Biomedical subjects

A Zanobetti

Publications and source records attributed to A Zanobetti.

At least 19 recordsLinked to original sources

PubMed2001

Are diabetics more susceptible to the health effects of airborne particles?

Convincing evidence now exists that particulate air pollution exacerbates heart and lung disease, leading to increased morbidity and mortality. The populations particularly susceptible to these exposures are still unclear. Recent work on potential mechanisms of action of particulate air pollution point to pathways also influenced by diabetes. We examined whether diabetes modified the effect of airborne particles by looking at the association of PM(10) with hospital admissions for heart and lung disease in persons with or without diabetes as a comorbidity. In addition we stratified by age within persons with and without diabetes. We used Medicare data for Cook County, Illinois for the years 1988-1994, and found that a 10 microg/m(3) increase in PM(10) was associated with a 2.01% (95% CI 1.40-2.62%) increase in admissions for heart disease with diabetes, but only a 0.94% (95% CI 0.61-1.28%) increase in persons without diabetes. Similar effect modification was not seen for lung diseases. When analyzing by age we found twice the PM(10)-associated risk for heart disease in diabetics than nondiabetics in both age groups. We found for pneumonia admissions that diabetes is an effect modifier in the younger age group, and for COPD in the older age group. We conclude that persons with diabetes are a susceptible population.

Aged↗

PubMed2001

Health effects of air pollution exposure on children and adolescents in São Paulo, Brazil.

Children and adolescents have been considered more susceptible to the effects of air pollution than adults. In order to investigate the responses of children of different ages to air pollution exposure, daily records of hospital admissions for children in five age groups (equal or less than 2 years of age, 3-5, 6-13, 14-19, and all ages together, i.e., from 0-19 years of age) were obtained from January 1993 to November 1997 in São Paulo, Brazil, and were compared to daily records of PM10, O3, SO2, CO and NO2 concentrations in ambient air. For each age group a generalized additive Poisson regression was fitted controlling for smooth functions of time, temperature, humidity, and days of the week, with an additional indicator for holidays. Polynomial distributed lag models were used to estimate the 7-day cumulative effect of each pollutant. Children 2 years or less were the most susceptible to the effects of all five pollutants with an increase of 9.4% (95% CI: 7.9,10.9) in respiratory admissions associated with each interquartile range increase in PM10. The oldest group was the second most susceptible to air pollutants, with each interquartile range increase in PM10 associated with a 5.1% (95% CI: 0.3,9.8) increase in respiratory admissions. An interquartile range increase in CO was associated with an 11.3% (95% CI: 5.9,16.8) increase in respiratory hospitalizations. When a multipollutant model was used, the effect of PM10 on respiratory admissions for all ages together was unchanged, while the SO2 and the other pollutants effect was substantially reduced. This study showed that daily respiratory hospital admissions for children and adolescents in São Paulo increased with air pollution, and that the largest effects were found for the youngest (2 years or less) and oldest (14-19 years) age groups.

Adolescent↗

PubMed2001

The time course of weather-related deaths.

We carried out time-series analysis in 12 U.S. cities to estimate both the acute effects and the lagged influence of weather on total daily deaths. We fit generalized additive Poisson regressions for each city using nonparametric smooth functions to control for long time trend and barometric pressure. We also controlled for day of the week. We estimated the effect and the lag structure of both temperature and humidity on the basis of a distributed lag model. In cold cities, both high and low temperatures were associated with increased deaths. In general, the effect of cold temperatures persisted for days, whereas the effect of high temperatures was restricted to the day of the death or the immediately preceding day and was twice as large as the cold effect. The hot temperature effect appears to be primarily harvesting. In hot cities, neither hot nor cold temperatures had much effect on deaths. The magnitude of the effect of hot temperature varied with central air conditioning use and the variance of summertime temperatures. We saw no clear pattern for humidity effect. These dissimilarities indicate that analysis of the impact of any climatic change should take into account regional weather differences and harvesting.

Age Factors↗

PubMed2001

The lag structure between particulate air pollution and respiratory and cardiovascular deaths in 10 US cities.

