Socioeconomic Exposure Research Articles

Exposure to diesel-related particulate matter, cortisol stress responsivity, and depressive symptoms in adolescents

Exposure to air pollution is associated with higher risk for psychopathology; however, the mechanisms underlying this association are not clear. Dysfunction of the hypothalamic-pituitary-adrenal (HPA) axis in response to stress has been implicated in depression. Here, we estimated annual exposure to particulate matter (PM) from diesel emissions in 170 9- to 15-year-old adolescents (56 % female) using their residential addresses and data from nearby monitoring sites. We obtained salivary cortisol samples from participants while they completed a social stress task and calculated area under the curve with respect to ground (AUCg) and with respect to increase (AUCi) in order to assess cortisol responsivity during stress. Participants also reported on their depressive symptoms and sleep disturbances. Greater exposure to diesel PM was associated with lower cortisol output (AUCg) during stress, which was associated with higher depressive symptoms, particularly for adolescents with more sleep disturbances. Importantly, these effects were independent of household and neighborhood socioeconomic disadvantage and exposure to early adversity. Thus, HPA-axis dysfunction may be one mechanism through which environmental pollutants affect adolescents’ mental health.

The role of school safety perceptions in childhood asthma diagnosis disparities.

Childhood racial and ethnic asthma disparities in the United States are the consequence of structural inequities such as those in socioeconomic status (SES) and exposure to violence, but little research has explored the role of the school environment in perpetuating racial and ethnic disparities. This study examines associations between perceptions of unsafe school environments (USEs) and subsequent asthma diagnosis for elementary-aged children and the extent to which USE mediates relations from SES and race/ethnicity to asthma diagnosis for Black, Latinx, and non-Latinx White children. We conducted a secondary analysis of data from the Early Childhood Longitudinal Study. Structural equation modeling was used to evaluate the association between USE and subsequent asthma diagnosis, controlling for child sex, and indirect effects of SES and race/ethnicity on asthma diagnosis via USE. For 6,532 children, USE in second grade positively predicted new asthma diagnoses in third grade and mediated the association between SES and new asthma diagnosis. SES and USE serially mediated the relationship between race/ethnicity and asthma diagnosis. Identifying as non-Latinx White was associated with a lower likelihood of subsequent asthma diagnosis via higher SES and lower USE while identifying as Black or Latinx was associated with a higher likelihood of subsequent asthma diagnosis via lower SES and higher USE. USE was associated with pediatric asthma diagnosis and mediated the relationship between SES and asthma diagnosis as well as race/ethnicity and asthma diagnosis. Findings highlight the need to consider USE as a social determinant of health for pediatric asthma. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Abstract PR-02: Uncovering molecular differences in pancreatic ductal adenocarcinoma tissues from Black and White patients in the US

