Distinct clinical trajectories of gastrointestinal progression among patients with systemic sclerosis.

OBJECTIVES: Systemic sclerosis (SSc) is heterogeneous in its clinical presentation. Common manifestations cluster together, defining unique subgroups. This investigation aims to characterize gastrointestinal (GI) phenotypes and determine whether they can be distinguished by temporal progression. METHODS: We examine a well-established SSc patient cohort with a modified Medsger GI severity score measured over time to determine heterogeneity in disease progression. Growth mixture models estimate each patient's phenotype and disease severity trajectory over time. We compare the characteristics of estimated phenotypes using non-parametric statistics and linear and logistic regression to compare patient characteristics between phenotypes while adjusting for disease duration. RESULTS: We examined 2696 SSc patients with at least two Medsger GI scores, identifying four unique phenotypes. The most common phenotype (n = 2325) ("Stable") had an average score of 1 that was consistent over time. Two phenotypes were progressive ["Early Progressive" (n = 142) and "Late Progressive" (n = 115)] with an initial average score of 1. The Early Progressive group increased initially and stabilized, and the Late Progressive group worsened slowly over time. A fourth phenotype ["Early Severe GI"; (n = 114)] had an initial average Medsger GI score just below 3 with high mortality and improving GI severity over time. CONCLUSIONS: Clinically distinct GI phenotypes exist among patients with SSc. These phenotypes are not only distinguished by GI and extra-intestinal SSc clinical complications, but they are also temporally distinct. Distinct autoantibody profiles are associated strongly with more severe GI disease.

Statin use and neurologic morbidity after coronary artery bypass grafting: A cohort study.

BACKGROUND: Statin use before surgery has been associated with reduced morbidity and mortality after vascular surgery. The effect of preoperative statin use on stroke and encephalopathy after coronary artery bypass grafting (CABG) is unclear. METHODS: A post hoc analysis was undertaken of a prospectively collected cohort of isolated CABG patients over a 10-year period at a single institution. Primary outcomes were stroke and encephalopathy. Univariable analyses identified risk factors for statin use, which were applied to a propensity score model using logistic regression and patients were divided into quintiles of propensity for statin use. Controlling for propensity score quintile, the odds ratio (OR) of combined stroke and encephalopathy (primary endpoint), cardiovascular mortality, myocardial infarction, and length of stay were compared between statin users and nonusers. RESULTS: There were 5,121 CABG patients, of whom 2,788 (54%) were taking statin medications preoperatively. Stroke occurred in 166 (3.2%) and encephalopathy in 438 (8.6%), contributing to 604 patients (11.8%) who met the primary endpoint. The unadjusted OR of stroke/encephalopathy in statin users was 1.053 (95% confidence interval [CI] 0.888-1.248, p = 0.582). Adjustment based on propensity score resulted in balance of stroke risk factors among quintiles. The propensity score-adjusted OR of stroke/encephalopathy in statin users was 0.958 (95% CI 0.784-1.170, p = 0.674). There were no significant differences in cardiovascular mortality, myocardial infarction, or length of stay between statin users and otherwise similar nonusers. CONCLUSIONS: In this large data cohort study, preoperative statin use was not associated with a decreased incidence of stroke and encephalopathy after coronary artery bypass grafting.

The effects of DonorNet 2007 on kidney distribution equity and efficiency.

