Exposure-response associations between chronic exposure to fine particulate matter and risks of hospital admission for major cardiovascular diseases: population based cohort study.

OBJECTIVE: To estimate exposure-response associations between chronic exposure to fine particulate matter (PM2.5) and risks of the first hospital admission for major cardiovascular disease (CVD) subtypes. DESIGN: Population based cohort study. SETTING: Contiguous US. PARTICIPANTS: 59 761 494 Medicare fee-for-service beneficiaries aged ≥65 years during 2000-16. Calibrated PM2.5 predictions were linked to each participant's residential zip code as proxy exposure measurements. MAIN OUTCOME MEASURES: Risk of the first hospital admission during follow-up for ischemic heart disease, cerebrovascular disease, heart failure, cardiomyopathy, arrhythmia, valvular heart disease, thoracic and abdominal aortic aneurysms, or a composite of these CVD subtypes. A causal framework robust against confounding bias and bias arising from errors in exposure measurements was developed for exposure-response estimations. RESULTS: Three year average PM2.5 exposure was associated with increased relative risks of first hospital admissions for ischemic heart disease, cerebrovascular disease, heart failure, cardiomyopathy, arrhythmia, and thoracic and abdominal aortic aneurysms. For composite CVD, the exposure-response curve showed monotonically increased risk associated with PM2.5: compared with exposures ≤5 µg/m3 (the World Health Organization air quality guideline), the relative risk at exposures between 9 and 10 µg/m3, which encompassed the US national average of 9.7 µg/m3 during the study period, was 1.29 (95% confidence interval 1.28 to 1.30). On an absolute scale, the risk of hospital admission for composite CVD increased from 2.59% with exposures ≤5 µg/m3 to 3.35% at exposures between 9 and 10 µg/m3. The effects persisted for at least three years after exposure to PM2.5. Age, education, accessibility to healthcare, and neighborhood deprivation level appeared to modify susceptibility to PM2.5. CONCLUSIONS: The findings of this study suggest that no safe threshold exists for the chronic effect of PM2.5 on overall cardiovascular health. Substantial benefits could be attained through adherence to the WHO air quality guideline.

Disparities in joint exposure to environmental and social stressors in urban households in Greater Boston.

OBJECTIVES: Understanding how environmental and social stressors cluster is critical to explaining and addressing health disparities. It remains unclear how these stressors cluster at fine spatial resolution in low to medium-income, urban households. We explored patterns of environmental and social exposures at the household-level and potential predictors of these joint exposures in two environmental justice communities in the Greater Boston area. METHODS: We recruited 150 households in Chelsea, MA and the Dorchester neighborhood of Boston, MA, between 2016 and 2019 and collected data on two domains: environmental and social stressor. For each domain, we fit Latent Class Analysis (LCA) models to exposure data to assess intra-domain variability, and cross-classified the resultant classes to identify joint exposure profiles. We compared differences in the distribution of these profiles by participants' demographic and household characteristics using χ2, Fisher's exact, Analysis of Variance, and Kruskal-Wallis tests. RESULTS: We identified two latent classes in each domain: High environmental (n = 90; 60.4%), Low environmental (n = 59; 39.6%), High Social (n = 31; 20.8%), and Low Social (n = 118; 79.2%). Cross-classification yielded four joint exposure profiles: Both Low (n = 46, 30.9%); Both High (n = 18, 12.1%); High environmental-Low Social (n = 72, 48.3%); and Low environmental-High Social (n = 13, 8.7%). Significant group differences were found by housing type (e.g., single-family vs. multi-family) (Fisher's exact p = 0.0016), housing tenure (p = 0.0007), and study site (p < 0.0001). We also observed differences by race/ethnicity, income, and education: households that were Hispanic/Latinx, below the poverty level, and with lower education were more likely to be in the Both High group. CONCLUSIONS: Our analyses confirmed that environmental and social stressors cluster in socially disadvantaged households. Housing type, housing tenure, and location of the residence were also strong predictors of cluster membership, with renter and multi-family residents at risk of high exposures to environmental and social stressors.

Measurement error correction for ambient PM2.5 exposure using stratified regression calibration: Effects on all-cause mortality.

