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Background Oncology rapidly shifted to telemedicine in response to the COVID‐19 pandemic. Telemedicine can increase access to healthcare, but recent research has shown disparities exist with telemedicine use during the pandemic. This study evaluated health disparities associated with telemedicine uptake during the COVID‐19 pandemic among cancer patients in a tertiary care academic medical center. Methods This retrospective cohort study evaluated telemedicine use among adult cancer patients who received outpatient medical oncology care within a tertiary care academic healthcare system between January and September 2020. We used multivariable mixed‐effects logistic regression models to determine how telemedicine use varied by patient race/ethnicity, primary language, insurance status, and income level. We assessed geospatial links between zip‐code level COVID‐19 infection rates and telemedicine use. Results Among 29,421 patient encounters over the study period, 8,541 (29%) were delivered via telemedicine. Several groups of patients were less likely to use telemedicine, including Hispanic (adjusted odds ratio [aOR] 0.86, p = 0.03), Asian (aOR 0.79, p = 0.002), Spanish‐speaking (aOR 0.71, p = 0.0006), low‐income (aOR 0.67, p < 0.0001), and those with Medicaid (aOR 0.66, p < 0.0001). Lower rates of telemedicine use were found in zip codes with higher rates of COVID‐19 infection. Each 10% increase in COVID‐19 infection rates was associated with an 8.3% decrease in telemedicine use (p = 0.002). Conclusions This study demonstrates racial/ethnic, language, and income‐level disparities with telemedicine use, which ultimately led patients with the highest risk of COVID‐19 infection to use telemedicine the least. Additional research to better understand actionable barriers will help improve telemedicine access among our underserved populations. At a tertiary care academic medical center, Hispanic, Asian, Spanish‐speaking, low‐income, and patients with Medicaid insurance were less likely to utilize telemedicine. Additionally, lower rates of telemedicine use were found in zip codes with higher rates of COVID‐19 infection. As such, there were significant racial/ethnic, language, and income‐level disparities with telemedicine use, which ultimately led cancer patients with the highest risk of COVID‐19 infection to use telemedicine the least.

Background Missing data in confounding variables present a frequent challenge in generating evidence using real-world data, including electronic health records (EHR). Our objective was to apply a recently published toolkit for characterizing missing data patterns and based on the toolkit results about likely missingness mechanisms, illustrate the decision-making process for analyses in an empirical case example. Methods We utilized the Structural Missing Data Investigations (SMDI) toolkit to characterize missing data patterns in the context of a pharmacoepidemiology study comparing cardiovascular outcomes of initiating sodium-glucose-cotransporter-2 inhibitors (SGLT2i) and dipeptidyl peptidase‐4 inhibitors (DPP‐4i) among older adults. The study used a linked EHR-Medicare claims dataset from Duke Health patients (2015–2017), focusing on partially observed confounders from EHR data (HbA1c lab and body mass index [BMI] values). Our analysis incorporated SMDI's descriptive functions and diagnostic tests to explore missingness patterns and determine missingness mitigation approaches. We used findings from these investigations to inform estimation of adjusted hazard ratios comparing the two classes of medications. Results High levels of missingness were noted for important confounding variables including HbA1c (63.6%) and BMI (16.5%). Diagnostic tests resulted in output that described: 1) the distributions of patient characteristics, exposure, and outcome between patients with or without an observed value of the partially observed covariate, 2) the ability to predict missingness based on observed covariates, and 3) estimate if the missingness of a partially observed covariate is differential with respect to the outcome. There was evidence that missingness could be sufficiently described using observed data, which allowed multiple imputation by chained equations using random forests to address missing confounder data in estimating treatment effects. Multiple imputation resulted in improved alignment of effect estimates with previous studies. Conclusions We were able to demonstrate the practical application of the SMDI toolkit in a real-world setting. Application of the SMDI toolkit and the resulting insights of potential missingness patterns can inform the choice of appropriate analytic methods and increase transparency of research methods in handling missing data. This type of approach can inform analytic decision making and may increase our ability to generate evidence from real-world data.

