A Marginalized Zero-Inflated Negative Binomial Model for Spatial Data: Modeling COVID-19 Deaths in Georgia.

Spatial count data with an abundance of zeros arise commonly in disease mapping studies. Typically, these data are analyzed using zero-inflated models, which comprise a mixture of a point mass at zero and an ordinary count distribution, such as the Poisson or negative binomial. However, due to their mixture representation, conventional zero-inflated models are challenging to explain in practice because the parameter estimates have conditional latent-class interpretations. As an alternative, several authors have proposed marginalized zero-inflated models that simultaneously model the excess zeros and the marginal mean, leading to a parameterization that more closely aligns with ordinary count models. Motivated by a study examining predictors of COVID-19 death rates, we develop a spatiotemporal marginalized zero-inflated negative binomial model that directly models the marginal mean, thus extending marginalized zero-inflated models to the spatial setting. To capture the spatiotemporal heterogeneity in the data, we introduce region-level covariates, smooth temporal effects, and spatially correlated random effects to model both the excess zeros and the marginal mean. For estimation, we adopt a Bayesian approach that combines full-conditional Gibbs sampling and Metropolis-Hastings steps. We investigate features of the model and use the model to identify key predictors of COVID-19 deaths in the US state of Georgia during the 2021 calendar year.

Association between gestational PFAS exposure and Children's adiposity in a diverse population.

Perfluoroalkyl substances (PFAS) are widely distributed suspected obesogens that cross the placenta. However, few data are available to assess potential fetal effects of PFAS exposure on children's adiposity in diverse populations. To address the data gap, we estimated associations between gestational PFAS concentrations and childhood adiposity in a diverse mother-child cohort. We considered 6 PFAS in first trimester blood plasma, measured using ultra-high-performance liquid chromatography with tandem mass spectrometry, collected from non-smoking women with low-risk singleton pregnancies (n = 803). Body mass index (BMI), waist circumference (WC), fat mass, fat-free mass, and % body fat were ascertained in 4-8 year old children as measures of adiposity. We estimated associations of individual gestational PFAS with children's adiposity and overweight/obesity, adjusted for confounders. There were more non-Hispanic Black (31.7 %) and Hispanic (42.6 %) children with overweight/obesity, than non-Hispanic white (18.2 %) and Asian/Pacific Islander (16.4 %) children (p < 0.0001). Perfluorooctane sulfonate (PFOS; 5.3 ng/mL) and perfluorooctanoic acid (2.0 ng/mL) had the highest median concentrations in maternal blood. Among women without obesity (n = 667), greater perfluoroundecanoic acid (PFUnDA) was associated with their children having higher WC z-score (ß = 0.08, 95%CI: 0.01, 0.14; p = 0.02), fat mass (ß = 0.55 kg, 95%CI: 0.21, 0.90; p = 0.002), and % body fat (ß = 0.01 %; 95%CI: 0.003, 0.01; p = 0.004), although the association of PFUnDA with fat mass attenuated at the highest concentrations. Among women without obesity, the associations of PFAS and their children's adiposity varied significantly by self-reported race-ethnicity, although the direction of the associations was inconsistent. In contrast, among the children of women with obesity, greater, PFOS, perfluorononanoic acid, and perfluorodecanoic acid concentrations were associated with less adiposity (n = 136). Our results suggest that specific PFAS may be developmental obesogens, and that maternal race-ethnicity may be an important modifier of the associations among women without obesity.

Exploring associations between prenatal exposure to multiple endocrine disruptors and birth weight with exposure continuum mapping.

