A Comparison of Measured Airborne and Self-Reported Secondhand Smoke Exposure in the MADRES Pregnancy Cohort Study.

INTRODUCTION: Secondhand smoke (SHS) exposure during pregnancy is linked to adverse birth outcomes, such as low birth weight and preterm birth. While questionnaires are commonly used to assess SHS exposure, their ability to capture true exposure can vary, making it difficult for researchers to harmonize SHS measures. This study aimed to compare self-reported SHS exposure with measurements of airborne SHS in personal samples of pregnant women. METHODS: SHS was measured on 48-hour integrated personal PM2.5 Teflon filters collected from 204 pregnant women, and self-reported SHS exposure measures were obtained via questionnaires. Descriptive statistics were calculated for airborne SHS measures, and analysis of variance tests assessed group differences in airborne SHS concentrations by self-reported SHS exposure. RESULTS: Participants were 81% Hispanic, with a mean (SD) age of 28.2 (6.0) years. Geometric mean (SD) personal airborne SHS concentrations were 0.14 (9.41) µg/m3. Participants reporting lower education have significantly higher airborne SHS exposure (p=0.015). Mean airborne SHS concentrations were greater in those reporting longer duration with windows open in the home. There was no association between airborne SHS and self-reported SHS exposure; however, asking about the number of smokers nearby in the 48-hour monitoring period was most correlated with measured airborne SHS (Two+ smokers: 0.30µg/m3 vs. One: 0.12µg/m3 and Zero: 0.15µg/m3; p=0.230). CONCLUSIONS: Self-reported SHS exposure was not associated with measured airborne SHS in personal PM2.5 samples. This suggests exposure misclassification using SHS questionnaires and the need for harmonized and validated questions to characterize this exposure in health studies. IMPLICATIONS: This study adds to the growing body of evidence that measurement error is a major concern in pregnancy research, particularly in studies that rely on self-report questionnaires to measure secondhand smoke (SHS) exposure. The study introduces an alternative method of SHS exposure assessment using objective optical measurements, which can help improve the accuracy of exposure assessment. The findings emphasize the importance of using harmonized and validated SHS questionnaires in pregnancy health research to avoid biased effect estimates. This study can inform future research, practice, and policy development to reduce SHS exposure and its adverse health effects.

Assessing household fine particulate matter (PM2.5) through measurement and modeling in the Bangladesh cook stove pregnancy cohort study (CSPCS).

Biomass fuel burning is a significant contributor of household fine particulate matter (PM2.5) in the low to middle income countries (LMIC) and assessing PM2.5 levels is essential to investigate exposure-related health effects such as pregnancy outcomes and acute lower respiratory infection in infants. However, measuring household PM2.5 requires significant investments of labor, resources, and time, which limits the ability to conduct health effects studies. It is therefore imperative to leverage lower-cost measurement techniques to develop exposure models coupled with survey information about housing characteristics. Between April 2017 and March 2018, we continuously sampled PM2.5 in three seasonal waves for approximately 48-h (range 46 to 52-h) in 74 rural and semi-urban households among the participants of the Bangladesh Cook Stove Pregnancy Cohort Study (CSPCS). Measurements were taken simultaneously in the kitchen, bedroom, and open space within the household. Structured questionnaires captured household-level information related to the sources of air pollution. With data from two waves, we fit multivariate mixed effect models to estimate 24-h average, cooking time average, daytime and nighttime average PM2.5 in each of the household locations. Households using biomass cookstoves had significantly higher PM2.5 concentrations than those using electricity/liquefied petroleum gas (626 µg/m3 vs. 213 µg/m3). Exposure model performances showed 10-fold cross validated R2 ranging from 0.52 to 0.76 with excellent agreement in independent tests against measured PM2.5 from the third wave of monitoring and ambient PM2.5 from a separate satellite-based model (correlation coefficient, r = 0.82). Significant predictors of household PM2.5 included ambient PM2.5, season, and types of fuel used for cooking. This study demonstrates that we can predict household PM2.5 with moderate to high confidence using ambient PM2.5 and household characteristics. Our results present a framework for estimating household PM2.5 exposures in LMICs, which are often understudied and underrepresented due to resource limitations.

