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Noroviruses are the most common cause of acute gastroenteritis across all ages worldwide. These pathogens are generally understood to exhibit a wintertime seasonality, though a systematic assessment of seasonal patterns has not been conducted in the era of modern diagnostics. We conducted a systematic review of the Pubmed Medline database for articles published between 1997 and 2011 to identify and extract data from articles reporting on monthly counts of norovirus. We conducted a descriptive analysis to document seasonal patterns of norovirus disease, and we also constructed multivariate linear models to identify factors associated with the strength of norovirus seasonality. The searched identified 293 unique articles, yielding 38 case and 29 outbreak data series. Within these data series, 52.7% of cases and 41.2% of outbreaks occurred in winter months, and 78.9% of cases and 71.0% of outbreaks occurred in cool months. Both case and outbreak studies showed an earlier peak in season-year 2002-03, but not in season-year 2006-07, years when new genogroup II type 4 variants emerged. For outbreaks, norovirus season strength was positively associated with average rainfall in the wettest month, and inversely associated with crude birth rate in both bivariate and multivariate analyses. For cases, none of the covariates examined was associated with season strength. When case and outbreaks were combined, average rainfall in the wettest month was positively associated with season strength. Norovirus is a wintertime phenomenon, at least in the temperate northern hemisphere where most data are available. Our results point to possible associations of season strength with rain in the wettest month and crude birth rate.

Despite substantial decreases in recent decades, acute gastroenteritis causes the second greatest burden of all infectious diseases worldwide. Noroviruses are a leading cause of sporadic cases and outbreaks of acute gastroenteritis across all age groups. We aimed to assess the role of norovirus as a cause of endemic acute gastroenteritis worldwide. We searched Embase, Medline, and Global Health databases from Jan 1, 2008, to March 8, 2014, for studies that used PCR diagnostics to assess the prevalence of norovirus in individuals with acute gastroenteritis. We included studies that were done continuously for 1 year or more from a specified catchment area (geographical area or group of people), enrolled patients who presented with symptoms of acute gastroenteritis, and used PCR-based diagnostics for norovirus on all stool specimens from patients with acute gastroenteritis. The primary outcome was prevalence of norovirus among all cases of gastroenteritis. We generated pooled estimates of prevalence by fitting linear mixed-effect meta-regression models. Of 175 articles included, the pooled prevalence of norovirus in 187 336 patients with acute gastroenteritis was 18% (95% CI 17–20). Norovirus prevalence tended to be higher in cases of acute gastroenteritis in community (24%, 18–30) and outpatient (20%, 16–24) settings compared with inpatient (17%, 15–19, p=0·066) settings. Prevalence was also higher in low-mortality developing (19%, 16–22) and developed countries (20%, 17–22) compared with high-mortality developing countries (14%, 11–16; p=0·058). Patient age and whether the study included years of novel strain emergence were not associated with norovirus prevalence. Norovirus is a key gastroenteritis pathogen associated with almost a fifth of all cases of acute gastroenteritis, and targeted intervention to reduce norovirus burden, such as vaccines, should be considered. The Foodborne Disease Burden Epidemiology Reference Group (FERG) of WHO and the Government of the Netherlands on behalf of FERG.

