BACKGROUND

Pediatric acute care utilization decreased dramatically during the coronavirus disease 2019 (COVID-19) pandemic. This study examined the association between the Child Opportunity Index (COI), a multidimensional neighborhood measure of childhood opportunity, and changes in acute care utilization at US pediatric hospitals during the COVID-19 pandemic compared with the previous 3 years.

METHODS

This observational study used administrative data across 41 US-based pediatric hospitals. Children aged 0 to 17 years with emergency department (ED) encounters during the study period were included. The COVID-19 pandemic time period (March 15, 2020–March 14, 2021) was the primary exposure. The primary outcome was the relative volume drop in ED encounters and observation/inpatient admissions through the ED by COI quintile.

RESULTS

Of 12 138 750 encounters, 3 705 320 (30.5%) were among the very low COI quintile. Overall, there was a 46.8% relative volume reduction in the pandemic period compared with the prepandmic period. This drop in volume occurred disproportionately among the very low COI quintile (51.1%) compared with the very high COI quintile (42.8%). The majority of clinical diagnosis groups demonstrated larger relative volume drops among the very low COI quintile.

CONCLUSIONS

Acute care utilization decreased the most among children from very low COI neighborhoods, narrowing previously described acute care utilization disparities. Additional study of patient perspectives on health care needs and access during this period is required to understand these changes.

What’s Known on the Subject:

Pediatric acute care utilization decreased dramatically during the coronavirus disease 2019 pandemic.

What This Study Adds:

A narrowing in previously described disparities in acute care utilization occurred during the coronavirus disease 2019 pandemic, though reasons behind this change in care-seeking behavior are unclear.

Disparities in pediatric acute care utilization in the United States are among many known indicators of health inequity. Non-Hispanic Black and Hispanic children and those with socioeconomic disadvantage, measured by parental income and public insurance status, have higher emergency department (ED) use rates and increased hospitalization for ambulatory care sensitive conditions (ACSCs) (conditions whose need for hospitalization are reduced with high-quality outpatient care).13  There is also growing evidence of similar associations with regional measures of socioeconomic status, such as neighborhood physical conditions, income inequality, and the Child Opportunity Index (COI).48 

The COI 2.0 is a publicly available, multidimensional measure of “neighborhood-based conditions and resources conducive to healthy child development,” a construct summarized as “opportunity.”9  It assigns a rating for overall opportunity, as well as 3 subdomains, educational opportunity, health and environmental opportunity, and social and economic opportunity, to each census tract. Residency in lower opportunity neighborhoods has been associated with increased pediatric hospitalization rates for asthma and ACSCs, increased pediatric urgent care utilization for ACSCs and injuries, and increased ED revisits and readmissions.5,6,10 

The coronavirus disease 2019 (COVID-19) pandemic dramatically impacted pediatric primary11,12  and acute care utilization patterns across the United States, with a 60% decrease in primary care office visits from March to April 2020, 42% decrease in ED visits from March to April 2020, and a 45% decrease in admissions in April 2020.1316  This drop in number of visits per 12-month-period, herein referred to as “annual volume,” was seen across a range of diagnoses, with a disproportionate decline for certain conditions such as respiratory illnesses.17,18  Though children from racial and ethnic minority groups and socioeconomically disadvantaged households have experienced a disproportionately higher burden of COVID-19 exposure and infection,19  previous studies suggest there was also a disproportionate decrease in ED utilization among vulnerable children (characterized by race, ethnicity, insurance status).18 

The objective of this study was to examine the association of neighborhood COI with changes in acute care utilization at US pediatric hospitals during the COVID-19 pandemic. We hypothesized that the volume drop among children living in lower COI areas would be less pronounced compared with children living in higher COI areas:

  1. overall because of presumed better access to preventive primary care and telehealth services among high COI groups; and

  2. for medical conditions that disproportionately impact disadvantaged groups (ie, trauma/injury, respiratory illnesses).5,2022 

This multicenter, observational study used data from the Pediatric Health Information System (PHIS) database, an administrative and billing database containing encounter-level data from 49 tertiary-care pediatric hospitals across the United States that is maintained by the Children’s Hospital Association (Lenexa, KS). Participating hospitals are located in 27 states and Washington, DC. Hospitals submit encounter-level data, including demographics, ZIP code, medications, and diagnoses based on International Classification of Disease, 10th Revision, Clinical Modification (ICD-10-CM) codes. Data were de-identified at the time of submission, and data quality and reliability were assured by joint efforts between the Children’s Hospital Association and participating hospitals. This study was deemed exempt by the institutional review board of Vanderbilt University Medical Center.

Children aged 0 to 17 years with ED encounters or observation/inpatient admission through the ED from March 15, 2017, to March 14, 2021, were included in the study. To identify the cohort of patients initially presenting for care at a participating hospital, we excluded direct admission and inpatient-to-inpatient transfer encounters. We also excluded 8 hospitals with incomplete data during the study period and encounters with missing or invalid ZIP codes (Supplemental Fig 3). Overall, excluded hospitals had a higher proportion of very low COI patients compared with included hospitals, but there was substantial hospital-level variability across included and excluded hospitals (Supplemental Table 4).

