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Video Abstract

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BACKGROUND AND OBJECTIVES:

Health care disparities are pervasive, but little is known about disparities in pediatric safety. We analyzed a national sample of hospitalizations to identify disparities in safety events.

METHODS:

In this population-based, retrospective cohort study of the 2019 Kids’ Inpatient Database, independent variables were race, ethnicity, and payer. Outcomes were Agency for Healthcare Research and Quality pediatric safety indicators (PDIs). Risk-adjusted odds ratios were calculated using white and private payer reference groups. Differences by payer were evaluated by stratifying race and ethnicity.

RESULTS:

Race and ethnicity of the 5 243 750 discharged patients were white, 46%; Hispanic, 19%; Black, 15%; missing, 8%; other race/multiracial, 7%, Asian American/Pacific Islander, 5%; and Native American, 1%. PDI rates (per 10 000 discharges) were 331.4 for neonatal blood stream infection, 267.5 for postoperative respiratory failure, 114.9 for postoperative sepsis, 29.5 for postoperative hemorrhage/hematoma, 5.6 for central-line blood stream infection, 3.5 for accidental puncture/laceration, and 0.7 for iatrogenic pneumothorax. Compared with white patients, Black and Hispanic patients had significantly greater odds in 5 of 7 PDIs; the largest disparities occurred in postoperative sepsis (adjusted odds ratio, 1.55 [1.38–1.73]) for Black patients and postoperative respiratory failure (adjusted odds ratio, 1.34 [1.21–1.49]) for Hispanic patients. Compared with privately insured patients, Medicaid-covered patients had significantly greater odds in 4 of 7 PDIs; the largest disparity occurred in postoperative sepsis (adjusted odds ratios, 1.45 [1.33–1.59]). Stratified analyses demonstrated persistent disparities by race and ethnicity, even among privately insured children.

CONCLUSIONS:

Disparities in safety events were identified for Black and Hispanic children, indicating a need for targeted interventions to improve patient safety in the hospital.

What’s Known on This Subject:

Pediatric safety events are associated with increased length of stay, morbidity, mortality, and resource utilization.

What This Study Adds:

Disparities were greatest in postoperative sepsis for Black patients and postoperative respiratory failure for Hispanic patients (versus white patients). The largest disparity for Medicaid-covered (versus privately insured) patients was in postoperative sepsis. Findings can prioritize efforts to inform interventions.

Despite decades of focus on eliminating medical errors, children continue to suffer substantial harms in hospital settings.1,2 These preventable harms disproportionately affect the most socially disadvantaged groups of children.3,4 Almost 20 years ago, the seminal report, Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care, highlighted existing disparities in health care quality and health outcomes by race and ethnicity and socioeconomic status.5 Several adult-focused studies have demonstrated disparities in safety events, including higher rates of health care-associated infections in patients of color.6,9 There is also mounting evidence of disparities in health care and outcomes for children, including adverse events for hospitalized children,10,16 suboptimal health care access and screening,17 lack of insurance coverage,18 and limited access to care and use of services.19,21 

Patient safety is the cornerstone of quality care, and safety grades were developed in 2012 to summarize hospitals’ overall performance in patient safety.22 A recent study sought to assess whether hospitals achieving better patient safety grades were safer for all patients, including patients across various racial and ethnic backgrounds and insurance coverage.23 Authors hypothesized that hospitals with higher safety grades would have narrower inequities; however, on the contrary, they found no overall associations between hospital safety grade and magnitude of Black–white disparities. The authors concluded, “these findings suggest that the hospitals most adept at achieving safe care overall are no better at identifying and narrowing inequities in the delivery of that care.”23 Another study found that, for 6 of the 11 adult-focused patient safety indicators, Black adult patients experienced significantly worse patient safety versus white patients in the same age group, of the same gender, and treated in the same hospital.9 The disconnect between overall safety achievements for some, but not all, in adult patients underscores the need to understand disparities in pediatric safety.

