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Background Prior observation has shown differences in COVID-19 hospitalization risk between SARS-CoV-2 variants, but limited information describes hospitalization outcomes. Methods Inpatients with COVID-19 at 5 hospitals in the eastern United States were included if they had hypoxia, tachypnea, tachycardia, or fever, and SARS-CoV-2 variant data, determined from whole-genome sequencing or local surveillance inference. Analyses were stratified by history of SARS-CoV-2 vaccination or infection. The average effect of SARS-CoV-2 variant on 28-day risk of severe disease, defined by advanced respiratory support needs, or death was evaluated using models weighted on propensity scores derived from baseline clinical features. Results Severe disease or death within 28 days occurred for 977 (29%) of 3369 unvaccinated patients and 269 (22%) of 1230 patients with history of vaccination or prior SARS-CoV-2 infection. Among unvaccinated patients, the relative risk of severe disease or death for Delta variant compared with ancestral lineages was 1.30 (95% confidence interval [CI]: 1.11–1.49). Compared with Delta, the risk for Omicron patients was.72 (95% CI:.59–.88) and compared with ancestral lineages was.94 (.78–1.1). Among Omicron and Delta infections, patients with history of vaccination or prior SARS-CoV-2 infection had half the risk of severe disease or death (adjusted hazard ratio:.40; 95% CI:.30–.54), but no significant outcome difference by variant. Conclusions Although risk of severe disease or death for unvaccinated inpatients with Omicron was lower than with Delta, it was similar to ancestral lineages. Severe outcomes were less common in vaccinated inpatients, with no difference between Delta and Omicron infections. [ABSTRACT FROM AUTHOR]

Seasonal influenza epidemics offer unique opportunities to study the invasion and re-invasion waves of a pathogen in a partially immune population. Detailed patterns of spread remain elusive, however, due to lack of granular disease data. Here we model high-volume city-level medical claims data and human mobility proxies to explore the drivers of influenza spread in the US during 2002–2010. Although the speed and pathways of spread varied across seasons, seven of eight epidemics likely originated in the Southern US. Each epidemic was associated with 1–5 early long-range transmission events, half of which sparked onward transmission. Gravity model estimates indicate a sharp decay in influenza transmission with the distance between infectious and susceptible cities, consistent with spread dominated by work commutes rather than air traffic. Two early-onset seasons associated with antigenic novelty had particularly localized modes of spread, suggesting that novel strains may spread in a more localized fashion than previously anticipated. [ABSTRACT FROM AUTHOR]

Epidemiology is concerned with determining the distribution and causes of disease. Throughout its history, epidemiology has drawn upon statistical ideas and methods to achieve its aims. Because of the exponential growth in our capacity to measure and analyze data on the underlying processes that define each person's state of health, there is an emerging opportunity for population-based epidemiologic studies to influence health decisions made by individuals in ways that take into account the individuals' characteristics, circumstances, and preferences. We refer to this endeavor as "individualized health." The present article comprises 2 sections. In the first, we describe how graphical, longitudinal, and hierarchical models can inform the project of individualized health. We propose a simple graphical model for informing individual health decisions using population-based data. In the second, we review selected topics in causal inference that we believe to be particularly useful for individualized health. Epidemiology and biostatistics were 2 of the 4 founding departments in the world's first graduate school of public health at Johns Hopkins University, the centennial of which we honor. This survey of a small part of the literature is intended to demonstrate that the 2 fields remain just as inextricably linked today as they were 100 years ago. [ABSTRACT FROM AUTHOR]

BACKGROUND: Population-based studies have estimated health risks of short-term exposure to fine particles using mass of PM2.5 (particulate matter ≤ 2.5 μm in aerodynamic diameter) as the indicator. Evidence regarding the toxicity of the chemical components of the PM2.5 mixture is limited. OBJECTIVE: In this study we investigated the association between hospital admission for cardiovascular disease (CVD) and respiratory disease and the chemical components of PM2.5 in the United States. METHODS: We used a national database comprising daily data for 2000-2006 on emergency hospital admissions for cardiovascular and respiratory outcomes, ambient levels of major PM2.5 chemical components [sulfate, nitrate, silicon, elemental carbon (EC), organic carbon matter (OCM), and sodium and ammonium ions], and weather. Using Bayesian hierarchical statistical models, we estimated the associations between daily levels of PM2.5 components and risk of hospital admissions in 119 U.S. urban communities for 12 million Medicare enrollees (≥ 65 years of age). RESULTS: In multiple-pollutant models that adjust for the levels of other pollutants, an interquartile range (IQR) increase in EC was associated with a 0.80% [95% posterior interval (PI), 0.34-1.27%] increase in risk of same-day cardiovascular admissions, and an IQR increase in OCM was associated with a 1.01% (95% PI, 0.04-1.98%) increase in risk of respiratory admissions on the same day. Other components were not associated with cardiovascular or respiratory hospital admissions in multiple-pollutant models. CONCLUSIONS: Ambient levels of EC and OCM, which are generated primarily from vehicle emissions, diesel, and wood burning, were associated with the largest risks of emergency hospitalization across the major chemical constituents of PM2.5. [ABSTRACT FROM AUTHOR]

Background: Little is known about the associations of previous standardized examination scores with scores on subsequent standardized examinations used to assess medical knowledge in internal medicine residencies.Objective: To examine associations of previous standardized test scores on subsequent standardized test scores.Design: Retrospective cohort study.Participants: One hundred ninety-five internal medicine residents.Methods: Bivariate associations of United States Medical Licensing Examination (USMLE) Steps and Internal Medicine In-Training Examination (IM-ITE) scores were determined. Random effects analysis adjusting for repeated administrations of the IM-ITE and other variables known or hypothesized to affect IM-ITE score allowed for discrimination of associations of individual USMLE Step scores on IM-ITE scores.Results: In bivariate associations, USMLE scores explained 17% to 27% of the variance in IME-ITE scores, and previous IM-ITE scores explained 66% of the variance in subsequent IM-ITE scores. Regression coefficients (95% CI) for adjusted associations of each USMLE Step with IM-ITE scores were USMLE-1 0.19 (0.12, 0.27), USMLE-2 0.23 (0.17, 0.30), and USMLE-3 0.19 (0.09, 0.29).Conclusions: No single USMLE Step is more strongly associated with IM-ITE scores than the others. Because previous IM-ITE scores are strongly associated with subsequent IM-ITE scores, appropriate modeling, such as random effects methods, should be used to account for previous IM-ITE administrations in studies for which IM-ITE score is an outcome. [ABSTRACT FROM AUTHOR]

