Table 1.
Numbers of residents in a city: a fictitious dataset
| 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%) |
aexposure was defined as actually using the free medical care service