The health effects associated with fine particulate matter (PM2.5) have attracted considerable public attention in recent decades. It has been verified that PM2.5 can damage the respiratory and cardiovascular systems and cause various diseases. While the association between diseases and PM2.5 has been widely studied, this work aims to analyze the association between PM2.5 and hospital visit rates for respiratory diseases in Taiwan. To this end, a disease mapping model that considers spatial effects is applied to estimate the association. The results show that there is a positive association between hospital visit rates and the PM2.5 concentrations in the Taiwanese population in 2012 after controlling for other variables, such as smoking rates and the number of hospitals in each region. This finding indicates that control of PM2.5 could decrease hospital visit rates for respiratory diseases in Taiwan.
Citation: Wang F, Chen T, Chang Q, Kao Y-W, Li J, Chen M, et al. (2021) Respiratory diseases are positively associated with PM2.5 concentrations in different areas of Taiwan. PLoS ONE 16(4): e0249694. https://doi.org/10.1371/journal.pone.0249694
Editor: Flavio Manoel Rodrigues Da Silva Júnior, Universidade Federal do Rio Grande - FURG, BRAZIL
Received: March 8, 2020; Accepted: March 23, 2021; Published: April 22, 2021
Copyright: © 2021 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The data of hospital visits were collected from the National Health Insurance Research Database (NHIRD). Users need to apply for data access at https://nhird.nhri.org.tw/.
Funding: YL was supported by the Research Funds for the Major Innovation Platform of Public Health & Disease Control and Prevention, Renmin University of China, 0001.
Competing interests: The authors have declared that no competing interests exist.
Every day, hundreds of millions of people worldwide suffer from various respiratory diseases. According to Feldman and Richards (2018) , lower respiratory infections alone are the fifth leading cause of death worldwide. After decades of research, scholars have identified certain causes of these diseases, such as genetic issues, infections, a nd smoking [2, 3]. In addition, air pollution has adverse health effects on the respiratory system and, thus, causes many respiratory diseases. In particular, patients of chronic respiratory diseases, such as chronic obstructive pulmonary disease (COPD) and the onset of asthma, are more vulnerable to air pollution . Furthermore, 91% of the world’s population lives in places where the air quality fails to meet the World Health Organization (WHO) standards , which is a global environmental problem. Specifically, a major health-damaging component of air pollution is fine particulate matter with a diameter less than 2.5 μm (PM2.5). These particles can penetrate the lungs and bloodstream unfiltered, causing respiratory diseases .
Studies on the association between PM2.5 and respiratory diseases have attracted considerable attention. An early study found that lung cancer mortality could increase 8% for every 10 μg/m3 increase in PM2.5 . Another study conducted in the United States found that respiratory deaths could increase by 1.68% for every 10 μg/m3 increase in 2-day averaged PM2.5 concentrations . Similar conclusions are drawn in studies from Europe and Japan [9, 10].
However, studies on the association between PM2.5 and respiratory diseases are rare in Taiwan. Owing to the prevalence of PM2.5 in Taiwan and the consequent health risks, it is of great importance to conduct more research to explore such associations. Meanwhile, owing to infectiousness, many respiratory diseases show the characteristics of spatial aggregation. Therefore, spatial factors are often considered in public health studies [7, 11]. In this work, a coherent generative model  is applied to explore the relationship between PM2.5 and respiratory diseases in Taiwan. As shown by , this model can provide more accurate estimates and tighter credible intervals than previous methods. In this work, we applied this model to investigate the relationship between PM2.5 and hospital visit rates for respiratory diseases in Taiwan. By controlling smoking rates and the number of hospitals in each region, a significantly positive effect from PM2.5 concentrations on the hospital visit rates for respiratory diseases was found.