BACKGROUND: Improved understanding of how prenatal exposure to environmental mixtures influences birth weight or other adverse outcomes is essential in protecting child health. OBJECTIVE: We illustrate a novel exposure continuum mapping (ECM) framework that combines the self-organizing map (SOM) algorithm with generalized additive modeling (GAM) in order to integrate spatially-correlated learning into the study mixtures of environmental chemicals. We demonstrate our method using biomarker data on chemical mixtures collected from a diverse mother-child cohort. METHODS: We obtained biomarker concentrations for 16 prevalent endocrine disrupting chemicals (EDCs) collected in the first-trimester from a large, ethnically/racially diverse cohort of healthy pregnant women (n = 604) during 2009-2012. This included 4 organochlorine pesticides (OCPs), 4 polybrominated diphenyl ethers (PBDEs), 4 polychlorinated biphenyls (PCBs), and 4 perfluoroalkyl substances (PFAS). We applied a two-stage exposure continuum mapping (ECM) approach to investigate the combined impact of the EDCs on birth weight. First, we analyzed our EDC data with SOM in order to reduce the dimensionality of our exposure matrix into a two-dimensional grid (i.e., map) where nodes depict the types of EDC mixture profiles observed within our data. We define this map as the 'exposure continuum map', as the gridded surface reflects a continuous sequence of exposure profiles where adjacent nodes are composed of similar mixtures and profiles at more distal nodes are more distinct. Lastly, we used GAM to estimate a joint-dose response based on the coordinates of our ECM in order to capture the relationship between participant location on the ECM and infant birth weight after adjusting for maternal age, race/ethnicity, pre-pregnancy body mass index (BMI), education, serum cotinine, total plasma lipids, and infant sex. Single chemical regression models were applied to facilitate comparison. RESULTS: We found that an ECM with 36 mixture profiles retained 70% of the total variation in the exposure data. Frequency analysis showed that the most common profiles included relatively low concentrations for most EDCs (~10%) and that profiles with relatively higher concentrations (for single or multiple EDCs) tended to be rarer (~1%) but more distinct. Estimation of a joint-dose response function revealed that lower birth weights mapped to locations where profile compositions were dominated by relatively high PBDEs and select OCPs. Higher birth weights mapped to locations where profiles consisted of higher PCBs. These findings agreed well with results from single chemical models. CONCLUSIONS: Findings from our study revealed a wide range of prenatal exposure scenarios and found that combinations exhibiting higher levels of PBDEs were associated with lower birth weight and combinations with higher levels of PCBs and PFAS were associated with increased birth weight. Our ECM approach provides a promising framework for supporting studies of other exposure mixtures.

Exploring the influence of short-term temperature patterns on temperature-related mortality: a case-study of Melbourne, Australia.

BACKGROUND: Several studies have identified the association between ambient temperature and mortality; however, several features of temperature behavior and their impacts on health remain unresolved. We obtain daily counts of nonaccidental all-cause mortality data in the elderly (65 + years) and corresponding meteorological data for Melbourne, Australia during 1999 to 2006. We then characterize the temporal behavior of ambient temperature development by quantifying the rates of temperature change during periods designated by pre-specified windows ranging from 1 to 30 days. Finally, we evaluate if the association between same day temperature and mortality in the framework of a Poisson regression and include our temperature trajectory variables in order to assess if associations were modified by the nature of how the given daily temperature had evolved. RESULTS: We found a positive significant association between short-term mortality risk and daily average temperature as mortality risk increased 6 % on days when temperatures were above the 90th percentile as compared to days in the referent 25-75th. In addition, we found that mortality risk associated with daily temperature varied by the nature of the temperature trajectory over the preceding twelve days and that peaks in mortality occurred during periods of high temperatures and stable trajectories and during periods of increasing higher temperatures and increasing trajectories. CONCLUSION: Our method presents a promising tool for improving understanding of complex temperature health associations. These findings suggest that the nature of sub-monthly temperature variability plays a role in the acute impacts of temperature on mortality; however, further studies are suggested.

Exploring associations between multipollutant day types and asthma morbidity: epidemiologic applications of self-organizing map ambient air quality classifications.

BACKGROUND: Recent interest in the health effects of air pollution focuses on identifying combinations of multiple pollutants that may be associated with adverse health risks. OBJECTIVE: Present a methodology allowing health investigators to explore associations between categories of ambient air quality days (i.e., multipollutant day types) and adverse health. METHODS: First, we applied a self-organizing map (SOM) to daily air quality data for 10 pollutants collected between January 1999 and December 2008 at a central monitoring location in Atlanta, Georgia to define a collection of multipollutant day types. Next, we conducted an epidemiologic analysis using our categories as a multipollutant metric of ambient air quality and daily counts of emergency department (ED) visits for asthma or wheeze among children aged 5 to 17 as the health endpoint. We estimated rate ratios (RR) for the association of multipollutant day types and pediatric asthma ED visits using a Poisson generalized linear model controlling for long-term, seasonal, and weekday trends and weather. RESULTS: Using a low pollution day type as the reference level, we found significant associations of increased asthma morbidity in three of nine categories suggesting adverse effects when combinations of primary (CO, NO2, NOX, EC, and OC) and/or secondary (O3, NH4, SO4) pollutants exhibited elevated concentrations (typically, occurring on dry days with low wind speed). On days with only NO3 elevated (which tended to be relatively cool) and on days when only SO2 was elevated (which likely reflected plume touchdowns from coal combustion point sources), estimated associations were modestly positive but confidence intervals included the null. CONCLUSIONS: We found that ED visits for pediatric asthma in Atlanta were more strongly associated with certain day types defined by multipollutant characteristics than days with low pollution levels; however, findings did not suggest that any specific combinations were more harmful than others. Relative to other health endpoints, asthma exacerbation may be driven more by total ambient pollutant exposure than by composition.

