Factors associated with tuberculosis treatment initiation among bacteriologically negative individuals evaluated for tuberculosis: an individual patient data meta-analysis.

Background: Globally, over one-third of pulmonary tuberculosis (TB) disease diagnoses are made based on clinical criteria after a negative diagnostic test result. Understanding factors associated with clinicians' decisions to initiate treatment for individuals with negative test results is critical for predicting the potential impact of new diagnostics. Methods: We performed a systematic review and individual patient data meta-analysis using studies conducted between January/2010 and December/2022 (PROSPERO: CRD42022287613). We included trials or cohort studies that enrolled individuals evaluated for TB in routine settings. In these studies participants were evaluated based on clinical examination and routinely-used diagnostics, and were followed for ≥1 week after the initial test result. We used hierarchical Bayesian logistic regression to identify factors associated with treatment initiation following a negative result on an initial bacteriological test (e.g., sputum smear microscopy, Xpert MTB/RIF). Findings: Multiple factors were positively associated with treatment initiation: male sex [adjusted Odds Ratio (aOR) 1.61 (1.31-1.95)], history of prior TB [aOR 1.36 (1.06-1.73)], reported cough [aOR 4.62 (3.42-6.27)], reported night sweats [aOR 1.50 (1.21-1.90)], and having HIV infection but not on ART [aOR 1.68 (1.23-2.32)]. Treatment initiation was substantially less likely for individuals testing negative with Xpert [aOR 0.77 (0.62-0.96)] compared to smear microscopy and declined in more recent years. Interpretation: Multiple factors influenced decisions to initiate TB treatment despite negative test results. Clinicians were substantially less likely to treat in the absence of a positive test result when using more sensitive, PCR-based diagnostics. Funding: National Institutes of Health. Research in context: Evidence before this study: In countries with a high burden of tuberculosis, over one-third of notified cases for pulmonary TB are diagnosed based on clinical criteria, without bacteriological confirmation of disease ('clinical diagnosis'). For these individuals with negative bacteriological test results, there is limited evidence on the factors associated with higher or lower rates of clinical diagnosis. In the context of individual clinical trials, some analyses have reported lower rates of treatment initiation for individuals testing negative on new cartridge-based PCR tests (e.g., Xpert MTB-RIF), as compared to individuals testing negative in sputum smear microscopy.Added value of this study: This study conducted a systematic review of studies that collected data on patient characteristics and treatment initiation decisions for individuals receiving a negative bacteriological test result as part of initial evaluation for TB. Patient-level data from 13 countries across 12 studies (n=15121) were analyzed in an individual patient data meta-analysis, to describe factors associated with clinicians' decisions to treat for TB disease. We identified significant associations between multiple clinical factors and the probability that a patient would be initiated on TB treatment, including sex, history of prior TB, reported symptoms (cough and night sweats), and HIV status. Controlling for other factors, patients testing negative on PCR-based diagnostics (e.g., Xpert MTB/RIF) were less likely to be initiated on treatment than those testing negative with smear microscopy.Implications of all the available evidence: Rates of clinical diagnosis for TB differ systematically as a function of multiple clinical factors and are lower for patients who test negative with new PCR-based diagnostics compared to earlier smear-based methods. This evidence can be used to refine diagnostic algorithms and better understand the implications of introducing new diagnostic tests for TB.

Identifying local foci of tuberculosis transmission in Moldova using a spatial multinomial logistic regression model.