BACKGROUND: Although numerous studies have demonstrated links between particulate matter (PM) and adverse health effects, the chemical components of the PM mixture that cause injury are unknown. OBJECTIVES: This work characterizes spatial and temporal variability of PM2.5 (PM with aerodynamic diameter < 2.5 µm) components in the United States; our objective is to identify components for assessment in epidemiologic studies. METHODS: We constructed a database of 52 PM2.5 component concentrations for 187 U.S. counties for 2000-2005. First, we describe the challenges inherent to analysis of a national PM2.5 chemical composition database. Second, we identify components that contribute substantially to and/or co-vary with PM2.5 total mass. Third, we characterize the seasonal and regional variability of targeted components. RESULTS: Strong seasonal and geographic variations in PM2.5 chemical composition are identified. Only seven of the 52 components contributed ≥ 1% to total mass for yearly or seasonal averages [ammonium (NH4+), elemental carbon (EC), organic carbon matter (OCM), nitrate (NO3-), silicon, sodium (Na+), and sulfate (SO42-)]. Strongest correlations with PM2.5 total mass were with NH4+ (yearly), OCM (especially winter), NO3- (winter), and SO42- (yearly, spring, autumn, and summer), with particularly strong correlations for NH4+ and SO42- in summer. Components that co-varied with PM2.5 total mass, based on daily detrended data, were NH4+, SO42- , OCM, NO3-, bromine, and EC. CONCLUSIONS: The subset of identified PM2.5 components should be investigated further to determine whether their daily variation is associated with daily variation of health indicators, and whether their seasonal and regional patterns can explain the seasonal and regional heterogeneity in PM10 (PM with aerodynamic diameter < 10 µm) and PM2.5 health risks. [ABSTRACT FROM AUTHOR]

Context Evidence on the health risks associated with short-term exposure to fine particles (particulate matter 2.5 μm in aerodynamic diameter [PM2.5]) is limited. Results from the new national monitoring network for PM2.5 make possible systematic research on health risks at national and regional scales. Objectives To estimate risks of cardiovascular and respiratory hospital admissions associated with short-term exposure to PM2.5 for Medicare enrollees and to explore heterogeneity of the variation of risks across regions. Design, Setting, and Participants A national database comprising daily time-series data daily for 1999 through 2002 on hospital admission rates (constructed from the Medicare National Claims History Files) for cardiovascular and respiratory outcomes and injuries, ambient PM2.5 levels, and temperature and dew-point temperature for 204 US urban counties (population >200 000) with 11.5 million Medicare enrollees (aged >65 years) living an average of 5.9 miles from a PM2.5 monitor. Main Outcome Measures Daily counts of county-wide hospital admissions for primary diagnosis of cerebrovascular, peripheral, and ischemic heart diseases, heart rhythm, heart failure, chronic obstructive pulmonary disease, and respiratory infection, and injuries as a control outcome. Results There was a short-term increase in hospital admission rates associated with PM2.5 for all of the health outcomes except injuries. The largest association was for heart failure, which had a 1.28% (95% confidence interval, 0.78%-1.78%) increase in risk per 10-μg/m3 increase in same-day PM2.5. Cardiovascular risks tended to be higher in counties located in the Eastern region of the United States, which included the Northeast, the Southeast, the Midwest, and the South. Conclusion Short-term exposure to PM2.5 increases the risk for hospital admission for cardiovascular and respiratory diseases. [ABSTRACT FROM AUTHOR]

This paper gives an overview of time series ideas and methods used in public health and biomedical research. A time series is a sequence of observations made over time. Examples in public health include daily ozone concentrations, weekly admissions to an emergency department, or annual expenditures on health care in the United States. Time series models are most commonly used in regression analysis to describe the dependence of the response at each time on predictor variables including covariates and possibly previous values in the series. For example, Bell et al. (2) use time series methods to regress daily mortality in U.S. cities on concentrations of particulate air pollution. Time series methods are necessary to make valid inferences from data by accounting for the correlation among repeated responses over time. [ABSTRACT FROM AUTHOR]

This article presents findings from updated analyses of data from 90 U.S. cities assembled for the National Morbidity, Mortality, and Air Pollution Study (NMMAPS). The data were analyzed with a generalized additive model (GAM) using the gamfunction in S-Plus (with default convergence criteria previously used and with more stringent criteria) and with a generalized linear model (GLM) with natural cubic splines. With the original method, the estimated effect of PM 10 (particulate matter 10μm in mass median aerodynamic diameter) on total mortality from nonexternal causes was a 0.41% increase per 10-μg/m 3 increase in PM 10 ; with the more stringent criteria, the estimate was 0.27%; and with GLM, the effect was 0.21%. The effect of PM 10 on respiratory and cardiovascular mortality combined was greater, but the pattern across models was similar. The findings of the updated analysis with regard to spatial heterogeneity across the 90 cities were unchanged from the original analyses. [ABSTRACT FROM AUTHOR]

Context Ozone has been associated with various adverse health effects, including increased rates of hospital admissions and exacerbation of respiratory illnesses. Although numerous time-series studies have estimated associations between day-to-day variation in ozone levels and mortality counts, results have been inconclusive. Objective To investigate whether short-term (daily and weekly) exposure to ambient ozone is associated with mortality in the United States. Design and Setting Using analytical methods and databases developed for the National Morbidity, Mortality, and Air Pollution Study, we estimated a national average relative rate of mortality associated with short-term exposure to ambient ozone for 95 large US urban communities from 1987-2000. We used distributed-lag models for estimating community-specific relative rates of mortality adjusted for time-varying confounders (particulate matter, weather, seasonality, and long-term trends) and hierarchical models for combining relative rates across communities to estimate a national average relative rate, taking into account spatial heterogeneity. Main Outcome Measure Daily counts of total non–injury-related mortality and cardiovascular and respiratory mortality in 95 large US communities during a 14-year period. Results A 10-ppb increase in the previous week’s ozone was associated with a 0.52% increase in daily mortality (95% posterior interval [PI], 0.27%-0.77%) and a 0.64% increase in cardiovascular and respiratory mortality (95% PI, 0.31%-0.98%). Effect estimates for aggregate ozone during the previous week were larger than for models considering only a single day’s exposure. Results were robust to adjustment for particulate matter, weather, seasonality, and long-term trends. Conclusions These results indicate a statistically significant association between short-term changes in ozone and mortality on average for 95 large US urban communities, which include about 40% of the total US p... [ABSTRACT FROM AUTHOR]

In this paper, we present national maps of relative rates of mortality associated with short-term exposure to particulate matter < 10 µm in aerodynamic diameter (PM[sub 10]). We report results for 88 of the largest metropolitan areas in the United States from 1987 to 1994 for all-cause mortality, combined cardiovascular and respiratory deaths, and other causes of mortality. Maximum likelihood estimates of the relative rate of mortality associated with PM[sub 10] and the degree of statistical uncertainty were obtained for each of the 88 cities by fitting a separate log-linear regression of the daily mortality rate on air pollution level and potential confounders. We obtained Bayesian estimates of the relative rates by fitting a hierarchical model that takes into account spatial correlation among the true city-specific relative rates. We found that daily variations of PM[sub 10] are positively associated with daily variations of mortality. In particular, the relative rate estimates of cardiovascular and respiratory mortality associated with PM[sub 10] are larger on average than the relative rate estimates of all-cause and other-cause mortality. The estimated increase in the relative rate of death from cardiovascular and respiratory mortality, all-cause mortality, and other-cause mortality were 0.31% (95% posterior interval, 0.15-0.5), 0.22% (95% posterior interval, 0.1-0.38), and 0.13% (95% posterior interval, -0.05 to 0.29), respectively. Bayesian estimates of the city-specific relative rates ranged from 0.23% to 0.35% for cardiovascular and respiratory mortality, from 0.18% to 0.27% for all causes, and from 0.10% to 0.20% for other causes of mortality. The spatial characterization of effects across cities offers the potential to identify factors that could influence the effect of PM[sub 10] on health, including particle characteristics, offering insights into mechanisms by which PM[sub 10] causes adverse health effects. [ABSTRACT FROM AUTHOR]

