogens, may be more important drivers of these changes than symptomatic diarrhea or growth faltering.FundingBill & Melinda Gates Foundation OPP1066146 and OPP1152146.

Using previously validated microbial source tracking markers, we detected and quantified fecal contamination from avian species and avian exposure, dogs, and humans on household cooking tables and floors. The association among contamination, infrastructure, and socioeconomic covariates was assessed using simple and multiple ordinal logistic regressions. The presence ofCampylobacterspp. in surface samples was linked to avian markers. Using molecular methods, animal feces were detected in 75.0% and human feces in 20.2% of 104 households. Floors were more contaminated than tables as detected by the avian markerAv4143, dog markerBactcan, and human markerBachum. Wood tables were consistently more contaminated than non-wood surfaces, specifically with the mitochondrial avian markersND5and CytB, fecal marker Av4143, and canine markerBactcan. Final multivariable models with socioeconomic and infrastructure characteristics included as covariates indicate that detection of avian feces and avian exposure was associated with the presence of chickens, maternal age, and length of tenancy, whereas detection of human markers was associated with unimproved water source. Detection ofCampylobacterin surface samples was associated with the avian fecal marker Av4143. We highlight the critical need to detect and measure the burden of animal fecal waste when evaluating household water, hygiene, and sanitation interventions, and the possibility of decreasing risk of exposure through the modification of surfaces to permit more effective household disinfection practices. Animals may be a more important source of household fecal contamination than humans in many low-resource settings, although interventions have historically focused almost exclusively on managing human waste.

Diarrheal disease, still a major cause of childhood illness, is caused by numerous, diverse infectious microorganisms, which are differentially sensitive to environmental conditions. Enteropathogen‐specific impacts of climate remain underexplored. Results from 15 studies that diagnosed enteropathogens in 64,788 stool samples from 20,760 children in 19 countries were combined. Infection status for 10 common enteropathogens—adenovirus, astrovirus, norovirus, rotavirus, sapovirus, Campylobacter, ETEC, Shigella, Cryptosporidium and Giardia—was matched by date with hydrometeorological variables from a global Earth observation dataset—precipitation and runoff volume, humidity, soil moisture, solar radiation, air pressure, temperature, and wind speed. Models were fitted for each pathogen, accounting for lags, nonlinearity, confounders, and threshold effects. Different variables showed complex, non‐linear associations with infection risk varying in magnitude and direction depending on pathogen species. Rotavirus infection decreased markedly following increasing 7‐day average temperatures—a relative risk of 0.76 (95% confidence interval: 0.69–0.85) above 28°C—while ETEC risk increased by almost half, 1.43 (1.36–1.50), in the 20–35°C range. Risk for all pathogens was highest following soil moistures in the upper range. Humidity was associated with increases in bacterial infections and decreases in most viral infections. Several virus species' risk increased following lower‐than‐average rainfall, while rotavirus and ETEC increased with heavier runoff. Temperature, soil moisture, and humidity are particularly influential parameters across all enteropathogens, likely impacting pathogen survival outside the host. Precipitation and runoff have divergent associations with different enteric viruses. These effects may engender shifts in the relative burden of diarrhea‐causing agents as the global climate changes., Key Points Many pathogens cause diarrhea in children and are affected in different ways by weather conditions like rainfall, temperature, and humidityDiarrhea‐causing bacteria infections increase in warm, humid weather and when soil is moist, though ETEC is sensitive to rainfall extremesHigh humidity decreases prevalence of several viruses, though only rotavirus increases in cold weather and following heavy surface runoff

