Seasonal confounding in air pollution and health time-series studies: effect on air pollution effect estimates

A major statistical challenge in air pollution and health time-series studies is to adequately control for confounding effects of time-varying covariates. Daily health outcome counts are most commonly analysed by Poisson regression models, adjusted for overdispersion, with air pollution levels included as a linear predictor and smooth functions for calendar time and weather variables to adjust for time-varying confounders. Various smoothers have been used so far, but the optimal strategy for choosing smoothers and their degree of smoothing remains controversial. In this work, we evaluate the performance of various smoothers with different criteria for choosing the degree of smoothing in terms of bias and efficiency of the air pollution effect estimate in a simulation study. The evaluated approaches were also applied to real mortality data from 22 European cities. The simulation study imitated a multi-city study. Data were generated from a fully parametric model. Model selection methods which optimize prediction may lead to increased biases in the air pollution effect estimate. Minimization of the absolute value of the sum of the partial autocorrelation function of the model's residuals (PACF), as a criterion to choose the degree of smoothness, gave the smallest biases. The penalized splines (PS) method with a large number of effective dfs (e.g. 8-12 per year) could be used as the basic, relatively conservative, analysis whereas the PS and natural splines in combination with PACF could be applied to provide a reasonable range of the effect estimate.     

A review of time-series studies used to evaluate the short-term effects of air pollution on human health

We review the methodology used in the analysis of time-series studies of ambient air pollution. Our focus is on mortality studies, in which daily counts of death are correlated with changes in air pollution. We first illustrate the methods by showing data from the 1950s, during which the effects of air pollution were much more pronounced, and then describe current methods that were developed to identify associations when the signal-to-noise ratio is much lower. We describe basic data sources, details of statistical methods, and current state of the art, especially as it refers to problems found recently with the fitting algorithm used in the generalized additive models. A summary of the findings from mortality studies is presented and the pre-eminent issues regarding methods, interpretation, and identification of susceptible populations are discussed. We conclude by describing possible biological mechanisms and suggesting other designs that will aid in the interpretation of data from studies of acute health effects.     

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.     

Particulate air pollution and asthma: a review of epidemiological and biological studies

The link between exposure to air pollution and exacerbation of asthma symptoms has been investigated by epidemiological study and by direct biological experimentation. In asthmatics, epidemiological studies generally show a positive correlation between the particulate fraction of air pollution and increased morbidity, although roles for other co-pollutants (for example, ozone) are implicated as well. Direct experimentation using air pollutants, especially particles, to investigate their effects on humans or on animal models of asthma provides corroboration of the epidemiology and has begun to identify the pathophysiological mechanisms involved. We begin this review with an overview of air pollution, followed by a survey of the epidemiological and experimental data regarding air pollution particles and asthma. We finish with a discussion of directions for future research.     

Time series methods in epidemiological studies on air pollution]

The time series methods in the epidemiological studies on air pollution are reviewed, illustrated by means of an autoregressive Poisson regression which was employed in the APHEA and EMECAM Projects. A listing is provided of the variations in the daily number of deaths of people over age 70 (all causes, CIE-9:001-799) in Barcelona, 1991-1995, with the average variations in the daily smog pollution levels. A Poisson regression is used insofar as the dependent random variable presumably follows such a probability distribution. As variable possibly leading to confusion, the impact of weather variables (daily temperature and relative humidity averages), seasonal, tendency-related behaviours and day of the year on the death rate are taken into account (all estimated on a determinist basis), in addition to any other variable which behaves in a way that it can be related to the dependent variable (i.e. flu epidemics). The relationship between the death rate and the confusing-causing variables is modelled on a non-linear basis, and the foreseeable lag times are also taken into account (i.e. by using explicative variable time lags). However, due to control not being perfect, it has been decided to opt for estimating an autoregressive Poisson model (adding in some different explicative variables time giving rise to a lag in the death rate) offsetting the residual autocorrelation. The main advantage of the method of analysis described above is that of making it possible to control confusing variables from a determinist standpoint with a software to which all of the groups taking part in this Project had access. This also affords the possibility of using this method in a set, standardized manner, facilitating the comparison of results and making an objective point analysis possible.     

