Model-based estimates of age-structured SARS-CoV-2 epidemiology in households.

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.

Assessment and comparison of model estimated and directly observed weather data for prediction of diarrhoea aetiology.

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.

Identification, mapping, and self-reported practice patterns of village doctors in Sitakunda subdistrict, Bangladesh.

Background: Informally trained health care providers, such as village doctors in Bangladesh, are crucial in providing health care services to the rural poor in low- and middle-income countries. Despite being one of the primary vendors of antibiotics in rural Bangladesh, village doctors often have limited knowledge about appropriate antibiotic use, leading to varied and potentially inappropriate dispensing and treatment practices. In this study, we aimed to identify, map, and survey village doctors in the Sitakunda subdistrict of Bangladesh to understand their distribution, practice characteristics, clinical behaviours, access to technologies, and use of these technologies for clinical decision-making. Methods: Using a 'snowball' sampling method, we identified and mapped 411 village doctors, with 371 agreeing to complete a structured survey. Results: The median distance between a residential household and the closest village doctor practice was 0.37 km, and over half of the practices (51.2%) were within 100 m of the major highway. Village doctors were predominately male (98.7%), with a median age of 39. After completing village doctor training, 39.4% had completed an internship, with a median of 15 years of practice experience. Village doctors reported seeing a median of 84 patients per week, including a median of five paediatric diarrhoea cases per week. They stocked a range of antibiotics, with ciprofloxacin and metronidazole being the most prescribed for diarrhoea. Most had access to phones with an internet connection and used online resources for clinical decision-making and guidance. Conclusions: The findings provide insights into the characteristics and practices of village doctors and point to the potential for internet and phone-based interventions to improve patient care and reduce inappropriate antibiotic use in this health care provider group.

Etiologies of bloody diarrhea in children presenting with acute gastroenteritis to US emergency departments.

Among 111 children presenting with bloody diarrhea in a multicenter study of molecular testing in US emergency departments, we found viral pathogens in 18%, bacteria in 48%, protozoa in 2%, and no pathogens detected in 38%.

Drivers of inappropriate use of antimicrobials in South Asia: A systematic review of qualitative literature.

Antimicrobial resistance is a global public health crisis. Effective antimicrobial stewardship requires an understanding of the factors and context that contribute to inappropriate use of antimicrobials. The goal of this qualitative systematic review was to synthesize themes across levels of the social ecological framework that drive inappropriate use of antimicrobials in South Asia. In September 2023, we conducted a systematic search using the electronic databases PubMed and Embase. Search terms, identified a priori, were related to research methods, topic, and geographic location. We identified 165 articles from the initial search and 8 upon reference review (n = 173); after removing duplicates and preprints (n = 12) and excluding those that did not meet eligibility criteria (n = 115), 46 articles were included in the review. We assessed methodological quality using the qualitative Critical Appraisal Skills Program checklist. The studies represented 6 countries in South Asia, and included data from patients, health care providers, community members, and policy makers. For each manuscript, we wrote a summary memo to extract the factors that impede antimicrobial stewardship. We coded memos using NVivo software; codes were organized by levels of the social ecological framework. Barriers were identified at multiple levels including the patient (self-treatment with antimicrobials; perceived value of antimicrobials), the provider (antimicrobials as a universal therapy; gaps in knowledge and skills; financial or reputational incentives), the clinical setting (lack of resources; poor regulation of the facility), the community (access to formal health care; informal drug vendors; social norms), and policy (absence of a regulatory framework; poor implementation of existing policies). This study is the first to succinctly identify a range of norms, behaviors, and policy contexts driving inappropriate use of antimicrobials in South Asia, emphasizing the importance of working across multiple sectors to design and implement approaches specific to the region.

Epidemiology of rotavirus in humans, animals, and the environment in Africa: a systematic review and meta-analysis.

