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Francesca Dominici

Publications and source records attributed to Francesca Dominici.

36 records · Page 2Linked to original sources
PubMed2004

Underestimation of standard errors in multi-site time series studies.

Multi-site time series studies of the association of air pollution with mortality and morbidity have figured prominently in the literature as comprehensive approaches for estimating short-term effects of air pollution on health. Hierarchical models are generally used to combine site-specific information and to estimate pooled air pollution effects while taking into account both within-site statistical uncertainty and across-site heterogeneity. Within a site, characteristics of time series data of air pollution and health (small pollution effects, missing data, and highly correlated predictors) make the modeling of all sources of uncertainty challenging. One potential consequence is underestimation of the statistical variance of the site-specific effects to be combined.In this paper, we investigate the impact of variance underestimation on the pooled relative rate estimate. We focused on two-stage normal-normal hierarchical models and on underestimation of the statistical variance at the first stage. By mathematical considerations and simulation studies, we found that variance underestimation did not affect the pooled estimate substantially. However, the pooled estimate was somewhat sensitive to variance underestimation when the number of sites was small and underestimation was severe. These simulation results are applicable to any two-stage normal-normal hierarchical model for combining information of site-specific results (including meta-analyses), and they can easily be extended to more general hierarchical formulations. We also examined the impact of variance underestimation on the national average relative rate estimate from the National Morbidity, Mortality and Air Pollution Study. We found that variance underestimation as large as 40% had little effect on the national average.

Air Pollutants↗
PubMed2004

Time-series studies of particulate matter.

Studies of air pollution and human health have evolved from descriptive studies of the early phenomena of large increases in adverse health effects following extreme air pollution episodes to time-series analyses based on the use of sophisticated regression models. In fact, advanced statistical methods are necessary to address the challenges inherent in the detection of a relatively small pollution risk in the presence of potential confounders. This paper reviews the history, methods, and findings of the time-series studies estimating health risks associated with short-term exposure to particulate matter (PM), though much of the discussion is applicable to epidemiological studies of air pollution in general. We review the critical role of epidemiological studies in setting regulatory standards and the history of PM epidemiology and time-series analysis. We also summarize recent time-series results and conclude with a discussion of current and future directions of time-series analysis of particulates, including research on mortality displacement and the resolution of results from cohort and time-series studies.

Air Pollutants↗
PubMed2004

Predicting patient-reported asthma outcomes for adults in managed care.

OBJECTIVE: To develop and evaluate a set of questionnaire-based screening tools to identify risk for 1-year adverse outcomes in adults with moderate to severe asthma. STUDY DESIGN: Prospective cohort study in 16 managed care organizations in the United States. PATIENTS AND METHODS: Patients (n = 4888) with moderate-to-severe asthma completed baseline and 1-year questionnaires (response rate, 79%). Adverse outcomes included hospitalization in the past year; emergency department (ED) visit in the past year; days of lost activity in the past month; a composite measure combining hospitalization, ED use, and lost days; and severe symptoms. Risk models were constructed for each of these 5 outcomes. Candidate predictors included baseline demographic characteristics, prior asthma healthcare use, access to care, symptoms, and treatment. Outcome variables were dichotomized, and logistic regression analysis was used to estimate the probability of 1-year outcomes. RESULTS: The patients' mean age was 45 years; 69% were female, and 83% were white. At 1-year follow-up, 9% had been hospitalized in the past year, 35% had used the ED, and 36% had reduced activity in the past month; 54% reported at least 1 of these, and 53% reported severe symptoms. Twenty-one items were retained for the 5 final risk models. Overall, the strongest predictors were comorbid illnesses and prior ED use. Model discrimination using receiver operating characteristic area ranged from 0.67 to 0.78 for predicting hospitalization, ED use, lost days, any one of these outcomes, and symptoms. CONCLUSIONS: The questionnaire-based risk models identified with good discrimination asthmatics at increased risk for a range of adverse outcomes. Risk models based on patient-reported data could be used to target individuals for intervention.

