GDP per capita and Life Expectancy
The World Health Organization defines health to be “a state of complete physical, mental and social wellbeing and not merely the absence of disease or infirmity1. A major health indicator of a population is its life expectancy, while the GDP per capita is a major indicator of its economic status. Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.2 While, GDP per capita is gross domestic product divided by midyear population3.
Many studies have shown that the health status of a population is frequently correlated with the economic status and income expenditure of the population. In the 1800’s income and life expectancy among the nations of the world were similar with low income (earning less than $2000.00 per year), poor health and life expectancy below 40 years. The current world population as of 2016 by the US census bureau was at 7.3 billion, about half of the world population 3.4 billion people still struggles to meet basic needs.
Living on less than $3.20 per day reflects poverty lines in lower-middle-income countries, while $5.50 a day reflects standards in upper-middle-income countries according to the World Bank4 .While rates of extreme poverty have declined substantially, falling from 36 percent in 1990, over 1.9 billion people, or 26.2 percent of the world’s population, were living on less than $3.20 per day in 2015. Close to 46 percent of the world’s population was living on less than $5.50 a day5. As of 2013 about 766 million people still live in extreme poverty (people living on less than $1.90 per day revised upward in 2015 from $1.25).
A better understanding of socio-economic patterns and health status of the world population can be gained using data from the following 5 representatives’ countries, namely Malawi, Bangladesh, India, China and the USA. The potential causative relationship between economics and health outcomes for each of these countries will be analyzed in this curriculum unit. At the extreme lower end of the socio-economic scale is Malawi, with a GDP per capita of about $338.00 and life expectancy of about 63 years. Malawi essentially captures the state of about 10% of the world population living in extreme poverty. An estimate of about 1.9 billion people live in poverty and have socio-economic characteristics similar to those of Bangladesh. Bangladesh has a GDP per capita of about $1500 and life expectancy of about 72 years. An estimated 3.5 billion of the world population are regarded as middle income with socio-economic characteristics similar to that of India. India has a GDP per capita of about $1900 and life expectancy of about 68 years. In contrast, an estimated 2 billion of the world population are regarded as high income and have socio-economic characteristics similar to China and the USA. China has a GDP per capita of $8800 and life expectancy of 76 years while the USA has a GDP per capita of about $59000 and life expectancy of 79 years.
The average per capita GDP in the LDCs is only $235; this figure is $24,522 on average for all developed countries and the average annual healthcare expenditure in LDCs is only $16 per person, whereas the average annual healthcare expenditure in high income countries is $1800 per person6. The average life expectancy in the LDCs is only 51 years, compared to 78 years in industrialized nations7. In African countries most affected by the global AIDS pandemic, life expectancy continues to decline – by 2010 the life expectancy in Botswana was only 27 years8.
The GINI Coefficient Factor
Generally, economic and health indicators suggest that people live longer in countries with a higher GDP per capita compared to countries with lower GDP per capita, however exceptions do occur. For instance, Bangladesh has a higher life expectancy than India, though India has a higher per capita GDP than Bangladesh. This discrepancy reflects that GDP per capita alone is insufficient to predict the health outcomes, including mortality and life expectancy, of any one country.
Other socio – economic factors, such as education and lifestyle choices, do play a major role in determining health outcomes in a population. Also, economic disparities that occur within countries measured by metrics such as the GINI coefficient can impact health outcomes. GINI coefficients can be used to measure the concentration of any distribution, not just the distributions of income. Higher concentrations translate into higher inequality. Lower concentrations mean lower inequality.9 The most recent GINI coefficient available for Bangladesh was 32.4 from 2016 while that of India was 35.4 from 201110. Comparing the GINI coefficient for Bangladesh and India suggests that Bangladesh has lower income inequality than India. This difference in inequality may be a significant factor in explaining why Bangladesh has a higher life expectancy than India, though India has a higher GDP per capita.
