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A model for allocating CDC’s HIV prevention resources in the United States

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Abstract

The Division of HIV/AIDS Prevention (DHAP) at the Centers for Disease Control and Prevention has an annual budget of approximately $325 million for funding HIV prevention programs in the U.S. The purpose of this paper is to thoroughly describe the methods used to develop a national HIV resource allocation model intended to inform DHAP on allocation strategies that might improve the overall effectiveness of HIV prevention efforts. The HIV prevention resource allocation problem consists of choosing how to apportion prevention resources among interventions and populations so that HIV incidence is minimized, given a budget constraint. We developed an epidemic model that projects HIV infections over time given a specific allocation scenario. The epidemic model is then embedded in a nonlinear mathematical optimization program to determine the allocation scenario that minimizes HIV incidence over a 5-year horizon. In our model, we consider the general U.S. population and specific at-risk populations. The at-risk populations include 15 subgroups structured by gender, race/ethnicity and HIV transmission risk group. HIV transmission risk groups include high-risk heterosexuals, men who have sex with men and injection drug users. We consider HIV screening interventions and interventions to reduce HIV-related risk behaviors. The output of the model is the optimal funding scenario indicating the amounts to be allocated to all combinations of populations and interventions. For illustrative purposes only, we provide a sample application of the model. In this example, the optimal allocation scenario is compared to the current baseline funding scenario to highlight how the current allocation of funds could be improved. In the baseline allocation, 29% of the annual budget is aimed at the general population, while the model recommends targeting 100% of the budget to the at-risk populations with no allocation targeted to the general population. Within the allocation to behavioral interventions the model recommends an increase in targeting diagnosed positives. Also, the model allocation suggests a greater focus on MSM and IDUs with a 72% of the annual budget allocated to them, while the baseline allocation for MSM and IDUs totals 37%. Incorporating future epidemic trends in the decision-making process informs the selection of populations and interventions that should be targeted. Improving the use of funds by targeting the interventions and population subgroups at greatest risk may lead to improved HIV outcomes. These models can also direct research by pointing to areas where the development of cost-effective interventions can have the most impact on the epidemic.

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References

  1. Hall HI et al (2008) Estimation of HIV Incidence in the United States. J Am Med Assoc 300(5):520–529

    Article  Google Scholar 

  2. Marks G et al (2005) Meta-analysis of high-risk sexual behavior in persons aware and unaware they are infected with HIV in the United States: Implications for HIV prevention programs. J Acquir Immune Defic Syndr 39:446–453

    Article  Google Scholar 

  3. Weinhardt LS et al (1999) Effects of HIV counseling and testing on sexual risk behavior: a meta-analytic review of published research, 1985–1997. Am J Public Health 89(9):1397–1405

    Article  Google Scholar 

  4. Marks G et al (2009) Understanding differences in HIV sexual transmission among Latino and black men who have sex with men: The Brothers y Hermanos study. AIDS Behav 13(4):682–690

    Article  Google Scholar 

  5. Bailey NTJ (1975) The Mathematical Theory of Infectious Diseases and its Applications, 2nd edn. Charles Griffin and Company, United Kingdom

    Google Scholar 

  6. Capasso V (1993) Mathematical Structures of Epidemic Systems. Springer, Berlin

    Book  Google Scholar 

  7. Edwards D, Shachter R, Owens DK (1998) A dynamic HIV-transmission model for evaluating the costs and benefits of vaccine programs. Interfaces 28(3):144–166

    Article  Google Scholar 

  8. Tchetgen E, Kaplan EH, Friedland GH (2001) Public Health Consequences of Screening Patients for Adherence to Highly Active Antiretroviral Therapy. J Acquir Immune Defic Syndr 26:118–129

    Article  Google Scholar 

  9. Zaric GS, Brandeau ML, Barnett PG (2000) Methadone maintenance treatment and HIV prevention: A cost effectiveness analysis. Manage Sci 25:1013–1031

    Article  Google Scholar 

  10. Supriatna AK, Possingham HP (1998) Optimal Harvesting for a Predator-Prey Metapopulation. Bull Math Biol 60(1):49–65

    Article  Google Scholar 

  11. Kaplan EH, Pollack HA (1998) Allocating HIV prevention resources. Socio-Economic Planning Sciences 32:257–263

    Article  Google Scholar 

  12. Luenberger, D.G., Introduction to dynamic systems : theory, models, and applications. 1979, New York: Wiley. 446.

  13. Zaric GS et al (1998) The effect of protease inhibitors on the spread of HIV and the development of drug resistance: a simulation study. Simulation 71:262–275

    Article  Google Scholar 

  14. Frontline Systems (2008) Large-Scale SQP Solver Engine Version 8.0 for Microsoft Excel. Incline Village, NV

  15. Schackman BR et al (2006) The lifetime cost of current human immunodeficiency virus care in the United States. Med Care 44(11):990–997

    Article  Google Scholar 

  16. Marks G, Crepaz N, Janssen RS (2006) Estimating sexual transmission of HIV from persons aware and unaware that they are infected with the virus in the USA. AIDS 20:1447–1450

    Article  Google Scholar 

  17. Weinhardt L (2005) HIV diagnosis and risk behavior. In: Kalichman S (ed) Positive prevention: reducing HIV transmission among people living with HIV/AIDS. New York, Kluwer Academic/Plenum, pp 29–63

    Google Scholar 

  18. Quinn TC (1997) Acute primary HIV infection. J Am Med Assoc 278(1):58–62

    Article  Google Scholar 

  19. Rapatski B, Suppe F, Yorke J (2005) HIV epidemics driven by late disease stage transmission. J Acquir Immune Defic Syndr 38(3):241–253