Using self-organizing maps to develop ambient air quality classifications: a time series example.

BACKGROUND: Development of exposure metrics that capture features of the multipollutant environment are needed to investigate health effects of pollutant mixtures. This is a complex problem that requires development of new methodologies. OBJECTIVE: Present a self-organizing map (SOM) framework for creating ambient air quality classifications that group days with similar multipollutant profiles. METHODS: Eight years of day-level data from Atlanta, GA, for ten ambient air pollutants collected at a central monitor location were classified using SOM into a set of day types based on their day-level multipollutant profiles. We present strategies for using SOM to develop a multipollutant metric of air quality and compare results with more traditional techniques. RESULTS: Our analysis found that 16 types of days reasonably describe the day-level multipollutant combinations that appear most frequently in our data. Multipollutant day types ranged from conditions when all pollutants measured low to days exhibiting relatively high concentrations for either primary or secondary pollutants or both. The temporal nature of class assignments indicated substantial heterogeneity in day type frequency distributions (~1%-14%), relatively short-term durations (<2 day persistence), and long-term and seasonal trends. Meteorological summaries revealed strong day type weather dependencies and pollutant concentration summaries provided interesting scenarios for further investigation. Comparison with traditional methods found SOM produced similar classifications with added insight regarding between-class relationships. CONCLUSION: We find SOM to be an attractive framework for developing ambient air quality classification because the approach eases interpretation of results by allowing users to visualize classifications on an organized map. The presented approach provides an appealing tool for developing multipollutant metrics of air quality that can be used to support multipollutant health studies.

Acute Kidney Injury and Subsequent Kidney Failure With Replacement Therapy Incidence in Older Adults With Advanced CKD: A Cohort Study of US Veterans.

Rationale & Objective: Advanced age is a major risk factor for chronic kidney disease (CKD) development, which has high heterogeneity in disease progression. Acute kidney injury (AKI) hospitalization rates are increasing, especially among older adults. Previous AKI epidemiologic analyses have focused on hospitalized populations, which may bias results toward sicker populations. This study examined the association between AKI and incident kidney failure with replacement therapy (KFRT) while evaluating age as an effect modifier of this relationship. Study Design: Retrospective cohort study. Setting & Participants: 24,133 Veterans at least 65 years old with incident CKD stage 4 from 2011 to 2013. Exposures: AKI, AKI severity, and age. Outcomes: KFRT and death. Analytical Approach: The Fine-Gray competing risk regression was used to model AKI and incident KFRT with death as a competing risk. A Cox regression was used to model AKI severity and death. Results: Despite a nonsignificant age interaction between AKI and KFRT, a clinically relevant combined effect of AKI and age on incident KFRT was observed. Compared with our oldest age group without AKI, those aged 65-74 years with AKI had the highest risk of KFRT (subdistribution HR [sHR], 14.9; 95% CI, 12.7-17.4), whereas those at least 85 years old with AKI had the lowest (sHR, 1.71; 95% CI, 1.22-2.39). Once Veterans underwent KFRT, their risk of death increased by 44%. A 2-fold increased risk of KFRT was observed across all AKI severity stages. However, the risk of death increased with worsening AKI severity. Limitations: Our study lacked generalizability, was restricted to ever use of medications, and used inpatient serum creatinine laboratory results to define AKI and AKI severity. Conclusions: In this national cohort, advanced age was protective against incident KFRT but not death. This is likely explained by the high frequency of deaths observed in this population (51.1%). Nonetheless, AKI and younger age are substantial risk factors for incident KFRT.

Older adults are at risk of acute kidney injury (AKI) and subsequent nonrecovery from AKI, resulting in long-term dialysis. Hospitalized patients have often been used in the past to study AKI. This could lead to biased conclusions when inferring from sicker populations. That is why we created a national cohort of 24,133 Veterans at least 65 years old with incident chronic kidney disease (CKD) stage 4 to examine the relationship between AKI and age and subsequent kidney failure with replacement therapy (KFRT). The data have showed that AKI and younger age are substantial risk factors for incident KFRT. As for older age, it appears to be protective against KFRT but not death. This is likely explained by the high frequency of deaths observed in our cohort.