To assess differences in the lag structure pattern between particulate matter < 10 microns/100 microns in diameter (PM10) and cause-specific mortality, we performed a time-series analysis in 10 US cities using generalized additive Poisson regressions in each city; nonparametric smooth functions were used to control for long time trend, weather, and day of the week. The PM10 effect was estimated based on its daily mean, 2-day moving average, and the cumulative 7-day effect by means of an unconstrained distributed lag model. A 10-microgram/m3 increase in the 7-day mean of PM10 was associated with increases in deaths due to pneumonia (2.7%, 95% confidence interval [CI]: 1.5, 3.9), chronic obstructive pulmonary disease (1.7%, 95% CI: 0.1, 3.3), and all cardiovascular diseases (1.0%, 95% CI: 0.6, 1.4). A 10-microgram/m3 increase in the 2-day mean of PM10 was associated with a 0.7% (95% CI: 0.3, 1.1) increase in deaths from myocardial infarction. When the distributed lag was assessed, two different patterns could be observed: respiratory deaths were more affected by air pollution levels on the previous days, whereas cardiovascular deaths were more affected by same-day pollution. These results contribute to the overall efforts so far in understanding how exposure to air pollution promotes adverse health effects.

Air Pollutants↗

PubMed2000

Do respiratory epidemics confound the association between air pollution and daily deaths?

Daily deaths are associated with air pollution. This association might be con*hhy;founded by uncontrolled risk factors. In order to estimate the potential confounding caused by respiratory epidemics of the association between air pollution and health effects, a time series study of air pollution and daily deaths was carried out. Daily records of deaths for all ages were obtained from five US cities: Chicago, IL; Detroit, MI; Minneapolis, MN; Pittsburgh, PA; and Seattle, WA. Daily levels of particles with a 50% cut-off aerodynamic diameter of 10 microm (PM10) and weather measurements were obtained. City-specific analysis was carried out using Poisson regression, adjusting for time trend, ambient temperature, dew point, barometric pressure and day of the week. A cubic polynomial was used for each epidemic period (> or =10 days of excessive pneumonia hospital admissions), and a dummy variable was used to control for isolated epidemic days. A 10-microg x m(-3) increase in PM10 concentration (lag 0-1) was associated with increased daily deaths in Chicago (0.81%, 95% confidence internal (CI) 0.54-1.09); Detroit (0.87%, 95% CI 0.60-1.15), Minneapolis (1.34%, 95% CI 0.78-1.90), Pittsburgh (0.84%, 95% CI 0.51-1.18) and Seattle (0.52%, 95% CI 0.11-0.94). When controlling for respiratory epidemics, small decreases in the PMlo effect were observed (Chicago 9%, Detroit 11%, Minneapolis 3%, Pittsburgh 5%, and Seattle 15%). The overall effect of PM10 concentration was 0.85% (95% CI 0.60-1.10) per 10 microg x m(-3) before controlling for epidemics and 0.78% (95% CI 0.51-1.05) after. This study showed that the association between air pollution and daily deaths is not due to failure to control for influenza or pneumonia epidemics.

Air Pollutants↗

PubMed2000

Generalized additive distributed lag models: quantifying mortality displacement.

There are a number of applied settings where a response is measured repeatedly over time, and the impact of a stimulus at one time is distributed over several subsequent response measures. In the motivating application the stimulus is an air pollutant such as airborne particulate matter and the response is mortality. However, several other variables (e.g. daily temperature) impact the response in a possibly non-linear fashion. To quantify the effect of the stimulus in the presence of covariate data we combine two established regression techniques: generalized additive models and distributed lag models. Generalized additive models extend multiple linear regression by allowing for continuous covariates to be modeled as smooth, but otherwise unspecified, functions. Distributed lag models aim to relate the outcome variable to lagged values of a time-dependent predictor in a parsimonious fashion. The resultant, which we call generalized additive distributed lag models, are seen to effectively quantify the so-called 'mortality displacement effect' in environmental epidemiology, as illustrated through air pollution/mortality data from Milan, Italy.

Journal Article↗

PubMed2000

Using meta-smoothing to estimate dose-response trends across multiple studies, with application to air pollution and daily death.

