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. 2022 Dec 23;30(13):36938–36951. doi: 10.1007/s11356-022-24803-x

Air pollution and coronary heart disease–related hospital visits in Beijing, China: time-series analysis using a generalized additive model

Yuan Gao 1, Weixuan Sheng 1, Yongtao Yang 2,✉
PMCID: PMC9780628  PMID: 36562963

Abstract

To investigate correlations between environmental and meteorological factors and frequency of presentation for coronary heart disease (CHD) in Beijing. Daily measurements of levels of six atmospheric pollutants were made, data relating to meteorological conditions collected, and CHD-related outpatient visits recorded from January 2015 to December 2019 in Beijing. A time-series analysis was made, using a generalized additive model with Poisson distribution, and R 3.6.3 software was used to estimate relationships among levels of atmospheric pollutants, ambient temperature, and visits occasioned by CHD. Results were controlled for time-dependent trend, other weather variables, day of the week, and holiday effects. Lag-response curves were plotted for specific and incremental cumulative effects of relative risk (RR). The aim was to correlate meteorological-environmental factors and the daily number of CHD-related hospital visits and to quantify the degree of correlation to identify any pathological associations. Response diagrams and three-dimensional diagrams of predicted exposure lag effects were constructed in order to evaluate relationships among the parameters of air pollution, temperature, and daily CHD visits. The fitted model was employed to predict the lag RR and 95% confidence interval (95% CI) for specific and incremental cumulative effects of random air pollutants at random concentrations. This model may then be used to predict effects on the outcome variable at any concentration of any defined pollutant, giving flexibility for public health purposes. The overall lag-response RR curves for the specific cumulative effects of the pollutants, particulate matter (PM)2.5, PM10, SO2, CO, and NO2, were statistically significant and for PM2.5, PM10, CO, and NO2, the overall lag-response RR curves for the incremental cumulative effect were statistically significant. When PM2.5, PM10, SO2, CO, and NO2 concentrations were above threshold values and the temperature was below 45 °F (reference value 70 °F), the number of CHD-related hospital visits increased with a time lag effect. The outpatient volume of CHD was predicted by the model to guide the flexible distribution of medical resources. Elevated PM2.5, PM10, SO2, CO, and NO2 concentrations in the atmosphere combined and low ambient temperature increased the risk of CHD with a time lag effect.

Keywords: Coronary heart disease, Environmental-meteorological factors, Generalized additive model, Time-series analysis

Background

Cardiovascular disease is a serious and increasing threat to global human health and its prevalence is of particular concern in China. A 2019 survey revealed rising rates of morbidity and mortality from cardiovascular diseases in China and estimated that 11 million patients suffer from coronary heart disease (CHD) (Chinese Cardiovascular Health and Disease Report Writing Group 2020). The global public health issue of air pollution is currently considered an interventionable risk factor for cardiovascular disease and increases the risk of fatal and non-fatal CHD (GBD 2017 Risk Factor Collaborators 2018).

Air pollutants cause various kinds of harm to the human body (Khorshed et al. 2021). Many studies have investigated the human response to exposure to air pollution. A meta-analysis of 34 studies reported a 2.5% increase in the risk of acute myocardial infarction (AMI) for every 10 μg/m3 increase in short-term exposure to PM2.5 (Mustafic et al. 2012). A study in South Korea showed that increases of 10 μg/m3 of PM2.5 and PM10 and 10 ppb of SO2, NO2, and CO were associated with a 0.93% increased risk of heart failure admission (Lee et al. 2021). Similarly, elevated concentrations of CO, NO2 and SO2 were also associated with increased risk of AMI. The effects of PM2.5 have attracted further scrutiny, and it has been shown that every 10 μg/m3 increase in short-term exposure to PM2.5 in Beijing caused a 0.46% rise in the daily number of patients hospitalized with AMI (Wu et al. 2019). Short-term PM2.5 exposure has also been associated with increased numbers of emergency visits due to other aspects of CHD (Xu et al. 2017). The Women’s Health Action Study in the USA showed that for every 10 μg/m3 increase in PM2.5 annual exposure, the risk of fatal and non-fatal CHD in menopausal women increased by 21% during 6 years of follow-up (Miller et al. 2007). An ESCAPE meta-analysis of 11 European cohort studies, which involved more than 100,000 participants with a median follow-up of 11.5 years, showed that for each 5 μg/m3 increase in PM2.5 exposure, CHD risk increased by 13%, and for each 10 μg/m3 increase in PM10 exposure, the increased risk was 12% (Cesaroni et al. 2014). Moreover, a prospective study based on mean 8 year follow-up of 120,000 Chinese adults showed a 43% increased risk of CHD for every 10 μg/m3 increase in PM2.5 exposure (Li et al. 2020).

