Author s response to reviews

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1 Author s response to reviews Title: Short-term effects of ambient fine particulate matter pollution on hospital visits for chronic obstructive pulmonary disease in Beijing, China Authors: Yaohua Tian (yaohua_tian@bjmu.edu.com) Xiao Xiang (doublex1990@qq.com) Juan Juan ( @qq.com) Jing Song (florasongjing@qq.com) Yaying Cao ( @qq.com) Chao Huang ( @qq.com) Man Li ( @qq.com) Yonghua Hu (yhhu@bjmu.edu.cn; yaohua_tian315@163.com) Version: 1 Date: 12 Feb 2018 Author s response to reviews: to Reviewers and Editor s comments on ENHE-D MS TITLE: Short-term effects of ambient fine particulate matter pollution on hospital visits for chronic obstructive pulmonary disease in Beijing, China. We appreciated the careful reading of our manuscript and valuable suggestions. We have carefully considered the comments and have revised the manuscript accordingly. The corresponding changes in the revised manuscript have been highlighted with red color. Reviewer comments: Reviewer #1: This paper investigate the associations between PM2.5 and hospital visits (inpatient and outpatient visits) for COPD. The results showed that an IQR increase in the concurrent day PM2.5 concentration was significantly associated with a 2.38% and 6.03% increase in daily outpatient visits and inpatient visits. The article is well written with few grammar errors and organized in a clear way. There are several comments that still need to be addressed.

2 1. Thought the results were significant, the research topic is very attractive. In addition, a few paper published recently by China CDC have reveal short term effects between PM2.5 and mortality of multiple diseases, which include COPD. We strongly suggested the authors review "Fine Particulate Air Pollution and Daily Mortality: A Nationwide Analysis in 272 Chinese Cities" which was published on <American Journal of Respiratory and Critical Care Medicine>. Thank you for this comment. Following your pertinent suggestion, we have reviewed the paper and cited it in the Introduction and Discussion: Our findings were supported by a recent national study done in 272 cities in China that reported significant effects of PM2.5 on mortality [1]. References 1. Chen R, Yin P, Meng X, Liu C, Wang L, Xu X, Ross JA, Tse LA, Zhao Z, Kan H et al. Fine Particulate Air Pollution and Daily Mortality. A Nationwide Analysis in 272 Chinese Cities. Am J Respir Crit Care Med. 2017;196(1): In Discussion, the authors mentioned that the risk estimates were greater for inpatient visit than for outpatient visit. The reviewer don't think the statement is appropriate from a statistical view. The factors causing the variations of inpatient visit and outpatient visit can be complicating, where a lot of latent variables are not investigated in the present study. The results are not comparable in terms of statistical associations. Similar statements are also needed to be revised. Thank you for this comment. In this study, we applied the same model for outpatient and inpatient visits, which is consistent with previous studies [1, 2]. The effect estimates for inpatient visits appeared to be greater than those for outpatient visits, and identical results were observed using stratified analyses by season, age, and sex. However, we acknowledged that the factors causing the variations of inpatient visit and outpatient visit can be complicating. We agreed with your point that it was not appropriate to directly compare the effect estimates for outpatient and inpatient visits. Following your pertinent suggestion, the relevant statements have been revised: The effect estimates for inpatient visits appeared to differ from those for outpatient visits. The factors causing the variations of inpatient visit and outpatient visit can be complicating. Future studies are warranted to explore the variations in the effects estimates for different morbidity outcomes.

