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Mark S Goldberg

Publications and source records attributed to Mark S Goldberg.

At least 19 recordsLinked to original sources
PubMed2006

An S-Plus function to calculate relative risks and adjusted means for regression models using natural splines.

We provide for generalized linear regression models that use natural cubic splines to model predictors an S-Plus function to calculate relative risks (RR), log relative risk (logRR), mean percent change (MPC) for continuous covariates modeled using a logarithmic link as well as adjusted means differences (MD) for the identity link. The function makes explicit use of the natural spline basis functions, the estimated coefficients for each natural spline basis function, and the fitted correlation matrix for the estimated coefficients and can thus accommodate any number of degrees of freedom. The main function produces a publication-quality graph of all of these quantities as compared to a user-specified reference value as well as the associated confidence limits. In another function, specific values of these statistics comparing a vector of values of the independent variable to the reference value can be calculated rather than plotted.

Regression Analysis↗
PubMed2006

Peering at peer review revealed high degree of chance associated with funding of grant applications.

BACKGROUND AND OBJECTIVES: There is a persistent degree of uncertainty and dissatisfaction with the peer review process underlining the need to validate the current grant awarding procedures. This study compared the CLassic Structured Scientific In-depth two reviewer critique (CLASSIC) with an all panel members' independent ranking method (RANKING). Eleven reviewers, reviewed 32 applications for a pilot project competition at a major university medical center. RESULTS: The degree of agreement between the two methods was poor (kappa = 0.36). The top rated project in each stream would have failed the funding cutoff with a frequency of 9 and 35%, depending on which pair of reviewers had been selected. Four of the top 10 projects identified by RANKING had a greater than 50% of not being funded by the CLASSIC ranking. Ten reviewers provided optimal consistency for the RANKING method. CONCLUSIONS: This study found that there is a considerable amount of chance associated with funding decisions under the traditional method of assigning the grant to two main reviewers. We recommend using the all reviewer ranking procedure to arrive at decisions about grant applications as this removes the impact of extreme reviews.

Canada↗
PubMed2005

Associations between ambient air pollution and daily mortality among persons with diabetes and cardiovascular disease.

BACKGROUND: Recent studies suggest that persons with diabetes and with cardiovascular disease may be at higher risk for the short-term effects of air pollution. We carried out this mortality time series study in Montreal, Quebec, Canada to confirm these observations and to determine whether diabetics who had other health conditions were also at higher risk of dying when air pollution increases. METHODS AND RESULTS: In one analysis, we related daily deaths from diabetes (using the underlying cause) to daily concentrations of particles and gaseous pollutants. In another analysis, we created subgroups by identifying subjects diagnosed 1 year before death with diabetes and other major health conditions from billing and prescription data from the universal Quebec Health Insurance Plan. The analysis made use of parametric log-linear Poisson models that were adjusted for long-term temporal trends and daily weather conditions. We found positive associations between most air pollutants and daily mortality from diabetes as well as among subjects diagnosed with diabetes 1 year before death. In the latter group of subjects, greater effects were found generally in the warm season and especially among subjects who had diabetes and who also had any cardiovascular disease, chronic coronary disease, and atherosclerosis. We did not find evidence of associations among persons who only had diabetes (i.e., did not also have cancer, cardiovascular disease, or lower respiratory disease). CONCLUSIONS: These data indicate that individuals with diabetes who also have cardiovascular disease may be susceptible to the short-term effects of air pollution.

Aged↗
PubMed2005

Assessing spatial variability of ambient nitrogen dioxide in Montréal, Canada, with a land-use regression model.

The purpose of this study was to derive a land-use regression model to estimate on a geographical basis ambient concentrations of nitrogen dioxide (NO2) in Montreal, Quebec, Canada. These estimates of concentrations of NO2 will be subsequently used to assess exposure in epidemiologic studies on the health effects of traffic-related air pollution. In May 2003, NO2 was measured for 14 consecutive days at 67 sites across the city using Ogawa passive diffusion samplers. Concentrations ranged from 4.9 to 21.2 ppb (median 11.8 ppb). Linear regression analysis was used to assess the association between logarithmic concentrations of NO2 and land-use variables derived using the ESRI Arc 8 geographic information system. In univariate analyses, NO2 was negatively associated with the area of open space and positively associated with traffic count on nearest highway, the length of highways within any radius from 100 to 750 m, the length of major roads within 750 m, and population density within 2000 m. Industrial land-use and the length of minor roads showed-no association with NO2. In multiple regression analyses, distance from the nearest highway, traffic count on the nearest highway, length of highways and major roads within 100 m, and population density showed significant associations with NO2; the best-fitting regression model had a R2 of 0.54. These analyses confirm the value of land-use regression modeling to assign exposures in large-scale epidemiologic studies.