Briefly, the index includes 29 indicators that impact children’s healthy development across 3 domains (education; health and development; social and economic). Each indicator is transformed to a z-score, standardized within and across census tracts, which is used to obtain a weighted average within each domain. The 3 average domain z-scores are then combined into an overall score using similar weighting strategies. Indicator weighting reflects how strongly the variable impacts children’s long-term health and economic outcomes. Child opportunity levels are created by ranking all US census tracts by overall average z-scores and dividing them into quintiles (very low, low, moderate, high, and very high) that represent 20% of the US child population.23  COI 2.0, typically measured at the census tract level, was mapped to the ZIP code level by the creators of COI and applied to data available in the PHIS database.24 

The primary exposure in this study was the COVID-19 pandemic time period, defined as March 15, 2020, to March 14, 2021. Outcomes and covariates from the exposure period were compared with the mean annual value across the prepandemic period, defined as rolling years starting March 15, 2017, to March 14, 2018, ending March 14, 2020.

The primary outcome was the relative volume reduction (decrease in ED and inpatient/observation admissions through the ED) within each COI quintile subgroup. Relative volume reduction was specifically defined as (mean annual volume in the prepandemic period–volume in the pandemic period/mean annual volume in the prepandmic period).

To further characterize changes in volume, the secondary outcome was relative volume reduction by COI quintile within clinical diagnosis groups on the basis of 3M All Patient Refined Diagnosis Related Groups (APR-DRGs).25  3M APR-DRG uses a proprietary algorithm to classify claims data into larger clinical groups on the basis of the ICD-10-CM code combinations.

Sociodemographic and clinical variables examined included age, sex, race and ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, Asian American, other non-Hispanic), presence of a complex chronic condition,26  hospital census region (Northeast, Midwest, South, West), distance from the hospital (in miles), encounter type (ED discharge, admitted through the ED), and primary payer (government, commercial, self-pay, other). Because race and ethnicity are social constructs, these variables were included in the analysis because they are strongly associated with differential opportunity in US society and are highly correlated with COI.27  In PHIS, race and ethnicity are included as 2 distinct variables, which were collapsed into a single variable.28  Hospitals submit race and ethnicity data for each visit according to hospital-specific practices, including parent/guardian self-report at the time of arrival or hospital registration.

Given the large number of ICD-10-CM codes and APR-DRGs, we used a 2-step modified Delphi method29  to establish consensus in further grouping the APR-DRGs into more clinically meaningful groups. Three expert authors (C.F., M.G., and E.F.) classified APR-DRGs into categories to examine changes in presentation to care by diagnosis, and 2 authors (T.R. and H.D.) served as moderators during the process. In round 1, APR-DRGs were independently categorized, primarily on the basis of service line, with the distinction between medical and surgical diagnoses preserved because of the differential impact of early policies suspending elective procedures.30  In round 2, the expert authors and moderators met virtually to “agree” or “disagree” with each APR-DRG grouping. Priority was also placed on distinguishing conditions that were known to show disparities in incidence (eg, trauma/injury, ACSCs),4,21  or that were identified as experiencing disproportionate volume changes during the COVID-19 pandemic in previous studies (eg, respiratory infection, mental health).17,31  The “Trauma/Injury’” category was defined using established ICD-10-CM diagnostic and external injury codes (S00–S99, T07, T14–28, T30–34, and T36–T78, *U01–*U03, V01–Y36, Y85–Y87, Y89). Groups were excluded if they did not meet an established criteria of 1000 annual encounters prepandemic. Additionally, APR-DRGs 540–640 relating to obstetrics, 950–952 relating to procedures unrelated to principal diagnosis, and 956 “Ungroupable” were all categorized as excluded from grouping. In the first modified Delphi round, 379 APR-DRGs were grouped, 54% of which met consensus. In round 2, 100% consensus was reached for a total of 23 categories covering 360 APR-DRGs.

We summarized demographic characteristics with frequencies and percentages using χ2 tests for categorical data and medians and interquartile ranges using Kruskal-Wallis tests for continuous variables in the overall population and by COI. We performed a 1way χ2 goodness-of-fit test to assess if the volume reduction in 2020 compared with the mean of the 3 prepandemic period years was distributed across all COI quintiles equally. All analyses were performed using SAS 9.4 (Cary, NC). P values <.05 were considered statistically significant.

The overall cohort, the prepandemic and pandemic cohorts combined, included 12 138 750 encounters among patients who were most often aged 0 to 2 years (37.0%), non-Hispanic White (36.0%), publicly insured (62.8%), and received care at a hospital in the South (36.8%). The overall cohort had higher representation from the very low COI (30.5%) quintile than the national distribution (by definition, 20% for each quintile).23  The majority of encounters were ED discharges (88.0%) (Table 1).

