COVID-19 Vaccine Policy Implementation and Differential Vaccine Uptake Trajectories in Chicago Communities.

BACKGROUND: Since the onset of the COVID-19 pandemic, multiple public health interventions have been implemented to respond to the rapidly evolving pandemic and community needs. This article describes the scope, timing, and impact of coordinated strategies for COVID-19 vaccine uptake in Chicago for the first year of vaccine distribution. METHODS: Using a series of interviews with public health officials and leaders of community-based organizations (CBOs) who participated in the implementation of the citywide COVID-19 vaccine outreach initiatives, we constructed a timeline of vaccine outreach initiatives. The timeline was matched to the vaccine uptake rates to explore the impact of the vaccine outreach initiatives by community area. Finally, we discussed the nature of policy initiatives and the level of vaccine uptake in relation to community characteristics. RESULTS: The Chicago Department of Public Health (CDPH) implemented myriad vaccine outreach strategies, including mass vaccination sites, improved access, and community-level vaccine campaigns. Protect Chicago+ was the primary vaccine outreach effort initiated by the CDPH, which identified 15 highly vulnerable community areas. More than 2.7 million (67%) Chicagoans completed the vaccine regimen by December 2021. Black (51.3%) Chicagoans were considerably less likely to be vaccinated than Asian (77.6%), White (69.8%), and Hispanic (63.6%) Chicago residents. In addition, there were significant spatial differences in the rate of COVID-19 vaccine completion: predominantly White and Hispanic communities, compared with Black communities, had higher rates of vaccine completion. CONCLUSIONS: The community outreach efforts to improve COVID-19 vaccine uptake in Chicago have shown the importance of community-engaged approaches in pandemic responses. Despite citywide efforts to build community infrastructure, Black communities had relatively lower levels of vaccine uptake than other communities. Broader social restructuring to mitigate disinvestment and residential segregation and to ameliorate medical mistrust will be needed to prepare for future pandemics and disasters.

Cure models with adaptive activation for modeling cancer survival.

We propose a class of cure rate models motivated by analysis of colon cancer and triple-negative breast cancer survival data. This class is indexed by an adaptive activation parameter and a function. We establish that the class is stochastically ordered in the activation parameter and also establish two identifiability results for this class. The first- and last-activation models are members of this class whereas many cure rate models proposed in the literature are also part of this class. We illustrate that while first- and last-activation models may perform poorly under model misspecifications, the proposed model with adaptive activation provides appropriate inference in these cases. We apply the proposed approach to assess treatment-sex interaction on cure rate in a colon cancer study and to assess role of tumor heterogeneity and ethnic disparity in breast cancer.

Minimally Invasive Pneumonectomy vs Open Pneumonectomy: Outcomes and Predictors of Conversion.

BACKGROUND: In the modern era, it is unknown if minimally invasive pneumonectomy for non-small cell lung cancer (NSCLC) provides a survival advantage over open pneumonectomy. METHODS: Patients who underwent pneumonectomy for NSCLC between 2015 and 2020 were queried from the National Cancer Database. Surgical approach was categorized as robot-assisted (RATS), video-assisted thoracoscopic (VATS), or open pneumonectomy on an intention-to-treat basis. Propensity score matching was performed to balance patient cohorts. Univariate and multivariate regression analyses were used to examine the association between surgical approach and 30 and 90-day mortality, and a Cox proportional hazards model was used to assess overall survival. RESULTS: Overall, 3784 patients were identified, including 73% open (n=2776), 19% VATS (n=725), and 8% RATS (n=283). The overall conversion rate from minimally invasive to open was 29.5% (n=298). After propensity matching 212 patients per cohort, there were no differences between open, VATS, and RATS 30-day (9.4% vs 8.5% vs 7.5%, p=0.807, respectively) or 90-day mortality (14.2% vs 12.3% vs 10.4%, p=0.516, respectively). Median overall survival was similar among open (48 months, 95% CI 35.6-64.1), VATS (51.0 months, 95% CI 34.9-72.3), and RATS approaches (50 months, 95% CI 42.6-NA, p=0.560). On multivariate analysis of the matched cohort, there was no association between approach and overall survival. RATS (OR 0.67, 95% CI 0.47-0.94, p=0.020) and neoadjuvant chemotherapy (OR 0.52, 95% CI 0.27-0.98, p=0.045) were found to be protective against conversion to open. CONCLUSIONS: Minimally-invasive pneumonectomy can be performed with short-term and long-term survival that are equivalent to open pneumonectomy.