BACKGROUND: Previous studies on the impact of measurement error for PM2.5 were mostly simulation studies, did not control for other pollutants, or used a single regression calibration model to correct for measurement error. However, the relationship between actual and error-prone PM2.5 concentration may vary by time and region. We aim to correct the measurement error of PM2.5 predictions using stratified regression calibration and investigate how the measurement error biases the association between PM2.5 and mortality in the Medicare Cohort. METHODS: The "gold-standard" measurements of PM2.5 were defined as daily monitoring data. We regressed daily monitoring PM2.5 on modeled PM2.5 using the simple linear regression by strata of season, elevation, census division and time period. Calibrated PM2.5 was calculated with stratum-specific calibration parameters ß0 (intercept) and ß1 (slope) for each strata and aggregated to annual level. Associations between calibrated and error-prone annual PM2.5 and all-cause mortality among Medicare beneficiaries were estimated with Quasi-Poisson regression models. RESULTS: Across 208 strata, the median of ß0 and ß1 were 0.62 (25% 0.0.20, 75% 1.06) and 0.93 (25% 0.87, 75% 0.99). From calibrated and error-prone PM2.5 data, we estimated that each 10 µg/m3 increase in PM2.5 was respectively associated with 4.9% (95%CI 4.6-5.2) and 4.6% (95%CI 4.4-4.9) increases in the mortality rate among Medicare beneficiaries, conditional on confounders. CONCLUSIONS: Regression calibration parameters of PM2.5 varied by time and region. Using error-prone measures of PM2.5 underestimated the association between PM2.5 and all-cause mortality. Modern exposure models produce relatively small bias.

Statistical implications of endogeneity induced by residential segregation in small-area modelling of health inequities.

Health inequities are assessed by health departments to identify social groups disproportionately burdened by disease and by academic researchers to understand how social, economic, and environmental inequities manifest as health inequities. To characterize inequities, group-specific small-area health data are often modeled using log-linear generalized linear models (GLM) or generalized linear mixed models (GLMM) with a random intercept. These approaches estimate the same marginal rate ratio comparing disease rates across groups under standard assumptions. Here we explore how residential segregation combined with social group differences in disease risk can lead to contradictory findings from the GLM and GLMM. We show that this occurs because small-area disease rate data collected under these conditions induce endogeneity in the GLMM due to correlation between the model's offset and random effect. This results in GLMM estimates that represent conditional rather than marginal associations. We refer to endogeneity arising from the offset, which to our knowledge has not been noted previously, as "offset endogeneity". We illustrate this phenomenon in simulated data and real premature mortality data, and we propose alternative modeling approaches to address it. We also introduce to a statistical audience the social epidemiologic terminology for framing health inequities, which enables responsible interpretation of results.

The immigrant birthweight paradox in an urban cohort: Role of immigrant enclaves and ambient air pollution.

BACKGROUND: Foreign-born Black and Latina women on average have higher birthweight infants than their US-born counterparts, despite generally worse socioeconomic indicators and prenatal care access, i.e., "immigrant birthweight paradox" (IBP). Residence in immigrant enclaves and associated social-cultural and economic benefits may be drivers of IBP. Yet, enclaves have been found to have higher air pollution, a risk factor for lower birthweight. OBJECTIVE: We investigated the association of immigrant enclaves and children's birthweight accounting for prenatal ambient air pollution exposure. METHODS: In the Boston-based Children's HealthWatch cohort of mother-child dyads, we obtained birthweight-for-gestational-age z-scores (BWGAZ) for US-born births, 2006-2015. We developed an immigrant enclave score based on census-tract percentages of foreign-born, non-citizen, and linguistically-isolated households statewide. We estimated trimester-specific PM2.5 concentrations and proximity to major roads based residential address at birth. We fit multivariable linear regressions of BWGAZ and examined effect modification by maternal nativity. Analyses were restricted to nonsmoking women and term births. RESULTS: Foreign-born women had children with 0.176 (95% CI: 0.092, 0.261) higher BWGAZ than US-born women, demonstrating the IBP in our cohort. Immigrant enclave score was not associated with BWGAZ, even after adjusting for air pollution exposures. However, this association was significantly modified by maternal nativity (pinteraction = 0.014), in which immigrant enclave score was positively associated with BWGAZ for only foreign-born women (0.090, 95% CI: 0.007, 0.172). Proximity to major roads was negatively associated with BWGAZ (-0.018 per 10 m, 95% CI: -0.032, -0.003) and positively correlated with immigrant enclave scores. Trimester-specific PM2.5 concentrations were not associated with BWGAZ. SIGNIFICANCE: Residence in immigrant enclaves was associated with higher birthweight children for foreign-born women, supporting the role of immigrant enclaves in the IBP. Future research of the IBP should account for immigrant enclaves and assess their spatial correlation with potential environmental risk factors and protective resources.

