Discovering disease trajectories from the Finnish Hospital Discharge Register with the MCL algorithm

Personalised medicine involves the use of individual information to determine the best medical treatment. Such information include the historical health records of the patient. In this thesis, the records used are part of the Finnish Hospital Discharge Register. This information is utilized to identify disease trajectories for individuals for the FINRISK cohorts. The techniques usually implemented to analyse longitudinal register data use Markov chains because of their capability to capture temporal relations. In this thesis a first order Markov chain is used to feed the MCL algorithm that identifies disease trajectories. These trajectories highlight the most prevalent diseases in the Finnish population: circulatory diseases, neoplasms and musculoskeletal disorders. Also, they defined high level interactions between other diseases, some of them showing an agreement with physiological interactions widely studied. For example, circulatory diseases and their thoroughly studied association with symptoms from the metabolic syndrome

Retrospective analysis of Mexican National Addictions Survey, 2008. Bias identification and correction.

Objective. To determine the presence of bias on the estimation of the consumption sometime in life of alcohol, tobacco or illegal drugs and inhalable substances, and to propose a correction for this in the case it is present. Materials and methods. Mexican National Addictions Surveys (NAS) 2002, 2008, and 2011 were analyzed to compare population estimations of consumption sometime in life of tobacco, alcohol or illegal drugs and inhalable substances. A couple of alternative approaches for bias correction were developed. Results. Estimated national prevalences of consumption sometime in life of alcohol and tobacco in the NAS 2008 are not plausible. There was no evidence of bias on the consumption sometime in life of illegal drugs and inhalable substances. New estimations for tobacco and alcohol consumption sometime in life were made, which resulted in plausible values when compared to other data available. Conclusion. Future analyses regarding tobacco and alcohol using NAS 2008 data will have to rely on these newly generated data weights, that are able to reproduce the new (plausible) estimations