Figure - available via license: Creative Commons Attribution 2.0 Generic
Content may be subject to copyright.
Source publication
HIV/AIDS resource allocation decisions are influenced by political, social, ethical and other factors that are difficult to quantify. Consequently, quantitative models of HIV/AIDS resource allocation have had limited impact on actual spending decisions. We propose a decision-support System for HIV/AIDS Resource Allocation (S4HARA) that takes into c...
Citations
... For example, S4HARA (System for HIV/AIDS resource allocation) is a four-step spreadsheet-based model for a rational resource allocation approach. Recommendations of the model are grounded in the cultural, social, and political context [32]. Furthermore, determining the overall performances of healthcare structures based on input-output relations plays a vital role in optimizing resource allocation and investment planning, as it contributes to reducing the uncertainty of future performance [33]. ...
Introduction
Artificial Intelligence (AI) represents a significant advancement in technology, and it is crucial for policymakers to incorporate AI thinking into policies and to fully explore, analyze and utilize massive data and conduct AI-related policies. AI has the potential to optimize healthcare financing systems. This study provides an overview of the AI application domains in healthcare financing.
Method
We conducted a scoping review in six steps: formulating research questions, identifying relevant studies by conducting a comprehensive literature search using appropriate keywords, screening titles and abstracts for relevance, reviewing full texts of relevant articles, charting extracted data, and compiling and summarizing findings. Specifically, the research question sought to identify the applications of artificial intelligence in health financing supported by the published literature and explore potential future applications. PubMed, Scopus, and Web of Science databases were searched between 2000 and 2023.
Results
We discovered that AI has a significant impact on various aspects of health financing, such as governance, revenue raising, pooling, and strategic purchasing. We provide evidence-based recommendations for establishing and improving the health financing system based on AI.
Conclusions
To ensure that vulnerable groups face minimum challenges and benefit from improved health financing, we urge national and international institutions worldwide to use and adopt AI tools and applications.
... Some studies note an apparent paradox: The practical impact of economic -models for priority setting is still limited even though these models have become more widespread. To make sense of this paradox, it is suggested in these studies that decision-makers may have limited understanding of the methodology underpinning decision analysis (Brookes et al., 2015;Wilkinson et al., 2016), implying a need to make economic evaluation more attuned to decision-makers' needs and local circumstances (Cromwell et al., 2015;Lasry et al., 2008;Peacock et al., 2010). In this way, in order to overcome misalignments between models and practices, health economic studies tend to advocate adjustments of methods and models. ...
Since the 1990s, the sociology of rationing has developed in explicit opposition to health economic and bioethical approaches to healthcare rationing. This implies a limited engagement with other disciplines and a limited impact on political debates. To bring the sociology of rationing into an interdisciplinary dialogue, it is important to understand the disciplines' analytical differences and similarities. Based on a critical interpretive literature synthesis, this article examines four disciplinary perspectives on healthcare rationing and priority setting: (1) Health economics, which seeks to develop decision models to provide for more rational resource allocation; (2) Bioethics, which seeks to develop normative principles and procedures to facilitate a just allocation of resources; (3) Health policy studies, which focus on issues of legitimacy and implementation of decision models; and lastly (4) Sociology, which analyses the uncertainty of rationing and the resulting value conflicts and negotiations. The article provides an analytical overview and suggestions on how to advance the impact of sociological arguments in future rationing debates: Firstly, we discuss how to develop the concepts and assumptions of the sociology of rationing. Secondly, we identify specific themes relevant for sociological inquiry, including the recurring problem of how to translate administrative priority setting decisions into clinical practice.
... We also study a widely-used equity metric, known as prevalence [70,71]. Here, we define the prevalence equity constraint to limit the absolute difference between the regional prevalence (cases per population in a region) and the country prevalence (cases per population over all regions) by the parameter k, and formulate it as follows: ...