Air Pollution, Neonatal Immune Responses, and Potential Joint Effects of Maternal Depression.

Prenatal maternal exposure to air pollution may cause adverse health effects in offspring, potentially through altered immune responses. Maternal psychosocial distress can also alter immune function and may increase gestational vulnerability to air pollution exposure. We investigated whether prenatal exposure to air pollution is associated with altered immune responses in cord blood mononuclear cells (CBMCs) and potential modification by maternal depression in 463 women recruited in early pregnancy (1999-2001) into the Project Viva longitudinal cohort. We estimated black carbon (BC), fine particulate matter (PM2.5), residential proximity to major roadways, and near-residence traffic density, averaged over pregnancy. Women reported depressive symptoms in mid-pregnancy (Edinburgh Postnatal Depression Scale) and depression history by questionnaire. Immune responses were assayed by concentrations of three cytokines (IL-6, IL-10, and TNF-α), in unstimulated or stimulated (phytohemagglutinin (PHA), cockroach extract (Bla g 2), house dust mite extract (Der f 1)) CBMCs. Using multivariable linear or Tobit regression analyses, we found that CBMCs production of IL-6, TNF-a, and IL-10 were all lower in mothers exposed to higher levels of PM2.5 during pregnancy. A suggestive but not statistically significant pattern of lower cord blood cytokine concentrations from ever (versus never) depressed women exposed to PM2.5, BC, or traffic was also observed and warrants further study.

Long-Term Association of Air Pollution and Hospital Admissions Among Medicare Participants Using a Doubly Robust Additive Model.

BACKGROUND: Studies examining the nonfatal health outcomes of exposure to air pollution have been limited by the number of pollutants studied and focus on short-term exposures. METHODS: We examined the relationship between long-term exposure to fine particulate matter with an aerodynamic diameter <2.5 micrometers (PM2.5), NO2, and tropospheric ozone and hospital admissions for 4 cardiovascular and respiratory outcomes (myocardial infarction, ischemic stroke, atrial fibrillation and flutter, and pneumonia) among the Medicare population of the United States. We used a doubly robust method for our statistical analysis, which relies on both inverse probability weighting and adjustment in the outcome model to account for confounding. The results from this regression are on an additive scale. We further looked at this relationship at lower pollutant concentrations, which are consistent with typical exposure levels in the United States, and among potentially susceptible subgroups. RESULTS: Long-term exposure to fine PM2.5 was associated with an increased risk of all outcomes with the highest effect seen for stroke with a 0.0091% (95% CI, 0.0086-0.0097) increase in the risk of stroke for each 1-µg/m3 increase in annual levels. This translated to 2536 (95% CI, 2383-2691) cases of hospital admissions with ischemic stroke per year, which can be attributed to each 1-unit increase in fine particulate matter levels among the study population. NO2 was associated with an increase in the risk of admission with stroke by 0.00059% (95% CI, 0.00039-0.00075) and atrial fibrillation by 0.00129% (95% CI, 0.00114-0.00148) per ppb and tropospheric ozone was associated with an increase in the risk of admission with pneumonia by 0.00413% (95% CI, 0.00376-0.00447) per parts per billion. At lower concentrations, all pollutants were consistently associated with an increased risk for all our studied outcomes. CONCLUSIONS: Long-term exposure to air pollutants poses a significant risk to cardiovascular and respiratory health among the elderly population in the United States, with the greatest increase in the association per unit of exposure occurring at lower concentrations.

Impact of Differential Privacy and Census Tract Data Source (Decennial Census Versus American Community Survey) for Monitoring Health Inequities.