Air pollution has been associated with daily mortality in numerous studies over the last decade. Although considerable attention has focused on issues of potential confounding in these associations, little has been done to address the question of what the shape of the dose-response relation looks like. The question of whether a threshold exists for these relations is of particular concern, with regard to both this application and many other epidemiologic questions. Nonparametric smoothing is widely used to control for the potentially nonlinear relations between covariates and daily deaths but has been little used to model the air pollution associations. Because sampling variability, among other factors, can introduce considerable noise into the estimates of linear dose-response curves, quantitative summaries have been widely used to come up with best linear fits. The same ability of meta-analytic techniques to average out noise applies to nonparametric smooth estimates in individual cities. We have developed a method of applying these techniques to combining nonparametric smooths. Using simulation studies, we show that this method can detect threshold and other nonlinear relations in epidemiologic studies, and we then apply it to analyze the association between PM10 and daily deaths in ten U.S. cities. We find that the association appears linear down to the lowest levels observed in the study. This method is generally applicable in settings where data from multiple studies can be combined.

Air Pollution↗

PubMed2000

Race, gender, and social status as modifiers of the effects of PM10 on mortality.

Interest has recently been focused on which populations are most at risk of premature mortality induced by air pollution. This coincides with greater concern about environmental justice. We analyzed total mortality in the four largest US cities with daily measurements of particulate matter less than 10 microns (PM10) and combined the results to determine whether race, sex, and education are potential modifiers of the effects of PM10 on mortality. We computed daily counts of deaths stratified by sex, race, and education in each city and investigated their associations with PM10 in a Poisson regression model. We combined the results by using inverse variance weighted averages. We found evidence of effect modification by sex, with the slope in female deaths one third larger than in male deaths, whereas for social factors and race we found only weak evidence of effect modification. In general, the effect modification appeared modest compared with other reports of substantial effect modification by medical conditions.

Air Pollution↗

PubMed2000

Airborne particles are a risk factor for hospital admissions for heart and lung disease.

We examined the association between particulate matter [less than/equal to] 10 microm; (PM(10)) and hospital admission for heart and lung disease in ten U.S. cities. Our three goals were to determine whether there was an association, to estimate how the association was distributed across various lags between exposure and response, and to examine socioeconomic factors and copollutants as effect modifiers and confounders. We fit a Poisson regression model in each city to allow for city-specific differences and then combined the city-specific results. We examined potential confounding by a meta-regression of the city-specific results. Using a model that considered simultaneously the effects of PM(10) up to lags of 5 days, we found a 2.5% [95% confidence interval (CI), 1.8-3. 3] increase in chronic obstructive pulmonary disease, a 1.95% (CI, 1. 5-2.4) increase in pneumonia, and a 1.27% increase (CI, 1-1.5) in CVD for a 10 microg/m(3) increase in PM(10). We found similar effect estimates using the mean of PM(10) on the same and previous day, but lower estimates using only PM(10) for a single day. When using only days with PM(10) < 50 mg/m(3), the effect size increased by [greater/equal to] 20% for all three outcomes. These effects are not modified by poverty rates or minority status. The results were stable when controlling for confounding by sulfur dioxide, ozone, and carbon monoxide. These results are consistent with previous epidemiology and recent mechanistic studies in animals and humans.

Aged↗

PubMed2000

Are there sensitive subgroups for the effects of airborne particles?

Recent studies have shown that particulate air pollution is a risk factor for hospitalization for heart and lung disease; however, little is known about what subpopulations are most sensitive to this pollutant. We analyzed Medicare hospital admissions for heart disease, chronic obstructive pulmonary disorders (COPD) and pneumonia in Chicago, Cook County, Illinois, between 1985 and 1994. We examined whether previous admissions or secondary diagnoses for selected conditions predisposed persons to having a greater risk from air pollution. We also considered effect modification by age, sex, and race. We found that the air-pollution-associated increase in hospital admissions for cardiovascular diseases was almost doubled in subjects with concurrent respiratory infections. The risk was also increased by a previous admission for conduction disorders. For COPD and pneumonia admissions, diagnosis of conduction disorders or dysrhythmias increased the risk of particulate matter < 10 microm in aerodynamic diameter (PM(10))-associated admissions. Persons with asthma had twice the risk of a PM(10)-associated pneumonia admission and persons with heart failure had twice the risk of PM(10)-induced COPD admissions. The PM(10) effect did not vary by sex, age, and race. These results suggest that patients with acute respiratory infections or defects in the electrical control of the heart are a risk group for particulate matter effects.

Aged↗

PubMed2000

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.

Adolescent↗

PubMed1999

Recommendations for the monitoring of short-term health effects of air pollution: lessons from the APHEA Multi Centre European Study.