Bayesian kernel machine regression for count data: modelling the association between social vulnerability and COVID-19 deaths in South Carolina.

The COVID-19 pandemic created an unprecedented global health crisis. Recent studies suggest that socially vulnerable communities were disproportionately impacted, although findings are mixed. To quantify social vulnerability in the US, many studies rely on the Social Vulnerability Index (SVI), a county-level measure comprising 15 census variables. Typically, the SVI is modelled in an additive manner, which may obscure non-linear or interactive associations, further contributing to inconsistent findings. As a more robust alternative, we propose a negative binomial Bayesian kernel machine regression (BKMR) model to investigate dynamic associations between social vulnerability and COVID-19 death rates, thus extending BKMR to the count data setting. The model produces a 'vulnerability effect' that quantifies the impact of vulnerability on COVID-19 death rates in each county. The method can also identify the relative importance of various SVI variables and make future predictions as county vulnerability profiles evolve. To capture spatio-temporal heterogeneity, the model incorporates spatial effects, county-level covariates, and smooth temporal functions. For Bayesian computation, we propose a tractable data-augmented Gibbs sampler. We conduct a simulation study to highlight the approach and apply the method to a study of COVID-19 deaths in the US state of South Carolina during the 2021 calendar year.

Geographic, Patient, and VA Medical Center Variation in the Receipt and Mode of Primary Care in a National Sample of Veterans with Diabetes during 2020.

While telemedicine infrastructure was in place within the Veterans Health Administration (VHA) healthcare system before the onset of the COVID-19 pandemic, geographically varying ordinances/closures disrupted vital care for chronic disease patients such as those with type 2 diabetes. We created a national cohort of 1,647,158 non-Hispanic White, non-Hispanic Black, and Hispanic veterans with diabetes including patients with at least one primary care visit and HbA1c lab result between 3.5% and 20% in the fiscal year (FY) 2018 or 2019. For each VAMC, the proportion of telehealth visits in FY 2019 was calculated. Two logistic Bayesian spatial models were employed for in-person primary care or telehealth primary care in the fourth quarter of the FY 2020, with spatial random effects incorporated at the VA medical center (MC) catchment area level. Finally, we computed and mapped the posterior probability of receipt of primary care for an "average" patient within each catchment area. Non-Hispanic Black veterans and Hispanic veterans were less likely to receive in-person primary care but more likely to receive tele-primary care than non-Hispanic white veterans during the study period. Veterans living in the most socially vulnerable areas were more likely to receive telehealth primary care in the fourth quarter of FY 2020 compared to the least socially vulnerable group but were less likely to receive in-person care. In summary, racial minorities and those in the most socially vulnerable areas were less likely to receive in-person primary care but more likely to receive telehealth primary care, potentially indicating a disparity in the impact of the pandemic across these groups.

Developing Catchment Area Data Dashboards for Cancer Centers: A Stakeholder- engaged Approach.

Background: Data dashboards that can communicate complex and diverse catchment area data effectively can transform cancer prevention and care delivery and strengthen community engagement efforts. Engaging stakeholders in data dashboard development, by seeking their inputs and collecting feedback, has the potential to maximize user-centeredness. Objective: To describe a systematic, stakeholder-driven, and theory-based approach for developing catchment area data visualization tools for cancer centers. Methods: Cancer-relevant catchment area data were identified from national- and state-level data sources (including cancer registries, national surveys, and administrative claims databases). A prototype tool for data visualization was designed, developed, and tested based on the OPT-In [ O rganize, P lan, T est, In tegrate] framework. A working group of multi-disciplinary experts collected stakeholder feedback through formative assessment to understand data and design preferences. Thematic areas, data elements, and the composition and placement of data visuals in the prototype were identified and refined by working group members. Visualizations were rendered in Tableau © and embedded in a public-facing website. A mixed-method approach was used to assess the understandability and actionability of the tool and to collect open-ended feedback that informed action items for improvisation. Results: We developed a visualization dashboard that illustrates cancer incidence and mortality, risk factor prevalence, healthcare access, and social determinants of health for the Hollings Cancer Center catchment area. Color-coded maps, time-series plots, and graphs illustrate these catchment area data. A total of 21 participants representing key stakeholders [general audience (n=4), community advisory board members and other representatives (n=7), and researchers (n=10)] were identified. The understandability and actionability scores exceeded the minimum (80%) threshold. Stakeholders' feedback confirmed that the tool is effective in communicating cancer data and is useful for education and advocacy. Themes that emerged from qualitative data suggest that additional changes to the tool such as a warm color palette, data source transparency, and the addition of analytical features (data overlaying and area-resolution selection) would further enhance the tool. Integration of communication efforts and messages within a broader context is in progress. Discussion: A catchment area data resource developed through a systematic, stakeholder- driven, and theory-based approach can meet (and surpass) benchmarks for understandability and actionability, and lead to an overall positive response from stakeholders. Creating channels for advocacy and forming community partnerships will be the next step necessary to promote policies and programs for improving cancer outcomes in the catchment areas.

