Publicly released health statistics play a central role in characterizing temporal trends and identifying structural changes in population health. However, disclosure limitation through suppression of small cell counts, as implemented in systems such as the Centers for Disease Control and Prevention Wide-ranging ONline Data for Epidemiologic Research (CDC WONDER), produces partially observed count data that complicate statistical inference. These challenges are particularly acute for rare outcomes and subgroup analyses, where suppression is widespread and varies across geographic regions, demographic populations, and time. We propose Bayesian ACCESS (Autoregressive Change-point and Clustering Estimation for Suppressed Count Series), a Bayesian hierarchical framework for inference on latent epidemic trajectories and their structural changes from disclosure-limited health statistics. The proposed model directly represents suppressed count data through a suppression-aware observation model, jointly infers multiple temporal change points and latent trajectories, and borrows information across related geographic and demographic populations through Bayesian nonparametric clustering while preserving meaningful heterogeneity. We apply Bayesian ACCESS to opioid-related overdose mortality data from CDC WONDER for U.S. states from 1999 to 2024. The analysis identifies distinct subgroup-specific epidemic trajectories and structural changes that would be difficult to characterize using publicly released health statistics without explicitly accounting for data suppression.

Buprenorphine retention is crucial for effective treatment of opioid use disorder (OUD), yet disparities in treatment discontinuation persist. This study aims to identify and quantify disparities in buprenorphine treatment retention using a machine learning framework adapted from Virtual Twins approach focusing on disparities related to sex, age, insurance type, geographic region, mental health status and community-level Social Vulnerability Index (SVI). Using a nationwide longitudinal cohort, we applied a two-stage machine learning approach. In Stage one, we trained classification models to estimate the counterfactual differences in treatment discontinuation across disparity types. Model performance was assessed using C-statistics and precision-recall curve. In Stage two, we employed regression models, decision trees and neural networks with Shapley Additive Explanations, to identify subgroups most vulnerable to the disparities and key contributing factors. Among 303,528 treatment episodes from 131,169 patients aged 18-85, 71% discontinued treatment within 180 days. Early medication adherence (3-month proportion of days covered) was the strongest predictor. Significant disparities emerged based on insurance, region, mental health status, age, and SVI. Higher discontinuation risk was observed among privately insured older adults, patients from high-SVI areas in the South without mental health diagnoses, and younger publicly insured individuals lacking psychiatric services. Psychiatric service utilization consistently mitigated discontinuation risks across subgroups. Limitations include the absence of race/ethnicity in the claims data, the inability to capture concurrent medications and initial buprenorphine dose, and lack of formal uncertainty estimates for the quantified disparities. The Virtual Twins analytical framework enabled identification of vulnerable subgroups and quantification of disparities attributable to specific risk factors. Interventions that prioritize early adherence, expand access to psychiatric services, and address structural barriers in high-SVI Southern communities and insurance-defined risk groups may improve equity in buprenorphine retention.

BACKGROUND:Vaccination effectively and safely prevents serious illness from coronavirus disease 2019 (COVID-19). This study examines the complex factors associated with vaccination uptake among economically disadvantaged adults calling the Texas/United Way 2-1-1 helpline. Results informed the development of strategies to increase COVID-19 vaccine uptake among vulnerable populations disproportionately impacted by COVID-19-related morbidity and mortality. MATERIAL AND METHODS:We conducted a cross-sectional survey among 2-1-1 adult callers (May 2021-September 2022). The survey examined demographic, sociocultural, healthcare access, and psychosocial factors, as well as mistrust in health agencies. We used multivariable logistic regression models to examine factors associated with vaccination uptake, and ordinal regression models to examine factors associated with psychosocial constructs significantly related to vaccination outcomes. RESULTS:The majority of respondents were from low-income households, had low education attainment, and were minorities. Among the 509 surveys, 67.2 % of participants reported vaccine uptake. Multivariable logistic regression analyses indicated significantly higher odds of vaccination among adults 50-64 years and 65 years and over (OR: 4.5, 95 % CI: 1.7-11.7 and OR: 7.2, 95 % CI: 1.8-28; P < 0.01). Respondents with higher scores on perceived susceptibility (OR: 1.4, 95 % CI: 1.0-1.8, p = 0.02), perceived safety of the vaccine (OR: 2.9, 95 % CI: 1.9-4.3, p < 0.01) and beliefs about vaccine effectiveness against COVID-19 (OR: 1.5, 95 % CI: 1.1-2.0, p = 0.02) and serious illness and hospitalization (OR: 1.4, 95 %: 1.0-1.8, p = 0.02) were significantly more likely to have received a vaccination. CONCLUSIONS:Our results indicate that among the sample of economically vulnerable 2-1-1 callers from primarily underserved minority groups, perceived vaccine safety and effectiveness (against COVID-19, serious illness, and hospitalization), and perceived susceptibility to COVID-19 are important factors associated with vaccine uptake. These findings underscore the importance of designing interventions to increase vaccination that address these constructs with powerful messages and strategies.