Adolescent↗
PubMed2004

The National Morbidity, Mortality, and Air Pollution Study. Part III: PM10 concentration-response curves and thresholds 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 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.

Air Pollution↗
PubMed2004

Time-series analysis of air pollution and mortality: a statistical review.

The Walter A Rosenblith New Investigator Award provided funding to explore new statistical approaches for air pollution research. This report reviews and summarizes the methodologic and substantive contributions to time-series analyses of air pollution and mortality that this award made possible. The review is organized according to the following general topics: (1) semiparametric methods for time-series analyses of air pollution and mortality; (2) explorations into the sensitivity of generalized additive models (GAMs*) applied to time-series data; (3) combining information in multisite time-series studies; (4) effects of misclassification of exposure; (5) mortality displacement; (6) shape of the concentration-response curve; and (7) ongoing projects and future directions. Appendix A includes abstracts of papers published as reports and in peer-reviewed journals.

Air Pollution↗
PubMed2003

Airborne particulate matter and mortality: timescale effects in four US cities.

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.

Air Pollution↗
PubMed2003

National maps of the effects of particulate matter on mortality: exploring geographical variation.

In this paper, we present national maps of relative rates of mortality associated with short-term exposure to particulate matter < 10 micro m in aerodynamic diameter (PM(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(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(10) are positively associated with daily variations of mortality. In particular, the relative rate estimates of cardiovascular and respiratory mortality associated with PM(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(10) on health, including particle characteristics, offering insights into mechanisms by which PM(10) causes adverse health effects.

Air Pollutants↗
PubMed2003

On the relationship between time-series studies, dynamic population studies, and estimating loss of life due to short-term exposure to environmental risks.

There is a growing concern that short-term exposure to combustion-related air pollution is associated with increased risk of death. This finding is based largely on time-series studies that estimate associations between daily variations in ambient air pollution concentrations and in the number of nonaccidental deaths within a community. Because these results are not based on cohort or dynamic population designs, where individuals are followed in time, it has been suggested that estimates of effect from these time-series studies cannot be used to determine the amount of life lost because of short-term exposures. We show that results from time-series studies are equivalent to estimates obtained from a dynamic population when each individual's survival experience can be summarized as the daily number of deaths. This occurs when the following conditions are satisfied: a) the environmental covariates vary in time and not between individuals; b) on any given day, the probability of death is small; c) on any given day and after adjusting for known risk factors for mortality such age, sex, smoking habits, and environmental exposures, each subject of the at-risk population has the same probability of death; d) environmental covariates have a common effect on mortality of all members of at-risk population; and e) the averages of individual risk factors, such as smoking habits, over the at-risk population vary smoothly with time. Under these conditions, the association between temporal variation in the environmental covariates and the survival experience of members of the dynamic population can be estimated by regressing the daily number of deaths on the daily value of the environmental covariates, as is done in time-series mortality studies. Issues in extrapolating risk estimates based on time-series studies in one population to estimate the amount of life lost in another population are also discussed.

Air Pollutants↗
PubMed2002

On the use of generalized additive models in time-series studies of air pollution and health.

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.

Air Pollution↗
PubMed2002

Asthma in older patients: factors associated with hospitalization.