Increasing Preterm Delivery and Small for Gestational Age Trends in South Carolina during the COVID-19 Pandemic.

Preterm delivery (PTD) complications are a major cause of childhood morbidity and mortality. We aimed to assess trends in PTD and small for gestational age (SGA) and whether trends varied between race-ethnic groups in South Carolina (SC). We utilized 2015-2021 SC vital records linked to hospitalization and emergency department records. PTD was defined as clinically estimated gestation less than (<) 37 weeks (wks.) with subgroup analyses of PTD < 34 wks. and < 28 wks. SGA was defined as infants weighing below the 10th percentile for gestational age. This retrospective study included 338,532 (243,010 before the COVID-19 pandemic and 95,522 during the pandemic) live singleton births of gestational age ≥ 20 wks. born to 260,276 mothers in SC. Generalized estimating equations and a change-point during the first quarter of 2020 helped to assess trends. In unadjusted analyses, pre-pandemic PTD showed an increasing trend that continued during the pandemic (relative risk (RR) = 1.04, 95% CI: 1.02-1.06). PTD < 34 wks. rose during the pandemic (RR = 1.07, 95% CI: 1.02-1.12) with a significant change in the slope. Trends in SGA varied by race and ethnicity, increasing only in Hispanics (RR = 1.02, 95% CI: 1.00-1.04) before the pandemic. Our study reveals an increasing prevalence of PTD and a rise in PTD < 34 wks. during the pandemic, as well as an increasing prevalence of SGA in Hispanics during the study period.

Radionuclide activity concentrations in forest surface fuels at the Savannah River Site.

BACKGROUND/OBJECTIVE: A study was undertaken at the United States Department of Energy's Savannah River Site (SRS), Aiken, South Carolina to investigate radionuclide activity concentrations in litter and duff from select areas at SRS. Litter (i.e. vegetative debris) and duff (i.e. highly decomposed vegetative debris) can often be the major fuels consumed during prescribed burns and have potential to release radiological contaminants into the environment. METHODS: Repeated samples from 97 locations were collected systematically across SRS and analyzed for radionuclide activity. Radionuclide activity concentrations found in litter and duff were compared. As spatial trends were of interest, spatial distributions of radionuclide activity concentrations found in litter and duff and spatial dependency amongst the data were explored. RESULTS: (7)Be, (40)K, and (137)Cs showed statistically significant proportional differences between litter and duff samples. Duff sample concentrations for (137)Cs (p < 0.0001) and (40)K (p = 0.0015) were statistically higher compared to litter samples. (7)Be activity concentrations were statistically higher in litter as compared to duff (p < 0.0001). For (40)K litter and duff samples, spatial correlation tests were not significant at p = 0.05 and the maps did not indicate any apparent high concentrations centered near possible radionuclide sources (i.e. SRS facilities). For (7)Be litter samples, significant spatial correlation was calculated (p = 0.0085). No spatial correlation was evident in the (7)Be duff samples (p = 1.0000) probably due to small sample size (n = 7). (137)Cs litter and duff samples showed significant spatial correlations (p < 0.0001 and p < 0.0001, respectively). CONCLUSIONS: To date, few studies characterize radionuclide activity concentrations in litter and duff, and to our knowledge none present spatial analysis. Key findings show that across SRS, (137)Cs is the primary radionuclide of concern, with the highest number of samples reported above MDC in litter (51.4%) and duff samples (83.2%). However, (137)Cs litter and duff spatial trends in the maps generated from the kriging parameters do not appear to directly link the areas with higher activity concentrations with SRS facilities. The results found herein provide valuable baseline monitoring data for future studies of forest surface fuels and can be used to evaluate changes in radioactivity in surface fuels in the southeast region of the U.S.

