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CAN WE AFFORD EXPANDED HIV SCREENING?

Erika G. Martin, A. David Paltiel , Rochelle P. Walensky and Bruce R. Schackman

Value in Health Supplemental Information


Expanded HIV Screening in the U.S.: What Will It Cost Government Discretionary and Entitlement Programs?  A Budget Impact Analysis

 

Introduction

 

This technical appendix supplements the methodological details in the manuscript text.  We provide additional information on the Disease Module and Screening Module, detail the source data for the Disease Module, describe the incident and prevalent cohorts modeled in the analysis and derive their population estimates, describe HIV screening protocols and detail how these costs were tabulated in the analysis, and clarify how we differentiated entitlement and discretionary costs.  Portions of the Disease Module description have been previously published elsewhere (1-4).

 

Disease Module Overview

 

The Cost-Effectiveness of Preventing AIDS Complications (CEPAC) Model is a widely-published computer-based model of chronic HIV disease (1-5).  It uses a Monte Carlo state-transition framework to track the natural history of illness in individuals.  “State-transition” means that the model characterizes patients’ natural history of illness as a sequence of monthly transitions from one health state to another.  “Monte Carlo” refers to a random number generator and set of estimated probabilities that are used to determine the sequence of transitions among health states.

 

Clinically, HIV-infected individuals experience a rapid rise in the HIV viral load within the first few weeks of exposure.  Acute infection may manifest with flu-like symptoms in up to half of patients.  However, this syndrome is self-limited and often does not present to medical attention or lead to HIV diagnosis.  With resolution of acute infection symptoms, viral load declines to a “steady state.”  Undetected HIV-infected individuals then remain in a period of asymptomatic infection with progressive immunologic decline and continued viral replication for almost a decade.  In the absence of HIV testing during the asymptomatic period, patients generally present to medical care with an opportunistic infection (OI) or other HIV-related illness such as renal insufficiency.  Patients whose HIV infection is detected may benefit from antiretroviral therapy (ART), which acts to suppress the viral load, increase immune system function, and deter poor HIV-related outcomes (6).

 

The CEPAC Model simulates the clinical disease process as follows.  Simulated patients undergo monthly transitions among mutually exclusive health states, categorized by primary HIV infection, chronic illness, acute illness, and death.  States are further subdivided into six unique CD4 strata (>500 cells/mm3, 301-500 cells/mm3, 201-300 cells/mm3, 101-200 cells/mm3, 50-100 cells/mm3, and ≤50 cells/mm3) and seven unique HIV RNA (viral load) strata (>100,000 copies/mL, 30,001-100,000 copies/mL, 10,001-30,000 copies/mL, 3,001-10,000 copies/mL, 501-3,000 copies/mL, 51-500 copies/mL, and ≤50 copies/mL).  Patients are at risk for the following opportunistic infections (OI) that lead to temporary states of acute illness:  Pneumocystis jiroveci pneumonia (PCP), toxoplasmosis, Mycobacterium avium complex (MAC) disease, disseminated fungal infection, cytomegalovirus, and bacterial and other infections.  Monthly probabilities of events include changes in CD4 counts and viral load, the development of OIs, adverse reactions to medications, and death.  The viral load level determines the rate of CD4 cell decline in the absence of antiretroviral therapy (ART) or in patients who have failed ART, and the CD4 count predicts the monthly risk of OIs. 

 

Investigators may specify the number, efficacy, and cost of sequential regimens of ART.  In the model, ART decreases viral load.  The reduction in viral load promotes CD4 rebound, thereby decreasing the risk of developing an OI. 

 

Summary statistics are collected for each simulated patient on all clinical events, the length of time spent in each health state, and the associated costs and quality of life.  A large cohort (usually around one million) is simulated until estimates are stable.  Investigators input user-defined initial population distributions of demographic and clinical characteristics, including age, sex, CD4 count, and viral load.  Investigators also specify the number of sequential lines of available ART, starting criteria for drug therapy, efficacy, and cost of treatment.  Other user-defined clinical inputs include the timing and effectiveness of prophylaxis and treatment for OIs and the frequency of ongoing laboratory monitoring (CD4 counts and HIV RNA tests) and routine clinical visits.  Table 4 lists additional details on these inputs.  There are over a thousand inputs in total and we do not reproduce them all here.  A full set of inputs is available to interested readers upon request.

