Short-term air pollution levels and sickle cell disease hospital encounters in South Carolina: A case-crossover analysis.

BACKGROUND: Sickle cell disease (SCD) is a genetic disorder and symptoms may be sensitive to environmental stressors. Although it has been hypothesized that exposure to outdoor air pollution could trigger acute SCD events, evidence is limited. METHODS: We obtained SCD administrative data on hospital encounters in South Carolina from 2002 to 2019. We estimated outdoor air pollutant (particulate matter<2.5 µm (PM2.5), ozone (O3), and PM2.5 elemental carbon (EC) concentrations at residential zip codes using spatio-temporal models. Using a random bi-directional, fixed-interval case-crossover study design, we investigated the relationship between air pollution exposure over 1-, 3-, 5-, 9-, and14-day periods with SCD hospital encounters. RESULTS: We studied 8410 patients with 144,129 hospital encounters. We did not observe associations among all patients with SCD and adults for PM2.5, O3, and EC. We observed positive associations among children for 9- and 14-day EC (OR: 1.05 (95% confidence interval (CI): 1.02, 1.08) and OR: 1.05 (95% CI: 1.02, 1.09), respectively) and 9- and 14-day O3 (OR: 1.04 (95%CI: 1.00, 1.08)) for both. CONCLUSIONS: Our findings suggest that short-term (within two-weeks) levels of EC and O3 and may be associated with SCD hospital encounters among children. Two-pollutant model results suggest that EC is more likely responsible for effects on SCD than O3. More research is needed to confirm our findings.

A comparison of PM2.5 exposure estimates from different estimation methods and their associations with cognitive testing and brain MRI outcomes.

BACKGROUND: Reported associations between particulate matter with aerodynamic diameter ≤2.5 µm (PM2.5) and cognitive outcomes remain mixed. Differences in exposure estimation method may contribute to this heterogeneity. OBJECTIVES: To assess agreement between PM2.5 exposure concentrations across 11 exposure estimation methods and to compare resulting associations between PM2.5 and cognitive or MRI outcomes. METHODS: We used Visit 5 (2011-2013) cognitive testing and brain MRI data from the Atherosclerosis Risk in Communities (ARIC) Study. We derived address-linked average 2000-2007 PM2.5 exposure concentrations in areas immediately surrounding the four ARIC recruitment sites (Forsyth County, NC; Jackson, MS; suburbs of Minneapolis, MN; Washington County, MD) using 11 estimation methods. We assessed agreement between method-specific PM2.5 concentrations using descriptive statistics and plots, overall and by site. We used adjusted linear regression to estimate associations of method-specific PM2.5 exposure estimates with cognitive scores (n = 4678) and MRI outcomes (n = 1518) stratified by study site and combined site-specific estimates using meta-analyses to derive overall estimates. We explored the potential impact of unmeasured confounding by spatially patterned factors. RESULTS: Exposure estimates from most methods had high agreement across sites, but low agreement within sites. Within-site exposure variation was limited for some methods. Consistently null findings for the PM2.5-cognitive outcome associations regardless of method precluded empirical conclusions about the potential impact of method on study findings in contexts where positive associations are observed. Not accounting for study site led to consistent, adverse associations, regardless of exposure estimation method, suggesting the potential for substantial bias due to residual confounding by spatially patterned factors. DISCUSSION: PM2.5 estimation methods agreed across sites but not within sites. Choice of estimation method may impact findings when participants are concentrated in small geographic areas. Understanding unmeasured confounding by factors that are spatially patterned may be particularly important in studies of air pollution and cognitive or brain health.

Air pollution and incident sarcoidosis in central Pennsylvania.

Sarcoidosis is a chronic granulomatous disease predominantly affecting the lungs and inducing significant morbidity and elevated mortality rate. The etiology of the disease is unknown but may involve exposure to an antigenic agent and subsequent inflammatory response resulting in granuloma formation. Various environmental and occupational risk factors have been suggested by previous observations, such as moldy environments, insecticides, and bird breeding. Our study investigated the association of air pollution with diagnosis of sarcoidosis using a case-control design. Penn State Health electronic medical records from 2005 to 2018 were examined for adult patients with (cases) and without (controls) an International Classification of Disease (ICD)-9 or -10 code for sarcoidosis. Patient addresses were geocoded and 24-hr residential-level air pollution concentrations were estimated using spatio-temporal models of particulate matter <2.5 µm (PM2.5), ozone, and PM2.5 elemental carbon (EC) and moving averages calculated. In total, 877 cases and 34,510 controls were identified. Logistic regression analysis did not identify significant associations between sarcoidosis incidence and air pollution exposure estimates. However, the odds ratio (OR) for EC for exposures occurring 7-10 years prior did approach statistical significance, and ORs exhibited an increasing trend for longer averaging periods. Data suggested a latency period of more than 6 years for PM2.5 and EC for reasons that are unclear. Overall, results for PM2.5 and EC suggest that long-term exposure to traffic-related air pollution may contribute to the development of sarcoidosis and emphasize the need for additional research and, if the present findings are substantiated, for public health interventions addressing air quality as well as increasing disease surveillance in areas with a large burden of PM2.5 and EC.