Conditional survival analysis of patients with resected non-small cell lung cancer.

Background: Conditional survival (CS) analyses provide an estimate of survival accounting for years already survived after treatment. We aim to evaluate the difference between actuarial and conditional survival in patients following lung resection for non-small cell lung cancer (NSCLC). In addition, CS analyses are used to examine whether prognosticators of survival change over time following surgery. Methods: Patients who underwent anatomic lung resection at a single institution for pathologic stage I-IIIA NSCLC between 2010 and 2021 were identified; those who underwent wedge resection for node-negative tumors ≤2 cm were also included. CS estimates were calculated as the probability of remaining disease-free after x years of nonrecurrence (CSx). Kaplan-Meier, log-rank, and Cox proportional hazard methods for examining CS were used for subgroup comparisons and assessing associations with baseline covariates. Results: Overall, 863 patients met the study inclusion criteria, with a median follow-up of 44.1 months. Conditional overall survival (OS) and disease-free survival (DFS) were greater than actuarial rates at all time points after surgery. At the time of resection, male sex (hazard ratio [HR], 1.33; 95% confidence interval [CI], 1.03 to 1.72; P = .032), tumor size >3 cm (HR, 1.17; 95% CI, 1.11-1.23; P < .001), node positivity (HR, 3.31; 95% CI, 2.52-4.33; P < .001), and American Joint Committee on Cancer stage (P < .001) were associated with DFS. However, if a patient lived 3 years without recurrence (CS3), these factors were no longer prognostic of DFS. Conclusions: Conditional survival analyses provide dynamic assessments of OS and DFS after NSCLC resection. After 3 years without recurrence, certain characteristics associated with DFS at the time of surgery no longer prognosticate recurrence.

A Model-free Approach for Testing Association.

The question of association between outcome and feature is generally framed in the context of a model based on functional and distributional forms. Our motivating application is that of identifying serum biomarkers of angiogenesis, energy metabolism, apoptosis, and inflammation, predictive of recurrence after lung resection in node-negative non-small cell lung cancer patients with tumor stage T2a or less. We propose an omnibus approach for testing association that is free of assumptions on functional forms and distributions and can be used as a general method. This proposed maximal permutation test is based on the idea of thresholding, is readily implementable and is computationally efficient. We demonstrate that the proposed omnibus tests maintain their levels and have strong power for detecting linear, nonlinear and quantile-based associations, even with outlier-prone and heavy-tailed error distributions and under nonparametric setting. We additionally illustrate the use of this approach in model-free feature screening and further examine the level and power of these tests for binary outcome. We compare the performance of the proposed omnibus tests with comparator methods in our motivating application to identify preoperative serum biomarkers associated with non-small cell lung cancer recurrence in early stage patients.

Bayesian Criterion Based Variable Selection.

Bayesian approaches for criterion based selection include the marginal likelihood based highest posterior model (HPM) and the deviance information criterion (DIC). The DIC is popular in practice as it can often be estimated from sampling based methods with relative ease and DIC is readily available in various Bayesian software. We find that sensitivity of DIC based selection can be high, in the range of 90 - 100%. However, correct selection by DIC can be in the range of 0 - 2%. These performances persist consistently with increase in sample size. We establish that both marginal likelihood and DIC asymptotically disfavor under-fitted models, explaining the high sensitivities of both criteria. However, mis-selection probability of DIC remains bounded below by a positive constant in linear models with g -priors whereas mis-selection probability by marginal likelihood converges to 0 under certain conditions. A consequence of our results is that not only the DIC cannot asymptotically differentiate between the data-generating and an over-fitted model, but, in fact, it cannot asymptotically differentiate between two over-fitted models as well. We illustrate these results in multiple simulation studies and in a biomarker selection problem on cancer cachexia of non-small cell lung cancer patients. We further study performances of HPM and DIC in generalized linear model as practitioners often choose to use DIC that is readily available in software in such non-conjugate settings.