BACKGROUND: Although older adults (> or =65 years) with asthma have higher rates of hospitalization and death from asthma than younger adults, the reasons for this are not known. OBJECTIVES: To determine whether patterns of care were less favorable for older than younger adults with asthma and to assess whether patient characteristics such as symptom severity and comorbid illnesses explain the higher rate of hospitalization. METHODS: Prospective cohort study of 6590 adults with asthma in 15 managed care organizations in the United States. Participants completed a survey of demographics, symptoms, health status, comorbid illnesses, treatment, access to care, self-care knowledge, physician specialty, and health care use. RESULTS: Among 6590 adults with asthma, 554 (8%) were 65 years or older and 1942 (29%) were aged 18 to 34 years. Older patients were more likely than younger patients to be men, white, non-Hispanic, and less educated. At baseline, older patients reported a greater frequency of asthma-related symptoms, such as daily cough (36% vs 22%, P<.001) and wheezing (27% vs 22%, P<.002). They were also more likely to report comorbid conditions, such as sinusitis (50% vs 38%), heartburn (35% vs 23%), chronic bronchitis (43% vs 16%), emphysema (19% vs 1%), congestive heart failure (8% vs 1%), and history of smoking (54% vs 34%) (all P<.001). Care appeared to be better for the older patients compared with the younger, including more frequent use of inhaled corticosteroids, greater self-management knowledge, and fewer reported barriers to care. In the follow-up year, older patients were approximately twice as likely to be hospitalized (14%) than were younger patients (7%) (P<.001). In multivariate analysis, however, older age was not predictive of future hospitalization (odds ratio, 1.05; 95% confidence interval, 0.68-1.61), after adjustment for sex, ethnicity, education, baseline asthma symptoms, health status, comorbid illnesses, and tobacco use. Factors independently associated with hospitalization included being female, nonwhite, less educated, and less physically healthy, and more frequent asthma symptoms. CONCLUSIONS: Although the older adults with asthma had greater respiratory symptoms and more comorbidity than their younger counterparts, chronologic age was not an independent risk for hospitalization. Appropriate care for older adults with asthma should address asthma symptoms and other chronic conditions.

Adult↗
PubMedDate not supplied

Revised analyses of the National Morbidity, Mortality, and Air Pollution Study: mortality among residents of 90 cities.

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 10 microm in mass median aerodynamic diameter) on total mortality from nonexternal causes was a 0.41% increase per 10-microg/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.

Air Pollution↗
PubMedDate not supplied

Testing the harvesting hypothesis by time-domain regression analysis. I: baseline analysis.

Although the association between air pollution and daily mortality is well established, the mechanisms by which air pollution results in excess mortality are not yet well understood. In particular, there exists debate over whether air pollution has a direct effect on mortality in the general population or simply shortens the life span of frail individuals, a hypothesis referred to as "harvesting." The goal of this investigation is to test the harvesting hypothesis using the time-domain regression method of Dominici et al. (2003a). We conducted simulations based on a two-compartment model that divides the population into a larger group of healthy individuals and a frail subpopulation. Death from air pollution is assumed to take place in two steps, by first moving from healthy population to the frail pool, then death with probability related to the level of air pollution. Using time-domain analysis, we seek to identify data patterns that would be characteristic of harvesting under different scenarios. For a pure harvesting model, time-domain analysis indicates that mortality is associated with a short-term air pollution episode of less than 2 d if the mean residency time in the frail pool is short. If both entrants and deaths depend on the level of air pollution and the rates of entry to and exit from the frail pool are about the same, the log relative risk estimates are essentially unchanged at all time scales. If pollution affects mortality in the frail pool more than entrants, larger effects will occur at shorter time scales.

Air Pollution↗
PubMedDate not supplied

Multisite time-series studies versus cohort studies: methods, findings, and policy implications.

Time-series and cohort studies of air pollution on human health have advanced greatly our understanding of the effects of air pollution on health since the earliest studies. Availability of large national databases and progress in computational tools and statistical methods have made possible the estimation of national average pollution effects and the exploration of potential sources of heterogeneity in the effects of air pollution across countries or regions. Interpretation of the findings needs to account for several challenges, including confounding and the resolution of seemingly conflicting results from time-series and cohort studies. This article presents an overview of the time-series and cohort studies' approaches for estimating the relative risk of mortality from particulate air pollution and discusses the statistical issues and challenges inherent in each of these studies. We also discuss policy relevant summaries in air pollution epidemiology, approaches for estimating the impact of particulate matter on mortality from time-series and cohort studies, and research opportunities under the National Medicare Cohort Study (NMCS).