Short-term Air Pollution Levels and Blood Pressure in Older Women.

BACKGROUND: Evidence of associations between daily variation in air pollution and blood pressure (BP) is varied and few prior longitudinal studies adjusted for calendar time. METHODS: We studied 143,658 postmenopausal women 50 to 79 years of age from the Women's Health Initiative (1993-2005). We estimated daily atmospheric particulate matter (PM) (in three size fractions: PM2.5, PM2.5-10, and PM10) and nitrogen dioxide (NO2) concentrations at participants' residential addresses using validated lognormal kriging models. We used linear mixed-effects models to estimate the association between air pollution concentrations and repeated measures of systolic and diastolic BP (SBP, DBP) adjusting for confounders and calendar time. RESULTS: Short-term PM2.5 and NO2 were each positively associated with DBP {0.10 mmHg [95% confidence interval (CI): 0.04, 0.15]; 0.13 mmHg (95% CI: 0.09, 0.18), respectively} for interquartile range changes in lag 3-5 day PM2.5 and NO2. Short-term NO2 was negatively associated with SBP [-0.21 mmHg (95%CI: -0.30, -0.13)]. In two-pollutant models, the NO2-DBP association was slightly stronger, but for PM2.5 was attenuated to null, compared with single-pollutant models. Associations between short-term NO2 and DBP were more pronounced among those with higher body mass index, lower neighborhood socioeconomic position, and diabetes. When long-term (annual) and lag 3-5 day PM2.5 were in the same model, associations with long-term PM2.5 were stronger than for lag 3-5 day. CONCLUSIONS: We observed that short-term PM2.5 and NO2 levels were associated with increased DBP, although two-pollutant model results suggest NO2 was more likely responsible for observed associations. Long-term PM2.5 effects were larger than short-term.

Long-term air pollution and risk of amyotrophic lateral sclerosis mortality in the Women's Health Initiative cohort.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder with no cure. Although the etiology of sporadic ALS is largely unknown, environmental exposures may affect ALS risk. OBJECTIVE: We investigated relationships between exposure to long-term ambient particulate matter (PM) and gaseous air pollution (AP) and ALS mortality. METHODS: Within the Women's Health Initiative (WHI) cohort of 161,808 postmenopausal women aged 50-79 years at baseline (1993-1998), we performed a nested case-control study of 256 ALS deaths and 2486 matched controls with emphasis on PM constituents (PM2.5, PM10, and coarse PM [PM10-2.5]) and gaseous pollutants (NOx, NO2, SO2, and ozone). Time-varying AP exposures estimates were averaged 5, 7.5, and 10 years prior to ALS death using both a GIS-based spatiotemporal generalized additive mixed model and ordinary kriging (empirical and multiple imputation, MI). Conditional logistic regression was used to estimate the relative risk of ALS death. RESULTS: In general, PM2.5 and PM10-related risks were not significantly elevated using either method. However, for PM10-2.5, odds ratios (ORs) were >1.0 for both methods at all time periods using MI and empirical data for PM10-2.5 (coarse) except for 5 and 7.5 years using the kriging method with covariate adjustment. CONCLUSION: This investigation adds to the body of information on long-term ambient AP exposure and ALS mortality. Specifically, the 2019 US Environmental Protection Agency (EPA) Integrated Science Assessment summarized the neurotoxic effects of PM2.5, PM10, and PM10-2.5. The conclusion was that evidence of an effect of coarse PM is suggestive but the data is presently not sufficient to infer a causal relationship. Further research on AP and ALS is warranted. As time from symptom onset to death in ALS is ∼2-4 years, earlier AP measures may also be of interest to ALS development. This is the first study of ALS and AP in postmenopausal women controlling for individual-level confounders.

Short-term and intermediate-term fine particulate air pollution are synergistically associated with habitual sleep variability in adolescents - A cross-sectional study.