INTRODUCTION: Globally, rotavirus infections are the most common cause of diarrhea-related deaths, especially among children under 5 years of age. This virus can be transmitted through the fecal-oral route, though zoonotic and environmental contributions to transmission are poorly defined. The purpose of this study is to determine the epidemiology of rotavirus in humans, animals, and the environment in Africa, as well as the impact of vaccination. METHODS: We searched PubMed, Web of Science, Africa Index Medicus, and African Journal Online, identifying 240 prevalence data points from 224 articles between 2009 and 2022. RESULTS: Human rotavirus prevalence among patients with gastroenteritis was 29.8% (95% CI, 28.1-31.5; 238710 participants), with similar estimates in children under 5 years of age, and an estimated case fatality rate of 1.2% (95% CI, 0.7-2.0; 10440 participants). Prevalence was estimated to be 15.4% and 6.1% in patients with non-gastroenteritis illnesses and apparently healthy individuals, respectively. Among animals, prevalence was 9.3% (95% CI, 5.7-13.7; 6115 animals), and in the environmental water sources, prevalence was 31.4% (95% CI, 17.7-46.9; 2530 samples). DISCUSSION: Our findings highlight the significant burden of rotavirus infection in Africa, and underscore the need for a One Health approach to limiting the spread of this disease.

Assessment of Model Estimated and Directly Observed Weather Data for Etiological Prediction of Diarrhea.

Recent advances in clinical prediction for diarrheal etiology in low- and middle-income countries have revealed that addition of weather data improves predictive performance. However, the optimal source of weather data remains unclear. We aim to compare model estimated satellite- and ground-based observational data with weather station directly-observed data for diarrheal prediction. We used clinical and etiological data from a large multi-center study of children with diarrhea to compare these methods. 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, directly observed weather station data approximates the modeled data, and given its ease of access, is likely adequate for prediction of diarrheal etiology in children in low- and middle-income countries.

Drivers of inappropriate use of antimicrobials in South Asia: A systematic review of qualitative literature.

Antimicrobial resistance is a global public health crisis. Effective antimicrobial stewardship requires an understanding of the factors and context that contribute to inappropriate use of antimicrobials. The goal of this qualitative systematic review was to synthesize themes across levels of the social ecological framework that drive inappropriate use of antimicrobials in South Asia. In September 2023, we conducted a systematic search using the electronic databases PubMed and Embase. Search terms, identified a priori, were related to research methods, topic, and geographic location. We identified 165 articles from the initial search and 8 upon reference review (n=173); after removing duplicates and preprints (n=12) and excluding those that did not meet eligibility criteria (n=115), 46 articles were included in the review. We assessed methodological quality using the qualitative Critical Appraisal Skills Program checklist. The studies represented 6 countries in South Asia, and included data from patients, health care providers, community members, and policy makers. For each manuscript, we wrote a summary memo to extract the factors that impede antimicrobial stewardship. We coded memos using NVivo software; codes were organized by levels of the social ecological framework. Barriers were identified at multiple levels including the patient (self-treatment with antimicrobials; perceived value of antimicrobials), the provider (antimicrobials as a universal therapy; gaps in knowledge and skills; financial or reputational incentives), the clinical setting (lack of resources; poor regulation of the facility), the community (access to formal health care; informal drug vendors; social norms), and policy (absence of a regulatory framework; poor implementation of existing policies). The findings highlight the importance of working across multiple sectors to design and implement approaches to antimicrobial stewardship in South Asia.

Derivation and External Validation of a Clinical Prediction Model for Viral Diarrhea Etiology in Bangladesh.

Background: Antibiotics are commonly overused for diarrheal illness in many low- and middle-income countries, partly due to a lack of diagnostics to identify viral cases, in which antibiotics are not beneficial. This study aimed to develop clinical prediction models to predict risk of viral-only diarrhea across all ages, using routinely collected demographic and clinical variables. Methods: We used a derivation dataset from 10 hospitals across Bangladesh and a separate validation dataset from the icddr,b Dhaka Hospital. The primary outcome was viral-only etiology determined by stool quantitative polymerase chain reaction. Multivariable logistic regression models were fit and externally validated; discrimination was quantified using area under the receiver operating characteristic curve (AUC) and calibration assessed using calibration plots. Results: Viral-only diarrhea was common in all age groups (<1 year, 41.4%; 18-55 years, 17.7%). A forward stepwise model had AUC of 0.82 (95% confidence interval [CI], .80-.84) while a simplified model with age, abdominal pain, and bloody stool had AUC of 0.81 (95% CI, .78-.82). In external validation, the models performed adequately although less robustly (AUC, 0.72 [95% CI, .70-.74]). Conclusions: Prediction models consisting of 3 routinely collected variables can accurately predict viral-only diarrhea in patients of all ages in Bangladesh and may help support efforts to reduce inappropriate antibiotic use.

Derivation and external validation of a clinical prognostic model identifying children at risk of death following presentation for diarrheal care.

Diarrhea continues to be a leading cause of death for children under-five. Amongst children treated for acute diarrhea, mortality risk remains elevated during and after acute medical management. Identification of those at highest risk would enable better targeting of interventions, but available prognostic tools lack validation. We used clinical and demographic data from the Global Enteric Multicenter Study (GEMS) to build clinical prognostic models (CPMs) to predict death (in-treatment, after discharge, or either) in children aged ≤59 months presenting with moderate-to-severe diarrhea (MSD), in Africa and Asia. We screened variables using random forests, and assessed predictive performance with random forest regression and logistic regression using repeated cross-validation. We used data from the Kilifi Health and Demographic Surveillance System (KHDSS) and Kilifi County Hospital (KCH) in Kenya to externally validate our GEMS-derived CPM. Of 8060 MSD cases, 43 (0.5%) children died in treatment and 122 (1.5% of remaining) died after discharge. MUAC at presentation, respiratory rate, age, temperature, number of days with diarrhea at presentation, number of people living in household, number of children <60 months old living in household, and how much the child had been offered to drink since diarrhea started were predictive of death both in treatment and after discharge. Using a parsimonious 2-variable prediction model, we achieved an area under the ROC curve (AUC) of 0.84 (95% CI: 0.82, 0.86) in the derivation dataset, and an AUC = 0.74 (95% CI 0.71, 0.77) in the external dataset. Our findings suggest it is possible to identify children most likely to die after presenting to care for acute diarrhea. This could represent a novel and cost-effective way to target resources for the prevention of childhood mortality.

Clinical Prediction Rule to Guide Diagnostic Testing for Shigellosis and Improve Antibiotic Stewardship for Pediatric Diarrhea.

Background: Diarrheal diseases are a leading cause of death for children aged <5 years. Identification of etiology helps guide pathogen-specific therapy, but availability of diagnostic testing is often limited in low-resource settings. Our goal is to develop a clinical prediction rule (CPR) to guide clinicians in identifying when to use a point-of-care (POC) diagnostic for Shigella in children presenting with acute diarrhea. Methods: We used clinical and demographic data from the Global Enteric Multicenter Study (GEMS) study to build predictive models for diarrhea of Shigella etiology in children aged ≤59 months presenting with moderate to severe diarrhea in Africa and Asia. We screened variables using random forests, and assessed predictive performance with random forest regression and logistic regression using 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 externally validate our GEMS-derived CPR. Results: Of the 5011 cases analyzed, 1332 (27%) had diarrhea of Shigella etiology. Our CPR had high predictive ability (area under the receiver operating characteristic curve = 0.80 [95% confidence interval, .79-.81]) using the top 2 predictive variables, age and caregiver-reported bloody diarrhea. We show that by using our CPR to triage who receives diagnostic testing, 3 times more Shigella diarrhea cases would have been identified compared to current symptom-based guidelines, with only 27% of cases receiving a POC diagnostic test. Conclusions: We demonstrate how a CPR can be used to guide use of a POC diagnostic test for diarrhea management. Using our CPR, available diagnostic capacity can be optimized to improve appropriate antibiotic use.

Derivation and external validation of a clinical prognostic model identifying children at risk of death following presentation for diarrheal care.

Diarrhea continues to be a leading cause of death for children under-five. Amongst children treated for acute diarrhea, mortality risk remains elevated during and after acute medical management. Identification of those at highest risk would enable better targeting of interventions, but available prognostic tools lack validation. We used clinical and demographic data from the Global Enteric Multicenter Study (GEMS) to build predictive models for death (in-treatment, after discharge, or either) in children aged ≤59 months presenting with moderate-to-severe diarrhea (MSD), in Africa and Asia. We screened variables using random forests, and assessed predictive performance with random forest regression and logistic regression using repeated cross-validation. We used data from the Kilifi Health and Demographic Surveillance System (KHDSS) and Kilifi County Hospital (KCH) in Kenya to externally validate our GEMS-derived clinical prognostic model (CPM). Of 8060 MSD cases, 43 (0.5%) children died in treatment and 122 (1.5% of remaining) died after discharge. MUAC at presentation, respiratory rate, age, temperature, number of days with diarrhea at presentation, number of people living in household, number of children <60 months old living in household, and how much the child had been offered to drink since diarrhea started were predictive of death both in treatment and after discharge. Using a parsimonious 2-variable prediction model, we achieve an AUC=0.84 (95% CI: 0.82, 0.86) in the derivation dataset, and an AUC=0.74 (95% CI 0.71, 0.77) in the external dataset. Our findings suggest it is possible to identify children most likely to die after presenting to care for acute diarrhea. This could represent a novel and cost-effective way to target resources for the prevention of childhood mortality.