Screening Module Overview

 

A separate Screening Module has been designed to simulate the clinical impact of counseling, testing, and referral (CTR) on incident and prevalent patients who are unaware of their infection status at the start of the simulation (3, 7).  The Screening Model is a front-end structure that determines when each simulated patient will become detected through screening or presentation to care with an OI.  For those detected through screening, the Screening Module additionally determines whether they were linked to care.  All patients (detected and undetected) have their clinical outcomes tracked through the Disease Module.  However, only those who have been successfully detected and linked to care are eligible for treatment and incur costs.  The Screening Module allows for input assumptions on testing frequency, linkage to care, rates of return for results, clinical characteristics of undetected populations, test performance characteristics (such as sensitivity and specificity), and cost. 

 

National testing rates were translated into monthly probabilities of test receipt using a binomial approximation.  For example, an annual test was simulated with a monthly test probability of 1/12.  We assumed that all undetected patients with an OI would present to care and learn of their infection status during the treatment episode.

 

Source Data for Disease Module

 

All clinical data for the Disease Module were derived from published literature, including the Multicenter AIDS Cohort Study (MACS), randomized clinical trials, and cohort studies.  Data from MACS were used to model the natural history of disease (8), including the probability of chronic AIDS death for each health state, the probability of primary infection with an OI, and the probability of death from an OI.  Cohort studies were used to derive the probability of secondary infection with an OI (9-16).  The monthly probability of non-AIDS deaths was calculated from the U.S. life expectancy tables (17).  The monthly baseline CD4 decline was derived from clinical literature (18).  The efficacy of each antiretroviral regimen was estimated from published clinical trials (19-25).  The full set of parameters is available from the authors upon request.

 

Patients initiated ART at CD4 < 350 or upon development of an OI, in accordance with DHHS guidelines (26).  Patients were offered six sequential lines of therapy, with decreasing effectiveness.  Patients who were observed to fail on one line of therapy were switched to the subsequent line in the following month.  Failure was defined as an observed increase in viral load or a 50 percent decrease in CD4 count for two consecutive months.  Data on the efficacy of the ART regimens (measured by rates of viral suppression) were derived from clinical trials (19-25).  We reduced the efficacy of ART regimens, as reported in clinical trials, by 15 percent to account for lower efficacy between observational cohorts and clinical trials (19, 27).  Prior to the start of each new regimen, patients received an HIV drug resistance test, in accordance with DHHS guidelines (26).  Patients received OI prophylaxis therapy in accordance with national recommendations (28).  Data on the efficacy of OI treatments and the probabilities of prophylaxis-related toxicity were derived from clinical trials (13, 29-36).  Viral load and CD4 counts were measured every three months, in accordance with the guidelines (26).

 

Characteristics of Incidence and Prevalent Cohorts

 

We modeled three distinct cohorts:  1) prevalent cases aware of their infection status at the start of the simulation (“prevalent aware”), 2) prevalent cases unaware of their infection at the start of the simulation (“prevalent unaware”), and 3) incident cases.  The first cohort was modeled using the Disease Model.  The latter two cohorts were modeled using both the Screening Model and Disease Model; patients in these cohorts were only eligible for treatment upon successful detection and linkage to care. We assumed that all incident cases are unaware of their infection until they are detected in the Screening Model. To calculate the number of patients eligible for government programs, we used national estimates of prevalence and incidence and subtracted the number of HIV-infected individuals likely to receive care through private insurance or the Veterans Administration (VA), as described below. 

 

 

Prevalent Cases Currently Aware of Their Infection.