Background Geospatial data science can be a powerful tool to aid the design, reach, efficiency, and impact of community-based intervention trials. The project titled Take Care Texas aims to develop and test an adaptive, multilevel, community-based intervention to increase COVID-19 testing and vaccination uptake among vulnerable populations in 3 Texas regions: Harris County, Cameron County, and Northeast Texas. Objective We aimed to develop a novel procedure for adaptive selections of census block groups (CBGs) to include in the community-based randomized trial for the Take Care Texas project. Methods CBG selection was conducted across 3 Texas regions over a 17-month period (May 2021 to October 2022). We developed persistent and recent COVID-19 burden metrics, using real-time SARS-CoV-2 monitoring data to capture dynamic infection patterns. To identify vulnerable populations, we also developed a CBG-level community disparity index, using 12 contextual social determinants of health (SDOH) measures from US census data. In each adaptive round, we determined the priority CBGs based on their COVID-19 burden and disparity index, ensuring geographic separation to minimize intervention “spillover.” Community input and feedback from local partners and health workers further refined the selection. The selected CBGs were then randomized into 2 intervention arms—multilevel intervention and just-in-time adaptive intervention—and 1 control arm, using covariate adaptive randomization, at a 1:1:1 ratio. We developed interactive data dashboards, which included maps displaying the locations of selected CBGs and community-level information, to inform the selection process and guide intervention delivery. Selection and randomization occurred across 10 adaptive rounds. Results A total of 120 CBGs were selected and followed the stepped planning and interventions, with 60 in Harris County, 30 in Cameron County, and 30 in Northeast Texas counties. COVID-19 burden presented substantial temporal changes and local variations across CBGs. COVID-19 burden and community disparity exhibited some common geographical patterns but also displayed distinct variations, particularly at different time points throughout this study. This underscores the importance of incorporating both real-time monitoring data and contextual SDOH in the selection process. Conclusions The novel procedure integrated real-time monitoring data and geospatial data science to enhance the design and adaptive delivery of a community-based randomized trial. Adaptive selection effectively prioritized the most in-need communities and allowed for a rigorous evaluation of community-based interventions in a multilevel trial. This methodology has broad applicability and can be adapted to other public health intervention and prevention programs, providing a powerful tool for improving population health and addressing health disparities.

Melanoma is projected to become the second most diagnosed cancer in the United States by 2040, emphasizing the importance of early detection for improving survival outcomes. Accurately estimating late-stage melanoma incidence at granular geographical levels is challenging due to its rarity and data sparsity. Instead of relying on aggregated reported health statistics on melanoma cases, which are commonly used in cancer surveillance research, we utilize the incidence-based individual case database, and propose a Bayesian Heterogeneous Spatio-Temporal Logistic Regression with Nonlinear Demographic Effect (BHSTLR-NDE) model to provide county-level annual estimates of late-stage melanoma incidence in Texas from 2000 to 2018, using data obtained through a request to the Texas Cancer Registry. The BHSTLR-NDE integrates county-level covariates, flexible case-level demographic effects, and a reduced-rank spatial modelling approach to effectively address data sparsity, spatio-temporal non-stationarity, and demographic heterogeneity. The superiority of the proposed BHSTLR-NDE over competing models is demonstrated through extensive simulation studies and an application to melanoma registry data. Results indicate that BHSTLR-NDE effectively borrows information across spatial, temporal, and demographic dimensions, improving estimation accuracy and robustness. These findings highlight the utility of the proposed modelling approach in enhancing cancer surveillance and guiding targeted interventions.

