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Background Cycles of incarceration, drug abuse, and poverty undermine ongoing public health efforts to reduce overdose deaths and the spread of infectious disease in vulnerable populations. Jail diversion programs aim to divert low-level drug offenders toward community care resources, avoiding criminal justice costs and disruptions in treatment for HIV, hepatitis C virus (HCV), and drug abuse. We sought to assess the health benefits and cost-effectiveness of a jail diversion program for low-level drug offenders. Methods and findings We developed a microsimulation model, calibrated to King County, Washington, that captured the spread of HIV and HCV infections and incarceration and treatment systems as well as preexisting interventions such as needle and syringe programs and opiate agonist therapy. We considered an adult population of people who inject drugs (PWID), people who use drugs but do not inject (PWUD), men who have sex with men, and lower-risk heterosexuals. We projected discounted lifetime costs and quality-adjusted life years (QALYs) over a 10-year time horizon with and without a jail diversion program and calculated resulting incremental cost-effectiveness ratios (ICERs) from the health system and societal perspectives. We also tracked HIV and HCV infections, overdose deaths, and jail population size. Over 10 years, the program was estimated to reduce HIV and HCV incidence by 3.4% (95% CI 2.7%–4.0%) and 3.3% (95% CI 3.1%–3.4%), respectively, overdose deaths among PWID by 10.0% (95% CI 9.8%–10.8%), and jail population size by 6.3% (95% CI 5.9%–6.7%). When considering healthcare costs only, the program cost $25,500/QALY gained (95% CI $12,600–$48,600). Including savings from reduced incarceration (societal perspective) improved the ICER to $6,200/QALY gained (95% CI, cost-saving $24,300). Sensitivity analysis indicated that cost-effectiveness depends on diversion program participants accessing community programs such as needle and syringe programs, treatment for substance use disorder, and HIV and HCV treatment, as well as diversion program cost. A limitation of the analysis is data availability, as fewer data are available for diversion programs than for more established interventions aimed at people with substance use disorder. Additionally, like any model of a complex system, our model relies on simplifying assumptions: For example, we simplified pathways in the healthcare and criminal justice systems, modeled an average efficacy for substance use disorder treatment, and did not include costs associated with homelessness, unemployment, and breakdown in family structure. Conclusions We found that diversion programs for low-level drug offenders are likely to be cost-effective, generating savings in the criminal justice system while only moderately increasing healthcare costs. Such programs can reduce incarceration and its associated costs, and also avert overdose deaths and improve quality of life for PWID, PWUD, and the broader population (through reduced HIV and HCV transmission)., Cora Bernard and co-workers investigate the health and societal value of diversion programs for people who misuse drugs., Author summary Why was this study done? Strategies for people with substance use disorder based on criminalization and aggressive law enforcement have created barriers to harm reduction while having little or no impact on the number of people who use drugs. Diversion programs redirect individuals with low-level drug and prostitution offenses away from the criminal justice system and into community-based services such as treatment for substance use disorder, housing, and mental health care. Our model-based analysis was designed to assess the health benefits and cost-effectiveness of a jail diversion program for low-level drug offenders. What did the researchers do and find? We developed a stochastic microsimulation model of adults in King County, Washington that captures sexual and injecting-based risk behaviors and partnerships and tracks the 10-year trajectories of people who inject drugs, people who use drugs but do not inject, and the lower-risk general population, with and without a diversion program. We projected HIV and hepatitis C virus (HCV) incidence, fatal overdoses, jail population size, and lifetime costs and quality-adjusted life years (QALYs) experienced in the population, with and without a diversion program. We estimated that the diversion program would reduce cumulative HIV and HCV incidence, fatal overdoses, and the jailed population, would lead to gains in QALYs, and would be highly cost-effective. What do these findings mean? A 2016 United Nations AIDS Programme (UNAIDS) report found that the status quo of criminalizing drug abuse is failing from the public safety and public health perspectives. Our study demonstrates that these perspectives can be aligned. Our analysis suggests that diversion programs are likely to substantially reduce spending in the criminal justice system while moderately increasing spending in the healthcare sector and are likely to be highly cost-effective. The spread of infectious disease stemming from drug injection and the global overcrowding of jails and prisons suggest that, more than ever, effective, cost-effective, and innovative approaches are required to address these crises. Diversion programs are one such program.

BackgroundEvaluations of public health interventions typically report benefits and harms aggregated over the population. However, benefits and harms are not always evenly distributed. Examining disaggregated outcomes enables decision makers to consider health benefits and harms accruing to both intended intervention recipients and others in the population.MethodsWe provide a graphical framework for categorizing and comparing public health interventions that examines the distribution of benefit and harm between and within population subgroups for a single intervention and compares distributions of harm and benefit for multiple interventions. We demonstrate the framework through a case study of a hypothetical increase in the price of meat (5%, 10%, 25%, or 50%) that, via elasticity of demand, reduces consumption and consequently reduces body mass index. We examine how inequalities in benefits and harms (measured by quality-adjusted life-years) are distributed across a population of white and black males and females.ResultsA 50% meat price increase would yield the greatest net benefit to the population. However, because of reduced consumption among low-weight individuals, black males would bear disproportionate harm relative to the benefit they receive. With increasing meat price, the distribution of harm relative to benefit becomes less “internal” to those receiving benefit and more “distributed” to those not receiving commensurate benefit. When we segment the population by sex only, this result does not hold.ConclusionsDisaggregating harms and benefits to understand their differential impact on subgroups can strongly affect which decision alternative is deemed optimal, as can the approach to segmenting the population. Our framework provides a useful tool for illuminating key tradeoffs relevant to harm-averse decision makers and those concerned with both equity and efficiency.

Purpose To estimate the basic reproduction ratio () of SARS-CoV-2 inside a correctional facility early in the COVID-19 pandemic. Methods We developed a dynamic transmission model for a large, urban jail in the United States. We used the next generation method to determine the basic reproduction ratio We included anonymized data of incarcerated individuals and correctional staff with confirmed COVID-19 infections in our estimation of the basic reproduction ratio () of SARS-CoV-2. Results The estimated is 8.44 (95% Credible Interval (CrI): 5.00-13.13) for the entire jail. Conclusions The high of SARS-CoV-2 in a large urban jail highlights the importance of including correctional facilities in public health strategies for COVID-19. In the absence of more aggressive mitigation strategies, correctional facilities will continue to contribute to community infections.

A safe supply of blood for transfusion is a critical component of the healthcare system in all countries. Most health systems manage the risk of transfusion-transmissible infections (TTIs) through a portfolio of blood safety interventions. These portfolios must be updated periodically to reflect shifting epidemiological conditions, emerging infectious diseases, and new technologies. However, the number of available blood safety portfolios grows exponentially with the number of available interventions, making it impossible for policymakers to evaluate all feasible portfolios without the assistance of a computer model. We develop a novel optimization model for evaluating blood safety portfolios that enables systematic comparison of all feasible portfolios of deferral, testing, and modification interventions to identify the portfolio that is preferred from a cost-utility perspective. We present structural properties that reduce the state space and required computation time in certain cases, and we develop a linear approximation of the model. We apply the model to retrospectively evaluate U.S. blood safety policies for Zika and West Nile virus for the years 2017, 2018, and 2019, defining donor groups based on season and geography. We leverage structural properties to efficiently find an optimal solution. We find that the optimal portfolio varies geographically, seasonally, and over time. Additionally, we show that for this problem the approximated model yields the same optimal solution as the exact model. Our method enables systematic identification of the optimal blood safety portfolio in any setting and any time period, thereby supporting decision makers in efforts to ensure the safety of the blood supply.