Environmental, social and behavioral risk factors in association with spatial clustering of childhood cancer incidence.

Childhood cancer incidence is known to vary by age, sex, and race/ethnicity, but evidence is limited regarding external risk factors. We aim to identify harmful combinations of air pollutants and other environmental and social risk factors in association with the incidence of childhood cancer based on 2003-2017 data from the Georgia Cancer Registry. We calculated the standardized incidence ratios (SIR) of Central Nervous System (CNS) tumors, leukemia and lymphomas based on age, gender and ethnic composition in each of the 159 counties in Georgia, USA. County-level information on air pollution, socioeconomic status (SES), tobacco smoking, alcohol drinking and obesity were derived from US EPA and other public data sources. We applied two unsupervised learning tools (self-organizing map [SOM] and exposure-continuum mapping [ECM]) to identify pertinent types of multi-exposure combinations. Spatial Bayesian Poisson models (Leroux-CAR) were fit with indicators for each multi-exposure category as exposure and SIR of childhood cancers as outcomes. We identified consistent associations of environmental (pesticide exposure) and social/behavioral stressors (low socioeconomic status, alcohol) with spatial clustering of pediatric cancer class II (lymphomas and reticuloendothelial neoplasms), but not for other cancer classes. More research is needed to identify the causal risk factors for these associations.

Examining Associations Between Neighborhood-Level Social Vulnerability and Care for Children With Sleep-Disordered Breathing.

OBJECTIVE: We aim to investigate the impact of neighborhood-level social vulnerability on otolaryngology care for children with obstructive sleep-disordered breathing (SDB). STUDY DESIGN: Retrospective cohort study. SETTING: A tertiary children's hospital. METHODS: Children aged 2 to 17 years with SDB were included. Residential addresses were geocoded with geographic information systems, and spatial overlays were used to assign census tract-level social vulnerability index (SVI) scores to each participant. Multivariable logistic regression models were used to estimate associations of neighborhood SVI scores and individual factors with attendance of otolaryngology referral appointment and interventions. RESULTS: The study included 397 patients (mean ± SD age, 5.9 ± 3.7 years; 51% male, n = 203). After adjustment for age and sex, children with higher overall SVI scores (odds ratio [OR], 0.40; 95% CI, 0.16-0.92) and higher socioeconomic vulnerability scores (OR, 0.34; 95% CI, 0.14-0.86) were less likely to attend their referral appointments. The odds of attending referrals were 83% lower (OR, 0.17; 95% CI, 0.09-0.34) for Black children and 73% lower (OR, 0.27; 95% CI, 0.11-0.65) for Hispanic children than for non-Hispanic White children. Medicaid beneficiaries had lower odds of attending their referrals (OR, 0.20; 95% CI, 0.08-0.48) than privately insured children. Overall SVI score was not associated with receiving recommended polysomnography or tonsillectomy. CONCLUSION: In our study, children living in areas of greater social vulnerability were less likely to attend their otolaryngology referral appointments for SDB evaluation, as were children of Black race, Hispanic ethnicity, and Medicaid beneficiaries. These results suggest that neighborhood conditions, as well as patient-level factors, influence patient access to SDB care.

Bayesian negative binomial regression with spatially varying dispersion: Modeling COVID-19 incidence in Georgia.

Overdispersed count data arise commonly in disease mapping and infectious disease studies. Typically, the level of overdispersion is assumed to be constant over time and space. In some applications, however, this assumption is violated, and in such cases, it is necessary to model the dispersion as a function of time and space in order to obtain valid inferences. Motivated by a study examining spatiotemporal patterns in COVID-19 incidence, we develop a Bayesian negative binomial model that accounts for heterogeneity in both the incidence rate and degree of overdispersion. To fully capture the heterogeneity in the data, we introduce region-level covariates, smooth temporal effects, and spatially correlated random effects in both the mean and dispersion components of the model. The random effects are assigned bivariate intrinsic conditionally autoregressive priors that promote spatial smoothing and permit the model components to borrow information, which is appealing when the mean and dispersion are spatially correlated. Through simulation studies, we show that ignoring heterogeneity in the dispersion can lead to biased and imprecise estimates. For estimation, we adopt a Bayesian approach that combines full-conditional Gibbs sampling and Metropolis-Hastings steps. We apply the model to a study of COVID-19 incidence in the state of Georgia, USA from March 15 to December 31, 2020.