Disparities in greenspace associated with sleep duration among adolescent children in Southern California.

More than half of adolescent children do not get the recommended 8 hours of sleep necessary for optimal growth and development. In adults, several studies have evaluated effects of urban stressors including lack of greenspace, air pollution, noise, nighttime light, and psychosocial stress on sleep duration. Little is known about these effects in adolescents, however, it is known that these exposures vary by socioeconomic status (SES). We evaluated the association between several environmental exposures and sleep in adolescent children in Southern California. Methods: In 2010, a total of 1476 Southern California Children's Health Study (CHS) participants in grades 9 and 10 (mean age, 13.4 years; SD, 0.6) completed a questionnaire including topics on sleep and psychosocial stress. Exposures to greenspace, artificial light at night (ALAN), nighttime noise, and air pollution were estimated at each child's residential address, and SES was characterized by maternal education. Odds ratios and 95% confidence intervals (95% CIs) for sleep outcomes were estimated by environmental exposure, adjusting for age, sex, race/ethnicity, home secondhand smoke, and SES. Results: An interquartile range (IQR) increase in greenspace decreased the odds of not sleeping at least 8 hours (odds ratio [OR], 0.86 [95% CI, 0.71, 1.05]). This association was significantly protective in low SES participants (OR, 0.77 [95% CI, 0.60, 0.98]) but not for those with high SES (OR, 1.16 [95%CI, 0.80, 1.70]), interaction P = 0.03. Stress mediated 18.4% of the association among low SES participants. Conclusions: Residing in urban neighborhoods of greater greenness was associated with improved sleep duration among children of low SES but not higher SES. These findings support the importance of widely reported disparities in exposure and access to greenspace in socioeconomically disadvantaged populations.

Water, Sanitation, and Hygiene (WaSH) insecurity in unhoused communities of Los Angeles, California.

BACKGROUND: Access to water and sanitation is a basic human right; however, in many parts of the world, communities experience water, sanitation, and hygiene (WaSH) insecurity. While WaSH insecurity is prevalent in many low and middle-income countries, it is also a problem in high-income countries, like the United States, as is evident in vulnerable populations, including people experiencing homelessness. Limited knowledge exists about the coping strategies unhoused people use to access WaSH services. This study, therefore, examines WaSH access among unhoused communities in Los Angeles, California, a city with the second-highest count of unhoused people across the nation. METHODS: We conducted a cross-sectional study using a snowball sampling technique with 263 unhoused people living in Skid Row, Los Angeles. We calculated frequencies and used multivariable models to describe (1) how unhoused communities cope and gain access to WaSH services in different places, and (2) what individual-level factors contribute to unhoused people's ability to access WaSH services. RESULTS: Our findings reveal that access to WaSH services for unhoused communities in Los Angeles is most difficult at night. Reduced access to overnight sanitation resulted in 19% of the sample population using buckets inside their tents and 28% openly defecating in public spaces. Bottled water and public taps are the primary drinking water source, but 6% of the sample reported obtaining water from fire hydrants, and 50% of the population stores water for night use. Unhoused people also had limited access to water and soap for hand hygiene throughout the day, with 17% of the sample relying on hand sanitizer to clean their hands. Shower and laundry access were among the most limited services available, and reduced people's ability to maintain body hygiene practices and limited employment opportunities. Our regression models suggest that WaSH access is not homogenous among the unhoused. Community differences exist; the odds of having difficulty accessing sanitation services is two times greater for those living outside of Skid Row (Adj OR: 2.52; 95% CI: 1.08-6.37) and three times greater for people who have been unhoused for more than six years compared to people who have been unhoused for less than a year (Adj OR: 3.26; 95% CI: 1.36-8.07). CONCLUSION: Overall, this study suggests a need for more permanent, 24-h access to WaSH services for unhoused communities living in Skid Row, including toilets, drinking water, water and soap for hand hygiene, showers, and laundry services.

Cohort profile: Bangladesh Cook Stove Pregnancy Cohort Study (CSPCS).

