Environmental Epidemiology最新文献

Background: Results from ecological studies have suggested that air pollution increases the risk of developing and dying from COVID-19. Drawing causal inferences from the measures of association reported in ecological studies is fraught with challenges given biases arising from an outcome whose ascertainment is incomplete, varies by region, time, and across sociodemographic characteristics, and cannot account for clustering or within-area heterogeneity. Through a series of analyses, we illustrate the dangers of using ecological studies to assess whether ambient air pollution increases the risk of dying from, or transmitting, COVID-19.

Methods: We performed an ecological analysis in the continental United States using county-level ambient concentrations of fine particulate matter (PM_2.5) between 2000 and 2016 and cumulative COVID-19 mortality counts through June 2020, December 2020, and April 2021. To show that spurious associations can be obtained in ecological data, we modeled the association between PM_2.5 and the prevalence of human immunodeficiency virus (HIV). We fitted negative binomial models, with a logarithmic offset for county-specific population, to these data. Natural cubic splines were used to describe the shape of the exposure-response curves.

Results: Our analyses revealed that the shape of the exposure-response curve between PM_2.5 and COVID-19 changed substantially over time. Analyses of COVID-19 mortality through June 30, 2021, suggested a positive linear relationship. In contrast, an inverse pattern was observed using county-level concentrations of PM_2.5 and the prevalence of HIV.

Conclusions: Our analyses indicated that ecological analyses are prone to showing spurious relationships between ambient air pollution and mortality from COVID-19 as well as the prevalence of HIV. We discuss the many potential biases inherent in any ecological-based analysis of air pollution and COVID-19.

Immune-mediated, noncommunicable diseases-such as autoimmune and inflammatory diseases-are chronic disorders, in which the interaction between environmental exposures and the immune system plays an important role. The prevalence and societal costs of these diseases are rising in the European Union. The EXIMIOUS consortium-gathering experts in immunology, toxicology, occupational health, clinical medicine, exposure science, epidemiology, bioinformatics, and sensor development-will study eleven European study populations, covering the entire lifespan, including prenatal life. Innovative ways of characterizing and quantifying the exposome will be combined with high-dimensional immunophenotyping and -profiling platforms to map the immune effects (immunome) induced by the exposome. We will use two main approaches that "meet in the middle"-one starting from the exposome, the other starting from health effects. Novel bioinformatics tools, based on systems immunology and machine learning, will be used to integrate and analyze these large datasets to identify immune fingerprints that reflect a person's lifetime exposome or that are early predictors of disease. This will allow researchers, policymakers, and clinicians to grasp the impact of the exposome on the immune system at the level of individuals and populations.

Background: Physical activity may increase the intake of air pollutants due to a higher ventilation rate, which may exacerbate the adverse health effects. This study investigated the combined effects of habitual exercise and long-term exposure to fine particulate matter (PM_2.5) on the incidence of dyslipidemia in a large longitudinal cohort in Taiwan.

Methods: A total of 121,948 adults (≥18 years) who received at least two medical examinations from 2001 to 2016 were recruited, yielding 407,821 medical examination records. A satellite-based spatiotemporal model was used to estimate the 2-year average PM_2.5 concentration (i.e., the year of and the year before the medical examination) at each participant's address. Information on habitual exercise within 1 month before the medical examination was collected using a standard self-administered questionnaire. A Cox regression model with time-dependent covariates was used to investigate the combined effects.

Results: Compared with inactivity, moderate and high levels of exercise were associated with a lower incidence of dyslipidemia, with hazard ratios (HRs) (95% confidence intervals [CIs]) of 0.91 (0.88, 0.94) and 0.73 (0.71, 0.75), respectively. Participants with a moderate (22.37-25.96 μg/m³) or high (>25.96 μg/m³) level of PM_2.5 exposure had a higher incidence of dyslipidemia than those with a low level of PM_2.5 exposure (≤22.37 μg/m³), with HRs (95% CIs) of 1.36 (1.32, 1.40), and 1.90 (1.81, 1.99), respectively. We observed a statistically significant, but minor, interaction effect of PM_2.5 exposure and exercise on the development of dyslipidemia, with an overall hazard ratios (95% CI) of 1.08 (1.05, 1.10), indicating that an incremental increase in the level of exercise was associated with an 8% increase in the risk of dyslipidemia associated with every 10 μg/m³ increase in PM_2.5 exposure. However, the negative association between habitual exercise and dyslipidemia remained, regardless of the level of PM_2.5 exposure, suggesting that the benefits of increased habitual exercise outweighed the adverse effects of the increase in PM_2.5 intake during exercise.

Conclusions: Increased levels of exercise and reduced levels of PM_2.5 exposures were associated with a lower incidence of dyslipidemia. Although an increase in habitual exercise slightly increased the risk of dyslipidemia associated with PM_2.5 exposure, the benefits of the increased habitual exercise outweighed the risks. Our findings suggest that habitual exercise is an effective approach for dyslipidemia prevention, even for people residing in relatively polluted areas.

Estimating long-term exposure to household air pollution is essential for quantifying health effects of chronic exposure and the benefits of intervention strategies. However, typically only a small number of short-term measurements are made. We compare different statistical models for combining these short-term measurements into predictions of a long-term average, with emphasis on the impact of temporal trends in concentrations and crossover in study design. We demonstrate that a linear mixed model that includes time adjustment provides the best predictions of long-term average, which have lower error than using household averages or mixed models without time, for a variety of different study designs and underlying temporal trends. In a case study of a cookstove intervention study in Honduras, we further demonstrate how, in the presence of strong seasonal variation, long-term average predictions from the mixed model approach based on only two or three measurements can have less error than predictions based on an average of up to six measurements. These results have important implications for the efficiency of designs and analyses in studies assessing the chronic health impacts of long-term exposure to household air pollution.

