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The Barriers to Excellent Health: Income and Health Inequality

Lisa Pietrangelo

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I. Statement of the Problem One of the most troubling trends of the 21st century is the nation’s declining health. With the increasing prevalence of fatty foods and the decreasing amount of exercise that people are getting, it is no surprise that poor health is threatening the youth of America. In early human history, subpar infant healthcare and a general lack of sufficient healthcare for the elderly contributed to a high birth rate and a high death rate. It is said that because of this phenomenon, parents had to bury their children. In a modern day industrialized society such as America, the birth rate and the death rate tend to be fairly low. The switch from parents burying their children to children burying their parents can be at least partially attributed to vast improvements in healthcare and an all around increase in individual health. However, social demography may soon reverse its current state of affairs, where parents die before their children, and soon it may tell the tale of children dying before their parents. Thus, these improvements in health may not continue. Former Under Secretary for Food, Nutrition, and Consumer Services Eric M. Bost stated in a Congressional Testimony “many children in this generation of children will not outlive their parents.” (Bost 2004) Joyce Lee, a pediatric endocrinologist stated “higher numbers of young and middle-age American adults are becoming obese at younger and younger ages.” (Warner 2010) However, the problem of the declining health of people at younger ages is clear. What is less clear is the impact how people perceive their family income has on how people perceive their own health and whether or not race plays a role in these changing perceptions. The goal of this study is to explain how perceptions of health change as perceptions of income changes. Additionally, the study aims to include race in the analysis, by explaining the relationship that it has with perception of income on health perceptions of Americans. The study is important because the findings may help policymakers in our nation’s Capitol understand more clearly how interconnected health and income perceptions really are and what must be done policy-wise to create a healthier America. Hopefully, this research can contribute to a deeper understanding of health and income as it intersects with race in a way that has not yet been explained and fuel the passion of other sociological researchers in a way that will get them interested in further research on health inequality in America. II. Review of Literature and Hypothesis Much of the current literature focuses on two key determinants of health, exercise and eating habits, rather than with perceptions of health and family income in dollars, rather than with perception of income. Such studies are important to review because they provide insight to what types of activities (or lack of activities) that people do and what types of food (or lack of foods) people eat that cause them to perceive that they are in poor health. Many researchers have already conducted studies that analyze how income affects the amount of exercise people get and what foods they eat, coming to the conclusion that with a decrease in income comes a decrease in health. Research indicates that people with a lower income have poorer health outcomes that are in part due to the fact that they exercise less than higher income people and in part due that they have reduced access to healthier food choices than higher income people. (Active Living by Design 2012; Aguilar 2010; Drewnoswki and Darmon 2004; Dutton 2005; Frazao, et al. 2007; Kawachi, Daniels and Robinson 2005; Laborde 2011; Lopez 2004; Marmot 2002; Parks 2003; Popkin, Siega-Riz and Haines 1996; Spalter-Roth, Lowenthal and Rubio 2005; U.S. Department of Health and Human Services 2012) These thirteen sources aim to elaborate on this phenomenon. Some of the research focuses on the independent variable being income and the dependent variable being amount of time spent exercising. A key finding supported by many of the articles indicates that lower income people are more likely to suffer from health

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problems due to a lack of exercise than higher income people (Active Living by Design 2012; Dutton 2005; Laborde 2011; U.S. Department of Health and Human Services 2012). Active Living by Design states “people from households with incomes below $15,000 are three times more likely to live a sedentary lifestyle than people from households with incomes above $50,000” (2002, pp. 1). Laborde states, “for every income bracket above $25,000 a year, moving up the income group consistently increases the minutes of exercise” (2011, pp.12). In Dutton (2005), the income groupings used were $20,000 per year or less and $20,000 per year or more, while The Department of Health and Human Services 2011 Health report indicates activity level on a scale relative to the poverty level. Regardless of the fact that they all conclude that a lower income is associated with poor health outcomes, it is important for this research to focus on the subjective perception of income rather than the objective quantitative level of income.