Existing compartmental models in epidemiology are limited in terms of optimizing the resource allocation to control an epidemic outbreak under disease growth uncertainty. In this study, we address this core limitation by presenting a multi-stage stochastic programming compartmental model, which integrates the uncertain disease progression and resource allocation to control an infectious disease outbreak. The proposed multi-stage stochastic program involves various disease growth scenarios and optimizes the distribution of treatment centers and resources while minimizing the total expected number of new infections and funerals. We define two new equity metrics, namely infection and capacity equity, and explicitly consider equity for allocating treatment funds and facilities over multiple time stages. We also study the multi-stage value of the stochastic solution (VSS), which demonstrates the superiority of the proposed stochastic programming model over its deterministic counterpart. We apply the proposed formulation to control the Ebola Virus Disease (EVD) in Guinea, Sierra Leone, and Liberia of West Africa to determine the optimal and fair resource-allocation strategies. Our model balances the proportion of infections over all regions, even without including the infection equity or prevalence equity constraints. Model results also show that allocating treatment resources proportional to population is sub-optimal, and enforcing such a resource allocation policy might adversely impact the total number of infections and deaths, and thus resulting in a high cost that we have to pay for the fairness. Our multi-stage stochastic epidemic-logistics model is practical and can be adapted to control other infectious diseases in meta-populations and dynamically evolving situations.
... We also study a widely-used equity metric, known as prevalence (Kedziora et al., 2019;Lasry et al., 2008). Here, we define the prevalence equity constraint to limit the absolute difference between the regional prevalence (cases per population in a region) and the country prevalence (cases per population over all regions) by the parameter k, and formulate it as follows: ...
Existing compartmental models in epidemiology are limited in terms of optimizing the resource allocation to control an epidemic outbreak under disease growth uncertainty. In this study, we address this core limitation by presenting a multi-stage stochastic programming compartmental model, which integrates the uncertain disease progression and resource allocation to control an infectious disease outbreak. The proposed multi-stage stochastic program involves various disease growth scenarios and optimizes the distribution of treatment centers and resources while minimizing the total expected number of new infections and funerals. We define two new equity metrics, namely infection and capacity equity, and explicitly consider equity for allocating treatment funds and facilities over multiple time stages. We also study the multi-stage value of the stochastic solution (VSS), which demonstrates the superiority of the proposed stochastic programming model over its deterministic counterpart. We apply the proposed formulation to control the Ebola Virus Disease (EVD) in Guinea, Sierra Leone, and Liberia of West Africa to determine the optimal and fair resource-allocation strategies. Our model balances the proportion of infections over all regions, even without including the infection equity or prevalence equity constraints. Model results also show that allocating treatment resources proportional to population is sub-optimal, and enforcing such a resource allocation policy might adversely impact the total number of infections and deaths, and thus resulting in a high cost that we have to pay for the fairness. Our multi-stage stochastic epidemic-logistics model is practical and can be adapted to control other infectious diseases in meta-populations and dynamically evolving situations.
... We also study a widely-used equity metric, known as prevalence (Kedziora et al., 2019;Lasry et al., 2008). Here, we define the prevalence equity constraint to limit the absolute difference between the regional prevalence (cases per population in a region) and the country prevalence (cases per population over all regions) by the parameter k, and formulate it as follows: ...
Existing compartmental models in epidemiology are limited in terms of optimizing the resource allocation to control an epidemic outbreak under disease growth uncertainty. In this study, we address this core limitation by presenting a multi-stage stochastic programming compartmental model, which integrates the uncertain disease progression and resource allocation to control an infectious disease outbreak. The proposed multi-stage stochastic program involves various disease growth scenarios and optimizes the distribution of treatment centers and resources while minimizing the total expected number of new infections and funerals. We define two new equity metrics, namely infection and capacity equity, and explicitly consider equity for allocating treatment funds and facilities over multiple time stages. We also study the multi-stage value of the stochastic solution (VSS), which demonstrates the superiority of the proposed stochastic programming model over its deterministic counterpart. We apply the proposed formulation to control the Ebola Virus Disease (EVD) in Guinea, Sierra Leone, and Liberia of West Africa to determine the optimal and fair resource-allocation strategies. Our model balances the proportion of infections over all regions, even without including the infection equity or prevalence equity constraints. Model results also show that allocating treatment resources proportional to population is sub-optimal, and enforcing such a resource allocation policy might adversely impact the total number of infections and deaths, and thus resulting in a high cost that we have to pay for the fairness. Our multi-stage stochastic epidemic-logistics model is practical and can be adapted to control other infectious diseases in meta-populations and dynamically evolving situations.
... In the context of syphilis prevention, resource allocation can be defined as the process of distributing funds or resources among syphilis prevention interventions or activities that are competing for the same budget. 3 Zaric and Brandeau 4 describe a 2-level model of resource allocation for disease prevention: a higher-level allocation of funds (such as the federal-level decision of how to allocate syphilis prevention resources across states) and a lower-level allocation of funds (such as the decisions by STD officials in a given state regarding how to allocate their funding across different interventions and subpopulations). Efficient allocations are needed at both levels to ensure the greatest health benefit in terms of preventing syphilis and related sequelae. ...
