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1.  Ambient Fine Particulate Matter, Nitrogen Dioxide, and Term Birth Weight in New York, New York 
American Journal of Epidemiology  2013;179(4):457-466.
Building on a unique exposure assessment project in New York, New York, we examined the relationship of particulate matter with aerodynamic diameter less than 2.5 μm and nitrogen dioxide with birth weight, restricting the population to term births to nonsmokers, along with other restrictions, to isolate the potential impact of air pollution on growth. We included 252,967 births in 2008–2010 identified in vital records, and we assigned exposure at the residential location by using validated models that accounted for spatial and temporal factors. Estimates of association were adjusted for individual and contextual sociodemographic characteristics and season, using linear mixed models to quantify the predicted change in birth weight in grams related to increasing pollution levels. Adjusted estimates for particulate matter with aerodynamic diameter less than 2.5 μm indicated that for each 10-µg/m3 increase in exposure, birth weights declined by 18.4, 10.5, 29.7, and 48.4 g for exposures in the first, second, and third trimesters and for the total pregnancy, respectively. Adjusted estimates for nitrogen dioxide indicated that for each 10-ppb increase in exposure, birth weights declined by 14.2, 15.9, 18.0, and 18.0 g for exposures in the first, second, and third trimesters and for the total pregnancy, respectively. These results strongly support the association of urban air pollution exposure with reduced fetal growth.
doi:10.1093/aje/kwt268
PMCID: PMC3908629  PMID: 24218031
air pollution; birth weight; nitrogen dioxide; particulate matter; pregnancy
2.  Cause-Specific Risk of Hospital Admission Related to Extreme Heat in Older Adults 
JAMA  2014;312(24):2659-2667.
IMPORTANCE
Heat exposure is known to have a complex set of physiological effects on multiple organ systems, but current understanding of the health effects is mostly based on studies investigating a small number of prespecified health outcomes such as cardiovascular and respiratory diseases.
OBJECTIVES
To identify possible causes of hospital admissions during extreme heat events and to estimate their risks using historical data.
DESIGN, SETTING, AND POPULATION
Matched analysis of time series data describing daily hospital admissions of Medicare enrollees (23.7 million fee-for-service beneficiaries [aged ≥65 years] per year; 85% of all Medicare enrollees) for the period 1999 to 2010 in 1943 counties in the United States with at least 5 summers of near-complete (>95%) daily temperature data.
EXPOSURES
Heat wave periods, defined as 2 or more consecutive days with temperatures exceeding the 99th percentile of county-specific daily temperatures, matched to non–heat wave periods by county and week.
MAIN OUTCOMES AND MEASURES
Daily cause-specific hospitalization rates by principal discharge diagnosis codes, grouped into 283 disease categories using a validated approach.
RESULTS
Risks of hospitalization for fluid and electrolyte disorders, renal failure, urinary tract infection, septicemia, and heat stroke were statistically significantly higher on heat wave days relative to matched non–heat wave days, but risk of hospitalization for congestive heart failure was lower (P < .05). Relative risks for these disease groups were 1.18 (95% CI, 1.12–1.25) for fluid and electrolyte disorders, 1.14 (95% CI, 1.06–1.23) for renal failure, 1.10 (95% CI, 1.04–1.16) for urinary tract infections, 1.06 (95% CI, 1.00–1.11) for septicemia, and 2.54 (95% CI, 2.14–3.01) for heat stroke. Absolute risk differences were 0.34 (95% CI, 0.22–0.46) excess admissions per 100 000 individuals at risk for fluid and electrolyte disorders, 0.25 (95% CI, 0.12–0.39) for renal failure, 0.24 (95% CI, 0.09–0.39) for urinary tract infections, 0.21 (95% CI, 0.01–0.41) for septicemia, and 0.16 (95% CI, 0.10–0.22) for heat stroke. For fluid and electrolyte disorders and heat stroke, the risk of hospitalization increased during more intense and longer-lasting heat wave periods (P < .05). Risks were generally highest on the heat wave day but remained elevated for up to 5 subsequent days.
CONCLUSIONS AND RELEVANCE
Among older adults, periods of extreme heat were associated with increased risk of hospitalization for fluid and electrolyte disorders, renal failure, urinary tract infection, septicemia, and heat stroke. However, the absolute risk increase was small and of uncertain clinical importance.
doi:10.1001/jama.2014.15715
PMCID: PMC4319792  PMID: 25536257
3.  Uncertainty in Propensity Score Estimation: Bayesian Methods for Variable Selection and Model Averaged Causal Effects 
Causal inference with observational data frequently relies on the notion of the propensity score (PS) to adjust treatment comparisons for observed confounding factors. As decisions in the era of “big data” are increasingly reliant on large and complex collections of digital data, researchers are frequently confronted with decisions regarding which of a high-dimensional covariate set to include in the PS model in order to satisfy the assumptions necessary for estimating average causal effects. Typically, simple or ad-hoc methods are employed to arrive at a single PS model, without acknowledging the uncertainty associated with the model selection. We propose three Bayesian methods for PS variable selection and model averaging that 1) select relevant variables from a set of candidate variables to include in the PS model and 2) estimate causal treatment effects as weighted averages of estimates under different PS models. The associated weight for each PS model reflects the data-driven support for that model’s ability to adjust for the necessary variables. We illustrate features of our proposed approaches with a simulation study, and ultimately use our methods to compare the effectiveness of surgical vs. nonsurgical treatment for brain tumors among 2,606 Medicare beneficiaries. Supplementary materials are available online.
doi:10.1080/01621459.2013.869498
PMCID: PMC3969816  PMID: 24696528
Bayesian statistics; causal inference; comparative effectiveness; model averaging; propensity score
4.  Association Between the Medicare Hospice Benefit and Health Care Utilization and Costs for Patients With Poor-Prognosis Cancer 
JAMA  2014;312(18):1888-1896.
