Search

Background Rotavirus vaccine performance appears worse in countries with high rotavirus genotype diversity. Evidence suggests diminished vaccine efficacy (VE) against G2P[4], which is heterotypic with existing monovalent rotavirus vaccine formulations. Most studies assessing genotype-specific VE have been underpowered and inconclusive. Methods We pooled individual-level data from 10 Phase II and III clinical trials of rotavirus vaccine containing G1 and P[8] antigens (RV1) conducted between 2000 and 2012. We estimated VE against both any-severity and severe (Vesikari score ≥11) rotavirus gastroenteritis (RVGE) using binomial and multinomial logistic regression models for non-specific VE against any RVGE, genotype-specific VE, and RV1-typic VE against genotypes homotypic, partially heterotypic, or fully heterotypic with RV1 antigens. We adjusted models for concomitant oral poliovirus and RV1 vaccination and the country's designated child mortality stratum. Results Analysis included 87 644 infants from 22 countries in the Americas, Europe, Africa, and Asia. For VE against severe RVGE, non-specific VE was 91% (95% confidence interval [CI]: 87–94%). Genotype-specific VE ranged from 96% (95% CI: 89–98%) against G1P[8] to 71% (43–85%) against G2P[4]. RV1-typic VE was 92% (95% CI: 84–96%) against partially heterotypic genotypes but 83% (67–91%) against fully heterotypic genotypes. For VE against any-severity RVGE, non-specific VE was 82% (95% CI: 75–87%). Genotype-specific VE ranged from 94% (95% CI: 86–97%) against G1P[8] to 63% (41–77%) against G2P[4]. RV1-typic VE was 83% (95% CI: 72–90%) against partially heterotypic genotypes but 63% (40–77%) against fully heterotypic genotypes. Conclusions RV1 VE is comparatively diminished against fully heterotypic genotypes including G2P[4].

To examine the hypothesis that longer distance from home-to-hospital is associated with worse outcomes among hospitalizations for community-acquired sepsis.A secondary analysis of data from the REasons for Geographic and Racial Differences in Stroke (REGARDS) prospective cohort of 30,239 white and Black US adults greater than or equal to 45 years old was conducted. Self-reported hospitalizations for serious infection between 2003 and 2012 fulfilling 2/4 systemic inflammatory response syndrome criteria were included. Estimated driving distance was derived from geocoded data and evaluated continuously and as quartiles of very close, close, far, very far (3.1, 3.1-5.8, 5.9-11.5, and11.5 miles respectively). The primary outcome was 30-day mortality while the secondary outcome was sequential organ failure assessment (SOFA) score on arrival.Of the 912 hospitalizations for community-acquired sepsis had adequate data for analysis. The median (interquartile range) estimated driving distance was 5.8 miles (3.1,11.7), and 54 (5.9%) experienced the primary outcome. Compared to living very close, participants living very far had a mortality odds ratio of 1.30 (95% CI 0.64,2.62) and presenting SOFA score difference of 0.33 (95% CI -0.03,0.68).Among a national sample of community-acquired sepsis hospitalizations, there was no significant association between home-to-hospital distance and either 30-day mortality or SOFA score on hospital presentation.

Surveillance research is of great importance for effective and efficient epidemiological monitoring of case counts and disease prevalence. Taking specific motivation from ongoing efforts to identify recurrent cases based on the Georgia Cancer Registry, we extend recently proposed "anchor stream" sampling design and estimation methodology. Our approach offers a more efficient and defensible alternative to traditional capture-recapture (CRC) methods by leveraging a relatively small random sample of participants whose recurrence status is obtained through a principled application of medical records abstraction. This sample is combined with one or more existing signaling data streams, which may yield data based on arbitrarily non-representative subsets of the full registry population. The key extension developed here accounts for the common problem of false positive or negative diagnostic signals from the existing data stream(s). In particular, we show that the design only requires documentation of positive signals in these non-anchor surveillance streams, and permits valid estimation of the true case count based on an estimable positive predictive value (PPV) parameter. We borrow ideas from the multiple imputation paradigm to provide accompanying standard errors, and develop an adapted Bayesian credible interval approach that yields favorable frequentist coverage properties. We demonstrate the benefits of the proposed methods through simulation studies, and provide a data example targeting estimation of the breast cancer recurrence case count among Metro Atlanta area patients from the Georgia Cancer Registry-based Cancer Recurrence Information and Surveillance Program (CRISP) database.

