Animal experiments and cross-sectional human studies have linked particulate matter (PM) with increased behavioral problems. We conducted a longitudinal study to examine whether the trajectories of delinquent behavior are affected by PM2.5 (PM with aerodynamic diameter ≤ 2.5 μm) exposures before and during adolescence. We used the parent-reported Child Behavior Checklist at age 9-18 with repeated measures every ~2-3 years (up to 4 behavioral assessments) on 682 children from the Risk Factors for Antisocial Behavior Study conducted in a multi-ethnic cohort of twins born in 1990-1995. Based on prospectively-collected residential addresses and a spatiotemporal model of ambient air concentrations in Southern California, monthly PM2.5 estimates were aggregated to represent long-term (1-, 2-, 3-year average) exposures preceding baseline and cumulative average exposure until the last assessment. Multilevel mixed-effects models were used to examine the association between PM2.5 exposure and individual trajectories of delinquent behavior, adjusting for within-family/within-individual correlations and potential confounders. We also examined whether psychosocial factors modified this association. The results sμggest that PM2.5 exposure at baseline and cumulative exposure during follow-up was significantly associated (p < 0.05) with increased delinquent behavior. The estimated effect sizes (per interquartile increase of PM2.5 by 3.12-5.18 μg/m3) were equivalent to the difference in delinquency scores between adolescents who are 3.5-4 years apart in age. The adverse effect was stronger in families with unfavorable parent-to-child relationships, increased parental stress or maternal depressive symptoms. Overall, these findings sμggest long-term PM2.5 exposure may increase delinquent behavior of urban-dwelling adolescents, with the resulting neurotoxic effect aggravated by psychosocial adversities.

Airborne exposures to polycyclic aromatic hydrocarbons (PAHs) are associated with adverse health outcomes. Because personal air measurements of PAHs are labor intensive and costly, spatial PAH exposure models are useful for epidemiological studies. However, few studies provide adequate spatial coverage to reflect intra-urban variability of ambient PAHs. In this study, we collected 39-40 weekly gas-phase PAH samples in southern California twice in summer and twice in winter, 2009, in order to characterize PAH source contributions and develop spatial models that can estimate gas-phase PAH concentrations at a high resolution. A spatial mixed regression model was constructed, including such variables as roadway, traffic, land-use, vegetation index, commercial cooking facilities, meteorology, and population density. Cross validation of the model resulted in an R2 of 0.66 for summer and 0.77 for winter. Results showed higher total PAH concentrations in winter. Pyrogenic sources, such as fossil fuels and diesel exhaust, were the most dominant contributors to total PAHs. PAH sources varied by season, with a higher fossil fuel and wood burning contribution in winter. Spatial autocorrelation accounted for a substantial amount of the variance in total PAH concentrations for both winter (56%) and summer (19%). In summer, other key variables explaining the variance included meteorological factors (9%), population density (15%), and roadway length (21%). In winter, the variance was also explained by traffic density (16%). In this study, source characterization confirmed the dominance of traffic and other fossil fuel sources to total measured gas-phase PAH concentrations while a spatial exposure model identified key predictors of PAH concentrations. Gas-phase PAH source characterization and exposure estimation is of high utility to epidemiologist and policy makers interested in understanding the health impacts of gas-phase PAHs and strategies to reduce emissions.

Background

Although studies have documented the association between heart rate variability (HRV) and ambient particulate exposures, the association between HRV, especially at night, and metal-rich, occupational particulate exposures remains unclear.

Objective

Our goal in this study was to investigate the association between long-duration HRV, including nighttime HRV, and occupational PM(2.5) exposures.

Methods

We used 24-hr ambulatory electrocardiograms (ECGs) to monitor 36 male boilermaker welders (mean age of 41 years) over a workday and nonworkday. ECGs were analyzed for HRV in the time domain; rMSSD (square root of the mean squared differences of successive intervals), SDNN (SD of normal-to-normal intervals over entire recording), and SDNN(i) (SDNN for all 5-min segments) were summarized over 24-hr, day (0730-2130 hours), and night (0000-0700 hours) periods. PM(2.5) (particulate matter with an aerodynamic diameter ResultsMean (+/- SD) PM(2.5) exposure was 0.73 +/- 0.50 mg/m(3) and ranged from 0.04 to 2.70 mg/m(3). We observed a consistent inverse exposure-response relationship, with a decrease in all HRV measures with increased PM(2.5) exposure. However, the decrease was most pronounced at night, where a 1-mg/m(3) increase in PM(2.5) was associated with a change of -8.32 [95% confidence interval (CI), -16.29 to -0.35] msec nighttime rMSSD, -14.77 (95% CI, -31.52 to 1.97) msec nighttime SDNN, and -8.37 (95% CI, -17.93 to 1.20) msec nighttime SDNN(i), after adjusting for nonworking nighttime HRV, age, and smoking.

Conclusion

Metal-rich particulate exposures were associated with decreased long-duration HRV, especially at night. Further research is needed to elucidate which particulate metal constituent is responsible for decreased HRV.