BACKGROUND: Both air pollution and poor sleep have been associated with increased risk of cardiovascular diseases. However, the association between air pollution and sleep health, especially among adolescents, is rarely investigated. METHODS: To investigate the association between fine particulate (PM2.5) air pollution and habitual sleep patterns, we analyzed data obtained from 246 adolescents who participated in the Penn State Child Cohort follow-up examination. We collected their individual-level 24-h (short-term) PM2.5 concentration by using a portable monitor. We estimated their residential-level PM2.5 concentration during the 60-day period prior to the examination (intermediate-term) using a kriging approach. Actigraphy was used to measure participants' sleep durations for seven consecutive nights. Habitual sleep duration (HSD) and sleep variability (HSV) were calculated as the mean and SD of the seven-night sleep duration. Multivariable-adjusted linear regression models were used to assess the association between PM2.5 exposures and HSD/HSV. An interaction between short-term and intermediate-term PM2.5 was created to explore their synergistic associations with HSD/HSV. RESULTS: Elevated short-term and intermediate-term PM2.5 exposure were significantly (p < 0.05) associated with higher HSV, but not HSD. Specifically, the mean (95% CI) increase in HSV associated with 1 SD higher 24-h (26.3 µg/m3) and 60-day average (2.2 µg/m3) PM2.5 were 14.6 (9.4, 14.8) and 4.9 (0.5, 9.2) minutes, respectively. In addition, there was a synergistic interaction (p = 0.08) between short-term and intermediate-term PM2.5 exposure on HSV, indicative that the association between intermediate-term PM2.5 and HSV became stronger as short-term PM2.5 increases, and vice versa. CONCLUSION: Short-term individual-level and intermediate-term residential-level PM2.5 exposures are adversely and synergistically associated with increased sleep variability, an indicator of instability of sleep quantity, in adolescents. Through such an association with sleep pattern, PM2.5 air pollution may increase long-term cardiometabolic risks.

Associations between air pollution, residential greenness, and glycated hemoglobin (HbA1c) in three prospective cohorts of U.S. adults.

BACKGROUND: While studies suggest impacts of individual environmental exposures on type 2 diabetes (T2D) risk, mechanisms remain poorly characterized. Glycated hemoglobin (HbA1c) is a biomarker of glycemia and diagnostic criterion for prediabetes and T2D. We explored associations between multiple environmental exposures and HbA1c in non-diabetic adults. METHODS: HbA1c was assessed once in 12,315 women and men in three U.S.-based prospective cohorts: the Nurses' Health Study (NHS), Nurses' Health Study II (NHSII), and Health Professionals Follow-up Study (HPFS). Residential greenness within 270 m and 1,230 m (normalized difference vegetation index, NDVI) was obtained from Landsat. Fine particulate matter (PM2.5) and nitrogen dioxide (NO2) were estimated from nationwide spatiotemporal models. Three-month and one-year averages prior to blood draw were assigned to participants' addresses. We assessed associations between single exposure, multi-exposure, and component scores from Principal Components Analysis (PCA) and HbA1c. Fully-adjusted models built on basic models of age and year at blood draw, BMI, alcohol use, and neighborhood socioeconomic status (nSES) to include diet quality, race, family history, smoking status, postmenopausal hormone use, population density, and season. We assessed interactions between environmental exposures, and effect modification by population density, nSES, and sex. RESULTS: Based on HbA1c, 19% of participants had prediabetes. In single exposure fully-adjusted models, an IQR (0.14) higher 1-year 1,230 m NDVI was associated with a 0.27% (95% CI: 0.05%, 0.49%) lower HbA1c. In basic component score models, a SD increase in Component 1 (high loadings for 1-year NDVI) was associated with a 0.19% (95% CI: 0.04%, 0.34%) lower HbA1c. CI's crossed the null in multi-exposure and fully-adjusted component score models. There was little evidence of associations between air pollution and HbA1c, and no evidence of effect modification. CONCLUSIONS: Among non-diabetic adults, environmental exposures were not consistently associated with HbA1c. More work is needed to elucidate biological pathways between the environment and prediabetes.

Impacts of long-term ambient particulate matter and gaseous pollutants on circulating biomarkers of inflammation in male and female health professionals.

