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Public Financing and Delivery of Hiv/Aids Care: Securing the Legacy of Ryan White Appendix A Technical Appendix: Estimating the Impact and Cost of Expanded HIV Care Programs INTRODUCTION The charge given the Committee on Public Financing and Delivery of HIV Care is to develop policy recommendations that would “mitigate the discontinuities and inefficiencies of current public funding systems” that support services for people living with HIV and “eliminate resulting disparities in access to care by filling identified financing and service gaps.” The Committee was specifically directed to consider as an option (including determining the expected costs, savings, and overall financial impact of) modifying Title XIX of the Social Security Act (Medicaid) to create an eligibility category based on HIV infection. To guide its deliberations, the Committee developed estimates of the likely impact (financial and on the health of the HIV-infected population) of alternative policy options. This appendix presents the methods and data the Committee used to model the impact of different financing options, and the results. Because of time constraints, the analysis was focused on the two financing options believed by the Committee to provide the best opportunity for meeting the goals it identified for a desirable system of care. Both of these options create an entitlement to care for those diagnosed with HIV who meet established income eligibility requirements. One option, the HIV Comprehensive Care Program (HIV-CCP), is a public insurance program funded entirely by the federal government and administered by the states. The other option, Optional Medicaid Eligibility Group with Increased Federal Match (Enhanced Medicaid), is a modification to the Medicaid program that provides for an enhanced federal match (70 percent on aver-
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Public Financing and Delivery of Hiv/Aids Care: Securing the Legacy of Ryan White age) to states that extend eligibility to individuals in the early stages of HIV disease. Both options are described in detail in Chapter 5. For brevity, “the Committee” is replaced by “we.” METHODS The approach to the analysis was to pose and then answer three broad questions for each option: What are the likely health benefits of implementing an alternative approach to public financing of HIV care in terms of mortality and life expectancy? In other words, what incremental gains in health does an additional investment in HIV care buy? What is the cost effectiveness of implementing an alternative approach? What is the cost of implementing the proposed alternative approach? MODELING OVERVIEW To answer these questions, we conducted an analysis that involved five steps: Estimate the number of people not currently receiving highly active antiretroviral therapy (HAART) who are likely to begin HAART with alternative methods for financing care. Estimate the cost and health implications over 10 years of each financing option, including the anticipated gain in life expectancy (adjusted for quality of life) and reduced mortality (premature deaths averted) among those who participate in the programs. Estimate the cost per quality-adjusted life-year (QALY) gained associated with enrollment in each financing option and compare the estimates to other investments in health. Estimate the short-term (first-year) cost implications for each public payer. Compare the results for each option to one another. Our approach was to gauge the potential increase in HAART use associated with a policy to expand access to HIV care by estimating how many individuals are currently in need of HAART and, of those, how many do not receive HAART. We refer to the number of people who need but do not receive HAART as the “HAART use deficit.” Our estimates of current HAART use are based primarily on data collected between 1996 and 1998, the beginning of the HAART era, which presents a limitation to our analysis.
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Public Financing and Delivery of Hiv/Aids Care: Securing the Legacy of Ryan White There is also data from 2000, however, that suggest that antiretroviral therapy (ART) (but not HAART) was used by about two-thirds of individuals with AIDS. This, along with unpublished data from a Kahn (2002) study on HAART use combined with ART use, suggests overall HAART use of approximately 45 percent. Recognizing that HAART use may have grown in the interim, especially for individuals with late-stage AIDS, we used a higher value of estimated HAART use (64 percent) based on an AIDS diagnosis by the 1987 definition. We then estimated the number of people who would receive HAART assuming implementation of a proposed change in public financing of HIV care. This estimate was based on program eligibility and enrollment, as well as the association of insurance status and ancillary services covered with