Blog 5

Title: Are rich people or poor people more likely to be ill? Lay perceptions, by social class and neighborhood, of inequalities in health

Authors: Sally Macintyre, Laura Mckay, Anne Ellaway,

Citation:

Macintyre, S., McKay, L., & Ellaway, A. (2005). Are rich people or poor people more likely to be ill? Lay perceptions, by social class and neighborhood, of inequalities in health. Social Science & Medicine, 60(2), 313-317.

 

This study compares how people in lower socioeconomic status (SES) believe how healthy they are, in contrast to, how people in high SES believe how healthy the lower SES are. This study asked direct questions, and used an area where the difference between the high and low SES was easily distinguishable. Even though the differences were clear between the two areas, neither one was extremely rich or extremely poor. The people surveyed in this research were from Glasgow, Scotland, and were from the ages 25, 45, and 65 years old. The people in each category were asked if those in lower SES were more likely to develop cancer, heart disease, mental illness, have accidents, be fitter, and to live longer in general. Each person was to choose more likely, less likely, or equally as likely. The most common answer was that the rich and poor were equally as likely to develop health problems. However, it was also a common theme to answer that the rich were more likely to be fitter and to live longer. In general, the poor were viewed to have a greater chance to develop heart disease, cancer, and mental illnesses. Although, people from a lower SES were less likely to admit that the poor would probably develop health problems easier than the rich. When the model looked at the how each sex answered, it showed that women were less likely to say that poor people were more likely to have accidents, develop diseases and to live longer. Men, on the other hand, were more likely to say that poor people were more likely to have health problems.

When looking at the how each age responded, it revealed that individuals in the 45 and 65 year age categories, were more likely to say that the poor are at a higher risk of having accidents compared to those in the 25 year age category. People in the 45 year category were much more likely than the other two, to say that the poor were more likely to develop heart disease, cancer, and mental illnesses. The 65 year olds in particular, were the least probable to say that the poor had a higher risk of heart disease than the rich.

The researchers involved with this study claim that they need to conduct more research sue to the limitations of this study. Some of the limitations include a small sample size, one suit of questions, and three small age groups. In addition, they couldn’t follow up on the people that they surveyed, and find out why they answered the way that they did.

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Blog 4

Title: Racial and social class gradients in life expectancy in contemporary California.

Authors: Christina Clarke, Tim Miller, Ellen Chang, Daixin Yin, Myles Cockburn, Scarlett Gomez

Citation:

Clarke, Miller, Chang, Yin, Cockburn, & Gomez. (2010). Racial and social class gradients in life expectancy in contemporary California. Social Science & Medicine, 70(9), 1373-1380.

 

This study looked at a three-year period from 1999-2001 in California, and it compared life expectancy to socioeconomic status (SES), race/ethnicity, age, and sex. These factors were also used to divide areas into blocks based on SES. During this time there were 689,036 individuals who passed away. To obtain information about these people, the researchers used the California Department of Health Services, and were able to access SES, race/ethnicity, age, sex, cause of death, and their address. They first divided each person into 6 racial categories including white, African-American, Native American, Hispanic, Asian, and other. They also made groups from the blocks the deceased lived on, and gave each block an average SES. The results show that males have a shorter life span than females by an average of 4.8 years. The largest difference (5.9 years) between males and females occurred in the lowest SES. The smallest difference (3.3 years) between male and female life expectancy is in the highest SES. Men in the lowest SES had a lower life expectancy than those in the highest SES by 7.0 years. Women in the lowest SES had a lower life expectancy than those in the highest SES by 4.4 years.

