Persist and Proliferate: Racial/Ethnic Disparities in Unmet Medical Needs among Older Adults during the COVID-19 Pandemic

• In Event: 41017 - COVID-19 and Inequalities in Health and Wellbeing

Abstract

Unmet medical needs profoundly impact individuals’ health and well-being, as well as society’s health equity and social justice. To investigate whether and how racial/ethnic disparities persisted during the COVID-19 pandemic, this study examines the association between race/ethnicity and unmet medical needs among 578,831 older adults aged 65 and above, using data from the Household Pulse Survey (HPS) spanning from April 23, 2020, to June 23, 2021. Logistic regression models are employed to analyze how race and ethnicity influence older individuals’ self-reported experiences of delayed or unmet medical care, while controlling for age, sex, marital status, education, income, and week number. Bivariate and multivariate analyses reveal that race and ethnicity significantly impact older adults’ likelihood of experiencing unmet medical needs during the pandemic. Specifically, compared to non-Hispanic Whites, Hispanic and non-Hispanic Black older adults were more likely to report unmet medical needs even after controlling for demographic characteristics and socioeconomic status, while non-Hispanic Asians were less likely to do so, and Asian-White disparities ceased to exist with control variables in the model. By introducing an interaction term “race/ethnicity * week number” and stratifying models by racial/ethnic group, it is highlighted that the COVID-19 pandemic presented a universal challenge irrespective of racial and ethnic backgrounds in the first three months; however, racial and ethnic disparities in unmet medical needs re-emerged and even widened as the pandemic progressed. These findings suggest the prioritized access to medical resources among Whites but the disproportionate negative impacts placed on Hispanic and Black groups, underscoring the persistence and exacerbation of structural racism in healthcare access during the pandemic. Future research should address issues of imputation and covariates with advanced imputation techniques and include more controlling variables in the model.

Author

• Tianqi Zhou, Syracuse University