Abstract
Introduction:
Exposure to air pollution has been linked to the mortality of heart failure. In this study, we sought to update the existing systematic review and meta-analysis, published in 2013, to further assess the association between air pollution and acute decompensated heart failure, including hospitalization and heart failure mortality.
Methods:
PubMed, Web of Science, EMBASE, and OVID databases were systematically searched till April 2022. We enrolled the studies regarding air pollution exposure and heart failure and extracted the original data to combine and obtain an overall risk estimate for each pollutant.
Results:
We analyzed 51 studies and 7,555,442 patients. Our results indicated that heart failure hospitalization or death was associated with increases in carbon monoxide (3.46% per 1 part per million; 95% CI 1.0233–1.046, P < 0.001), sulfur dioxide (2.20% per 10 parts per billion; 95% CI 1.0106–1.0335, P < 0.001), nitrogen dioxide (2.07% per 10 parts per billion; 95% CI 1.0106–1.0335, P < 0.001), and ozone (0.95% per 10 parts per billion; 95% CI 1.0024–1.0166, P < 0.001) concentrations. Increases in particulate matter concentration were related to heart failure hospitalization or death (PM2.5 1.29% per 10 μg/m3, 95% CI 1.0093–1.0165, P < 0.001; PM10 1.30% per 10 μg/m3, 95% CI 1.0102–1.0157, P < 0.001).
Conclusion:
The increase in the concentration of all pollutants, including gases (carbon monoxide, sulfur dioxide, nitrogen dioxide, ozone) and particulate matter [(PM2.5), (PM10)], is positively correlated with hospitalization rates and mortality of heart failure.
Systematic review registration:
https://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42021256241.
Introduction
Heart failure (HF) is a group of clinical syndromes characterized by dyspnea or fatigue caused by ventricular filling or ejection disorders or both (1), which is the ultimate destination of all cardiovascular diseases and is the leading cause of global morbidity and mortality (2). The incidence of HF is rising steadily worldwide, but the prognosis is still poor (3). More than 64 million people are suffering from HF in the world, with an estimated prevalence of 1–2% among adults in developed countries (4).
Air pollution is a worldwide problem affecting human health (5). It has been recognized as an independently detrimental factor to respiratory and cardiovascular diseases (6). According to the report of the World Health Organization, almost 7 million people die due to air pollution every year, indicating air pollution to be the world's largest environmental health risk (7). Particulate matters (PM) and gases are the main components of air pollution that have been reported to induce stroke (8), myocardial infarction (9), HF, atrial fibrillation, and sudden cardiac death (10) in multiple populations. Air pollution-induced cardiovascular events have received more attention than other related toxicity, such as pulmonary diseases (11). Epidemiological studies suggest an association between short-term fluctuations in ambient air pollution and the risk of hospitalization for acute cardiovascular events, including acute decompensated HF (12). In 2013, Shah et al. (13) reviewed and meta-analyzed the studies on air pollution and HF published from 1948 to 2012. The results showed that the increase in the concentration of all pollutants, except ozone, is positively related to the incidence and mortality of HF patients. However, all studies, but one in this meta-analysis, were conducted in developed countries. Moreover, over 22 studies, including 4 million participants and 14 studies in developing countries, focusing on the relationship between air pollution and HF have been published after Shah's study (13).
Thus, we updated the systematic review and meta-analysis to reassess the relationship between air pollutants and HF outcomes (hospitalization and death). This systematic review and meta-analysis were performed according to the guidelines of the Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA) criteria (Supplementary Table S1). This meta-analysis has been registered with PROSPERO (ID: CRD42021256241).
Methods
Search strategy
Literature was searched in four databases, including PubMed, Web of Science, EMBASE, and OVID databases, from their inception until 30 April 2022. The following keywords were included for the search: “heart failure,” “air pollution,” “particulate matter,” “ozone,” “carbon monoxide,” “sulfur dioxide,” “nitrogen dioxide,” and “carbon dioxide.”
