Contributions to distributional regression models. Applications in biomedicine

The present thesis makes statistical contributions in the field of frequentist and Bayesian distributional regression (DR) for univariate and bivariate responses. This work also proposes the inclusion of functional data into the DR models. The proposed methodologies are applied to several real biomedical studies, with emphasis in diabetes research

Spatial Patterns in the Rate of Alcohol Withdrawal Syndrome in Galicia (Spain)

AbstractIn the last twenty years there have been many studies that have considered geographic area as a health determinant. The analysis of the impact of these geographical effects is of importance to capture possible spatial heterogeneities. The present ecological study was aimed at investigating spatial trends in the rate of alcohol withdrawal syndrome (AWS rate) in a defined community. To take into account other potential confounding factors, we used Structured Additive Regression (STAR) models with Poisson response, which allows flexible modeling of spatial and non-linear effects. The analysis showed different results of various socio-demographic effects on the response when including the spatial trends in the model

Longer-term results of a Universal Electronic Consultation Program at the Cardiology Department of a Galician Healthcare Area

Background: Telemedicine models play a key role in organizing the growing demand for care and healthcare accessibility, but there are not described longer-term results in health care. Our objective is to assess the longer-term results (delay time in care, accessibility and hospital admissions) of an electronic consultation (e-consultation) outpatient care management program. Methods: Epidemiological and clinical data were obtained from the 41,258 patients referred by primary care to the cardiology department from January 1, 2010, to December 31, 2019. Until 2012, all patients were attended in an in-person-consultation (2010-2012). In 2013, we instituted an e-consultation program (2013-2019) for all primary care referrals to cardiologists that preceded patients’ in-person consultations when considered. We used an interrupted time series regression approach to investigate the impact of the e-consultation program on i) delay time (days) in care and ii) hospital admissions. We also analyzed iii) total number and referral rate (population adjusted referred rate) in both periods (in-person consultation and e-consultation); and iv) the accessibility was measured as number of consultations and variation according to distance from municipality and reference hospital center. Results: During the e-consultation period, the demand for cardiology care increased (7.2 ±2.4% vs. 10.1 ±4.8% per 1,000 inhabitants, p<0.001) and referrals from different areas were equalized. The reduction in delay to consultation during the in-person consultation (-0.96, 95%CI [-0.951, -0.966], p<0.001) was maintained with e-consultations (-0.064, 95%CI [0.043, 0.085], p<0.001). After the implementation of e-consultation, we observed that the increasing of hospital admission observed in the in-person consultation (IRR: 1,011;95% CI [1,003-1,018]), was stabilized (IRR:1,000; 95% CI [0.985, 1.015]; p=0.874). Conclusions: Implementing e-consultations in the outpatient management model may improve accessibility of care for patients furthest from the referral hospital. After e-consultations were implemented, the upward trend of hospital admissions observed during the in-person consultation period was stabilized with a slight downward trend