Environmental Research 136 (2015) 196–204

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Systematic review and meta-analysis of the adverse health effects of ambient PM2.5 and PM10 pollution in the Chinese population Feng Lu a,b, Dongqun Xu b, Yibin Cheng b, Shaoxia Dong b, Chao Guo a, Xue Jiang c, Xiaoying Zheng a,n a

Institute of Population Research, Peking University, Beijing 100871, China Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100050, China c Peking University Third Hospital, Beijing 100191, China b

art ic l e i nf o

a b s t r a c t

Article history: Received 9 April 2014 Received in revised form 5 June 2014 Accepted 9 June 2014

Introduction: As the largest developing country, China has some of the worst air quality in the world. Heavy smog in January 2013 led to unprecedented public concern about the health impact of exposure to particulate matter. Conducting health impact assessments of particulate matter has thus become an urgent task for public health practitioners. Combined estimates of the health effects of exposure to particulate matter from quantitative reviews could provide vital information for epidemiology-based health impact assessments, but estimates for the Chinese population are limited. Methods: On December 31, 2013, we systematically searched the PubMed, Web of Science, and China National Knowledge Infrastructure databases using as keywords names of 127 major cities in Mainland China, Hong Kong, and Taiwan. From among the 1464 articles identified, 59 studies were manually screened. Random-effects or fixed-effects models were used to combine their risk estimates, the funnel plots with Egger test were performed to evaluate the publication bias and Meta regression were run to explore the association between exposure to particulate matter with aerodynamic diameters less than 10 and 2.5 mm (PM10 and PM2.5) and the resulting health effects by the Comprehensive Meta Analysis. Results: In terms of short-term effects, the combined excess risks of total non-accidental mortality, mortality due to cardiovascular disease, and mortality due to respiratory disease were 0.36% (95% confidence interval [95%CI]: 0.26%, 0.46%), 0.36% (95%CI: 0.24%, 0.49%), and 0.42% (95%CI: 0.28%, 0.55%), for each 10 μg/m3 increase in PM10. A 10 μg/m3 increase in PM2.5 was associated with a 0.40% (95%CI: 0.22%, 0.59%) increase in total non-accidental mortality, a 0.63% (95%CI: 0.35%, 0.91%) increase in mortality due to cardiovascular disease, and a 0.75% (95%CI: 01.39%, 1.11%) increase in mortality due to respiratory disease. For constituent-specific mortality, increases of 0.40–3.11% were associated with an increase of 10 ng/m3 for nickel in PM. The summary estimate ranges of hospital utilization were 0.08%
0.72% and – 0.58%
1.32% for a 10 μg/m3 increase in PM10 and PM2.5. In terms of long-term effects, a 10 μg/m3 increase of PM10 corresponded to 23–67% increase in the risk of mortality. Conclusion: Short exposures to PM10 and PM2.5 are associated with increases in mortality, but evidence of constituent-associated health effects, long-term effects and morbidity in China is still inadequate. & Elsevier Inc. All rights reserved.

Keywords: Particulate matter Meta-analysis China Health effects

1. Introduction As the largest developing country, China has some of the worst air quality in the world(Kan et al.,2012). In January 2013, a hazardous dense haze covered 1.4 million km2 of China and affected more than 800 million people(Xu et al., 2013), subsequently causing unprecedented public concern about the health impacts of

n Correspondence to: Institute of Population Research, Peking University, No.5 Yiheyuan Road Haidian District, Beijing 100871, China. E-mail address: [email protected] (X.Y. Zheng).

http://dx.doi.org/10.1016/j.envres.2014.06.029 0013-9351/& Elsevier Inc. All rights reserved.

air pollution. Particulate matter (PM) pollution is closely related to haze and among various air pollutants has been most strongly linked to adverse health effects (Pope and Dockery, 2006). The Global Burden of Disease Study showed that in 2010, ambient PM pollution was the fourth leading risk factor for disability-adjusted life-years (DALYs) in China, resulting in 25.2 million DALYs (Yang et al., 2013). Moreover, there is increasing evidence of the effects of PM with aerodynamic diameters less than 10 and 2.5 mm (PM10 and PM2.5) on cardiovascular disease (CVD) and respiratory disease (RD) (Samoli et al., 2005; Lepeule et al., 2012; Krewski et al., 2009; Kloog et al., 2014).