HAART use. Current and anticipated HAART use were then compared to calculate the incremental gain in HAART use expected as a result of the creation of a new entitlement to care. We used a disease state-transition (Markov) model of HIV disease progression adapted from Kahn et al. (2001) to estimate the health and financial impact of providing greater access to HAART. This model portrays a population of individuals with HIV disease classified into five increasingly severe disease states: asymptomatic with a CD4 cell count >500, asymptomatic CD4 200–500, symptomatic CD4 200–500, AIDS by the 1993 definition only (including CD4 < 200), and AIDS by the 1987 clinical definition. The model specifies transition probabilities between disease states and to death, per time period. These probabilities are derived from published empirical studies. The model thus predicts how the mix of HIV disease states evolves over time for the specified infected population. The transition probabilities are reduced for individuals on HAART, based on a structured review of HAART clinical trials which used disease progression or surrogate markers as endpoints. Thus, increased insurance coverage, such as with HIV-CCP, slows disease progression by increasing the likelihood of HAART use. The original model produced three clinical outcomes. New AIDS diagnoses represent the progression from any pre-AIDS state to AIDS (by the CDC’s 1993 definition). Deaths include all causes, as generally reported. Life years are cumulative years of life for all HIV-infected individuals, unadjusted for quality of life. The model was updated by the Committee to calculate QALYs, based on the most recent reviews of the utility of HIV health states. The model also calculates the costs of providing HIV medical care, by assigning to each individual in each time period a set of costs reflecting the Committee’s estimates of the costs of HIV medical care by severity of illness (see separate section on costs). We also estimated the health and cost implications of ancillary services provided by one financing option (HIV-CCP). This financing option has a
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Public Financing and Delivery of Hiv/Aids Care: Securing the Legacy of Ryan White benefit package that includes case management, mental health care, and treatment of substance abuse. For each service, we estimated from previously reported estimates the unmet need, the costs of meeting that need, and, for the latter two, expected increases in quality of life. We estimated the cost per QALY for each option using the standard cost–utility ratio. The numerator includes societal costs for medical care and ancillary services under each financing alternative, minus the same costs with current financing. In the denominator is the gain in QALYs as compared with the current situation. We estimated the first-year financial impact on the budgets of the federal government, collectively on the budgets of the states, and on the cost of care for the uninsured. This was done based on how services are currently financed, expected increases in cost, and specified changes in federal matching rates. Finally, we compared the financing options on key outcome measures. This comparison indicated the incremental differences in costs, health gains, and cost per health gain. All costs are adjusted to 2002 using the medical component of the United States consumer price index. All future costs and health outcomes are discounted to 2002 using a discount rate of 3 percent per year. FINANCING OPTIONS We defined three financing options: maintaining the system as it currently exists, a federally funded eligibility expansion with a comprehensive benefit package (HIV-CCP), and a state-option eligibility expansion with 70 percent federal match (Enhanced Medicaid). Descriptions follow of the three options that focus on characteristics that we explicitly modeled. The “current” option is based on the most recent and representative data, as described in the Inputs section. To facilitate adjustment of costs for specific services for the alternative financing options, we characterized current costs by type of service. The services included HAART, viral resistance testing, HIV monitoring labs, outpatient visits (adjusted for specifically listed outpatient services), other medications, inpatient care, emergency care, substance abuse treatment, mental health care, case management, dental care, obstetrics/gynecology, home health/visiting nurse care, and prevention counseling. Although the list is extensive, we did not include services such as housing, food, transportation, child care, and legal advocacy, which can also be necessary depending on the circumstances of the individual. Utilization was set to levels reported in current literature (primarily the HIV Cost and Services Utilization Study [HCSUS]), and other sources as well, reflecting the current mix of insurance and associated benefits packages.