BACKGROUND: Multidrug resistant tuberculosis (MDR-TB) represents a major public health concern in the Republic of Moldova, with an estimated 31% of new and 56% of previously treated TB cases having MDR disease in 2022. A recent genomic epidemiology study of incident TB occurring in 2018 and 2019 found that 92% of MDR-TB was the result of transmission. The MDR phenotype was concentrated among two M. tuberculosis (Mtb) lineages: L2.2.1 (Beijing) and L4.2.1 (Ural). METHODS: We developed and applied a hierarchical Bayesian multinominal logistic regression model to Mtb genomic, spatial, and epidemiological data collected from all individuals with diagnosed TB in Moldova in 2018 and 2019 to identify locations in which specific Mtb strains are being transmitted. We then used a logistic regression model to estimate locality-level factors associated with local transmission. FINDINGS: We found differences in the spatial distribution and degree of local concentration of disease due to specific strains of Beijing and Ural lineage Mtb. Foci of transmission for four strains of Beijing lineage Mtb, predominantly of the MDR-TB phenotype, were located in several regions, but largely concentrated in Transnistria. In contrast, transmission of Ural lineage Mtb had less marked patterns of spatial aggregation, with a single strain (also of the MDR phenotype) spatially clustered in southern Transnistria. We found a 30% (95% credible interval 2%-80%) increase in odds of a locality being a transmission cluster for each increase of 100 persons per square kilometer, while higher local tuberculosis incidence and poverty were not associated with a locality being a transmission focus. INTERPRETATION: Our results identified localities where specific Mtb transmission networks were concentrated and quantified the association between locality-level factors and focal transmission. This analysis revealed Transnistria as the primary area where specific Mtb strains (predominantly of the MDR-TB phenotype) were locally transmitted and suggests that targeted intensified case finding in this region may be an attractive policy option. FUNDING: Funding for this work was provided by the National Institute of Allergy and Infectious Diseases at the US National Institutes of Health.

A scalar-on-quantile-function approach for estimating short-term health effects of environmental exposures.

Environmental epidemiologic studies routinely utilize aggregate health outcomes to estimate effects of short-term (eg, daily) exposures that are available at increasingly fine spatial resolutions. However, areal averages are typically used to derive population-level exposure, which cannot capture the spatial variation and individual heterogeneity in exposures that may occur within the spatial and temporal unit of interest (eg, within a day or ZIP code). We propose a general modeling approach to incorporate within-unit exposure heterogeneity in health analyses via exposure quantile functions. Furthermore, by viewing the exposure quantile function as a functional covariate, our approach provides additional flexibility in characterizing associations at different quantile levels. We apply the proposed approach to an analysis of air pollution and emergency department (ED) visits in Atlanta over 4 years. The analysis utilizes daily ZIP code-level distributions of personal exposures to 4 traffic-related ambient air pollutants simulated from the Stochastic Human Exposure and Dose Simulator. Our analyses find that effects of carbon monoxide on respiratory and cardiovascular disease ED visits are more pronounced with changes in lower quantiles of the population's exposure. Software for implement is provided in the R package nbRegQF.

Estimated population-level impact of pneumococcal conjugate vaccines against all-cause pneumonia mortality among unvaccinated age groups in five Latin American countries.

BACKGROUND: Pneumococcal conjugate vaccines (PCVs) provide strong direct protection in children, while limited data are available on their indirect effect on mortality among older age groups. This multi-country study aimed to assess the population-level impact of pediatric PCVs on all-cause pneumonia mortality among ≥5 years of age, and invasive pneumococcal disease (IPD) cases in Chile. METHODS: Demographic and mortality data from Argentina, Brazil, Chile, Colombia, and Mexico were collected considering the ≥ 5-year-old population, from 2000-2019, with 1,795,789 deaths due to all-cause pneumonia. IPD cases in Chile were also evaluated. Time series models were employed to evaluate changes in all-cause pneumonia deaths during the post-vaccination period, with other causes of death used as synthetic controls for unrelated temporal trends. RESULTS: No significant change in death rates due to all-cause pneumonia was detected following PCV introduction among most age groups and countries. The proportion of IPD cases caused by vaccine serotypes decreased from 29% (2012) to 6% (2022) among ≥65 years in Chile. DISCUSSION: While an effect of PCV against pneumonia deaths (a broad clinical definition that may not be specific enough to measure indirect effects) was not detected, evidence of indirect PCV impact was observed among vaccine-type-specific IPD cases.

A scaled kernel density estimation prior for dynamic borrowing of historical information with application to clinical trial design.

