Midlife non-Hispanic white mortality in the United States is rising, particularly in small metro and rural counties. This article responds to calls for county-level studies. We examine social determinants of morbidity and mortality among adult non-Hispanic whites in Yavapai County, Arizona, as part of an integrative study. We report overall mortality trends in Yavapai County using CDC Wonder data and then examine social determinants of reported physical health and mental distress in Yavapai County data using 6 years (2011–2016) of the Arizona Behavioral Risk Factor Surveillance System (BRFSS). The BRFSS includes 1,024 non-Hispanic white respondents aged 25–64. We also present data from the recently established Yavapai County Overdose Fatality Review Board (YCOFRB). Mortality trends indicate that suicide and drug and alcoholrelated mortality have all increased since 1999. These increases affect all 5-year age groups from 25 to 64 and both men and women. BRFSS data show that low education and unemployment, but not number of children or home ownership, are significantly associated with worse reported health and frequent mental distress in multivariate analyses. The YCOFRB point to the importance of homelessness and mental health. The mortality crisis in Yavapai County is not restricted to midlife or to drug-related deaths. The unemployed and those with low levels of education are particularly at risk. There is a need for integrative approaches that use local data to elucidate social determinants of morbidity and mortality and to reveal structural determinants.
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Demonstrating a systems approach for integrating disparate data streams to inform decisions on children’s environmental health
The use of systems science methodologies to understand complex environmental and human health relationships is increasing. Requirements for advanced datasets, models, and expertise limit current application of these approaches by many environmental and public health practitioners.
A conceptual system-of-systems model was applied for children in North Carolina counties that includes example indicators of children’s physical environment (home age, Brownfield sites, Superfund sites), social environment (caregiver’s income, education, insurance), and health (low birthweight, asthma, blood lead levels). The web-based Toxicological Prioritization Index (ToxPi) tool was used to normalize the data, rank the resulting vulnerability index, and visualize impacts from each indicator in a county. Hierarchical clustering was used to sort the 100 North Carolina counties into groups based on similar ToxPi model results. The ToxPi charts for each county were also superimposed over a map of percentage county population under age 5 to visualize spatial distribution of vulnerability clusters across the state.
Data driven clustering for this systems model suggests 5 groups of counties. One group includes 6 counties with the highest vulnerability scores showing strong influences from all three categories of indicators (social environment, physical environment, and health). A second group contains 15 counties with high vulnerability scores driven by strong influences from home age in the physical environment and poverty in the social environment. A third group is driven by data on Superfund sites in the physical environment.
This analysis demonstrated how systems science principles can be used to synthesize holistic insights for decision making using publicly available data and computational tools, focusing on a children’s environmental health example. Where more traditional reductionist approaches can elucidate individual relationships between environmental variables and health, the study of collective, system-wide interactions can enable insights into the factors that contribute to regional vulnerabilities and interventions that better address complex real-world conditions.
- Award ID(s):
- 1937012
- NSF-PAR ID:
- 10362986
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- BMC Public Health
- Volume:
- 22
- Issue:
- 1
- ISSN:
- 1471-2458
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Objective The current paper examines the intersection between social vulnerability, individual risk, and social/psychological resources with adult suicidality during the COVID‐19 pandemic.
Method Data come from a national sample (
n = 10,368) of U.S. adults. Using an online platform, information was gathered during the third week of March 2020, and post‐stratification weighted to proportionally represent the U.S. population in terms of age, gender, race/ethnicity, income, and geography.Results Nearly 15 percent of sampled respondents were categorized as high risk, scoring 7+ on the Suicide Behaviors Questionnaire‐Revised (SBQ‐R). This level of risk varied across social vulnerability groupings: Blacks, Native Americans, Hispanics, families with children, unmarried, and younger respondents reported higher SBQ‐R scores than their counterparts (
p < .000). Regression results confirm these bivariate differences and also reveal that risk factors (food insecurity, physical symptoms, and CES‐D symptomatology) are positive and significantly related to suicidality (p < .000). Additionally, resource measures are significant and negatively related to suicidality (p < .000).Conclusions These results provide some insight on the impact COVID‐19 is having on the general U.S. population. Practitioners should be prepared for what will likely be a significant mental health fall‐out in the months and years ahead.