IMPORTANCE
More patients with cancer use hospice currently than ever before, but there are indications that care intensity outside of hospice is increasing, and length of hospice stay decreasing. Uncertainties regarding how hospice affects health care utilization and costs have hampered efforts to promote it.
OBJECTIVE
To compare utilization and costs of health care for patients with poor-prognosis cancers enrolled in hospice vs similar patients without hospice care.
DESIGN, SETTING, AND PARTICIPANTS
Matched cohort study of patients in hospice and nonhospice care using a nationally representative 20% sample of Medicare fee-for-service beneficiaries who died in 2011. Patients with poor-prognosis cancers (eg, brain, pancreatic, metastatic malignancies) enrolled in hospice before death were matched to similar patients who died without hospice care.
EXPOSURES
Period between hospice enrollment and death for hospice beneficiaries, and the equivalent period of nonhospice care before death for matched nonhospice patients.
MAIN OUTCOMES AND MEASURES
Health care utilization including hospitalizations and procedures, place of death, cost trajectories before and after hospice start, and cumulative costs, all during the last year of life.
RESULTS
Among 86 851 patients with poor-prognosis cancers, median time from first poor-prognosis diagnosis to death was 13 months (interquartile range [IQR], 3–34), and 51 924 (60%) entered hospice before death. Matching yielded a cohort balanced on age, sex, region, time from poor-prognosis diagnosis to death, and baseline care utilization, with 18 165 patients in the hospice group and 18 165 in the nonhospice group.
After matching, 11% of nonhospice and 1% of hospice beneficiaries who had cancer-directed therapy after exposure were excluded. Median hospice duration was 11 days. Nonhospice beneficiaries had significantly greater health care utilization, largely for acute conditions not directly related to cancer and higher overall costs.
CONCLUSIONS AND RELEVANCE
In this sample of Medicare fee-for-service beneficiaries with poor-prognosis cancer, those receiving hospice care vs not (control), had significantly lower rates of hospitalization, intensive care unit admission, and invasive procedures at the end of life, along with significantly lower total costs during the last year of life.
doi:10.1001/jama.2014.14950
PMCID: PMC4274169  PMID: 25387186
5.  Does exposure prediction bias health effect estimation? The relationship between confounding adjustment and exposure prediction 
Epidemiology (Cambridge, Mass.)  2014;25(4):583-590.
In environmental epidemiology, we are often faced with two challenges. First, an exposure prediction model is needed to estimate the exposure to an agent of interest, ideally at the individual level. Second, when estimating the health-effect associated with the exposure, confounding adjustment is needed in the health-effects regression model. The current literature addresses these two challenges separately. That is, methods that account for measurement error in the predicted exposure often fail to acknowledge the possibility of confounding, while methods designed to control confounding often fail to acknowledge that the exposure has been predicted. In this paper, we consider exposure prediction and confounding adjustment in a health-effects regression model simultaneously. By using theoretical arguments and simulation studies, we show that the bias of a health-effect estimate is influenced by the exposure prediction model, the type of confounding adjustment used in the health-effects regression model, and the relationship between these two. Moreover, we argue that even with a health-effects regression model that properly adjusts for confounding, the use of a predicted exposure can bias the health-effect estimate unless all confounders included in the health-effects regression model are also included in the exposure prediction model. While these results of this paper were motivated by studies of environmental contaminants, they apply more broadly to any context where an exposure needs to be predicted.
doi:10.1097/EDE.0000000000000099
PMCID: PMC4206696  PMID: 24815302
6.  Particulate Matter Matters 
Science (New York, N.Y.)  2014;344(6181):257-259.
Quasi-experimental evidence is needed on the relations between human health and airborne particulate matter.
doi:10.1126/science.1247348
PMCID: PMC4206184  PMID: 24744361
7.  Linking Student Performance in Massachusetts Elementary Schools with the “Greenness” of School Surroundings Using Remote Sensing 
PLoS ONE  2014;9(10):e108548.
Various studies have reported the physical and mental health benefits from exposure to “green” neighborhoods, such as proximity to neighborhoods with trees and vegetation. However, no studies have explicitly assessed the association between exposure to “green” surroundings and cognitive function in terms of student academic performance. This study investigated the association between the “greenness” of the area surrounding a Massachusetts public elementary school and the academic achievement of the school’s student body based on standardized tests with an ecological setting. Researchers used the composite school-based performance scores generated by the Massachusetts Comprehensive Assessment System (MCAS) to measure the percentage of 3rd-grade students (the first year of standardized testing for 8–9 years-old children in public school), who scored “Above Proficient” (AP) in English and Mathematics tests (Note: Individual student scores are not publically available). The MCAS results are comparable year to year thanks to an equating process. Researchers included test results from 2006 through 2012 in 905 public schools and adjusted for differences between schools in the final analysis according to race, gender, English as a second language (proxy for ethnicity and language facility), parent income, student-teacher ratio, and school attendance. Surrounding greenness of each school was measured using satellite images converted into the Normalized Difference Vegetation Index (NDVI) in March, July and October of each year according to a 250-meter, 500-meter, 1,000-meter, and 2000-meter circular buffer around each school. Spatial Generalized Linear Mixed Models (GLMMs) estimated the impacts of surrounding greenness on school-based performance. Overall the study results supported a relationship between the “greenness” of the school area and the school-wide academic performance. Interestingly, the results showed a consistently positive significant association between the greenness of the school in the Spring (when most Massachusetts students take the MCAS tests) and school-wide performance on both English and Math tests, even after adjustment for socio-economic factors and urban residency.
