Individual and joint effects of prenatal PM2.5 and maternal stress on child temperament.

Prenatal fine particulate matter (PM2.5) and maternal psychological functioning have been associated with child cognitive outcomes, though their independent and joint impacts on earlier behavioral outcomes remains less studied. We used data from 382 mother-child pairs from a prospective birth cohort in Mexico City. Temperament was measured at 24 months using the Carey Toddler Temperament Scale (TTS). Exploratory factor analysis (EFA) was used to update the factor structure of the TTS. During pregnancy, mothers completed the Crisis in Family Systems-Revised, Edinburgh Depression Scale, pregnancy-specific anxiety scale, and the Perceived Stress Scale. Pregnancy PM2.5 was assessed using estimates from a satellite-based exposure model. We assessed the association between prenatal maternal stress and PM2.5 on temperament, in both independent and joint models. Quantile g-computation was used to estimate the joint associations. Models were adjusted for maternal age, SES, education, child sex, and child age. In EFA, we identified three temperament factors related to effortful control, extraversion, and negative affect. Our main results showed that higher levels of PM2.5 and several of the maternal psychological functioning measures were related to both effortful control and negative affect in the child, both individually and as a mixture. For instance, a one quartile increase in the prenatal mixture was associated with higher negative affect scores in the child (0.34, 95% CI: 0.16, 0.53). We observed modification of these associations by maternal SES, with associations seen only among lower SES participants for both effortful control (-0.45, 95% CI: -0.70, -0.20) and negative affect outcomes (0.60, 95% CI: 0.35, 0.85). Prenatal PM2.5 and maternal psychological functioning measures were associated with toddler temperament outcomes, providing evidence for impacts of chemical and non-chemical stressors on early child health.

Short-term exposure to PM2.5 and 1.5 million deaths: a time-stratified case-crossover analysis in the Mexico City Metropolitan Area.

BACKGROUND: Satellite-based PM2.5 predictions are being used to advance exposure science and air-pollution epidemiology in developed countries; including emerging evidence about the impacts of PM2.5 on acute health outcomes beyond the cardiovascular and respiratory systems, and the potential modifying effects from individual-level factors in these associations. Research on these topics is lacking in low and middle income countries. We aimed to explore the association between short-term exposure to PM2.5 with broad-category and cause-specific mortality outcomes in the Mexico City Metropolitan Area (MCMA), and potential effect modification by age, sex, and SES characteristics in such associations. METHODS: We used a time-stratified case-crossover study design with 1,479,950 non-accidental deaths from the MCMA for the period of 2004-2019. Daily 1 × 1 km PM2.5 (median = 23.4 µg/m3; IQR = 13.6 µg/m3) estimates from our satellite-based regional model were employed for exposure assessment at the sub-municipality level. Associations between PM2.5 with broad-category (organ-system) and cause-specific mortality outcomes were estimated with distributed lag conditional logistic models. We also fit models stratifying by potential individual-level effect modifiers including; age, sex, and individual SES-related characteristics namely: education, health insurance coverage, and job categories. Odds ratios were converted into percent increase for ease of interpretation. RESULTS: PM2.5 exposure was associated with broad-category mortality outcomes, including all non-accidental, cardiovascular, cerebrovascular, respiratory, and digestive mortality. A 10-µg/m3 PM2.5 higher cumulative exposure over one week (lag06) was associated with higher cause-specific mortality outcomes including hypertensive disease [2.28% (95%CI: 0.26%-4.33%)], acute ischemic heart disease [1.61% (95%CI: 0.59%-2.64%)], other forms of heart disease [2.39% (95%CI: -0.35%-5.20%)], hemorrhagic stroke [3.63% (95%CI: 0.79%-6.55%)], influenza and pneumonia [4.91% (95%CI: 2.84%-7.02%)], chronic respiratory disease [2.49% (95%CI: 0.71%-4.31%)], diseases of the liver [1.85% (95%CI: 0.31%-3.41%)], and renal failure [3.48% (95%CI: 0.79%-6.24%)]. No differences in effect size of associations were observed between age, sex and SES strata. CONCLUSIONS: Exposure to PM2.5 was associated with non-accidental, broad-category and cause-specific mortality outcomes beyond the cardiovascular and respiratory systems, including specific death-causes from the digestive and genitourinary systems, with no indication of effect modification by individual-level characteristics.