BACKGROUND: Systemic inflammation may serve as a biological mechanism linking air pollution to poor health but supporting evidence from studies of long-term pollutant exposure and inflammatory cytokines is inconsistent. OBJECTIVE: We studied associations between multiple particulate matter (PM) and gaseous air pollutants and pro- and anti-inflammatory cytokines within two nationwide cohorts of men and women. METHODS: Data were obtained from 16,151 women in the Nurses' Health Study and 7,930 men in the Health Professionals' Follow-up Study with at least one measure of circulating adiponectin, C-Reactive Protein (CRP), Interleukin-6 (IL-6) or soluble tumor necrosis-factor receptor-2 (sTNFR-2). Exposure to PM with aerodynamic diameter ≤2.5, 2.5-10, and ≤10 µm (PM2.5, PM2.5-10, PM10) and nitrogen dioxide (NO2) was estimated using spatio-temporal models and were linked to participants' addresses at the time of blood draw. Averages of the 1-, 3-, and 12-months prior to blood draw were examined. Associations between each biomarker and pollutant were estimated from linear regression models adjusted for individual and contextual covariates. RESULTS: In adjusted models, we observed a 2.72% (95% CI: 0.43%, 5.95%), 3.11% (-0.12%, 6.45%), and 3.67% (0.19%, 7.26%) increase in CRP associated with a 10 µg/m3 increase in 1-, 3-, and 12- month averaged NO2 in women. Among men, there was a statistically significant 5.96% (95% CI: 0.07%, 12.20%), 6.99% (95% CI: 0.29%, 14.15%), and 8.33% (95% CI: 0.35%, 16.94%) increase in CRP associated with a 10 µg/m3 increase in 1-, 3-, and 12-month averaged PM2.5-10, respectively. Increasing PM2.5-10 was associated with increasing IL-6 and sTNFR-2 among men over shorter exposure durations. There were no associations with exposures to PM2.5 or PM10, or with adiponectin. Findings were robust to sensitivity analyses restricting to disease-free controls and non-movers. CONCLUSIONS: Across multiple long-term pollutant exposures and inflammatory markers, associations were generally weak. Focusing on specific pollutant-inflammatory mechanisms may clarify pathways.

Gaseous air pollutants and DNA methylation in a methylome-wide association study of an ethnically and environmentally diverse population of U.S. adults.

Epigenetic mechanisms may underlie air pollution-health outcome associations. We estimated gaseous air pollutant-DNA methylation (DNAm) associations using twelve subpopulations within Women's Health Initiative (WHI) and Atherosclerosis Risk in Communities (ARIC) cohorts (n = 8397; mean age 61.3 years; 83% female; 46% African-American, 46% European-American, 8% Hispanic/Latino). We used geocoded participant address-specific mean ambient carbon monoxide (CO), nitrogen oxides (NO2; NOx), ozone (O3), and sulfur dioxide (SO2) concentrations estimated over the 2-, 7-, 28-, and 365-day periods before collection of blood samples used to generate Illumina 450 k array leukocyte DNAm measurements. We estimated methylome-wide, subpopulation- and race/ethnicity-stratified pollutant-DNAm associations in multi-level, linear mixed-effects models adjusted for sociodemographic, behavioral, meteorological, and technical covariates. We combined stratum-specific estimates in inverse variance-weighted meta-analyses and characterized significant associations (false discovery rate; FDR<0.05) at Cytosine-phosphate-Guanine (CpG) sites without among-strata heterogeneity (PCochran's Q > 0.05). We attempted replication in the Cooperative Health Research in Region of Augsburg (KORA) study and Normative Aging Study (NAS). We observed a -0.3 (95% CI: -0.4, -0.2) unit decrease in percent DNAm per interquartile range (IQR, 7.3 ppb) increase in 28-day mean NO2 concentration at cg01885635 (chromosome 3; regulatory region 290 bp upstream from ZNF621; FDR = 0.03). At intragenic sites cg21849932 (chromosome 20; LIME1; intron 3) and cg05353869 (chromosome 11; KLHL35; exon 2), we observed a -0.3 (95% CI: -0.4, -0.2) unit decrease (FDR = 0.04) and a 1.2 (95% CI: 0.7, 1.7) unit increase (FDR = 0.04), respectively, in percent DNAm per IQR (17.6 ppb) increase in 7-day mean ozone concentration. Results were not fully replicated in KORA and NAS. We identified three CpG sites potentially susceptible to gaseous air pollution-induced DNAm changes near genes relevant for cardiovascular and lung disease. Further harmonized investigations with a range of gaseous pollutants and averaging durations are needed to determine the effect of gaseous air pollutants on DNA methylation and ultimately gene expression.

Epigenetically mediated electrocardiographic manifestations of sub-chronic exposures to ambient particulate matter air pollution in the Women's Health Initiative and Atherosclerosis Risk in Communities Study.

