Table 2.
Effect modification by city-level characteristics for the association between extreme temperature and preterm birth.
| Extreme heat |
Extreme cold |
|||
|---|---|---|---|---|
| Potential factors | MI-PTB | S-PTB | MI-PTB | S-PTB |
| β (95%CI) | β (95%CI) | β (95%CI) | β (95%CI) | |
| Population | ||||
| Resident population (10,000 people) | 0.22 (-0.34, 0.77) | 0.09 (-0.18, 0.35) | 0.06 (-0.13, 0.24) | 0.09 (-0.10, 0.28) |
| Population density (people/km2) | 0.11 (-0.20, 0.42) | 0.05 (-0.40, 0.50) | 0.06 (-0.11, 0.22) | 0.07 (-0.10, 0.24) |
| Economy | ||||
| GDP per capita (CNY) | -0.13 (-0.43, 0.17) | -0.16 (-0.30, -0.01) | -0.06 (-0.24, 0.12) | -0.05 (-0.57, 0.47) |
| Engel coefficient (%) | 0.03 (-0.41, 0.48) | 0.11 (-0.32, 0.53) | 0.09 (-0.25, 0.42) | 0.10 (-0.38, 0.59) |
| Unemployment rate (%) | 0.02 (-0.49, 0.53) | -0.07 (-0.70, 0.57) | 0.07 (-0.15, 0.29) | 0.13 (-0.26, 0.52) |
| Medical resource | ||||
| Number of health institutions (n) | 0.05 (-0.56, 0.66) | -0.05 (-0.56, 0.47) | 0.07 (-0.17, 0.32) | -0.07 (-0.32, 0.18) |
| Hospital beds per 1000 people (n) | -0.12 (-0.47, 0.23) | -0.06 (-0.25, 0.13) | -0.25 (-0.50, -0.01) | -0.06 (-0.57, 0.46) |
| Physicians per 1000 people (n) | -0.10 (-0.44, 0.24) | -0.05 (-0.20, 0.09) | -0.09 (-0.38, 0.21) | 0.13 (-0.26, 0.51) |
| Environment | ||||
| NDVI | -0.06 (-0.59, 0.47) | -0.32 (-0.14, 0.75) | -0.08 (-0.45, 0.29) | -0.11 (-0.28, 0.05) |
| Air quality (%) | -0.11 (-0.31, 0.09) | -0.08 (-0.24, 0.08) | -0.05 (-0.21, 0.11) | -0.06 (-0.28, 0.15) |
Abbreviation: MI-PTB, medically induced preterm birth. S-PTB, spontaneous preterm birth. NDVI, Normalized Difference Vegetation Index.