Identifying Variation in Physical Health Behaviors and Depressive Symptoms among Religiosity Clusters of African American Adults in the United States.

Religiosity is an important factor in the lives of many African Americans, who suffer a greater health burden than their White counterparts. In this study, we examined associations between dimensions of religiosity with health behaviors and depressive symptoms in a sample of African American adults in the United States. Participants (N = 2086) completed five measures of religiosity (religious involvement, positive and negative religious coping, scriptural influence, belief in illness as punishment for sin) and measures of several health behaviors, cancer screening behaviors, and depressive symptoms. Using cluster analysis to examine the deep structure of religiosity, three clusters emerged: Positive Religious, Negative Religious, and Low Religious. In general, the Positive Religious group engaged in more healthy behaviors (e.g., fruit and vegetable consumption, fecal occult blood test) and fewer risky health behaviors (e.g., smoke and consume alcohol), and reported fewer depressive symptoms than did the Negative Religious and/or Low Religious groups. Theoretical implications and implications for interventions by clergy and mental health professionals are discussed.

Explaining the Varying Patterns of COVID-19 Deaths Across the United States: 2-Stage Time Series Clustering Framework.

BACKGROUND: Socially vulnerable communities are at increased risk for adverse health outcomes during a pandemic. Although this association has been established for H1N1, Middle East respiratory syndrome (MERS), and COVID-19 outbreaks, understanding the factors influencing the outbreak pattern for different communities remains limited. OBJECTIVE: Our 3 objectives are to determine how many distinct clusters of time series there are for COVID-19 deaths in 3108 contiguous counties in the United States, how the clusters are geographically distributed, and what factors influence the probability of cluster membership. METHODS: We proposed a 2-stage data analytic framework that can account for different levels of temporal aggregation for the pandemic outcomes and community-level predictors. Specifically, we used time-series clustering to identify clusters with similar outcome patterns for the 3108 contiguous US counties. Multinomial logistic regression was used to explain the relationship between community-level predictors and cluster assignment. We analyzed county-level confirmed COVID-19 deaths from Sunday, March 1, 2020, to Saturday, February 27, 2021. RESULTS: Four distinct patterns of deaths were observed across the contiguous US counties. The multinomial regression model correctly classified 1904 (61.25%) of the counties' outbreak patterns/clusters. CONCLUSIONS: Our results provide evidence that county-level patterns of COVID-19 deaths are different and can be explained in part by social and political predictors.

Modeling the differences in the time-series profiles of new COVID-19 daily confirmed cases in 3,108 contiguous U.S. counties: A retrospective analysis.

OBJECTIVE: The COVID-19 pandemic in the U.S. has exhibited a distinct multiwave pattern beginning in March 2020. Paradoxically, most counties do not exhibit this same multiwave pattern. We aim to answer three research questions: (1) How many distinct clusters of counties exhibit similar COVID-19 patterns in the time-series of daily confirmed cases? (2) What is the geographic distribution of the counties within each cluster? and (3) Are county-level demographic, socioeconomic and political variables associated with the COVID-19 case patterns? MATERIALS AND METHODS: We analyzed data from counties in the U.S. from March 1, 2020 to January 2, 2021. Time series clustering identified clusters in the daily confirmed cases of COVID-19. An explanatory model was used to identify demographic, socioeconomic and political variables associated with the outbreak patterns. RESULTS: Three patterns were identified from the cluster solution including counties in which cases are still increasing, those that peaked in the late fall, and those with low case counts to date. Several county-level demographic, socioeconomic, and political variables showed significant associations with the identified clusters. DISCUSSION: The pattern of the outbreak is related both to the geographic location within the U.S. and several variables including population density and government response. CONCLUSION: The reported pattern of cases in the U.S. is observed through aggregation of the daily confirmed COVID-19 cases, suggesting that local trends may be more informative. The pattern of the outbreak varies by county, and is associated with important demographic, socioeconomic, political and geographic factors.

