ABSTRACT
BACKGROUND: there has been a long-standing, consistent use worldwide of Healthcare Administrative Databases (HADs) for epidemiological purposes, especially to identify acute and chronic health conditions. These databases are able to reflect health-related conditions at a population level through disease-specific case-identification algorithms that combine information coded in multiple HADs. In Italy, in the past 10 years, HAD-based case-identification algorithms have experienced a constant increase, with a significant extension of the spectrum of identifiable diseases. Besides estimating incidence and/or prevalence of diseases, these algorithms have been used to enroll cohorts, monitor quality of care, assess the effect of environmental exposure, and identify health outcomes in analytic studies. Despite the rapid increase in the use of case-identification algorithms, information on their accuracy and misclassification rate is currently unavailable for most conditions. OBJECTIVES: to define a protocol to systematically review algorithms used in Italy in the past 10 years for the identification of several chronic and acute diseases, providing an accessible overview to future users in the Italian and international context. METHODS: PubMed will be searched for original research articles, published between 2007 and 2017, in Italian or English. The search string consists of a combination of free text and MeSH terms with a common part on HADs and a disease-specific part. All identified papers will be screened for eligibility by two independent reviewers. All articles that used/defined an algorithm for the identification of each disease of interest using Italian HADs will be included. Algorithms with exclusive use of death certificates, pathology register, general practitioner or pediatrician data will be excluded. Pertinent papers will be classified according to the objective for which the algorithm was used, and only articles that used algorithms with "primary objectives" (I disease occurrence; II population/cohort selection; III outcome identification) will be considered for algorithm extraction. The HADs used (hospital discharge records, drug prescriptions, etc.), ICD-9 and ICD-10 codes, ATC classification of drugs, follow-back periods, and age ranges applied by the algorithms will be collected. Further information on specific accuracy measures from external validations, sensitivity analyses, and the contribution of each source will be recorded. This protocol will be applied for 16 different systematic reviews concerning eighteen diseases (Hypothyroidism, Hyperthyroidism, Diabetes mellitus, Type 1 diabetes mellitus, Acute myocardial infarction, Ischemic heart disease, Stroke, Hypertension, Heart failure, Congenital heart anomalies, Parkinson's disease, Multiple sclerosis, Epilepsy, Chronic obstructive pulmonary disease, Asthma, Inflammatory bowel disease, Celiac disease, Chronic kidney failure). CONCLUSION: this protocol defines a standardized approach to extensively examine and compare all experiences of case identification algorithms in Italy, on the 18 abovementioned diseases. The methodology proposed may be applied to other systematic reviews concerning diseases not included in this project, as well as other settings, including international ones. Considering the increasing availability of healthcare data, developing standard criteria to describe and update characteristics of published algorithms would be of great use to enhance awareness in the choice of algorithms and provide a greater comparability of results.
Subject(s)
Acute Disease , Algorithms , Chronic Disease , Databases, Factual , Health Services Administration , Research Design , Systematic Reviews as Topic , Humans , ItalyABSTRACT
BACKGROUND: Parkinson's Disease (PD), Multiple Sclerosis (MS), and Epilepsy are three highly impactful health conditions affecting the nervous system. PD, MS, and epilepsy cases can be identified by means of Healthcare Administrative Databases (HADs) to estimate the occurrence of these diseases, to better monitor the adherence to treatments, and to evaluate patients' outcomes. Nevertheless, the absence of a validated and standardized approach makes it hard to quantify case misclassification. OBJECTIVES: to identify and describe all PD, MS, and epilepsy case-identification algorithms by means of Italian HADs, through the review of papers published in the past 10 years. METHODS: 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. 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, follow-back 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. RESULTS: the search strategy led to the identification of 70 papers for PD, 154 for MS, and 100 for epilepsy, of which 3 papers for PD, 6 for MS, and 5 for epilepsy were considered pertinent. Most articles were published in the last three years (2014-2017) and focused on a region-wide setting. Out of all pertinent articles, 3 original algorithms for PD, 4 for MS, and 4 for epilepsy were identified. The Drug Prescription Database (DPD) and Hospital Discharge record Database (HDD) were used by almost all PD, MS, and epilepsy case-identification algorithms. The Exemption from healthcare Co-payment Database (ECD) was used by all PD and MS case-identification algorithms, while only 1 epilepsy case-identification algorithm used this source. All epilepsy case-identification algorithms were based on at least a combination of electroencephalogram (EEG) and drug prescriptions. An external validation had been performed by 2 papers for MS, 2 for epilepsy, and only 1 for PD. CONCLUSION: the results of our review highlighted the scarce use of HADs for the identification of cases affected by neurological diseases in Italy. While PD and MS algorithms are not so heterogeneous, epilepsy case-identification algorithms have increased in complexity over time. Further validations are needed to better understand the specific characteristics of these algorithms.