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: BACKGROUND: Administrative databases are widely available and have been extensively used to provide estimates of chronicdisease prevalence for the purpose of surveillance of both geographical and temporal trends. There are,however, other sources of data available, such as medical records from primary care and national surveys. Inthis paper we compare disease prevalence estimates obtained from these three different data sources. METHODS: Data from general practitioners (GP) and administrative transactions for health services were collected fromfive Italian regions (Veneto, Emilia Romagna, Tuscany, Marche and Sicily) belonging to all the threemacroareas of the country (North, Center, South). Crude prevalence estimates were calculated by data sourceand region for diabetes, ischaemic heart disease, heart failure and chronic obstructive pulmonary disease(COPD). For diabetes and COPD, prevalence estimates were also obtained from a national health survey.When necessary, estimates were adjusted for completeness of data ascertainment. RESULTS: Crude prevalence estimates of diabetes in administrative databases (range: from 4.8% to 7.1%) were lowerthan corresponding GP (6.2%-8.5%) and survey-based estimates (5.1%-7.5%). Geographical trends weresimilar in the three sources and estimates based on treatment were the same, while estimates adjusted forcompleteness of ascertainment (6.1%-8.8%) were slightly higher. For ischaemic heart disease administrativeand GP data sources were fairly consistent, with prevalence ranging from 3.7% to 4.7% and from 3.3% to4.9%, respectively. In the case of heart failure administrative estimates were consistently higher than GPs'estimates in all five regions, the highest difference being 1.4% vs 1.1%. For COPD the estimates fromadministrative data, ranging from 3.1% to 5.2%, fell into the confidence interval of the Survey estimates infour regions, but failed to detect the higher prevalence in the most Southern region (4.0% in administrativedata vs 6.8% in survey data). The prevalence estimates for COPD from GP data were consistently higher thanthe corresponding estimates from the other two sources. CONCLUSION: This study supports the use of data from Italian administrative databases to estimate geographic differences inpopulation prevalence of ischaemic heart disease, treated diabetes, diabetes mellitus and heart failure. Thealgorithm for COPD used in this study requires further refinement.BMC Public Health 01/2013; 13(1):15. · 2.08 Impact Factor
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ABSTRACT: This article describes the methodological challenges associated with disease-based international comparison of health system performance and how they have been addressed in the EuroHOPE (European Health Care Outcomes, Performance and Efficiency) project. The project uses linkable patient-level data available from national sources of Finland, Hungary, Italy, The Netherlands, Norway, Scotland and Sweden. The data allow measuring the outcome and the use of resources in uniformly-defined patient groups using standardized risk adjustment procedures in the participating countries. The project concentrates on five important disease groups: acute myocardial infarction (AMI), ischemic stroke, hip fracture, breast cancer and very low birth weight and preterm infants (VLBWI). The essentials of data gathering, the definition of the episode of care, the developed indicators concerning baseline statistics, treatment process, cost and outcomes are described. The preliminary results indicate that the disease-based approach is attractive for international performance analyses, because it produces various measures not only at country level but also at regional and hospital level across countries. The possibility of linking hospital discharge register to other databases and the availability of comprehensive register data will determine whether the approach can be expanded to other diseases and countries.Health Policy 05/2013; · 1.51 Impact Factor
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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. · 1.77 Impact Factor