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.

Background The rising life expectancy at birth marks a significant victory for modern medicine and public health initiatives. Advances in medical technology, improved healthcare services, enhanced nutrition, and greater awareness of health and wellness have all contributed to prolonging the average human lifespan. In recent decades, many countries, particularly in Europe, have seen life expectancy at birth surpass previous historical limits. This increase significantly affects public health, economics, and social structures. As populations age, the demand for healthcare services, long-term care, and pensions increases, adding strain on both public and private sectors. These shifts necessitate modifications in social services, healthcare delivery, and pension plans to accommodate an older demographic. Therefore, accurate forecasts of life expectancy at birth are crucial for governments and organizations to make well-informed decisions and effectively plan for future demographic changes. Forecasting life expectancy at birth has become central to demographic research due to its pivotal role in planning and policy formulation.

Methods Numerous studies have introduced various methods and techniques to predict future life expectancy, utilizing a broad array of statistical tools and models. These methods range from simple linear projections based on historical data to complex models that incorporate multiple variables and sophisticated statistical techniques. Despite the availability of numerous forecasting methods, there is a notable gap in the literature concerning systematic evaluations of these techniques, as most studies focus on creating new forecasting methods or refining existing models to increase predictive accuracy. This study addresses this gap by examining the performance of two principal econometric models, namely the Autoregressive Integrated Moving Average (ARIMA) and the Vector Error Correction (VEC) model, in predicting future life expectancy at birth in Europe. These models represent different approaches to time series forecasting, each with distinct assumptions and capabilities. This research used Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the Diebold-Mariano (DM) tests to compare the forecast accuracy of the two models.

Results The results from RMSE and MAE showed that the ARIMA model better predicted future life expectancy at birth for half of the European countries in this study, while the VEC model was more accurate for the other half. The DM test indicated statistically significant differences in forecast accuracy between the ARIMA and VEC models in some European countries.

Conclusions This study aimed to determine which model delivers the most accurate and reliable predictions by comparing these models in predicting future life expectancy at birth across several European countries. Such insights are crucial for policymakers, planners, and researchers developing health systems and social policies tailored to an aging European population.

Background Recently, the demand for care has risen, while in contrast, healthcare resources remain limited. These resources include health expenditure, the number of physicians, nurses, and hospital beds. Many studies have revealed that healthcare resources are one of the most critical factors contributing to a population's health status. The healthcare system plays a key role in transforming these resources into health outcomes, which are widely used as indicators to measure population health and the performance of healthcare systems. Previous work has primarily investigated the relationship between health expenditure or the number of doctors and population health. However, the association between healthcare resources as a whole has yet to be widely examined.

Methods This study employed multilevel regression to examine the impact of healthcare resources and healthcare systems on population health across 25 European countries. The healthcare systems of these countries can be broadly categorized into Beveridge-type and Bismarck-type systems. Additionally, this study used descriptive statistics and Welch's t-test to observe the allocation patterns of healthcare resources and to compare the performance of the two types of healthcare systems.

Results The regression analysis indicated that health expenditure per capita and the number of physicians and nurses were positively correlated with life expectancy at birth, while the number of hospital beds was negatively correlated with it. In contrast, infant mortality was negatively associated with health expenditure per capita and the number of physicians and nurses, and positively associated with the number of hospital beds. However, the healthcare systems did not show statistical significance in the models for life expectancy at birth or infant mortality. Welch's t-test revealed that the Beveridge-type healthcare system performed better than the Bismarck-type system.

Conclusions The findings suggest that oversupplying healthcare resources could harm health outcomes. Countries using the same healthcare systems tend to allocate healthcare resources similarly, and these allocations might affect the performance of the healthcare systems. Thus, policymakers should consider this when deciding how to allocate funding to healthcare resources and when selecting or shifting to a healthcare model for their countries.

Background Worldwide, healthcare systems are struggling with rising health expenditures and inefficient healthcare systems. Moreover, an aging population, longer lifespans, and infectious diseases put more pressure on healthcare systems. Healthcare's performance, efficiency, and effectiveness need to be evaluated to prevent a further rise in health expenditure. In Europe, healthcare systems can be broadly categorized into two models: the Beveridge model and the Bismarck model. These two healthcare systems' performance, efficiency, and effectiveness have been discussed over the past decade. Many researchers have tried to determine which healthcare system performs better and more effectively. However, a clear conclusion has yet to be reached.

Methods This research compared the performance and effectiveness of 27 European countries using Beveridge-type and Bismarck-type healthcare systems from 2000 to 2020. This research used life expectancy at birth as a health outcome indicator to represent population health and evaluate the performance of two healthcare systems. The health expenditure per capita PPP was used as medical spending. A Welch's t-test was first employed to measure the performance of the two healthcare systems. This study then applied economic evaluation to compare the effectiveness of the two healthcare systems.

Results Welch’s t-test results demonstrated that life expectancy at birth in countries using the Beveridge-type healthcare system was statistically significantly higher than in countries using the Bismarck-type healthcare system. The effectiveness ratio of the period from 2000 to 2020 showed that the Beveridge-type healthcare system has a higher value of medical spending than the Bismarck-type healthcare system.

Conclusions The results revealed that the Beveridge-type healthcare system performed better and was more cost-effective than the Bismarck-type healthcare system. Policymakers should be careful when choosing or shifting a healthcare model for their countries.