Abstract Background: Pancreatic cancer (PC) is among the leading causes of cancer-related deaths in the United States, with a 5-year survival rate of approximately 13%. Previous studies reported higher PC incidence and mortality rates among Black or African American (BAA) patients in comparison to White patients. Disadvantaged socioeconomic status (SES) and environmental exposure to chemicals are factors contributing to racial disparities in pancreatic cancer. Whether and how biological pathways play roles in disparities in pancreatic cancer is unclear. The understanding of molecular features associated racial backgrounds in pancreatic cancer will have significant impacts on both scientific inquiry on cancer biology and clinical management of pancreatic malignancy. In this study, we examined oncogene alterations and PD-L1 expression in pancreatic cancer patients of diverse racial backgrounds. Methods: We conducted our study by analyzing clinical test results of patients through a collaboration with Tempus Labs. In total, 4,249 pancreatic cancer patients’ (White: 3797 and BAA: 452) information were included in this study. DNA target panel sequencing was performed to identify genomic mutations. Genomic microsatellite instability (MSI) and tumor mutational burdens (TMB) were assessed. We also evaluated PD-L1 expression by immunohistochemistry. Results: DNA sequencing analysis revealed differential gene mutation profiles between White and BAA patients. We observed higher rates of KRAS (79.2% vs. 74.2%, q=0.14), TP53 (69.9% vs. 61.9%, q=0.014), KMT2C (6.9% vs. 2.3%, q=0.001), RNF43 (6% vs. 3.7%, q=0.14), and KMT2D (4.4% vs. 2.8%, q=0.3) mutations among BAA patients compared to White patients whereas fewer GNAS (0.2% vs 1.6%, q=0.14) mutation occur in the BAA group. Importantly, the KRAS mutant allele KRASG12R was significantly enriched in BAA compared to White patients (14.4% vs. 10.1%). Next, we compared the TMB levels and found that BAA patients showed higher average TMB levels when compared to White patients. Further, we determined the expression of PD-L1 between the two racial groups and observed BAA patients had more frequent PD-L1 overexpression than White patients (13.2% vs. 8.6%, p<0.05). Interestingly, the race associated difference in PD-L1 expression was even more remarkable in female groups (14.1% vs. 8%, p<0.05). Conclusion: We observed differences in mutation profiles of oncogenes between BAA and White patients, suggesting that such molecular-level changes may contribute to the higher incidence among BAA patients. Additionally, higher frequencies of PD-L1 upregulation in BAA patients implicate racial backgrounds influence tumor microenvironment regulation in pancreatic cancer patients. It is imperative to improve racial diversity in translational and clinical studies. Citation Format: Saurabh Mandal, Emily A Teslow, Minxuan Huang, Swathi Sridhar, Howard Crawford, Melissa C Stoppler, Albert Levin, Ling Huang. Uncovering molecular differences in pancreatic ductal adenocarcinoma tissues from Black and White patients in the US [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr PR-02.

Using geographic effect measure modification to examine socioeconomic-related surface temperature disparities in New York City.

Lower socioeconomic (SES) communities are more likely to be situated in urban heat islands and have higher heat exposures than their higher SES counterparts, and this inequality is expected to intensify due to climate change. To examine the relationship between surface temperatures and SES in New York City (NYC) by employing a novel analytical approach. Through incorporating modifiable features, this study aims to identify potential locations where mitigation interventions can be implemented to reduce heat disparities associated with SES. Using the 2013-2017 American Community Survey, U.S Landsat-8 Analysis Ready Data surface temperatures (measured on 8/12/2016), and the NYC Land Cover Dataset at the census tract level (2098 tracts), this study examines the association between two components of tract-level SES (percentage of individuals living below the poverty line and the percentage of individuals without a high school degree) and summer day surface temperature in NYC. First, we examine this association with an unrestricted NYC linear regression, examining the city-wide association between the two SES facets and summer surface temperature, with additional models adjusting for altitude, shoreline, and nature-cover. Then, we assess geographic effect measure modification by employing the same models to three supplemental regression model strategies (borough-restricted and community district-restricted linear regressions, and geographically weighted regression (GWR)) that examined associations within smaller intra-city areas. All regression strategies identified areas where lower neighborhood SES composition is associated with higher summer day surface temperatures. The unrestricted NYC regressions revealed widespread disparities, while the borough-restricted and community district-restricted regressions identified specific political boundaries within which these disparities existed. The GWR, addressing spatial autocorrelation, identified significant socioeconomic heat disparities in locations such as northwest Bronx, central Brooklyn, and uptown Manhattan. These findings underscore the need for targeted policies and community interventions, including equitable urban planning and cooling strategies, to mitigate heat exposure in vulnerable neighborhoods. This study redefines previous research on urban socioeconomic disparities in heat exposure by investigating both modifiable (nature cover) and non-modifiable (altitude and shoreline) built environment factors affecting local temperatures at the census tract level in New York City. Through a novel analytical approach, the research aims to highlight intervention opportunities to mitigate heat disparities related to socioeconomic status. By examining the association between surface temperatures and socioeconomic status, as well as investigating different geographic and governmental scales, this study offers actionable insights for policymakers and community members to address heat exposure inequalities effectively across different administrative boundaries. The objective is to pinpoint potential sites for reducing socioeconomic heat exposure disparities at various geographic and political levels.

