The Impact of Medicaid Expansion on HIV-Positive Men and Women in the Southern United States

By Sarah E. Rudasill
2016, Vol. 8 No. 02 | pg. 2/3 |

Research Question

The purpose of this research was to determine the approximate number of HIV-positive individuals who would qualify for Medicaid under a hypothetical expansion in ten southern states: North Carolina, South Carolina, Georgia, Florida, Alabama, Tennessee, Kentucky, Arkansas, Mississippi, and Louisiana. Although two of these states – Kentucky and Arkansas – have since voted to expand Medicaid to their constituencies, the 2013 data precedes these decisions and thus provides a snapshot in time of the impact of a Medicaid expansion.

After calculating the number of individuals who did not previously qualify for Medicaid but would now qualify under the Affordable Care Act’s financial and categorical changes to the program, a determination of the demographic characteristics of those most likely to benefit was conducted. After describing the magnitude and composition of the potential newly insured pool, a cost-benefit analysis was conducted to determine the financial impact of the expansion. Upon determining whether the benefits of the expansion justified the costs, policy recommendations based upon previous state Medicaid expansions were made to maximize the number of HIV-positive individuals diagnosed and treated in the South. The ultimate goal was to develop a comprehensive analysis of the consequences of a hypothetical Medicaid expansion in the South in order to inform each state’s health care policy decisions.

Methods

Upon initial research into state HIV data, it became clear that none of the ten states collected detailed data about the socioeconomic status of those diagnosed with HIV. In the mandatory reporting system, most states only included the number of diagnoses, the estimation of persons living with HIV by county or public health region, and demographic information, including race, gender, age, area of residence, and mode of transmission. Not a single state recorded the income levels of HIV-positive individuals, thereby missing a key opportunity to collect information that could be used for research on policies impacting the HIV-positive population.

To overcome this obstacle, the American FactFinder tool of the U.S. Census Bureau was utilized to access income data by county for each of the ten states. The three-year, 2013 American Community Survey (ACS) was specifically used to estimate income quintiles by county. The ACS is a nationwide survey of communities that accounts for all households and other living situations, including group homes, correctional facilities, and collegiate housing. Therefore, the survey is a valuable tool for capturing the diverse living situations of Americans.

After collecting income data by county for each state, the CDC nationwide data was then utilized to determine each state’s Medicaid eligibility levels as a percentage of the FPL in 2013 (Table 2). To avoid overestimations, the percentage of the FPL for HIV-positive individuals with children was used, since all ten states excluded childless adults from Medicaid. The percentages ranged from 13 percent of the FPL in Alabama to 105 percent of the FPL in Tennessee, with an average of 40 percent of the FPL. Each state’s health department was then searched for data regarding the number of HIV-positive individuals living in each county, and the resulting data was overlaid on the county income data.

Table 2. Income eligibility levels as a percentage of the Federal Poverty Line (FPL) by Medicaid category by state

State Medicaid (0-1 yrs) Medicaid (ages 1-5) Medicaid (ages 6-18) Pregnant Women Parents Other Adults Expansion in 2014
North Carolina 210% 210% 133% 196% 45% 0% No
South Carolina 208% 208% 208% 194% 62% 0% No
Georgia 205% 149% 133% 220% 35% 0% No
Tennessee 195% 142% 133% 195% 105% 0% No
Florida 206% 140% 133% 191% 30% 0% No
Alabama 141% 141% 141% 141% 13% 0% No
Mississippi 194% 143% 133% 194% 22% 0% No
Louisiana 212% 212% 212% 133% 19% 0% No
Kentucky 195% 159% 159% 195% 57% 0% Yes
Arkansas 211% 211% 211% 209% 16% 0% Yes
Average 198% 172% 160% 187% 40% 0% -

The income and HIV-positive individual data by county were utilized to estimate the number of HIV-positive individuals living under the state’s Medicaid eligibility line, as well as the total number living under 138 percent of the FPL for the proposed Medicaid expansion and 300 percent of the FPL to discern qualification for ADAP. Since the Medicaid expansion would cover those earning under 138 percent of the FPL who were not currently receiving Medicaid, the total number under 138 percent was subtracted by the total number under the state’s current eligibility level to find the total number of HIV-positive individuals who would be assisted by an expansion. However, the calculation includes those over the age of 65, who would qualify for Medicare rather than Medicaid.