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Public Financing and Delivery of Hiv/Aids Care: Securing the Legacy of Ryan White The HIV-CCP option is a highly incentivized expansion in eligibility, accompanied by a 100 percent federal financing match, a strengthened benefit package, and higher outpatient reimbursement rates. Eligibility is based on having an income that is below 250 percent of the federal poverty level (FPL)—$22,150 for an individual in 2002. Enrollment is assumed to be high due to incentives to both providers (higher reimbursement) and patients (better benefits and better paid providers). The benefits package is richer than current average Medicaid benefits because unrestricted coverage of three key ancillary services (case management, mental health, and substance abuse treatment) is included. Outpatient reimbursement is increased 20 percent as compared with Medicaid to be comparable to Medicare, plus 5 percent on average for Centers of Excellence. For the HIV-CCP, increases in utilization are in three areas. First, there is an increase in ancillary services, due to improved coverage. Second, some individuals who were previously out of care enter into regular care. Third, HAART use (and viral resistance testing) increases because being insured is associated with higher HAART use. We assume that because of enhanced reimbursement, HAART use will equal that seen with private insurance. HAART use further increases because of the improved coverage of ancillary services, which have been independently associated with higher HAART use. Because these services largely help to address problems associated with poverty, and because low income is independently associated with lower HAART use, we refer to these gains as partially alleviating the poverty effect. The Enhanced Medicaid option is a state-discretion expansion in Medicaid eligibility, accompanied by a 70 percent federal financing match, with no change in benefits or reimbursement. Eligibility is also based on having an income below 250 percent of the FPL. Enrollment is lower than with the HIV-CCP entitlement due to much lower incentives to providers and patients. Increases in utilization are just for individuals who begin to use HAART (and viral resistance testing) as a result of becoming insured. Without enhanced reimbursement, HAART use among enrollees is assumed to equal that reported for Medicaid. INPUTS We conducted extensive literature searches to identify inputs for this model of HIV disease and health services use, and consulted with a number of experts. For most inputs, data were available that directly provided input values or could be readily adapted for that purpose. All cost data were adjusted to 2002 dollars using the medical care consumer price index. For those inputs lacking data, we relied on expert judgment, including discussion within the Committee, and chose values that tended to understate the
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Public Financing and Delivery of Hiv/Aids Care: Securing the Legacy of Ryan White impact of the modeled policies. Due to uncertainty in inputs, we specified uncertainty ranges and conducted sensitivity analyses. The following discussion is divided into four categories: HIV population characteristics, HAART use, HIV clinical services and costs, and effects of financing options. The text parallels Table A-1. HIV Population and Characteristics According to Fleming et al. (2002), approximately 950,000 individuals are infected with HIV. Among those infected, Fleming further estimates that approximately 670,000 are aware of infection. Of these, approximately 360,000 have AIDS (CD4 < 200, or AIDS-defining condition). Based on data from in-care populations, we assume that 150,000 of these have AIDS by clinical criteria and 210,000 by CD4 < 200 (Bozzette et al., 1998). As individuals are more likely to be aware of HIV infection later in disease (e.g., if infected for longer and/or symptomatic), we assumed a greater number of aware individuals in each more severe disease state. Information regarding income level and insurance status of HIV-infected individuals was obtained from HCSUS, a national probability sample of people with HIV in care. According to Bozzette et al. (1998), the majority (72 percent) of those with HIV are low income (household income < $25,000). Among these low-income individuals, nearly 25 percent are uninsured and 61 percent rely on Medicaid or Medicare. In contrast, of those with incomes > $25,000, most (78 percent) rely on private insurance (Bozzette et al., 