Incorporating historical data into a current data analysis can improve estimation of parameters shared across both datasets and increase the power to detect associations of interest while reducing the time and cost of new data collection. Several methods for prior distribution elicitation have been introduced to allow for the data-driven borrowing of historical information within a Bayesian analysis of the current data. We propose scaled Gaussian kernel density estimation (SGKDE) prior distributions as potentially more flexible alternatives. SGKDE priors directly use posterior samples collected from a historical data analysis to approximate probability density functions, whose variances depend on the degree of similarity between the historical and current datasets, which are used as prior distributions in the current data analysis. We compare the performances of the SGKDE priors with some existing approaches using a simulation study. Data from a recently completed phase III clinical trial of a maternal vaccine for respiratory syncytial virus are used to further explore the properties of SGKDE priors when designing a new clinical trial while incorporating historical data. Overall, both studies suggest that the new approach results in improved parameter estimation and power in the current data analysis compared to the considered existing methods.

Hierarchical Bayesian modeling of heterogeneous outcome variance in cluster randomized trials.

BACKGROUND: Heterogeneous outcome correlations across treatment arms and clusters have been increasingly acknowledged in cluster randomized trials with binary endpoints, where analytical methods have been developed to study such heterogeneity. However, cluster-specific outcome variances and correlations have yet to be studied for cluster randomized trials with continuous outcomes. METHODS: This article proposes models fitted in the Bayesian setting with hierarchical variance structure to quantify heterogeneous variances across clusters and explain it with cluster-level covariates when the outcome is continuous. The models can also be extended to analyzing heterogeneous variances in individually randomized group treatment trials, with arm-specific cluster-level covariates, or in partially nested designs. Simulation studies are carried out to validate the performance of the newly introduced models across different settings. RESULTS: Simulations showed that overall the newly introduced models have good performance, reporting low bias and approximately 95% coverage for the intraclass correlation coefficients and regression parameters in the variance model. When variances are heterogeneous, our proposed models had improved model fit over models with homogeneous variances. When used to analyze data from the Kerala Diabetes Prevention Program study, our models identified heterogeneous variances and intraclass correlation coefficients across clusters and examined cluster-level characteristics associated with such heterogeneity. CONCLUSION: We proposed new hierarchical Bayesian variance models to accommodate cluster-specific variances in cluster randomized trials. The newly developed methods inform the understanding of how an intervention strategy is implemented and disseminated differently across clusters and can help improve future trial design.

Environmental surveillance for COVID-19 using SARS-CoV-2 RNA concentration in wastewater - a study in District East, Karachi, Pakistan.

Background: Wastewater-based surveillance is used to track the temporal patterns of the SARS-CoV-2 virus in communities. Viral RNA particle detection in wastewater samples can indicate an outbreak within a catchment area. We describe the feasibility of using a sewage network to monitor SARS-CoV-2 trend and use of genomic sequencing to describe the viral variant abundance in an urban district in Karachi, Pakistan. This was among the first studies from Pakistan to demonstrate the surveillance for SARS-CoV-2 from a semi-formal sewage system. Methods: Four sites draining into the Lyari River in District East, Karachi, were identified and included in the current study. Raw sewage samples were collected early morning twice weekly from each site between June 10, 2021 and January 17, 2022, using Bag Mediated Filtration System (BMFS). Secondary concentration of filtered samples was achieved by ultracentrifugation and skim milk flocculation. SARS-CoV-2 RNA concentrations in the samples were estimated using PCR (Qiagen ProMega kits for N1 & N2 genes). A distributed-lag negative binomial regression model within a hierarchical Bayesian framework was used to describe the relationship between wastewater RNA concentration and COVID-19 cases from the catchment area. Genomic sequencing was performed using Illumina iSeq100. Findings: Among the 151 raw sewage samples included in the study, 123 samples (81.5%) tested positive for N1 or N2 genes. The average SARS-CoV-2 RNA concentrations in the sewage samples at each lag (1-14 days prior) were associated with the cases reported for the respective days, with a peak association observed on lag day 10 (RR: 1.15; 95% Credible Interval: 1.10-1.21). Genomic sequencing showed that the delta variant dominated till September 2022, while the omicron variant was identified in November 2022. Interpretation: Wastewater-based surveillance, together with genomic sequencing provides valuable information for monitoring the community temporal trend of SARS-CoV-2. Funding: PATH, Bill & Melinda Gates Foundation, and Global Innovation Fund.

Incorporating Efficacy Data from Initial Trials Into Subsequent Evaluations: Application to Vaccines Against Respiratory Syncytial Virus.