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et al., 2013) and these have focused mainly on the impact of PM10 on mortality. Therefore, the aim of the present study is to provide up-to-date estimates of the magnitude of adverse health effects of PM10 and PM2.5 pollution in the Chinese population. To that end, epidemiological evidence dating back to the 1990s, including data on mortality and hospital utilization for all non-accidental causes, CVD, and RD caused by ambient PM10 and PM2.5 pollution in all age groups, was systematically assessed.

Given quantitative evidence of the health effects of exposure to PM pollution, researchers in many countries have conducted systematic reviews that efficiently integrate existing information and provide data for rational decision making (Mulrow, 1994; Bell et al., 2013; Mehta et al., 2013; Pieters et al., 2012; Biggeri et al., 2005). Concentration–response functions (CRFs) drawn by metaanalysis could provide key information for health impact assessments. For example, the World Health Organization (WHO) Air Quality Guidelines were determined based on health impact assessments (World Health Organization, 2006). However, the exposure–response functions of other countries cannot simply be transferred to the Chinese context because of differences in particle composition and population characteristics, and quantitative review of the adverse health effects of PM pollution in the Chinese population is still limited. To date only four meta-analyzes with pooled effect estimates for PM have been published in English (Kan et al., 2005; Aunan and Pan , 2004; Lai et al., 2013; Shang

2. Materials and methods 2.1. Literature search and data extraction We searched the English-language databases PubMed (National Library of Medicine, Bethesda, MD, USA) and Web of Science (Thompson Scientific, Philadelphia, PA, USA) and the Chinese-

Studies returned by searching in PubMed, Web of Science, and CNKI n = 398 Epidemiological studies involved the health

No

Unrelated (other pollutants, other countries,

impact of PM10/PM2.5 in China

n = 167

non-health studies, thermal stress, and others)

Yes

n = 231

Original studies expressed quantitative

Reviews, indoor or occupational exposure,

exposure–response relationships between

No

studies without a city-specific quantitative

ambient PM10/PM2.5 and health outcomes in a

n = 104

relationship, and interaction research only

city* Yes

with stratified results n = 126

Study population was healthy people of all

No

ages

n = 17

Yes

n = 109

Health outcomes were mortality and hospital utilization for all non-accidental causes,

No n = 32

Research on non-cardiopulmonary disease, single or subclassification of cardiovascular and respiratory diseases

cardiovascular disease, or respiratory disease Yes

Studies of specific high-risk groups (e.g., smokers, patients, infants, children, elders)

n = 76

Among duplicate or similar articles with the same location, study period, pollutants, health

No

outcomes, and data source, the most recently published articles were included

Studies with repeat data and studies that lacked sufficient information or eligible data

n = 21 Reference list search for identified papers

Yes

n = 55 Yes Yes

Studies included in the meta-analysis (n = 59) n=4

In English (n = 35)

n = 1,066

Literature screened according to the appraisal criteria

In Chinese (n = 24)

Fig. 1. Systematic screening process for literature review.

198

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language databases of the China National Knowledge Infrastructure for epidemiological literature on the adverse health effects of PM10 and PM2.5 air pollution in China published between 1990 and 2013. Combinations of the following keywords were used: (1) particulate, particle, PM10, PM2.5; (2) adverse effect, epidemiology, health, disease, mortality, death, morbidity, hospital admission, admission, emergency visit, emergency attendance, outpatient visit, outpatient attendance; (3) timeseries, time-series or time series, case-crossover, cohort; (4) China, Chinese, Taiwan, Hong Kong, river delta, and the names of 127 Chinese cities at the prefecture level or above with populations of more than 1,000,000. The search strategies indentified potential studies conducted in China that examined the health effects of exposure to PM10 and PM2.5. We also searched the reference lists of identified papers for additional papers. Bibliographic reference lists were manually selected for metaanalysis using the following inclusion criteria: (1) All epidemiological studies, in both English and Chinese, involved the health impact of exposure to PM10 and PM2.5 in the Chinese population; (2) original studies expressed quantitative exposure–response relationships between ambient PM10/PM2.5 and health outcomes (e.g., relative risk, odds ratios, excess risk [ER], or hazard ratios and their 95% confidence intervals [95%CI]); (3) subjects were not from specific high-risk groups (e.g., smokers or children); (4) the health outcomes were mortality and hospital utilization for all non-accidental causes, CVD, and RD; and (5) studies were not identical or similar in terms of location, study period, pollutants, health outcomes, or data source. If more than two articles of the same study period, location, and population had the same pollutants, health outcomes, and data source, the most recently published articles would be included. The systematic screening steps are summarized in Fig. 1. For the selected studies, the title, authors, location, publication year, study period, study design, number of events, pollutants and health outcomes, and specific risk estimates were extracted and entered into a Microsoft Excel database (Version 2007 Microsoft, Redmond, WA, USA). All risk estimates were expressed as a standardized increment in pollutant concentration that were calculated according to the following formulas used by Shah et al., (2013) Only single-pollutant model results were included, and we selected lags following the protocol summarized previously by Atkinson et al., (2012) for short-term risk estimates.