doi:10.1371/journal.pone.0108548
PMCID: PMC4195655  PMID: 25310542
8.  Evidence on Vulnerability and Susceptibility to Health Risks Associated With Short-Term Exposure to Particulate Matter: A Systematic Review and Meta-Analysis 
American Journal of Epidemiology  2013;178(6):865-876.
Although there is strong evidence that short-term exposure to particulate matter is associated with health risks, less is known about whether some subpopulations face higher risks. We identified 108 papers published after 1995 and summarized the scientific evidence regarding effect modification of associations between short-term exposure to particulate matter and the risk of death or hospitalization. We performed a meta-analysis of estimated mortality associations by age and sex. We found strong, consistent evidence that the elderly experience higher risk of particular matter­–associated hospitalization and death, weak evidence that women have higher risks of hospitalization and death, and suggestive evidence that those with lower education, income, or employment status have higher risk of death. Meta-analysis showed a statistically higher risk of death of 0.64% (95% confidence interval (CI): 0.50, 0.78) for older populations compared with 0.34% (95% CI: 0.25, 0.42) for younger populations per 10 μg/m3 increase of particulate matter with aerodynamic diameter ≤10 μm. Women had a slightly higher risk of death of 0.55% (95% CI: 0.41, 0.70) compared with 0.50% (95% CI: 0.34, 0.54) for men, but these 2 risks were not statistically different. Our synthesis on modifiers for risks associated with particulate matter can aid the design of air quality policies and suggest directions for future research. Studies of biological mechanisms could be informed by evidence of differential risks by population, such as by sex and preexisting conditions.
doi:10.1093/aje/kwt090
PMCID: PMC3775545  PMID: 23887042
age; effect modifiers; hospital admissions; mortality; particulate matter; PM10; PM2.5; socioeconomic status
9.  Heat-Related Mortality and Adaptation to Heat in the United States 
Environmental Health Perspectives  2014;122(8):811-816.
Background: In a changing climate, increasing temperatures are anticipated to have profound health impacts. These impacts could be mitigated if individuals and communities adapt to changing exposures; however, little is known about the extent to which the population may be adapting.
Objective: We investigated the hypothesis that if adaptation is occurring, then heat-related mortality would be decreasing over time.
Methods: We used a national database of daily weather, air pollution, and age-stratified mortality rates for 105 U.S. cities (covering 106 million people) during the summers of 1987–2005. Time-varying coefficient regression models and Bayesian hierarchical models were used to estimate city-specific, regional, and national temporal trends in heat-related mortality and to identify factors that might explain variation across cities.
Results: On average across cities, the number of deaths (per 1,000 deaths) attributable to each 10°F increase in same-day temperature decreased from 51 [95% posterior interval (PI): 42, 61] in 1987 to 19 (95% PI: 12, 27) in 2005. This decline was largest among those ≥ 75 years of age, in northern regions, and in cities with cooler climates. Although central air conditioning (AC) prevalence has increased, we did not find statistically significant evidence of larger temporal declines among cities with larger increases in AC prevalence.
Conclusions: The population has become more resilient to heat over time. Yet even with this increased resilience, substantial risks of heat-related mortality remain. Based on 2005 estimates, an increase in average temperatures by 5°F (central climate projection) would lead to an additional 1,907 deaths per summer across all cities.
Citation: Bobb JF, Peng RD, Bell ML, Dominici F. 2014. Heat-related mortality and adaptation to heat in the United States. Environ Health Perspect 122:811–816; http://dx.doi.org/10.1289/ehp.1307392
doi:10.1289/ehp.1307392
PMCID: PMC4123027  PMID: 24780880
10.  Acute effects of ambient ozone on mortality in Europe and North America: results from the APHENA study 
The “Air Pollution and Health: A Combined European and North American Approach” (APHENA) project is a collaborative analysis of multi-city time-series data on the association between air pollution and adverse health outcomes. The main objective of APHENA was to examine the coherence of findings of time-series studies relating short-term fluctuations in air pollution levels to mortality and morbidity in 125 cities in Europe, the US, and Canada. Multi-city time-series analysis was conducted using a two-stage approach. We used Poisson regression models controlling for overdispersion with either penalized or natural splines to adjust for seasonality. Hierarchical models were used to obtain an overall estimate of excess mortality associated with ozone and to assess potential effect modification. Potential effect modifiers were city-level characteristics related to exposure to other ambient air pollutants, weather, socioeconomic status, and the vulnerability of the population. Regionally pooled risk estimates from Europe and the US were similar; those from Canada were substantially higher. The pooled estimated excess relative risk associated with a 10 µg/m3 increase in 1 h daily maximum O3 was 0.26 % (95 % CI, 0.15 %, 0.37 %). Across regions, there was little consistent indication of effect modification by age or other effect modifiers considered in the analysis. The findings from APHENA on the effects of O3 on mortality in the general population were comparable with previously reported results and relatively robust to the method of data analysis. Overall, there was no indication of strong effect modification by age or ecologic variables considered in the analysis.
doi:10.1007/s11869-012-0180-9
PMCID: PMC3668792  PMID: 23734168
Ozone; Mortality; Time-series; Multi-city; Cardiovascular; Respiratory
11.  A national case-crossover analysis of the short-term effect of PM2.5 on hospitalizations and mortality in subjects with diabetes and neurological disorders 
Environmental Health  2014;13:38.