TABLE 1

Demographic and Clinical Characteristics of Cohort by Year, March 15 to March 14

Total (N = 12 138 750)2017 (3 467 662)2018 (n = 3 347 005)2019 (n = 3 495 121)2020 (n = 1 828 962)
Age, median (IQR) 5.0 (1.0–10.0) 4.0 (1.0–10.0) 4.0 (1.0–10.0) 4.0 (1.0–10.0) 5.0 (1.0–12.0) 
Age group, y, n (%)      
 0–2 4 493 564 (37.0) 1 295 888 (37.4) 1 260 592 (37.7) 1 292 281 (37.0) 644 803 (35.3) 
 3–5 2 229 410 (18.4) 646 179 (18.6) 621 932 (18.6) 662 549 (19.0) 298 750 (16.3) 
 6–12 3 377 295 (27.8) 978 351 (28.2) 926 928 (27.7) 974 816 (27.9) 497 200 (27.2) 
 13–17 2 038 481 (16.8) 547 244 (15.8) 537 553 (16.1) 565 475 (16.2) 388 209 (21.2) 
Sex      
 Female 5 765 497 (47.5) 1 639 852 (47.3) 1 586 680 (47.4) 1 652 991 (47.3) 885 974 (48.4) 
 Male 6 369 354 (52.5) 1 826 336 (52.7) 1 758 823 (52.5) 1 841 648 (52.7) 942 547 (51.5) 
 Unknown 3899 (0.0) 1474 (0.0) 1502 (0.0) 482 (0.0) 441 (0.0) 
Race and ethnicity, n (%)      
 Non-Hispanic White 4 375 712 (36.0) 1 223 478 (35.3) 1 207 664 (36.1) 1 235 140 (35.3) 709 430 (38.8) 
 Non-Hispanic Black 3 117 548 (25.7) 897 255 (25.9) 870 177 (26.0) 903 552 (25.9) 446 564 (24.4) 
 Hispanic 3 373 056 (27.8) 990 197 (28.6) 918 811 (27.5) 978 470 (28.0) 485 578 (26.5) 
 Asian American 353 584 (2.9) 95 182 (2.7) 98 715 (2.9) 109 176 (3.1) 50 511 (2.8) 
 Other Non-Hispanic 664 182 (5.5) 190 791 (5.5) 180 562 (5.4) 192 847 (5.5) 99 982 (5.5) 
 Missing 254 668 (2.1) 70 759 (2.0) 71 076 (2.1) 75 936 (2.2) 36 897 (2.0) 
Any CCC, n (%) 845 249 (7.0) 214 819 (6.2) 224 278 (6.7) 240 664 (6.9) 165 488 (9.0) 
Hospital census region, n (%)      
 Northeast 1 137 939 (9.4) 319 968 (9.2) 314 805 (9.4) 326 073 (9.3) 177 093 (9.7) 
 Midwest 3 831 387 (31.6) 1 119 853 (32.3) 1 077 715 (32.2) 1 102 274 (31.5) 531 545 (29.1) 
 South 4 465 460 (36.8) 1 237 470 (35.7) 1 218 492 (36.4) 1 288 056 (36.9) 721 442 (39.4) 
 West 2 703 964 (22.3) 790 371 (22.8) 735  993 (22.0) 778 718 (22.3) 398 882 (21.8) 
Distance, miles, median (IQR)a 9.8 (5.2–19.0) 9.4 (5.0–18.0) 9.7 (5.2–18.7) 9.8 (5.2–19.1) 11.0 (5.7–21.4) 
Encounter type, n (%)      
 ED discharge 10 686 120 (88.0) 3 092 379 (89.2) 2 960 738 (88.5) 3 081 888 (88.2) 1 551 115 (84.8) 
 Admission through ED 1 452 630 (12.0) 375 283 (10.8) 386 267 (11.5) 413 233 (11.8) 277 847 (15.2) 
Payer      
 Government 7 625 192 (62.8) 2 171 380 (62.6) 2 111 751 (63.1) 2 211 534 (63.3) 1 130 527 (61.8) 
 Commercial 3 651 396 (30.1) 1 000 664 (28.9) 1 010 836 (30.2) 1 052 457 (30.1) 587 439 (32.1) 
 Self-pay 575 767 (4.7) 155 803 (4.5) 158 958 (4.7) 181 832 (5.2) 79 174 (4.3) 
 Other/missing 286 395 (2.4) 139 815 (4.0) 65 460 (2.0) 49 298 (1.4) 31 822 (1.7) 
COI, n (%)      
 Very high 2 110 783 (17.4) 588 311 (17.0) 582 503 (17.4) 602 132 (17.2) 337 837 (18.5) 
 High 1 859 616 (15.3) 514 784 (14.8) 510 990 (15.3) 537 088 (15.4) 296 754 (16.2) 
 Moderate 2 088 822 (17.2) 593 842 (17.1) 572 985 (17.1) 600 955 (17.2) 321 040 (17.6) 
 Low 2 374 209 (19.6) 680 221 (19.6) 652 627 (19.5) 687 578 (19.7) 353 783 (19.3) 
 Very low 3 705 320 (30.5) 1 090 504 (31.4) 1 027 900 (30.7) 1 067 368 (30.5) 519 548 (28.4) 