BACKGROUND: When a randomized controlled trial fails to demonstrate statistically significant efficacy against the primary endpoint, a potentially costly new trial would need to be conducted to receive licensure. Incorporating data from previous trials might allow for more efficient follow-up trials to demonstrate efficacy, speeding the availability of effective vaccines. METHODS: Based on the outcomes from a failed trial of a maternal vaccine against respiratory syncytial virus (RSV), we simulated data for a new Bayesian group-sequential trial. We analyzed the data either ignoring data from the previous trial (i.e., weakly informative prior distributions) or using prior distributions incorporating the historical data into the analysis. We evaluated scenarios where efficacy in the new trial was the same, greater than, or less than that in the original trial. For each scenario, we evaluated the statistical power and type I error rate for estimating the vaccine effect following interim analyses. RESULTS: When we used a stringent threshold to control the type I error rate, analyses incorporating historical data had a small advantage over trials that did not. If control of type I error is less important (e.g., in a postlicensure evaluation), the incorporation of historical data can provide a substantial boost in efficiency. CONCLUSIONS: Due to the need to control the type I error rate in trials used to license a vaccine, incorporating historical data provides little additional benefit in terms of stopping the trial early. However, these statistical approaches could be promising in evaluations that use real-world evidence following licensure.

Effects of air pollution on adverse birth outcomes and pregnancy complications in the U.S. state of Kansas (2000-2015).

Neonatal mortality and morbidity are often caused by preterm birth and lower birth weight. Gestational diabetes mellitus (GDM) and gestational hypertension (GH) are the most prevalent maternal medical complications during pregnancy. However, evidence on effects of air pollution on adverse birth outcomes and pregnancy complications is mixed. Singleton live births conceived between January 1st, 2000, and December 31st, 2015, and reached at least 27 weeks of pregnancy in Kansas were included in the study. Trimester-specific and total pregnancy exposures to nitrogen dioxide (NO2), particulate matter with an aerodynamic diameter less than 2.5 µm (PM2.5), and ozone (O3) were estimated using spatiotemporal ensemble models and assigned to maternal residential census tracts. Logistic regression, discrete-time survival, and linear models were applied to assess the associations. After adjustment for demographics and socio-economic status (SES) factors, we found increases in the second and third trimesters and total pregnancy O3 exposures were significantly linked to preterm birth. Exposure to the second and third trimesters O3 was significantly associated with lower birth weight, and exposure to NO2 during the first trimester was linked to an increased risk of GDM. O3 exposures in the first trimester were connected to an elevated risk of GH. We didn't observe consistent associations between adverse pregnancy and birth outcomes with PM2.5 exposure. Our findings indicate there is a positive link between increased O3 exposure during pregnancy and a higher risk of preterm birth, GH, and decreased birth weight. Our work supports limiting population exposure to air pollution, which may lower the likelihood of adverse birth and pregnancy outcomes.

Predictors of early life residential mobility in urban and rural Pennsylvania children with acute lymphoblastic leukemia and implications for environmental exposure assessment.

BACKGROUND: Residential mobility can introduce exposure misclassification in pediatric epidemiology studies using birth address only. OBJECTIVE: We examined whether residential mobility varies by sociodemographic factors and urbanicity/rurality among children with cancer. METHODS: Our study included 400 children born in Pennsylvania during 2002-2015 and diagnosed with leukemia at ages 2-7 years. Addresses were obtained from state registries at birth and diagnosis. We considered three aspects of mobility between birth and diagnosis: whether a child moved, whether a mover changed census tract, and distance moved. We evaluated predictors of these aspects in urban- and rural-born children using chi-square, t-tests, and regression analyses. RESULTS: Overall, 58% of children moved between birth and diagnosis; suburban/rural-born children were more likely to move than urban-born children (67% versus 57%). The mean distance moved was 16.7 km in suburban/rural-born and 14.8 km in urban-born movers. In urban-born children, moving between birth and diagnosis was associated with race, education, participation in the Nutrition Program for Women, Infants and Children (WIC), and census tract-level income (all χ2 p < 0.01). Urban-born movers tended to be born in a census tract with a higher Social Vulnerability Index than non-movers (t-test p < 0.01). No factors were statistically significantly associated with any of the residential mobility metrics in suburban/rural-born children, although the sample size was small. IMPACT STATEMENT: In this study of a vulnerable population of children with cancer, we found that rural-born children were more likely to move than urban-born children, however, the frequency of movers changing census tracts was equivalent. Mobility in urban-born children, but not rural-born, was associated with several social factors, although the sample size for rural-born children was small. Mobility could be an important source of misclassification depending on the spatial heterogeneity and resolution of the exposure data and whether the social factors are related to exposures or health outcomes. Our results highlight the importance of considering differences in mobility between urban and rural populations in spatial research.