PURPOSE: The Cook Stove Pregnancy Cohort Study (CSPCS) was designed to assess the effects of biomass fuel use on household air pollution (HAP) as well as the effects of HAP (fine particulate matter, PM2.5) on birth outcomes and acute lower respiratory infection (ALRI) among infants in Bangladesh. PARTICIPANTS: We recruited 903 women within 18 weeks of pregnancy from rural and semiurban areas of Bangladesh between November 2016 and March 2017. All women and their infants (N=831 pairs) were followed until 12 months after delivery and a subset have undergone respiratory and gut microbiota analysis. METHODS: Questionnaires were administered to collect detailed sociodemographic, medical, nutritional and behavioural information on the mother-child dyads. Anthropometric measurements and biological samples were also collected, as well as household PM2.5 concentrations. FINDINGS TO DATE: Published work in this cohort showed detrimental effects of biomass fuel and health inequity on birth outcomes. Current analysis indicates high levels of household PM2.5 being associated with cooking fuel type and infant ALRI. Lastly, we identified distinct gut and respiratory microbial communities at 6 months of age. FUTURE PLANS: This study provides an economical yet effective framework to conduct pregnancy cohort studies determining the health effects of adverse environmental exposures in low-resource countries. Future analyses in this cohort include assessing the effect of indoor PM2.5 levels on (1) physical growth, (2) neurodevelopment, (3) age of first incidence and frequency of ALRI in infants and (4) the development of the respiratory and gut microbiome. Additional support has allowed us to investigate the effect of in utero exposure to metals on infant neurodevelopment in the first year of life.

Changes in preterm birth and stillbirth during COVID-19 lockdowns in 26 countries.

Preterm birth (PTB) is the leading cause of infant mortality worldwide. Changes in PTB rates, ranging from -90% to +30%, were reported in many countries following early COVID-19 pandemic response measures ('lockdowns'). It is unclear whether this variation reflects real differences in lockdown impacts, or perhaps differences in stillbirth rates and/or study designs. Here we present interrupted time series and meta-analyses using harmonized data from 52 million births in 26 countries, 18 of which had representative population-based data, with overall PTB rates ranging from 6% to 12% and stillbirth ranging from 2.5 to 10.5 per 1,000 births. We show small reductions in PTB in the first (odds ratio 0.96, 95% confidence interval 0.95-0.98, P value <0.0001), second (0.96, 0.92-0.99, 0.03) and third (0.97, 0.94-1.00, 0.09) months of lockdown, but not in the fourth month of lockdown (0.99, 0.96-1.01, 0.34), although there were some between-country differences after the first month. For high-income countries in this study, we did not observe an association between lockdown and stillbirths in the second (1.00, 0.88-1.14, 0.98), third (0.99, 0.88-1.12, 0.89) and fourth (1.01, 0.87-1.18, 0.86) months of lockdown, although we have imprecise estimates due to stillbirths being a relatively rare event. We did, however, find evidence of increased risk of stillbirth in the first month of lockdown in high-income countries (1.14, 1.02-1.29, 0.02) and, in Brazil, we found evidence for an association between lockdown and stillbirth in the second (1.09, 1.03-1.15, 0.002), third (1.10, 1.03-1.17, 0.003) and fourth (1.12, 1.05-1.19, <0.001) months of lockdown. With an estimated 14.8 million PTB annually worldwide, the modest reductions observed during early pandemic lockdowns translate into large numbers of PTB averted globally and warrant further research into causal pathways.

Outdoor artificial light at night, air pollution, and risk of childhood acute lymphoblastic leukemia in the California Linkage Study of Early-Onset Cancers.