In 2007, UNOS released DonorNet 2007 (DN07) in hope of improving allocation equity and efficiency. We hypothesized that hard-to-place organs might be less efficiently handled through this regimented process. We analyzed associations between DN07 and center-level equity, number of refusals per organ and cold ischemia time (CIT). A total of 8244 kidney transplants between 1/2006 and 12/2006 (pre-DN07) were compared with 6029 transplants between 5/2007 and 2/2008 (post-DN07). Distribution equity was assessed by the Gini coefficient, changes in the number of refusals and CIT by negative binomial regression and discard rates by logistic regression. We estimated quantile-specific differences in CIT by bootstrapping. We found no significant change in center-level distribution equity after DN07. Number of refusals per organ increased by 20% (adjusted rate ratio (1.12)1.20(1.28), p < 0.001) at the patient level and 11% (ARR (1.07)1.11(1.16), p < 0.001) at the center level. Regression models of CIT showed no global change in CIT associated with DN07, but those kidneys with the longest CIT pre-DN07 had statistically significantly longer CIT post-DN07. The discard rate also increased significantly (ARR (1.06)1.11(1.17), p < 0.001). DN07 has not improved equity or efficiency in allocation of deceased donor kidneys, and may be harming the allocation of hard-to-place kidneys.

Bayesian distributed lag models: estimating effects of particulate matter air pollution on daily mortality.

A distributed lag model (DLagM) is a regression model that includes lagged exposure variables as covariates; its corresponding distributed lag (DL) function describes the relationship between the lag and the coefficient of the lagged exposure variable. DLagMs have recently been used in environmental epidemiology for quantifying the cumulative effects of weather and air pollution on mortality and morbidity. Standard methods for formulating DLagMs include unconstrained, polynomial, and penalized spline DLagMs. These methods may fail to take full advantage of prior information about the shape of the DL function for environmental exposures, or for any other exposure with effects that are believed to smoothly approach zero as lag increases, and are therefore at risk of producing suboptimal estimates. In this article, we propose a Bayesian DLagM (BDLagM) that incorporates prior knowledge about the shape of the DL function and also allows the degree of smoothness of the DL function to be estimated from the data. We apply our BDLagM to its motivating data from the National Morbidity, Mortality, and Air Pollution Study to estimate the short-term health effects of particulate matter air pollution on mortality from 1987 to 2000 for Chicago, Illinois. In a simulation study, we compare our Bayesian approach with alternative methods that use unconstrained, polynomial, and penalized spline DLagMs. We also illustrate the connection between BDLagMs and penalized spline DLagMs. Software for fitting BDLagM models and the data used in this article are available online.

Left insular stroke is associated with adverse cardiac outcome.

BACKGROUND: After stroke, 10% of patients have adverse cardiac outcomes. Left insular damage may contribute to this by impairing sympathovagal balance (associated with cardiac structural damage and arrhythmias). METHODS: The authors conducted a prospective study of 32 patients with left insular stroke (Group 1) and 84 patients with non-insular stroke/TIA (Group 2). Adverse cardiac outcomes (cardiac death, myocardial infarction, angina, and heart failure) were assessed over 1 year. Myocardial wall motion was investigated with transesophageal echocardiography. RESULTS: Group 1's cardiac outcome relative risk (RR) compared with Group 2 was 1.75 (95% CI: 1.02, 3.00, p = 0.05). Left insular stroke remained an independent predictor of cardiac outcome in multivariate analyses. Sensitivity analysis excluding TIA and angina showed similar results. For Group 1 patients without symptomatic coronary artery disease (SCAD), cardiac outcome RR = 4.06 (95% CI: 1.83, 9.01, p = 0.002). For Group 1 with SCAD, RR = 0.36 (95% CI: 0.06, 2.13, p = 0.14). Cardiac wall motion impairment was also associated with left insular stroke independent of CAD or nonischemic heart disease. Right insular stroke was not associated with adverse cardiac outcomes or cardiac wall motion impairment. CONCLUSIONS: Left insular stroke is associated with an increased risk of adverse cardiac outcome and decreased cardiac wall motion compared to stroke in other locations and TIA. This was particularly marked in patients without symptomatic coronary artery disease (SCAD). In patients with SCAD, the cardioprotective effect of medications, especially beta-blockers alone or combined with ischemic preconditioning, may explain the lack of association in this subgroup.

Is there cognitive decline 1 year after CABG? Comparison with surgical and nonsurgical controls.