Background Understanding how infectious disease transmission varies from person to person, including associations with age and contact behavior, can help design effective control strategies. Within households, transmission may be highly variable because of differing transmission risks by age, household size, and individual contagiousness. Our aim was to disentangle those factors by fitting mathematical models to SARS-CoV-2 household survey and serologic data. Methods We surveyed members of 3,381 Utah households from January-April 2021 and performed SARS-CoV-2 antibody testing on all available members. We paired these data with a probabilistic model of household importation and transmission composed of a novel combination of transmission variability and age- and size-structured heterogeneity. We calculated maximum likelihood estimates of mean and variability of household transmission probability between household members in different age groups and different household sizes, simultaneously with importation probability and probabilities of false negative and false positive test results. Results 12.8% of individual participants, residing in 17.4% of the participating households, showed serologic evidence of prior infection or reported a prior positive test on the survey. Serologically positive individuals in younger age groups were less likely than older adults to have tested positive during their infection according to our survey results. Our model results suggested that adolescents and young adults (ages 13–24) acquired SARS-CoV-2 infection outside the household at a rate substantially higher than younger children and older adults. Our estimate of the household secondary attack rate (HSAR) among adults aged 45 and older exceeded HSARs to and/or from younger age groups at a given household size. We found lower HSAR in households with more members, independent of age differences. The age-specific HSAR patterns we found could not be explained by age-dependent biological susceptibility and transmissibility alone, suggesting that age groups contacted each other at different rates within households. Conclusions We disentangled several factors contributing to age-specific infection risk, including non-household exposure, within-household exposure to specific age groups, and household size. Within-household contact rate differences played a significant role in driving household transmission epidemiology. These findings provide nuanced insights for understanding community outbreak patterns and mechanisms of differential infection risk.

Background Many populations are exposed to arsenic, lead, and manganese. These metals influence immune function. We evaluated the association between exposure to single and multiple metals, including arsenic, lead, and manganese, to humoral immunity as measured by antibody concentrations to diphtheria and tetanus toxoid among vaccinated Bangladeshi children. Additionally, we examined if this association was potentially mediated by nutritional status. Methods Antibody concentrations to diphtheria and tetanus were measured in children’s serum at age 5 ( n  = 502). Household drinking water was sampled to quantify arsenic (W-As) and manganese (W-Mn), whereas lead was measured in blood (B-Pb). Exposure samples were taken during pregnancy, toddlerhood, and early childhood. Multiple linear regression models (MLRs) with single or combined metal predictors were used to determine the association with antibody outcomes. MLR results were transformed to units of percent change in outcome per doubling of exposure to improve interpretability. Structural equation models (SEMs) were used to further assess exposure to metal mixtures. SEMs regressed a latent exposure variable ( Metals ), informed by all measured metal variables (W-As, W-Mn, and B-Pb), on a latent outcome variable ( Antibody ), informed by measured antibody variables (diphtheria and tetanus). Weight-for-age z-score (WFA) at age 5 was evaluated as a mediator. Results Diphtheria antibody was negatively associated with W-As during pregnancy in MLR, but associations were attenuated after adjusting for W-Mn and B-Pb (− 2.9% change in diphtheria antibody per doubling in W-As, 95% confidence interval [CI]: − 7%, 1.5%). Conversely, pregnancy levels of B-Pb were positively associated with tetanus antibody, even after adjusting for W-As and W-Mn (13.3%, 95% CI: 1.7%, 26.3%). Overall, null associations were observed between W-Mn and antibody outcomes. Analysis by SEMs showed that the latent Metals mixture was significantly associated with the latent Antibody outcome (β = − 0.16, 95% CI: − 0.26, − 0.05), but the Metals variable was characterized by positive and negative loadings of W-As and B-Pb, respectively. Sex-stratified MLR and SEM analyses showed W-As and B-Pb associations were exclusive to females. Mediation by WFA was null, indicating Metals only had direct effects on Antibody . Conclusions We observed significant modulation of vaccine antibody concentrations among children with pregnancy and early life exposures to drinking water arsenic and blood lead. We found distinct differences by child sex, as only females were susceptible to metal-related modulations in antibody levels. Weight-for-age, a nutritional status proxy, did not mediate the association between the metal mixture and vaccine antibody.