Diarrheal disease remains a major cause of childhood mortality and morbidity causing poor health and economic outcomes. In low-resource settings, young children are exposed to numerous risk factors for enteric pathogen transmission within their dwellings, though the relative importance of different transmission pathways varies by pathogen species. The objective of this analysis was to model associations between five household-level risk factors&mdash, water, sanitation, flooring, caregiver education, and crowding&mdash, and infection status for endemic enteric pathogens in children in five surveillance studies. Data were combined from 22 sites in which a total of 58,000 stool samples were tested for 16 specific enteropathogens using qPCR. Risk ratios for pathogen- and taxon-specific infection status were modeled using generalized linear models along with hazard ratios for all-cause diarrhea in proportional hazard models, with the five household-level variables as primary exposures adjusting for covariates. Improved drinking water sources conferred a 17% reduction in diarrhea risk, however, the direction of its association with particular pathogens was inconsistent. Improved sanitation was associated with a 9% reduction in diarrhea risk with protective effects across pathogen species and taxa of around 10&ndash, 20% risk reduction. A 9% reduction in diarrhea risk was observed in subjects with covered floors, which were also associated with decreases in risk for zoonotic enteropathogens. Caregiver education and household crowding showed more modest, inconclusive results. Combining data from diverse sites, this analysis quantified associations between five household-level exposures on risk of specific enteric infections, effects which differed by pathogen species but were broadly consistent with hypothesized transmission mechanisms. Such estimates may be used within expanded water, sanitation, and hygiene (WASH) programs to target interventions to the particular pathogen profiles of individual communities and prioritize resources.

Undernourished children are metabolically immature relative to those growing normally across 3 diverse epidemiologic contexts., Malnutrition continues to affect the growth and development of millions of children worldwide, and chronic undernutrition has proven to be largely refractory to interventions. Improved understanding of metabolic development in infancy and how it differs in growth-constrained children may provide insights to inform more timely, targeted, and effective interventions. Here, the metabolome of healthy infants was compared to that of growth-constrained infants from three continents over the first 2 years of life to identify metabolic signatures of aging. Predictive models demonstrated that growth-constrained children lag in their metabolic maturity relative to their healthier peers and that metabolic maturity can predict growth 6 months into the future. Our results provide a metabolic framework from which future nutritional programs may be more precisely constructed and evaluated.

Improving understanding of the pathogen-specific seasonality of enteric infections is critical to informing policy on the timing of preventive measures and to forecast trends in the burden of diarrhoeal disease. Data obtained from active surveillance of cohorts can capture the underlying infection status as transmission occurs in the community. The purpose of this study was to characterise rotavirus seasonality in eight different locations while adjusting for age, calendar time and within-subject clustering of episodes by applying an adapted Serfling model approach to data from a multi-site cohort study. In the Bangladesh and Peru sites, within-subject clustering was high, with more than half of infants who experienced one rotavirus infection going on to experience a second and more than 20% experiencing a third. In the five sites that are in countries that had not introduced the rotavirus vaccine, the model predicted a primary peak in prevalence during the dry season and, in three of these, a secondary peak during the rainy season. The patterns predicted by this approach are broadly congruent with several emerging hypotheses about rotavirus transmission and are consistent for both symptomatic and asymptomatic rotavirus episodes. These findings have practical implications for programme design, but caution should be exercised in deriving inferences about the underlying pathways driving these trends, particularly when extending the approach to other pathogens.

The performance of eight microbial source tracking (MST) markers was evaluated in a low-resource, tropical community located in Iquitos, Peru. Fecal samples from humans, dogs, cats, rats, goats, buffalos, guinea-pigs, chickens, ducks, pigeons, and parrots were collected (n = 117). All samples were tested with human (BacHum, HF183-Taqman), dog (BactCan), pig (Pig-2-Bac), and avian (LA35, Av4143, ND5, cytB) markers using quantitative PCR (qPCR). Internal validity metrics were calculated using all animal fecal samples, as well as animal fecal samples contextually relevant for the Peruvian Amazon. Overall, Pig-2-Bac performed best, with 100% sensitivity and 88.5% specificity to detect the correct fecal source. Human-associated markers showed a sensitivity of 80.0% and 76.7%, and specificity of 66.2% and 67.6%. When limiting the analysis to contextually relevant animal fecal samples for the Peruvian Amazon, Av143 surpassed cytB with 95.7% sensitivity and 81.8% specificity. BactCan demonstrated 100% sensitivity and 47.4% specificity. The gene copy number detected by BacHum and HF183-Taqman were positively correlated (Pearson's correlation coefficient: 0.785), as well as avian markers cytB with Av4143 (Pearson's correlation coefficient: 0.508) and nd5 (Pearson's correlation coefficient: 0.949). These findings suggest that markers such as Av4143, Pig2Bac, cytb and BacHum have acceptable performance to be impactful in source attribution studies for zoonotic enteric disease transmission in this and similar low-resource communities., Graphical abstract Unlabelled Image, Highlights • First report of six fecal microbial source tracking markers and two avian mitochondrial microbial source tracking markers in the Peruvian Amazon. • Pig-2-Bac was the best performing marker, with 100% sensitivity and 88.5% specificity. • Based on the sensitivity and specificity, it is possible to use Av413, cytb and HF183-Taqman markers for the attribution of fecal contamination and exposure in this setting.