RR (standardised) = RR Increment(10)/Increment(original) (original)

(1)

ER(%) = (RR − 1) × 100%

(2)

2.2. Meta-analysis Effect estimates from the selected studies were summarized using the inverse variance method, in which the overall effect estimate is the average of the individual study effect estimates that is weighted by the inverse of the study variance. In this meta-analysis, we first examined the heterogeneity due to each study using the standard I2 test. We considered the existence of heterogeneity at the 10% level of significance and I2 exceeding 30%; based on this, either fixed-effects or random-effects models were used to assess the pooled estimates. In addition, we used funnel plots with the Egger test of asymmetry at an alpha level of 0.1 to evaluate publication bias (Egger et al., 1997) and used the trim-and-fill method to adjust the asymmetry (Duval and Tweedie, 2000) Moreover, a leave-oneout sensitivity analysis was performed by iteratively removing one study at a time to confirm that our findings were not being driven by any single study.

All analyzes were conducted using Comprehensive Meta-Analysis (Version 2.0, Biostat, Englewood, NJ, USA).

3. Results After a systematic search and review of the English and Chinese literatures, 59 studies reported from 1999 to 2013 were identified and included in the meta-analysis. The studies involved 22 cities in mainland China, Hong Kong, and Taiwan (Fig. 2). Of the studies, 43 used a time-series design, 13 used a case-crossover design, and 3 were cohort studies incorporating more than 218 million events across the whole of China. Reported 24-hour mean concentrations were 52–174 μg/m3 for PM10 and 39–177 μg/m3 for PM2.5, which exceed the WHO air quality guidelines of 50 μg/m3 (PM10) and 25 μg/m3 (PM2.5) (World Health Organization, 2005). 3.1. Mortality To estimate the effects of short-term exposure to PM10 and PM2.5, we examined 36 time-series and case-crossover studies of 17 Chinese cities. The ER of mortality for each city by study is shown in Table 1. For each 10 μg/m3 increase in PM10 concentration, the risk of total non-accidental mortality increased by 0.36% (95%CI: 0.26%, 0.46%), the risk of mortality due to CVD increased by 0.36% (95%CI: 0.24%, 0.49%), and the risk of mortality due to RD increased by 0.42% (95%CI: 0.28%, 0.55%). For each 10 μg/m3 increase in PM2.5 concentration, the combined ERs of mortality due to all non-accidental causes, mortality due to CVD, and mortality due to RD were 0.40% (95%CI: 0.22%, 0.59%), 0.63% (95%CI: 0.35%, 0.91%), and 0.75% (95%CI: 0.39%, 1.11%), respectively. For constituent-specific mortality, the quantitative estimates were only reported in Hong Kong and Xi′an. Increases of 3.11% (95%CI: 0.59%, 5.70%) and 1.42%( 95%CI: 0.41%, 2.45%) for mortality due to RD in Hong Kong were associated with an increase of 10 ng/m3 for nickel and vanadium in PM10 (Wong et al. 2012). In Xi′an, a 10 ng/m3 increase of nickel in PM2.5 was associated with 0.40%( 95%CI: 0.00%, 0.80%), 0.60%( 95%CI:  0.10%, 1.20%), 0.90%( 95%CI: 0.20%, 1.70%) increase in mortality due to all non-accidental causes, mortality due to CVD, and mortality due to RD; and an 10 μg/m3 increase of nitrate in PM2.5 was associated with 2.45%(95%CI: 1.10%, 3.79%) increase in total non-accidental mortality (Cao et al. 2012). Estimates of the long-term impacts of exposure to PM pollution were reported by three cohort studies in Shenyang, Taiyuan, Tianjin and Rizhao for exposure to PM10 only. One study showed that a 10 μg/m3 increase in the annual average concentration of PM10 corresponded to a 24% (95%CI: 22%, 27%) increase in the risk of all-cause mortality and a 23% (95%CI: 19%, 26%) increase in mortality due to CVD (Zhang et al., 2014). Two cohort studies in Shenyang reported a 55% (95%CI: 51%, 60%) increase in the risk of mortality due to CVD (Zhang et al., 2011) and a 67% (95%CI: 60%, 74%) increase in the risk of mortality due to RD (Dong et al., 2012). 3.2. Morbidity The association between PM pollution and hospital utilization (including hospital admissions, emergency room visits, and outpatient visits, mainly for CVD and RD) was reported by 22 studies in Beijing, Guangzhou, Hong Kong, Jinan, Jinchang, Lanzhou, Shanghai, and Urumqi. For PM10 (Table 2), the pooled ER of CVD among five cities from seven studies was 0.37% (95%CI: 0.17%, 0.56%), the ER of RD among five cities from six studies was 0.51% (95%CI: 0.23%, 0.79%), and the ER of all causes in Shanghai was 0.18% (95%CI: –0.15%, 0.52%) for hospital admission. A 10 μg/m3 increase in PM10 concentration

