The Effects Race and Income on HIV/AIDS Infection
Is HIV/AIDS More Prevalent in African Americans?
Final Project
STAT 5990 / HINF 6030 course
Masters in Health Informatics
By
ABEL MEDHANIE GEBREYESUS - B00498815
SUNIL NAIR - B00492855
Dalhousie Health Informatics
Halifax, Nova Scotia
December 12, 2007
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Abstract
Objective: Are the African-American people more susceptible to HIV/AIDS infection
and be hospitalized? Does race and level of income have an impact on increasing a
chance of HIV/AIDS infection and as a result of having poor mortality? These are the
questions we would try to answer by this statistical analysis.
Methods: This is a population based study, where data were collected through National
Hospital Discharge Survey (NHDS). The NHDS dataset has discharge records from all
participating U.S hospitals. This study includes 125,165 discharge data of HIV/AIDS
infected patients hospitalized in the year 2005. A Multivariate analysis using Poisson
distribution was conducted in order to examine the relationship of race and income level
on high incidence of HIV/AIDS infections in African-American population.
Results: Although African Americans (blacks) represent only the 13% of the United
States population, of the total patients hospitalized due to HIV/AIDS, 55.87% of those
were blacks (p-Value=0.02). 92.54% of those hospitalized were either not insured or
were covered by government insurance coverage, which could belong to a low income
group (p-Value<.0001).
Conclusion: Race and Income has a significant influence on susceptibility to HIV/AIDS
infections; Afro-Americans (Blacks) are 1.33 times more likely to be infected than
whites. A significant finding is that the income level didn't change race's effect on HIV
infections. Race has a significant effect on HIV infections or is an important predictor of
incidence of HIV infections independent of the income. In other words, irrespective of
the income level being black and poor increases the changes of being infected with
HIV/AIDS.
1. Introduction
In the Global summary of AIDS epidemic published in December 2007, the WHO estimates that
there are 33.2 million people living with HIV in this world today. There are more than 6800 new
infections per day, and almost 96% of those are from the low and middle income countries. [1] A
plethora of research work has been carried out in underdeveloped and developing countries that
suggest an association between poverty and a higher incidence rate of HIV/AIDS. [2] In the
industrialized nations most of the research has been focused towards associating the infection
with race, sexual behavior, and drug abuse with increase prevalence of HIV infection.
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According to official estimates, around 1.2 million American are living with HIV. As a result,
more and more people are hospitalized. Among those around 1.2 million Americans, who are
living with HIV/AIDS are, the most affected are those of African Americans, in terms of race.
According to a Center for Disease Control (CDC) fact sheet, “in the United States, the
HIV/AIDS epidemic is a health crisis for African Americans. At all stages of HIV/AIDS—from
infection with HIV to death with AIDS—blacks are disproportionately affected compared with
members of other races and ethnicities”.
In the United States in 2002, HIV/AIDS was among the top 3 causes of death for African
American men aged 25–54 years and among the top 4 causes of death for African American
women aged 25–54 years. It was the number 1 cause of death for African American women aged
25–34 years [3]
Therefore we will determine the influence of race and income on increased incidence of
HIV/AIDS in Afro-Americans. Based on this, the null hypothesis is: There is no effect of race
and income on HIV infection. This means that we are going to test whether or not there's a
difference of HIV infection count or rate among different races, at the same time, we are going to
check whether or not there is an association between the HIV infection count/rate and income, as
we control the patients' age, sex and Marital Status.
2. Literature Review
BASTARDO et al in their study: “Relationship between Quality of Life, Social Support and
Disease-related Factors in HIV-infected Persons in Venezuela”, examined the relationships
among health-related quality of life (HRQL), social support, sociodemographic factors and
disease-related factors in persons infected with the HIV living in Venezuela. This exploratory
study was designed to assess the HRQL and levels of social support of HIV-infected persons
living in Venezuela; and examine the relationships of quality of life, social support and
demographic and disease-related factors in such persons. The researchers conclude that there is
an important association between social support and HRQL in HIV-infected persons in
Venezuela.