Urban Stress and its Association with Symptoms of Depression, Fatigue, and Sleep Disruption in Women in Mexico City.

Women in urban neighborhoods often face disproportionately higher levels of environmental and social stressors; however, the health effects from urban stressors remains poorly understood. We aimed to evaluate the association between urban stress and symptoms of depression, fatigue, and sleep disruption in a cohort of 460 women in Mexico City. To assess urban stress, women were administered the Urban Annoyances (Nuisances Environnementales) scale. Six constructs were summarized to create an overall index. Depressive symptoms were assessed using the Edinburgh Depression Scale; the Patient-Reported Outcomes Information System scales were used to assess sleep disruption and fatigue. Linear regression models were used to estimate the association with continuous symptoms comparing women with high urban stress to those with lower levels. Models were adjusted for socioeconomic status, education, age, social support, and previous depressive symptoms. High urban stress was associated with greater depressive symptoms (ß: 1.77; 95%CI: 0.83, 2.71), fatigue (ß: 2.47; 95%CI: 0.87, 4.07), and sleep disruption (ß: 2.14; 95%CI: 0.54, 3.73). Urban stress plays an important role in women's psychological and physical health, highlighting the importance of including these measures in environmental health studies. Urban interventions, such as promoting alternative transport options, should additionally be addressed to improve health of urban populations.

Long-term exposure to ambient fine particulate matter and carotid intima media thickness at bilateral, left and right in adults from Mexico City: Results from GEA study.

BACKGROUND: PM2.5 exposure has been associated with intima-media thickness (cIMT) increase. However, very few studies distinguished between left and right cIMT in relation to PM2.5 exposure. AIM: To evaluate associations between chronic exposure to PM2.5 and cIMT at bilateral, left, and right in adults from Mexico City. METHODS: This study comprised 913 participants from the control group, participants without personal or family history of cardiovascular disease, of the Genetics of Atherosclerosis Disease Mexican study (GEA acronym in Spanish), recruited at the Instituto Nacional de Cardiología Ignacio Chávez from June 2008 to January 2013. To assess the associations between chronic exposure to PM2.5 (per 5 µg/m3 increase) at different lag years (1-4 years) and cIMT (bilateral, left, and right) we applied distributed lag non-linear models (DLNMs). RESULTS: The median and interquartile range for cIMT at bilateral, left, and right, were 630 (555, 735), 640 (550, 750), and 620 (530, 720) µm, respectively. Annual average PM2.5 exposure was 26.64 µg/m3, with median and IQR, of 24.46 (23.5-25.46) µg/m3. Results from DLNMs adjusted for age, sex, body mass index, low-density lipoproteins, and glucose, showed that PM2.5 exposure for year 1 and 2, were positively and significantly associated with right-cIMT [6.99% (95% CI: 3.67; 10.42) and 2.98% (0.03; 6.01), respectively]. Negative associations were observed for PM2.5 at year 3 and 4 and right-cIMT; however only year 3 was statistically significant [-2.83% (95% CI: 5.12; -0.50)]. Left-cIMT was not associated with PM2.5 exposure at any lag year. The increase in bilateral cIMT followed a similar pattern as that observed for right-cIMT, but with lower estimates. CONCLUSIONS: Our results suggest different susceptibility between left and right cIMT associated with PM2.5 exposure highlighting the need of measuring both, left and right cIMT, regarding ambient air pollution in epidemiological studies.

Predicting fine-scale daily NO2 over Mexico City using an ensemble modeling approach.