However, time series analysis on air pollutants and CHD-related hospital visits is rare. A generalized additive model (GAM) was considered appropriate for the current analysis since pathogenic effects of air pollution and meteorological factors exhibited a time lag prior to onset and persisted after removal of the stimulus. This means that health consequences often show complex and non-linear relationships with stimuli. The model presented allows a preliminary prediction of the types of environmental pathogenic agents and their influence on the incidence of CHD to be made. The current study was conducted to analyze the influence of various atmospheric pollutant concentrations on the number of visits made by patients with CHD in Beijing during a 5-year period (2015–2019) using GAM.

Materials and methods

Study site

Beijing, the capital of the People’s Republic of China (39° 56′ N, 116° 20′ E) is located to the North of the North China Plain. The terrain is high in the northwest and low in the southeast. The west, north, and northeast are surrounded by mountains on three sides, and the southeast comprises a plain that slopes gradually towards the Bohai Sea. Local air pollution conditions, combined with the local terrain, climate, and seasonal changes which produce limited air flow, prolong the contact time between people and airborne pathogenic pollutants.

Clinical data

From January 1, 2015, to December 31, 2019, daily visits for CHD were recorded in the affiliated Beijing Shijitan Hospital of Capital Medical University. Data relating to date of visit, gender, outpatient history, home address, and diagnosis were collated. CHD was identified using the criteria of the International Classification of Diseases (ICD-10), including the following sub-categories: diabetic CHD E14.551, non-CHD myocardial infarction I21.901, CHD I25.101, silent CHD (asymptomatic CHD) I25.652, and CHD arrhythmia I49.902. Records of the first hospital visit only by a given patient were included in order to achieve standardization of records. Subsequent visits by the same patient within 1 week were excluded. Cases were reviewed according to the above inclusion and exclusion criteria, and a total of 241,476 patients enrolled.

Meteorological data

Beijing Meteorological Bureau supplied the meteorological data which may be downloaded from Beijing, People’s Republic of China Weather History | Weather Underground.

Daily mean temperature (Fahrenheit, °F), dew (Fahrenheit, °F), relative humidity (%), mean wind speed (miles per hour, mph), mean atmospheric pressure (mm Hg), and total precipitation (inches) were recorded between January 1, 2015, and December 31, 2019.

Concentrations of air pollutants, including aerodynamic diameter of particulate matter less than or equal to 2.5 μm (PM2.5), aerodynamic diameter of particulate matter less than or equal to 10 μm (PM10), sulfur dioxide (SO2), Carbon monoxide (CO), nitrogen dioxide (NO2), and ozone (O3) were measured daily by 35 environmental monitoring stations in Beijing. Measurements were combined by Beijing Municipal Ecology and Environment Bureau into mean daily concentrations to represent the exposure levels of residents. And Air Quality Index (AQI) which converts the concentration of air pollutants into a unified index was also recorded daily. It takes the primary pollutant index to mark the air quality. The higher the AQI index, the higher the level, the more serious the pollution and the more obvious the impact on human health.

