Skip to main content

Effect of ambient fine particulates (PM2.5) on hospital admissions for respiratory and cardiovascular diseases in Wuhan, China

Abstract

Background

Positive associations between ambient PM2.5 and cardiorespiratory disease have been well demonstrated during the past decade. However, few studies have examined the adverse effects of PM2.5 based on an entire population of a megalopolis. In addition, most studies in China have used averaged data, which results in variations between monitoring and personal exposure values, creating an inherent and unavoidable type of measurement error.

Methods

This study was conducted in Wuhan, a megacity in central China with about 10.9 million people. Daily hospital admission records, from October 2016 to December 2018, were obtained from the Wuhan Information center of Health and Family Planning, which administrates all hospitals in Wuhan. Daily air pollution concentrations and weather variables in Wuhan during the study period were collected. We developed a land use regression model (LUR) to assess individual PM2.5 exposure. Time-stratified case-crossover design and conditional logistic regression models were adopted to estimate cardiorespiratory hospitalization risks associated with short-term exposure to PM2.5. We also conducted stratification analyses by age, sex, and season.

Results

A total of 2,806,115 hospital admissions records were collected during the study period, from which we identified 332,090 cardiovascular disease admissions and 159,365 respiratory disease admissions. Short-term exposure to PM2.5 was associated with an increased risk of a cardiorespiratory hospital admission. A 10 μg/m3 increase in PM2.5 (lag0–2 days) was associated with an increase in hospital admissions of 1.23% (95% CI 1.01–1.45%) and 1.95% (95% CI 1.63–2.27%) for cardiovascular and respiratory diseases, respectively. The elderly were at higher PM-induced risk. The associations appeared to be more evident in the cold season than in the warm season.

Conclusions

This study contributes evidence of short-term effects of PM2.5 on cardiorespiratory hospital admissions, which may be helpful for air pollution control and disease prevention in Wuhan.

Background

Air pollution has remained an important global health issue [1]. Numerous epidemiological studies have proven that PM2.5, particulate matter with an aerodynamic diameter less than 2.5 μm, is a critical contributor that leads to increased mortality and morbidity [2, 3]. According to the analysis of the Global Burden of Diseases Study, approximately 2.94 million deaths and 10.5 million disability-adjusted life years (DALYs) globally are attributable to ambient particulate matter pollution, making it the eighth leading risk for death [4].

Previous studies have provided strong evidence of the harmful effects of PM2.5 on cardiorespiratory diseases [5, 6]. Although several large-scale studies, conducted in western developed countries [7, 8], have examined the associations between air pollution and cardiorespiratory hospital admissions, these results may not be applicable to developing countries due to local climate conditions, PM chemical components and population susceptibility. In China, several large-scale analyses have been conducted [9, 10]. However, the populations of these studies were obtained by specific sampling methods and may not represent the entire population. Some single-center epidemiological studies have been conducted in several large cities in China [11, 12], but the hospital admissions data of these studies were collected from a limited number of hospitals, which may introduce selection bias. Therefore, studies that examine the association between PM2.5 and cardiorespiratory hospital admissions based on all citizens of a large city are needed to better understand the real impact of ambient fine particulate matter in China.

Exposure assessment methods are crucial for epidemiological studies. Commonly used air pollution assessment methods include monitoring data derived from fixed stations, Dispersion Models (DM), atmospheric Chemical Transport Models (CTMs), and Land Use Regression (LUR) [13, 14]. Most studies have estimated individual exposure to air pollution using the ambient concentrations derived from fixed stations, which lacks spatial and temporal resolution. Conventional DM and CTMs require various data with high precision, which makes the simulation process complicated and high-cost [15]. Compared with the above methods, LUR models, which use land use, geographic, and traffic characteristics to explain spatial variations of air pollution concentrations, have proven to be cost-effective methods of air pollution exposure assessment. With the development of geographic information system (GIS) technology, LUR has achieved great success, mainly in Europe and North America [16, 17]. In China, however, only a few studies have applied LUR models in epidemiological research.

Therefore, this study was conducted based on the admission data of all hospitals in Wuhan, from October 2016 to December 2018. Considering Wuhan’s universal access to hospital healthcare and the availability of these records, the impact of PM2.5 on the entire population can be assessed. Furthermore, LUR models were developed to better capture individual PM2.5 exposure. The objective of this analysis was to examine the association between PM2.5 and cardiorespiratory hospital admissions.

Methods

Study area

With a land area of 8569.15 km2 and a population of about 10.9 million (Wuhan Statistical Yearbook-2018), Wuhan (29.58°N to 31.22°N and 113.41°E to 115.05°E) is the capital city of Hubei Province and a megacity in central China. Due to its subtropical, monsoon climate, Wuhan has typical weather featured in distinct seasons and abundant rainfall. The major sources of air pollution in the city are biomass and coal combustion, steel manufacture, smelting, and vehicle emissions [18].

