Longevity and Education

This article is about the disparities of longevity between levels of education and racial groups. The paper examines the impact of race and education on past and present life expectancy trends. It also attempts to dispel the illusion of grandeur that most Americans appear to have about life expectancy. With advances in medicine, technology, genetics, and other health related fields, many just stop examining the data when they hear that life expectancy is going up. This article is an attempt to educate on why life expectancy should be examined past its face value.

Whilst life expectancy is going up in general, a look below the surface provides data the shows an alarming divide between many subgroups in the US. Racial, ethnic, educational, and income differences are all directly linked to causing variations in longevity. In this article the variations in educational attainment will be the main topic. Whist educational attainment is only one of multiple factors in determining socioeconomic status, it is shown to have a heavy influence on all other variables. The rationale behind this article is to provide evidence on education’s influence in longevity, then provide possible solutions for policy makers going forward regarding the wide gap seen between varying ethnic groups. The methodology behind this experiment was to examine the death certificates of Americans and stratify the deaths by sex, age, race, and level of education completed. Then compare the information they found to national averages and statistics, as well as previous studies done on similar topics.

The results of this experiment yielded some interesting findings. The first thing found was that regardless of their level of education completed, it was found that women in the United States live longer than men at every age and ethnic group. It was also found that the Caucasian group, at every age and educational level, outlived members of the African American group. The Hispanic group was found to have the highest reported life expectancy at birth among any of the racial and ethnic categories. However, this is reported with caution as the rates of immigration and emigration cause fluctuations in the data. Another major result was that education has powerful associations in the duration of life for all races, ethnicities, and ages. It was found that there is approximately a 10-year difference when comparing the life expectancy of the most educated with the least educated of a group.

Overall, it can be concluded that education is a very important factor when determining the longevity of a group. Implementing educational enhancements for all ages and races would contribute to reducing the large gap in life expectancy that currently exists.

Olshansky, S. Jay, et al. “Differences in life expectancy due to race and educational differences are widening, and many may not catch up.” Health Affairs 31.8 (2012): 1803-1813.

Advertisements

Longevity and Education

This article is about the disparities of longevity between levels of education and racial groups. The paper examines the impact of race and education on past and present life expectancy trends. It also attempts to dispel the illusion of grandeur that most Americans appear to have about life expectancy. With advances in medicine, technology, genetics, and other health related fields, many just stop examining the data when they hear that life expectancy is going up. This article is an attempt to educate on why life expectancy should be examined past its face value.

Whilst life expectancy is going up in general, a look below the surface provides data the shows an alarming divide between many subgroups in the US. Racial, ethnic, educational, and income differences are all directly linked to causing variations in longevity. In this article the variations in educational attainment will be the main topic. Whist educational attainment is only one of multiple factors in determining socioeconomic status, it is shown to have a heavy influence on all other variables. The rationale behind this article is to provide evidence on education’s influence in longevity, then provide possible solutions for policy makers going forward regarding the wide gap seen between varying ethnic groups. The methodology behind this experiment was to examine the death certificates of Americans and stratify the deaths by sex, age, race, and level of education completed. Then compare the information they found to national averages and statistics, as well as previous studies done on similar topics.

The results of this experiment yielded some interesting findings. The first thing found was that regardless of their level of education completed, it was found that women in the United States live longer than men at every age and ethnic group. It was also found that the Caucasian group, at every age and educational level, outlived members of the African American group. The Hispanic group was found to have the highest reported life expectancy at birth among any of the racial and ethnic categories. However, this is reported with caution as the rates of immigration and emigration cause fluctuations in the data. Another major result was that education has powerful associations in the duration of life for all races, ethnicities, and ages. It was found that there is approximately a 10-year difference when comparing the life expectancy of the most educated with the least educated of a group.

Overall, it can be concluded that education is a very important factor when determining the longevity of a group. Implementing educational enhancements for all ages and races would contribute to reducing the large gap in life expectancy that currently exists.

Mortality and Socioeconomic levels in the U.S.

This article is about the inverse association between risk of disease and socioeconomic level. This is quickly becoming one of the most prevalent topics observed in public health. Often when one thinks of socioeconomic level, regardless of how it is measured, it is equated to things such as property of an individual. It is also used in determining medical care, quality of food/housing, and other necessities. This article argues that while these things are correct, in industrialized countries it is important to consider the distribution of wealth overall. Studies are showing that as countries become more egalitarian in relation to economic distribution, life expectancy will increase.

The rationale behind this study is; examining the factors above in the United States will yield results in mortality trends and how socioeconomic position effects one’s overall health. The research was conducted by looking income inequality censuses in the 80’s and 90’s. Annual Household income was also calculated and divided into approximately 20 groups. This was then used to bracket incomes into percentiles and extrapolate the data in regard to where it fell in said percentile.

After analyzing the research, a significant correlation was found between household income and mortality. The less income a household had, the higher the rate of mortality for all causes. Another interesting result was that lower levels of income were also associated with higher levels of smoking, homicide, violent crime, imprisonment, and unemployment. A couple of interesting conclusions can be drawn from the article. One is that the variations of income inequality are significantly associated with mortality trends, social indicators, and numerous health outcomes. This is very important when looking at things such as economic policies. This information proves that economic policies can have major implications on the health of countries. Going forward citizens should be wary of policies that increase the inequality of income distribution as they can deteriorate the health of the overall population.

Overall the article was interesting and insightful. The information was presented in a relatively clear way and the researchers related it to the problem well. The multitude of graphs and tables also help to provide a good visual representation as means to better understand the data. However, while the bulk of the article was written well, there were a couple of points that could stand to be clarified. At the end of the paper, towards the conclusions, the authors went off on a tangent about an ecological fallacy. I felt the relevance of including this could have been better explained to the reader.

Kaplan, George A., et al. “Inequality in income and mortality in the United States: analysis of mortality and potential pathways.” Bmj 312.7037 (1996): 999-1003.

Mortality and Life Expectancy

This article examines the changes in mortality and life expectancy based on education status. The authors examined socioeconomic factors in the 80’s and 90’s and the role it played on the longevity of someone’s life. The main factor looked at is educational status, however, income and availability of resources were also examined.

The rationale behind this experiment is to determine how mortality effects different ethnic groups in relation to their educational status. By doing this one can determine whether education or different ethnic background plays a bigger role in the mortality rate of a group. To begin the experimental data was obtained on mortality trends and education. Life expectancy trends were also obtained for various age-sex-race-educational groups. This data was then analyzed by computing education and life expectancy against one’s own race-sex group and the entire population. Also, specific causes of mortality were taken into account and analyzed within the data. This analysis resulted in a couple of interesting findings. The first major finding was that life expectancy for higher educational groups increased exponentially more than lower educational groups, regardless of factors like ethnicity and sex. When analyzed with other cultural factors it was found that the growing life expectancy educational gap was most pronounced in women. Another interesting result obtained was the disparity between race-sex groups was falling. The data began to grow increasingly more dependent on the level of education and less on a person’s ethnicity.

In conclusion, the experiment shows your mom wasn’t kidding when she said school is important. The level of educational attainment of someone is directly correlated to the longevity of their life. This also proves that socioeconomic factors such as GDP and income increase life expectancy. It is possible to infer this because higher education leads to better paying jobs, otherwise the motivation for educational obtainment would be next to nonexistent. Another conclusion drawn was that disease related morbidity rates were lower for people in the higher educational bracket. This is an interesting finding as one would assume disease wouldn’t be biased towards an education level.

Overall, I thought this experiment was okay. It had areas that were interesting and some that were confusing and poorly explained. The experiment started as a look on morbidity and life expectancy due to education. However, it quickly turned to an examination of morbidity rates due to specific diseases and failed to make much of a connection on why education plays a role. I feel that the abstract failed to inform the reader of the main purpose of the experiment.

Meara, Ellen R., Seth Richards, and David M. Cutler. “The gap gets bigger: changes in mortality and life expectancy, by education, 1981–2000.” Health Affairs 27.2 (2008): 350-360.