Introduction:Buprenorphine is effective in reducing opioid-related morbidity and mortality; however, many patients discontinue treatment prematurely. While previous research has focused on individual-level predictors of retention, the influence of community context remains underexplored. This study aims to examine how community-level factors, such as social vulnerability, availability of buprenorphine-waivered providers and access to behavioural health services, affect the duration of buprenorphine treatment episodes for individuals with opioid use disorder (OUD). Methods:We conducted a retrospective cohort study using longitudinal claims data from 2006 to 2022. Adults aged 18 and older who initiated buprenorphine treatment for OUD and maintained continuous enrolment were included. Treatment episodes were defined by refill continuity, monitored until a gap of greater than 14 days occurred. Patient ZIP codes were linked to Social Vulnerability Index scores, provider density and behavioural health facility availability. The primary outcome was treatment duration, classified into short-term (0-3 months), medium-term (3-6 months), extended medium-term (6-12 months) and long-term (>12 months). Associations were identified using stepwise multinomial logistic regression. Results:From the 131 169 individuals (303 528 buprenorphine treatment episodes), most of them were males (60.8%), aged 18-34 years (47.7%), and commercially insured (76.0%). For medium-term duration, individuals living in low-vulnerable areas (OR: 1.14 (1.36-1.47)), with high provider density (1.07 (1.02-1.12)) and high mental health service availability (1.20 (1.15-1.25)) were associated with longer retention. Effect size increased for extended medium-term durations, including low vulnerability (1.73 (1.67-1.80)) and high mental health service availability (1.31 (1.26-1.37)). In long-term treatment episodes, those living in low-vulnerable areas (1.95 (1.89-2.02)), high provider density (1.1 (1.06-1.15)) and high mental health services access (1.58 (1.52-1.64)) presented an increased effect size in the odds. Conclusions:Social vulnerability, provider availability and mental health service access are significantly associated with buprenorphine treatment duration. Addressing these access inequities could improve retention for individuals with OUD.

Background:Melanoma currently ranks as the fifth leading cancer diagnosis and is projected to become the second most common cancer in the United States by 2040. Melanoma detected at earlier stages may be treated with less-risky and less-costly therapeutic options. Objective:This study aims to analyze temporal and spatial trends in melanoma incidence by stage at diagnosis (overall, early, and late) in Texas from 2000 to 2018, focusing on demographic and geographic variations to identify high-risk populations and regions for targeted prevention efforts. Methods:We used melanoma incidence data from all 254 Texas counties from the Texas Cancer Registry (TCR) from 2000 to 2018, aggregated by county and year. Among these, 250 counties reported melanoma cases during the period. Counties with no cases reported in a certain year were treated as having no cases. Melanoma cases were classified by SEER Summary Stage and stratified by the following four key covariates: age, sex, race and ethnicity, and stage at diagnosis. Incidence rates (IRs) were calculated per 100,000 population, and temporal trends were analyzed using joinpoint regression to determine average annual percentage changes (AAPCs) with 95% CIs for the whole time period (2000-2018), the most recent 10-year period (2009-2018), and the most recent 5-year period (2014-2018). Heat map visualizations were developed to assess temporal trends by patient age, year of diagnosis, stage at diagnosis, sex, and race and ethnicity. Spatial cluster analysis was conducted using Getis-Ord Gi* statistics to identify county-level geographic clusters of high and low melanoma incidence by stage at diagnosis. Results:A total of 82,462 melanoma cases were recorded, of which 74.7% (n=61,588) were early stage, 11.3% (n=9,352) were late stage, and 14% (n=11,522) were of unknown stage. Most cases were identified as males and non-Hispanic White individuals. Melanoma IRs increased from 2000 to 2018, particularly among older adults (60+ years; AAPC range 1.20%-1.84%; all P values were <.001), males (AAPC 1.59%; P<.001), and non-Hispanic White individuals (AAPC of 3.24% for early stage and 2.38% for late stage; P<.001 for early stage and P = .03 for late state). Early-stage diagnoses increased while the rates of late-stage diagnoses remained stable for the overall population. The spatial analysis showed that urban areas had higher early-stage incidence rates (P=.06), whereas rural areas showed higher late-stage incidence rates (P=.05), indicating possible geographic-based differences in access to dermatologic care. Conclusions:Melanoma incidence in Texas increased over the study time period, with the most-at-risk populations being non-Hispanic White individuals, males, and individuals aged 50 years and older. The stable rates of late-stage melanoma among racial and ethnic minority populations and rural populations highlight potential differences in access to diagnostic care. Future prevention efforts may benefit from increasing access to dermatologic care in areas with higher rates of late-stage melanoma at diagnosis.

