From Theory to Practice: Implementation of a Resource Allocation Model in Health Departments

Abstract

Objective To develop a resource allocation model to optimize health departments’ Centers for Disease Control and Prevention (CDC)–funded HIV prevention budgets to prevent the most new cases of HIV infection and to evaluate the model’s implementation in 4 health departments. Design, Settings, and Participants We developed a linear programming model combined with a Bernoulli process model that allocated a fixed budget among HIV prevention interventions and risk subpopulations to maximize the number of new infections prevented. The model, which required epidemiologic, behavioral, budgetary, and programmatic data, was implemented in health departments in Philadelphia, Chicago, Alabama, and Nebraska. Main Outcome Measures The optimal allocation of funds, the site-specific cost per case of HIV infection prevented rankings by intervention, and the expected number of HIV cases prevented. Results The model suggested allocating funds to HIV testing and continuum-of-care interventions in all 4 health departments. The most cost-effective intervention for all sites was HIV testing in nonclinical settings for men who have sex with men, and the least cost-effective interventions were behavioral interventions for HIV-negative persons. The pilot sites required 3 to 4 months of technical assistance to develop data inputs and generate and interpret the results. Although the sites found the model easy to use in providing quantitative evidence for allocating HIV prevention resources, they criticized the exclusion of structural interventions and the use of the model to allocate only CDC funds. Conclusions Resource allocation models have the potential to improve the allocation of limited HIV prevention resources and can be used as a decision-making guide for state and local health departments. Using such models may require substantial staff time and technical assistance. These model results emphasize the allocation of CDC funds toward testing and continuum-of-care interventions and populations at highest risk of HIV transmission.

Job burnout and turnover intention among Chinese primary healthcare staff: the mediating effect of satisfaction

“… 31 32 To fully use health resources and to improve the healthcare system’s overall social impacts, governments and concerned departments should emphasise more attention to optimise medical resources allocation. 33 Under market economy conditions, public hospital managers should also establish and consummate hospital operation and management systems. As an occupation with high risk, pressure and skill, healthcare staffs deserve a high payment.…”

Improving Linkage, Retention, and Reengagement in HIV Care in 12 Metropolitan Areas

“…Findings were used to construct specific local goals and objectives. To aid the planning process, some grantees conducted extensive situation analyses (Carey et al, 2015), used detailed analyses of existing HIV behavioral surveillance data to pinpoint gaps (German, Linton, Cassidy-Stewart, & Flynn, 2014), or conducted resource allocation modeling (Holtgrave et al, 2016; Kessler et al, 2013; Lasry, Sansom, Hicks, & Uzunangelov, 2012; Ryan et al, 2014; Yaylali et al, 2016). Other grantees improved use of data systems to assess linkage-to-care successes, barriers, and trends (Das et al, 2013).…”

HIV supply chains in low- and middle-income countries: overview and research opportunities

“…A first observation from Table 4 is that almost all of the selected papers focus on the strategic decision level. The strategic decision that is considered most is how to distribute available funds among different HIV prevention programs and different populations that are mainly affected by HIV (Earnshaw et al , 2007; Kassa, 2018; Kessler et al , 2013; Lasry et al , 2007; Lasry et al , 2011; Malvankar and Zaric, 2011; Rauner et al , 2005; Richter et al , 1999; Vieira et al , 2003; Yaylali et al , 2016; Zaric and Brandeau, 2001a, 2007). Optimizing this resource allocation problem on a more detailed level, Kassa (2018), Lasry et al (2007) and Malvankar and Zaric (2011) take into account the different levels at which decisions about the allocation of funds are made.…”

“…CPLEX). The optimization models developed in the selected papers include linear programming (Earnshaw et al , 2007; Juusola and Brandeau, 2016; Kassa, 2018; Malvankar and Zaric, 2011; McCoy and Johnson, 2014; Yaylali et al , 2016; Zaric and Brandeau, 2007), nonlinear programming (i.e. Alistar et al , 2014; Kok et al , 2015; Lasry et al , 2011, 2007; Richter et al , 1999), mixed-integer programming (de Vries et al , 2020; Deo and Sohoni, 2015; Jónasson et al , 2017; Núñez Ares et al , 2016), quadratic programming (McCoy and Johnson, 2014), nonlinear integer programming (Barrow et al , 2020) and nonconvex programming (Kassa, 2018).…”

“…Since most of the papers focus on HIV prevention, the value dimension mainly focuses on the number of infections averted and the number of new infections (i.e. Alistar et al , 2014; Earnshaw et al , 2007; Juusola and Brandeau, 2016; Kassa, 2018; Kessler et al , 2013; Kok et al , 2015; Lasry et al , 2011, 2007; Malvankar and Zaric, 2011; McCoy and Johnson, 2014; Richter et al , 1999; Yaylali et al , 2016; Zaric and Brandeau, 2001a, 2007). This dimension also includes the quality-adjusted-life-years (QALYs) (Alistar et al , 2014; Deo et al , 2015; Juusola and Brandeau, 2016; Zaric and Brandeau, 2001a), the number of HIV-positive people receiving treatment (Barrow et al , 2020; Jónasson et al , 2017), the number of patients receiving their test results (Deo and Sohoni, 2015; Jónasson et al , 2017), the number of persons served (de Vries et al , 2020; Núñez Ares et al , 2016; Yaylali et al , 2016), patient adherence (McCoy and Johnson, 2014), the number of patients achieving viral suppression (Barrow et al , 2020) and the accessibility of HIV services (de Vries et al , 2020; Núñez Ares et al , 2016).…”