Case ascertainment

Daily hospitalization records were obtained from the Wuhan Information center of Health and Family Planning (http://wjw.wuhan.gov.cn/) between Oct 1, 2016 and Dec 31, 2018. The Wuhan Information center of Health and Family Planning is a hospital authority within the municipal Bureau of Health, to which all the hospitals in Wuhan have to report their information of hospital infrastructure, medical service and management. All of the public hospitals (university affiliated hospitals, regional hospitals, provincial hospitals and so on), a total of 59 municipal hospitals, were included in this study. From each record, we extracted de-identified patient age, sex, home address, and primary diagnosis. The diagnoses were made by licensed specialized physicians according to current clinical guidelines. Cardiorespiratory hospital admissions in the present study were identified based on the primary diagnosis according to the International Classification of Diseases, 10th Revision (ICD-10): total cardiovascular disease (CVD, I00–I99), hypertension (I10–I15), coronary heart disease (CHD, I20–I25), stroke (I60–I69), total respiratory disease (J00–J99), and chronic obstructive pulmonary disease (COPD, J41–J44). A total of 2,806,115 hospital admission records were collected during the study period, from which we identified 332,090 for total cardiovascular diseases and 159,365 for total respiratory diseases. Specific inclusion and exclusion criteria are outlined in Additional file 1: Figure S1. The present study is considered exempt from institutional review board approval since the data used were collected for administrative purposes without any personal identifiers.

Air pollutant data

During the study period, the air pollution data were collected from the Wuhan Environmental Protection Bureau (http://hbj.wuhan.gov.cn/), which has established 20 ambient air quality monitoring stations in the 13 districts of Wuhan city. To calculate daily 24-h concentrations, ≥ 75% of the 1-h values must have been available on that particular day; To calculate the annual concentration, there must be at least 324 daily values available. Four stations were excluded because the above criteria were not met. Finally, daily 24-h average concentration data for PM2.5 (unit, μg/m3), sulfur dioxide (SO2) (unit, μg/m3), nitrogen dioxide (NO2) (unit, μg/m3), and carbon monoxide (CO) (unit, mg/m3) during the study period were collected from 16 air quality monitoring stations (Fig. 1). Daily meteorological data including mean temperature (°C) and relative humidity (%) during the study period were collected from the China Meteorological Data Network (http://data.cma.cn/).

Fig. 1
figure1

Study area and the results of LUR models. a Geographical location of Wuhan in China. b Spatial distribution of mean PM2.5 estimations across Wuhan city from October 1, 2016 to December 31, 2018. c Scatter plot correlating the measured and predicted PM2.5 values from 16 monitoring stations

LUR model

In this study, LUR models were constructed by combining measurements of PM2.5 from fixed-site monitors with a range of geographic predictors. The detailed model-building process is described in the supplementary materials (Additional file 2: Table S1).

Final models were represented with 1 km spatial resolution. Kriging interpolation was used to transform predicted PM2.5 data from monitors into concentration maps (Fig. 1). We then extrapolated annual-mean PM2.5 concentrations from the LUR model to daily levels, following the method described in previous studies [19]. Briefly, we geocoded individual addresses and assigned the annual average PM2.5 concentrations from the LUR models to each individual. Daily PM2.5 concentrations assigned to each subject were adjusted by the ratio of daily-specific PM2.5 concentrations to the estimated annual average PM2.5 concentrations at the nearest monitor.

Statistical design

The case-crossover (CCO) design was first proposed by Maclure [20] to study transient effects on the risk of acute events. As each subject serves as his or her own control, this type of study controls for the influence of self-confounding variables that remain constant.

In this study, we performed a time-stratified case-crossover study design to evaluate associations between short-term PM2.5 exposures and cardiorespiratory hospital admissions in Wuhan. The case day was defined as the day of hospital admission and the control days were identified by matching the day of the week (DOW) within the same year and month. By virtue of this design, the potential confounding effects of long-term trends and seasonality can be largely eliminated.

Analytic model

We used a conditional logistic regression (CLR) model to obtain estimates of the odds ratios (ORs) and 95% confidence intervals (CIs) for the effect of PM2.5 exposures on cardiorespiratory hospital admissions. To control covariates, we applied a natural cubic spline (NCS) function with 3 degrees of freedom (df) for both temperature and humidity to eliminate nonlinear confounding effects.

Considering that a single-day lag model might underestimate the association [21], the cumulative effects were estimated using different lag structures, including both single-day (lag0 to lag6) and several days’ moving averages (lag0–1 to lag0–6). Linearity for exposure–response relationship between PM2.5 and cardiorespiratory admissions was further checked by smoothing the PM2.5 terms using the NCS function (with 3 df).

Furthermore, we conducted stratification analyses by age (< 45, 45–54, 55–64, 65–74, and > 74 years), sex (male and female), and season (warm: April to September; cold: October to March of the next year) to explore the potential effect modifiers on the associations between PM2.5 and cause-specific hospital admissions deriving from the single pollutant model. The Z-test was applied to test the statistical significance of differences by gender or season [22].

Sensitivity analysis

To check the robustness of our main results, we conducted several sensitivity analyses by: (1) fitting two-pollutant models by additionally adjusting for air pollutants (NO2, SO2, CO) collected from the monitoring stations closest to patients’ homes; (2) conducting a symmetric CCO design (days: ± 7, 14) [23]; and (3) changing the degrees of freedom of meteorological variables (2–4 df).

All of the analyses were conducted using R, version 3.5.1. We used the “survival” package for CLR analysis. Two-sided tests were conducted, and effects with p < 0.05 were considered to be statistically significant. All results of model estimates are reported as ORs and 95% confidence intervals (CIs) associated with each 10 μg/m3 increase in PM2.5 concentrations.

Results

A total of 491,455 hospital admissions, of which 332,090 were for total cardiovascular diseases and 159,365 for total respiratory diseases, were recorded from October 1, 2016 to December 31, 2018 in Wuhan (Table 1). The mean age of cardiovascular disease admissions was 63.69 years (SD = 17.58) and that of respiratory diseases admissions was 68.17 years (SD = 10.42). For both cardiovascular and respiratory admissions, older people over 74 years old accounted for the largest proportion, and the number of males was higher than that of females during the study period.