Accumulating evidence from European cities indicates that current levels of ambient air pollution are likely to be associated with detectable effects on daily mortality and hospital admissions. Public health authorities everywhere are concerned about the possible effects of air pollution on the health of their populations but there are no guidelines for how these effects can efficiently be monitored. Furthermore, decisions about air pollution monitoring tend to be made without reference to the epidemiological requirements of health monitoring. The APHEA project (Air Pollution and Health a European Approach) investigated the short-term health effects of air pollution in 15 European cities. Experience gained in this project provides a basis for recommendations to public health and environmental authorities concerning the requirements for a basic health monitoring system. This paper considers the theoretical and practical aspects of a monitoring system and makes recommendations concerning 1) the minimum data set required, 2) the methods of statistical analysis and presentation and 3) Europe-wide coordination of monitoring.

Air Pollution↗

PubMed1998

Time-series analysis of air pollution and cause-specific mortality.

Ten large European cities provided data on daily air pollution as well as mortality from respiratory and cardiovascular mortality. We used Poisson autoregressive models that controlled for trend, season, influenza epidemics, and meteorologic influences to assess the short-term effects of air pollution at each city. We then compared and pooled the city-specific results in a meta-analysis. The pooled relative risks of daily deaths from cardiovascular conditions were 1.02 [95% confidence interval (CI) = 1.01-1.04] for a 50 microg/m3 increment in the concentration of black smoke and 1.04 (95% CI = 1.01-1.06) for an increase in sulfur dioxide levels in western European cities. For respiratory diseases, these figures were 1.04 (95% CI = 1.02-1.07) and 1.05 (95% CI = 1.03-1.07), respectively. These associations were not found in the five central European cities. Eight-hour averages of ozone were also moderately associated with daily mortality in western European cities (relative risk = 1.02; 95% CI = 1.00-1.03 for cardiovascular conditions and relative risk = 1.06; 95% CI = 1.02-1.10 for respiratory conditions). Nitrogen dioxide did not show consistent relations with daily mortality. These results are similar to previously published data and add credence to the causal interpretation of these associations at levels of air pollution close to or lower than current European standards.

Aged↗

PubMed1996

Short term effects of air pollution on health: a European approach using epidemiologic time series data: the APHEA protocol.

BACKGROUND AND OBJECTIVES: Results from several studies over the past five years have shown that the current levels of pollutants in Europe and North America have adverse short term effects on health. The APHEA project aims to quantifying these in Europe, using standardised methodology. The project protocol and analytical methodology are presented here. DESIGN: Daily time series data were gathered for several air pollutants (sulphur dioxide; particulate matter, measured as total particles or as the particle fraction with an aerodynamic diameter smaller than a certain cut off, or as black smoke; nitrogen dioxide; and ozone) and health outcomes (the total and cause specific number of deaths and emergency hospital admissions). The data included fulfilled the quality criteria set by the APHEA protocol. SETTING: Fifteen European cities from 10 different countries with a total population over 25 million. METHODOLOGY: The APHEA collaborative group decided on a specific methodological procedure to control for confounding effects and evaluate the hypothesis. At the same time there was sufficient flexibility to allow local characteristics to be taken into account. The procedure included modelling of all potential confounding factors (that is, seasonal and long term patterns, meteorological factors, day of the week, holidays, and other unusual events), choosing the "best" air pollution models, and applying diagnostic tools to check the adequacy of the models. The final analysis used autoregressive Poisson models allowing for overdispersion. Effects were reported as relative risks contrasting defined increases in the corresponding pollutant levels. Each participating group applied the analyses to their own data. CONCLUSIONS: This methodology enabled results from many different European settings to be considered collectively. It represented the best available compromise between feasibility, comparability, and local adaptibility when using aggregated time series data not originally collected for the purpose of epidemiological studies.

Air Pollutants↗

PubMed1996

Short term effects of urban air pollution on respiratory health in Milan, Italy, 1980-89.