 

The CDC estimates that there are 1,106,000 cases of HIV in the U.S., of whom 21% do not know their infection status (37).  This yields 874,000 prevalent cases aware of their infection (“prevalent aware”) and a maximum of 232,000 prevalent cases who could potentially be identified through a screening program (“prevalent unaware”).  The VA cares for approximately 33,400 cases (38).  Of patients enrolled in HIVRN sites, 15.4% have private insurance (pooled across years) (39).  The HIVRN sample excludes veterans.  We calculate the number of “prevalent aware” cases with private insurance as 15.4%*(874,000-33,400) = 129,000.  The number of “prevalent aware” cases currently eligible for care through discretionary or entitlement programs is the total population (874,000) minus those receiving care through the VA (33,400) and private insurance (129,000), which equals 711,000. 

 

The actual number of individuals who seek government-financed care may be larger than our estimate if there is a decrease in the number of individuals with private or VA insurance, or if some individuals are under-insured.  We address this variable with a sensitivity analysis in which we increase the population eligible for testing and care by 10 percent.  The upper bound for the prevalent aware estimate is 747,000.

 

Prevalent Cases Currently Unaware of Their Infection.

 

We assumed that the fraction of “prevalent unaware” cases that would be eligible for care through the VA or private insurance would be similar to the payer distribution of “prevalent aware” cases.  Patients without private insurance may have fewer encounters with the healthcare system, in which they might receive an HIV test.  If this were true, “prevalent unaware” cases would be more likely to be eligible for discretionary or entitlement programs, compared to “prevalent aware” cases.  Our assumption of a similar payer distribution would thereby result in a conservative estimate of the budgetary impact to public payers.  We addressed this issue in a sensitivity analysis, in which we increased the number of cases eligible for publicly financed healthcare by 10 percent.

 

 To estimate the number of “prevalent unaware” cases eligible for care through the VA upon detection, we assumed that the probability of awareness of HIV status among veterans is similar to the U.S. population (79%).  The 33,400 “prevalent aware” VA cases would correspond to 33,400*(0.79/0.21) = 8,900 “prevalent unaware” VA cases.  As described above, we estimated the number of “prevalent unaware” cases with private insurance as 15.4%*(232,000-8,900) = 34,400.  The number of “prevalent unaware” cases currently eligible for care through discretionary or entitlement programs is the total population (232,000) minus those likely to receive care through the VA (8,900) and private insurance (34,000), which equals 189,000.  In our sensitivity analysis, we used an upper bound of 198,000.

 

Incident Cases.

 

We used the CDC’s recently updated estimates of national HIV incidence (revised from 40,000 to 56,300 new cases annually) (40).  If the fraction of incident cases receiving care through the VA and private insurance is similar to the fraction among “prevalent aware” cases, then 2,200 of incident cases would be eligible for VA care and 8,300 would be eligible for private insurance upon detection.  The number of incident cases currently eligible for care after detection through discretionary or entitlement programs is the total population of incident cases (56,300) minus those likely to receive care through the VA (2,200) and private insurance (8,300), which equals 45,800.  The number of individuals who seek a government-financed test may be larger if it is difficult in practice to exclude those with alternate insurance sources from public testing facilities.  In our sensitivity analysis, we used an upper bound of 48,100.

 

In contrast to the two static prevalent cohorts, a new incident cohort arrives during each budget year.  To estimate the budgetary impact of incident cohorts, one run was performed to capture annual costs for a single newly-infected cohort.  These costs were repeated for each subsequent budget year, with the arrival of a new incident cohort.  The total cost in each year was the summation of the annual costs of the cohorts.  Similar to cost calculations on the prevalent unaware cohort, we only tabulated costs of incident cases who have been identified.  New cases are not linked to care in the model until detection.

 

 

Characteristics of Individuals Eligible for Screening

 

We tabulated HIV screening costs for non-elderly adults eligible for discretionary or entitlement programs only (aged 19 to 64) in order to reflect the revised guidelines’ focus on this group (41).  We tabulated treatment costs for all adults (aged 19 or greater) eligible for these government programs, including the elderly.  As less than 3 percent of HIV-infected individuals are 65 or older (42), seniors comprise a small share of total treatment costs.  However, the effectiveness of current therapies makes it possible for those infected and identified to live into late adulthood, thereby incurring costs to Medicare. 