Spatiotemporal models to estimate ambient exposures at high spatiotemporal resolutions are crucial in large-scale air pollution epidemiological studies that follow participants over extended periods. Previous models typically rely on central-site monitoring data and/or covered short periods, limiting their applications to long-term cohort studies. Here we developed a spatiotemporal model that can reliably predict nitrogen oxide concentrations with a high spatiotemporal resolution over a long time span (>20 years). Leveraging the spatially extensive highly clustered exposure data from short-term measurement campaigns across 1-2 years and long-term central site monitoring in 1992-2013, we developed an integrated mixed-effect model with uncertainty estimates. Our statistical model incorporated nonlinear and spatial effects to reduce bias. Identified important predictors included temporal basis predictors, traffic indicators, population density, and subcounty-level mean pollutant concentrations. Substantial spatial autocorrelation (11-13%) was observed between neighboring communities. Ensemble learning and constrained optimization were used to enhance reliability of estimation over a large metropolitan area and a long period. The ensemble predictions of biweekly concentrations resulted in an R² of 0.85 (RMSE: 4.7 ppb) for NO₂ and 0.86 (RMSE: 13.4 ppb) for NO_x. Ensemble learning and constrained optimization generated stable time series, which notably improved the results compared with those from initial mixed-effects models.

OBJECTIVE: To examine the association of sleep disturbance with Parkinson disease (PD) during 10+ years of follow-up among postmenopausal women, 50 to 79 years of age at baseline. METHODS: Longitudinal data on 130,502 study-eligible women (mean ± standard deviation baseline age = 63.16 ± 7.20 y) from the Womens Health Initiative Clinical Trials and Womens Health Initiative Observational Study were analyzed. The cohort was followed for 15.88 ± 6.50 years, yielding 2,829 (2.17%) PD cases. Sleep disturbance (habitual sleep duration, insomnia symptoms, obstructive sleep apnea risk factors, sleep aids among those with WHI Insomnia Rating Scale scores (WHIIRS) > 9) was measured at baseline and one follow-up time by September 12, 2005. Cox proportional hazards models evaluated relationships controlling for sociodemographic, lifestyle, and health characteristics. RESULTS: PD was significantly associated with long sleep duration (≥9 h) versus a benchmark of 7 to 8 hours (hazard ratio [HR] = 1.296, 95% confidence interval [CI]: 1.153-1.456), WHIIRS (>9 vs ≤9) (HR = 1.114, 95% CI:1.023-1.214), and use of sleep aids (yes vs no) (HR = 1.332, 95% CI:1.153-1.539) among those with WHIIRS > 9. Compared with 7 to 8 hours, short (<7 h) sleep duration was unrelated to PD. Finally, the presence of obstructive sleep apnea risk factors was not associated with PD. CONCLUSIONS: Among postmenopausal women, sleep disturbance was associated with approximately 10% to 30% increased PD risk after ∼16 years follow-up. Prospective cohort studies with objective exposures and adjudicated outcomes that include men and women of diverse backgrounds are required to confirm and extend these findings.

Mounting evidence indicates that early-life exposure to particulate air pollutants pose threats to children's cognitive development, but studies about the neurotoxic effects associated with exposures during adolescence remain unclear. We examined whether exposure to ambient fine particles (PM2.5) at residential locations affects intelligence quotient (IQ) during pre-/early- adolescence (ages 9-11) and emerging adulthood (ages 18-20) in a demographically-diverse population (N = 1,360) residing in Southern California. Increased ambient PM2.5 levels were associated with decreased IQ scores. This association was more evident for Performance IQ (PIQ), but less for Verbal IQ, assessed by the Wechsler Abbreviated Scale of Intelligence. For each inter-quartile (7.73 μg/m3) increase in one-year PM2.5 preceding each assessment, the average PIQ score decreased by 3.08 points (95% confidence interval = [-6.04, -0.12]) accounting for within-family/within-individual correlations, demographic characteristics, family socioeconomic status (SES), parents' cognitive abilities, neighborhood characteristics, and other spatial confounders. The adverse effect was 150% greater in low SES families and 89% stronger in males, compared to their counterparts. Better understanding of the social disparities and sexual dimorphism in the adverse PM2.5-IQ effects may help elucidate the underlying mechanisms and shed light on prevention strategies.

Exposure to high air temperature in late pregnancy is increasingly recognized as a risk factor for preterm birth (PTB). However, the combined effects of heatwaves with air pollution and green space are still unexplored. In the context of climate change, investigating the interaction between environmental factors and identifying communities at higher risk is important to better understand the etiological mechanisms and design targeted interventions towards certain women during pregnancy. To examine the combined effects of heatwaves, air pollution and green space exposure on the risk of PTB. California birth certificate records for singleton births (2005-2013) were obtained. Residential zip code-specific daily temperature during the last week of gestation was used to create 12 definitions of heatwave with varying temperature thresholds and durations. We fit multi-level Cox proportional hazard models with time to PTB as the outcome and gestational week as the temporal unit. Relative risk due to interaction (RERI) was applied to estimate the additive interactive effect of air pollution and green space on the effect of heatwaves on PTB. In total, 1,967,300 births were included in this study. For PM2.5, PM10 and O3, we found positive additive interactions (RERIs >0) between heatwaves and higher air pollution levels. Combined effects of heatwaves and green space indicated negative interactions (RERIs <0) for less intense heatwaves (i.e., shorter duration or relatively low temperature), whereas there were potential positive interactions (RERIs >0) for more intense heatwaves. This study found synergistic harmful effects for heatwaves with air pollution, and potential positive interactions with lack of green space on PTB. Implementing interventions, such as heat warning systems and behavioral changes, targeted toward pregnant women at risk for high air pollution and low green space exposures may optimize the benefits of reducing acute exposure to extreme heat before delivery.