We analyzed a national data base of air pollution and mortality for the 88 largest U.S. cities for the period 1987 - 1994, to estimate relative rates of mortality associated with airborne particulate matter smaller than 10 microns (PM10) and the form of the relationship between PM10 concentration and mortality. To estimate city-specific relative rates of mortality associated with PM10, we built log-linear models that included nonparametric adjustments for weather variables and longer term trends. To estimate PM10 mortality dose-response curves, we modeled the logarithm of the expected value of daily mortality as a function of PM10 using natural cubic spines with unknown numbers and location of knots. We also developed spatial models to investigate the heterogeneity of relative mortality rates and of the shapes of PM10 mortality dose-response curves across cities and geographical regions. To determine whether variability in effect estimates can be explained can be explained by city-specific factors, we explored the dependence of relative mortality rates on mean pollution levels, demographic variables, reliability of the pollution data, and specific constituents of particulate matter. We implemented estimation with simulation-based methods, including data augmentation to impute the missing data of the city-specific covariates and the reversible jump Markov chain Monte Carlo (RJMCMC) to sample from the posterior distribution of the parameters in the hierarchical spline model. We found that previous-day PM10 concentrations were positively associated with total mortality in most the locations, with a .5% increment for a 10 μgm/¹ increase in PM10. The effect was strongest in the Northeast region, where the increase in the death rate was twice as high as the average for the other cities. [ABSTRACT FROM AUTHOR]

Episodes of extremely hot or cold temperatures are associated with increased mortality. Time-series analyses show an association between temperature and mortality across a range of less extreme temperatures. In this paper, the authors describe the temperature-mortality association for 11 large eastern US cities in 1973-1994 by estimating the relative risks of mortality using log-linear regression analysis for time-series data and by exploring city characteristics associated with variations in this temperature-mortality relation. Current and recent days' temperatures were the weather components most strongly predictive of mortality, and mortality risk generally decreased as temperature increased from the coldest days to a certain threshold temperature, which varied by latitude, above which mortality risk increased as temperature increased. The authors also found a strong association of the temperature-mortality relation with latitude, with a greater effect of colder temperatures on mortality risk in more-southern cities and of warmer temperatures in more-northern cities. The percentage of households with air conditioners in the south and heaters in the north, which serve as indicators of socioeconomic status of the city population, also predicted weather-related mortality. The model developed in this analysis is potentially useful for projecting the consequences of climate-change scenarios and offering insights into susceptibility to the adverse effects of weather. Am J Epidemiol 2002;155:80-7. [ABSTRACT FROM AUTHOR]

Reports over the last decade of association between levels of particles in outdoor air and daily mortality counts have raised concern that air pollution shortens life, even at concentrations within current regulatory limits. Criticisms of these reports have focused on the statistical techniques that are used to estimate the pollution—mortality relationship and the inconsistency in findings between cities. We have developed analytical methods that address these concerns and combine evidence from multiple locations to gain a unified analysis of the data. The paper presents log-linear regression analyses of daily time series data from the largest 20 US cities and introduces hierarchical regression models for combining estimates of the pollution—mortality relationship across cities. We illustrate this method by focusing on mortality effects of PM[sub 10] (particulate matter less than 10 µm in aerodynamic diameter) and by performing univariate and bivariate analyses with PM[sub 10] and ozone (O[sub 3]) level. In the first stage of the hierarchical model, we estimate the relative mortality rate associated with PM[sub 10] for each of the 20 cities by using semiparametric log-linear models. The second stage of the model describes between-city variation in the true relative rates as a function of selected city-specific covariates. We also fit two variations of a spatial model with the goal of exploring the spatial correlation of the pollutant-specific coefficients among cities. Finally, to explore the results of considering the two pollutants jointly, we fit and compare univariate and bivariate models. All posterior distributions from the second stage are estimated by using Markov chain Monte Cario techniques. In univariate analyses using concurrent day pollution values to predict 3 mortality, we find that an increase of 10 µg m[sup -3] in PM[sub 10] on average in the USA is associated with a 0.48% increase in mortality (95% interval: 0.05, 0.92)... [ABSTRACT FROM AUTHOR]

Numerous studies have shown a positive association between daily mortality and particulate air pollution, even at concentrations below regulatory limits. These findings have motivated interest in the shape of the exposure-response relation. The authors have developed flexible modeling strategies for time-series data that include spline and threshold exposure-response models; they apply these models to daily time-series data for the 20 largest US cities for 1987–1994, using the concentration of particulate matter <10 μm in aerodynamic diameter (PM10) as the exposure measure. The spline model showed a linear relation without indication of threshold for PM10 and relative risk of death for all causes and cardiorespiratory causes; by contrast, for other causes, the risk did not increase until approximately 50 μg/m3 PM10. For all-cause mortality, a linear model without threshold was preferred to the threshold model and to the spline model, using the Akaike information criterion (AIC). The findings were similar for cardiovascular and respiratory deaths combined. By contrast, for causes other than cardiovascular and respiratory, a threshold model was more competitive with a threshold value estimated at 65 μg/m3. These findings indicate that linear models without a threshold are appropriate for assessing the effect of particulate air pollution on daily mortality even at current levels. Am J Epidemiol 2000;152:397–406. [ABSTRACT FROM PUBLISHER]

Our objective was to evaluate the relationship between the "Make The Call, Don't Miss a Beat" national mass media campaign and emergency medical services (EMS) use among women with possible heart attack symptoms. We linked campaign TV public service advertisement data with national EMS activation data for 2010 to 2014. We identified EMS activations (i.e., responses) for possible heart attack symptoms and for unintentional injuries for both women and men. We estimated the impact of the campaign on the fraction of the 1.7 to 15.9 million activations of women with possible heart attack symptoms compared with 1.9 million female activations for unintentional injuries within each EMS agency and month using quasi-binomial logistic regression controlling for time and state. Of the 3,175 U S. counties, 90% were exposed to the campaign. However, less than 2% of U.S. counties reached moderate TV exposure (≥300 gross rating points) during the entire campaign period. We did not observe an increase in the fraction of female activations for possible heart attack during periods or in counties with higher campaign exposure. This mass media campaign that relied heavily on TV public service advertisements was not associated with increased EMS use by women with possible heart attack symptoms, even among counties that were more highly exposed to the campaign advertisements. [ABSTRACT FROM AUTHOR]

Background: Air pollution in cities has been linked to increased rates of mortality and morbidity in developed and developing countries. Although these findings have helped lead to a tightening of air-quality standards, their validity with respect to public health has been questioned. Methods: We assessed the effects of five major outdoor-air pollutants on daily mortality rates in 20 of the largest cities and metropolitan areas in the United States from 1987 to 1994. The pollutants were particulate matter that is less than 10 μm in aerodynamic diameter (PM10), ozone, carbon monoxide, sulfur dioxide, and nitrogen dioxide. We used a two-stage analytic approach that pooled data from multiple locations. Results: After taking into account potential confounding by other pollutants, we found consistent evidence that the level of PM10 is associated with the rate of death from all causes and from cardiovascular and respiratory illnesses. The estimated increase in the relative rate of death from all causes was 0.51 percent (95 percent posterior interval, 0.07 to 0.93 percent) for each increase in the PM10 level of 10 μg per cubic meter. The estimated increase in the relative rate of death from cardiovascular and respiratory causes was 0.68 percent (95 percent posterior interval, 0.20 to 1.16 percent) for each increase in the PM10 level of 10 μg per cubic meter. There was weaker evidence that increases in ozone levels increased the relative rates of death during the summer, when ozone levels are highest, but not during the winter. Levels of the other pollutants were not significantly related to the mortality rate. Conclusions: There is consistent evidence that the levels of fine particulate matter in the air are associated with the risk of death from all causes and from cardiovascular and respiratory illnesses. These findings strengthen the rationale for controlling the levels of respirable particles in outdoor air. (N Engl J Med 2000;343:1742-9.) [ABSTRACT FROM AUTHOR]

Purpose: To assess factors associated with gender disparities in cataract surgery volume and evaluate how these differences have changed over time., Setting: Cataract surgeons in the 2012 to 2018 Medicare database., Design: Retrospective study., Methods: The association of provider gender with the number of cataract surgeries per office visit billed was assessed with negative binomial regression models, controlling for calendar year, years in practice, hospital affiliation, geographic region, rurality, density of ophthalmologists, and the national percentile of Area Deprivation Index (ADI) score for the practice location., Results: There were 8480 cataract surgeons, most of whom were male (78%). Male surgeons worked in more deprived areas with a higher ADI (median: 40 vs 33, P < .001). Female surgeons performed fewer cataracts per year (140 [95% CI, 126-154] vs 276 [95% CI, 263-288], P < .001) and billed fewer office visits (1038 [95% CI, 1008-1068] vs 1505 [95% CI, 1484-1526], P < .001). In multivariate analysis, the number of cataract surgeries per office visit was greater for males compared with females in all years in the South (average incidence rate ratio 1.80), Midwest (1.50), and West (1.53), but not in the Northeast (1.16). The relative rate of cataract surgeries between male and female surgeons in each region did not change significantly over time from 2012 to 2018 ( P > .05 in each region)., Conclusions: Gender disparities in cataract volume among male and female surgeons have remained unchanged over time from 2012 to 2018. The higher cataract volume among male surgeons may be explained in part by provider practice location. Further studies are needed to better understand and address gender disparities., (Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of ASCRS and ESCRS.)

Policy Points Social determinants of health are an important predictor of future health care costs. Medicaid must partner with other sectors to address the underlying causes of its beneficiaries' poor health and high health care spending., Context: Social determinants of health are an important predictor of future health care costs but little is known about their impact on Medicaid spending. This study analyzes the role of social determinants of health (SDH) in predicting future health care costs for adult Medicaid beneficiaries with similar past morbidity burdens and past costs., Methods: We enrolled into a prospective cohort study 8,892 adult Medicaid beneficiaries who presented for treatment at an emergency department or clinic affiliated with two hospitals in Washington, DC, between September 2017 and December 31, 2018. We used SDH information measured at enrollment to categorize our participants into four social risk classes of increasing severity. We used Medicaid claims for a 2-year period; 12 months pre- and post-study enrollment to measure past and future morbidity burden according to the Adjusted Clinical Groups system. We also used the Medicaid claims data to characterize total annual Medicaid costs one year prior to and one year after study enrollment., Results: The 8,892 participants were primarily female (66%) and Black (91%). For persons with similar past morbidity burdens and past costs (p < 0.01), the future morbidity burden was significantly higher in the upper two social risk classes (1.15 and 2.04, respectively) compared with the lowest one. Mean future health care spending was significantly higher in the upper social risk classes compared with the lowest one ($2,713, $11,010, and $17,710, respectively) and remained significantly higher for the two highest social risk classes ($1,426 and $3,581, respectively), given past morbidity burden and past costs (p < 0.01). When we controlled for future morbidity burden (measured concurrently with future costs), social risk class was no longer a significant predictor of future health care costs., Conclusions: SDH are statistically significant predictors of future morbidity burden and future costs controlling for past morbidity burden and past costs. Further research is needed to determine whether current payment systems adequately account for differences in the care needs of highly medically and socially complex patients., (© 2022 Milbank Memorial Fund.)

Background: Little is known about the relationship between social determinants of health (SDH) and medication adherence among Medicaid beneficiaries with hypertension., Methods: We conducted a posthoc subgroup analysis of 3044 adult Medicaid beneficiaries who enrolled in a parent prospective cohort study and had a diagnosis of hypertension based on their Medicaid claims during a 24-month period before study enrollment. We calculated the proportion of days covered by at least one antihypertensive medication during the first 12 months after study enrollment using the prescription claims data. We measured numerous SDH at the time of study enrollment and we categorized our hypertension cohort into 4 social risk groups based on their response profiles to the SDH variables. We compared the mean proportion of days covered by the different levels of the SDH factors. We modeled the odds of being covered by an antihypertensive medication daily throughout the follow-up period by social risk group, adjusted for age, sex, and disease severity using a generalized linear model., Results: The nonrandom sample was predominately Black (93%), female (62%) and had completed high school (77%). The mean proportion of days covered varied significantly by different SDH, such as food insecurity (49%-56%), length of time living at present place (47%-57%), smoking status (50%-56%), etc. Social risk group was a significant predictor of medication adherence. Participants in the 2 groups with the most social risks were 36% (adjusted odds ratio=0.64 [95% CI, 0.53-0.78]) and 20% (adjusted odds ratio=0.80 [95% CI, 0.70-0.93]) less adherent to their hypertension therapy compared with participants in the group with the fewest social risks., Conclusions: Social risks are associated with lower antihypertensive medication adherence in the Medicaid population.

Introduction: This study evaluates the association of multidimensional social determinants of health (SDoH) with non-adherence to diabetic retinopathy examinations., Research Design and Methods: This was a post-hoc subgroup analysis of adults with diabetes in a prospective cohort study of enrollees in the Washington, DC Medicaid program. At study enrollment, participants were given a comprehensive SDoH survey based on the WHO SDoH model. Adherence to recommended dilated diabetic retinopathy examinations, as determined by qualifying Current Procedural Terminology codes in the insurance claims, was defined as having at least one eye examination in the 2-year period following study enrollment., Results: Of the 8943 participants enrolled in the prospective study, 1492 (64% female, 91% non-Hispanic Black) were included in this post-hoc subgroup analysis. 47.7% (n=712) were adherent to the recommended biennial diabetic eye examinations. Not having a regular provider (eg, a primary care physician) and having poor housing conditions (eg, overcrowded, inadequate heating) were associated with decreased odds of adherence to diabetic eye examinations (0.45 (95% CI 0.31 to 0.64) and 0.70 (95% CI 0.53 to 0.94), respectively) in the multivariate logistic regression analysis controlling for age, sex, race/ethnicity, overall health status using the Chronic Disability Payment System, diabetes severity using the Diabetes Complications Severity Index, history of eye disease, and history of diabetic eye disease treatment., Conclusions: A multidimensional evaluation of SDoH revealed barriers that impact adherence to diabetic retinopathy examinations. Having poor housing conditions and not having a regular provider were associated with poor adherence. A brief SDoH assessment could be incorporated into routine clinical care to identify social risks and connect patients with the necessary resources to improve adherence to diabetic retinopathy examinations., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Study Objective: We evaluate the relationship between social determinants of health and emergency department (ED) visits in the Medicaid Cohort of the District of Columbia., Methods: We conducted a retrospective cohort analysis of 8,943 adult Medicaid beneficiaries who completed a social determinants of health survey at study enrollment. We merged the social determinants of health data with participants' Medicaid claims data for up to 24 months before enrollment. Using latent class analysis, we grouped our participants into 4 distinct social risk classes based on similar responses to the social determinants of health questions. We classified ED visits as primary care treatable or ED care needed, using the Minnesota algorithm. We calculated the adjusted log relative primary care treatable and ED care needed visit rates among the social risk classes by using generalized linear mixed-effects models., Results: The majority (71%) of the 49,111 ED visits made by the 8,943 participants were ED care needed. The adjusted log relative rate of both primary care treatable and ED care needed visit rates increased with each higher (worse) social risk class compared with the lowest class. Participants in the highest social risk class (ie, unemployed and many social risks) had a log relative primary care treatable and ED care needed rate of 39% (range 28% to 50%) and 29% (range 21% to 38%), respectively, adjusted for age, sex, and illness severity., Conclusion: There is a strong relationship between social determinants of health and ED utilization in this Medicaid sample that is worth investigating in other Medicaid samples and patient populations., (Copyright © 2020 American College of Emergency Physicians. Published by Elsevier Inc. All rights reserved.)