Statistical analysis

A time-series analysis using a generalized additive model with Poisson distribution was analyzed by R3.6.3 software to estimate the relationship between levels of air pollutants, temperature, and CHD-related hospital visits. Results were controlled for time trend, other weather variables, day of the week, and effects of public holidays. Mgcv and dlnm packages were used to plot the lag-response curves for specific cumulative effects and incremental cumulative effects of relative risk (RR). In this context, a specific cumulative effect refers to the influence of pollutant concentrations on a given day on subsequent hospital visits, and an incremental cumulative effect refers to the prior exposure over a number of days on subsequent hospital visits within a single day. Response diagrams and three-dimensional diagrams of predicted exposure lag effect were constructed. Relationships between parameters of air pollutants and daily numbers of CHD visits were summarized.

The fitted model was used to predict the lag RR and 95% confidence interval (95% CI) of specific and incremental cumulative effects for random air pollutant and random concentration.

Model building

A generalized additive model is a generalized linear model with a linear predictor, involving the cross-basis (Gasparrini et al. 2017; Gasparrini 2016: Gasparrini 2014; Gasparrini et al. 2013) and smooth functions of covariates. The model accommodates flexible specification of the dependence of the response on the covariates but by specifying only “smooth functions,” rather than detailed parametric relationships, simplifies the analysis. This model has been used previously to study environmental meteorological factors and the incidence of non-coronary heart disease (Sheng et al. 2022). The cross-basis of different air pollutant use time lags is fitted, and other meteorological factors are used as covariates. Ambient temperature has been recognized as influencing cardiovascular disease and was included on a cross-basis function for fitting and prediction (Tian et al. 2012). The specific modeling steps of R in the current study are as follows:

  • Yt ~ Poisson(μt)

  • Log μt = β0 + β1 cb.Xt + β2 cb.temp + s(t, df) + s(Zt, df) + DOW

  • Yt: the number of visits for CHD at day t

  • μt: expected number of visits for CHD at day t

  • β0: the intercept

  • β: the vector of coefficients

  • t: time variable

  • cb.Xt: cross-basis of atmospheric pollutant concentration at day t

  • cb.temp: cross-basis of temperature at day t

  • s: spline smoothing function

  • Zt: meteorological factor

  • df: degree of freedom

  • DOW: the day of the week and public holidays are represented as categorical variables to control short-term fluctuations (Yang et al. 2020a, b)

Sensitivity analyses of the main results were performed by changing the degrees of freedom (df) for time variable and 3 df for meteorological factors. The influence of these factors on the number of patients with non CHD was analyzed (Gestro et al. 2017; Chen et al. 2020; Tian et al. 2020).

Ethics

The Ethics Review Board of Beijing Shijitan Hospital approved the investigation and ensured that the safety, health, and rights of patients involved were protected.

Results

Descriptive statistics for meteorological variables, air pollutants, and hospital visits

Values for meteorological variables, levels of air pollutants, and hospital visits for CHD are shown in Table 1.

Table 1.

Meteorological variables, levels of air pollutants, and hospital visits

Factor Unit Mean SD Minimum Maximum Percentiles
25 50 75
Temperature ℉ 57.061 20.2100 6.4 91.1 36.975 59.450 75.900
Dew ℉ 35.534 24.9941 -30.8 78.9 14.375 36.950 57.500
Humidity % 50.457 19.7208 8.0 98.3 34.400 50.300 65.900
Wind mph 4.636 1.8876 1.3 15.6 3.300 4.300 5.600
Pressure mmHg 15.251 2.0410 7.5 30.6 14.800 15.000 15.100
Precipitation in 0.0629 0.35244 0.00 10.34 0.0000 0.0000 0.0000
AQI 103.40 64.567 23 500 57.00 87.00 135.00
PM2.5 μg/m3 60.64 56.648 3 477 23.00 45.00 78.25
PM10 μg/m3 87.92 65.304 0 550 44.00 72.00 111.00
SO2 μg/m3 8.14 9.330 2 84 3.00 5.00 9.00
CO mg/m3 1.004 0.8212 0.2 8.0 0.500 0.800 1.100
NO2 μg/m3 44.09 21.827 4 155 29.00 39.00 54.00
O3 μg/m3 98.78 63.335 2 311 53.00 83.00 141.00
Male 69.68 41.154 4 220 25 76 102
Female 62.56 35.452 4 186 27 64 89
Number 132.24 75.279 8 352 51 141 191
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The time series pattern for CHD-related hospital visits and air pollutants during the study period is presented in Fig. 1.