Evaluation of a new low-cost particle sensor as an internet-of-things device for outdoor air quality monitoring.

Many low-cost particle sensors are available for routine air quality monitoring of PM2.5, but there are concerns about the accuracy and precision of the reported data, particularly in humid conditions. The objectives of this study are to evaluate the Sensirion SPS30 particulate matter (PM) sensor against regulatory methods for measurement of real-time particulate matter concentrations and to evaluate the effectiveness of the Intelligent AirTM sensor pack for remote deployment and monitoring. To achieve this, we co-located the Intelligent AirTM sensor pack, developed at Clemson University and built around the Sensirion SPS30, to collect data from July 29, 2019, to December 12, 2019, at a regulatory site in Columbia, South Carolina. When compared to the Federal Equivalent Methods, the SPS30 showed an average bias adjusted R2 = 0.75, mean bias error of -1.59, and a root mean square error of 2.10 for 24-hour average trimmed measurements over 93 days, and R2 = 0.57, mean bias error of -1.61, and a root mean square error of 3.029, for 1-hr average trimmed measurements over 2300 hours when the central 99% of data was retained with a data completeness of 75% or greater. The Intelligent AirTM sensor pack is designed to promote long-term deployment and includes a solar panel and battery backup, protection from the elements, and the ability to upload data via a cellular network. Overall, we conclude that the SPS30 PM sensor and the Intelligent AirTM sensor pack have the potential for greatly increasing the spatial density of particulate matter measurements, but more work is needed to understand and calibrate sensor measurements.Implications: This work adds to the growing body of research that indicates that low-cost sensors of particulate matter (PM) for air quality monitoring has a promising future, and yet much work is left to be done. This work shows that the level of data processing and filtering effects how the low-cost sensors compare to existing federal reference and equivalence methods: more data filtering at low PM levels worsens the data comparison, while longer time averaging improves the measurement comparisons. Improvements must be made to how we handle, calibrate, and correct PM data from low-cost sensors before the data can be reliably used for air quality monitoring and attainment.

Geographical distribution of the health crisis of war in the Tigray region of Ethiopia.

War destroys health facilities and displaces health workers. It has a devastating impact on population health, especially in vulnerable populations. We assess the geographical distribution of the impact of war on healthcare delivery by comparing the pre-November 2020 and the November to June 2021 status of health facilities in the Tigray region of Ethiopia. Data were collected from February 2021 to June 2021, during an active civil war and an imposed communication blackout in Tigray. Primary data were collected and verified by multiple sources. Data include information on health facility type, geocoding and health facility status (fully functional (FF), partially functional (PF), not functional, no communication). Only 3.6% of all health facilities (n=1007), 13.5% of all hospitals and health centres (n=266), and none of the health posts (n=741), are functional. Destruction varies by geographic location; only 3.3% in Western, 3.3% in South Eastern, 6.5% in North Western, 8% in Central, 14.6% in Southern, 16% in Eastern and 78.6% in Mekelle are FF. Only 9.7% of health centres, 43.8% of general hospitals and 21.7% of primary hospitals are FF. None of the health facilities are operating at prewar level even when classified as FF or PF due to lack of power and water or essential devices looted or destroyed, while they still continue operating. The war in Tigray has clearly had a direct and devastating impact on healthcare delivery. Restoration of the destroyed health facilities needs to be a priority agenda of the international community.

Racial Distribution of Neighborhood-Level Social Deprivation in a Retrospective Cohort of Prostate Cancer Survivors.

