Projecting health-ageing trajectories in Europe using a dynamic microsimulation model

Abstract

The extent of the challenges and opportunities that population ageing presents depends heavily on the population’s health. Hence, for the development of appropriate strategies that enable countries to adopt the emerging demographic and epidemiological realities, information on future health trajectories of elderly population is a natural requirement. This study presents an innovative methodological framework for projecting the health of individuals using a dynamic microsimulation model that considers interactions between sociodemographic characteristics, health, mortality, bio-medical and behavioral risk factors. The model developed, called ATHLOS-Mic, is used to project the health of cohorts born before 1960 for the period 2015–2060 for selected European Countries using SHARE data to illustrate the possible effects of some selected risk factors and education on future health trajectories. Results show that, driven by a better educational attainment, each generation will be healthier than the previous one at same age. Also, we see that, on average, an individual of our base population will live about 18 more years since the start of the projection period, but only 5 years in good health. Finally, we find that a scenario that removes the effect of having a low level of education on individual health has the largest impact on the projected average health, the average number of years lived per person, and the average number of years lived in good health.

Telemonitoring of elderly with hypertension and type 2 diabetes at the primary care level: Protocol for a multicentric randomized controlled pilot study

“…In previous studies, older multimorbid patients were often excluded because they could not use modern technologies, were not motivated to participate in the study, or needed additional support from family members ( 1 , 5 , 7 ). According to recent projections, the global population of people over 65 years of age will more than double by 2050, while the proportion of elderly people in the global population will increase from 9% to 16% ( 34 ). Therefore, exploring novel approaches such as telemonitoring to promote patient-centred care focused on quality of life and self-management support is essential.…”

Decoding health status transitions of over 200 000 patients with traumatic brain injury from preceding injury to the injury event

“…The methods presented here describe a non-hypothesis-driven approach for detecting health status at injury events and combining them with health status results preceding the injury. This approach offers an explanation for the challenges associated with injury diagnosis, classification, and surveillance, which can be confounded by population health heterogeneity and epigenetic ambiguity 46 . With these challenges in mind, we conducted an impartial and interpretable assessment of health status transitions in TBI accounting for 2,600 individual diagnoses encoded using the ICD-10 3 in a retrospective cohort of people of all ages, biological sex, socioeconomic standing, and place of living who had universally-funded access to healthcare.…”

Health Status Transitions Related to Traumatic Brain Injury.

“…In this paper, we have described a method for aligning health status transition in TBI, a disorder of significant public health concern and a major cause of disability worldwide 4,45 . The methods presented here describe an innovative non-hypothesis-driven approach for detecting health status at injury events and then combining health status results preceding the injury; this offers an explanation for the challenges associated with injury diagnosis, classification, and surveillance, which can be confounded by population health heterogeneity and epigenetic ambiguity 46 . With these challenges in mind, we conducted an impartial and interpretable assessment of health status transitions in TBI accounting for 2,600 individual diagnoses encoded using the ICD-10 3 in a retrospective cohort of people of all ages, biological sex, socioeconomic standing, and place of living who had non-differential access to healthcare.…”