IntroductionHealthcare resources are often crucial but limited, requiring careful consideration and informed allocation based on population needs and potential healthcare access. In resource allocation settings, availability and accessibility of resources should be examined simultaneously. The two-step floating catchment area (2SFCA) method has been previously used to evaluate spatial accessibility to healthcare resources and services, and to address health-related disparities. The 2SFCA methods have regained significant popularity during the COVID-19 pandemic, as their application proved crucial in addressing priority public health data analysis, modeling, and accessibility challenges. However, comprehensive comparisons of the 2SFCA method input parameters in the context of public health concerns in Texas are lacking. Our study aims to (a) perform a comparative analysis of 2SFCA input parameters on patterns of spatial accessibility and (b) identify a 2SFCA method to guide evaluation of equitable allocation of scarce mental health resources for children and adolescents in Texas.MethodsWe used the Texas Child Psychiatry Access Network (CPAN) data to assess county-level, regional patterns in access to pediatric psychiatric care, and to identify areas to expand CPAN to mitigate access-related disparities. Using the 2SFCA method, we further compared accessibility patterns across two kernel density distance decay functions for 10 catchment area specifications.ResultsAs expected, spatial accessibility measures, such as the spatial accessibility ratio (SPAR), are sensitive to input parameters, particularly the catchment area. However, across all catchment area thresholds, two clusters of counties in southern and central Texas had particularly low accessibility, highlighting the opportunity for expanding the provider network in these areas.DiscussionIdentifying areas with low accessibility can help public health initiatives prioritize regions in need of improved services and resources. The incorporation of additional data on supply capacity and care-seeking behavior would aid in the refinement of estimates for spatial accessibility at the regional level and within larger urban centers.

Objective: Lowell, Massachusetts, has been severely impacted by the opioid-related overdose crisis. Utilizing emergency medical services (EMS) data can inform local interventions by identifying opioid-related incidents (ORIs) with shorter lags in reporting. Our objective was to identify spatial and temporal variation in ORI and investigate its association with underlying socioeconomic indicators by coupling EMS data with Bayesian spatial-temporal analyses. Methods: We obtained data on ORI occurrences within the City of Lowell from January 2011 to June 2022 from Pridestar Trinity EMS. The ORI occurrences were aggregated by month and census tracts. We gathered American Community Survey 5-year estimates (2011-2022) for census-tract percentages of white, black, Hispanic, poverty, unemployed, bachelor’s degree, and rent-burdened populations. Using these data, we constructed a Bayesian spatial-temporal Poisson model to identify associations between quarterly ORI rates and these tract-level measures, along with seasonal effects. Results: ORI rates in Lowell rose from 20 per 10,000 people in 2011 to 93 per 10,000 people in 2018, stabilizing around 60 per 10,000 people from 2019 to 2021, with annual peaks between July through September. Downtown Lowell had consistently higher ORI rates, which extended north-south after 2016. Census tracts with higher percentage of black (relative risk = 1.008; 95% credible interval [1.002, 1.014]) and Hispanic populations (1.014 [1.009, 1.018]) were associated with higher ORI rates. Higher rent burden (1.103 [1.095, 1.11]) and poverty rates (1.02 [1.015, 1.025]) were positively associated with ORI rates, while unemployment rates were inversely associated. Conclusions: ORI rates in Lowell were associated with community-level sociodemographic factors and exhibited clear seasonal patterns. These findings could inform local prevention and response planning strategies for near-real-time ORI spike detection in communities to mitigate the impact of opioid overdose.

