Corporate social responsibility and financial profile of Spanish private hospitals.

In the context of the recent economic crisis, the financial situation of the Spanish health sector has become a major concern for responsible actors from the public and private spheres, because of a decline in public spending and increased demand due to population growth and ageing. The public health system seeks collaborative synergies with the private health system to achieve better health care results, cut waiting lists and cope with financial pressure. The private health sector currently owns 452 hospitals in Spain (57% of the country's total). This paper analyses the financial statements of hospital companies in the Spanish private healthcare system, using compositional data (CoDa) methodology. It identifies a significant relationship between financial statement structure and corporate social responsibility (CSR). Relevant CSR indicators include a sustainability report according to the Global Reporting Initiative (GRI), accreditation by the Joint Commission International (JCI) and ISO 14001, 50001 and 26000 accreditations. These indicators reduce asset turnover and increase debt quality: the proportion of long-term debt within total liability. From a methodological perspective, the paper proposes the first application CoDa in statistical models to predict financial statements from non-financial variables, and the first use of CoDa in partial-least-squares structural equation models (PLS-SEM). Our methodological approach reduces the asymmetry, redundancy and outliers encountered in standard statistical models that predict financial ratios.

Compositional analysis of dietary patterns.

Instead of looking at individual nutrients or foods, dietary pattern analysis has emerged as a promising approach to examine the relationship between diet and health outcomes. Despite dietary patterns being compositional (i.e. usually a higher intake of some foods implies that less of other foods are being consumed), compositional data analysis has not yet been applied in this setting. We describe three compositional data analysis approaches (compositional principal component analysis, balances and principal balances) that enable the extraction of dietary patterns by using control subjects from the Spanish multicase-control (MCC-Spain) study. In particular, principal balances overcome the limitations of purely data-driven or investigator-driven methods and present dietary patterns as trade-offs between eating more of some foods and less of others.

Using structural equation models to evaluate the magnitude of measurement error in blood pressure.

This article aims to compare alternative methods for estimating the quality of blood pressure measurements. Traditional within-subject variance estimates in mixed analysis of variance models are compared to multiple-group multitrait-multimethod models, which are a particular case of mean-and-covariance-structure confirmatory factor analysis models. Confirmatory factor analysis models belong to the family of structural equation models and were specifically developed to analyse psychosociological traits measured by tests or surveys, but they have also proved suitable for evaluating the quality of blood pressure measurements. Confirmatory factor analysis models are less restrictive and provide more detailed information than traditional approaches, enable researchers to compute weighted averages of individual measures with optimal measurement quality, make it easier to correct the biasing effects of measurement error on the results of substantive studies, and make the use of equivalent replicated measures unnecessary under certain conditions.