A Cost Comparison Between Mohs Micrographic Surgery and Conventional Excision for the Treatment of Head and Neck Melanomas In Situ and Thin Melanomas.

BACKGROUND: Variation in operative setting and surgical technique exists when treating specialty site melanomas. There are limited data comparing costs among surgical modalities. OBJECTIVE: To evaluate the costs of head and neck melanoma surgery performed with Mohs micrographic surgery or conventional excision in the operating room or office-based settings. METHODS: A retrospective cohort study was performed on patients aged 18 years and older with surgically treated head and neck melanoma in 2 cohorts, an institutional cohort and an insurance claims cohort, for the years 2008-2019. The primary outcome was total cost of care for a surgical encounter, provided in the form of insurance reimbursement data. A generalized linear model was used to adjust for covariates affecting differences between treatment groups. RESULTS: In the institutional and insurance claims cohorts, average adjusted treatment cost was highest in the conventional excision-operating room treatment group, followed by the Mohs surgery and conventional excision-office setting ( p < .001). CONCLUSION: These data demonstrate the important economic role the office-based setting has for head and neck melanoma surgery. This study allows cutaneous oncologic surgeons to better understand the costs of care involved in head and neck melanoma treatment. Cost awareness is important for shared decision-making discussions with patients.

Incidence of Timely Outpatient Follow-Up Care After Emergency Department Encounters for Acute Heart Failure.

BACKGROUND: Patients who are discharged from the emergency department (ED) after an encounter for acute heart failure are at high risk for return hospitalization. These patients may benefit from timely outpatient follow-up care to reassess volume status, adjust medications, and reinforce self-care strategies. This study examines the incidence of outpatient follow-up care after ED encounters for acute heart failure and describes patient characteristics associated with obtaining timely follow-up care. METHODS: We conducted a retrospective cohort study using an administrative claims database for a large US commercial insurer, from January 1, 2012 to June 30, 2019. Participants included adult patients discharged from the ED with principal diagnosis of acute heart failure. The primary outcome was obtaining an in-person outpatient clinic visit for heart failure within 30 days. We also examined the competing risk of all-cause hospitalization within 30 days and without an intervening outpatient clinic visit. We estimated competing risk regression models to identify patient characteristics associated with obtaining outpatient follow-up and report cause-specific hazard ratios. RESULTS: The cohort included 52 732 patients, with mean age of 73.9 years (95% CI, 73.8-74.0) and 27 395 (52.0% [95% CI, 51.5-52.4]) female patients. Within 30 days of the ED encounter, 12 279 (23.2%) patients attended an outpatient clinic visit for heart failure, with 8382 (15.9%) patients hospitalized before they could obtain an outpatient clinic visit. In the adjusted analysis, patients that were younger, women, reporting non-Hispanic Black race, and had fewer previous clinic visits were less likely to obtain outpatient follow-up care. CONCLUSIONS: Few patients obtain timely outpatient follow-up after ED visits for heart failure, although nearly 20% require hospitalization within 30 days. Improved transitions following discharge from the ED may represent an opportunity to improve outcomes for patients with acute heart failure.

A regression framework for a probabilistic measure of cost-effectiveness.

To make informed health policy decisions regarding a treatment, we must consider both its cost and its clinical effectiveness. In past work, we introduced the net benefit separation (NBS) as a novel measure of cost-effectiveness. The NBS is a probabilistic measure that characterizes the extent to which a treated patient will be more likely to experience benefit as compared to an untreated patient. Due to variation in treatment response across patients, uncovering factors that influence cost-effectiveness can assist policy makers in population-level decisions regarding resource allocation. In this paper, we introduce a regression framework for NBS in order to estimate covariate-specific NBS and find determinants of variation in NBS. Our approach is able to accommodate informative cost censoring through inverse probability weighting techniques, and addresses confounding through a semiparametric standardization procedure. Through simulations, we show that NBS regression performs well in a variety of common scenarios. We apply our proposed regression procedure to a realistic simulated data set as an illustration of how our approach could be used to investigate the association between cancer stage, comorbidities and cost-effectiveness when comparing adjuvant radiation therapy and chemotherapy in post-hysterectomy endometrial cancer patients.