First, we performed a preliminary screening of the titles and abstracts. Then, we further evaluated the full texts of potentially eligible studies. We manually searched the reference lists of all the included studies. Literature selection and study quality assessment were completed by two independent authors (Y.Y. and Y.G.), and conflicts between the two authors were resolved after a discussion with an arbitrator (Y.P.).
Inclusion and exclusion criteria
Articles that met the following criteria were included: (1) original human epidemiological studies reported associations between air pollution exposure and HF hospitalization rate or mortality up to and including lag (day) 7, confidence interval (95%CI); (2) case-crossover, time series (both assessed by generalized linear regression models); (3) focused on exposure of outdoor (ambient) air pollution, but not of indoor air pollution; and (4) published in English. Studies were excluded if they were (1) animal or experimental studies; (2) case reports, comments, or reviews; or (3) studies that reported an unclear increment of air pollutant concentrations.
Data extraction
Data were extracted from all the selected studies, including (1) study characteristics (first author, published year, study location, and period); (2) study populations (sample size and range of age); (3) outcomes [hospitalization rates and mortality, lag (days)]; (4) air pollution measurement method and increment of air pollution used in effect evaluation, including per interquartile range (IQR), standard deviation (SD), or per 10 μg/m3; and (5) effect estimates of the association between air pollution and HF (OR, RR, with 95% CI). The effect estimates of the single-pollutant model were extracted.
Quality assessment
The Newcastle–Ottawa Quality Assessment Scale (NOS) checklist was applied to assess the quality of the studies enrolled in this study. Two authors (Y.Y. and Y.G.) worked independently, and inconsistencies in the quality assessment were resolved through discussion with an arbitrator (Y.P.). The score ranges from 0 to 9 points. A higher score indicates a higher study quality. A study with a score of ≥7 was regarded as high quality; otherwise, the study was considered low quality (14).
Data synthesis
We synthesized the data extracted from the included studies by the following formula. The relative risk (RR) is combined into a standardized pollutant concentration increment before the meta-analysis as follows: 10 μg/m3 for PM2.5 and PM10; 10 parts per billion for nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3); and 1 part per million for carbon monoxide (CO).
Many studies provide multiple estimates of a single lag, such as lag 0 or lag 1, which are merged separately. The shortest lag is used to evaluate the overall risk estimate. A few studies provided only cumulative lag (e.g., lag 0–1 or lag 0–2), which is not suitable for merging in a single lag analysis but is used to determine the overall risk estimates.
The original study data were further stratified by study design (case-crossover vs. time series), age (all ages vs. 60 years), and outcome (hospitalization vs. mortality). Assuming the prevalence of air pollution exposure is 100%, we used our overall risk estimate and formula to calculate the population-attributable risk for each pollutant as follows:
Statistical analysis
Stata version 15.0 (StataCorp., College Station, TX, USA) software was used to calculate the impact of each pollutant on the HF hospitalization rate or mortality and the 95% CI. The significance of the pooled OR and RR was determined by the Z test (15), and a p-value of < 0.05 was considered statistically significant.
The heterogeneity test was performed by the standard I2 test. If I2 ≥ 50% (P ≤ 0.10), it was considered that there was heterogeneity among the studies, and then, the Dersimonian–Laird random-effects model was used to combine the values. If I2 < 50% (P > 0.10), the research was regarded as homogeneous, and then, the Mantel–Haenszel fixed-effects model was performed to merge values. We expected that the heterogeneity between studies was due to different study designs, analysis methods, different lagging exposures, and geographic and population differences.
The sensitivity analysis was performed by eliminating individual studies one by one to evaluate the stability of the results. The funnel plot and Egger linear regression were used to test for publication bias, and if there was publication bias, the cut-and-compensation method was used to correct the bias.
