Short-term effects of air pollutants on daily mortality in the Stockholm county - A spatiotemporal analysis

Spatio-temporal variation in prostate cancer testing in Stockholm: A population-based study

Prostate cancer screening using prostate-specific antigen (PSA) testing is controversial but remains prevalent in many countries. There is little information in Sweden or elsewhere on the spatial variation in PSA testing. This study aims to describe the spatio-temporal variation in PSA testing prior to a prostate cancer diagnosis in the Stockholm region at the municipality and small area levels. A population-based register study comprised men aged 40 years and over living in the Stockholm region during 2007-2016. For Stockholm in 2016, we reported the proportion of men who had a PSA test for the preceding one, two, five and ten years by ten-year age groups. The age-standardised proportion of men having a PSA test was reported for municipalities by calendar years. We used spatial smoothing for calculating the age-standardised proportion of men having a PSA test in a small area for each calendar year. In 2016, 74.0% and 77.8% of men aged 60-69 and 70-79 years respectively had taken a PSA test in the previous ten years. The municipalities of Danderyd and Ekerö showed high proportions of PSA testing. A marked heterogeneity in such proportions within each municipality was observed. The odds ratio for having a PSA test for those born in Sweden was 2.22 (95% CI 2.00-2.52). Opportunistic PSA testing is widespread with three quarters of men in their sixties and seventies having had a test in the preceding decade. We found evidence for marked geographical heterogeneity, where more affluent and metropolitan areas had higher levels of testing. Variations in PSA testing was associated with socio-economic position and demographic factors including education, income and country of birth.

Short-Term Interaction Effects of PM2.5 and O3 on Daily Mortality: A Time-Series Study of Multiple Cities in China

In recent years, PM2.5 and O3 have been the two main pollutants affecting public health in China, but the interaction of the two pollutants on human health remains unclear. A two-stage analytical approach was used to investigate the relationships of PM2.5-O3 co-pollution with nonaccidental, cardiovascular, and respiratory mortality levels across 14 cities in China. We first utilized a generalized additive model (GAM) to determine the city-specific associations of PM2.5 and O3 with daily mortality. The associations were then combined at the national and regional levels using meta-analysis. To investigate the potential interactions between the two pollutants and cause-specific mortality, we performed stratified analyses by co-pollutant exposure levels and the synergy index (SI) (SI > 1 indicates a synergistic interaction). The effect of changes in the two pollutants' concentrations (in 10 μg/m3 increases) on mortality was assessed. The stratification analysis results suggested that each 10 μg/m3 increase in PM2.5 at lag0-1 (lag01) in the low, moderate, and high strata of the O3 concentrations increased nonaccidental mortality by 0.07% (95% confidence interval: -0.03%, 0.17%), 0.33% (0.13%, 0.53%), and 0.68% (0.30%, 1.06%), respectively, with significant between-group differences (p < 0.001). Moreover, each 10 μg/m3 increase in O3 (lag01) in the low, moderate, and high strata of the PM2.5 concentrations increased nonaccidental mortality by 0.15% (-0.06%, 0.36%), 0.53% (0.19%, 0.87%), and 0.75% (0.14%, 1.36%), respectively, with significant between-group differences (p < 0.001). We also found substantial synergistic interactions between the two pollutants and nonaccidental, cardiovascular, and respiratory mortality levels, with SI values of 1.48, 1.51, and 1.33, respectively. Additionally, a subgroup analysis revealed that the interaction of these two pollutants on nonaccidental mortality were greater in South China compared to elsewhere, and during the warm season compared to during the cold season. Our findings suggested that the simultaneous control of PM2.5 and O3 within the context of combined air pollution could significantly decrease the disease risk, especially in southern China and during the warm season.