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Fig. 2. Cities involved in this study and number of articles included for each city.

corresponded to a 0.08% (95%CI: –0.08%, 0.23%) increase in total emergency room visits in Shanghai and 0.60% (95%CI: 0.30%, 0.80%) and 0.30% (95%CI: 0.07%, 0.54%) increases in emergency room visits for CVD and RD, respectively, in Beijing. With regard to outpatient visits, the ER of all causes in Shanghai was 0.11% (95%CI: –0.03%, 0.26%), and the combined ER of RD in Jinan and Shenzhen was 0.72% (95%CI: 0.02%, 1.41%). For PM2.5, associations with total hospital utilization were reported for Shanghai only: ERs were 0.44% (95%CI: 0.16%, 0.72%) for emergency room visits and –0.58% (95%CI: –1.61%, 0.41%) for outpatient visits (Wang et al., 2013). As for specific causes, the combined ER of CVD in Lanzhou and Hong Kong was 0.56% (95%CI: 0.36%, 0.75%) for hospital admission (Yang, 2013; Qiu et al., 2013), and the ER in Beijing was 0.50% (95%CI: 0.10%, 0.90%) for emergency room visits (Guo et al., 2009). An association with hospital utilization for RD was found in only one city. ERs were 0.80% (95%CI: 0.44%, 1.14%) in Lanzhou79, 1.32% (95%CI: 1.02%, 1.61%) in Beijing54, and 0.35% (95%CI: 0.12%, 1.64%) in Guangzhou (Ma et al., 2012) for hospital admission, emergency room visits, and outpatient visits, respectively.

3.3. Heterogeneity and publication bias The heterogeneity in the meta-analysis was large in magnitude and was most evident for the pollutant–outcome pair of PM10 and mortality due to CVD (I2 ¼ 62%). Publication bias was noted for all pollutant–outcome pairs except PM2.5–RD mortality (Egger′s test for asymmetry, P ¼0.6740.10) and PM10–RD hospital admission (Egger's test for asymmetry, P ¼0.30 40.10). Adjusting for asymmetry using the trim-and-fill method did not alter the direction of the effect but did reduce the effect size (Table 3).

3.4. Sensitivity analysis and meta-regression To evaluate the robustness of the correlation results, we performed a leave-one-out sensitivity analysis that involved iteratively removing one study at a time and recalculating the combined estimates. The combined ER remained stable, indicating that our results were not being driven by any single study. Meta-regression of mean daily PM concentrations and reported ERs indicated an inverse linear association between PM10 concentration and mortality due to RD (n ¼14, β ¼ –8.2  10  3, P¼ 0.000; Fig. 3). The impact of city and study method as moderators in the regression analysis was not significant (P Z0.05).