Acute lymphoblastic leukemia (ALL) is the most common type of cancer in children (age 0-14 years); however, the etiology remains incompletely understood. Several environmental exposures have been linked to risk of childhood ALL, including air pollution. Closely related to air pollution and human development is artificial light at night (ALAN), which is believed to disrupt circadian rhythm and impact health. We sought to evaluate outdoor ALAN and air pollution on risk of childhood ALL. The California Linkage Study of Early-Onset Cancers is a large population-based case-control in California that identifies and links cancer diagnoses from the California Cancer Registry to birth records. For each case, 50 controls with the same year of birth were obtained from birth records. A total of 2,782 ALL cases and 139,100 controls were identified during 2000-2015. ALAN was assessed with the New World Atlas of Artificial Night Sky Brightness and air pollution with an ensemble-based air pollution model of particulate matter smaller than 2.5 microns (PM2.5). After adjusting for known and suspected risk factors, the highest tertile of ALAN was associated with an increased risk of ALL in Hispanic children (odds ratio [OR] = 1.15, 95% confidence interval [CI] 1.01-1.32). There also appeared to be a borderline association between PM2.5 level and risk of ALL among non-Hispanic White children (OR per 10 µg/m3 = 1.24, 95% CI 0.98-1.56). We observed elevated risk of ALL in Hispanic children residing in areas of greater ALAN. Further work is needed to understand the role of ALAN and air pollution in the etiology of childhood ALL in different racial/ethnic groups.

A geospatial assessment of industrial releases and pediatric neuroblastic tumours at diagnosis: A retrospective case series.

Environmental risk factors associated with malignancy of pediatric neuroblastic tumours are not well-known and few studies have examined the relationship between industrial emissions and neuroblastic tumour diagnosis. A retrospective case series of 310 patients was evaluated at a tertiary hospital in Toronto, Canada between January 2008, and December 2018. Data from the National Pollutant Release Inventory (NPRI) were used to estimate exposure for a dozen chemicals with known or suspected carcinogenicity or embryotoxicity. Comparative analysis and predictive logistic regression models for malignant versus benign neuroblastic tumours included variables for residential proximity, number, and type of industries, mean total emissions within 2 km, and inverse distance weighted (IDW) quantity of chemical-specific industrial emissions estimated within 10 and 50 km of cases. No significant difference was seen between malignant and benign cases with respect to the mean nearest residential distance to industry, the number or type of industry, or the mean total quantity of industrial emissions within a 2 km radius of residential location of cases. However, there were statistically significant differences in the interpolated IDW emissions of dioxins and furans released between 1993 and 2019 within 10 km. Concentrations were significantly higher in malignant neuroblastic tumours at 1.65 grams (g) toxic equivalent (TEQ) (SD 2.01 g TEQ) compared to benign neuroblastic tumours at 1.13 g TEQ (SD 0.84 g TEQ) (p = 0.05). Within 50 km 3 years prior to diagnosis, malignant cases were exposed to higher levels of aluminum, benzene, and nitrogen dioxide (p = 0.02, p = 0.04, and p = 0.02 respectively). Regression analysis of the IDW emissions within a 50 km radius revealed higher odds of exposure to benzene for malignant neuroblastic tumours (OR = 1.03, CI: 1.01-1.05, p = 0.01). These preliminary findings suggest a potential role of industrial emissions in the development of malignant pediatric neuroblastic tumours and underscore the need for further research to investigate these associations.

Estimating traffic noise over a large urban area: An evaluation of methods.

Unlike air pollution, traffic-related noise remains unregulated and has been under-studied despite evidence of its deleterious health impacts. To characterize population exposure to traffic noise, both acoustic-based numerical models and data-driven statistical approaches can generate estimates over large urban areas. The aim of this work is to formally compare the performances of the most common traffic noise models by evaluating their estimates for different categories of roads and validating them against a unique dataset of measured noise in Long Beach, California. Specifically, a statistical land use regression model, an extreme gradient boosting machine learning model (XGB), and three numerical/acoustic traffic noise models: the US Noise Model (FHWA-TNM2.5), a commercial noise model (CadnaA), and an open-source European model (Harmonoise) were optimized and compared. The results demonstrate that XGB and CadnaA were the most effective models for estimating traffic noise, and they are particularly adept at differentiating noise levels on different categories of road.

Exposure models for particulate matter elemental concentrations in Southern California.

