Background and objective:
The stratification of the general population according to health needs allows to provide better-tailored services. A simple score called Multisource Comorbidity Score (MCS) has been developed and validated for predicting several outcomes. The aim of this study was to evaluate whether the predictive ability of the MCS improves by incorporating socioeconomic data (as measured by a deprivation index).

Methods:
Beneficiaries of the Italian National Health Service who in the index year (2018) were aged 50–85 years and were resident in the Sicily region for at least 2 years were identified. The 1-year mortality was the outcome of interest. For each individual, the MCS was calculated according to his/her clinical profile, and the deprivation index of the census unit level of the individual’s residence was collected. Akaike information criterion and Bayesian information criterion statistics were used to compare the goodness of fit of the model that included only MCS and the model that also contained the deprivation index. The models were further compared by means of the area under the receiver operating characteristic curve (AUC).

Results:
The final cohort included 1,062,221 individuals, with a mortality rate of 15.6 deaths per 1,000 person-years. Both MCS and deprivation index were positively associated with mortality. The goodness of fit statistics of the two models were very similar. For MCS only and MCS plus deprivation index models, Akaike information criterion were 19,566 and 19,578, respectively, whereas Bayesian information criterion were 19,528 and 19,516, respectively. The AUC values were 0.78 for both models.

Conclusion:
The present study shows that socioeconomic features as measured by the deprivation index did not improve the capability of MCS in predicting 1-year risk of death. Future studies are needed to investigate other sources of data to enhance the risk stratification of populations.