4. Discussion For this quantitative review, we systematically collected and summarized epidemiological evidence of morbidity and mortality associated with all causes and cardio-respiratory disease. The results showed positive correlations between mortality and morbidity, as reflected in hospital utilization, and PM exposure. With regard to short-term effects based on time-series or case-crossover studies of the Chinese population, the pooled ERs of total nonaccidental mortality and mortality due to CVD were 0.40% and 0.63% for PM2.5, lower than that of mortality due to RD (0.75%), and the combined ERs of non-accidental mortality, mortality due to CVD, and mortality due to RD for PM10 (0.36%, 0.36%, 0.42%, respectively) were lower than those for PM2.5. The pattern for hospital admission was the same as that for mortality for PM10: the overall ER of hospital admission for CVD (0.37%) was lower than that for RD (0.51%). Some individual constituents of PM Such as nickel and nitrate might more toxic than others, and the reported ERs for long-term exposure to PM10 were larger than that for short-term exposure. However, the studies regarding health

200

Table 1 ER of mortality due to short-term exposure to PM and detailed information for each city by studya. Referenceb

Study period

Study design

ER (%) of total non-accidental mortality (95%CI)

ER (%) of mortality due to CVD (95%CI)

ER (%) of mortality due to RD (95%CI)

Anshan Beijing Beijing Beijing Beijing Beijing Beijing Beijing Guangzhou Guangzhou Hangzhou Hong Kong Hong Kong Hong Kong Kaohsiung Lanzhou Shanghai Shanghai Shanghai Shanghai Shanghai Shanghai Suzhou Taichung Taipei Taiyuan Tianjin Tianjin Tianjin Wuhan Wuhan

2004–2006 2009–2010 2007–2008 2008–2009 2005–2009 2004–2008 2003–2008 2003 2006–2009 2004–2008 2002–2004 1985–1995 1996–2002 1995–1998 1994–2000 2004–2007 2001–2008 2001–2004 2004–2005 2002 2000–2002 2000–2001 2002–2007 1993–2006 1994–1999 2004 2006–2010 2006–2009 2005–2007 2005–2006 2001–2004

Case-crossover Time-series Case-crossover Time-series Time-series Time-series Time-series Time-series Time-series Time-series Case-crossover Time-series Time-series Time-series Case-crossover Time-series Time-series Time-series Time-series Time-series Case-crossover Time-series Time-series Time-series Case-crossover Time-series Time-series Time-series Time-series Case-crossover Time-series

0.24(-0.03.0.51) 0.25(0.17.0.33) – 0.15(0.05.0.24) 0.13(0.08.0.17) – – – 1.26(0.86.1.66) 0.94(0.79.1.09) – 0.09(-0.12.0.30) 0.53(0.26.0.81) – 0.00(-0.81.0.82) 0.69(0.28.1.10) 0.15(0.07.0.23) 0.26(0.14.0.37) 0.14(0.02.0.26) 0.57(0.18.0.96) 0.70(0.30.1.10) 0.30(0.10.0.50) – 0.39(0.19.0.58)  0.16(-0.93.0.63) – 0.01(-0.40.0.42) 0.42(0.26.0.58) 0.45(0.21.0.69) – 0.43(0.24.0.62) 0.36(0.26.0.46)R

0.67(0.29.1.04) 0.24(0.12.0.35) 0.36(  0.07.0.78) 0.14(0.00.0.28) 0.14(0.00.0.27) 0.20(0.01.0.39) 0.16(0.14.0.18) 0.40(0.20.0.60) 1.79(1.11.2.47) 1.23(0.98.1.48) 0.61(0.28.0.94) -0.26(-0.67.0.18) 0.61(0.11.1.10) 0.30(  0.20.0.80)  0.44(  2.16.1.32) – – 0.27(0.10.0.44) 0.24(0.08.0.40) 1.08(0.33.1.83) 0.70(0.20.1.20) 0.30(0.00.0.60)  1.38(  1.69,  1.07) –  0.38(  1.88.1.10) 4.80(0.20.9.60) 1.02(0.48.1.56) 0.41(0.21.0.62) 0.60(0.29.0.91) – 0.57(0.31.0.84) 0.36(0.24.0.49)R

