"Health insurance companies accumulate a great wealth of historical data, including policyholder characteristics (age, residence, etc.), death event information, and the frequency and type of medical claims made by each individual, which can be used as an indirect measure of subject's health status. At the same time, the aging processes occurring in most of developed countries generate an interest in assessing the relationship between emergency care demand and survival rate, especially for policyholders over 65. Theoretically, the amount needed by insurance companies to cover the costs of policyholders requiring frequent emergency care should be compensated by a lower survival rate, but this compensation is ambiguous due to heterogeneity between subjects. Indeed, aging and mortality rates are influenced by subject-specific socioeconomic and biological variables, which may vary considerably between individuals, and within a single subject. Consequently, there is both a medical and economic necessity to assess how the individual medical demand of elderly subjects will evolve over time, as their age-related high rate of chronic disease make them require additional medical resources. On one hand, insurance prices are measured in terms of premiums, so individual health status must be considered in order to allow the elderly to sign actuarially fair contracts. On the other hand, an insurance company providing pensions and insurance needs to plan for unexpected costs derived from people having lifespans above mean expectations. Under this interdependency scheme, the joint models for longitudinal and time-to-event data proposed in this thesis provide useful tools to properly address the underlying relationship between the emergency medical demand and the hazard for death event. This thesis makes a contribution to the statistical methodology in the field of joint modeling techniques, which were applied to a large longitudinal dataset, the HI Dataset, provided by a Spanish medical insurance company. From this dataset, we collected those subjects over 65 and living in Barcelona (Spain). For each subject, we have the historical emergency claims information within the study window, as well as the time-to-event information. The longitudinal outcome is of discrete nature, usually restricted to a small range of non-negative integer values which are affected by some degree of overdispersion, i.e. the observed variance exceeds the mean. Additionally, counts with a large number of zeros become quite common due to the incomplete nature of health insurance data. All those subjects entering after the age of 65 are considered as delayed entries, and their time-to-event data are subject to left truncation in addition to the potential (non-informative) right censoring. Then, the implemented joint models must account for the special characteristics of our observed longitudinal response, departing from the common Gaussian responses, together with the specific time-to-event data pattern. There are three main tasks at hand in the joint analysis of the two considered outcomes: 1. To implement a joint model which allows for the handling of longitudinal counts, also considering potential subject-specific overdispersion by means of a model which considers a zero excess (zero inflation). Moreover, survival times can also be subject to both left truncation and right censoring. 2. To assess the functional form to associate each individual's expected longitudinal response with their death risk, investigating the effect of the cumulative longitudinal response on the current death hazard. 3. As a central focus of this thesis, to propose the existence of a time-dependent relationship between the longitudinal process and the time-to-event outcome. This is defined using Bayesian P-splines in order for any specific shape to be conferred. All the analyses included in this thesis have been implemented under the Bayesian framework, in the R and JAGS free-software environments." -- TDX.

Although many books currently available describe statistical models and methods for analyzing longitudinal data, they do not highlight connections between various research threads in the statistical literature. Responding to this void, Longitudinal Data Analysis provides a clear, comprehensive, and unified overview of state-of-the-art theory

The field's bestselling reference, updated with the latest tools, data, techniques, and the latest recommendations from the Second Panel on Cost-Effectiveness in Health and Medicine Cost-Effectiveness Analysis in Health is a practical introduction to the tools, methods, and procedures used worldwide to perform cost-effective research. Covering every aspect of a complete cost-effectiveness analysis, this book shows you how to find which data you need, where to find it, how to analyze it, and how to prepare a high-quality report for publication. Designed for the classroom or the individual learner, the material is presented in simple and accessible language for those who lack a biostatistics or epidemiology background, and each chapter includes real-world examples and "tips and tricks" that highlight key information. Exercises throughout allow you to test your understanding with practical application, and the companion website features downloadable data sets for students, as well as lecture slides and a test bank for instructors. This new third edition contains new discussion on meta-analysis and advanced modeling techniques, a long worked example using visual modeling software TreeAge Pro, and updated recommendations from the U.S. Public Health Service's Panel on Cost-Effectiveness in Health and Medicine. This is the second printing of the 3rd Edition, which has been corrected and revised for 2018 to reflect the latest standards and methods. Cost-effectiveness analysis is used to evaluate medical interventions worldwide, in both developed and developing countries. This book provides process-specific instruction in a concise, structured format to give you a robust working knowledge of common methods and techniques. Develop a thoroughly fleshed-out research project Work accurately with costs, probabilities, and models Calculate life expectancy and quality-adjusted life years Prepare your study and your data for publication Comprehensive analysis skills are essential for students seeking careers in public health, medicine, biomedical research, health economics, health policy, and more. Cost-Effectiveness Analysis in Health walks you through the process from a real-world perspective to help you build a skillset that's immediately applicable in the field.