Is model-estimated PM2.5 exposure equivalent to station-observed in mortality risk assessment? A literature review and meta-analysis

Model-estimated air pollution exposure assessments have been extensively employed in the evaluation of health risks associated with air pollution. However, few studies synthetically evaluate the reliability of model-estimated PM2.5 products in health risk assessment by comparing them with ground-based monitoring station air quality data. In response to this gap, we undertook a meticulously structured systematic review and meta-analysis. Our objective was to aggregate existing comparative studies to ascertain the disparity in mortality effect estimates derived from model-estimated ambient PM2.5 exposure versus those based on monitoring station-observed PM2.5 exposure. We conducted searches across multiple databases, namely PubMed, Scopus, and Web of Science, using predefined keywords. Ultimately, ten studies were included in the review. Of these, seven investigated long-term annual exposure, while the remaining three studies focused on short-term daily PM2.5 exposure. Despite variances in the estimated Exposure-Response (E-R) associations, most studies revealed positive associations between ambient PM2.5 exposure and all-cause and cardiovascular mortality, irrespective of the exposure being estimated through models or observed at monitoring stations. Our meta-analysis revealed that all-cause mortality risk associated with model-estimated PM2.5 exposure was in line with that derived from station-observed sources. The pooled Relative Risk (RR) was 1.083 (95% CI: 1.047, 1.119) for model-estimated exposure, and 1.089 (95% CI: 1.054, 1.125) for station-observed sources (p = 0.795). In conclusion, most model-estimated air pollution products have demonstrated consistency in estimating mortality risk compared to data from monitoring stations. However, only a limited number of studies have undertaken such comparative analyses, underscoring the necessity for more comprehensive investigations to validate the reliability of these model-estimated exposure in mortality risk assessment.

Geospatial analysis of short-term exposure to air pollution and risk of cardiovascular diseases and mortality-A systematic review

The cardiovascular risk associated with short-term ambient air pollution exposure is well-documented. However, recent advancements in geospatial techniques have provided new insights into this risk. This systematic review focuses on short-term exposure studies that applied advanced geospatial pollution modelling to estimate cardiovascular disease (CVD) risk and accounted for additional unconventional neighbourhood-level confounders to analyse their modifier effect on the risk. Four databases were investigated to select publications between 2018 and 2023 that met the inclusion criteria of studying the effect of particulate matter (PM2.5 and PM10), SO2, NOx, CO, and O3 on CVD mortality or morbidity, utilizing pollution modelling techniques, and considering spatial and temporal confounders. Out of 3277 publications, 285 were identified for full-text review, of which 34 satisfied the inclusion criteria for qualitative analysis, and 12 of them were chosen for additional quantitative analysis. Quality assessment revealed that 28 out of 34 included articles scored 4 or above, indicating high quality. In 30 studies, advanced pollution modelling techniques were used, while in 4 only simpler methods were applied. The most pertinent confounders identified were socio-demographic variables (e.g., socio-economic status, population percentage by race or ethnicity) and neighbourhood-level built environment variables (e.g., urban/rural area, percentage of green space, proximity to healthcare), which exhibited varying modifier effects depending on the context. In the quantitative analysis, only PM 2.5 showed a significant positive association to all-cause CVD-related hospitalisation. Other pollutants did not show any significant effect, likely due to the high inter-study heterogeneity and a limited number of cases. The application of advanced geospatial measurement and modelling of air pollution exposure, as well as its risk, is increasing. This review underscores the importance of accounting for unconventional neighbourhood-level confounders to enhance the understanding of the CVD risk associated with short-term pollution exposure.

Association Between Nitrogen Dioxide Pollution and Cause-Specific Mortality in China: Cross-Sectional Time Series Study

BACKGROUND

Nitrogen dioxide (NO2) has been frequently linked to a range of diseases and associated with high rates of mortality and morbidity worldwide. However, there is limited evidence regarding the risk of NO2 on a spectrum of causes of mortality. Moreover, adjustment for potential confounders in NO2 analysis has been insufficient, and the spatial resolution of exposure assessment has been limited.

OBJECTIVE

This study aimed to quantitatively assess the relationship between short-term NO2 exposure and death from a range of causes by adjusting for potential confounders in Guangzhou, China, and determine the modifying effect of gender and age.