Results
Search process and study characteristics
As can be seen in Figure 1, there were 3,110 studies enrolled in the initial search. After removing duplicates and screening titles, 549 studies underwent in-depth review, and 51 studies matched the inclusion criteria. In all, 30 studies used a time series design (16–45), 20 studies used a case-crossover design (46–65), and 1 used both study designs (66). Of the 51 studies, 19 were from developing countries and 32 from developed countries. Specifically, 21 are in the Americas (17 in the United States, 2 in Canada, and 2 in Brazil), 20 in Asia (16 in China, 1 in Thailand, 1 in South Korea, and 2 in Japan), 7 in Europe (4 in Italy, 2 in the United Kingdom, and 1 in the Netherlands), and 3 in Oceania (2 in Australia and 1 in New Zealand). The general characteristics of the studies included in the meta-analysis and details of the quality assessment are displayed in Table 1 and Supplementary Table S2, respectively.
Figure 1
Table 1
| References | Location | Published | Period | Study design | Data source | Population | Number of events | Outcome |
|---|---|---|---|---|---|---|---|---|
| Schwartz et al. (17) | USA | 1995 | 1986–1989 | Time-series | Medicare data | ≥65 years | 38,862 | HA |
| Poloniecki et al. (18) | UK | 1997 | 1987–1994 | Time-series | Hospital episode records | ≥65 years | 62,853 | HA |
| Morris et al. (19) | USA | 1998 | 1986–1989 | Time-series | Medicare data | ≥65 years | 49,640 | HA |
| Wong et al. (20) | Hong Kong | 1999 | 1994–1995 | Time-series | Hospital data admission registry | All | NR | HA |
| Wong et al. (21) | Hong Kong | 1999 | 1995–1997 | Time-series | Hospital authority data | ≥65 years | NR | HA |
| Hoek et al. (22) | Netherlands | 2001 | 1986–1994 | Time-series | Death certificates | All | 45,333 | Mortality |
| Kwon et al. (66) | South Korea | 2001 | 1994–1998 | Case-crossover and time-series | Mortality records | ≥65 years | 1,807 | Mortality |
| Ye et al. (23) | Japan | 2001 | 1980–1995 | Time-series | Ministry of Health | ≥65 years | 4,469 | HA |
| McGowan et al. (24) | New Zealand | 2002 | 1988–1998 | Time-series | Hospital data admission registry | All | 5,146 | HA |
| Goldberg et al. (25) | Canada | 2003 | 1984–1993 | Time-series | Billing and prescription data | ≥65 years | 16,794 | Mortality |
| Koken et al. (26) | USA | 2003 | 1993–1997 | Time-series | Agency for Healthcare Research and Quality | All | 1,860 | HA |
| Bateson et al. (46) | USA | 2004 | 1988–1991 | Case-crossover | Medicare and medicaid data | All | 26,923 | Mortality |
| Metzger et al. (27) | USA | 2004 | 1993–2000 | Time-series | Billing data | All | 20,073 | HA |
| Wellenius et al. (47) | USA | 2005 | 1987–1999 | Case-crossover | Medicare and medicaid data | ≥65 years | 55,019 | HA |
| Martins et al. (28) | Brazil | 2006 | 1996–2001 | Time-series | Department of Data Analysis of the Unified Health System | ≥65 years | 24,476 | HA |
| Dominici et al. (29) | USA | 2006 | 1999–2002 | Time-series | Medicare data | ≥65 years | 986,392 | HA |
| Wellenius et al. (48) | USA | 2006 | 1986–1999 | Case-crossover | Medicare and Medicaid data | All | 292,918 | HA |
| Barnett et al. (49) | Australia and New Zealand | 2006 | 1998–2001 | Case-crossover | Government health departments (Australia) and Ministry of Health (NZ) | ≥65 years | NR | HA |
| Lee et al. (30) | Taiwan | 2008 | 1996–2004 | Time-series | National Health Institute registry | All | 13,475 | HA |
| Peel et al. (50) | USA | 2007 | 1993–2000 | Case-crossover | Billing records | >64 years | 20,073 | HA |
| Forastiere et al. (51) | Italy | 2008 | 1997–2004 | Case-crossover | Regional registries of cause of death | All | 9,569 | Mortality |
| Yang et al. (52) | Taiwan | 2008 | 1996–2004 | Case-crossover | National Health Institute registry | All | 24,240 | HA |
