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: To test the hypothesis that scores of a validated measure of physician empathy are associated with clinical outcomes for patients with diabetes mellitus. This retrospective correlational study included 20,961 patients with type 1 or type 2 diabetes mellitus from a population of 284,298 adult patients in the Local Health Authority, Parma, Italy, enrolled with one of 242 primary care physicians for the entire year of 2009. Participating physicians' Jefferson Scale of Empathy scores were compared with occurrence of acute metabolic complications (hyperosmolar state, diabetic ketoacidosis, coma) in diabetes patients hospitalized in 2009. Patients of physicians with high empathy scores, compared with patients of physicians with moderate and low empathy scores, had a significantly lower rate of acute metabolic complications (4.0, 7.1, and 6.5 per 1,000 patients, respectively, P < .05). Logistic regression analysis showed physicians' empathy scores were associated with acute metabolic complications: odds ratio (OR) = 0.59 (95% confidence interval [CI], 0.37-0.95, contrasting physicians with high and low empathy scores). Patients' age (≥69 years) also contributed to the prediction of acute metabolic complications: OR = 1.7 (95% CI, 1.2-1.4). Physicians' gender and age, patients' gender, type of practice (solo, association), geographical location of practice (mountain, hills, plain), and length of time the patient had been enrolled with the physician were not associated with acute metabolic complications. These results suggest that physician empathy is significantly associated with clinical outcome for patients with diabetes mellitus and should be considered an important component of clinical competence.Academic medicine: journal of the Association of American Medical Colleges 07/2012; 87(9):1243-9. · 2.34 Impact Factor
- Health Policy Newsletter. 01/2005;
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ABSTRACT: Quantifying population health is important for public health policy. Since national disease registers recording clinical diagnoses are often not available, pharmacy data were frequently used to identify chronic conditions (CCs) in populations. However, most approaches mapping prescribed drugs to CCs are outdated and unambiguous. The aim of this study was to provide an improved and updated mapping approach to the classification of medications. Furthermore, we aimed to give an overview of the proportions of patients with CCs in Switzerland using this new mapping approach. The database included medical and pharmacy claims data (2011) from patients aged 18 years or older. Based on prescription drug data and using the Anatomical Therapeutic Chemical (ATC) classification system, patients with CCs were identified by a medical expert review. Proportions of patients with CCs were calculated by sex and age groups. We constructed multiple logistic regression models to assess the association between patient characteristics and having a CC, as well as between risk factors (diabetes, hyperlipidemia) for cardiovascular diseases (CVD) and CVD as one of the most prevalent CCs. A total of 22 CCs were identified. In 2011, 62% of the 932[prime]612 subjects enrolled have been prescribed a drug for the treatment of at least one CC. Rheumatologic conditions, CVD and pain were the most frequent CCs. 29% of the persons had CVD, 10% both CVD and hyperlipidemia, 4% CVD and diabetes, and 2% suffered from all of the three conditions. The regression model showed that diabetes and hyperlipidemia were strongly associated with CVD. Using pharmacy claims data, we developed an updated and improved approach for a feasible and efficient measure of patients' chronic disease status. Pharmacy drug data may be a valuable source for measuring population's burden of disease, when clinical data are missing. This approach may contribute to health policy debates about health services sources and risk adjustment modelling.BMC Public Health 10/2013; 13(1):1030. · 2.08 Impact Factor