1998). Information regarding disease stage and insurance status was also obtained from HCSUS (not shown in table). Individuals with clinical AIDS are about two-thirds as likely to have private insurance as are asymptomatic individuals, and are correspondingly more likely to have Medicaid. This reflects the impoverishing effects of severe AIDS as well as the AIDS disability requirement for HIV-associated Medicaid eligibility. HAART Use and Need Estimates of the current prevalence of HAART use are drawn from a number of sources. Studies of HIV-infected populations (AIDS and HIV non-AIDS) in New York State and in three metropolitan areas used local data sources (HIV/AIDS surveillance, lab reporting, Medicaid and AIDS Drug Assistance Program [ADAP] billing claims) from 2001 in a framework endorsed by the Health Resources and Services Administration (HRSA) to estimate participation in HIV care (Kahn, 2002). Studies of Medicaid and ADAP populations were conducted for 1998 in four heavily HIV-affected states (Kahn et al., 2002). Additional older estimates provide
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Public Financing and Delivery of Hiv/Aids Care: Securing the Legacy of Ryan White TABLE A-1 Analysis Inputs HIV Population Baseline Estimate Sources Infected 950,000 Fleming et al., 2002 Aware 670,000 Fleming et al., 2002 HIV Population Characteristics Baseline Estimate Sources By clinical stage (among aware) CD4 > 500 0.08 Bozzette et al., 1998; CD4 499—350 0.15 Expert Judgment CD4 349—200 0.24 CD4 199—50 0.31 CD4 < 50 0.22 By income level Proportion <$25,000 0.72 Bozzette et al., 1998 Proportion >$25,000 0.28 By insurance status, among aware Proportion Medicaid, other public (including Medicare) 0.50 Bozzette et al., 1998; Expert Judgment Proportion uninsured 0.25 Proportion private 0.25 HAART Use Baseline Estimate Sources Current use Total current antiretroviral (ARV) use 230,000 Kahn, 2002; Kahn et al., 2002; Moorman et al., 1998; Palella et al., 1998 By clinical stage ARV current use (CD4 50–200) 0.40 Kahn, 2002; Kahn et al., 2002; Moorman et al., 1998; Palella et al., 1998 By income odds ratio (OR) getting ARV if < $25,000 0.60 Andersen et al., 2002 By insurance status OR getting ARV if uninsured 0.74 Andersen et al., 2002 OR getting ARV if Medicaid alone 0.83 OR getting ARV if Medicare—other 0.82 OR getting ARV if HMO insurance 0.90
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Public Financing and Delivery of Hiv/Aids Care: Securing the Legacy of Ryan White HIV Clinical Costsb Use Cost per person year (ppy) Sources HAART (CD4 50–200) $9,222 Schackman et al., 2002; Expert Judgment Other medicines 1.00 $3,980 Aldridge et al., 2002; Bozzette et al., 2001 Prevention counseling 1.00 $272 Holtgrave et al., 2002 Monitoring labs 1.00 $682 Schackman et al., 2002 Outpatient medical 1.00 $1,629 Bozzette et al., 1998; Bozzette et al., 2001; Shapiro et al., 1999 Sexually transmitted disease, tuberculosis, and hepatitis screening 1.00 $14 IOM, 1997; Gable et al., 1996; HepNet Hepatitis C InfoCenter, 2003 Inpatient medical 1.00 $4,246 Bozzette et al., 1998; Bozzette et al., 2001 Emergency department 0.33 $846 Bozzette et al., 1998 Dental 1.00 $513 Bozzette et al., 1998; Capilouto et al., 1991 Obstetrics/gynecology 0.20 $446 Bozzette et al., 1998 Home health/visiting nurses 0.20 $5,000 London et al., 2001; MetLife, 2002 Baseline Use Gain in Use Due to Improved Coverage OR for ARV Use Cost ppy Substance abuse treatment 0.075 0.075 1.700 $6,193 Ashman et al., 2002; Burnam et al., 2001; Conover and Whetten-Goldstein, 2002; Finkelstein and Tiger, 2002; Lo et al., 2002; Marx, 2002; Messeri et al., 2002; Sherer et al., 2002; Strathdee et al., 1998; Zaric et al., 2000 Mental health 0.220 0.09 1.400 $1,380 Ashman et al., 2002; Burnam et al., 2001; Lo et al., 2002; Messeri et al., 2002; Sambamoorthi et al., 2000; Sherer et al., 2002; Turner et al., 2001