Predicting unsafe driving risk among commercial truck drivers using machine learning: Lessons learned from the surveillance of 20 million driving miles.

The emergence of sensor-based Internet of Things (IoT) monitoring technologies have paved the way for conducting large-scale naturalistic driving studies, where continuous kinematic driver-based data are generated, capturing crash/near-crash safety critical events (SCEs) and their precursors. However, it is unknown whether the SCEs risk can be predicted to inform driver decisions in the medium term (e.g., hours ahead) since the literature has focused on SCE predictions either for a given road segment or for automated breaking applications, i.e., immediately before the event. In this paper, we examine the SCE data generated from 20+ million miles-driven by 496 commercial truck drivers to address three main questions. First, whether SCEs can be predicted using disparate driving-related data sources. Second, if so, what the relative importance of the different predictors examined is. Third, whether the prediction models can be generalized to new drivers and future time periods. We show that SCEs can be predicted 30 min in advance, using machine learning techniques and dependent variables capturing the driver's characteristics, weather conditions, and day/time categories, where an area under the curve (AUC) up to 76% can be achieved. Moreover, the predictive performance remains relatively stable when tested on new (i.e., not in the training set) drivers and a future two-month time period. Our results can inform dispatching and routing applications, and lead to the development of technological interventions to improve driver safety.

Association between maternal outdoor physical exercise and the risk of preterm birth: a case-control study in Wuhan, China.

BACKGROUND: China had the second largest proportion of preterm birth (PTB) internationally. However, only 11% of pregnant women in China meet international guidelines for maternal physical activity, a significantly lower proportion than that in Western countries. This study aims to examine the association between outdoor physical exercise during pregnancy and PTB among Chinese women in Wuhan, China. METHODS: A case-control study was conducted among 6656 pregnant women (2393 cases and 4263 controls) in Wuhan, China from June 2011 to June 2013. Self-reported measures of maternal physical exercise (frequency per week and per day in minutes) were collected. Adjusted odds ratios were estimated using Bayesian hierarchical logistic regression and a generalized additive mixed model (GAMM). RESULTS: Compared to women not involved in any physical activity, those who participated in physical exercise 1-2 times, 3-4 times, and over five times per week had 20% (aOR: 0.80, 95% credible interval [95% CI]: 0.68-0.92), 30% (aOR: 0.70, 95% CI: 0.60-0.82), and 32% (aOR: 0.68, 95% CI: 0.59-0.78) lower odds of PTB, respectively. The Bayesian GAMM showed that increasing physical exercise per day was associated with lower risk of PTB when exercise was less than 150 min per day; however, this direction of association is reversed when physical exercise was more than 150 min per day. CONCLUSION: Maternal physical exercise, at a moderate amount and intensity, is associated with lower PTB risk. More data from pregnant women with high participation in physical exercise are needed to confirm the reported U-shape association between the physical exercise and risk of preterm birth.

Investigating a disease outbreak.

Teams of epidemiological and medical "detectives" are working to get a coronavirus pandemic under control. Ronald D. Fricker, Jr and Steven E. Rigdon walk us through a typical investigation.

Comparing the Performance of Charlson and Elixhauser Comorbidity Indices to Predict In-Hospital Mortality Among a Chinese Population.

