The case-crossover design is widely used in environmental epidemiology as an effective alternative to the conventional time-series regression design to estimate short-term associations of environmental exposures with a range of acute events. This tutorial illustrates the implementation of the time-stratified case-crossover design to study aggregated health outcomes and environmental exposures, such as particulate matter air pollution, focusing on adjusting covariates and investigating effect modification using conditional Poisson regression. Time-varying confounders can be adjusted directly in the conditional regression model accounting for the adequate lagged exposure–response function. Time-invariant covariates at the subpopulation level require reshaping the typical time-series data set into a long format and conditioning out the covariate in the expanded stratum set. When environmental exposure data are available at geographical units, the stratum set should combine time and spatial dimensions. Moreover, it is possible to examine effect modification using interaction models. The time-stratified case-crossover design offers a flexible framework to properly account for a wide range of covariates in environmental epidemiology studies.

Exposure to outdoor air pollution has been consistently associated with asthma. In this study, we reviewed the epidemiological studies published within the last 5 years on the association between outdoor air pollution and exacerbation and onset of asthma. A large number of studies have been published within the last 5 years. Short‐term exposure to outdoor air pollution is associated with exacerbation of pre‐existing asthma, manifested as worsening of symptoms and increasing of asthma‐related emergency room visits and hospital admissions. Furthermore, increasing evidence suggests that long‐term exposure to outdoor air pollution can result in onset of asthma. Children are more susceptible to outdoor air pollution. Future studies should be conducted to explore the mechanisms underlying the association between air pollutants and onset of asthma, including gene involvement. In addition, disentangling the effect of a mixture of air pollutants and identifying the key components of air pollution will complete the existing evidence. More importantly, a better understanding is required on the future impact of air pollution on asthma under a changing climate.

Climate change can directly impact temperature-related excess deaths and might subsequently change the seasonal variation in mortality. In this study, we aimed to provide a systematic and comprehensive assessment of potential future changes in the seasonal variation, or seasonality, of mortality across different climate zones. In this modelling study, we collected daily time series of mean temperature and mortality (all causes or non-external causes only) via the Multi-Country Multi-City Collaborative (MCC) Research Network. These data were collected during overlapping periods, spanning from Jan 1, 1969 to Dec 31, 2020. We projected daily mortality from Jan 1, 2000 to Dec 31, 2099, under four climate change scenarios corresponding to increasing emissions (Shared Socioeconomic Pathways [SSP] scenarios SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). We compared the seasonality in projected mortality between decades by its shape, timings (the day-of-year) of minimum (trough) and maximum (peak) mortality, and sizes (peak-to-trough ratio and attributable fraction). Attributable fraction was used to measure the burden of seasonality of mortality. The results were summarised by climate zones. The MCC dataset included 126 809 537 deaths from 707 locations within 43 countries or areas. After excluding the only two polar locations (both high-altitude locations in Peru) from climatic zone assessments, we analysed 126 766 164 deaths in 705 locations aggregated in four climate zones (tropical, arid, temperate, and continental). From the 2000s to the 2090s, our projections showed an increase in mortality during the warm seasons and a decrease in mortality during the cold seasons, albeit with mortality remaining high during the cold seasons, under all four SSP scenarios in the arid, temperate, and continental zones. The magnitude of this changing pattern was more pronounced under the high-emission scenarios (SSP3-7.0 and SSP5-8.5), substantially altering the shape of seasonality of mortality and, under the highest emission scenario (SSP5-8.5), shifting the mortality peak from cold seasons to warm seasons in arid, temperate, and continental zones, and increasing the size of seasonality in all zones except the arid zone by the end of the century. In the 2090s compared with the 2000s, the change in peak-to-trough ratio (relative scale) ranged from 0·96 to 1·11, and the change in attributable fraction ranged from 0·002% to 0·06% under the SSP5-8.5 (highest emission) scenario. A warming climate can substantially change the seasonality of mortality in the future. Our projections suggest that health-care systems should consider preparing for a potentially increased demand during warm seasons and sustained high demand during cold seasons, particularly in regions characterised by arid, temperate, and continental climates. The Environment Research and Technology Development Fund of the Environmental Restoration and Conservation Agency, provided by the Ministry of the Environment of Japan.