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Public Financing and Delivery of Hiv/Aids Care: Securing the Legacy of Ryan White Baseline Use Gain in Use Due to Improved Coverage OR for ARV Use Cost ppy Case management 0.600 0.15 1.500 $826 Katz et al., 2001; Lo et al., 2002; Magnus et al., 2001; Marx, 2002; Messeri, 2002; Sherer, 2002 Federal Matching Rates for Medicaid Sources Florida 58.83 DHHS, 2003 Georgia 59.60 Illinois 50.00 New York 50.00 Texas 59.99 Effects of Financing Baseline Estimate Sources Proportion eligible Publicly insured/in care 0.92 Expert Judgment Publicly insured/not in care 0.975 Expert Judgment Uninsured/in care 0.53 Expert Judgment Uninsured/not in care 0.50 Expert Judgment Enrollment rates Publicly insured/in care 0.90 Expert Judgment Publicly insured/not in care 0.40 Expert Judgment Uninsured/in care 0.90 Expert Judgment Uninsured/not in care 0.30 Expert Judgment If enrolled, in care Publicly insured/in care 1 Expert Judgment Publicly insured/not in care 0.75 Expert Judgment Uninsured/in care 1 Expert Judgment Uninsured/not in care 0.75 Expert Judgment Enrollment rate adjustment, Enhanced Medicaid program 0.667 Expert Judgment Health Effects Baseline Estimate Sources Utility deficit due to advanced disease 0.12–0.24 Tengs and Wallace, 2000 Utility change (drop) for being on HAART –.03 Expert Judgment
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Public Financing and Delivery of Hiv/Aids Care: Securing the Legacy of Ryan White Health Effects Baseline Estimate Sources Utility gain for being on HAART (symptom reduction) 0.06–0.13 Expert Judgment; see text Utility adjustment for receiving substance abuse treatment 0.1 Zaric, 2000; Expert Judgment Utility adjustment for receiving mental health treatment 0.05 Simon et al., 2001; Wang et al., 2002; Expert Judgment Increase in Service Utilization Substance abuse treatment 0.075 Zaric, 2000; Expert Judgment Mental health treatment 0.09 Simon et al., 2001; Wang et al., 2002; Expert Judgment Case management 0.15 Expert Judgment nationally representative data of individuals in care (Bozzette et al., 1998) and individuals in private and public HIV specialty clinics (Moorman et al., 1998; Palella et al., 1998). Based on these data, we estimated that 230,000 individuals are on HAART, including 40 percent of those with a nadir CD4 count between 50 and 199. To determine the association of HAART use with income level and insurance status used data from HCSUS (Andersen et al., 2000). Though this data is from 1996, somewhat more recent nationwide data (from 1997–1998) and analyses of data from the state and local levels suggest the persistence of income and insurance effects found by Andersen et al. (Bhattacharya et al., 2003; Goldman et al., 2003; Kahn, 2002; Kahn et al., 2002; Goldman et al., 2001; Hsu et al., 2001). Low-income individuals (family income < $25,000) were less likely to be on HAART (odds ratio [OR] = 0.6). The odds of being on HAART also varied by insurance status, from 0.74 among those with no insurance, to 0.83 among Medicaid recipients, to 0.90 among those with health maintenance organization (HMO) insurance (reference group is those with private fee-for-service insurance) (Andersen et al., 2000). We defined HAART need based on HIV disease stage. All those with AIDS “need” HAART. Although there are many legitimate reasons not to provide HAART when someone has AIDS, and many patients may decline HAART, clinical guidelines suggest offering and using HAART. Among
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Public Financing and Delivery of Hiv/Aids Care: Securing the Legacy of Ryan White those with HIV disease with a CD4 count of 200 to 350 (often symptomatic), we assume that half need HAART, consistent with the guidelines’ suggestion for flexibility in this range. For those even earlier in disease, we define need as the small percentage estimated to be currently using HAART, which at that stage is not recommended (DHHS, 2003). HIV Clinical Services and Costs To determine HAART costs, estimates were reviewed from HCSUS data, state and territorial ADAPs, and the 1999 Red Book average wholesale price (AWP). We used the last of these, further adjusted to reflect Medicaid drug pricing (15 percent below average manufacturer price, which is 20 percent below AWP on average) (Schackman et al., 2002; DHHS, 2000). Thus, we estimated total per-person annual cost of HAART to be $9,222. According to Committee estimates, individuals with HIV need a number of clinical services. Among individuals who have developed AIDS (CD4 < 199), in addition to benefiting from HAART, we estimate that all need the provision of medications beyond HAART as appropriate, such as opportunistic infection prophylaxis (DHHS, 2003). We also estimate that all individuals with HIV, regardless of disease stage, need prevention counseling, monitoring labs, inpatient and outpatient medical care, sexually transmitted disease (STD) screening and treatment, and dental care (100 percent need for each). Among a smaller proportion of individuals with HIV, there is a need for additional clinical services. Based on estimates from HCSUS data, we estimate that a third of individuals with HIV need coverage for