Total (N = 12 138 750)2017 (3 467 662)2018 (n = 3 347 005)2019 (n = 3 495 121)2020 (n = 1 828 962)
Age, median (IQR) 5.0 (1.0–10.0) 4.0 (1.0–10.0) 4.0 (1.0–10.0) 4.0 (1.0–10.0) 5.0 (1.0–12.0) 
Age group, y, n (%)      
 0–2 4 493 564 (37.0) 1 295 888 (37.4) 1 260 592 (37.7) 1 292 281 (37.0) 644 803 (35.3) 
 3–5 2 229 410 (18.4) 646 179 (18.6) 621 932 (18.6) 662 549 (19.0) 298 750 (16.3) 
 6–12 3 377 295 (27.8) 978 351 (28.2) 926 928 (27.7) 974 816 (27.9) 497 200 (27.2) 
 13–17 2 038 481 (16.8) 547 244 (15.8) 537 553 (16.1) 565 475 (16.2) 388 209 (21.2) 
Sex      
 Female 5 765 497 (47.5) 1 639 852 (47.3) 1 586 680 (47.4) 1 652 991 (47.3) 885 974 (48.4) 
 Male 6 369 354 (52.5) 1 826 336 (52.7) 1 758 823 (52.5) 1 841 648 (52.7) 942 547 (51.5) 
 Unknown 3899 (0.0) 1474 (0.0) 1502 (0.0) 482 (0.0) 441 (0.0) 
Race and ethnicity, n (%)      
 Non-Hispanic White 4 375 712 (36.0) 1 223 478 (35.3) 1 207 664 (36.1) 1 235 140 (35.3) 709 430 (38.8) 
 Non-Hispanic Black 3 117 548 (25.7) 897 255 (25.9) 870 177 (26.0) 903 552 (25.9) 446 564 (24.4) 
 Hispanic 3 373 056 (27.8) 990 197 (28.6) 918 811 (27.5) 978 470 (28.0) 485 578 (26.5) 
 Asian American 353 584 (2.9) 95 182 (2.7) 98 715 (2.9) 109 176 (3.1) 50 511 (2.8) 
 Other Non-Hispanic 664 182 (5.5) 190 791 (5.5) 180 562 (5.4) 192 847 (5.5) 99 982 (5.5) 
 Missing 254 668 (2.1) 70 759 (2.0) 71 076 (2.1) 75 936 (2.2) 36 897 (2.0) 
Any CCC, n (%) 845 249 (7.0) 214 819 (6.2) 224 278 (6.7) 240 664 (6.9) 165 488 (9.0) 
Hospital census region, n (%)      
 Northeast 1 137 939 (9.4) 319 968 (9.2) 314 805 (9.4) 326 073 (9.3) 177 093 (9.7) 
 Midwest 3 831 387 (31.6) 1 119 853 (32.3) 1 077 715 (32.2) 1 102 274 (31.5) 531 545 (29.1) 
 South 4 465 460 (36.8) 1 237 470 (35.7) 1 218 492 (36.4) 1 288 056 (36.9) 721 442 (39.4) 
 West 2 703 964 (22.3) 790 371 (22.8) 735  993 (22.0) 778 718 (22.3) 398 882 (21.8) 
Distance, miles, median (IQR)a 9.8 (5.2–19.0) 9.4 (5.0–18.0) 9.7 (5.2–18.7) 9.8 (5.2–19.1) 11.0 (5.7–21.4) 
Encounter type, n (%)      
 ED discharge 10 686 120 (88.0) 3 092 379 (89.2) 2 960 738 (88.5) 3 081 888 (88.2) 1 551 115 (84.8) 
 Admission through ED 1 452 630 (12.0) 375 283 (10.8) 386 267 (11.5) 413 233 (11.8) 277 847 (15.2) 
Payer      
 Government 7 625 192 (62.8) 2 171 380 (62.6) 2 111 751 (63.1) 2 211 534 (63.3) 1 130 527 (61.8) 
 Commercial 3 651 396 (30.1) 1 000 664 (28.9) 1 010 836 (30.2) 1 052 457 (30.1) 587 439 (32.1) 
 Self-pay 575 767 (4.7) 155 803 (4.5) 158 958 (4.7) 181 832 (5.2) 79 174 (4.3) 
 Other/missing 286 395 (2.4) 139 815 (4.0) 65 460 (2.0) 49 298 (1.4) 31 822 (1.7) 
COI, n (%)      
 Very high 2 110 783 (17.4) 588 311 (17.0) 582 503 (17.4) 602 132 (17.2) 337 837 (18.5) 
 High 1 859 616 (15.3) 514 784 (14.8) 510 990 (15.3) 537 088 (15.4) 296 754 (16.2) 
 Moderate 2 088 822 (17.2) 593 842 (17.1) 572 985 (17.1) 600 955 (17.2) 321 040 (17.6) 
 Low 2 374 209 (19.6) 680 221 (19.6) 652 627 (19.5) 687 578 (19.7) 353 783 (19.3) 
 Very low 3 705 320 (30.5) 1 090 504 (31.4) 1 027 900 (30.7) 1 067 368 (30.5) 519 548 (28.4) 

