Only a few studies on selected populations have checked criterion validity by comparing drugs categories head-to-head with their diagnoses-based analogues. It was designed to predict future health costs and thus restricted to chronic diseases. For example the “Rxrisk” model developed by Fishmann included 55 therapeutic categories. Improvements now include a wider range of drugs, new scores, and extended application to various populations (pediatric, Medicare and Medicaid, veterans, European countries). Most medication-based classification systems are derived from the chronic disease score (CDS) developed by Von Korff et al., with a fair prediction of hospitalization, mortality, the number of ambulatory visits and costs. Thence the growing interest in measures based on drug prescription data, often routinely collected by insurers they may also provide information on well-controlled diseases, which are frequently under-declared by physicians. In Switzerland, as in many other countries, such records are missing mainly because data collection is time-consuming, costly and not always reliable. In the USA, Medicare and Medicaid databases and some private health insurance or maintenance organizations routinely record ambulatory diagnoses. Ĭurrent patient classification systems are mainly based on diagnoses information. Although the increased use of electronic medical records (EMR) by primary physicians has the potential to collect clinical information in large populations, the identification of a particular disease within an EMR often remains far from straightforward. National health surveys have been conducted to estimate the prevalence of chronic illnesses but such expensive and time-consuming studies are generally not feasible on an ongoing basis. Outpatient morbidity information is scarcer except for cancer registers and contagious infections, which are subject to mandatory declaration. Most developed countries have minimal data sets on inpatient morbidity and causes of death. Demographic variables do not account sufficiently for the discrepancy in health service use and costs, overestimating cost variations between care providers and misidentifying outliers. However, due to insufficient concordance with their diagnoses-based analogues, their use for morbidity indicators is limited.īuilding health indicators, managing health care and prevention, and adjusting for insurers’ risks require the assessment of morbidity burdens. Most categories provide insurers with health status information that could be exploited for healthcare expenditure prediction or ambulatory cost control, especially when ambulatory diagnoses are not available. For 22 conditions, drugs-based information identified accurately a subset of the population defined by diagnoses. In addition, they exhibited good reproducibility and allowed prevalence estimates in accordance with national estimates. After accounting for inpatient under-coding, fifteen conditions agreed sufficiently with their diagnoses-based counterparts to be considered alternative strategies to diagnoses. ResultsĮighty percent used a drug associated with at least one of the 60 morbidity categories derived from drugs dispensation. The reproducibility of the drugs-based morbidity measure over the 2 years was assessed for all enrollees. ![]() The agreement between the two approaches was measured by weighted kappa coefficients. They were followed throughout 20 and hospitalized at least once during this period. We compared drugs-based categories with their diagnoses-based analogues using anonymous data on 108,915 individuals insured with one of four companies. The objective of the study was to test the accuracy of drugs-based morbidity groups derived from the World Health Organization Anatomical Therapeutic Chemical Classification of drugs by checking them against diagnoses-based groups. But most published tools use national drug nomenclatures and offer no head-to-head comparisons between drugs-related and diagnoses-based categories. ![]() ![]() Pharmacy-based case mix measures are an alternative source of information to the relatively scarce outpatient diagnoses data.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |