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.

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.

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.

Evaluating the effects between metal mixtures and serum vaccine antibody concentrations in children: a prospective birth cohort study.

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 (ß = - 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.

A Prospective Cohort Study Examining the Associations of Maternal Arsenic Exposure With Fetal Loss and Neonatal Mortality.

Arsenic crosses the placenta, possibly increasing the risk of adverse reproductive outcomes. We aimed to examine the association between maternal arsenic exposure and fetal/neonatal survival using data from a prospective cohort study of 1,616 maternal-infant pairs recruited at a gestational age of ≤16 weeks in Bangladesh (2008-2011). Arsenic concentration in maternal drinking water was measured at enrollment. Extended Cox regression (both time-dependent coefficients and step functions) was used to estimate the time-varying association between maternal arsenic exposure and fetal/neonatal death (all mortality between enrollment and 1 month after birth). In a sensitivity analysis, we assessed gestational arsenic exposure using maternal urine samples taken at enrollment. We observed 203 fetal losses and 20 neonatal deaths. Higher arsenic exposure was associated with a slightly decreased mortality rate up to the middle of the second trimester, and then the mortality rate switched directions around 20 weeks' gestation. In the step function model, the hazard ratios for combined mortality (fetal loss and neonatal death) per unit increase in the natural log of drinking water arsenic concentration (µg/L) ranged from 1.35 (95% CI: 1.08, 1.69) in weeks 25-28 to 0.81 (95% CI: 0.65, 1.02) in weeks 9-12. This nonlinear association suggests that arsenic may exert survival pressure on developing fetuses, potentially contributing to survival bias, and may also indicate that arsenic toxicity differs by fetal developmental stage.

Effect of a high dosage opioid prior authorization policy on prescription opioid use, misuse, and overdose outcomes.

BACKGROUND: High dosage opioid use is a risk factor for opioid-related overdose commonly cited in guidelines, recommendations, and policies. In 2012, the Oregon Medicaid program developed a prior authorization policy for opioid prescriptions above 120 mg per day morphine equivalent dose (MED). This study aimed to evaluate the effects of that policy on utilization, prescribing patterns, and health outcomes. METHODS: Using administrative claims data from Oregon and a control state (Colorado) between 2011 and 2013, we used difference-in-differences analyses to examine changes in utilization, measures of high risk opioid use, and overdose after introduction of the policy. We also evaluated opioid utilization in a cohort of individuals who were high dosage opioid users before the policy. RESULTS: Following implementation of Oregon's high dosage policy, the monthly probability of an opioid fill over 120 mg MED declined significantly by 1.7 percentage points (95% confidence interval [CI]; -2.0% to -1.4%), whereas it increased significantly by 1.0 percentage points (95% CI 0.4% to 1.7%) for opioid fills < 61 mg MED. Fills of medications used to treat neuropathic pain also increased by 1.2 percentage points (95% CI 0.7% to 1.8%). The monthly probability of multiple pharmacy use declined by 0.1 percentage points (-0.2% to -0.0) following the prior authorization, but there were no significant changes in ED encounters or hospitalizations for opioid overdose. Among individuals who were using a high dosage opioid before the policy, there was a 20.3 percentage point (95% CI -15.3% to -25.3%) decline in estimated probability of having a high dosage fill after the policy. CONCLUSIONS: Oregon's prior authorization policy was effective at reducing high dosage opioid prescriptions. While multiple pharmacy use also declined, we found no impact on opioid overdose were observed.

Using prescription monitoring program data to characterize out-of-pocket payments for opioid prescriptions in a state Medicaid program.

BACKGROUND: Out-of-pocket payment for prescription opioids is believed to be an indicator of abuse or diversion, but few studies describe its epidemiology. Prescription drug monitoring programs (PDMPs) collect controlled substance prescription fill data regardless of payment source and thus can be used to study this phenomenon. OBJECTIVE: To estimate the frequency and characteristics of prescription fills for opioids that are likely paid out-of-pocket by individuals in the Oregon Medicaid program. RESEARCH DESIGN: Cross-sectional analysis using Oregon Medicaid administrative claims and PDMP data (2012 to 2013). SUBJECTS: Continuously enrolled nondually eligible Medicaid beneficiaries who could be linked to the PDMP with two opioid fills covered by Oregon Medicaid. MEASURES: Patient characteristics and fill characteristics for opioid fills that lacked a Medicaid pharmacy claim. Fill characteristics included opioid name, type, and association with indicators of high-risk opioid use. RESULTS: A total of 33 592 Medicaid beneficiaries filled a total of 555 103 opioid prescriptions. Of these opioid fills, 74 953 (13.5%) could not be matched to a Medicaid claim. Hydromorphone (30%), fentanyl (18%), and methadone (15%) were the most likely to lack a matching claim. The 3 largest predictors for missing claims were opioid fills that overlapped with other opioids (adjusted odds ratio [aOR] 1.37; 95% confidence interval [CI], 1.34-1.4), long-acting opioids (aOR 1.52; 95% CI, 1.47-1.57), and fills at multiple pharmacies (aOR 1.45; 95% CI, 1.39-1.52). CONCLUSIONS: Prescription opioid fills that were likely paid out-of-pocket were common and associated with several known indicators of high-risk opioid use.

Climatic Drivers of Diarrheagenic Escherichia coli Incidence: A Systematic Review and Meta-analysis.

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.

Norovirus genotype profiles associated with foodborne transmission, 1999-2012.

Worldwide, noroviruses are a leading cause of gastroenteritis. They can be transmitted from person to person directly or indirectly through contaminated food, water, or environments. To estimate the proportion of foodborne infections caused by noroviruses on a global scale, we used norovirus transmission and genotyping information from multiple international outbreak surveillance systems (Noronet, CaliciNet, EpiSurv) and from a systematic review of peer-reviewed literature. The proportion of outbreaks caused by food was determined by genotype and/or genogroup. Analysis resulted in the following final global profiles: foodborne transmission is attributed to 10% (range 9%%-11%) of all genotype GII.4 outbreaks, 27% (25%-30%) of outbreaks caused by all other single genotypes, and 37% (24%%-52%) of outbreaks caused by mixtures of GII.4 and other noroviruses. When these profiles are applied to global outbreak surveillance data, results indicate that ≈14% of all norovirus outbreaks are attributed to food.

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.

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%.

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.

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 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 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).

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.

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.

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.