Objectives. To investigate how census tract (CT) estimates of mortality rates and inequities are affected by (1) differential privacy (DP), whereby the public decennial census (DC) data are injected with statistical "noise" to protect individual privacy, and (2) uncertainty arising from the small number of different persons surveyed each year in a given CT for the American Community Survey (ACS).Methods. We compared estimates of the 2008-2012 average annual premature mortality rate (death before age 65 years) in Massachusetts using CT data from the 2010 DC, 2010 DC with DP, and 2008-2012 ACS 5-year estimate data.Results. For these 3 denominator sources, the age-standardized premature mortality rates (per 100 000) for the total population respectively equaled 166.4 (95% confidence interval [CI] = 162.2, 170.6), 166.4 (95% CI = 162.2, 170.6), and 166.3 (95% CI = 162.1, 170.5), and inequities in the range from best to worst quintile for CT racialized economic segregation were from 103.4 to 260.1, 102.9 to 258.7, and 102.8 to 262.4. Similarity of results across CT denominator sources held for analyses stratified by gender and race/ethnicity.Conclusions. Estimates of health inequities at the CT level may not be affected by use of 2020 DP data and uncertainty in the ACS data.

Real-time indoor PM2.5 monitoring in an urban cohort: Implications for exposure disparities and source control.

Fine particulate matter (PM2.5) concentrations are highly variable indoors, with evidence for exposure disparities. Real-time monitoring coupled with novel statistical approaches can better characterize drivers of elevated PM2.5 indoors. We collected real-time PM2.5 data in 71 homes in an urban community of Greater Boston, Massachusetts using Alphasense OPC-N2 monitors. We estimated indoor PM2.5 concentrations of non-ambient origin using mass balance principles, and investigated their associations with indoor source activities at the 0.50 to 0.95 exposure quantiles using mixed effects quantile regressions, overall and by homeownership. On average, the majority of indoor PM2.5 concentrations were of non-ambient origin (≥77%), with a higher proportion at increasing quantiles of the exposure distribution. Major source predictors of non-ambient PM2.5 concentrations at the upper quantile (0.95) were cooking (1.4-23 µg/m3) and smoking (15 µg/m3, only among renters), with concentrations also increasing with range hood use (3.6 µg/m3) and during the heating season (5.6 µg/m3). Across quantiles, renters in multifamily housing experienced a higher proportion of PM2.5 concentrations from non-ambient sources than homeowners in single- and multifamily housing. Renters also more frequently reported cooking, smoking, spray air freshener use, and second-hand smoke exposure, and lived in units with higher air exchange rate and building density. Accounting for these factors explained observed PM2.5 exposure disparities by homeownership, particularly in the upper exposure quantiles. Our results suggest that renters in multifamily housing may experience higher PM2.5 exposures due to a combination of behavioral and building factors that are amenable to intervention.

Racial Disparities in Associations between Neighborhood Demographic Polarization and Birth Weight.

Neighborhood demographic polarization, or the extent to which a privileged population group outnumbers a deprived group, can affect health by influencing social dynamics. While using birth records from 2001 to 2013 in Massachusetts (n = 629,675), we estimated the effect of two demographic indices, racial residential polarization (RRP) and economic residential polarization (ERP), on birth weight outcomes, which are established predictors of the newborn's future morbidity and mortality risk. Higher RRP and ERP was each associated with higher continuous birth weight and lower odds for low birth weight and small for gestational age, with evidence for effect modification by maternal race. On average, per interquartile range increase in RRP, the birth weight was 10.0 g (95% confidence interval: 8.0, 12.0) higher among babies born to white mothers versus 6.9 g (95% CI: 4.8, 9.0) higher among those born to black mothers. For ERP, it was 18.6 g (95% CI: 15.7, 21.5) higher among those that were born to white mothers versus 1.8 g (95% CI: -4.2, 7.8) higher among those born to black mothers. Racial and economic polarization towards more privileged groups was associated with healthier birth weight outcomes, with greater estimated effects in babies that were born to white mothers than those born to black mothers.

Multi-media biomarkers: Integrating information to improve lead exposure assessment.

