Decision making for HIV prevention and treatment scale up: bridging the gap between theory and practice

A Game Theoretic Analysis of Competition Between Vaccine and Drug Companies during Disease Contraction and Recovery

Background

Infectious diseases such as COVID-19 and HIV/AIDS are behaviorally challenging for persons, vaccine and drug companies, and donors.

Methods

In 3 linked games in which a disease may or may not be contracted, N persons choose risky or safe behavior (game 1). Two vaccine companies (game 2) and 2 drug companies (game 3) choose whether to develop vaccines and drugs. Each person chooses whether to buy 1 vaccine (if no disease contraction) or 1 drug (if disease contraction). A donor subsidizes vaccine and drug developments and purchases. Nature probabilistically chooses disease contraction, recovery versus death with and without each drug, and whether vaccines and drugs are developed successfully. COVID-19 data are used for parameter estimation.

Results

Each person chooses risky behavior if its utility outweighs safe behavior, accounting for nature’s probability of disease contraction which depends on how many are vaccinated. Each person buys a vaccine or drug if the companies produce them and if their utilities (accounting for side effects and virus mutation) outweigh the costs, which may be subsidized by a sponsor.

Discussion

Drug purchases depend on nature’s recovery probability exceeding the probability in the absence of a drug. Each company develops and produces a vaccine or drug if nature’s probability of successful development is high, if sufficiently many persons buy the vaccine or drug at a sales price that sufficiently exceeds the production price, and if the donor sponsors.

Conclusion

Accounting for all players’ interlinked decisions allowing 14 outcomes, which is challenging without a game theoretic analysis, the donor maximizes all persons’ expected utilities at the societal level to adjust how persons’ purchases and the companies’ development and production are subsidized.

Highlights

Decisions of persons, the pharmaceutical industry, and donors in disease contraction and recovery assuming virus mutation

BACKGROUND

The article develops an eight-period game between N persons and a pharmaceutical company. The choices of a donor and Nature are parametric.

METHODS

Persons choose between safe and risky behavior, and whether or not to buy drugs. The pharmaceutical company chooses whether or not to develop drugs. The donor chooses parametrically whether to subsidize drug purchases and drug developments. Nature chooses disease contraction, recovery, death, and virus mutation. The game is solved with backward induction.

RESULTS

The conditions are specified for each of seven outcomes ranging from safe behavior to risky behavior and buying no or one or both drugs. The seven outcomes distribute themselves across three outcomes for the pharmaceutical company, which are to develop no drugs, develop one drug, and develop two drugs if the virus mutates. For these three outcomes the donor's expected utility is specified.

CONCLUSION

HIV/AIDS data is used to present a procedure for parameter estimation. The players' strategic choices are exemplified. The article shows how strategic interaction between persons and a pharmaceutical company, with parametric choices of a donor and Nature, impact whether persons choose risky or safe behavior, whether a pharmaceutical company develops no drugs or one drug, or two drugs if a virus mutates, and the impact of subsidies by a donor.

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

OBJECTIVE

This study reviews optimization models for human immunodeficiency virus (HIV) and acquired immunodeficiency syndrome (AIDS) resource allocation.

METHODS

We searched 2 databases for peer-reviewed articles published from January 1985 through August 2019 that describe optimization models for resource allocation in HIV/AIDS. We included models that consider 2 or more competing HIV/AIDS interventions. We extracted data on selected characteristics and identified similarities and differences across models. We also assessed the quality of mathematical disease transmission models based on the best practices identified by a 2010 task force.

RESULTS

The final qualitative synthesis included 23 articles that used 14 unique optimization models. The articles shared several characteristics, including the use of dynamic transmission modeling to estimate health benefits and the inclusion of specific high-risk groups in the study population. The models explored similar HIV/AIDS interventions that span primary and secondary prevention and antiretroviral treatment. Most articles were focused on sub-Saharan African countries (57%) and the United States (39%). There was notable variation in the types of optimization objectives across the articles; the most common was minimizing HIV incidence or maximizing infections averted (87%). Articles that utilized mathematical modeling of HIV disease and transmission displayed variable quality.

CONCLUSIONS

This systematic review of the literature identified examples of optimization models that have been applied in different settings, many of which displayed similar features. There were similarities in objective functions across optimization models, but they did not align with global HIV/AIDS goals or targets. Future work should be applied in countries facing the largest declines in HIV/AIDS funding.

Humanitarian medical supply chain in disaster response

Purpose

Following a large-scale disaster, medical assistance is a critical component of the emergency response. The paper aims to discuss this issue.

Design/methodology/approach

Academic and practitioner literature is used to develop a framework studying the effectiveness of the humanitarian medical supply chain (HMSC). The framework is validated by using the findings of interviews conducted with experts and the case study of a serious humanitarian medical crisis (Ebola outbreak in 2014).