ObjectivesTo estimate the impact of various mitigation strategies on COVID-19 transmission in a U.S. jail beyond those offered in national guidelines.MethodsWe developed a stochastic dynamic transmission model of COVID-19 in one large urban U.S. jail among staff and incarcerated individuals. We divided the outbreak into four intervention phases: the start of the outbreak, depopulation of the jail, increased proportion of people in single cells, and asymptomatic testing. We used the next generation method to estimate the basic reproduction ratio, R0, in each phase. We estimated the fraction of new cases, hospitalizations, and deaths averted by these interventions along with the standard measures of sanitization, masking, and social distancing interventions.ResultsFor the first outbreak phase, the estimated R0 was 8.23 (95% CrI: 5.01-12.90), and for the subsequent phases, R0,phase 2 = 3.58 (95% CrI: 2.46-5.08), R0,phase 3 = 1.72 (95% CrI: 1.41-2.12), and R0,phase 4 = 0.45 (95% CrI: 0.32-0.59). In total, the jail’s interventions prevented approximately 83% of projected cases and hospitalizations and 89% of deaths over 83 days.ConclusionsDepopulation, single celling, and asymptomatic testing within jails can be effective strategies to mitigate COVID-19 transmission in addition to standard public health measures.Policy ImplicationsDecision-makers should prioritize reductions in the jail population, single celling, and testing asymptomatic populations, as additional measures to manage COVID-19 within correctional settings.

UNAIDS' 90-90-90 goal for 2020 is for 90% of HIV-infected people to know their status, 90% of infected individuals to receive antiretroviral therapy (ART), and 90% of those on ART to achieve viral suppression. To achieve these ambitious goals, effective care delivery programs are needed. In this paper we present a case study showing how HIV care can be improved by viewing the patient care process as a production process and applying methods of process improvement analysis. We examine the continuum of HIV care at a hospital-based HIV clinic in Kingston, Jamaica. We perform qualitative analysis to identify key programmatic, personnel, and clinical areas for process improvement. We then perform quantitative analysis. We develop a stochastic model of the care process which we use to evaluate the effects of potential process improvements on the number of patients who receive ART and the number who achieve viral suppression. We also develop a model for optimal investment of a fixed budget among interventions aimed at improving the care cascade and we use the model to determine the optimal investment among three interventions that the clinic could invest in. By viewing the patient care process as a production process and applying qualitative and quantitative process improvement analysis, our case study illustrates how clinics can identify the best ways to maximize clinical outcomes. Our methods are generalizable to other HIV care clinics as well as to clinics that provide care for other chronic conditions (e.g., diabetes, hepatitis B, or opioid use disorder).

Background. Network meta-analyses (NMAs) that compare treatments for a given condition allow physicians to identify which treatments have higher or lower probabilities of reducing the risks of disease complications or increasing the risks of treatment side effects. Translating these data into personalized treatment plans requires integration of NMA data with patient-specific pretreatment risk estimates and preferences regarding treatment objectives and acceptable risks. Methods. We introduce a modeling framework to integrate data probabilistically from NMAs with data on individualized patient risk estimates for disease outcomes, treatment preferences (such as willingness to incur greater side effects for increased life expectancy), and risk preferences. We illustrate the modeling framework by creating personalized plans for antipsychotic drug treatment and evaluating their effectiveness and cost-effectiveness. Results. Compared with treating all patients with the drug that yields the greatest quality-adjusted life-years (QALYs) on average (amisulpride), personalizing the selection of antipsychotic drugs for schizophrenia patients over the next 5 years would be expected to yield 0.33 QALYs (95% credible interval [crI]: 0.30–0.37) per patient at an incremental cost of $4849/QALY gained (95% crI: dominant–$12,357), versus 0.29 and 0.04 QALYs per patient when accounting for only risks or preferences, respectively, but not both. Limitations. The analysis uses a linear, additive utility function to reflect patient treatment preferences and does not consider potential variations in patient time discounting. Conclusions. Our modeling framework rigorously computes what physicians normally have to do mentally. By integrating 3 key components of personalized medicine—evidence on efficacy, patient risks, and patient preferences—the modeling framework can provide personalized treatment decisions to improve patient health outcomes.

Eradicating hunger and malnutrition is a key development goal of the twenty first century. This paper addresses the problem of optimally identifying seed varieties to reliably increase crop yield within a risk-sensitive decision making framework. Specifically, a novel hierarchical machine learning mechanism for predicting crop yield (the yield of different seed varieties of the same crop) is introduced. This prediction mechanism is then integrated with a weather forecasting model and three different approaches for decision making under uncertainty to select seed varieties for planting so as to balance yield maximization and risk. The model was applied to the problem of soybean variety selection given in the 2016 Syngenta Crop Challenge. The prediction model achieved a median absolute error of 235 kg/ha and thus provides good estimates for input into the decision models. The decision models identified the selection of soybean varieties that appropriately balance yield and risk as a function of the farmer’s risk aversion level. More generally, the models can support farmers in decision making about which seed varieties to plant.

BACKGROUND Oral HIV preexposure prophylaxis (PrEP) has been recommended as a means of HIV prevention among people who inject drugs (PWIDs) but, at current prices, is unlikely to be cost-effective for all PWID. OBJECTIVE To determine the cost-effectiveness of alternative strategies for enrolling PWID in PrEP. DESIGN Dynamic network model that captures HIV transmission and progression among PWID in a representative US urban center. OUTCOME MEASURES HIV infections averted, discounted costs and quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios. INTERVENTION We assume 25% PrEP coverage and investigate four strategies: first, random PWID are enrolled (Unselected Enrollment); second, individuals are randomly selected and enrolled together with their partners (Enroll Partners); third, individuals with the highest number of sexual and needle-sharing partnerships are enrolled (Most Partners); fourth, individuals with the greatest number of infected partners are enrolled (Most Positive Partners). RESULTS PrEP can achieve significant health benefits: compared with the status quo of no PrEP, the strategies gain 1114 QALYs (Unselected Enrollment), 2194 QALYs (Enroll Partners), 2481 QALYs (Most Partners), and 3046 QALYs (Most Positive Partners) over 20 years in a population of approximately 8500 people. The incremental cost-effectiveness ratio of each strategy compared with the status quo (cost per QALY gained) is $272 000 (Unselected Enrollment), $158 000 (Enroll Partners), $124 000 (Most Partners), and $101 000 (Most Positive Partners). All strategies except Unselected Enrollment are cost-effective according to WHO criteria. CONCLUSION Selection of high-risk PWID for PrEP can improve the cost-effectiveness of PrEP for PWID.