All P values, χ2 tests across all 4 years looking at column percentages, are significant <.001. CCC, complex chronic condition;26  IQR, interquartile range.

a

Distance calculated as miles from the center of patient’s ZIP code to the center of hospital’s ZIP code.

During the prepandemic period, the very low COI quintile compared with the very high COI quintile was predominantly younger (38.6% vs 36.9% 0–2 years, P < .001), non-Hispanic Black (46.5% vs 15.1%, P < .001), and had government insurance (80.5% vs 51.7%, P < .001). Compared with the very high COI quintile, the very low COI quintile had a lower rate of complex chronic conditions (5.6% vs 7.4%, P < .001) and a higher frequency of ED discharge encounters (90.8% vs 87.1%, P < .001) (Table 2).

TABLE 2

Demographic Characteristics by COI Quintile, Prepandemic Period (2017–2019)

2017–2019
Very HighHighModerateLowVery LowPa
Age group, y, n (%)      <.001 
 0–2 576 707 (36.9) 788  893 (39.0) 671 743 (38.0) 580 880 (32.8) 1 230 538 (38.6)  
 3–5 289 944 (18.6) 377 165 (18.7) 328 142 (18.6) 333 138 (18.8) 602 271 (18.9)  
 6–12 438 739 (28.1) 551 258 (27.3) 488 849 (27.7) 529 580 (29.9) 871 669 (27.4)  
 13–17 257 472 (16.5) 303 110 (15.0) 279 048 (15.8) 329 348 (18.6) 481 294 (15.1)  
Sex, N (%)      <.001 
 Female 735 363 (47.1) 954 357 (47.2) 836 920 (47.3) 827 891 (46.7) 1 524 992 (47.9)  
 Male 826 982 (52.9) 1 064 810 (52.7) 930 262 (52.6) 944 690 (53.3) 1 660 063 (52.1)  
 Unknown 517 (0.0) 1259 (0.1) 600 (0.0) 365 (0.0) 717 (0.0)  
Race and ethnicity, n (%)       
 Non-Hispanic White 785 165 (50.2) 618 515 (30.6) 731 027 (41.4) 1 131 811 (63.8) 399 764 (12.5)  
 Non-Hispanic Black 235 388 (15.1) 466 900 (23.1) 320 317 (18.1) 167 169 (9.4) 1 481 210 (46.5)  
 Hispanic 360 918 (23.1) 730 958 (36.2) 535 798 (30.3) 205 398 (11.6) 1 054 406 (33.1)  
 Asian American 48 053 (3.1) 49 772 (2.5) 47 847 (2.7) 101 425 (5.7) 55 976 (1.8)  
 Other Non-Hispanic 93 934 (6.0) 111 444 (5.5) 97 373 (5.5) 119 863 (6.8) 141 586 (4.4)  
 Missing 39 404 (2.5) 42 837 (2.1) 35 420 (2.0) 47 280 (2.7) 52 830 (1.7)  
Any CCC, n (%) 115 541 (7.4) 138 917 (6.9) 129 158 (7.3) 118 128 (6.7) 178 017 (5.6) <.001 
Distance, miles, median (IQR)b 13.3 (7.6–23.3) 9.3 (5.5–23.0) 12.2 (6.6–23.3) 13.2 (8.4–18.6) 5.8 (3.5–10.5) <.001 
Encounter type, n (%)      <.001 
 ED discharge 1 360 575 (87.1) 1 779 271 (88.1) 1 545 593 (87.4) 1 555 397 (87.7) 2 894 169 (90.8)  
 Admitted through ED 202 287 (12.9) 241 155 (11.9) 222 189 (12.6) 217 549 (12.3) 291 603 (9.2)  
Payer      <.001 
 Government 808 458 (51.7) 1 448 579 (71.7) 1 124 763 (63.6) 548 752 (31.0) 2 564 113 (80.5)  
 Commercial 653 685 (41.8) 403 291 (20.0) 519 834 (29.4) 1 136 093 (64.1) 351 054 (11.0)  
 Self-pay 70 904 (4.5) 108 850 (5.4) 86 909 (4.9) 58 536 (3.3) 171 394 (5.4)  
 Other/missing 29 815 (1.9) 59 706 (3.0) 36 276 (2.1) 29 565 (1.7) 99 211 (3.1)  
2017–2019
Very HighHighModerateLowVery LowPa
Age group, y, n (%)      <.001 
 0–2 576 707 (36.9) 788  893 (39.0) 671 743 (38.0) 580 880 (32.8) 1 230 538 (38.6)  
 3–5 289 944 (18.6) 377 165 (18.7) 328 142 (18.6) 333 138 (18.8) 602 271 (18.9)  
 6–12 438 739 (28.1) 551 258 (27.3) 488 849 (27.7) 529 580 (29.9) 871 669 (27.4)  
 13–17 257 472 (16.5) 303 110 (15.0) 279 048 (15.8) 329 348 (18.6) 481 294 (15.1)  
Sex, N (%)      <.001 
 Female 735 363 (47.1) 954 357 (47.2) 836 920 (47.3) 827 891 (46.7) 1 524 992 (47.9)  
 Male 826 982 (52.9) 1 064 810 (52.7) 930 262 (52.6) 944 690 (53.3) 1 660 063 (52.1)  
 Unknown 517 (0.0) 1259 (0.1) 600 (0.0) 365 (0.0) 717 (0.0)  
Race and ethnicity, n (%)       
 Non-Hispanic White 785 165 (50.2) 618 515 (30.6) 731 027 (41.4) 1 131 811 (63.8) 399 764 (12.5)  
 Non-Hispanic Black 235 388 (15.1) 466 900 (23.1) 320 317 (18.1) 167 169 (9.4) 1 481 210 (46.5)  
 Hispanic 360 918 (23.1) 730 958 (36.2) 535 798 (30.3) 205 398 (11.6) 1 054 406 (33.1)  
 Asian American 48 053 (3.1) 49 772 (2.5) 47 847 (2.7) 101 425 (5.7) 55 976 (1.8)  
 Other Non-Hispanic 93 934 (6.0) 111 444 (5.5) 97 373 (5.5) 119 863 (6.8) 141 586 (4.4)  
 Missing 39 404 (2.5) 42 837 (2.1) 35 420 (2.0) 47 280 (2.7) 52 830 (1.7)  
Any CCC, n (%) 115 541 (7.4) 138 917 (6.9) 129 158 (7.3) 118 128 (6.7) 178 017 (5.6) <.001 
Distance, miles, median (IQR)b 13.3 (7.6–23.3) 9.3 (5.5–23.0) 12.2 (6.6–23.3) 13.2 (8.4–18.6) 5.8 (3.5–10.5) <.001 
Encounter type, n (%)      <.001 
 ED discharge 1 360 575 (87.1) 1 779 271 (88.1) 1 545 593 (87.4) 1 555 397 (87.7) 2 894 169 (90.8)  
 Admitted through ED 202 287 (12.9) 241 155 (11.9) 222 189 (12.6) 217 549 (12.3) 291 603 (9.2)  
Payer      <.001 
 Government 808 458 (51.7) 1 448 579 (71.7) 1 124 763 (63.6) 548 752 (31.0) 2 564 113 (80.5)  
 Commercial 653 685 (41.8) 403 291 (20.0) 519 834 (29.4) 1 136 093 (64.1) 351 054 (11.0)  
 Self-pay 70 904 (4.5) 108 850 (5.4) 86 909 (4.9) 58 536 (3.3) 171 394 (5.4)  
 Other/missing 29 815 (1.9) 59 706 (3.0) 36 276 (2.1) 29 565 (1.7) 99 211 (3.1)  

CCC, complex chronic condition;26  IQR, interquartile range. —, not applicable.

a

P values look across COI distributions at the column percentages.

b

Distance calculated as miles from the center of patient’s ZIP code to the center of hospital’s ZIP code.