China is implementing intensive policies on electric vehicles to control air pollution in urban regions, especially the Beijing–Tianjin–Hebei (BTH) region, one of the most polluted areas in China. The development of electric vehicles will lead to an increase in electricity demand. Because electricity is mostly generated by thermal power in China, primary energy consumption will also increase. This study sets two scenarios: with the electric vehicle policies scenario (REN) and without the electric vehicle policies scenario (FOS) to compare electric vehicle policy’s impact. We quantified the health benefits of the electric vehicle policies in the BTH region by using an integrated assessment framework. Compared with scenario FOS, the local PM_2.5 emission will reduce by 11.38%, 15.12%, 22.27%, and the concentration will reduce by 18.84%, 20.04%, and 19.57% in Beijing, Tianjin, Hebei separately by 2030 in REN. The electric vehicle policies can avoid 23.5 million morbidities and 4.6 thousand mortalities and save CNY 20.65 billion using the value of statistical life and 1.5 million work loss days in 2030 in REN. Our results show that electric vehicle policy can bring a remarkably positive benefit to public health and the economy.

Several methods have been used to assess the seasonality of health outcomes in epidemiological studies. However, little information is available on the methods to study the changes in seasonality before and after adjusting for environmental or other known seasonally varying factors. Such investigations will help us understand the role of these factors in seasonal variation in health outcomes and further identify currently unknown or unmeasured risk factors. This tutorial illustrates a statistical procedure for examining the seasonality of health outcomes and their changes, after adjusting for potential environmental drivers by assessing and comparing shape, timings and size. We recommend a three-step procedure, each carried out and compared before and after adjustment: (i) inspecting the fitted seasonal curve to determine the broad shape of seasonality; (ii) identifying the peak and trough of seasonality to determine the timings of seasonality; and (iii) estimating the peak-to-trough ratio and attributable fraction to measure the size of seasonality. Reporting changes in these features on adjusting for potential drivers allows readers to understand their role in seasonality and the nature of any residual seasonal pattern. Furthermore, the proposed approach can be extended to other health outcomes and environmental drivers.

A causal link between air pollution exposure and cardiovascular events has been suggested. However fewer studies have investigated the shape of the associations at low levels of air pollution and identified the most important temporal window of exposure. Here we assessed long-term associations between particulate matter < 2.5 µm (PM_2.5) at low concentrations and multiple cardiovascular endpoints using the UK Biobank cohort. Using data on adults (aged > 40) from the UK Biobank cohort, we investigated the associations between 1-year, 3-year and 5-year time-varying averages of PM_2.5 and incidence of major adverse cardiovascular events (MACE), myocardial infarction (MI), heart failure, atrial fibrillation and flutter and cardiac arrest. We also investigated outcome subtypes for MI and stroke. Events were defined as hospital inpatient admissions. We fitted Cox proportional hazard regression models applying extensive control for confounding at both individual and area level. Finally, we assessed the shape of the exposure-response functions to assess effects at low levels of exposure. We analysed data from 377,736 study participants after exclusion of prevalent subjects. The average follow-up (2006-2021) was 12.9 years. We detected 19,353 cases of MACE, 6,562 of acute MI, 6,278 of heart failure, 1,258 for atrial fibrillation and flutter, and 16,327 for cardiac arrest. Using a 5-year exposure window, we detected positive associations (for 5 μg/m³ increase in PM_2.5) for 5-point MACE of [1.12 (95 %CI: 1.00-1.26)], heart failure [1.22 (1.00-1.50)] and cardiac arrest [1.16 (1.03-1.31)]. We did not find any association with acute MI, while non-ST-elevation MI was associated with the 1-year exposure window [1.52 (1.12-2.07)]. The assessment of the shape of the exposure-response relationships suggested that risk is approximately linear for most of the outcomes. We found positive associations between long-term exposure to PM_2.5 and multiple cardiovascular outcomes for different exposure windows. The cardiovascular risk tends to rise even at exposure concentrations below 12-15 μg/m³, indicating high risk below UK national and international thresholds.