BACKGROUND: Short-duration exposure to ambient particulate matter (PM) air pollution is associated with cardiac autonomic dysfunction and prolonged ventricular repolarization. However, associations with sub-chronic exposures to coarser particulates are relatively poorly characterized as are molecular mechanisms underlying their potential relationships with cardiovascular disease. MATERIALS AND METHODS: We estimated associations between monthly mean concentrations of PM < 10 µm and 2.5-10 µm in diameter (PM10; PM2.5-10) with time-domain measures of heart rate variability (HRV) and QT interval duration (QT) among U.S. women and men in the Women's Health Initiative and Atherosclerosis Risk in Communities Study (nHRV = 82,107; nQT = 76,711). Then we examined mediation of the PM-HRV and PM-QT associations by DNA methylation (DNAm) at three Cytosine-phosphate-Guanine (CpG) sites (cg19004594, cg24102420, cg12124767) with known sensitivity to monthly mean PM concentrations in a subset of the participants (nHRV = 7,169; nQT = 6,895). After multiply imputing missing PM, electrocardiographic and covariable data, we estimated associations using attrition-weighted, linear, mixed, longitudinal models adjusting for sociodemographic, behavioral, meteorological, and clinical characteristics. We assessed mediation by estimating the proportions of PM-HRV and PM-QT associations mediated by DNAm. RESULTS: We found little evidence of PM-HRV association, PM-QT association, or mediation by DNAm. CONCLUSIONS: The findings suggest that among racially/ethnically and environmentally diverse U.S. populations, sub-chronic exposures to coarser particulates may not exert appreciable, epigenetically mediated effects on cardiac autonomic function or ventricular repolarization. Further investigation in better-powered studies is warranted, with additional focus on shorter duration exposures to finer particulates and non-electrocardiographic outcomes among relatively susceptible populations.

Exploring persistent racial/ethnic disparities in lead exposure among American children aged 1-5 years: results from NHANES 1999-2016.

OBJECTIVE: The aim of this study was to determine whether long-standing racial disparities in lead exposure still exists for children age 1-5 years old. We examined if blood lead levels were higher among non-Hispanic Black children and others compared to non-Hispanic White children. METHODS: Data from the National Health and Nutrition Examination Survey (NHANES) from 1999-2016 were used. Geometric mean blood lead levels (BLLs) were compared by race/ethnicity using log-transformed simple linear regression. Associations between race and elevated BLL were assessed using weighted Chi-square tests. Log-transformed multiple weighted linear regression was used to assess what factors affected BLLs. RESULTS: A total of 6772 children were included in this study. In 1999-2000, the geometric mean BLL for non-Hispanic Black children was 3.08 µg/dL, compared to 2.03 µg/dL for non-Hispanic White children (p = 0.01). The difference in geometric mean BLL between non-Hispanic Black children and non-Hispanic White children continued to be statistically significant in later years (all p < 0.05) until 2015-2016 (0.89 µg/dL vs 0.74 µg/dL, p = 0.17). Log-transformed linear regression showed that being non-Hispanic Black and having low family income were independently associated with higher BLL. CONCLUSION: Although lead exposure in the general population continued to decline for all racial/ethnic groups, non-Hispanic Black children still had higher BLL than non-Hispanic White children. In more recent years, the racial/ethnic gap was lesser but persisted. Racial/ethnic disparity in childhood BLL could be partially explained by socio-economic factors.

Long-term exposure to particulate air pollution and brachial artery flow-mediated dilation in the Old Order Amish.

BACKGROUND: Atmospheric particulate matter (PM) has been associated with endothelial dysfunction, an early marker of cardiovascular risk. Our aim was to extend this research to a genetically homogenous, geographically stable rural population using location-specific moving-average air pollution exposure estimates indexed to the date of endothelial function measurement. METHODS: We measured endothelial function using brachial artery flow-mediated dilation (FMD) in 615 community-dwelling healthy Amish participants. Exposures to PM < 2.5 µm (PM2.5) and PM < 10 µm (PM10) were estimated at participants' residential addresses using previously developed geographic information system-based spatio-temporal models and normalized. Associations between PM exposures and FMD were evaluated using linear mixed-effects regression models, and polynomial distributed lag (PDL) models followed by Bayesian model averaging (BMA) were used to assess response to delayed effects occurring across multiple months. RESULTS: Exposure to PM10 was consistently inversely associated with FMD, with the strongest (most negative) association for a 12-month moving average (- 0.09; 95% CI: - 0.15, - 0.03). Associations with PM2.5 were also strongest for a 12-month moving average but were weaker than for PM10 (- 0.07; 95% CI: - 0.13, - 0.09). Associations of PM2.5 and PM10 with FMD were somewhat stronger in men than in women, particularly for PM10. CONCLUSIONS: Using location-specific moving-average air pollution exposure estimates, we have shown that 12-month moving-average estimates of PM2.5 and PM10 exposure are associated with impaired endothelial function in a rural population.

