Sickness Absence and Record Linkage Using Primary Healthcare, Hospital and Occupational Databases

Objective: Charlson comorbidity index (CCI) has been adapted to primary care (PC) patients to determine chronic illness costs. We retrospectively evaluated its ability to predict sickness absence, hospital admissions and in-hospital mortality among 1,826,190 workers followed during the period 2007-2009.

Methods: The electronic administrative databases DIRAYA

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and MBDS contain information of diseases and conditions of patients attended in primary care and hospital settings, respectively. We retrospectively used available information in the DIRAYA medical record database to calculate CCI adapted to PC (CCIPC), and analyzed its relation to sickness absence, hospital admissions and in-hospital mortality.

Results: The models including age, gender, province of residence, hospital size and CCIPC calculated in PC setting were predictive of every outcome: sick leave (their number and duration), hospital admissions (number and length of hospital stays) and in-hospital mortality, when measured in terms of adjusted Odds Ratios and 95% confidence limits. Area under the curve for ROC predictive models was maximal for in-hospital mortality (0.9254).

Conclusion: The adapted CCIPC was predictive of all outcomes related to sick leave, hospital admissions, and in-hospital mortality among a large sample of Spanish workers. If the goal is to compare outcomes across centers and regions for specific diseases and causes of sickness absence, CCIPC is a promising option worthy of prospective testing. The future availability of information through Big Data can increase the external validity of these results if at the same time biases that threaten the internal validity of the results are avoided.