Across the entire cohort, there was a 46.8% reduction in volume in the pandemic period compared with the mean volume during the prepandemic period. With this substantial drop in volume, there was a change across all demographic groups in the cohort seeking care in the pandemic compared with the prepandemic time period (Table 1).

The drop in volume occurred disproportionately among the very low COI quintile (51.1% relative volume reduction) compared with the very high COI quintile (42.8% relative volume reduction), P < .001 (Fig 1).

FIGURE 1

Relative volume reduction by COI quintile. Vertical bar, overall volume reduction.

FIGURE 1

Relative volume reduction by COI quintile. Vertical bar, overall volume reduction.

Close modal

There was notable variation in the relative volume reduction by COI among each individual hospital, with some hospitals’ relative volume reductions tightly clustered and others experiencing a wide range of volume reduction by COI. Thirty of the 41 included hospitals experienced the greatest relative volume reduction in the very low COI quintile. There was no major outlier driving the overall results (Fig 2).

FIGURE 2

Relative volume reduction by COI quintile and individual hospital. Vertical bar, overall volume reduction; Dot, relative volume reduction by COI quintile.

FIGURE 2

Relative volume reduction by COI quintile and individual hospital. Vertical bar, overall volume reduction; Dot, relative volume reduction by COI quintile.

Close modal

The largest percentage volume drop occurred among the infectious respiratory (74.8%), infectious gastrointestinal (64.0%), and noninfectious respiratory (63.8%) diagnosis groups. The majority of clinical diagnosis groups demonstrated a significant difference in volume distribution by COI during the prepandemic and pandemic periods (Supplemental Table 5), with a larger relative volume drop among children in the very low COI quintile, as was seen in the overall cohort. The opposite pattern appeared among 2 groups: patients with infectious respiratory (78.8% very high versus 73.8% very low, P < .001) and toxicologic medical (36.4% very high versus 20.3% very low, P < .001) diseases, with a larger relative volume drop among the very high COI quintile (Table 3).

TABLE 3

Volume Reduction by Clinical Diagnosis Group, Overall and Relative Reduction by COI Quintile