Exposure assessment traditionally relies on biomarkers that measure chemical concentrations in individual biological media (i.e., blood, urine, etc.). However, chemicals distribute unevenly among different biological media; thus, each medium provides incomplete information about body burden. We propose that machine learning and statistical approaches can create integrated exposure estimates from multiple biomarker matrices that better represent the overall body burden, which we term multi-media biomarkers (MMBs). We measured lead (Pb) in blood, urine, hair and nails from 251 Italian adolescents aged 11-14 years from the Public Health Impact of Metals Exposure (PHIME) cohort. We derived aggregated MMBs from the four biomarkers and then tested their association with Wechsler Intelligence Scale for Children (WISC) IQ scores. We used three approaches to derive the Pb MMB: one supervised learning technique, weighted quantile sum regression (WQS), and two unsupervised learning techniques, independent component analysis (ICA) and non-negative matrix factorization (NMF). Overall, the Pb MMB derived using WQS was most consistently associated with IQ scores and was the only method to be statistically significant for Verbal IQ, Performance IQ and Total IQ. A one standard deviation increase in the WQS MMB was associated with lower Verbal IQ (ß [95% CI] = -2.2 points [-3.7, -0.6]), Performance IQ (-1.9 points [-3.5, -0.4]) and Total IQ (-2.1 points [-3.8, -0.5]). Blood Pb was negatively associated with only Verbal IQ, with a one standard deviation increase in blood Pb being associated with a -1.7 point (95% CI: [-3.3, -0.1]) decrease in Verbal IQ. Increases of one standard deviation in the ICA MMB were associated with lower Verbal IQ (-1.7 points [-3.3, -0.1]) and lower Total IQ (-1.7 points [-3.3, -0.1]). Similarly, an increase of one standard deviation in the NMF MMB was associated with lower Verbal IQ (-1.8 points [-3.4, -0.2]) and lower Total IQ (-1.8 points [-3.4, -0.2]). Weights highlighting the contributions of each medium to the MMB revealed that blood Pb was the largest contributor to most MMBs, although the weights varied from more than 80% for the ICA and NMF MMBs to between 30% and 54% for the WQS-derived MMBs. Our results suggest that MMBs better reflect the total body burden of a chemical that may be acting on target organs than individual biomarkers. Estimating MMBs improved our ability to estimate the full impact of Pb on IQ. Compared with individual Pb biomarkers, including blood, a Pb MMB derived using WQS was more strongly associated with IQ scores. MMBs may increase statistical power when the choice of exposure medium is unclear or when the sample size is small. Future work will need to validate these methods in other cohorts and for other chemicals.

Fine Particulate Air Pollution and Birthweight: Differences in Associations Along the Birthweight Distribution.

BACKGROUND: Maternal exposure to fine particulate air pollution (PM2.5) during pregnancy is associated with lower newborn birthweight, which is a risk factor for chronic disease. Existing studies typically report the average association related with PM2.5 increase, which does not offer information about potentially varying associations at different points of the birthweight distribution. METHODS: We retrieved all birth records in Massachusetts between 2001 and 2013 then restricted our analysis to full-term live singletons (n = 775,768). Using the birthdate, gestational age, and residential address reported at time of birth, we estimated the average maternal PM2.5 exposure during pregnancy of each birth. PM2.5 predictions came from a model that incorporates satellite, land use, and meteorologic data. We applied quantile regression to quantify the association between PM2.5 and birthweight at each decile of birthweight, adjusted for individual and neighborhood covariates. We considered effect modification by indicators of individual and neighborhood socioeconomic status (SES). RESULTS: PM2.5 was negatively associated with birthweight. An interquartile range increase in PM2.5 was associated with a 16 g [95% confidence interval (CI) = 13, 19] lower birthweight on average, 19 g (95% CI = 15, 23) lower birthweight at the lowest decile of birthweight, and 14 g (95% CI = 9, 19) lower birthweight at the highest decile. In general, the magnitudes of negative associations were larger at lower deciles. We did not find evidence of effect modification by individual or neighborhood SES. CONCLUSIONS: In full-term live births, PM2.5 and birthweight were negatively associated with more severe associations at lower quantiles of birthweight.

County-level radon exposure and all-cause mortality risk among Medicare beneficiaries.