Travel surveillance uncovers dengue virus dynamics and introductions in the Caribbean.

Dengue is the most prevalent mosquito-borne viral disease in humans, and cases are continuing to rise globally. In particular, islands in the Caribbean have experienced more frequent outbreaks, and all four dengue virus (DENV) serotypes have been reported in the region, leading to hyperendemicity and increased rates of severe disease. However, there is significant variability regarding virus surveillance and reporting between islands, making it difficult to obtain an accurate understanding of the epidemiological patterns in the Caribbean. To investigate this, we used travel surveillance and genomic epidemiology to reconstruct outbreak dynamics, DENV serotype turnover, and patterns of spread within the region from 2009-2022. We uncovered two recent DENV-3 introductions from Asia, one of which resulted in a large outbreak in Cuba, which was previously under-reported. We also show that while outbreaks can be synchronized between islands, they are often caused by different serotypes. Our study highlights the importance of surveillance of infected travelers to provide a snapshot of local introductions and transmission in areas with limited local surveillance and suggests that the recent DENV-3 introductions may pose a major public health threat in the region.

Investigation of Sources of Fluorinated Compounds in Private Water Supplies in an Oil and Gas-Producing Region of Northern West Virginia.

Per- and polyfluoroalkyl substances (PFASs) are a class of toxic organic compounds that have been widely used in consumer applications and industrial activities, including oil and gas production. We measured PFAS concentrations in 45 private wells and 8 surface water sources in the oil and gas-producing Doddridge, Marshall, Ritchie, Tyler, and Wetzel Counties of northern West Virginia and investigated relationships between potential PFAS sources and drinking water receptors. All surface water samples and 60% of the water wells sampled contained quantifiable levels of at least one targeted PFAS compound, and four wells (8%) had concentrations above the proposed maximum contaminant level (MCL) for perfluorooctanoic acid (PFOA). Individual concentrations of PFOA and perfluorobutanesulfonic acid exceeded those measured in finished public water supplies. Total targeted PFAS concentrations ranged from nondetect to 36.8 ng/L, with surface water concentrations averaging 4-fold greater than groundwater. Semiquantitative, nontargeted analysis showed concentrations of emergent PFAS that were potentially higher than targeted PFAS. Results from a multivariate latent variable hierarchical Bayesian model were combined with insights from analyses of groundwater chemistry, topographic characteristics, and proximity to potential PFAS point sources to elucidate predictors of PFAS concentrations in private wells. Model results reveal (i) an increased vulnerability to contamination in upland recharge zones, (ii) geochemical controls on PFAS transport likely driven by adsorption, and (iii) possible influence from nearby point sources.

Spatially-targeted tuberculosis screening has limited impact beyond household contact tracing in Lima, Peru: A model-based analysis.

Mathematical models have suggested that spatially-targeted screening interventions for tuberculosis may efficiently accelerate disease control, but empirical data supporting these findings are limited. Previous models demonstrating substantial impacts of these interventions have typically simulated large-scale screening efforts and have not attempted to capture the spatial distribution of tuberculosis in households and communities at a high resolution. Here, we calibrate an individual-based model to the locations of case notifications in one district of Lima, Peru. We estimate the incremental efficiency and impact of a spatially-targeted interventions used in combination with household contact tracing (HHCT). Our analysis reveals that HHCT is relatively efficient with a median of 40 (Interquartile Range: 31.7 to 49.9) household contacts required to be screened to detect a single case of active tuberculosis. However, HHCT has limited population impact, producing a median incidence reduction of only 3.7% (Interquartile Range: 5.8% to 1.9%) over 5 years. In comparison, spatially targeted screening (which we modeled as active case finding within high tuberculosis prevalence areas 100 m2 grid cell) is far less efficient, requiring evaluation of ≈12 times the number of individuals as HHCT to find a single individual with active tuberculosis. Furthermore, the addition of the spatially targeted screening effort produced only modest additional reductions in tuberculosis incidence over the 5 year period (≈1.3%) in tuberculosis incidence. In summary, we found that HHCT is an efficient approach for tuberculosis case finding, but has limited population impact. Other screening approaches which target areas of high tuberculosis prevalence are less efficient, and may have limited impact unless very large numbers of individuals can be screened.

