The Impact of the COVID-19 Pandemic on Tobacco Treatment Program Implementation at NCI-Designated Cancer Centers.

INTRODUCTION: The COVID-19 pandemic disrupted cancer screening and treatment delivery, but COVID-19's impact on tobacco cessation treatment for cancer patients who smoke has not been widely explored. METHODS: We conducted a sequential cross-sectional analysis of data collected from 34 NCI-designated cancer centers participating in NCI's Cancer Center Cessation Initiative (C3I), across three reporting periods: one prior to COVID-19 (January-June 2019) and two during the pandemic (January-June 2020, January-June 2021). Using McNemar's Test of Homogeneity, we assessed changes in services offered and implementation activities over time. RESULTS: The proportion of centers offering remote treatment services increased each year for Quitline referrals (56%, 68%, and 91%; p=.000), telephone counseling (59%, 79%, and 94%; p=.002), and referrals to Smokefree TXT (27%, 47%, and 56%; p=.006). Centers offering video-based counseling increased from 2020 to 2021 (18% to 59%; p=.006), Fewer than 10% of centers reported laying off tobacco treatment staff. Compared to early 2020, in 2021 C3I centers reported improvements in their ability to maintain staff and clinician morale, refer to external treatment services, train providers to deliver tobacco treatment, and modify clinical workflows. DISCUSSION: The COVID-19 pandemic necessitated rapid transition to new telehealth program delivery of tobacco treatment for patients with cancer. C3I cancer centers adjusted rapidly to challenges presented by the pandemic, with improvements reported in staff morale and ability to train providers, refer patients to tobacco treatment, and modify clinical workflows. These factors enabled C3I centers to sustain evidence-based tobacco treatment implementation during and beyond the COVID-19 pandemic. IMPLICATIONS: This work describes how NCI-designated cancer centers participating in the Cancer Center Cessation Initiative (C3I) adapted to challenges to sustain evidence-based tobacco use treatment programs during the COVID-19 pandemic. This work offers a model for resilience and rapid transition to remote tobacco treatment services delivery and proposes a policy and research agenda for telehealth services as an approach to sustaining evidence-based tobacco treatment programs.

Comparing mental and physical health of U.S. veterans by VA healthcare use: implications for generalizability of research in the VA electronic health records.

OBJECTIVE: The Department of Veterans Affairs' (VA) electronic health records (EHR) offer a rich source of big data to study medical and health care questions, but patient eligibility and preferences may limit generalizability of findings. We therefore examined the representativeness of VA veterans by comparing veterans using VA healthcare services to those who do not. METHODS: We analyzed data on 3051 veteran participants age ≥ 18 years in the 2019 National Health Interview Survey. Weighted logistic regression was used to model participant characteristics, health conditions, pain, and self-reported health by past year VA healthcare use and generate predicted marginal prevalences, which were used to calculate Cohen's d of group differences in absolute risk by past-year VA healthcare use. RESULTS: Among veterans, 30.4% had past-year VA healthcare use. Veterans with lower income and members of racial/ethnic minority groups were more likely to report past-year VA healthcare use. Health conditions overrepresented in past-year VA healthcare users included chronic medical conditions (80.6% vs. 69.4%, d = 0.36), pain (78.9% vs. 65.9%; d = 0.35), mental distress (11.6% vs. 5.9%; d = 0.47), anxiety (10.8% vs. 4.1%; d = 0.67), and fair/poor self-reported health (27.9% vs. 18.0%; d = 0.40). CONCLUSIONS: Heterogeneity in veteran sociodemographic and health characteristics was observed by past-year VA healthcare use. Researchers working with VA EHR data should consider how the patient selection process may relate to the exposures and outcomes under study. Statistical reweighting may be needed to generalize risk estimates from the VA EHR data to the overall veteran population.

The impact of COVID-19 pandemic on depression and anxiety symptoms: Findings from the United Arab Emirates Healthy Future (UAEHFS) cohort study.

BACKGROUND: Significant concerns about mental health were raised during the COVID-19 pandemic. We investigated the prevalence of depression and anxiety symptoms among the participants of the United Arab Emirates Healthy Future Study (UAEHFS); a national cohort study. We further explored the change in the prevalence of depression symptoms among those with comparable pre-pandemic data. METHODS: A sample of UAEHFS participants were invited to complete a COVID-19 online questionnaire during the first wave of the pandemic. Depression and anxiety symptoms were assessed using the Patient Health Questionnaire Depression Scale (PHQ-8) and the Generalized Anxiety Disorder-7 Scale (GAD-7) respectively. Unpaired analyses were done to examine the effect of COVID-19 on depression and anxiety symptoms during the pandemic. Paired analysis was conducted to examine the change in depression symptoms. RESULTS: During the pandemic, we reported a prevalence of 32.8% (95% CI: 27.0, 39.1) for depression and 26.4% (95% CI: 21.0, 32.6) for anxiety symptoms. Younger people reported higher levels of depression (40.4%) and anxiety (34.5%) symptoms. Females reported higher levels of depression (36.5%) and anxiety (32.7%) symptoms. In paired analysis, the prevalence of depression symptoms during the pandemic was 34% (95% CI: 26.5, 42.4) compared to 29.9% (95% CI: 22.7, 38.1) before the pandemic. No statistically significant difference was observed, p-value = 0.440. Adjusted multivariate logistic regression models for PHQ-8 and GAD-7 during the pandemic showed that participants, who were experiencing flu-like symptoms, had higher odds of reporting depression symptoms compared to those without symptoms. Additionally, age was significantly negatively associated with anxiety symptoms. CONCLUSIONS: Overall, we found that depression and anxiety symptoms were more prevalent among young people and females. However, we did not find a significant change in the prevalence of depression symptoms among those with comparable pre-pandemic data. Identifying vulnerable groups and understanding trajectories through longitudinal studies would help with planning for effective mental health interventions for the current and future pandemics.

