Repeated Exposure to COVID-19 Misinformation: A Longitudinal Analysis of Prevalence and Predictors in a Community Sample.

Belief in health misinformation can affect individual health decisions and actions. Repeated exposure to the same misinformation strengthens its impact, yet little is known about how commonly repeated exposure occurs. To estimate the prevalence, we tracked exposure to 5 inaccurate COVID-19 claims every week for up to 23 consecutive weeks in a racially diverse panel of adults (n = 213). Repeated exposure was common: across the 5 claims, 10%-43% of respondents reported hearing the misinformation in ≥ 3 different weeks. Frontline workers were more likely than other community members to experience repeated exposure, with adjusted incidence rate ratios (IRRs) ranging from 1.8 to 4.9 across the 4 items. Repeated exposure was most common among older adults. Adjusted IRR for those ages ≥ 50 versus 18-29 years ranged from 1.8 to 2.5 per misinformation claim. Public health planning efforts to counter health misinformation should anticipate multiple exposures to the same false claim, especially in certain subgroups.

Impact of COVID-19 pandemic on breast cancer screening in a large midwestern United States academic medical center.

BACKGROUND: Access to breast screening mammogram services decreased during the COVID-19 pandemic. Our objectives were to estimate: 1) the COVID-19 affected period, 2) the proportion of pandemic-associated missed or delayed screening encounters, and 3) pandemic-associated patient attrition in screening encounters overall and by sociodemographic subgroup. METHODS: We included screening mammogram encounter EPIC data from 1-1-2019 to 12-31-2022 for females ≥40 years old. We used Bayesian State Space models to describe weekly screening mammogram counts, modeling an interruption that phased in and out between 3-1-2020 and 9-1-2020. We used the posterior predictive distribution to model differences between a predicted, uninterrupted process and the observed screening mammogram counts. We estimated associations between race/ethnicity and age group and return screening mammogram encounters during the pandemic among those with 2019 encounters using logistic regression. RESULTS: Our analysis modeling weekly screening mammogram counts included 231,385 encounters (n = 127,621 women). Model-estimated screening mammograms dropped by >98% between 03-15-2020 and 05-24-2020 followed by a return to pre-pandemic levels or higher with similar results by race/ethnicity and age group. Among 79,257 women, non-Hispanic (NH) Asians, NH Blacks, and Hispanics had significantly (p < .05) lower odds of screening encounter returns during 2020-2022 vs. NH Whites with odds ratios (ORs) from 0.70 to 0.91. Among 79,983 women, those 60-69 had significantly higher odds of any return screening encounter during 2020-2022 (OR = 1.28), while those ≥80 and 40-49 had significantly lower odds (ORs 0.77, 0.45) than those 50-59 years old. A sensitivity analysis suggested a possible pre-existing pattern. CONCLUSIONS: These data suggest a short-term pandemic effect on screening mammograms of ~2 months with no evidence of disparities. However, we observed racial/ethnic disparities in screening mammogram returns during the pandemic that may be at least partially pre-existing. These results may inform future pandemic planning and continued efforts to eliminate mammogram screening disparities.

The impact of individual-level income predicted from the BRFSS on the association between insurance status and overall survival among adults with cancer from the SEER program.

INTRODUCTION: Among patients with cancer in the United States, Medicaid insurance is associated with worse outcomes than private insurance and with similar outcomes as being uninsured. However, prior studies have not addressed the impact of individual-level socioeconomic status, which determines Medicaid eligibility, on the associations of Medicaid status and cancer outcomes. Our objective was to determine whether differences in cancer outcomes by insurance status persist after accounting for individual-level income. METHODS: The Surveillance, Epidemiology, and End Results (SEER) database was queried for 18-64 year-old individuals with cancer from 2014-2016. Individual-level income was imputed using a model trained on Behavioral Risk Factors Surveillance Survey participants including covariates also present in SEER. The association of 1-year overall survival and insurance status was estimated with and without adjustment for estimated individual-level income and other covariates. RESULTS: A total of 416,784 cases in SEER were analyzed. The 1-yr OS for patients with private insurance, Medicaid insurance, and no insurance was 88.7%, 76.1%, and 73.7%, respectively. After adjusting for all covariates except individual-level income, 1-year OS differences were worse with Medicaid (-6.0%, 95% CI = -6.3 to -5.6) and no insurance (-6.7%, 95% CI = -7.3 to -6.0) versus private insurance. After also adjusting for estimated individual-level income, the survival difference for Medicaid patients was similar to privately insured (-0.4%, 95% CI = -1.9 to 1.1) and better than uninsured individuals (2.1%, 95% CI = 0.7 to 3.4). CONCLUSIONS: Income, rather than Medicaid status, may drive poor cancer outcomes in the low-income and Medicaid-insured population. Medicaid insurance coverage may improve cancer outcomes for low-income individuals.

Associations between Medicaid enrollment and diagnosis stage and survival among pediatric cancer patients.

BACKGROUND: Medicaid-associated disparities in childhood and adolescent (pediatric) cancer diagnosis stage and survival have been reported. However, a key limitation of prior studies is the assessment of health insurance at a single time point. To evaluate Medicaid-associated disparities more robustly, we used Surveillance, Epidemiology, and End Results (SEER)-Medicaid linked data to examine diagnosis stage and survival disparities in those (i) Medicaid-enrolled and (ii) with discontinuous and continuous Medicaid enrollment. METHODS: SEER-Medicaid linked data from 2006 to 2013 were obtained on cases diagnosed from 0 to 19 years. Medicaid enrollment was classified as enrolled versus not enrolled, with further classifications as continuous when enrolled 6 months before through 6 months after diagnosis, and discontinuous when not enrolled continuously for this period. We used multinomial logistic and Cox proportional hazards regression models to determine associations between enrollment measures, diagnosis stage, and cancer death adjusted for covariates. RESULTS: Among 21,502 cases, a higher odds of distant stage diagnoses were observed in association with Medicaid enrollment (odds ratio [OR] = 1.56, 95% confidence interval [CI]: 1.48-1.65), with the highest odds for discontinuous enrollment (OR = 2.0, 95% CI: 1.86-2.15). Among 30,654 cases, any Medicaid enrollment, continuous enrollment, and discontinuous enrollment were associated with 1.68 (95% CI: 1.35-2.10), 1.66 (95% CI: 1.35-2.05), and 1.89 (95% CI: 1.54-2.33) times higher hazards of cancer death versus no enrollment, respectively. CONCLUSIONS: Medicaid enrollment, particularly discontinuous enrollment, is associated with a higher  distant stage diagnosis odds and risk of death. This study supports the critical need for consistent health insurance coverage in children and adolescents.