emergency department visits. Furthermore, we estimate that a fifth would benefit from obstetrics/gynecology services, home health/visiting nurses (according to data from HCSUS), and food services (based on data from a San Francisco study). A small percentage would also benefit from transportation services (also based on the data from San Francisco). Utilization of case management, substance abuse treatment, and mental health treatment was estimated from published estimates of unmet need for care. We estimated an increase from 15 to 30 percent in use of substance abuse services among injection drug users (IDUs), who constitute half of individuals with HIV/AIDS, based on published data (Sherer et al., 2002) and expert judgment, including Committee member experience with offering substance abuse treatment to IDUs. We estimated an increase of 9 percent in mental health treatment, representing an estimated 18 percent unmet need and a 50 percent likelihood of seeking needed care (Burnam et al., 2001; Expert Judgment). We estimated a 15 percent increase in case management, based on expressed unmet need (Sherer et al., 2002). Costs for
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Public Financing and Delivery of Hiv/Aids Care: Securing the Legacy of Ryan White TABLE A-12 Univariate Sensitivity Analysis Values Outcomes: QALYs Gained; Societal Cost; Cost/QALY Variable Base Case (low, high) Low Valuea High Value Base case NA 129,385 $5.56 billion $42,972 HIV Population Characteristics Aware 670,000 (536,000; 804,000)b 103,508 $4.45 billion —c 155,262 $6.67 billion — By clinical stage among aware Base case (CD4>500/CD4 499–350/CD4 349–200/AIDS 93/AIDS 87) 0.07/0.15/0.24/0.31/0.22 CD4 349–200 portion (0.07/0.18/0.19/0.33/0.22, 0.07/0.12/0.29/0.29/0.22) 129,582 $5.60 billion $43,207 129,114 $5.51 billion $42,669 AIDS 93 portion (0.07/0.15/0.27/0.25/0.25, 0.07/0.15/0.21/0.37/0.19) 131,256 $5.53 billion $42,122 127,514 $5.59 billion $43,808
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Public Financing and Delivery of Hiv/Aids Care: Securing the Legacy of Ryan White Values Outcomes: QALYs Gained; Societal Cost; Cost/QALY Variable Base Case (low, high) Low Valuea High Value By insurance status, among aware Base case (public/uninsured/private) 0.50/0.25/0.25 Low private 0.53/0.27/0.20 130,928 127,843 High private 0.47/0.23/0.30 $5.72 billion $43,742 $5.39 billion $42,161 HAART Use Individuals currently on HAART—total 230,000 (184,000; 276,000) 108,141 $5.41 billion $50,031 150,627 $5.21 billion $34,601 Individuals currently on HAART—public and uninsured 167,650 (134,120; 201,180) 152,367 $6.72 billion $38,097 82,419 $4.40 billion $53,391 Relative risk of receiving HAART if family income <$25,000 0.60 (0.5; 0.7) 153,326 $5.94 billion $38,715 112,283 $5.26 billion $46,825
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Public Financing and Delivery of Hiv/Aids Care: Securing the Legacy of Ryan White Relative risk of receiving HAART in program versus private insurance 1.0 (0.92, NAd) 100,965 $5.05 billion $49,973 NA Relative risk of getting HAART if receiving case management 1.5 (1.2, 1.8) 116,291 $5.33 billion $45,837 142,675 $5.78 billion $40,490 Relative risk of getting HAART if receiving mental health treatment 1.4 (1.2, 1.6) 124,108 $5.47 billion $44,607 134,662 $5.65 billion $41,944 Relative risk of getting HAART if receiving substance abuse treatment 1.7 (1.3, 3.0) 120,590 $5.41 billion $44,840 158,114 $6.00 billion $37,967 HIV Clinical Costs Clinical care, individuals not on HAARTe (per person-year) $18,705 ($16,834; $20,576) — $5.51 billion $42,582 — $5.61 billion $43,361 HAART, in the program $9,222 ($8,300; $10,144) — $5.15 billion $39,830 — $5.97 billion $46,113 Non-HAART clinical care, individuals on HAART $12,373 ($11,136; $13,611) — $5.09 billion $39,340 — $6.03 billion $46,603
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Public Financing and Delivery of Hiv/Aids Care: Securing the Legacy of Ryan White Values Outcomes: QALYs Gained; Societal Cost; Cost/QALY Variable Base Case (low, high) Low Valuea High Value Case management $826 ($661; $991) — $5.50 billion $42,452 — $5.62 billion $43,401 Mental health treatment $1,380 ($1,104; $1,656) — $5.50 billion $42,541 — $5.62 billion $43,402 Substance abuse treatment $6,193 ($5,574; $6,812)f — $5.46 billion $42,167 — $5.66 billion $43,777 Increase in Service Utilization Substance abuse treatment 0.075 (0.06; 0.09) 126,387 $5.35 billion $42,343 132,383 $5.77 billion $43,572 Mental health treatment 0.09 (0.07; 0.11) 127,387 $5.50 billion $43,160 131,384 $5.62 billion $42,789
