A latent variable frailty model is built for data coming from a neonatal study conducted to investigate whether the presence of a particular hospital service given to families with premature babies has a positive effect on their care requirements within the first year of life. The predicted value of the latent frailty term from information obtained from the family in advance of the birth furnishes an overall measure of the quality of health of the baby. This identifies families at risk. Maximum likelihood and Bayesian approaches are used to estimate the effect of the variables on the value of the latent baby frailty and for prediction of health complications. It is found that these give much the same estimates of regression coefficients, but that the variance components are the more difficult to estimate. We indicate how the findings from the model may be presented as a scorecard for predicting frailty, and so be useful to doctors working in hospital neonatal units. New information about a baby is automatically combined with the current score to provide an up-to-date score, so that rapid decisions for taking appropriate action are made more possible. A diagnostic procedure is proposed to assess how well the independence assumptions of the model are met in fitting to this data. It is concluded that the frailty model provides an informative summary of the data from this neonatal study.