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Title: Predicting the Risk of Hospitalisations from a Longitudinal Perspective. The Northern Finland 1966 Birth Cohort.
Author: Gonzalez-Izquierdo, Arturo
ISNI:       0000 0001 3503 9501
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2008
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This thesis contains the analysis of data on hospitalisations that people on the Northern Finland Birth Cohort 1966, had during the Iifecourse The cohort consists of information obtained from 12,231 children and their mothers living in the provinces of Oulu and Lapland with expected delivery dates in the year 1966. The document presents a detail explanation of the theoretical and practical aspects around the study of hospitalisations viewed from a general epidemiological perspective.. The objective of the investigation was focused on identifying factors from lifecourse affecting the risk of being hospitalised. The 9ccurrence of hospital admissions is analysed considering time to event, length, type (all diagnoses taken into account) and number. Factors from the lifecourse potentially associated to such information were selected from a new set of combined characteristics such as clinical and biological, demographic, socioeconomic, and behavioural, neurobehavioral and developmental. Multivariate statistical methods for the reduction of dimensionality were used in the variable selection Exploratory and descriptive techniques were used to identify patterns of occurrence per group of diagnosis, determining the stratification of subsequent studies. Poisson regression was applied to study predictors for the number of hospitalisations per period of life. Binary and multinomial logistic regressions were applied to identify factors affecting repeated hospitalisations. Finally, survival analyses, in partiCUlar competing risks models, were used to study risk factors influencing the occurrence within specific groups of diagnosis. The study consists of an analysis of a complex structure of multi-factorial associations between hospitalisations and their possible predictors. The data comes from a large prospective cohort and the time sequence of factors is very well defined. It provides epidemiological evidence at an individual level and very precise information on patterns of hospital admissions.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available