Importance Opioid use disorder (OUD) is a significant cause of morbidity and mortality in the US, yet many individuals with OUD do not receive treatment. Objective To assess the cost-effectiveness of OUD treatments and association of these treatments with outcomes in the US. Design and Setting This model-based cost-effectiveness analysis included a US population with OUD. Interventions Medication-assisted treatment (MAT) with buprenorphine, methadone, or injectable extended-release naltrexone; psychotherapy (beyond standard counseling); overdose education and naloxone distribution (OEND); and contingency management (CM). Main Outcomes and Measures Fatal and nonfatal overdoses and deaths throughout 5 years, discounted lifetime quality-adjusted life-years (QALYs), and costs. Results In the base case, in the absence of treatment, 42 717 overdoses (4132 fatal, 38 585 nonfatal) and 12 660 deaths were estimated to occur in a cohort of 100 000 patients over 5 years, and 11.58 discounted lifetime QALYs were estimated to be experienced per person. An estimated reduction in overdoses was associated with MAT with methadone (10.7%), MAT with buprenorphine or naltrexone (22.0%), and when combined with CM and psychotherapy (range, 21.0%-31.4%). Estimated deceased deaths were associated with MAT with methadone (6%), MAT with buprenorphine or naltrexone (13.9%), and when combined with CM, OEND, and psychotherapy (16.9%). MAT yielded discounted gains of 1.02 to 1.07 QALYs per person. Including only health care sector costs, methadone cost $16 000/QALY gained compared with no treatment, followed by methadone with OEND ($22 000/QALY gained), then by buprenorphine with OEND and CM ($42 000/QALY gained), and then by buprenorphine with OEND, CM, and psychotherapy ($250 000/QALY gained). MAT with naltrexone was dominated by other treatment alternatives. When criminal justice costs were included, all forms of MAT (with buprenorphine, methadone, and naltrexone) were associated with cost savings compared with no treatment, yielding savings of $25 000 to $105 000 in lifetime costs per person. The largest cost savings were associated with methadone plus CM. Results were qualitatively unchanged over a wide range of sensitivity analyses. An analysis using demographic and cost data for Veterans Health Administration patients yielded similar findings. Conclusions and Relevance In this cost-effectiveness analysis, expanded access to MAT, combined with OEND and CM, was associated with cost-saving reductions in morbidity and mortality from OUD. Lack of widespread MAT availability limits access to a cost-saving medical intervention that reduces morbidity and mortality from OUD. Opioid overdoses in the US likely reached a record high in 2020 because of COVID-19 increasing substance use, exacerbating stress and social isolation, and interfering with opioid treatment. It is essential to understand the cost-effectiveness of alternative forms of MAT to treat OUD.

When allocating limited vaccines to control an infectious disease, policy makers frequently have goals relating to individual health benefits (e.g., reduced morbidity and mortality) as well as population-level health benefits (e.g., reduced transmission and possible disease eradication). We consider the optimal allocation of a limited supply of a preventive vaccine to control an infectious disease, and four different allocation objectives: minimize new infections, deaths, life years lost, or quality-adjusted life years (QALYs) lost due to death. We consider an SIR model with n interacting populations, and a single allocation of vaccine at time 0. We approximate the model dynamics to develop simple analytical conditions characterizing the optimal vaccine allocation for each objective. We instantiate the model for an epidemic similar to COVID-19 and consider n=2 population groups: one group (individuals under age 65) with high transmission but low mortality and the other group (individuals age 65 or older) with low transmission but high mortality. We find that it is optimal to vaccinate younger individuals to minimize new infections, whereas it is optimal to vaccinate older individuals to minimize deaths, life years lost, or QALYs lost due to death. Numerical simulations show that the allocations resulting from our conditions match those found using much more computationally expensive algorithms such as exhaustive search. Sensitivity analysis on key parameters indicates that the optimal allocation is robust to changes in parameter values. The simple conditions we develop provide a useful means of informing vaccine allocation decisions for communicable diseases.

We examine the problem of allocating a limited supply of vaccine for controlling an infectious disease with the goal of minimizing the effective reproduction number Re. We consider an SIR model with two interacting populations and develop an analytical expression that the optimal vaccine allocation must satisfy. With limited vaccine supplies, we find that an all-or-nothing approach is optimal. For certain special cases, we determine the conditions under which the optimal Re is below 1. We present an example of vaccine allocation for COVID-19 and show that it is optimal to vaccinate younger individuals before older individuals to minimize Re if less than 59% of the population can be vaccinated. The analytical conditions we develop provide a simple means of determining the optimal allocation of vaccine between two population groups to minimize Re.

Economic evaluations of infectious disease control interventions frequently use dynamic compartmental epidemic models. Such models capture heterogeneity in risk of infection by stratifying the population into discrete risk groups, thus approximating what is typically continuous variation in risk. An important open question is whether and how different risk stratification choices influence model predictions of intervention effects. We develop equivalent Susceptible-Infected-Susceptible (SIS) dynamic transmission models: an unstratified model, a model stratified into a high-risk and low-risk group, and a model with an arbitrary number of risk groups. Absent intervention, the models produce the same overall prevalence of infected individuals in steady state. We consider an intervention that either reduces the contact rate or increases the disease clearance rate. We develop analytical and numerical results characterizing the models and the effects of the intervention. We find that there exist multiple feasible choices of risk stratification, contact distribution, and within- and between-group contact rates for models that stratify risk. We show analytically and empirically that these choices can generate different estimates of intervention effectiveness, and that these differences can be significant enough to alter conclusions from cost-effectiveness analyses and change policy recommendations. We conclude that the choice of how to discretize risk in compartmental epidemic models can influence predicted effectiveness of interventions. Therefore, analysts should examine multiple alternatives and report the range of results.

BACKGROUND: To achieve the goal of HIV viral suppression, provision of medication alone is not sufficient. Concomitant frameworks to evaluate HIV care delivery programs are needed. METHODS: This study examined the care continuum at a hospital-based HIV clinic in Kingston, Jamaica using a modified HIV continuum of care, with an increased focus on viral load indicators (viral load samples taken, results returned, and viral suppression). A statistical analysis of patient flow through the care continuum to identify gaps in program delivery was performed. Key programmatic areas for process improvement and the utility of this approach for viral load suppression interpretation were identified. RESULTS: Between 2010 and 2015, more than 1600 patients had been registered for care and more than 1000 had accessed antiretroviral therapy at this location. Consistent trends in programme performance were seen from 2010–2012. Although declines in the proportion of viral load samples taken and results returned occurred because of laboratory failures in 2013, the trend of increasing numbers and proportions of virally suppressed patients continued. Statistical analysis indicated that improvements in laboratory quality (fraction of viral load samples returned with accurate test results) could increase viral load suppression among patients at the clinic by up to 17%. CONCLUSIONS: Refining care delivery processes can significantly improve HIV viral load suppression rates. Expanding monitoring frameworks to include all of the essential processes that affect final outcome indicators can provide valuable insight into trends of outcome indicators and programme performance.

