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Table 1 Features of the studies imported to the meta-analysis

From: Association between PM2.5 and risk of hospitalization for myocardial infarction: a systematic review and a meta-analysis

No

Author/ publication year

Country

City

Design

Study period (month)

Lag exposure

Case population (n)

Adjustment

Quality score

1

(Peters et al., 2001) [28]

USA

Boston

Case-Crossover

5

Lag0

772

Day of the week, season, and meteorological parameters on the same time scales

High

2

(Peters et al., 2005) [29]

Germany

Augsburg

Case -Crossover

24

Lag0, Lag5, Lag0–4

851

Temperature, humidity, days of the week, pressure

High

3

(Sullivan et al., 2005) [30]

USA

Washington

Case-Crossover

72

Lag0

5793

Relative humidity and temperature

High

4

(Pop et al., 2006) [31]

USA

Utah

Case-Crossover

120

Lag0, Lag3

3910

Temperature

Low

5

(Zanobetti &Schwartz

et al. 2006) [32]

USA

Boston

Time Series

48

Lag0, Lag0–1

15,578

Temperature, days of the week

High

6

(Barnett et al., 2006) [33]

Australia

Auckland, Brisbane, Canberra, Christchurch, Melbourne, Perth, Sydney

Case-Crossover

36

Lag0–1

56,036

Day of week, pressure, holidays, temperature, humidity and others

High

7

(Ueda et al., 2009) [34]

Japanese

Fukuoka, Kawasaki, Kobe, Nagoya, Osaka, Sapporo, Sakai, Sendai and Tokyo

Time Series

24

Lag0, Lag1

67,897

Days of the week, seasonality, relative humidity, ambient, and temperature

Low

8

(Stieb et al., 2009) [35]

Canada

Edmonton, Halifax, Montreal, Ottawa, Saint John, Vancouver and Toronto

Time Series

120

Lag0, Lag1, Lag2

63,184

Seasonal cycles, temperature, and humidity

High

9

(Belleudi et al., 2010) [36]

Italy

Rome

Case-Crossover

56

Lag0, Lag6

7520

Influenza, population reduction, epidemics, pressure, and Temperature

Low

10

(Zanobetti &Schwartz 2009) [37]

USA

112 cities (The biggest cities are California, New York City, Los Angeles, Chicago, Illinois and New York)

Time Series

72

Lag0–1

397,894

Long-term trend, seasonality, temperature, days of the week

High

11

(Rich et al., 2010) [38]

USA

New Jersey

Case-Crossover

24

Lag0

5864

Weather and days of the week

High

12

(Berglind et al., 2010)a [39]

Sweden

Boston

Case-Crossover

24

Lag0

772

Relative humidity and temperature

Low

13

(Berglind et al., 2010) b [39]

Sweden

Seattle

Case-Crossover

24

Lag0

5793

Relative humidity and temperature

Low

14

(Berglind et al., 2010) c [39]

Sweden

Augsburg

Case-Crossover

24

Lag0

691

Temperature and relative humidity

Low

15

(Mate et al., 2010) [40]

Spain

Madrid

Time Series

24

Lag6

1096

Days of the week, trend, seasonality, influenza and temperature

High

16

(von Klot et al., 2011) [41]

Germany

Augsburg

Case-Crossover

48

Lag0

960

Days of the week and temperature

High

17

(Chang et al., 2013) [42]

Taiwan

Taipei

Case-Crossover

48

Lag0

14,353

Temperature and relative humidity

High

18

(Rosenthal et al., 2013) [43]

Finland

Helsinki

Case-Crossover

96

Lag0, Lag1, Lag2, Lag3,Lag0–3

629

Temperature and humidity

High

19

(Talbott et al., 2014) [21]

USA

Florida

Case-Crossover

96

Lag0, Lag1, Lag2, Lag0–2

135,421

Maximum apparent temperature and ozone

Low

20

(Gardner et al., 2014) [44]

USA

New York

Case-Crossover

36

Lag0–1,Lag0–2, Lag0–3, Lag0–4

677

Relative humidity and temperature

High

21

(Milojevic et al., 2014) [45]

UK

London

Case-Crossover

72

Lag0–4

452,343

Temperature, days of the week

High

22

(Wichmann et al., 2014) [46]

Sweden

Gothenburg

Case-Crossover

300

Lag0, Lag1, Lag0–1

28,215

Relative humidity, temperature and public holiday

High

23

(Wang et al., 2015) [47]

Canada

Calgary, Edmonton

Case-Crossover

132

Lag(0,1.2.3,4)

22,628

daily average of temperature, dew point temperature and wind speed

Low

24

(Zang et al., 2016) [48]

China

Chaoyang

Case-Crossover

12

Lag(0,1,2,3,4,5)

2749

meteorological conditions and/or other gaseous pollutants

High

25

(Argacha et al., 2016) [49]

Belgian

Belgian

Case-Crossover

48

Lag0

11,428

Day of the week, temperature

High

26

(Baneras et al., 2017) [20]

Spain

Barcelona

Time Series

24

Lag0

4141

Seasonal, meteorological factors, and time-calendar variables

High

27

(Akbarzadeh et al., 2018) [50]

Iran

Tehran

Case-Crossover

24

Lag0–1

208

Temperature and humidity

Low

28

(Yu et al., 2018) [51]

China

Changzhou

Time Series

24

Lag(0,1,2,3,4,5,6), Lag(0–1,0-2,0-3,0-4,0-5,0–6)

5545

Temperature, days of the week, relative humidity, seasonal trends

Low