Findings

The factors affecting the effectiveness of the HMSC are identified.

Research limitations/implications

To get an expert opinion on the major logistical challenges of the medical assistance in emergencies only 11 interviews with practitioners were conducted.

Originality/value

While the existing academic literature discusses the distribution of various supplies needed by the affected population, limited research focuses specifically on studying the HMSC aspect of the response. This paper closes this gap by describing the HMSC in the case of disaster response, and identifying the factors affecting its effectiveness, especially focusing on the factors that are unique to the medical aspect of the humanitarian supply chain.

Implementing parallel spreadsheet models for health policy decisions: The impact of unintentional errors on model projections

Background

Spreadsheet software is increasingly used to implement systems science models informing health policy decisions, both in academia and in practice where technical capacity may be limited. However, spreadsheet models are prone to unintentional errors that may not always be identified using standard error-checking techniques. Our objective was to illustrate, through a methodologic case study analysis, the impact of unintentional errors on model projections by implementing parallel model versions.

Methods

We leveraged a real-world need to revise an existing spreadsheet model designed to inform HIV policy. We developed three parallel versions of a previously validated spreadsheet-based model; versions differed by the spreadsheet cell-referencing approach (named single cells; column/row references; named matrices). For each version, we implemented three model revisions (re-entry into care; guideline-concordant treatment initiation; immediate treatment initiation). After standard error-checking, we identified unintentional errors by comparing model output across the three versions. Concordant model output across all versions was considered error-free. We calculated the impact of unintentional errors as the percentage difference in model projections between model versions with and without unintentional errors, using +/-5% difference to define a material error.

Results

We identified 58 original and 4,331 propagated unintentional errors across all model versions and revisions. Over 40% (24/58) of original unintentional errors occurred in the column/row reference model version; most (23/24) were due to incorrect cell references. Overall, >20% of model spreadsheet cells had material unintentional errors. When examining error impact along the HIV care continuum, the percentage difference between versions with and without unintentional errors ranged from +3% to +16% (named single cells), +26% to +76% (column/row reference), and 0% (named matrices).

Conclusions

Standard error-checking techniques may not identify all errors in spreadsheet-based models. Comparing parallel model versions can aid in identifying unintentional errors and promoting reliable model projections, particularly when resources are limited.

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

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.

Perceptions about data-informed decisions: an assessment of information-use in high HIV-prevalence settings in South Africa

Background

Information-use is an integral component of a routine health information system and essential to influence policy-making, program actions and research. Despite an increased amount of routine data collected, planning and resource-allocation decisions made by health managers for managing HIV programs are often not based on data. This study investigated the use of information, and barriers to using routine data for monitoring the prevention of mother-to-child transmission of HIV (PMTCT) programs in two high HIV-prevalence districts in South Africa.

Methods

We undertook an observational study using a multi-method approach, including an inventory of facility records and reports. The performance of routine information systems management (PRISM) diagnostic ‘Use of Information’ tool was used to assess the PMTCT information system for evidence of data use in 57 health facilities in two districts. Twenty-two in-depth interviews were conducted with key informants to investigate barriers to information use in decision-making. Participants were purposively selected based on their positions and experience with either producing PMTCT data and/or using data for management purposes. We computed descriptive statistics and used a general inductive approach to analyze the qualitative data.

Results

Despite the availability of mechanisms and processes to facilitate information-use in about two-thirds of the facilities, evidence of information-use (i.e., indication of some form of information-use in available RHIS reports) was demonstrated in 53% of the facilities. Information was inadequately used at district and facility levels to inform decisions and planning, but was selectively used for reporting and monitoring program outputs at the provincial level. The inadequate use of information stemmed from organizational issues such as the lack of a culture of information-use, lack of trust in the data, and the inability of program and facility managers to analyze, interpret and use information.

Conclusions

Managers’ inability to use information implied that decisions for program implementation and improving service delivery were not always based on data. This lack of data use could influence the delivery of health care services negatively. Facility and program managers should be provided with opportunities for capacity development as well as practice-based, in-service training, and be supported to use information for planning, management and decision-making.

Perceptions about data-informed decisions: an assessment of information-use in high HIV-prevalence settings in South Africa

BACKGROUND

Information-use is an integral component of a routine health information system and essential to influence policy-making, program actions and research. Despite an increased amount of routine data collected, planning and resource-allocation decisions made by health managers for managing HIV programs are often not based on data. This study investigated the use of information, and barriers to using routine data for monitoring the prevention of mother-to-child transmission of HIV (PMTCT) programs in two high HIV-prevalence districts in South Africa.