STUDY OBJECTIVE: To investigate the association between daily urban air pollution and acute effects on respiratory health. STUDY DESIGN: Time series analysis following the procedure defined in the APHEA protocol. SETTING: City of Milan, Italy, from 1980-89. Two air pollutants, total suspended particulates (TSP) and sulphur dioxide (SO2), and two health outcomes, deaths and hospital admissions were considered. The last was analysed according to two age groups. SUBJECTS: Daily deaths and general hospital admissions for respiratory causes in residents who died in Milan or were admitted to local hospitals in that city. MAIN RESULTS: There was an increased risk of respiratory death and of hospital admission associated with increased concentrations of SO2 and TSP. The relative risks were similar for both pollutants, and were higher for respiratory deaths than for hospital admissions. No changes in relation to season were seen in the SO2 effect on respiratory deaths, but there was a suggestion of a higher effect on hospital admissions in the cool months. The seasonal pattern of the TSP effect was inconsistent: for mortality it was higher in the warm period while for hospital admissions it seemed to be higher in the cool months. This last result might be due to chance, although some role could have been played by the hospital admission data on all general admissions for respiratory causes (ICD-9: 460-519) as these are a much less specific end point. CONCLUSION: In Milan, a positive association was found between the daily SO2 or TSP concentrations and the number of deaths or hospital admissions for respiratory causes. This confirms results from other European and North American cities.

Acute Disease↗

PubMed1995

[Air pollution and daily mortality among Milan residents, 1980-89. Preliminary results].

Short term effects of air pollutant levels on daily mortality were studied, using time series approach, in Milan from the year 1980 to 1989. The Poisson regression with autocorrelated residuals was applied. This paper reports preliminary results of the analyses on the association between total daily number of deaths and the 24h mean concentrations, on the same day, for two air pollutants: sulphur dioxide and total suspended particulate. A positive and curvilinear relation (with a logarithmic shape) has been found between total daily mortality and concentration levels for each pollutant. This result confirm a steeper increase in mortality at low concentrations, lower than the international standards in use at the moment.

Air Pollutants↗

PubMed1995

[Time series analysis in environmental epidemiology: short-term effects of air pollution on mortality and morbidity].

This work gives an overview of design and analysis of temporal studies using aggregated data in air pollution epidemiology. In the last years time series are often used to study the short-term association between ambient air pollution levels and aggregated health data. Health endpoints are usually daily mortality and/or daily hospital admission data from routine health registers. Air quality data are commonly obtained from one (or a few) fixed site monitoring stations. To detect the temporal association between the time-pattern in air pollution and the time-pattern in health data particular attention needs to be given to the autocorrelation structure, to the seasonality and long term trend in the data, and to the weather variables. Poisson regression with autocorrelated residuals is the suitable statistical method to analyze time studies. Furthermore, the pollutant variable can be analyzed at different lag-times to account for short latency periods in the manifestation of diseases. Studies with temporal aggregated data show the same disadvantages of the ecologic studies, although, in this case, confounding is less of a problem. Temporal studies usually are based on a large database, so that sufficient power can be achieved to detect even weak associations. Finally, the exposure information on subjects is often better characterized by short-term fluctuations in ambient air quality than is the case in geographic aggregations.

Air Pollutants↗

PubMedDate not supplied

Air pollution and cause-specific mortality in Milan, Italy, 1980-1989.

In several studies, investigators have reported associations among air pollution, weather, and daily deaths, usually from all causes. In the current study, we focused on the difference in lag time between exposure to total suspended particulates or extreme weather and cause-specific mortality in an effort to understand the potential underlying mechanism. We used a robust Poisson regression in a generalized additive model to investigate the association between air pollution and daily mortality. We used a loess smooth function to model season, weather, and humidity; indicator variables for hot days were also used. To examine the relationship in a currently meaningful range, we excluded all days with a total suspended particulate concentration higher than 200 microg/m3. We found a significant association on the concurrent day, both for respiratory infection deaths (11% increase/100 microg/m3 increase in total suspended particulate; 95% confidence interval = 5, 17) and for heart-failure deaths (7% increase; 95% confidence interval = 3, 11). The associations with myocardial infarction (i.e., 10% increase; 95% confidence interval = 3, 18) and chronic obstructive pulmonary disease (12% increase, 95% confidence interval = 6, 17) were found for the means of 3 and 4 d prior to death. We observed an effect of cold weather at lag 1 for respiratory infections and an effect of hot weather at lag 0 for heart failure and myocardial infarctions. The association for all causes and cause-specific deaths was almost identical to that noted previously in Philadelphia, Pennsylvania. Smoothed functions of total suspended particulates suggested a higher slope at lower concentrations, and this finding may account for differences noted between European and U.S. studies. Given that both the dependence between weather and daily mortality and the lag between exposure and death varies by cause of death, analyses by specific causes of death would be very useful in the future.

Air Pollution↗