Background. Positive associations have been noted between temperature and diarrhea incidence, but considerable uncertainty surrounds quantitative estimates of this relationship because of pathogen-specific factors and a scarcity of data on the influence of meteorological factors on the risk of disease. Quantifying these relationships is important for disease prevention and climate change adaptation. Methods. To address these issues, we performed a systematic literature review of studies in which at least 1 full year of data on the monthly incidence of diarrheagenic Escherichia coli were reported. We characterized seasonal patterns of disease incidence from 28 studies. In addition, using monthly time- and location-specific weather data for 18 studies, we performed univariate Poisson models on individual studies and a meta-analysis, using a generalized estimating equation, on the entire data set. Results. We found an 8% increase in the incidence of diarrheagenic E. coli (95% confidence interval, 5%–11%; P < .0001) for each 1°C increase in mean monthly temperature. We found a modest positive association between 1-month-lagged mean rainfall and incidence of diarrheagenic E. coli, which was not statistically significant when we controlled for temperature. Conclusions. These results suggest that increases in ambient temperature correspond to an elevated incidence of diarrheagenic E. coli and underscore the need to redouble efforts to prevent the transmission of these pathogens in the face of increasing global temperatures.

Nearly 150 million children under-5 years of age were stunted in 2020. We aimed to develop a clinical prediction rule (CPR) to identify children likely to experience additional stunting following acute diarrhea, to enable targeted approaches to prevent this irreversible outcome. We used clinical and demographic data from the Global Enteric Multicenter Study (GEMS) to build predictive models of linear growth faltering (decrease of ≥0.5 or ≥1.0 in height-for-age -score [HAZ] at 60-day follow-up) in children ≤59 months presenting with moderate-to-severe diarrhea, and community controls, in Africa and Asia. We screened variables using random forests, and assessed predictive performance with random forest regression and logistic regression using fivefold cross-validation. We used the Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development (MAL-ED) study to (1) re-derive, and (2) externally validate our GEMS-derived CPR. Of 7639 children in GEMS, 1744 (22.8%) experienced severe growth faltering (≥0.5 decrease in HAZ). In MAL-ED, we analyzed 5683 diarrhea episodes from 1322 children, of which 961 (16.9%) episodes experienced severe growth faltering. Top predictors of growth faltering in GEMS were: age, HAZ at enrollment, respiratory rate, temperature, and number of people living in the household. The maximum area under the curve (AUC) was 0.75 (95% confidence interval [CI]: 0.75, 0.75) with 20 predictors, while 2 predictors yielded an AUC of 0.71 (95% CI: 0.71, 0.72). Results were similar in the MAL-ED re-derivation. A 2-variable CPR derived from children 0-23 months in GEMS had an AUC = 0.63 (95% CI: 0.62, 0.65), and AUC = 0.68 (95% CI: 0.63, 0.74) when externally validated in MAL-ED. Our findings indicate that use of prediction rules could help identify children at risk of poor outcomes after an episode of diarrheal illness. They may also be generalizable to all children, regardless of diarrhea status. This work was supported by the National Institutes of Health under Ruth L. Kirschstein National Research Service Award NIH T32AI055434 and by the National Institute of Allergy and Infectious Diseases (R01AI135114).

Traditional clinical prediction models focus on parameters of the individual patient. For infectious diseases, sources external to the patient, including characteristics of prior patients and seasonal factors, may improve predictive performance. We describe the development of a predictive model that integrates multiple sources of data in a principled statistical framework using a post-test odds formulation. Our method enables electronic real-time updating and flexibility, such that components can be included or excluded according to data availability. We apply this method to the prediction of etiology of pediatric diarrhea, where 'pre-test’ epidemiologic data may be highly informative. Diarrhea has a high burden in low-resource settings, and antibiotics are often over-prescribed. We demonstrate that our integrative method outperforms traditional prediction in accurately identifying cases with a viral etiology, and show that its clinical application, especially when used with an additional diagnostic test, could result in a 61% reduction in inappropriately prescribed antibiotics.