 

Data from the Henry J. Kaiser Family Foundation (KFF) were used to derive the number of non-elderly adults eligible for a government-financed HIV test (43).  Of the 261,400,000 non-elderly individuals (including children) in the U.S., 16% (41,800,000) are enrolled in Medicaid or other public insurance and 17% (44,400,000) are uninsured.  Of the 78,600,000 children in the U.S., 29% (22,800,000) have Medicaid or other publicly-funded care and 11% (8,600,000) are uninsured.  Subtracting the number of children from the former estimates yields 19,000,000 non-elderly adults (aged 19 to 64) enrolled in Medicaid or other public health programs and 35,800,000 non-elderly uninsured adults.   

 

We used data from the VA (44) to remove the number of non-elderly veterans from this estimate.  The VA estimates there are 7,800,000 veterans enrolled in the VA healthcare system, of whom 39% (3,100,000) are elderly.  We assumed that the number of veterans under 19 years of age was negligible.  Subtracting the VA non-elderly population (4,700,000) from the KFF estimates yielded a total of 50,100,000 non-elderly adults in the U.S. who would be eligible for an HIV test financed through a public program.  In practice, program coordinators at HIV prevention centers may be unable to either exclude those with private or VA insurance from HIV testing or obtain reimbursement through these sources.  We addressed this in a sensitivity analysis, in which we increased the number of cases eligible for government-financed testing to 55,100,000.

 

Calculation of Screening Costs

 There are several reasons why HIV-infected individuals may not access care:  first, they may not know they are infected, and second, they may know their infection status but have not been linked to care.  We account for both of these issues in the screening module.  As discussed above, 21 percent of individuals do not know they have been infected (37).  This is incorporated into our estimates through the N calculations described above.  Some individuals receive a test but do not return for results, and this test return varies by test type (45).  Of those who are identified, clinical data suggest that approximately 80 percent are linked to care (46-48).  Incomplete test receipt and linkage to care is incorporated into the screening module input parameters and is described in greater detail below.

Screening Protocols.

 

In the base case, we modeled a one-step rapid test with two steps for a positive result, as described in prior cost-effectiveness analyses (49-51).  After a patient consents to testing, a provider collects a specimen and a laboratory technician processes the sample.  If the result is positive, a second specimen is collected and sent to an outside laboratory for a Western blot confirmatory analysis.  Patients must return to receive results during a second visit.  All patients receive post-test counseling at the time of their test results.  For HIV-positive patients, post-test counseling includes assistance with identifying follow-up care.  Approximately three percent of patients do not wait to receive results of their rapid test during the initial visit (45), which we indicate in Table 1 as a 97 percent “test return rate.”  Of those who receive their positive rapid test results, four-fifths are successfully linked to follow-up HIV specialty care (46, 48), which we indicate in Table 1 as an 80 percent “probability detected case linked to care.”  We assumed confirmatory tests had perfect sensitivity and specificity, an assumption used in other cost analyses (51).

 

In a sensitivity analysis, we modeled a two-step enzyme-linked immunosorbent assay (ELISA) with four steps for a positive initial result (49-51).  In contrast to the rapid test, patients do not receive preliminary results during the first visit.  Upon consent, a provider collects a specimen which is sent to an outside laboratory for analysis.  If the specimen tests positive, a technician will repeat the ELISA twice (for a total of three ELISA tests) and perform a subsequent Western blot.  Patients must return for a second visit to receive their results and post-test counseling.  Due to the two-visit process, there is a higher rate of non-return for the ELISA than the rapid test:  approximately three-fourths of those with positive readings and two-thirds of those with negative readings return for their results (45).  Table 1 differentiates between the test return rates for positive and negative results.  We assumed that those who received their positive ELISA test results had the same probability of being linked to care (80%) as those who received their positive rapid test results.

 

Screening Costs.