Advancing geospatial preconception health research in primary care through medical informatics and artificial intelligence

Established life course approaches suggest that health status in adulthood can be influenced by events that occurred during the prenatal developmental period. Yet, interventions such as diet and lifestyle changes performed during pregnancy have had a small impact on both maternal and offspring health outcomes. Currently, there is a growing body of literature that highlights the importance of maternal health before conception (months or years before pregnancy occurs) for the future health of offspring. While some studies have explored factors such as maternal body composition, nutrition, and lifestyle in this area, location-based environmental and socioeconomic exposures before conception may also contribute to future offspring health. In this line, the study of a patient's geographic history presents a promising avenue. To foster research in this direction, the integration of geospatial health, medical informatics and artificial intelligence techniques offers great potential. Importantly, novel sources of big health data sets such as electronic health records registered at the primary care level provide a unique framework due to its inherent longitudinal nature. Nonetheless, a number of privacy, ethical, and technical challenges need to be overcome for this kind of longitudinal analysis to mature and succeed. In the long-term, we support the vision of incorporating a patient's geographic history into her clinical history to equip clinicians with useful contextual information to explore.

Nonlinear effect of urban noise pollution on depression of the elderly in China based on the Bayesian machine learning method

With the intensification of urbanisation and population ageing globally, it is crucial to understand the effect of urban noise pollution on the mental health of the ageing population. This research employed the Bayesian machine learning method to investigate the nonlinear effect of urban noise pollution on depression scores in the elderly (DSE) population in China. Based on individual survey data, the China Health and Retirement Longitudinal Study (CHARLS) data from 2011 to 2020, and corresponding temporally monitored annual urban equivalent sound pressure level (ESPL) data in China, propensity score matching (PSM) was used to identify the causal effect of urban ESPL on DSE. The quantile regression model (QRM), the Bayesian additive regression tree model (BARTM), and the Bayesian causal forest method (BCFM) were used to detect the nonlinear influencing mechanism of urban ESPL on DSE. The PSM analysis which can balance confounding influence revealed a significant disparity in depression scores between ageing individuals exposed to higher ESPL and those residing in more tranquil settings, suggesting a definitive causal relationship. Furthermore, the QRM results highlight a significantly increasing effect of ESPL on DSE at higher quantiles (0.4528 and 0.5709 at the 75th and 85th percentiles, respectively). The BARTM identifies a ‘U-shaped’ dose–response curve between ESPL and DSE, with low ESPL as beneficial, but exposure above 53.67 dB(A) representing noise pollution, especially past 55.85 dB(A), significantly increasing DSE. Specifically, when ESPL ranges from 53.67 to 55.68 dB(A) with other variables held constant, DSE increases by an average of 0.28 for every 1 dB(A) rise. Beyond 55.68 dB(A), the average DSE increase is 0.85 for each additional 1 dB(A) in ESPL. A notable spatial heterogeneity was observed in the local effects of annual urban ESPL on DSE. The BCFM results indicate that socio-economic exposure level can modulate the effect of ESPL on DSE.

Heat stress risk at an intra-urban level: A case study of Delhi, India

Cities throughout the globe are expanding due to rapid urbanization. The risk associated with heat on residents is increasing and thus requires interventions at an intraurban level. This study aims to determine the spatial distribution of the heat stress risk index (HSRI) in an urban area of Delhi (India). This study illustrates a novel technique using meteorological variables simulated from a numerical weather prediction urban canopy model (WRF-UCM) to generate the Universal Thermal Climate Index (UTCI) in the city of Delhi at a resolution of 333 m. The estimated UTCI is then optimally blended with the socioeconomic vulnerability and exposure indices generated through the identification of suitable parameters via principal component analysis (PCA). The analysis based on the high-resolution UTCI risk index showed that within the existing framework, 88 % of the city falls under a heat stress risk above the “Moderate to Highest” category of stress values, which explicitly affects the population and requires urgent interventions for the development of a resilient and sustainable city. The study also revealed that the areas with the highest UTCIs may not correspond to the zones related to the highest heat stress risk. This suggests that the HSRI rather than the UTCI alone should be the optimal indicator for consideration by stakeholders when developing policies to mitigate heat stress-related issues in urban areas under changing climate.