IntroductionSchool policy can encourage sun safe habits, such as wearing hats and applying sunscreen. However, sun safety policies (SSP) have not been formally assessed for Texas independent school districts (ISDs), particularly in counties with the highest melanoma incidence relative risk (RR). This study aims to assess the presence, strength, and intent of SSPs across Texas ISDs located in counties with the highest and lowest melanoma incidence. We also identify factors correlated with stronger SSP.MethodsEleven components of SSPs from 102 ISDs were evaluated in this cross-sectional study by examining school district websites, official documents, social media, media appearances, statements by school officials, and the Texas Education Agency's online database. Coders were trained to score each policy's content, presence, and strength.ResultsPolicies for sunscreen use and hats existed in 94% (n = 96) and 92% (n = 94) of ISDs, respectively. In counties with the highest melanoma incidence RR, 30% (n = 15) and 44% (n = 22) of ISDs allocated resources for sun safety and outdoor shade, compared to 2% (n = 1) and 3% (n = 2) in low-risk counties. No ISDs had SSPs on UV protective clothing, accountability, or modeling sun safety behaviors. SSP strength was positively correlated with percentage of school nurses (ρ = 0.564, P < 0.001), community median household income (ρ = 0.431, P < 0.001), percentage of female students (ρ = 0.461, P < 0.001), and tax rate (ρ = 0.366, P = 0.0002). Negative correlations were found with percentage of central staff administration (ρ = -0.523, P < 0.001) and graduation rates (ρ = -0.335, P < 0.001).ConclusionOur findings underscore the need for interventions to strengthen SSPs across Texas.

Objectives: Extracorporeal cardiopulmonary resuscitation (eCPR) is a promising treatment that could improve survival for refractory out-of-hospital (OHCA) patients. Healthcare systems may choose to start eCPR in the prehospital setting to optimize time to eCPR initiation and decrease low-flow time. We used geospatial modeling to evaluate different eCPR catchment strategies for a forthcoming prehospital eCPR program in Houston, Texas. Methods: We studied OHCAs treated by the Houston Fire Department from 2013 to 2021. We included OHCA patients aged 18-65 years old with an initial shockable rhythm that did not have prehospital return of spontaneous circulation (ROSC). Based on the geolocation that each OHCA occurred, we used geospatial modeling to identify eCPR candidates using four mapping strategies based on distance/drive time from the eCPR center: 1) 15-minute drive time, 20-minute drive time, 10-mile drive distance, and 15-mile drive distance. Results: Of 18,501 OHCAs during the study period, 881 met the eCPR inclusion criteria. Compared to non-eCPR candidates, eCPR candidates were younger (median age 52.3 years vs 62.7 years, p < 0.01) and had a higher proportion of males (76.6% v 59.8%, p < 0.01). Of eCPR candidate OHCAs, OHCAs occurred more frequently during the weekdays and the daytime, with 5:00 PM being the most common time. Using geospatial modeling and based on drive time, 219 OHCAs (24.9% of 881) were within a 15-minute drive, and 454 (51.5%) were within a 20-minute drive. Using drive distance, 383 eCPR candidates (43.5%) were within 10 miles, and 703 (79.8%) were within 15 miles. Conclusions: Using geospatial modeling, we demonstrated a process to estimate potential eCPR patient volumes for a geographic region. Geospatial modeling represents a viable strategy for healthcare systems to delineate eCPR catchment areas.