Background Coronavirus disease 2019 (COVID-19) testing is a critical component of public health surveillance and pandemic control, especially among the unvaccinated, as the nation resumes in-person activities. This study examined the relationships between COVID-19 testing rates, testing positivity rates, and vaccination coverage across US counties. Methods Data from the Health and Human Services’ Community Profile Report and 2016–2020 American Community Survey 5-Year Estimates were used. A total of 3114 US counties were analyzed from January through September 2021. Associations among the testing metrics and vaccination coverage were estimated using multiple linear regression models with fixed effects for states and adjusted for county demographics. COVID-19 testing rates (polymerase chain reaction [PCR] testing per 1000), testing positivity (percentage of all PCR tests that were positive), and vaccination coverage (percentage of county population that was fully vaccinated) were determined. Results Nationally, median daily COVID-19 testing rates were highest in January and September (35.5 and 34.6 tests per capita, respectively) and lowest in July (13.2 tests per capita). Monthly testing positivity was between 0.03 and 0.12 percentage points lower for each percentage points of vaccination coverage, and monthly testing rates were between 0.08 and 0.22 tests per capita higher for each percentage point of vaccination coverage. Conclusions The quantity of COVID-19 testing was associated with vaccination coverage, implying counties having populations with relatively lower protection against the virus are conducting less testing than counties with relatively more protection. Monitoring testing practices in relation to vaccination coverage may be used to monitor the sufficiency of COVID-19 testing based on population susceptibility to the virus.

BACKGROUND: Estimates of rotavirus vaccine effectiveness (VE) in the United States appear higher in years with more rotavirus activity. We hypothesized rotavirus VE is constant over time but appears to vary as a function of temporal variation in local rotavirus cases and/or misclassified diagnoses. METHODS: We analyzed 6 years of data from eight US surveillance sites on 8- to 59-month olds with acute gastroenteritis symptoms. Children’s stool samples were tested via enzyme immunoassay (EIA); rotavirus-positive results were confirmed with molecular testing at the US Centers for Disease Control and Prevention. We defined rotavirus gastroenteritis cases by either positive on-site EIA results alone or positive EIA with Centers for Disease Control and Prevention confirmation. For each case definition, we estimated VE against any rotavirus gastroenteritis, moderate-to-severe disease, and hospitalization using two mixed-effect regression models: the first including year plus a year–vaccination interaction, and the second including the annual percent of rotavirus-positive tests plus a percent positive–vaccination interaction. We used multiple overimputation to bias-adjust for misclassification of cases defined by positive EIA alone. RESULTS: Estimates of annual rotavirus VE against all outcomes fluctuated temporally, particularly when we defined cases by on-site EIA alone and used a year–vaccination interaction. Use of confirmatory testing to define cases reduced, but did not eliminate, fluctuations. Temporal fluctuations in VE estimates further attenuated when we used a percent positive–vaccination interaction. Fluctuations persisted until bias-adjustment for diagnostic misclassification. CONCLUSIONS: Both controlling for time-varying rotavirus activity and bias-adjusting for diagnostic misclassification are critical for estimating the most valid annual rotavirus VE.

Transmission heterogeneity, whereby a disproportionate fraction of pathogen transmission events result from a small number of individuals or geographic locations, is an inherent property of many, if not most, infectious disease systems. For vector-borne diseases, transmission heterogeneity is inferred from the distribution of the number of vectors per host, which could lead to significant bias in situations where vector abundance and transmission risk at the household do not correlate, as is the case with dengue virus (DENV). We used data from a contact tracing study to quantify the distribution of DENV acute infections within human activity spaces (AS), the collection of residential locations an individual routinely visits, and quantified measures of virus transmission heterogeneity from two consecutive dengue outbreaks (DENV-4 and DENV-2) that occurred in the city of Iquitos, Peru. Negative-binomial distributions and Pareto fractions showed evidence of strong overdispersion in the number of DENV infections by AS and identified super-spreading units (SSUs): i.e. AS where most infections occurred. Approximately 8% of AS were identified as SSUs, contributing to more than 50% of DENV infections. SSU occurrence was associated more with DENV-2 infection than with DENV-4, a predominance of inapparent infections (74% of all infections), households with high Aedes aegypti mosquito abundance, and high host susceptibility to the circulating DENV serotype. Marked heterogeneity in dengue case distribution, and the role of inapparent infections in defining it, highlight major challenges faced by reactive interventions if those transmission units contributing the most to transmission are not identified, prioritized, and effectively treated.