| Bell et al. (31) | USA | 2009 | 1999–2005 | Time-series | Medicare data | All | 1,142,928 | HA |
| Haley et al. (53) | USA | 2009 | 2001–2005 | Case-crossover | NYSDOH registry | All | 170,502 | HA |
| Stieb et al. (32) | Canada | 2009 | 1999–2000 | Time-series | Emergency department registry | All | 32,313 | HA |
| Ueda et al. (33) | Japan | 2009 | 2002–2004 | Time-series | Ministry of Health | ≥65 years | 17,548 | Mortality |
| Zanobetti et al. (34) | USA | 2009 | 2000–2003 | Time-series | Medicare data | All | 238,587 | HA |
| Colais et al. (54) | Italy | 2012 | 2001–2005 | Case-crossover | Hospital discharge registry | ≥65 years | 55,339 | HA |
| Belleudi et al. (55) | Italy | 2010 | 2001–2005 | Case-crossover | Hospital discharge registry | ≥65 years | 17,561 | HA |
| Hsieh et al. (56) | China | 2013 | 2006–2010 | case-crossover | The National Health Insurance | All | 20,776 | HA |
| Yang et al. (35) | China | 2014 | 2008–2012 | Time-series | Guangzhou Emergency Center | All | 3,375 | HA |
| Milojevic et al. (57) | UK | 2014 | 2003–2009 | Case-crossover | the Myocardial Ischaemia National Audit Project (MINAP) database | All | 37,033 | Mortality |
| Chen et al. (58) | China | 2015 | 2006–2010 | Case-crossover | The National Health Insurance | All | 64,002 | HA |
| Weber et al. (59) | USA | 2016 | 2004–2006 | Case-crossover | The New York State Planning and Research Cooperative System (SPARCS) | ≥35 | 342,411 | HA |
| Vaduganathan et al. (60) | Italy | 2016 | 2004–2007 | Case-crossover | The Hospital Discharge Database | All | 6,000 | HA |
| Dabass et al. (61) | USA | 2016 | 1999–2011 | Case-crossover | The Pennsylvania Department of Health Vital Statistics Division | All | 4,358 | Mortality |
| Xu et al. (36) | China | 2017 | 2013 | Time-series | Emergency department registry | All | 56,221 | HA |
| Liu et al. (62) | China | 2018 | 2014–2015 | Case-crossover | the National Hospital Performance Evaluation Project of the National Healthcare Data Center of China | ≥18 | 105,501 | HA |
| Hsu et al. (37) | USA | 2017 | 1991–2006 | Time-series | The NYS Department of Health's Statewide Planning and Research Cooperative System | All | 999,264 | HA |
| Li et al. (38) | China | 2018 | 2010–2012 | Time-series | Beijing Medical Claim Data for Employees | ≥18 | 15,256 | HA |
| Huynh et al. (39) | Australian | 2018 | 2009–2012 | Time-series | The Clinical Informatics and Business Intelligence Unit of the Department of Health and Human Services of Tasmania. | All | 1,246 | HA |
| Li et al. (63) | China | 2018 | 2013–2017 | Case-crossover | The Beijing Municipal Commission of Health and Family Planning Information Center | >18 | 58,393 | HA |
| Zhang et al. (64) | USA | 2018 | 2014–2016 | Case-crossover | The inpatient SPARCS database | All | 1,917,823 | HA |
| Pothirat et al. (40) | Thailand | 2019 | 2016–2017 | Time-series | Hospital registry records | All | 859 | HA |
| Amsalu et al. (16) | China | 2019 | 2013–2017 | Time-series | the Beijing Public Health Information Center | ≥18 | 58,432 | HA |
| Tian et al. (41) | China | 2019 | 2014–2017 | Time-series | The Urban Employee Basic Medical Insurance (UEBMI) | ≥18 | 4,383 | HA |
| Wu et al. (42) | China | 2019 | 2014–2017 | time-series | China Center for Disease Control and Prevention | All | 1,782 | Mortality |
| Feng et al. (43) | China | 2019 | 2013 | time-series | The Beijing Medical Research Data | All | 56,212 | HA |
| Qiu et al. (65) | USA | 2020 | 2000–2012 | Case-crossover | Center for Medicare and Medicaid Services | >64 | 204,774 | HA |
| Gu et al. (44) | China | 2020 | 2013–2017 | time-series | Hospital Quality Monitoring System of China | All | 87,052 | HA |
| Pamplona et al. (45) | Brazil | 2020 | 2000–2013. | time-series | The Unified Health System database (DA TASUS) | >60 | 135,589 | HA |
Contextual details of studies included in the meta-analysis.