Fig. 1.

Fig. 1

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Time-series plot showing the daily numbers of CHD-related hospital visits and daily levels of air pollutants

Over time, the number of CHD-related hospital visits decreased. Fewer visits were made in summer, when air pollutants were at their lowest levels, than in winter, showing a seasonal trend. Overall, levels of air pollutants improved during the 4 years of the study period.

Lag-effect (specific and incremental cumulative effects)

Based on the generalized additive model and after controlling the effects of meteorological factors and time trends, the study constructed a cross-basis function of atmospheric pollutant concentration and put them into the model to analyze the relationship between atmospheric pollutant concentration, time lag effect, and the number of CHD incidence accurately. Ambient temperature was an important association with cardiovascular disease, so a cross-basis function of the temperature was also used to analyze.

The peak value of the overall specific cumulative effect was found to be on day 3, and there was a lag duration of 11 days for every 10 unit increase in AQI. The peak value of the overall incremental cumulative effect was found on day 12 with a lag duration of 30 days (Fig. 2).

Fig. 2.

Fig. 2

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The overall lag-response RR curves of specific and incremental cumulative effects with per 10 units increases for AQI

As the AQI rose, the lag effect was prolonged (Fig. 3).

Fig. 3.

Fig. 3

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Response and three-dimensional diagrams of predicted exposure lag effect for AQI

Lag-effects (specific and incremental cumulative effects) for other pollutants showed as Figs. 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, and 12 are presented in Table 2.

Fig. 4.

Fig. 4

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The overall lag-response RR curves of specific and incremental cumulative effects with per 10 units increases for PM2.5

Fig. 5.

Fig. 5

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Response and three-dimensional diagrams of predicted exposure lag effect for PM2.5

Fig. 6.

Fig. 6

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The overall lag-response RR curves of specific and incremental cumulative effects with per 10 units increases for PM10

Fig. 7.

Fig. 7

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Response and three-dimensional diagrams of predicted exposure lag effect for PM10

Fig. 8.

Fig. 8

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The overall lag-response RR curves of specific and incremental cumulative effects with per 5 units increases for SO2

Fig. 9.

Fig. 9

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The overall lag-response RR curves of specific and incremental cumulative effects with per 1 unit increases for CO

Fig. 10.

Fig. 10

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Response and three-dimensional diagrams of predicted exposure lag effect for CO

Fig. 11.

Fig. 11

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The overall lag-response RR curves of specific and incremental cumulative effects with per 10 units increases for NO2

Fig. 12.

Fig. 12

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Response and three-dimensional diagrams of predicted exposure lag effect for NO2

Table 2.

Lag-effects (specific and incremental cumulative effects) for additional pollutants

Pollutants Amount of increase Peak value of the overall specific cumulative effect/lag duration (day) peak value of the overall incremental cumulative effect/lag duration (day) Schematic diagram (figure)
AQI 10 3/11 12/30 2, 3
PM2.5 10 0/13 11/30 4, 5
PM10 10 0/13 15/30 6, 7
SO2 5 21/13 * 8, –
CO 1 4/11 12/26 9, 10
NO2 10 0/13 15/35 11, 12
O3 10 * * –, –
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*Not statistically significant

For temperatures below 45 °F (compared with the 70 °F reference value), there was a double-peak phenomenon with a lag duration of 4–5 days. Peak values were found on days 3–4 and days 12–13 (Fig. 13).

Fig. 13.

Fig. 13

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Response and three-dimensional diagrams of predicted exposure lag effect for temperature in the model corresponding to AQI (reference value: 70 °F). R.2 of GAM model is 0.815

Using the fitted model to predict the lag RR and 95% confidence interval (95% CI) of specific and incremental cumulative effects for random AQI

According to the air quality evaluation standard issued by the Ministry of Environmental Protection of China in 2012, air quality is divided into six levels according to AQI values, as shown in Table 3.

Table 3.