Background
Diabetes and neurological disorders are a growing burden among the elderly, and may also make them more susceptible to particulate air matter with aerodynamic diameter less than 2.5 μg (PM2.5). The same biological responses thought to effect cardiovascular disease through air pollution-mediated systemic oxidative stress, inflammation and cerebrovascular dysfunction could also be relevant for diabetes and neurodegenerative diseases.
Methods
We conducted multi-site case-crossover analyses of all-cause deaths and of hospitalizations for diabetes or neurological disorders among Medicare enrollees (>65 years) during the period 1999 to 2010 in 121 US communities. We examined whether 1) short-term exposure to PM2.5 increases the risk of hospitalization for diabetes or neurological disorders, and 2) the association between short-term exposure to PM2.5 and all-cause mortality is modified by having a previous hospitalization of diabetes or neurological disorders.
Results
We found that short term exposure to PM2.5 is significantly associated with an increase in hospitalization risks for diabetes (1.14% increase, 95% CI: 0.56, 1.73 for a 10 μg/m3 increase in the 2 days average), and for Parkinson’s disease (3.23%, 1.08, 5.43); we also found an increase in all-cause mortality risks (0.64%, 95% CI: 0.42, 0.85), but we didn’t find that hospitalization for diabetes and neurodegenerative diseases modifies the association between short term exposure to PM2.5 and all-cause mortality.
Conclusion
We found that short-term exposure to fine particles increased the risk of hospitalizations for Parkinson’s disease and diabetes, and of all-cause mortality. While the association between short term exposure to PM2.5 and mortality was higher among Medicare enrollees that had a previous admission for diabetes and neurological disorders than among Medicare enrollees that did not had a prior admission for these diseases, the effect modification was not statistically significant. We believe that these results provide useful insights regarding the mechanisms by which particles may affect the brain. A better understanding of the mechanisms will enable the development of new strategies to protect individuals at risk and to reduce detrimental effects of air pollution on the nervous system.
doi:10.1186/1476-069X-13-38
PMCID: PMC4064518  PMID: 24886318
PM2.5; Diabetes; Neurological disorders; Mortality risk; Hospitalizations
12.  Heat-related Emergency Hospitalizations for Respiratory Diseases in the Medicare Population 
Rationale: The heat-related risk of hospitalization for respiratory diseases among the elderly has not been quantified in the United States on a national scale. With climate change predictions of more frequent and more intense heat waves, it is of paramount importance to quantify the health risks related to heat, especially for the most vulnerable.
Objectives: To estimate the risk of hospitalization for respiratory diseases associated with outdoor heat in the U.S. elderly.
Methods: An observational study of approximately 12.5 million Medicare beneficiaries in 213 United States counties, January 1, 1999 to December 31, 2008. We estimate a national average relative risk of hospitalization for each 10°F (5.6°C) increase in daily outdoor temperature using Bayesian hierarchical models.
Measurements and Main Results: We obtained daily county-level rates of Medicare emergency respiratory hospitalizations (International Classification of Diseases, Ninth Revision, 464–466, 480–487, 490–492) in 213 U.S. counties from 1999 through 2008. Overall, each 10°F increase in daily temperature was associated with a 4.3% increase in same-day emergency hospitalizations for respiratory diseases (95% posterior interval, 3.8, 4.8%). Counties’ relative risks were significantly higher in counties with cooler average summer temperatures.
Conclusions: We found strong evidence of an association between outdoor heat and respiratory hospitalizations in the largest population of elderly studied to date. Given projections of increasing temperatures from climate change and the increasing global prevalence of chronic pulmonary disease, the relationship between heat and respiratory morbidity is a growing concern.
doi:10.1164/rccm.201211-1969OC
PMCID: PMC3734617  PMID: 23491405
chronic obstructive pulmonary disease; hospitalization; hot temperature; respiratory tract infections; weather
13.  Reduced hierarchical models with application to estimating health effects of simultaneous exposure to multiple pollutants 
Summary
Hierarchical models (HM) have been used extensively in multisite time series studies of air pollution and health to estimate health effects of a single pollutant adjusted for other pollutants and other time-varying factors. Recently, Environmental Protection Agency (EPA) has called for research quantifying health effects of simultaneous exposure to many air pollutants. However, straightforward application of HM in this context is challenged by the need to specify a random-effect distribution on a high-dimensional vector of nuisance parameters. Here we introduce reduced HM as a general statistical approach for analyzing correlated data with many nuisance parameters. For reduced HM we first calculate the integrated likelihood of the parameter of interest (e.g. excess number of deaths attributed to simultaneous exposure to high levels of many pollutants), and we then specify a flexible random-effect distribution directly on this parameter. Simulation studies show that the reduced HM performs comparably to the full HM in many scenarios, and even performs better in some cases, particularly when the multivariate random-effect distribution of the full HM is misspecified. Methods are applied to estimate relative risks of cardiovascular hospital admissions associated with simultaneous exposure to elevated levels of particulate matter and ozone in 51 US counties during 1999–2005.
doi:10.1111/rssc.12006
PMCID: PMC3864808  PMID: 24357883
Air pollution; Multilevel models; Multisite time series data; Nuisance parameters; Random effects
14.  Completing the Results of the 2013 Boston Marathon 
PLoS ONE  2014;9(4):e93800.