Currently available medication for treating many chronic diseases is often effective only for a subgroup of patients, and biomarkers accurately assessing whether an individual belongs to this subgroup typically do not exist. In such settings, physicians learn about the effectiveness of a drug primarily through experimentation—i.e., by initiating treatment and monitoring the patient’s response. Precise guidelines for discontinuing treatment are often lacking or left entirely to the physician’s discretion. We introduce a framework for developing adaptive, personalized treatments for such chronic diseases. Our model is based on a continuous-time, multi-armed bandit setting where drug effectiveness is assessed by aggregating information from several channels: by continuously monitoring the state of the patient, but also by (not) observing the occurrence of particular infrequent health events, such as relapses or disease flare-ups. Recognizing that the timing and severity of such events provide critical information for treatment decisions is a key point of departure in our framework compared with typical (bandit) models used in healthcare. We show that the model can be analyzed in closed form for several settings of interest, resulting in optimal policies that are intuitive and may have practical appeal. We illustrate the effectiveness of the methodology by developing a set of efficient treatment policies for multiple sclerosis, which we then use to benchmark several existing treatment guidelines. The online appendix is available at https://doi.org/10.1287/mnsc.2017.2793 . This paper was accepted by Noah Gans, stochastic models and simulation.

ObjectivesWe aim to estimate the impact of various mitigation strategies on COVID-19 transmission in a US jail beyond those offered in national guidelines.DesignWe developed a stochastic dynamic transmission model of COVID-19.SettingOne anonymous large urban US jail.ParticipantsSeveral thousand staff and incarcerated individuals.InterventionsThere were four intervention phases during the outbreak: the start of the outbreak, depopulation of the jail, increased proportion of people in single cells and asymptomatic testing. These interventions were implemented incrementally and in concert with one another.Primary and secondary outcome measuresThe basic reproduction ratio, R0, in each phase, as estimated using the next generation method. The fraction of new cases, hospitalisations and deaths averted by these interventions (along with the standard measures of sanitisation, masking and social distancing interventions).ResultsFor the first outbreak phase, the estimated R0 was 8.44 (95% credible interval (CrI): 5.00 to 13.10), and for the subsequent phases, R0,phase 2=3.64 (95% CrI: 2.43 to 5.11), R0,phase 3=1.72 (95% CrI: 1.40 to 2.12) and R0,phase 4=0.58 (95% CrI: 0.43 to 0.75). In total, the jail’s interventions prevented approximately 83% of projected cases, hospitalisations and deaths over 83 days.ConclusionsDepopulation, single celling and asymptomatic testing within jails can be effective strategies to mitigate COVID-19 transmission in addition to standard public health measures. Decision makers should prioritise reductions in the jail population, single celling and testing asymptomatic populations as additional measures to manage COVID-19 within correctional settings.

Injection drug users (IDUs) are at high risk of acquiring and spreading various blood-borne infections including human immunodeficiency virus (HIV), hepatitis C virus (HCV), hepatitis B virus (HBV) and a number of sexually transmitted infections. These infections can spread among IDUs via risky sexual and needle-sharing contacts. To accurately model the spread of such contagions among IDUs, we build a bi-layer network that captures both types of risky contacts. We present methodology for inferring important model parameters, such as those governing network structure and dynamics, from readily available data sources (e.g., epidemiological surveys). Such a model can be used to evaluate the efficacy of various programs that aim to combat drug addiction and contain blood-borne diseases among IDUs. The model is especially useful for evaluating interventions that exploit the structure of the contact network. To illustrate, we instantiate a network model with data collected by a needle and syringe program in Chicago. We model sexual and needle-sharing contacts and the consequent spread of HIV and HCV. We use the model to evaluate the potential effects of a peer education (PE) program under different targeting strategies. We show that a targeted PE program would avert significantly more HIV and HCV infections than an untargeted program, highlighting the importance of reaching individuals who are centrally located in contact networks when instituting prevention programs.

The operating room is a major cost and revenue center for most hospitals. Thus, more effective operating room management and scheduling can provide significant benefits. In many hospitals, the post-anesthesia care unit (PACU), where patients recover after their surgical procedures, is a bottleneck. If the PACU reaches capacity, patients must wait in the operating room until the PACU has available space, leading to delays and possible cancellations for subsequent operating room procedures. We develop a generalizable optimization and machine learning approach to sequence operating room procedures to minimize delays caused by PACU unavailability. Specifically, we use machine learning to estimate the required PACU time for each type of surgical procedure, we develop and solve two integer programming models to schedule procedures in the operating rooms to minimize maximum PACU occupancy, and we use discrete event simulation to compare our optimized schedule to the existing schedule. Using data from Lucile Packard Children's Hospital Stanford, we show that the scheduling system can significantly reduce operating room delays caused by PACU congestion while still keeping operating room utilization high: simulation of the second half of 2016 shows that our model could have reduced total PACU holds by 76% without decreasing operating room utilization. We are currently working on implementing the scheduling system at the hospital.

Structural assumptions in infectious disease models, such as the choice of network or compartmental model type or the inclusion of different types of heterogeneity across individuals, might affect model predictions as much as or more than the choice of input parameters. We explore the potential implications of structural assumptions on HIV model predictions and policy conclusions. We illustrate the value of inference robustness assessment through a case study of the effects of a hypothetical HIV vaccine in multiple population subgroups over eight related transmission models, which we sequentially modify to vary over two dimensions: parameter complexity (e.g., the inclusion of age and HCV comorbidity) and contact/simulation complexity (e.g., aggregated compartmental vs. individual/disaggregated compartmental vs. network models). We find that estimates of HIV incidence reductions from network models and individual compartmental models vary, but those differences are overwhelmed by the differences in HIV incidence between such models and the aggregated compartmental models (which aggregate groups of individuals into compartments). Complexities such as age structure appear to buffer the effects of aggregation and increase the threshold of net vaccine effectiveness at which aggregated models begin to overestimate reductions. The differences introduced by parameter complexity in estimated incidence reduction also translate into substantial differences in cost-effectiveness estimates. Parameter complexity does not appear to play a consistent role in differentiating the projections of network models.

