Optimal allocation of HIV prevention funds for state health departments

Abstract

ObjectiveTo estimate the optimal allocation of Centers for Disease Control and Prevention (CDC) HIV prevention funds for health departments in 52 jurisdictions, incorporating Health Resources and Services Administration (HRSA) Ryan White HIV/AIDS Program funds, to improve outcomes along the HIV care continuum and prevent infections.MethodsUsing surveillance data from 2010 to 2012 and budgetary data from 2012, we divided the 52 health departments into 5 groups varying by number of persons living with diagnosed HIV (PLWDH), median annual CDC HIV prevention budget, and median annual HRSA expenditures supporting linkage to care, retention in care, and adherence to antiretroviral therapy. Using an optimization and a Bernoulli process model, we solved for the optimal CDC prevention budget allocation for each health department group. The optimal allocation distributed the funds across prevention interventions and populations at risk for HIV to prevent the greatest number of new HIV cases annually.ResultsBoth the HIV prevention interventions funded by the optimal allocation of CDC HIV prevention funds and the proportions of the budget allocated were similar across health department groups, particularly those representing the large majority of PLWDH. Consistently funded interventions included testing, partner services and linkage to care and interventions for men who have sex with men (MSM). Sensitivity analyses showed that the optimal allocation shifted when there were differences in transmission category proportions and progress along the HIV care continuum.ConclusionThe robustness of the results suggests that most health departments can use these analyses to guide the investment of CDC HIV prevention funds into strategies to prevent the most new cases of HIV.

Optimization Models for HIV/AIDS Resource Allocation: A Systematic Review

“…Eight articles used the Optima optimization software package, [36][37][38][39][40][41][42][43] whereas 4 articles used 2 unique resource allocation models developed to inform the Centers for Disease Control and Prevention's allocation of HIV/AIDS prevention funding. [44][45][46][47] Thus, only 14 unique optimization techniques were identified through our review. In our qualitative analysis, we considered each study on its own and then jointly with other studies that used the same model to underscore similarities.…”

“…Five studies (22%) used other methods such as adding up the cumulative benefits of interventions based on published cost-effectiveness ratios 53 and using other mathematical estimation methods (eg, health production functions). 17,46,47,54 Table 3 lists the qualitative data extracted from each study that was reviewed. Only 2 studies considered disease dynamics in the general population exclusively; the remaining 21 studies looked at the general population in addition to specific high-risk groups, such as men who have sex with men (MSM), female sex workers (FSWs) and their clients, injection drug users (IDUs), and migrant laborers.…”

Pre-Exposure Prophylaxis (PrEP) Awareness Is Low Among Heterosexual People of Color Who Might Benefit From PrEP in Philadelphia

“…A recent cost effectiveness study indicated that primary prevention interventions for heterosexuals are best suited to primary care settings. 18 Thus, interventions that encourage providers to follow PrEP guidelines with patients of color are encouraged.…”

Optimizing health resource allocation for improving timely HIV diagnosis in China

“…Mathematical modelling effectively identifies the optimized resource allocation for HIV interventions in different settings [25][26][27][28][29]. For example, in developing settings, such as sub-Saharan Africa, McGillen et al [25] used a mathematical model to guide domestic and international funders in understanding local HIV epidemics and relevant drivers for HIV pre-vention.…”

“…For example, in developing settings, such as sub-Saharan Africa, McGillen et al [25] used a mathematical model to guide domestic and international funders in understanding local HIV epidemics and relevant drivers for HIV pre-vention. In a developed setting like the United States, Yaylali et al [26] developed an economic model to determine the most effective way to allocate government funds across HIVrelated interventions and populations at risk (MSM, PWID and heterosexuals) to prevent the maximal number of new cases annually. This paper aims to develop a mathematical model to determine the optimized allocation of health resources for HIV testing across 14 populations in China.…”