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Public Financing and Delivery of Hiv/Aids Care: Securing the Legacy of Ryan White Case management 0.15 (0.12; 0.18) — $5.50 billion $42,542 — $5.62 billion $43,401 Eligibility and Enrollment Proportion Eligible By insurance status In care Public 0.92 (0.87; 0.97)g 121,393 $5.24 billion 137,378 $5.88 billion Uninsured 0.53 (0.42; 0.64) $43,176 $42,784 Not in care Public 0.975 (0.93; 1.0)h 126,756 $5.42 billion 131,308 $5.66 billion Uninsured 0.50 (0.4; 0.6) $42,760 $43,139 Enrollment Ratesi In care Publicly insured 0.90 (0.45; 0.99) 84,518 138,358 Uninsured 0.90 (0.45; 0.99) $3.77 billion $44,649 $5.92 billion $42,773 Not in care Publicly insured 0.40 (0.20; 0.60) 109,559 149,211 Uninsured 0.30 (0.15; 0.45) $4.57 billion $41,680 $6.55 billion $43,927
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Public Financing and Delivery of Hiv/Aids Care: Securing the Legacy of Ryan White Values Outcomes: QALYs Gained; Societal Cost; Cost/QALY Variable Base Case (low, high) Low Valuea High Value If enrolled, in care Not in care (previously) Publicly insured 0.75 (0.6; 0.9) 121,454 137,315 Uninsured 0.75 (0.6; 0.9) $5.16 billion $42,503 $5.96 billion $43,386 Health Effects Utility deficit due to advanced disease 0.12–0.24 (–10%, +10%) 127,128 — $43,735 131,642 — $42,235 Utility change (drop) for being on HAART (side effects, inconvenience) –0.03 (0.0; –0.06)j 141,340 — $39,337 117,430 — $47,347 Utility gain for being on HAART (symptom reduction) 0.06–0.13 (–10%, +10%) 125,665 — $44,244 133,105 — $41,771
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Public Financing and Delivery of Hiv/Aids Care: Securing the Legacy of Ryan White Utility adjustment for receiving substance abuse treatment 0.1 (0.08; 0.12) 126,387 — $43,991 132,383 — $41,999 Utility adjustment for receiving mental health treatment 0.05 (0.04; 0.06) 127,587 — $43,578 131,184 — $42,383 aLow value/high value indicates the value of the input variable. bUnless otherwise indicated sensitivity analyses were done using a range of ± 20%. cA dash (—) indicates same value as base case. dFor HAART use in program, no upper bound is specified because use at the level of private insurance (base case) is considered a maximum. eCost of care not on HAART is for those individuals who would be on HAART with the program, to facilitate comparison with subsequent rows. fA range of ± 10% was used for this variable. gA range of ± 5% was used for this variable. hA range of ± 5% was used for this variable. iA range of ± 50% (with a maximum of 0.99) was used for all of the variables in this section. jA range of ± 100% was used for this variable.
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Public Financing and Delivery of Hiv/Aids Care: Securing the Legacy of Ryan White of care data, and estimated health utility gains from treatment. Generally, we used ranges of ±10 percent for the high/low values when the original input was from a well-done and directly relevant study or source, up to ±20 percent to determine the high/low values when the original input was from a published but less definitive source or sources, and ±50 percent or more when published data were scarce and we relied on expert judgment to estimate the input. Each sensitivity analysis estimated three outputs (QALY gain, societal cost, and cost per QALY gained, all discounted) for the low and high input values. We found that no single variable had an unexpected impact on the results of the model and, in general, the variations in outputs resulting from the analyses were modest, especially for cost effectiveness. Predictably, total population with HIV along with HAART use and cost produced the widest range of results. Varying the total HIV population aware affected total program cost proportionately (i.e., ±20 percent) but did not affect cost per QALY gained. If, however, all newly aware were asymptomatic and not candidates for HAART over the five years, a 20 percent gain in awareness would generate much smaller gains in QALYs (to 134,181) and costs ($6.2 billion) and a small rise in cost per QALY gained (to $46,076) (not in table). Different disease distributions among the aware had little effect. Different insurance status distributions affected cost and QALYs gained a little, and cost per QALY gained almost not at all. Varying current HAART use among those publicly insured or uninsured by ±20 percent produced relatively substantial changes in terms of both total cost and QALYs gained, and moderate variation in