Sentence examples for morbidity models were from inspiring English sources

This part of the study supplements Part I in which different approaches for developing predictive morbidity models were reviewed in a unitary framework from a theoretical point of view.

In other words, single-morbidity models are more precise in predicting ER visits.

A further co-morbidity model was constructed to determine the odds of "moderate/poor" compared to "good/excellent" SRH for each chronic condition as an index condition plus any one or more other conditions adjusted for age, social class depression and anti-depressant use.

To analyze the effect of the production system on morbidity, separate multivariable models were fit with total morbidity, BRD, enteritis, otitis and arthritis as binary outcome variables.

Scores of predictive morbidity models can be built, from the most mundane – back pain correlated to sleep deprivation – to the most critical involving heart conditions linked to various lifestyle factors.

Separate models were also developed for maternal morbidity and transfusion.

These models were complicated and had high morbidity and mortality rates.

All models were adjusted for age, sex and co-morbidities (using equivalent weightings from the Charlson co-morbidity index) (Charlson et al., 1987).

Models were run for each of the six morbidities separately.

Finally, 2 additional multivariate models were computed to assess the effect of frailty on worsening morbidity.

Ordinal logistic regression models were used to assess the influence of risk factors on morbidity.