Mesura de la influència dels determinants de la salut en l'evolució de la tuberculosi

Identifier:  TDX:2244

Handle: https://hdl.handle.net/20.500.11797/TDX2244

Authors:  Oliach Güell, Mª José

Abstract:
Background. Tuberculosis is related to biological factors, as well as, to other health factors. Objective. The purpose of this thesis is to analyse the contribution of health determinants to understand changes in the epidemiological behaviour of tuberculosis in Spain. The relative importance of each health determinant effect is evaluated. It is shown that the evolution of tuberculosis is not only related to health determinants, but also to non-health factors. Methodology. (1) Review of the literature and data collection; (2) time series cointegration analysis to describe the consistency of each of the determinants and to confirm its long-term relationships with the disease; and (3) cross-sectional data analysis through structural equations models that allow to measure latent multidimensional phenomena, which are not directly measurable, such as the propensity to tuberculosis. The results confirmed our hypothesis: A) Time series analysis: (1) the unprotected population is more susceptible against tuberculosis. In contrast, a welfare society is more resistant against the disease; (2) organic and multi-organic disorders facilitate the progression of tuberculosis; (3) the size of the budget devoted to health, a better health care financing system and effective health care planning strategies contribute to a lower incidence of tuberculosis and more efficient use of resources; (4) harmful lifestyles and adverse weather conditions increase the use of health services and the incidence of tuberculosis. B) Structural equation modeling: both the hypothesized multiple indicators multiple causes (MIMIC) model and the path model show significant differences in the relative weight of the determinants in explaining tuberculosis indicators. Interestingly, the impact of environment and lifestyles variables on tuberculosis is four times higher than the impact of biological health variables.