HA, hospital admissions; NR, not report.
Exposure to air pollution and the rate of HF hospitalization or mortality
As can be seen in Figure 2, HF hospitalization or mortality was positively correlated with all air pollution, which was consistent with the results of Shah's (13) meta-analysis, except O3. HF hospitalization or mortality was increased by 3.46% (95%CI 1.0233–1.046, P < 0.001) per increase of 1 part per million of CO. Each 10 ppb increase in SO2, O3, and NO2, respectively, was associated with 2.20% (95%CI 1.0106–1.0335, P < 0.001), 0.24% (95% CI 1.0024–1.0166, P < 0.001), and 2.07% (95% CI 1.0106–1.0335, P < 0.001) increases in the risk of HF-related hospitalization or mortality. PM2.5 (1.29%, 95%CI 1.0093–1.0165, P < 0.001) and PM10 (1.30%, 95%CI 1.0102–1.0157, P < 0.001) were found to be positively associated with HF hospitalization or mortality (Supplementary Figures S1–S6). In addition, we conducted a subgroup analysis based on study design and age (Figure 3). There was no change in effect direction across all pollutants in these analyses (Supplementary Table S4).
Figure 2
Figure 3
Sensitivity analysis and publication bias
Sensitivity analysis showed that the relationship between PM2.5 exposure and HF hospitalization or mortality was influenced by Amsalu et al. (16) (Supplementary Figure S13). The pooled standardized RR was changed to 1.0142 (95% CI: 1.0102–1.0182) after removing Amsalu's study. The association between PM10 exposure and HF hospitalization or mortality was affected by Morris et al. (19) (Supplementary Figure S14). After excluding, the pooled RR was changed to 1.0186 (1.0133–1.024). The sensitivity bias was also found in CO [Bell et al.'s (31), Supplementary Figure S16] and NO2 [Ye et al.'s (23), Supplementary Figure S15]. We recalculated the pooled RR and 95% CI after removing those studies (Supplementary Table S3). The sensitivity analysis of SO2 and O3 did not change by excluding each study, suggesting that the results were stable (Supplementary Figures S17, S18).
There was publication bias on studies of PM2.5, CO, and NO2 (Supplementary Figures S7, S10, S11), since the p-value of Begg's test was < 0.05. Other pollutants have no substantial publication bias (Egger's test, p > 0.05, Supplementary Figures 8, 9, 12). After using trimming and filling methods to adjust asymmetry, the effect value was slightly lower than that before correction, but the direction of effect estimation does not change (Table 2).
