Modelling and Predicting eHealth Usage in Europe: A Multidimensional Approach From an Online Survey of 13,000 European Union Internet Users

BACKGROUND More advanced methods and models are needed to evaluate the participation of patients and citizens in the shared health care model that eHealth proposes. OBJECTIVE The goal of our study was to design and evaluate a predictive multidimensional model of eHealth usage. METHODS We used 2011 survey data from a sample of 13,000 European citizens aged 16-74 years who had used the Internet in the previous 3 months. We proposed and tested an eHealth usage composite indicator through 2-stage structural equation modelling with latent variables and measurement errors. Logistic regression (odds ratios, ORs) to model the predictors of eHealth usage was calculated using health status and sociodemographic independent variables. RESULTS The dimensions with more explanatory power of eHealth usage were health Internet attitudes, information health Internet usage, empowerment of health Internet users, and the usefulness of health Internet usage. Some 52.39% (6811/13,000) of European Internet users' eHealth usage was more intensive (greater than the mean). Users with long-term health problems or illnesses (OR 1.20, 95% CI 1.12-1.29) or receiving long-term treatment (OR 1.11, 95% CI 1.03-1.20), having family members with long-term health problems or illnesses (OR 1.44, 95% CI 1.34-1.55), or undertaking care activities for other people (OR 1.58, 95% CI 1.40-1.77) had a high propensity toward intensive eHealth usage. Sociodemographic predictors showed that Internet users who were female (OR 1.23, 95% CI 1.14-1.31), aged 25-54 years (OR 1.12, 95% CI 1.05-1.21), living in larger households (3 members: OR 1.25, 95% CI 1.15-1.36; 5 members: OR 1.13, 95% CI 0.97-1.28; ≥6 members: OR 1.31, 95% CI 1.10-1.57), had more children <16 years of age (1 child: OR 1.29, 95% CI 1.18-1.14; 2 children: OR 1.05, 95% CI 0.94-1.17; 4 children: OR 1.35, 95% CI 0.88-2.08), and had more family members >65 years of age (1 member: OR 1.33, 95% CI 1.18-1.50; ≥4 members: OR 1.82, 95% CI 0.54-6.03) had a greater propensity toward intensive eHealth usage. Likewise, users residing in densely populated areas, such as cities and large towns (OR 1.17, 95% CI 1.09-1.25), also had a greater propensity toward intensive eHealth usage. Educational levels presented an inverted U shape in relation to intensive eHealth usage, with greater propensities among those with a secondary education (OR 1.08, 95% CI 1.01-1.16). Finally, occupational categories and net monthly income data suggest a higher propensity among the employed or self-employed (OR 1.07, 95% CI 0.99-1.15) and among the minimum wage stratum, earning ≤€1000 per month (OR 1.66, 95% CI 1.48-1.87). CONCLUSIONS We provide new evidence of inequalities that explain intensive eHealth usage. The results highlight the need to develop more specific eHealth practices to address different realities.