Persuasive COVID-19 vaccination campaigns on Facebook and nationwide vaccination coverage in Ukraine, India, and Pakistan.

Social media platforms have a wide and influential reach, and as such provide an opportunity to increase vaccine uptake. To date, there is no large-scale, robust evidence on the offline effects of online messaging campaigns. We aimed to test whether pre-tested, persuasive messaging campaigns from UNICEF, disseminated on Facebook, influenced COVID-19 vaccine uptake in Ukraine, India, and Pakistan. In Ukraine, we deployed a stepped-wedge randomized controlled trial (RCT). Half of the 24 oblasts (provinces) received five weeks of the intervention, the other half ten weeks of the intervention. In India, an RCT with an augmented synthetic control was conducted in five states (Bihar, Chhattisgarh, Jharkhand, Madhya Pradesh, Rajasthan), whereby 40 out of 174 districts were randomized to receive six weeks of intervention. In Pakistan we deployed a pre-post design, whereby 25 city districts received six weeks of the intervention. Weekly COVID-19 vaccination data was sourced through government databases. Using Poisson regression models, the association between the intervention and vaccine uptake was estimated. In Ukraine we conducted a survey among Facebook users at three time points during the RCT, to ascertain vaccination intentions and trust in vaccines. The campaigns reached more than 110 million Facebook users and garnered 2.9 million clicks. In Ukraine, we found that the intervention did not affect oblast-level vaccination coverage (Relative Risk (RR): 0.93, 95% Confidence Interval (CI) 0.86-1.01). Similarly, in India and Pakistan we found no effect of our intervention (India: RR 0.85, 95% CI 0.70-1.04; Pakistan: RR 0.64, 95% CI 0.01-29.9). The survey among Facebook users in Ukraine showed that trust in vaccines and information sources was an important predictor of vaccination status and intention to get vaccinated. Our campaigns on Facebook had a wide reach, which did not translate in shifting behaviours. Timing and external events may have limited the effectiveness of our interventions.

Season of Conception and Risk of Cerebral Palsy.

Importance: Cerebral palsy (CP) is the most prevalent neuromotor disability in childhood, but for most cases the etiology remains unexplained. Seasonal variation in the conception of CP may provide clues for their potential etiological risk factors that vary across seasons. Objective: To evaluate whether the month or season of conception is associated with CP occurrence. Design, Setting, and Participants: This statewide cohort study examined more than 4 million live births that were registered in the California birth records during 2007 to 2015 and were linked to CP diagnostic records (up to year 2021). Statistical analyses were conducted between March 2022 and January 2023. Exposures: The month and season of conception were estimated based on the child's date of birth and the length of gestation recorded in the California birth records. Main Outcomes and Measures: CP status was ascertained from the diagnostic records obtained from the Department of Developmental Services in California. Poisson regression was used to estimate the relative risk (RR) and 95% CI for CP according to the month or the season of conception, adjusting for maternal- and neighborhood-level factors. Stratified analyses were conducted by child's sex and neighborhood social vulnerability measures, and the mediating role of preterm birth was evaluated. Results: Records of 4 468 109 children (51.2% male; maternal age: 28.3% aged 19 to 25 years, 27.5% aged 26 to 30 years; maternal race and ethnicity: 5.6% African American or Black, 13.5% Asian, 49.8% Hispanic or Latinx of any race, and 28.3% non-Hispanic White) and 4697 with CP (55.1% male; maternal age: 28.3% aged 19 to 25 years, 26.0% aged 26 to 30 years; maternal race and ethnicity: 8.3% African American or Black, 8.6% Asian, 54.3% Hispanic or Latinx of any race, and 25.8% non-Hispanic White) were analyzed. Children conceived in winter (January to March) or spring (April to June) were associated with a 9% to 10% increased risk of CP (winter: RR, 1.09 [95% CI, 1.01-1.19]; spring: RR, 1.10 [95% CI, 1.02-1.20]) compared with summer (July to September) conceptions. Analyses for specific months showed similar results with children conceived in January, February, and May being at higher risk of CP. The associations were slightly stronger for mothers who lived in neighborhoods with a high social vulnerability index, but no child sex differences were observed. Only a small portion of the estimated association was mediated through preterm birth. Conclusions and Relevance: In this cohort study in California, children conceived in winter and spring had a small increase in CP risk. These findings suggest that seasonally varying environmental factors should be considered in the etiological research of CP.