Although there has been work in pediatrics to categorize1,2 and reduce safety events for children,24,25 we need to better identify disparities in pediatric safety events to inform improvement interventions. Some recent work has revealed disparities in rates of triggers and adverse events for hospitalized children14 and disparities in sepsis management,15 yet inequities in standard safety measures have not been assessed for hospitalized children. In the early 2000s, the Agency for Healthcare Research and Quality (AHRQ) developed patient safety indicators, initially focused on adult populations, and subsequently developed pediatric-specific quality indicators (PDI) for hospitalized children.26 These PDIs classify safety events using administrative data in children and are used by hospitals to help identify and benchmark safety problems that need further investigation, as well as for public reporting, trending, and pay-for-performance initiatives. Our study objective, therefore, was to analyze a national sample of pediatric hospitalizations for disparities in pediatric safety using established AHRQ PDI measures. Specifically, we aimed to identify disparities in these preventable safety events in hospitalized children, on the basis of race and ethnicity and/or payer status.

This is a population-based, retrospective cohort study. Data were obtained from the 2019 Kids’ Inpatient Database (KID), a sample of pediatric hospitalizations sponsored and maintained by the AHRQ Healthcare Cost and Utilization Project (HCUP) and includes discharges from only 2019. KID data sets are released approximately every 3 years, with data on up to 7.5 million weighted cases. The data set includes public hospitals and academic medical centers and excludes rehabilitation and long-term acute care hospitals. The KID database includes a 10% sample of newborns and 80% sample of other pediatric discharges from 4000 US hospitals. Because KID includes newborn deliveries, >80% of patients are <1 year old. All diagnoses and procedures are coded using the International Classification of Diseases, 10th Revision, Clinical Modification. The 2019 KID included ∼3 million hospitalizations, accounting for 80% of national pediatric hospitalizations from >3900 US hospitals in 49 states plus the District of Columbia. We included patients aged 0 to 17 years old. The Children’s National Hospital institutional review board reviewed the protocol and determined it to be exempt.

AHRQ PDIs were the dependent variables. PDIs included hospital-level measures to detect potential safety problems that occur during a patient’s hospital stay, and incorporated risk adjustments for each indicator. The 7 PDIs include accidental puncture or laceration, iatrogenic pneumothorax, postoperative events (hemorrhage or hematoma, sepsis, and respiratory failure), and blood stream infections (BSIs) (BSIs in neonates and central venous catheter-related BSIs). The PDIs are publicly available (https://qualityindicators.ahrq.gov/Downloads/Modules/PDI/V2021/TechSpecs/PDI_2021_ICD10_techspecs_pdf.zip). Each PDI has a defined unique “at-risk” population that is described in the Technical Reports denominator definitions.27 AHRQ PDIs are not inherently part of KID. PDI analytic methods were applied to the KID database in this study.

Race and ethnicity and payer status were the independent variables. Race and ethnicity is a social construct variable and was selected for this study to identify health disparities and inequities.28 Race and ethnicity data in KID are collected in accordance with hospitals’ policies, which can vary by hospital and state; KID uses a single, aggregate variable, with ethnicity taking precedence over race in hospitals reporting both. Race and ethnicity in KID consists of Asian American and Pacific Islander, Black, Hispanic, Native American, white, other, missing, and unknown. Other includes other races and/or multiracial for included states, and was included in the analysis because it is 1 of the fastest growing groups of children in the United States.29 Insurance status was classified as Medicaid, private insurance, self-pay, Medicare, no charge, and other.

For this analysis, model inclusion was based on sufficient sample size. The final racial and ethnic groups included in the analysis were Asian American/Pacific Islander, Black, Hispanic, white, and other. Patients classified as missing and unknown were excluded because of inability to interpret categories due to variability in the definition by state. Patients classified as Native American were excluded because of an inability to interpret findings due to an inadequate sample size. Patients with Medicare insurance or categorized as self-pay, no charge, and other groups were excluded because of inadequate sample sizes or variability in state definitions.