Table 1 Basic characteristics of cardiovascular disease and respiratory disease admissions in Wuhan (Oct 1, 2016 to Dec 31, 2018)

During the study period, the mean daily average concentrations were 48.2 μg/m3 for PM2.5 and the mean daily average temperature and humidity were 16.3 °C and 80% respectively (Additional file 3: Table S2), reflecting the subtropical climate in Wuhan. Figure 2 shows seasonal trends of PM2.5 concentrations, with high values in winter and low values in summer. The PM2.5 exposure of most patients was within China’s ambient air quality standards (AQS), but it exceeded the World Health Organization (WHO) air quality guidelines (AQG).

Fig. 2
figure2

Distribution of individual PM2.5 exposures on the day of admission. Data points represent the PM2.5 concentrations of individuals. The density plot on the right margin of the y-axis visualizes the distribution of the number of subjects over the PM2.5 concentration. The dashed lines denote the Grade II criteria set by the Chinese AQS (75 μg/m3) and the WHO AQG (25 μg/m3)

PM2.5 showed similar lag patterns for it’s impact on total cardiorespiratory hospital admissions (Fig. 3). Detailed risk estimates are listed in the supplementary materials (Additional file 5: Table S4). For single-day lags, a weakened lagging effect of PM2.5 was observed from lag0 to lag6. Significant harmful effects were shown on lag0–lag2 with respect to the risk of admissions for all cardiorespiratory diseases, and the highest risks were found at lag0, except for hypertension. For the cumulative lag day effect, we found significant positive associations in all analyzed hospital admissions, while the greatest effects for all diseases were observed at lag0–2. Thus, in the subsequent analyses, we mainly chose lag0–2 as the exposure period to evaluate the acute effects of ambient particulate matter. The moving average lag model usually had higher estimates than that of single-day exposure. Each 10 μg/m3 increase in PM2.5 at lag0–2 was associated with a 1.2% (95% CI 1.0%–1.4%) increment in daily hospital admissions for total CVD and a 2.0% (95% CI 1.6%–2.3%) increment for total respiratory diseases (Additional file 5: Table S4). The effect estimates remained stable in the symmetric CCO design (Additional file 6: Table S5) and with different degrees of freedom for smoothing of meteorological variables (2–4 df) (Additional file 7: Table S6).

Fig. 3
figure3

OR (95% CI) of hospital admission for total and cause-specific cardiorespiratory disease per 10 μg/m3 increase of PM2.5 with different lag patterns in single-pollutant models

For subgroup analysis, we examined the associations between PM2.5 and cardiorespiratory hospital admissions at lag0–2, classified by age, gender, and admission season (Fig. 4). In age-specific analyses, positive associations were found in all age groups for respiratory admission. Stronger effects of PM2.5 on both cardiovascular and respiratory admission were observed in the elderly (over 65 years). However, the hazard effects among people aged > 74 years were slightly lower than that of people aged 65–74 years in some cause-specific diseases (COPD, coronary heart disease, and stroke). In addition, COPD patients aged 45–54 years were at the greatest risk, with ORs of 1.042 (95% CI 1.010–1.075) (Additional file 8: Table S7). In sex-specific analyses, exposures to PM2.5 showed significant effects on both genders, except hypertension, but gender differences in PM-associated risks were statistically insignificant. In season-specific analyses, we found a greater effect of PM2.5 for all cardiorespiratory diseases in the cold season than in the warm season.

Fig. 4
figure4

OR (95% CI) of hospital admission per 10 μg/m3 increase of PM2.5 stratified by age, gender, and season at admission at lag0–2

There was a clear dose–response relationship between PM2.5 concentration and hospital admissions for both cardiovascular and respiratory diseases (Fig. 5). Both results exhibited generally similar patterns. The relationship was approximately linear, with a tiny fluctuation at lower concentrations (< 100 μg/m3) and a sharper response at higher concentrations.

Fig. 5
figure5

Concentration response relationship of PM2.5 concentrations with hospital admissions for cardiorespiratory diseases at lag0–2. The solid line represents the predicted OR, and the dotted lines represent the 95% CI

The risk estimates of PM2.5 with a three-day moving average (lag0–2) in two-pollutant models were summarized in Table 2, adjusting for other air pollutants (NO2, SO2 and CO). Overall, the effects of PM2.5 remained stable after adjusting for gaseous pollutants for total cardiorespiratory diseases, while the effect estimates of PM2.5 in two-pollutant model were slightly smaller than in single-pollutant model. Notably, for three gaseous pollutants, the adverse effects were observed on most cardiorespiratory diseases after adjusting for PM2.5.

Table 2 Odds ratio (95% CIs) of admissions from total CVD, respiratory, COPD, hypertension and stroke per 10 μg/m3 increase in a 3-day moving average (lag 0–2) concentration of PM2.5, with and without adjustment for pollutants

Discussion

To the best of our knowledge, this is the first study in China that has examined the adverse effects of PM2.5 on hospital admissions based on an entire population of a megalopolis using LUR models. Evidence gained in this study showed a significant PM2.5-associated risk on cardiovascular diseases (including hypertension, CHD, and stroke) and respiratory diseases (including COPD), with robust outcomes after adjustment for other gaseous pollutants. Regarding subgroup analyses, the relationship estimates of different age groups varied from each other, while risk estimates were higher in the cold season. These findings provide strong evidence of associations between ambient PM2.5 and cardiorespiratory hospital admissions in Wuhan, and might help public agencies to develop strategies for air pollution control and disease prevention.