0.21(-0.82.1.24) 0.29(0.03.0.54) – 0.29(0.02.0.58) 0.12(0.06.0.19) – 0.1(0.06.0.15) – 0.93(0.03.1.83) 0.97(0.62.1.32) – 0.02(-0.33.0.36) 0.83(0.23.1.44) 0.80(0.10.1.40) 0.34(  2.76.3.56) – – 0.27(-0.01.0.56) 0.22(  0.08.0.52) 1.23(0.40.2.06) – 0.50(  0.1.1.10) – –  0.45(  3.09.2.30) – – 0.76(0.29.1.23) 0.82(0.04.1.61) 0.50(0.40.0.60) 0.87(0.34.1.41) 0.42(0.28.0.55)R

Beijing Beijing Chongqing Guangzhou Shanghai Shanghai Shanghai Shanghai Shenyang Xi′an

2007–2008 2004–2009 1995 2007–2008 2007–2008 2001–2008 2004–2005 2002 2006–2008 2004–2008

Case-crossover Time-series Time-series Case-crossover Time-series Time-series Time-series Time-series Case-crossover Time-series

– – 0.00(-0.72.0.68) 0.90(0.55.1.26) 0.57(0.12.1.01) 0.17(0.02.0.35) 0.30(0.06.0.54) 0.85(0.32.1.39) 0.49(0.19.0.79) 0.20(0.07.0.33) 0.40(0.22.0.59)R

0.78(0.07.1.49) – – 1.22(0.63.1.68) 0.78(0.10.1.43) – 0.39(0.12.0.66) 1.54(0.37.2.72) 0.53(0.09.0.97) 0.27(0.08.0.46) 0.63(0.35.0.91)R

– 0.69(0.54.0.85) – 0.97(0.16.0.79) 0.07(-1.29.1.38) – 0.71(0.05.1.37) 2.02(1.14.2.91) 0.97(0.01.1.94) 0.19(-0.20.0.59) 0.75(0.39.1.11)R

ER: Excess risk, PM: Particulate matter, CI: Confidence interval, CVD: Cardiovascular disease, RD: Respiratory disease, R: Random effect. a b

ERs for mortality are presented as % (95%CI) per 10 μg/m3 increase in PM10 and PM2.5. References are arranged in the order appeared in the context, some of which seemed out of order in the field because they are from the same reference with the previous one,

F. Lu et al. / Environmental Research 136 (2015) 196–204

PM10 Chen et al. (2010) Yang et al. (2013) Dong et al. (2013) Zhang et al. (2012) Xue et al. (2012) Zhang et al. (2012) Zhang et al. (2011) Yang and Pan (2008) Yu et al. (2012) Huang et al. 2012) Ren et al. (2007) Wong CMWong et al. (2012) Wong et al. (2010) Wong et al. 2002) Tsai et al. (2003) Zhang et al. (2011) Chen et al. (2013) Wong et al. (2010) Huang et al. (2009) Song et al. (2008) Jia et al. (2004) Kan and Chen (2003) Yang et al. (2010) Tsai et al. (2010) Yang et al. 2004) Zhang et al. (2008) Wang et al., (2013) Li et al., 2013) Zhang et al. (2010) Liu et al. 2012) Wong et al. (2010) Combined estimate PM2.5 [288] Li PLi et al. 2013) Venners et al., (2003) Yang et al. (2012) Geng et al. (2013) Chen et al. (2013) Huang et al. (2009) Song et al. 2008) Ma et al. (2011) Huang et al. (2012) Combined estimate

City

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Table 2 ER of various types of hospital utilization due to PM10 exposure and detailed information by studya. Referenceb

City

Study period

Study design

ER (%) of all causes

ER (%) of CVD

ER (%) of RD

Hospital admissions Wong et al. (2010) Wong et al. (1999) Lan (2013) Zhang et al. (2011) Zheng et al. (2012) Sun et al. (2010) Chen et al. (2010) Bao et al. (2013) Xiaokaiti et al. (2013) Combined estimate

