Child wasting and concurrent stunting in low- and middle-income countries.

Sustainable Development Goal 2.2-to end malnutrition by 2030-includes the elimination of child wasting, defined as a weight-for-length z-score that is more than two standard deviations below the median of the World Health Organization standards for child growth1. Prevailing methods to measure wasting rely on cross-sectional surveys that cannot measure onset, recovery and persistence-key features that inform preventive interventions and estimates of disease burden. Here we analyse 21 longitudinal cohorts and show that wasting is a highly dynamic process of onset and recovery, with incidence peaking between birth and 3 months. Many more children experience an episode of wasting at some point during their first 24 months than prevalent cases at a single point in time suggest. For example, at the age of 24 months, 5.6% of children were wasted, but by the same age (24 months), 29.2% of children had experienced at least one wasting episode and 10.0% had experienced two or more episodes. Children who were wasted before the age of 6 months had a faster recovery and shorter episodes than did children who were wasted at older ages; however, early wasting increased the risk of later growth faltering, including concurrent wasting and stunting (low length-for-age z-score), and thus increased the risk of mortality. In diverse populations with high seasonal rainfall, the population average weight-for-length z-score varied substantially (more than 0.5 z in some cohorts), with the lowest mean z-scores occurring during the rainiest months; this indicates that seasonally targeted interventions could be considered. Our results show the importance of establishing interventions to prevent wasting from birth to the age of 6 months, probably through improved maternal nutrition, to complement current programmes that focus on children aged 6-59 months.

Early-childhood linear growth faltering in low- and middle-income countries.

Globally, 149 million children under 5 years of age are estimated to be stunted (length more than 2 standard deviations below international growth standards)1,2. Stunting, a form of linear growth faltering, increases the risk of illness, impaired cognitive development and mortality. Global stunting estimates rely on cross-sectional surveys, which cannot provide direct information about the timing of onset or persistence of growth faltering-a key consideration for defining critical windows to deliver preventive interventions. Here we completed a pooled analysis of longitudinal studies in low- and middle-income countries (n = 32 cohorts, 52,640 children, ages 0-24 months), allowing us to identify the typical age of onset of linear growth faltering and to investigate recurrent faltering in early life. The highest incidence of stunting onset occurred from birth to the age of 3 months, with substantially higher stunting at birth in South Asia. From 0 to 15 months, stunting reversal was rare; children who reversed their stunting status frequently relapsed, and relapse rates were substantially higher among children born stunted. Early onset and low reversal rates suggest that improving children's linear growth will require life course interventions for women of childbearing age and a greater emphasis on interventions for children under 6 months of age.

Causes and consequences of child growth faltering in low-resource settings.

Growth faltering in children (low length for age or low weight for length) during the first 1,000 days of life (from conception to 2 years of age) influences short-term and long-term health and survival1,2. Interventions such as nutritional supplementation during pregnancy and the postnatal period could help prevent growth faltering, but programmatic action has been insufficient to eliminate the high burden of stunting and wasting in low- and middle-income countries. Identification of age windows and population subgroups on which to focus will benefit future preventive efforts. Here we use a population intervention effects analysis of 33 longitudinal cohorts (83,671 children, 662,763 measurements) and 30 separate exposures to show that improving maternal anthropometry and child condition at birth accounted for population increases in length-for-age z-scores of up to 0.40 and weight-for-length z-scores of up to 0.15 by 24 months of age. Boys had consistently higher risk of all forms of growth faltering than girls. Early postnatal growth faltering predisposed children to subsequent and persistent growth faltering. Children with multiple growth deficits exhibited higher mortality rates from birth to 2 years of age than children without growth deficits (hazard ratios 1.9 to 8.7). The importance of prenatal causes and severe consequences for children who experienced early growth faltering support a focus on pre-conception and pregnancy as a key opportunity for new preventive interventions.

Predicting the binding of small molecules to nuclear receptors using machine learning.