BACKGROUND: It is widely assumed that decline in cognition after coronary artery bypass grafting (CABG) is related to use of the cardiopulmonary bypass pump. Because most studies have not included comparable control groups, it remains unclear whether postoperative cognitive changes are specific to cardiopulmonary bypass, general aspects of surgery, or vascular pathologies of the aging brain. METHODS: This nonrandomized study included four groups: CABG patients (n = 140); off-pump coronary surgery (n = 72); nonsurgical cardiac controls (NSCC) with diagnosed coronary artery disease but no surgery (n = 99); and heart healthy controls (HHC) with no cardiac risk factors (n = 69). Subjects were evaluated at baseline (preoperatively), 3 months, and 12 months. Eight cognitive domains and a global cognitive score, as well as depressive and subjective symptoms were analyzed. RESULTS: At baseline, patients with coronary artery disease (CABG, off-pump, and NSCC) had lower performance than the HHC group in several cognitive domains. By 3 months, all groups had improved. From 3 to 12 months, there were minimal intrasubject changes for all groups. No consistent differences between the CABG and off-pump patients were observed. CONCLUSIONS: Compared with heart healthy controls (HHC), the groups with coronary artery disease had lower cognitive test scores at baseline. There was no evidence that the cognitive test performance of coronary artery bypass grafting (CABG) patients differed from that of control groups with coronary artery disease over a 1-year period. This study emphasizes the need for appropriate control groups for interpreting longitudinal changes in cognitive performance after CABG.

Considerations in the evaluation of surrogate endpoints in clinical trials. summary of a National Institutes of Health workshop.

We report on recommendations from a National Institutes of Health Workshop on methods for evaluating the use of surrogate endpoints in clinical trials, which was attended by experts in biostatistics and clinical trials from a broad array of disease areas. Recent advances in biosciences and technology have increased the ability to understand, measure, and model biological mechanisms; appropriate application of these advances in clinical research settings requires collaboration of quantitative and laboratory scientists. Biomarkers, new examples of which arise rapidly from new technologies, are used frequently in such areas as early detection of disease and identification of patients most likely to benefit from new therapies. There is also scientific interest in exploring whether, and under what conditions, biomarkers may substitute for clinical endpoints of phase III trials, although workshop participants agreed that these considerations apply primarily to situations where trials using clinical endpoints are not feasible. Evaluating candidate biomarkers in the exploratory phases of drug development and investigating surrogate endpoints in confirmatory trials require the establishment of a statistical and inferential framework. As a first step, participants reviewed methods for investigating the degree to which biomarkers can explain or predict the effect of treatments on clinical endpoints measured in clinical trials. They also suggested new approaches appropriate in settings where biomarkers reflect only indirectly the important processes on the causal path to clinical disease and where biomarker measurement errors are of concern. Participants emphasized the need for further research on development of such models, whether they are empirical in nature or attempt to describe mechanisms in mathematical terms. Of special interest were meta-analytic models for combining information from multiple studies involving interventions for the same condition. Recommendations also included considerations for design and conduct of trials and for assemblage of databases needed for such research. Finally, there was a strong recommendation for increased training of quantitative scientists in biologic research as well as in statistical methods and modeling to ensure that there will be an adequate workforce to meet future research needs.

The evaluation of multiple surrogate endpoints.

Surrogate endpoints are desirable because they typically result in smaller, faster efficacy studies compared with the ones using the clinical endpoints. Research on surrogate endpoints has received substantial attention lately, but most investigations have focused on the validity of using a single biomarker as a surrogate. Our paper studies whether the use of multiple markers can improve inferences about a treatment's effects on a clinical endpoint. We propose a joint model for a time to clinical event and for repeated measures over time on multiple biomarkers that are potential surrogates. This model extends the formulation of Xu and Zeger (2001, in press) and Fawcett and Thomas (1996, Statistics in Medicine 15, 1663-1685). We propose two complementary measures of the relative benefit of multiple surrogates as opposed to a single one. Markov chain Monte Carlo is implemented to estimate model parameters. The methodology is illustrated with an analysis of data from a schizophrenia clinical trial.