In recent years, there has been growing interest in developing air pollution prediction models to reduce exposure measurement error in epidemiologic studies. However, efforts for localized, fine-scale prediction models have been predominantly focused in the United States and Europe. Furthermore, the availability of new satellite instruments such as the TROPOsopheric Monitoring Instrument (TROPOMI) provides novel opportunities for modeling efforts. We estimated daily ground-level nitrogen dioxide (NO2) concentrations in the Mexico City Metropolitan Area at 1-km2 grids from 2005 to 2019 using a four-stage approach. In stage 1 (imputation stage), we imputed missing satellite NO2 column measurements from the Ozone Monitoring Instrument (OMI) and TROPOMI using the random forest (RF) approach. In stage 2 (calibration stage), we calibrated the association of column NO2 to ground-level NO2 using ground monitors and meteorological features using RF and extreme gradient boosting (XGBoost) models. In stage 3 (prediction stage), we predicted the stage 2 model over each 1-km2 grid in our study area, then ensembled the results using a generalized additive model (GAM). In stage 4 (residual stage), we used XGBoost to model the local component at the 200-m2 scale. The cross-validated R2 of the RF and XGBoost models in stage 2 were 0.75 and 0.86 respectively, and 0.87 for the ensembled GAM. Cross-validated rootmean-squared error (RMSE) of the GAM was 3.95 µg/m3. Using novel approaches and newly available remote sensing data, our multi-stage model presented high cross-validated fits and reconstructs fine-scale NO2 estimates for further epidemiologic studies in Mexico City.

Daily exposure to PM2.5 and 1.5 million deaths: A time-stratified case-crossover analysis in the Mexico City Metropolitan Area.

Background: Satellite-based PM2.5 predictions are being used to advance exposure science and air-pollution epidemiology in developed countries; including emerging evidence about the impacts of PM2.5 on acute health outcomes beyond the cardiovascular and respiratory systems, and the potential modifying effects from individual-level factors in these associations. Research on these topics is lacking in Latin America. Methods: We used a time-stratified case-crossover study design with 1,479,950 non-accidental deaths from Mexico City Metropolitan Area for the period of 2004-2019. Daily 1×1 km PM2.5 (median=23.4 µg/m3; IQR=13.6 µg/m3) estimates from our satellite-based regional model were employed for exposure assessment at the sub-municipality level. Associations between PM2.5 with broad-category (organ-system) and cause-specific mortality outcomes were estimated with distributed lag conditional logistic models. We also fit models stratifying by potential individual-level effect modifiers including; age, sex, and individual SES-related characteristics namely: education, health insurance coverage, and job categories. Results: PM2.5 exposure was associated with higher total non-accidental, cardiovascular, cerebrovascular, respiratory, and digestive mortality. A 10-µg/m3 PM2.5 higher cumulative exposure over one week (lag06) was associated with higher cause-specific mortality outcomes including hypertensive disease [2.28% (95%CI: 0.26%-4.33%)], acute ischemic heart disease [1.61% (95%CI: 0.59%-2.64%)], other forms of heart disease [2.39% (95%CI: -0.35%-5.20%)], hemorrhagic stroke [3.63% (95%CI: 0.79%-6.55%)], influenza and pneumonia [4.91% (95%CI: 2.84%-7.02%)], chronic respiratory disease [2.49% (95%CI: 0.71%-4.31%)], diseases of the liver [1.85% (95%CI: 0.31%-3.41%)], and renal failure [3.48% (95%CI: 0.79%-6.24%)]. No differences in effect size of associations were observed between SES strata. Conclusions: Exposure to PM2.5 was associated with mortality outcomes beyond the cardiovascular and respiratory systems, including specific death-causes from the digestive and genitourinary systems, with no indications of effect modification by individual SES-related characteristics.

Prediction of daily mean and one-hour maximum PM2.5 concentrations and applications in Central Mexico using satellite-based machine-learning models.

