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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