Leveraging machine learning predictive biomarkers to augment the statistical power of clinical trials with baseline magnetic resonance imaging.

A key factor in designing randomized clinical trials is the sample size required to achieve a particular level of power to detect the benefit of a treatment. Sample size calculations depend upon the expected benefits of a treatment (effect size), the accuracy of measurement of the primary outcome, and the level of power specified by the investigators. In this study, we show that radiomic models, which leverage complex brain MRI patterns and machine learning, can be utilized in clinical trials with protocols that incorporate baseline MR imaging to significantly increase statistical power to detect treatment effects. Akin to the historical control paradigm, we propose to utilize a radiomic prediction model to generate a pseudo-control sample for each individual in the trial of interest. Because the variability of expected outcome across patients can mask our ability to detect treatment effects, we can increase the power to detect a treatment effect in a clinical trial by reducing that variability through using radiomic predictors as surrogates. We illustrate this method with simulations based on data from two cohorts in different neurologic diseases, Alzheimer's disease and glioblastoma multiforme. We present sample size requirements across a range of effect sizes using conventional analysis and models that include a radiomic predictor. For our Alzheimer's disease cohort, at an effect size of 0.35, total sample size requirements for 80% power declined from 246 to 212 for the endpoint cognitive decline. For our glioblastoma multiforme cohort, at an effect size of 1.65 with the endpoint survival time, total sample size requirements declined from 128 to 74. This methodology can decrease the required sample sizes by as much as 50%, depending on the strength of the radiomic predictor. The power of this method grows with increased accuracy of radiomic prediction, and furthermore, this method is most helpful when treatment effect sizes are small. Neuroimaging biomarkers are a powerful and increasingly common suite of tools that are, in many cases, highly predictive of disease outcomes. Here, we explore the possibility of using MRI-based radiomic biomarkers for the purpose of improving statistical power in clinical trials in the contexts of brain cancer and prodromal Alzheimer's disease. These methods can be applied to a broad range of neurologic diseases using a broad range of predictors of outcome to make clinical trials more efficient.

Changes in Supplemental Nutrition Assistance Program Policies and Diabetes Prevalence: Analysis of Behavioral Risk Factor Surveillance System Data From 2004 to 2014.

OBJECTIVE: Food insecurity is associated with diabetes. The Supplemental Nutrition Assistance Program (SNAP) is the largest U.S. government food assistance program. Whether such programs impact diabetes trends is unclear. The objective of this study was to evaluate the association between changes in state-level policies affecting SNAP participation and county-level diabetes prevalence. RESEARCH DESIGN AND METHODS: We evaluated the association between change in county-level diabetes prevalence and changes in the U.S. Department of Agriculture SNAP policy index-a measure of adoption of state-level policies associated with increased SNAP participation (higher value indicating adoption of more policies associated with increased SNAP participation; range 1-10)-from 2004 to 2014 using g-computation, a robust causal inference methodology. The study included all U.S. counties with diabetes prevalence data available from the Centers for Disease Control and Prevention's U.S. Diabetes Surveillance System. RESULTS: The study included 3,135 of 3,143 U.S. counties. Mean diabetes prevalence increased from 7.3% (SD 1.3) in 2004 to 9.1% (SD 1.8) in 2014. The mean SNAP policy index increased from 6.4 (SD 0.9) to 8.2 (SD 0.6) in 2014. After accounting for changes in demographic-, economic-, and health care-related variables and the baseline SNAP policy index, a 1-point absolute increase in the SNAP policy index between 2004 and 2014 was associated with a 0.050 (95% CI 0.042-0.057) percentage point lower diabetes prevalence per year. CONCLUSIONS: State policies aimed at increasing SNAP participation were independently associated with a lower rise in diabetes prevalence between 2004 and 2014.

Trends in Patient Volume by Hospital Type and the Association of These Trends With Time to Cancer Treatment Initiation.