 

The assignment of screening costs is displayed graphically in Figure 3.  All patients incurred the initial screening costs.  HIV-infected individuals in the acute state (the first two months of infection) were assumed to remain undetected because they do not have detectable antibodies (6), unless they received a false positive antibody result and were subsequently identified by confirmatory testing.  This occurred with probability 0.1%, obtained by subtracting the test specificity (99.9%) from 1.  A small fraction (0.4%, determined by the test sensitivity) received a false negative result.  All individuals incurred the costs of post-test counseling upon receipt of their positive or negative test results.  Patients testing positive on the initial test incurred costs for additional laboratory testing, post-test counseling, and linkage to care.  All costs were derived from micro-cost data presented by Farnham (51), adjusted for inflation using the medical care component of the CPI (52).  Government payers incurred additional administration costs for patients who did not return for their results, which included mailing reminder letters or making phone calls.  We assumed these costs totaled 30 minutes of staff time and $1 for materials (mailings and phone calls). 

 

Screening costs for HIV-negative individuals were calculated deterministically based on test characteristics.  All patients incurred the initial screening costs.  The small fraction (0.1%, determined by the test specificity) receiving a false positive result incurred costs for subsequent confirmatory testing and post-test counseling but not linkage to care.  Patients testing negative on the initial test incurred post-test counseling costs if they received their results.  Patients who did not return for their results incurred an administration cost.  All costs except for the non-return cost were derived from Farnham (51).

 

Differentiating Between Discretionary and Entitlement Costs

As described in the manuscript, we assumed that the treatment costs for all currently undetected patients (incident and “prevalent unaware”) were incurred by discretionary programs until they developed an OI or attained age 65, at which point their care was financed through an entitlement program.  We used these markers as a proxy for gaining eligibility for Medicaid or Medicare on the basis of disability or age. 

 

We allowed currently detected patients (“prevalent aware”) to start the simulation in either a discretionary or entitlement program.  As with the incident and “prevalent unaware” cohorts, patients enrolled in a discretionary program became eligible for an entitlement program when they developed an OI or attained age 65.  We used national data to assign the fraction of the “prevalent aware” cohort to each program.  Among patients currently enrolled in HIVRN, 28.7% receive care financed by RW or are uninsured, and 55.9% receive care through Medicare or Medicaid (39).  Using the calculation method described above, we estimated that 241,000 of the “prevalent aware” cases were eligible for care through a discretionary program at the start of the simulation, and that the remaining 470,000 were immediately eligible for care through Medicare or Medicaid.

 

We differentiated between discretionary and entitlement costs using two sets of simulations.  The first set of simulations tracked all costs.  The second set of simulations used identical inputs except that costs terminated in the month following detection with an OI or attainment of age 65.  This second set of simulations represented costs to discretionary programs.  The difference between these cost outlays represents the costs to entitlement programs. 

 


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Table 4.  Inputs and source data for efficacy and cost of antiretroviral regimens, OI treatments, and routine outpatient care.

 

Variable

Baseline Value

Reference

Efficacy of antiretroviral therapy (% HIV RNA suppressed to < 400 copies/mL at 48 weeks)

1st line

73

(19)

2nd line

62

(21)

3rd line     

52

(21)

4th line    

55

(20)

5th line     

34

(24)

6th line      OBR only

13

(24)

Cost of antiretroviral therapy ($USD/month)

 

 

1st line     

1,278

(53)

2nd line     

1,953

(53, 54)

3rd line     

1,961

(53, 54)

4th line   

2,480

(53, 54)

5th line     

3,801

(53, 54)

6th line     

1,738

(53, 54)

Opportunistic infections and routine care

 

 

Timing of prophylaxis and treatment for OIs

National guidelines

(28)

Frequency of ongoing laboratory monitoring

Every 3 months

(28)

Frequency of routine clinical visits

Every 3 months

(28)

 

 


Figure 3. Assignment of HIV screening costs for budget impact analysis.

Picture1

 

 

 

 


Value in Health Article Supplementary Information
 

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