Spatial variations in the associations of mental distress with sleep insufficiency in the United States: a county-level spatial analysis.

In this research, we conducted hierarchical multiple regression and complex sample general linear model (CSGLM) to expand knowledge on factors contributing to mental distress, particularly from a geographic perspective. Based on the Getis-Ord G* hot-spot analysis, geographic distribution of both FMD and insufficient sleep showed several contiguous hotspots in southeast regions. Moreover, in the hierarchical regression, even after accounting for potential covariates and multicollinearity, a significant association between FMD and insufficient sleep was found, explaining that mental distress increases with increasing insufficient sleep (R2 = 0.835). In the CSGLM, a R2 value of 0.782 indicated that the CSGLM procedure provided concrete evidence that FMD was significantly related to sleep insufficiency even after taking complex sample designs and weighting adjustments in the BRFSS into account. This geographic association between FMD and insufficient sleep based on cross-county study has not been reported before in the literature. These findings suggest a need for further investigation on geographic disparity on mental distress and insufficient sleep and have novel implications in our understanding of the etiology of mental distress.

Using community-based geographical information system (GIS) to recruit older Asian Americans in an Alzheimer's disease study.

OBJECTIVE: This study aims to show the usefulness of incorporating a community-based geographical information system (GIS) in recruiting research participants for the Asian Cohort for Alzheimer's Disease (ACAD) study for using the subgroup of Korean American (KA) older adults. The ACAD study is the first large study in the USA and Canada focusing on the recruitment of Chinese, Korean and Vietnamese older adults to address the issues of under-representation of Asian Americans in clinical research. METHODS: To promote clinical research participation of racial/ethnic minority older adults with and without dementia, we used GIS by collaborating with community members to delineate boundaries for geographical clusters and enclaves of church and senior networks, and KA serving ethnic clinics. In addition, we used socioeconomic data identified as recruitment factors unique to KA older adults which was analysed for developing recruitment strategies. RESULTS: GIS maps show a visualisation of the heterogeneity of the sociodemographic characteristics and the resources of faith-based organisations and KA serving local clinics. We addressed these factors that disproportionately affect participation in clinical research and successfully recruited the intended participants (N=60) in the proposed period. DISCUSSION: Using GIS maps to locate KA provided innovative inroads to successful research outreach efforts for a pilot study that may be expanded to other underserved populations across the USA in the future. We will use this tool subsequently on a large-scale clinical genetic epidemiology study. POLICY IMPLICATION: This approach responds to the call from the National Institute on Aging to develop strategies to improve the health status of older adults in diverse populations. Our study will offer a practical guidance to health researchers and policymakers in identifying understudied and hard-to-reach specific Asian American populations for clinical studies or initiatives. This would further contribute in reducing the health and research disparity gaps among older minority populations.

An ecological study on the spatially varying association between adult obesity rates and altitude in the United States: using geographically weighted regression.

In this research, we evaluated the relationship between obesity rates and altitude using a cross-county study design. We applied a geographically weighted regression (GWR) to examine the spatially varying association between adult obesity rates and altitude after adjusting for four predictor variables including physical activity. A significant negative relationship between altitude and adult obesity rates were found in the GWR model. Our GWR model fitted the data better than OLS regression (R2 = 0.583), as indicated by an improved R2 (average R2 = 0.670; range: 0.26-0.77) and a lower Akaike Information Criteria (AIC) value (14,736.88 vs. 15,386.59 in the OLS model). These approaches, evidencing spatial varying associations, proved very useful to refine interpretations of the statistical output on adult obesity. This study underscored the geographic variation in relationships between adult obesity rates and mean county altitude in the United States. Our study confirmed a varying overall negative relationship between county-level adult obesity rates and mean county altitude after taking other confounding factors into account.

A spatial epidemiology case study of mentally unhealthy days (MUDs): air pollution, community resilience, and sunlight perspectives.

