Phd in SCHOOL OF STATISTICAL SCIENCES

Thesis title: Gender Gap in Health and Survival: Age-Cause Contributions and Healthy Life Expectancy

The focus of my thesis is on the gender gap in health and survival. The first aim of my dissertation is to shed light on the evolution of the gender gap in survival in recent decades by examining patterns of contributions of ages and causes of death to the gender gap in life expectancy. The analyses cover a wide range of causes of death and include long time periods for several European and non-European countries. Advanced statistical techniques have been applied to evaluate changes in contributions of ages and causes of death to the gender gap in life expectancy over time (e.g. Functional Data Analysis) by using aggregated-level data (e.g. Human Mortality Database, Human Causes-of-Death Database, WHO Causes-of-Death Database). The second aim of my dissertation is to investigate gender differences in the associations between health expectancy and potential risk factors, focusing on healthy life expectancy as life expectancy free from specific diseases (not commonly studied), rather than disability-free life expectancy. In particular, I analyse the sex difference in the extent of the reduction of cancer-free life expectancy due to the (co-) occurrence of risk factors, such as smoking, obesity, and physical inactivity. Additionally, I investigate gender disparities in the association between depression-free life expectancy and two distinctive marital statuses: married and widowed. Multistate life table approach (by using Interpolated Markov Chain software and R function ELECT) is applied to measure healthy and unhealthy life expectancy in recent cohorts of older Americans of the Health and Retirement Study, over a large follow-up. First, our findings reveal that with mortality delay, the most relevant age contributors to the gender gap in mortality, with regards to both premature mortality and old-age mortality (e.g., due to cancer, cardiovascular diseases and external causes), indeed shifted towards older ages, but the shift was not rigid. On the contrary, it could involve, depending on the cause of death and the country, either a compression of the most relevant age-contributors or a dispersion. In addition, this dissertation shows the benefits of the demographic application of Functional Data Analysis approach (FDA), revealing that the age- and cause-specific contributions to the GGLE act almost entirely on only two dimensions: level (greater/smaller contribution) and age pattern (location of the curves across ages). The location of the cause-specific mortality differences across the age spectrum had a greater impact on the gender gap in life expectancy than the magnitude of the cause-specific differences. Findings also revealed that, while the presence of individual and multiple behavioral risk factor, such as smoking, obesity, physical inactivity, attenuates cancer-free life expectancy disadvantage of men compare to women, social factors such as being widowed (rather than being married) amplifies depression-free life expectancy disadvantage of men compared to women. Finally, this dissertation offers insights into the complex factors that contribute to the gender gap in life and health expectancy and highlights the need for continued efforts to improve health outcomes for both men and women across all ages and causes of death. Manuscripts included in this dissertation: Study I Feraldi, A., and Zarulli, V. (2022). Patterns in age and cause of death contribution to the sex gap in life expectancy: a comparison among ten countries. Genus, 78(1), 1-22. Study II Feraldi, A., Zarulli, V., Mazzuco, S., and Giudici, C. (2023). Functional Data Analysis Approach in Population Studies: An Application to the Gender Gap in Life Expectancy (Under review in Quality & Quantity - Methods for modelling and understanding population changes). Study III Feraldi, A., Giudici, C., and Brouard N. (2023). Gender Gap in Cancer-Free Life Expectancy: The Association with Smoking, Obesity and Physical Inactivity (Results will be partly presented at the Population Association of America 2023 Annual Conference). Study IV Feraldi, A., Giudici, C., and Brouard N. (2023). Estimating Gender Gap in Depression-Free Life Expectancy among 50 and older American widowed: An application using Interpolated Markov Chain Approach (Under review in Statistical Methods & Applications).