Global, regional, and national estimates of tuberculosis incidence and case detection among incarcerated individuals from 2000 to 2019: a systematic analysis.

BACKGROUND: People who are incarcerated are at high risk of developing tuberculosis. We aimed to estimate the annual global, regional, and national incidence of tuberculosis among incarcerated populations from 2000 to 2019. METHODS: We collected and aggregated data for tuberculosis incidence and prevalence estimates among incarcerated individuals in published and unpublished literature, annual tuberculosis notifications among incarcerated individuals at the country level, and the annual number of incarcerated individuals at the country level. We developed a joint hierarchical Bayesian meta-regression framework to simultaneously model tuberculosis incidence, notifications, and prevalence from 2000 to 2019. Using this model, we estimated trends in absolute tuberculosis incidence and notifications, the incidence and notification rates, and the case detection ratio by year, country, region, and globally. FINDINGS: In 2019, we estimated a total of 125 105 (95% credible interval [CrI] 93 736-165 318) incident tuberculosis cases among incarcerated individuals globally. The estimated incidence rate per 100 000 person-years overall was 1148 (95% CrI 860-1517) but varied greatly by WHO region, from 793 (95% CrI 430-1342) in the Eastern Mediterranean region to 2242 (1515-3216) in the African region. Global incidence per 100 000 person-years between 2000 and 2012 among incarcerated individuals decreased from 1884 (95% CrI 1394-2616) to 1205 (910-1615); however, from 2013 onwards, tuberculosis incidence per 100 000 person-years was stable, from 1183 (95% CrI 876-1596) in 2013 to 1148 (860-1517) in 2019. In 2019, the global case detection ratio was estimated to be 53% (95% CrI 42-64), the lowest over the study period. INTERPRETATION: Our estimates suggest a high tuberculosis incidence rate among incarcerated individuals globally with large gaps in tuberculosis case detection. Tuberculosis in incarcerated populations must be addressed with interventions specifically tailored to improve diagnoses and prevent transmission as a part of the broader global tuberculosis control effort. FUNDING: National Institutes of Health.

High Ambient Temperature in Pregnancy and Risk of Childhood Acute Lymphoblastic Leukemia.

Background: High ambient temperature is increasingly common due to climate change and is associated with risk of adverse pregnancy outcomes. Acute lymphoblastic leukemia (ALL) is the most common malignancy in children, the incidence is increasing, and in the United States it disproportionately affects Latino children. We aimed to investigate the potential association between high ambient temperature in pregnancy and risk of childhood ALL. Methods: We used data from California birth records (1982-2015) and California Cancer Registry (1988-2015) to identify ALL cases diagnosed <14 years and 50 times as many controls matched by sex, race/ethnicity, and date of last menstrual period. Ambient temperatures were estimated on a 1-km grid. Association between ambient temperature and ALL was evaluated per gestational week, restricted to May-September, adjusting for confounders. Bayesian meta-regression was applied to identify critical exposure windows. For sensitivity analyses, we evaluated a 90-day pre-pregnancy period (assuming no direct effect before pregnancy) and constructed an alternatively matched dataset for exposure contrast by seasonality. Findings: Our study included 6,258 ALL cases and 307,579 controls. The peak association between ambient temperature and risk of ALL was observed in gestational week 8, where a 5 °C increase was associated with an odds ratio of 1.09 (95% confidence interval 1.04-1.14) and 1.05 (95% confidence interval 1.00-1.11) among Latino and non-Latino White children, respectively. The sensitivity analyses supported this. Interpretation: Our findings suggest an association between high ambient temperature in early pregnancy and risk of childhood ALL. Further replication and investigation of mechanistic pathways may inform mitigation strategies.