Background Malaria is a leading cause of morbidity and mortality among HIV-infected pregnant women in sub-Saharan Africa: at least 1 million pregnancies among HIV-infected women are complicated by co-infection with malaria annually, leading to increased risk of premature delivery, severe anaemia, delivery of low birth weight infants, and maternal death. Current guidelines recommend either daily cotrimoxazole (CTX) or intermittent preventive treatment with sulfadoxine–pyrimethamine (IPTp-SP) for HIV-infected pregnant women to prevent malaria and its complications. The cost-effectiveness of CTX compared to IPTp-SP among HIV-infected pregnant women was assessed. Methods A microsimulation model of malaria and HIV among pregnant women in five malaria-endemic countries in sub-Saharan Africa was constructed. Four strategies were compared: (1) 2-dose IPTp-SP at current IPTp-SP coverage of the country (“2-IPT Low”); (2) 3-dose IPTp-SP at current coverage (“3-IPT Low”); (3) 3-dose IPTp-SP at the same coverage as antiretroviral therapy (ART) in the country (“3-IPT High”); and (4) daily CTX at ART coverage. Outcomes measured include maternal malaria, anaemia, low birth weight (LBW), and disability-adjusted life years (DALYs). Sensitivity analyses assessed the effect of adherence to CTX. Results Compared with the 2-IPT Low Strategy, women receiving CTX had 22.5% fewer LBW infants (95% CI 22.3–22.7), 13.5% fewer anaemia cases (95% CI 13.4–13.5), and 13.6% fewer maternal malaria cases (95% CI 13.6–13.7). In all simulated countries, CTX was the preferred strategy, with incremental cost-effectiveness ratios ranging from cost-saving to $3.9 per DALY averted from a societal perspective. CTX was less effective than the 3-IPT High Strategy when more than 18% of women stopped taking CTX during the pregnancy. Conclusion In malarious regions of sub-Saharan Africa, daily CTX for HIV-infected pregnant women regardless of CD4 cell count is cost-effective compared with 3-dose IPTp-SP as long as more than 82% of women adhere to daily dosing. Electronic supplementary material The online version of this article (doi:10.1186/s12936-017-2047-x) contains supplementary material, which is available to authorized users.

How long should a patient with a treatable chronic disease wait for more effective treatments before accepting the best available treatment? We develop a framework to guide optimal treatment decisions for a deteriorating chronic disease when treatment technologies are improving over time. We formulate an optimal stopping problem using a discrete-time, finite-horizon Markov decision process. The goal is to maximize a patient’s quality-adjusted life expectancy. We derive structural properties of the model and analytically solve a three-period treatment decision problem. We illustrate the model with the example of treatment for chronic hepatitis C virus (HCV). Chronic HCV affects 3–4 million Americans and has been historically difficult to treat, but increasingly effective treatments have been commercialized in the past few years. We show that the optimal treatment decision is more likely to be to accept currently available treatment—despite expectations for future treatment improvement—for patients who have high-risk history, who are older, or who have more comorbidities. Insights from this study can guide HCV treatment decisions for individual patients. More broadly, our model can guide treatment decisions for curable chronic diseases by finding the optimal treatment policy for individual patients in a heterogeneous population.

Operations research (OR)-based analyses have the potential to improve decision making for many important, real-world health care problems. However, junior scholars often avoid working on practical applications in health because promotion and tenure processes tend to value theoretical studies more highly than applied studies. This paper discusses the author's experiences in using OR to inform and influence decisions in health and provides a blueprint for junior researchers who wish to find success by taking a similar path. This involves selecting good problems to study, forming productive collaborations with domain experts, developing appropriate models, identifying the most salient results from an analysis, and effectively disseminating findings to decision makers. The paper then suggests how journals, funding agencies, and senior academics can encourage such work by taking a broader and more informed view of the potential role and contributions of OR to solving health care problems. Making room in academia for the application of OR in health follows in the tradition begun by the founders of operations research: to work on important real-world problems where operations research can contribute to better decision making.

Background. Current guidelines for economic evaluations of health interventions define relevant outcomes as those accruing to individuals receiving interventions. Little consensus exists on counting health impacts on current and future fertility and childbearing. Our objective was to characterize current practices for counting such health outcomes. Methods. We developed a framework characterizing health interventions with direct and/or indirect effects on fertility and childbearing and how such outcomes are reported. We identified interventions spanning the framework and performed a targeted literature review for economic evaluations of these interventions. For each article, we characterized how the potential health outcomes from each intervention were considered, focusing on quality-adjusted life-years (QALYs) associated with fertility and childbearing. Results. We reviewed 108 studies, identifying 7 themes: 1) Studies were heterogeneous in reporting outcomes. 2) Studies often selected outcomes for inclusion that tend to bias toward finding the intervention to be cost-effective. 3) Studies often avoided the challenges of assigning QALYs for pregnancy and fertility by instead considering cost per intermediate outcome. 4) Even for the same intervention, studies took heterogeneous approaches to outcome evaluation. 5) Studies used multiple, competing rationales for whether and how to include fertility-related QALYs and whose QALYs to include. 6) Studies examining interventions with indirect effects on fertility typically ignored such QALYs. 7) Even recent studies had these shortcomings. Limitations include that the review was targeted rather than systematic. Conclusions. Economic evaluations inconsistently consider QALYs from current and future fertility and childbearing in ways that frequently appear biased toward the interventions considered. As the Panel on Cost-Effectiveness in Health and Medicine updates its guidelines, making the practice of cost-effectiveness analysis more consistent is a priority. Our study contributes to harmonizing methods in this respect.

Background. The National Salt Reduction Initiative, in which food producers agree to lower sodium to levels deemed feasible for different foods, is expected to significantly reduce sodium intake if expanded to a large sector of food manufacturers. Objective. Given recent data on the relationship between sodium intake, hypertension, and associated cardiovascular disease at a population level, we sought to examine risks and benefits of the program. Design. To estimate the impact of further expanding the initiative on hypertension, myocardial infarction (MI) and stroke incidence, and related mortality, given food consumption patterns across the United States, we developed and validated a stochastic microsimulation model of hypertension, MI, and stroke morbidity and mortality, using data from food producers on sodium reduction among foods, linked to 24-hour dietary recalls, blood pressure, and cardiovascular histories from the National Health and Nutrition Examination Survey. Results. Expansion of the initiative to ensure all restaurants and manufacturers reach agreed-upon sodium targets would be expected to avert from 0.9 to 3.0 MIs (a 1.6%–5.4% reduction) and 0.5 to 2.8 strokes (a 1.1%–6.2% reduction) per 10,000 Americans per year over the next decade, after incorporating consumption patterns and variations in the effect of sodium reduction on blood pressure among different demographic groups. Even high levels of consumer addition of table salt or substitution among food categories would be unlikely to neutralize this benefit. However, if recent epidemiological associations between very low sodium and increased mortality are causal, then older women may be at risk of increased mortality from excessively low sodium intake. Conclusions: An expanded National Salt Reduction Initiative is likely to significantly reduce hypertension and hypertension-related cardiovascular morbidity but may be accompanied by potential risks to older women.