Air Pollution↗
PubMedDate not supplied

Cancer incidence among female flight attendants: a meta-analysis of published data.

BACKGROUND: Flight attendants are exposed to cosmic ionizing radiation and other potential cancer risk factors, but only recently have epidemiological studies been performed to assess the risk of cancer among these workers. The aim of the present work was to evaluate the incidence of various types of cancer among female cabin attendants by combining cancer incidence estimates reported in published studies. METHODS: All follow-up studies reporting standardized incidence ratio (SIR) for cancer among female flight attendants were obtained from online databases and analyzed. A metaanalysis was performed by applying Bayesian hierarchical models, which take into account studies that reported SIR = 0 and natural heterogeneity of study-specific SIRs. RESULTS: A total of seven published studies reporting SIR for several cancer types were extracted. Meta-analysis showed a significant excess of melanoma (meta-SIR 2.15, 95% posterior interval [PI] 1.56-2.88) and breast carcinoma (meta-SIR 1.40; PI 1.19-1.65) and a slight but not significant excess of cancer incidence across types (meta-SIR 1.11, PI 0.98-1.25). CONCLUSIONS: Although further studies are necessary to clarify the exact role of occupational exposure, all airlines should, as some companies do, estimate radiation dose, organize the schedules of crew members in order to reduce further exposure in highly exposed flight attendants, inform crew members about health risks, and give special protection to pregnant women.

Aircraft↗
PubMedDate not supplied

Is risk-adjustor selection more important than statistical approach for provider profiling? Asthma as an example.

OBJECTIVES: To examine how the selections of different risk adjustors and statistical approaches affect the profiles of physician groups on patient satisfaction. DATA SOURCES: Mailed patient surveys. Patients with asthma were selected randomly from each of 20 California physician groups between July 1998 and February 1999. A total of 2515 patients responded. RESEARCH DESIGN: A cross-sectional study. Patient satisfaction with asthma care was the performance indicator for physician group profiling. Candidate variables for risk-adjustment model development included sociodemographic, clinical characteristics, and self-reported health status. Statistical strategies were the ratio of observed-to-expected rate (OE), fixed effects (FE), and the random effects (RE) approaches. Model performance was evaluated using indicators of discrimination (C-statistic) and calibration (Hosmer-Lemeshow chi2). Ranking impact of using different risk adjustors and statistical approaches was based on the changes in absolute ranking (AR) and quintile ranking (QR) of physician group performance and the weighted kappa for quintile ranking. RESULTS: Variables that added significantly to the discriminative power of risk-adjustment models included sociodemographic (age, sex, prescription drug coverage), clinical (asthma severity), and health status (SF-36 PCS and MCS). Based on an acceptable goodness-of-fit (P > 0.1)and higher C-statistics, models adjusting for sociodemographic, clinical, and health status variables (Model S-C-H) using either the FE or RE approach were more favorable. However, the C-statistic (=0.68) was only fair for both models. The influence of risk-adjustor selection on change of performance ranking was more salient than choice of statistical strategy (AR: 50%-80% v. 20%-55%; QR: 10%-30% v. 0%-10%). Compared to the model adjusting for sociodemographic and clinical variables only and using OE approach, the Model S-C-H using RE approach resulted in 70% of groups changing in AR and 25% changing in QR (weighted kappa: 0.88). Compared to the Consumer Assessment of Health Plans model, the Model S-C-H using RE approach resulted in 65% of groups changing in AR and 20% changing in QR (weighted kappa: 0.88). CONCLUSIONS: In comparing the performance of physician groups on patient satisfaction with asthma care, the use of sociodemographic, clinical, and health status variables maximized risk-adjustment model performance. Selection of risk adjustors had more influence on ranking profiles than choice of statistical strategies. Stakeholders employing provider profiling should pay careful attention to the selection of both variables and statistical approach used in risk-adjustment.

Adult↗