Background: A better understanding of neighborhood-level factors' contribution is needed in order to increase the precision of cancer control interventions that target geographic determinants of cancer health disparities. This study characterized the distribution of neighborhood deprivation in a racially diverse cohort of prostate cancer survivors. Methods: A retrospective cohort of 253 prostate cancer patients who were treated with radical prostatectomy from 2011 to 2019 was established at the Medical University of South Carolina. Individual-level data on clinical variables (e.g., stage, grade) and race were abstracted. Social Deprivation Index (SDI) and Healthcare Professional Shortage (HPS) status was obtained from the Robert Graham Center and assigned to participants based on their residential census tract. Data were analyzed with descriptive statistics and multivariable logistic regression. Results: The cohort of 253 men consisted of 168 white, 81 African American, 1 Hispanic and 3 multiracial men. Approximately 49% of 249 men lived in areas with high SDI (e.g., SDI score of 48 to 98). The mean for SDI was 44.5 (+27.4), and the range was 97 (1−98) for all study participants. African American men had a significantly greater likelihood of living in a socially deprived neighborhood compared to white men (OR = 3.7, 95% C.I. 2.1−6.7, p < 0.01), while men who lived in areas with higher HPS shortage status were significantly more likely to live in a neighborhood that had high SDI compared to men who lived in areas with lower HPS shortages (OR = 4.7, 95% C.I. = 2.1−10.7, p < 0.01). African Americans had a higher likelihood of developing biochemical reoccurrence (OR = 3.7, 95% C.I. = 1.7−8.0) compared with white men. There were no significant association between SDI and clinical characteristics of prostate cancer. Conclusions: This study demonstrates that SDI varies considerably by race among men with prostate cancer treated with radical prostatectomy. Using SDI to understand the social environment could be -particularly useful as part of precision medicine and precision public health approaches and could be used by cancer centers, public health providers, and other health care specialists to inform operational decisions about how to target health promotion and disease prevention efforts in catchment areas and patient populations.

Spatial and temporal trends in social vulnerability and COVID-19 incidence and death rates in the United States.

BACKGROUND: Socially vulnerable communities may be at higher risk for COVID-19 outbreaks in the US. However, no prior studies examined temporal trends and differential effects of social vulnerability on COVID-19 incidence and death rates. Therefore, we examined temporal trends among counties with high and low social vulnerability to quantify disparities in trends over time. METHODS: We conducted a longitudinal analysis examining COVID-19 incidence and death rates from March 15 to December 31, 2020, for each US county using data from USAFacts. We classified counties using the Social Vulnerability Index (SVI), a percentile-based measure from the Centers for Disease Control and Prevention, with higher values indicating more vulnerability. Using a Bayesian hierarchical negative binomial model, we estimated daily risk ratios (RRs) comparing counties in the first (lower) and fourth (upper) SVI quartiles, adjusting for rurality, percentage in poor or fair health, percentage female, percentage of smokers, county average daily fine particulate matter (PM2.5), percentage of primary care physicians per 100,000 residents, daily temperature and precipitation, and proportion tested for COVID-19. RESULTS: At the outset of the pandemic, the most vulnerable counties had, on average, fewer cases per 100,000 than least vulnerable SVI quartile. However, on March 28, we observed a crossover effect in which the most vulnerable counties experienced higher COVID-19 incidence rates compared to the least vulnerable counties (RR = 1.05, 95% PI: 0.98, 1.12). Vulnerable counties had higher death rates starting on May 21 (RR = 1.08, 95% PI: 1.00,1.16). However, by October, this trend reversed and the most vulnerable counties had lower death rates compared to least vulnerable counties. CONCLUSIONS: The impact of COVID-19 is not static but can migrate from less vulnerable counties to more vulnerable counties and back again over time.

Using Bayesian time-stratified case-crossover models to examine associations between air pollution and "asthma seasons" in a low air pollution environment.

Many areas of the United States have air pollution levels typically below Environmental Protection Agency (EPA) regulatory limits. Most health effects studies of air pollution use meteorological (e.g., warm/cool) or astronomical (e.g., solstice/equinox) definitions of seasons despite evidence suggesting temporally-misaligned intra-annual periods of relative asthma burden (i.e., "asthma seasons"). We introduce asthma seasons to elucidate whether air pollutants are associated with seasonal differences in asthma emergency department (ED) visits in a low air pollution environment. Within a Bayesian time-stratified case-crossover framework, we quantify seasonal associations between highly resolved estimates of six criteria air pollutants, two weather variables, and asthma ED visits among 66,092 children ages 5-19 living in South Carolina (SC) census tracts from 2005 to 2014. Results show that coarse particulates (particulate matter <10 µm and >2.5 µm: PM10-2.5) and nitrogen oxides (NOx) may contribute to asthma ED visits across years, but are particularly implicated in the highest-burden fall asthma season. Fine particulate matter (<2.5 µm: PM2.5) is only associated in the lowest-burden summer asthma season. Relatively cool and dry conditions in the summer asthma season and increased temperatures in the spring and fall asthma seasons are associated with increased ED visit odds. Few significant associations in the medium-burden winter and medium-high-burden spring asthma seasons suggest other ED visit drivers (e.g., viral infections) for each, respectively. Across rural and urban areas characterized by generally low air pollution levels, there are acute health effects associated with particulate matter, but only in the summer and fall asthma seasons and differing by PM size.