Air quality evaluation standard

Air quality AQI Grade
1 0–50 Superior
2 51–100 Good
3 101–150 Mild contamination
4 151–200 Medium contamination
5 201–300 Heavy contamination
6  > 300 Extremely heavy contamination
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Specific and incremental cumulative lag RR values and 95% confidence intervals (95% CI) with AQI values of 50, 100, 150, 200, and 300 were obtained by GAM prediction fitted with data from 2015 to 2019 (Figs. 14 and 15). Peaks for specific cumulative effect values were found on day 3 with a lag duration of 11 days. The peak value of incremental cumulative effect was found on day 12 with a lag duration of 30 days.

Fig. 14.

Fig. 14

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Specific cumulative effects of AQI and calculation of RR and 95% CI

Fig. 15.

Fig. 15

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Incremental cumulative effects of AQI and calculation of RR and 95% CI

Discussion

As the capital of the People’s Republic of China, Beijing has a population of 14.93 million people. It is typical for northern cities, concentrations of pollutants, such as particulate matter and SO2, increase during the burning of winter heating fuel. There is significant correlation between atmospheric pollutant concentrations and meteorological factors with higher wind speeds acting to disperse pollutants. Similarly, higher temperatures lead to lower pollutant concentrations at ground level, as warm air rises. Most cities in Southern China have small seasonal temperature variations, and winter heating is not needed giving significantly lower concentrations of air pollutants than Beijing (Wang et al. 2019). It is important to investigate correlations between meteorological-environmental factors and frequency of presentation for CHD in Beijing. Following implementation of air pollution prevention and control measures in 2015, there has been a continuous improvement in the air quality and changes in pollutant composition (China releases air quality improvement report 2019). The current study focused on correlating air pollution with numbers of CHD-related hospital visits post-2015 implementation of pollutant reduction measures.

Guidelines of the European Society of Cardiology regarding diagnosis and management of chronic coronary syndromes (CCS) indicate that the impacts of environmental factors, such as air pollution or environmental noise, are at unprecedented levels (Knuuti et al. 2020). Indeed, the link between fatal and non-fatal CHD and air pollution is well-documented (LI Jing et al. 2021). Previous work has demonstrated an association between increased concentrations of CO, NO2, and SO2 and AMI, and no link has been shown for O3 concentration (Mustafic et al. 2012). Pathogenic mechanisms whereby atmospheric pollution promotes CHD remain unclear. The presence of PM in the lungs may cause oxidative stress and inflammation which becomes systemic and promotes thrombosis, blood hypercoagulability, endothelial dysfunction, atherosclerosis, insulin resistance, and dyslipidemia, all of which contribute to the condition of CHD (Fiordelisi et al. 2017). In addition, PM exposure may activate the pulmonary autonomic nervous system (ANS) arc, mediated by the transient receptor potential (TRP) channel, resulting in ANS imbalance leading to vasoconstriction, endothelial dysfunction, hypertension, platelet aggregation, accelerated heartbeat, and arrhythmia. Such a state is responsible for promoting cardiovascular and cerebrovascular diseases (Chin 2015). Animal studies have demonstrated a link between long-term exposure to PM2.5, especially that emitted by automobiles, and myocardial ischemia which may promote systemic inflammation, changes in endothelial function, and increased susceptibility to thrombosis (Eydgahi and Singh 2006).

3D diagrams presented in the current work show that a temperature lower than 45 °F (reference temperature: 70 °F) produces a double-peak phenomenon during the lag period with peaks at days 3–4 and days 12–13 and a lag duration of 4–5 days. Many studies have demonstrated a temperature effect which influences the occurrence of cardiovascular and cerebrovascular diseases. Underlying mechanisms may involve stimulation of the sympathetic nervous and blood coagulation systems. The sympathetic nervous system influences the renin-angiotensin system (RAS) and inflammatory response, and low temperatures promote vasoconstriction, raising arterial pressure and catecholamine levels. Increased catecholamine levels increase the heart rate, putting more strain on the cardiovascular system and increasing the propensity for CHD (Zhang et al. 2014; Hsu et al. 2016).