OBJECTIVE: Earlier comorbidity measures have been developed or validated using the North American population. This study aims to compare five Charlson or Elixhauser comorbidity indices to predict in-hospital mortality using a large electronic medical record database from Shanxi, China. METHODS: Using the primary diagnosis code and surgery procedure codes, we identified four hospitalized patient cohorts, hospitalized between 2013 and 2017, in Shanxi, China, as follows: congestive heart failure (CHF, n=41,577), chronic renal failure (CRF, n=40,419), diabetes (n=171,355), and percutaneous coronary intervention (PCI, n=39,097). We used logistic regression models and c-statistics to evaluate the in-hospital mortality predictive performance of two multiple comorbidity indicator variables developed by Charlson in 1987 and Elixhauser in 1998 and three single numeric scores by Quan in 2011, van Walraven in 2009, and Moore 2017. RESULTS: Elixhauser comorbidity indicator variables had consistently higher c-statistics (0.824, 0.843, 0.904, 0.853) than all other four comorbidity measures, across all four disease cohorts. Moore's comorbidity score outperformed the other two score systems in CHF, CRF, and diabetes cohorts (c-statistics: 0.776, 0.832, 0.869), while van Walraven's score outperformed all others among PCI patients (c-statistics: 0.827). CONCLUSION: Elixhauser comorbidity indicator variables are recommended, when applied to large Chinese electronic medical record databases, while Moore's score system is appropriate for relatively small databases.

A Review of Data Analytic Applications in Road Traffic Safety. Part 2: Prescriptive Modeling.

In the first part of the review, we observed that there exists a significant gap between the predictive and prescriptive models pertaining to crash risk prediction and minimization, respectively. In this part, we review and categorize the optimization/ prescriptive analytic models that focus on minimizing crash risk. Although the majority of works in this segment of the literature are related to the hazardous materials (hazmat) trucking problems, we show that (with some exceptions) many can also be utilized in non-hazmat scenarios. In an effort to highlight the effect of crash risk prediction model on the accumulated risk obtained from the prescriptive model, we present a simulated example where we utilize four risk indicators (obtained from logistic regression, Poisson regression, XGBoost, and neural network) in the k-shortest path algorithm. From our example, we demonstrate two major designed takeaways: (a) the shortest path may not always result in the lowest crash risk, and (b) a similarity in overall predictive performance may not always translate to similar outcomes from the prescriptive models. Based on the review and example, we highlight several avenues for future research.

A Review of Data Analytic Applications in Road Traffic Safety. Part 1: Descriptive and Predictive Modeling.

This part of the review aims to reduce the start-up burden of data collection and descriptive analytics for statistical modeling and route optimization of risk associated with motor vehicles. From a data-driven bibliometric analysis, we show that the literature is divided into two disparate research streams: (a) predictive or explanatory models that attempt to understand and quantify crash risk based on different driving conditions, and (b) optimization techniques that focus on minimizing crash risk through route/path-selection and rest-break scheduling. Translation of research outcomes between these two streams is limited. To overcome this issue, we present publicly available high-quality data sources (different study designs, outcome variables, and predictor variables) and descriptive analytic techniques (data summarization, visualization, and dimension reduction) that can be used to achieve safer-routing and provide code to facilitate data collection/exploration by practitioners/researchers. Then, we review the statistical and machine learning models used for crash risk modeling. We show that (near) real-time crash risk is rarely considered, which might explain why the optimization models (reviewed in Part 2) have not capitalized on the research outcomes from the first stream.

Ambient fine particulate matter and ozone higher than certain thresholds associated with myopia in the elderly aged 50 years and above.

Although myopia has been largely ignored among the elderly population, there is an increased risk of myopia with advancing age. Ambient air pollution is one potential contributor to vision impairments, but few epidemiological studies have demonstrated such an association. This cross-sectional survey collected the information of 33,626 subjects aged ≥50 years in six developing countries during 2007-2010. Myopia was identified based on questions related to symptoms of myopia. The annual concentrations of fine particulate matter (PM2.5) and ozone (O3) were estimated with the satellite data and chemical transport model. We examined the associations between the two pollutants and myopia using mixed-effect Poisson regression models with robust variance estimation (sandwich estimation). We observed J-shaped associations between the two pollutants and myopia, and identified 12 and 54 µg/m3 as the threshold concentrations. The adjusted prevalence ratio was 1.12 (95% CI: 1.05, 1.21) and 1.26 (95% CI: 1.14, 1.38) for each standard deviation (SD) increase in PM2.5 and O3 concentrations above their threshold, respectively. In addition, the interaction analysis suggested a synergistic interaction of these two pollutants on myopia in the additive model, with a synergistic index of 1.81 (Bootstrapping 95% CI: 0.92, 4.94). Our results indicate that long-term exposures to PM2.5 and O3 might be important environmental risk factors of myopia in the elderly, and suggest that more efforts should be taken to reduce airborne PM2.5 and O3 levels to protect vision health.