METHODS

We undertook an observational study using a multi-method approach, including an inventory of facility records and reports. The performance of routine information systems management (PRISM) diagnostic 'Use of Information' tool was used to assess the PMTCT information system for evidence of data use in 57 health facilities in two districts. Twenty-two in-depth interviews were conducted with key informants to investigate barriers to information use in decision-making. Participants were purposively selected based on their positions and experience with either producing PMTCT data and/or using data for management purposes. We computed descriptive statistics and used a general inductive approach to analyze the qualitative data.

RESULTS

Despite the availability of mechanisms and processes to facilitate information-use in about two-thirds of the facilities, evidence of information-use (i.e., indication of some form of information-use in available RHIS reports) was demonstrated in 53% of the facilities. Information was inadequately used at district and facility levels to inform decisions and planning, but was selectively used for reporting and monitoring program outputs at the provincial level. The inadequate use of information stemmed from organizational issues such as the lack of a culture of information-use, lack of trust in the data, and the inability of program and facility managers to analyze, interpret and use information.

CONCLUSIONS

Managers' inability to use information implied that decisions for program implementation and improving service delivery were not always based on data. This lack of data use could influence the delivery of health care services negatively. Facility and program managers should be provided with opportunities for capacity development as well as practice-based, in-service training, and be supported to use information for planning, management and decision-making.

Policy makers, the international community and the population in the prevention and treatment of diseases: case study on HIV/AIDS

A four-period game is developed between a policy maker, the international community, and the population. This research supplements, through implementing strategic interaction, earlier research analyzing "one player at a time". The first two players distribute funds between preventing and treating diseases. The population reacts by degree of risky behavior which may cause no disease, disease contraction, recovery, sickness/death. More funds to prevention implies less disease contraction but higher death rate given disease contraction. The cost effectiveness of treatment relative to prevention, country specific conditions, and how the international community converts funds compared with the policy maker in a country, are illustrated. We determine which factors impact funding, e.g. large probabilities of disease contraction, and death given contraction, and if the recovery utility and utility of remaining sick or dying are far below the no disease utility. We also delineate how the policy maker and international community may free ride on each other's contributions. The model is tested against empirical data for 43 African countries. The results show consistency between the theoretical model and empirical estimates. The paper argues for the need to create commitment mechanisms to ensure that free riding by both countries and the international community is avoided.

Getting it right when budgets are tight: Using optimal expansion pathways to prioritize responses to concentrated and mixed HIV epidemics

Background

Prioritizing investments across health interventions is complicated by the nonlinear relationship between intervention coverage and epidemiological outcomes. It can be difficult for countries to know which interventions to prioritize for greatest epidemiological impact, particularly when budgets are uncertain.

Methods

We examined four case studies of HIV epidemics in diverse settings, each with different characteristics. These case studies were based on public data available for Belarus, Peru, Togo, and Myanmar. The Optima HIV model and software package was used to estimate the optimal distribution of resources across interventions associated with a range of budget envelopes. We constructed “investment staircases”, a useful tool for understanding investment priorities. These were used to estimate the best attainable cost-effectiveness of the response at each investment level.

Findings

We find that when budgets are very limited, the optimal HIV response consists of a smaller number of ‘core’ interventions. As budgets increase, those core interventions should first be scaled up, and then new interventions introduced. We estimate that the cost-effectiveness of HIV programming decreases as investment levels increase, but that the overall cost-effectiveness remains below GDP per capita.

Significance

It is important for HIV programming to respond effectively to the overall level of funding availability. The analytic tools presented here can help to guide program planners understand the most cost-effective HIV responses and plan for an uncertain future.

Geographic area-based rate as a novel indicator to enhance research and precision intervention for more effective HIV/AIDS control

Ending the HIV epidemic needs additional methods to better assess the incidence and prevalence of HIV infection. In this study, a new indicator - G-rate was developed for the evaluation of HIV epidemics across regions with regard to geographic area size. Different from the commonly used incidence and prevalence rates that assess the HIV epidemic with reference to population (termed as P rate in this study), G rate measures the number of new infections (incidence) or cases (prevalence) over a unit land area in one year. We demonstrated the utility of G rates using officially reported data on new HIV infections and persons living with HIV in the United States during 2000-2012. Findings of our analysis indicate that relative to P rates, G rates indicated a quicker increase in the HIV epidemic in the United States during the study period. In 2012, 4.6 persons were newly infected and 101.4 persons lived with HIV per 1000 km2 land area. The five states with both highest P prevalence rates and highest G prevalence rates were Florida, Maryland, New York, New Jersey and Washington DC, which included New Jersey ranked 8th by P rate and excluded Massachusetts ranked 5th by G rate. In conclusion, adding G rates extends the conventional measurement system that consists of case count and P rate. Combining G rates with P rates provides a new approach for information extraction to support precision intervention strategy toward the goal of creating an AIDS-Free Generation.