Objective: To develop distinct social risk profiles based on social determinants of health (SDH) information and to determine whether these social risk groups varied in terms of health, health care utilization, and costs., Methods: We prospectively enrolled 8943 beneficiaries insured by the District of Columbia Medicaid program between September 2017 and December 2018. Participants completed a SDH survey and we obtained their Medicaid claims data for a 2-year period before study enrollment. We used latent class analysis (LCA) to identify distinct social risk profiles based on their SDH responses. We assessed the relationship among different SDH as well as the relationship among the social risk classes and health, health care use and costs., Results: The majority of SDH were moderately to strongly correlated with one another. LCA yielded 4 distinct social risk groups. Group 1 reported the least social risks with the most employed. Group 2 was distinguished by financial strain and housing instability with fewer employed. Group 3 were mostly unemployed with limited car and internet access. Group 4 had the most social risks and most unemployed. The social risk groups demonstrated meaningful differences in health, acute care utilization, and health care costs with group 1 having the best health outcomes and group 4 the worst (P<0.05)., Conclusions: LCA is a practical method of aggregating correlated SDH data into a finite number of distinct social risk groups. Understanding the constellation of social challenges that patients face is critical when attempting to address their social needs and improve health outcomes., Competing Interests: The authors declare no conflict of interest., (Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Risk factors for progression of coronavirus disease 2019 (COVID-19) to severe disease or death are underexplored in U.S. cohorts., Objective: To determine the factors on hospital admission that are predictive of severe disease or death from COVID-19., Design: Retrospective cohort analysis., Setting: Five hospitals in the Maryland and Washington, DC, area., Patients: 832 consecutive COVID-19 admissions from 4 March to 24 April 2020, with follow-up through 27 June 2020., Measurements: Patient trajectories and outcomes, categorized by using the World Health Organization COVID-19 disease severity scale. Primary outcomes were death and a composite of severe disease or death., Results: Median patient age was 64 years (range, 1 to 108 years); 47% were women, 40% were Black, 16% were Latinx, and 21% were nursing home residents. Among all patients, 131 (16%) died and 694 (83%) were discharged (523 [63%] had mild to moderate disease and 171 [20%] had severe disease). Of deaths, 66 (50%) were nursing home residents. Of 787 patients admitted with mild to moderate disease, 302 (38%) progressed to severe disease or death: 181 (60%) by day 2 and 238 (79%) by day 4. Patients had markedly different probabilities of disease progression on the basis of age, nursing home residence, comorbid conditions, obesity, respiratory symptoms, respiratory rate, fever, absolute lymphocyte count, hypoalbuminemia, troponin level, and C-reactive protein level and the interactions among these factors. Using only factors present on admission, a model to predict in-hospital disease progression had an area under the curve of 0.85, 0.79, and 0.79 at days 2, 4, and 7, respectively., Limitation: The study was done in a single health care system., Conclusion: A combination of demographic and clinical variables is strongly associated with severe COVID-19 disease or death and their early onset. The COVID-19 Inpatient Risk Calculator (CIRC), using factors present on admission, can inform clinical and resource allocation decisions., Primary Funding Source: Hopkins inHealth and COVID-19 Administrative Supplement for the HHS Region 3 Treatment Center from the Office of the Assistant Secretary for Preparedness and Response.

Background Current approaches fail to separate patients at high versus low risk for ventricular arrhythmias owing to overreliance on a snapshot left ventricular ejection fraction measure. We used statistical machine learning to identify important cardiac imaging and time-varying risk predictors. Methods and Results Three hundred eighty-two cardiomyopathy patients (left ventricular ejection fraction ≤35%) underwent cardiac magnetic resonance before primary prevention implantable cardioverter defibrillator insertion. The primary end point was appropriate implantable cardioverter defibrillator discharge or sudden death. Patient characteristics; serum biomarkers of inflammation, neurohormonal status, and injury; and cardiac magnetic resonance-measured left ventricle and left atrial indices and myocardial scar burden were assessed at baseline. Time-varying covariates comprised interval heart failure hospitalizations and left ventricular ejection fractions. A random forest statistical method for survival, longitudinal, and multivariable outcomes incorporating baseline and time-varying variables was compared with (1) Seattle Heart Failure model scores and (2) random forest survival and Cox regression models incorporating baseline characteristics with and without imaging variables. Age averaged 57±13 years with 28% women, 66% white, 51% ischemic, and follow-up time of 5.9±2.3 years. The primary end point (n=75) occurred at 3.3±2.4 years. Random forest statistical method for survival, longitudinal, and multivariable outcomes with baseline and time-varying predictors had the highest area under the receiver operating curve, median 0.88 (95% CI, 0.75-0.96). Top predictors comprised heart failure hospitalization, left ventricle scar, left ventricle and left atrial volumes, left atrial function, and interleukin-6 level; heart failure accounted for 67% of the variation explained by the prediction, imaging 27%, and interleukin-6 2%. Serial left ventricular ejection fraction was not a significant predictor. Conclusions Hospitalization for heart failure and baseline cardiac metrics substantially improve ventricular arrhythmic risk prediction.

Study Objective: We determine the validity of self-reported prescription filling among emergency department (ED) patients., Methods: We analyzed a subgroup of 1,026 patients enrolled in a randomized controlled trial who were prescribed at least 1 medication at ED discharge, were covered by Medicaid insurance, and completed a telephone interview 1 week after the index ED visit. We extracted all pharmacy and health care use claims information from a state Medicaid database for all subjects within 30 days of their index ED visit. We used the pharmacy claims as the criterion standard and evaluated the accuracy of self-reported prescription filling obtained during the follow-up interview by estimating its sensitivity, specificity, positive likelihood ratio and negative likelihood ratio tests. We also examined whether the accuracy of self-reported prescription filling varied significantly by patient and clinical characteristics., Results: Of the 1,635 medications prescribed, 74% were filled according to the pharmacy claims. Subjects reported filling 90% of prescriptions for a difference of 16% (95% confidence interval [CI] 14% to 18%). The self-reported data had high sensitivity (0.96; 95% CI 0.95 to 0.97) but low specificity (0.30; 95% CI 0.26 to 0.34). The positive likelihood ratio (1.37; 95% CI 1.29 to 2.46) and negative likelihood ratio (0.13; 95% CI 0.09 to 0.17) tests indicate that self-reported data are not a good indicator of prescription filling but are a moderately good indicator of nonfulfillment. Several factors were significantly associated with lower sensitivity (drug class and over-the-counter medications) and specificity (drug class, as needed, site and previous ED use)., Conclusion: Self-reported prescription filling is overestimated and associated with few factors., (Copyright © 2013 American College of Emergency Physicians. Published by Mosby, Inc. All rights reserved.)