BackgroundCigarette smoking is a risk factor for mortality in several genitourinary cancers, likely due to accumulation of carcinogens in urine. However, in prostate cancer (PC) the link has been less studied. We evaluated differences in prostate cancer-specific mortality (PCSM) between current smokers, past smokers, and never smokers diagnosed with PC.MethodsThis was a retrospective cohort study of PCSM in men diagnosed with PC between 2000 and 2015 treated in the US Veterans Affairs health care system, using competing risk regression analyses.ResultsThe cohort included 73,668 men (current smokers: 22,608 (30.7%), past smokers: 23,695 (32.1%), and never smokers: 27,365 (37.1%)). Median follow-up was 5.9 years. Current smoker patients were younger at presentation (median age current: 63, never: 66; p < 0.001), and had more advanced disease stage (stage IV disease current: 5.3%, never: 4.3%; p < 0.04). The 10-year incidence of PCSM was 5.2%, 4.8%, and 4.5% for current, past, and never smokers, respectively. On competing risk regression, current smoking was associated with increased PCSM (subdistribution hazard ratio: 1.14, 95% confidence interval: (1.05–1.24), p = 0.002), whereas past smoking was not. Hierarchical regression suggests that this increased risk was partially attributable to tumor characteristics.ConclusionsSmoking at the time of diagnosis is associated with a higher risk of dying from PC as well as other causes of death. In contrast, past smoking was not associated with PCSM suggesting that smoking may be a modifiable risk factor. PC diagnosis may be an important opportunity to discuss smoking cessation.

BackgroundIncreasing evidence indicates an association between statins and reduced prostate cancer-specific mortality (PCSM). However, significant bias may exist in these studies. One particularly challenging bias to assess is the healthy user effect, which may be quantified by screening patterns. We aimed to evaluate the association between statin use, screening, and PCSM in a dataset with detailed longitudinal information.MethodsWe used the Veterans Affairs Informatics and Computing Infrastructure to assemble a cohort of patients diagnosed with prostate cancer (PC) between 2000 and 2015. We collected patient-level demographic, comorbidity, and tumor data. We also assessed markers of preventive care utilization including cholesterol and prostate specific antigen (PSA) screening rates. Patients were considered prediagnosis statin users if they had at least one prescription one or more years prior to PC diagnosis. We evaluated PCSM using hierarchical Fine-Gray regression models and all-cause mortality (ACM) using a cox regression model.ResultsThe final cohort contained 68,432 men including 40,772 (59.6%) prediagnosis statin users and 27,660 (40.4%) nonusers. Prediagnosis statin users had higher screening rates than nonusers for cholesterol (90 vs. 69%, p < 0.001) and PSA (76 vs. 67%, p < 0.001). In the model which excluded screening, prediagnosis statin users had improved PCSM (SHR 0.90, 95% CI 0.84–0.97; p = 0.004) and ACM (HR 0.96, 95% CI 0.93–0.99; p = 0.02). However, after including cholesterol and PSA screening rates, prediagnosis statin users and nonusers showed no differences in PCSM (SHR 0.98, 95% CI 0.91–1.06; p = 0.59) or ACM (HR 1.02, 95% CI 0.98–1.05; p = 0.25).ConclusionWe found that statin users tend to have more screening than nonusers. When we considered screening utilization, we observed no relationship between statin use before a prostate cancer diagnosis and prostate cancer mortality.

BackgroundThere is concern that African-American men (AA) with low-risk prostate cancer may present with more aggressive disease and thus may not be candidates for active surveillance (AS). However, it is uncertain if poorer outcomes are due to disparities in access to medical care rather than true biological differences.MethodsObservational cohort study of patients diagnosed with low-risk PC—Gleason score ≤6, clinical tumor stage ≤2A, and prostate specific antigen (PSA) level ≤10—at US Department of Veterans Affairs between January 1, 2001 and October 31, 2015 and treated with radical prostatectomy. Outcomes included upgrading to Gleason Grade Group 2 (GG2), GG ≥ 3, PSA recurrence, pathologic tumor stage ≥3, positive surgical margins, and all-cause mortality.ResultsA total of 2857 men (AA: 835 White: 2022) with a median follow-up of 7.1 years. Overall, there was no significant difference between AA and White men in upgrading to GG ≥ 3 (RR = 1.18, p = 0.43), tumor stage ≥3 (RR = 0.95, p = 0.74), positive surgical margins (RR = 1.14, p = 0.20), PSA recurrence (SHR = 1.26, p = 0.06), and all-cause mortality (SHR = 1.26, p = 0.16). However, there was a significant increase in upgrading for AA to GG2 (RR = 1.49, p < 0.01).ConclusionsThere was no significant difference in most adverse pathologic outcomes between AA and White patients. However, GG2 upgrading was more common in AA men. The implication is that AA may need to undergo additional evaluation, such as a biopsy MRI, before initiating AS. Whether the increase in GG2 upgrading will lead to poorer long-term clinical outcomes such as metastasis and PCSM also requires further investigation.