BACKGROUND: Radon is an inert gas formed from the decay of naturally-occurring materials in the earth's crust. It infiltrates into homes from soil, water, and construction materials. Its decay products are radionuclides, which attach to ambient particles. Residential radon is one of the leading risk factors for lung cancer. The scarce evidence for associations with other mortality causes originates mostly from occupational studies. METHODS: In a cohort study with 14 years of follow-up (2000-2013), we evaluated the association between chronic radon exposure and all-cause mortality, and explored whether there are subpopulations who are more vulnerable to radon effects. We included 87,296,195 person-years of follow-up from all Medicare beneficiaries in the Mid-Atlantic and Northeastern U.S. states. We examined the association between the logarithm of county-averaged radon (ln(Rn)) and mortality and assessed effect modification by chronic conditions. RESULTS: An interquartile range increase in the ln(Rn) was associated with a 2·62% increase (95% CI 2·52%; 2·73%) in mortality, independent of PM2.5 exposure. Larger mortality risks were observed among individuals with respiratory, cardiovascular and metabolic diseases, with the highest associations observed among those with diabetes (4·98% increase), heart failure (4·58% increase), and chronic obstructive pulmonary disease (4·49% increase). CONCLUSION: We found an increased risk for all-cause mortality associated with increased radon exposure. The risk was enhanced among susceptible individuals with chronic conditions. We believe this is the first cohort study to identify populations at higher risk for non-malignant health consequences of radon exposure. Due to the limitations in exposure assessment and availability of individual confounders, these findings should be interpreted with caution.

Association between prenatal particulate air pollution exposure and telomere length in cord blood: Effect modification by fetal sex.

INTRODUCTION: In utero particulate matter exposure produces oxidative stress that impacts cellular processes that include telomere biology. Newborn telomere length is likely critical to an individual's telomere biology; reduction in this initial telomere setting may signal increased susceptibility to adverse outcomes later in life. We examined associations between prenatal particulate matter with diameter ≤2.5 µm (PM2.5) and relative leukocyte telomere length (LTL) measured in cord blood using a data-driven approach to characterize sensitive windows of prenatal PM2.5 effects and explore sex differences. METHODS: Women who were residents of Mexico City and affiliated with the Mexican Social Security System were recruited during pregnancy (n = 423 for analyses). Mothers' prenatal exposure to PM2.5 was estimated based on residence during pregnancy using a validated satellite-based spatio-temporally resolved prediction model. Leukocyte DNA was extracted from cord blood obtained at delivery. Duplex quantitative polymerase chain reaction was used to compare the relative amplification of the telomere repeat copy number to single gene (albumin) copy number. A distributed lag model incorporating weekly averages for PM2.5 over gestation was used in order to explore sensitive windows. Sex-specific associations were examined using Bayesian distributed lag interaction models. RESULTS: In models that included child's sex, mother's age at delivery, prenatal environmental tobacco smoke exposure, pre-pregnancy BMI, gestational age, birth season and assay batch, we found significant associations between higher PM2.5 exposure during early pregnancy (4-9 weeks) and shorter LTL in cord blood. We also identified two more windows at 14-19 and 34-36 weeks in which increased PM2.5 exposure was associated with longer LTL. In stratified analyses, the mean and cumulative associations between PM2.5 and shortened LTL were stronger in girls when compared to boys. CONCLUSIONS: Increased PM2.5 during specific prenatal windows was associated with shorter LTL and longer LTL. PM2.5 was more strongly associated with shortened LTL in girls when compared to boys. Understanding sex and temporal differences in response to air pollution may provide unique insight into mechanisms.

Police-Related Deaths and Neighborhood Economic and Racial/Ethnic Polarization, United States, 2015-2016.

OBJECTIVES: To estimate the association between rates of police-related deaths and neighborhood residential segregation (by income, race/ethnicity, or both combined) in the United States. METHODS: We identified police-related deaths that occurred in the United States (2015-2016) using a data set from the Guardian newspaper. We used census data to estimate expected police-related death counts for all US census tracts and to calculate the Index of Concentration at the Extremes as a segregation measure. We used multilevel negative binomial models for the analyses. RESULTS: Overall, police-related death rates were highest in neighborhoods with the greatest concentrations of low-income residents (vs high-income residents) and residents of color (vs non-Hispanic White residents). For non-Hispanic Blacks, however, the risk was greater in the quintile of neighborhoods with the highest concentration of non-Hispanic White residents than in certain neighborhoods with relatively higher concentrations of residents of color (the third and fourth quintiles). CONCLUSIONS: Neighborhood context matters-beyond individual race/ethnicity-for understanding, preventing, and responding to the occurrence of police-related deaths. Public Health Implications. Efforts to monitor, prevent, and respond to police-related deaths should consider neighborhood context, including levels of segregation by income and race/ethnicity.