Long-term exposure to outdoor air pollution and the prevalence of asthma: meta-analysis of multi-community prevalence studies

The effect of outdoor air pollution on variations in asthma prevalence is unclear. We have conducted a meta-analysis of multi-community studies to evaluate and quantify the evidence for an association between community levels of pollution and asthma prevalence. We identified peer-reviewed articles reporting associations between community levels of air pollution and the prevalence of wheeze symptom or asthma diagnosis which were based on five or more communities. Associations were investigated by meta-analysis and by analysis of the direction and statistical significance of estimates. We identified 21 studies of more than five communities (range 6 to 62). The proportion of studies reporting at least one significantly positive association was 43% but, of the total of 178 pollution-outcome estimates, only 11% were statistically significantly positive. Thirteen studies reported associations with pollution analysed as a quantitative variable, and these results were meta-analysed. For period prevalence (mainly wheeze symptom), the results were null: Random effects estimates (95% confidence intervals) for the odds ratios of nitrogen dioxide, particulate matter with aerodynamic diameter <10 μm, ozone and sulphur dioxide were 1.00 (0.95, 1.06), 1.00 (0.94, 1.07), 1.01 (0.96, 1.07) and 1.03 (0.97, 1.09), respectively. For lifetime prevalence (mainly asthma diagnosis), the random effects estimates were similar: 1.00 (0.99, 1.01), 0.99 (0.96, 1.02), 1.06 (0.98, 1.14) and 1.00 (0.96, 1.05), respectively. We found no evidence of an association between community levels of outdoor air pollution and asthma prevalence.     

Systematic review of epidemiological studies on health effects associated with management of solid waste

Background  

Management of solid waste (mainly landfills and incineration) releases a number of toxic substances, most in small quantities and at extremely low levels. Because of the wide range of pollutants, the different pathways of exposure, long-term low-level exposure, and the potential for synergism among the pollutants, concerns remain about potential health effects but there are many uncertainties involved in the assessment. Our aim was to systematically review the available epidemiological literature on the health effects in the vicinity of landfills and incinerators and among workers at waste processing plants to derive usable excess risk estimates for health impact assessment.     

中国环境保护重点城市空气质量指数时空变化特征

  目的   研究不同温度范围PM₁₀对成都市≥65岁老年人过早死亡累积滞后效应的影响。  方法   利用四分位间距对气温进行分层,运用分布滞后非线性Quasipoisson回归模型,分层分析2013 — 2016年不同温度范围PM₁₀对成都老年人过早死亡的累积滞后效应及其对死亡影响的持续作用时间。  结果   调整温度后,PM₁₀的短期波动对成都老年人过早死亡的影响向后延迟2～5 d,其累积滞后死亡影响持续3～5 d,PM₁₀每增加10μg/m³可使老年人非意外全死因死亡累积滞后效应增加0.2%（95 % CI = 0.0 %～0.3 %）。在高温和中等温度范围PM₁₀对老年人的过早死亡存在显著的累积滞后影响,但在低温范围PM₁₀对老年人的过早死亡影响无统计学意义（P > 0.05）;在高温范围PM₁₀每增加10μg/m³全死因（非意外）和心血管病累积滞后死亡风险分别增加3.5 %（95 % CI = 0.1 %～7.0 %）和3.3 %（95 % CI = 0.2 %～6.4 %）;在中温范围PM₁₀每增加10μg/m³全死因（非意外）、心血管病和癌症累积滞后死亡风险分别增加0.3 %（95 % CI = 0.1 %～0.5 %）、0.3 %（95% CI = 0.1 %～0.6 %）和0.5 %（95% CI = 0.2 %～0.9 %）;所有温度范围PM₁₀对呼吸疾病死亡的滞后累积效应均无统计学意义（P > 0.05）。  结论   成都PM₁₀对老年人过早死亡影响存在显著的温度差异。     

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.     

室外空气污染对儿童呼吸系统健康的影响

目的 探讨空气污染对儿童呼吸系统健康的危害，根据本溪市历年空气质量监测结果，对不同污染区的儿童健康状况进行调查。方法 采取整群抽样方法，在本溪市轻、中、重污染区随机抽取小学和幼儿园各一所，全部幼童为调查对象。结果 本溪市儿童持续咳嗽、咯痰、哮喘、哮喘现患、喘鸣及喘鸣样症状的发生率分别为11．89％，5．44％，1．15％，0．62％，6．98％，5．83％。幼儿呼吸系统各病症发生率均高于学龄儿童的发生率；除幼儿男性持续咳嗽发生率低于女性的发生率外(OR=0．69，95％CI=0．49～0．98)，性别间差异无统计学意义。重、中污染区儿童的发生率高于轻污染区儿童的发生率，其中重污染区儿童喘鸣样症状的发生率高于轻污染区儿童的发生率(OR=1．50，95％CI=1．01～2．25)。沿街居住可使儿童呼吸系统疾病及症状的发生率增高，随着住房与交通干线距离的接近，儿童呼吸系统疾病的发生率也呈现增高趋势。易感因素与室外空气污染的联合作用较强，呼吸系统各种疾病的发生率均以受其他因素影响的易感儿童为高，无其他因素影响的非易感儿童发生率为低。结论 室外空气污染是儿童呼吸系统健康的影响因素。     