Fine particulate matter exposure and olfactory dysfunction among urban-dwelling older US adults.

OBJECTIVES: The olfactory nerve is anatomically susceptible to injury from pollution in inspired air, but there are no large-scale epidemiologic studies investigating this relationship. METHODS: Cross-sectional study using data from the National Social Life, Health, and Aging Project, a representative sample of home-dwelling US adults age 57-85 years. Olfactory function was tested using a validated 5-item odor identification test (Sniffin' Sticks). Exposure to fine particulate matter (PM2.5) at each respondent's home was estimated as 1-12 month moving averages prior to olfactory assessment using validated spatio-temporal models. RESULTS: Olfactory dysfunction was significantly associated with PM2.5 exposures averaged over 3-12 months in urban-dwelling respondents. The strongest effect was for 6 month average exposure (per 1-IQR increase in PM2.5: OR 1.28, 95% CI 1.05, 1.55) adjusting for age, gender, race/ethnicity, education, cognition, comorbidity, smoking, and the season. Interestingly, the most deleterious effects were observed among the youngest respondents, 57-64 years old, and those living in the northeast and south. CONCLUSIONS: We show for the first time that air pollution exposure is associated with poor olfaction among urban-living, older US adults.

Variability of Uncrossmatched Blood Use by Helicopter EMS Programs in the United States.

STUDY OBJECTIVE: Some helicopter emergency medical services (HEMS) maintain an independent supply of blood for use during transport, although practice is variable and not well described. We aimed to characterize the blood-carrying practices by HEMS programs across the United States. METHODS: Online surveys were sent to the leadership of the 261 HEMS programs nationwide listed in the 2011 Atlas and Database of Air Medical Services (ADAMS) database. We examined blood-carrying practices in aggregate, including typical transport time, proportion of scene transports, and local population density. A GIS (Geographic Information System) and multivariable logistic regression models were used to estimate the impact of characteristics of local practice on each program's decision to carry blood. RESULTS: A total of 235 (91%) programs responded to the survey, representing 857 of the 929 (92.2%) HEMS rotor wing aircraft nationwide. Fifty-nine (25.3%) programs independently carried blood. A higher proportion of interfacility transports (OR 1.023; 95% CI 1.010-1.036) and decreased local population density (OR 1.006; 95% CI 1.001-1.011) were associated with increased odds of carrying blood. Transport time (OR 1.006; 95% CI 0.991-1.020) and number of transports (OR 1.000; CI 1.000-1.000) were not associated with a program's blood carrying practices. There was no effect of local practices on a program's decision to carry blood (OR 1.002; 95% CI 0.980-1.026). CONCLUSION: There is great variability in the utilization of blood by HEMS programs in the United States. Programs that serve more rural areas and programs with a larger percentage of interfacility transports are more likely to independently carry blood.

The association of long-term exposure to PM2.5 on all-cause mortality in the Nurses' Health Study and the impact of measurement-error correction.

BACKGROUND: Long-term exposure to particulate matter less than 2.5 µm in diameter (PM2.5) has been consistently associated with risk of all-cause mortality. The methods used to assess exposure, such as area averages, nearest monitor values, land use regressions, and spatio-temporal models in these studies are subject to measurement error. However, to date, no study has attempted to incorporate adjustment for measurement error into a long-term study of the effects of air pollution on mortality. METHODS: We followed 108,767 members of the Nurses' Health Study (NHS) 2000-2006 and identified all deaths. Biennial mailed questionnaires provided a detailed residential address history and updated information on potential confounders. Time-varying average PM2.5 in the previous 12-months was assigned based on residential address and was predicted from either spatio-temporal prediction models or as concentrations measured at the nearest USEPA monitor. Information on the relationships of personal exposure to PM2.5 of ambient origin with spatio-temporal predicted and nearest monitor PM2.5 was available from five previous validation studies. Time-varying Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95 percent confidence intervals (95%CI) for each 10 µg/m(3) increase in PM2.5. Risk-set regression calibration was used to adjust estimates for measurement error. RESULTS: Increasing exposure to PM2.5 was associated with an increased risk of mortality, and results were similar regardless of the method chosen for exposure assessment. Specifically, the multivariable adjusted HRs for each 10 µg/m(3) increase in 12-month average PM2.5 from spatio-temporal prediction models were 1.13 (95%CI:1.05, 1.22) and 1.12 (95%CI:1.05, 1.21) for concentrations at the nearest EPA monitoring location. Adjustment for measurement error increased the magnitude of the HRs 4-10% and led to wider CIs (HR = 1.18; 95%CI: 1.02, 1.36 for each 10 µg/m(3) increase in PM2.5 from the spatio-temporal models and HR = 1.22; 95%CI: 1.02, 1.45 from the nearest monitor estimates). CONCLUSIONS: These findings support the large body of literature on the adverse effects of PM2.5, and suggest that adjustment for measurement error be considered in future studies where possible.