BACKGROUND: Machine-learning algorithms are becoming popular techniques to predict ambient air PM2.5 concentrations at high spatial resolutions (1 × 1 km) using satellite-based aerosol optical depth (AOD). Most machine-learning models have aimed to predict 24 h-averaged PM2.5 concentrations (mean PM2.5) in high-income regions. Over Mexico, none have been developed to predict subdaily peak levels, such as the maximum daily 1-h concentration (max PM2.5). OBJECTIVE: Our goal was to develop a machine-learning model to predict mean PM2.5 and max PM2.5 concentrations in the Mexico City Metropolitan Area from 2004 through 2019. METHODS: We present a new modeling approach based on extreme gradient boosting (XGBoost) and inverse-distance weighting that uses AOD, meteorology, and land-use variables. We also investigated applications of our mean PM2.5 predictions that can aid local authorities in air-quality management and public-health surveillance, such as the co-occurrence of high PM2.5 and heat, compliance with local air-quality standards, and the relationship of PM2.5 exposure with social marginalization. RESULTS: Our models for mean and max PM2.5 exhibited good performance, with overall cross-validated mean absolute errors (MAE) of 3.68 and 9.20 µg/m3, respectively, compared to mean absolute deviations from the median (MAD) of 8.55 and 15.64 µg/m3. In 2010, everybody in the study region was exposed to unhealthy levels of PM2.5. Hotter days had greater PM2.5 concentrations. Finally, we found similar exposure to PM2.5 across levels of social marginalization. SIGNIFICANCE: Machine learning algorithms can be used to predict highly spatiotemporally resolved PM2.5 concentrations even in regions with sparse monitoring. IMPACT: Our PM2.5 predictions can aid local authorities in air-quality management and public-health surveillance, and they can advance epidemiological research in Central Mexico with state-of-the-art exposure assessment methods.

A spatiotemporal reconstruction of daily ambient temperature using satellite data in the Megalopolis of Central Mexico from 2003 to 2019.

While weather stations generally capture near-surface ambient air temperature (Ta) at a high temporal resolution to calculate daily values (i.e., daily minimum, mean, and maximum Ta), their fixed locations can limit their spatial coverage and resolution even in densely populated urban areas. As a result, data from weather stations alone may be inadequate for Ta-related epidemiology particularly when the stations are not located in the areas of interest for human exposure assessment. To address this limitation in the Megalopolis of Central Mexico (MCM), we developed the first spatiotemporally resolved hybrid satellite-based land use regression Ta model for the region, home to nearly 30 million people and includes Mexico City and seven more metropolitan areas. Our model predicted daily minimum, mean, and maximum Ta for the years 2003-2019. We used data from 120 weather stations and Land Surface Temperature (LST) data from NASA's MODIS instruments on the Aqua and Terra satellites on a 1 × 1 km grid. We generated a satellite-hybrid mixed-effects model for each year, regressing Ta measurements against land use terms, day-specific random intercepts, and fixed and random LST slopes. We assessed model performance using 10-fold cross-validation at withheld stations. Across all years, the root-mean-square error ranged from 0.92 to 1.92 K and the R 2 ranged from .78 to .95. To demonstrate the utility of our model for health research, we evaluated the total number of days in the year 2010 when residents ≥65 years old were exposed to Ta extremes (above 30°C or below 5°C). Our model provides much needed high-quality Ta estimates for epidemiology studies in the MCM region.

Exposure to PM2.5 and Obesity Prevalence in the Greater Mexico City Area.

