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Table 1 Information of Selected Studies

From: The effect of smoke-free legislation on the mortality rate of acute myocardial infarction: a meta-analysis

Author

Study location

National study

Effective date of a law

Study period

Target people

Comprehensiveness of a law

Previous ban in place

Size of Population at risk

AMI deaths cases

RR value

95% Confidence interval

AMI definition

Source of data

Measure /Statistical Method

Control variable

Europe

Joan R Villalbí2011

Spain

Yes

January 1, 2006

January 12,004- December 31,2006

Ages ≥35 years

Workplaces (with exemptions for bars, cafes, restaurants, night clubs and discos)

Yes

–

68,862

0.90

0.88–0.92

ICD-10: CM 055

the National Statistics Institute (INE in its Spanish acronym)

Poisson regression

Age, gender

Ages ≥35 years, male

–

40,080

0.90

0.88–0.93

Ages ≥35 years, female

–

28,782

0.90

0.87–0.92

January 1, 2004- December 31,2007

Ages ≥35 years

–

90,382

0.86

0.84–0.88

Ages ≥35 years, male

–

52,583

0.86

0.83–0.88

Ages ≥35 years, female

–

37,799

0.86

0.84–0.89

FernandoAguero2013

Girona, Spain.

No

January 1, 2006

January 1, 2002-December 31, 2008

Ages 35–74 years

All indoor public places and workplaces but allowed some exceptions in hospitality venues

Yes

–

891

0.82

0.71–0.94

ICD-9: 410–414, 798;

ICD-10:I20–I25, I513, R960, R961, R98, R99

The REGICOR Study, conducted in six counties of the Girona province in the north east of Spain

Poisson regression model

Age, sex, smoking status, existing trend, and seasonality

Ages 35–74 years, female

–

200

0.72

0.52–0.97

Ages 35–74 years, male

–

691

0.85

0.72–0.99

Ages 35–64 years

–

359

0.94

0.76–1.17

Ages 65–74 years

–

532

0.74

0.62–0.89

Sericea Stallings-Smith 2013

Ireland

Yes

March 29, 2004

January 1, 2000-December 31,2007

Ages ≥35 years

Workplaces including restaurants, bars, and pubs

Yes

1,900,000

–

0.97

0.92–1.02

ICD-9:410; ICD-10:I21

Central Statistics Office (CSO) Ireland.

Poisson linear regression model with interrupted time-series analysis

Time trend, season, influenza, and smoking prevalence

Ages ≥35 years, male

–

0.97

0.91–1.02

Ages ≥35 years, female

–

0.97

0.91–1.03

Ages 35–64 years

–

0.97

0.86–1.07

Ages 65–84 years

–

0.94

0.89–1.00

Ages> 85 years

–

1.01

0.94–1.09

United States

Kanaka D. Shetty2011

United States

Yes

1995

1990–2004

All

All workplaces except bars and restaurants

Yes

–

2,018,548

1.02

0.99–1.05

ICD-9:410; ICD-10:I21

Multiple Cause of Death (MCD) database

Region-level fixed effects multivariate linear regression model

Secular trends and regions

Melanie S. Dove 2010

Massachusetts, US

No

July 2004

January 11,999- -December 31, 2006

Ages ≥35 years

All workplaces, including restaurants and bars

No

2,507,320

20,806

0.90

0.86–0.95

ICD-10:I21

Massachusetts Registry of Vital Records and Statistics

Poisson regression model adjusted for a linear time term

Long-term trend, season, particulate matter less than 2.5 lm aerodynamic diameter (PM2.5),10–12 and influenza

Ages > 35 years

Yes

835,597

6176

1.01

0.92–1.11

Ages > 35 years

–

3,342,917

26,982

0.93

0.89–0.97

Ages > 35 years, male

1,548,463

13,595

0.95

0.89–1.01

Ages > 35 years, female

1,794,454

13,387

0.90

0.85–0.96

Ages 35–64 years

2,482,755

4162

0.92

0.82–1.04

Ages 65–75 years

427,830

4657

0.99

0.89–1.11

Ages> 75 years

432,332

18,163

0.91

0.86–0.96

McAlister 2010â‘ 

Jefferson County, Texas, USA

No

Autumn of 2000

1996–2005

All

Unclear

–

250,000

–

0.84

0.77–0.91

ICD-10:I21

Texas Department of State Health Services

Bivariate piecewise linear regression model

None

Brad Rodu2012â‘¡

Utah, US

No

January 1, 1995

1991–1995

Ages > 45 years

Enclosed indoor places of public access, bars exempted

No

488,000

767

0.92

0.90–0.94

ICD-9:410; ICD-10:I21

Compressed Mortality File from the National Center for Health Statistics (NCHS)

Test for difference between two independent proportions

None

California, US

January 1, 1995

1991–1995

Enclosed workplaces and restaurants without bars

Yes

9,100,000

17,656

0.98

0.97–0.99

South Dakota, US

July 1, 2002

1999–2003

Most workplaces, bars and casinos exempted

No

287,000

686

1.09

0.95–1.25

Delaware, US

November 27, 2002

1999–2003

Most indoor public places, including restaurants and bars

No

305,000

433

0.92

0.90–0.93

Florida, US

July 1, 2003

2000–2004

Most indoor public places, including restaurants and bars

No

720,000

10,073

0.91

0.91–0.92

New York, US

July 24, 2003

2000–2004

All workplaces, including restaurants and bars

Yes

720,000

10,347

0.88

0.83–0.93

Carl Bartecchi 2006

Pueblo, United States

No

July 1, 2003

January 1, 2002 - December 31,2004

All

Inside the workplace and all buildings open to the public

Yes

147,751

–

0.77

0.64–0.93

ICD-9:410

Health Statistics Section of the Colorado Department of Public Health and Environment

Poisson regression model, with the test of linear contrasts between pre-ordinance and post-ordinance changes.

Time, location, time-by location

interaction, and harmonics to account for seasonality

Other locations

Tania 2016â‘¢

São Paulo city, Brazil

No

August 1, 2009

January 2005- December, 2010

All

Prohibited the use of cigarettes and other tobacco products in closed and semi-closed places, public and private, with the exception of residences, places of religious worship where smoking is part of the ceremony and sites designated for the consumption of tobacco products.

Yes

–

39,177

0.95

0.93–0.96

ICD-10: I21,I22,I23,I24

Mortality Information System (SIM)

Autoregressive Integrated Moving Average with exogenous variables (ARIMAX) with Interrupted Time Series Analysis (ITSA)

Total hospital admission, carbon monoxide, minimum temperature and air relative humidity

TQ Thach

Hong Kong, China

No

1 January 2007

1 January 2001–31 December 2011

All

Workplaces including restaurants, bars, and pubs

No

–

–

0.87

0.81–0.94

ICD-10:I21

Hong Kong Special Administrative Region (SAR) Government Census and Statistics Department

Poisson regression model

Age, gender

  1. Note:â‘ â‘¡â‘¢In these articles, the relative risk of AMI mortality were not provided. So based on provided AMI death, AMI mortality and population data, we calculated the RR value of AMI mortality