Environmental enteric dysfunction (EED) is associated with chronic undernutrition. Efforts to identify minimally invasive biomarkers of EED reveal an expanding number of candidate analytes. An analytic strategy is reported to select among candidate biomarkers and systematically express the strength of each marker’s association with linear growth in infancy and early childhood. 180 analytes were quantified in fecal, urine and plasma samples taken at 7, 15 and 24 months of age from 258 subjects in a birth cohort in Peru. Treating the subjects’ length-for-age Z-score (LAZ-score) over a 2-month lag as the outcome, penalized linear regression models with different shrinkage methods were fitted to determine the best-fitting subset. These were then included with covariates in linear regression models to obtain estimates of each biomarker’s adjusted effect on growth. Transferrin had the largest and most statistically significant adjusted effect on short-term linear growth as measured by LAZ-score–a coefficient value of 0.50 (0.24, 0.75) for each log2 increase in plasma transferrin concentration. Other biomarkers with large effect size estimates included adiponectin, arginine, growth hormone, proline and serum amyloid P-component. The selected subset explained up to 23.0% of the variability in LAZ-score. Penalized regression modeling approaches can be used to select subsets from large panels of candidate biomarkers of EED. There is a need to systematically express the strength of association of biomarkers with linear growth or other outcomes to compare results across studies., Author summary Childhood undernutrition is widespread throughout the world and has severe, long-lasting health impacts. Substances measured in blood, urine and stool could be used as biomarkers to identify children undergoing growth failure before these impacts occur. However, it is not yet known which of the many markers that can be identified are accurate and clinically useful predictors of poor growth in infants and children. This study used a large number of candidate biomarkers of immune activation, metabolism and hormones and applied statistical methods to narrow them down from 110 different substances, to the 36 best predictors of growth in 258 Peruvian infants. It also estimated how large the effect of each of these markers was on height two months later. The biomarker with the largest effect was transferrin, a glycoprotein that can be measured in blood samples. 15-month old children with elevated transferrin were around two thirds of a centimeter taller on average at 17 months than those with low levels. Transferrin and other proteins, glycoproteins, hormones and antibodies that this study identified, can be measured easily and affordably in standard laboratories making them feasible to be used broadly as prognostic markers as part of child health and nutrition programs in under-resourced settings.

Background: Environmental enteropathy (EE) is associated with chronic undernutrition. Efforts to identify minimally invasive biomarkers of EE reveal an expanding number of candidate analytes. An analytic strategy is reported to select among candidate biomarkers and systematically express the strength of each marker's association with linear growth in infancy and early childhood. Methods: 180 analytes were quantified in fecal, urine and plasma samples taken at 7, 15 and 24 months of age from 258 subjects in a birth cohort in Peru. Treating the subjects' length-for-age Z-score (LAZ-score) over a 2-month lag as the outcome, penalized linear regression models with different shrinkage methods were fitted to determine the best-fitting subset. These were then included with covariates in linear regression models to obtain estimates of each biomarker's adjusted effect on growth. Findings: Transferrin had the largest and most statistically significant adjusted effect on short-term linear growth as measured by LAZ-score - a coefficient value of 0.76 (0.48, 1.05) for each log2 increase in plasma transferrin concentration relative to the sample mean. Other biomarkers with large effect size estimates included 4-hydroxyproline, tumor necrosis factor receptor 2, beta-2-microglobulin, proline, serum amyloid P-component and adiponectin. The selected subset explained up to 26.0% of the variability in LAZ-score. Interpretation: Penalized regression modeling approaches can be used to select subsets from large panels of candidate biomarkers of EE. There is a need to systematically express the strength of association of biomarkers with linear growth or other select outcomes to compare results across studies. Funding: Bill & Melinda Gates Foundation. Declaration of Interest: The authors report no conflicts of interest. The information has not previously been presented in any meetings. Ethical Approval: Ethical approval for MAL-ED was given by the Johns Hopkins Institutional Review Board as well as the Ethics Committee of Asociacion Benefica PRISMA, and the Regional Health Department of Loreto.