Air Pollutants↗
PubMed2003

Occupational exposures to extremely low frequency magnetic fields and postmenopausal breast cancer.

BACKGROUND: The association between occupational exposure to extremely low frequency magnetic fields (ELF-MF) and risk of postmenopausal breast cancer was assessed in a case-control study. METHODS: Breast cancer cases were compared to cancer controls. Interviewers elicited information on risk factors and on lifetime work history. Industrial hygienists assigned to each job average duration of exposure to ELF-MF at four levels of intensities ("none," <0.2 microT; "low," 0.2-<0.5microT; "medium," 0.5-<1microT; "high," > or =1-10microT). Unconditional logistic regression was used to estimate adjusted odds ratios (OR) and 95% confidence intervals (95% CI). RESULTS: A total number of 608 cases and 667 controls participated. Adjusting for accepted breast cancer risk factors, we found an OR of 1.13 for lifetime occupational exposure to ELF-MF at medium or high intensities. Risks were larger for exposures before age 35 (OR = 1.40), and statistically significant for exposures before 35 among cases with progesterone receptor positive tumors (OR = 1.56, 95% CI=1.02-2.39). CONCLUSIONS: There appears to be a small increased risk for breast cancer among postmenopausal women exposed occupationally to ELF-MF.

Aged↗
PubMed2003

Associations between ambient air pollution and daily mortality among persons with congestive heart failure.

We conducted a mortality time series study to investigate the association between daily mortality for congestive heart failure (CHF), and daily concentrations of particles and gaseous pollutants in the ambient air of Montreal, Quebec, during the period 1984-1993. In addition, using data from the universal Quebec Health Insurance Plan, we identified individuals >/=65 years of age who, one year before death, had a diagnosis of CHF. Fixed-site air pollution monitors in Montreal provided daily mean levels of pollutants. We regressed the logarithm of daily counts of mortality on the daily mean levels of each pollutant, after accounting for seasonal and subseasonal fluctuations in the mortality time series, non-Poisson dispersion, weather variables, and other gaseous and particle pollutants. Using cause of death information, we did not find any associations between daily mortality for CHF and any air pollutants. The analyses of CHF defined from the medical record showed positive associations with coefficient of haze, the extinction coefficient, SO(2), and NO(2). For example, the mean percent increase in daily mortality for an increase in the coefficient of haze across the interquartile range was 4.32% (95% CI: 0.95-7.80%) and for NO(2) it was 4.08% (95% CI: 0.59-7.68%). These effects were generally higher in the warm season.

Aged↗
PubMed2003

A time-series study of air pollution, socioeconomic status, and mortality in Vancouver, Canada.

We evaluated the relationship between daily levels of particulate and gaseous phase pollutants and mortality within a dynamic cohort of approximately 550,000 individuals whose vital status was ascertained between 1986 and 1999. Time-series methods were applied to evaluate whether there were differential pollutant effects on daily aggregated numbers of deaths in the cohort that was stratified into quintiles of income as defined by the 1991 and 1996 Canadian censuses. The percent change in all-cause, cardiovascular, respiratory, and cancer daily mortality was calculated in relation to short-term changes in levels of a number of particulate (PM(2.5), PM(10-2.5), total suspended particle co-efficient of haze PM(10), SO(4)) and gaseous (O(3), CO, SO(2), NO(2)) pollutants. The estimated effects of air pollution on mortality were adjusted for day of week effects, and several meteorologic variables including temperature, change in barometric pressure, and relative humidity. Several gaseous pollutants were associated with an increased risk of mortality. Specifically for an increase equivalent to the difference between the 90th and 10th percentiles, the estimated percent change in daily mortality based on the 3-day average of NO(2), and SO(2) was 4.0% and 1.3%, respectively. The corresponding changes in mortality associated with SO(2) were much higher when analyses were restricted to death from respiratory disease. Specifically, a difference between the 90th and 10th percentiles was associated with a 5.6% (95% CI= -0.7% to 12.3%). The daily mean coarse fraction (PM(10-2.5)) was associated with increased cardiovascular mortality (estimated change=5.9%, 95% CI=1.1-10.8%). PM(2.5) was not found to be an important predictor of mortality. For NO(2), CO, and SO(2), there was some suggestion of increased risk of all-cause and cardiovascular mortality at lower levels of socioeconomic status. However, these results should be interpreted cautiously due to the small number of deaths observed within each stratum of socioeconomic status.

