Indirect costs of myocardial infarction in Portugal

Contribution of high-technology procedures to public healthcare expenditures: the case of ischemic heart disease in Portugal, 2002-2015

The magnitude of the impact of technological innovations on healthcare expenditure is unclear. This paper estimated the impact of high-technology procedures on public healthcare expenditure for patients with ischemic heart disease (IHD) in Portugal. The Blinder-Oaxaca decomposition method was applied to Portuguese NHS administrative data for IHD discharges during two periods, 2008-2015 vs. 2002-2007 (N = 434,870). We modelled per episode healthcare expenditures on the introduction of new technologies, adjusting for GDP, patient age, and comorbidities. The per episode healthcare expenditure was significantly higher in 2008-2015 compared to 2002-2007 for IHD discharges. The increase in the use of high-technology procedures contributed to 28.6% of this growth among all IHD patients, and to 18.4%, 6.8%, 11.1%, and 29.2% for acute myocardial infarction, unstable angina, stable angina, and other IHDs, respectively. Changes in the use of stents and embolic protection and/or coronary brachytherapy devices were the largest contributors to expenditure growth. High-technology procedures were confirmed as a key driver of public healthcare expenditure growth in Portugal, contributing to more than a quarter of this growth.

Identification of Health Expenditures Determinants: A Model to Manage the Economic Burden of Cardiovascular Disease

The purpose of this paper is to investigate the determinants influencing the costs of cardiovascular disease in the regional health service in Italy’s Apulia region from 2014 to 2016. Data for patients with acute myocardial infarction (AMI), heart failure (HF), and atrial fibrillation (AF) were collected from the hospital discharge registry. Generalized linear models (GLM), and generalized linear mixed models (GLMM) were used to identify the role of random effects in improving the model performance. The study was based on socio-demographic variables and disease-specific variables (diagnosis-related group, hospitalization type, hospital stay, surgery, and economic burden of the hospital discharge form). Firstly, both models indicated an increase in health costs in 2016, and lower spending values for women (p < 0.001) were shown. GLMM indicates a significant increase in health expenditure with increasing age (p < 0.001). Day-hospital has the lowest cost, surgery increases the cost, and AMI is the most expensive pathology, contrary to AF (p < 0.001). Secondly, AIC and BIC assume the lowest values for the GLMM model, indicating the random effects’ relevance in improving the model performance. This study is the first that considers real data to estimate the economic burden of CVD from the regional health service’s perspective. It appears significant for its ability to provide a large set of estimates of the economic burden of CVD, providing information to managers for health management and planning.