METHODS

A time series study was conducted on 413,703 deaths that occurred in Guangzhou during the period of 2010 to 2018. The causes of death were classified into 10 categories and 26 subcategories. We utilized a generalized additive model with quasi-Poisson regression analysis using a natural cubic splines function with lag structure of 0 to 4 days to estimate the potential lag effect of NO2 on cause-specific mortality. We estimated the percentage change in cause-specific mortality rates per 10 μg/m3 increase in NO2 levels. We stratified meteorological factors such as temperature, humidity, wind speed, and air pressure into high and low levels with the median as the critical value and analyzed the effects of NO2 on various death-causing diseases at those high and low levels. To further identify potentially vulnerable subpopulations, we analyzed groups stratified by gender and age.

RESULTS

A significant association existed between NO2 exposure and deaths from multiple causes. Each 10 μg/m3 increment in NO2 density at a lag of 0 to 4 days increased the risks of all-cause mortality by 1.73% (95% CI 1.36%-2.09%) and mortality due to nonaccidental causes, cardiovascular disease, respiratory disease, endocrine disease, and neoplasms by 1.75% (95% CI 1.38%-2.12%), 2.06% (95% CI 1.54%-2.59%), 2.32% (95% CI 1.51%-3.13%), 2.40% (95% CI 0.84%-3.98%), and 1.18% (95% CI 0.59%-1.78%), respectively. Among the 26 subcategories, mortality risk was associated with 16, including intentional self-harm, hypertensive disease, and ischemic stroke disease. Relatively higher effect estimates of NO2 on mortality existed for low levels of temperature, relative humidity, wind speed, and air pressure than with high levels, except a relatively higher effect estimate was present for endocrine disease at a high air pressure level. Most of the differences between subgroups were not statistically significant. The effect estimates for NO2 were similar by gender. There were significant differences between the age groups for mortality due to all causes, nonaccidental causes, and cardiovascular disease.

CONCLUSIONS

Short-term NO2 exposure may increase the risk of mortality due to a spectrum of causes, especially in potentially vulnerable populations. These findings may be important for predicting and modifying guidelines for NO2 exposure in China.

Association between short-term exposure to air pollutants and cause-specific daily mortality in Italy. A nationwide analysis

BACKGROUND/AIM

Daily air pollution has been linked with mortality from urban studies. Associations in rural areas are still unclear and there is growing interest in testing the role that air pollution has on other causes of death. This study aims to evaluate the association between daily air pollution and cause-specific mortality in all 8092 Italian municipalities.

METHODS

Natural, cardiovascular, cardiac, ischemic, cerebrovascular, respiratory, metabolic, diabetes, nervous and psychiatric causes of death occurred in Italy were extracted during 2013-2015. Daily ambient PM₁₀, PM_2.5 and NO₂ concentrations were estimated through machine learning algorithms. The associations between air pollutants and cause-specific mortality were estimated with a time-series approach using a two-stage analytic protocol where area-specific over-dispersed Poisson regression models where fit in the first stage, followed by a meta-analysis in the second. We tested for effect modification by sex, age class and the degree of urbanisation of the municipality.

RESULTS

We estimated a positive association between PM₁₀ and PM_2.5 and the mortality from natural, cardiovascular, cardiac, respiratory and nervous system causes, but not with metabolic or psychiatric causes of death. In particular, mortality from nervous diseases increased by 4.55% (95% CI: 2.51-6.63) and 9.64% (95% CI: 5.76-13.65) for increments of 10 μg/m³ in PM₁₀ and PM_2.5 (lag 0-5 days), respectively. NO₂ was positively associated with respiratory (6.68% (95% CI: 1.04-12.62)) and metabolic (7.30% (95% CI: 1.03-13.95)) mortality for increments of 10 μg/m³ (lag 0-5). Higher associations with natural mortality were found among the elderly, while there were no differential effects between sex or between rural and urban areas.