Background Rotavirus vaccine effects appear lower in countries with high child mortality rates and high diversity of rotavirus genotypes. Some evidence suggests diminished vaccine efficacy (VE) against the G2P[4] genotype, which is heterotypic with existing monovalent rotavirus vaccine formulations. Most studies assessing genotype-specific VE have been underpowered and inconclusive. Methods We pooled individual-level data from ten Phase II and III clinical trials of monovalent rotavirus vaccine containing G1 and P[8] antigens (RV1). We estimated VE against any-severity and severe (Vesikari score ≥11) rotavirus gastroenteritis (RVGE) using binomial and multinomial logistic regression models for three types of VE: non-specific VE against any RVGE; genotype-specific VE against specific genotypes; and RV1-typic VE against genotypes homotypic, partially heterotypic, or fully heterotypic with the RV1 G1 and P[8] antigens. Models were adjusted for oral poliovirus vaccination concomitant with RV1 vaccination and the country’s child mortality stratum. Results A total of 87,644 infants from 22 countries in the Americas, Europe, Africa, and Asia were included in analysis. VE against severe RVGE was 91% (95% confidence interval (CI): 87-94%). Genotype-specific VE ranged from 96% (95% CI: 89-98%) against homotypic G1P[8] to 71% (95% CI: 43-85%) against fully-heterotypic G2P[4]. VE against severe RVGE caused by partially heterotypic genotypes (92% (95% CI: 84-96%)) was similar to VE against the homotypic genotype, but VE against fully heterotypic genotypes was lower (83% (95% CI: 67-91%)). VE against any-severity RVGE was 82% (95% CI: 75-87%). Genotype-specific VE estimates against any-severity RVGE ranged from 94% (95% CI: 86-97%) against G1P[8] to 63% (95% CI: 41-77%) against G2P[4]. VE against any-severity RVGE was lower (83% (95% CI: 72-90%) against partially heterotypic genotypes, but lowest (63% (95% CI: 40-77%)) against fully heterotypic genotypes. Conclusion RV1 VE is diminished against fully heterotypic genotypes including G2P[4]. Disclosures Benjamin Lopman, PhD, Epidemiological Research and Methods, LLC: Advisor/Consultant.

Purpose Skin cancer is the most common, yet oftentimes preventable, cancer type in the United States. Exposure to ultraviolet radiation from sunlight is the most prominent environmental risk factor for skin cancer. Besides environmental exposure, demographic characteristics such as race, age, and socioeconomic status may make some groups more vulnerable. An exploratory spatial clustering method is described for identifying clusters of vulnerability to skin cancer incidence and mortality based on composite indices, which combine data from environmental and demographic risk factors. Methods Based on county-level ultraviolet data and demographic risk factors, two vulnerability indices for skin cancer were generated using an additive percentile rank approach. With these indices, univariate local Moran's I spatial autocorrelation identified significant clusters, or hotspots, of neighboring counties with high overall vulnerability indices. Clusters were identified separately for skin cancer incidence and mortality. Results Counties with high vulnerabilities were spatially distributed across the United States in a pattern that generally increased to the South and West. Clusters of counties with high skin cancer incidence vulnerability were mostly observed in Utah and Colorado, even with highly conservative levels of significance. Meanwhile, clusters for skin cancer mortality vulnerability were observed in southern Alabama and west Florida as well as across north Alabama, north Georgia and up through the Tennessee-North Carolina area. Conclusions Future skin cancer research and screening initiatives may use these innovative composite vulnerability indices and identified clusters to better target resources based on anticipated risk from underlying demographic and environmental factors.

