Prevalence of chronic kidney disease in France: methodological considerations and pitfalls with the use of Health claims databases.

Abstract

Health policy-making require careful assessment of chronic kidney disease (CKD) epidemiology to develop efficient and cost-effective care strategies. The aim of the present study was to use the RENALGO-EXPERT algorithm to estimate the global prevalence of CKD in France. An expert group developed the RENALGO-EXPERT algorithm based on healthcare consumption. This algorithm has been applied to the French National Health claims database (SNDS), where no biological test findings are available to estimate a national CKD prevalence for the years 2018-2021. The CONSTANCES cohort (+219 000 adults aged 18-69 with one CKD-EPI eGFR) was used to discuss the limit of using health claims data. Between 2018 and 2021, the estimated prevalence in the SNDS increased from 8.1% to 10.5%. The RENALGO-EXPERT algorithm identified 4.5% of the volunteers in the CONSTANCES as CKD. The RENALGO-EXPERT algorithm had a positive predictive value of 6.2% and negative predictive value of 99.1% to detect an eGFR<60ml/min/1.73m². Half of 252 false positive cases (ALGO+, eGFR>90) had been diagnosed with kidney disease during hospitalization, and the other half based on healthcare consumption suggestive of a 'high-risk' profile; 95% of the 1661 false negatives (ALGO-, eGFR<60) had an eGFR between 45 and 60ml/min, half had medication and two-thirds had biological exams possibly linked to CKD. Half of them had a hospital stay during the period but none had a diagnosis of kidney disease. Our result is in accordance with other estimations of CKD prevalence in the general population. Analysis of diverging cases (FP and FN) suggests using health claims data have inherent limitations. Such an algorithm can identify patients whose care pathway is close to the usual and specific CKD pathways. It does not identify patients who have not been diagnosed or whose care is inappropriate or at early stage with stable GFR.