Projected Increase in Heatwaves under 1.5 and 2.0 °C Warming Levels Will Increase the Socio-Economic Exposure across China by the Late 21st Century

The impending challenge posed by escalating heatwave events due to projected global warming scenarios of 1.5 and 2.0 °C underscores the critical need for a comprehensive understanding of their impact on human health and socio-economic realms. This study delves into the anticipated implications of elevated global temperatures, specifically the 1.5 and 2.0 °C warming scenarios under the SSP2-4.5 and SSP5-8.5 pathways, on population and GDP exposure to heatwaves in China. We also evaluated the aggregated impacts of climate, population, and GDP and their interactions on future socio-economic exposure across China. We leveraged data sourced from the climatic output of Coupled Model Intercomparison Project Phase 6 (CMIP6) for heatwave analysis and integrated population and GDP projections under divergent Shared Socio-economic Pathways (SSPs), including SSP2-4.5 (low emission) and SSP5-8.5 (high-emission). Results indicate a drastic surge in the number of heatwave days under both warming scenarios, particularly in regions like Xinjiang (XJ), North China (NC), and South China (SC) subregions, with a notable disparity in the elevation of heatwave days among different levels. There is an alarming surge in population exposure, escalating approximately 7.94–8.70 times under the 1.5 °C warming scenario and markedly increasing by 14.48–14.75 times by the 2100s relative to the baseline (1985–2014) under the more extreme 2.0 °C warming level. Likewise, the study unveils a substantial elevation in GDP exposure, ranging from 40.65 to 47.21 times under the 1.5 °C warming level and surging dramatically by 110.85–113.99 times under the 2.0 °C warming level. Further analyses disclose that the climate effect predominantly influences changes in population exposure, constituting 72.55–79.10% of the total change. Meanwhile, the interaction effect notably shapes GDP exposure alterations, contributing 77.70–85.99% to the total change. The comprehensive investigation into alterations in population and GDP exposure under varying warming scenarios, coupled with the quantification of each contributing factor, holds paramount importance in mitigating the detrimental repercussions of heatwaves on both human life and socio-economic landscapes.

Projections and uncertainty analysis of socioeconomic exposure to compound dry and hot events under 1.5℃ and 2.0℃ warming levels across China

Projections and uncertainty analysis of socioeconomic exposure to compound dry and hot events under 1.5℃ and 2.0℃ warming levels across China

The health implications of neighborhood networks based on daily mobility in US cities

A large body of research has been dedicated to understanding the neighborhood conditions that impact health, which outcomes are affected, and how these associations vary by demographic and socioeconomic neighborhood and individual characteristics. This literature has focused mostly on the neighborhoods in which individuals reside, thus failing to recognize that residents across race/ethnicity and class spend a non-trivial amount of their time in neighborhoods far from their residential settings. To address this gap, we use mobile phone data from the company SafeGraph to compare racial inequality in neighborhood socioeconomic advantage exposure across three scales: the neighborhoods that residents live in, their adjacent neighborhoods, and the neighborhoods that they regularly visit. We found that the socioeconomic advantage levels in neighborhood networks differ from the levels at the residential and adjacent scales across all ethnoracial neighborhoods. Furthermore, socioeconomic advantage at the network level is associated with diabetes and hypertension prevalence above and beyond its impact at the residential and adjacent levels. We also find ethnoracial differences in these associations, with greater beneficial consequences of network socioeconomic advantage exposure on hypertension and diabetes for white neighborhoods. Future social determinants of health research needs to reconceptualize exposure to include the larger neighborhood network that a community is embedded in based on where their residents travel to and from.