Dynamics of eastern equine encephalitis virus during the 2019 outbreak in the Northeast United States.

Eastern equine encephalitis virus (EEEV) causes a rare but severe disease in horses and humans and is maintained in an enzootic transmission cycle between songbirds and Culiseta melanura mosquitoes. In 2019, the largest EEEV outbreak in the United States for more than 50 years occurred, centered in the Northeast. To explore the dynamics of the outbreak, we sequenced 80 isolates of EEEV and combined them with existing genomic data. We found that, similar to previous years, cases were driven by multiple independent but short-lived virus introductions into the Northeast from Florida. Once in the Northeast, we found that Massachusetts was important for regional spread. We found no evidence of any changes in viral, human, or bird factors which would explain the increase in cases in 2019, although the ecology of EEEV is complex and further data is required to explore these in more detail. By using detailed mosquito surveillance data collected by Massachusetts and Connecticut, however, we found that the abundance of Cs. melanura was exceptionally high in 2019, as was the EEEV infection rate. We employed these mosquito data to build a negative binomial regression model and applied it to estimate early season risks of human or horse cases. We found that the month of first detection of EEEV in mosquito surveillance data and vector index (abundance multiplied by infection rate) were predictive of cases later in the season. We therefore highlight the importance of mosquito surveillance programs as an integral part of public health and disease control.

Incorporating efficacy data from initial trials into subsequent evaluations: Application to vaccines against respiratory syncytial virus.

Background: When a randomized controlled trial fails to demonstrate statistically significant efficacy against the primary endpoint, a potentially costly new trial would need to be conducted to receive licensure. Incorporating data from previous trials might allow for the conduct of more efficient follow-up trials to demonstrate efficacy, speeding the availability of effective vaccines. Methods: Based on the outcomes from a failed trial of a maternal vaccine against respiratory syncytial virus (RSV), we simulated data for a new Bayesian group-sequential trial. The data were analyzed either ignoring data from the previous trial (i.e., weakly informative prior distributions) or using prior distributions that incorporate the historical data into the analysis. We evaluated scenarios where the efficacy in the new trial was the same, greater than, or less than the efficacy in the original trial. For each scenario, we evaluated the statistical power and type I error rate for estimating the vaccine effect following interim analyses. Results: If a stringent threshold is used to control the type I error rate, the analyses that incorporated historical data had a small advantage over trials that did not. If control of type I error is less important (e.g., in a post-licensure evaluation), the incorporation of historical data can provide a substantial boost in efficiency. Conclusions: Due to the need to control the type I error rate in trials used to license a vaccine, the incorporation of historical data provides little additional benefit in terms of stopping the trial early. However, these statistical approaches could be promising in evaluations that use real-world evidence following licensure.

Social Vulnerability and Groundwater Vulnerability to Contamination From Unconventional Hydrocarbon Extraction in the Appalachian Basin.

Unconventional oil and gas (UOG) development, made possible by horizontal drilling and high-volume hydraulic fracturing, has been fraught with controversy since the industry's rapid expansion in the early 2000's. Concerns about environmental contamination and public health risks persist in many rural communities that depend on groundwater resources for drinking and other daily needs. Spatial disparities in UOG risks can pose distributive environmental injustice if such risks are disproportionately borne by marginalized communities. In this paper, we analyzed groundwater vulnerability to contamination from UOG as a physically based measure of risk in conjunction with census tract level sociodemographic characteristics describing social vulnerability in the northern Appalachian Basin. We found significant associations between elevated groundwater vulnerability and lower population density, consistent with UOG development occurring in less densely populated rural areas. We also found associations between elevated groundwater vulnerability and lower income, higher proportions of elderly populations, and higher proportion of mobile homes, suggesting a disproportionate risk burden on these socially vulnerable groups. We did not find a statistically significant association between elevated groundwater vulnerability and populations of racial/ethnic minorities in our study region. Household surveys provided empirical support for a relationship between sociodemographic characteristics and capacity to assess and mitigate exposures to potentially contaminated water. Further research is needed to probe if the observed disparities translate to differences in chemical exposure and adverse health outcomes.