Nuclear receptors (NRs) are important biological targets of endocrine-disrupting chemicals (EDCs). Identifying chemicals that can act as EDCs and modulate the function of NRs is difficult because of the time and cost of in vitro and in vivo screening to determine the potential hazards of the 100 000s of chemicals that humans are exposed to. Hence, there is a need for computational approaches to prioritize chemicals for biological testing. Machine learning (ML) techniques are alternative methods that can quickly screen millions of chemicals and identify those that may be an EDC. Computational models of chemical binding to multiple NRs have begun to emerge. Recently, a Nuclear Receptor Activity (NuRA) dataset, describing experimentally derived small-molecule activity against various NRs has been created. We have used the NuRA dataset to develop an ensemble of ML-based models to predict the agonism, antagonism, binding and effector binding of small molecules to nine different human NRs. We defined the applicability domain of the ML models as a measure of Tanimoto similarity to the molecules in the training set, which enhanced the performance of the developed classifiers. We further developed a user-friendly web server named 'NR-ToxPred' to predict the binding of chemicals to the nine NRs using the best-performing models for each receptor. This web server is freely accessible at http://nr-toxpred.cchem.berkeley.edu. Users can upload individual chemicals using Simplified Molecular-Input Line-Entry System, CAS numbers or sketch the molecule in the provided space to predict the compound's activity against the different NRs and predict the binding mode for each.

What Happens on Call Doesn't Stay on Call. The Effects of In-house Call on Acute Care Surgeons' Sleep and Burnout: Results of the Surgeon Performance (SuPer) Trial.

OBJECTIVE: We sought to quantify the effects of in-house call(IHC) on sleep patterns and burnout among acute care surgeons (ACS). BACKGROUND: Many ACS take INC, which leads to disrupted sleep and high levels of stress and burnout. METHODS: Physiological and survey data of 224 ACS with IHC were collected over 6 months. Participants continuously wore a physiological tracking device and responded to daily electronic surveys. Daily surveys captured work and life events as well as feelings of restfulness and burnout. The Maslach Burnout Inventory (MBI) was administered at the beginning and end of the study period. RESULTS: Physiological data were recorded for 34,135 days, which includes 4389 nights of IHC. Feelings of moderate, very, or extreme burnout occurred 25.7% of days and feelings of being moderately, slightly, or not at all rested occurred 75.91% of days. Decreased amount of time since the last IHC, reduced sleep duration, being on call, and having a bad outcome all contribute to greater feelings of daily burnout ( P <0.001). Decreased time since last call also exacerbates the negative effect of IHC on burnout ( P <0.01). CONCLUSIONS: ACS exhibit lower quality and reduced amount of sleep compared with an age-matched population. Furthermore, reduced sleep and decreased time since the last call led to increased feelings of daily burnout, accumulating in emotional exhaustion as measured on the MBI. A reevaluation of IHC requirements and patterns as well as identification of countermeasures to restore homeostatic wellness in ACS is essential to protect and optimize our workforce.

Associations of Prenatal First Trimester Essential and Nonessential Metal Mixtures with Body Size and Adiposity in Childhood.

BACKGROUND: Prenatal nonessential metals may contribute to postnatal adiposity, whereas essential metals may have metabolic benefits. We evaluated joint and individual associations between prenatal metals and childhood adiposity. METHODS: We measured concentrations of six nonessential (arsenic, barium, cadmium, cesium, lead, and mercury) and four essential (magnesium, manganese, selenium, and zinc) metals in first trimester maternal blood from a prebirth cohort. We collected anthropometric measures in early childhood, mid-childhood, and early adolescence including subscapular+tricep skinfold thickness (mm) (N = 715-859), waist circumference (cm) (N = 717-882), and body mass index (BMI) (z-score) (N = 716-875). We measured adiposity in mid-childhood and early adolescence using bone densitometry total- and trunk- fat mass index (kg/m 2 ) (N = 511-599). We estimated associations using adjusted quantile g-computation and linear regression. RESULTS: The nonessential metal mixture was associated with higher total (ß = 0.07, 95% CI = 0.01, 0.12) and trunk fat mass index (ß = 0.12, CI = 0.02, 0.22), waist circumference (ß = 0.01, CI = 0.00, 0.01), and BMI (ß = 0.24, CI = 0.07, 0.41) in mid-childhood, and total fat mass index (ß = 0.07, CI = 0.01, 0.14), and BMI (ß = 0.19, CI = 0.02, 0.37) in early adolescence. The essential metal mixture was associated with lower early adolescence total-(ß = -0.11, CI = -0.17, -0.04) and trunk- fat mass index (ß = -0.13, CI = -0.21, -0.05), subscapular+tricep skinfold thickness (ß = -0.02, CI = -0.03, -0.00), waist circumference (ß = -0.003, CI = -0.01, -0.00), and BMI (ß = -0.16, CI = -0.28, -0.04). Cadmium and cesium were individually associated with childhood adiposity at different timepoints. CONCLUSIONS: Prenatal first-trimester essential metals were associated with lower childhood adiposity, whereas nonessential metals were associated with higher adiposity into adolescence.