Capture-recapture methods are widely applied in estimating the number (N) of prevalent or cumulatively incident cases in disease surveillance. Here, we focus the bulk of our attention on the common case in which there are two data streams. We propose a sensitivity and uncertainty analysis framework grounded in multinomial distribution-based maximum likelihood, hinging on a key dependence parameter that is typically non-identifiable but is epidemiologically interpretable. Focusing on the epidemiologically meaningful parameter unlocks appealing data visualizations for sensitivity analysis and provides an intuitively accessible framework for uncertainty analysis designed to leverage the practicing epidemiologist’s understanding of the implementation of the surveillance streams as the basis for assumptions driving estimation of N. By illustrating the proposed sensitivity analysis using publicly available HIV surveillance data, we emphasize both the need to admit the lack of information in the observed data and the appeal of incorporating expert opinion about the key dependence parameter. The proposed uncertainty analysis is an empirical Bayes-like approach designed to more realistically acknowledge variability in the estimated N associated with uncertainty in an expert’s opinion about the non-identifiable parameter, together with the statistical uncertainty. We demonstrate how such an approach can also facilitate an appealing general interval estimation procedure to accompany capture-recapture methods. Simulation studies illustrate the reliable performance of the proposed approach for quantifying uncertainties in estimating N in various contexts. Finally, we demonstrate how the recommended paradigm has the potential to be directly extended for application to data from more than two surveillance streams.

BackgroundExposure to PM2.5 arising from solid fuel combustion is estimated to result in approximately 2.3 million premature deaths and 90 million lost disability-adjusted life years annually. ‘Clean’ cooking interventions attempting to mitigate this burden have had limited success in reducing exposures to levels that may yield improved health outcomes.ObjectivesThis paper reports exposure reductions achieved by a liquified petroleum gas (LPG) stove and fuel intervention for pregnant mothers in the Household Air Pollution Intervention Network (HAPIN) randomized controlled trial.MethodsThe HAPIN trial included 3,195 households primarily using biomass for cooking in Guatemala, India, Peru, and Rwanda. 24-hour exposures to PM2.5, carbon monoxide (CO), and black carbon (BC) were measured for pregnant women once before randomization into control (n=1605) and LPG arms (n=1590) and twice thereafter (aligned with trimester). Changes in exposure were estimated by directly comparing exposures between intervention and control arms and by using linear mixed-effect models to estimate the impact of the intervention on exposure levels.ResultsMedian exposures of PM2.5, BC, and CO post-randomization in the intervention arm were lower by 66% (70.7 versus 24.0 μg/m3), 71% (9.6 versus 2.8 μg/m3), and 83% (1.2 versus ppm), respectively, compared to the control arm. Exposure reductions were similar across research locations. Post-intervention PM2.5 exposures in the intervention arm were at the lower end of what has been reported for LPG and other clean fuel interventions, with 69% of PM2.5 samples falling below the WHO Annual Interim Target 1 of 35 μg/m3.DiscussionThis study indicates that an LPG intervention with high displacement of traditional cooking can reduce exposures to levels thought to be associated with health benefits. Success in reducing exposures was likely due to strong performance of, and high adherence to the intervention.

Elevated blood pressure (BP) is a leading risk factor for the global burden of disease. Household air pollution (HAP), resulting from the burning of biomass fuels, may be an important cause of elevated BP in resource-poor communities. We examined the exposure-response relationship of personal exposures to HAP -fine particulate matter (PM

