This research was performed in accordance with the Declaration of Helsinki. The Saint Louis University Ethics Committee, called the Saint Louis University Institutional Review Board, deemed this work to be exempt from requiring ethics approval and deemed this work as non-human subjects research because data was de-identified, retrospective and patients did not actively participate.
De-identified medical record data were obtained from the Saint Louis University-SSM HealthCare System’s Virtual Data Warehouse (VDW), a database developed from electronic health records following removal of patient and provider identifiers. The VDW captures clinical encounter data starting in 1/1/2008 from academic and non-academic ambulatory and inpatient settings in a Mid-western, multi-state health care system. The health care system covers rural and urban locations from the southern half of Wisconsin, Southern Illinois, the St. Louis, Missouri metropolitan area, mid-Missouri, and the Oklahoma City, Oklahoma metropolitan areas.
The VDW includes patients from birth to > 90 years of age who have private or public health insurance as well as uninsured who utilize health care services within the SSM Health system. The VDW is updated monthly and includes over 10.3 million unique patients who had at least one encounter in the health care system. As a member site of the Health Care Systems Research Network (www.hcsrn.org), VDW variables are defined according to HCSRN specifications. VDW variables are created from ICD-9 and ICD-10 codes, current procedural terminology (CPT) codes, pharmacy orders, laboratory orders and results, vital signs, provider type, clinic type, referrals, and demographics.
Eligibility
Patients were eligible if they had a new patient well visit or one or more established patient well visits anytime between 7/1/2018 to 6/30/2021 (n = 798,571 patients). Our objective was to evaluate changes in well-visits at the system level. We did not intend to follow a specific group of patients over time. Our approach allowed us to capture use by new patients, often entering the health care system for pregnancy and subsequent pediatric well-visits.
Age was defined at each visit for each patient and the distribution calculated among all well visits. We used CPT and ICD-9/10 V and Z codes for age-group specific new patient or established patient well visits (see supplementary e-Table 1). Other descriptive sample variables, calculated at the patient level, included gender, race, and average number of visits per patient.
Outcome
The outcome variable of interest was the total number of well visits in each month from 7/1/2018 through 6/1/2021.
Analytic approach
To analyze temporal trends and identify changes in trends in the number of well visits per month before COVID-19 and during COVID-19, a joinpoint regression analysis on the log-transformed monthly counts was conducted using Joinpoint software version 4.9.0.0 [10, 11]. Using permutation tests to determine the most parsimonious model, these regression models determine the optimal number of joinpoints corresponding to changes in the slope of monthly counts over time. Based on starting and ending months of each segment identified by joinpoints, the monthly percent change (MPC) and 95% confidence interval in well visit count was calculated. Main analyses were conducted based on HEDIS-defined age categories (< 1, 1–4, 5–11, 12–17, 18–39, 40–64, and ≥ 65 years) that differ in guideline concordant, well visit frequency. Supplementary analyses (see Supplementary material) were conducted by gender and age as well as race (White vs. Black) and age. The focus of this analysis is describing temporal changes within age groups; no between group comparisons were made.