Methodological issues on the use of administrative data in healthcare research: the case of heart failure hospitalizations in Lombardy region, 2000 to 2012

Abstract

BackgroundAdministrative data are increasingly used in healthcare research. However, in order to avoid biases, their use requires careful study planning. This paper describes the methodological principles and criteria used in a study on epidemiology, outcomes and process of care of patients hospitalized for heart failure (HF) in the largest Italian Region, from 2000 to 2012.MethodsData were extracted from the administrative data warehouse of the healthcare system of Lombardy, Italy. Hospital discharge forms with HF-related diagnosis codes were the basis for identifying HF hospitalizations as clinical events, or episodes. In patients experiencing at least one HF event, hospitalizations for any cause, outpatient services utilization, and drug prescriptions were also analyzed.ResultsSeven hundred one thousand, seven hundred one heart failure events involving 371,766 patients were recorded from 2000 to 2012. Once all the healthcare services provided to these patients after the first HF event had been joined together, the study database totalled about 91 million records. Principles, criteria and tips utilized in order to minimize errors and characterize some relevant subgroups are described.ConclusionsThe methodology of this study could represent the basis for future research and could be applied in similar studies concerning epidemiology, trend analysis, and healthcare resources utilization.

Statistical Challenges of Administrative and Transaction Data

“…However, to ensure the accuracy of the information, a twofold strategy may be pursued: to link administrative data with suitable clinical registries or surveys (Gasperoni et al, 2017) and to check appropriateness of clinical questions that may be answered by these data, since 'the quality is not a property of the data set itself' but depends on the 'use to which it is put'. As an example, specific guidelines for dealing with administrative databases when acute and/or chronic cardiovascular diseases are considered are provided in Gasperoni et al (2017), Barbieri et al (2010), Mazzali et al (2016) and Ieva et al (2015).…”

Marked point process models for the admissions of heart failure patients

“…Furthermore, in ref. the authors showed that heart failure is a highly heterogeneous condition and thus various criteria were utilized to characterize different Heart Failure (HF) subgroups, leading to a sharper distinction of patients in four subgroups, which will be called HF groups, due to the classification of patient's disease. More precisely, the authors (see ref.…”

“…More precisely, the authors (see ref. ) rely on the type and number of patient's diagnosis coded with ICD‐9‐CM (International Classification of Diseases, 9th revision, Clinical Modification) and on two slightly different criteria, which are the so called AHRQ and HCC , in order to finally cluster heart failure people.…”

Regional variation in hospitalisation and mortality in heart failure: comparison of England and Lombardy using multistate modelling

“…The HF extract used in this study was part of a bigger dataset of all patients admitted in the region selecting patients with incident events between January 2006 and December 2012. For a detailed description of the extraction criteria to construct the data set of interest see Mazzali et al [17]. In brief, the Lombardy project's aims are to study the epidemiology, short-and medium-term outcomes and the variations in processes of care for patients hospitalized for HF.…”

“…First, some Lombardy HF admissions might actually have been for non-HF diagnoses and so should have been excluded from our cohort. The extraction criteria described in [17] are less restrictive than used in England, so this is possible to an extent. Second, some England non-HF admissions might actually have been for HF and so should have been included in our cohort.…”