 

Socioeconomic Factors and Life Expectancy

An article done by Mahfuz Kabir examines the relationship of socioeconomic factors on life expectancy. More specifically, the article attempts to explain the life expectancy trends found in developing countries along with some of their anomalies. Life expectancy is a widely used indicator for overall development of a country. It is linked to various different socioeconomic factors that developing countries should be striving to improve. By looking at this statistic, it provides a rough estimate of the direction in which multiple programs in that country are heading. The rationale behind this article was to determine which factors contribute most to increasing a countries life expectancy. If a specific factor could be isolated, a country could use this information to devote more resources towards it and improve their efficiency. The methodology was to first gather information about the correlation of the varying socioeconomic factors and examine their trends in regard to longevity of life. After establishing the relationships between factors, the author proceeded to use the information to create equations in which the data could be extrapolated to graphs. This data is also tested for statistical significance, allowing the author to draw some interesting results. The first major result found was that life expectancy has a strong positive correlation to income/GDP, but only to a certain extent. The data showed that its relationship was strongest the worse off economically a person was. However, as that person gained wealth, the relationship of GDP/income on life expectancy began to dissipate. Essentially the poorer you are, the more increasing your economic situation matters. Another interesting result was that conventional socioeconomic factors were increasing in some countries, but the overall life expectancy was still going down. When examining this further the author was able to conclude that, while these factors are a good indicator of life expectancy, they are not all that is directly responsible in determining its trend. They way improvements to the various economic factors are made can be just as important. An example of this would be health care spending, improvement to a healthcare building is not nearly as effective as hiring more doctors to come to the underprivileged countries.

Overall the article was insightful and interesting to read. However, the graphs could have been explained better as to help the reader understand the data more.

Kabir, M. “Determinants of Life Expectancy in Developing Countries.” The Journal of Developing Areas, vol. 41 no. 2, 2008, pp. 185-204. Project MUSEdoi:10.1353/jda.2008.0013

 

 

$Money Matters$

The value of money, by definition, is defined with materialistic measures. Its usefulness lies in what it can help you obtain and often we are taught that “it isn’t everything”. However, socioeconomic factors may be more deeply intertwined with our lives then we realize. The value of a dollar may affect the value and longevity of your life.

The article of discussion for this post is “Income distribution and life expectancy”. A study done in Britain was questioning whether developed countries could increase the life expectancy of their populations through methods of income redistribution. Researchers were considering the causal significance between mortality rates and income studies done in the 1970’s-1980’s. The study was largely grounded in the lower to middle class, as the research was non-linear and showed the biggest differences between the lower 60% of the spectrum. The other 40%, which was mainly comprised of middle through upper classes, was less statistically significant. This was due to the fact that eventually the amount of money you made simply stopped playing a factor in the span of one’s life. The rationale behind this experiment was to test the theory that; if income was redistributed more evenly, then life expectancy of those in less fortunate situations would rise. while those with more socioeconomic resources would hardly see any effect. This line of thinking was supported when looking at cross-sectional studies, which suggested that a more egalitarian method of wealth distribution yielded a more stable life expectancy throughout that countries population.

The Major results in this article were that; overall, there is a strong and clear relation between a society’s income distribution and average life expectancy. These results stemmed from associations in analyses between cross sectional data and data covering changes over time. This led to a couple of conclusions being suggested, however, the strongest evidence was for income distribution effecting mortality rates. Figures in the article also serve to bolster this viewpoint and provide evidence that income changes are most influential to changes in health of the least well off. Another piece of interesting evidence to back this conclusion is a correlation between Britain and Japan. In the 1970’s both countries had relatively similar Life expectancy’s and income distributions. However, since the 70’s Britain’s income distribution has grown more one-sided, while Japan has become consistently more egalitarian. When looking at the other half of the equation they discovered Japan had trended way above Britain and in fact, had the highest average life expectancy in the world. The increase in the distribution of the wealth was found to be the key factor as to why this happened. In conclusion, this article provides statistical evidence that the amount of money you earn can indeed effect how long you live.

Wilkinson, Richard G. “Income distribution and life expectancy.” BMJ: British Medical Journal 304.6820 (1992): 165.