CONCLUSIONS

Short-term exposure to particulate matter was associated with mortality from nervous diseases. Mortality from metabolic diseases was associated with NO₂ exposure. Other associations are confirmed and updated, including the contribution of lowly urbanised areas. Health effects were also found in suburban and rural areas.

Short term exposure to air pollution and mortality in the US: a double negative control analysis

RATIONALE

Studies examining the association of short-term air pollution exposure and daily deaths have typically been limited to cities and used citywide average exposures, with few using causal models.

OBJECTIVES

To estimate the associations between short-term exposures to fine particulate matter (PM_2.5), ozone (O₃), and nitrogen dioxide (NO₂) and all-cause and cause-specific mortality in multiple US states using census tract or address exposure and including rural areas, using a double negative control analysis.

METHODS

We conducted a time-stratified case-crossover study examining the entire population of seven US states from 2000-2015, with over 3 million non-accidental deaths. Daily predictions of PM_2.5, O₃, and NO₂ at 1x1 km grid cells were linked to mortality based on census track or residential address. For each pollutant, we used conditional logistic regression to quantify the association between exposure and the relative risk of mortality conditioning on meteorological variables, other pollutants, and using double negative controls.

RESULTS

A 10 μg/m³ increase in PM_2.5 exposure at the moving average of lag 0-2 day was significantly associated with a 0.67% (95%CI: 0.34-1.01%) increase in all-cause mortality. 10 ppb increases in NO₂ or O₃ exposure at lag 0-2 day were marginally associated with and 0.19% (95%CI: -0.01-0.38%) and 0.20 (95% CI-0.01, 0.40), respectively. The adverse effects of PM_2.5 persisted when pollution levels were restricted to below the current global air pollution standards. Negative control models indicated little likelihood of omitted confounders for PM_2.5, and mixed results for the gases. PM_2.5 was also significantly associated with respiratory mortality and cardiovascular mortality.

CONCLUSIONS

Short-term exposure to PM_2.5 and possibly O₃ and NO₂ are associated with increased risks for all-cause mortality. Our findings delivered evidence that risks of death persisted at levels below currently permissible.

Air Pollution and Human Health: Investigating the Moderating Effect of the Built Environment

Air pollution seriously threatens human health and even causes mortality. It is necessary to explore effective prevention methods to mitigate the adverse effect of air pollution. Shaping a reasonable built environment has the potential to benefit human health. In this context, this study quantified the built environment, air pollution, and mortality at 1 km × 1 km grid cells. The moderating effect model was used to explore how built environment factors affect the impact of air pollution on cause-specific mortality and the heterogeneity in different areas classified by building density and height. Consequently, we found that greenness played an important role in mitigating the effect of ozone (O3) and nitrogen dioxide (NO2) on mortality. Water area and diversity of land cover can reduce the effect of fine particulate matter (PM2.5) and NO2 on mortality. Additionally, gas stations, edge density (ED), perimeter-area fractal dimension (PAFRAC), and patch density (PD) can reduce the effect of NO2 on mortality. There is heterogeneity in the moderating effect of the built environment for different cause-specific mortality and areas classified by building density and height. This study can provide support for urban planners to mitigate the adverse effect of air pollution from the perspective of the built environment.

Associations between short-term ambient ozone exposure and cause-specific mortality in rural and urban areas of Jiangsu, China