Therefore, the data in Table 1 from the CDC was used to remove the proportion of the HIV-positive population above 65 years. The end result was an estimation of the total number of individuals expected to be impacted by a Medicaid expansion. The number of individuals shifted from the ADAP program to Medicaid upon qualification under the new standards was also calculated, thereby yielding cost savings for the ADAP program. This was accomplished by subtracting the number of HIV-positive individuals living under 138 percent of the FPL from the total number living under 300 percent of the FPL. Since average ADAP expenditures are $12,648 per year, the expected cost savings if each individual switched to Medicaid could be calculated (Lefert et al., 2013).

Table 1. Demographics of the HIV-positive population by state

State Female Male Black White Hispanic Multiracial Above 65 years
North Carolina 30% 70% 65% 25% 6% 2% 5%
South Carolina 30% 70% 71% 23% 4% 2% 1%
Mississippi 33% 67% 74% 21% 3% 3% 4.4%
Georgia 26% 74% 70% 21% 6% 3% 2%
Florida 30% 70% 49% 29% 20% 2% 3%
Alabama 28% 72% 64% 30% 2% 3% 5%
Tennessee 26% 74% 57% 37% 4% 2% 3.45%
Kentucky 19% 81% 33% 58% 6% 3% 5%
Louisiana 30% 70% 68% 27% 4% 1% 3.3%
Arkansas 24% 76% 44% 48% 5% 3% 0.5%
Average 28% 72% 59% 32% 6% 2% 3%

Upon calculating the total number of newly-qualifying HIV-positive individuals by county and state, the CDC’s demographic data of HIV-positive individuals by state, found in Table 1, was utilized to determine the approximate composition of the population impacted by a Medicaid expansion. A racial and gender breakdown of the affected group was conducted, with a focus exclusively on those of working age.

After studying the demographics of those impacted, a cost-benefit analysis of an expansion within the ten southern states was conducted. The benefits of increased economic productivity, savings for ADAP, and the value of the additional quality-adjusted life years for the HIV-positive population enrolled in antiretroviral therapy were quantified. There was also discussion of the economic benefits of reduced transmission and improvements in mental, social, and emotional health, which could not be quantified. The benefits were then compared to the costs of an expansion, including the expense of medications and all subsequent medical treatment, as well as the social costs of food support, housing assistance, and Social Security Disability Insurance.

There are a number of potential errors inherent in this method, although all efforts were made to ensure an underestimation of the number of HIV-positive individuals impacted and an overestimation of the costs of the program. First, the critical assumption that all HIV-positive adults had children was made when estimating the number impacted by a Medicaid expansion. This was necessary because each state excluded childless, working adults from Medicaid, but simply calculating the number of HIV-positive individuals living under 138 percent of the FPL would capture both the childless adults and those with children who make under the state-determined income level and thus are already on Medicaid. While this method underestimates the number who could benefit from the expansion, it was necessary for analysis because of the previously discussed poor socioeconomic data collection of each state.

Table 3. Estimated minimum number of HIV-positive adults impacted by a Medicaid expansion and their demographics by state

State Maximum Number of HIV-Positive Individuals Assisted Maximum Percentage of HIV-Positive Individuals Assisted
North Carolina 7359 30.3%
South Carolina 5010 33.8%
Mississippi 3202 30.6%
Georgia 8992 19.4%
Florida 19785 20.3%
Alabama 3712 27.8%
Tennessee 3257 20.3%
Kentucky 1364 23.6%
Louisiana 3944 20.7%
Arkansas 1081 23.4%
Totals 57705 22.9%

To account for the tremendous underestimation, an estimation of the maximum number of HIV-positive individuals assisted was calculated without making this critical assumption. The overestimation via double counting of those with children may be partially counteracted by undiagnosed and untreated individuals, who when empowered by the potential for newfound financial assistance for life-saving medications, enroll in the Medicaid expansion. Therefore, a range was produced from the absolute minimum to the estimated maximum for those assisted.