Hong Kong Hong Kong Jinchang Lanzhou Lanzhou Lanzhou Shanghai Urumqi Urumqi

1996–2002 1994–1995 2006–2008 2004–2007 2001–2005 2001–2005 2005–2007 2005–2009 2005–2009

Time-series Time-series Time-series Time-series Case-crossover Time-series Time-series Time-series Time-series

– – – – – – 0.18(-0.15.0.52) – – –

0.58(0.36.0.80) 0.60(0.20.1.10) 0.47(0.02.0.94) 0.21(0.02.0.40) 0.20(0.10.0.30) – 0.23(-0.03.0.48) – 5.70(2.10.8.80) 0.37(0.17.0.56)R

0.60(0.40.0.80) 1.60(1.00.2.20) 0.47(0.15.0.80) – – 0.17(0.04.0.03) 0.01(-0.26.0.28) 0.63(0.46.1.65) – 0.51(0.23.0.79)R

Emergency room visits Leitte et al. (2011) Su et al. (2010) Guo et al. (2008) Cao JCao et al. (2009) Jia et al. (2009) Combined estimate

Beijing Beijing Beijing Shanghai Shanghai

2004–2006 2004–2005 2004–2006 2005–2007 2002–2004

Time-series Case-crossover Case-crossover Time-series Time-series

– – – 0.01(-0.09.0.10) 0.17(0.01.0.34) 0.08(-0.08.0.23)R

– – 0.60(0.30.0.80) – – –

0.22(-0.11.0.54) 0.40(0.10.0.80) – – – 0.30(0.07.0.54)F

Outpatient visits Wang et al. (2008) Peng et al. (2011) Cao JCao et al. (2009) Combined estimate

Jinan Shenzhen Shanghai

2002–2005 2008 2005–2007

Time-series Time-series Time-series

– – 0.11(  0.03.0.26) –

– – – –

0.40(0.20.0.50) 1.11(0.61.1.62) – 0.72(0.02.1.41)R

ER: Excess risk, PM: Particulate matter, CVD: Cardiovascular disease, RD: Respiratory disease, R: Random effect. F: Fixed effect. a

ERs for mortality are presented as % (95%CI) per 10 μg/m3 increase in PM10 and PM2.5. References are arranged in the order appeared in the context, some of which seemed out of order in the field because they are from the same reference with the previous one. b

effects of PM constituents, and the cohort-based evidence of the long-term impact were still not sufficient to summarize the exposure–response relationship in China. The overall magnitudes of effects found in our meta-analysis are comparable with those found in studies conducted in other parts of the world. In terms of mortality, Pope and Dockery (2006) summarized some key meta-analyzes and multicity studies of short-term PM exposure and mortality; the results observed in our analysis are slightly lower than their estimates. Chen et al. (2012) reported that a 10 μg/m3 increase in PM10 was associated with a 0.35% increase in non-accidental mortality, a 0.44% increase in mortality due to CVD, and a 0.56% increase in mortality due to RD in 16 Chinese cities, which is similar to our findings. As for morbidity, the combined estimates summarized in this review are near the lower end of the range for European countries and the United States. For example, ERs of 0.59–4.23% per 10 μg/m3 for hospital admission due to RD were summarized by the US Environmental Protection Agency (US-EPA, 1996) to develop air quality criteria for

PM. And ERs of 1.3% per 10 μg/m3 PM10 for hospital admission due to CVD and RD, as epidemiology-basedexposure–response functions, were used by Künzli et al. (2000) to quantify the effects of air pollution in Europe. Like the pooled estimates, ER values for various pollution– outcome pairs in our meta-analysis were larger for RD than for CVD. This is in line with a Health Effects Institute report on developing countries in Asia (HEI, 2010) and a meta-analysis of European studies WHO (2004). However, the patterns are less consistent in different studies. For instance, Son et al., (2012) reported the opposite pattern in Korea: the ER for CVD (0.92%) was slightly higher than that for RD (0.78%). This discrepancy may be due to the difference in components and level of PM pollution, local population sensitivity, and age distribution. In addition, the effects of PM2.5 for various rubrics are stronger than those of PM10. This phenomenon has been observed in some epidemiological studies (Janssen et al., 2013; Beelen et al., 2014). A probable reason is that compared to PM10, PM2.5 can penetrate deeper into the