Abstract Prior studies have demonstrated that certain populations including older patients, racial/ethnic minority groups, and women are underrepresented in clinical trials. We performed a retrospective analysis of patients with non-Hodgkin lymphoma (NHL) seen at MD Anderson Cancer Center (MDACC) to investigate the association between trial participation, race/ethnicity, travel distance, and neighborhood socioeconomic status (nSES). Using patient addresses, we ascertained nSES variables on educational attainment, income, poverty, racial composition, and housing at the census tract (CT) level. We also performed geospatial analysis to determine the geographic distribution of clinical trial participants and distance from patient residence to MDACC. We examined 3146 consecutive adult patients with NHL seen between January 2017 and December 2020. The study cohort was predominantly male and non-Hispanic White (NHW). The most common insurance types were private insurance and Medicare; only 1.1% of patients had Medicaid. There was a high overall participation rate of 30.5%, with 20.9% enrolled in therapeutic trials. In univariate analyses, lower participation rates were associated with lower nSES including higher poverty rates and living in crowded households. Racial composition of CT was not associated with differences in trial participation. In multivariable analysis, trial participation varied significantly by histology, and participation declined nonlinearly with age in the overall, follicular lymphoma, and diffuse large B-cell lymphoma (DLBCL) models. In the DLBCL subset, Hispanic patients had lower odds of participation than White patients (odds ratio, 0.36; 95% confidence interval, 0.21-0.62; P = .001). In our large academic cohort, race, sex, insurance type, and nSES were not associated with trial participation, whereas age and diagnosis were.

INTRODUCTION:Chronic diseases are primary causes of mortality and disability in the U.S. Although individual-level indices to assess the burden of multiple chronic diseases exist, there is a lack of quantitative tools at the population level. This gap hinders the understanding of the geographical distribution and impact of chronic diseases, crucial for effective public health strategies. This study aims to construct a Chronic Disease Burden Index (CDBI) for evaluating county-level disease burden, to identify geographic and temporal patterns, and investigate the association between CDBI and social vulnerability. METHODS:A total of 20 health measures from CDC's PLACES database (2018-2021) were used to construct annual county-level CDBIs through principal component analysis. Geographic hotspots of chronic disease burden were identified using Getis-Ord Gi*. Multinomial logistic regression models and bivariate maps were used to assess the association between CDBI and CDC's social vulnerability index. Analyses were conducted in 2023-2024. RESULTS:Counties with high chronic disease burden were predominantly clustered in the southern U.S. High persistent chronic disease burden was prevalent in Kentucky and West Virginia, while increased burden was observed in Ohio and Texas. Chronic disease burden was highly associated with social vulnerability index (ORQ5 vs Q1=7.6, 95% CI: [6.6, 8.8]), with nonmetro-urban counties experiencing elevated CDBI (OR=14.6, 95% CI: [9.7, 21.9]). CONCLUSIONS:The CDBI offers an effective tool for assessing chronic disease burden at the population level. Identifying high-burden and vulnerable communities is a crucial first step toward facilitating resource allocation to enhance equitable healthcare access and advancing understanding of health disparities.

Previous studies have shown that individuals living in areas with persistent poverty (PP) experience worse cancer outcomes compared to those living in areas with transient or no persistent poverty (nPP). The association between PP and melanoma outcomes remains unexplored. We hypothesized that melanoma patients living in PP counties (defined as counties with ≥ 20

The border city of El Paso, Texas, and its water utility, El Paso Water, initiated a SARS-CoV-2 wastewater monitoring program to assess virus trends and the appropriateness of a wastewater monitoring program for the community. Nearly weekly sample collection at four wastewater treatment facilities (WWTFs), serving distinct regions of the city, was analyzed for SARS-CoV-2 genes using the CDC 2019-Novel coronavirus Real-Time RT-PCR diagnostic panel. Virus concentrations ranged from 86.7 to 268,000 gc/L, varying across time and at each WWTF. The lag time between virus concentrations in wastewater and reported COVID-19 case rates (per 100,00 population) ranged from 4-24 days for the four WWTFs, with the strongest trend occurring from November 2021 - June 2022. This study is an assessment of the utility of a geographically refined SARS-CoV-2 wastewater monitoring program to supplement public health efforts that will manage the virus as it becomes endemic in El Paso.