Importance: Increasing demand for cancer care may be outpacing the capacity of hospitals to provide timely treatment, particularly at referral centers such as National Cancer Institute (NCI)-designated and academic centers. Whether the rate of patient volume growth has strained hospital capacity to provide timely treatment is unknown. Objective: To evaluate trends in patient volume by hospital type and the association between a hospital's annual patient volume growth and time to treatment initiation (TTI) for patients with cancer. Design, Setting, and Participants: This retrospective, hospital-level, cross-sectional study used longitudinal data from the National Cancer Database from January 1, 2007, to December 31, 2016. Adult patients older than 40 years who had received a diagnosis of 1 of the 10 most common incident cancers and initiated their treatment at a Commission on Cancer-accredited hospital were included. Data were analyzed between December 19, 2019, and March 27, 2020. Exposures: The mean annual rate of patient volume growth at a hospital. Main Outcomes and Measures: The main outcome was TTI, defined as the number of days between diagnosis and the first cancer treatment. The association between a hospital's mean annual rate of patient volume growth and TTI was assessed using a linear mixed-effects model containing a patient volume × time interaction. The mean annual change in TTI over the study period by hospital type was estimated by including a hospital type × time interaction term. Results: The study sample included 4 218 577 patients (mean [SD] age, 65.0 [11.4] years; 56.6% women) treated at 1351 hospitals. From 2007 to 2016, patient volume increased 40% at NCI centers, 25% at academic centers, and 8% at community hospitals. In 2007, the mean TTI was longer at NCI and academic centers than at community hospitals (NCI: 50 days [95% CI, 48-52 days]; academic: 43 days [95% CI, 42-44 days]; community: 37 days [95% CI, 36-37 days]); however, the mean annual increase in TTI was greater at community hospitals (0.56 days; 95% CI, 0.49-0.62 days) than at NCI centers (-0.73 days; 95% CI, -0.95 to -0.51 days) and academic centers (0.14 days; 95% CI, 0.03-0.26 days). An annual volume growth rate of 100 patients, a level observed at less than 1% of hospitals, was associated with a mean increase in TTI of 0.24 days (95% CI, 0.18-0.29 days). Conclusions and Relevance: In this cross-sectional study, from 2007 to 2016, across the studied cancer types, patients increasingly initiated their cancer treatment at NCI and academic centers. Although increases in patient volume at these centers outpaced that at community hospitals, faster growth was not associated with clinically meaningful treatment delays.

Net benefit separation and the determination curve: A probabilistic framework for cost-effectiveness estimation.

Considerations regarding clinical effectiveness and cost are essential in comparing the overall value of two treatments. There has been growing interest in methodology to integrate cost and effectiveness measures in order to inform policy and promote adequate resource allocation. The net monetary benefit aggregates information on differences in mean cost and clinical outcomes; the cost-effectiveness acceptability curve was developed to characterize the extent to which the strength of evidence regarding net monetary benefit changes with fluctuations in the willingness-to-pay threshold. Methods to derive insights from characteristics of the cost/clinical outcomes besides mean differences remain undeveloped but may also be informative. We propose a novel probabilistic measure of cost-effectiveness based on the stochastic ordering of the individual net benefit distribution under each treatment. Our approach is able to accommodate features frequently encountered in observational data including confounding and censoring, and complements the net monetary benefit in the insights it provides. We conduct a range of simulations to evaluate finite-sample performance and illustrate our proposed approach using simulated data based on a study of endometrial cancer patients.

Impact of Regression to the Mean on the Synthetic Control Method: Bias and Sensitivity Analysis.

To make informed policy recommendations from observational panel data, researchers must consider the effects of confounding and temporal variability in outcome variables. Difference-in-difference methods allow for estimation of treatment effects under the parallel trends assumption. To justify this assumption, methods for matching based on covariates, outcome levels, and outcome trends-such as the synthetic control approach-have been proposed. While these tools can reduce bias and variability in some settings, we show that certain applications can introduce regression to the mean (RTM) bias into estimates of the treatment effect. Through simulations, we show RTM bias can lead to inflated type I error rates and bias toward the null in typical policy evaluation settings. We develop a novel correction for RTM bias that allows for valid inference and show how this correction can be used in a sensitivity analysis. We apply our proposed sensitivity analysis to reanalyze data concerning the effects of California's Proposition 99, a large-scale tobacco control program, on statewide smoking rates.