Association of Socio-economic and Media Exposure on Inequality of Micronutrients Intake among Children in Odisha: A District Level Analysis from NFHS-5 (2019-21)

Association of Socio-economic and Media Exposure on Inequality of Micronutrients Intake among Children in Odisha: A District Level Analysis from NFHS-5 (2019-21)

Demographic and socioeconomic predictors of religious/spiritual beliefs and behaviours in a prospective cohort study (ALSPAC) in Southwest England: Results from the offspring generation

Background We explored associations between possible demographic and socioeconomic causes of religious/spiritual beliefs and behaviours (RSBB) in the offspring generation of the Avon Longitudinal Study of Parents and Children (ALSPAC). Methods We examined approximately 4,450 offspring aged 28 years with RSBB data from a prospective birth cohort study (ALSPAC) in Southwest England. Three RSBB outcome measures were assessed: religious belief (belief in God/a divine power; yes/not sure/no), religious affiliation (Christian/none/other) and religious attendance (frequency of attendance at a place of worship). We explored age- and sex-adjusted associations between 35 demographic and socioeconomic exposures and each of the three RSBB outcomes using multinomial regression. Exposure-sex interactions were also examined. Results Some sociodemographic factors were associated with RSBB in this cohort; for instance, being female and from an ethnicity other than White were associated with increased religiosity across all domains. For many other exposures, however, associations were frequently null or inconsistent, often depending on the specific exposure and outcome combination. As an example, higher educational attainment was associated with higher rates of religious attendance, but not with religious belief or affiliation; in contrast, higher income was associated with lower levels of religiosity. No consistent interactions between sex and the exposures on RSBB were found. Effect sizes were also rather weak, with most pseudo-R 2 values below 0.5% and a maximum of 1.2%. Conclusions The results highlight that several demographic and socioeconomic factors are associated with RSBB in this cohort. However, the number of these associations, and their magnitude, is smaller than comparable results from the parental generation of these offspring, suggesting that patterns of sociodemographic factors associated with RSBB differ between these generations. In addition to describing these associations, this paper will help inform future studies using these data, particularly regarding the choice of potential sociodemographic confounders.

Serum signature of antibodies to Toxoplasma gondii, rubella virus, and cytomegalovirus in females with bipolar disorder: A cross-sectional study

Background and aimImmunity alterations have been observed in bipolar disorder (BD). However, whether serum positivity of antibodies to Toxoplasma gondii (T gondii), rubella, and cytomegalovirus (CMV) shared clinical relevance with BD, remains controversial. This study aimed to investigate this association. MethodsAntibody seropositivity of IgM and IgG to T gondii, rubella virus, and CMV of females with BD and controls was extracted based on medical records from January 2018 to January 2023. Family history, type of BD, onset age, and psychotic symptom history were also collected. Results585 individuals with BD and 800 healthy controls were involved. Individuals with BD revealed a lower positive rate of T gondii IgG in the 10–20 aged group (OR = 0.10), and a higher positive rate of rubella IgG in the 10–20 (OR = 5.44) and 20–30 aged group (OR = 3.15). BD with family history preferred a higher positive rate of T gondii IgG (OR = 24.00). Type-I BD owned a decreased positive rate of rubella IgG (OR = 0.37) and an elevated positive rate of CMV IgG (OR = 2.12) compared to type-II BD, while BD with early onset showed contrast results compared to BD without early onset (Rubella IgG, OR = 2.54; CMV IgG, OR = 0.26). BD with psychotic symptom history displayed a lower positive rate of rubella IgG (OR = 0.50). LimitationsAbsence of male evidence and control of socioeconomic status and environmental exposure. ConclusionsDifferential antibody seropositive rates of T gondii, rubella, and cytomegalovirus in BD were observed.