Objectives: The objective was to characterize service completion times by patient, clinical, temporal, and crowding factors for different phases of emergency care using quantile regression (QR)., Methods: A retrospective cohort study was conducted on 1-year visit data from four academic emergency departments (EDs; N = 48,896-58,316). From each ED's clinical information system, the authors extracted electronic service information (date and time of registration; bed placement, initial contact with physician, disposition decision, ED discharge, and disposition status; inpatient medicine bed occupancy rate); patient demographics (age, sex, insurance status, and mode of arrival); and clinical characteristics (acuity level and chief complaint) and then used the service information to calculate patients' waiting room time, treatment time, and boarding time, as well as the ED occupancy rate. The 10th, 50th, and 90th percentiles of each phase of care were estimated as a function of patient, clinical, temporal, and crowding factors using multivariate QR. Accuracy of models was assessed by comparing observed and predicted service completion times and the proportion of observations that fell below the predicted 10th, 50th, and 90th percentiles., Results: At the 90th percentile, patients experienced long waiting room times (105-222 minutes), treatment times (393-616 minutes), and boarding times (381-1,228 minutes) across the EDs. We observed a strong interaction effect between acuity level and temporal factors (i.e., time of day and day of week) on waiting room time at all four sites. Acuity level 3 patients waited the longest across the four sites, and their waiting room times were most influenced by temporal factors compared to other acuity level patients. Acuity level and chief complaint were important predictors of all phases of care, and there was a significant interaction effect between acuity and chief complaint. Patients with a psychiatric problem experienced the longest treatment times, regardless of acuity level. Patients who presented with an injury did not wait as long for an ED or inpatient bed. Temporal factors were strong predictors of service completion time, particularly waiting room time. Mode of arrival was the only patient characteristic that substantially affected waiting room time and treatment time. Patients who arrived by ambulance had shorter wait times but longer treatment times compared to those who did not arrive by ambulance. There was close agreement between observed and predicted service completion times at the 10th, 50th, and 90th percentile distributions across the four EDs., Conclusions: Service completion times varied significantly across the four academic EDs. QR proved to be a useful method for estimating the service completion experience of not only typical ED patients, but also the experience of those who waited much shorter or longer. Building accurate models of ED service completion times is a critical first step needed to identify barriers to patient flow, begin the process of reengineering the system to reduce variability, and improve the timeliness of care provided.

Study Objective: We determine the effect of crowding on emergency department (ED) waiting room, treatment, and boarding times across multiple sites and acuity groups., Methods: This was a retrospective cohort study that included ED visit and inpatient medicine occupancy data for a 1-year period at 4 EDs. We measured crowding at 30-minute intervals throughout each patient's ED stay. We estimated the effect of crowding on waiting room time, treatment time, and boarding time separately, using discrete-time survival analysis with time-dependent crowding measures (ie, number waiting, number being treated, number boarding, and inpatient medicine occupancy rate), controlling for patient demographic and clinical characteristics., Results: Crowding substantially delayed patients' waiting room and boarding times but not treatment time. During the day shift, when the number boarding increased from the 50th to the 90th percentile, the adjusted median waiting room time (range 26 to 70 minutes) increased by 6% to 78% (range 33 to 82 minutes), and the adjusted median boarding time (range 250 to 626 minutes) increased by 15% to 47% (range 288 to 921 minutes), depending on the site. Crowding delayed the care of high-acuity level 2 patients at all sites. During crowded periods (ie, 90%), the adjusted median waiting room times of high-acuity level 2 patients were 3% to 35% higher than during normal periods, depending on the site and crowding measure., Conclusion: Using discrete-time survival analysis, we were able to dynamically measure crowding throughout each patient's ED visit and demonstrate its deleterious effect on the timeliness of emergency care, even for high-acuity patients.

Background: Prospective cohort studies constitute the major source of evidence about the mortality effects of chronic exposure to particulate air pollution. Additional studies are needed to provide evidence on the health effects of chronic exposure to particulate matter < or = 2.5 microm in aerodynamic diameter (PM(2.5)) because few studies have been carried out and the cohorts have not been representative., Objectives: This study was designed to estimate the relative risk of death associated with long-term exposure to PM(2.5) by region and age groups in a U.S. population of elderly, for the period 2000-2005., Methods: By linking PM(2.5) monitoring data to the Medicare billing claims by ZIP code of residence of the enrollees, we have developed a new retrospective cohort study, the Medicare Cohort Air Pollution Study. The study population comprises 13.2 million participants living in 4,568 ZIP codes having centroids within 6 miles of a PM(2.5) monitor. We estimated relative risks adjusted by socioeconomic status and smoking by fitting log-linear regression models., Results: In the eastern and central regions, a 10-microg/m(3) increase in 6-year average of PM(2.5) is associated with 6.8% [95% confidence interval (CI), 4.9-8.7%] and 13.2% (95% CI, 9.5-16.9) increases in mortality, respectively. We found no evidence of an association in the western region or for persons > or = 85 years of age., Conclusions: We established a cohort of Medicare participants for investigating air pollution and mortality on longer-term time frames. Chronic exposure to PM(2.5) was associated with mortality in the eastern and central regions, but not in the western United States.

The authors investigated whether short-term effects of fine particulate matter with an aerodynamic diameter < or =2.5 microm (PM(2.5)) on risk of cardiovascular and respiratory hospitalizations among the elderly varied by region and season in 202 US counties for 1999-2005. They fit 3 types of time-series models to provide evidence for 1) consistent particulate matter effects across the year, 2) different particulate matter effects by season, and 3) smoothly varying particulate matter effects throughout the year. The authors found statistically significant evidence of seasonal and regional variation in estimates of particulate matter effect. Respiratory disease effect estimates were highest in winter, with a 1.05% (95% posterior interval: 0.29, 1.82) increase in hospitalizations per 10-microg/m(3) increase in same-day PM(2.5). Cardiovascular diseases estimates were also highest in winter, with a 1.49% (95% confidence interval: 1.09, 1.89) increase in hospitalizations per 10-microg/m(3) increase in same-day PM(2.5), with associations also observed in other seasons. The strongest evidence of a relation between PM(2.5) and hospitalizations was in the Northeast for both respiratory and cardiovascular diseases. Heterogeneity of PM(2.5) effects on hospitalizations may reflect seasonal and regional differences in emissions and in particles' chemical constituents. Results can help guide development of hypotheses and further epidemiologic studies on potential heterogeneity in the toxicity of constituents of the particulate matter mixture.

