Table 6. Results from the random intercept model (a measure of variation) for the timing of death at the district level using multilevel logistic regression analysis.
| Random effect | Model_1a | Model_2b | Model_3c | Model_4d |
|---|---|---|---|---|
| District level variance (SE) | 0.47(0.11) | 0.39(0.08) | 0.36(0.08) | 0.35(0.07) |
| P_values | <0.001 | <0.001 | <0.001 | <0.001 |
| ICC (%) | 12.3% | 10.6% | 9.9% | 9.6% |
| Explained variance (PVC) (%) | Reference | 15.2% | 21.1% | 46.0% |
| MOR (95%CI) | 1.90(1.68,2.27) | 1.81(1.63,2.07) | 1.76(1.58,2.01) | 1.75(1.60,2.04) |
| Model fit statics | ||||
| AIC | 7500 | 7153 | 7512 | 7111 |
| BIC | 7591 | 7216 | 7551 | 7206 |
SE = Standard Error; DIC = Deviance Information Criterion; ICC = Intra-Class Correlation; PCV = Percentage Change in Variance; MOR = Median Odds Ratio; CI = Confidence Interval; AIC = Akaike’s Information Criterion; BIC = Schwarz’s Bayesian Information Criteria.
Model_1a is the empty model, a baseline model without any determinant variable
Model_2b is adjusted for individual-level factors
Model_3c is adjusted for community-level factors
Model_4d is the final model adjusted for the individual- and community-level factors