Assessment of Regional Variability in COVID-19 Outcomes Among Patients With Cancer in the United States

Key Points Question To what extent are spatiotemporal trends in the COVID-19 pandemic in the United States associated with outcomes for patients with cancer infected with SARS-CoV-2? Findings This cohort study of 4749 patients with cancer and COVID-19 found no significant differences in outcomes across the 9 US census divisions. Overall, outcomes significantly improved between March and December 2020, and treatment at cancer centers in less densely populated counties was associated with better outcomes. Meaning These findings suggest that understanding the heterogeneity in COVID-19 outcomes between cancer centers could guide resource allocation and help the oncology community improve COVID-19 outcomes for this patient population.

Approved Project Title

Regional Variability and Urban-Rural Health Disparities in Outcomes of Patients with Cancer and COVID-19 Approved Project PIs
Jessica Hawley and Clara Hwang 1 (a) Manuscript title Assessment of US Regional Variability in COVID-19 Outcomes Among Patients With Cancer 3 Objectives State specific objectives, including any prespecified hypotheses H0: Clinical outcomes and US census region are independent in patients with cancer and COVID-19 in adjusted models (no difference in death (30-day all-cause mortality) or composite endpoint (death, rate of ICU admission, rate of mechanical ventilation). HA: Clinical outcomes and US census region are associated in patients with cancer and COVID-19. 4 Study design Retrospective, multi-center cohort study 5 Setting Cohort study of patients with current or past history of cancer and lab-confirmed SARS-CoV-2 infection from >120 participating CCC19 sites. Data analyzed from March 15, 2020 -November 30, 2020. Registry built and maintained as electronic REDCap database at VUMC. 6 Participants (a) Give the eligibility criteria, and the sources and methods of selection of participants. Describe methods of follow-up Subjects included in this analysis were those with laboratory-confirmed SARS-CoV-2 infection and past or current history of invasive malignancy, from the U.S., and entered into the CCC19 database between 3/17/2020 and 11/30/2020 with follow-up data reported through 12/31/2020. We excluded records of presumptive COVID-19 cases, patients < 18 years of age, patients with incomplete follow-up data, cases of non-invasive cancers and premalignant conditions or non-melanoma noninvasive skin cancers, cases from outside the U.S., and records with quality score >4. (b) For matched studies, give matching criteria and number of exposed and unexposed Not a matched study. Variables of interest are defined by the CCC19 database registry and entered by each local site's data abstractor.
10 Study size Explain how the study size was arrived at Case volume dependent on data abstracters at each site.
11 Quantitative variables Explain how quantitative variables will be handled in the analyses. If applicable, describe which groupings will be chosen and why As per above 12 Statistical methods (a) Describe all statistical methods, including those to be used to control for confounding Covariates (listed above under potential confounders) and binary outcomes will be summarized across 9 census subregions using standard descriptive statistics. Multivariable generalized linear mixed-effects models (with a logit link for binary outcomes and center-level random effects) will be used to (1) estimate adjusted covariate-outcome associations and (2) estimate adjusted subregion-level outcome rates overall and at 3-month time intervals. For secondary outcomes, the model will include an offset for (log) follow-up time. A list of potential tables and figures is provided below. (b) Describe any methods that will be used to examine subgroups and interactions None (c) Explain how missing data will be addressed Multiple imputation will be used to impute missing and unknown data for all variables included in the analysis, with some exceptions: unknown ECOG performance score and unknown cancer status will not be imputed and treated as a separate category in analyses. Imputation will be performed on the largest dataset possible (that is, after removing test cases and other manual exclusions, but before applying specific exclusion criteria). At least 10 imputations will be generated.
(d) If applicable, explain how loss to follow-up will be addressed Excluded if no data on 30-day follow-up form. We will extend follow-up time (through 12/31/2020) to ensure that follow-up for the primary outcome, 30-day mortality, is complete to the best of our ability.

Approved Project Variables
Once the project design and SAP have been approved for your project, this document will be used to specify the exact outcomes and variables to be used in your analysis. Please provide as much information as you can, including the existing variable name if you know it. Use of existing variables will decrease the amount of time that it takes to get your project to the analysis phase, but we will endeavor to add any needed derived variables based on your project needs. Existing variables can be found in two places within the GitHub repo: the Data Dictionary ("CCC19_DataDictionary.csv"), which includes the native variables found in the survey, and the list of Derived Variables ("CCC19_Derived_Variables_Spreadsheet.xlsx"). The composite outcome reflected the occurrence of any of the following: admission to an intensive care unit, receipt of mechanical ventilation, and total all-cause mortality. Analyses of the composite outcome were limited to 4,561 patients within non-missing data. b Odds ratios greater than 1 indicate higher odds of 30-day all-cause mortality. c Odds ratios greater than 1 indicate higher odds of admission to an intensive care unit, receipt of mechanical ventilation, or total all-cause mortality. d Adjusted for age, sex, race and ethnicity, smoking status, obesity, cardiovascular comorbidities, pulmonary comorbidities, renal disease, diabetes mellitus, type of malignancy, cancer status, Eastern Cooperative Oncology Group performance status, anti-COVID-19 treatments, and month of COVID-19 diagnosis. P values for evaluating the null hypothesis of equality in odds ratios across census divisions (8 degrees of freedom): 30-day mortality, 0.42; composite outcome, 0.73.