Context: Health risks of fine particulate matter of 2.5 microm or less in aerodynamic diameter (PM2.5) have been studied extensively over the last decade. Evidence concerning the health risks of the coarse fraction of greater than 2.5 microm and 10 microm or less in aerodynamic diameter (PM10-2.5) is limited., Objective: To estimate risk of hospital admissions for cardiovascular and respiratory diseases associated with PM10-2.5 exposure, controlling for PM2.5., Design, Setting, and Participants: Using a database assembled for 108 US counties with daily cardiovascular and respiratory disease admission rates, temperature and dew-point temperature, and PM10-2.5 and PM2.5 concentrations were calculated with monitoring data as an exposure surrogate from January 1, 1999, through December 31, 2005. Admission rates were constructed from the Medicare National Claims History Files, for a study population of approximately 12 million Medicare enrollees living on average 9 miles (14.4 km) from collocated pairs of PM10 and PM2.5 monitors., Main Outcome Measures: Daily counts of county-wide emergency hospital admissions for primary diagnoses of cardiovascular or respiratory disease., Results: There were 3.7 million cardiovascular disease and 1.4 million respiratory disease admissions. A 10-microg/m3 increase in PM10-2.5 was associated with a 0.36% (95% posterior interval [PI], 0.05% to 0.68%) increase in cardiovascular disease admissions on the same day. However, when adjusted for PM2.5, the association was no longer statistically significant (0.25%; 95% PI, -0.11% to 0.60%). A 10-microg/m3 increase in PM10-2.5 was associated with a nonstatistically significant unadjusted 0.33% (95% PI, -0.21% to 0.86%) increase in respiratory disease admissions and with a 0.26% (95% PI, -0.32% to 0.84%) increase in respiratory disease admissions when adjusted for PM2.5. The unadjusted associations of PM2.5 with cardiovascular and respiratory disease admissions were 0.71% (95% PI, 0.45%-0.96%) for same-day exposure and 0.44% (95% PI, 0.06% to 0.82%) for exposure 2 days before hospital admission., Conclusion: After adjustment for PM2.5, there were no statistically significant associations between coarse particulates and hospital admissions for cardiovascular and respiratory diseases.

Background: Lack of knowledge regarding particulate matter (PM) characteristics associated with toxicity is a crucial research gap. Short-term effects of PM can vary by location, possibly reflecting regional differences in mixtures. A report by Lippmann et al. [Lippmann et al., Environ Health Perspect 114:1662-1669 (2006)] analyzed mortality effect estimates from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) for 1987-1994. They found that average concentrations of nickel or vanadium in PM2.5 (PM with aerodynamic diameter < 2.5 microm) positively modified the lag-1 day association between PM10 and all-cause mortality., Objective: We reestimated the relationship between county-specific lag-1 PM10 (PM with aerodynamic diameter < 10 microm) effects on mortality and county-specific nickel or vanadium PM2.5 average concentrations using 1987-2000 effect estimates. We explored whether such modification is sensitive to outliers., Methods: We estimated long-term average county-level nickel and vanadium PM2.5 concentrations for 2000-2005 for 72 U.S. counties representing 69 communities. We fitted Bayesian hierarchical regression models to investigate whether county-specific short-term effects of PM10 on mortality are modified by long-term county-specific nickel or vanadium PM2.5 concentrations. We conducted sensitivity analyses by excluding individual communities and considering log-transformed data., Results: Our results were consistent with those of Lippmann et al. However, we found that when counties included in the NMMAPS New York community were excluded from the sensitivity analysis, the evidence of effect modification of nickel or vanadium on the short-term effects of PM10 mortality was much weaker and no longer statistically significant., Conclusions: Our analysis does not contradict the hypothesis that nickel or vanadium may increase the risk of PM to human health, but it highlights the sensitivity of findings to particularly influential observations.

Evaluation of the public health impact of air quality regulations, referred to as accountability research, is increasingly viewed as a necessary component of responsible governmental policy interventions. The authors present an example of accountability assessment based on evaluating change in the short-term effect of airborne particles over a period of increasingly stringent regulation that might have changed the chemical composition and toxicity of these particles. They used updated data and methods of the National Morbidity Mortality Air Pollution Study to estimate national average relative rates of the effects of particulate matter

We propose a method for diagnosing confounding bias under a model that links a spatially and temporally varying exposure and health outcome. We decompose the association into orthogonal components, corresponding to distinct spatial and temporal scales of variation. If the model fully controls for confounding, the exposure effect estimates should be equal at the different temporal and spatial scales. We show that the overall exposure effect estimate is a weighted average of the scale-specific exposure effect estimates. We use this approach to estimate the association between monthly averages of fine particles (PM2.5) over the preceding 12 months and monthly mortality rates in 113 US counties from 2000 to 2002. We decompose the association between PM2.5 and mortality into 2 components: (1) the association between "national trends" in PM2.5 and mortality; and (2) the association between "local trends," defined as county-specific deviations from national trends. This second component provides evidence as to whether counties having steeper declines in PM2.5 also have steeper declines in mortality relative to their national trends. We find that the exposure effect estimates are different at these 2 spatiotemporal scales, which raises concerns about confounding bias. We believe that the association between trends in PM2.5 and mortality at the national scale is more likely to be confounded than is the association between trends in PM2.5 and mortality at the local scale. If the association at the national scale is set aside, there is little evidence of an association between 12-month exposure to PM2.5 and mortality.

Background: Although numerous studies have demonstrated links between particulate matter (PM) and adverse health effects, the chemical components of the PM mixture that cause injury are unknown., Objectives: This work characterizes spatial and temporal variability of PM(2.5) (PM with aerodynamic diameter < 2.5 microm) components in the United States; our objective is to identify components for assessment in epidemiologic studies., Methods: We constructed a database of 52 PM(2.5) component concentrations for 187 U.S. counties for 2000-2005. First, we describe the challenges inherent to analysis of a national PM(2.5) chemical composition database. Second, we identify components that contribute substantially to and/or co-vary with PM(2.5) total mass. Third, we characterize the seasonal and regional variability of targeted components., Results: Strong seasonal and geographic variations in PM(2.5) chemical composition are identified. Only seven of the 52 components contributed >/= 1% to total mass for yearly or seasonal averages [ammonium (NH(4) (+)), elemental carbon (EC), organic carbon matter (OCM), nitrate (NO(3) (-)), silicon, sodium (Na(+)), and sulfate (SO(4) (2-))]. Strongest correlations with PM(2.5) total mass were with NH(4) (+) (yearly), OCM (especially winter), NO(3) (-) (winter), and SO(4) (2-) (yearly, spring, autumn, and summer), with particularly strong correlations for NH(4) (+) and SO(4) (2-) in summer. Components that co-varied with PM(2.5) total mass, based on daily detrended data, were NH(4) (+), SO(4) (2-) (,) OCM, NO(3) (2-), bromine, and EC., Conclusions: The subset of identified PM(2.5) components should be investigated further to determine whether their daily variation is associated with daily variation of health indicators, and whether their seasonal and regional patterns can explain the seasonal and regional heterogeneity in PM(10) (PM with aerodynamic diameter < 10 microm) and PM(2.5) health risks.