Due to a scarcity of routine monitoring of speciated particulate matter (PM), there has been limited capability to develop exposure models that robustly estimate component-specific concentrations. This paper presents the largest such study conducted in a single urban area. Using samples that were collected at 220 locations over two seasons, quasi-ultrafine (PM0.2), accumulation mode fine (PM0.2-2.5), and coarse (PM2.5-10) particulate matter concentrations were used to develop spatiotemporal regression, machine learning models that enabled predictions of 24 elemental components in eight Southern California communities. We used supervised variable selection of over 150 variables, largely from publicly available sources, including meteorological, roadway and traffic characteristics, land use, and dispersion model estimates of traffic emissions. PM components that have high oxidative potential (and potentially large health effects) or are otherwise important markers for major PM sources were the primary focus. We present results for copper, iron, and zinc (as non-tailpipe vehicle emissions); elemental carbon (diesel emissions); vanadium (ship emissions); calcium (soil dust); and sodium (sea salt). Spatiotemporal linear regression models with 17 to 36 predictor variables including meteorology; distance to different classifications of roads; intersections and off ramps within a given buffer distance; truck and vehicle traffic volumes; and near-roadway dispersion model estimates produced superior predictions over the machine learning approaches (cross validation R-squares ranged from 0.76 to 0.92). Our models are easily interpretable and appear to have more effectively captured spatial gradients in the metallic portion of PM than other comparably large studies, particularly near roadways for the non-tailpipe emissions. Furthermore, we demonstrated the importance of including spatiotemporally resolved meteorology in our models as it helped to provide key insights into spatial patterns and allowed us to make temporal predictions.

A Review of Road Traffic-Derived Non-Exhaust Particles: Emissions, Physicochemical Characteristics, Health Risks, and Mitigation Measures.

Implementation of regulatory standards has reduced exhaust emissions of particulate matter from road traffic substantially in the developed world. However, nonexhaust particle emissions arising from the wear of brakes, tires, and the road surface, together with the resuspension of road dust, are unregulated and exceed exhaust emissions in many jurisdictions. While knowledge of the sources of nonexhaust particles is fairly good, source-specific measurements of airborne concentrations are few, and studies of the toxicology and epidemiology do not give a clear picture of the health risk posed. This paper reviews the current state of knowledge, with a strong focus on health-related research, highlighting areas where further research is an essential prerequisite for developing focused policy responses to nonexhaust particles.

Social inequality influences the impact of household air pollution on birth outcomes.

BACKGROUND: Studies demonstrated associations between maternal exposure to household air pollution from cooking and increased risk of adverse birth outcomes in offspring; however, the modifying effect of socioeconomic status (SES) on this association has not been explored. OBJECTIVES: In a cohort of pregnant women with 800 single live births between 2016 and 2017 in rural and semi urban areas of Bangladesh, we tested the hypotheses that kitchen location and cooking fuel type affect birth outcomes (birth weight, low birth weight [LBW] and small for gestational age [SGA]) and these associations vary by SES. METHODS: Demographic characteristics including SES, kitchen location and fuel type were assessed in prenatal visits. Neonatal anthropometric measurements were recorded within 72 h of births. We performed multivariable linear and logistic regressions adjusting for potential confounders to test the study hypotheses. RESULTS: For newborns from households with indoor kitchens, adjusted mean birth weight was 65.13 g (95% confidence interval [CI]: -118.37, -11.90) lower and the odds of LBW and SGA were 58% (odds ratio [OR]:1.58, 95% CI: 1.12, 2.24) and 41% (OR: 1.41, 95% CI: 1.05, 1.92) higher compared to those born in households with outdoor kitchens. We found SES significantly modified the associations between kitchen location and birth outcomes in households using biomass fuels. Newborns from low SES households with indoor kitchens had 89 g lower birth weight and a higher odds of being born with LBW (OR: 2.08, 95% CI 1.23, 3.58), and SGA (OR: 1.70, 95% CI 1.06, 2.76) than those born in high SES households using outdoor kitchens. CONCLUSIONS: In areas with poor access or affordability to clean fuel such as in our study population, cooking in an outdoor kitchen can reduce the burden of LBW and SGA, particularly for low SES households. Promoting outdoor kitchens is a possible intervention strategy to mitigate adverse birth outcomes.