Table 2
| Gaseous pollutants | Particulate matter | |||||
|---|---|---|---|---|---|---|
| Carbon monoxide (ppm) | Nitrogen dioxide (ppb) | Sulfur dioxide (ppb) | Ozone (ppb) | PM2.5 (μg/m3) | PM10 (μg/m3) | |
| Increment | 1 ppm | 10 ppb | 10 ppb | 10 ppb | 10 μg/m3 | 10 μg/m3 |
| Median pollutant concentration (IQR) | 1.14 (0.89–1.23) | 16.45 (14.09–24.07) | 2.24 (1.12–5.44) | 36.28 (30.50–39.66) | 47.15 (12.99–60.58) | 64.86 (39.49–110.83) |
| Range (min–max) | 0.20–1.49 | 12.55–41.37 | 0.98–11.79 | 20.2–43.60 | 2.9–102.1 | 36.47–131.50 |
| Number of studies | 20 | 18 | 19 | 19 | 28 | 27 |
| Heterogeneity, I2 | 0.89 | 0.92 | 0.83 | 0.87 | 0.88 | 0.91 |
| Population-attributable risk (PAR), % (95% CI) | 3.34 (2.28–4.40) | 2.63 (1.36–2.70) | 2.15 (1.05–3.24) | 0.94 (0.24–1.63) | 1.27 (0.92–1.62) | 1.28 (1.01–1.55) |
| Publication bias | ||||||
| Egger regression test, p-value | 0.001 (< 0.05) | 0.002 (< 0.05) | 0.132 | 0.143 | 0.000 (< 0.05) | 0.078 |
| Non-adjusted RR (95% CI)§ | 1.035 (1.023–1.046) | 1.021 (1.014–1.028) | 1.022 (1.011–1.034) | 1.010 (1.002 −1.017) | 1.013 (1.009–1.017) | 1.013 (1.010–1.016) |
| Adjusted RR (95% CI)¶ | 1.018 (1.006–1.030) | 1.008 (1.000–1.015) | / | / | 1.008 (1.004–1.012) | / |
| Number of studies adjusted | 8 | 8 | / | / | 8 | / |
Heterogeneity, population-attributable risk, and assessment for publication bias stratified by gaseous and particulate air pollutants.
ppb, parts per billion; PM, particulate matter; PAR, population-attributable risk; IQR, interquartile range. Median pollutant concentration (IQR) derived from the average daily pollutant concentrations reported per study. Range of the average pollutant concentrations across the studies from minimum to maximum. PAR reported per ten-unit increment in air pollutant concentration, except for CO where per one-unit increment. Calculated as PAR = X (RR – 1)/[X (RR – 1) + 1], where X indicates prevalence exposure (assumed to be 100% here).
Risk estimates derived from the pooled analysis of studies.
Risk estimates after adjustment for publication bias using the trim and fill method.
Discussion
This updated systematic review and meta-analysis enrolled 51 human epidemiological studies conducted in 11 countries. We found that the increase in four gas pollutants (CO, NO2, SO2, and O3) and two particulate matters (PM2.5 and PM10) was positively correlated with the HF hospitalization or mortality rate, regardless of the overall effect or lag effect. These results suggest that air pollution exposure was the risk factor for hospitalization or death in patients with HF. Among all the included pollutants, CO exposure had the greatest impact on the risk of hospitalization or death in patients with HF, while O3 exposure seemed to be the weakest one.
In 1995, Schwartz et al. (17) conducted the first human epidemiological study on air pollution exposure and the HF hospitalization rate in the United States and found that PM10 and CO, but not SO2 and O3, were positively correlated with HF. Since then, many studies explored the relationship between air pollution and HF. These studies showed inconsistent results. In 2013, a meta-analysis by Shah (13) showed that air pollution had a close temporal association with HF hospitalization and mortality. However, only one of the 35 studies was conducted in developing countries, making it difficult to assess the impact in developing countries. In Shah's study, exposures to PM2.5, PM10, SO2, NO2, and CO were positively correlated with HF hospitalization and mortality. However, exposure to ozone did not present this impact. Moreover, there were few studies on air pollution and HF in developing countries at that time. Only five studies in Shah's meta-analysis were from developing countries. In our meta-analysis, we updated the literature to 30 April 2022 and added 22 studies, including 14 studies from developing countries. We enrolled 7,555,442 cases, which was much more than the 3,374,700 participants in Shah's study (13). These made our evidentiary weight stronger.
