Using pharmacy data to identify those with chronic conditions in Emilia Romagna, Italy.
ABSTRACT Automated pharmacy data have been used to develop a measure of chronic disease status in the general population. The objectives of this project were to refine and apply a model of chronic disease identification using Italian automated pharmacy data; to describe how this model may identify patterns of morbidity in Emilia Romagna, a large Italian region; and to compare estimated prevalence rates using pharmacy data with those available from a 2000 Emilia Romagna disease surveillance study.
Using the Chronic Disease Score, a list of chronic conditions related to the consumption of drugs under the Italian pharmaceutical dispensing system was created. Clinical review identified medication classes within the Italian National Therapeutic Formulary that were linked to the management of each chronic condition. Algorithms were then tested on pharmaceutical claims data from Emilia Romagna for 2001 to verify the applicability of the classification scheme.
Thirty-one chronic condition drug groups (CCDGs) were identified. Applying the model to the pharmacy data, approximately 1.5 million individuals (37.1%) of the population were identified as having one or more of the 31 CCDGs. The 31 CCDGs accounted for 77% (E556 million) of 2001 pharmaceutical expenditures. Cardiovascular diseases, rheumatological conditions, chronic respiratory illness, gastrointestinal diseases and psychiatric diseases were the most frequent chronic conditions. External validation comparing rates of the diseases found through using pharmacy data with those of a 2000 Emilia Romagna disease surveillance study showed similar prevalence of illness.
Using Italian automated pharmacy data, a measure of population-based chronic disease status was developed. Applying the model to pharmaceutical claims from Emilia Romagna 2001, a large proportion of the population was identified as having chronic conditions. Pharmacy data may be a valuable alternative to survey data to assess the extent to which large populations are affected by chronic conditions.
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ABSTRACT: Increasingly, researchers and health specialists are obtaining information on chronic illnesses from self-reports. This study validates self-reports of two major health conditions, hypertension and diabetes, based on a recent survey in Taiwan (SEBAS 2000). These data, based on a large, nationally representative sample of respondents aged 54 and older, include both self-reported health information and a physical examination. Average blood pressure readings, laboratory measures of glycosylated hemoglobin, and information on whether the respondent was taking medication for hypertension or diabetes are used to validate respondents' reports of high blood pressure and diabetes. The resulting comparisons reveal that self-reports vastly underestimate the prevalence of hypertension, but yield a reasonably accurate estimate of the prevalence of diabetes. Significant correlates of the accuracy of the self-reports include age, education, time of the most recent health exam, and cognitive function.Journal of Clinical Epidemiology 03/2003; 56(2):148-54. · 5.33 Impact Factor
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ABSTRACT: Develop and estimate the RxRisk model, a risk assessment instrument that uses automated ambulatory pharmacy data to identify chronic conditions and predict future health care cost. The RxRisk model's performance in predicting cost is compared with a demographic-only model, the Ambulatory Clinical Groups (ACG), and Hierarchical Coexisting Conditions (HCC) ICD-9-CM diagnosis-based risk assessment instruments. Each model's power to forecast health care resource use is assessed. Health services utilization and cost data for approximately 1.5 million individuals enrolled in five mixed-model Health Maintenance Organizations (HMOs) from different regions in the United States. Retrospective cohort study using automated managed care data. SUBJECTS All persons enrolled during 1995 and 1996 in Group Health Cooperative of Puget Sound, HealthPartners of Minnesota and the Colorado, Ohio and Northeast Regions of Kaiser-Permanente. MEASURES RxRisk, an algorithm that classifies prescription drug fills into chronic disease classes for adults and children. HCCs produce the most accurate forecasts of total costs than either RxRisk or ACGs but RxRisk performs similarly to ACGs. Using the R(2) criteria HCCs explain 15.4% of the prospective variance in cost, whereas RxRisk explains 8.7% and ACGs explain 10.2%. However, for key segments of the cost distribution the differences in forecasting power among HCCs, RxRisk, and ACGs are less obvious, with all three models generating similar predictions for the middle 60% of the cost distribution. HCCs produce more accurate forecasts of total cost, but the pharmacy-based RxRisk is an alternative risk assessment instrument to several diagnostic based models and depending on the nature of the application may be a more appropriate option for medical risk analysis.Medical Care 02/2003; 41(1):84-99. · 3.23 Impact Factor