Spatio-temporal modeling of particulate air pollution in the conterminous United States using geographic and meteorological predictors.

BACKGROUND: Exposure to atmospheric particulate matter (PM) remains an important public health concern, although it remains difficult to quantify accurately across large geographic areas with sufficiently high spatial resolution. Recent epidemiologic analyses have demonstrated the importance of spatially- and temporally-resolved exposure estimates, which show larger PM-mediated health effects as compared to nearest monitor or county-specific ambient concentrations. METHODS: We developed generalized additive mixed models that describe regional and small-scale spatial and temporal gradients (and corresponding uncertainties) in monthly mass concentrations of fine (PM2.5), inhalable (PM10), and coarse mode particle mass (PM(2.5-10)) for the conterminous United States (U.S.). These models expand our previously developed models for the Northeastern and Midwestern U.S. by virtue of their larger spatial domain, their inclusion of an additional 5 years of PM data to develop predictions through 2007, and their use of refined geographic covariates for population density and point-source PM emissions. Covariate selection and model validation were performed using 10-fold cross-validation (CV). RESULTS: The PM2.5 models had high predictive accuracy (CV R2=0.77 for both 1988-1998 and 1999-2007). While model performance remained strong, the predictive ability of models for PM10 (CV R2=0.58 for both 1988-1998 and 1999-2007) and PM(2.5-10) (CV R2=0.46 and 0.52 for 1988-1998 and 1999-2007, respectively) was somewhat lower. Regional variation was found in the effects of geographic and meteorological covariates. Models generally performed well in both urban and rural areas and across seasons, though predictive performance varied somewhat by region (CV R2=0.81, 0.81, 0.83, 0.72, 0.69, 0.50, and 0.60 for the Northeast, Midwest, Southeast, Southcentral, Southwest, Northwest, and Central Plains regions, respectively, for PM2.5 from 1999-2007). CONCLUSIONS: Our models provide estimates of monthly-average outdoor concentrations of PM2.5, PM10, and PM(2.5-10) with high spatial resolution and low bias. Thus, these models are suitable for estimating chronic exposures of populations living in the conterminous U.S. from 1988 to 2007.

Exposure measurement error in PM2.5 health effects studies: a pooled analysis of eight personal exposure validation studies.

BACKGROUND: Exposure measurement error is a concern in long-term PM2.5 health studies using ambient concentrations as exposures. We assessed error magnitude by estimating calibration coefficients as the association between personal PM2.5 exposures from validation studies and typically available surrogate exposures. METHODS: Daily personal and ambient PM2.5, and when available sulfate, measurements were compiled from nine cities, over 2 to 12 days. True exposure was defined as personal exposure to PM2.5 of ambient origin. Since PM2.5 of ambient origin could only be determined for five cities, personal exposure to total PM2.5 was also considered. Surrogate exposures were estimated as ambient PM2.5 at the nearest monitor or predicted outside subjects' homes. We estimated calibration coefficients by regressing true on surrogate exposures in random effects models. RESULTS: When monthly-averaged personal PM2.5 of ambient origin was used as the true exposure, calibration coefficients equaled 0.31 (95% CI:0.14, 0.47) for nearest monitor and 0.54 (95% CI:0.42, 0.65) for outdoor home predictions. Between-city heterogeneity was not found for outdoor home PM2.5 for either true exposure. Heterogeneity was significant for nearest monitor PM2.5, for both true exposures, but not after adjusting for city-average motor vehicle number for total personal PM2.5. CONCLUSIONS: Calibration coefficients were <1, consistent with previously reported chronic health risks using nearest monitor exposures being under-estimated when ambient concentrations are the exposure of interest. Calibration coefficients were closer to 1 for outdoor home predictions, likely reflecting less spatial error. Further research is needed to determine how our findings can be incorporated in future health studies.

Novel Quality Control Metric for the Pharmacotherapy of Major Depressive Disorder: Measuring Guideline Concordance and Its Impact on Symptom Severity.