Sheep anthrax outbreak in Muddaballi Village, Karnataka, India: a comprehensive study of factors

Anthrax, caused by Bacillus anthracis, remains a persistent threat to global public health and livestock industries. This study investigates a sheep anthrax outbreak in Muddaballi Village, Karnataka, India. Muddaballi has a population of 2,450 people and a livestock population of 2,279 animals, including 756 cows, 82 buffaloes, 1,129 sheep, and 312 goats. The village experienced anthrax outbreaks in 2016, 2021, and 2023, with significant sheep mortalities: 4 out of 200 sheep in 2016, 25 out of 150 sheep in 2021, and multiple incidents in 2023 affecting different farmers with mortality rates ranging from 4 to 20 sheep. The ecological characteristics, including an average annual temperature of 27.0°C and annual rainfall of 587 mm, alongside proximity to the Tungabhadra reservoir, played a crucial role in spore survival and transmission. The study highlights socioeconomic factors and Risk Exposure and Mitigation Behaviour (REMB) as critical determinants of anthrax incidence, with farmers reporting strong agreement on the influence of these factors (Likert scale: 4-5). The integration of AI tools for early detection and rapid response, comprehensive disease surveillance, and ensuring vaccine accessibility are essential measures. Training initiatives for local veterinarians and farmers are also crucial to mitigate future outbreaks. This multifaceted approach is vital for effective anthrax prevention and control in Muddaballi and similar regions.

Time and space co-ordinates of Hodgkin's lymphoma in Sardinia, Italy

BackgroundThe epidemiological investigation of Hodgkin's lymphoma (HL) among the genetically peculiar population of the Italian island of Sardinia might provide interesting etiological clues. MethodsWe used the database of 1974–2003 incident cases of hematological malignancies in Sardinia and Bayesian methods to explore the time trend and geographic spread of HL incidence by sex, and age whether ≤44 or ≥45 years. We also tested its association with several socio-economic and environmental risk factors. ResultsThe age- and sex-standardized (world population) incidence rate of HL was 2.6 per 100,000 (95% CI, 2.5–2.8). Over the study period, HL incidence increased linearly in both sexes and among those aged ≤44 years but not above that age. Cases clustered among young women in a central-western area covering four bordering administrative units (13 cases vs. 5.7 expected, P = 0.002). The posterior probability of excess HL cases aged ≤44 years was elevated only in a commune in the suburban area of the region's capital. Cases aged ≥45 years were uniformly spread over the region. Among the risk factors we explored, urban residence was associated with an elevated and goat farming with a decreased risk of HL occurrence. We did not observe a link with socio-economic deprivation, environmental exposures, or multiple sclerosis. The geographic spread of COVID-19 was also unrelated to past HL incidence. ConclusionsOur results prompt further in-depth investigation into the previously undetected cluster and the nature of the observed associations.

Independent and joint effects of neighborhood-level environmental and socioeconomic exposures on body mass index in early childhood: The environmental influences on child health outcomes (ECHO) cohort

Past studies support the hypothesis that the prenatal period influences childhood growth. However, few studies explore the joint effects of exposures that occur simultaneously during pregnancy. To explore the feasibility of using mixtures methods with neighborhood-level environmental exposures, we assessed the effects of multiple prenatal exposures on body mass index (BMI) from birth to age 24 months. We used data from two cohorts: Healthy Start (n = 977) and Maternal and Developmental Risks from Environmental and Social Stressors (MADRES; n = 303). BMI was measured at delivery and 6, 12, and 24 months and standardized as z-scores. We included variables for air pollutants, built and natural environments, food access, and neighborhood socioeconomic status (SES). We used two complementary statistical approaches: single-exposure linear regression and quantile-based g-computation. Models were fit separately for each cohort and time point and were adjusted for relevant covariates. Single-exposure models identified negative associations between NO2 and distance to parks and positive associations between low neighborhood SES and BMI z-scores for Healthy Start participants; for MADRES participants, we observed negative associations between O3 and distance to parks and BMI z-scores. G-computations models produced comparable results for each cohort: higher exposures were generally associated with lower BMI, although results were not significant. Results from the g-computation models, which do not require a priori knowledge of the direction of associations, indicated that the direction of associations between mixture components and BMI varied by cohort and time point. Our study highlights challenges in assessing mixtures effects at the neighborhood level and in harmonizing exposure data across cohorts. For example, geospatial data of neighborhood-level exposures may not fully capture the qualities that might influence health behavior. Studies aiming to harmonize geospatial data from different geographical regions should consider contextual factors when operationalizing exposure variables.

