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Using Time Series Analysis to Forecast the Future Life Expectancy Trends in Europe by Considering Macro-Level Determinants
Blekinge Institute of Technology, Faculty of Engineering, Department of Industrial Economics.ORCID iD: 0000-0003-4620-7472
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Background Recently, there has been an increasing interest in forecasting the future trends of the population. Knowing the population trends is very important for policymakers to plan for their healthcare, social service, and pension systems. Life expectancy has increased more than double in many countries over the past two centuries. Many countries have increased the retirement age by linking it with life expectancy. Most previous research only used past life expectancy trends to forecast the future direction of life expectancy or included lifestyle behaviors such as smoking, drinking, and so on to predict the future trend. However, life expectancy is a health outcome indicator to represent population health. When discussing population health, it should be seen from a macro-level perspective. Prior studies revealed that economic growth, unemployment, and population density were macro-level determinants that can influence population health. Thus, forecasting life expectancy by considering these determinants could project it closer to reality.

Methods This research employed Vector Error Correction Model (VECM) by considering macro-level determinants to predict future life expectancy at birth trends. Moreover, the generalized impulse response function (GIRF) was applied to explain the reaction of life expectancy at birth to those macro-level determinants, and the generalized forecast error variance decomposition (GFEVD) was analyzed to measure the amount of information of those macro-level determinants contribute to future life expectancy at birth.

Results GIRF analysis revealed that life expectancy at birth has a positive relationship with economic growth and unemployment in many countries. However, population density has a positive relationship in some countries and a negative relationship in some countries. The results of GFEVD indicated that in many European countries, economic growth and unemployment have a medium effect on explaining future life expectancy at birth. At the same time, population density has little effect on explaining future life expectancy at birth. Conclusions The findings suggested that the population will live longer in many countries. Hence, policymakers must plan their healthcare, pension, and social services systems well to support the increasing demand in the future.

Keywords [en]
Time series analysis, Vector Error Correction Model (VECM), forecasting, impulse response function, life expectancy at birth, Europe
National Category
Economics
Research subject
Industrial Economics a nd Managemen
Identifiers
URN: urn:nbn:se:bth-26119OAI: oai:DiVA.org:bth-26119DiVA, id: diva2:1852610
Available from: 2024-04-18 Created: 2024-04-18 Last updated: 2024-04-25Bibliographically approved
In thesis
1. Enhancing the Performance and Efficiency of Healthcare Systems Using Industrial Economic Principles and Statistical Techniques
Open this publication in new window or tab >>Enhancing the Performance and Efficiency of Healthcare Systems Using Industrial Economic Principles and Statistical Techniques
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Optimizing healthcare systems has become more crucial in recent years due to escalating healthcare demands and economic constraints. This dissertation employed industrial economic principles and advanced statistical methods to analyze the performance and efficiency of healthcare systems in Europe. The study provided a detailed analysis of how healthcare systems can enhance service delivery and maintain cost-effectiveness by integrating industrial economic theories with empirical data. The dissertation was organized into a series of analyses, each focusing on different aspects of healthcare system performance, resource allocation, operational efficiency, and forecasting future health demands, as well as evaluating the accuracy of these forecasting models. Statistical techniques such as time series and multilevel regression analyses were used to examine the interplay between healthcare resources, healthcare systems, and health outcomes across European nations. The two main healthcare models in Europe, the Beveridge and Bismarck models, were compared in terms of performance, efficiency, and resource allocation. The main findings revealed that effective resource allocation and efficient management practices can significantly enhance the performance of healthcare systems. The study indicated that a healthcare system's efficiency depends on its ability to adjust resource allocation to changes in demographic and economic conditions. Additionally, this dissertation forecasted future demands for healthcare services, social security benefits, and pensions by incorporating macro-level determinants such as economic growth, unemployment rates, and population density into the forecasting models. The accuracy of these models provided valuable insights for policymakers to effectively plan for future healthcare, social security, and pension needs. Moreover, this dissertation employed an economic evaluation to compare the cost-effectiveness of Beveridge-type and Bismarck-type healthcare systems over the past twenty years. An effectiveness ratio was applied to measure the relationship between inputs (medical spending) and outputs (health outcomes). These effectiveness ratios demonstrated which healthcare system yields better health outcomes for each dollar spent. Furthermore, the findings indicated that the efficiency of healthcare systems varies from country to country, highlighting the challenges of adopting a universal approach to healthcare policy. This dissertation contributes to the academic field by demonstrating how industrial economic principles can be applied to improve the performance and efficiency of healthcare systems. It offered a framework for evaluating healthcare performance and efficiency, which can inform future reforms to achieve sustainable, high-quality healthcare services. This study promotes a dynamic approach to healthcare planning that adapts to technological advancements and demographic changes to enhance population health. 

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2024. p. 370
Series
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 10
Keywords
Healthcare system efficiency, industrial economics, performance measurement, healthcare resource allocation, healthcare model comparison, forecasting healthcare demand
National Category
Health Care Service and Management, Health Policy and Services and Health Economy
Research subject
Industrial Economics a nd Managemen
Identifiers
urn:nbn:se:bth-26122 (URN)978-91-7295-483-0 (ISBN)
Public defence
2024-06-13, J1630, Blekinge Institute of Technology, 371 79 Karlskrona, Karlskrona, 13:00 (English)
Opponent
Supervisors
Available from: 2024-04-19 Created: 2024-04-18 Last updated: 2024-05-23Bibliographically approved

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