A Systematic Review of Case-Identification Algorithms Based on Italian Healthcare Administrative Databases for Three Relevant Diseases of the Digestive and Genitourinary System: Inflammatory Bowel Diseases, Celiac Disease, and Chronic Kidney Disease.

Abstract

to identify and describe all Inflammatory Bowel Disease (IBD), Celiac Disease (CD), and Chronic Kidney Disease (CKD) case-identification algorithms by means of Italian Healthcare Administrative Databases (HADs), through a review of papers published in the past 10 years. this study is part of a project that systematically reviewed case-identification algorithms for 18 acute and chronic conditions by means of HADs in Italy. PubMed was searched for original articles, published between 2007 and 2017, in Italian or English. The search string consisted of a combination of free text and MeSH terms with a common part that focused on HADs and a disease-specific part. All identified papers were screened by two independent reviewers; exclusion criteria were the following: no details of algorithms reported, algorithm not developed in the Italian context, exclusive use of data from the death certificate register, or from general practitioner or pediatrician databases. Pertinent papers were classified according to the objective for which the algorithm had been used, and only articles that used algorithms for primary objectives (I disease occurrence, II population/cohort selection, III outcome identification) were considered for algorithm extraction. The HADs used (hospital discharge records, drug prescriptions, etc.), ICD-9 and ICD-10 codes, ATC classification of drugs, followback periods, and age ranges applied by the algorithms have been reported. Further information on specific objective(s), accuracy measures, sensitivity analyses and the contribution of each HAD, have also been recorded. the search string led to the identification of 98 articles for IBD, 42 articles for CD, and 390 for CKD. By screening the references, one paper for IBD was added. Finally, this led to 5, 9, and 8 pertinent papers respectively for IBD, CD, and CKD. Considering the papers on IBD and CD, specific age selections were applied to focus on children and young adult populations. When a selection on age was applied for CKD, instead, it mostly considered individuals aged more than 18 years. Three algorithms for IBD, 4 for CD, and 5 for CKD were extracted from papers and characterized. Drug prescription databases were used for both IBD and CKD algorithms, whereas the hospital discharge database and co-payment exemption database were used for IBD and CD. Pathology records and specialist visit databases were also used for CD and CKD, respectively. For each disease only one algorithm applied criteria for the exclusion of prevalent cases. External validation was performed only for Crohn's disease among IBDs, in one algorithm. the results of this review indicate that case identification for IBD and CD from routinely collected data can be considered feasible and can be used to perform different kinds of epidemiological studies. The same is not true for CKD, which requires further efforts, mainly to improve the detection of early stage patients.