Age-related differences in the association between financial hardship and weight change during the COVID-19 pandemic.

Objective: To examine the association of financial hardship with weight changes in the US during the COVID-19 pandemic. Methods: We used data from the COVID-19's Unequal Racial Burden survey, a nationally representative, cross-sectional, online survey of diverse adults living in the US, 12/2020-2/2021. This study included 1000 Asian, Black, Latino (half Spanish-speaking), and White adults and 500 American Indian or Alaska Native, Native Hawaiian or Pacific Islander, and multiracial adults (5500 total). Age-specific (18-39, 40-59, ≥60) associations between financial hardship domains and weight change were estimated using multinomial logistic regression, adjusted for demographic and health characteristics. Results: Financial hardship during the COVID-19 pandemic was prevalent across all age groups (18-39: 76.2 %; 40-59: 75.6 %; ≥60: 50.6 %). Among adults aged 18-39 and ≥ 60 years old, food insecurity was significantly associated with weight loss (18-39: aOR = 1.42, 95 % CI = 1.04, 1.95; ≥60: aOR = 3.67, 95 % CI = 1.50, 8.98). Among all age groups, unmet healthcare expenses was also associated with weight loss (18-39: aOR = 1.31, 95 % CI = 1.01, 1.70; 40-59: aOR = 1.49, 95 % CI = 1.06, 2.08; ≥60: aOR = 1.73, 95 % CI = 1.03, 2.91). Among adults aged 18-39 and ≥ 60 years old, lost income was significantly associated with weight gain (18-39: aOR = 1.36, 95 % CI = 1.09-1.69; ≥60: aOR = 1.46, 95 % CI = 1.04, 2.06), and among adults 40-59 years old, experiencing increased debt was significantly associated with weight gain (aOR = 1.50, 95 % CI = 1.13, 1.99). Conclusions: For those aged 18-39 and ≥ 60 years old experiencing financial hardship during the COVID-19 pandemic was associated with both weight loss and weight gain. Less correlation was observed among adults aged 40-59.

Racial and ethnic disparities in the association between financial hardship and self-reported weight change during the first year of the pandemic in the U.S.

Studies have shown that financial hardship can impact weight change; however, it is unclear what the economic impact of the COVID-19 pandemic has had on weight change in U.S. adults, or whether racial-ethnic groups were impacted differentially. We estimated the association between financial hardship and self-reported weight change using data from the cross-sectional COVID-19's Unequal Racial Burden (CURB) survey, a nationally representative online survey of 5,500 American Indian/Alaska Native, Asian, Black/African American, Latino (English- and Spanish-speaking), Native Hawaiian/Pacific Islander, White, and multiracial adults conducted from 12/2020 to 2/2021. Financial hardship was measured over six domains (lost income, debt, unmet general expenses, unmet healthcare expenses, housing insecurity, and food insecurity). The association between each financial hardship domain and self-reported 3-level weight change variable were estimated using multinomial logistic regression, adjusting for sociodemographic and self-reported health. After adjustment, food insecurity was strongly associated with weight loss among American Indian/Alaska Native (aOR = 2.18, 95% CI = 1.05-4.77), Black/African American (aOR = 1.77, 95% CI = 1.02-3.11), and Spanish-speaking Latino adults (aOR = 2.32, 95% CI = 1.01-5.35). Unmet healthcare expenses were also strongly associated with weight loss among Black/African American, English-speaking Latino, Spanish-speaking Latino, and Native Hawaiian/Pacific Islander adults (aORs = 2.00-2.14). Other domains were associated with weight loss and/or weight gain, but associations were not as strong and less consistent across race-ethnicity. In conclusion, food insecurity and unmet healthcare expenses during the pandemic were strongly associated with weight loss among racial-ethnic minority groups. Using multi-dimensional measures of financial hardship provides a comprehensive assessment of the effects of specific financial hardship domains on weight change among diverse racial-ethnic groups.

Opportunities, challenges, and future directions for simulation modeling the effects of structural racism on cancer mortality in the United States: a scoping review.

PURPOSE: Structural racism could contribute to racial and ethnic disparities in cancer mortality via its broad effects on housing, economic opportunities, and health care. However, there has been limited focus on incorporating structural racism into simulation models designed to identify practice and policy strategies to support health equity. We reviewed studies evaluating structural racism and cancer mortality disparities to highlight opportunities, challenges, and future directions to capture this broad concept in simulation modeling research. METHODS: We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Scoping Review Extension guidelines. Articles published between 2018 and 2023 were searched including terms related to race, ethnicity, cancer-specific and all-cause mortality, and structural racism. We included studies evaluating the effects of structural racism on racial and ethnic disparities in cancer mortality in the United States. RESULTS: A total of 8345 articles were identified, and 183 articles were included. Studies used different measures, data sources, and methods. For example, in 20 studies, racial residential segregation, one component of structural racism, was measured by indices of dissimilarity, concentration at the extremes, redlining, or isolation. Data sources included cancer registries, claims, or institutional data linked to area-level metrics from the US census or historical mortgage data. Segregation was associated with worse survival. Nine studies were location specific, and the segregation measures were developed for Black, Hispanic, and White residents. CONCLUSIONS: A range of measures and data sources are available to capture the effects of structural racism. We provide a set of recommendations for best practices for modelers to consider when incorporating the effects of structural racism into simulation models.