The second critical assumption made during the cost-benefit analysis was that all HIV-positive individuals who are eligible for the Medicaid expansion will also completely enroll in all other forms of social support: food stamps, housing assistance, and Social Security Disability Insurance. While all of these individuals earn less than 138 percent of the FPL and thus qualify for support from many of these programs, it is unreasonable to assume that all of these individuals will remain on these social support programs for the entirety of the average additional 23.26 years of life gained. The cost-benefit analysis was calculated as if all individuals would require support for the entire duration of their gained life-years, however, because this guarantees that the research has successfully accounted for the maximum possible cost of a Medicaid expansion.

The one calculation that leads to a slight overestimation of the number of individuals impacted by the Medicaid expansion is that pregnant women already qualify for Medicaid at the expanded income eligibility levels. Therefore, pregnant women are double counted because they are included in the number impacted by an expansion despite already qualifying for Medicaid. However, this figure should have a negligible impact on the outcome because pregnant women constitute only a small proportion of the HIV-positive population. Although no data exists on the proportion of HIV-positive individuals who are pregnant, since pregnancy is a short-term condition, pregnant women only remain on Medicaid for short periods of time until they become reclassified as adults with children under the Medicaid qualification scheme. Therefore, in comparison to the underestimation of adults who would benefit from a Medicaid expansion, the double-counting of pregnant women is negligible.

Results

The first objective was to determine the approximate number of HIV-positive individuals in the South who currently do not qualify for Medicaid but would qualify for health insurance under a Medicaid expansion to effectively 138% of the federal poverty line. After overlaying the Census data with state health department data and estimating each county’s expected beneficiaries for each of the ten states, it was found that an absolute minimum of 41,312 HIV-positive individuals would qualify for Medicaid after an expansion of the income requirements and removal of the categorical eligibility requirement. This subset represents 16.4% of the entire HIV-positive population of the South and thus carries substantial impact in reducing the South’s new title as the HIV epicenter of the United States. States with less generous Medicaid policies, such as Mississippi and Alabama, would provide assistance to over one quarter of their HIV-positive populations, while states with more generous policies like Tennessee would assist only a small fraction of their population.

Table 4. Estimated maximum number of HIV-positive adults impacted by a Medicaid expansion

State Total Number of HIV Positive Individuals Minimum Estimated Adults Helped by Medicaid Expansion Minimum Percentage of HIV-positive individuals assisted Estimated Females Estimated Males Black White Hispanic Multiracial
North Carolina 24320 4682 19.3% 1386 3297 3034 1185 300 112
South Carolina 14843 2458 16.6% 745 1714 1741 575 91 44
Mississippi 10473 2677 25.6% 878 1799 1971 552 67 80
Georgia 46380 6712 14.5% 1716 4995 4665 1416 369 228
Florida 97597 15484 15.9% 4668 10815 7510 4413 3140 297
Alabama 13328 3362 25.2% 952 2410 2162 1002 81 104
Tennessee 16063 779 4.8% 202 577 446 289 28 12
Kentucky 5782 800 13.8% 154 647 262 463 46 25
Louisiana 19052 3401 17.9% 1024 2378 2320 918 126 34
Arkansas 4617 956 20.7% 233 723 418 457 51 26
Totals 252455 41312 16.4% 11957 29354 24526 11270 4298 964

It is important to acknowledge that this figure is the absolute minimum number of HIV-positive individuals in the South who could be assisted by an expansion. If we calculate the estimated maximum number of individuals who could be assisted by not making the critical assumption that all HIV-positive individuals have children, we find that this number increases to a potential 57,705 HIV-positive individuals who could qualify for Medicaid, which represents 22.9% of the entire HIV-positive population in the South. It is likely that the true figure falls within the range from the absolute minimum of 41,312 to the estimated maximum of 57,705, with approximately one-fifth of the HIV-positive population of the South eligible for a Medicaid expansion.