Additionally, research suggests that lower income people are more likely to have significant barriers that prevent them from being able to exercise than higher income people. Some barriers include a lack of money to join health facilities, lack of transportation to get to such a facility, being forced to live in unsafe neighborhood that prevents them from exercising outside, living in a place with poor access to parks and playgrounds, and living near an environmentally unsafe space that limits their ability to stay outside for long. (Active Living by Design 2012; Dutton 2005; Aguilar 2010; Marmot 2002; Parks 2003) Interestingly, many articles claim that certain barriers are more influential than others. For example, Active Living by Design (2012) indicated that the lack of good transportation is the key factor that limits low-income people’s ability to travel their desired routes, while Dutton (2005) claims that the most common barrier is health problems that prevent these people from exercising. Additionally, these barriers are not due to ignorance, but rather, due to a lack of material conditions, as the poor accessibility is one of the reasons that lower income people, regardless of whether or not they want to exercise, have trouble actually doing so (Aguilar 2010; Marmot 2002). Parks (2003) expands on this, linking income to location; those who live in the suburbs are more likely to exercise than those who live in rural areas. It can be assumed that those who live in rural areas are less likely to have easy and quick access to health facilities or even the means to get there. A problem with the barriers to exercise is that it is hard to accurately understand every single barrier because it is hard to list every possible barrier to exercise that exists. For example, a barrier that may be prominent in one study may not even be apparent in another. When focusing research more subjectively, such as through the variable perception of income, respondents are more likely to consider not only health, but also all dimensions of their life when responding to the survey question and thus their own perceptions may be more accurate. The other type of research focuses on income as the independent variable and food choices as the dependent variable. Such research indicates that the food choices that people make are often unhealthy regardless of income. (Drewnoswki and Darmon 2004; Frazao, et al. 2007; Popkin, Siega-Riz and Haines 1996) One problem, however, was that the studies were often more convoluted and had different research goals than the studies done regarding exercise and income. For example, Popkin, Siega-Riz and Haines (1996) focuses on how the diets of different groups of people have changed through the years, rather than how income has affected food choices, concluding that dietary quality has improved in all income groups from 1965 to 1991. Frazao et. al (2007) focuses on how spending patterns would change if purchasing power were increased for those who are on food stamps, illustrating how food spending, though it increases as income increases, shares a similar composition amongst all income groups. Drewnowski and Darmon (2004) further indicated that because sugary and fatty foods are often the cheapest, those types of foods are purchased more frequently than other healthier options. All studies agreed that the current American diet of processed foods,

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laced with added sugars and trans fats, is an unhealthy one and such a diet is often much cheaper and therefore more acceesible to the majority of Americans than one rich in fresh fruits, fresh vegetables, lean protiens, and whole grains. There were also studies done that focus on how race impacts health. Both Kawachi, Daniels and Robinson (2005) and Spalter-Roth, Lowenthal and Rubio (2005) indicate that the racial disparaties in health outcomes are very much evident by minorities’ reduced access to healthcare and the fact that they receive much poorer care. The studies, however, are very cursory and do not single out only race as a factor for the change in health outcomes, but rather focus on how race is connected to much broader socioeconomic factors. Additionally, the articles explain how minorities are more likely to be of lower socioeconomic class than whites and thus may have worse health outcomes not because of race but because of class. Lopez (2004) focuses primarily on self-rated health and income inquality, concluding that income inequality in major metropolitan areas in America has a negative effect on health. The study found that as “self-rated health was a good predictor of morbidity and mortality,” (Lopez 2004, pp. 2409) it could be used in analysis instead of using more typically seen objective measures that are harder to obtain. Thus, this research indicates that the many key determinants of health, such as exercise and food choices, that most certainly have an effect on people’s health must be accounted for when people self-rate their own health. However, there has not been research that focuses on perception of family income rather than acual family income, so this study aims to build up the research to understand the perceptions of health and perceptions of income as they intersect.

These studies all provide excellent starting points for this research paper, with two hypotheses. (1) People who perceive that their family income is above average are more likely to rate their condition of health as excellent or good than people who perceive that their family income is below average, who are more likely to rate their condition of health as fair or poor. (2) Minority respondents are more likely to perceive their health in a negative way, rating their condition of health as either fair or poor, than white respondents, who are more likely to perceive their health in a positive way, rating their condition of health as either good or excellent. When controlling for race, perception of income still affects how people perceive their own health.