Personalized online ensemble machine learning with applications for dynamic data streams.

In this work we introduce the personalized online super learner (POSL), an online personalizable ensemble machine learning algorithm for streaming data. POSL optimizes predictions with respect to baseline covariates, so personalization can vary from completely individualized, that is, optimization with respect to subject ID, to many individuals, that is, optimization with respect to common baseline covariates. As an online algorithm, POSL learns in real time. As a super learner, POSL is grounded in statistical optimality theory and can leverage a diversity of candidate algorithms, including online algorithms with different training and update times, fixed/offline algorithms that are not updated during POSL's fitting procedure, pooled algorithms that learn from many individuals' time series, and individualized algorithms that learn from within a single time series. POSL's ensembling of the candidates can depend on the amount of data collected, the stationarity of the time series, and the mutual characteristics of a group of time series. Depending on the underlying data-generating process and the information available in the data, POSL is able to adapt to learning across samples, through time, or both. For a range of simulations that reflect realistic forecasting scenarios and in a medical application, we examine the performance of POSL relative to other current ensembling and online learning methods. We show that POSL is able to provide reliable predictions for both short and long time series, and it's able to adjust to changing data-generating environments. We further cultivate POSL's practicality by extending it to settings where time series dynamically enter and exit.

Associations between prenatal phthalate exposure and childhood epigenetic age acceleration.

BACKGROUND: Phthalates, a group of pervasive endocrine-disrupting chemicals found in plastics and personal care products, have been associated with a wide range of developmental and health outcomes. However, their impact on biomarkers of aging has not been characterized. We tested associations between prenatal exposure to 11 phthalate metabolites on epigenetic aging in children at birth, 7, 9, and 14 years of age. We hypothesized that prenatal phthalate exposure will be associated with epigenetic age acceleration measures at birth and in early childhood, with patterns dependent on sex and timing of DNAm measurement. METHODS: Among 385 mother-child pairs from the CHAMACOS cohort, we measured DNAm at birth, 7, 9, and 14 years of age, and utilized adjusted linear regression to assess the association between prenatal phthalate exposure and Bohlin's Gestational Age Acceleration (GAA) at birth and Intrinsic Epigenetic Age Acceleration (IEAA) throughout childhood. Additionally, quantile g-computation was utilized to assess the effect of the phthalate mixture on GAA at birth and IEAA throughout childhood. RESULTS: We found a negative association between prenatal di (2-ethylhexyl) phthalate (DEHP) exposure and IEAA among males at age 7 (-0.62 years; 95% CI:-1.06 to -0.18), and a marginal negative association between the whole phthalate mixture and GAA among males at birth (-1.54 days, 95% CI: -2.79 to -0.28), while most other associations were nonsignificant. CONCLUSIONS: Our results suggest that prenatal exposure to certain phthalates is associated with epigenetic aging in children. Additionally, our findings suggest that the influence of prenatal exposures on epigenetic age may only manifest during specific periods of child development, and studies relying on DNAm measurements solely from cord blood or single time points may overlook potential relationships.

Evaluation of an on-site sanitation intervention against childhood diarrhea and acute respiratory infection 1 to 3.5 years after implementation: Extended follow-up of a cluster-randomized controlled trial in rural Bangladesh.