For many communicable diseases, knowledge of the underlying contact network through which the disease spreads is essential to determining appropriate control measures. When behavior change is the primary intervention for disease prevention, it is important to understand how to best modify network connectivity using the limited resources available to control disease spread. We describe and compare four algorithms for selecting a limited number of links to remove from a network: two “preventive” approaches (edge centrality, R0 minimization), where the decision of which links to remove is made prior to any disease outbreak and depends only on the network structure; and two “reactive” approaches (S–I edge centrality, optimal quarantining), where information about the initial disease states of the nodes is incorporated into the decision of which links to remove. We evaluate the performance of these algorithms in minimizing the total number of infections that occur over the course of an acute outbreak of disease. We consider different network structures, including both static and dynamic Erdos–Renyi random networks with varying levels of connectivity, a real-world network of residential hotels connected through injection drug use, and a network exhibiting community structure. We show that reactive approaches outperform preventive approaches in averting infections. Among reactive approaches, removing links in order of S–I edge centrality is favored when the link removal budget is small, while optimal quarantining performs best when the link removal budget is sufficiently large. The budget threshold above which optimal quarantining outperforms the S–I edge centrality algorithm is a function of both network structure (higher for unstructured Erdos–Renyi random networks compared to networks with community structure or the real-world network) and disease infectiousness (lower for highly infectious diseases). We conduct a value-of-information analysis of knowing which nodes are initially infected by comparing the performance improvement achieved by reactive over preventive strategies. We find that such information is most valuable for moderate budget levels, with increasing value as disease spread becomes more likely (due to either increased connectedness of the network or increased infectiousness of the disease).

Background The risks of HIV transmission associated with the opioid epidemic make cost-effective programs for people who inject drugs (PWID) a public health priority. Some of these programs have benefits beyond prevention of HIV—a critical consideration given that injection drug use is increasing across most United States demographic groups. To identify high-value HIV prevention program portfolios for US PWID, we consider combinations of four interventions with demonstrated efficacy: opioid agonist therapy (OAT), needle and syringe programs (NSPs), HIV testing and treatment (Test & Treat), and oral HIV pre-exposure prophylaxis (PrEP). Methods and findings We adapted an empirically calibrated dynamic compartmental model and used it to assess the discounted costs (in 2015 US dollars), health outcomes (HIV infections averted, change in HIV prevalence, and discounted quality-adjusted life years [QALYs]), and incremental cost-effectiveness ratios (ICERs) of the four prevention programs, considered singly and in combination over a 20-y time horizon. We obtained epidemiologic, economic, and health utility parameter estimates from the literature, previously published models, and expert opinion. We estimate that expansions of OAT, NSPs, and Test & Treat implemented singly up to 50% coverage levels can be cost-effective relative to the next highest coverage level (low, medium, and high at 40%, 45%, and 50%, respectively) and that OAT, which we assume to have immediate and direct health benefits for the individual, has the potential to be the highest value investment, even under scenarios where it prevents fewer infections than other programs. Although a model-based analysis can provide only estimates of health outcomes, we project that, over 20 y, 50% coverage with OAT could avert up to 22,000 (95% CI: 5,200, 46,000) infections and cost US$18,000 (95% CI: US$14,000, US$24,000) per QALY gained, 50% NSP coverage could avert up to 35,000 (95% CI: 8,900, 43,000) infections and cost US$25,000 (95% CI: US$7,000, US$76,000) per QALY gained, 50% Test & Treat coverage could avert up to 6,700 (95% CI: 1,200, 16,000) infections and cost US$27,000 (95% CI: US$15,000, US$48,000) per QALY gained, and 50% PrEP coverage could avert up to 37,000 (22,000, 58,000) infections and cost US$300,000 (95% CI: US$162,000, US$667,000) per QALY gained. When coverage expansions are allowed to include combined investment with other programs and are compared to the next best intervention, the model projects that scaling OAT coverage up to 50%, then scaling NSP coverage to 50%, then scaling Test & Treat coverage to 50% can be cost-effective, with each coverage expansion having the potential to cost less than US$50,000 per QALY gained relative to the next best portfolio. In probabilistic sensitivity analyses, 59% of portfolios prioritized the addition of OAT and 41% prioritized the addition of NSPs, while PrEP was not likely to be a priority nor a cost-effective addition. Our findings are intended to be illustrative, as data on achievable coverage are limited and, in practice, the expansion scenarios considered may exceed feasible levels. We assumed independence of interventions and constant returns to scale. Extensive sensitivity analyses allowed us to assess parameter sensitivity, but the use of a dynamic compartmental model limited the exploration of structural sensitivities. Conclusions We estimate that OAT, NSPs, and Test & Treat, implemented singly or in combination, have the potential to effectively and cost-effectively prevent HIV in US PWID. PrEP is not likely to be cost-effective in this population, based on the scenarios we evaluated. While local budgets or policy may constrain feasible coverage levels for the various interventions, our findings suggest that investments in combined prevention programs can substantially reduce HIV transmission and improve health outcomes among PWID., In a modeling study, Cora Bernard and colleagues examine the cost-effectiveness of opioid agonist therapy (OAT), needle-syringe exchange programs (NSP), screening and intensive antiretroviral therapy programs (Test & Treat), and oral HIV pre-exposure prophylaxis (PrEP) for prevention of HIV in people who inject drugs., Author summary Why was this study done? The US drug-injecting population is growing, with consequent deaths from opioid overdose (30,000 deaths in 2014 alone) as well as increased HIV transmission. Cost-effective HIV prevention programs for people who inject drugs are essential to the long-term health outcomes for this population and other high-risk groups in the US. Our model-based analysis was designed to identify high-value portfolios of HIV prevention programs targeted to people who inject drugs in the US. What did the researchers do and find? We adapted and extended a dynamic compartmental model that tracks multiple HIV risk groups in the US. We projected the lifetime costs and benefits resulting from combinations of opioid agonist therapy, needle and syringe exchange programs, enhanced HIV screening and antiretroviral therapy programs, and oral HIV pre-exposure prophylaxis (PrEP) for people who inject drugs. We estimated that opioid agonist therapy, which reduces injecting frequency and results in multiple, immediate quality-of-life improvements, can be cost-effective, while less expensive programs like needle and syringe exchange can provide additional value when used in combination. PrEP was not likely to be cost-effective in this population in most of the scenarios we evaluated. What do these findings mean? Several prevention interventions—opioid agonist therapy, needle and syringe exchange programs, and enhanced HIV testing and treatment services—have the potential to prevent HIV transmission in PWID and improve health outcomes while meeting commonly accepted thresholds for cost-effectiveness. Opioid agonist therapy may reduce HIV transmission and also improve quality of life for people who inject drugs; both effects contribute to its favorable cost-effectiveness. Some prevention programs for people who inject drugs are controversial, and access is limited. We demonstrate that some of these programs can be both effective and cost-effective investments that have the potential to reduce not only the spread of HIV but also the size of the injecting population.

Effective treatment for tuberculosis (TB) patients on first-line treatment involves triaging those with drug-resistant (DR) TB to appropriate treatment alternatives. Patients likely to have DR TB are identified using results from repeated inexpensive sputum-smear (SS) tests and expensive but definitive drug sensitivity tests (DST). Early DST may lead to high costs and unnecessary testing; late DST may lead to poor health outcomes and disease transmission. We use a partially observable Markov decision process (POMDP) framework to determine optimal DST timing. We develop policy-relevant structural properties of the POMDP model. We apply our model to TB in India to identify the patterns of SS test results that should prompt DST if transmission costs remain at status-quo levels. Unlike previous analyses of personalized treatment policies, we take a societal perspective and consider the effects of disease transmission. The inclusion of such effects can significantly alter the optimal policy. We find that an optimal DST policy could save India approximately $1.9 billion annually.