III. Method: Sample, Measures, and Procedure Using secondary data analysis from the 2010 General Social Survey (GSS) dataset, I conducted univariate, bivariate and multivariate analyses on the independent, dependent, and control variables using the statistical analysis software Statistical Product and Service Solutions (SPSS). The GSS is a survey that is conducted regularly by the National Opinion Research Center in Chicago, partially funded by the National Science Foundation, with the purpose of providing reliable and valid data for social scientists to analyze. (Babbie 2012) The information the GSS gathers about the “2,044 people sampled can be taken as an accurate reflection of…all (non-institutionalized, English-speaking) American adults (18 years of age or older).” (Babbie 2012, p. 26) According to Babbie 2012:

“The U.S. Postal Service and the U.S. Census Bureau have made lists of household units available for 72% of the U.S. population. With the aid of computers, random samples of households are drawn from the lists for inclusion in the GSS sample. For the 28% of the population not on household lists, random samples of census tracts and enumeration districts are drawn. Once tracts and districts are selected, housing units are randomized and selected for inclusion in the GSS sample.” (p. 12)

The sampling design is what contributes to the fact that the data is generalizable, as the design is responsible for a sampling error of only a few percentage points. Another thing to note about the GSS is the fact that the data were collected using a split-ballot design, which means that some questions were asked to a random susbsample of households, while other

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questions were asked to the other households; these questions, although still generalizable, are likely to have a higher percentage of sampling error. (Babbie 2012)

I used three different variables for the analysis. The independent variable that I used was RECFIN, a recoded version of FINRELA. The original question asked respondents “Compared with American families in general, would you say your family income is far below average, below average, average, above average, or far above average?” The responses for FINRELA were coded as follows: 0: Inapplicable, 1: Far below average, 2: Below average, 3: Average, 4: Above average, 5: Far above average, 8: Don’t know, 9: No answer. I recoded the responses 1 and 2 to read 1: Below average, 3 to read 2: Average, 4 and 5 to read 3: Above average, while 0, 8, and 9 stayed the same. I recoded the variable because combining two responses, for example, far below average and below average, to one more general response, for example, below average, makes the data easier to understand and analyze.

The dependent variable that I used was HEALTH. The question asked respondents “Would you say that in general your health is excellent, good, fair or poor?” The responses were coded as follows: 0: Inapplicable, 1: Excellent, 2: Good, 3: Fair, 4: Poor, 8: Don’t Know, 9: No answer. This variable did not need to be recoded. The control variable that I used was RACE. The question asked respondents their race. The responses were coded as follows: 0: Inapplicable, 1: White, 2: Black, 3: Other. This variable did not need to be recoded, either.

In SPSS, I conducted a univariate analysis of the dependent variable HEALTH. To do this, I ran a frequency table. I conducted a bivariate analysis of the independent variable RECFIN and dependent variable HEALTH. I conducted another bivariate analysis of the control variable RACE and the dependent variable HEALTH. Lastly, I conducted a multivariate analysis of the independent variable RECFIN, the dependent variable HEALTH and the control variable RACE. For these three analyses, I ran three separate cross tabulations, two bivariate crosstabs and one multivariate crosstab. IV. Results

Attached figure 1 is a pie graph that represents the distribution of the dependent variable, HEALTH. The pie graph shows that 25.4% of respondents believe that their condition of health is excellent. The majority of respondents, 47.08%, believe that their condition of health is good. 22.0% of respondents believe that their condition of health is fair, while only 5.49% of respondents believe that their condition of health is poor.

Attached table 1 is a bivariate analysis of the independent variable RECFIN, and the dependent variable HEALTH. The table shows that among respondents who believe that their family income is below average, 17.6% of rated their condition of health as excellent, 41.4% rated their condition of health as good, 31.2% rated their condition of health as fair, and 9.9% rated their health as poor. Among respondents who believe that their family income is average, 26.7% rated their condition of health as excellent, 50.4% rated their condition of health as good, 18.7% rated their condition of health as fair, and 4.2% rated their condition of health as poor. Among respondents who believe that their family income is above average, 26.0% rated their condition of health as excellent, 49.5% rated their condition of health as good, 13.2% rated their condition of health as fair, and 1.2% rated their condition of health as poor. The column percentages change, indicating that there is a relationship between variables. People who perceive their income as above average are more likely to perceive their health more positively. The relationship between peoples opinion on their family income and peoples opinion on their health is statistically significant (chi-square = 203.851, p < 0.001). These numbers mean that it is with 99.9% confidence that the results

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found in this sample are not due to chance and there is a relationship between variables. Therefore, the first hypothesis is supported.