All analyses were conducted using sampling weights, as per HCUP guidance.30 Unadjusted PDI rates were calculated per 10 000 children at risk, accompanied by 95% confidence intervals. Risk-adjustment varied by PDI type, based on AHRQ severity adjustments, which adjusted for age, gender, age–gender interaction, Diagnosis Related Groups, and comorbidities. None of the risk adjustments provided by AHRQ, however, includes race and ethnicity or payer status. We modified available AHRQ multivariable logistic regression models by adding race and ethnicity or insurance, and present adjusted rates for each stratum, as well as adjusted odds ratios.

For this analysis, the reference groups were the white or private payer groups. We did not select the racial category of white for the reference group as a normative or race-neutral benchmark,31,32 but rather because they are historically socially advantaged groups.

Separate models were used for race and ethnicity and payer because these variables were highly associated in a subanalysis. Therefore, to better elucidate potential associations, we conducted analyses for primary insurance stratified by race and ethnicity, and for race and ethnicity stratified by payer. Tests of interaction between race and ethnicity and insurance revealed no significant interaction. All statistical analyses were performed with SAS v.9.4 (SAS Institute, Cary, North Carolina), with P values <.05 being considered statistically significant.

For the 5 243 747 hospitalizations analyzed, 48% of patients were female, and almost 80% were <1 year of age (Table 1). With respect to race and ethnicity, 46% of patients were classified as white; 19%, Hispanic; 15%, Black; 7%, other race/multiracial; 5%, Asian American or Pacific Islander; <1%, Native American; and 8%, missing or unknown. For insurance coverage, 47% of patients were insured by Medicaid; 44% by private insurance; 5% were self-pay; 3%, other; 0.3% by Medicare; and 0.1% were missing.

TABLE 1

Sociodemographic Characteristics of Study Population

CharacteristicN%
Age (y)   
 <1 4 021 312 76.7 
 1–4 371 832 7.1 
 5–9 244 539 4.7 
 10–14 321 993 6.1 
 15–17 284 071 5.4 
Sex   
 Male 2 714 706 51.8 
 Female 2 529 043 48.2 
Race and ethnicity   
 White 2 420 623 46.2 
 Hispanic 999 143 19.1 
 Black 761 991 14.5 
 Other 339 462 6.5 
 Asian American or Pacific Islander 249 033 4.8 
 Native American 39 798 0.8 
 Missing 433 702 8.3 
Payer status   
 Medicaid 2 490 502 47.5 
 Private insurance 2 329 512 44.4 
 Self-pay 242 083 4.6 
 Other 155 643 3.0 
 Medicare 13 942 0.3 
 No charge 4822 0.1 
 Missing 7247 0.1 
CharacteristicN%
Age (y)   
 <1 4 021 312 76.7 
 1–4 371 832 7.1 
 5–9 244 539 4.7 
 10–14 321 993 6.1 
 15–17 284 071 5.4 
Sex   
 Male 2 714 706 51.8 
 Female 2 529 043 48.2 
Race and ethnicity   
 White 2 420 623 46.2 
 Hispanic 999 143 19.1 
 Black 761 991 14.5 
 Other 339 462 6.5 
 Asian American or Pacific Islander 249 033 4.8 
 Native American 39 798 0.8 
 Missing 433 702 8.3 
Payer status   
 Medicaid 2 490 502 47.5 
 Private insurance 2 329 512 44.4 
 Self-pay 242 083 4.6 
 Other 155 643 3.0 
 Medicare 13 942 0.3 
 No charge 4822 0.1 
 Missing 7247 0.1 

The eligible number of hospitalized children at risk for each PDI (denominator) ranged from 67 264 for neonatal BSI to 3 147 612 for accidental puncture or laceration. Overall PDI rates (per 10 000 discharges) were 331.4 for neonatal BSI (the highest PDI rate), 267.5 for postoperative respiratory failure, 114.9 for postoperative sepsis, 29.5 for postoperative hemorrhage/hematoma, 5.6 for central-line BSI, 3.5 for accidental puncture/laceration, and 0.7 for iatrogenic pneumothorax (the lowest PDI rate). Table 2 shows at-risk denominators and unadjusted rates for each PDI by race and ethnicity and payer status.