Background Opioid-related overdose mortality has remained at crisis levels across the United States, increasing 5-fold and worsened during the COVID-19 pandemic. The ability to provide forecasts of opioid-related mortality at granular geographical and temporal scales may help guide preemptive public health responses. Current forecasting models focus on prediction on a large geographical scale, such as states or counties, lacking the spatial granularity that local public health officials desire to guide policy decisions and resource allocation. Objective The overarching objective of our study was to develop Bayesian spatiotemporal dynamic models to predict opioid-related mortality counts and rates at temporally and geographically granular scales (ie, ZIP Code Tabulation Areas [ZCTAs]) for Massachusetts. Methods We obtained decedent data from the Massachusetts Registry of Vital Records and Statistics for 2005 through 2019. We developed Bayesian spatiotemporal dynamic models to predict opioid-related mortality across Massachusetts’ 537 ZCTAs. We evaluated the prediction performance of our models using the one-year ahead approach. We investigated the potential improvement of prediction accuracy by incorporating ZCTA-level demographic and socioeconomic determinants. We identified ZCTAs with the highest predicted opioid-related mortality in terms of rates and counts and stratified them by rural and urban areas. Results Bayesian dynamic models with the full spatial and temporal dependency performed best. Inclusion of the ZCTA-level demographic and socioeconomic variables as predictors improved the prediction accuracy, but only in the model that did not account for the neighborhood-level spatial dependency of the ZCTAs. Predictions were better for urban areas than for rural areas, which were more sparsely populated. Using the best performing model and the Massachusetts opioid-related mortality data from 2005 through 2019, our models suggested a stabilizing pattern in opioid-related overdose mortality in 2020 and 2021 if there were no disruptive changes to the trends observed for 2005-2019. Conclusions Our Bayesian spatiotemporal models focused on opioid-related overdose mortality data facilitated prediction approaches that can inform preemptive public health decision-making and resource allocation. While sparse data from rural and less populated locales typically pose special challenges in small area predictions, our dynamic Bayesian models, which maximized information borrowing across geographic areas and time points, were used to provide more accurate predictions for small areas. Such approaches can be replicated in other jurisdictions and at varying temporal and geographical levels. We encourage the formation of a modeling consortium for fatal opioid-related overdose predictions, where different modeling techniques could be ensembled to inform public health policy.

Artificial light at night (ALAN) is a growing environmental hazard with economic, ecological, and public health implications. Previous studies suggested a higher burden of light pollution and related adverse effects in disadvantaged communities. It is critical to characterize the geographic distribution and temporal trend of ALAN and identify associated demographic and socioeconomic factors at the population level to lay the foundation for environmental and public health monitoring and policy-making. We used satellite data from the Black Marble suite to characterize ALAN in all counties in contiguous US and reported considerable variations in ALAN spatiotemporal patterns between 2012 and 2019. As expected, ALAN levels were generally higher in metropolitan and coastal areas; however, several rural counties in Texas experienced remarkable increase in ALAN since 2012, while population-level ALAN burden also increased substantially in many metropolitan areas. Importantly, we found that during this period, although the overall ALAN levels in the USA declined modestly, the temporal trend of ALAN varied across areas with different racial/ethnic compositions: counties with a higher percentage of racial/ethnic minority groups, particularly Hispanic populations, exhibited significantly less decline. As a result, the differences in ALAN levels, as measured by the Black Marble product, across racial/ethnic groups became larger between 2012 and 2019. In conclusion, our study documented variations in ALAN spatiotemporal patterns across America and identified multiple population correlates of ALAN patterns that warrant further investigations. Future studies should identify underlying factors (e.g., economic development and decline, urban planning, and transition to newer lighting technologies such as light emitting diodes) that may have contributed to ALAN disparities in the USA.

Wastewater is a discarded human by-product, but its analysis may help us understand the health of populations. Epidemiologists first analyzed wastewater to track outbreaks of poliovirus decades ago, but so-called wastewater-based epidemiology was reinvigorated to monitor SARS-CoV-2 levels while bypassing the difficulties and pit falls of individual testing. Current approaches overlook the activity of most human viruses and preclude a deeper understanding of human virome community dynamics. Here, we conduct a comprehensive sequencing-based analysis of 363 longitudinal wastewater samples from ten distinct sites in two major cities. Critical to detection is the use of a viral probe capture set targeting thousands of viral species or variants. Over 450 distinct pathogenic viruses from 28 viral families are observed, most of which have never been detected in such samples. Sequencing reads of established pathogens and emerging viruses correlate to clinical data sets of SARS-CoV-2, influenza virus, and monkeypox viruses, outlining the public health utility of this approach. Viral communities are tightly organized by space and time. Finally, the most abundant human viruses yield sequence variant information consistent with regional spread and evolution. We reveal the viral landscape of human wastewater and its potential to improve our understanding of outbreaks, transmission, and its effects on overall population health.