IntroductionDespite clear linkages between provision of clean water and improvements in child health, limited information exists about the health impacts of large water infrastructure improvements in low-income settings. Billions of dollars are spent annually to improve urban water supply, and rigorous evaluation of these improvements, especially targeting informal settlements, is critical to guide policy and investment strategies. Objective measures of infection and exposure to pathogens, and measures of gut function, are needed to understand the effectiveness and impact of water supply improvements.Methods and analysisIn the PAASIM study, we examine the impact of water system improvements on acute and chronic health outcomes in children in a low-income urban area of Beira, Mozambique, comprising 62 sub-neighborhoods and ∼26,300 households. This prospective matched cohort study follows 548 mother-child dyads from late pregnancy through 12 months of age. Primary outcomes include measures of enteric pathogen infections, gut microbiome composition, and source drinking water microbiological quality, measured at the child’s 12 month visit. Additional outcomes include diarrhea prevalence, child growth, previous enteric pathogen exposure, child mortality, and various measures of water access and quality. Our analyses will compare a) subjects living in sub-neighborhoods with the improved water to those living in sub-neighborhoods without these improvements; and b) subjects with household water connections on their premises to those without such a connection. This study will provide critical information to understand how to optimize investments for improving child health, filling the information gap about the impact of piped water provision to low-income urban households, using novel gastrointestinal disease outcomes.Ethics and disseminationThe study was approved by the Emory University Institutional Review Board and the National Bio-Ethics Committee for Health in Mozambique. The pre-analysis is published on the Open Science Framework platform (https://osf.io/4rkn6/). Results will be shared with relevant stakeholders locally, and through publications.STRENGTHS AND LIMITATIONS OF THE STUDYThis matched cohort study of an urban water supply improvement project will provide critical information about the health impacts of providing piped water and household connections to low-income households.We employ rigorous measures of exposure and novel and objective outcome measures, including gut microbiome composition and molecular detection of enteric pathogens.The study design allows for examination of both neighborhood and household-level effects of water supply improvements.As a natural experiment, we are unable to randomize the intervention, leading to potential residual confounding.We are unable to examine the impacts of all aspects of the city-wide water improvement project, due to lack of comparable populations, and instead focus only on the low income neighborhoods.

Contact tracing is an essential public health tool for controlling epidemic disease outbreaks such as the COVID-19 pandemic. Digital contact tracing using real-time locations or proximity of individuals can be used to significantly speed up and scale up contact tracing. In this article, we present our project, REACT, for REAal-time Contact Tracing and risk monitoring via privacy-enhanced tracking of users' locations and symptoms. With privacy enhancement that allows users to control and refine the precision with which their information will be collected and used, REACT will enable: 1) contact tracing of individuals who are exposed to infected cases and identification of hot-spot locations, 2) individual risk monitoring based on the locations they visit and their contact with others; and 3) community risk monitoring and detection of early signals of community spread. We will briefly describe our ongoing work and the approaches we are taking as well as some challenges we encountered in deploying the app.

BackgroundAir pollution and neighborhood socioeconomic status (nSES) have been shown to affect cognitive decline in older adults. In previous studies, nSES acts as both a confounder and an effect modifier between air pollution and cognitive decline.ObjectivesThis study aims to examine the individual and joint effects of air pollution and nSES on cognitive decline on adults 50 years and older in Metro Atlanta, USA.MethodsPerceived memory and cognitive decline was assessed in 11,897 participants aged 50+ years from the Emory Healthy Aging Study (EHAS) using the cognitive function instrument (CFI). Three-year average air pollution concentrations for 12 pollutants and 16 nSES characteristics were matched to participants using census tracts. Individual exposure linear regression and LASSO models explore individual exposure effects. Environmental mixture modeling methods including, self-organizing maps (SOM), Bayesian kernel machine regression (BKMR), and quantile-based G-computation explore joint effects, and effect modification between air pollutants and nSES characteristics on cognitive decline.ResultsParticipants living in areas with higher air pollution concentrations and lower nSES experienced higher CFI scores (beta: 0.121; 95% CI: 0.076, 0.167) compared to participants living in areas with low air pollution and high nSES. Additionally, the BKMR model showed a significant overall mixture effect on cognitive decline, indicating synergy between air pollution and nSES. These joint effects explain protective effects observed in single-pollutant linear regression models, even after adjustment for confounding by nSES (e.g., an IQR increase in CO was associated with a 0.038-point decrease (95% CI: -0.06, -0.01) in CFI score).DiscussionObserved protective effects of single air pollutants on cognitive decline can be explained by joint effects and effect modification of air pollutants and nSES. Researchers must consider nSES as an effect modifier if not a co-exposure to better understand the complex relationships between air pollution and nSES in urban settings.