Materials and methods
In this work, the research objective is to investigate the influence of PM2.5 on hospital visit rates for respiratory diseases in Taiwan. The data of hospital visits were collected for different diseases in 349 third-level administrative regions of Taiwan in 2012 from the National Health Insurance Research Database (NHIRD, https://nhird.nhri.org.tw/). Then, the hospital visit rates for respiratory diseases are defined as hospital visits for respiratory disease divided by the total number of hospital visits for all diseases. Here, respiratory diseases are defined as diseases corresponding to ICD-9 codes 460–466 and 470–478, which include acute respiratory diseases, upper respiratory infections, upper respiratory tract infections, among others. The detailed information for codes 460–466 and 470–478 is listed in Table 1.
The raw data of PM2.5 (unit: μg/m3) are collected from the Taiwanese Central Weather Bureau (https://www.cwb.gov.tw/). The concentrations of PM2.5 are recorded in 70 meteorological stations across Taiwan in 2012. Each station recorded the raw PM2.5 concentration every hour of every day in 2012. For each station, the recordings throughout the year were averaged. Since 70 meteorological stations do not correspond to the 349 regions, the Kriging technique  was further applied to interpolate the PM2.5 value for each region.
To better detect the influence of PM2.5 on respiratory diseases, we collected the smoking rate and number of hospitals in the 349 regions in Taiwan as control variables. The smoking rate data were obtained from the Adult Smoking Behavior Survey, a survey conducted by the Health Promotion Administration in Taiwan. Here, the smoking rate is defined as the percentage of people over 18 years of age who have previously smoked more than a total of 100 cigarettes and have used tobacco products in the past 30 days. The number of hospitals were collected from the Taiwanese NHIRD for the 349 regions in 2012. Here, the number of hospitals is defined as the total number of clinics, district hospitals, regional hospitals, and medical centers in each region.
We applied the coherent and generative disease mapping model (CG model)  to investigate the relationship between hospital visit rates for respiratory diseases and PM2.5. In the past literature of disease mapping models, most research has focused on relative risks, which required the use of internal standardization to calculate the expected number of observations and thus, made the models incoherent and not generative [11, 14–18]. On the contrary, the CG model replaced relative risk with disease incidence, and thus, behaved incoherently and generatively. Consequently, it achieved tighter credible intervals. Thus, in the present work, the CG model was used to estimate the hospital visit rates for respiratory diseases.
To better detect the influence of PM2.5 on respiratory diseases, we considered two covariates, namely, the smoking rate and number of hospitals, as control variables. The existing literature has shown that the smoking rate and number of hospitals could influence hospital visit rates for respiratory diseases. A meta-analysis of longitudinal studies, including 216 articles from 1985 to 2013, showed that there were substantial increases in the risks of lung cancer, COPD, and asthma among adult smokers . In addition, the smoking rate was often used as a covariate in previous studies. For example, the Cox proportional hazards model was used to study the association between air pollution and respiratory mortality in Japan , which involved adjusting for smoking status. The number of hospitals in each region, as a representation of a region’s economic status, could also influence the hospital visit rates of diseases . Therefore, we included the smoking rate and number of hospitals in the CG model.
The structure of the CG model used in this study was as follows. Assume there were a total of I = 349 regions in Taiwan. For region i, Yi was the number of hospital visits for respiratory diseases and ni was the total number of hospital visits for all diseases. Accordingly, the hospital visit rate for respiratory diseases in region i was defined as pi = Yi/ni. Then, the CG model was written as (1)
The number of hospital visits for respiratory disease in each region was assumed to follow a Poisson distribution with an expected value of ni pi. The logit transformation of pi was then modeled using a linear relationship with the PM2.5 concentration, the smoking rate, the number of hospitals, and a spatial random effect ϕi. To spatially model ϕi, the classic conditionally autoregressive (CAR) distribution was used as a prior [14, 20]. By applying the CAR distribution, each region’s neighboring values were considered to smooth the local rates.