Obesity and approaches to weight in an urban African-American community.

OBJECTIVES: To describe the prevalence of obesity, associated factors, and current approaches to weight in an inner city African-American community. DESIGN: In-home survey by community health interviewers. SETTING: Baltimore, Maryland. PARTICIPANTS: 2196 community residents identified in a probability sample of census blocks. MAIN OUTCOME MEASURES: Self-reported height and weight and calculated Body Mass Index (BMI), category of BMI, and stated weight goals. RESULTS: Sixty percent of participants were overweight (BMI> or =25 kg/m2), and 31% were obese (BMI> or =30 kg/m2). In multivariate analysis, women, those earning $15,000-30,000, and those aged 45-60 were more likely to be obese; less likely to be obese were smokers, daily drinkers, and those with "good" or "excellent" health. Sixty-one percent of obese participants reported trying to lose weight, while 36% of normal weight participants were trying to gain weight. Of those trying to lose weight, 35% were using recommended approaches, and 26% received "the professional help they needed to control their weight." CONCLUSIONS: Although obesity was prevalent, few were using recommended weight loss strategies and a significant minority of normal weight participants were trying to gain weight, indicating a need for improved weight management and obesity prevention in the African-American community.

Latent class model diagnosis.

In many areas of medical research, such as psychiatry and gerontology, latent class variables are used to classify individuals into disease categories, often with the intention of hierarchical modeling. Problems arise when it is not clear how many disease classes are appropriate, creating a need for model selection and diagnostic techniques. Previous work has shown that the Pearson chi 2 statistic and the log-likelihood ratio G2 statistic are not valid test statistics for evaluating latent class models. Other methods, such as information criteria, provide decision rules without providing explicit information about where discrepancies occur between a model and the data. Identifiability issues further complicate these problems. This paper develops procedures for assessing Markov chain Monte Carlo convergence and model diagnosis and for selecting the number of categories for the latent variable based on evidence in the data using Markov chain Monte Carlo techniques. Simulations and a psychiatric example are presented to demonstrate the effective use of these methods.

Fine particulate air pollution and mortality in 20 U.S. cities, 1987-1994.

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 microm 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 microg 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 microg 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.

The National Morbidity, Mortality, and Air Pollution Study. Part I: Methods and methodologic issues.

The Health Effects Institute, established in 1980, is an independent and unbiased source of information on the health effects of motor vehicle emissions. HEI supports research on all major pollutants, including regulated pollutants (such as carbon monoxide, ozone, nitrogen dioxide, and particulate matter) and unregulated pollutants (such as diesel engine exhaust, methanol, and aldehydes). To date, HEI has supported more than 200 projects at institutions in North America and Europe and has published over 100 research reports. Typically, HEI receives half its funds from the US Environmental Protection Agency and half from 28 manufacturers and marketers of motor vehicles and engines in the US. Occasionally, funds from other public and private organizations either support special projects or provide resources for a portion of an HEI study. Regardless of funding sources, HEI exercises complete autonomy in setting its research priorities and in reaching its conclusions. An independent Board of Directors governs HEI. The Institute's Research and Review Committees serve complementary scientific purposes and draw distinguished scientists as members. The results of HEI-funded studies are made available as Research Reports, which contain both the Investigators' Report and the Review Committee's evaluation of the work's scientific quality and regulatory relevance.

Estimating particulate matter-mortality dose-response curves and threshold levels: an analysis of daily time-series for the 20 largest US cities.

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 microm 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 microg/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 microg/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.

Multivariate continuation ratio models: connections and caveats.