Our study found that short-term exposure to PM2.5 was positively correlated with hospital admissions for respiratory illnesses. However, as PM2.5 concentrations in Wuhan were much higher, the magnitude of the risk estimates in our study was generally lower than in prior reports [24, 25]. One multicity study found a strong PM2.5-related admission risk at lag2–5 in five European cities, in which a 12.4 μg/m3 increase in PM2.5 was associated with a 6.4% increase in respiratory hospital admissions [25]. Several explanations might account for the spatial heterogeneity of air pollution-associated health effects. First, as shown in the previous study, the exposure–response relationship between health outcomes and fine particulate matter is relatively steep at low levels of exposure and flattens out at higher exposures [26]. Individuals vulnerable to air pollution might have developed symptoms and gone to the hospital before air pollution concentrations reached a high level [27]. Second, compared with the developed countries, Wuhan has a younger age structure, making it less sensitive to exposure to air pollutants. Moreover, different climate conditions, PM2.5 compositions and different lifestyle patterns are possible explanations.

The lag effects of short-term exposure to air pollution are of wide interest in air pollution epidemiology. In this study, PM2.5 exhibited a similar lagged pattern for overall cardiorespiratory admission as well as in different subgroups. For single-day lag models, the estimates for PM2.5 were the highest at lag0 day and decreased in later lag days, in line with previous studies [10, 28]. This temporal pattern suggests that exposure to PM2.5 may increase the risk of hospital admission within hours of exposure. A multi-city study in England and Wales found an elevated risk for myocardial infarction by PM10 and NO2 at lag1–6 h, with excess risks of 1.2% (95% CI 0.3–2.1%) and 1.1% (95% CI 0.3–2.1%) respectively, per 10 μg/m3 increase [29]. Another study from Japan also found that hourly changes in particulate matter (0 to < 6 h) were positively associated with the risk of cardiovascular and cerebrovascular disease [30]. In the present investigation, we found that a moving average lag model usually had higher estimates than that of single-day exposure, with the greatest effects observed at lag0–2. Similar results have also been observed in other continents [8, 31]. In New England, a study found that the highest harmful effects of PM2.5 exposure were at lag0–5, for each 10 μg/m3 increment, associated with an increase of 4.31, 3.95, and 2.56% percentage change in the hospital admission rates for myocardial infarction, congestive heart failure, and ischemic stroke respectively [8]. Another study in Denmark found the highest ultrafine particle associated risk for stroke at lag0–4 [31]. The variation in days of moving average pattern could be due to the combined effects of many complex factors such as different types of disease, individual behavioral patterns, air pollution components, and the differences in study design. These findings suggest that the effects of air pollution across several days impact daily hospital admissions. There is also experimental support for this pattern, as a toxicological study reported that acute lung inflammation, induced by particle instillation, took up to 4 days to resolve [32]. Considering that the time scale extends over several days, a moving average lag model might be a better exposure metric than a single-day lag in air pollution epidemiological studies. These results provide solid evidence about the importance of the timing of air pollution exposure.

This study explored the demographic-specific associations between PM2.5 and hospital admissions for respiratory and cardiovascular diseases. Similar to other studies, a higher susceptibility to PM2.5 was found among the elderly (over 65 years old). Such elderly high-risk association is widely accepted due to the weaker immune systems and potential for more chronic medical conditions. In addition, interesting deviations from this pattern were observed for specific subgroups of disease. The risk of COPD, coronary heart disease, and stroke in this study peaked in the middle-age group. For cause-specific cardiovascular diseases, CHD and stroke, the adverse effects were slightly higher in the 65–74 years group than in the over-74 years group. This result may be the consequence of a “harvesting effect” in which susceptible residents might have developed symptoms and died before reaching the age of 75 [33]. Notably, for the COPD patients, stronger associations were found in those aged 45–54 years old, with each 10 μg/m3 increase in PM2.5 corresponding to a 4.25% (95% CI 1.02%–7.58%) increase in hospital admissions. This finding seems inconsistent to prior study results. In a cohort study conducted in the United States, a higher risk for hospital admission for COPD was found in age group ≥ 76 [34]. More recently, a review of 30 epidemiological studies on air pollution and the morbidity of COPD and asthma found no evidence for the effect of any pollutants on hospital utilization in people aged 15–64 [35]. The variability in these results could be due to possible differences in selection strategy of the study population. As COPD is largely encountered in the elderly, previous studies tended to select people ≥ 65 years old as the study population, or divided age into categorical variables based on 15 and 65 years old [34, 36], which limited the power to examine the relationship between COPD and air pollutants in specific age groups. Further investigations are still needed to explore vulnerable populations.

The assessment of gender differences has been of wide interest in air pollution epidemiology. In the current study, although statistical significance of in gender difference was not observed for PM-associated risks for hospital admission, there were still slight deviations in the magnitude of risk estimates in males and females. For total respiratory disease, slightly higher risk estimates were found in females. For specific cardiovascular diseases, coronary heart disease and stroke, we found that males were at slightly higher risk for hospital admissions. Consistent with the results of the present study, a pooled analysis from 33 Chinese communities reported that the effect of ambient air pollution exposure on the prevalence of stroke and CVD was much higher in men than in women [37]. Another multi-country study in the United States suggested that women might be more susceptible to PM2.5-related hospitalizations for respiratory causes [24]. However, these differences could be related to factors such as chemical components and exposure patterns of local populations. The findings of the current study indicated that gender difference tendencies for PM-associated risk may vary among different diseases. The underlying pathology and mechanism of these discrepancies should be further explored in future investigations so as to protect vulnerable subpopulations from PM pollution.