BACKGROUND: Diarrhea and acute respiratory infection (ARI) are leading causes of death in children. The WASH Benefits Bangladesh trial implemented a multicomponent sanitation intervention that led to a 39% reduction in the prevalence of diarrhea among children and a 25% reduction for ARI, measured 1 to 2 years after intervention implementation. We measured longer-term intervention effects on these outcomes between 1 to 3.5 years after intervention implementation, including periods with differing intensity of behavioral promotion. METHODS AND FINDINGS: WASH Benefits Bangladesh was a cluster-randomized controlled trial of water, sanitation, hygiene, and nutrition interventions (NCT01590095). The sanitation intervention included provision of or upgrades to improved latrines, sani-scoops for feces removal, children's potties, and in-person behavioral promotion. Promotion was intensive up to 2 years after intervention initiation, decreased in intensity between years 2 to 3, and stopped after 3 years. Access to and reported use of latrines was high in both arms, and latrine quality was significantly improved by the intervention, while use of child feces management tools was low. We enrolled a random subset of households from the sanitation and control arms into a longitudinal substudy, which measured child health with quarterly visits between 1 to 3.5 years after intervention implementation. The study period therefore included approximately 1 year of high-intensity promotion, 1 year of low-intensity promotion, and 6 months with no promotion. We assessed intervention effects on diarrhea and ARI prevalence among children <5 years through intention-to-treat analysis using generalized linear models with robust standard errors. Masking was not possible during data collection, but data analysis was masked. We enrolled 720 households (360 per arm) from the parent trial and made 9,800 child observations between June 2014 and December 2016. Over the entire study period, diarrheal prevalence was lower among children in the sanitation arm (11.9%) compared to the control arm (14.5%) (prevalence ratio [PR] = 0.81, 95% CI 0.66, 1.00, p = 0.05; prevalence difference [PD] = -0.027, 95% CI -0.053, 0, p = 0.05). ARI prevalence did not differ between sanitation (21.3%) and control (22.7%) arms (PR = 0.93, 95% CI 0.82, 1.05, p = 0.23; PD = -0.016, 95% CI -0.043, 0.010, p = 0.23). There were no significant differences in intervention effects between periods with high-intensity versus low-intensity/no promotion. Study limitations include use of caregiver-reported symptoms to define health outcomes and limited data collected after promotion ceased. CONCLUSIONS: The observed effect of the WASH Benefits Bangladesh sanitation intervention on diarrhea in children appeared to be sustained for at least 3.5 years after implementation, including 1.5 years after heavy promotion ceased. Existing latrine access was high in the study setting, suggesting that improving on-site latrine quality can deliver health benefits when latrine use practices are in place. Further work is needed to understand how latrine adoption can be achieved and sustained in settings with low existing access and how sanitation programs can adopt transformative approaches of excreta management, including safe disposal of child and animal feces, to generate a hygienic home environment. TRIAL REGISTRATION: ClinicalTrials.gov; NCT01590095; https://clinicaltrials.gov/ct2/show/NCT01590095.

Development and validation of prediction models for gestational diabetes treatment modality using supervised machine learning: a population-based cohort study.