Of next greatest concern is the demographic composition of the pool of individuals who would be assisted by a Medicaid expansion in the South. In fact, the changing demographics of the at-risk population for HIV discussed in previous literature are reflected in the demographics of those who would be assisted. Nearly 60% of those who positioned to benefit from the expansion are African American, with white Americans constituting just over 25% and Hispanic Americans just over 10% of the assisted population.

Table 5. AIDS Drugs Assistance Program (ADAP) savings by state

State Number Removed from ADAP ADAP Expenditures per person ADAP Savings
North Carolina 4929 $ 12,648.00 $ 62,341,100.70
South Carolina 2483 $ 12,648.00 $ 31,405,360.80
Mississippi 2801 $ 12,648.00 $ 35,421,402.60
Georgia 6849 $ 12,648.00 $ 86,620,377.66
Florida 15963 $ 12,648.00 $ 201,894,917.02
Alabama 3539 $ 12,648.00 $ 44,759,550.58
Tennessee 807 $ 12,648.00 $ 10,202,890.24
Kentucky 842 $ 12,648.00 $ 10,655,761.63
Louisiana 3517 $ 12,648.00 $ 44,487,750.85
Arkansas 961 $ 12,648.00 $ 12,150,980.11
Totals 42690
$ 539,940,092.19

This estimation demonstrates that those who are now most susceptible to contracting the virus – low-income, African Americans residing in the rural South – would also be the best positioned to benefit from this expansion. In addition to race, the gender breakdown of those directly assisted is striking. Over 70% of those eligible under a Medicaid expansion are male, which is reflective of current rates of infection as well as the fact that current Medicaid policies are more generous toward mothers than males who are either fathers or childless adults. Finally, only a small proportion of those assisted in a Medicaid expansion would be the elderly; 97% of those who qualify for the expansion are of working age.

The next objective addresses the expected benefits and costs of a Medicaid expansion for this minimum pool of over 41,000 HIV-positive individuals. Summing the quantifiable benefits, which included the direct value of additional quality life years, savings for the AIDS Drug Assistance Program, and increased labor productivity, it was found that total benefits are $ 22,019,644,538.21 under the government’s valuation of life or $ 54,505,723,628.15 under Stanford University’s private estimation of the value of human life. Other benefits, while not quantifiable but relevant in a cost-benefit discussion, include reduced transmission rates stemming from treatment access, increased willingness to pursue testing and treatment due to drug affordability, and improved mental and social health.

Table 6. Quantified Benefits of a Medicaid Expansion for the HIV-positive population in the South

Benefits Government Definition Stanford Definition
Life Gained (12.94 QALYs, discounted) $ 20,560,809,446.01 $ 53,046,888,535.96
Increased Labor Productivity $ 918,895,000.00 $ 918,895,000.00
ADAP Savings $ 539,940,092.19 $ 539,940,092.19
Total Benefits $ 22,019,644,538.21 $ 54,505,723,628.15

Considering the costs, the discounted expense of lifetime HIV treatment at a discount rate of 3% for the additional 23.26 years of life is $206,656 per HIV-positive individual assisted. If all 41,312 individuals who qualify under the Medicaid expansion utilize the program, the maximum costs are $8,537,297,427. However, in addition to the direct costs of medication, legislatures must take into account the additional social expenditures of the life extensions for the HIV-positive population. Since those who would qualify under the Medicaid expansion are low-income, they frequently qualify for other social programs, including the Supplemental Nutritional Assistance Program, Section 8 housing assistance from Housing and Urban Development, and Social Security Disability Insurance.