... The purpose of this 2-phase approach was to illustrate a scenario in which a district's syphilis burden was inversely correlated with the effectiveness of its syphilis prevention activities. Specifically, in phase 1, each district with a less effective syphilis prevention programs (districts 10-18) would have a higher syphilis rate than its corresponding district with a more effective syphilis prevention program (districts [1][2][3][4][5][6][7][8][9]. For example district 10 would have a higher syphilis rate in phase 1 compared with district 1, its corresponding district. ...
Background:
Improvements in resource allocation can increase the benefits of federally-funded sexually transmitted disease (STD) prevention activities. The purpose of this study was to illustrate how different strategies for allocating federal funds to sub-national districts for syphilis prevention might affect the incidence of syphilis at the national level.
Methods:
We modeled syphilis rates by district and year using an equation based on a previous analysis of state-level syphilis elimination funding and syphilis case rates from 1998 to 2005 in the United States. We used the model to illustrate the potential impact of three different strategies for allocating supplemental federal funds to sub-national districts to support syphilis prevention activities a hypothetical country with 18 sub-national districts. The three strategies were based on each district's (1) population size, (2) syphilis incidence rate, or (3) number of syphilis cases. The hypothetical country was similar to the United States in overall population and syphilis burden.
Results:
Without the supplemental federal funds, there would be an estimated 48,600 incident infections annually in the hypothetical country. With the supplemental federal funds, the annual number of infections would be reduced to 27,800 with a population-based allocation of funding to each district, 26,700 with a rate-based allocation, and 24,400 with a case-based allocation of funding.
Conclusions:
Allocating federal STD prevention funds to districts based on burden of disease can be an efficient strategy, although this efficiency may be reduced or eliminated when high burden districts have less ability to provide adequate STD prevention services than lower burden districts.
... There are often formidable barriers to integrating operations research findings into the decision-making process. The way decisions are made is therefore an important area of inquiry in the operations research literature [2,43]. Lasry et al. [44] addressed this by developing the spreadsheet model System for HIV/AIDS Resource Allocation (S4HARA), which combines principles of efficient resource allocation with non-quantifiable political, social, and ethical factors influencing decision-making process. ...
Realizing the full individual and population-wide benefits of antiretroviral therapy for human immunodeficiency virus (HIV) infection requires an efficient mechanism of HIV-related health service delivery. We developed a system dynamics model of the continuum of HIV care in Vancouver, Canada, which reflects key activities and decisions in the delivery of antiretroviral therapy, including HIV testing, linkage to care, and long-term retention in care and treatment. To measure the influence of operational interventions on population health outcomes, we incorporated an HIV transmission component into the model. We determined optimal resource allocations among targeted and routine testing programs to minimize new HIV infections over five years in Vancouver. Simulation scenarios assumed various constraints informed by the local health policy. The project was conducted in close collaboration with the local health care providers, Vancouver Coastal Health Authority and Providence Health Care.
... With regard to improving health in developing countries, both the types of decisions to be made and the measurement of the objectives can vary. While most objectives relate to improving overall health, metrics can include disability-adjusted life years (DALYs) (Lasry et al. 2008), quality-adjusted life years (QALYs) (Zaric and Brandeau 2001), and the number of infections (Brandeau et al. 2003). Additionally, models specific to developing countries must consider unique characteristics such as road infrastructure (Balcik et al. 2008;Rahman and Smith 2000), spatial accessibility (Carr and Jallah 2008;Wilson and Blower 2005), nomadic populations (Ndiaye and Alfares 2008), and rainy seasons (Oppong 1996). ...
... While many complex models have been developed for addressing public health concerns in developing countries, few are developed specifically for implementation. Lasry et al. (2008) develop a resource allocation model for HIV prevention. The model is implemented in a spreadsheet-based decision support tool, S4HARA. ...
... The model is implemented in a spreadsheet-based decision support tool, S4HARA. The tool deconstructs decision making into four interrelated stages including prioritization of HIV prevention methods and allocation of funds (Lasry et al. 2008). ...