Aged↗
PubMed2003

Factors associated with pattern of care before surgery for breast cancer in Quebec between 1992 and 1997.

BACKGROUND: Practice guidelines for breast cancer emphasize the importance of establishing an accurate diagnosis using a minimum number of procedures and selecting optimal treatment regimens. Understanding the determinants of waiting time is essential to develop optimum interventions to reduce delay. OBJECTIVES: The purpose of this study is to estimate the extent to which variability in 1) the number of procedures before surgery and 2) waiting time from initial procedure to surgery are explainable by factors related to the woman, to the provider, and to the care setting. RESEARCH DESIGN: Records of physicians' fee-for-service claims were obtained for 23,370 women undergoing breast cancer surgery in Quebec between 1992 and 1997. Multilevel logistic regression was used to determine predictors of having multiple procedures before surgery. Hierarchical linear regression models were used to identify predictors of waiting time, separately for women with lymph node involvement and without this involvement. RESULTS: Overall, 23% of the women had 3 or more procedures before surgery with significant variation found across hospitals and surgeons. Number of procedures was a strong predictor of waiting time. Waiting time also varied by stage, age, comorbidity, a history of benign disease, surgical setting, calendar time, month of initial procedure, and hospital teaching status. CONCLUSION: Although variability in waiting time was more strongly influenced by the characteristics of the women rather than by physician- or hospital-related factors, most variation remained unexplained by the factors included in this study. To reduce overall waiting time, strategies would need to be systemically applied.

Adult↗
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

Fine particulate air pollution and all-cause mortality within the Harvard Six-Cities Study: variations in risk by period of exposure.

PURPOSE: We used Poisson regression methods to examine the relation between temporal changes in the levels of fine particulate air pollution (PM(2.5)) and the risk of mortality among participants of the Harvard Six Cities longitudinal study. METHODS: Our analyses were based on 1430 deaths that occurred between 1974 and 1991 in a cohort that accumulated 105,714 person-years of follow-up. For each city, indices of PM(2.5) were derived using daily samples. Individual level data were collected on several risk factors including: smoking, education, body mass index (BMI), and occupational exposure to dusts. Time-dependent indices of PM(2.5) were created across 13 calendar periods (< 1979, 1979, 1980, em leader, 1989, >/= 1990) to explore whether recent or chronic exposures were more important predictors of mortality. RESULTS: The relative risk (RR) of mortality calculated using Poisson regression based on average city-specific exposures that remained constant during follow-up was 1.31 [95% confidence interval (CI) = 1.12-1.52] per 18.6 microg/m(3) of PM(2.5). This result was similar to the risk calculated using the Cox model (RR = 1.26, 95% CI = 1.08-1.46). The RR of mortality was attenuated when the Poisson regression model included a time-dependent estimate of exposure (RR = 1.19, 95% CI = 1.04-1.36). There was little variation in RR across time-dependent indices of PM(2.5). CONCLUSIONS: The attenuated risk of mortality that was observed with a time-dependent index of PM(2.5) is due to the combined influence of city-specific variations in mortality rates and decreasing levels of air pollution that occurred during follow-up. The RR of mortality associated with PM(2.5) did not depend on when exposure occurred in relation to death, possibly because of little variation between the time-dependent city-specific exposure indices.

Air Pollutants↗
PubMed2002

Association between alcohol consumption and postmenopausal breast cancer: results of a case-control study in Montreal, Quebec, Canada.