The main objective of this spatial epidemiologic research is to gain greater insights into the geographic dimension displayed by the different duration of mentally unhealthy days (MUDs) across U.S. counties. Mentally unhealthy days (MUDs) are studied in entire cross counties for year of 2014. Using Behavioural Risk Factor Surveillance System (BRFSS) data in 2014, we examine main factors of mental health hazard including health behaviour, clinical care, socioeconomic and physical environment, demographic, community resilience, and extreme climatic conditions. In this study, we take complex design factors such as clustering, stratification and sample weight in the BRFSS data into account by using Complex Samples General Linear Model (CSGLM). Then, spatial regression models, spatial lag and error models, are applied to examine spatial dependencies and heteroscedasticity. Results of the geographic analyses indicate that counties with lower air pollution (PM2.5), higher community resilience (social, economic, infrastructure, and institutional resilience), and higher sunlight exposure had significantly lower average number of MUDs reported in the past 30 days. These findings suggest that policy makers should take air pollution, community resilience, and sunlight exposure into account when designing environmental and health policies and allocating resources to more effectively manage mental health problems.

Assessing community vulnerability to floods and hurricanes along the Gulf Coast of the United States.

Hurricanes and flooding have affected millions of people and generated massive economic losses over the past several decades. Geographic information system (GIS) methods are employed in this paper to analyse coastal communities' vulnerability to these two hazards along the Gulf Coast of the United States. Specifically, two types of quantitative indicators are developed: (i) exposure to hurricanes and flooding, based on information from multiple sources; and a social vulnerability index, constructed using census data. These indices are combined to depict the spatial patterns of overall community vulnerability to hurricanes and flooding along the US Gulf Coast. The results of this study can potentially inform disaster management agencies, county governments, and municipalities in areas at heightened risk. Furthermore, the demonstration of the geographic distribution of community vulnerability can assist decision-makers in prioritising to-do items and designing policies and plans for the more effective allocation of resources. The paper ends by discussing the study's limitations and its practical implications.

Using geographically weighted regression for social inequality analysis: association between mentally unhealthy days (MUDs) and socioeconomic status (SES) in U.S. counties.

This research explores geographic variability of factors on social inequality related to mental health in the United States using county-level data in 2014. First, we account for complex design factors in Behavioural Risk Factor Surveillance System (BRFSS) data such as clustering, stratification, and sample weight using Complex Samples General Linear Model (CSGLM). Then, three variables are used in the model as indicators of social inequality, low socioeconomic status (SES): unemployment, education status, and social association status. A geographically weighted regression analysis is applied to examine the spatial variations in the associations of mentally unhealthy days (MUDs) with the indicators of SES in the United States. The results demonstrate that unemployment and education level show global positive and negative influences respectively on MUDs. Social association status ranged from positive to negative across the United States, implying some geographic clustering. These findings suggest that social and health policies should be adjusted to address the different effects of indicators of social inequality on mental health across different social characteristics of communities to more effectively manage mental health problems.

An Ecological Study on the Spatially Varying Relationship between County-Level Suicide Rates and Altitude in the United States.

Suicide is a serious but preventable public health issue. Several previous studies have revealed a positive association between altitude and suicide rates at the county level in the contiguous United States. We assessed the association between suicide rates and altitude using a cross-county ecological study design. Data on suicide rates were obtained from a Web-based Injury Statistics Query and Reporting System (WISQARS), maintained by the U.S. National Center for Injury Prevention and Control (NCIPC). Altitude data were collected from the United States Geological Survey (USGS). We employed an ordinary least square (OLS) regression to model the association between altitude and suicide rates in 3064 counties in the contiguous U.S. We conducted a geographically weighted regression (GWR) to examine the spatially varying relationship between suicide rates and altitude after controlling for several well-established covariates. A significant positive association between altitude and suicide rates (average county rates between 2008 and 2014) was found in the dataset in the OLS model (R² = 0.483, p < 0.001). Our GWR model fitted the data better, as indicated by an improved R² (average: 0.62; range: 0.21–0.64) and a lower Akaike Information Criteria (AIC) value (13,593.68 vs. 14,432.14 in the OLS model). The GWR model also significantly reduced the spatial autocorrelation, as indicated by Moran’s I test statistic (Moran’s I = 0.171; z = 33.656; p < 0.001 vs. Moran’s I = 0.323; z = 63.526; p < 0.001 in the OLS model). In addition, a stronger positive relationship was detected in areas of the northern regions, northern plain regions, and southeastern regions in the U.S. Our study confirmed a varying overall positive relationship between altitude and suicide. Future research may consider controlling more predictor variables in regression models, such as firearm ownership, religion, and access to mental health services.