In recent decades, healthcare has become increasingly expensive, creating pressure on healthcare providers to cut costs while maintaining or improving quality. Operations research can play an important role in supporting such efforts. A key challenge faced by hospital planners is scheduling and management of operating rooms, as operating rooms typically provide highly specialized care, require significant resources, and contribute significantly to a hospital’s bottom line. We describe recent work on hospital operating room management at Lucile Packard Children’s Hospital Stanford. We describe preliminary outcomes of three projects aimed at improving the efficiency of the hospital’s operating rooms: machine learning to improve surgical case length estimation; queuing analysis to improve operational efficiency; and integer programming to schedule cases to reduce surgical delays.

During the 20th century, deaths from a range of serious infectious diseases decreased dramatically due to the development of safe and effective vaccines. However, infant immunization coverage has increased only marginally since the 1960s, and many people remain susceptible to vaccine-preventable diseases. “Catch-up vaccination” for age groups beyond infancy can be an attractive and effective means of immunizing people who were missed earlier. However, as newborn vaccination rates increase, catch-up vaccination becomes less attractive: the number of susceptible people decreases, so the cost to find and vaccinate each unvaccinated person may increase; in addition, the number of infected individuals decreases, so each unvaccinated person faces a lower risk of infection. This article presents a general framework for determining the optimal time to discontinue a catch-up vaccination program. We use a cost-effectiveness framework: we consider the cost per quality-adjusted life year gained of catch-up vaccination efforts as a function of newborn immunization rates over time and consequent disease prevalence and incidence. We illustrate our results with the example of hepatitis B catch-up vaccination in China. We contrast results from a dynamic modeling approach with an approach that ignores the impact of vaccination on future disease incidence. The latter approach is likely to be simpler for decision makers to understand and implement because of lower data requirements.

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.

In an era of limited healthcare budgets, mathematical models can be useful tools to identify cost-effective programs and to support policymakers in informed decision making. This paper reports results of our work carried out over several years with the Asian Liver Center at Stanford University, a nonprofit outreach and advocacy organization that is an international leader in the fight against hepatitis B and liver cancer. Hepatitis B is a vaccine-preventable viral disease that, if untreated, can lead to death from cirrhosis and liver cancer. Infection with hepatitis B is a major public health problem, particularly in Asian populations. We used new combinations of decision analysis and Markov models to analyze the cost-effectiveness of several interventions to combat hepatitis B in the United States and China. The results of our OR-based analyses have helped change United States public health policy on hepatitis B screening for millions of people and have helped encourage policymakers in China to enact legislation to provide free catch-up vaccination for hundreds of millions of children. These policies are an important step in eliminating health disparities, reducing discrimination, and ensuring that millions of people who need it can now receive hepatitis B vaccination or lifesaving treatment.

Background Rising atmospheric carbon dioxide concentrations are anticipated to decrease the zinc and iron concentrations of crops. The associated disease burden and optimal mitigation strategies remain unknown. We sought to understand where and to what extent increasing carbon dioxide concentrations may increase the global burden of nutritional deficiencies through changes in crop nutrient concentrations, and the effects of potential mitigation strategies. Methods and findings For each of 137 countries, we incorporated estimates of climate change, crop nutrient concentrations, dietary patterns, and disease risk into a microsimulation model of zinc and iron deficiency. These estimates were obtained from the Intergovernmental Panel on Climate Change, US Department of Agriculture, Statistics Division of the Food and Agriculture Organization of the United Nations, and Global Burden of Disease Project, respectively. In the absence of increasing carbon dioxide concentrations, we estimated that zinc and iron deficiencies would induce 1,072.9 million disability-adjusted life years (DALYs) globally over the period 2015 to 2050 (95% credible interval [CrI]: 971.1–1,167.7). In the presence of increasing carbon dioxide concentrations, we estimated that decreasing zinc and iron concentrations of crops would induce an additional 125.8 million DALYs globally over the same period (95% CrI: 113.6–138.9). This carbon-dioxide-induced disease burden is projected to disproportionately affect nations in the World Health Organization’s South-East Asia and African Regions (44.0 and 28.5 million DALYs, respectively), which already have high existing disease burdens from zinc and iron deficiencies (364.3 and 299.5 million DALYs, respectively), increasing global nutritional inequalities. A climate mitigation strategy such as the Paris Agreement (an international agreement to keep global temperatures within 2°C of pre-industrial levels) would be expected to avert 48.2% of this burden (95% CrI: 47.8%–48.5%), while traditional public health interventions including nutrient supplementation and disease control programs would be expected to avert 26.6% of the burden (95% CrI: 23.8%–29.6%). Of the traditional public health interventions, zinc supplementation would be expected to avert 5.5%, iron supplementation 15.7%, malaria mitigation 3.2%, pneumonia mitigation 1.6%, and diarrhea mitigation 0.5%. The primary limitations of the analysis include uncertainty regarding how food consumption patterns may change with climate, how disease mortality rates will change over time, and how crop zinc and iron concentrations will decline from those at present to those in 2050. Conclusions Effects of increased carbon dioxide on crop nutrient concentrations are anticipated to exacerbate inequalities in zinc and iron deficiencies by 2050. Proposed Paris Agreement strategies are expected to be more effective than traditional public health measures to avert the increased inequality., In a modeling study Christopher Weyant and colleagues predict reduced crop nutritional content and subsequent health disparities due to increased carbon dioxide levels caused by climate change., Author summary Why was this study done? Increasing atmospheric carbon dioxide concentrations are anticipated to decrease the zinc and iron concentrations of some crops. The disease burden induced by changes in zinc and iron concentrations in crops, and optimal mitigation strategies, are unknown. What did the researchers do and find? We developed a country-level microsimulation model of zinc and iron deficiency to project the health effects of zinc- and iron-deficiency-related diseases in terms of disability-adjusted life years accumulated over the period 2015 to 2050, worldwide. We estimated that carbon-dioxide-induced reductions in the zinc and iron concentrations of crops would be expected to induce approximately 125.8 million disability-adjusted life years globally over the period from 2015 to 2050, disproportionately affecting South-East Asian and sub-Saharan African countries. We also estimated that a climate mitigation strategy concordant with the Paris Agreement would avert a greater portion of this burden (approximately 48.2%) than traditional public health interventions (approximately 26.6%). What do these findings mean? The effects of increased carbon dioxide concentrations on crop nutrient concentrations may increase existing inequalities in nutritional deficiencies by 2050. Climate mitigation strategies, such as the Paris Agreement, may be more effective than traditional public health interventions in averting this increased inequality.