Does socioeconomic and environmental burden affect vulnerability to extreme air pollution and heat? A case-crossover study of mortality in California.

Extreme heat and air pollution is associated with increased mortality. Recent evidence suggests the combined effects of both is greater than the effects of each individual exposure. Low neighborhood socioeconomic status ("socioeconomic burden") has also been associated with increased exposure and vulnerability to both heat and air pollution. We investigated if neighborhood socioeconomic burden or the combination of socioeconomic and environmental exposures ("socioenvironmental burden") modified the effect of combined exposure to extreme heat and particulate air pollution on mortality in California. We used a time-stratified case-crossover design to assess the impact of daily exposure to extreme particulate matter <2.5 μm (PM2.5) and heat on cardiovascular, respiratory, and all-cause mortality in California 2014-2019. Daily average PM2.5 and maximum temperatures based on decedent's residential census tract were dichotomized as extreme or not. Census tract-level socioenvironmental and socioeconomic burden was assessed with the CalEnviroScreen (CES) score and a social deprivation index (SDI), and individual educational attainment was derived from death certificates. Conditional logistic regression was used to estimate associations of heat and PM2.5 with mortality with a product term used to evaluate effect measure modification. During the study period 1,514,292 all-cause deaths could be assigned residential exposures. Extreme heat and air pollution alone and combined were associated with increased mortality, matching prior reports. Decedents in census tracts with higher socioenvironmental and socioeconomic burden experienced more days with extreme PM2.5 exposure. However, we found no consistent effect measure modification by CES or SDI on combined or separate extreme heat and PM2.5 exposure on odds of total, cardiovascular or respiratory mortality. No effect measure modification was observed for individual education attainment. We did not find evidence that neighborhood socioenvironmental- or socioeconomic burden significantly influenced the individual or combined impact of extreme exposures to heat and PM2.5 on mortality in California. We investigated the effect measure modification by socioeconomic and socioenvironmental of the co-occurrence of heat and PM2.5, which adds support to the limited previous literature on effect measure modification by socioeconomic and socioenvironmental burden of heat alone and PM2.5 alone. We found no consistent effect measure modification by neighborhood socioenvironmental and socioeconomic burden or individual level SES of the mortality association with extreme heat and PM2.5 co-exposure. However, we did find increased number of days with extreme PM2.5 exposure in neighborhoods with high socioenvironmental and socioeconomic burden. We evaluated multiple area-level and an individual-level SES and socioenvironmental burden metrics, each estimating socioenvironmental factors differently, making our conclusion more robust.

Assessing PM2.5 exposure bias towards deprived areas in England using a new indicator

In many countries higher concentrations of harmful air pollutants coincide with more deprived areas of society, where prevalence of health impacts associated with air pollution are greater. However, the impact of policies aimed at mitigating air pollution on the bias in exposure across deprivation groups rarely feature in policy development.We introduce the Indicator of Exposure Bias (IoEB) to the UK Integrated Assessment Model to quantify this bias, providing a method for comparing different scenarios and sectors. We analyse the bias in exposure to PM2.5 concentrations across deprivation levels within England currently and for future scenarios, and by sectors. While England's Index of Multiple Deprivation (IMD) and the Health domain are used as measures of deprivation here, the indicator can be applied to any pair of spatial socio-economic status and environmental exposure metrics.An IoEB of 0.88 µg.m−3 was calculated for the 2018 baseline, indicating a bias in exposures towards more deprived areas ranked using the IMD. All future scenarios considered here lead to reductions in population exposure and a reduction in the bias towards more deprived areas, with the greatest reduction of 59 % achieved by focusing on urban sources of primary PM2.5. The total bias in exposure towards more deprived areas is mitigated by an opposite bias for the transboundary contribution, therefore the bias in exposure of total PM2.5 does not accurately reflect that associated to UK anthropogenic emissions. The bias varies significantly between sectors, with non-industrial combustion and road transport the greatest contributors.Reaching England's 2040 PM2.5 exposure target is likely to deliver substantially greater health benefits for the more deprived members of society than the less deprived, reducing health inequality. The bias is sensitive to the measure of deprivation used, demonstrated using the Health domain.