TABLE 2

Unadjusted Rates for Pediatric Quality Indicators (PDI) by Race and Ethnicity and Payer Status

PDIN At RiskRate Per 10 000 Discharges (95% CI)P
PDI 01 accidental puncture or laceration Overall 3 147 612 3.46 (3.26–3.67) NA 
Race and ethnicity .097 
Black 510 265 2.99 (2.51–3.46) 
Hispanic 603 869 3.43 (2.97–3.9) 
Asian American or Pacific Islander 133 641 3.29 (2.32–4.26) 
Native American 26 772 1.97 (0.29–3.66) 
Other Race or multiple race 200 764 4.11 (3.23–5) 
White 1 430 132 3.65 (3.33–3.96) 
Missing 242 169 3.14 (2.44–3.85) 
Payer .017 
Medicare 8711 4.51 (0.05–8.97) 
Medicaid 1 596 496 3.47 (3.18–3.76) 
Private insurance 1 311 016 3.42 (3.1–3.73) 
Self-pay 122 794 2.07 (1.27–2.88) 
No charge 3272 4.14 (0–11.1) 
Other 100 878 4.91 (3.54–6.28) 
Missing 4446 15.22 (3.76–26.68) 
PDI 05 iatrogenic pneumothorax Overall 2 885 802 0.7 (0.6–0.79)  
Race and ethnicity .026 
Black 454 839 0.45 (0.26–0.65) 
Hispanic 558 508 0.58 (0.38–0.78) 
Asian American or Pacific Islander 121 178 0.78 (0.28–1.28) 
Native American 24 677 0 (0–0) 
Other race or multiple race 182 681 0.74 (0.34–1.13) 
White 1 324 671 0.84 (0.68–0.99) 
Missing 219 248 0.68 (0.33–1.02) 
Payer .017 
Medicare 8042 3.44 (0–7.5) 
Medicaid 1 457 163 0.64 (0.51–0.77) 
Private insurance 1 208 374 0.68 (0.54–0.83)  
Self-pay 113 439 0.71 (0.22–1.2)  
No charge 3076 0 (0–0) 
Other 91 635 1.62 (0.79–2.44) 
Missing 4072 0 (0–0) 
PDI 08 postoperative hemorrhage or hematoma Overall 116 192 29.51 (26.39–32.63)  
Race and ethnicity .005 
Black 13 910 45.59 (34.39–56.78) 
Hispanic 23 614 25.94 (19.46–32.43) 
Asian American or Pacific Islander 4447 33.63 (16.62–50.65) 
Native American 815 48.38 (0.74–96.02) 
Other race or multiple race 6818 41.16 (25.96–56.36) 
White 59 649 26.67 (22.53–30.8) 
Missing 6939 17.55 (7.7–27.4) 
Payer <.0001 
Medicare 371 0 (0–0) 
Medicaid 51 688 35.83 (30.68–40.98) 
Private insurance 54 845 21.58 (17.69–25.46) 
Self-pay 2331 51.99 (22.79–81.19) 
No charge 105 0 (0–0) 
Other 6651 40.95 (25.6–56.3) 
Missing 202 0 (0–0) 
PDI 09 postoperative respiratory failure Overall 101 883 267.54 (257.63–277.45)  
Race and ethnicity <.0001 
Black 12 188 307.24 (276.61–337.88) 
Hispanic 20 527 298.75 (275.46–322.04) 
Asian American or Pacific Islander 3841 269.49 (218.28–320.7) 
Native American 706 266.02 (147.33–384.7) 
Other race or multiple race 5916 341.44 (295.17–387.72) 
White 52 739 217.64 (205.19–230.1) 
Missing 5965 445.75 (393.38–498.12) 
Payer <.0001 
Medicare 356 110.16 (1.73–218.58) 
Medicaid 44 815 307.21 (291.24–323.19) 
Private insurance 48 598 231.37 (218.01–244.74) 