Visualization of different variables
Fig 1 showed a histogram of hospital visit rates for respiratory diseases in all 349 regions in Taiwan. It was evident that the hospital visit rates for respiratory diseases in most regions lied in the range of 0.1 to 0.4. The highest hospital visit rate for respiratory diseases was 0.45 for Shuishang Village, Chiayi County, located in midwestern Taiwan. The lowest hospital visit rate for respiratory diseases was 0 observed in Dabu Village, also in Chiayi County.
This figure illustrates the distribution of hospital visit rates for respiratory diseases in 349 third-level administrative regions of Taiwan in 2012. As shown, the hospital visit rates vary in different regions.
To explore the spatial distributions of PM2.5 concentrations, smoking rates, the number of hospitals, and the hospital visit rates for respiratory diseases, we summarized the variable information of 349 third-level administrative regions into 22 second-level administrative regions in Taiwan. Specifically, for each second-level administrative region, we calculated the average values of PM2.5 concentrations (after Kriging), smoking rates, hospital numbers, and hospital visit rates for respiratory diseases in all third-level administrative regions under the region’s jurisdiction. Table 2 listed the corresponding results for all 22 second-level administrative regions, as well as their geographical locations in Taiwan.
The geographical locations of each region in Taiwan are also reported.
We first focused on the distribution of PM2.5 concentrations. As shown in Table 2, in general, the southwestern and southern regions had relatively higher PM2.5 concentrations than other regions. Moreover, the highest PM2.5 concentration in 2012 was 40.165 μg/m3 in Kaohsiung City, a metropolis located in southern Taiwan. The lowest PM2.5 concentration was 13.3 μg/m3 in Taitung City, located in eastern Taiwan. The average concentration of PM2.5 across 349 regions in Taiwan was 28.9 μg/m3.
Among all 349 third-level administrative regions in Taiwan, the mean of smoking rate was 0.18. As shown in Table 2, the central regions had higher smoking rates than others. Specifically, the highest smoking rate was 0.230 in Nantou County, located in central Taiwan, and the lowest smoking rate was 0.144 in Tainan City and Tainan County, located in southeastern Taiwan. Finally, we investigated the distribution of hospital numbers across 349 regions in Taiwan. Table 2 showed that the number of hospitals varied a lot from region to region. Specifically, Banqiao City in Taipei County had the largest number of hospitals (1145), while Daren Village in Taitung County, located in eastern Taiwan, had only 1 hospital.
Table 3 showed the basic statistical summaries of the abovementioned four variables. The coefficient of variation (CV) was used to demonstrate the dispersion of each variable’s frequency distribution. As shown in Table 3, the number of hospitals had the highest CV at 1.52, indicating its scattered characteristics, shown in Table 2. Specifically, the range of hospital numbers lies in the range between 1 to 1145 among all 349 regions in Taiwan. Except for number of hospitals, the coefficients of variation of the other three variables were almost the same and all less than 0.3.
Correlational relationship between different variables
To explore the relationship among the four variables, Fig 2 showed the Pearson correlation coefficient between different variables. PM2.5 had a positive association with the hospital visit rate (0.04), but it was not significant (p-value = 0.5). Moreover, the number of hospitals was significantly negatively associated (-0.12) with the hospital visit rate (p-value = 0.02), but the smoking rate showed no significant association (0.07) with the hospital visit rate (p-value = 0.16).
HVRRD refers to the hospital visit rate for respiratory diseases. The symbol X indicates that the two variables’ correlation coefficient is not significant with a p-value greater than 0.05.
To further explore the relationship between PM2.5 and the hospital visit rates for respiratory diseases, regions with PM2.5 bigger than a threshold were selected to calculate their correlations with the corresponding hospital visit rates. The thresholds were selected according to the PM2.5 pollution levels set by Taiwan. The corresponding results were shown in Fig 3. It was obvious that, as PM2.5 increased, its correlation with hospital visit rate also became larger. When the pollution level of PM2.5 was bigger than 8, the correlation reached 0.208.