Inability to get needed health care during the COVID-19 pandemic among a nationally representative, diverse population of U.S. adults with and without chronic conditions.

BACKGROUND: Delays in health care have been observed in the U.S. during the COVID-19 pandemic; however, the prevalence of inability to get needed care and potential disparities in health care access have yet to be assessed. METHODS: We conducted a nationally representative, online survey of 5,500 American Indian/Alaska Native, Asian, Black/African American, Latino (English- and Spanish-speaking), Native Hawaiian/Pacific Islander, White, and multiracial adults between 12/2020-2/2021 (baseline) and 8/16/2021-9/9/2021 (6-month follow-up). Participants were asked "Since the start of the pandemic, was there any time when you did not get medical care that you needed?" Those who responded "Yes" were asked about the type of care and the reason for not receiving care. Poisson regression was used to estimate the association between sociodemographics and inability to receive needed care; all analyses were stratified by chronic condition status. Chronic conditions included: chronic obstructive pulmonary disease (COPD), heart conditions, type 2 diabetes, chronic kidney disease or on dialysis, sickle cell disease, cancer, and immunocompromised state (weakened immune system). RESULTS: Overall, 20.0% of participants at baseline and 22.7% at follow-up reported not getting needed care. The most common reasons for being unable to get needed care included fear of COVID-19 (baseline: 44.1%; follow-up: 47.2%) and doctors canceled appointment (baseline: 25.3%; follow-up: 14.1%). Routine care (baseline: 59.9%; follow-up: 62.6%) and chronic care management (baseline: 31.5%; follow-up: 30.1%) were the most often reported types of delayed care. Fair/poor self-reported physical health was significantly associated with being unable to get needed care despite chronic condition status (≥ 1 chronic condition: aPR = 1.36, 95%CI = 1.04-1.78); no chronic conditions: aPR = 1.52, 95% CI = 1.28-1.80). The likelihood of inability to get needed care differed in some instances by race/ethnicity, age, and insurance status. For example, uninsured adults were more likely to not get needed care (≥ 1 chronic condition: aPR = 1.76, 95%CI = 1.17-2.66); no chronic conditions: aPR = 1.25, 95% CI = 1.00-1.56). CONCLUSIONS: Overall, about one fifth of participants reported being unable to receive needed care at baseline and follow-up. Delays in receiving needed medical care may exacerbate existing conditions and perpetuate existing health disparities among vulnerable populations who were more likely to have not received needed health care during the pandemic.

Telehealth access, willingness, and barriers during the COVID-19 pandemic among a nationally representative diverse sample of U.S. adults with and without chronic health conditions.

INTRODUCTION: During the COVID-19 pandemic, telehealth services represented a critical tool in maintaining continuity and access to care for adults in the USA. However, despite improvements in access and utilization during the pandemic, disparities in telehealth utilization have persisted. It is unclear what role access and willingness to use telehealth play in telehealth disparities. METHODS: We used data from the nationally representative COVID-19's Unequal Racial Burden (CURB) survey, an online survey conducted between December 2020 and February 2021, n = 5500. Multivariable Poisson regression was used to estimate the prevalence of perceived telehealth access and willingness to use telehealth services among adults with and without chronic conditions. RESULTS: Overall, 60.1% of adults with and 38.7% of adults without chronic conditions reported having access to telehealth. After adjustment, adults with chronic conditions were more likely to report telehealth access (adjusted prevalence ratio [aPR] = 1.35, 95% confidence interval [CI] = 1.21-1.50). Most adults with and without chronic conditions reported being willing to use telehealth services (85.1% and 79.8%, respectively), and no significant differences in willingness were observed across chronic condition status (aPR = 1.03, 95% CI = 0.95-1.13). Perceived telehealth access appeared to be a predictor of telehealth willingness in both groups (chronic conditions: aPR = 1.22, 95% CI = 0.97-1.54; no chronic conditions: aPR = 1.37, 95% CI = 1.22-1.54). The prevalence of perceived barriers to telehealth was low, with the majority reporting no barriers (chronic conditions = 51.4%; no chronic conditions = 61.4%). DISCUSSION: Perceived access to telehealth was associated with telehealth willingness. Investing in approaches that increase telehealth accessibility and awareness is needed to improve access to telehealth for adults with and without chronic conditions.