Mapping rates of inpatient hospitalizations related to mental disorders in the state of Missouri: A conditional autoregressive model with zip code-level data.

Nearly one in five American adults suffers from mental illness in a given year. Mental health conditions are known to be spatially clustered, but no prior work has examined the clustering of mental health related hospitalizations. This analysis uses Bayesian hierarchical models to predict rates of inpatient hospitalizations attributed to mental disorders within zip codes in Missouri, USA. Eight separate models were run, and all models yielded similar estimates for the average rate of mental health related hospitalizations (around 13 per 1000 population). The percent of families receiving food stamps and percent of vacant housing were found to be significantly associated with hospitalization rates, after controlling for age, gender, and race. These rates were also significantly spatially clustered (Moran's I > 0.3 and p < 0.05 for all models). Health professionals can use these results to prioritize regions throughout the state that have the greatest need for mental health service providers and interventions.

Ambient PM2.5 and O3 and their combined effects on prevalence of presbyopia among the elderly: A cross-sectional study in six low- and middle-income countries.

BACKGROUND: Ambient air pollutant directly contacts with the eyes, however, the effect of ambient fine particulate matter (PM2.5) and ozone (O3) on vision impairment, such as presbyopia, has been kept largely unknown. METHODS: We surveyed a total of 36,620 participants aged 50 years and above in six low- and middle-income countries. Ambient annual concentrations of PM2.5 and O3 for the residential community were estimated using satellite data and chemical transport model. A mixed effects model was utilized to assess the effects of ambient PM2.5 and O3 on presbyopia, as well as their combined effects. RESULTS: A total of 13,841 presbyopia cases were identified among the participants with a prevalence rate of 41.17%. For both PM2.5 and O3, we found a J-shaped exposure-response relationship with the threshold being identified at 15 µg/m3 for PM2.5 and 55 µg/m3 for O3. The odds ratio (OR) of presbyopia was 1.15 (95% CI: 1.09, 1.21) for each 10 µg/m3 increase in PM2.5 above 15 µg/m3 and 1.37 (95% CI: 1.23, 1.54) for O3 above 55 µg/m3 after adjusting for various potential confounding factors. There appeared to be a synergistic interaction between ambient PM2.5 and O3 on presbyopia in the additive model, the combined effect was significantly larger than the sum of their individual effects, with a synergistic index of 2.39. CONCLUSION: This study supports that exposures to ambient PM2.5 and O3 might be important risk factors of presbyopia among old adults, and simultaneously exposure to high level of the two pollutants could intensify their individual effects.

A respiratory health survey of a subsurface smoldering landfill.

In late 2010, a subsurface smoldering event was detected in the Bridgeton Sanitary Landfill in St. Louis County, Missouri. This was followed by complaints from nearby residents of foul odors emanating from the landfill. In 2016 a health survey was conducted of residents near the landfill and, as a comparison, other regions of St. Louis County. The survey was a two-stage cluster sample, where the first stage was census blocks, and the second stage was households within the census blocks. The health survey, which was conducted by face-to-face interviews of residents both near the landfill and away from the landfill, focused mainly on respiratory symptoms and diseases such as asthma and chronic obstructive pulmonary disease. The differences in the prevalence of asthma (26.7%, 95% CI 19.8-34.1 landfill vs 24.7%, 95% CI 15.7-33.6 comparison) and COPD (13.7%, 95% CI 7.2-20.3 landfill vs 12.5%, 95% CI 6.4-18.7 comparison) between the two groups were not statistically significant. Landfill households reported significantly more "other respiratory conditions," (17.6%, 95% CI 11.1-24.1 landfill vs 9.5%, 95% CI 4.8-14.3 comparison) and attacks of shortness of breath (33.9%, 95% CI 25.1-42.8 landfill vs 17.9%, 95% CI 12.3-23.5). Frequency of odor perceptions and level of worry about neighborhood environmental issues was higher among landfill households (p < 0.001). We conclude that the results do not support the hypothesis that people living near the Bridgeton Landfill have elevated respiratory or related illness compared to those people who live beyond the vicinity of the landfill.

