key: cord-0993039-db9yfn8d
authors: Whaley, Christopher M.; Pera, Megan F.; Cantor, Jonathan; Chang, Jennie; Velasco, Julia; Hagg, Heather K.; Sood, Neeraj; Bravata, Dena M.
title: Changes in Health Services Use Among Commercially Insured US Populations During the COVID-19 Pandemic
date: 2020-11-05
journal: JAMA Netw Open
DOI: 10.1001/jamanetworkopen.2020.24984
sha: 76ca553ef97c4ce56223b68b7663f3544ec7d716
doc_id: 993039
cord_uid: db9yfn8d

IMPORTANCE: The coronavirus disease 2019 (COVID-19) pandemic has placed unprecedented strain on patients and health care professionals and institutions, but the association of the pandemic with use of preventive, elective, and nonelective care, as well as potential disparities in use of health care, remain unknown. OBJECTIVE: To examine changes in health care use during the first 2 months of the COVID-19 pandemic in March and April of 2020 relative to March and April of 2019 and 2018, and to examine whether changes in use differ by patient’s zip code–level race/ethnicity or income. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study analyzed health insurance claims for patients from all 50 US states who receive health insurance through their employers. Changes in use of preventive services, nonelective care, elective procedures, prescription drugs, in-person office visits, and telemedicine visits were examined during the first 2 months of the COVID-19 pandemic in 2020 relative to existing trends in 2019 and 2018. Disparities in the association of the pandemic with health care use based on patient’s zip code–level race and income were also examined. RESULTS: Data from 5.6, 6.4, and 6.8 million US individuals with employer-sponsored insurance in 2018, 2019, and 2020, respectively, were analyzed. Patient demographics were similar in all 3 years (mean [SD] age, 34.3 [18.6] years in 2018, 34.3 [18.5] years in 2019, and 34.5 [18.5] years in 2020); 50.0% women in 2018, 49.5% women in 2019, and 49.5% women in 2020). In March and April 2020, regression-adjusted use rate per 10 000 persons changed by −28.2 (95% CI, −30.5 to −25.9) and −64.5 (95% CI, −66.8 to −62.2) for colonoscopies; −149.1 (95% CI, −162.0 to −16.2) and −342.1 (95% CI, −355.0 to −329.2) for mammograms; −60.0 (95% CI, −63.3 to −54.7) and −118.1 (95% CI, −112.4 to −113.9) for hemoglobin A(1c) tests; −300.5 (95% CI, −346.5 to −254.5) and −369.0 (95% CI, −414.7 to −323.4) for child vaccines; −4.6 (95% CI, −5.3 to −3.9) and −10.9 (95% CI, −11.6 to −10.2) for musculoskeletal surgery; −1.1 (95% CI, −1.4 to −0.7) and −3.4 (95% CI, −3.8 to −3.0) for cataract surgery; −13.4 (95% CI, −14.6 to −12.2) and −31.4 (95% CI, −32.6 to −30.2) for magnetic resonance imaging; and −581.1 (95% CI, −612.9 to −549.3) and −1465 (95% CI, −1496 to −1433) for in-person office visits. Use of telemedicine services increased by 227.9 (95% CI, 221.7 to 234.1) per 10 000 persons and 641.6 (95% CI, 635.5 to 647.8) per 10 000 persons. Patients living in zip codes with lower-income or majority racial/ethnic minority populations experienced smaller reductions in in-person visits (≥80% racial/ethnic minority zip code: 200.0 per 10 000 [95% CI, 128.9-270.1]; 79%-21% racial/ethnic minority zip code: 54.2 per 10 000 [95% CI, 33.6-74.9]) but also had lower rates of adoption of telemedicine (≥80% racial/ethnic minority zip code: −71.6 per 10 000 [95% CI, −87.6 to −55.5]; 79%-21% racial/ethnic minority zip code: −15.1 per 10 000 [95% CI, −19.8 to −10.4]). CONCLUSIONS AND RELEVANCE: In this cross-sectional study of a large US population with employer-sponsored insurance, the first 2 months of the COVID-19 pandemic were associated with dramatic reductions in the use of preventive and elective care. Use of telemedicine increased rapidly but not enough to account for reductions in in-person primary care visits. Race and income disparities at the zip code level exist in use of telemedicine.

This study used medical and pharmacy claims data from employers who purchased access to the Castlight Health platform, which provides price transparency, wellness and other health benefits tools. This analysis did not include the digital tools provided by Castlight Health, but instead used the claims data that participating employers provide to Castlight as a way to implement the digital tools. For each of approximately 200 self-insured employers that provide access to this tool, the claims data covers all in-network procedures that are reimbursed through insurance. The claims data includes reimbursement amounts, procedure codes, and patient diagnoses. The data also includes demographic (e.g., geographic location, age, gender) and employer information (e.g., industry). We did not have access to individual-level data, but instead data aggregated to the year-month-state-gender-age group category level.

One potential concern is that the population included in this sample may not be representative of the broader U.S. population. To assess differences between our study population and the broader commercially insured population, we used data from the American Community Survey (ACS). 2018 is the most recent year available in the ACS, and so we limited our comparison to 2018. We limited the ACS sample to individuals who receive insurance through an employer or union and are under the age of 65. We applied the nationally representative population weights in the ACS data. As shown in Table A1 , the Castlight population is similar in gender, age, and geographic distribution to the ACS population.