Attached table 2 is a bivariate analysis of the control variable RACE, and the dependent variable HEALTH. The table shows that among White respondents, 26.9% of rated their condition of health as excellent, 46.3% rated their condition of health as good, 21.4% rated their condition of health as fair, and 5.4% rated their health as poor. Among Black respondents, 19.1% rated their condition of health as excellent, 50.9% rated their condition of health as good, 23.5% rated their condition of health as fair, and 6.5% rated their condition of health as poor. Among respondents from other races, 22.5% rated their condition of health as excellent, 47.8% rated their condition of health as good, 25.0% rated their condition of health as fair, and 4.7% rated their condition of health as poor. The column percentages change, indicating that there is a relationship between variables. White people are more likely to perceive their health positively than both black people and people from other races. The relationship between respondents’ race and their perception of their health is statistically significant (chi-square = 15.273, p < 0.05). These numbers mean that it is with 95% confidence that the results found in this sample are not due to chance and there is a relationship between variables. Therefore, the second hypothesis is supported.

Attached table 3 is a multivariate analysis using the independent variable RECFIN, the dependent variable HEALTH, and the control variable RACE. The table shows that among white respondents who believe that their family income is below average, 18.0% reported their condition of health as excellent, 41.0% as good, 30.8% as fair, and 10.2% as poor. Among white respondents who believe that their family income is average, 29.3% reported their condition of health as excellent, 48.5% as good, 19.1% as fair, and 4.1% as poor. Among white respondents who believe that their family income is above average, 36.5% reported their condition of health as excellent, 49.3% as good, 12.8% as fair, and 1.4% as poor.

Additionally, table 3 shows that among black respondents who believe that their family income is below average, 15.0% reported their condition of health as excellent, 43.7% as good, 30.1% as fair, and 11.2% as poor. Among black respondents who believe that their family income is average, 20.4% reported their condition of health as excellent, 58.7% as good, 17.4% as fair, and 3.5% as poor. Among black respondents who believe that their family income is above average, 34.8% reported their condition of health as excellent, 50.0% as good, 15.2% as fair, and 0.0% as poor.

Table 3 also shows that among all non-white and non-black respondents who believe that their family income is below average, 18.8% reported their condition of health as excellent, 40.2% as good, 35.9% as fair, and 5.1% as poor. Among all non-white and non-black respondents who believe that their family income is average, 21.9% reported their condition of health as excellent, 54.4% as good, 17.5% as fair, and 6.1% as poor. Among all non-white and non-black respondents who believe that their family income is above average, 31.0% reported their condition of health as excellent, 52.4% as good, 16.7% as fair, and 0.0% as poor. Therefore, when race was controlled, perception of income affected people’s perception of health. The results indicate that the relationship between perception of income and perception of health is still statistically significant (see attached table 3 for chi-square values).

V. Discussion Both my hypotheses were supported. The first hypothesis, that people who perceive that their family income is above average are more likely to rate their condition of health as excellent or good than people who perceive that their family income is below average, who are more likely to rate their condition of health as fair or poor, was supported by the first

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bivariate analysis of RECFIN and HEALTH (see attached table 1). The results were expected, as current literature indicates that a lower income has a negative effect on health outcomes. The variables used in this research, perceptions of health and perceptions of income, correlate with actual health outcomes and actual monetary income, therefore, those who perceive their family income as lower are more likely to perceive their health as poor or fair and vice versa.

The second hypothesis, that minority respondents are more likely to perceive their health in a negative way, rating their condition of health as either fair or poor, than white respondents, who are more likely to perceive their health in a positive way, rating their condition of health as either good or excellent, was found to be correct as well. The hypothesis was posed as such because it is widely known that there are a higher percentage of minorities in poverty than there are white people in poverty. The bivariate analysis of RACE and HEALTH (see attached table 2) indicated that black people and people of other races are more likely to perceive their health negatively than white people. This statement fits with research in the sense that minority respondents are more likely to belong to a lower socioeconomic class than white respondents and therefore are more likely to perceive their health negatively.