Among both sexes, life expectancy was the highest for those among Asian descent; African Americans had the lowest life expectancy; whites and Hispanics fell in the middle range. The neighborhood SES was significant for African American and white females. It showed a six to seven-year decrease in life expectancy for African American and white females living in the lowest SES compared to those of the same race/ethnicity living in the higher SES. There were comparable results for males, but the greatest difference occurred for Asian men. When the researchers analyzed age, they found interesting results. African American men ages 25-29 in the lowest SES had a mortality rate five times higher than the average. They also found that Asian women also 25-29 years old living in the lowest SES was a third of the average. Another interesting result found was the evidence of a “cross-over” between the lowest and highest SES. This result showed that as people aged the mortality rate increased in the higher SES compared to the lower SES. However, this only appeared in Asian and Hispanic races/ethnicities. This study showed a clear difference between the highest and lowest SES. There was a clear difference between the younger ages living in the highest and lowest SES, but as they got older the difference continued to get smaller.

Blog 3

Title: Growing Disparities in Life Expectancy

Authors: Joyce Manchester, Julie Topoleski, and the United States. Congressional Budget Office

Citation:

Manchester, J., Topoleski, J., & United States. Congressional Budget Office. (2008). Growing disparities in life expectancy (Economic and budget issue brief). Washington, D.C.: United States Congressional Budget Office.

 

This study talks about how the life expectancy has grown since 1950, but it states that there is a growing difference between life expectancies between the low-income and the high-income individuals, as well as the more and less educated individuals. In 2004, the life expectancy for individuals at birth has grown about 10 years since 1950. The study explains that in history, life expectancy was also dependent on socioeconomic status and education, but recently it has played an even bigger factor than in the past. In 1980, the life expectancy at birth was 2.8 years longer for those with a high socioeconomic status, whereas, by the year 2000, that has risen to 4.5 years difference between low and high income. The life expectancy difference between a high and low income at age 65 in 1980 was 0.3 years, by 2000 it had risen to 1.6 years. From 1990 to 2000, the gap between the life expectancy of those with a high school education and those with a college degree increased by 30%. The increase in the difference was due to the increase in life expectancy to individuals with a college degree, whereas those with only a high school diploma had no increase in life expectancy. Educational attainment also plays a role in the differing mortality rate in cancer and heart disease. When mortality due to diseases associated with smoking come into focus, it also adds to the difference in the mortality rates between the two educational levels.

On the international scale, Great Britain is following the same trend as the United States. Those with a high socioeconomic status have been increasing in life expectancy compared to those with a lower status. Men in professional professions have gained 5.7 years of life expectancy at birth and 2.6 years of life at 65 years old from 1970 to 1990. This is in stark contrast to those in unskilled professions. Men in these professions only gained 1.7 years at birth and 0.9 years at 65 years of age. However, for women, the gaps in life expectancy were decreasing until 1991, which is when the gap starts to increase. But this is not the case across the world. In Canada, the gap between life expectancies is decreasing. This is interesting because both Canada and Great Britain provide universal health care, so researchers cannot blame access to health care as the reason behind the life expectancy gaps. Some possible reasons for the gap between socioeconomic status include, smoking, obesity, adherence to medical treatment, healthy lifestyles, and use of health care.

Blog 2

Title: Is Wealthier Always Healthier? The Impact of National Income Level, Inequality, And Poverty on Public Health in Latin America.

Authors: Brian Biggs, Lawrence King, Sanjay Basu, and David Stuckler

Citation:

Biggs, King, Basu, & Stuckler. (2010). Is wealthier always healthier? the impact of national income level, inequality, and poverty on public health in latin america. Social Science & Medicine, 71(2), 266-273.

 

In this study GDP was compared to life expectancy, infant mortality, and tuberculosis mortality, to determine if “Wealthier is Healthier.” In this study data was used from twenty-two countries in Latin America from 1960 to 2007. In previous studies, it has been determined that when a country has a higher GDP, a single dollar more in hand, does not make as much of a difference as it would to a country with a lower GDP. It is the same concept as taking a $100 from a millionaire, where they would barely notice the money missing, compared to taking the same amount from a college student, who would notice immediately because they need the money more. Many people have debated the correlation between income inequality and public health. Although in a different study they found that over 70% of societies analyzed there was worse health in areas with more social inequality. Most studies use cross-sectional data, or they use small scale longitudinal data for their studies. However, this study only focuses on a small section of developing countries and uses longitudinal data as well as a time series regression to analyze the income versus life expectancy. This study specifically studies GDP (income), poverty, and inequality to life expectancy, tuberculosis (TB) mortality, and infant mortality.