Rare-disease registries can be useful for studying the associations between environmental exposures and disease severity, but often require the addition of a healthy comparison control group. Defining a surrogate control group, matched and balanced on potentially confounding variables, would allow for the comparison of exposure distributions with cases from a registry. In the present study, we assess whether controls selected externally, using stratification score (SS) matching, can serve as effective proxies for internal controls. In addition, we use methyl paraben (MEPB) to compare the estimated associations between an externally matched sample and case–control frequencies in a cohort with internally matched controls. We started with 225 eligible cases of autism spectrum disorder (ASD) from Childhood Autism Risks from Genetics and the Environment (CHARGE), 241 internal controls from CHARGE, and 265 external controls from the National Health and Nutrition Examination Survey (NHANES) cycles 2005–2016. We calculated the SSs using demographic covariates and matched 1:1 using a caliper method without a replacement. The distribution of the covariates and the mean squared error of the paired differences (MSEpaired) in the SSs between the internal and external group were similar (MSEpaired = 0.007 and 0.011, respectively). The association between MEPB and ASD compared to the controls was similar between the externally matched SS pairs and the original frequency matched cohort. Controls selected externally, via SS matching, can provide a comparable bias reduction to that provided by the internal controls, and therefore may be a useful strategy in situations when the internal controls are not available.

COVID-19 testing is a critical component of public health surveillance and pandemic control, especially among the unvaccinated, as the nation resumes in-person activities.This study examined the relationships between COVID-19 testing rates, testing positivity rates and vaccination coverage across US counties.Data from the Health and Human Services' Community Profile Report and 2016-2020 American Community Survey 5-Year Estimates were used. 3,114 US counties were analyzed from January through September 2021. Associations among the testing metrics and vaccination coverage were estimated using multiple linear regression models with fixed effects for states and adjusted for county demographics. COVID-19 testing rates (PCR testing per 1,000), testing positivity (percentage of all PCR tests that were positive), and vaccination coverage (percentage county population that was fully vaccinated).Nationally, median daily COVID-19 testing rates were highest in January and September (35.5 and 34.6 tests per capita, respectively) and lowest in July (13.2 tests per capita). Monthly testing positivity was between 0.03 and 0.12 percentage points (pp) lower for each pp of vaccination coverage, and monthly testing rates were between 0.08 and 0.22 tests per capita higher for each pp of vaccination coverage.The quantity of COVID-19 testing was associated with vaccination coverage, implying counties having populations with relatively lower protection against the virus are conducting less testing than counties with relatively more protection. Monitoring testing practices in relation to vaccination coverage may be used to monitor the sufficiency of COVID-19 testing based on population susceptibility to the virus.

BACKGROUND The onset of the COVID-19 pandemic necessitated the rapid transition of many types of substance use disorder (SUD) treatments to telehealth formats, despite limited information about what makes treatment effective in this novel format. OBJECTIVE This study aims to examine the feasibility and effectiveness of virtual intensive outpatient programming (IOP) treatment for SUD in the context of a global pandemic, while considering the unique challenges posed to data collection during an unprecedented public health crisis. METHODS The study is based on a longitudinal study with a baseline sample of 3642 patients who enrolled in intensive outpatient addiction treatment (in-person, hybrid, or virtual care) from January 2020 to March 2021 at a large substance use treatment center in the United States. The analytical sample consisted of patients who completed the 3-month postdischarge outcome survey as part of routine outcome monitoring (n=1060, 29.1% response rate). RESULTS No significant differences were detected by delivery format in continuous abstinence (χ22=0.4, P=.81), overall quality of life (F2,826=2.06, P=.13), financial well-being (F2,767=2.30, P=.10), psychological well-being (F2,918=0.72, P=.49), and confidence in one’s ability to stay sober (F2,941=0.21, P=.81). Individuals in hybrid programming were more likely to report a higher level of general health than those in virtual IOP (F2,917=4.19, P=.01). CONCLUSIONS Virtual outpatient care for the treatment of SUD is a feasible alternative to in-person-only programming, leading to similar self-reported outcomes at 3 months postdischarge. Given the many obstacles presented throughout data collection during a pandemic, further research is needed to better understand under what conditions telehealth is an acceptable alternative to in-person care.