Geographic Variation in Mentally Unhealthy Days: Air Pollution and Altitude Perspectives.

Ha, Hoehun. Geographic variation in mentally unhealthy days: air pollution and altitude perspectives. High Alt Med Biol. 18:258-266, 2017. BACKGROUND: Mental health incorporates our emotional, psychological, and social well-being and it is critical at each phase of life, from youth and preadulthood through adulthood. METHODS: We assessed the association between mentally unhealthy days (MUDs), air pollutant concentrations, and altitude on the basis of cross-county studies. Data on poor mental health days for the United States were based on health-related telephone surveys conducted by the Behavioral Risk Factor Surveillance System (BRFSS). Average annual regional air pollution data were obtained from Center for Disease Control and Prevention (CDC) WONDER Environmental data, and altitude data were collected from the U.S. Geological Survey (USGS). RESULTS: In the data set (across 2589 U.S. counties for 2011), even after accounting for potential confounding variables and multicollinearity, a significant association between altitude, air pollution, and poor mental health days was found, explaining that poor mental health days increase with increasing air pollution concentrations and with decreasing altitude (R2 = 0.663, p < 0.001). Controlling for socioeconomic (e.g., education and employment) and social (including social relationship and crime) factors did not change these findings. CONCLUSIONS: In this study, we found that counties with lower air pollution and higher altitude had significantly lower average number of MUDs reported within the past 30 days. This association has not been reported before in the literature. These findings suggest a need for further investigation into the extent that air quality and altitude may serve as significant factors for mental health and have major implications in our understanding of the etiology of mental health by medical professionals.

Analysis of Pollution Hazard Intensity: A Spatial Epidemiology Case Study of Soil Pb Contamination.

Heavy industrialization has resulted in the contamination of soil by metals from anthropogenic sources in Anniston, Alabama. This situation calls for increased public awareness of the soil contamination issue and better knowledge of the main factors contributing to the potential sources contaminating residential soil. The purpose of this spatial epidemiology research is to describe the effects of physical factors on the concentration of lead (Pb) in soil in Anniston AL, and to determine the socioeconomic and demographic characteristics of those residing in areas with higher soil contamination. Spatial regression models are used to account for spatial dependencies using these explanatory variables. After accounting for covariates and multicollinearity, results of the analysis indicate that lead concentration in soils varies markedly in the vicinity of a specific foundry (Foundry A), and that proximity to railroads explained a significant amount of spatial variation in soil lead concentration. Moreover, elevated soil lead levels were identified as a concern in industrial sites, neighborhoods with a high density of old housing, a high percentage of African American population, and a low percent of occupied housing units. The use of spatial modelling allows for better identification of significant factors that are correlated with soil lead concentrations.

Analysis of heavy metal sources in soil using kriging interpolation on principal components.

Anniston, Alabama has a long history of operation of foundries and other heavy industry. We assessed the extent of heavy metal contamination in soils by determining the concentrations of 11 heavy metals (Pb, As, Cd, Cr, Co, Cu, Mn, Hg, Ni, V, and Zn) based on 2046 soil samples collected from 595 industrial and residential sites. Principal Component Analysis (PCA) was adopted to characterize the distribution of heavy metals in soil in this region. In addition, a geostatistical technique (kriging) was used to create regional distribution maps for the interpolation of nonpoint sources of heavy metal contamination using geographical information system (GIS) techniques. There were significant differences found between sampling zones in the concentrations of heavy metals, with the exception of the levels of Ni. Three main components explaining the heavy metal variability in soils were identified. The results suggest that Pb, Cd, Cu, and Zn were associated with anthropogenic activities, such as the operations of some foundries and major railroads, which released these heavy metals, whereas the presence of Co, Mn, and V were controlled by natural sources, such as soil texture, pedogenesis, and soil hydrology. In general terms, the soil levels of heavy metals analyzed in this study were higher than those reported in previous studies in other industrial and residential communities.