Exposure to PM2.5 has been associated with the prevalence of obesity. In the Greater Mexico City Area (GMCA), both are ranked among the highest in the world. Our aim was to analyze this association in children, adolescents, and adults in the GMCA. We used data from the 2006 and 2012 Mexican National Surveys of Health and Nutrition (ENSANUT). Participants' past-year exposure to ambient PM2.5 was assessed using land use terms and satellite-derived aerosol optical depth estimates; weight and height were measured. We used survey-adjusted logistic regression models to estimate the odds ratios (ORs) of obesity (vs. normal-overweight) for every 10 µg/m3 increase in annual PM2.5 exposure for children, adolescents, and adults. Using a meta-analysis approach, we estimated the overall odds of obesity. We analyzed data representing 19.3 million and 20.9 million GMCA individuals from ENSANUT 2006 and 2012, respectively. The overall pooled estimate between PM2.5 exposure and obesity was OR = 1.96 (95% CI: 1.21, 3.18). For adolescents, a 10 µg/m3 increase in PM2.5 was associated with an OR of 3.53 (95% CI: 1.45, 8.58) and 3.79 (95% CI: 1.40, 10.24) in 2006 and 2012, respectively. More studies such as this are recommended in Latin American cities with similar air pollution and obesity conditions.

Associations between daily ambient temperature and sedentary time among children 4-6 years old in Mexico City.

BACKGROUND: Sedentary behavior is a worldwide public health concern. There is consistent and growing evidence linking sedentary behavior to mortality and morbidity. Early monitoring and assessment of environmental factors associated with sedentary behaviors at a young age are important initial steps for understanding children's sedentary time and identifying pertinent interventions. OBJECTIVE: This study examines the association between daily temperature (maximum, mean, minimum, and diurnal variation) and all-day sedentary time among 4-6 year old children in Mexico City (n = 559) from the year 2013 to 2015. METHODS: We developed a spatiotemporally resolved hybrid satellite-based land use regression temperature model and calculated percent daily sedentary time from aggregating 10-second epoch vertical counts captured by accelerometers that participants wore for one week. We modeled generalized additive models (GAMs), one for each temperature type as a covariate (maximum, mean, minimum, and diurnal variation). All GAMs included percent all-day sedentary time as the outcome and participant-level random intercepts to account for repeated measures of sedentary time. Our models were adjusted for demographic factors and environmental exposures. RESULTS: Daily maximum temperature, mean temperature, and diurnal variation have significant negative linear relationships with all-day sedentary time (p<0.01). There is no significant association between daily minimum temperature and all-day sedentary time. Children have on average 0.26% less daily sedentary time (approximately 2.2 minutes) for each 1°C increase in ambient maximum temperature (range 7.1-30.2°C), 0.27% less daily sedentary time (approximately 2.3 minutes) for each 1°C increase in ambient mean temperature (range 4.3-22.2°C), and 0.23% less daily sedentary time (approximately 2.0 minutes) for each 1°C increase in diurnal variation (range 3.0-21.6°C). CONCLUSIONS: These results are contrary to our hypothesis in which we expected a curvilinear relationship between temperature (maximum, mean, minimum, and diurnal variation) and sedentary time. Our findings suggest that temperature is an important environmental factor that influences children's sedentary behavior.

Children's acute respiratory symptoms associated with PM2.5 estimates in two sequential representative surveys from the Mexico City Metropolitan Area.

BACKGROUND: Respiratory diseases are a major component of morbidity in children and their symptoms may be spatially and temporally exacerbated by exposure gradients of fine particulate matter (PM2.5) in large polluted urban areas, like the Mexico City Metropolitan Area (MCMA). OBJECTIVES: To analyze the association between satellite-derived and interpolated PM2.5 estimates with children's (≤9 years old) acute respiratory symptoms (ARS) in two probabilistic samples representing the MCMA. METHODS: We obtained ARS data from the 2006 and 2012 National Surveys for Health and Nutrition (ENSaNut). Two week average exposure to PM2.5 was assessed for each household with spatial estimates from a hybrid model with satellite measurements of aerosol optical depth (AOD-PM2.5) and also with interpolated PM2.5 measurements from ground stations, from the Mexico City monitoring network (MNW-PM2.5). We used survey-adjusted logistic regressions to analyze the association between PM2.5 estimates and ARS reported on children. RESULTS: A total of 1,005 and 1,233 children were surveyed in 2006 and 2012 representing 3.1 and 3.5 million children, respectively. For the same years and over the periods of study, the estimated prevalence of ARS decreased from 49.4% (95% CI: 44.9,53.9%) to 37.8% (95% CI: 34,41.7%). AOD-PM2.5 and MNW-PM2.5 estimates were associated with significantly higher reports of ARS in children 0-4 years old [OR2006 = 1.29 (95% (CI): 0.99,1.68) and OR2006 = 1.24 (95% CI: 1.08,1.42), respectively]. We observed positive non-significant associations in 2012 in both age groups and in 2006 for children 5-9 years old. No statistically significant differences in health effect estimates of PM2.5 were found comparing AOD-PM2.5 or MNW-PM2.5 for exposure assessment. CONCLUSIONS: Our findings suggest that PM2.5 is a risk factor for the prevalence of ARS in children and expand the growing evidence of the utility of new satellite AOD-based methods for estimating health effects from acute exposure to PM2.5.

