TABLE 2.
Results of the OLS model and SLM (China, 2018).
| Variables | OLS | SLM | ||
|---|---|---|---|---|
| Coefficient | p-value | Coefficient | p-value | |
| ρ | — | — | 0.72 | 0.001 |
| Intercept | 91.0 | 0.169 | 96.7 | 0.016 |
| AT | −1.0 | 0.041 | −0.7 | 0.013 |
| ARH | 0.4 | 0.072 | 0.3 | 0.046 |
| IR | 0.3 | 0.060 | 0.2 | 0.015 |
| PM | −1.7 | 0.190 | −2.1 | 0.012 |
| PLP | 0.5 | 0.327 | 0.1 | 0.884 |
| R 2 | 0.421 | — | 0.710 | — |
| LLR | −96.126 | — | −88.805 | — |
| AIC | 204.253 | — | 191.611 | — |
| Moran’s I a | 0.195 | 0.008 | −0.092 | 0.372 |
a
The residual of model.
OLS, ordinary least squares model; SLM, spatial lag model, the dependent variable for all was age-standardized prevalence of rheumatic diseases. AT, average temperature; ARH, average relative humidity; IR, illiteracy rate; PM, proportion of men; PLP, proportion of living with partner; ρ, spatial autoregressive parameter; LLR, log-likelihood ratio; AIC, Akaike information criterion.