In this study, higher short-term effects of PM2.5 on cardiorespiratory hospital admissions were found during the cold period. This may be due to the seasonal variation of PM2.5 in Wuhan, with a high concentration in winter and a lower one in summer (Fig. 2), combined with a sharper response at higher concentrations in the exposure response curve (Fig. 5). Relatively low temperatures in the winter can accelerate the conversion of particles, while low wind speed restricts air pollutants from dispersing [38]. The seasonal finding in this study echoes a study in Hong Kong [39], which found an increased risk of respiratory mortality in the cold season when PM10 concentrations were up to 80 μg/m3. Two previous large-scale analyses from the United States also found larger PM2.5-induced risks of hospitalizations for cardiovascular and respiratory diseases in cold months (winter or spring) [24, 40].

In two-pollutant analyses, the associations of PM2.5 with total cardiovascular and respiratory diseases remained robust after adjustment for gaseous pollutants (NO2, SO2 and CO), suggesting PM2.5 seemingly has independent impact on total cardiorespiratory hospital admission. We also observed that the effect estimates of PM2.5 in two-pollutant model were slightly smaller than in single-pollutant model. In addition, after adjusting for PM2.5, gaseous pollutants themselves have an adverse impact on most cardiorespiratory diseases. Many studies have indicated similar results with this one [41,42,43]. Possible reason could be the confounding effect of gaseous pollutants. The observed effects of PM2.5 using single-pollutant models might be partly due to the exposure of gaseous pollutants, while the independent effect of gaseous pollutants has been proved [44]. However, these findings should be interpreted with caution, because the high correlation amongst the pollutants may render the model partly unstable.

Compared with previous studies, this study has several strengths. First, we obtained hospitalization data from a total of 59 hospitals in Wuhan to evaluate the PM-admission relationships. Given Wuhan’s universal access to hospital health care, the potential for selection bias was minimized and the results can be directly generalized to the whole city. Second, the adoption of LUR model increases the accuracy when assessing the spatial variations in individual PM2.5 exposures and in detecting possible associations. This study has some limitations as well. First, we linked PM2.5 to cardiorespiratory diseases by date of hospital admission rather than by the date of symptom onset. This may have introduced a non-differential error in exposure measurement and underestimated the effect estimates. Second, while the exposure modeling methods employed in this study added new information in comparison with most previous studies, the deficiency of PM2.5 exposure data from occupation, commuting, and pollution originating from indoor sources may have further attenuated our effect estimates. Third, although the two-pollutant models were fitted to examine the robustness of the association between PM2.5 and hospital admissions, the collinearity between pollutants limited the ability to separate the independent effect of PM2.5.

Conclusion

This study provides evidence regarding the short-term health impacts of PM2.5 exposure as well as identifies sensitive subpopulations in Wuhan. We find that the cumulative effect of short-term PM2.5 exposure are higher than that of single day. The risk estimates of different age groups vary from each other, while the elderly are still at higher risk for most diseases. Besides, the higher PM-induced risk during the cold season cannot be ignored. These findings extend our knowledge related to the effects of higher levels of exposure and may help public agencies to develop strategies for air pollution control.

Availability of data and materials

The data that support the findings of this study are available from the Wuhan Information Center of Health and Family Planning, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the Wuhan Information Center of Health and Family Planning.

Abbreviations

AQG:

Air quality guidelines

AQS:

Ambient air quality standards

CCO:

Case-crossover

CHD:

Coronary heart disease

CI:

Confidence interval

CO:

Carbon monoxide

COPD:

Chronic obstructive pulmonary disease

CTM:

Atmospheric chemical transport model

CVD:

Cardiovascular disease

DALYs:

Disability-adjusted life-years

DOW:

Day of the week

DF:

Degrees of freedom

DM:

Dispersion models

GIS:

Geographic information system

LUR:

Land use regression

NCS:

Natural cubic spline

NO2 :

Nitrogen dioxide

OR:

Odds ratio

PM10 :

Particulate matter with an aerodynamic diameter of 10 μm or less

PM2.5 :

Particulate matter with an aerodynamic diameter of 2.5 μm or less

SD:

Standard deviations

SO2 :

Sulfur dioxide

WHO:

World Health Organization

References

  1. 1.

    Watts N, Amann M, Ayeb-Karlsson S, Belesova K, Bouley T, Boykoff M, Byass P, Cai WJ, Campbell-Lendrum D, Chambers J, et al. The Lancet Countdown on health and climate change: from 25 years of inaction to a global transformation for public health. Lancet. 2018;391:581–630. https://doi.org/10.1016/s0140-6736(17)32464-9.

    Article  PubMed  Google Scholar 

  2. 2.

    Beelen R, Raaschou-Nielsen O, Stafoggia M, Andersen ZJ, Weinmayr G, Hoffmann B, Wolf K, Samoli E, Fischer P, Nieuwenhuijsen M, et al. Effects of long-term exposure to air pollution on natural-cause mortality: an analysis of 22 European cohorts within the multicentre ESCAPE project. Lancet. 2014;383:785–95. https://doi.org/10.1016/s0140-6736(13)62158-3.

    CAS  Article  PubMed  Google Scholar 

  3. 3.

    Liu C, Chen R, Sera F, Vicedo-Cabrera AM, Guo YM, Tong SL, Coelho M, Saldiva PHN, Lavigne E, Matus P, et al. Ambient particulate air pollution and daily mortality in 652 cities. N Engl J Med. 2019;381:705–15. https://doi.org/10.1056/NEJMoa1817364.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Stanaway JD, Afshin A, Gakidou E, Lim SS, Abate D, Abate KH, Abbafati C, Abbasi N, Abbastabar H, Abd-Allah F, et al. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392:1923–94. https://doi.org/10.1016/s0140-6736(18)32225-6.

