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Authors Christos H. Skiadas and Charilaos Skiadas present a method for estimating the Total Loss of Healthy Life Years based on the first exit time theory for a stochastic process, the resulting Health State Function, and the Deterioration Function estimated as the curvature of the health state function. This methodology has been applied in numerous cases in the UK, Scotland, and Sweden, providing evidence to support their theory. The study demonstrates that both the World Health Organization (WHO) and European Union (EU) estimates of healthy life expectancy can be derived from this method. While the WHO system considers severe and moderate causes when estimating the loss of healthy life years, the EU system calculates total loss without such differentiation. By utilizing only death and population data, this methodology offers estimators for both scenarios without requiring survey data. These estimates serve as valuable tools to test and enhance existing survey-based methodologies. Despite convergence between WHO and EU systems, differences persist due to varying survey methodologies and data analysis approaches. Two prominent schools working in this field include the Institute for Health Metrics and Evaluation (IHME) led by Christopher J.L. Murray globally, and the European Health and Life Expectancy Information System (EHLEIS) with Jean-Marie Robine leading a team from EU member states.
SummaryAuthors Christos H. Skiadas and Charilaos Skiadas have a way to figure out how many healthy years of life are lost using a special theory. This method looks at how our health changes over time and has been used in different countries like the UK, Scotland, and Sweden. It helps us know how long we can stay healthy based on rules from the World Health Organization (WHO) and European Union (EU). The WHO system looks at different types of health problems when counting lost years, while the EU system just counts all lost years together. This method uses information about deaths and populations to make guesses without needing surveys.
Definitions
- Estimating: Guessing or figuring out something
- Stochastic: Random or unpredictable
- Function: A rule that connects one thing to another
- Curvature: How much something is bent or curved
- Deterioration: Getting worse over time
- Expectancy: How long we can expect something to last
Introduction:
In recent years, there has been a growing interest in estimating the total loss of healthy life years due to various health conditions. This is an important measure as it allows for a better understanding of the impact of diseases and injuries on overall population health. In this research paper, authors Christos H. Skiadas and Charilaos Skiadas present a novel method for estimating the Total Loss of Healthy Life Years (TLY) based on first exit time theory for a stochastic process.
Background:
The concept of healthy life expectancy has been widely used by organizations such as the World Health Organization (WHO) and European Union (EU) to measure population health. However, these estimates are often derived from survey data which can be costly and time-consuming to collect. Moreover, there may be variations in survey methodologies and data analysis approaches which can lead to discrepancies in estimates.
Methodology:
The methodology proposed by Skiadas and Skiadas utilizes only death and population data to estimate TLY without requiring any survey data. The key components of this method include the Health State Function (HSF), Deterioration Function (DF), and first exit time theory for a stochastic process.
The HSF is defined as the probability that an individual will remain in good health until their next birthday given their current age, sex, and country-specific mortality rates. The DF is then estimated as the curvature of the HSF which represents how quickly an individual's health deteriorates with age.
Results:
This methodology has been applied in various cases in countries such as UK, Scotland, and Sweden with promising results. It was found that both WHO and EU estimates of healthy life expectancy can be derived from this method. However, differences still exist due to varying survey methodologies used by different organizations.
Comparison with Other Methodologies:
Two prominent schools working in this field include the Institute for Health Metrics and Evaluation (IHME) led by Christopher J.L Murray globally, and the European Health and Life Expectancy Information System (EHLEIS) with Jean-Marie Robine leading a team from EU member states. While these organizations also provide estimates for healthy life expectancy, their methodologies differ from the one proposed by Skiadas and Skiadas.
The IHME uses a composite measure called Disability-Adjusted Life Years (DALYs) which combines years of life lost due to premature death and years lived with disability. On the other hand, EHLEIS uses survey data to estimate healthy life expectancy. However, both these methods have limitations such as high costs for data collection or reliance on subjective self-reported health measures.
Conclusion:
In conclusion, the method proposed by Skiadas and Skiadas offers a cost-effective alternative for estimating TLY without requiring any survey data. It has been successfully applied in various cases and provides estimators for both WHO and EU scenarios. This methodology can serve as a valuable tool to test and enhance existing survey-based methodologies used by organizations like WHO and EU. Further research in this area could lead to more accurate estimates of population health status globally.
We propose a method of estimating the Total Loss of Healthy Life Years based on the first exit time theory for a stochastic process, the resulting Health State Function and the Deterioration Function estimated as the curvature of the health state function. We have done many applications in UK and Scotland and Sweden supporting our theory. Furthermore it was proven that both the WHO and EU estimates of the healthy life expectancy can result from our method. The WHO system takes into account the severe and moderate causes in estimating the loss of healthy life years; instead the EU system calculates the total loss of healthy life years. For both cases our methodology provides both estimators from only death and population data. The advantages of our method are straightforward. We do not need survey data to make the calculations. The resulting estimates should be used to test and improve the existing survey based methodologies. While the WHO and EU systems tend to approach each other differences continue to appear based on the methodology of the related surveys and the analysis of data. Two main schools are working to these directions one based on USA the Institute for Health Metrics and Evaluation (IHME) headed by Christopher J.L. Murray and contributors in all over the world and the European Health and Life Expectancy Information System (EHLEIS) with Jean-Marie Robine and a team from the EU member states. Keywords: Deterioration, Loss of healthy years, HALE, DALE, World Health Organization, WHO, European Union, EU, EHEMU, IHME, EHLEIS, Healthy life expectancy, Life expectancy.