Objective: Studies suggest that people with major depressive disorder (MDD) often receive treatment that is not concordant with practice guidelines. To evaluate this, we (1) developed a guideline concordance algorithm for MDD pharmacotherapy (GCA-8), (2) scored it using clinical data, and (3) compared its explanation of patient-reported symptom severity to a traditional concordance measure.Methods: This study evaluated 1,403 adults (67% female, 85% non-Hispanic/Latino White, mean age 43 years) with non-psychotic MDD (per ICD-10 codes), from the Penn State Psychiatry Clinical Assessment and Rating Evaluation System (PCARES) registry (visits from February 1, 2015, to April 13, 2021). We (1) scored 1-year concordance using the Canadian Network for Mood and Anxiety Treatments (CANMAT) guidelines and deviation from 8 pharmacotherapy-related criteria and (2) examined associations between concordance and Patient Health Questionnaire depression module (PHQ-9) scores.Results: The mean GCA-8 score was 6.37 (standard deviation [SD] = 1.30; 8.00 = perfect concordance). Among those who switched drugs (n = 671), 81% (n = 542) did not have their dose increased to the recommended maximum before switching. In our adjusted analyses, we found that a 1 SD increase in the GCA-8 was associated with a 0.78 improvement in the mean PHQ-9 score (P < .001). The comparison concordance measure was not associated with the mean PHQ-9 score (ß = -0.20; P = .20; R2 = 0.53), and adding the GCA-8 score significantly improved the model (R2 = 0.54; Vuong test P = .008).Conclusions: By measuring naturalistic MDD pharmacotherapy guideline concordance with the GCA-8, we revealed potential treatment gaps and an inverse association between guideline concordance and MDD symptom severity.

Short-term exposures to temperature and risk of sudden cardiac death in women: A case-crossover analysis in the Nurses' Health Study.

Background: Sudden cardiac death (SCD) is a major source of mortality and is the first manifestation of heart disease for most cases. Thus, there is a definite need to identify risk factors for SCD that can be modified on the population level. Short-term exposures to temperature have been implicated as a potential risk factor. Our objective was to determine if short-term temperature exposures were associated with increased risk of SCD in a US-based time-stratified case-crossover study. Methods: A total of 465 cases of SCD were identified among participants of the prospective Nurses' Health Study (NHS). Control days were selected from all other matching days of the week within the same month as the case day. Average ambient temperature on the current day (Lag0) and preceding 27 days (Lags1-27) was determined at the residence level using 800-m resolution estimates. Conditional logistic distributed lag nonlinear models (DLNMs) were used to assess the relative risk (RR) of the full range of temperature exposures over the lag period. Results: Warmer exposures in the days before event and colder temperatures 21-28 days prior were associated with increased risks of SCD. These results were driven by associations in regions other than the Northeast and among married women. Conclusions: Both warm and cold ambient temperatures are suggestively associated with risks of SCD among middle-aged and older women living across the United States.

Associations of fine particulate matter with incident cardiovascular disease; comparing models using ZIP code-level and individual-level fine particulate matter and confounders.

BACKGROUND: PM2.5 has been positively associated with cardiovascular disease (CVD) incidence. Most evidence has come from cohorts and administrative databases. Cohorts typically have extensive information on potential confounders and residential-level exposures. Administrative databases are usually more representative but typically lack information on potential confounders and often only have exposures at coarser geographies (e.g., ZIP code). The weaknesses in both types of studies have been criticized for potentially jeopardizing the validity of their findings for regulatory purposes. METHODS: We followed 101,870 participants from the US-based Nurses' Health Study (2000-2016) and linked residential-level PM2.5 and individual-level confounders, and ZIP code-level PM2.5 and confounders. We used time-varying Cox proportional hazards models to examine associations with CVD incidence. We specified basic models (adjusted for individual-level age, race and calendar year), individual-level confounder models, and ZIP code-level confounder models. RESULTS: Residential- and ZIP code-level PM2.5 were strongly correlated (Pearson r = 0.88). For residential-level PM2.5, the hazard ratio (HR, 95 % confidence interval) per 5 µg/m3 increase was 1.06 (1.01, 1.11) in the basic and 1.04 (0.99, 1.10) in the individual-level confounder model. For ZIP code-level PM2.5, the HR per 5 µg/m3 was 1.04 (0.99, 1.08) in the basic and 1.02 (0.97, 1.08) in the ZIP code-level confounder model. CONCLUSION: We observed suggestive positive, but not statistically significant, associations between long-term PM2.5 and CVD incidence, regardless of the exposure or confounding model. Although differences were small, associations from models with individual-level confounders and residential-level PM2.5 were slightly stronger than associations from models with ZIP code-level confounders and PM2.5.