Table 2. Discrimination and model-fit statistics from simple logistic regression.
| Health outcome | Predictor | P-value | AUC | AIC | BIC |
| Hypertension (total N=6663) |
Birth months | 0.58 | 0.522 | 8539 | 8621 |
| Highest month (Jan vs. the rest)* | 0.12 | 0.506 | 8526 | 8540 | |
| Sex | 0.60 | 0.503 | 8528 | 8542 | |
| Age (continuous) | <0.0001 | 0.643 | 8124 | 8138 | |
| Age >50 | <0.0001 | 0.585 | 8285 | 8299 | |
| Coronary heart disease (total N=6472) |
Birth months | 0.05 | 0.539 | 6020 | 6102 |
| Highest month (April vs. the rest)* | 0.03 | 0.510 | 6016 | 6029 | |
| Sex | <0.0001 | 0.598 | 5873 | 5886 | |
| Age (continuous) | <0.0001 | 0.621 | 5846 | 5859 | |
| Age >50 | <0.0001 | 0.572 | 5889 | 5902 | |
| Stroke (total N=6845) |
Birth months | 0.66 | 0.523 | 7452 | 7534 |
| Highest month (July vs. the rest)* | 0.25 | 0.505 | 7440 | 7453 | |
| Sex | 0.94 | 0.509+ | 7441 | 7455 | |
| Age (continuous) | <0.0001 | 0.598 | 7300 | 7313 | |
| Age >50 | <0.0001 | 0.556 | 7344 | 7358 | |
| Diabetes (total N=7971) |
Birth months | 0.59 | 0.525 | 7118 | 7202 |
| Highest month (Nov vs. the rest)* | 0.02 | 0.510 | 7102 | 7116 | |
| Sex | <0.0001 | 0.530 | 7092 | 7106 | |
| Age (continuous) | <0.0001 | 0.597 | 6974 | 6988 | |
| Age >50 | <0.0001 | 0.566 | 6977 | 6991 | |
| Chronic kidney disease (total N=4002) |
Birth months | 0.57 | 0.530 | 4286 | 4361 |
| Highest month (March vs. the rest)* | 0.04 | 0.511 | 4271 | 4284 | |
| Sex | 0.003 | 0.528 | 4267 | 4279 | |
| Age (continuous) | <0.0001 | 0.796 | 3457 | 3470 | |
| Age >50 | <0.0001 | 0.602 | 3991 | 4003 |
Each predictor is separately modeled as a univariate covariate in Simple logistic regression.
Birth month (1-12) is included as a categorical covariate (via 11 dummies); sex is binary; and age (in years) is included as a continuous or binary covariate (>50 vs. ≤ 50 years old).
*Highest month (vs. rest as binary variable) is selected post-hoc, so results may suffer optimism bias.
P-value is computed from Wald Chi-square test; degrees of freedom=11 for birth month and 1 for all others.
AUC, area under the ROC curve, is a discrimination statistic; 0.5 means random discrimination and 1 means perfect discrimination.
AIC, Akaike information criteria, is a measure of the relative quality of a statistical model for a given set of data: a lower value means a better model fit.
BIC, Bayesian information criteria, is a Bayesian extension of AIC: a lower value means a better model fit.
AIC and BIC should be compared within the same outcome due to different Ns and amount of information.
+Estimation issue so we fitted the model with Y=stroke or TIA, and averaged the AUC of 0.511 and 0.507.