Using pharmacy data to identify those with chronic conditions in Emilia Romagna, Italy

Area of Science:

• Public Health
• Epidemiology
• Health Informatics

Background:

• Automated pharmacy data offers a potential tool for assessing chronic disease prevalence.
• Previous methods for chronic disease identification relied on different data sources.

Purpose of the Study:

• To refine and apply a model for identifying chronic diseases using Italian automated pharmacy data.
• To describe morbidity patterns in the Emilia Romagna region.
• To compare prevalence rates from pharmacy data with existing surveillance data.

Main Methods:

• Developed a list of chronic conditions (31 chronic condition drug groups - CCDGs) linked to drug consumption in Italy.
• Utilized the Chronic Disease Score and clinical review of medication classes.
• Tested algorithms on Emilia Romagna pharmaceutical claims data from 2001.

Main Results:

• Identified 31 CCDGs, with cardiovascular, rheumatological, respiratory, gastrointestinal, and psychiatric diseases being most frequent.
• Approximately 37.1% of the population (1.5 million individuals) had at least one identified CCDG.
• These CCDGs represented 77% of 2001 pharmaceutical expenditures.
• Pharmacy data-derived prevalence rates closely matched those from a 2000 disease surveillance study.

Conclusions:

• A robust measure of population-based chronic disease status was developed using Italian automated pharmacy data.
• The model successfully identified a significant proportion of the population with chronic conditions in Emilia Romagna.
• Automated pharmacy data presents a valuable alternative to survey data for population health assessments.