3 References 1. Pan HH, Chen CT, Sun HL, Ku MS, Liao PF, Lu KH, Sheu JN, Huang JY, Pai JY, Lue KH. Comparison of the effects of air pollution on outpatient and inpatient visits for asthma: a population-based study in Taiwan. PLoS ONE. 2014;9(5). 2. Tian Y, Xiang X, Juan J, Sun K, Song J, Cao Y, Hu Y. Fine particulate air pollution and hospital visits for asthma in Beijing, China. Environ Pollut. 2017;230: Just as authors mentioned that, the meteorology and air quality data was collected from one site, which may be not representative for the whole city. The reviewer is just wondering why the study only used PM2.5 reports by U.S. embassy since both national air quality (1) and municipal sites (over 10) provide more detailed air quality reports for public reference. Thank you for this comment. In China, PM2.5 was gradually introduced in the national air quality monitoring network since January 2013 [1]. However, this study was conducted between January 1, 2010, and June 30, Therefore, PM2.5 measurements from the National Air Pollution Monitoring System for the study period were not available and thus we only used the PM2.5 reports by U.S. embassy. Following your pertinent suggestion, we have described the details on PM2.5 measurements in the Method: Data on hourly PM2.5 concentrations were collected from the reports published by the U.S. embassy, which established an ambient air quality monitoring station on the rooftop of embassy building located in Chaoyang district, Beijing. The PM2.5 levels obtained from the monitor have been demonstrated to exhibit approximately the same trend as city-wide PM2.5 levels [2]. To reduce exposure misclassification, the maximum distance between the monitor and hospital visits considered was approximately 40 kilometers (km) [3, 4]. Approximately 79.2% of Beijing s total population lived within a 40-km radius of the monitor. All areas of high population density (>5000 people/km2), 97.8% (44/45) of the tertiary hospitals and 79.3% (69/87) of the secondary hospitals in Beijing located within a 40-km radius of the monitor [5]. Previous studies have indicated that the monitoring data could be used as a proxy for population exposure among individuals living < 40 km from the monitor [5-7]. A map on Beijing along with the station location was shown in the figure below. This figure was included in the Additional file 1: Figure S1. We also have addressed this limitation in the Discussion: The use of PM2.5 concentrations derived entirely from a fixed-site monitoring station as a proxy for personal exposure is expected to lead to exposure misclassification, which may underestimate the effects of air pollution [8].

4 References 1. Chen R, Yin P, Meng X, Liu C, Wang L, Xu X, Ross JA, Tse LA, Zhao Z, Kan H et al. Fine Particulate Air Pollution and Daily Mortality. A Nationwide Analysis in 272 Chinese Cities. Am J Respir Crit Care Med. 2017;196(1): Wang JF, Hu MG, Xu CD, Christakos G, Zhao Y. Estimation of citywide air pollution in Beijing. PLoS ONE. 2013;8(1):8. 3. Tian Y, Xiang X, Juan J, Sun K, Song J, Cao Y, Hu Y. Fine particulate air pollution and hospital visits for asthma in Beijing, China. Environ Pollut. 2017;230: Tian Y, Xiang X, Wu Y, Cao Y, Song J, Sun K, Liu H, Hu Y. Fine Particulate Air Pollution and First Hospital Admissions for Ischemic Stroke in Beijing, China. Sci Rep. 2017;7(1): Xie W, Li G, Zhao D, Xie X, Wei Z, Wang W, Wang M, Liu W, Sun J, Jia Z et al. Relationship between fine particulate air pollution and ischaemic heart disease morbidity and mortality. Heart. 2015;101(4): Wellenius GA, Burger MR, Coull BA, Schwartz J, Suh HH, Koutrakis P, Schlaug G, Gold DR, Mittleman MA. Ambient air pollution and the risk of acute ischemic stroke. Arch Intern Med. 2012;172(3): Dockery DW, Luttmann-Gibson H, Rich DQ, Link MS, Mittleman MA, Gold DR, Koutrakis P, Schwartz JD, Verrier RL. Association of air pollution with increased incidence of ventricular tachyarrhythmias recorded by implanted cardioverter defibrillators. Environ Health Perspect. 2005;113(6): Goldman GT, Mulholland JA, Russell AG, Strickland MJ, Klein M, Waller LA, Tolbert PE. Impact of exposure measurement error in air pollution epidemiology: effect of error type in time-series studies. Environ Health. 2011;10(61): Reviewer #2: This paper is concise, focusing on chronic obstructive pulmonary disease (COPD) only in the capital of China, Beijing, based on 2.5 year data. This study reports city-specific COPD risk estimates attributable to ambient fine particulate matter (PM2.5), comparing about 3.5 million outpatients to about 0.1 million inpatients. With a huge sample size, this paper can provide new evidence on the association between PM2.5 and COPD. However, I believe that more detail descriptions of several things below are warranted.