As a result of these economic disparities, there could be a huge difference in life expectancy even between countries on the same income level, depending on how the money is distributed and how it is used11. Simply put, for the most part the rich tend to live longer, whether comparing between rich and poor nations or comparing low income individual to high income earners within the same economy. Understandably, analyzing multiple economic and health indicators will provide a more robust understanding of causative relationships between economies and health outcomes. However, giving the scope and pacing of the New Haven Precalculus curriculum, this curriculum unit will be restricted to looking at data on GDP per capita and life expectancy of the 5 representative countries listed above. This curriculum unit, however, should not be limited to a Precalculus class alone, the curriculum unit could be expanded upon. Incorporating this unit in to a Statistics or Economic course would allow for further analysis of additional economic and health indicators.
Economic Development, Biotechnological Advances and Health Impact
The overall health of all nations of the world has improved significantly as a result of medical advances and the successful diffusion and adoption of such technology from more advanced countries to less developed ones. However, the goal of bioengineering to meet global health challenges given resource constraints remains elusive. There remains a large disparity between developing and developed countries of the world in terms of economic power and access to human centered biotechnological advancements.
Additionally, income inequality within a country among its different socio-economic groups strongly influences the populations ability to have access to state-of-the-art health care. Medical research and biotechnology development does not come cheap. Though the goal of medical research is to improve health coupled with it is also the goal of profit maximization of entrepreneurs. Investors in research and development of biotechnology designs not only look forward to recouping their investment, but also to make profit. Unfortunately, not all research results in technologies that are cost effective to be massively produced or will be adopted by society, sometimes due to cultural values and religious beliefs.
All of these complexities raise both economic and ethical challenges in the research, development, dissemination and adoption of human centered biotechnology. On one hand, an argument can be made that biotechnology designs should be cost effective; developed with the purchasing ability, cultural values and beliefs of the consumer of the product in mind. While, on the other hand, a major ethical concern that will be considered in this unit is: what should be the role of government and society in providing access to quality and timely health care for all socio-economic group within her population.
Through to the 1950s conditions such as stroke, heart attack, cancer and diabetes were regarded as degenerative diseases, but statistical analysis of their frequency pattern and distribution provided evidence that many of these conditions had a major environmental component. For example, death certificate rates for cancers of the stomach and lung rose so sharply between 1950 and 1973 that major environmental factors must have been at work generating these diseases in different populations12.
China, for instance witnessed rapid economic growth during the 1980s and 1990s that transformed the country and lifted millions of its citizens out of poverty. The economic boom, however, has been accompanied by environmental degradation, including a severe deterioration in the water quality of the country’s rivers and lakes. Extensive use of fertilizers by farmers and industrial wastewater dumping by manufacturing firms have rendered the water in many lakes and rivers unfit for human consumption13. China’s water monitoring system indicates that roughly 70% of the river water is unsafe for human consumption, although many farmers in rural areas still rely on these sources for drinking water 14. Concurrent with the decline in water quality in China’s lakes and rivers, the country experienced an increase in rural cancer rates during the 1990s. Stomach cancer and liver cancer now represent China’s fourth and sixth leading causes of death and, in combination with other digestive tract cancers (such as esophageal), account for 11% of all fatalities and nearly 1 million deaths annually in China.15
The use of sophisticated statistical methods was first applied to the study of noninfectious disease after world war 2 to analyze the patterns and associations of diseases in large populations. The emergence of clinical epidemiology marked one of the most important successes of the medical sciences in the 20th century16. By 1950 there was a global trend towards better health, health started to improve even in the poorest countries partly due to the economic development in many countries and partly due to medical advancements that by this time could be applied everywhere, e.g. antibiotics17.
In fact, a better understanding of the physiology and disorders of pregnancy together with improved prenatal care and obstetric skills has led to a steady reduction in maternal mortality. In an industrial country, few children now die of child-hood infection; the major pediatric problems are genetic and congenital disorders, which account for about 40 percent of admissions in pediatric wards, and behavioral problems18. Infant death are below ten per thousand births throughout Europe and the USA whereas over a 100 infants die per thousand in countries like Liberia and Ethiopia. Many of these premature babies would have survived with access to incubators, but modern incubators are complex and expensive. A standard incubator in an American hospital might cost more than $40,000. In addition to the expensive cost of the incubators, the knowledge, skill, right environmental conditions and utilities such as electricity needed to maintain the incubators are lacking in most developing countries of the world.