We develop semiparametric estimation methods for a pair of regressions that characterize the first and second moments of clustered discrete survival times. In the first regression, we represent discrete survival times through univariate continuation indicators whose expectations are modeled using a generalized linear model. In the second regression, we model the marginal pairwise association of survival times using the Clayton-Oakes cross-product ratio (Clayton, 1978, Biometrika 65, 141-151; Oakes, 1989, Journal of the American Statistical Association 84, 487-493). These models have recently been proposed by Shih (1998, Biometrics 54, 1115-1128). We relate the discrete survival models to multivariate multinomial models presented in Heagerty and Zeger (1996, Journal of the American Statistical Society 91, 1024-1036) and derive a paired estimating equations procedure that is computationally feasible for moderate and large clusters. We extend the work of Guo and Lin (1994, Biometrics 50, 632-639) and Shih (1998) to allow covariance weighted estimating equations and investigate the impact of weighting in terms of asymptotic relative efficiency. We demonstrate that the multinomial structure must be acknowledged when adopting weighted estimating equations and show that a naive use of GEE methods can lead to inconsistent parameter estimates. Finally, we illustrate the proposed methodology by analyzing psychological testing data previously summarized by TenHave and Uttal (1994, Applied Statistics 43, 371-384) and Guo and Lin (1994).

Exposure measurement error in time-series studies of air pollution: concepts and consequences.

Misclassification of exposure is a well-recognized inherent limitation of epidemiologic studies of disease and the environment. For many agents of interest, exposures take place over time and in multiple locations; accurately estimating the relevant exposures for an individual participant in epidemiologic studies is often daunting, particularly within the limits set by feasibility, participant burden, and cost. Researchers have taken steps to deal with the consequences of measurement error by limiting the degree of error through a study's design, estimating the degree of error using a nested validation study, and by adjusting for measurement error in statistical analyses. In this paper, we address measurement error in observational studies of air pollution and health. Because measurement error may have substantial implications for interpreting epidemiologic studies on air pollution, particularly the time-series analyses, we developed a systematic conceptual formulation of the problem of measurement error in epidemiologic studies of air pollution and then considered the consequences within this formulation. When possible, we used available relevant data to make simple estimates of measurement error effects. This paper provides an overview of measurement errors in linear regression, distinguishing two extremes of a continuum-Berkson from classical type errors, and the univariate from the multivariate predictor case. We then propose one conceptual framework for the evaluation of measurement errors in the log-linear regression used for time-series studies of particulate air pollution and mortality and identify three main components of error. We present new simple analyses of data on exposures of particulate matter < 10 microm in aerodynamic diameter from the Particle Total Exposure Assessment Methodology Study. Finally, we summarize open questions regarding measurement error and suggest the kind of additional data necessary to address them.

Community health survey in an urban African-American neighborhood: distribution and correlates of elevated blood pressure.

While considerable improvements have been made over the last 30 years in hypertension (HTN) awareness, treatment, and control, a recent reversal of these trends has been documented with African-American adults, particularly among those continuing to suffer from uncontrolled hypertension and its adverse consequences. This paper presents data from a cross-sectional representative survey of the health status of an urban African-American community. The study was designed in partnership with community leadership to improve HTN care and control. The baseline survey was a face-to-face interview (including blood pressure [BP] measurements) of 2,196 adults residing in randomly selected blocks in the Sandtown-Winchester neighborhood in Baltimore City. These sample data were compared with national data from the NHANES III survey, and demonstrated similar awareness of hypertension. However, hypertension control rates among treated hypertensives were significantly lower in the study community (28%) than in the national survey (44%). Compared with normotensive individuals, those with HTN were significantly older, had less education, were less likely to be employed, and had lower annual incomes. Individuals with HTN were also significantly more likely to rate their health as poor/fair, to report a history of heart disease, stroke, diabetes, kidney disease, obesity, high cholesterol, and lack of exercise, as well as to be at greater risk of alcoholism or alcohol problems. Hypertensive individuals (88% with reported prior history, 12% newly detected) were significantly more likely to have a usual source of care, have seen a health professional in the last 12 months, and to be extremely satisfied with the provider; however, 20% of individuals with hypertension reported no health insurance. These data indicate the need for focused interventions to enhance hypertension maintenance of care and adherence to treatment.