HIV Treatment and Prevention: A Simple Model to Determine Optimal Investment

OBJECTIVE

To create a simple model to help public health decision makers determine how to best invest limited resources in HIV treatment scale-up and prevention.

METHOD

A linear model was developed for determining the optimal mix of investment in HIV treatment and prevention, given a fixed budget. The model incorporates estimates of secondary health benefits accruing from HIV treatment and prevention and allows for diseconomies of scale in program costs and subadditive benefits from concurrent program implementation. Data sources were published literature. The target population was individuals infected with HIV or at risk of acquiring it. Illustrative examples of interventions include preexposure prophylaxis (PrEP), community-based education (CBE), and antiretroviral therapy (ART) for men who have sex with men (MSM) in the US. Outcome measures were incremental cost, quality-adjusted life-years gained, and HIV infections averted.

RESULTS

Base case analysis indicated that it is optimal to invest in ART before PrEP and to invest in CBE before scaling up ART. Diseconomies of scale reduced the optimal investment level. Subadditivity of benefits did not affect the optimal allocation for relatively low implementation levels. The sensitivity analysis indicated that investment in ART before PrEP was optimal in all scenarios tested. Investment in ART before CBE became optimal when CBE reduced risky behavior by 4% or less. Limitations of the study are that dynamic effects are approximated with a static model.

CONCLUSIONS

Our model provides a simple yet accurate means of determining optimal investment in HIV prevention and treatment. For MSM in the US, HIV control funds should be prioritized on inexpensive, effective programs like CBE, then on ART scale-up, with only minimal investment in PrEP.

Optimizing an HIV testing program using a system dynamics model of the continuum of care

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.

Performance of a mathematical model to forecast lives saved from HIV treatment expansion in resource-limited settings

BACKGROUND

International guidelines recommend HIV treatment expansion in resource-limited settings, but funding availability is uncertain. We evaluated the performance of a model that forecasts lives saved through continued HIV treatment expansion in Haiti.

METHODS

We developed a computer-based, mathematical model of HIV disease and used incidence density analysis of patient-level Haitian data to derive model parameters for HIV disease progression. We assessed the internal validity of model predictions and internally calibrated model inputs when model predictions did not fit the patient-level data. We then derived uncertain model inputs related to diagnosis and linkage to care, pretreatment retention, and enrollment on HIV treatment through an external calibration process that selected input values by comparing model predictions to Haitian population-level data. Model performance was measured by fit to event-free survival (patient level) and number receiving HIV treatment over time (population level).

RESULTS

For a cohort of newly HIV-infected individuals with no access to HIV treatment, the model predicts median AIDS-free survival of 9.0 years precalibration and 6.6 years postcalibration v. 5.8 years (95% confidence interval, 5.1-7.0) from the patient-level data. After internal validation and calibration, 16 of 17 event-free survival measures (94%) had a mean percentage deviation between model predictions and the empiric data of <6%. After external calibration, the percentage deviation between model predictions and population-level data on the number on HIV treatment was <1% over time.

CONCLUSIONS

Validation and calibration resulted in a good-fitting model appropriate for health policy decision making. Using local data in a policy model-building process is feasible in resource-limited settings.

HIV epidemic control-a model for optimal allocation of prevention and treatment resources

With 33 million people living with human immunodeficiency virus (HIV) worldwide and 2.7 million new infections occurring annually, additional HIV prevention and treatment efforts are urgently needed. However, available resources for HIV control are limited and must be used efficiently to minimize the future spread of the epidemic. We develop a model to determine the appropriate resource allocation between expanded HIV prevention and treatment services. We create an epidemic model that incorporates multiple key populations with different transmission modes, as well as production functions that relate investment in prevention and treatment programs to changes in transmission and treatment rates. The goal is to allocate resources to minimize R 0, the reproductive rate of infection. We first develop a single-population model and determine the optimal resource allocation between HIV prevention and treatment. We extend the analysis to multiple independent populations, with resource allocation among interventions and populations. We then include the effects of HIV transmission between key populations. We apply our model to examine HIV epidemic control in two different settings, Uganda and Russia. As part of these applications, we develop a novel approach for estimating empirical HIV program production functions. Our study provides insights into the important question of resource allocation for a country's optimal response to its HIV epidemic and provides a practical approach for decision makers. Better decisions about allocating limited HIV resources can improve response to the epidemic and increase access to HIV prevention and treatment services for millions of people worldwide.

Coverage, context and targeted prevention: optimising our impact

Development of efficacious interventions is only the first step in achieving population level impact. Efficacious interventions impact infection levels in the population only if they are implemented at the right scale. Coverage must be prioritised across subpopulations based on the diversity and clustering of infections and risk in society, and expanded rapidly without delay. It is important to prioritise those who are most likely to transmit infection first.