Examining inequality in healthcare utilization during pandemic disruptions
Lian, J.; Pei, S.; Gao, J.; Zhong, L.
Show abstract
ImportanceHealth crisis like recent COVID-19 pandemic has continuously disrupted healthcare utilization, particularly among non-COVID-19 patients. It remains unclear whether these disruptions were experienced equally across populations or if they disproportionately impacted specific demographic groups. ObjectiveTo assess inequality in healthcare utilization during the COVID-19 pandemic, stratified by demographic characteristics (age, race and ethnicity, income, and education) and medical specialties (emergency medicine, anesthesiology, and cardiology) across U.S. states. Design, Setting, and ParticipantsThis retrospective observational study analyzed electronic health records (EHRs) from millions of anonymized patients across U.S. states between 2019 and 2022. The Gini coefficient was used to quantify disparities in healthcare utilization. A regression discontinuity design was employed to assess the effect of declining telehealth usage on inequality. Main Outcomes and MeasuresTemporal inequality in healthcare utilization by demographic group and specialty, measured monthly using the Gini coefficient. ResultsCompared with the pre-pandemic period (2019), there was an approximate 45% decline in patient visits during the pandemic (2020-2022). Inequality in healthcare utilization increased consistently across demographic groups, with Gini coefficients rising by 14.5% (age), 2.3% (race and ethnicity), 7.0% (income), and 4.8% (education). States that maintained high levels of telehealth service usage experienced smaller increases in inequality. Conclusions and RelevanceThe pandemics significantly exacerbated disparities in healthcare utilization among non-COVID-19 patients. These findings underscore the importance of sustaining adaptive healthcare delivery strategies, such as telehealth, to mitigate systemic inequities during long-term public health emergencies.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Digitalization impacts the COVID-19 pandemic and the stringency of government measures 93%
- A Comprehensive County Level Framework to Identify Factors Affecting Hospital Capacity and Predict Future Hospital Demand 92%
- Can tracking mobility be used as a public health tool against COVID-19 following the expiration of stay-at-home mandates? 92%
Similar papers in this journal
- Diversity and inclusion: A hidden additional benefit of Open Data 91%
- A proposed de-identification framework for a cohort of children presenting at a health facility in Uganda 91%
- Development and preliminary testing of Health Equity Across the AI Lifecycle (HEAAL): A framework for healthcare delivery organizations to mitigate the risk of AI solutions worsening health inequities 91%
Similar papers in this journal
- A scoping review of fair machine learning techniques when using real-world data 93%
- Demonstrating the Consequences of Learning Missingness Patterns in Early Warning Systems for Preventative Health Care: A Novel Simulation and Solution 92%
- Natural language processing for scalable feature engineering and ultra-high-dimensional confounding adjustment in healthcare database studies 90%
Similar papers in this journal
- Development and Evaluation of MADDIE: Method to Acquire Delivery Date Information from Electronic Health Records 91%
- Synthetic Data Generation in Healthcare: A Scoping Review of reviews on domains, motivations, and future applications 90%
- Emergence and Evolution of Big Data Analytics in HIV Research: Bibliometric Analysis of Federally Sponsored Studies 2000-2019 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.