Another Canadian study by Canadian HIV/AIDS Legal Network says: “The links between
poverty and HIV/AIDS go in two directions. In one direction, poverty contributes to people’s
vulnerability to HIV, exacerbates the impact HIV/AIDS has upon them, and leads to greater
illness and early death. Going in the other direction, the experience of HIV/AIDS by individuals,
households and communities leads to an intensification of poverty. As a result, HIV/AIDS
frequently impoverishes people in such a way as to intensify the epidemic itself”
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In another detailed study, a U.S. Department of Health and Human Services, Office of the
Surgeon General’s report indicates, many African Americans live in segregated neighborhoods,
and poor African Americans tend to live among other African Americans who are poor. “Poor
neighborhoods have few resources, a disadvantage reflected in high unemployment rates,
homelessness, crime, and substance abuse,” it underlines. The report says: “African Americans
are more likely than whites to live in severe poverty, with incomes at or below 50 percent of the
poverty threshold; the African American rate of severe poverty is more than three times the
white rate. Children and youth are especially affected; while the national poverty rate for U.S.
children is nearly 20 percent, almost 37 percent of African Americans 18 and younger live in
poor families.” This report also stated about the relationship between such poverty and poor
health conditions in African Americans.
The study by Tony L. Whitehead from University of Maryland is also noted, “in the United
States, ethnic minority groups, particularly African Americans (and Hispanics) suffer
disproportionately in morbidity and mortality from the human immunodeficiency virus and the
acquired immunodeficiency syndrome (HIV/AIDS).” In his “Urban Low-Income African
American Men, HIV/AIDS, and Gender Identity” indicates African Americans are more prone to
infection and the living style of blacks is hustle oriented with low income survival.
The HIV/AIDS epidemic is concentrated in poor communities, where African Americans are
disproportionately represented. From 2000 to 2004, new AIDS cases in the United States
increased by less than one percent; however, new cases in the South, where poor and black
communities are found largely, increased by nine percent. Jennifer Kates and Alicia Carbaugh of
the Kaiser Family Foundation (February 2006) showed that the picture is gloomy for African
Americans in the HIV/AIDS situation.
3. Method
Data Source
NHDS data are collected from a sample of inpatient records acquired from a national sample of
participating hospitals in the U.S. Our data set consisted of discharge records of HIV/AIDS
patients as per the ICD-9 disease classification code. There were 125,165 (n) HIV/AIDS related
discharges in the year 2005.
Design
Since this is a survey data, we needed to weight each observation to get the outcome - HIV
counts. For this study we have focused on Poisson regression model (PRM) in order to examine
the count of HIV infection in our sample. The Poisson regression models are basic models for
count data analysis. The GENMOD procedure of SAS was employed to do the PRM. The
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/DIST=POISSON option tells SAS to use the Poisson distribution. It is essential to do a good
diagnostic check of whether or not the Poisson distribution is a good fit for this count outcome.
We performed the Goodness–of-fit test by investigating the Value/DF value for the model
deviance (the measure of discrepancy between observed and fitted values). For large samples
like the NHDS dataset, a model with a good fit to the data will have a Value/DF value close to 1,
and as we found out this value in the first model is bigger than 1, suggesting a somewhat poor fit.
We found that the existing model is over-dispersed; therefore we refit the model with a Pearson
scaling factor to adjust the over-dispersion. We used the over-dispersed Poisson models to analyze
the effects of race and income on HIV infection, controlling the other effects such as patients’
age, sex, region and marriage. The Value/DF value is 1 at the row of “Scaled Pearson X2” in the
second model as seen in the table below.
Criteria for Assessing Goodness of Fit
Criterion DF Value Value/DF
Deviance 861 142383.6668 165.3701
Scaled Deviance 861 473.7625 0.5502
Pearson Chi-Square 861 258763.2600 300.5380
Scaled Pearson X2 861 861.0000 1.0000
Log Likelihood 1735.6473
The variables used:
# Variable Type Label
4 Age Number Age in years, months or days
5 Sex Number patient sex
6 Race Number Patient race
14 Owner Number Ownership of hospital
12 Region Number Geographic region of hospital
15 Weight Number Analysis weight
7 Marstat Number Marital status
As age is normally distributed it was left as a continuous variable. As the NHDS datasets does not carry
the income data, we have used the Ownership of the hospital as an indicative of the level of income of the
patients. Ownership of hospital indicates whether the hospital is private, government or is non-profit
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charitable trust management. Typically, poor patients frequent the non-profit charitable hospitals who
cannot afford to purchase private medical insurance.
We have conducted a full Poisson model including all chosen variables. We also have tried to fit the
model without race to check how the coefficient of income changes and another model without income to
check how the coefficient of race changes so that we could analyze the influence between race and
income on HIV infection occurrences. Sex, age and marital status do not appear to be influential. There is
no difference between married patients and others that we called 'single' even though the p-value of
marriage is <0.0001 which comes from ‘undisclosed’ group and ‘singles’ group's comparison. We have
excluded marital status because of the large number of ‘undisclosed’ as this could be due to the fact that
patients were reluctant to disclose their sexual preferences.