The 2013 Boston marathon was disrupted by two bombs placed near the finish line. The bombs resulted in three deaths and several hundred injuries. Of lesser concern, in the immediate aftermath, was the fact that nearly 6,000 runners failed to finish the race. We were approached by the marathon's organizers, the Boston Athletic Association (BAA), and asked to recommend a procedure for projecting finish times for the runners who could not complete the race. With assistance from the BAA, we created a dataset consisting of all the runners in the 2013 race who reached the halfway point but failed to finish, as well as all runners from the 2010 and 2011 Boston marathons. The data consist of split times from each of the 5 km sections of the course, as well as the final 2.2 km (from 40 km to the finish). The statistical objective is to predict the missing split times for the runners who failed to finish in 2013. We set this problem in the context of the matrix completion problem, examples of which include imputing missing data in DNA microarray experiments, and the Netflix prize problem. We propose five prediction methods and create a validation dataset to measure their performance by mean squared error and other measures. The best method used local regression based on a K-nearest-neighbors algorithm (KNN method), though several other methods produced results of similar quality. We show how the results were used to create projected times for the 2013 runners and discuss potential for future application of the same methodology. We present the whole project as an example of reproducible research, in that we are able to make the full data and all the algorithms we have used publicly available, which may facilitate future research extending the methods or proposing completely different approaches.
doi:10.1371/journal.pone.0093800
PMCID: PMC3984103  PMID: 24727904
15.  Model Feedback in Bayesian Propensity Score Estimation 
Biometrics  2013;69(1):263-273.
Summary
Methods based on the propensity score comprise one set of valuable tools for comparative effectiveness research and for estimating causal effects more generally. These methods typically consist of two distinct stages: 1) a propensity score stage where a model is fit to predict the propensity to receive treatment (the propensity score), and 2) an outcome stage where responses are compared in treated and untreated units having similar values of the estimated propensity score. Traditional techniques conduct estimation in these two stages separately; estimates from the first stage are treated as fixed and known for use in the second stage. Bayesian methods have natural appeal in these settings because separate likelihoods for the two stages can be combined into a single joint likelihood, with estimation of the two stages carried out simultaneously. One key feature of joint estimation in this context is “feedback” between the outcome stage and the propensity score stage, meaning that quantities in a model for the outcome contribute information to posterior distributions of quantities in the model for the propensity score. We provide a rigorous assessment of Bayesian propensity score estimation to show that model feedback can produce poor estimates of causal effects absent strategies that augment propensity score adjustment with adjustment for individual covariates. We illustrate this phenomenon with a simulation study and with a comparative effectiveness investigation of carotid artery stenting vs. carotid endarterectomy among 123,286 Medicare beneficiaries hospitlized for stroke in
doi:10.1111/j.1541-0420.2012.01830.x
PMCID: PMC3622139  PMID: 23379793
Bayesian estimation; causal inference; comparative effectiveness; model feedback; propensity score
16.  Associations of PM2.5 Constituents and Sources with Hospital Admissions: Analysis of Four Counties in Connecticut and Massachusetts (USA) for Persons ≥ 65 Years of Age 
Environmental Health Perspectives  2013;122(2):138-144.
Background: Epidemiological studies have demonstrated associations between short-term exposure to PM2.5 and hospital admissions. The chemical composition of particles varies across locations and time periods. Identifying the most harmful constituents and sources is an important health and regulatory concern.
Objectives: We examined pollutant sources for associations with risk of hospital admissions for cardiovascular and respiratory causes.
Methods: We obtained PM2.5 filter samples for four counties in Connecticut and Massachusetts and analyzed them for PM2.5 elements. Source apportionment was used to estimate daily PM2.5 contributions from sources (traffic, road dust, oil combustion, and sea salt as well as a regional source representing coal combustion and other sources). Associations between daily PM2.5 constituents and sources and risk of cardiovascular and respiratory hospitalizations for the Medicare population (> 333,000 persons ≥ 65 years of age) were estimated with time-series analyses (August 2000–February 2004).
Results: PM2.5 total mass and PM2.5 road dust contribution were associated with cardiovascular hospitalizations, as were the PM2.5 constituents calcium, black carbon, vanadium, and zinc. For respiratory hospitalizations, associations were observed with PM2.5 road dust, and sea salt as well as aluminum, calcium, chlorine, black carbon, nickel, silicon, titanium, and vanadium. Effect estimates were generally robust to adjustment by co-pollutants of other constituents. An interquartile range increase in same-day PM2.5 road dust (1.71 μg/m3) was associated with a 2.11% (95% CI: 1.09, 3.15%) and 3.47% (95% CI: 2.03, 4.94%) increase in cardiovascular and respiratory admissions, respectively.
Conclusions: Our results suggest some particle sources and constituents are more harmful than others and that in this Connecticut/Massachusetts region the most harmful particles include black carbon, calcium, and road dust PM2.5.