Background Histo-blood group antigens (HBGAs) such as fucosyltransferase (FUT)2 and 3 may act as innate host factors that differentially influence susceptibility of individuals and their offspring to pediatric enteric infections. Methods In 3 community-based birth cohorts, FUT2 and FUT3 statuses were ascertained for mother-child dyads. Quantitative polymerase chain reaction panels tested 3663 diarrheal and 18 148 asymptomatic stool samples for 29 enteropathogens. Cumulative diarrhea and infection incidence were compared by child (n = 520) and mothers’ (n = 519) HBGA status and hazard ratios (HRs) derived for all-cause diarrhea and specific enteropathogens. Results Children of secretor (FUT2 positive) mothers had a 38% increased adjusted risk of all-cause diarrhea (HR = 1.38; 95% confidence interval (CI), 1.15–1.66) and significantly reduced time to first diarrheal episode. Child FUT2 and FUT3 positivity reduced the risk for all-cause diarrhea by 29% (HR = 0.81; 95% CI, 0.71–0.93) and 27% (HR = 0.83; 95% CI, 0.74–0.92), respectively. Strong associations between HBGAs and pathogen-specific infection and diarrhea were observed, particularly for noroviruses, rotaviruses, enterotoxigenic Escherichia coli, and Campylobacter jejuni/coli. Conclusions Histo-blood group antigens affect incidence of all-cause diarrhea and enteric infections at magnitudes comparable to many common disease control interventions. Studies measuring impacts of interventions on childhood enteric disease should account for both child and mothers’ HBGA status., The histo-blood group antigens of children and their mothers are determinants of diarrhea in early childhood. This study estimates the innate immunity provided by HBGA for principal etiologies of diarrhea and demonstrates altered risk for bacterial and viral enteropathogens.

Poor child gut health, resulting from a lack of access to an improved toilet or clean water, has been proposed as a biological mechanism underlying child stunting and oral vaccine failure. Characteristics related to household sanitation, water use, and hygiene were measured among a birth cohort of 270 children from peri-urban Iquitos Peru. These children had monthly stool samples and urine samples at four time points and serum samples at (2–4) time points analyzed for biomarkers related to intestinal inflammation and permeability. We found that less storage of fecal matter near the household along with a reliable water connection were associated with reduced inflammation, most prominently the fecal biomarker myeloperoxidase (MPO) (no sanitation facility compared with those with an onsite toilet had −0.43 log MPO, 95% confidence interval [CI]: −0.74, −0.13; and households with an intermittent connection versus those with a continuous supply had +0.36 log MPO, 95% CI: 0.08, 0.63). These results provide preliminary evidence for the hypothesis that children less than 24 months of age living in unsanitary conditions will have elevated gut inflammation.

Background Longitudinal and time series analyses are needed to characterize the associations between hydrometeorological parameters and health outcomes. Earth Observation (EO) climate data products derived from satellites and global model-based reanalysis have the potential to be used as surrogates in situations and locations where weather-station based observations are inadequate or incomplete. However, these products often lack direct evaluation at specific sites of epidemiological interest. Methods Standard evaluation metrics of correlation, agreement, bias and error were applied to a set of ten hydrometeorological variables extracted from two quasi-global, commonly used climate data products – the Global Land Data Assimilation System (GLDAS) and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) - to evaluate their performance relative to weather-station derived estimates at the specific geographic locations of the eight sites in a multi-site cohort study. These metrics were calculated for both daily estimates and 7-day averages and for a rotavirus-peak-season subset. Then the variables from the two sources were each used as predictors in longitudinal regression models to test their association with rotavirus infection in the cohort after adjusting for covariates. Results The availability and completeness of station-based validation data varied depending on the variable and study site. The performance of the two gridded climate models varied considerably within the same location and for the same variable across locations, according to different evaluation criteria and for the peak-season compared to the full dataset in ways that showed no obvious pattern. They also differed in the statistical significance of their association with the rotavirus outcome. For some variables, the station-based records showed a strong association while the EO-derived estimates showed none, while for others, the opposite was true. Conclusion Researchers wishing to utilize publicly available climate data – whether EO-derived or station based - are advised to recognize their specific limitations both in the analysis and the interpretation of the results. Epidemiologists engaged in prospective research into environmentally driven diseases should install their own weather monitoring stations at their study sites whenever possible, in order to circumvent the constraints of choosing between distant or incomplete station data or unverified EO estimates., Highlights • Earth observation data has potential for use in epidemiology but is seldom validated. • The performance of two EO products varied depending on numerous factors. • Epidemiologists should use their own weather stations to record meteorological data. • Daily estimates of meteorological variables are associated with rotavirus infection. • Choice of climate data source should be determined by the research question.