Decisions regarding the best use of scarce health resources become increasingly complex in developing countries due to high disease incidence, poor healthcare system infrastructure, and other societal factors. We develop a resource allocation model for the design of an Indoor Residual Spraying (IRS) program for malaria prevention in developing countries. Due to the seasonal nature of malaria risk factors, the model addresses dynamic resource allocation based on the risk characteristics. Using the model as a framework, a decision support tool for IRS operations is constructed. With a small numerical example we demonstrate the value of the tool for evaluating complexities and tradeoffs in the allocation of limited resources for an IRS program and the impact of heuristic decision making.
... The trade-offs between efficiency and equity are among these criteria, and have long been emphasized in the field of HIV/AIDS treatment and prevention [23,24]. Several mathematical frameworks, including mathematical programming, have been proposed to incorporate equity considerations into resource allocation in the public sector [25][26][27][28][29]. Several of these models and tools have been applied to paradigmatic HIV/AIDS policy examples [30][31][32][33][34]. Our goal in this paper is to present a simple mathematical model which assesses the impact of health interventions according to two comparable dimensions of efficiency and equity. ...
Background
We determine efficient, equitable and mixed efficient-equitable allocations of a male circumcision (MC) intervention reducing female to male HIV transmission in South Africa (SA), as a case study of an efficiency-equity framework for resource allocation in HIV prevention.
Methods
We present a mathematical model developed with epidemiological and cost data from the nine provinces of SA. The hypothetical one-year-long MC intervention with a budget of US$ 10 million targeted adult men 15–49 years of age in SA. The intervention was evaluated according to two criteria: an efficiency criterion, which focused on maximizing the number of HIV infections averted by the intervention, and an equity criterion (defined geographically), which focused on maximizing the chance that each male adult individual had access to the intervention regardless of his province.
Results
A purely efficient intervention would prevent 4,008 HIV infections over a year. In the meantime, a purely equitable intervention would avert 3,198 infections, which represents a 20% reduction in infection outcome as compared to the purely efficient scenario. A half efficient-half equitable scenario would prevent 3,749 infections, that is, a 6% reduction in infection outcome as compared to the purely efficient scenario.
Conclusions
This paper provides a framework for resource allocation in the health sector which incorporates a simple equity metric in addition to efficiency. In the specific context of SA with a MC intervention for the prevention of HIV, incorporation of geographical equity only slightly reduces the overall efficiency of the intervention.
... Therefore, we are unable to suggest a way of weighting the importance of the various techniques described. We have proposed elsewhere a method that incorporates formal quantitative resource allocation modelling as well as the more qualitative influential factors into the decision-making process (Lasry et al. 2008). Table 3 lists the types of players that exert an influence on resource allocation decisions and highlights the number of code occurrences associated with each type of player that appeared in the transcribed interviews. ...
... We found 29 references containing all three terms detailing mathematical models intended to be useful for decision-making. However, only one of the references consider the factors identified in this study and these are clearly important to decision-making (Lasry et al. 2008). The factors and processes uncovered in this research may lead to improved methods of resource allocation. ...
... Resource allocation methods that include a thorough understanding of the current allocation process, the stakeholders involved and their influence on the allocation are likely to yield recommendations that are useful and attuned to the context within which resource allocation decisions are made. The current allocation of resources in KwaDukuza and the results of an improved allocation are discussed elsewhere (Lasry et al. 2008). In general, a cost-effectiveness-based approach applied in KwaDukuza would encourage increasing the allocation to condom distribution and treatment of sexually transmitted infections (Lasry et al. 2008). ...
through a descriptive study, we determined the factors that influence the decision-making process for allocating funds to HIV/AIDS prevention and treatment programmes, and the extent to which formal decision tools are used in the municipality of KwaDukuza, South Africa.
we conducted 35 key informant interviews in KwaDukuza. The interview questions addressed specific resource allocation issues while allowing respondents to speak openly about the complexities of the HIV/AIDS resource allocation process.
donors have a large influence on the decision-making process for HIV/AIDS resource allocation. However, advocacy groups, governmental bodies and local communities also play an important role. Political power, culture and ethics are among a set of intangible factors that have a strong influence on HIV/AIDS resource allocation. Formal methods, including needs assessment, best practice approaches, epidemiologic modelling and cost-effectiveness analysis are sometimes used to support the HIV/AIDS resource allocation process. Historical spending patterns are an important consideration in future HIV/AIDS allocation strategies.
several factors and groups influence resource allocation in KwaDukuza. Although formal economic and epidemiologic information is sometimes used, in most cases other factors are more important for resource allocation decision-making. These other factors should be considered in any attempts to improve the resource allocation processes.
