Personalized Lung Cancer Screening – Acceptability among Primary Care Providers

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Cost-effectiveness evaluation of the 2021 US Preventive Services Task Force recommendation for lung cancer screening

IMPORTANCE: The US Preventive Services Task Force (USPSTF) issued its 2021 recommendation on lung cancer screening, which lowered the starting age for screening from 55 to 50 years and the minimum cumulative smoking exposure from 30 to 20 pack-years relative to its 2013 recommendation. Although costs are expected to increase because of the expanded screening eligibility criteria, it is unknown whether the new guidelines for lung cancer screening are cost-effective. OBJECTIVE: To evaluate the cost-effectiveness of the 2021 USPSTF recommendation for lung cancer screening compared with the 2013 recommendation and to explore the cost-effectiveness of 6 alternative screening strategies that maintained a minimum cumulative smoking exposure of 20 pack-years and an ending age for screening of 80 years but varied the starting ages for screening (50 or 55 years) and the number of years since smoking cessation (≤15, ≤20, or ≤25). DESIGN, SETTING, AND PARTICIPANTS: A comparative cost-effectiveness analysis using 4 independently developed microsimulation models that shared common inputs to assess the population-level health benefits and costs of the 2021 recommended screening strategy and 6 alternative screening strategies compared with the 2013 recommended screening strategy. The models simulated a 1960 US birth cohort. Simulated individuals entered the study at age 45 years and were followed up until death or age 90 years, corresponding to a study period from January 1, 2005, to December 31, 2050. EXPOSURES: Low-dose computed tomography in lung cancer screening programs with a minimum cumulative smoking exposure of 20 pack-years. MAIN OUTCOMES AND MEASURES: Incremental cost-effectiveness ratio (ICER) per quality-adjusted life-year (QALY) of the 2021 vs 2013 USPSTF lung cancer screening recommendations as well as 6 alternative screening strategies vs the 2013 USPSTF screening strategy. Strategies with a mean ICER lower than $100 000 per QALY were deemed cost-effective. RESULTS: The 2021 USPSTF recommendation was estimated to be cost-effective compared with the 2013 recommendation, with a mean ICER of$72 564 (range across 4 models, $59 493-$85 837) per QALY gained. The 2021 recommendation was not cost-effective compared with 6 alternative strategies that used the 20 pack-year criterion. Strategies associated with the most cost-effectiveness included those that expanded screening eligibility to include a greater number of former smokers who had not smoked for a longer duration (ie, ≤20 years and ≤25 years since smoking cessation vs ≤15 years since smoking cessation). In particular, the strategy that screened former smokers who quit within the past 25 years and began screening at age 55 years was associated with screening coverage closest to that of the 2021 USPSTF recommendation yet yielded greater cost-effectiveness, with a mean ICER of $66 533 (range across 4 models,$55 693-$80 539). CONCLUSIONS AND RELEVANCE: This economic evaluation found that the 2021 USPSTF recommendation for lung cancer screening was cost-effective; however, alternative screening strategies that maintained a minimum cumulative smoking exposure of 20 pack-years but included individuals who quit smoking within the past 25 years may be more cost-effective and warrant further evaluation.Accepted manuscrip

Worst-Case Conditional Value-at-Risk Minimization for Hazardous Materials Transportation

Despite significant advances in risk management, the routing of hazardous materials (hazmat) has relied on relatively simplistic methods. In this paper, we apply an advanced risk measure, called conditional value-at-risk (CVaR), for routing hazmat trucks. CVaR offers a flexible, risk-averse, and computationally tractable routing method that is appropriate for hazmat accident mitigation strategies. The two important data types in hazmat transportation are accident probabilities and accident consequences, both of which are subject to many ambiguous factors. In addition, historical data are usually insufficient to construct a probability distribution of accident probabilities and consequences. This motivates our development of a new robust optimization approach for considering the worst-case CVaR (WCVaR) under data uncertainty. We study important axioms to ensure that both the CVaR and WCVaR risk measures are coherent and appropriate in the context of hazmat transportation. We also devise a computational method for WCVaR and demonstrate the proposed WCVaR concept with a case study in a realistic road network

Comparative Effectiveness of Up To Three Lines of Chemotherapy Treatment Plans for Metastatic Colorectal Cancer

Modern chemotherapy agents transformed standard care for metastatic colorectal cancer (mCRC) but raised concerns about the financial burden of the disease. We studied comparative effectiveness of treatment plans that involve up to three lines of therapies and impact of treatment sequencing on health and cost outcomes. We employed a Markov model to represent the dynamically changing health status of mCRC patients and used Monte-Carlo simulation to evaluate various treatment plans consistent with existing guidelines. We calibrated our model by a meta-analysis of published data from an extensive list of clinical trials and measured the effectiveness of each plan in terms of cost per quality-adjusted life year. We examined the sensitivity of our model and results with respect to key parameters in two scenarios serving as base case and worst case for patients’ overall and progression-free survivals. The derived efficient frontiers included seven and five treatment plans in base case and worst case, respectively. The incremental cost-effectiveness ratio (ICER) ranged between $26,260 and$152,530 when the treatment plans on the efficient frontiers were compared against the least costly efficient plan in the base case, and between $21,256 and$60,040 in the worst case. All efficient plans were expected to lead to fewer than 2.5 adverse effects and on average successive adverse effects were spaced more than 9 weeks apart from each other in the base case. Based on ICER, all efficient treatment plans exhibit at least 87% chance of being efficient. Sensitivity analyses show that the ICERs were most dependent on drug acquisition cost, distributions of progression-free and overall survivals, and health utilities. We conclude that improvements in health outcomes may come at high incremental costs and are highly dependent in the order treatments are administered

Supplemental Methods 1 from Acceptability of Personalized Lung Cancer Screening Program Among Primary Care Providers

Supplemental Methods 1: Survey</p

Supplemental Table 2 from Acceptability of Personalized Lung Cancer Screening Program Among Primary Care Providers

Supplemental Table 2 shows a logistic regression analysis investigating various provider characteristics (age, gender, race, profession, years in practice, patients per week, work in a residency training site, work setting) as they influence responses to the question of whether or not providers recommend lung cancer screening for eligible patients.</p

Supplemental Table 3 from Acceptability of Personalized Lung Cancer Screening Program Among Primary Care Providers

Supplemental Table 3 shows a logistic regression analysis investigating various provider characteristics (age, gender, race, profession, years in practice, patients per week, work in a residency training site, work setting) as they influence responses to the question of whether or not providers consider increased time requirement as a barrier to personalized lung cancer screening implementation.</p

Supplemental Table 4 from Acceptability of Personalized Lung Cancer Screening Program Among Primary Care Providers

Supplemental Table 4 shows provider level of agreement between 3 different proposed uses of biomarkers for lung cancer screening: as an adjuvant to the current screening, as a standalone means of screening, or as a guide to uncertain findings to the current screening methods.</p

The Impact of Model Assumptions on Personalized Lung Cancer Screening Recommendations

BACKGROUND: Recommendations regarding personalized lung cancer screening are being informed by natural-history modeling. Therefore, understanding how differences in model assumptions affect model-based personalized screening recommendations is essential.DESIGN: Five Cancer Intervention and Surveillance Modeling Network (CISNET) models were evaluated. Lung cancer incidence, mortality, and stage distributions were compared across 4 theoretical scenarios to assess model assumptions regarding 1) sojourn times, 2) stage-specific sensitivities, and 3) screening-induced lung cancer mortality reductions. Analyses were stratified by sex and smoking behavior.RESULTS: Most cancers had sojourn times &lt;5 y (model range [MR]; lowest to highest value across models: 83.5%-98.7% of cancers). However, cancer aggressiveness still varied across models, as demonstrated by differences in proportions of cancers with sojourn times &lt;2 y (MR: 42.5%-64.6%) and 2 to 4 y (MR: 28.8%-43.6%). Stage-specific sensitivity varied, particularly for stage I (MR: 31.3%-91.5%). Screening reduced stage IV incidence in most models for 1 y postscreening; increased sensitivity prolonged this period to 2 to 5 y. Screening-induced lung cancer mortality reductions among lung cancers detected at screening ranged widely (MR: 14.6%-48.9%), demonstrating variations in modeled treatment effectiveness of screen-detected cases. All models assumed longer sojourn times and greater screening-induced lung cancer mortality reductions for women. Models assuming differences in cancer epidemiology by smoking behaviors assumed shorter sojourn times and lower screening-induced lung cancer mortality reductions for heavy smokers.CONCLUSIONS: Model-based personalized screening recommendations are primarily driven by assumptions regarding sojourn times (favoring longer intervals for groups more likely to develop less aggressive cancers), sensitivity (higher sensitivities favoring longer intervals), and screening-induced mortality reductions (greater reductions favoring shorter intervals).IMPLICATIONS: Models suggest longer screening intervals may be feasible and benefits may be greater for women and light smokers.HIGHLIGHTS: Natural-history models are increasingly used to inform lung cancer screening, but causes for variations between models are difficult to assess.This is the first evaluation of these causes and their impact on personalized screening recommendations through easily interpretable metrics.Models vary regarding sojourn times, stage-specific sensitivities, and screening-induced lung cancer mortality reductions.Model outcomes were similar in predicting greater screening benefits for women and potentially light smokers. Longer screening intervals may be feasible for women and light smokers.</p

Supplemental Table 1 from Acceptability of Personalized Lung Cancer Screening Program Among Primary Care Providers

Supplemental Table 1. Respondent demographics</p