Maternal Early-Life Risk Factors and Later Gestational Diabetes Mellitus: A Cross-Sectional Analysis of the UAE Healthy Future Study (UAEHFS).

Limited studies have focused on maternal early-life risk factors and the later development of gestational diabetes mellitus (GDM). We aimed to estimate the GDM prevalence and examine the associations of maternal early-life risk factors, namely: maternal birthweight, parental smoking at birth, childhood urbanicity, ever-breastfed, parental education attainment, parental history of diabetes, childhood overall health, childhood body size, and childhood height, with later GDM. This was a retrospective cross-sectional study using the UAE Healthy Future Study (UAEHFS) baseline data (February 2016 to April 2022) on 702 ever-married women aged 18 to 67 years. We fitted a Poisson regression to estimate the risk ratio (RR) for later GDM and its 95% confidence interval (CI). The GDM prevalence was 5.1%. In the fully adjusted model, females with low birthweight were four times more likely (RR 4.04, 95% CI 1.36-12.0) and females with a parental history of diabetes were nearly three times more likely (RR 2.86, 95% CI 1.10-7.43) to report later GDM. In conclusion, maternal birthweight and parental history of diabetes were significantly associated with later GDM. Close glucose monitoring during pregnancy among females with either a low birth weight and/or parental history of diabetes might help to prevent GDM among this high-risk group.

Substance use disorders and COVID-19: An analysis of nation-wide Veterans Health Administration electronic health records.

BACKGROUND: Substance use disorders (SUD) elevate the risk for COVID-19 hospitalization, but studies are inconsistent on the relationship of SUD to COVID-19 mortality. METHODS: Veterans Health Administration (VHA) patients treated in 2019 and evaluated in 2020 for COVID-19 (n=5,556,315), of whom 62,303 (1.1%) tested positive for COVID-19 (COVID-19+). Outcomes were COVID-19+ by 11/01/20, hospitalization, ICU admission, or death within 60 days of a positive test. Main predictors were any ICD-10-CM SUDs, with substance-specific SUDs (cannabis, cocaine, opioid, stimulant, sedative) explored individually. Logistic regression produced unadjusted and covariate-adjusted odds ratios (OR; aOR). RESULTS: Among COVID-19+ patients, 19.25% were hospitalized, 7.71% admitted to ICU, and 5.84% died. In unadjusted models, any SUD and all substance-specific SUDs except cannabis use disorder were associated with COVID-19+(ORs=1.06-1.85); adjusted models produced similar results. Any SUD and all substance-specific SUDs were associated with hospitalization (aORs: 1.24-1.91). Any SUD, cocaine and opioid disorder were associated with ICU admission in unadjusted but not adjusted models. Any SUD, cannabis, cocaine, and stimulant disorders were inversely associated with mortality in unadjusted models (OR=0.27-0.46). After adjustment, associations with mortality were no longer significant. In ad hoc analyses, adjusted odds of mortality were lower among the 49.9% of COVID-19+ patients with SUD who had SUD treatment in 2019, but not among those without such treatment. CONCLUSIONS: In VHA patients, SUDs are associated with COVID-19 hospitalization but not COVID-19 mortality. SUD treatment may provide closer monitoring of care, ensuring that these patients received needed medical attention, enabling them to ultimately survive serious illness.

COVID-19 outcomes in hospitalized patients with active cancer: Experiences from a major New York City health care system.

BACKGROUND: The authors sought to study the risk factors associated with severe outcomes in hospitalized coronavirus disease 2019 (COVID-19) patients with cancer. METHODS: The authors queried the New York University Langone Medical Center's records for hospitalized patients who were polymerase chain reaction-positive for severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) and performed chart reviews on patients with cancer diagnoses to identify patients with active cancer and patients with a history of cancer. Descriptive statistics were calculated and multivariable logistic regression was used to determine associations between clinical, demographic, and laboratory characteristics with outcomes, including death and admission to the intensive care unit. RESULTS: A total of 4184 hospitalized SARS CoV-2+ patients, including 233 with active cancer, were identified. Patients with active cancer were more likely to die than those with a history of cancer and those without any cancer history (34.3% vs 27.6% vs 20%, respectively; P < .01). In multivariable regression among all patients, active cancer (odds ratio [OR], 1.89; CI, 1.34-2.67; P < .01), older age (OR, 1.06; CI, 1.05-1.06; P < .01), male sex (OR for female vs male, 0.70; CI, 0.58-0.84; P < .01), diabetes (OR, 1.26; CI, 1.04-1.53; P = .02), morbidly obese body mass index (OR, 1.87; CI, 1.24-2.81; P < .01), and elevated D-dimer (OR, 6.41 for value >2300; CI, 4.75-8.66; P < .01) were associated with increased mortality. Recent cancer-directed medical therapy was not associated with death in multivariable analysis. Among patients with active cancer, those with a hematologic malignancy had the highest mortality rate in comparison with other cancer types (47.83% vs 28.66%; P < .01). CONCLUSIONS: The authors found that patients with an active cancer diagnosis were more likely to die from COVID-19. Those with hematologic malignancies were at the highest risk of death. Patients receiving cancer-directed therapy within 3 months before hospitalization had no overall increased risk of death. LAY SUMMARY: Our investigators found that hospitalized patients with active cancer were more likely to die from coronavirus disease 2019 (COVID-19) than those with a history of cancer and those without any cancer history. Patients with hematologic cancers were the most likely among patients with cancer to die from COVID-19. Patients who received cancer therapy within 3 months before hospitalization did not have an increased risk of death.