Derivation and external validation of clinical prediction rules identifying children at risk of linear growth faltering.

Background: 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. Methods: 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 z-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. Results: 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. Conclusions: 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. Funding: 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).

Incidence Rate and Risk Factors Associated with Travelers' Diarrhea in International Travelers Departing from Utah, USA.

Despite knowledge on the causes and prevention strategies for travelers' diarrhea (TD), it continues to be one of the most common illnesses experienced by U.S. international travelers. However, studies of risk factors associated with TD among U.S. travelers are limited. In this study, we aimed to determine the incidence rate of TD, the proportion of travelers who experience TD, and to identify risk factors associated with TD. In this cross-sectional study, we collected and analyzed data from anonymous posttravel questionnaires submitted by international travelers recruited during their pretravel visit at two travel clinics in Salt Lake City, Utah, from October 2016 to March 2020. Of 571 travelers who completed posttravel surveys, 484 (85%) answered the TD question, of which 111 (23%) reported TD, for an incidence rate of 1.1 episodes per 100 travel-days (95% confidence interval [CI]: 0.9-1.4). In a multivariable model, visiting Southeast Asian (odds ratio [OR]: 2.60; 95% CI: 1.45-4.72) and African (OR: 2.06; 95% CI: 1.09-3.93]) WHO regions, having 10 or more individuals in the group (OR: 3.91; 95% CI: 1.50-11.32]), longer trip duration (OR: 1.01; 95% CI: 1.00-1.02), visiting both urban and rural destinations (OR: 1.94; 95% CI: 1.01-3.90), and taking medications/supplements to prevent TD (OR: 2.74; 95% CI: 1.69-4.47) were statistically significantly associated with increased odds of reporting TD. TD continues to be common in international travelers from the United States. Our findings provide insights regarding travelers' behaviors regarding TD in international travelers from high-income countries and shows the need for additional research into prevention strategies for travelers' diarrhea.

Improving Antibiotic Stewardship for Diarrheal Disease With Probability-Based Electronic Clinical Decision Support: A Randomized Crossover Trial.

Importance: Inappropriate use of antibiotics for diarrheal illness can result in adverse effects and increase in antimicrobial resistance. Objective: To determine whether the diarrheal etiology prediction (DEP) algorithm, which uses patient-specific and location-specific features to estimate the probability that diarrhea etiology is exclusively viral, impacts antibiotic prescriptions in patients with acute diarrhea. Design, Setting, and Participants: A randomized crossover study was conducted to evaluate the DEP incorporated into a smartphone-based electronic clinical decision-support (eCDS) tool. The DEP calculated the probability of viral etiology of diarrhea, based on dynamic patient-specific and location-specific features. Physicians were randomized in the first 4-week study period to the intervention arm (eCDS with the DEP) or control arm (eCDS without the DEP), followed by a 1-week washout period before a subsequent 4-week crossover period. The study was conducted at 3 sites in Bangladesh from November 17, 2021, to January 21, 2021, and at 4 sites in Mali from January 6, 2021, to March 5, 2021. Eligible physicians were those who treated children with diarrhea. Eligible patients were children between ages 2 and 59 months with acute diarrhea and household access to a cell phone for follow-up. Interventions: Use of the eCDS with the DEP (intervention arm) vs use of the eCDS without the DEP (control arm). Main Outcomes and Measures: The primary outcome was the proportion of children prescribed an antibiotic. Results: A total of 30 physician participants and 941 patient participants (57.1% male; median [IQR] age, 12 [8-18] months) were enrolled. There was no evidence of a difference in the proportion of children prescribed antibiotics by physicians using the DEP (risk difference [RD], -4.2%; 95% CI, -10.7% to 1.0%). In a post hoc analysis that accounted for the predicted probability of a viral-only etiology, there was a statistically significant difference in risk of antibiotic prescription between the DEP and control arms (RD, -0.056; 95% CI, -0.128 to -0.01). No known adverse effects of the DEP were detected at 10-day postdischarge. Conclusions and Relevance: Use of a tool that provides an estimate of etiological likelihood did not result in a significant change in overall antibiotic prescriptions. Post hoc analysis suggests that a higher predicted probability of viral etiology was linked to reductions in antibiotic use. Trial Registration: Clinicaltrials.gov Identifier: NCT04602676.