The CG model was then applied to estimate the hospital visit rates of all respiratory diseases corresponding to ICD-9 codes 460–466 and 470–478. The model results were shown in Table 4. As shown, the mean estimated coefficients for PM2.5 concentrations, the smoking rate and the number of hospitals were all positive. The corresponding 95% credible intervals for the three covariates were all larger than zero. These results suggested that, the three covariates all had significant positive effects on the hospital visit rate for respiratory diseases.
The estimated mean and 95% credible intervals for different variables are reported.
We then investigated the influences of PM2.5 on the hospital visit rates related to specific respiratory diseases. To this end, the top two diseases with the highest hospital visit rates in 2012 were considered as examples. They were acute upper respiratory infections of multiple or unspecified sites and acute bronchitis and bronchiolitis, corresponding to ICD-9 codes 465 and 466, respectively. The CG models were then applied separately to the hospital visit rates for these two diseases, and the results were shown in Table 5. In general, the modeling results for these two diseases were in accordance with those for the hospital visit rates of all respiratory diseases in Table 4. As shown, for either disease, the PM2.5 concentration, number of hospitals, and smoking rate all showed significantly positive relationships with its hospital visit rate.
Summary of findings
In this work, we aimed to investigate the relationship between PM2.5 and hospital visit rates for respiratory diseases in Taiwan. Although the PM2.5 concentrations in Taiwan were far below the WHO standard, we still found that different regions in Taiwan had quite different PM2.5 concentrations. Therefore, it was still of great importance to explore the influence of PM2.5 concentrations on hospital visit rates for respiratory diseases. To this end, we first calculated the Pearson correlation coefficient between PM2.5 concentrations and hospital visit rates for respiratory diseases, which was positive, but not significant. Then, the Pearson correlation coefficients for the two variables under different pollution levels were calculated. The corresponding results indicated that relationship between PM2.5 and hospital visit rates for respiratory diseases became stronger at higher levels of PM2.5. However, it should be noted that the Pearson correlation could only test the linear correlations between two variables without considering other variables. Therefore, the associations between PM2.5 concentrations and the hospital visit rate for respiratory diseases should be further investigated via modelling approach.
To this end, we then applied the CG disease mapping model on the respiratory disease data to investigate the relationship between PM2.5 concentrations and hospital visit rates for respiratory diseases. We discussed the results of the CG disease mapping model from the following perspectives.
The effect of PM2.5 concentrations.
By applying the disease mapping model and controlling smoking rates and the number of hospitals in each region, we found that PM2.5 concentrations had a significantly positive effect on the hospital visit rates for respiratory diseases. Specifically, every 1 μg/m3 increase in PM2.5 concentrations would cause a 1.316 (i.e., e0.84 − 1 = 1.316) increase in the odds ratio of hospital visit rates for respiratory diseases while controlling other variables.
The effects of smoking rate.
By using the CG disease mapping model, a significant positive effect was observed for the smoking rate. That was, when the smoking rate increased, the hospital visit rate for respiratory diseases would also increase. Specifically, every 1 percent (i.e., 0.01) increase in the smoking rate would cause a 0.072 (i.e., (e2.11 − 1) × 0.01 = 0.072) increase in the odds ratio of hospital visit rates for respiratory diseases while controlling other variables. This finding for smoking rate was consistent with the existing literature [3, 10, 19].
The effect of hospital number.
The CG model also detected a significant positive effect from the number of hospitals on the hospital visit rate for respiratory diseases. The positive influence of hospital number might be related to the economic development. When the economy of a region was more developed, it usually had more hospitals. In other words, the hospitals were more accessible to the region’s residents. Therefore, the residents in these regions would be more concerned about their health and more willing to go to hospital than residents in other regions, both of which would lead to the number of hospitals positively influencing the hospital visit rate.
The smoothness for hospital visit rates.