Use of predictive spatial modeling to reveal that primary cancers have distinct central nervous system topography patterns of brain metastasis.

OBJECTIVE: Brain metastasis is the most common intracranial neoplasm. Although anatomical spatial distributions of brain metastasis may vary according to primary cancer subtype, these patterns are not understood and may have major implications for treatment. METHODS: To test the hypothesis that the spatial distribution of brain metastasis varies according to cancer origin in nonrandom patterns, the authors leveraged spatial 3D coordinate data derived from stereotactic Gamma Knife radiosurgery procedures performed to treat 2106 brain metastases arising from 5 common cancer types (melanoma, lung, breast, renal, and colorectal). Two predictive topographic models (regional brain metastasis echelon model [RBMEM] and brain region susceptibility model [BRSM]) were developed and independently validated. RESULTS: RBMEM assessed the hierarchical distribution of brain metastasis to specific brain regions relative to other primary cancers and showed that distinct regions were relatively susceptible to metastasis, as follows: bilateral temporal/parietal and left frontal lobes were susceptible to lung cancer; right frontal and occipital lobes to melanoma; cerebellum to breast cancer; and brainstem to renal cell carcinoma. BRSM provided probability estimates for each cancer subtype, independent of other subtypes, to metastasize to brain regions, as follows: lung cancer had a propensity to metastasize to bilateral temporal lobes; breast cancer to right cerebellar hemisphere; melanoma to left temporal lobe; renal cell carcinoma to brainstem; and colon cancer to right cerebellar hemisphere. Patient topographic data further revealed that brain metastasis demonstrated distinct spatial patterns when stratified by patient age and tumor volume. CONCLUSIONS: These data support the hypothesis that there is a nonuniform spatial distribution of brain metastasis to preferential brain regions that varies according to cancer subtype in patients treated with Gamma Knife radiosurgery. These topographic patterns may be indicative of the abilities of various cancers to adapt to regional neural microenvironments, facilitate colonization, and establish metastasis. Although the brain microenvironment likely modulates selective seeding of metastasis, it remains unknown how the anatomical spatial distribution of brain metastasis varies according to primary cancer subtype and contributes to diagnosis. For the first time, the authors have presented two predictive models to show that brain metastasis, depending on its origin, in fact demonstrates distinct geographic spread within the central nervous system. These findings could be used as a predictive diagnostic tool and could also potentially result in future translational and therapeutic work to disrupt growth of brain metastasis on the basis of anatomical region.

Environmental Influences on Sleep in the California Teachers Study Cohort.

Only two-thirds of Americans meet the recommended 7 hours of sleep nightly. Insufficient sleep and circadian disruption have been associated with adverse health outcomes, including diabetes and cardiovascular disease. Several environmental disruptors of sleep have been reported, such as artificial light at night (ALAN) and noise. These studies tended to evaluate exposures individually. We evaluated several spatially derived environmental exposures (ALAN, noise, green space, and air pollution) and self-reported sleep outcomes obtained in 2012-2015 in a large cohort of 51,562 women in the California Teachers Study. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for sleep duration and latency. After adjusting for age, race/ethnicity, chronotype, use of sleep medication, and self-reported trouble sleeping, ALAN (per 5 millicandela (mcd)/m2 luminance, OR = 1.13, 95% CI: 1.07, 1.20) and air pollution (per 5 µg/m3 PM2.5, OR = 1.06, 95% CI: 1.04, 1.09) were associated with shorter sleep duration (<7 hours), and noise was associated with longer latency (>15 minutes) (per 10 decibels, OR = 1.05, 95% CI: 1.01, 1.10). Green space was associated with increased duration (per 0.1 units, OR = 0.41, 95% CI: 0.28, 0.60) and decreased latency (per 0.1 units, OR = 0.55, 95% CI: 0.39, 0.78). Further research is necessary to understand how these and other exposures (e.g., diet) perturb an individuals' inherited sleep patterns and contribute to downstream health outcomes.

Using dynamic time warping self-organizing maps to characterize diurnal patterns in environmental exposures.