Short-term effects of ambient temperature on non-external and cardiovascular mortality among older adults of metropolitan areas of Mexico.

Multi-city studies assessing the association between acute exposure to temperature and mortality in Latin American are limited. To analyze the short-term effect of changes in temperature (increase and decrease) on daily non-external and cardiovascular mortality from 1998 to 2014, in people 65 years old and over living in 10 metropolitan areas of Mexico. Analyses were performed through Poisson regression models with distributed lag non-linear models. Statistical comparison of minimum mortality temperature (MMT) and city-specific cutoffs of 24-h temperature mean values (5th/95th and 1st/99th percentiles) were used to obtain the mortality relative Risk (RR) for cold/hot and extreme cold/extreme hot, respectively, for the same day and lags of 0-3, 0-7, and 0-21 days. A meta-analysis was conducted to synthesize the estimates (RRpooled). Significant non-linear associations of temperature-mortality relation were found in U or inverted J shape. The best predictors of mortality associations with cold and heat were daily temperatures at lag 0-7 and lag 0-3, respectively. RRpooled of non-external causes was 6.3% (95%CI 2.7, 10.0) for cold and 10.2% (95%CI 4.4, 16.2) for hot temperatures. The RRpooled for cardiovascular mortality was 7.1% (95%CI 0.01, 14.7) for cold and 7.1% (95%CI 0.6, 14.0) for hot temperatures. Results suggest that, starting from the MMT, the changes in temperature are associated with an increased risk of non-external and specific causes of mortality in elderly people. Generally, heat effects on non-external and specific causes of mortality occur immediately, while cold effects occur within a few days and last longer.

Cardiovascular and Cerebrovascular Mortality Associated With Acute Exposure to PM2.5 in Mexico City.

BACKGROUND AND PURPOSE: Acute exposure to particulate matter with aerodynamic diameter <2.5 µm (PM2.5) is associated with acute cardiovascular and cerebrovascular mortality. The aim of this study was to evaluate these associations with specific causes of cardiovascular and cerebrovascular mortality in Mexico City. METHODS: We obtained daily mortality records for Mexico City from 2004 to 2013 for cardiovascular and cerebrovascular causes in people ≥25 and ≥65 years old. Exposure to PM2.5 was assessed with daily estimates from a new hybrid spatiotemporal model using satellite measurements of aerosol optical depth PM2.5 and compared to ground level PM2.5 measurements with missing data estimated with generalized additive models PM2.5. We fitted Poisson regression models with distributed lags for all mortality outcomes. RESULTS: An increase of 10 µg/m3 in aerosol optical depth PM2.5 was associated with increased cardiovascular (1.22%; 95% confidence interval, 0.17-2.28) and cerebrovascular mortality (3.43%; 95% confidence interval, 0.10-6.28) for lag days 0 to 1 (lag 0-1). Stronger effects were identified for hemorrhagic stroke and people ≥65 years. Associations were slightly smaller using generalized additive models PM2.5. CONCLUSIONS: These results support the evidence that acute exposure to PM2.5 is associated with increased risk of specific cardiovascular and cerebrovascular mortality causes.