    Article  Google Scholar 

  5. 5.

    Dai L, Zanobetti A, Koutrakis P, Schwartz JD. Associations of fine particulate matter species with mortality in the United States: a multicity time-series analysis. Environ Health Perspect. 2014;122:837–42. https://doi.org/10.1289/ehp.1307568.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Pererz L, Medina-Ramon M, Kunzli N, Alastuey A, Pey J, Perez N, Garcia R, Tobias A, Querol X, Sunyer J. Size fractionate particulate matter, vehicle traffic, and case-specific daily mortality in Barcelona Spain. Environ Sci Technol. 2009;43:4707–14. https://doi.org/10.1021/es8031488.

    CAS  Article  Google Scholar 

  7. 7.

    Afoakwah C, Nghiem S, Scuffham P, Huynh Q, Marwick T, Byrnes J. Impacts of air pollution on health: evidence from longitudinal cohort data of patients with cardiovascular diseases. Eur J Health Econ. 2020;21:1025–38. https://doi.org/10.1007/s10198-020-01198-5.

    Article  PubMed  Google Scholar 

  8. 8.

    Qiu XY, Wei YG, Wang Y, Di Q, Sofer T, Abu Awad Y, Schwartz J. Inverse probability weighted distributed lag effects of short-term exposure to PM2.5 and ozone on CVD hospitalizations in New England Medicare participants—exploring the causal effects. Environ Res. 2020;182:7. https://doi.org/10.1016/j.envres.2019.109095.

    CAS  Article  Google Scholar 

  9. 9.

    Liu H, Tian Y, Xiang X, Juan J, Song J, Cao Y, Huang C, Li M, Hu Y. Ambient particulate matter concentrations and hospital admissions in 26 of China’s largest cities: a case-crossover study. Epidemiology. 2018;29:649–57. https://doi.org/10.1097/ede.0000000000000869.

    Article  PubMed  Google Scholar 

  10. 10.

    Tian YH, Liu H, Liang TL, Xiang X, Li M, Juan J, Song J, Cao YY, Wang XW, Chen LB, et al. Ambient air pollution and daily hospital admissions: a nationwide study in 218 Chinese cities. Environ Pollut. 2018;242:1042–9. https://doi.org/10.1016/j.envpol.2018.07.116.

    CAS  Article  PubMed  Google Scholar 

  11. 11.

    Chen DY, Zhang FX, Yu CH, Jiao AQ, Xiang QQ, Yu Y, Mayvaneh F, Hu KJ, Ding Z, Zhang YQ. Hourly associations between exposure to ambient particulate matter and emergency department visits in an urban population of Shenzhen, China. Atmos Environ. 2019;209:78–85. https://doi.org/10.1016/j.atmosenv.2019.04.021.

    CAS  Article  Google Scholar 

  12. 12.

    Wang XY, Wang WC, Jiao SL, Yuan J, Hu CP, Wang L. The effects of air pollution on daily cardiovascular diseases hospital admissions in Wuhan from 2013 to 2015. Atmos Environ. 2018;182:307–12. https://doi.org/10.1016/j.atmosenv.2018.03.036.

    CAS  Article  Google Scholar 

  13. 13.

    Hennig F, Sugiri D, Tzivian L, Fuks K, Moebus S, Jockel KH, Vienneau D, Kuhlbusch TAJ, de Hoogh K, Memmesheimer M, et al. Comparison of land-use regression modeling with dispersion and chemistry transport modeling to assign air pollution concentrations within the Ruhr Area. Atmosphere. 2016;7:19. https://doi.org/10.3390/atmos7030048.

    CAS  Article  Google Scholar 

  14. 14.

    Ozkaynak H, Baxter LK, Dionisio KL, Burke J. Air pollution exposure prediction approaches used in air pollution epidemiology studies. J Eposure Sci Environ Epidemiol. 2013;23:566–72. https://doi.org/10.1038/jes.2013.15.

    CAS  Article  Google Scholar 

  15. 15.

    Solomos S, Amiridis V, Zanis P, Gerasopoulos E, Sofiou FI, Herekakis T, Brioude J, Stohl A, Kahn RA, Kontoes C. Smoke dispersion modeling over complex terrain using high resolution meteorological data and satellite observations—the Fire Hub platform. Atmos Environ. 2015;119:348–61. https://doi.org/10.1016/j.atmosenv.2015.08.066.

    CAS  Article  Google Scholar 

  16. 16.

    Abernethy RC, Allen RW, McKendry IG, Brauer M. A land use regression model for ultrafine particles in Vancouver Canada. Environ Sci Technol. 2013;47:5217–25. https://doi.org/10.1021/es304495s.

    CAS  Article  PubMed  Google Scholar 

  17. 17.

    Eeftens M, Beelen R, de Hoogh K, Bellander T, Cesaroni G, Cirach M, Declercq C, Dedele A, Dons E, de Nazelle A, et al. Development of land use regression models for PM2.5, PM2.5 absorbance, PM10 and PMcoarse in 20 European study areas; results of the ESCAPE Project. Environ Sci Technol. 2012;46:11195–205. https://doi.org/10.1021/es301948k.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Wang S, Yu SC, Yan RC, Zhang QY, Li PF, Wang LQ, Liu WP, Zheng XJ. Characteristics and origins of air pollutants in Wuhan, China, based on observations and hybrid receptor models. J Air Waste Manag Assoc. 2017;67:739–53. https://doi.org/10.1080/10962247.2016.1240724.