Purpose Cancer patients frequently utilize the emergency department (ED) for a variety of diagnoses both related to and unrelated to their cancer, yet ED outcomes for cancer patients are not well documented. This study sought to define risks and identify predictors for inpatient admission and hospital mortality among cancer patients presenting to the ED. Patients and Methods We utilized the National Emergency Department Sample to identify patients with and without a diagnosis of cancer presenting to the ED between January 2016 and December 2018. We used multivariable mixed‐effects logistic regression models to assess the influence of cancer on outcomes of hospital admission after the ED visit and hospital mortality for the whole patient cohort and individual presenting diagnoses. Results There were 340 million weighted ED visits, of which 8.3 million (2.3%) were associated with a cancer diagnosis. Compared to non‐cancer patients, patients with cancer had an increased risk of inpatient admission (64.7% vs. 14.8%; p < 0.0001) and hospital mortality (4.6% vs. 0.5%; p < 0.0001). For each of the top 15 presenting diagnoses, cancer patients had increased risks of hospitalization (odds ratio [OR] range 2.0–13.2) or death (OR range 2.1–14.4). Although our dataset does not contain reliable estimation of stage, cancer site was the most robust individual predictor associated with the risk of hospitalization or death compared to other clinical or system‐related factors. Conclusions Cancer patients in the ED have high risks for hospital admission and death when compared to patients without cancer. Cancer patients represent a distinct population and may benefit from cancer‐specific risk stratification or focused interventions to improve outcomes. On average, 2.8 million (2.3%) patients visit the emergency department. For each of the top 15 presenting diagnoses cancer patients had increased risks of hospitalization and death compared to non‐cancer patients. Among cancer patients, cancer site was the most robust individual predictor associated with risk of hospitalization or death.

BackgroundThere is conflicting evidence regarding the association between androgen deprivation therapy (ADT) for prostate cancer (PC) and the risk of developing stroke and thromboembolic events. Our study evaluated the association between ADT use and development of stroke, transient ischemic attack (TIA), deep vein thrombosis (DVT), and pulmonary embolism (PE) in a homogenous group of men with PC treated with definitive radiation therapy (RT) after controlling for multiple sources of confounding.MethodsObservational cohort study of patients diagnosed with PC at the US Department of Veterans Affairs between 1 January 2001 and October 31, 2015 and treated with definitive RT. Exposure was initiation of ADT within 1 year of PC diagnosis. Primary outcomes were development of stroke, TIA, DVT, or PE.Results44,246 men with median follow-up of 6.8 years. The overall cumulative incidences of stroke, TIA, DVT, and PE at 10 years were 6.0, 3.0, 3.4, and 1.9%, respectively. In the multivariable competing risks model, there was a significant association between ADT and stroke (subdistribution hazard ratio (SHR) = 1.19, 95% CI = 1.09–1.30, p < 0.01), TIA (SHR = 1.24, 95% CI = 1.08–1.41, p < 0.01), and DVT (SHR = 1.18, 95% CI = 1.04–1.34, p < 0.01). ADT was only associated with PE in men receiving ADT for > 1 year (SHR = 1.34, 95% CI = 1.06–1.69, p-value = 0.03).ConclusionWe observed an increase in the risk of stroke, TIA, and DVT in men receiving ADT and an increased risk of PE in men receiving long-term ADT. These results highlight concerns regarding long-term risks of ADT on stroke and thromboembolic events in the treatment of PC.