On the interpretation of epidemiological studies of ambient air pollution

Studies of the effects of ambient air pollution on human health are complicated by the fact that individuals are exposed to a complex mixture of toxic and non-toxic substances that vary in their make-up in space and in time. It is impossible using standard epidemiological designs to uniquely identify any individual component of air pollution as a causal agent of a health effect because of simultaneous exposure to all or a subset of ambient pollutants. Nevertheless, associations observed for a particular pollutant can be interpreted as a marker of toxicity of the mixture, and regulatory action on judiciously selected pollutants can benefit health, as such actions can modify the complex mixture. This paper argues that we may be able to further our understanding of the effects of air pollution on human health by investigating whether particular mixtures are more toxic through (1) conducting and reporting results for all measured components; (2) possibly characterizing the complex mixture by developing synoptic categorizations of daily air pollution, through cluster or latent profile analysis; (3) a careful analysis of differences in the mixtures between locations; and (4) toxicological studies and controlled studies on humans that make use of factorial designs for various components of the air pollution mixture.     

Comparing meta-analysis and ecological-longitudinal analysis in time-series studies. A case study of the effects of air pollution on mortality in three Spanish cities

STUDY OBJECTIVE: The objective of this paper is to introduce a different approach, called the ecological-longitudinal, to carrying out pooled analysis in time series ecological studies. Because it gives a larger number of data points and, hence, increases the statistical power of the analysis, this approach, unlike conventional ones, allows the complementation of aspects such as accommodation of random effect models, of lags, of interaction between pollutants and between pollutants and meteorological variables, that are hardly implemented in conventional approaches. DESIGN: The approach is illustrated by providing quantitative estimates of the short-term effects of air pollution on mortality in three Spanish cities, Barcelona, Valencia and Vigo, for the period 1992-1994. Because the dependent variable was a count, a Poisson generalised linear model was first specified. Several modelling issues are worth mentioning. Firstly, because the relations between mortality and explanatory variables were non-linear, cubic splines were used for covariate control, leading to a generalised additive model, GAM. Secondly, the effects of the predictors on the response were allowed to occur with some lag. Thirdly, the residual autocorrelation, because of imperfect control, was controlled for by means of an autoregressive Poisson GAM. Finally, the longitudinal design demanded the consideration of the existence of individual heterogeneity, requiring the consideration of mixed models. MAIN RESULTS: The estimates of the relative risks obtained from the individual analyses varied across cities, particularly those associated with sulphur dioxide. The highest relative risks corresponded to black smoke in Valencia. These estimates were higher than those obtained from the ecological-longitudinal analysis. Relative risks estimated from this latter analysis were practically identical across cities, 1.00638 (95% confidence intervals 1.0002, 1.0011) for a black smoke increase of 10 microg/m(3) and 1.00415 (95% CI 1.0001, 1.0007) for a increase of 10 microg/m(3) of sulphur dioxide. Because the statistical power is higher than in the individual analysis more interactions were statistically significant, especially those among air pollutants and meteorological variables. CONCLUSIONS: Air pollutant levels were related to mortality in the three cities of the study, Barcelona, Valencia and Vigo. These results were consistent with similar studies in other cities, with other multicentric studies and coherent with both, previous individual, for each city, and multicentric studies for all three cities.     

A meta-analysis of time-series studies of ozone and mortality with comparison to the national morbidity, mortality, and air pollution study

BACKGROUND: Although many time-series studies of ozone and mortality have identified positive associations, others have yielded null or inconclusive results, making the results of these studies difficult to interpret. METHODS: We performed a meta-analysis of 144 effect estimates from 39 time-series studies, and estimated pooled effects by lags, age groups, cause-specific mortality, and concentration metrics. We compared results with pooled estimates from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS), a time-series study of 95 large U.S. urban centers from 1987 to 2000. RESULTS: Both meta-analysis and NMMAPS results provided strong evidence of a short-term association between ozone and mortality, with larger effects for cardiovascular and respiratory mortality, the elderly, and current-day ozone exposure. In both analyses, results were insensitive to adjustment for particulate matter and model specifications. In the meta-analysis, a 10-ppb increase in daily ozone at single-day or 2-day average of lags 0, 1, or 2 days was associated with an 0.87% increase in total mortality (95% posterior interval = 0.55% to 1.18%), whereas the lag 0 NMMAPS estimate is 0.25% (0.12% to 0.39%). Several findings indicate possible publication bias: meta-analysis results were consistently larger than those from NMMAPS; meta-analysis pooled estimates at lags 0 or 1 were larger when only a single lag was reported than when estimates for multiple lags were reported; and heterogeneity of city-specific estimates in the meta-analysis were larger than with NMMAPS. CONCLUSIONS: This study provides evidence of short-term associations between ozone and mortality as well as evidence of publication bias.     