OverallPrepandemic Period AveragePandemic PeriodAverage Volume Drop% Volume Drop% Volume Drop by COI and Group
GroupN%nnn%Very HighHighModerateLowVery LowPa
Overall groupsb 1 2070 544 100.00 3 418 800 1 814 145 1 604 655 46.9 43.0 43.2 45.7 47.6 51.2 <.001 
Infectious respiratory 2 595 092 21.50 797 979 201 156 596 823 74.8 78.8 74.4 74.3 74 73.8 <.001 
Infectious GI 682 987 5.66 203 241 73 265 129 976 64.0 64 62.2 63.8 64.3 64.6 <.001 
Respiratory 730 247 6.05 217 225 78 573 138 652 63.8 64.7 62 62.9 63.3 64.9 <.001 
Infectious other 1 556 393 12.89 443 030 227 302 215 728 48.7 46 45 46.9 48 52.8 <.001 
ENT–medical 268 843 2.23 75 499 42 347 33 152 43.9 44.7 43.4 43.3 44.1 44 <.001 
Orthopedic 55 084 0.46 15 338 9070 6268 40.9 37.5 38.5 39 42.4 44.3 <.001 
Other–medical 964 737 7.99 266 880 164 096 102 784 38.5 36.7 35.2 36.2 37.7 42.1 <.001 
Hematology–medical 110 020 0.91 30 401 18 817 11 584 38.1 39.9 36.6 36.6 37.5 39.2 <.001 
Neurology–medical 399 985 3.31 109 682 70 940 38 742 35.3 33.1 31.8 33.5 35.8 39.9 <.001 
Nutrition/metabolic 102 274 0.85 27 922 18 508 9414 33.7 35.2 31.4 33.2 33.3 34.9 <.001 
GI–medical 820 520 6.80 223 960 148 640 75 320 33.6 32.9 31.3 32.4 34 36.1 <.001 
Other–surgical 30 628 0.25 8338 5614 2724 32.7 30.8 26.8 25.4 34.5 41.9 <.001 
Oncologic 10 547 0.09 2857 1977 880 30.8 30.8 30 23 33.8 36.4 .048 
Cardiovascular–medical 220 091 1.82 58 959 43 213 15 746 26.7 23.8 23 25.6 26.2 31.3 <.001 
Trauma/injury 2 839 701 23.53 760 246 558 964 201 282 26.5 20.9 21.6 25.2 28 33.6 <.001 
Toxicologic–medical 24 240 0.20 6446 4903 1543 23.9 36.4 23.7 21.1 25.3 20.3 <.001 
GU–medical 200 859 1.66 52 846 42 321 10 525 19.9 14.7 14.8 18 23.3 24.8 <.001 
Mental health 286 724 2.38 74 341 63 701 10 640 14.3 8.1 12.1 18.1 23.7 <.001 
Neurology–surgical 10 524 0.09 2711 2390 0321 11.9 2.7 5.3 20.1 9.9 19.2 <.001 
Endocrine–medical 63 453 0.53 16 285 14 598 1687 10.4 14.7 12.8 14.7 5.3 .207 
Neonatal 12 710 0.11 3216 3062 154 4.8 1.4 2.7 1.2 6.3 10.6 <.001 
GI–surgical 75 678 0.63 19 146 18 240 906 4.7 −3.4 3.1 4.8 9.3 9.2 <.001 
GU–surgical 9207 0.08 2253 2448 (195) −8.7 −19.4 −9.1 −5.7 −2.2 −7.9 .002 
OverallPrepandemic Period AveragePandemic PeriodAverage Volume Drop% Volume Drop% Volume Drop by COI and Group
GroupN%nnn%Very HighHighModerateLowVery LowPa
Overall groupsb 1 2070 544 100.00 3 418 800 1 814 145 1 604 655 46.9 43.0 43.2 45.7 47.6 51.2 <.001 
Infectious respiratory 2 595 092 21.50 797 979 201 156 596 823 74.8 78.8 74.4 74.3 74 73.8 <.001 
Infectious GI 682 987 5.66 203 241 73 265 129 976 64.0 64 62.2 63.8 64.3 64.6 <.001 
Respiratory 730 247 6.05 217 225 78 573 138 652 63.8 64.7 62 62.9 63.3 64.9 <.001 
Infectious other 1 556 393 12.89 443 030 227 302 215 728 48.7 46 45 46.9 48 52.8 <.001 
ENT–medical 268 843 2.23 75 499 42 347 33 152 43.9 44.7 43.4 43.3 44.1 44 <.001 
Orthopedic 55 084 0.46 15 338 9070 6268 40.9 37.5 38.5 39 42.4 44.3 <.001 
Other–medical 964 737 7.99 266 880 164 096 102 784 38.5 36.7 35.2 36.2 37.7 42.1 <.001 
Hematology–medical 110 020 0.91 30 401 18 817 11 584 38.1 39.9 36.6 36.6 37.5 39.2 <.001 
Neurology–medical 399 985 3.31 109 682 70 940 38 742 35.3 33.1 31.8 33.5 35.8 39.9 <.001 
Nutrition/metabolic 102 274 0.85 27 922 18 508 9414 33.7 35.2 31.4 33.2 33.3 34.9 <.001 
GI–medical 820 520 6.80 223 960 148 640 75 320 33.6 32.9 31.3 32.4 34 36.1 <.001 
Other–surgical 30 628 0.25 8338 5614 2724 32.7 30.8 26.8 25.4 34.5 41.9 <.001 
Oncologic 10 547 0.09 2857 1977 880 30.8 30.8 30 23 33.8 36.4 .048 
Cardiovascular–medical 220 091 1.82 58 959 43 213 15 746 26.7 23.8 23 25.6 26.2 31.3 <.001 
Trauma/injury 2 839 701 23.53 760 246 558 964 201 282 26.5 20.9 21.6 25.2 28 33.6 <.001 
Toxicologic–medical 24 240 0.20 6446 4903 1543 23.9 36.4 23.7 21.1 25.3 20.3 <.001 
GU–medical 200 859 1.66 52 846 42 321 10 525 19.9 14.7 14.8 18 23.3 24.8 <.001 
Mental health 286 724 2.38 74 341 63 701 10 640 14.3 8.1 12.1 18.1 23.7 <.001 
Neurology–surgical 10 524 0.09 2711 2390 0321 11.9 2.7 5.3 20.1 9.9 19.2 <.001 
Endocrine–medical 63 453 0.53 16 285 14 598 1687 10.4 14.7 12.8 14.7 5.3 .207 
Neonatal 12 710 0.11 3216 3062 154 4.8 1.4 2.7 1.2 6.3 10.6 <.001 
GI–surgical 75 678 0.63 19 146 18 240 906 4.7 −3.4 3.1 4.8 9.3 9.2 <.001 
GU–surgical 9207 0.08 2253 2448 (195) −8.7 −19.4 −9.1 −5.7 −2.2 −7.9 .002 

Prepandemic period, March 15, 2017, to March 14, 2020; pandemic period, March 15, 2020, to March 14, 2021. ENT, ear, nose, and throat; GI, gastrointestinal; GU, genitourinary.

a

P values are χ2 tests comparing the proportion of volume by COI in prepandemic period to pandemic period.

b

Removed transplant, cardiovascular–surgical, excluded, and ungroupable groups, causing the percentage volume drop for “Overall Groups” (46.9%) to be slightly higher than for the entire cohort (46.8%).

In this observational, multicenter study of acute care utilization at US children’s hospitals from March 2017 to March 2021, we found a 46.8% reduction in hospital utilization during the COVID-19 pandemic compared with the same time period during the 3 previous years. The relative reduction in volume was highest among the very low COI quintile (51.1%) and lowest among the very high COI quintile (42.8%). Overall, this pattern persisted across clinical diagnosis groupings, with the largest drops seen among the infectious gastrointestinal and respiratory groups.

The observed trend in volume reduction is in contrast to our a priori hypothesis that the largest relative volume drop in ED utilization would occur among higher COI groups. Drivers of this disproportionate drop in the lowest COI group may include changes in health care access and other infrastructure during the pandemic, changes in health care-seeking behaviors, and/or changes in the epidemiology of illnesses that especially impact children from families living in low opportunity communities.