5 Major comments: 1. The target population of this study? The author indicated in line 13-14, "The objective of this study was to explore the short-term effect of PM2.5 on COPD in Beijing, China." This sounds a general population but the study followed out- and in-patients. The author should clarify the target population and the meaning of outpatients. Thank you for this comment. Following your pertinent suggestion, we have changed the description of The objective of this study was to explore the short-term effect of PM2.5 on COPD in Beijing, China into The objective of this study was to explore the short-term effects of PM2.5 on outpatient and inpatient visits for COPD in Beijing, China. We also have added the description of outpatients in the Methods: The outpatient visit was defined as a patient visit to a physician's office, clinic, or hospital outpatient department [1]. References 1. Pan HH, Chen CT, Sun HL, Ku MS, Liao PF, Lu KH, Sheu JN, Huang JY, Pai JY, Lue KH. Comparison of the effects of air pollution on outpatient and inpatient visits for asthma: a population-based study in Taiwan. PLoS ONE. 2014;9(5). 2. Misclassification of PM2.5. How serious? Lines indicates just one monitoring station. Even if data validation of this station has been done, it does not inform misclassification in exposure assignment. A map on Beijing should be provided along with the station location and incidences of the in- and out-patients. This map is to show distances between the station and outpatients and inpatients, which can show how serious the misclassification in PM2.5. Thank you for this comment. Following your pertinent suggestion, we have described the details on PM2.5 measurements in the Method: Data on hourly PM2.5 concentrations were collected from the reports published by the U.S. embassy, which established an ambient air quality monitoring station on the rooftop of embassy building located in Chaoyang district, Beijing. The PM2.5 levels obtained from the monitor have been demonstrated to exhibit approximately the same trend as city-wide PM2.5 levels [1]. To reduce exposure misclassification, the maximum distance between the monitor and hospital visits considered was approximately 40 kilometers (km) [2, 3]. Approximately 79.2% of Beijing s total population lived within a 40-km radius of the monitor. All areas of high population density (>5000 people/km2), 97.8% (44/45) of the

6 tertiary hospitals and 79.3% (69/87) of the secondary hospitals in Beijing located within a 40-km radius of the monitor [4]. Previous studies have indicated that the monitoring data could be used as a proxy for population exposure among individuals living < 40 km from the monitor [4-6]. We also have addressed this limitation in the Discussion: The use of PM2.5 concentrations derived entirely from a fixed-site monitoring station as a proxy for personal exposure is expected to lead to exposure misclassification, which may underestimate the effects of air pollution [7]. We also have provided a map on Beijing along with the station location, shown in the figure below. This figure was included in the Additional file 1: Figure S1. References 1. Wang JF, Hu MG, Xu CD, Christakos G, Zhao Y. Estimation of citywide air pollution in Beijing. PLoS ONE. 2013;8(1):8. 2. Tian Y, Xiang X, Juan J, Sun K, Song J, Cao Y, Hu Y. Fine particulate air pollution and hospital visits for asthma in Beijing, China. Environ Pollut. 2017;230: Tian Y, Xiang X, Wu Y, Cao Y, Song J, Sun K, Liu H, Hu Y. Fine Particulate Air Pollution and First Hospital Admissions for Ischemic Stroke in Beijing, China. Sci Rep. 2017;7(1): Xie W, Li G, Zhao D, Xie X, Wei Z, Wang W, Wang M, Liu W, Sun J, Jia Z et al. Relationship between fine particulate air pollution and ischaemic heart disease morbidity and mortality. Heart. 2015;101(4): Wellenius GA, Burger MR, Coull BA, Schwartz J, Suh HH, Koutrakis P, Schlaug G, Gold DR, Mittleman MA. Ambient air pollution and the risk of acute ischemic stroke. Arch Intern Med. 2012;172(3): Dockery DW, Luttmann-Gibson H, Rich DQ, Link MS, Mittleman MA, Gold DR, Koutrakis P, Schwartz JD, Verrier RL. Association of air pollution with increased incidence of ventricular tachyarrhythmias recorded by implanted cardioverter defibrillators. Environ Health Perspect. 2005;113(6): Goldman GT, Mulholland JA, Russell AG, Strickland MJ, Klein M, Waller LA, Tolbert PE. Impact of exposure measurement error in air pollution epidemiology: effect of error type in time-series studies. Environ Health. 2011;10(61): Exposure differences between out- and in-patients?

