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.
- SourceAvailable from: Thomas Szucs
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- "We found that the largest proportions of persons suffered from pain, rheumatologic conditions and cardiovascular diseases, including hypertension. These findings are in line with previous studies in Italy estimating prevalence of chronic diseases showing highest rates in cardiovascular diseases and rheumatologic conditions [12,13]. On the other hand, our results are different from the Dutch estimates based on the PCG-model showing a low prevalence rate in rheumatologic conditions . "
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. DOI:10.1186/1471-2458-13-1030 · 2.26 Impact Factor
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- "An alternative method for monitoring health problems is the identification of conditions from associated prescriptions. The information on medications recorded in the EHRs may be more complete and several studies have used this approach to estimate the prevalence of certain chronic diseases [15-17]. Additionally, every four years a survey on the health of the population is undertaken in the Basque Country (“Encuesta de Salud de la CAPV”, ESCAV) ; its findings allow us to monitor self-perceived health and the prevalence of health problems in our community as reported by the population itself. "
ABSTRACT: Background Chronic diseases are an increasing threat to people’s health and to the sustainability of health organisations. Despite the need for routine monitoring systems to assess the impact of chronicity in the population and its evolution over time, currently no single source of information has been identified as suitable for this purpose. Our objective was to describe the prevalence of various chronic conditions estimated using routine data recorded by health professionals: diagnoses on hospital discharge abstracts, and primary care prescriptions and diagnoses. Methods The ICD-9-CM codes for diagnoses and Anatomical Therapeutic Chemical (ATC) codes for prescriptions were collected for all patients in the Basque Country over 14 years of age (n=1,964,337) for a 12-month period. We employed a range of different inputs: hospital diagnoses, primary care diagnoses, primary care prescriptions and combinations thereof. Data were collapsed into the morbidity groups specified by the Johns Hopkins Adjusted Clinical Groups (ACGs) Case-Mix System. We estimated the prevalence of 12 chronic conditions, comparing the results obtained using the different data sources with each other and also with those of the Basque Health Interview Survey (ESCAV). Using the different combinations of inputs, Standardized Morbidity Ratios (SMRs) for the considered diseases were calculated for the list of patients of each general practitioner. The variances of the SMRs were used as a measure of the dispersion of the data and were compared using the Brown-Forsythe test. Results The prevalences calculated using prescription data were higher than those obtained from diagnoses and those from the ESCAV, with two exceptions: malignant neoplasm and migraine. The variances of the SMRs obtained from the combination of all the data sources (hospital diagnoses, and primary care prescriptions and diagnoses) were significantly lower than those using only diagnoses. Conclusions The estimated prevalence of chronic diseases varies considerably depending of the source(s) of information used. Given that administrative databases compile data registered for other purposes, the estimations obtained must be considered with caution. In a context of increasingly widespread computerisation of patient medical records, the complementary use of a range of sources may be a feasible option for the routine monitoring of the prevalence of chronic diseases.BMC Health Services Research 10/2012; 12(1):365. DOI:10.1186/1472-6963-12-365 · 1.71 Impact Factor
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- "Therefore, our study provides a highly generalizable and up to date estimate of the impact of the disease in our Country. We also used a well-validated and reliable method for the identification of individuals with diabetes and the classification of complications/comorbidities registered in hospital, facilitating the prospective monitoring of temporal trends in the years to come   . "
ABSTRACT: Background and aims: To estimate the impact of diabetes and its complications, overall and in different age classes, on the likelihood of hospital admission for specific causes. Methods and results: We carried out a record-linkage analysis of administrative registers including data on 8,940,420 citizens in 21 Local Health Authorities in Italy. Individuals with pharmacologically treated diabetes (!2 prescriptions of antidiabetic agents during the year 2008) were paired in a 1:1 proportion with those who did not receive such drugs (controls) based on propensity-score matching. Odds Ratios (ORs) of hospitalization for macro and micro-vascular conditions in individuals with diabetes as compared to controls were estimated. The system identified 498,825 individuals with diabetes pharmacologically treated (prevalence of 5.6%). Prevalence of diabetes in people aged <14 years, 14e39 years, 40e65 years, and !65 years was 0.1%, 0.6%, 6.4%, and 18.2%, respectively. Overall, 23.9% of subjects with diabetes and 11.5% of controls had had at least a hospital admission during 12 months for the causes considered. Diabetes increased the likelihood of hospitalization by two to six times for the different causes examined. In absolute terms, diabetes was responsible for an excess of over 12,000 hospital admissions per 100,000 individuals/year. Conclusion: Despite the availability of effective treatments to prevent or delay major compli-cations, diabetes still places an enormous burden on both patients and the health care system. Given the continuous rise in diabetes prevalence both in middle-aged and elderly individuals, (A. Nicolucci). a v a i l a b l e a t w w w . s c i e n c e d i r e c t . c o m j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / n m c d Nutrition, Metabolism & Cardiovascular Diseases (2012) 22, 605e612 0939-4753/$ -see front matter ª we can expect an additional, hardly sustainable increase in the demand for health care in the near future.Nutrition, metabolism, and cardiovascular diseases: NMCD 01/2012; 22:605-612. · 3.32 Impact Factor