Bayesian varying coefficient kernel machine regression to assess neurodevelopmental trajectories associated with exposure to complex mixtures.

Exposure to environmental mixtures can exert wide-ranging effects on child neurodevelopment. However, there is a lack of statistical methods that can accommodate the complex exposure-response relationship between mixtures and neurodevelopment while simultaneously estimating neurodevelopmental trajectories. We introduce Bayesian varying coefficient kernel machine regression (BVCKMR), a hierarchical model that estimates how mixture exposures at a given time point are associated with health outcome trajectories. The BVCKMR flexibly captures the exposure-response relationship, incorporates prior knowledge, and accounts for potentially nonlinear and nonadditive effects of individual exposures. This model assesses the directionality and relative importance of a mixture component on health outcome trajectories and predicts health effects for unobserved exposure profiles. Using contour plots and cross-sectional plots, BVCKMR also provides information about interactions between complex mixture components. The BVCKMR is applied to a subset of data from PROGRESS, a prospective birth cohort study in Mexico city on exposure to metal mixtures and temporal changes in neurodevelopment. The mixture include metals such as manganese, arsenic, cobalt, chromium, cesium, copper, lead, cadmium, and antimony. Results from a subset of Programming Research in Obesity, Growth, Environment and Social Stressors data provide evidence of significant positive associations between second trimester exposure to copper and Bayley Scales of Infant and Toddler Development cognition score at 24 months, and cognitive trajectories across 6-24 months. We also detect an interaction effect between second trimester copper and lead exposures for cognition at 24 months. In summary, BVCKMR provides a framework for estimating neurodevelopmental trajectories associated with exposure to complex mixtures.

Contribution of socioeconomic factors to the variation in body-mass index in 58 low-income and middle-income countries: an econometric analysis of multilevel data.

BACKGROUND: Most epidemiological studies have not simultaneously quantified variance in health within and between populations. We aimed to estimate the extent to which basic socioeconomic factors contribute to variation in body-mass index (BMI) across different populations. METHODS: We pooled data from the cross-sectional Demographic and Health Surveys (2005-16) for 15-49 year old women with complete data for anthropometric measures in 58 low-income and middle-income countries (LMICs). We compared estimates from multilevel variance component models for BMI before and after adjusting for age and socioeconomic factors (place of residence, education, household wealth, and marital status). The hierarchical structure of the sample included three levels with women at level 1, communities at level 2, and countries at level 3. The primary outcome was BMI. We did a sensitivity analysis using the 2002-03 World Health Surveys. FINDINGS: Of 1 212 758 women nested within 64 764 communities and 58 countries, we found that most unexplained variation for BMI was attributed to between-individual differences (80%) and the remaining was between-population differences (14% for countries and 6% for communities). Socioeconomic factors explained a large proportion of between-population variance in BMI (14·8% for countries and 47·1% for communities), but only about 2% of interindividual variance. In country-specific models, we found substantial variation in the magnitude of between-individual differences (variance estimates ranging from 7·6 to 31·4, or 86·0-98·6% of the total variation) and the proportion explained by socioeconomic factors (0·1-6·4%). The disproportionately large unexplained between-individual variance in BMI was consistently found in additional analyses including more comprehensive set of predictor variables, both men and women, and populations from low-income and high-income countries. INTERPRETATION: Our findings on variance decomposition in BMI and explanation by socioeconomic factors at population and individual levels indicate that inferential questions that target within and between populations are importantly inter-related and should be considered simultaneously. FUNDING: None.

The School Inner-City Asthma Intervention Study: Design, rationale, methods, and lessons learned.