The field's bestselling reference, updated with the latest tools, data, techniques, and the latest recommendations from the Second Panel on Cost-Effectiveness in Health and Medicine Cost-Effectiveness Analysis in Health is a practical introduction to the tools, methods, and procedures used worldwide to perform cost-effective research. Covering every aspect of a complete cost-effectiveness analysis, this book shows you how to find which data you need, where to find it, how to analyze it, and how to prepare a high-quality report for publication. Designed for the classroom or the individual learner, the material is presented in simple and accessible language for those who lack a biostatistics or epidemiology background, and each chapter includes real-world examples and "tips and tricks" that highlight key information. Exercises throughout allow you to test your understanding with practical application, and the companion website features downloadable data sets for students, as well as lecture slides and a test bank for instructors. This new third edition contains new discussion on meta-analysis and advanced modeling techniques, a long worked example using visual modeling software TreeAge Pro, and updated recommendations from the U.S. Public Health Service's Panel on Cost-Effectiveness in Health and Medicine. This is the second printing of the 3rd Edition, which has been corrected and revised for 2018 to reflect the latest standards and methods. Cost-effectiveness analysis is used to evaluate medical interventions worldwide, in both developed and developing countries. This book provides process-specific instruction in a concise, structured format to give you a robust working knowledge of common methods and techniques. Develop a thoroughly fleshed-out research project Work accurately with costs, probabilities, and models Calculate life expectancy and quality-adjusted life years Prepare your study and your data for publication Comprehensive analysis skills are essential for students seeking careers in public health, medicine, biomedical research, health economics, health policy, and more. Cost-Effectiveness Analysis in Health walks you through the process from a real-world perspective to help you build a skillset that's immediately applicable in the field.

This open access book examines health trajectories and health transitions at different stages of the life course, including childhood, adulthood and later life. It provides findings that assess the role of biological and social transitions on health status over time. The essays examine a wide range of health issues, including the consequences of military service on body mass index, childhood obesity and cardiovascular health, socio-economic inequalities in preventive health care use, depression and anxiety during the child rearing period, health trajectories and transitions in people with cystic fibrosis and oral health over the life course. The book addresses theoretical, empirical and methodological issues as well as examines different national contexts, which help to identify factors of vulnerability and potential resources that support resilience available for specific groups and/or populations. Health reflects the ability of individuals to adapt to their social environment. This book analyzes health as a dynamic experience. It examines how different aspects of individual health unfold over time as a result of aging but also in relation to changing socioeconomic conditions. It also offers readers potential insights into public policies that affect the health status of a population.

Introduction. There is increasing concern with rising health care costs. Assessing cost-of-illness reveals the major determinants of costs to society. Weaknesses in current methodology include a lack of comprehensiveness, insufficient precision, the absence of information on reliability and validity and the omission of key stakeholders' perspectives. The objectives of this thesis were to assess the reliability of patient self-reports of health services utilization and to utilize a prospective model to generate a comprehensive and precise estimate of the cost of asthma per patient from multiple perspectives. Methods. The Pharmacy Medication Monitoring Program (PMMP) collects longitudinal data from patients recruited in community pharmacies. Respiratory patients reported medication consumption, health services use, out-of-pocket costs, quality-of-life and other information during 3 telephone interviews conducted at 1, 3 and 6 months after enrollment. Self-reports of health services utilization were compared to Ontario Health Insurance Plan (OHIP) records in 83 subjects and the cost of asthma was assessed in 1,279 patients. Results. A gradient agreement between self-reports and OHIP records was observed, ranging from substantial for admissions to slight for emergency room visits. Age, disease severity and occupation were significant determinants of total costs. The annual cost to society among adults ranged from $1,633 per mildly ill patient (95\% CI \$1,282, $1,984) to \$4,223 per severely ill patient (95% CI $3,849, \$4,598). The costs in children under 4 years were higher than older children but lower than adults. Indirect costs were responsible for 43% of total costs. The largest components of direct costs were hospitalizations, 47%, and medications, 40%. Conclusions. Interventions aimed at reducing productivity losses and admissions may result in savings to society, the health care system and the patient. Estimates stratified by age, severity and occupation can serve as benchmarks and prove useful for case mix and capitation planning. The quality of clinical and health policy decision-making may be enhanced by cost-of-illness estimates that are comprehensive, precise and expressed from multiple perspectives. The PMMP is a potentially useful model for outcomes research and health economic assessments.