Background: There is increasing evidence that a complex interplay of factors within environments in which children grows up, contributes to children's suboptimal mental health and cognitive development. The concept of the life-course exposome helps to study the impact of the physical and social environment, including social inequities, on cognitive development and mental health over time.

Methods: Equal-Life develops and tests combined exposures and their effects on children's mental health and cognitive development. Data from eight birth-cohorts and three school studies (N = 240.000) linked to exposure data, will provide insights and policy guidance into aspects of physical and social exposures hitherto untapped, at different scale levels and timeframes, while accounting for social inequities. Reasoning from the outcome point of view, relevant stakeholders participate in the formulation and validation of research questions, and in the formulation of environmental hazards. Exposure assessment combines GIS-based environmental indicators with omics approaches and new data sources, forming the early-life exposome. Statistical tools integrate data at different spatial and temporal granularity and combine exploratory machine learning models with hypothesis-driven causal modeling.

Conclusions: Equal-Life contributes to the development and utilization of the exposome concept by (1) integrating the internal, physical and social exposomes, (2) studying a distinct set of life-course effects on a child's development and mental health (3) characterizing the child's environment at different developmental stages and in different activity spaces, (4) looking at supportive environments for child development, rather than merely pollutants, and (5) combining physical, social indicators with novel effect markers and using new data sources describing child activity patterns and environments.

Background: Associations between mortality and exposure to ambient air pollution are usually explored using concentrations of residential outdoor fine particulate matter (PM_2.5) to estimate individual exposure. Such studies all have an important limitation in that they do not capture data on individual mobility throughout the day to areas where concentrations may be substantially different, leading to possible exposure misclassification. We examine the possible role of outdoor PM_2.5 concentrations at work for a large population-based mortality cohort.

Methods: Using the 2001 Canadian Census Health and Environment Cohort (CanCHEC), we created a time-weighted average that incorporates employment hours worked in the past week and outdoor PM_2.5 concentration at work and home. We used a Cox proportional hazard model with a 15-year follow-up (2001 to 2016) to explore whether inclusion of workplace estimates had an impact on hazard ratios for mortality for this cohort.

Results: Hazard ratios relying on outdoor PM_2.5 concentration at home were not significantly different from those using a time-weighted estimate, for the full cohort, nor for those who commute to a regular workplace. When exploring cohort subgroups according to neighborhood type and commute distance, there was a notable but insignificant change in risk of nonaccidental death for those living in car-oriented neighborhoods, and with commutes greater than 10 km.

Conclusions: Risk analyses performed with large cohorts in low-pollution environments do not seem to be biased if relying solely on outdoor PM_2.5 concentrations at home to estimate exposure.

Background: Climate vulnerability of the unborn can contribute to adverse birth outcomes, in particular, but it is still not well understood. We investigated the association between ambient temperature and stillbirth risk among a historical population in northern Sweden (1880-1950).

Methods: We used digitized parish records and daily temperature data from the study region covering coastal and inland communities some 600 km north of Stockholm, Sweden. The data included 141,880 births, and 3,217 stillbirths, corresponding to a stillbirth rate of 22.7 (1880-1950). The association between lagged temperature (0-7 days before birth) and stillbirths was estimated using a time-stratified case-crossover design. Incidence risk ratios (IRR) with 95% confidence intervals were computed, and stratified by season and sex.

Results: We observed that the stillbirth risk increased both at low and high temperatures during the extended summer season (April to September), at -10°C, and the IRR was 2.3 (CI 1.28, 4.00) compared to the minimum mortality temperature of +15°C. No clear effect of temperature during the extended winter season (October to March) was found. Climate vulnerability was greater among the male fetus compared to the female counterparts.

Conclusion: In this subarctic setting before and during industrialization, both heat and cold during the warmer season increased the stillbirth risk. Urbanization and socio-economic development might have contributed to an uneven decline in climate vulnerability of the unborn.

Background: Ambient air pollution is an important environmental exposure and has been linked with impaired cognitive function. Few studies have investigated its impact on children's academic performance on a nationwide level. We hypothesize that higher ambient air pollution concentrations will be associated with lower average academic test scores.

Methods: We investigated three prevalent ambient air pollutants: PM_2.5, NO₂ and ozone, and their associations with the average academic test scores, at the Geographic School District (GSD) level, of the third to eighth grade students in the United States from 2010 to 2016. We applied multivariate linear regression and controlled for urbanicity, socioeconomic status, student racial/ethnic compositions, and individual intercepts for each district-grade level and each year.

Results: We found that an interquartile range increase in PM_2.5 concentrations was associated with a 0.007 (95% confidence interval: 0.005, 0.009) SD lower average math test scores, and a 0.004 (95% confidence interval: 0.002, 0.005) SD lower average English language/arts test scores. Similar associations were observed for NO₂ and ozone on math, and for NO₂ on English language/arts. The magnitudes of these associations are equivalent to the effects of short-term reductions of thousands of dollars in district median household income. The reductions in test scores were larger for GSDs with higher socioeconomic status, though most associations remained negative at all socioeconomic levels.

Conclusions: Our results show that ambient air pollution within a GSD is associated with lower academic performance among children. Further improving air quality may benefit children's overall academic achievement and socioeconomic attainment across the lifespan.