Citation: Bell ML, Ebisu K, Leaderer BP, Gent JF, Lee HJ, Koutrakis P, Wang Y, Dominici F, Peng RD. 2014. Associations of PM2.5 constituents and sources with hospital admissions: analysis of four counties in Connecticut and Massachusetts (USA) for persons ≥ 65 years of age. Environ Health Perspect 122:138–144; http://dx.doi.org/10.1289/ehp.1306656
doi:10.1289/ehp.1306656
PMCID: PMC3915260  PMID: 24213019
17.  The Effect of Air Pollution Control on Life Expectancy in the United States: An Analysis of 545 US counties for the period 2000 to 2007 
Background
In recent years (2000 to 2007), ambient levels of fine particulate matter (PM2.5) have continued to decline as a result of interventions, but the decline has been at a slower rate than previous years (1980 to 2000). Whether these more recent and slower declines of PM2.5 levels continue to improve life expectancy and whether they benefit all populations equally is unknown.
Methods
We assembled a dataset for 545 U.S. counties consisting of yearly county-specific average PM2.5, yearly county-specific life expectancy, and several potentially confounding variables measuring socioeconomic status, smoking prevalence and demographic characteristics for the years 2000 and 2007. We used regression models to estimate the association between reductions in PM2.5 and changes in life expectancy for the period 2000 to 2007.
Results
A decrease of 10 µg/m3 in the concentration of PM2.5 was associated with an increase in mean life expectancy of 0.35 years SD= 0.16 years, p = 0.033). This association was stronger in more urban and densely populated counties.
Conclusions
Reductions in PM2.5 were associated with improvements in life expectancy for the period 2000 to 2007. Air pollution control in the last decade has continued to have a positive impact on public health.
doi:10.1097/EDE.0b013e3182770237
PMCID: PMC3521092  PMID: 23211349
18.  Comparing Exposure Metrics for the Effects of Fine Particulate Matter on Emergency Hospital Admissions 
A crucial step in an epidemiological study of the effects of air pollution is to accurately quantify exposure of the population. In this paper, we investigate the sensitivity of the health effects estimates associated with short-term exposure to fine particulate matter with respect to three potential metrics for daily exposure: ambient monitor data, estimated values from a deterministic atmospheric chemistry model, and stochastic daily average human exposure simulation output. Each of these metrics has strengths and weaknesses when estimating the association between daily changes in ambient exposure to fine particulate matter and daily emergency hospital admissions. Monitor data is readily available, but is incomplete over space and time. The atmospheric chemistry model output is spatially and temporally complete, but may be less accurate than monitor data. The stochastic human exposure estimates account for human activity patterns and variability in pollutant concentration across microenvironments, but requires extensive input information and computation time. To compare these metrics, we consider a case study of the association between fine particulate matter and emergency hospital admissions for respiratory cases for the Medicare population across three counties in New York. Of particular interest is to quantify the impact and/or benefit to using the stochastic human exposure output to measure ambient exposure to fine particulate matter. Results indicate that the stochastic human exposure simulation output indicates approximately the same increase in relative risk associated with emergency admissions as using a chemistry model or monitoring data as exposure metrics. However, the stochastic human exposure simulation output and the atmospheric chemistry model both bring additional information which helps to reduce the uncertainly in our estimated risk.
doi:10.1038/jes.2013.39
PMCID: PMC3805672  PMID: 23942393
Health effects; air polluation; particulate matter; ambient monitoring data; CMAQ; exposure models; SHEDS; PM2.5
19.  Residential exposure to aircraft noise and hospital admissions for cardiovascular diseases: multi-airport retrospective study 
Objective To investigate whether exposure to aircraft noise increases the risk of hospitalization for cardiovascular diseases in older people (≥65 years) residing near airports.
Design Multi-airport retrospective study of approximately 6 million older people residing near airports in the United States. We superimposed contours of aircraft noise levels (in decibels, dB) for 89 airports for 2009 provided by the US Federal Aviation Administration on census block resolution population data to construct two exposure metrics applicable to zip code resolution health insurance data: population weighted noise within each zip code, and 90th centile of noise among populated census blocks within each zip code.
Setting 2218 zip codes surrounding 89 airports in the contiguous states.
Participants 6 027 363 people eligible to participate in the national medical insurance (Medicare) program (aged ≥65 years) residing near airports in 2009.
Main outcome measures Percentage increase in the hospitalization admission rate for cardiovascular disease associated with a 10 dB increase in aircraft noise, for each airport and on average across airports adjusted by individual level characteristics (age, sex, race), zip code level socioeconomic status and demographics, zip code level air pollution (fine particulate matter and ozone), and roadway density.
Results Averaged across all airports and using the 90th centile noise exposure metric, a zip code with 10 dB higher noise exposure had a 3.5% higher (95% confidence interval 0.2% to 7.0%) cardiovascular hospital admission rate, after controlling for covariates.
Conclusions Despite limitations related to potential misclassification of exposure, we found a statistically significant association between exposure to aircraft noise and risk of hospitalization for cardiovascular diseases among older people living near airports.
doi:10.1136/bmj.f5561
PMCID: PMC3805481  PMID: 24103538
20.  Comprehensive Smoking Bans and Acute Myocardial Infarction Among Medicare Enrollees in 387 US Counties: 1999–2008 
American Journal of Epidemiology  2012;176(7):642-648.