Telemedicine procedures were identified as claims with procedure codes in the following set: ('99441','99442','99443','99444','99421','99422','99423','98970','98971','98972','G2061','G2062',' G2063') , claims with a procedure code modifier in ('95','GT','GQ'), or a place of service code equal to 2.

To examine the association between health care utilization and the first month of the COVID-19's declaration of a national emergency in the U.S., we estimate a regression model that quantifies the change in health care utilization in March 2020, relative to previous periods. We defined healthcare utilization by measuring the number of persons per 10,000 persons who received each of the 145 services grouped by the IBM Watson Health procedure categories. We defined utilization rates ( ) for procedure received by patients (age and gender-level) who live in state during time period (year and month) . Ages are categorized as 0-2, 3-18, 19-26, 27-45, and 46-64 . For colonoscopies, we restricted the denominator population to persons ages 46 to 64; for mammograms, women ages 46 to 64; and for infant vaccines, children ages 0 to 2.

With these utilization measures we estimated a regression model of the form = + ℎ2020 + 1 + 2 + 3 + 4 + 5 ℎ + In this model, the ℎ2020 term is an indicator for the March 2020 time period. We included fixed effects controls for the age categories, gender, state, year, and month. We estimated this model separately for the 10 procedures of interest. For the model where we pooled across procedures, we included a fixed effect for procedure ( 6 ). We estimated this model using linear regressions with Stata version 16.

For the models that assess differences by the five-digit zip code level income and race, we first linked the utilization/ measures to zip code. We then used data from the 2018 American Community Survey (ACS) on zip code level household income and race. For income, we defined zip code mean income relative the federal poverty line (FPL) for a family of four ($26,200) . We categorized mean zip code income as below 200% of FPL ($52,400), between 200% and 400% ($104,800) and above 400% of FPL. For race, we categorized zip codes as those with 80% or more non-white residents, 79% to 21% non-white residents, and 80% or more white residents (and 20% or fewer non-white residents).

To measure the differences in changes in health care utilization after the national emergency declaration based on zip code income, we estimated a regression model of the form = + 1 ℎ2020 + 2 1 + 3 2 + 4 ℎ2020 × 1 + 5 ℎ2020 × 2 + 1 + 2 + 3 + 4 + 5 ℎ +

In this model, 1 represents zip codes with mean household income below 200% of FPL and 2 represents zip codes with household income between 200% and 400% of FPL. The omitted category is zip codes with mean household income 400% or more of FPL. The regression model includes indicator controls for the income level, plus interaction terms between each income level and the March 2020 indicator.

To measure the differences in changes in health care utilization after the national emergency declaration based on the percent of the zip code that is non-white, we estimated a regression model of the form = + 1 ℎ2020 + 2 1 + 3 2 + 4 ℎ2020 × 1 + 5 ℎ2020 × 2 + 1 + 2 + 3 + 4 + 5 ℎ +

In this model, 1 represents zip codes 80% or more non-white residents and 2 represents zip codes with between 79% and 21% non-white residents. The omitted category is zip codes with 80% or more white residents. The regression model includes indicator controls for the race category, plus interaction terms between each race category and the March 2020 indicator.

Note: Colonoscopy population limited to ages 46-64, mammogram population limited to women ages 46-64, vaccine population limited to children ages 0-2, and labor and delivery population limited to women ages 19-45. We also examined the industry distribution of the Castlight population, across 30 industries. As presented in eTable 2, most enrollees receive insurance through an employer in the telecommunications industry. 4119.7% This table shows regression-adjusted differences in use of office visits and telemedicine in March and April 2020, relative to the 2018 to 2020 time period. The dependent variable in each column is the monthly number of persons per 10,000 eligible persons with the respective procedure. Regression models include fixed effect controls for year and month, state, patient gender, and age category (categorized as 0-2, 3-18, 19-26, 27-45, 46-64) . 95% Confidence intervals in parentheses. ** p<0.01, * p<0.05

(1)

(2) (3) (4) (5) (6) Colonoscopy Mammogram HbA1C Vaccines Office visits Telemedicine Panel A: Income Differences Pre Covid difference in utilization for persons in zip codes with income below 200% of FPL, relative to persons in zip codes with income above 400% FPL -15.01*** -80.21*** -392.0*** 9.293*** -302.1*** -2.311* (-16.05 to -13.98) (-85.27 to -75.14) (-412.0 to -371.9) (7.864 to 10.72) (-312.8 to -291.5) (-4.628 to 0.00636) Pre Covid difference in utilization for persons in zip codes with income between 201-400% of FPL, relative to persons in zip codes with income above 400% FPL -8.443*** -46.75*** -104.3*** 0.348 -146.5*** -0.655

We categorized mean zip code income as below 200% of FPL ($52,400), between 200% and 400% ($104,800) and above 400% of FPL. For race, we categorized zip codes as those with 80% or more non-white residents, 79% to 21% non-white residents, and 80% or more white residents (and 20% or fewer non-white residents). The dependent variable in each column is the monthly number of persons per 10,000 eligible persons with the respective procedure. Regression models include fixed effect controls for year and month, state, patient gender, and age category