It has been suggested that as much as 95 percent of medical technology donated to developing countries breaks within the first five years of use.19 This shows that while technology may be successful and effective in one region of the world it may not be effective in other regions of the world. For technology transfer and adoption to be successful between regions of the world, there has to be similarities in terms of infrastructure, available skills, knowledge and economic empowerment. Many developing countries are still living in dire poverty with dysfunctional health care systems and extremely limited access to basic medical care. In contrast, the developed world is constantly clamoring more for the latest advancement in technology that would save more lives, improve health status and improve quality and delivery of health care.
Numerous studies have shown that there is a strong correlation between economic development and technological advancement, as a result, low income country continue to be at a disadvantage. Take for instance the case of Malawi: The Global Funds to Fight AIDS, TB and Malaria saw it fit only to give funding of $1 a day for anti-retroviral treatment for just 25,000 of 900,000 Malawians infected with AIDS over a five-year period. A death warrant from the international community for the people of Malawi. Sadly, one sixth of humanity lives as Malawians the poorest of the poor-too ill, hungry or destitute to step on the first rung of the development ladder. There is a valid ethical question about what could possibly be the rational for this decision by the international community.
Rising Costs and Unintended Consequences of Biotechnological Advances
In developed economies, technological advancements such as organ transplantation, diagnostic imaging systems, genetic manipulation are big ticket items that have significantly contributed to rising medical costs. While it is true that new technologies on the average improve the quality of medical care by improving health outcomes, this is not true of every technology in clinical use. Many new technologies are ineffective or redundant and do not improve health outcome. It is however difficult to discriminate between effective and ineffective technologies when they are being introduced.
Empirical evidence suggests that medical technology accounts for about 10 to 40% of the increase in health care expenditures over time, albeit some technologies may actually reduce costs by replacing more expensive alternatives or preventing expensive health consequences, but the overall effect is to increase costs.20 Industrial countries continues to grapple with the problem of the spiraling costs of health care resulting from technological development, public expectations, and in particular the rapidly increasing size of their elderly populations. As the size of the aging population increases, many societies may have to face the ethical question of rationing medical care. Unfortunately, one approach to resolving the rising cost of health care has been to restrict access to health care for a growing segment of the population, primarily the uninsured, while preserving the best available care to those fortunate enough to have coverage.
Other issues arising from technological advancement are their unintended consequences, some of these unintended consequences have been beneficial while others have been detrimental. For example, one of the most versatile technology that has been developed is the Ultrasound technology developed in 1794. The Ultrasound technology was originally developed as a navigation tool by an Italian biologist. This biologist’s curiosity on finding how bats find their way in the dark led to the discovery of Sonar; the ability to use sound waves and the echoes they generate would prove to be of immense benefit during World War I to correctly pinpoint the location of German submarines. Doctors also engaged the use of ultrasound in surgery as they discovered that heat generated from sound waves could destroy tissues. Ultrasound would also become useful as a diagnostic tool when a chemist in 1949 employed the technology to locate gallstones in dogs. With the development of the ultrasound physicians began to navigate the human body bouncing sound waves off the internal organs.
Ultrasound thus became a very promising tool to learn more about pregnancy and monitor high risk pregnancy without the risk of exposing the fetus to dangerous x -ray radiation21.“In 1959, the Scottish obstetrician Ian Donald used the new technology on a woman who happened to be pregnant and noticed that the fetus returned echoes as well. If Donald suspected that knowledge would translate into fetal selection and subtraction, he probably envisioned women attempting to avoid debilitating sex- linked diseases like hemophilia. (When the first sex-selective abortions had been performed in Denmark using amniocentesis four years earlier, indeed, they were done for that reason - and discriminated against males as a result.) He could have hardly guessed that ultrasound would one day contribute to a sex ratio imbalance involving over 160 million "missing" females in Asia and elsewhere”22. This major unintended consequence of the ultrasound technology has led to abnormal sex ratio in the human population.