Extreme floods pose multiple direct and indirect health risks. These risks include contamination of water, food, and the environment, often causing outbreaks of diarrheal disease. Evidence regarding the effects of flooding on individual diarrhea-causing pathogens is limited, but is urgently needed in order to plan and implement interventions and prioritize resources before climate-related disasters strike. This study applied a causal inference approach to data from a multisite study that deployed broadly inclusive diagnostics for numerous high-burden common enteropathogens. Relative risks (RRs) of infection with each pathogen during a flooding disaster that occurred at one of the sites&mdash, Loreto, Peru&mdash, were calculated from generalized linear models using a comparative interrupted time series framework with the other sites as a comparison group and adjusting for background seasonality. During the early period of the flood, increased risk of heat-stable enterotoxigenic E. coli (ST-ETEC) was identified (RR = 1.73 [1.10, 2.71]) along with a decreased risk of enteric adenovirus (RR = 0.36 [0.23, 0.58]). During the later period of the flood, sharp increases in the risk of rotavirus (RR = 5.30 [2.70, 10.40]) and sapovirus (RR = 2.47 [1.79, 3.41]) were observed, in addition to increases in transmission of Shigella spp. (RR = 2.86 [1.81, 4.52]) and Campylobacter spp. (RR = 1.41 (1.01, 1.07). Genotype-specific exploratory analysis reveals that the rise in rotavirus transmission during the flood was likely due to the introduction of a locally atypical, non-vaccine (G2P[4]) strain of the virus. Policy-makers should target interventions towards these pathogens&mdash, including vaccines as they become available&mdash, in settings where vulnerability to flooding is high as part of disaster preparedness strategies, while investments in radical, transformative, community-wide, and locally-tailored water and sanitation interventions are also needed.

We present an example of a tool for quantifying the burden, the population in need of intervention and resources need to contribute for the control of soil-transmitted helminth (STH) infection at multiple administrative levels for the region of Latin America and the Caribbean (LAC). The tool relies on published STH prevalence data along with data on the distribution of several STH transmission determinants for 12,273 sub-national administrative units in 22 LAC countries taken from national censuses. Data on these determinants was aggregated into a single risk index based on a conceptual framework and the statistical significance of the association between this index and the STH prevalence indicators was tested using simple linear regression. The coefficient and constant from the output of this regression was then put into a regression formula that was applied to the risk index values for all of the administrative units in order to model the estimated prevalence of each STH species. We then combine these estimates with population data, treatment thresholds and unit cost data to calculate total control costs. The model predicts an annual cost for the procurement of preventive chemotherapy of around US$ 1.7 million and a total cost of US$ 47 million for implementing a comprehensive STH control programme targeting an estimated 78.7 million school-aged children according to the WHO guidelines throughout the entirety of the countries included in the study. Considerable savings to this cost could potentially be made by embedding STH control interventions within existing health programmes and systems. A study of this scope is prone to many limitations which restrict the interpretation of the results and the uses to which its findings may be put. We discuss several of these limitations.

The Inter-American Development Bank (IDB) (http://www.iadb.org/en/inter-american-developmentbank,2837.html) is the regional, multilateral development bank for Latin America and the Caribbean (LAC). The largest source of external development financing for the region, the IDB support efforts by LAC countries to reduce poverty and inequality with the aim of bringing about development in a sustainable, climate-friendly way. Of the IDB’s 48 member states, 26 are borrowing countries which own majority shares in the institution. In 2010, the IDB approved an unprecedented 170 projects and US$ 570 million in grant financing. The IDB’s Social Protection and Health (SPH) division falls under the social sector and has a total of 56 staff working in the fields of health and social protection with the majority based in country offices of the borrowing countries themselves. The SPH division’s priorities in social protection are: (i) early childhood development (ECD), with focus on infants up to 3 years of age; (ii) youth at risk (in coordination with other business units); and (iii) poverty alleviation programmes.