7 The 2nd question is about the distance between monitoring station and patients. Another issue regarding the exposure to PM2.5 is the difference in ambient PM2.5 concentrations between outand in-patients. Inpatients would be in hospital, being less exposed to the ambient PM concentrations. This study however assumes no difference in exposures to ambient PM. The author should consider this. Without it, this study cannot differentiate out- and in-patients. Thank you for this comment. The maximum distance between the monitor and hospital visits considered was approximately 40 kilometers (km) [1, 2]. Previous studies have indicated that the monitoring data could be used as a proxy for population exposure among individuals living < 40 km from the monitor [3-5]. In this study, we used ambient PM2.5 levels on the day of hospital visits (outpatient and inpatient visits) as individual exposure. This strategy is consistent with previous studies [1, 6-8]. However, we also acknowledged the difference in ambient PM2.5 concentrations between out- and in-patients. Following your pertinent suggestion, we have addressed this limitation in the Discussion: In this study, we used ambient PM2.5 levels on the day of hospital visits (outpatient and inpatient visits) as individual exposure. This strategy is consistent with previous studies [1, 6-8]. However, inpatients would be in hospital, being less exposed to the ambient PM2.5 concentrations. This exposure measurement error would tend to bias the effect estimates downward [9]. References 1. Tian Y, Xiang X, Juan J, Sun K, Song J, Cao Y, Hu Y. Fine particulate air pollution and hospital visits for asthma in Beijing, China. Environ Pollut. 2017;230: Tian Y, Xiang X, Wu Y, Cao Y, Song J, Sun K, Liu H, Hu Y. Fine Particulate Air Pollution and First Hospital Admissions for Ischemic Stroke in Beijing, China. Sci Rep. 2017;7(1): Xie W, Li G, Zhao D, Xie X, Wei Z, Wang W, Wang M, Liu W, Sun J, Jia Z et al. Relationship between fine particulate air pollution and ischaemic heart disease morbidity and mortality. Heart. 2015;101(4): Wellenius GA, Burger MR, Coull BA, Schwartz J, Suh HH, Koutrakis P, Schlaug G, Gold DR, Mittleman MA. Ambient air pollution and the risk of acute ischemic stroke. Arch Intern Med. 2012;172(3): Dockery DW, Luttmann-Gibson H, Rich DQ, Link MS, Mittleman MA, Gold DR, Koutrakis P, Schwartz JD, Verrier RL. Association of air pollution with increased incidence of ventricular tachyarrhythmias recorded by implanted cardioverter defibrillators. Environ Health Perspect. 2005;113(6):

8 6. Pan HH, Chen CT, Sun HL, Ku MS, Liao PF, Lu KH, Sheu JN, Huang JY, Pai JY, Lue KH. Comparison of the effects of air pollution on outpatient and inpatient visits for asthma: a population-based study in Taiwan. PLoS ONE. 2014;9(5). 7. Dominici F, Peng RD, Bell ML, Pham L, McDermott A, Zeger SL, Samet JM. Fine particulate air pollution and hospital admission for cardiovascular and respiratory diseases. JAMA. 2006;295(10): Peng RD, Chang HH, Bell ML, McDermott A, Zeger SL, Samet JM, Dominici F. Coarse particulate matter air pollution and hospital admissions for cardiovascular and respiratory diseases among Medicare patients. JAMA. 2008;299(18): Goldman GT, Mulholland JA, Russell AG, Strickland MJ, Klein M, Waller LA, Tolbert PE. Impact of exposure measurement error in air pollution epidemiology: effect of error type in time-series studies. Environ Health. 2011;10(61): Confounders for inpatients? Can we justify the same model for out-and in-patients? This study reports higher associations for inpatients compared to outpatients. However, inpatients are older than outpatients overall, and thus age could be a confounder. In the model, the author can add another term on age or apply different models. Inpatients would have primary causes of their hospitalizations. How many (in %) were hospitalized due to pulmonary disease? A table on this info (categorized by the ICD-10 code) would be useful to understand why they are at higher risk for COPD. Thank you for this comment. In this study, only hospital visits with a primary diagnosis of COPD (ICD-10 codes J40 J44) were included. We applied the same model for outpatient and inpatient visits, which is consistent with previous studies [1, 2]. We observed higher effect estimates for inpatients compared to outpatients. We also have conducted stratification analyses to explore potential effect modification of COPD risk by age (18 64 years and 65 years). The same trend remained. However, the factors causing the variations of inpatient visit and outpatient visit can be complicating. We agreed with your point that it was not appropriate to directly compare the effect estimates for outpatient and inpatient visits. Following your pertinent suggestion, the relevant statements have been revised: The effect estimates for inpatient visits appeared to differ from those for outpatient visits. The factors causing the variations of inpatient visit and outpatient visit can be complicating. Future studies are warranted to explore the variations in the effects estimates for different morbidity outcomes.