Self-pay 2036 179.38 (121.72–237.03) 
No charge 94 301.79 (0–647.53) 
Other 5811 307.32 (262.94–351.69) 
Missing 173 156.05 (0–340.51) 
PDI 10 postoperative sepsis Overall 212 719 114.9 (110.37–119.42)  
Race and ethnicity <.0001 
Black 29 233 173.03 (158.09–187.98) 
Hispanic 44 327 123.52 (113.23–133.8) 
Asian American or Pacific Islander 7488 85.95 (65.05–106.86) 
Native American 1683 153.19 (94.52–211.86) 
Other race or multiple race 12 911 132.41 (112.69–152.13) 
White 104 210 92.51 (86.7–98.33) 
Missing 12 868 128.65 (109.18–148.12) 
Payer <.0001 
Medicare 715 186.65 (87.43–285.88) 
Medicaid 100 078 143.87 (136.49–151.24) 
Private insurance 94 633 80.76 (75.06–86.46) 
Self-pay 5422 149.24 (116.96–181.51) 
No charge 263 107.88 (0–232.7) 
Other 11 189 124.14 (103.62–144.65) 
Missing 420 95.29 (2.35–188.23) 
PDI 12 central venous catheter-related BSI Overall 2 535 923 5.56 (5.27–5.85)  
Race and ethnicity .001 
Black 425 839 6.74 (5.96–7.52) 
Hispanic 469 072 5.95 (5.25–6.64) 
Asian American or Pacific Islander 109 031 3.71 (2.57–4.85) 
Native American 21 422 6.22 (2.88–9.56) 
Other race or multiple race 162 994 5.84 (4.67–7.02)  
White 1 148 153 5.18 (4.77–5.6) 
Missing 199 411 5.02 (4.04–6.01) 
Payer <.0001 
Medicare 7309 14.62 (5.86–23.38) 
Medicaid 1 294 425 6.4 (5.97–6.84) 
Private insurance 1 055 731 4.24 (3.85–4.64) 
Self-pay 93 142 4.37 (3.03–5.71) 
No charge 2618 25.84 (6.4–45.29) 
Other 79 292 9.33 (7.2–11.45) 
Missing 3406 3.88 (0–10.5) 
NQI 03 neonatal BSI Overall 67 264 331.39 (317.86–344.92)  
Race and ethnicity <.0001 
Black 15 535 384.24 (354.01–414.46) 
Hispanic 11 333 394.16 (358.33–429.98) 
Asian American or Pacific Islander 2654 355.58 (285.13–426.03) 
Native American 498 313.7 (160.53–466.87) 
Other race or multiple race 5109 369.3 (317.59–421.01) 
White 26 843 265.95 (246.7–285.2) 
Missing 5293 326.67 (278.78–374.57) 
Payer .056 
Medicare 154 421.75 (104.18–739.32) 
Medicaid 36 108 342.63 (323.86–361.39) 
Private insurance 27 121 313.99 (293.23–334.74) 
Self-pay 1212 268.27 (177.29–359.25) 
No charge 38 0 (0–0) 
Other 2561 385.66 (311.08–460.24) 
Missing 71 362.9 (0–796.63) 
PDIN At RiskRate Per 10 000 Discharges (95% CI)P
PDI 01 accidental puncture or laceration Overall 3 147 612 3.46 (3.26–3.67) NA 
Race and ethnicity .097 
Black 510 265 2.99 (2.51–3.46) 
Hispanic 603 869 3.43 (2.97–3.9) 
Asian American or Pacific Islander 133 641 3.29 (2.32–4.26) 
Native American 26 772 1.97 (0.29–3.66) 
Other Race or multiple race 200 764 4.11 (3.23–5) 
White 1 430 132 3.65 (3.33–3.96) 
Missing 242 169 3.14 (2.44–3.85) 
Payer .017 
Medicare 8711 4.51 (0.05–8.97) 