cost effectiveness ($34,000 to $54,000 per QALY gained). Variations in the relative risk of getting HAART due to use of ancillary services had only small effects. HIV clinical costs had only modest effects on cost per QALY gained. The inputs with the greatest impact were cost of HAART per person-year and of non-HAART clinical costs for those on HAART. These sensitivity analyses by ±20 percent produced no change in QALYs and some variation in total cost. It produced one of the widest variations in cost effectiveness, however, from $39,000 to $46,000. Only non-HAART clinical cost for those on HAART had a greater variation in terms of cost effectiveness, from $39,340 to $46,603. Changes in utilization of ancillary services had very small effects. For eligibility and enrollment, the largest variation in outputs resulted from inputs where the value was based on expert judgment. For example, uncertainty in enrollment led to variation in costs of $3.8 to $5.9 billion, though very little change in cost effectiveness due to similar changes in QALYs gained. This is essentially a program scaling effect. Changes in health inputs had small effects due to fairly narrow uncertainty (e.g., for utility decrement due to disease status) or only limited
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Public Financing and Delivery of Hiv/Aids Care: Securing the Legacy of Ryan White impact on results (e.g., utility gain for one ancillary service). The largest effect was for the utility decrement associated with HAART use, due to complete lack of data, with variation in QALYs gained from 117,000 to 141,000, and for cost effectiveness from $39,000 to $47,000 per QALY gained. Additional and more nuanced sensitivity analysis would be useful to explore the implications of variations of other assumptions, for example, differing distributions of HAART use by disease stage, particularly late AIDS. Time and resource constraints, however, dictated that we focus on those analyses where the outcomes could materially affect the findings of the model. REFERENCES Aldridge C, Davis D, Doyle A, Kates J, Chou L. 2002. National ADAP Monitoring Project Annual Report 2002. National Association of State and Territorial AIDS Directors, AIDS Treatment DATA Network and Kaiser Family Foundation: Washington, DC. Andersen R, Bozzette S, Shapiro M, St Clair P, Morton S, Crystal S, Goldman D, Wenger N, Gifford A, Leibowitz A, Asch S, Berry S, Nakazono T, Heslin K, Cunningham W. 2000. Access of vulnerable groups to antiretroviral therapy among persons in care for HIV disease in the United States. HIV Cost and Services Utilization Study Consortium. Health Services Research 35(2):389–416. Ashman JJ, Conviser R, Pounds MB. 2002. Associations between HIV-positive individuals’ receipt of ancillary services and medical care receipt and retention. AIDS Care 14(Suppl. 1):S109–S118. Bhattacharya J, Goldman D, Sood N. 2003. The link between public and private insurance and HIV-related mortality. Journal of Health Economics 22:1105–1122. Bozzette SA, Berry SH, Duan N, Frankel MR, Leibowitz AA, Lefkowitz D, Emmons CA, Senterfitt JW, Berk ML, Morton SC, Shapiro MF. 1998.The care of HIV-infected adults in the United States. HIV Cost and Services Utilization Study Consortium. New England Journal of Medicine 339(26):1897–1904. Bozzette SA, Joyce G, McCaffrey DF, Leibowitz AA, Morton SC, Berry SH, Rastegar A, Timberlake D, Shapiro MF, Goldman DP. 2001. Expenditures for the care of HIV-infected patients in the era of highly active antiretroviral therapy. New England Journal of Medicine 344(11):817–823. Burnam MA, Bing EG, Morton SC, Sherbourne C, Fleishman JA, London AS, Vitiello B, Stein M, Bozzette SA, Shapiro MF. 2001.Use of mental health and substance abuse treatment services among adults with HIV in the United States. Archives of General Psychiatry 58(8):729–736. Capilouto EI, Piette J, White BA, Fleishman J. 1991. Perceived need for dental care among persons living with acquired immunodeficiency syndrome. Medical Care 29(8):745–754. Conover CJ, Whetten-Goldstein K. 2002. The impact of ancillary services on primary care use and outcomes for HIV/AIDS patients with public insurance coverage. AIDS Care 14(Suppl. 1):S59–S71. DHHS (U.S. Department of Health and Human Services). 2000. AIDS Drug Assistance Program Cost Containment Strategies. OEI-05-99-00610. Washington, DC: DHHS Office of Inspector General. DHHS. 2003. Federal Matching Shares for Medicaid. Washington, DC: DHHS.
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Representative terms from entire chapter: