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Abstract Background In 2018, the Infectious Diseases Society of America (IDSA) published guidelines for diagnosis and treatment of Clostridioides (formerly Clostridium) difficile infection (CDI). However, there is little guidance regarding which treatments are cost-effective. Methods We used a Markov model to simulate a cohort of patients presenting with an initial CDI diagnosis. We used the model to estimate the costs, effectiveness, and cost-effectiveness of different CDI treatment regimens recommended in the recently published 2018 IDSA guidelines. The model includes stratification by the severity of the initial infection, and subsequent likelihood of cure, recurrence, mortality, and outcomes of subsequent recurrences. Data sources were taken from IDSA guidelines and published literature on treatment outcomes. Outcome measures were discounted quality-adjusted life-years (QALYs), costs, and incremental cost-effectiveness ratios (ICERs). Results Use of fidaxomicin for nonsevere initial CDI, vancomycin for severe CDI, fidaxomicin for first recurrence, and fecal microbiota transplantation (FMT) for subsequent recurrence (strategy 44) cost an additional $478 for 0.009 QALYs gained per CDI patient, resulting in an ICER of $31 751 per QALY, below the willingness-to-pay threshold of $100 000/QALY. This is the optimal, cost-effective CDI treatment strategy. Conclusions Metronidazole is suboptimal for nonsevere CDI as it is less beneficial than alternative strategies. The preferred treatment regimen is fidaxomicin for nonsevere CDI, vancomycin for severe CDI, fidaxomicin for first recurrence, and FMT for subsequent recurrence. The most effective treatments, with highest cure rates, are also cost-effective due to averted mortality, utility loss, and costs of rehospitalization and/or further treatments for recurrent CDI. We estimated the cost-effectiveness of different treatment regimens for Clostridioides difficile infection (CDI), per Infectious Diseases Society of America 2018 guidelines. Optimal treatment is vancomycin for severe CDI, fidaxomicin for first recurrence, and fecal microbiota transplant for subsequent recurrence.

Background In January 2020, an outbreak of atypical pneumonia caused by a novel coronavirus, SARS-CoV-2, was reported in Wuhan, China. On Jan 23, 2020, the Chinese government instituted mitigation strategies to control spread. Most modeling studies have focused on projecting epidemiological outcomes throughout the pandemic. However, the impact and optimal timing of different mitigation approaches have not been well-studied. Methods We developed a mathematical model reflecting SARS-CoV-2 transmission dynamics in an age-stratified population. The model simulates health and economic outcomes from Dec 1, 2019 through Mar 31, 2020 for cities including Wuhan, Chongqing, Beijing, and Shanghai in China. We considered differences in timing and duration of three mitigation strategies in the early phase of the epidemic: city-wide quarantine on Wuhan, travel history screening and isolation of travelers from Wuhan to other Chinese cities, and general social distancing. Results Our model estimated that implementing all three mitigation strategies one week earlier would have averted 35% of deaths in Wuhan (50% in other cities) with a 7% increase in economic impacts (16-18% in other cities). One week’s delay in mitigation strategy initiation was estimated to decrease economic cost by the same amount, but with 35% more deaths in Wuhan and more than 80% more deaths in the other cities. Of the three mitigation approaches, infections and deaths increased most rapidly if initiation of social distancing was delayed. Furthermore, social distancing of working-age adults was most critical to reducing COVID-19 outcomes versus social distancing among children and/or the elderly. Conclusions Optimizing the timing of epidemic mitigation strategies is paramount and involves weighing trade-offs between preventing infections and deaths and incurring immense economic impacts. City-wide quarantine was not as effective as city-wide social distancing due to its much higher daily cost than social distancing. Under typical economic evaluation standards, the optimal timing for the full set of control measures would have been much later than Jan 23, 2020 (status quo).

University students have higher average number of contacts than the general population. Students returning to university campuses may exacerbate COVID-19 dynamics in the surrounding community. We developed a dynamic transmission model of COVID-19 in a mid-sized city currently experiencing a low infection rate. We evaluated the impact of 20,000 university students arriving on September 1 in terms of cumulative COVID-19 infections, time to peak infections, and the timing and peak level of critical care occupancy. We also considered how these impacts might be mitigated through screening interventions targeted to students. If arriving students reduce their contacts by 40% compared to pre-COVID levels, the total number of infections in the community increases by 115% (from 3,515 to 7,551), with 70% of the incremental infections occurring in the general population, and an incremental 19 COVID-19 deaths. Screening students every 5 days reduces the number of infections attributable to the student population by 42% and the total COVID-19 deaths by 8. One-time mass screening of students prevents fewer infections than 5-day screening, but is more efficient, requiring 196 tests needed to avert one infection instead of 237. University students are highly inter-connected with the surrounding off-campus community. Screening targeted at this population provides significant public health benefits to the community through averted infections, critical care admissions, and COVID-19 deaths.

The COVID-19 pandemic presented significant challenges in educational settings. Schools implemented a variety of COVID-19 mitigation strategies, some of which were controversial due to potential disruptions to in-person learning. We developed an agent-based model of COVID-19 in a US high school setting to evaluate potential trade-offs between preventing COVID-19 infections versus avoiding in-person learning loss under different mitigation policies in a post-Omicron context. Mitigation policies included isolation alone and in combination with quarantine of exposed students, weekly testing of all students or testing of exposed students (‘test-to-stay’) under different scenarios of mask use and booster dose uptake. Outcomes were simulated over an 11 week trimester. We found that requiring a full 5 or 10 day quarantine of exposed students reduced COVID-19 infections by five to sevenfold relative to isolation alone, but at a cost of nearly 40% learning days lost. Test-to-stay achieved nearly the same level of infection reduction with lower levels of learning loss. Weekly testing also reduced COVID-19 infections but was less effective and incurred higher learning loss than test-to-stay. Universal masking and increased vaccination not only reduced infections at no cost to learning but also synergized with other strategies to reduce trade-offs.

Proactive therapeutic drug monitoring of infliximab is a marginally cost-effective strategy for Crohn’s disease, whereas reactive therapeutic drug monitoring is cost-effective. As the cost of infliximab decreases, a proactive strategy of dosing infliximab becomes more cost-effective. AbstractBackgroundTherapeutic drug monitoring (TDM) is increasingly performed for Infliximab (IFX) in patients with Crohn’s disease (CD). Reactive TDM is a cost-effective strategy to empiric IFX dose escalation. The cost-effectiveness of proactive TDM is unknown. The aim of this study is to assess the cost-effectiveness of proactive vs reactive TDM in a simulated population of CD patients on IFX.MethodsWe developed a stochastic simulation model of CD patients on IFX and evaluated the expected health costs and outcomes of a proactive TDM strategy compared with a reactive strategy. The proactive strategy measured IFX concentration and antibody status every 6 months, or at the time of a flare, and dosed IFX to a therapeutic window. The reactive strategy only did so at the time of a flare.ResultsThe proactive strategy led to fewer flares than the reactive strategy. More patients stayed on IFX in the proactive vs reactive strategy (63.4% vs 58.8% at year 5). From a health sector perspective, a proactive strategy was marginally cost-effective compared with a reactive strategy (incremental cost-effectiveness ratio of $146,494 per quality-adjusted life year), assuming a 40% of the wholesale price of IFX. The results were most sensitive to risk of flaring with a low IFX concentration and the cost of IFX.ConclusionsAssuming 40% of the average wholesale acquisition cost of biologic therapies, proactive TDM for IFX is marginally cost-effective compared with a reactive TDM strategy. As the cost of infliximab decreases, a proactive monitoring strategy is more cost-effective.

The use of open-source programming languages, such as R, in health decision sciences is growing and has the potential to facilitate model transparency, reproducibility, and shareability. However, realizing this potential can be challenging. Models are complex and primarily built to answer a research question, with model sharing and transparency relegated to being secondary goals. Consequently, code is often neither well documented nor systematically organized in a comprehensible and shareable approach. Moreover, many decision modelers are not formally trained in computer programming and may lack good coding practices, further compounding the problem of model transparency. To address these challenges, we propose a high-level framework for model-based decision and cost-effectiveness analyses (CEA) in R. The proposed framework consists of a conceptual, modular structure and coding recommendations for the implementation of model-based decision analyses in R. This framework defines a set of common decision model elements divided into five components: (1) model inputs, (2) decision model implementation, (3) model calibration, (4) model validation, and (5) analysis. The first four components form the model development phase. The analysis component is the application of the fully developed decision model to answer the policy or the research question of interest, assess decision uncertainty, and/or to determine the value of future research through value of information (VOI) analysis. In this framework, we also make recommendations for good coding practices specific to decision modeling, such as file organization and variable naming conventions. We showcase the framework through a fully functional, testbed decision model, which is hosted on GitHub for free download and easy adaptation to other applications. The use of this framework in decision modeling will improve code readability and model sharing, paving the way to an ideal, open-source world.

Bovine tuberculosis (bTB) is a chronic disease of cattle that is difficult to control and eradicate in part due to the costly nature of surveillance and poor sensitivity of diagnostic tests. Like many countries, bTB prevalence in Uruguay has gradually declined to low levels due to intensive surveillance and control efforts over the past decades. In low prevalence settings, broad-based surveillance strategies based on routine testing may not be the most cost-effective way for controlling between-farm bTB transmission, while targeted surveillance aimed at high-risk farms may be more efficient for this purpose. To investigate the efficacy of targeted surveillance, we developed an integrated within- and between-farm bTB transmission model utilizing data from Uruguay’s comprehensive animal movement database. A genetic algorithm was used to fit uncertain parameter values, such as the animal-level sensitivity of skin testing and slaughter inspection, to observed bTB epidemiological data. Of ten alternative surveillance strategies evaluated, a strategy based on eliminating testing in low-risk farms resulted in a 40% reduction in sampling effort without increasing bTB incidence. These results can inform the design of more cost-effective surveillance programs to detect and control bTB in Uruguay and other countries with low bTB prevalence.

Antimicrobial use contributes to the growing public health challenge of infectious diseases that are resistant to all but a few remaining treatments via natural selection. When few treatment options remain, should the last effective treatment be reserved for controlling larger outbreaks in the future? In “Dynamics of Drug Resistance: Optimal Control of an Infectious Disease,” N. Chehrazi, L. E. Cipriano, and E. A. Enns formulate this important policy question as a control problem with two state variables—disease prevalence and the level of treatment resistance—for an established family of SIS infectious disease models with resistance. They prove that when the disease transmission rate is constant, it is optimal to treat everyone until the level of resistance is so high that it is no longer economical to treat anyone. Public health policies and social distancing can cause a nonconstant disease transmission rate; in these cases, it may be optimal to preserve the drug for relatively larger outbreaks or to use the drug to treat some, but not all, infected individuals. Antimicrobial resistance is a significant public health threat. In the United States alone, two million people are infected, and 23,000 die each year from antibiotic-resistant bacterial infections. In many cases, infections are resistant to all but a few remaining drugs. We examine the case in which a single drug remains and solve for the optimal treatment policy for a susceptible–infected–susceptible infectious disease model, incorporating the effects of drug resistance. The problem is formulated as an optimal control problem with two continuous state variables: the disease prevalence and drug’s “quality” (the fraction of infections that are drug-susceptible). The decision maker’s objective is to minimize the discounted cost of the disease to society over an infinite horizon. We provide a new generalizable solution approach that allows us to thoroughly characterize the optimal treatment policy analytically. We prove that the optimal treatment policy is a bang-bang policy with a single switching time. The action/inaction regions can be described by a single boundary that is strictly increasing when viewed as a function of drug quality, indicating that, when the disease transmission rate is constant, the policy of withholding treatment to preserve the drug for a potentially more serious future outbreak is not optimal. We show that the optimal value function and/or its derivatives are neither C 1 nor Lipschitz continuous, suggesting that numerical approaches to this family of dynamic infectious disease models may not be computationally stable. Furthermore, we demonstrate that relaxing the standard assumption of a constant disease transmission rate can fundamentally change the shape of the action region, add a singular arc to the optimal control, and make preserving the drug for a serious outbreak optimal. In addition, we apply our framework to the case of antibiotic-resistant gonorrhea.

Engagement in lifelong HIV care is critical for both patient and public health, yet there are limited resources to invest in improving HIV outcomes. We systematically reviewed evidence on the cost-effectiveness of retention and re-engagement interventions. We searched five databases for peer-reviewed studies published between 2010 and 2020. We assessed reporting and methods quality, extracted data on target populations, interventions, and cost-effectiveness, and evaluated overall strength of evidence. Eleven studies met inclusion criteria, and eight had moderate-high quality. Cost-effectiveness estimates ranged from cost-saving to over$1,000,000/quality-adjusted life year (QALY) gained. Of the 73 cost-effectiveness ratios reported, 64% were < $ 100,000/QALY gained. Interventions were more likely to be cost-effective when targeted to high-risk groups, implemented in locations where baseline retention levels were low, and when used in combination with other high-impact HIV interventions (such as prevention). Overall, existing evidence is moderately strong that retention and/or re-engagement interventions can be cost-effective in high-income countries.

Despite extensive evidence of work as a key social determinant of hypertension, risk prediction equations incorporating this information are lacking. Such limitations hinder clinicians’ ability to tailor patient care and comprehensively address hypertension risk factors. This study examined whether including work characteristics in hypertension risk equations improves their predictive accuracy. Using occupation ratings from the Occupational Information Network database, we measured job demand, job control, and supportiveness of supervisors and coworkers for occupations in the United States economy. We linked these occupation-based measures with the employment status and health data of participants in the Coronary Artery Risk Development in Young Adults (CARDIA) study. We fit logistic regression equations to estimate the probability of hypertension onset in five years among CARDIA participants with and without variables reflecting work characteristics. Based on the Harrell’s c- and Hosmer–Lemeshow’s goodness-of-fit statistics, we found that our logistic regression models that include work characteristics predict hypertension onset more accurately than those that do not incorporate these variables. We also found that the models that rely on occupation-based measures predict hypertension onset more accurately for White than Black participants, even after accounting for a sample size difference. Including other aspects of work, such as workers’ experience in the workplace, and other social determinants of health in risk equations may eliminate this discrepancy. Overall, our study showed that clinicians should examine workers’ work-related characteristics to tailor hypertension care plans appropriately.