The data used in this study was located from the World Development Indicators, World Health Organization, and the Socio-Economic Database for Latin America and the Caribbean. The study only looked at those families in extreme poverty to provide a better representation of those in poverty. In the results it showed that poverty has a significant correlation with all the health measures used. GDP also had a strong correlation to all the health measurements. However, inequality had almost no correlation to any of the health measurements, except a weak correlation with TB. GDP was shown to have the strongest correlation to life expectancy and infant mortality. For every 1% increase in GDP it would increase life expectancy by .06 years, and it would reduce infant mortality by 1.17%. Although, the GDP correlation to TB was not near as significant as the other two health factors. When inequality was decreasing or constant, each 1% increase in GDP, correlated to a 1.51% decrease in infant mortality, a 1.79% reduction in TB mortality, and an increase in life expectancy by .14 years. The main question that this study was not truly found, but instead they were left with a “it depends” answer. “Wealthier means healthier” only when the wealth is distributed equally, but GDP will still remain the largest factor in most areas of health.

Blog 1

Title: Relationship Between the Remaining Years of Healthy Life Expectancy in Older Age and National Income Level, Educational Attainment, and Improved Water Quality

Authors: Jong In Kim and Gukbin Kim

Pages: 402-417 (16 pages long)

Summary of Article:

This article is comparing life expectancy to multiple factors including income levels, education, and water quality. Specifically, it talks about the healthy life expectancy (HLE), which simply means how many healthy years remaining after reaching 65 years of age. Although many studies have looked at life expectancy, very few have looked at the different income levels. The difference in income levels result in varying degrees of how much health care an individual will receive. Another contributing factor to life expectancy is clean drinking water. It has not yet been proven that clean drinking water helps improve the HLE, but it is certain that it provides another level of protection to a long life. There is a strong positive correlation between income levels and educational attainment; however, the relationship between educational attainment and life expectancy is still to be determined. This study has compared the socioeconomic factors (national income level, water quality, and education level) of 148 countries to each life expectancy.

It is important to remember that this study measures the HLE which only accounts for the healthy years lived, not including those in which an elderly person has been crippled with disease. This study also omits individual factors, such as heredity, when conducting research. The hypothesis of the study states that the connections between drinking water, income level, and education can differ in countries that are well developed and countries that are underdeveloped. These factors can predict the HLE and play an important part of the overall life expectancy. To research this hypothesis the researchers decided to use existing data found from multiple credible sources (there was no actual experiment as this would be very difficult and it could be considered unethical).

This study found that, for the most part, the HLE increased as the other factors increased. The biggest problem against a long-life expectancy is socioeconomic inequality. This study has determined that the water quality, income level, and education play a crucial role in determining a life expectancy. In this study those with the lower income and education levels were significantly less healthy than people with a higher income and education levels. There also appeared to be a large gap between the intermediate level and the elevated level of income and education. Even though people in the middle range were healthier than those in the lowest range, there is a large gap between them and those with a high level of income and education. Regarding the water quality, there is still a high amount of water diarrhea deaths in developing countries. The UN has finally decided that having access to good water quality is a human right and is working on fixing it, now that it has proven to lower life expectancy. In conclusion, all three factors have proven to have a crippling effect on life expectancy.

Citation:

Kim, Jong In. (2016). Relationship Between the Remaining Years of Healthy Life Expectancy in Older Age and National Income Level, Educational Attainment, and Improved Water Quality. International Journal of Aging and Human Development, 83(4), 402-418.