    CAS  Article  PubMed  Google Scholar 

  19. 19.

    Zhang XT, Fan CF, Ren Z, Feng H, Zuo SS, Hao JY, Liao JL, Zou YL, Ma L. Maternal PM2.5 exposure triggers preterm birth: a cross-sectional study in Wuhan China. Global Health Res Policy. 2020;5:11. https://doi.org/10.1186/s41256-020-00144-5.

    Article  Google Scholar 

  20. 20.

    Maclure M. The case-crossover design—a method for studying transient effects on the risk of acute events. Am J Epidemiol. 1991;133:144–53. https://doi.org/10.1093/oxfordjournals.aje.a115853.

    CAS  Article  PubMed  Google Scholar 

  21. 21.

    Xu Q, Li X, Wang S, Wang C, Huang FF, Gao Q, Wu LJ, Tao LX, Guo J, Wang W, Guo XH. Fine particulate air pollution and hospital emergency room visits for respiratory disease in urban areas in Beijing, China, in 2013. PLoS ONE. 2016;11:17. https://doi.org/10.1371/journal.pone.0153099.

    CAS  Article  Google Scholar 

  22. 22.

    Altman DG, Bland JM. Statistics notes—interaction revisited: the difference between two estimates. Bmj-Br Med J. 2003;326:219–219. https://doi.org/10.1136/bmj.326.7382.219.

    Article  Google Scholar 

  23. 23.

    Carracedo-Martinez E, Taracido M, Tobias A, Saez M, Figueiras A. Case-crossover analysis of air pollution health effects: a systematic review of methodology and application. Environ Health Perspect. 2010;118:1173–82. https://doi.org/10.1289/ehp.0901485.

    Article  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Bell ML, Son JY, Peng RD, Wang Y, Dominici F. Ambient PM2.5 and risk of hospital admissions do risks differ for men and women? Epidemiology. 2015;26:575–9. https://doi.org/10.1097/ede.0000000000000310.

    Article  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Lanzinger S, Schneider A, Breitner S, Stafoggia M, Erzen I, Dostal M, Pastorkova A, Bastian S, Cyrys J, Zscheppang A, et al. Ultrafine and fine particles and hospital admissions in central Europe results from the UFIREG study. Am J Respir Crit Care Med. 2016;194:1233–41. https://doi.org/10.1164/rccm.201510-2042OC.

    Article  PubMed  Google Scholar 

  26. 26.

    Chen R, Yin P, Meng X, Liu C, Wang L, Xu X, Ross JA, Tse LA, Zhao Z, Kan H, Zhou M. Fine particulate air pollution and daily mortality a nationwide analysis in 272 Chinese cities. Am J Respir Crit Care Med. 2017;196:73–81. https://doi.org/10.1164/rccm.201609-1862OC.

    CAS  Article  PubMed  Google Scholar 

  27. 27.

    Costa AF, Hoek G, Brunekreef B, Ponce de Leon ACM. Air pollution and deaths among elderly residents of Sao Paulo, Brazil: an analysis of mortality displacement. Environ Health Perspect. 2017;125:349–54. https://doi.org/10.1289/ehp98.

    CAS  Article  PubMed  Google Scholar 

  28. 28.

    Yin P, He GJ, Fan MY, Chiu KY, Fan MR, Liu C, Xue A, Liu T, Pan YH, Mu Q, Zhou MG. Particulate air pollution and mortality in 38 of China’s largest cities: time series analysis. Bmj-Br Med J. 2017;356:12. https://doi.org/10.1136/bmj.j667.

    Article  Google Scholar 

  29. 29.

    Bhaskaran K, Hajat S, Armstrong B, Haines A, Herrett E, Wilkinson P, Smeeth L. The effects of hourly differences in air pollution on the risk of myocardial infarction: case crossover analysis of the MINAP database. Br Med J. 2011. https://doi.org/10.1136/bmj.d5531.

    Article  Google Scholar 

  30. 30.

    Yorifuji T, Suzuki E, Kashima S. Cardiovascular emergency hospital visits and hourly changes in air pollution. Stroke. 2014;45:1264–8. https://doi.org/10.1161/strokeaha.114.005227.

    Article  PubMed  Google Scholar 

  31. 31.

    Andersen ZJ, Olsen TS, Andersen KK, Loft S, Ketzel M, Raaschou-Nielsen O. Association between short-term exposure to ultrafine particles and hospital admissions for stroke in Copenhagen, Denmark. Eur Heart J. 2010;31:2034–40. https://doi.org/10.1093/eurheartj/ehq188.

    CAS  Article  PubMed  Google Scholar 

  32. 32.

    Lay JC, Bennett WD, Ghio AJ, Bromberg PA, Costa DL, Kim CS, Koren HS, Devlin RB. Cellular and biochemical response of the human lung after intrapulmonary instillation of ferric oxide particles. Am J Respir Cell Mol Biol. 1999;20:631–42. https://doi.org/10.1165/ajrcmb.20.4.3355.

    CAS  Article  PubMed  Google Scholar 

  33. 33.

    Chen C, Zhu PF, Lan L, Zhou L, Liu RC, Sun QH, Ban J, Wang WT, Xu DD, Li TT. Short-term exposures to PM25 and cause-specific mortality of cardiovascular health in China. Environ Res. 2018;161:188–94. https://doi.org/10.1016/j.envres.2017.10.046.

    CAS  Article  PubMed  Google Scholar 

  34. 34.

    Zanobetti A, Bind M-AC, Schwartz J. Particulate air pollution and survival in a COPD cohort. Environ Health. 2008. https://doi.org/10.1186/1476-069x-7-48.