Objectives. To propose a novel Bayesian spatial-temporal approach to identify and quantify severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing disparities for small area estimation. Methods. In step 1, we used a Bayesian inseparable space-time model framework to estimate the testing positivity rate (TPR) at geographically granular areas of the census block groups (CBGs). In step 2, we adopted a rank-based approach to compare the estimated TPR and the testing rate to identify areas with testing deficiency and quantify the number of needed tests. We used weekly SARS-CoV-2 infection and testing surveillance data from Cameron County, Texas, between March 2020 and February 2022 to demonstrate the usefulness of our proposed approach. Results. We identified the CBGs that had experienced substantial testing deficiency, quantified the number of tests that should have been conducted in these areas, and evaluated the short- and long-term testing disparities. Conclusions. Our proposed analytical framework offers policymakers and public health practitioners a tool for understanding SARS-CoV-2 testing disparities in geographically small communities. It could also aid COVID-19 response planning and inform intervention programs to improve goal setting and strategy implementation in SARS-CoV-2 testing uptake. (Am J Public Health. 2023;113(1):40-48. https://doi.org/10.2105/AJPH.2022.307127).

Background Cameron County, a low-income south Texas-Mexico border county marked by severe health disparities, was consistently among the top counties with the highest COVID-19 mortality in Texas at the onset of the pandemic. The disparity in COVID-19 burden within Texas counties revealed the need for effective interventions to address the specific needs of local health departments and their communities. Publicly available COVID-19 surveillance data were not sufficiently timely or granular to deliver such targeted interventions. An agency-academic collaboration in Cameron used novel geographic information science methods to produce granular COVID-19 surveillance data. These data were used to strategically target an educational outreach intervention named “Boots on the Ground” (BOG) in the City of Brownsville (COB). Objective This study aimed to evaluate the impact of a spatially targeted community intervention on daily COVID-19 test counts. Methods The agency-academic collaboration between the COB and UTHealth Houston led to the creation of weekly COVID-19 epidemiological reports at the census tract level. These reports guided the selection of census tracts to deliver targeted BOG between April 21 and June 8, 2020. Recordkeeping of the targeted BOG tracts and the intervention dates, along with COVID-19 daily testing counts per census tract, provided data for intervention evaluation. An interrupted time series design was used to evaluate the impact on COVID-19 test counts 2 weeks before and after targeted BOG. A piecewise Poisson regression analysis was used to quantify the slope (sustained) and intercept (immediate) change between pre- and post-BOG COVID-19 daily test count trends. Additional analysis of COB tracts that did not receive targeted BOG was conducted for comparison purposes. Results During the intervention period, 18 of the 48 COB census tracts received targeted BOG. Among these, a significant change in the slope between pre- and post-BOG daily test counts was observed in 5 tracts, 80% (n=4) of which had a positive slope change. A positive slope change implied a significant increase in daily COVID-19 test counts 2 weeks after targeted BOG compared to the testing trend observed 2 weeks before intervention. In an additional analysis of the 30 census tracts that did not receive targeted BOG, significant slope changes were observed in 10 tracts, of which positive slope changes were only observed in 20% (n=2). In summary, we found that BOG-targeted tracts had mostly positive daily COVID-19 test count slope changes, whereas untargeted tracts had mostly negative daily COVID-19 test count slope changes. Conclusions Evaluation of spatially targeted community interventions is necessary to strengthen the evidence base of this important approach for local emergency preparedness. This report highlights how an academic-agency collaboration established and evaluated the impact of a real-time, targeted intervention delivering precision public health to a small community.