BACKGROUND: Gestational diabetes (GDM) is prevalent and benefits from timely and effective treatment, given the short window to impact glycemic control. Clinicians face major barriers to choosing effectively among treatment modalities [medical nutrition therapy (MNT) with or without pharmacologic treatment (antidiabetic oral agents and/or insulin)]. We investigated whether clinical data at varied stages of pregnancy can predict GDM treatment modality. METHODS: Among a population-based cohort of 30,474 pregnancies with GDM delivered at Kaiser Permanente Northern California in 2007-2017, we selected those in 2007-2016 as the discovery set and 2017 as the temporal/future validation set. Potential predictors were extracted from electronic health records at different timepoints (levels 1-4): (1) 1-year preconception to the last menstrual period, (2) the last menstrual period to GDM diagnosis, (3) at GDM diagnosis, and (4) 1 week after GDM diagnosis. We compared transparent and ensemble machine learning prediction methods, including least absolute shrinkage and selection operator (LASSO) regression and super learner, containing classification and regression tree, LASSO regression, random forest, and extreme gradient boosting algorithms, to predict risks for pharmacologic treatment beyond MNT. RESULTS: The super learner using levels 1-4 predictors had higher predictability [tenfold cross-validated C-statistic in discovery/validation set: 0.934 (95% CI: 0.931-0.936)/0.815 (0.800-0.829)], compared to levels 1, 1-2, and 1-3 (discovery/validation set C-statistic: 0.683-0.869/0.634-0.754). A simpler, more interpretable model, including timing of GDM diagnosis, diagnostic fasting glucose value, and the status and frequency of glycemic control at fasting during one-week post diagnosis, was developed using tenfold cross-validated logistic regression based on super learner-selected predictors. This model compared to the super learner had only a modest reduction in predictability [discovery/validation set C-statistic: 0.825 (0.820-0.830)/0.798 (95% CI: 0.783-0.813)]. CONCLUSIONS: Clinical data demonstrated reasonably high predictability for GDM treatment modality at the time of GDM diagnosis and high predictability at 1-week post GDM diagnosis. These population-based, clinically oriented models may support algorithm-based risk-stratification for treatment modality, inform timely treatment, and catalyze more effective management of GDM.

Evaluating the Effects of Opioid Prescribing Policies on Patient Outcomes in a Safety-net Primary Care Clinic.

BACKGROUND: After decades of liberal opioid prescribing, multiple efforts have been made to reduce reliance upon opioids in clinical care. Little is known about the effects of opioid prescribing policies on outcomes beyond opioid prescribing. OBJECTIVE: To evaluate the combined effects of multiple opioid prescribing policies implemented in a safety-net primary care clinic in San Francisco, CA, in 2013-2014. DESIGN: Retrospective cohort study and conditional difference-in-differences analysis of nonrandomized clinic-level policies. PATIENTS: 273 patients prescribed opioids for chronic non-cancer pain in 2013 at either the treated (n=151) or control clinic (n=122) recruited and interviewed in 2017-2018. INTERVENTIONS: Policies establishing standard protocols for dispensing opioid refills and conducting urine toxicology testing, and a new committee facilitating opioid treatment decisions for complex patient cases. MAIN MEASURES: Opioid prescription (active prescription, mean dose in morphine milligram equivalents [MME]) from electronic medical charts, and heroin and opioid analgesics not prescribed to the patient (any use, use frequency) from a retrospective interview. KEY RESULTS: The interventions were associated with a reduction in mean prescribed opioid dose in the first three post-policy years (year 1 conditional difference-in-differences estimate: -52.0 MME [95% confidence interval: -109.9, -10.6]; year 2: -106.2 MME [-195.0, -34.6]; year 3: -98.6 MME [-198.7, -23.9]; year 4: -72.6 MME [-160.4, 3.6]). Estimates suggest a possible positive association between the interventions and non-prescribed opioid analgesic use (year 3: 5.2 absolute percentage points [-0.1, 11.2]) and use frequency (year 3: 0.21 ordinal frequency scale points [0.00, 0.47]) in the third post-policy year. CONCLUSIONS: Clinic-level opioid prescribing policies were associated with reduced dose, although the control clinic achieved similar reductions by the fourth post-policy year, and the policies may have been associated with increased non-prescribed opioid analgesic use. Clinicians should balance the urgency to reduce opioid prescribing with potential harms from rapid change.

Evaluating the robustness of targeted maximum likelihood estimators via realistic simulations in nutrition intervention trials.