    Google Scholar 

  20. Pilcher C et al (2004) Brief but efficient: acute HIV infection and the sexual transmission of HIV. J Infect Dis 189(10):1785–1792

    Article  Google Scholar 

  21. The White House Office of National AIDS Policy (July 2010) National HIV/AIDS strategy for the United States. Washington, DC, pp 60

  22. Campsmith ML et al (2010) Undiagnosed HIV prevalence among adults and adolescents in the United States at the end of 2006. J Acquir Immune Defic Syndr 53(5):619–624

    Google Scholar 

  23. Farnham PG et al (2008) Comparing the costs of HIV screening strategies and technologies in health-care settings. Public Health Rep 123(Suppl 3):51–62

    Google Scholar 

  24. Shrestha RK et al (2008) Cost-effectiveness of finding new HIV diagnoses using rapid HIV testing in community-based organizations. Public Health Rep 123(Suppl 3):94–100

    Google Scholar 

  25. Centers for Disease Control and Prevention (2008) Subpopulation estimates from the HIV incidence surveillance system --- United States, 2006. Morbidity and mortality. Weekly Rep 57(36):985–989

    Google Scholar 

  26. Campsmith M, Rhodes P, Hall I (2009) Estimated prevalence of undiagnosed HIV infection: US, end of 2006, in 16th Conference on Retroviruses and Opportunistic Infections (CROI 2009). Montreal, QC. p. Abstract No.1036

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The findings and conclusions in this paper are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention.

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Correspondence to Arielle Lasry.

Appendix A

Appendix A

1.1 Identifying the effective contact rates

We identify incidence estimates in each subpopulation i at the start of the horizon and denote it by I i (0) [25]. We then define I iD (0) and I iU (0), the incidence resulting from unsafe contact with those diagnosed and the incidence resulting from unsafe contact with those that are undiagnosed, respectively. Over all subpopulations, 21% of those who are HIV infected are currently undiagnosed [26] yet they contribute 48% of the annual incidence—resulting in a transmission rate that is more than three times higher than that of those who have been diagnosed with HIV [16]. Investment in HIV screening leads to diagnoses thereby moving people from the undiagnosed compartment into the less risky diagnosed compartment.

For every combination of ij subpopulations, we estimate, p ij , the proportion of new infections in subpopulation i that results from unsafe contact with subpopulation j. The matrix of p ij values is multiplied by the I iU (0) vector resulting in I ijU (0), the number of new infections in the susceptible subpopulation i that result from unsafe contact with the undiagnosed subpopulation j. Also, the matrix of p ij values is multiplied by the I iD (0) vector resulting in I ijD (0), the number of new infections in susceptible subpopulation i that result from unsafe contact with diagnosed subpopulation j.

Then, knowing the size of each subpopulation compartment, we identify λ Uij and λ Dij as follows:

$$ \mathop{I}\nolimits_{{ijU}} (1) = \frac{{{\lambda_{{ijU}}} \cdot {U_j}(0) \cdot {S_i}(0)}}{{{N_j}(0)}} \Rightarrow {\lambda_{{ijU}}} = \frac{{\mathop{\text{I}}\nolimits_{{ijU}} (1) \cdot {N_j}(0)}}{{{U_j}(0) \cdot {S_i}(0)}} $$
$$ \mathop{\hbox{I}}\nolimits_{{ijD}} (1) = \frac{{{\lambda_{{ijD}}} \cdot {D_j}(0) \cdot {S_i}(0)}}{{{N_j}(0)}} \Rightarrow {\lambda_{{ijD}}} = \frac{{\mathop{\text{I}}\nolimits_{{ijD}} (1) \cdot {N_j}(0)}}{{{D_j}(0) \cdot {S_i}(0)}} $$

We assume that I i Eq. 1 can be approximated by I i (0). In the calculation of new infections, the denominator which expresses the size of the partner subpopulation, I j (t), includes all those who have ceased engaging in risky behavior following a behavior change intervention.

These contact rates combine the impact of all factors that contribute to infection transmission such as number of partnerships, number of acts, transmission probabilities and infection co-factors together into one rate. Defining contact rates from incidence averts the needs to untangle the factors that contribute to an effective transmission.

λ Uij and λ Dij are fixed throughout the time horizon; benefits from investing in prevention programs result from reducing the size of the susceptible and infected compartments.

Our estimates of HIV incidence are based on HIV surveillance data that reflect epidemic trends occurring under current conditions, including the current expenditure of prevention funds. Given that our contacts rates are derived from incidence estimates, an adjustment is required to remove the effects of the existing allocation from the contact rates. The goal is to find contact rates that yield the latest HIV incidence estimates under the existing allocation. To do this, we define a linear optimization model where all contact rates as set as decision variables, we then set the amounts allocated to all interventions (i.e. decision variables referred to in Eq. 11) as per the existing allocation. The objective is to minimize the squared sum of the difference between the HIV incidence estimates in each subpopulation and the number of new infections in each subpopulation given the existing allocation of funds. The optimal solution sets the number of new infections in each subpopulation under the existing allocation of funds equal to the HIV incidence estimates and defines the final contact rates for the resource allocation model.

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Lasry, A., Sansom, S.L., Hicks, K.A. et al. A model for allocating CDC’s HIV prevention resources in the United States. Health Care Manag Sci 14, 115–124 (2011). https://doi.org/10.1007/s10729-010-9147-2

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