Healthcare claims and health interview survey data for chronic disease surveillance: agreement and comparative validity of prevalence indicators for 20 chronic conditions in a general population sample in France.

Healthcare claims data are increasingly used to derive chronic condition (CC) surveillance indicators, although comparative evidence with self-reported data remains scarce. We explored the agreement and comparative validity (concurrent and predictive) of 20 CC prevalence indicators independently constructed using the French National Health Data System (SNDS) and Health, Health Care, and Insurance Survey (ESPS 2010-2014). Individual data from 5039 ESPS participants aged ≥25 years, representative of the 2010 French general population, were linked to the SNDS. Follow-up data included a 2014 health self-assessment and 5-year mortality. We considered 20 CCs with corresponding SNDS case-identifying algorithms and self-reported information from ESPS, including most cardiovascular diseases and frequent cancers. Kappa statistics assessed agreement between CC indicators across databases. Polytomous and dichotomous logistic regression assessed determinants of disagreement between sources and associations of indicators with health outcomes (concurrent and predictive validity). Prevalence values were much higher with survey data except for hypertension, diabetes, thyroid disorders, epilepsy, and most cancers for which they were closer (±20%) to claims data. Agreement between CC indicators varied from the strongest (hypertension, diabetes, thyroid disorders, most cancers) to the weakest (cardiac rhythm disorders, peptic ulcer, chronic liver diseases). Sex, age, and multimorbidity strongly influenced agreement. Most claims database indicators were more strongly associated with health outcomes. Health interview surveys and healthcare claims-derived indicators are not interchangeable given their specific determinants. Since no general rule applies to all CCs, the advantages and disadvantages of each data source should be closely considered in case-to-case analysis.