A measurement error model for time-series studies of air pollution and mortality.

One barrier to interpreting the observational evidence concerning the adverse health effects of air pollution for public policy purposes is the measurement error inherent in estimates of exposure based on ambient pollutant monitors. Exposure assessment studies have shown that data from monitors at central sites may not adequately represent personal exposure. Thus, the exposure error resulting from using centrally measured data as a surrogate for personal exposure can potentially lead to a bias in estimates of the health effects of air pollution. This paper develops a multi-stage Poisson regression model for evaluating the effects of exposure measurement error on estimates of effects of particulate air pollution on mortality in time-series studies. To implement the model, we have used five validation data sets on personal exposure to PM10. Our goal is to combine data on the associations between ambient concentrations of particulate matter and mortality for a specific location, with the validation data on the association between ambient and personal concentrations of particulate matter at the locations where data have been collected. We use these data in a model to estimate the relative risk of mortality associated with estimated personal-exposure concentrations and make a comparison with the risk of mortality estimated with measurements of ambient concentration alone. We apply this method to data comprising daily mortality counts, ambient concentrations of PM10measured at a central site, and temperature for Baltimore, Maryland from 1987 to 1994. We have selected our home city of Baltimore to illustrate the method; the measurement error correction model is general and can be applied to other appropriate locations.Our approach uses a combination of: (1) a generalized additive model with log link and Poisson error for the mortality-personal-exposure association; (2) a multi-stage linear model to estimate the variability across the five validation data sets in the personal-ambient-exposure association; (3) data augmentation methods to address the uncertainty resulting from the missing personal exposure time series in Baltimore. In the Poisson regression model, we account for smooth seasonal and annual trends in mortality using smoothing splines. Taking into account the heterogeneity across locations in the personal-ambient-exposure relationship, we quantify the degree to which the exposure measurement error biases the results toward the null hypothesis of no effect, and estimate the loss of precision in the estimated health effects due to indirectly estimating personal exposures from ambient measurements.

Template mixture models for direct cortical electrical interference data.

This paper introduces a statistical approach for high-level spatial analysis when there is little prior information about the shape or location of the region of interest in the underlying image and limited spatial resolution of the available data. Our work was motivated by a functional brain mapping technique called direct cortical electrical interference (DCEI) that gives binary observations at multiple sites throughout the brain. We estimate an underlying, binary spatial response function using a mixture of an unknown number of simple geometrical shapes (e.g. circles) with unknown centers and sizes to be estimated. Inference is made using reversible jump Markov chain Monte Carlo. The approach is illustrated with simulated examples and a real example with DCEI data.

The National Morbidity, Mortality, and Air Pollution Study. Part II: Morbidity and mortality from air pollution in the United States.

BACKGROUND: Epidemiologic time-series studies conducted in a number of cities have identified, in general, an association between daily changes in concentration of ambient particulate matter (PM) and daily number of deaths (mortality). Increased hospitalization (a measure of morbidity) among the elderly for specific causes has also been associated with PM. These studies have raised concerns about public health effects of particulate air pollution and have contributed to regulatory decisions in the United States. However, scientists have pointed out uncertainties that raise questions about the interpretation of these studies. One limitation to previous time-series studies of PM and adverse health effects is that the evidence for an association is derived from studies conducted in single locations using diverse analytic methods. Statistical procedures have been used to combine the results of these single location studies in order to produce a summary estimate of the health effects of PM. Difficulties with this approach include the process by which cities were selected to be studied, the different analytic methods applied to each single study, and the variety of methods used to measure or account for variables included in the analysis. These individual studies were also not able to account for the effects of gaseous air pollutants in a systematic manner.