Chronic viral diseases such as human immunodeficiency virus (HIV) and hepatitis B virus (HBV) afflict millions of people worldwide. A key public health challenge in managing such diseases is identifying infected, asymptomatic individuals so that they can receive antiviral treatment. Such treatment can benefit both the treated individual (by improving quality and length of life) and the population as a whole (through reduced transmission). We develop a compartmental model of a chronic, treatable infectious disease and use it to evaluate the cost and effectiveness of different levels of screening and contact tracing. We show that: (1) the optimal strategy is to get infected individuals into treatment at the maximal rate until the incremental health benefits balance the incremental cost of controlling the disease; (2) as one reduces the disease prevalence by moving people into treatment (which decreases the chance that they will infect others), one should increase the level of contact tracing to compensate for the decreased effectiveness of screening; (3) as the disease becomes less prevalent, it is optimal to spend more per case identified; and (4) the relative mix of screening and contact tracing at any level of disease prevalence is such that the marginal efficiency of contact tracing (cost per infected person found) equals that of screening if possible (e.g., when capacity limitations are not binding). We also show how to determine the cost-effective equilibrium level of disease prevalence (among untreated individuals), and we develop an approximation of the path of the optimal prevalence over time. Using this, one can obtain a close approximation of the optimal solution without having to solve an optimal control problem. We apply our methods to an example of hepatitis B virus.

ObjectivePersonalised medicine seeks to select and modify treatments based on individual patient characteristics and preferences. We sought to develop an automated strategy to select and modify blood pressure treatments, incorporating the likelihood that patients with different characteristics would benefit from different types of medications and dosages and the potential severity and impact of different side effects among patients with different characteristics.Design, setting and participantsWe developed a Markov decision process (MDP) model to incorporate meta-analytic data and estimate the optimal treatment for maximising discounted lifetime quality-adjusted life-years (QALYs) based on individual patient characteristics, incorporating medication adjustment choices when a patient incurs side effects. We compared the MDP to current US blood pressure treatment guidelines (the Eighth Joint National Committee, JNC8) and a variant of current guidelines that incorporates results of a major recent trial of intensive treatment (Intensive JNC8). We used a microsimulation model of patient demographics, cardiovascular disease risk factors and side effect probabilities, sampling from the National Health and Nutrition Examination Survey (2003–2014), to compare the expected population outcomes from adopting the MDP versus guideline-based strategies.Main outcome measuresCosts and QALYs for the MDP-based treatment (MDPT), JNC8 and Intensive JNC8 strategies.ResultsCompared with the JNC8 guideline, the MDPT strategy would be cost-saving from a societal perspective with discounted savings of US$1187 per capita (95% CI 1178 to 1209) and an estimated discounted gain of 0.06 QALYs per capita (95% CI 0.04 to 0.08) among the US adult population. QALY gains would largely accrue from reductions in severe side effects associated with higher treatment doses later in life. The Intensive JNC8 strategy was dominated by the MDPT strategy.ConclusionsAn MDP-based approach can aid decision-making by incorporating meta-analytic evidence to personalise blood pressure treatment and improve overall population health compared with current blood pressure treatment guidelines.

Estimating the potential health benefits and expenditures of a partially effective HIV vaccine is an important consideration in the debate about whether HIV vaccine research should continue. We developed an epidemic model to estimate HIV prevalence, new infections, and the cost-effectiveness of vaccination strategies in the U.S. Vaccines with modest efficacy could prevent 300,000–700,000 HIV infections and save $30 billion in healthcare expenditures over 20 years. Targeted vaccination of high-risk individuals is economically efficient, but difficulty in reaching these groups may mitigate these benefits. Universal vaccination is cost-effective for vaccines with 50%-efficacy and price similar to other infectious disease vaccines.

Purpose. Mathematical and simulation models are increasingly used to plan for and evaluate health sector responses to disasters, yet no clear consensus exists regarding best practices for the design, conduct, and reporting of such models. The authors examined a large selection of published health sector disaster response models to generate a set of best practice guidelines for such models. Methods . The authors reviewed a spectrum of published disaster response models addressing public health or health care delivery, focusing in particular on the type of disaster and response decisions considered, decision makers targeted, choice of outcomes evaluated, modeling methodology, and reporting format. They developed initial recommendations for best practices for creating and reporting such models and refined these guidelines after soliciting feedback from response modeling experts and from members of the Society for Medical Decision Making. Results . The authors propose 6 recommendations for model construction and reporting, inspired by the most exemplary models: health sector disaster response models should address real-world problems, be designed for maximum usability by response planners, strike the appropriate balance between simplicity and complexity, include appropriate outcomes that extend beyond those considered in traditional cost-effectiveness analyses, and be designed to evaluate the many uncertainties inherent in disaster response. Finally, good model reporting is particularly critical for disaster response models. Conclusions . Quantitative models are critical tools for planning effective health sector responses to disasters. The proposed recommendations can increase the applicability and interpretability of future models, thereby improving strategic, tactical, and operational aspects of preparedness planning and response.

Russia has one of the world's fastest growing HIV epidemics, and HIV screening has been widespread. Whether such screening is an effective use of resources is unclear. We used epidemiologic and economic data from Russia to develop a Markov model to estimate costs, quality of life and survival associated with a voluntary HIV screening programme compared with no screening in Russia. We measured discounted lifetime health-care costs and quality-adjusted life years (QALYs) gained. We varied our inputs in sensitivity analysis. Early identification of HIV through screening provided a substantial benefit to persons with HIV, increasing life expectancy by 2.1 years and 1.7 QALYs. At a base-case prevalence of 1.2%, once-per-lifetime screening cost $13,396 per QALY gained, exclusive of benefit from reduced transmission. Cost-effectiveness of screening remained favourable until prevalence dropped below 0.04%. When HIV-transmission-related costs and benefits were included, once-per-lifetime screening cost $6910 per QALY gained and screening every two years cost $27,696 per QALY gained. An important determinant of the cost-effectiveness of screening was effectiveness of counselling about risk reduction. Early identification of HIV infection through screening in Russia is effective and cost-effective in all but the lowest prevalence groups.

A co-epidemic arises when the spread of one infectious disease stimulates the spread of another infectious disease. Recently, this has happened with human immunodeficiency virus (HIV) and tuberculosis (TB). We develop two variants of a co-epidemic model of two diseases. We calculate the basic reproduction number (R0), the disease-free equilibrium, and the quasi-disease-free equilibria, which we define as the existence of one disease along with the complete eradication of the other disease, and the co-infection equilibria for specific conditions. We determine stability criteria for the disease-free and quasi-disease-free equilibria. We present an illustrative numerical analysis of the HIV-TB co-epidemics in India that we use to explore the effects of hypothetical prevention and treatment scenarios. Our numerical analysis demonstrates that exclusively treating HIV or TB may reduce the targeted epidemic, but can subsequently exacerbate the other epidemic. Our analyses suggest that coordinated treatment efforts that include highly active antiretroviral therapy for HIV, latent TB prophylaxis, and active TB treatment may be necessary to slow the HIV-TB co-epidemic. However, treatment alone may not be sufficient to eradicate both diseases. Increased disease prevention efforts (for example, those that promote condom use) may also be needed to extinguish this co-epidemic. Our simple model of two synergistic infectious disease epidemics illustrates the importance of including the effects of each disease on the transmission and progression of the other disease.