Additionally, when controlling for race, perception of income is still the major factor that affects how people perceive their own health. It is clear that lower income people of any race are more likely to perceive their health negatively than higher income people of any race. This data appear both valid and reliable, as it is analyzed from the widely accepted GSS. According to Babbie (2010), validity “is a term describing the measure that accurately reflects the concept it intended to measure” (pp. 153). The concern of most research is that the variables chosen do not measure what they are supposed to measure, however, in this research, it is evident that the variables RECFIN and HEALTH, measuring people’s opinion on family income and their condition of health, respectively, adequately measure people’s perceptions of income and perceptions of health. Additionally, causal validity, or internal validity, “refers to the possibility that the conclusions drawn from experimental results may not accurately reflect what has gone on in the experiment itself” (Babbie 2010, pp. 240). Because the variable RACE was controlled for, it can be assumed that a relationship between the variables actually exists and thus RECFIN is an adequate predictor of HEALTH. Another issue that many researchers face is reliability, which “is a matter of whether or not a particular technique, applied repeatedly to the same object, will yield the same results each time” (Babbie 2010, pp. 150). Because the GSS has been conducted year after year, yielding results that change only as the social atmosphere changes, it is widely accepted by social scientists, as a reliable source of data for analysis and therefore, reliability in this research study is not a problem. Lastly, the research is most certainly generalizable to the American population, due to the fact that the data is from the generalizable GSS that uses a sampling design to ensure that the data is representative of the American public.

This research study most definitely contributed to general sociological knowledge about social life, as it focused on two variables related to perception and how they interact. As no studies existed that analyzed the effect that perception of income has on perception of health existed, the findings were certainly valuable in understanding this relationship between variables, as people who perceive their income as lower are more likely to perceive their health negatively. However, this study was lacking in the sense that there are no variables in GSS that address how people feel about different potential solutions for reducing health outcome inequality. For example, had there been a variable that allowed respondents to indicate how much exercise they would prefer to get, one that indicated how much exercise they actually get, and one that let respondents indicate how they could potentially reach their exercise goals, it may have been easier to understand how barriers to good health function in

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the real world and the potential ways to address these barriers. The current GSS variables are not enough to ensure proper research on the solutions to health inequality. Thus, though the GSS sample is certainly representative of 18+, English-speaking Americans, the fact that it does not contain many variables that may prove useful in new research is a problem. Therefore, the main way to improve this research would be to conduct more comprehensive studies that deal with many different health-related and income-related variables that were not apparent in the GSS.

Many current research studies are lacking in the sense that they too focus on how income affects health, rather than what should be done policy-wise to ensure that lower income people can meet their exercise recommendations and fruit and vegetable recommendations. Two studies (Marmot 2002, Adler 2002) that focus on income and overall health suggest that to improve health disparities, income equality is necessary. Reducing the income inequality will reduce health outcome inequality. In general, sociological researchers should focus less on research similar to what has already been conducted on the effects of income on health and more on policy recommendations that can help to fix the problem of lower income people having worse health outcomes than their higher income counterparts, be it through reducing barriers to exercise or solving the problem of food deserts. To reduce income inequality so that health inequality is reduced should be the focus of today’s sociologists.

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Drewnoswki, Adam, and Nicole Darmon. "Food Choices and Diet Cost: An Economic Analysis." American Society for Nutritional Sciences. Washington D.C.: The Journal of Nutrition, 2004. 900-904.

Dutton, Gareth, Jolene Johnson, Dori Whitehead, Jamie Bodenlos, Phillip Brantley. "Barriers to Physical Activity Among Predominantly Low-Income African-American Patients with Type 2 Diabetes." Diabetes Care 28, no. 5 (May 2005): 1209-1210.

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Parks, S E, R A Housemann, R C Brownson. "Differential correlates of physical activity in urban and rural adults of various socioeconomic backgrounds in the United States." Journal of Epidemiol Community Heath 57 (2003): 29-35.

Popkin, Barry, Anna Maria Siega-Riz, and Pamela Haines. "Comparison of Dietary Trends Among Racial and Socioeconomic Groups in the United States." The New England Journal of Medicine, September 1996: 716-720.

Spalter-Roth, Roberta, Terri Ann Lowenthal, and Mercedes Rubio. Race, Ethnicity and the Health of Americans. Prod. American Sociological Association. Washington D.C., July 2005.

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Warner, Jennifer. Baby Boomers May Outlive Their Kids. April 9, 2010. http://children.webmd.com/news/20100409/baby-boomers-may-outlive-their-kids (accessed April 24, 2013).

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Attachments

Figure 1: Univariate analysis of dependent variable HEALTH.

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Table 1: Bivariate Analysis of independent variable RECFIN, and dependent variable HEALTH.

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Table 2: Bivariate analysis of control variable RACE, and dependent variable HEALTH.

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Table 3: Multivariate analysis of independent variable RECFIN, dependent variable HEALTH, and

control variable RACE.