La vie métaformatée : prolégomènes à l'exo-sphère

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal

Changements épidémiologiques au Canada : un regard sur les causes de décès des personnes âgées de 65 ans et plus, 1979-2007

La mortalité aux jeunes âges devenant de plus en plus faible, l’augmentation de l’espérance de vie est de plus en plus dépendante des progrès en mortalité aux âges avancés. L’espérance de vie à 65 ans et à 85 ans n’a cependant pas connu un rythme de progression continu depuis les trois dernières décennies. Ces changements dans l’évolution de l’espérance de vie proviennent de changements dans les tendances de certaines causes de décès et de leurs interactions. Ce mémoire analyse la contribution des causes de décès aux changements qu’a connus l’espérance de vie, mais aussi l’évolution spécifique des taux de mortalité liés aux principales causes de décès au Canada entre 1979 et 2007. Finalement, une analyse de l’implication de ces changements dans un contexte de transition épidémiologique sera réalisée, par un questionnement sur le fait que l’on assiste ou non au passage de certaines pathologies dominantes à d’autres. La réponse à ce questionnement se trouve dans l’étude de l’évolution par âge et dans le temps des causes de décès. Les résultats montrent que les progrès en espérance de vie à 65 ans et à 85 ans sont encore majoritairement dus à la diminution de la mortalité par maladies cardiovasculaires. Toutefois, ces dernières causes de décès ne sont pas les seules à contribuer aux progrès en espérance de vie, puisque les taux de mortalité dus aux dix principales causes de décès au Canada ont connu une diminution, bien qu’elles n’aient pas toutes évolué de la même manière depuis 1979. On ne semble ainsi pas passer d’un type de pathologies dominantes à un autre, mais à une diminution générale de la mortalité par maladies chroniques et à une diversification plus importante des causes de décès à des âges de plus en plus avancés, notamment par la diminution des «grandes» causes de décès.With the decrease of mortality at younger ages, gain in life expectancy is heavily dependent on the progress in old age mortality. However, over the last three decades, life expectancies at 65 and 85 years old have not experienced a constant rate of progress. Changes in life expectancy progress come from changes in specific causes of death trends and their interactions. The present thesis studies the contribution of causes of death on the changes in life expectancies and the trends in death rates of specific causes of death in Canada between 1979 and 2007. An analysis of those changes in an epidemiological transition context has also been done by questioning whether or not we are witnessing a shift from certain dominant diseases to others. This questioning will be answered by studying variation in the causes of death by age and over time. The results of this study show that progress in life expectancies at 65 and 85 years old are still mainly due to the decrease in cardiovascular mortality. However, cardiovascular diseases are not the only causes of death to contribute to the progress in life expectancy. Since 1979, mortality rates from the ten leading causes of death in Canada have all declined but in different ways. Thus, there does not seem to be a shift in the dominant causes of death towards others in Canada, but there is a general mortality decline from chronic diseases and a greater diversification of causes of death at older ages

Modelling and forecasting healthy life expectancy. A Compositional Data Analysis approach

Will the extra years of life gained by the increase in life expectancy be lived in good or bad health? As forecasts support social, economic and medical decisions, as well as individuals' choices, there is a clear rationale for forecasting healthy life expectancy. However, only a limited number of models are available to forecast healthy life expectancy. Some are based on multistate modelling, which can be data-demanding and requires separate forecasts of transition rates for mortality within different health statuses and the incidence rate. We here suggest a less data demanding model to forecast mortality and health prevalence simultaneously. The model is based on the Sullivan method, which uses cross-sectional data, and Compositional Data Analysis. The method is applied to Swedish female mortality aged 65 and above. We show that deaths have been shifted towards older ages and not-limited, leading to an increase in both life expectancy and disability-free life expectancy

Outsurvival as a measure of the inequality of lifespans between two populations

Background: Inequality in lifespans between two populations, e.g., males and females or people with low and high socioeconomic status, is a focus of demographic, economic, and sociological research and of public policy analysis. Such inequality is usually measured by differences in life expectancy. Objective: We aim to devise a cogent measure of how much distributions of lifespans differ between two populations. Results: We propose an outsurvival statistic, φ (phi), that measures the probability that an individual from a population with low life expectancy will live longer than an individual from a population with high life expectancy. This statistic can also be interpreted as an underdog probability - the chance that a random value from a distribution with a low mean will exceed a random value from a distribution with a higher mean. Contribution: Our outsurvival probability complements life-expectancy differences to provide a more nuanced view of the inequality of lifespans between two populations. Our mathematically equivalent underdog probability provides an intuitive and widely applicable perspective on the more general question of how disparate two distributions are

Understanding Differences in Cancer Survival between Populations: A New Approach and Application to Breast Cancer Survival Differentials between Danish Regions

Large variations in cancer survival have been recorded between populations, e.g., between countries or between regions in a country. To understand the determinants of cancer survival differentials between populations, researchers have often applied regression analysis. We here propose the use of a non-parametric decomposition method to quantify the exact contribution of specific components to the absolute difference in cancer survival between two populations. Survival differences are here decomposed into the contributions of differences in stage at diagnosis, population age structure, and stage-and-age-specific survival. We demonstrate the method with the example of differences in one-year and five-year breast cancer survival between Denmark’s five regions. Differences in stage at diagnosis explained 45% and 27%, respectively, of the one- and five-year survival differences between Zealand and Central Denmark for patients diagnosed between 2008 and 2010. We find that the introduced decomposition method provides a powerful complementary analysis and has several advantages compared with regression models: No structural or distributional assumptions are required; aggregated data can be used; and the use of absolute differences allows quantification of the survival that could be gained by improving, for example, stage at diagnosis relative to a reference population, thus feeding directly into health policy evaluation