Advances in measurement technology are producing increasingly time-resolved environmental exposure data. We aim to gain new insights into exposures and their potential health impacts by moving beyond simple summary statistics (e.g., means, maxima) to characterize more detailed features of high-frequency time series data. This study proposes a novel variant of the Self-Organizing Map (SOM) algorithm called Dynamic Time Warping Self-Organizing Map (DTW-SOM) for unsupervised pattern discovery in time series. This algorithm uses DTW, a similarity measure that optimally aligns interior patterns of sequential data, both as the similarity measure and training guide of the neural network. We applied DTW-SOM to a panel study monitoring indoor and outdoor residential temperature and particulate matter air pollution (PM2.5) for 10 patients with asthma from 7 households near Salt Lake City, UT; the patients were followed for up to 373 days each. Compared to previous SOM algorithms using timestamp alignment on time series data, the DTW-SOM algorithm produced fewer quantization errors and more detailed diurnal patterns. DTW-SOM identified the expected typical diurnal patterns in outdoor temperature which varied by season, as well diurnal patterns in PM2.5 which may be related to daily asthma outcomes. In summary, DTW-SOM is an innovative feature engineering method that can be applied to highly time-resolved environmental exposures assessed by sensors to identify typical diurnal (or hourly or monthly) patterns and provide new insights into the health effects of environmental exposures.

The international Perinatal Outcomes in the Pandemic (iPOP) study: protocol.

Preterm birth is the leading cause of infant death worldwide, but the causes of preterm birth are largely unknown. During the early COVID-19 lockdowns, dramatic reductions in preterm birth were reported; however, these trends may be offset by increases in stillbirth rates. It is important to study these trends globally as the pandemic continues, and to understand the underlying cause(s). Lockdowns have dramatically impacted maternal workload, access to healthcare, hygiene practices, and air pollution - all of which could impact perinatal outcomes and might affect pregnant women differently in different regions of the world. In the international Perinatal Outcomes in the Pandemic (iPOP) Study, we will seize the unique opportunity offered by the COVID-19 pandemic to answer urgent questions about perinatal health. In the first two study phases, we will use population-based aggregate data and standardized outcome definitions to: 1) Determine rates of preterm birth, low birth weight, and stillbirth and describe changes during lockdowns; and assess if these changes are consistent globally, or differ by region and income setting, 2) Determine if the magnitude of changes in adverse perinatal outcomes during lockdown are modified by regional differences in COVID-19 infection rates, lockdown stringency, adherence to lockdown measures, air quality, or other social and economic markers, obtained from publicly available datasets. We will undertake an interrupted time series analysis covering births from January 2015 through July 2020. The iPOP Study will involve at least 121 researchers in 37 countries, including obstetricians, neonatologists, epidemiologists, public health researchers, environmental scientists, and policymakers. We will leverage the most disruptive and widespread "natural experiment" of our lifetime to make rapid discoveries about preterm birth. Whether the COVID-19 pandemic is worsening or unexpectedly improving perinatal outcomes, our research will provide critical new information to shape prenatal care strategies throughout (and well beyond) the pandemic.

W-TSS: A Wavelet-Based Algorithm for Discovering Time Series Shapelets.

Many approaches to time series classification rely on machine learning methods. However, there is growing interest in going beyond black box prediction models to understand discriminatory features of the time series and their associations with outcomes. One promising method is time-series shapelets (TSS), which identifies maximally discriminative subsequences of time series. For example, in environmental health applications TSS could be used to identify short-term patterns in exposure time series (shapelets) associated with adverse health outcomes. Identification of candidate shapelets in TSS is computationally intensive. The original TSS algorithm used exhaustive search. Subsequent algorithms introduced efficiencies by trimming/aggregating the set of candidates or training candidates from initialized values, but these approaches have limitations. In this paper, we introduce Wavelet-TSS (W-TSS) a novel intelligent method for identifying candidate shapelets in TSS using wavelet transformation discovery. We tested W-TSS on two datasets: (1) a synthetic example used in previous TSS studies and (2) a panel study relating exposures from residential air pollution sensors to symptoms in participants with asthma. Compared to previous TSS algorithms, W-TSS was more computationally efficient, more accurate, and was able to discover more discriminative shapelets. W-TSS does not require pre-specification of shapelet length.