Several recently developed methods have the potential to harness machine learning in the pursuit of target quantities inspired by causal inference, including inverse weighting, doubly robust estimating equations and substitution estimators like targeted maximum likelihood estimation. There are even more recent augmentations of these procedures that can increase robustness, by adding a layer of cross-validation (cross-validated targeted maximum likelihood estimation and double machine learning, as applied to substitution and estimating equation approaches, respectively). While these methods have been evaluated individually on simulated and experimental data sets, a comprehensive analysis of their performance across real data based simulations have yet to be conducted. In this work, we benchmark multiple widely used methods for estimation of the average treatment effect using ten different nutrition intervention studies data. A nonparametric regression method, undersmoothed highly adaptive lasso, is used to generate the simulated distribution which preserves important features from the observed data and reproduces a set of true target parameters. For each simulated data, we apply the methods above to estimate the average treatment effects as well as their standard errors and resulting confidence intervals. Based on the analytic results, a general recommendation is put forth for use of the cross-validated variants of both substitution and estimating equation estimators. We conclude that the additional layer of cross-validation helps in avoiding unintentional over-fitting of nuisance parameter functionals and leads to more robust inferences.

Chronic Disease Comorbidities Among Injured Patients in Cameroon: A Retrospective Cohort Study.

INTRODUCTION: Chronic diseases are increasing but underdiagnosed in low-income and middle-income countries (LMICs), where injury mortality is already disproportionately high. We estimated prevalence of known chronic disease comorbidities and their association with outcomes among injured patients in Cameroon. MATERIALS AND METHODS: Injured patients aged ≥15 y presenting to four Cameroonian hospitals between October 2017 and January 2020 were included. Our explanatory variable was known chronic disease; prevalence was age-standardized. Outcomes were overall in-hospital mortality and admission or transfer from the emergency department (ED). Associations between known chronic disease and outcomes were evaluated using logistic regression adjusted for age, gender, estimated injury severity score (eISS), hospital, and household socioeconomic status. Unadjusted eISS-stratified and age-stratified outcomes were also compared via chi-squared tests. RESULTS: Of 7509 injured patients, 370 (4.9%) reported at least one known chronic disease; age-standardized prevalence was 8.4% (95% confidence interval [CI] 7.5%-9.2%). Patients with known chronic disease had higher mortality (4.6% versus 1.5%, adjusted odds ratio [aOR]: 2.61 [95% CI: 1.25-5.47], P = 0.011) and were more likely to be admitted or transferred from the ED (38.7% versus 19.8%, aOR: 1.40 [95% CI: 1.02-1.92], P = 0.038) compared to those without known comorbidities. Crude differences in mortality (11.3% versus 3.3%, P = 0.002) and hospital admission or transfer (63.8% versus 46.6%, P = 0.011) were most notable for patients with eISS 16-24. CONCLUSIONS: Despite underdiagnosis among Cameroonians, we demonstrated worse injury outcomes among those with known chronic diseases. Integrating chronic disease screening with injury care may help address underdiagnosis in Cameroon. Future work should assess whether chronic disease prevention in LMICs could improve injury outcomes.

Extreme heat and its association with social disparities in the risk of spontaneous preterm birth.

BACKGROUND: Climate change is increasing the frequency and intensity of heatwaves. Prior studies associate high temperature with preterm birth. OBJECTIVES: We tested the hypotheses that acute exposure to extreme heat was associated with higher risk of live spontaneous preterm birth (≥20 and <37 completed weeks), and that risks were higher among people of colour and neighbourhoods with heat-trapping landcover or concentrated racialised economic disadvantage. METHODS: We conducted a retrospective cohort study of people giving birth between 2007 and 2011 in Harris County, Texas (Houston metropolitan area) (n = 198,013). Exposures were daily ambient apparent temperature (ATmax in 5°C increments) and dry-bulb temperatures (Tmax and Tmin >historical [1971-2000] summertime 99th percentile) up to a week prior for each day of pregnancy. Survival analysis controlled for individual-level risk factors, secular and seasonal trends. We considered race/ethnicity, heat-trapping neighbourhood landcover and Index of Concentration at the Extremes as effect modifiers. RESULTS: The frequency of preterm birth was 10.3%. A quarter (26.8%) of people were exposed to ATmax ≥40°C, and 22.8% were exposed to Tmax and Tmin >99th percentile while at risk. The preterm birth rate among the exposed was 8.9%. In multivariable models, the risk of preterm birth was 15% higher following extremely hot days (hazard ratio [HR] 1.15 (95% confidence interval [CI] 1.01, 1.30) for ATmax ≥40°C vs. <20°C; HR 1.15 (95% CI 1.02, 1.28) for Tmax and Tmin >99th percentile). Censoring at earlier gestational ages suggested stronger associations earlier in pregnancy. The risk difference associated with extreme heat was higher in neighbourhoods of concentrated racialised economic disadvantage. CONCLUSIONS: Ambient heat was associated with spontaneous preterm birth, with stronger associations earlier in pregnancy and in racially and economically disadvantaged neighbourhoods, suggesting climate change may worsen existing social inequities in preterm birth rates.