Exposure to ambient PM2.5 associated with overall and domain-specific disability among adults in six low- and middle-income countries.

BACKGROUND: Exposure to particulate matter pollution is associated with various cardiopulmonary diseases, which are closely related with disability. The direct relationship between air pollution and disability, however, has not been fully explored. METHODS: We used data from 45,625 participants in the Study on global AGEing and adult health in six low- and middle-income countries. The 12-item version of the World Health Organization Disability Assessment Schedule (WHODAS 2.0) was used to measure the disability with six domains (cognition, mobility, self-care, getting along, life activities, and participation in society). Participants' community addresses were used to estimate annual concentration of PM2.5 using satellite data. We used linear mixed models to examine the effects of PM2.5 on overall and domain-specific WHODAS scores. RESULTS: Exposure to PM2.5 was significantly associated with greater disability score (a higher score implies a greater disability); each 10µg/m3 increase corresponded to 0.72 (95% CI: 0.22, 1.22) increase in overall disability score. Compared with low PM2.5 level (<14.33µg/m3), moderate (14.33-27.83µg/m3) and high exposure levels (>27.83µg/m3) were associated with 3.43 (95% CI: 1.43, 5.43) and 3.72 (95% CI: 1.59, 5.86) increase in disability scores. Among the six domains, cognition, mobility and getting along were found to be associated with PM2.5. Stratified analyses found that women and older subjects were more sensitive to this effect. CONCLUSION: Exposure to ambient PM2.5 might be one risk factor of disability in the low- and middle-income countries, women and older adults are the vulnerable population; and among the six domains, cognition, mobility and getting along are more relevant to this effect.

Association of perfluoroalkyl substances exposure with impaired lung function in children.

Previous studies have demonstrated associations between serum levels of perfluoroalkyl substances (PFASs) and asthma or asthma related-biomarkers. However, no studies have reported a possible relationship between PFASs exposure and lung function among children. The objective of the present study is to test the association between PFASs exposure and lung function in children from a high exposure area by using a cross-sectional case-control study, which included 132 asthmatic children and 168 non-asthmatic controls recruited from 2009 to 2010 in the Genetic and Biomarkers study for Childhood Asthma. Structured questionnaires were administered face-to-face. Lung function was measured by spirometry. Linear regression models were used to examine the influence of PFASs on lung function. The results showed that asthmatics in our study had significantly higher serum PFAS concentrations than healthy controls. Logistic regression models showed a positive association between PFASs and asthma, with adjusted odds ratios (ORs) ranging from 0.99 (95% confidence interval [CI]: 0.80-1.21) to 2.76 (95% CI: 1.82-4.17). Linear regression modeling showed serum PFASs levels were significantly negatively associated with three pulmonary function measurements (forced vital capacity: FVC; forced expiratory volume in 1s: FEV1; forced expiratory flow 25-75%: FEF25-75) among children with asthma, the adjusted coefficients between lung function and PFASs exposure ranged from -0.055 (95%CI: -0.100 to -0.010) for FVC and perfluorooctane sulfonate (PFOS) to -0.223 (95%CI: -0.400 to -0.045) for FEF25-75 and perfluorooctanoic acid (PFOA). PFASs were not, however, significantly associated with pulmonary function among children without asthma. In conclusion, this study suggests that serum PFASs are associated with decreased lung function among children with asthma.