Methodological issues in studies of air pollution and reproductive health

In the past decade there have been an increasing number of scientific studies describing possible effects of air pollution on perinatal health. These papers have mostly focused on commonly monitored air pollutants, primarily ozone (O₃), particulate matter (PM), sulfur dioxide (SO₂), carbon monoxide (CO), and nitrogen dioxide (NO₂), and various indices of perinatal health, including fetal growth, pregnancy duration, and infant mortality. While most published studies have found some marker of air pollution related to some types of perinatal outcomes, variability exists in the nature of the pollutants and outcomes associated. Synthesis of the findings has been difficult for various reasons, including differences in study design and analysis. A workshop was held in September 2007 to discuss methodological differences in the published studies as a basis for understanding differences in study findings and to identify priorities for future research, including novel approaches for existing data. Four broad topic areas were considered: confounding and effect modification, spatial and temporal exposure variations, vulnerable windows of exposure, and multiple pollutants. Here we present a synopsis of the methodological issues and challenges in each area and make recommendations for future study. Two key recommendations include: (1) parallel analyses of existing data sets using a standardized methodological approach to disentangle true differences in associations from methodological differences among studies; and (2) identification of animal studies to inform important mechanistic research gaps. This work is of critical public health importance because of widespread exposure and because perinatal outcomes are important markers of future child and adult health.     

Systematic review and metaanalysis of air pollution exposure and risk of diabetes

The present systematic review and metaanalysis of published observational studies was conducted to assess the health effects of exposure to air pollution on diabetes risk. Online databases were searched through January 2013, and the reference lists of pertinent articles reporting observational studies in humans were examined. Pooled relative risks and 95 % confidence intervals were calculated with a random-effects model. Exposure to air pollution was associated with slight increase in risk of diabetes and susceptibility of people with diabetes to air pollution. These results were consistent between time-series, case-crossover and cohort studies and between studies conducted in North America and Europe. The association between exposure to air pollution and diabetes was stronger for gaseous pollutants than for particulate matter. Our metaanalysis suggests that exposure to air pollution may be a risk factor for diabetes and increase susceptibility of people with diabetes to air pollution.     

Systematic review of epidemiological studies of exposure to tritium.

Tritium (3H) is a radioactive isotope of hydrogen. A number of factors combine to create a good deal of interest in the risks arising from exposure to tritium of both workers and members of the public. Tritium is ubiquitous in environmental and biological systems and is very mobile due to its occurrence as tritiated water. In this paper we systematically review epidemiological data relating to tritium exposure with a view to assessing the risk of such exposure using those studies that are potentially informative. The usefulness of the available studies of cancer and other adverse health effects in workforces and members of the general public is often impaired by a lack of tritium-specific dose data, low doses and small numbers of cases. A number of workforce studies have been identified in which tritium-specific individual doses have been estimated, although none of them, as presently reported, enable reliable inferences to be made on risks associated with exposure to tritium. In general, the available epidemiological studies on the offspring of radiation workers or on pregnancy outcome in areas subject to releases of tritium do not contain enough detail to estimate risks from tritium exposure. Although the studies presently reported are uninformative on risks from tritium, a number of the occupationally exposed cohorts would be potentially informative, particularly if data were suitably combined.     

Residential outdoor air pollution and lung function in schoolchildren

BACKGROUND: Long-term exposure to outdoor air pollution has typically been estimated on the aggregate level, and more individual measures of exposure are needed. We investigated the associations with lung function of residential outdoor air pollution in early life, total lifetime, and days before lung function test. METHODS: In 2001-2002, spirometry was performed in 2307 9- and 10-year-old children who had lived in Oslo, Norway, since birth. Outdoor air pollution exposure for each child was assessed by the EPISODE dispersion model, calculating hourly concentrations of nitrogen dioxide (NO2), particulate matter (PM) with aerodynamic diameter less than 10 microm (PM10) and 2.5 microm (PM2.5). We applied linear regression analysis stratified by sex. RESULTS: Early and lifetime exposures to outdoor air pollution were associated with reduced peak expiratory flow and reduced forced expiratory flow at 25% and 50% of forced vital capacity, especially in girls. One interquartile increase of lifetime exposure to NO2, PM10, and PM2.5 was associated with change in adjusted peak respiratory flow of, respectively, -79 mL/s (95% confidence interval = -128 to -31), -66 mL/s (-110 to -23), and -58 mL/s (-94 to -21). We also found short-term effects of NO2 that became stronger with increasing time lags, but no short-term effects of PM. When we included short- and long-term NO2 exposures simultaneously, only the long-term effect remained. We found no effect on forced volumes. Adjusting for a contextual socioeconomic factor diminished the associations. CONCLUSIONS: Short- and long-term residential exposures to traffic-related pollutants in Oslo were associated with reduced peak expiratory flow and forced expiratory flow at 25% and 50% in 9- to 10-year-old children, especially in girls, with weaker associations after adjusting for a contextual socioeconomic factor.