OBJECTIVES: To evaluate sleep and affective (mood/anxiety) disorders as clinical predictors of incident Parkinsons disease (PD) among women ≥65 years of age. METHODS: We performed secondary analyses with available data from the Womens Health Initiative Clinical Trials and Observational Study linked to Medicare claims. Sleep, mood and anxiety disorders at baseline were defined using diagnostic codes. Incident PD was defined using self-reported PD, first PD diagnosis, use of PD medications, and/or deaths attributed to PD. Cox regression was applied to estimate hazard ratios (HR) with 95 % confidence intervals (CI), controlling for socio-demographic/lifestyle/health characteristics. Time-to-event was calculated from baseline (1993-1998) to year of PD event, loss to follow-up, death, or December 31, 2018, whichever came first. RESULTS: A total of 53,996 study-eligible WHI participants yielded 1756 (3.25 %) PD cases over ~14.39 (±6.18) years of follow-up. The relative risk for PD doubled among women with affective disorders (HR = 2.05, 95 % CI: 1.84, 2.27), mood disorders (HR = 2.18, 95 % CI: 1.97, 2.42) and anxiety disorders (HR = 1.97, 95 % CI: 1.75, 2.22). Sleep disorders alone (without affective) were not significantly associated with PD risk (HR = 0.85, 95 % CI: 0.69, 1.04), whereas affective disorders alone (without sleep) (HR = 1.93, 95 % CI: 1.72, 2.17) or in combination with sleep disorders (HR = 2.18, 95 % CI: 1.85, 2.56) were associated with twice the PD risk relative to no sleep/affective disorders. LIMITATIONS: Observational design; Selection bias; Information bias; Generalizability. CONCLUSIONS: Among older women, joint sleep/affective disorders and affective disorders alone are strong clinical predictors of incident PD over 14 years.

OBJECTIVES: The relationship between optimism and cognitive functioning is not fully understood. We examined the association of optimism with risk of mild cognitive impairment (MCI) and dementia in the Womens Health Initiative Memory Study (WHIMS). METHODS: Optimism was measured by the Life Orientation Test-Revised (LOT-R) total score, and optimism and pessimism subscales. A panel of experts adjudicated cognitive endpoints based on annual cognitive assessments. We used cox proportional hazard regression models to examine the association of LOT-R total score and optimism and pessimism sub-scores with MCI/dementia. We also examined the relationship between vascular disease, LOT-R total score, optimism and pessimism, and cognition. RESULTS: Mean age was 70.5 (SD = 3.9) years. The sample (N = 7249) was 87% white, and 29.8% of participants had < 12 years of education. Total LOT-R score (HR = 0.96, 95% CI: 0.94, 0.98, p < 0.001) was associated with lower risk of combined MCI or dementia. More pessimism (HR = 1.08, 95% CI: 1.05, 1.11, p < 0.0001) was associated with higher risk of MCI or dementia after adjustment for ethnicity, education, vascular disease, and depression. No significant relationships emerged from the optimism subscale. CONCLUSION: These data suggest that less pessimism, but not more optimism, was associated with a lower risk of MCI and dementia.

Few studies have assessed extreme temperatures' impact on gestational diabetes mellitus (GDM). We examined the relation between GDM risk with weekly exposure to extreme high and low temperatures during the first 24 weeks of gestation and assessed potential effect modification by microclimate indicators.

Methods

We utilized 2008-2018 data for pregnant women from Kaiser Permanente Southern California electronic health records. GDM screening occurred between 24 and 28 gestational weeks for most women using the Carpenter-Coustan criteria or the International Association of Diabetes and Pregnancy Study Groups criteria. Daily maximum, minimum, and mean temperature data were linked to participants' residential address. We utilized distributed lag models, which assessed the lag from the first to the corresponding week, with logistic regression models to examine the exposure-lag-response associations between the 12 weekly extreme temperature exposures and GDM risk. We used the relative risk due to interaction (RERI) to estimate the additive modification of microclimate indicators on the relation between extreme temperature and GDM risk.

Results

GDM risks increased with extreme low temperature during gestational weeks 20--24 and with extreme high temperature at weeks 11-16. Microclimate indicators modified the influence of extreme temperatures on GDM risk. For example, there were positive RERIs for high-temperature extremes and less greenness, and a negative RERI for low-temperature extremes and increased impervious surface percentage.

Discussion

Susceptibility windows to extreme temperatures during pregnancy were observed. Modifiable microclimate indicators were identified that may attenuate temperature exposures during these windows, which could in turn reduce the health burden from GDM.