Table 7. Costs of a Medicaid Expansion for the HIV-positive population in the South

Costs Undiscounted (per person) Discounted (per person) Total
Treatment $ 411,000.00 $ 206,656.00 $ 8,537,297,427.00
Food Stamps $ 80,107.44 $ 40,279.03 $ 1,663,992,621.46
Housing Assistance $ 165,582.36 $ 83,256.90 $ 3,439,478,738.34
Social Security $ 339,130.80 $ 170,519.24 $ 7,044,428,755.54
Total Costs $ 995,820.60 $ 500,711.17 $ 20,685,197,542.34

Since data does not exist to predict the usage rates of these services by HIV-positive individuals, the cost-benefit analysis took into account the total costs of each social program if every HIV-positive individual receiving Medicaid under the expansion utilized the average benefit package. Therefore, the maximum possible expenditure on food assistance is $1,663,992,621.46, housing assistance is $3,439,478,738.34, and Social Security insurance is $ 7,044,428,755.54 for an ultimate total cost for all 41,312 individuals of $20,685,197,542.34.

Suggested Reading from Inquiries Journal

As the nation’s largest health insurance program, Medicaid plays a huge role in the current health care reform debate. The program serves over 50 million people and has total outlays equaling over $280 billion[i]. Medicaid is much more than simply a program for the poor. It may also serve those who qualify for Supplemental... MORE»
Advertisement
During the 2008 Presidential Election, voters designated health insurance reform as a key issue for their future president to work on. With 46.3 million Americans uninsured in 2008, voters demanded change, and upon his election... MORE»
The value proposition in the commercial setting is the functional relationship of quality and price. It is held to be a utility maximizing function of the relationship between buyer and seller. Its proponents assert that translation of the value proposition into the health care system of the United States is a necessary and perhaps... MORE»
A country of approximately 37 million people, Kenya has struggled to build a health system that can effectively deliver quality health services to its population. Access to health care varies widely throughout the country and is determined on numerous factors, though in particular, major divides exist between rural and urban communities... MORE»
Submit to Inquiries Journal, Get a Decision in 10-Days

Inquiries Journal provides undergraduate and graduate students around the world a platform for the wide dissemination of academic work over a range of core disciplines.

Representing the work of students from hundreds of institutions around the globe, Inquiries Journal's large database of academic articles is completely free. Learn more | Blog | Submit

Follow IJ

Latest in Health Science

2022, Vol. 14 No. 03
The use of synthetic opioids in the United States in the past 30 years has created an epidemic the likes of which our healthcare and law enforcement systems have never before encountered. Although some opioid analogs, like fentanyl, were developed... Read Article »
2021, Vol. 13 No. 10
Romanies are one of history’s most misunderstood ethnic populations. Since medieval times, they have faced slavery, forced assimilation, sterilization, genocide, and other forms of ethnic cleansing. Their cultural and historical persecution... Read Article »
2021, Vol. 13 No. 09
The calcium-binding protein apoaequorin has been studied for its possible indication to improve human cognition and memory. Faculty at Quincy Bioscience developed Prevagen with this in mind, claiming its apoaequorin-formulated supplement may decrease... Read Article »
2021, Vol. 13 No. 05
Areas of the world found to harbor the people with exceptional lifespans are known as a Longevity Blue Zone (LBZ). LBZ’s are areas around the world that have an unusual concentration of centenarians. This paper investigates the link between... Read Article »
2020, Vol. 12 No. 12
Although spirituality has been an essential part of healing for most of mankind, modern medicine is more likely to embrace a mechanistic view of the human body where illness is an engineering problem and the body is the sum of discrete parts, rather... Read Article »
2020, Vol. 12 No. 10
Ketamine, described by the chemical formula C13H16ClNO, is most commonly associated with adolescent and adult recreational drug users and ravers who abuse this drug to experience a euphoric and dissociative state. Although this drug is a federal... Read Article »
2020, Vol. 12 No. 10
The ketogenic diet, or keto diet for short, is a fad diet that has gained significant attention in recent years as a popular weight loss approach. The diet is characterized by a depletion of carbohydrates which in turn place the body in a state... Read Article »

What are you looking for?

FROM OUR BLOG

Finding Balance in Graduate School
How to Select a Graduate Research Advisor
How to Use Regression Analysis Effectively