Restrictions on smoking in public places have become increasingly widespread in the United States, particularly since the year 2005. National-scale studies in Europe and local-scale studies in the United States have found decreases in hospital admissions for acute myocardial infarction (AMI) following smoking bans. The authors analyzed AMI admission rates for the years 1999–2008 in 387 US counties that enacted comprehensive smoking bans across 9 US states, using a study population of approximately 6 million Medicare enrollees aged 65 years or older. Effects of smoking bans on AMI admissions were estimated by using Poisson regression with linear and nonlinear adjustment for secular trend and random effects at the county level. Under the assumption of linearity in the secular trend of declining AMI, smoking bans were associated with a statistically significant ban-associated decrease in admissions for AMI in the 12 months following the ban. However, the estimated effect was attenuated to nearly zero when the assumption of linearity in the underlying trend was relaxed. This analysis demonstrates that estimation of potential health benefits associated with comprehensive smoking bans is challenged by the need to adjust for nonlinearity in secular trend.
doi:10.1093/aje/kws267
PMCID: PMC3530376  PMID: 22986145
environmental tobacco smoke; mixed-effects models; secondhand smoke; smoking bans
21.  Short-term Exposure to Particulate Matter Constituents and Mortality in a National Study of U.S. Urban Communities 
Environmental Health Perspectives  2013;121(10):1148-1153.
Background: Although the association between PM2.5 mass and mortality has been extensively studied, few national-level analyses have estimated mortality effects of PM2.5 chemical constituents. Epidemiologic studies have reported that estimated effects of PM2.5 on mortality vary spatially and seasonally. We hypothesized that associations between PM2.5 constituents and mortality would not vary spatially or seasonally if variation in chemical composition contributes to variation in estimated PM2.5 mortality effects.
Objectives: We aimed to provide the first national, season-specific, and region-specific associations between mortality and PM2.5 constituents.
Methods: We estimated short-term associations between nonaccidental mortality and PM2.5 constituents across 72 urban U.S. communities from 2000 to 2005. Using U.S. Environmental Protection Agency (EPA) Chemical Speciation Network data, we analyzed seven constituents that together compose 79–85% of PM2.5 mass: organic carbon matter (OCM), elemental carbon (EC), silicon, sodium ion, nitrate, ammonium, and sulfate. We applied Poisson time-series regression models, controlling for time and weather, to estimate mortality effects.
Results: Interquartile range increases in OCM, EC, silicon, and sodium ion were associated with estimated increases in mortality of 0.39% [95% posterior interval (PI): 0.08, 0.70%], 0.22% (95% PI: 0.00, 0.44), 0.17% (95% PI: 0.03, 0.30), and 0.16% (95% PI: 0.00, 0.32), respectively, based on single-pollutant models. We did not find evidence that associations between mortality and PM2.5 or PM2.5 constituents differed by season or region.
Conclusions: Our findings indicate that some constituents of PM2.5 may be more toxic than others and, therefore, regulating PM total mass alone may not be sufficient to protect human health.
Citation: Krall JR, Anderson GB, Dominici F, Bell ML, Peng RD. 2013. Short-term exposure to particulate matter constituents and mortality in a national study of U.S. urban communities. Environ Health Perspect 121:1148–1153; http://dx.doi.org/10.1289/ehp.1206185
doi:10.1289/ehp.1206185
PMCID: PMC3801200  PMID: 23912641
22.  The effect of primary organic particles on emergency hospital admissions among the elderly in 3 US cities 
Environmental Health  2013;12:68.
Background
Fine particle (PM2.5) pollution related to combustion sources has been linked to a variety of adverse health outcomes. Although poorly understood, it is possible that organic carbon (OC) species, particularly those from combustion-related sources, may be partially responsible for the observed toxicity of PM2.5. The toxicity of the OC species may be related to their chemical structures; however, few studies have examined the association of OC species with health impacts.
Methods
We categorized 58 primary organic compounds by their chemical properties into 5 groups: n-alkanes, hopanes, cyclohexanes, PAHs and isoalkanes. We examined their impacts on the rate of daily emergency hospital admissions among Medicare recipients in Atlanta, GA and Birmingham, AL (2006–2009), and Dallas, TX (2006–2007). We analyzed data in two stages; we applied a case-crossover analysis to simultaneously estimate effects of individual OC species on cause-specific hospital admissions. In the second stage we estimated the OC chemical group-specific effects, using a multivariate weighted regression.
Results
Exposures to cyclohexanes of six days and longer were significantly and consistently associated with increased rate of hospital admissions for CVD (3.40%, 95%CI = (0.64, 6.24%) for 7-d exposure). Similar increases were found for hospitalizations for ischemic heart disease and myocardial infarction. For respiratory related hospital admissions, associations with OC groups were less consistent, although exposure to iso-/anteiso-alkanes was associated with increased respiratory-related hospitalizations.
Conclusions
Results suggest that week-long exposures to traffic-related, primary organic species are associated with increased rate of total and cause-specific CVD emergency hospital admissions. Associations were significant for cyclohexanes, but not hopanes, suggesting that chemical properties likely play an important role in primary OC toxicity.
doi:10.1186/1476-069X-12-68
PMCID: PMC3765898  PMID: 23981468
Emergency hospital admissions; Fine particles; Medicare; Primary organic particles
23.  Bayesian hierarchical distributed lag models for summer ozone exposure and cardio-respiratory mortality 
Environmetrics  2005;16(5):547-562.