The Effects of Using a Clinical Prediction Rule to Prioritize Diagnostic Testing on Transmission and Hospital Burden: A Modeling Example of Early Severe Acute Respiratory Syndrome Coronavirus 2.

BACKGROUND: Prompt identification of infections is critical for slowing the spread of infectious diseases. However, diagnostic testing shortages are common in emerging diseases, low resource settings, and during outbreaks. This forces difficult decisions regarding who receives a test, often without knowing the implications of those decisions on population-level transmission dynamics. Clinical prediction rules (CPRs) are commonly used tools to guide clinical decisions. METHODS: Using early severe acute respiratory syndrome coronavirus disease 2 (SARS-CoV-2) as an example, we used data from electronic health records to develop a parsimonious 5-variable CPR to identify those who are most likely to test positive. To consider the implications of gains in daily case detection at the population level, we incorporated testing using the CPR into a compartmentalized model of SARS-CoV-2. RESULTS: We found that applying this CPR (area under the curve, 0.69; 95% confidence interval, .68-.70) to prioritize testing increased the proportion of those testing positive in settings of limited testing capacity. We found that prioritized testing led to a delayed and lowered infection peak (ie, "flattens the curve"), with the greatest impact at lower values of the effective reproductive number (such as with concurrent community mitigation efforts), and when higher proportions of infectious persons seek testing. In addition, prioritized testing resulted in reductions in overall infections as well as hospital and intensive care unit burden. CONCLUSION: We highlight the population-level benefits of evidence-based allocation of limited diagnostic capacity.SummaryWhen the demand for diagnostic tests exceeds capacity, the use of a clinical prediction rule to prioritize diagnostic testing can have meaningful impact on population-level outcomes, including delaying and lowering the infection peak, and reducing healthcare burden.

A modular approach to integrating multiple data sources into real-time clinical prediction for pediatric diarrhea.

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.

Robust Testing in Outpatient Settings to Explore COVID-19 Epidemiology: Disparities in Race/Ethnicity and Age, Salt Lake County, Utah, 2020.

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.

Urinary polycyclic aromatic hydrocarbons concentrations and hepatitis B antibody serology in the United States (NHANES, 2003-2014).

BACKGROUND: Polycyclic aromatic hydrocarbons (PAHs) are environmental contaminants that are hepatotoxic and immunotoxic. PAH exposure may modulate hepatitis B immunology. OBJECTIVE: We used data from 6 cycles of the National Health and Nutrition Examination Survey (2003-2014) to evaluate the associations between urinary PAH metabolites and hepatitis B serology. METHODS: This analysis included individuals who self-reported receiving ≥3 doses of hepatitis B vaccine and urinary PAH metabolites (i.e. 1-napthol, 2-napthol, 3-fluorene, 2-fluorene, 1-phenanthrene, 1-pyrene, and total PAH [sum of all metabolites]). Separate logistic regression models assessed the association between hepatitis B vaccination status (i.e. individuals who were immune due to vaccination or susceptible) and tertiles of urinary PAH. Models were adjusted for age, gender, race/ethnicity, survey cycle, family income to poverty ratio, BMI, country of birth, serum cotinine, and urinary creatinine. RESULTS: Among participants who reported receiving ≥3 doses of vaccine and had no antibodies indicating a history of hepatitis B infection and/or current hepatitis B infection, dose-response relationships were observed where individuals with the lowest odds of serology indicating a response to the hepatitis B vaccine (i.e., anti-HBs+, anti-HBc-, and HBsAg-) were in the highest tertile of 2-Napthol (adjusted Odds Ratio [aOR]: 0.70, 95% confidence interval [CI]: 0.54, 0.91), 3-Napthol (aOR: 0.68, 95% CI: 0.53, 0.87), 2-Fluorene (aOR: 0.61, 95% CI: 0.54, 0.86), 1-Phenanthrene (aOR: 0.79, 95% CI: 0.65, 0.97), 1-Pyrene (aOR): 0.68, 95% CI: 0.55, 0.83), and total PAH (aOR: 0.73, 95% CI: 0.56, 0.95) had the compared to the lowest tertile. CONCLUSION: This cross-sectional study supports a hypothesis that PAH exposures experienced by the general US population may modulate hepatitis B vaccine induced immunity. Given the ubiquity of PAH exposures in the US, additional research is warranted to explore the effects of chronic PAH exposures on hepatitis B related humoral immunity.