4. Results and discussion
While it is widely recognized that poverty, or low income, is associated with poor health, even in
rich societies, the nature of the relationship between income and health status is not clearly
understood. Especially, in big health issues and crises, like HIV/AIDS, it is important to look at
the relationship of income and the possibility of HIV infections.
But, who is poor or rich is very relative term and defined differently by different social or
economic orientations. However, for this study, we are going to classify using simple definition.
Although it is still a matter of controversy, we look at the ownership variable. As all of the
subjects or the population is hospitalized, we counted those who go to the proprietary hospitals,
coded as 1, labeled as high income and those who hospitalized in government owned or in non-
profit organizations, coded as 2 and 3, respectively, labeled as low income AIDS patients.
A number of studies focus specifically on measures of very low income, or poverty. They find
that persistent poverty appears to be most damaging for health. Those people who are
persistently poor have worse health outcomes than those who experience poverty only
occasionally or not at all (BENZEVAL et al). In our study, parallel to income, race is also taken
into consideration. In this case, we will examine which race is more infected and what is the
income level to this race and based on such results, we will conclude how the relationship is of
income affected races to be more infected.
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Descriptive Statistics of
Variables Used in the Model
Variable Frequency Percent
Sex
Male 77839 62.19
Female 47326 37.81
Region
Northwest 42169 33.69
Midwest 12948 10.34
South 54867 43.84
West 15181 12.13
Race
Black 69924 55.87
White 30780 24.59
Others 6210 4.96
Unknown 18251 14.58
Income/Ownership
of Hospital
High 9340 7.46
Low 115825 92.54
Marital Status
Married 11494 9.18
Single 65815 52.58
Undisclosed 47856 38.23
The data has 62.19 % male and 37.81% female population. These include from 15 years old to
75 years old, and the peak age of hospitalization due to HIV/AIDs is 45. As the age increases,
the hospitalization also increases until it reaches 45 years old. Then, it declines after 45.
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Peak age of hospitalization
Although African Americans (blacks) represent only the 13% of the United States population,
this specific group is affected more than any races with 55.87% of blacks are hospitalized. This
shows there is clear difference among different races; and it shows how the blacks are affected
by HIV/AIDS more than any other races. For instance, whites are around 80 of the United States
population. However, only 24.59% of them are hospitalized.
In another study also, the National Institutes of Health stated that although African Americans
represent only the 13% of the United States population, this specific group is affected more than
any races with approximately 46% of new HIV infections and 50% of reported AIDS cases.
Another interesting factor of the result, most of these African Americans also depend on
government funded health insurance coverage, like on Medicare and Medicaid. This factor also
can show their status to some extent. As most of them are depending on the government funded
health insurance, they have less income in their daily life. Although it needs more study these
interrelated factors has some indication on weather there is a direct relationship between income
and infection.
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Among those infected and hospitalized African American, the majority are single, in terms of
their marital status. They are almost one third of the total hospitalized African Americans with
30.99%. This trend is also the same in whites, because among those 24.59 hospitalized whites,
10.89% are singles. Taking into account this information, both in blacks and whites, singles are
more affected. This could be most of them are homosexuals. Or the data may put homosexuals,
lesbians or other form of partners as singles.
Whites Blacks Others
Married 3.03 5.02 1.14
Single 10.89 30.99 3.23
Widowed 0.04 1.30 0.46
Divorced 1.77 0.29 0.40
Separated 0.70 2.46 0.04
Not stated 8.15 15.80 14.28
Total % 24.59 55.87 19.54
Adjust for Over-dispersion
The exponential of the estimate (coefficient) represents the difference or ratio of the expected
HIV infections between the two levels (compared category vs. reference). For example, race is
declared as a categorical predictor in the model, we need to compare blacks, with others and
whites.
Compared category vs. Referenced category
Effects/Predictors Estimate Exponential p-Value
Race
Black 0.29 1.33 0.02
Others -0.38 0.68 0.12
Whites (reference) . . .
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From above table, we see that the predicted mean count of HIV infections for blacks is about
1.33 times that for whites (controlling for income and the other variables in the model), which is
significant (p-value =0.02) at alpha=0.05 level, while the predicted mean HIV infections for
others is about 0.68 times than that for whites, but the difference is not significant (p=0.12).
Since the p-Value for both race and income are <0.05, we can reject the hypothesis that race and
income does not have an influence on increased incidence HIV/AIDS.