By considering the spatial effects in the CG disease mapping model, the observed hospital visit rates for respiratory diseases could be smoothed. To illustrate this ideas, we took the region of Dabu Village in Chiayi County as an example. Specifically, the raw number of hospital visits for respiratory diseases in Dabu Village in 2012 was zero. However, this did not mean that the residents in Dabu Village were immune to respiratory diseases. In fact, the estimated hospital visit rate for respiratory diseases in Dabu Village was 0.251, which was smoothed by its neighborhood regions. This finding verified the advantages of using disease mapping models.
Results for two specific respiratory diseases.
This study further investigated the influences of PM2.5 on the hospital visit rates of two specific respiratory diseases, i.e., acute upper respiratory infections of multiple or unspecified sites and acute bronchitis and bronchiolitis. By using the CG disease mapping model on the respiratory data for the two diseases, similar modeling results were obtained. However, the degree of influence caused by the same variable behaved slightly differently for different diseases. As for PM2.5 concentrations, by controlling other variables in the model for disease with ICD-9 code 465, every 1 μg/m3 increase in PM2.5 concentrations would increase the odds ratio by 1.293 on average; while in the model for disease with ICD-9 code 466, every 1 μg/m3 increase in PM2.5 concentrations would increase the odds ratio by 1.226 on average. Therefore, when compared with the influence of PM2.5 on the hospital visit rates of all respiratory diseases, these two diseases were impacted less by PM2.5 concentrations.
Comparison with the literature.
Our results were consistent with those of the existing literature. First, our findings regarding the positive association between PM2.5 and respiratory diseases were also found in such places as the United States, Europe, and Japan [7–10]. However, these prior studies mainly focused on disease mortality, rather than hospital admission rates. For example, Zanobetti and Schwartz  conducted studies in the United States, and found that respiratory deaths could increase by 1.68% for every 10 μg/m3 increase in 2-day averaged PM2.5 concentrations. In Taiwan, similar results were obtained by some previous studies. For example, Liwei Lai , focusing on the health risk of PM2.5 in Kaoping region in Taiwan, found that, after controlling seasonal and time effects, the monthly trend of respiratory hospital admissions was moderately related to monthly averaged PM2.5 concentrations. Another study was conducted by Tsai et al. , who aimed to detect the influence of PM2.5 on hospital admissions for respiratory diseases in Taiwan. Using a case-crossover approach for data in 2006 to 2010, they found that hospital admissions of respiratory diseases were positively associated with PM2.5 levels. However, different from our definition for respiratory diseases, Tsai et al.  studied only three kinds of respiratory diseases, that was, pneumonia, asthma, and COPD.
There were several limitations in our study. First, as pointed out in Liwei Lai , hospital admissions included only residents who had health insurance and went to clinics or hospitals for medical treatment. However, there were also people who had symptoms but did not go to clinics or hospitals, leading to a missing data problem. Second, we investigated only the effect of PM2.5 on respiratory diseases using data collected in one particular year (i.e., 2012), and found a significant positive influence. In the future, data with a longer time span should be analyzed to verify whether this conclusion holds over time. Third, owing to limited data sources, we employed only two covariates, namely, smoking rate and number of hospitals, as controlled variables. In further studies, more covariates, such as PM10 and NO2, could be included in the disease mapping model to obtain more reliable results. Finally, the relationship between PM2.5 and hospital visit rates for respiratory diseases was investigated on a yearly accumulated level. However, when longitude data were available, some spatiotemporal disease mapping models can be applied to extract both spatial effects and temporal effects.
In conclusion, a significantly positive effect caused by PM2.5 concentrations was found for hospital admissions of respiratory diseases by using a disease mapping model. Suggested by this work, the harm of PM2.5 on respiratory diseases was vividly shown in Taiwan. It could be regarded as a reminder to the whole society that immediate actions should be taken to deal with the air pollution of PM2.5 in Taiwan.
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