Our results showed a high degree of heterogeneity, which might be due to the different pollutant detection methods and ethnicity and populations in different studies. Therefore, we aggregated estimates of effects based on standardized increments, which could help reduce heterogeneity. In a sensitivity analysis, we found that the direction remained unchanged and the correlation was positive after removing some studies.
Studies showed that PM2.5 is a critical factor for overall HF progression by regulating lung oxidative stress, inflammation, and RV remodeling (67). PM2.5 exposure triggered oxidative stress in the heart and systemic inflammation (68) and inhibited vascular endothelial repair capacity (69). It was reported that air pollution aggravated aortic endothelial dysfunction in HF rats (69). In addition, CO aggressively binds to hemoglobin with an affinity 200 times greater than that of oxygen, resulting in a reduced fraction of oxygenated hemoglobin in the bloodstream (70). This weakens the blood's capacity to deliver oxygen to the tissues (71). Thus, chronic exposure to high levels of CO will induce tissue hypoxia. Some studies indicated that exposure to SO2 was sufficient to disrupt excitatory synaptic inputs to cardiac vagal neurons, the reflexive control part of heart rate, and induce tachycardia (72). Taken together, many studies reported the relationship between air pollution and HF. However, the molecular mechanism is still ambiguous.
There were several limitations to our study. First, part of the data came from routine administrative sources, which might introduce bias due to coding errors and misclassification. Second, there were three kinds of pollutants (PM2.5, SO2, and NO2) that had publication bias despite the direction of the adjusted overall effect remaining unchanged. Third, we found significant heterogeneity across all pollutants, which indicated the differences in population demographics, sample size, and patient characteristics.
Heart failure is a common and fatal disease. We found that exposure to all pollutants, including ozone, was positively associated with HF hospitalization and mortality. However, due to the limited number of studies on short-term effects, caution should still be taken in interpreting our results.
Statements
Data availability statement
The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors.
Author contributions
X-hC, PY, and Y-hP designed the study, coordinated the study, and directed its implementation. Y-sY and Y-yG collected data and conducted the follow-up work. Y-sY and J-fZ wrote the manuscript. All authors have read and approved the final manuscript.
Funding
This study was funded by the National Natural Science Foundation of China under grant no. 81700243, the subject of Jiangsu Province Hospital of Traditional Chinese Medicine under grant no. Y2020CX42, and the graduate training innovation project of Jiangsu Province (grant nos. SJCX21_0780 and KYCX21_1714).
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2022.948765/full#supplementary-material
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Summary
Keywords
heart failure, air pollution, particulate matter, gas pollutant, meta-analysis
Citation
Yang Y, Pei Y, Gu Y, Zhu J, Yu P and Chen X (2023) Association between short-term exposure to ambient air pollution and heart failure: An updated systematic review and meta-analysis of more than 7 million participants. Front. Public Health 10:948765. doi: 10.3389/fpubh.2022.948765
Received
20 May 2022
Accepted
29 December 2022
Published
23 January 2023
Volume
10 - 2022
Edited by
Mariana Matera Veras, University of São Paulo, Brazil
Reviewed by
Asaad Sharhani, Ahvaz Jundishapur University of Medical Sciences, Iran; Peng Yin, Chinese Center for Disease Control and Prevention, China
Updates
Copyright
© 2023 Yang, Pei, Gu, Zhu, Yu and Chen.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Peng Yu ✉ yupengdoctor@126.comXiao-hu Chen ✉ chenxhdoctor1962@163.com
†These authors have contributed equally to this work
This article was submitted to Environmental Health and Exposome, a section of the journal Frontiers in Public Health
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