Patterns of healthcare utilization according to health equity determinants during the first year of the pandemic at Johns Hopkins Medicine

Abstract

Objectives Rapid telehealth adoption happened at the onset of the coronavirus disease 2019 (COVID-19) pandemic, resulting in a move from in-person predominant to telehealth predominant care delivery. Later, in person visits rebounded with telehealth options remaining. This study aimed to assess differences in healthcare utilization during this changing landscape in terms of health equity determinants. Materials and Methods This was an observational cohort study of Johns Hopkins Medicine (JHM) patients. We analyzed utilization of video, telephone, and in-person patient-provider visits by eligible patients between March 16, 2019 and December 31, 2020. Percent changes in average weekly patient-provider visits from pre-pandemic (March 16, 2019-June 30, 2019) to early 2020 pandemic (March 16, 2020-June 30, 2020) and from pre-pandemic (July 1, 2019-December 31, 2019) to late 2020 pandemic (July 1, 2020-December 31, 2020). We used a quantile cut off technique to describe disproportionately smaller or greater drops in visits during the first year of the pandemic among health equity determinant groups and according to visit specialty, when compared to the total population. Results There was a 39% drop in patient-provider visits from the pre-pandemic to the early 2020 pandemic period, and a 24% drop from pre-pandemic to the late 2020 pandemic period. We discovered 21 groups according to health equity determinates and visit departments with patterns of disproportionately smaller or greater drops in visits during the first year of the pandemic, when compared to the total population: Pattern 1—smaller drop in visits early and late 2020 (age 45-64, Medicare insurance, high poverty and high unemployment; mental health and medical specialty visits –P < .001); Pattern 2—greater drop in visits early 2020 only (age 65-84; OB/GYN and surgical specialty visits—P < .001); Pattern 3—greater drop in visits early and late 2020 (age 0-5, age 6-17, age 85+, Asian race, Hispanic or Latino ethnicity, private insurance—P < .001); and Pattern 4—smaller drop in visits in early 2020 when compared to late 2020. The age 18-44 group showed a smaller drop in visits early 2020 and then visit levels similar to the total population late 2020. Primary care visits were similar to the total population early 2020 and then a smaller drop in visits late 2020 (P < .001). Discussion Our study provides evidence of health equity determinant groups having disproportionally smaller or greater drops in visits during the first year of the pandemic. The observed differences may have been influenced by changing telehealth offerings during the first year of the pandemic. Groups with disproportionately smaller drops in visits early 2020 (Pattern #1 and age 18-44 group in Pattern #4), suggests more success with adopting telehealth among those groups. Whereas groups with disproportionately greater drops in visits early 2020 (Pattern #2 and Pattern #3), suggests less success with telehealth adoption. For Pattern #4, more clarification is needed on how changes in telehealth offerings contributed to the downward trend in visits observed from early to late 2020. Conclusion We describe 4 main patterns to characterize groups with disproportionately smaller or greater drops in visits during the first year of the pandemic. While this work did not specifically study vulnerable populations, these patterns set the stage for further studies of such groups.