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Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) the causal agent for COVID-19, is a communicable disease spread through close contact. It is known to disproportionately impact certain communities due to both biological susceptibility and inequitable exposure. In this study, we investigate the most important health, social, and environmental factors impacting the early phases (before July, 2020) of per capita COVID-19 transmission and per capita all-cause mortality in US counties. We aggregate county-level physical and mental health, environmental pollution, access to health care, demographic characteristics, vulnerable population scores, and other epidemiological data to create a large feature set to analyze per capita COVID-19 outcomes. Because of the high-dimensionality, multicollinearity, and unknown interactions of the data, we use ensemble machine learning and marginal prediction methods to identify the most salient factors associated with several COVID-19 outbreak measure. Our variable importance results show that measures of ethnicity, public transportation and preventable diseases are the strongest predictors for both per capita COVID-19 incidence and mortality. Specifically, the CDC measures for minority populations, CDC measures for limited English, and proportion of Black- and/or African-American individuals in a county were the most important features for per capita COVID-19 cases within a month after the pandemic started in a county and also at the latest date examined. For per capita all-cause mortality at day 100 and total to date, we find that public transportation use and proportion of Black- and/or African-American individuals in a county are the strongest predictors. The methods predict that, keeping all other factors fixed, a 10% increase in public transportation use, all other factors remaining fixed at the observed values, is associated with increases mortality at day 100 of 2012 individuals (95% CI [1972, 2356]) and likewise a 10% increase in the proportion of Black- and/or African-American individuals in a county is associated with increases total deaths at end of study of 2067 (95% CI [1189, 2654]). Using data until the end of study, the same metric suggests ethnicity has double the association as the next most important factors, which are location, disease prevalence, and transit factors. Our findings shed light on societal patterns that have been reported and experienced in the U.S. by using robust methods to understand the features most responsible for transmission and sectors of society most vulnerable to infection and mortality. In particular, our results provide evidence of the disproportionate impact of the COVID-19 pandemic on minority populations. Our results suggest that mitigation measures, including how vaccines are distributed, could have the greatest impact if they are given with priority to the highest risk communities.more » « less
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Abstract Unregulated private wells are understudied potential sources of community-acquired Legionnaires’ disease. Here we conducted a comprehensive survey of 44 homes supplied by private wells in Wake County, North Carolina, quantifying Legionella spp. DNA, Legionella pneumophila DNA, and total bacterial 16S rRNA genes via real-time polymerase chain reaction in hot and cold drinking water samples, along with culturable L. pneumophila via IDEXX Legiolert in cold drinking water samples. Legionella spp. DNA, L. pneumophila DNA and culturable L. pneumophila were detected in 100, 65·5 and 15·9% of the 44 homes, respectively, and culturable levels were comparable to some municipal surveys applying the same methods. Total coliforms and Escherichia coli were monitored as representative faecal indicators and were found in 20·4 and 0·0% of homes. Within certain sample types, Legionella spp. and L. pneumophila gene copy numbers were positively associated with total bacteria (i.e. total 16S rRNA genes) and water softener use, but were not associated with faecal indicator bacteria, inorganic water parameters or other well characteristics. These findings confirm that occurrence of Legionella and L. pneumophila is highly variable in private wells.
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Abstract Objective Infancy is both a critical window for hypothalamic–pituitary–adrenal (HPA) axis development, and a sensitive period for social–emotional influences. We hypothesized that the social–emotional quality of maternal–infant interactions are associated with methylation of HPA‐axis gene
NR3C1 later in childhood.Methods Using a subsample of 114 mother‐infant pairs from the Avon Longitudinal Study of Parents and Children (ALSPAC), linear regression models were created to predict variance in methylation of seven selected CpG sites from
NR3C1 in whole blood at age 7 years, including the main predictor variable of the first principal component score of observed maternal–infant interaction quality (derived from the Thorpe Interaction Measure at 12 months of age) and covariates of cell‐type proportion, maternal financial difficulties and marital status at 8 months postnatal, child birthweight, and sex.Results CpG site cg27122725 methylation was negatively associated with warmer, more positive maternal interaction with her infant (
β = 0.19,p = .02,q = 0.13). In sensitivity analyses, the second highest quartile of maternal behavior (neutral, hesitant behavior) was positively associated with cg12466613 methylation. The other five CpG sites were not significantly associated with maternal–infant interaction quality.Conclusions Narrow individual variation of maternal interaction with her infant is associated with childhood methylation of two CpG sites on
NR3C1 that may be particularly sensitive to environmental influences. Infancy may be a sensitive period for even small influences from the social–emotional environment on the epigenetic determinants of HPA‐axis function.