Table 3 Heterogeneity and publication bias for pollutant–outcome pairsc. Outcome

Pollutant

Mortality Total PM10 CVD PM10 RD PM10 Total PM2.5 CVD PM2.5 RD PM2.5 Hospital admission CVD PM10 RD PM10

No. of estimates

Summary ER (%) (95%CI)a

Heterogeneity I2 (%)

P value for Egger's test

Adjusted ER (%) (95%CI)b

22 27 20 8 7 7

0.36(0.26.0.46) 0.36(0.24.0.49) 0.42(0.28.0.55) 0.40(0.22.0.59) 0.63(0.35.0.91) 0.75(0.39.1.11)

36 62 19 20 9 25

0.01 0.05 0.00 0.08 0.01 0.67

0.21 (0.10.0.32) 0.18 (0.05.0.31) 0.20 (0.08.0.33) 0.24 (0.04.0.44) 0.35 (0.06.0.65) 0.62 (0.21.1.02)

7 6

0.37(0.17.0.56) 0.51(0.23.0.79)

52 47

0.03 0.30

0.31 (0.09.0.53) 0.37 (0.07.0.68)

ER: Excess risk, CI: confidence interval, PM: Particulate matter, CVD: Cardiovascular disease, RD: Respiratory disease. a b c

Risk estimates derived from pooled analysis of studies. Risk estimates after adjustment for publication bias using the trim-and-fill method. Some pollutant–outcome pairs are not shown because of an insufficient number of studies.

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Fig. 3. Meta-regression analysis of daily mean PM10 concentrations and the ER of mortality in different studies.

airways of the respiratory tract and can reach the alveoli, where it is effectively retained in the lung parenchyma (Valavanidis et al., 2008). Generally speaking, a linear dose–response association between PM air pollution and mortality has been found in Europe (Samoli et al., 2005) and the United States (Daniels et al., 2000); that is, the incremental effect of an increase in PM on mortality is the same for all PM exposures. But in our meta-regression we found a downward trend in the ER of mortality due to RD with an increase in daily mean PM10 concentration in the range of 50–150 μg/m3 that is different from the common range of daily mean PM exposures less than 50 μg/m3 in Europe and the United

States. This indicates that the shape of dose–response curves might tend to be more stable and represent a departure from the linear dose–response relation in this range. This might be related to a saturation mechanism, in which underlying biochemical and cellular processes become saturated with small doses of a toxic component (Pope et al., 2009). Or perhaps our analysis lacked an adequate number of pollutant–outcome pairs to identify the real relationship. Further research is needed to more reliably determine whether there are nonlinearities in the relationship between PM exposure and mortality (Roberts 2006). In summary, the evidence reviewed here indicates that shortterm exposure to PM is associated with increases in mortality and

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morbidity. The pooled ER estimates of health effects, including all non-accidental causes, CVD, and RD, are comparable in magnitude and pattern to findings reported elsewhere. The common estimates derived from this study could be applied to epidemiologybased assessments of health risk and public health impact related to PM pollution in China. However, several limitations of our study should be considered. First, we did not include cross-sectional studies to evaluate the long-term impacts of PM pollution, as their low power for causal inference could have compromised the accuracy of the estimates. Second, we combined estimates for singlepollutant models, which do not consider the potential synergistic effects of multiple pollutants or adjust for collinearity (DominiciDominici et al., 2010). Other factors that might have affected response to PM pollution, including age, sex, temperature, humidity, and season, were also not taken into account. Third, evidence of long-term effects and hospital utilization, especially emergency room visits and outpatient visits, is still insufficient for metaanalysis. And there is not sufficient evidence to quantitatively assign different CRFs to different chemical composition of PM. Therefore, we suggest that more cohort-based studies in China be developed on the long-term effects of PM exposure, and more studies be conducted on the differential toxicities of PM constituents and the relationship between morbidity and PM exposure. And the effects of meteorological and demographic factors should be thoroughly analyzed in future research.

Acknowledgments This work was supported by the National Key Project (973) of Study on Interaction Mechanism of Environment and Genetic of Birth Defect in China (No. 2007CB5119001), National Yang Zi Scholar program.

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