Longitudinal Effects of a Sanitation Intervention on Environmental Fecal Contamination in a Cluster-Randomized Controlled Trial in Rural Bangladesh.

Household latrine access generally is not associated with reduced fecal contamination in the environment, but its long-term effectiveness has not been measured. We conducted an environmental assessment nested within the WASH Benefits Bangladesh randomized controlled trial (NCT01590095). We quantified E. coli and fecal coliforms in samples of stored drinking water, child hands, mother hands, soil, and food among a random sample of households from the sanitation and control arms of the trial. Samples were collected during eight quarterly visits approximately 1-3.5 years after intervention initiation. Overall, there were no substantial differences in environmental fecal contamination between households enrolled in the sanitation and control arms. Statistically significant reductions were found in stored water and child hands after pooling across sampling rounds, but the effects were small and not consistent across rounds. In addition, we assessed potential effect modification of intervention effects by follow-up time, season, wealth, community-level latrine density and coverage, population density, and domestic animal ownership. While the intervention had statistically significant effects within some subgroups, there were no consistent patterns of effect modification. Our findings support a growing consensus that on-site latrines are insufficient to prevent fecal contamination in the rural household environment.

Exposure to arsenic at different life-stages and DNA methylation meta-analysis in buccal cells and leukocytes.

BACKGROUND: Arsenic (As) exposure through drinking water is a global public health concern. Epigenetic dysregulation including changes in DNA methylation (DNAm), may be involved in arsenic toxicity. Epigenome-wide association studies (EWAS) of arsenic exposure have been restricted to single populations and comparison across EWAS has been limited by methodological differences. Leveraging data from epidemiological studies conducted in Chile and Bangladesh, we use a harmonized data processing and analysis pipeline and meta-analysis to combine results from four EWAS. METHODS: DNAm was measured among adults in Chile with and without prenatal and early-life As exposure in PBMCs and buccal cells (N = 40, 850K array) and among men in Bangladesh with high and low As exposure in PBMCs (N = 32, 850K array; N = 48, 450K array). Linear models were used to identify differentially methylated positions (DMPs) and differentially variable positions (DVPs) adjusting for age, smoking, cell type, and sex in the Chile cohort. Probes common across EWAS were meta-analyzed using METAL, and differentially methylated and variable regions (DMRs and DVRs, respectively) were identified using comb-p. KEGG pathway analysis was used to understand biological functions of DMPs and DVPs. RESULTS: In a meta-analysis restricted to PBMCs, we identified one DMP and 23 DVPs associated with arsenic exposure; including buccal cells, we identified 3 DMPs and 19 DVPs (FDR < 0.05). Using meta-analyzed results, we identified 11 DMRs and 11 DVRs in PBMC samples, and 16 DMRs and 19 DVRs in PBMC and buccal cell samples. One region annotated to LRRC27 was identified as a DMR and DVR. Arsenic-associated KEGG pathways included lysosome, autophagy, and mTOR signaling, AMPK signaling, and one carbon pool by folate. CONCLUSIONS: Using a two-step process of (1) harmonized data processing and analysis and (2) meta-analysis, we leverage four DNAm datasets from two continents of individuals exposed to high levels of As prenatally and during adulthood to identify DMPs and DVPs associated with arsenic exposure. Our approach suggests that standardizing analytical pipelines can aid in identifying biological meaningful signals.

Acute myocardial infarction associated with abacavir and tenofovir based antiretroviral drug combinations in the United States.