9 References 1. Pan HH, Chen CT, Sun HL, Ku MS, Liao PF, Lu KH, Sheu JN, Huang JY, Pai JY, Lue KH. Comparison of the effects of air pollution on outpatient and inpatient visits for asthma: a population-based study in Taiwan. PLoS ONE. 2014;9(5). 2. Tian Y, Xiang X, Juan J, Sun K, Song J, Cao Y, Hu Y. Fine particulate air pollution and hospital visits for asthma in Beijing, China. Environ Pollut. 2017;230: Difference in between developing and developed countries in relation to the study objective? Line 42 says about developing countries, where PM2.5 is more severe. Considering sources of PM2.5, it is unclear developing countries would have higher PM2.5 than developed countries. If so, references should be provided. The terms are more related to economic status than PM2.5 concentrations. Health care system or availability, nutrition profile, etc could be also related to the terms. The author needs to clarify in what sense this study considered developing countries. Thank you for this comment. Previous studies on the associations between PM2.5 and COPD were primarily conducted in the developed countries, and evidence is limited in the developing countries, where PM2.5 pollution is generally more severe [1]. We agreed with your point that sources of PM2.5, health care system or availability, and nutrition profile would have effects on the association. However, data on these variables were not available in our study. In this study, we examined the association between PM2.5 and hospital visits for COPD in Beijing, China, which has the highest ambient PM2.5 levels worldwide. Following your pertinent suggestion, we have cited several references and clarified in what sense this study considered developing countries in the Introduction: However, none of these studies were conducted in developing countries where PM2.5 pollution is generally more severe [1]. China, the largest developing country, has the highest ambient PM2.5 levels worldwide [2]. References 1. Cohen AJ, Brauer M, Burnett R, Anderson HR, Frostad J, Estep K, Balakrishnan K, Brunekreef B, Dandona L, Dandona R et al. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study Lancet. 2017;389(10082): Kan H, Chen R, Tong S. Ambient air pollution, climate change, and population health in China. Environ Int. 2012;42:10-19.

10 6. Seasonal analysis The smoothing on calendar time is to remove seasonal or long-term trend in COPD. For the warm or cool season analysis, please specify how the smoothing on calendar time was applied. Just smoothing on warm season only or smoothing on year-round and then extracting warm season only? Justify the method done for this study. The study period is Jan 2010 to June Following the definition of warm (Apr to Sept) and cool (Oct to Mar) seasons, it would be better up to March 2012 or Sept 2012 (if available). Thank you for this comment. In subgroup analyses, we just used a penalized spline function of calendar time on warm or cool season to accommodate the long-term trend in hospital visits for COPD, which is consistent with previous studies [1, 2]. Following your pertinent suggestion, this issue has been addressed in the Methods: A penalized spline function of calendar time on warm or cool season was used to accommodate the long-term trend in hospital visits for COPD, which is consistent with previous studies [1, 2]. Following your pertinent suggestion, we have done a sensitivity analysis between January 2010 and March The results of the sensitivity analyses were similar to our primary estimates, indicating the robustness of our findings (data were shown in the Table below). Percentage change 95% CI P-value Outpatient visits Season <0.001 Cool Warm Inpatient visits Season <0.001 Cool Warm References 1. Yin P, Chen R, Wang L, Meng X, Liu C, Niu Y, Lin Z, Liu Y, Liu J, Qi J et al. Ambient Ozone Pollution and Daily Mortality: A Nationwide Study in 272 Chinese Cities. Environ Health Perspect. 2017;125(11). 2. Tian Y, Xiang X, Juan J, Song J, Cao Y, Huang C, Li M, Hu Y. Short-term Effect of Ambient Ozone on Daily Emergency Room Visits in Beijing, China. Sci Rep. 2018;8(1):

11 Minor comments: 7. Line 84: Beta represents in relation to unit increase in PM2.5, not per IQR. Thank you for this comment. Following your pertinent suggestion, we have changed the description into: β represents log-relative risk of COPD morbidity in relation to unit increase in PM2.5 concentrations. 8. Line 94: morbidity- hospital visit. Thank you for this comment. Following your pertinent suggestion, we have changed the description into: association between COPD hospital visits and PM Table 3: For easy comparisons with other studies under discussion, it would be better if % change per 10 ug/m3 is provided in addition to the IQR. Thank you for this comment. Following your pertinent suggestion, we have provided a table representing the percentage changes in daily COPD hospital visits associated with per 10 ug/m3 increase in PM2.5 concentrations in the Additional file 1: Table S1. Table S1. Percentage changes with 95% CIs in outpatient and inpatient visits for chronic obstructive pulmonary disease (COPD) associated with per 10 ug/m3 increase in fine particulate matter (PM2.5) concentration for different lag structures. Hospital service Lag days Percentage change 95% CI P Outpatient visits Lag 0 days <2e-16 Lag 1 days Lag 2 days <2e-16 Lag 3 days <2e-16 Lag 0-1 days <2e-16 Lag 0-2 days <2e-16 Lag 0-3 days <2e-16 Inpatient visits Lag 0 days <2e-16 Lag 1 days e-11 Lag 2 days Lag 3 days Lag 0-1 days <2e-16

12 Lag 0-2 days <2e-16 Lag 0-3 days <2e Provide a scatter plot on PM2.5 concentration (x-axis) and counts of out- and in-patients in different color. Thank you for this comment. Following your pertinent suggestion, we have provided a scatter plot on PM2.5 concentration (x-axis) and counts of out- and in-patients in different color in the Additional file 1: Figure S2.