SUMMARY
In this article we develop Bayesian hierarchical distributed lag models for estimating associations between daily variations in summer ozone levels and daily variations in cardiovascular and respiratory (CVDRESP) mortality counts for 19 large U.S. cities included in the National Morbidity, Mortality and Air Pollution Study (NMMAPS) for the summers of 1987–1994.
In the first stage, we define a semi-parametric distributed lag Poisson regression model to estimate city-specific relative rates of CVDRESP mortality associated with short-term exposure to summer ozone. In the second stage, we specify a class of distributions for the true city-specific relative rates to estimate an overall effect by taking into account the variability within and across cities. We perform the calculations with respect to several random effects distributions (normal, t-student, and mixture of normal), thus relaxing the common assumption of a two-stage normal–normal hierarchical model. We assess the sensitivity of the results to: (i) lag structure for ozone exposure; (ii) degree of adjustment for long-term trends; (iii) inclusion of other pollutants in the model; (iv) heat waves; (v) random effects distributions; and (vi) prior hyperparameters.
On average across cities, we found that a 10ppb increase in summer ozone level over the previous week is associated with a 1.25 per cent increase in CVDRESP mortality (95 per cent posterior regions: 0.47, 2.03). The relative rate estimates are also positive and statistically significant at lags 0, 1 and 2. We found that associations between summer ozone and CVDRESP mortality are sensitive to the confounding adjustment for PM10, but are robust to: (i) the adjustment for long-term trends, other gaseous pollutants (NO2, SO2 and CO); (ii) the distributional assumptions at the second stage of the hierarchical model; and (iii) the prior distributions on all unknown parameters.
Bayesian hierarchical distributed lag models and their application to the NMMAPS data allow us to estimate of an acute health effect associated with exposure to ambient air pollution in the last few days on average across several locations. The application of these methods and the systematic assessment of the sensitivity of findings to model assumptions provide important epidemiological evidence for future air quality regulations.
doi:10.1002/env.721
PMCID: PMC3697867  PMID: 23825932
Bayesian hierarchical model; distributed lag model; ozone; cardiovascular and respiratory mortality
24.  A Meta-Analysis and Multisite Time-Series Analysis of the Differential Toxicity of Major Fine Particulate Matter Constituents 
American Journal of Epidemiology  2012;175(11):1091-1099.
Health risk assessments of particulate matter less than 2.5 μm in diameter (PM2.5) often assume that all constituents of PM2.5 are equally toxic. While investigators in previous epidemiologic studies have evaluated health risks from various PM2.5 constituents, few have conducted the analyses needed to directly inform risk assessments. In this study, the authors performed a literature review and conducted a multisite time-series analysis of hospital admissions and exposure to PM2.5 constituents (elemental carbon, organic carbon matter, sulfate, and nitrate) in a population of 12 million US Medicare enrollees for the period 2000–2008. The literature review illustrated a general lack of multiconstituent models or insight about probabilities of differential impacts per unit of concentration change. Consistent with previous results, the multisite time-series analysis found statistically significant associations between short-term changes in elemental carbon and cardiovascular hospital admissions. Posterior probabilities from multiconstituent models provided evidence that some individual constituents were more toxic than others, and posterior parameter estimates coupled with correlations among these estimates provided necessary information for risk assessment. Ratios of constituent toxicities, commonly used in risk assessment to describe differential toxicity, were extremely uncertain for all comparisons. These analyses emphasize the subtlety of the statistical techniques and epidemiologic studies necessary to inform risk assessments of particle constituents.
doi:10.1093/aje/kwr457
PMCID: PMC3491972  PMID: 22510275
meta-analysis; nitrates; particulate matter; risk assessment; soot; sulfates
25.  Variations in Surgical Outcomes Associated with Hospital Compliance with Safety Practices 
Surgery  2012;151(5):651-659.
Background
The Leapfrog Group aims to improve patient safety by promoting hospital compliance with National Quality Forum (NQF) safe practices. It is unknown, however, whether implementation of these safety practices improve outcomes following high-risk operations.
Methods
We conducted a cross-sectional analysis of 658 nationwide hospitals that responded to the 2005 Leapfrog Group Hospital Quality & Safety survey. A total of 79,462 patients were identified from Medicare claims data who underwent a pancreatectomy, hepatectomy, esophagectomy, open aortic aneurysm repair, colectomy or gastrectomy procedure from 2004 through 2006. Random-effects logistic regression models were used to estimate the association between hospital compliance with NQF safe practices and risk-adjusted odds of complications, failure rate to rescue, and mortality after adjusting for patient and hospital level confounders.
Results
Of the 658 hospitals that responded to surveys, 41% had fully implemented NQF safe practices and 59% reported partial compliance with these standards. Compared to hospitals with partial NQF compliance, we found significant evidence that hospitals with full compliance had an increased likelihood of diagnosing a complication following any of the six high-risk operations (OR: 1.13; 95%CI: 1.03–1.25), but had a decreased likelihood of failure to rescue (OR: 0.82; 95%CI: 0.71–0.96), and a decreased odds of mortality (OR: 0.80; 95%CI: 0.71–0.91).
Conclusions
Despite having a higher rate of postoperative complications, hospitals fully complying with safe practices were associated with lower failure to rescue and reduced mortality following high-risk operations. These results highlight the importance of having hospitals systems in place to promote safety and manage postoperative complications.
doi:10.1016/j.surg.2011.12.001
PMCID: PMC3414538  PMID: 22261296

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