One possible explanation is that this pattern may reflect the disparate impact of the pandemic on access to care. Despite the expansion of telehealth services that occurred during the pandemic,32,33  decreased access to office-based primary care11,12,30  among higher COI groups may have resulted in increased reliance on the ED to evaluate acute illness, more similar to typical care-seeking pattern of lower COI groups. Alternatively, higher rates of ED visits among pediatric practices with higher telemedicine use suggests that access to telehealth among this population may have facilitated triage and appropriate presentation for emergency care.34  Conversely, children from lower COI neighborhoods may have experienced decreased ability to access acute care because of decreased access to public transportation or having parents and caregivers who were working as essential workers and could not leave their jobs or work remotely.3537  Payers have a vested interest in understanding how ambulatory care and telehealth access may have driven changes in expensive ED and inpatient utilization during the pandemic.

Another possible explanation is that individuals in lower COI households were more hesitant to seek care during this time period. A survey study conducted by Macy et al in May 2020 found increased hesitancy to seek care among families who were non-White, non-English speaking, publicly insured, from a neighborhood with a high COVID-19 positivity rate, and from a very low/low COI neighborhood.38  Such avoidance can lead to undervaccination, increased risk of serious illness, and poor chronic illness control. Thus, the drivers of hesitancy and effective interventions to overcome hesitancy should be prioritized.

Both of these hypotheses assume a change in care-seeking behavior rather than a change in true incidence of disease. Alternatively, as suggested by the largest volume decreases among infectious respiratory and gastrointestinal groups in our study and a disproportionate decline in respiratory infections reported in previous studies,12,17,18,39  better infection control practices such as masking and social distancing may have alleviated need for assessment in the hospital and/or admission among a population that historically relies more heavily on acute care for respiratory and infectious illnesses. Thus, enhanced infection control practices may be beneficial to keep in place long term even after the COVID-19 pandemic to lessen longstanding health disparities.

The greater overall volume decrease in conditions that are traditionally associated with ED utilization among lower COI patients (infectious gastrointestinal conditions, respiratory conditions, etc)5,6  is 1 likely driver of the higher relative volume drop in the very low COI quintile across the cohort. Interestingly, the infectious respiratory group did experience lower relative volume reduction among the very low COI quintile as we hypothesized. This suggests that families from very high COI neighborhoods may have had greater access to and/or better-quality outpatient or telehealth management of ACSCs.33,40  Additionally, this may indicate an increased ability to practice social distancing and infection prevention measures among this group. Key interventions for optimizing care of ACSCs should continue to be identified and scaled broadly to lessen such disparities.

There are several limitations of this study. First, the hospitals included in the PHIS database are all tertiary care children’s hospitals; thus, our results may not be representative of trends at all hospitals. Second, we were unable to determine if patients accessed care elsewhere during the study period. Knowing if patients were being seen at other, non-PHIS hospitals or nonhospital-based settings (eg, urgent care, primary care) may reveal different trends in utilization among COI groups. Data regarding primary care, telehealth, and non-PHIS, hospital-based visits would also allow for better understanding of observed trends. Third, examining COI at the ZIP code level may result in misclassification of a small proportion of families into the wrong COI quintile. However, we expect this misclassification to occur equally during the prepandemic and pandemic periods and therefore have minimal impact on our primary outcome: the relative change in volume among these 2 time periods. Finally, this is a descriptive, hypothesis-generating study. The lack of patient-reported and/or qualitative data limits conclusive explanations for the identified trends. All of these limitations represent areas for future study.

The significant decrease in acute care utilization at children’s hospitals during the COVID-19 pandemic compared with 2017 to 2019 occurred disproportionately among patients from very low COI ZIP codes. This was consistent across the majority of clinical diagnosis groupings despite hospital-to-hospital variation in underlying population distribution and volume reduction during the time period. Overall, our results suggest a narrowing in previously described disparities in acute care utilization during this time period. Further study of patient perspectives, specific disease patterns, association between regional COVID-19 disease prevalence and policies, and future trends are needed to better understand the observed patterns in ED/inpatient volume. Understanding the reasons for the observed trends is especially important to ensure that appropriate systemic investments are made to target drivers of the observed care utilization patterns and promote equitable access to care and health outcomes during future surges.

Drs Fritz, Fleeger, and Goyal conceptualized and designed the study, participated in the 2-step modified Delphi process, interpreted the data, and drafted and revised the manuscript; Dr Richardson and Ms De Souza conceptualized and designed the study, participated in the 2-step modified Delphi process, completed the statistical analyses, and revised the manuscript for important intellectual content; Drs Kaiser, Sills, Cooper, Parikh, Puls, DeLaroche, Hogan, Pantell, Kornblith, Heller, and Bigham participated in data interpretation and revised the manuscript for important intellectual content; and all authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

FUNDING: No external funding.

POTENTIAL CONFLICT OF INTEREST: Dr Pantell receives support from the Agency for Healthcare Research and Quality under award #K12HS026383 and the National Center for Advancing Translational Sciences under award #KL2TR001870. He also reported receiving grants from the National Institutes of Health during the conduct of the study. The remaining authors have no conflicts to disclose.

This observational study assessed relative changes in acute care utilization by Child Opportunity Index level, a multidimensional neighborhood measure, during the coronavirus disease 2019 pandemic.

     
  • ACSC

    ambulatory care sensitive condition

  •  
  • APR-DRG

    All Patient Refined Diagnosis Related Group

  •  
  • ED

    emergency department

  •  
  • COI

    Child Opportunity Index

  •  
  • COVID-19

    coronavirus disease 2019

  •  
  • ICD-10-CM

    International Classification of Disease, 10th Revision, Clinical Modification

  •  
  • PHIS

    Pediatric Health Information System

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Supplementary data