Factors associated with self-reported diagnosed asthma in urban and rural Malawi: Observations from a population-based study of non-communicable diseases.

The growing burden of asthma in low- and middle-income countries has been linked to urbanisation and lifestyle changes. However, this burden has not been well characterised in adults. Therefore, we investigated the prevalence of self-reported diagnosed asthma and associated factors in urban and rural adults in Malawi, Southern Africa. Within a cross-sectional population-based survey to determine the burden and risk factors for non-communicable diseases (NCDs) in the city of Lilongwe and rural Karonga district, we collected information on self-reported previously diagnosed asthma and asthma-related symptoms using an interviewer-led questionnaire. Other data collected included: demographic characteristics, socioeconomic status indicators, NCD comorbidities, environmental exposures, and anthropometric measurements. We used multivariable logistic regression models to explore factors associated with self-reported asthma adjusting for variables associated with the outcome in univariable analysis. Findings were corrected for multiple comparisons using the Bonferroni method. We analysed data from 30,483 adult participants (54.6% urban,45.4% rural and 61.9% female). A prior asthma diagnosis was reported in 5.1% of urban and 4.5% of rural participants. In urban females, being obese (>30 kg/m2) compared to normal weight (18.5-24.9 kg/m2) was associated with greater odds of asthma (OR = 1.59, 95% CI [1.26-2.01], p<0.001), after adjusting for confounders. We observed associations between previously diagnosed heart disease and asthma in female participants which remained significant in rural females after Bonferroni correction (OR = 2.30,95%CI [1.32-4.02], p = 0.003). Among rural males, current smokers had reduced odds of diagnosed asthma (OR = 0.46,95%CI [0.27-0.79], p = 0.004) compared to those who had never smoked. In Malawi the prevalence of self-reported diagnosed asthma was greatest in females and urban dwellers. Notably, our findings indicate relationships between excess body weight as well as comorbidities and diagnosed asthma in females. Future investigations using longitudinally collected data and clinical measurements of asthma are needed to better understand these associations.

Demographic and socioeconomic predictors of religious/spiritual beliefs and behaviours in a prospective cohort study (ALSPAC) in Southwest England: Results from the offspring generation.

We explored associations between possible demographic and socioeconomic causes of religious/spiritual beliefs and behaviours (RSBB) in the offspring generation of the Avon Longitudinal Study of Parents and Children (ALSPAC). We examined approximately 4,450 offspring aged 28 years with RSBB data from a prospective birth cohort study (ALSPAC) in Southwest England. Three RSBB outcome measures were assessed: religious belief (belief in God/a divine power; yes/not sure/no), religious affiliation (Christian/none/other) and religious attendance (frequency of attendance at a place of worship). We explored age- and sex-adjusted associations between 35 demographic and socioeconomic exposures and each of the three RSBB outcomes using multinomial regression. Exposure-sex interactions were also examined. Some sociodemographic factors were associated with RSBB in this cohort; for instance, being female and from an ethnicity other than White were associated with increased religiosity across all domains. For many other exposures, however, associations were frequently null or inconsistent, often depending on the specific exposure and outcome combination. As an example, higher educational attainment was associated with higher rates of religious attendance, but not with religious belief or affiliation; in contrast, higher income was associated with lower levels of religiosity. No consistent interactions between sex and the exposures on RSBB were found. Effect sizes were also rather weak, with most pseudo- R 2 values below 0.5% and a maximum of 1.2%. The results highlight that several demographic and socioeconomic factors are associated with RSBB in this cohort. However, the number of these associations, and their magnitude, is smaller than comparable results from the parental generation of these offspring, suggesting that patterns of sociodemographic factors associated with RSBB differ between these generations. In addition to describing these associations, this paper will help inform future studies using these data, particularly regarding the choice of potential sociodemographic confounders.