Without manipulation both sex ratio at birth and population sex ratio are remarkably constant in human populations. Though small alterations do occur naturally for example, a small excess of male births with 105–107 male births for every 100 female births. This slight excess of male births was first documented in 1710 by John Graunt and colleagues for the population of London23 , and many studies of human populations have confirmed their finding. A key study of births for the period 1962 to 1980 in 24 countries in Europe showed a sex ratio of 105– 107, with a median of 105.924.“The tradition of son preference, however, has distorted these natural sex ratios in large parts of Asia and North Africa. This son preference is manifest in sex-selective abortion and in discrimination in care practices for girls, both of which lead to higher female mortality. Differential gender mortality has been a documented problem for decades and led to reports in the early 1990s of 100 million ‘‘missing women’’ across the developing world. Since that time, improved health care and conditions for women have resulted in reductions in female mortality, but these advances have now been offset by a huge increase in the use of sex-selective abortion, which became available in the mid-1980s. Largely as a result of this practice, there are now an estimated 80 million missing females in India and China alone.
The large cohorts of ‘‘surplus’’ males now reaching adulthood are predominantly of low socioeconomic class, and concerns have been expressed that their lack of marriageability, and consequent marginalization in society, may lead to antisocial behavior and violence, threatening societal stability and security.”25.
Vital statistics, such as the number of live births, and the number of deaths (including infant deaths) by sex, age, and cause are essential in order to accurately assess the health of populations and make decisions regarding health resources; unfortunately, these data are lacking from many areas. Data from many countries are not complete for instance deaths are recorded only in certain areas, while in others all areas are covered but not all deaths are recorded. Average rates of coverage vary widely, from only 10% in Africa, to over 90% in Europe.26
Because mortality does not give a complete picture of the burden of disease borne by individuals in different populations, the Disability adjusted life year (DALY) metric was proposed by the 1990 global burden to measure disease burden. The DALYs is a composite measure that shows the effect of premature mortality and the prevalence and the severity of ill-health in populations. DALYs are calculated by adding the number of years of life lost (YLL) to the number of years lived with disability (YLDs) for a certain disease or disorder27. DALY is the summary measure used to give an indication of overall burden of disease. One DALY represents the loss of the equivalent of one year of full health. Using DALYs, the burden of diseases that cause premature death but little disability (such as drowning or measles) can be compared to that of diseases that do not cause death but do cause disability (such as cataract causing blindness).28
The burden of disease, expressed in DALYs per 1000 population, has decreased in all regions during the period of 2000-2016, with the WHO African region having attained the largest decline (44%). This region, however, still bore the highest burden in 2016, 587 DALYs per 1000 population. This is over two-fold the burden of disease in the region with the lowest DALY rates (270 per 1000 population) in 2016: the WHO Western Pacific region. All regions have seen modest reductions (5-9%) in the contribution of premature death (measured by years of life lost or YLL), to overall burden of disease (measured by total DALYs) between 2000 and 2016. Globally, 29% of total DALYs were caused by communicable, maternal, neonatal and nutritional causes in 2016, a decline from 43% in 2000. The WHO African region has strikingly high proportion (61%) of DALYs due to communicable, maternal, neonatal and nutritional causes compared to other regions. In the WHO European Region and WHO Western Pacific Region, at least 80% DALYs were due to noncommunicable diseases. Road injuries caused a loss of nearly 83 million years of full health among the world’s population in 2016; making it among the 5 leading causes of DALYs. The proportion of total DALYs borne by children under 15 years old globally declined from 47% in 2000 to 32% in 2016, reflecting the massive reduction in deaths among children under 5 years old during this period. Almost all (87%) of DALYs borne by children under 15 years old, however, were caused by premature death, the remaining 13% were caused by ill health and disability. Adults aged 15-59 years old bore 36% of total DALYs in 2016 (up from 31% in 2000), and people aged 60 years and older bore the remaining 33% (up from 22% in 200029.
The trend from the result above shows that disease burden are shifting from communicable disease to non-communicable disease, and from premature death to years lived with disability particularly in high income populations. This is in line with expectations, since high income countries are at the forefront of bio- technological advancement. On the other hand, low income countries in sub-Saharan Africa, the dominant causes of disease burden still largely remain communicable, maternal, neonatal, and nutritional disorders. This is rather unfortunate, but does not defy expectations because bio-technological advancements are a rarity in this region of the world.