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

Effects of the interaction between cold spells and fine particulate matter on mortality risk in Xining: a case-crossover study at high altitude

Background

With global climate change, the health impacts of cold spells and air pollution caused by PM2.5 are increasingly aggravated, especially in high-altitude areas, which are particularly sensitive. Exploring their interactions is crucial for public health.

Methods

We collected time-series data on meteorology, air pollution, and various causes of death in Xining. This study employed a time-stratified case-crossover design and conditional logistic regression models to explore the association between cold spells, PM2.5 exposure, and various causes of death, and to assess their interaction. We quantitatively analyzed the interaction using the relative excess odds due to interaction (REOI), attributable proportion due to interaction (AP), and synergy index (S). Moreover, we conducted stratified analyses by average altitude, sex, age, and educational level to identify potential vulnerable groups.

Results

We found significant associations between cold spells, PM2.5, and various causes of death, with noticeable effects on respiratory disease mortality and COPD mortality. We identified significant synergistic effects (REOI>0, AP > 0, S > 1) between cold spells and PM2.5 on various causes of death, which generally weakened with a stricter definition of cold spells and longer duration. It was estimated that up to 9.56% of non-accidental deaths could be attributed to concurrent exposure to cold spells and high-level PM2.5. High-altitude areas, males, the older adults, and individuals with lower educational levels were more sensitive. The interaction mainly varied among age groups, indicating significant impacts and a synergistic action that increased mortality risk.

Conclusion

Our study found that in high-altitude areas, exposure to cold spells and PM2.5 significantly increased the mortality risk from specific diseases among the older adults, males, and those with lower educational levels, and there was an interaction between cold spells and PM2.5. The results underscore the importance of reducing these exposures to protect public health.

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.

Ecological Study of Urbanicity and Self-reported Poor Mental Health Days Across US Counties

Geography may influence mental health by inducing changes to social and physical environmental and health-related factors. This understanding is largely based on older studies from Western Europe. We sought to quantify contemporary relationships between urbanicity and self-reported poor mental health days in US counties. We performed regression on US counties (n = 3142) using data from the County Health Rankings and Roadmaps. Controlling for state, age, income, education, and race/ethnicity, large central metro counties reported 0.24 fewer average poor mental health days than small metro counties (t = - 5.78, df = 423, p < .001). Noncore counties had 0.07 more average poor mental health days than small metro counties (t = 3.06, df = 1690, p = 0.002). Better mental health in large central metro counties was partly mediated by differences in the built environment, such as better food environments. Poorer mental health in noncore counties was not mediated by considered mediators.

Are there joint effects of different air pollutants and meteorological factors on mental disorders? A machine learning approach

Exposure to air pollutants is considered to be associated with mental disorders (MD). Few studies have addressed joint effect of multiple air pollutants and meteorological factors on admissions of MD. We examined the association between multiple air pollutants (PM_2.5, PM₁₀, O₃, SO₂, and NO₂), meteorological factors (temperature, precipitation, relative humidity, and sunshine time), and MD risk in Yancheng, China. Associations were estimated by a generalized linear regression model (GLM) adjusting for time trend, day of the week, and patients' average age. Empirical weights of environmental exposures were judged by a weighted quantile sum (WQS) model. A machine learning approach, Bayesian kernel machine regression (BKMR), was used to assess the overall effect of mixed exposures. We calculated excess risk (ER) and 95% confidence interval (CI) for each exposure. According to the effect of temperature on MD, we divided the exposure of all factors into different temperature groups. In the high temperature group, GLM found that for every 10 μg/m³ increase in O₃, PM_2.5 and PM₁₀ exposure, the ERs were 1.926 (95%CI 0.345, 3.531), 1.038 (95%CI 0.024, 2.062), and 0.780 (95% CI 0.052, 1.512) after adjusting for covariates. Temperature, relative humidity, and sunshine time also reported significant results. The WQS identified O₃ and temperature (above the threshold) had the highest weights among air pollutants and meteorological factors. BKMR found a significant positive association between mixed exposure and MD risks. In the low temperature group, only O₃ and temperature (below the threshold) showed significant results. These findings provide policymakers and practitioners with important scientific evidence for possible interventions. The association between different exposures and MD risk warrants further study.

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.

[Adaptation to altitude in respiratory diseases]

The frequency of high-altitude sojourns (for work, leisure, air travel or during car/train journeys) justifies the question of their tolerance, especially in people with pre-existing respiratory disease. Reduced barometric pressure and abrupt variations in temperature and inhaled air density may be responsible for modifications affecting the respiratory system and, in fine, oxygenation. These modifications may compromise altitude tolerance, further worsen respiratory dysfunction and render physical exercise more difficult. In obstructive lung disease, altitude is associated with gas exchange impairment, increased ventilation at rest and during exercise and heightened pulmonary artery pressure through hypoxic vasoconstriction, all of which may worsen dyspnea and increase the risk of altitude intolerance (acute mountain sickness, AMS). The most severe patients require rigorous evaluation, and hypoxic testing can be proposed. People with mild to moderate intermittent asthma can plan high altitude sojourns, provided that they remain under control at night and during exercise, and follow an adequate action plan in case of exacerbation. Respiratory disease patients with pulmonary artery hypertension (PAH) and chemoreflex control abnormalities need to be identified as at risk of altitude intolerance.

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.

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.

Is there a link between air pollution and mental disorders?

Several studies have demonstrated the association between air pollution and different medical conditions including respiratory and cardiovascular diseases. Air pollutants might have a role also in the etiology of mental disorders in the light of their toxicity on central nervous system. Purpose of the present manuscript was to review and summarize available data about an association between psychiatric disorders and air pollution. A research in the main database sources has been conducted to identify relevant papers about the topic. Different air pollutants and in particular PM and nitric oxides have been associated with poor mental health; long exposition to PM_2.5 has been associated with an increased risk of new onset of depressive symptoms (Cohen's effect size d: 0.05-0.81), while increased concentration of nitric dioxide in summer with worsening of existing depressive conditions (Cohen's effect size d: 0.05-1.77). However, the interpretation of these finding should take into account the retrospective design of most of studies, different periods of observations, confounding factors such as advanced age or medical comorbidity. Further studies with rigorous methodology are needed to confirm the results of available literature about this topic.

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.