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Table 1 Numbers of residents in a city: a fictitious dataset

From: Assessing the impact of natural policy experiments on socioeconomic inequalities in health: how to apply commonly used quantitative analytical methods?

Education (n)

Sex (n)

Policy allocation (n)

Self-assessed health

Before the policy

(Health t1,%)

After the policy

(Health t2, %)

Low (10000)

Male

(5000)

Exposeda (1250)

Poor

333 (27%)

221 (18%)

Good

917 (73%)

1029 (82%)

Unexposed (3750)

Poor

1000 (27%)

950 (25%)

Good

2750 (73%)

2800 (75%)

Female

(5000)

Exposed (3750)

Poor

500 (13%)

333 (9%)

Good

3250 (87%)

3417 (91%)

Unexposed (1250)

Poor

167 (13%)

159 (13%)

Good

1083 (87%)

1091 (87%)

High (10000)

Male

(5000)

Exposed (625)

Poor

83 (13%)

46 (7%)

Good

542 (87%)

579 (93%)

Unexposed (4375)

Poor

584 (13%)

467 (11%)

Good

3791 (87%)

3908 (89%)

Female

(5000)

Exposed (1875)

Poor

125 (7%)

70 (4%)

Good

1750 (93%)

1805 (96%)

Unexposed (3125)

Poor

208 (7%)

166 (5%)

Good

2917 (93%)

2959 (95%)

  1. aexposure was defined as actually using the free medical care service