Most previous studies on the acute health effects of ozone are limited to urban areas, largely due to the paucity of air pollutant measurements in rural areas. We here estimated the county-specific daily maximum 8-h average ozone concentration in Jiangsu Province, China during 2015-2018, using a recently developed spatiotemporal machine learning model at a spatial resolution of 0.1° × 0.1° (∼11 × 11 km). Counties were equally divided into urban and rural groups based on the median of the percentage of urban residents across Jiangsu counties obtained from the National Population Census in 2010. We first conducted time-series analyses to estimate the county-specific effect of ozone using generalized linear models, then pooled the effect estimates by random-effects modeling. A 10 μg/m³ increase in the 4-day moving average (lag 0-3) of ambient ozone exposure was associated with increases of 0.66% (95% confidence interval [CI] 0.36%-0.95%) in daily nonaccidental mortality in rural areas and 0.42% in urban areas (95% CI, 0.27%-0.56%). Short-term ambient ozone exposure was associated with an increased risk of mortality caused by chronic obstructive pulmonary disease, hypertension, ischemic heart disease, and stroke. Our finding suggests that both urban and rural residents suffer adverse health effects from short-term ozone exposure.

A tutorial on the case time series design for small-area analysis

Background

The increased availability of data on health outcomes and risk factors collected at fine geographical resolution is one of the main reasons for the rising popularity of epidemiological analyses conducted at small-area level. However, this rich data setting poses important methodological issues related to modelling complexities and computational demands, as well as the linkage and harmonisation of data collected at different geographical levels.

Methods

This tutorial illustrated the extension of the case time series design, originally proposed for individual-level analyses on short-term associations with time-varying exposures, for applications using data aggregated over small geographical areas. The case time series design embeds the longitudinal structure of time series data within the self-matched framework of case-only methods, offering a flexible and highly adaptable analytical tool. The methodology is well suited for modelling complex temporal relationships, and it provides an efficient computational scheme for large datasets including longitudinal measurements collected at a fine geographical level.

Results

The application of the case time series for small-area analyses is demonstrated using a real-data case study to assess the mortality risks associated with high temperature in the summers of 2006 and 2013 in London, UK. The example makes use of information on individual deaths, temperature, and socio-economic characteristics collected at different geographical levels. The tutorial describes the various steps of the analysis, namely the definition of the case time series structure and the linkage of the data, as well as the estimation of the risk associations and the assessment of vulnerability differences. R code and data are made available to fully reproduce the results and the graphical descriptions.

Conclusions

The extension of the case time series for small-area analysis offers a valuable analytical tool that combines modelling flexibility and computational efficiency. The increasing availability of data collected at fine geographical scales provides opportunities for its application to address a wide range of epidemiological questions.

Variation and dispersal of PM10 and PM2.5 during COVID-19 lockdown over Kolkata metropolitan city, India investigated through HYSPLIT model

The higher concentration of PM10 and PM2.5 in the lower atmosphere is severely harmful for human health and it also makes visibility diminution along with weather and climate modifications. The main objective is to find out the spatiotemporal variation and dispersal of PM10 and PM2.5 along with COVID-19 infection in the dusty city Kolkata. The consecutive two years PM10 and PM2.5 data of different stations have been obtained from State Pollution Control Board, Govt. of West Bengal. Forward trajectory analysis has been done through HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory) model to find the path and direction of air particles. The result showed that the various meteorological or environmental factors (such as temperature, relative humidity, wind, wind speed, pressure and gusty wind) and geographical location regulate the spatiotemporal variation of PM10 and PM2.5. These factors like high temperature with relative humidity and strong wind influence to disperse the particulate matters from north to south direction from city to outside during summer in Kolkata metropolitan city. During summer (both pre and lockdown years), the height of particles is extended up to 1000 m owing to active atmospheric ventilation whereas in winter it is confined within 100 m. The HYSPLIT model clearly specified that the particles dispersed from south, south-west to north and north east direction due to strong wind. The constant magnification of PM10 and PM2.5 in the lower atmosphere leads to greater frequency of COVID-19 infections and deaths. In Kolkata, the one of the crucial reasons of high infection and deaths (COVID-19) is co-morbidity of people.