The methodological development of this paper is motivated by a common problem in econometrics where we are interested in estimating the difference in the average expenditures between two populations, say with and without a disease, as a function of the covariates. For example, let Y(1) and Y(2) be two non-negative random variables denoting the health expenditures for cases and controls. Smooth Quantile Ratio Estimation (SQUARE) is a novel approach for estimating Delta=E[Y(1)] - E[Y(2)] by smoothing across percentiles the log-transformed ratio of the two quantile functions. Dominici et al. (2005) have shown that SQUARE defines a large class of estimators of Delta, is more efficient than common parametric and nonparametric estimators of Delta, and is consistent and asymptotically normal. However, in applications it is often desirable to estimate Delta(x)=E[Y(1)|x]--E[Y(2)|x], that is, the difference in means as a function of x. In this paper we extend SQUARE to a regression model and we introduce a two-part regression SQUARE for estimating Delta(x) as a function of x. We use the first part of the model to estimate the probability of incurring any costs and the second part of the model to estimate the mean difference in health expenditures, given that a nonzero cost is observed. In the second part of the model, we apply the basic definition of SQUARE for positive costs to compare expenditures for the cases and controls having 'similar' covariate profiles. We determine strata of cases and control with 'similar' covariate profiles by the use of propensity score matching. We then apply two-part regression SQUARE to the 1987 National Medicare Expenditure Survey to estimate the difference Delta(x) between persons suffering from smoking-attributable diseases and persons without these diseases as a function of the propensity of getting the disease. Using a simulation study, we compare frequentist properties of two-part regression SQUARE with maximum likelihood estimators for the log-transformed expenditures.

This study examined the association between the Joint Commission on Accreditation of Healthcare Organizations (JCAHO) accreditation scores and the Agency for Healthcare Research and Quality's Inpatient Quality Indicators and Patient Safety Indicators (IQIs/PSIs). JCAHO accreditation data from 1997 to 1999 were matched with institutional IQI/PSI performance from 24 states in the Healthcare Cost and Utilization Project. Most institutions scored high on JCAHO measures despite IQI/PSI performance variation with no significant relationship between them. Principal component analysis found 1 factor each of the IQIs/PSIs that explained the majority of variance on the IQIs/PSIs. Worse performance on the PSI factor was associated with worse performance on JCAHO scores (P=.02). No significant relationships existed between JCAHO categorical accreditation decisions and IQI/PSI performance. Few relationships exist between JCAHO scores and IQI/PSI performance. There is a need to continuously reevaluate all measurement tools to ensure they are providing the public with reliable, consistent information about health care quality and safety.

Time-series studies have linked daily variations in nonaccidental deaths with daily variations in ambient particulate matter air pollution, while controlling for qualitatively larger influences of weather and season. Although time-series analyses typically include nonlinear terms for weather and season, questions remain as to whether models to date have completely controlled for these important predictors. In this paper, the authors use two flexible versions of distributed lag models to control extensively for the confounding effects of weather and season. One version builds on the current approach to controlling for weather, while the other version offers a new approach. The authors conduct a comprehensive sensitivity analysis of the particulate matter-mortality relation by applying these methods to the recently updated National Morbidity, Mortality, and Air Pollution Study database that comprises air pollution, weather, and mortality time series from 1987 to 2000 for 100 US cities. They combine city-specific estimates of the short-term effects of particulate matter on mortality using a Bayesian hierarchical model. They conclude that, within the broad classes of models considered, national average estimates of particulate matter relative risk are consistent with previous estimates from this study and are robust to model specification for weather and seasonal confounding.

Time series models relating short-term changes in air pollution levels to daily mortality counts typically assume that the effects of air pollution on the log relative rate of mortality do not vary with time. However, these short-term effects might plausibly vary by season. Changes in the sources of air pollution and meteorology can result in changes in characteristics of the air pollution mixture across seasons. The authors developed Bayesian semiparametric hierarchical models for estimating time-varying effects of pollution on mortality in multisite time series studies. The methods were applied to the database of the National Morbidity and Mortality Air Pollution Study, which includes data for 100 US cities, for the period 1987-2000. At the national level, a 10-microg/m(3) increase in particulate matter less than 10 microm in aerodynamic diameter at a 1-day lag was associated with 0.15% (95% posterior interval (PI): -0.08, 0.39), 0.14% (95% PI: -0.14, 0.42), 0.36% (95% PI: 0.11, 0.61), and 0.14% (95% PI: -0.06, 0.34) increases in mortality for winter, spring, summer, and fall, respectively. An analysis by geographic region found a strong seasonal pattern in the Northeast (with a peak in summer) and little seasonal variation in the southern regions of the country. These results provide useful information for understanding particle toxicity and guiding future analyses of particle constituent data.

Numerous studies have shown a positive association between daily mortality and particulate air pollution, even at concentrations below regulatory limits. These findings have motivated interest in the shape of the concentration-response relation. We developed flexible modeling strategies for time-series data that include spline and threshold concentration-response models. We applied these models to daily time-series data for the 20 largest US cities for 1987 through 1994, using concentration of particulate matter less than 10 microm in aerodynamic diameter (PM10*) as the exposure measure. The spline model showed a linear relation without indicating a threshold for the relative risks of death for all causes (total deaths) and for cardiovascular-respiratory causes in relation to PM10 concentration. By contrast, for causes other than cardiovascular-respiratory, the relative risk did not increase until the concentration reached approximately 50 microg/m3 PM10. For total mortality, a linear model without threshold was preferred to the threshold model and to the spline model, using the value of the Akaike information criterion (AIC). The findings were similar for combined cardiovascular and respiratory deaths. These findings indicate that linear models without a threshold are appropriate for assessing the effect of particulate air pollution on daily mortality even at current ambient levels.

While time-series studies have consistently provided evidence for an effect of particulate air pollution on mortality, uncertainty remains as to the extent of the life-shortening implied by those associations. In this paper, the authors estimate the association between air pollution and mortality using different timescales of variation in the air pollution time series to gain further insight into this question. The authors' method is based on a Fourier decomposition of air pollution time series into a set of independent exposure variables, each representing a different timescale. The authors then use this set of variables as predictors in a Poisson regression model to estimate a separate relative rate of mortality for each exposure timescale. The method is applied to a database containing information on daily mortality, particulate air pollution, and weather in four US cities (Pittsburgh, Pennsylvania; Minneapolis, Minnesota; Seattle, Washington; and Chicago, Illinois) from the period 1987-1994. The authors found larger relative rates of mortality associated with particulate air pollution at longer timescale variations (14 days-2 months) than at shorter timescales (1-4 days). These analyses provide additional evidence that associations between particle indexes and mortality do not imply only an advance in the timing of death by a few days for frail individuals.

The widely used generalized additive models (GAM) method is a flexible and effective technique for conducting nonlinear regression analysis in time-series studies of the health effects of air pollution. When the data to which the GAM are being applied have two characteristics--1) the estimated regression coefficients are small and 2) there exist confounding factors that are modeled using at least two nonparametric smooth functions--the default settings in the gam function of the S-Plus software package (version 3.4) do not assure convergence of its iterative estimation procedure and can provide biased estimates of regression coefficients and standard errors. This phenomenon has occurred in time-series analyses of contemporary data on air pollution and mortality. To evaluate the impact of default implementation of the gam software on published analyses, the authors reanalyzed data from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) using three different methods: 1) Poisson regression with parametric nonlinear adjustments for confounding factors; 2) GAM with default convergence parameters; and 3) GAM with more stringent convergence parameters than the default settings. The authors found that pooled NMMAPS estimates were very similar under the first and third methods but were biased upward under the second method.