OBJECTIVES: To determine the association between postmenopausal breast cancer and prior consumption of alcoholic beverages. METHODS: This case-control study, conducted in all Montreal hospitals between 1996 and 1997, included 556 postmenopausal women (age 50-75 years) who had a new histologically confirmed diagnosis of primary, malignant breast cancer. Control subjects (577) were selected from other histologically confirmed sites of cancer. A detailed history of alcohol consumption and other risk factors was obtained by interview. Indices reflecting alcohol consumption were developed and unconditional logistic regression was used to estimate adjusted odds ratios (OR) and 95% confidence intervals (CI). RESULTS: Current regular drinkers of any type of alcohol were at an increased risk of breast cancer (OR = 1.5; 95% CI 1.0-2.2). For all beverages considered, current regular drinkers showed higher risks than ever regular drinkers. The risk of breast cancer was highest among women who reported exclusive drinking of wine on a weekly or daily basis (e.g. current regular drinking: OR = 2.3; 95% CI 1.2-4.3). Women who started to drink wine on or before the age of 40 were at a 2.5 times increased risk (95% CI 1.4-4.4). CONCLUSIONS: Our findings provide further support for a positive association between the risk of postmenopausal breast cancer and alcohol consumption.

Aged↗
PubMedDate not supplied

Overview of the reanalysis of the Harvard Six Cities Study and American Cancer Society Study of Particulate Air Pollution and Mortality.

This article provides an overview of the Reanalysis Study of the Harvard Six Cities and the American Cancer Society (ACS) studies of particulate air pollution and mortality. The previous findings of the studies have been subject to debate. In response, a reanalysis team, comprised of Canadian and American researchers, was invited to participate in an independent reanalysis project to address the concerns. Phase I of the reanalysis involved the design of data audits to determine whether each study conformed to the consistency and accuracy of their data. Phase II of the reanalysis involved conducting a series of comprehensive analyses using alternative statistical methods. Alternative models were also used to identify covariates that may confound or modify the association of particulate air pollution as well as identify sensitive population subgroups. The audit demonstrated that the data in the original analyses were of high quality, as were the risk estimates reported by the original investigators. The sensitivity analysis illustrated that the mortality risk estimates reported in both studies were found to be robust against alternative Cox models. Detailed investigation of the covariate effects found a significant modifying effect of education and a relative risk of mortality associated with fine particles and declining education levels. The study team applied spatial analytic methods to the ACS data, resulting in various levels of spatial autocorrelations supporting the reported association for fine particles mortality of the original investigators as well as demonstrating a significant association between sulfur dioxide and mortality. Collectively, our reanalysis suggest that mortality may be attributable to more than one component of the complex mixture of ambient air pollutants for U.S. urban areas.

Air↗
PubMedDate not supplied

Selection of ecologic covariates in the American Cancer Society study.

The American Cancer Society (ACS) Study of the effects of long-term exposure to ambient air pollution on mortality used metropolitan areas to assign exposures to individual cohort members (Pope et al., 1995); these authors did not, however, control for any other place-specific variables in their analysis. Consequently, the study has been criticized on the basis that the association observed between air pollution and mortality may be confounded by other unmeasured ecologic covariates. To address this criticism, the reanalysis team selected a set of place-specific variables that measured determinants of health ranging from the biophysical environment to the social environment and the healthcare system. This article outlines the process by which place-specific ecologic covariates were selected; data measuring these variables were obtained and geographic boundaries for places were delineated. Issues involved in obtaining and using geographically based ecological data are examined within the context of the reanalysis of the ACS study. Both the ecological fallacy and the atomistic fallacy are addressed and an argument is made for the importance of studying the effects of place-specific variables that are integral or contextual in nature. Issues relating to the Modifiable Areal Unit Problem (MAUP) are explored with reference to using ZIP codes and data from a variety of sources. It is argued that differences in the geographical scale of variability for various pollutants may prove to be the key to distinguishing between their relative impacts on health and that multilevel analyses are essential for understanding the impact of social and environmental determinants of health. A number of determinants of health are then briefly examined in terms of their association with mortality, the appropriateness of their being measured at the metropolitan scale, and the availability of data for the 1980s from U.S. sources. Finally, the article presents the database of place-specific ecologic covariates that was incorporated into the ACS models during the reanalysis in order to account for the influence that place may have above and beyond ambient air pollution.

Air Pollutants↗
PubMedDate not supplied

Controlling for potential confounding by occupational exposures.