Medicaid 1 596 496 3.47 (3.18–3.76) 
Private insurance 1 311 016 3.42 (3.1–3.73) 
Self-pay 122 794 2.07 (1.27–2.88) 
No charge 3272 4.14 (0–11.1) 
Other 100 878 4.91 (3.54–6.28) 
Missing 4446 15.22 (3.76–26.68) 
PDI 05 iatrogenic pneumothorax Overall 2 885 802 0.7 (0.6–0.79)  
Race and ethnicity .026 
Black 454 839 0.45 (0.26–0.65) 
Hispanic 558 508 0.58 (0.38–0.78) 
Asian American or Pacific Islander 121 178 0.78 (0.28–1.28) 
Native American 24 677 0 (0–0) 
Other race or multiple race 182 681 0.74 (0.34–1.13) 
White 1 324 671 0.84 (0.68–0.99) 
Missing 219 248 0.68 (0.33–1.02) 
Payer .017 
Medicare 8042 3.44 (0–7.5) 
Medicaid 1 457 163 0.64 (0.51–0.77) 
Private insurance 1 208 374 0.68 (0.54–0.83)  
Self-pay 113 439 0.71 (0.22–1.2)  
No charge 3076 0 (0–0) 
Other 91 635 1.62 (0.79–2.44) 
Missing 4072 0 (0–0) 
PDI 08 postoperative hemorrhage or hematoma Overall 116 192 29.51 (26.39–32.63)  
Race and ethnicity .005 
Black 13 910 45.59 (34.39–56.78) 
Hispanic 23 614 25.94 (19.46–32.43) 
Asian American or Pacific Islander 4447 33.63 (16.62–50.65) 
Native American 815 48.38 (0.74–96.02) 
Other race or multiple race 6818 41.16 (25.96–56.36) 
White 59 649 26.67 (22.53–30.8) 
Missing 6939 17.55 (7.7–27.4) 
Payer <.0001 
Medicare 371 0 (0–0) 
Medicaid 51 688 35.83 (30.68–40.98) 
Private insurance 54 845 21.58 (17.69–25.46) 
Self-pay 2331 51.99 (22.79–81.19) 
No charge 105 0 (0–0) 
Other 6651 40.95 (25.6–56.3) 
Missing 202 0 (0–0) 
PDI 09 postoperative respiratory failure Overall 101 883 267.54 (257.63–277.45)  
Race and ethnicity <.0001 
Black 12 188 307.24 (276.61–337.88) 
Hispanic 20 527 298.75 (275.46–322.04) 
Asian American or Pacific Islander 3841 269.49 (218.28–320.7) 
Native American 706 266.02 (147.33–384.7) 
Other race or multiple race 5916 341.44 (295.17–387.72) 
White 52 739 217.64 (205.19–230.1) 
Missing 5965 445.75 (393.38–498.12) 
Payer <.0001 
Medicare 356 110.16 (1.73–218.58) 
Medicaid 44 815 307.21 (291.24–323.19) 
Private insurance 48 598 231.37 (218.01–244.74) 
Self-pay 2036 179.38 (121.72–237.03) 
No charge 94 301.79 (0–647.53) 
Other 5811 307.32 (262.94–351.69) 
Missing 173 156.05 (0–340.51) 
PDI 10 postoperative sepsis Overall 212 719 114.9 (110.37–119.42)  
Race and ethnicity <.0001 
Black 29 233 173.03 (158.09–187.98) 
Hispanic 44 327 123.52 (113.23–133.8) 
Asian American or Pacific Islander 7488 85.95 (65.05–106.86) 
Native American 1683 153.19 (94.52–211.86) 
Other race or multiple race 12 911 132.41 (112.69–152.13) 
White 104 210 92.51 (86.7–98.33) 
Missing 12 868 128.65 (109.18–148.12) 
Payer <.0001 
Medicare 715 186.65 (87.43–285.88) 