A systematic literature review of deep learning neural network for time series air quality forecasting

Rapid progress of industrial development, urbanization and traffic has caused air quality reduction that negatively affects human health and environmental sustainability, especially among developed countries. Numerous studies on the development of air quality forecasting model using machine learning have been conducted to control air pollution. As such, there are significant numbers of reviews on the application of machine learning in air quality forecasting. Shallow architectures of machine learning exhibit several limitations and yield lower forecasting accuracy than deep learning architecture. Deep learning is a new technology in computational intelligence; thus, its application in air quality forecasting is still limited. This study aims to investigate the deep learning applications in time series air quality forecasting. Owing to this, literature search is conducted thoroughly from all scientific databases to avoid unnecessary clutter. This study summarizes and discusses different types of deep learning algorithms applied in air quality forecasting, including the theoretical backgrounds, hyperparameters, applications and limitations. Hybrid deep learning with data decomposition, optimization algorithm and spatiotemporal models are also presented to highlight those techniques' effectiveness in tackling the drawbacks of individual deep learning models. It is clearly stated that hybrid deep learning was able to forecast future air quality with higher accuracy than individual models. At the end of the study, some possible research directions are suggested for future model development. The main objective of this review study is to provide a comprehensive literature summary of deep learning applications in time series air quality forecasting that may benefit interested researchers for subsequent research.

Short-term exposure to nitrogen dioxide and mortality: A systematic review and meta-analysis

BACKGROUND

Ambient air pollution has been characterized as a leading cause of mortality worldwide and has been associated with cardiovascular and respiratory diseases. There is increasing evidence that short-term exposure to nitrogen dioxide (NO₂), is related to adverse health effects and mortality.

METHODS

We conducted a systematic review of short-term NO₂ and daily mortality, which were indexed in PubMed and Embase up to June 2021. We calculated random-effects estimates by different continents and globally, and tested for heterogeneity and publication bias.

RESULTS

We included 87 articles in our quantitative analysis. NO₂ and all-cause as well as cause-specific mortality were positively associated in the main analysis. For all-cause mortality, a 10 ppb increase in NO₂ was associated with a 1.58% (95%CI 1.28%-1.88%, I² = 96.3%, Eggers' test p < 0.01, N = 57) increase in the risk of death. For cause-specific mortality, a 10 ppb increase in NO₂ was associated with a 1.72% (95%CI 1.41%-2.04%, I² = 87.4%, Eggers' test p < 0.01, N = 42) increase in cardiovascular mortality and a 2.05% (95%CI 1.52%-2.59%, I² = 78.5%, Eggers' test p < 0.01, N = 38) increase in respiratory mortality. In the sensitivity analysis, the meta-estimates for all-cause mortality, cardiovascular and respiratory mortality were nearly identical. The heterogeneity would decline to varying degrees through regional and study-design stratification.

CONCLUSIONS

This study provides evidence of an association between short-term exposure to NO₂, a proxy for traffic-sourced air pollutants, and all-cause, cardiovascular and respiratory mortality.

PM2.5 concentration prediction during COVID-19 lockdown over Kolkata metropolitan city, India using MLR and ANN models

Kolkata is the third densely populated city of India and Kolkata stands in the World's 25 most polluted cities along with 10 worse polluted cities in India. The relevant study claims that due to the imposition of lockdown during COVID-19 pandemic, the atmospheric pollution level has been significantly reduced over the metropolitan city Kolkata like other cities of the world. The main objective of this study is to predict the concentration of PM_2.5 using multiple linear regression (MLR) and artificial neural network (ANN) models and similarly, to compare the accuracy level of two models. The concentration of PM_2.5 data has been obtained from state pollution control board, Govt. of West Bengal and daily meteorological data have been collected from the world weather website. The results show that non-linear artificial neural network model is more rational compared with multiple linear regression model due to its high precision and accuracy level (in respect to RMSE, MAE and R²). In this research artificial neural network (ANN) model exhibited higher accuracy during the training and testing phases (root mean square error (RMSE), mean absolute error (MAE) and R² indicate 3.74, 1.14 and 0.91 respectively in training phase and 2.55, 4.32 and 0.69 in testing phase respectively). This model (ANN)) can be applied to predict the concentration of PM_2.5 during the execution of urban air quality management plan.