Background Despite guideline recommendations, utilization of low‐dose computed tomography (LDCT) for lung cancer screening remains low. The driving factors behind these low rates and the real‐world effect of LDCT utilization on lung cancer outcomes remain limited. Methods We identified patients diagnosed with non‐small cell lung cancer (NSCLC) from 2015 to 2017 within the Veterans Health Administration. Multivariable logistic regression assessed the influence of LDCT screening on stage at diagnosis. Lead time correction using published LDCT lead times was performed. Cancer‐specific mortality (CSM) was evaluated using Fine–Gray regression with non‐cancer death as a competing risk. A lasso machine learning model identified important predictors for receiving LDCT screening. Results Among 4664 patients, mean age was 67.8 with 58‐month median follow‐up, 95% CI = [7–71], and 118 patients received ≥1 screening LDCT before NSCLC diagnosis. From 2015 to 2017, LDCT screening increased (0.1%–6.6%, mean = 1.3%). Compared with no screening, patients with ≥1 LDCT were more than twice as likely to present with stage I disease at diagnosis (odds ratio [OR] 2.16 [95% CI 1.46–3.20]) and less than half as likely to present with stage IV (OR 0.38 [CI 0.21–0.70]). Screened patients had lower risk of CSM even after adjusting for LDCT lead time (subdistribution hazard ratio 0.60 [CI 0.42–0.85]). The machine learning model achieved an area under curve of 0.87 and identified diagnosis year and region as the most important predictors for receiving LDCT. White, non‐Hispanic patients were more likely to receive LDCT screening, whereas minority, older, female, and unemployed patients were less likely. Conclusions Utilization of LDCT screening is increasing, although remains low. Consistent with randomized data, LDCT‐screened patients were diagnosed at earlier stages and had lower CSM. LDCT availability appeared to be the main predictor of utilization. Providing access to more patients, including those in diverse racial and socioeconomic groups, should be a priority. In this retrospective cohort study from 2015 to 2017, we found that low‐dose computed tomography (LDCT) screening usage remains low (1.3%), significantly increases the odds of stage I diagnosis (odds ratio [OR] 2.16), decreases odds of stage IV diagnosis (OR 0.38), and decreases risk of cancer‐specific mortality (subdistribution hazard ratio 0.60). LDCT usage appears to be driven by regional and temporal differences LDCT access. Increasing access for all patients should be a priority toward reducing lung cancer mortality.

Abstract Evidence is accumulating that individuals with cancer diagnoses exhibit Alzheimer’s disease (AD) and related dementia (ADRD) risk profiles that differ from the general population of U.S. older adults. In this study we used SEER-Medicare data to compare the relative risk of AD/ADRD between individuals with slow-progressive cancers and the non-cancer general population. The study cohort included individuals age 65+ (N=2,023,054) with a primary diagnosis (1999-2017) of one of nine slow progressive cancers (breast, colorectal, prostate, uterine, kidney, ovarian, and urinary bladder cancers, as well as lymphomas and melanoma) and no clinical record of AD/ADRD prior to cancer diagnosis. This cohort was then matched by age to a comparable non-cancer population (N=1,142,641). The hazard ratios of AD/ADRD for each cancer compared to the non-cancer cohort were evaluated individually in 29 age-specific groups for each cancer type. All cancers had similar patterns of dependence for post-cancer AD/ADRD risks. We found that the presence of cancer was associated with higher risk of AD/ADRD at age at diagnosis 65-75; the relative risks decline with age at diagnoses becoming protective at advanced ages. Furthermore, for any given age at diagnosis the relative risk of AD/ADRD (i.e., cancer vs. non-cancer) also declines with time. Detailed discussion of possible causes of these effects including cancer treatment, genetic variation, possible trade-off effects, common risk and protective factors, possibly lower administration and adherence of AD/ADRD diagnostic procedures for individuals with cancer, and the roles of competing risks (first of all due to death cases) is presented.

Colorectal cancer (CRC) is the second leading cancer-related cause of death in the United States. However, survivorship has been increasing. Both cancer survivors and underserved populations experience unique health-related challenges and disparities that may exist among long-term CRC survivors as it relates to routine preventive care, specifically pneumococcal (PNM) vaccination. The aim of this study was to explore the relationship between long-term CRC survival and the receipt of PNM vaccine among Hispanic Medicare recipients compared with non-Hispanic populations. This study is a cross-sectional analysis of the Surveillance, Epidemiology, and End Results (SEER)-Medicare claims data examining ethnic differences in the receipt of PNM vaccination among long-term CRC survivors. Multivariable logistic regression models considered Hispanic ethnicity while controlling for sociodemographic characteristics, comorbidity score, age, tumor stage, and SEER registry. Our sample revealed 32,501 long-term CRC survivors, and 1509 identified as Hispanic (4.64%) based on an established SEER algorithm. In total, 16,252 CRC survivors, or 50.00% of our sample, received a PNM vaccination. We found that Hispanic CRC survivors had 10% decreased odds of having received a PNM vaccine compared with non-Hispanic survivors (P=.03). Disparities likely exist in the utilization of PNM vaccination among long-term CRC survivors. Among Medicare beneficiaries, the use of claims data regarding PNM vaccination highlights the relatively poor utilization of guideline-directed preventive care.