Many domestic and foreign studies have shown that air pollution levels are related to the mortality from and hospitalization due to cardiovascular and cerebrovascular diseases (Hsu et al. 2016; Yi et al. 2010; Chen et al. 2012). However, few of these studies have regarded the number of outpatient visits as the outcome. In some countries, it is necessary to make an appointment with a doctor in advance, and many patients cannot immediately see a doctor when they show symptoms. Therefore, the outpatient records recorded by hospitals may not truly and reliably reflect the incidence of disease at that time and place. These two factors limit the study of the effect of air pollutants on outpatient visits in some countries. By contrast, before the Covid-19 pandemic, the “first come, first served” principle of hospital visits in Beijing means that no prior appointment is necessary, giving an opportunity to monitor more closely any impact of changes in air pollution on presentation with CHD (Cao et al. 2009). Outpatient records at Beijing Shijitan Hospital are relatively complete, allowing connections to be made between numbers of outpatient visits and the effects of air pollution on cardiovascular and cerebrovascular diseases.

Due to numerous external confounding factors leading to individual morbidity, the current GAM model results in control of the over-dispersion effect to a certain extent but not in complete elimination.

The current work presents an analytical model with potential utility for prediction of atmospheric conditions likely to lead to increased numbers of hospital visits related to CHD. The use of GAM for analysis of non-linear relationships enables lag periods, taking into account fluctuations in pollutant levels and influences of prior exposure to pollutants. We propose that the use of such a model would allow the Chinese meteorological office to make advance predictions of when hospital visits due to CHD are likely to be at their highest. The meteorological office would then be in a position to notify Beijing hospitals and ensure that facilities were in readiness for increased numbers of patients. Such an approach would hugely improve the efficiency of public health preparedness and lead to better appropriation of resources.

Conclusion

Elevated PM2.5, PM10, SO2, CO, and NO2 concentrations in the atmosphere and low temperature were linked to increased risk of CHD with a time lag effect.

The overall lag-response RR curves for the specific cumulative effect of PM2.5, PM10, SO2, CO, and NO2 were statistically significant. The overall lag-response RR curves for incremental cumulative effects of PM2.5, PM10, CO, and NO2 were statistically significant. When PM2.5, PM10, SO2, CO, and NO2 concentrations were above threshold values and the temperature was below 45 °F (reference value 70 °F), the number of CHD visits increased after a time lag. Knowledge of air pollution conditions, such as AQI, and the lag period before onset of CHD may be used to guide the allocation of medical resources. Our next research will integrate various pollutants and temperature to guide the distribution of medical resources.

In conclusion, it is suggested that patients at risk of CHD should be given guidance to reduce their exposure to air pollution and minimize cardiovascular damage. Medical institutions should increase allocation of outpatient resources when atmospheric conditions indicate increased population susceptibility to CHD.

Acknowledgements

The authors would like to express their gratitude to Editsprings (https://www.editsprings.cn) for the expert linguistic services provided.

Abbreviations

CHD

Coronary heart disease

RR

Relative risk

95% CI

95% Confidence interval

PM

Particulate matter

AMI

Acute myocardial infarction

RAS

Renin-angiotensin system

GAM

Generalized additive model

DLNMs

Distributed lag non-linear models

DLMs

Distributed lag linear models

AQI

Air Quality Index

CCS

Chronic coronary syndromes

ANS

Autonomic nervous system

TRP

Transient receptor potential

Author contribution

Drs. Yuan Gao and Yongtao Yang had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Dr. Yuan Gao.

Acquisition, analysis, or interpretation of data: Drs. Yuan Gao and Weixuan Sheng.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Drs. Weixuan Sheng and Yuan Gao.

Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

The Ethics Review Board of Beijing Shijitan Hospital approved the investigation and ensured that the safety, health and rights of patients involved were protected.

Consent for publication

The authors give the consent for this study to be published in International Journal of Health Geographics.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher's note

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

Contributor Information

Yuan Gao, Email: xiaomoyu198502@126.com.

Weixuan Sheng, Email: swx0214@126.com.

Yongtao Yang, Email: 13911150451@139.com.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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