Asthma is the most common chronic disease of childhood in the United States, causes significant morbidity, particularly in the inner-city, and accounts for billions of dollars in health care utilization. Home environments are established sources of exposure that exacerbate symptoms and home-based interventions are effective. However, elementary school children spend 7 to 12h a day in school, primarily in one classroom. From the observational School Inner-City Asthma Study we learned that student classroom-specific exposures are associated with worsening asthma symptoms and decline in lung function. We now embark on a randomized, blinded, sham-controlled school environmental intervention trial, built on our extensively established school/community partnerships, to determine the efficacy of a school-based intervention to improve asthma control. This factorial school/classroom based environmental intervention will plan to enroll 300 students with asthma from multiple classrooms in 40 northeastern inner-city elementary schools. Schools will be randomized to receive either integrated pest management versus control and classrooms within these schools to receive either air purifiers or sham control. The primary outcome is asthma symptoms during the school year. This study is an unprecedented opportunity to test whether a community of children can benefit from school or classroom environmental interventions. If effective, this will have great impact as an efficient, cost-effective intervention for inner city children with asthma and may have broad public policy implications.

The Association Between Air Pollution and Onset of Depression Among Middle-Aged and Older Women.

Despite recently reported associations between air pollution and acute psychiatric outcomes, the association with depression onset has not, to our knowledge, been previously examined. We conducted a prospective cohort study among 41,844 women in the Nurses' Health Study, in the United States. The women had an average age of 66.6 (standard deviation, 7.6) years, were depression-free in 1996, and were followed through 2008. May-September ozone exposures were predicted by interpolating concentrations from the 5 nearest monitors. One-, 2-, and 5-year average concentrations of particulate matter with an aerodynamic diameter less than or equal to 2.5 µm (PM2.5) were predicted at each participant's residence using a spatiotemporal model. We defined depression as report of doctor's diagnosis or use of antidepressant medication. We estimated adjusted hazard ratios with time-varying Cox models. Hazard ratios for both pollutants were elevated (per 10-parts-per-billion increase in ozone, hazard ratio (HR) = 1.06; 95% confidence interval (CI): 1.00, 1.12; per 10-µg/m3 increase in 1-year PM2.5, HR = 1.08; 95% CI: 0.97, 1.20). Associations were stronger when only antidepressant use was used to define cases (for ozone, HR = 1.08; 95% CI: 1.02, 1.14; for PM2.5, HR = 1.12; 95% CI: 1.00, 1.25). To our knowledge, these results represent the first identification of a possible association between both long-term ozone and PM2.5 exposure and depression onset. Although the stronger association specifically with antidepressant use may reflect that this endpoint better captures the onset time and milder cases, our findings should be interpreted with caution.

Local Residential Segregation Matters: Stronger Association of Census Tract Compared to Conventional City-Level Measures with Fatal and Non-Fatal Assaults (Total and Firearm Related), Using the Index of Concentration at the Extremes (ICE) for Racial, Economic, and Racialized Economic Segregation, Massachusetts (US), 1995-2010.

Research on residential segregation and health, primarily conducted in the USA, has chiefly employed city or regional measures of racial segregation. To test our hypothesis that stronger associations would be observed using local measures, especially for racialized economic segregation, we analyzed risk of fatal and non-fatal assault in Massachusetts (1995-2010), since this outcome is strongly associated with residential segregation. The segregation metrics comprised the Index of Concentration at the Extremes (ICE), the Index of Dissimilarity, and poverty rate, with measures computed at both the census tract and city/town level. Key results were that larger associations between fatal and non-fatal assaults and residential segregation occurred for models using the census tract vs. city/town measures, with the greatest associations observed for racialized economic segregation. For fatal assaults, comparing the bottom vs. top quintiles, the incidence rate ratio (and 95% confidence interval (CI)) in models using the census tract measures equaled 3.96 (95% CI 3.10, 5.06) for the ICE for racialized economic segregation, 3.26 (95% CI 2.58, 4.14) for the ICE for income, 3.14 (95% CI 2.47, 3.99) for poverty, 2.90 (95% CI 2.21, 3.81) for the ICE for race/ethnicity, and only 0.93 (95% CI 0.79, 1.11) for the Index of Dissimilarity; in models that included both census tract and city/town ICE measures, this risk ratio for the ICE for racialized economic segregation was higher at the census tract (3.29; 95% CI 2.43, 4.46) vs. city/town level (1.61; 95% CI 1.12, 2.32). These results suggest that, at least in the case of fatal and non-fatal assaults, research on residential segregation should employ local measures, including of racialized economic segregation, to avoid underestimating the adverse impact of segregation on health.