    Article  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Zhang SQ, Li GX, Tian L, Guo Q, Pan XC. Short-term exposure to air pollution and morbidity of COPD and asthma in East Asian area: a systematic review and meta-analysis. Environ Res. 2016;148:15–23. https://doi.org/10.1016/j.envres.2016.03.008.

    CAS  Article  PubMed  Google Scholar 

  36. 36.

    Medina-Ramon M, Zanobetti A, Schwartz J. The effect of ozone and PM10 on hospital admissions for pneumonia and chronic obstructive pulmonary disease: a national multicity study. Am J Epidemiol. 2006;163:579–88. https://doi.org/10.1093/aje/kwj078.

    Article  PubMed  Google Scholar 

  37. 37.

    Dong GH, Qian ZM, Wang J, Chen WQ, Ma WJ, Trevathan E, Xaverius PK, DeClue R, Wiese A, Langston M, et al. Associations between ambient air pollution and prevalence of stroke and cardiovascular diseases in 33 Chinese communities. Atmos Environ. 2013;77:968–73. https://doi.org/10.1016/j.atmosenv.2013.06.034.

    CAS  Article  Google Scholar 

  38. 38.

    Dai W, Gao J, Cao G, Ouyang F. Chemical composition and source identification of PM25 in the suburb of Shenzhen China. Atmos Res. 2013;122:391–400. https://doi.org/10.1016/j.atmosres.2012.12.004.

    CAS  Article  Google Scholar 

  39. 39.

    Wong CM, Ma S, Hedley AJ, Lam TH. Effect of air pollution on daily mortality in Hong Kong. Environ Health Perspect. 2001;109:335–40. https://doi.org/10.2307/3454891.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Bell ML, Ebisu K, Peng RD, Walker J, Samet JM, Zeger SL, Dominici F. Seasonal and regional short-term effects of fine particles on hospital admissions in 202 US Counties, 1999–2005. Am J Epidemiol. 2008;168:1301–10. https://doi.org/10.1093/aje/kwn252.

    Article  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Milojevic A, Wilkinson P, Armstrong B, Bhaskaran K, Smeeth L, Hajat S. Short-term effects of air pollution on a range of cardiovascular events in England and Wales: case-crossover analysis of the MINAP database, hospital admissions and mortality. Heart. 2014;100(14):1093–8. https://doi.org/10.1136/heartjnl-2013-304963.

    Article  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Chen MD, Qiu H, Wang LY, Zhou L, Zhao F. Attributable risk of cardiovascular hospital admissions due to coarse particulate pollution: a multi-city time-series analysis in southwestern China. Atmos Environ. 2019;218:8. https://doi.org/10.1016/j.atmosenv.2019.117014.

    CAS  Article  Google Scholar 

  43. 43.

    Orellano P, Reynoso J, Quaranta N, Bardach A, Ciapponi A. Short-term exposure to particulate matter (PM10 and PM2.5), nitrogen dioxide (NO2), and ozone (O-3) and all-cause and cause-specific mortality: systematic review and meta-analysis. Environ Int. 2020;142:15. https://doi.org/10.1016/j.envint.2020.105876.

    CAS  Article  Google Scholar 

  44. 44.

    Mills IC, Atkinson RW, Kang S, Walton H, Anderson HR. Distinguishing the associations between daily mortality and hospital admissions and nitrogen dioxide from those of particulate matter: a systematic review and meta-analysis. BMJ Open. 2016. https://doi.org/10.1136/bmjopen-2015-010751.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

Not applicable.

Author information

Affiliations

Authors

Contributions

ZR and XL analyzed the data, interpreted the data, and drafted the manuscript; TL collected the data and revised the manuscript; DC conducted statistical analysis, and revised the manuscript. JK, WX, SJ, and YH contributed to data collection and manuscript preparation. LJ provided important comments while developing the manuscript; LM took overall responsibility for the design, implementation and analysis of the study. The authors read and approved the final manuscript.

Corresponding authors

Correspondence to Jingling Liao or Lu Ma.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that there are no conflicts of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1:

Figure S1. Flow chart of the selection process for the study population.

Additional file 2:

Table S1. Description of Developed LUR Models for PM2.5 in different year.

Additional file 3:

Table S2. Descriptive statistics of air pollutant concentration and meteorological factor in Wuhan, 2016.10 -2018.12.

Additional file 4:

Table S3. Spearman correlations among environmental variables in Wuhan, 2016.10 -2018.12.

Additional file 5:

Table S4. Odds ratio (95% CIs) of admissions at various exposure days, associated with per 10 μg/m3 increase of PM2.5.

Additional file 6:

Table S5. Odds ratio (95% CIs) of admissions at various exposure days, associated with per 10 μg/m3 increase of PM2.5, using symmetric CCO design.

Additional file 7:

Table S6. Sensitive analyses of odds ratio (95% CIs) of admissions at lag0~2 under varying degrees of freedom (df) for, associated with per 10 μg/m3 increase of PM2.5.

Additional file 8:

Table S7. Odds ratio (95% CIs) of cardiorespiratory hospital admissions stratified by age, gender and season, associated with per 10 μg/m3 increase of PM2.5.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ren, Z., Liu, X., Liu, T. et al. Effect of ambient fine particulates (PM2.5) on hospital admissions for respiratory and cardiovascular diseases in Wuhan, China. Respir Res 22, 128 (2021). https://doi.org/10.1186/s12931-021-01731-x

Download citation

Keywords

  • Air pollution
  • Particulate matter
  • Spatial epidemiology
  • Case-crossover study