Occupational exposure is an important potential confounder in air pollution studies because it is plausible that individuals who live in highly polluted areas also work in more polluted environments. While the original investigators made some efforts to control for possible confounding by occupational variables, it was felt that these could be improved upon. The reanalysis team attempted to control for occupational confounding by supplementing the original data sets with two new variables, an indicator of the "dirtiness" of a subject's job and an indicator of possible exposure to occupational lung carcinogens. The attribution of these variables was based on the job title recorded by the original investigators and on the judgment of our experts concerning typical exposure patterns in different occupations. We fitted Cox proportional-hazards models identical to those that had been used by the original investigators while also including one or both of the new occupational covariates in the models. In none of the analyses did the inclusion of the occupational variables materially change the results. It would therefore appear that, in general, the results reported by the original investigators were not distorted by inadequate control of occupational variables. We also carried out some analyses using the dirtiness index as a stratification variable to assess effect modification. There was some indication, albeit inconsistent, that the effect of air pollution on mortality was greater among subjects with dirty jobs than among those with clean jobs.

Adult↗
PubMedDate not supplied

Spatial analysis of the air pollution-mortality relationship in the context of ecologic confounders.

Lack of control for confounding by ecological covariates that may relate to sulfate air pollution and mortality was a key criticism of the two studies that were the focus of the Particle Reanalysis Project. To assess the validity of this criticism, we address the question: "Does sulfate air pollution exert health effects when the impact of other individual and ecologic variables thought to influence health is taken into account?" A related question arises from the possibility of autocorrelation in the mortality risks and ecologic covariates. Failure to control for autocorrelation can lead to false positive significance tests and may indicate bias resulting from a missing variable or group of variables. We control for more than 25 individual risk factors and for 20 ecologic variables representing environmental, socioeconomic, demographic, health- care, and lifestyle determinants of health in a two-stage multilevel analysis. Four modeling strategies are used to control for spatial autocorrelation. Of the 20 ecologic variables tested, only sulfate and sulfur dioxide are significant in models that incorporate spatial autocorrelation. Accounting for autocorrelation also reduces the size and certainty of the sulfate effect on mortality when compared to results generated from Cox models where independent observations are assumed. Confidence limits for the sulfate relative risk include unity in models that simultaneously control for sulfur dioxide and autocorrelation.

Air Pollution↗
PubMedDate not supplied

Retrospective data quality audits of the Harvard Six Cities and American Cancer Society studies.

The Harvard Six Cities (6-Cities) and American Cancer Society (ACS) studies are longitudinal cohort mortality studies of large populations that provided important information about the human health effects associated with long-term exposure to fine particulate air pollution. Possible changes to federal regulation of particulates prompted a review of data collection methods, analysis, and reported results from these two studies. This article describes the methodology used to conduct quality assurance audits of both studies and summarizes the audit findings. Statistically based, randomly selected samples of 250 health questionnaires and 250 death certificates from each study were audited against data from analysis files. In cases where study-specific data could not be located, validation was performed using information and data from other sources. Some errors were found in programming and data transformation in both studies, but none affected the results of the original investigations. Both audits confirmed that the published studies are an accurate representation of the collected data. The audits also underscored the importance of adequate attention to documentation and record-keeping practices during the conduct of all studies and proper archiving at their conclusion.

Air Pollution↗
PubMedDate not supplied

Spatial regression models for large-cohort studies linking community air pollution and health.

Cohort study designs are often used to assess the association between community-based ambient air pollution concentrations and health outcomes, such as mortality, development and prevalence of disease, and pulmonary function. Typically, a large number of subjects are enrolled in the study in each of a small number of communities. Fixed-site monitors are used to determine long-term exposure to ambient pollution. The association between community average pollution levels and health is determined after controlling for risk factors of the health outcome measured at the individual level (i.e., smoking). We present a new spatial regression model linking spatial variation in ambient air pollution to health. Health outcomes can be measured as continuous variables (pulmonary function), binary variables (prevalence of disease), or time-to-event data (survival or development of disease). The model incorporates risk factors measured at the individual level, such as smoking, and at the community level, such as air pollution. We demonstrate that the spatial autocorrelation in community health outcomes, an indication of not fully characterizing potentially confounding risk factors to the air pollution--health association, can be accounted for through the inclusion of location in the deterministic component of the model assessing the effects of air pollution on health or through a distance-decay spatial autocorrelation function in the stochastic component of the model, or both. We present a statistical approach that can be implemented for very large cohort studies. Our methods are illustrated with an analysis of the American Cancer Society cohort to determine whether the prevalence of heart disease is associated with concentrations of sulfate particles. From a statistical point of view, it appears that a location surface in the deterministic component of the model was preferred to a distance-decay autocorrelation structure in the model's stochastic component.

Air Pollution↗