Recent advances in clinical prediction for diarrhoeal aetiology in low- and middle-income countries have revealed that the addition of weather data to clinical data improves predictive performance. However, the optimal source of weather data remains unclear. We aim to compare the use of model estimated satellite- and ground-based observational data with weather station directly observed data for the prediction of aetiology of diarrhoea. We used clinical and etiological data from a large multi-centre study of children with moderate to severe diarrhoea cases to compare their predictive performances. We show that the two sources of weather conditions perform similarly in most locations. We conclude that while model estimated data is a viable, scalable tool for public health interventions and disease prediction, given its ease of access, directly observed weather station data is likely adequate for the prediction of diarrhoeal aetiology in children in low- and middle-income countries.

Abstract Background Severe dehydration due to acute infectious diarrhea remains a leading cause of death among young children worldwide. Diarrhea with severe dehydration is a clinical syndrome with distinct management per the World Health Organization (WHO) Integrated Management of Childhood Illness (IMCI) and the WHO Global Task Force on Cholera Control (GTFCC) guidelines. We sought to characterize the pathogens causing severe dehydration using data from the Global Enteric Multicenter Study. Methods We used the IMCI and GTFCC guidelines to define severe dehydration and quantitative polymerase chain reaction–based attribution models to assign the etiology of diarrhea associated with severe dehydration. Results The IMCI or GTFCC guidelines classified 2284 of the 5304 (43%) cases with moderate-to-severe diarrhea as having severe dehydration. In one-third of the cases with severe dehydration, no pathogens were attributed. The top pathogens attributed to children with guidelines-classified severe dehydration varied by age and were similar among those requiring intravenous hydration and hospitalization. Rotavirus (30.9%), Cryptosporidium (12.0%), and heat-stable (ST) enterotoxigenic Escherichia coli (ETEC) (10.3%) were the most common pathogens for ages 0–11 months, while Shigella/enteroinvasive E coli (EIEC) (25.8%), rotavirus (19.3%), and ST-ETEC (10.9%) were the most common for ages 12–23 months. Shigella/EIEC (25.9%), Vibrio cholerae (10.4%), and rotavirus (9.2%) were the most common among ages 24–59 months. Conclusions The findings inform prioritization of pathogens, in addition to V cholerae, that cause severe dehydration for future preventive and treatment efforts. The schema for prioritization is driven primarily by age stratifications. Diarrhea with severe dehydration causes significant morbidity and mortality in low- and middle-income countries. Attributable causes of severe dehydration varied by age and by definition of severe dehydration, informing pathogen prioritization for prevention and treatment strategies.

Objective US-based descriptions of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection have focused on patients with severe disease. Our objective was to describe characteristics of a predominantly outpatient population tested for SARS-CoV-2 in an area receiving comprehensive testing. Methods We extracted data on demographic characteristics and clinical data for all patients (91% outpatient) tested for SARS-CoV-2 at University of Utah Health clinics in Salt Lake County, Utah, from March 10 through April 24, 2020. We manually extracted data on symptoms and exposures from a subset of patients, and we calculated the adjusted odds of receiving a positive test result by demographic characteristics and clinical risk factors. Results Of 17 662 people tested, 1006 (5.7%) received a positive test result for SARS-CoV-2. Hispanic/Latinx people were twice as likely as non-Hispanic White people to receive a positive test result (adjusted odds ratio [aOR] = 2.0; 95% CI, 1.3-3.1), although the severity at presentation did not explain this discrepancy. Young people aged 0-19 years had the lowest rates of receiving a positive test result for SARS-CoV-2 (<4 cases per 10 000 population), and adults aged 70-79 and 40-49 had the highest rates of hospitalization per 100 000 population among people who received a positive test result (16 and 11, respectively). Conclusions We found disparities by race/ethnicity and age in access to testing and in receiving a positive test result among outpatients tested for SARS-CoV-2. Further research and public health outreach on addressing racial/ethnic and age disparities will be needed to effectively combat the coronavirus disease 2019 pandemic in the United States.