By Dmitry Kaminskiy,
General Partner, Deep Knowledge Group
A study that dates back to 1950 shows that on average, global life expectancy increased by 9.7 years in a decade. Since 2000, however, the increase in global life expectancy has been just 1.9 years per decade—around 80% less than the gains achieved in the 1940s.
How come our predecessors were making faster progress with basic hygiene and public health measures? It is alarming to witness the opposite trend of what our generation would expect in the era of the Fourth Industrial Revolution.
Since 1900, the global average life expectancy has more than doubled and is now above 70 years. The global slowdown in life expectancy gains since year 2000 is not attributed to the lack of technology or its ability to lengthen our lives. Technology is merely an enabler, and it is upon us not only to utilize it in research and development, but also to make it widely available in the form of goods and services that empower everyday consumers to prolong their lifespan and healthspan.
It is time we took the longevity sector seriously.
Why is life expectancy important?
Life expectancy is the most important indicator for analysing population health since it covers mortality across the whole life span. It provides information on the average age of death in a population. Life expectancy is estimated on age-specific mortality rates for the population in question, which necessitates enumeration data for the population’s size and the number of fatalities at each age.
In recent decades, there has been a significant shift in the population’s age structure: the number and proportion of individuals who are over 65 has grown significantly. More individuals are likely to be living with dementia and other chronic diseases that make them more sensitive to risk factors and reliant on health and social care services.
We haven’t seen enough progress in scientific advancements related to hard-to-solve medical problems such as chronic conditions, especially age-related disabilities. As Disability-Adjusted Life Years (DALYs) evolve, we can observe how our society’s health and wellbeing is changing with the times. The changes in DALYs offer a unique glimpse into where further improvement in global quality of living can be made. Such factors contribute to temporary limitations of life expectancy extension.
Moreover, Health Adjusted Life Year (HALE) and Quality-Adjusted Life Years (QALY) have seen a dramatic evolution in recent years, transitioning from an abstract concept to a metric that deeply impacts healthcare decision-making. This shift reflects the increasing importance placed on weighing quality of life against financial costs when it comes to medical treatments and interventions.
It is also a prevalent misperception that higher life expectancy is only attributable to lower child mortality rates. During recent years, mortality rates among younger individuals made no significant impact on life expectancy trends, while making tiny positive contributions before 2010.
What’s causing the slowdown in increasing life expectancy?
It is impossible to trace the recent slowdown in improvement in life expectancy to a single cause, and it is likely that a variety of variables must be addressed concurrently. One of those variables that has a crucial impact in the current situation is healthcare possibilities targeted at older individuals.
The main contributing factors to life expectancy currently are cardiovascular, metabolic, and neurodegenerative disorders. They have the strongest impact on the most vulnerable proportion of the population – the elderly. As this proportion of the population increases, the lack of tangible solutions for their health problems will further shorten life expectancies across the globe.
One of the greatest ways to improve life expectancy is to exert more effort in the longevity industry. We need to properly establish the longevity sector with a focus on the ageing population.
Artificial Intelligence plays a significant role in this process. Most of the longevity related products and services require aggregation and analysis of large and often continuously updated datasets, which creates a great start for machine learning-like applications. Products and services can then be built with the active use of different data-driven solutions. InsurTech, P4 medicine and fitness products are some of the most obvious examples here. All of these sectors require aggregation and analysis of big data in order to provide meaningful and personalised solutions.