Improving brain age estimates with deep learning leads to identification of novel genetic factors associated with brain aging.

To study genetic factors associated with brain aging, we first need to quantify brain aging. Statistical models have been created for estimating the apparent age of the brain, or predicted brain age (PBA), using imaging data. Recent studies have refined these models to obtain a more accurate PBA, but research has yet to demonstrate the scientific value of doing so. Here, we show that a more accurate PBA leads to better characterization of genetic factors associated with brain aging. We trained a convolutional neural network (CNN) model on 16,998 UK Biobank subjects to derive PBA, then conducted a genome-wide association study on the PBA, in which we identified single nucleotide polymorphisms from four independent loci significantly associated with brain aging, three of which were novel. By comparing association results based on the CNN-derived PBA to those based on a linear regression-derived PBA, we concluded that a more accurate PBA enables the discovery of novel genetic associations. Our results may be valuable for identifying other lifestyle factors associated with brain aging.

Contribution of tailpipe and non-tailpipe traffic sources to quasi-ultrafine, fine and coarse particulate matter in southern California.

Exposure to traffic-related air pollution (TRAP) in the near-roadway environment is associated with multiple adverse health effects. To characterize the relative contribution of tailpipe and non-tailpipe TRAP sources to particulate matter (PM) in the quasi-ultrafine (PM0.2), fine (PM2.5) and coarse (PM2.5-10) size fractions and identify their spatial determinants in southern California (CA). Month-long integrated PM0.2, PM2.5 and PM2.5-10 samples (n = 461, 265 and 298, respectively) were collected across cool and warm seasons in 8 southern CA communities (2008-9). Concentrations of PM mass, elements, carbons and major ions were obtained. Enrichment ratios (ER) in PM0.2 and PM10 relative to PM2.5 were calculated for each element. The Positive Matrix Factorization model was used to resolve and estimate the relative contribution of TRAP sources to PM in three size fractions. Generalized additive models (GAMs) with bivariate loess smooths were used to understand the geographic variation of TRAP sources and identify their spatial determinants. EC, OC, and B had the highest median ER in PM0.2 relative to PM2.5. Six, seven and five sources (with characteristic species) were resolved in PM0.2, PM2.5 and PM2.5-10, respectively. Combined tailpipe and non-tailpipe traffic sources contributed 66%, 32% and 18% of PM0.2, PM2.5 and PM2.5-10 mass, respectively. Tailpipe traffic emissions (EC, OC, B) were the largest contributor to PM0.2 mass (58%). Distinct gasoline and diesel tailpipe traffic sources were resolved in PM2.5. Others included fuel oil, biomass burning, secondary inorganic aerosol, sea salt, and crustal/soil. CALINE4 dispersion model nitrogen oxides, trucks and intersections were most correlated with TRAP sources. The influence of smaller roadways and intersections became more apparent once Long Beach was excluded. Non-tailpipe emissions constituted ~8%, 11% and 18% of PM0.2, PM2.5 and PM2.5-10, respectively, with important exposure and health implications. Future efforts should consider non-linear relationships amongst predictors when modeling exposures. Implications: Vehicle emissions result in a complex mix of air pollutants with both tailpipe and non-tailpipe components. As mobile source regulations lead to decreased tailpipe emissions, the relative contribution of non-tailpipe traffic emissions to near-roadway exposures is increasing. This study documents the presence of non-tailpipe abrasive vehicular emissions (AVE) from brake and tire wear, catalyst degradation and resuspended road dust in the quasi-ultrafine (PM0.2), fine and coarse particulate matter size fractions, with contributions reaching up to 30% in PM0.2 in some southern California communities. These findings have important exposure and policy implications given the high metal content of AVE and the efficiency of PM0.2 at reaching the alveolar region of the lungs and other organ systems once inhaled. This work also highlights important considerations for building models that can accurately predict tailpipe and non-tailpipe exposures for population health studies.