Race 95% Confidence Interval (0.04, 0.528) at alpha=0.05
High Income 95% Confidence Interval (-1.35, -058) at alpha=0.05
The Type III likelihood ratio tests for the predictors in the model (similar to F-tests in an analysis
of variance setting) allowing us to get an idea of whether the effects (categorical or continuous)
are significant or not in overall. The p-value for race is 0.0027 in the Type 3 analysis, which tells
us that race is a significant effect or predictor on the mean count of HIV infections in overall.
Check whether race will change without
income in model
Effects/Predictors Estimate Exponential p-Value
Race
Black 0.33 1.39 0.01
Others -0.30 0.68 0.23
Whites (reference) . . .
Finally we wanted to check whether the two variables race and income are related and whether
controlling one will have any impact on the other. When we did not consider Income, the
estimate for blacks was 0.33 and its p-value was 0.01. The ratio was 1.39, slightly higher than
that in model 2 (1.33, controlling income). With this we can conclude that income didn’t change
race’s effect on HIV infections significantly. In other words, race and income are significant
predictors on the HIV infections independently. And there is no influence of level of income on
the race having an effect as a pre-disposing factor to HIV infections.
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5. Conclusion
The primary focus of our investigation was to determine the association of income level and race
with higher incidence of HIV/AIDS infection. We would examine the effect of different
determinants, for example, is it because of particular communities poverty, their socio-economic
status, their ethnicity or their gender that they are more predisposed to getting HIV/AIDS. The
research findings suggests that there is an important association between race and income level
(poverty) that place people at risk of HIV infection and subsequent disease progression. Among
the group of race, Blacks have a much higher risk of infection than the other groups combined.
We also established that most HIV/AIDS patients are from a low income group. The mortality
rate of low-income HIV-positive people is higher than higher incomes and more education. [4]
The main reason for this finding could be that low-income HIV-positive patients are more likely
to be covered by Medicaid or Medicare or are uninsured.
HIV/AIDS analytical study is a highly complex methodology because of the continuously
changing nature of the disease and the varied characteristics of the different at-risk groups, and
also because the epidemic is high in the most marginalized of communities. Accessing these
communities for research purposes can itself be a formidable challenge. There are several other
challenges faced by the HIV/AIDS researchers in developed nations, like in the US. Generally
healthcare and hospital datasets does not contain direct information on income. Most of income
related data is gathered by direct interviews.
Perhaps the striking observation from our study was that the level of income has no relation to
race as far as the HIV infections are concerned. What this means is that an African-American
who belongs to a higher income group could still be more susceptible to HIV/AIDS infection.
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Bibliography
[1] 2007 AIDS epidemic update. UNAIDS. November 2007.
http://www.unaids.org/en/HIV_data/2007EpiUpdate/default.asp
[2] Anderson RN, Smith BL. Deaths: leading causes for 2002. National Vital Statistics Reports
2005;53(17): 67–70.
[3] CDC. “Racial/ethnic disparities in diagnoses of HIV/AIDS---33 states, 2001--2004.” MMWR
2006;55:121--5.
[4] CDC. “Racial/ethnic disparities in diagnoses of HIV/AIDS---33 states, 2001--2004.” MMWR
2006;55:121--5.
[5] Fleming, Patricia (07/01/2006). \"The Epidemiology of HIV/AIDS in Women in the Southern
United States\". Sexually transmitted diseases (0148-5717), 33 (7), p. S32.
[6] Bastardo, Y.M., and Kimberlin, C.L., Relationship between quality of life, social support and
disease-related factors in HIV-infected persons in Venezuela, AIDS Care, Volume 12, Number
5, 1 October 2000 , pp. 673-684(12)
[7] http://www.aidslaw.ca/publications/interfaces/downloadFile.php?ref=107
[8] http://mentalhealth.samhsa.gov/cre/ch3_current_status.asp
[9] ibid
[10] ibid
[11] Whitehead, TONY L., Urban Low-Income African American Men, HIV/AIDS, and Gender
Identity, Department Of Anthropology, University Of Maryland
[12] ibid
[13 Jennifer Kates and Alicia Carbaugh of the Kaiser Family Foundation; African Americans
and HIV/AIDS, February 2006
[14] BENZEVAL, MICHAELA; TAYLOR JAYNE and KEN JUDGE, Evidence on the
Relationship between Low Income and Poor Health: Is the Government Doing Enough?
Fiscal Studies (2000) vol. 21, no. 3, pp. 375–399
[15] Journal of Health Care for the Poor and Underserved, United Press International reports
(United Press International, 11/1
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