INTRODUCTION: Although individual antiretroviral drugs have been shown to be associated with elevated cardiovascular disease (CVD) risk, data are limited on the role of antiretroviral drug combinations. Therefore, we sought to investigate CVD risk associated with antiretroviral drug combinations. METHODS: Using an administrative health-plan dataset, risk of acute myocardial infarction (AMI) associated with current exposure to antiretroviral drug combinations was assessed among persons living with HIV receiving antiretroviral therapy (ART) across the U.S. from October 2009 through December 2014. To account for confounding-by-indication and for factors simultaneously acting as causal mediators and confounders, we applied inverse probability of treatment weighted marginal structural models to longitudinal data of patients. RESULTS: Over 114,417 person-years (n = 73,071 persons) of ART exposure, 602 cases of AMI occurred at an event rate of 5.26 (95% CI: 4.86, 5.70)/1000 person-years. Of the 14 antiretroviral drug combinations studied, persons taking abacavir-lamivudine-darunavir had the highest incidence rate (IR: 11/1000; 95% CI: 7.4-16.0) of AMI. Risk (HR; 95% CI) of AMI was elevated for current exposure to abacavir-lamivudine-darunavir (1.91; 1.27-2.88), abacavir-lamivudine-atazanavir (1.58; 1.08-2.31), and tenofovir-emtricitabine-raltegravir (1.35; 1.07-1.71). Tenofovir-emtricitabine-efavirenz was associated with reduced risk (0.65; 0.54-0.78). Abacavir-lamivudine-darunavir was associated with increased risk of AMI beyond that expected of abacavir alone, likely attributable to darunavir co-administration. We did not find an elevated risk of AMI when abacavir-lamivudine was combined with efavirenz or raltegravir. CONCLUSION: The antiretroviral drug combinations abacavir-lamivudine-darunavir, abacavir-lamivudine-atazanavir and tenofovir-emtricitabine-raltegravir were found to be associated with elevated risk of AMI, while tenofovir-emtricitabine-efavirenz was associated with a lower risk. The AMI risk associated with abacavir-lamivudine-darunavir was greater than what was previously described for abacavir, which could suggest an added risk from darunavir. The results should be confirmed in additional studies.

Early Evidence of Inactivated Enterovirus 71 Vaccine Impact Against Hand, Foot, and Mouth Disease in a Major Center of Ongoing Transmission in China, 2011-2018: A Longitudinal Surveillance Study.

BACKGROUND: Enterovirus 71 (EV71) is a major causative agent of hand, foot, and mouth disease (HFMD), associated with severe manifestations of the disease. Pediatric immunization with inactivated EV71 vaccine was initiated in 2016 in the Asia-Pacific region, including China. We analyzed a time series of HFMD cases attributable to EV71, coxsackievirus A16 (CA16), and other enteroviruses in Chengdu, a major transmission center in China, to assess early impacts of immunization. METHODS: Reported HFMD cases were obtained from China's notifiable disease surveillance system. We compared observed postvaccination incidence rates during 2017-2018 with counterfactual predictions made from a negative binomial regression and a random forest model fitted to prevaccine years (2011-2015). We fit a change point model to the full time series to evaluate whether the trend of EV71 HFMD changed following vaccination. RESULTS: Between 2011 and 2018, 279 352 HFMD cases were reported in the study region. The average incidence rate of EV71 HFMD in 2017-2018 was 60% (95% prediction interval [PI], 41%-72%) lower than predicted in the absence of immunization, corresponding to an estimated 6911 (95% PI, 3246-11 542) EV71 cases averted over 2 years. There were 52% (95% PI, 42%-60%) fewer severe HFMD cases than predicted. However, the incidence rate of non-CA16 and non-EV71 HFMD was elevated in 2018. We identified a significant decline in the trend of EV71 HFMD 4 months into the postvaccine period. CONCLUSIONS: We provide the first real-world evidence that programmatic vaccination against EV71 is effective against childhood HFMD and present an approach to detect early vaccine impact or intended consequences from surveillance data.