Medicaid 100 078 143.87 (136.49–151.24) 
Private insurance 94 633 80.76 (75.06–86.46) 
Self-pay 5422 149.24 (116.96–181.51) 
No charge 263 107.88 (0–232.7) 
Other 11 189 124.14 (103.62–144.65) 
Missing 420 95.29 (2.35–188.23) 
PDI 12 central venous catheter-related BSI Overall 2 535 923 5.56 (5.27–5.85)  
Race and ethnicity .001 
Black 425 839 6.74 (5.96–7.52) 
Hispanic 469 072 5.95 (5.25–6.64) 
Asian American or Pacific Islander 109 031 3.71 (2.57–4.85) 
Native American 21 422 6.22 (2.88–9.56) 
Other race or multiple race 162 994 5.84 (4.67–7.02)  
White 1 148 153 5.18 (4.77–5.6) 
Missing 199 411 5.02 (4.04–6.01) 
Payer <.0001 
Medicare 7309 14.62 (5.86–23.38) 
Medicaid 1 294 425 6.4 (5.97–6.84) 
Private insurance 1 055 731 4.24 (3.85–4.64) 
Self-pay 93 142 4.37 (3.03–5.71) 
No charge 2618 25.84 (6.4–45.29) 
Other 79 292 9.33 (7.2–11.45) 
Missing 3406 3.88 (0–10.5) 
NQI 03 neonatal BSI Overall 67 264 331.39 (317.86–344.92)  
Race and ethnicity <.0001 
Black 15 535 384.24 (354.01–414.46) 
Hispanic 11 333 394.16 (358.33–429.98) 
Asian American or Pacific Islander 2654 355.58 (285.13–426.03) 
Native American 498 313.7 (160.53–466.87) 
Other race or multiple race 5109 369.3 (317.59–421.01) 
White 26 843 265.95 (246.7–285.2) 
Missing 5293 326.67 (278.78–374.57) 
Payer .056 
Medicare 154 421.75 (104.18–739.32) 
Medicaid 36 108 342.63 (323.86–361.39) 
Private insurance 27 121 313.99 (293.23–334.74) 
Self-pay 1212 268.27 (177.29–359.25) 
No charge 38 0 (0–0) 
Other 2561 385.66 (311.08–460.24) 
Missing 71 362.9 (0–796.63) 

CI, confidence interval; NA, not applicable; NQI, Neonatal Quality Indicator.

Compared with white patients, Black and Hispanic patients had significantly greater odds of 5 and 4 of the 7 PDIs, respectively (Table 3). Black patients had the largest disparity in postoperative sepsis, and Hispanic patients had the largest disparity in postoperative respiratory failure (Fig 1). No disparities were observed for accidental puncture or laceration, and Black and Hispanic children had significant lower adjusted odds of iatrogenic pneumothorax than white children.

TABLE 3

Adjusted Rates and Odds Ratios for Pediatric Quality Indicators (PDI) by Race and Ethnicity

PDIAdjusted Rate Per 10 000 (95% CI)PAdjusted Odds Ratio
(95% CI)
Accidental puncture or laceration (PDI 01) White 0.6 (0.49–0.75) .342 NA 
Black 0.55 (0.43–0.7)  NA 
Hispanic 0.54 (0.43–0.68)  NA 
Asian American or Pacific Islander 0.64 (0.45–0.91)  NA 
Other race/multiple race 0.68 (0.51–0.9)  NA 
Iatrogenic pneumothorax (PDI 05) White 0.32 (0.24–0.44) .047 Reference 
Black 0.19 (0.11–0.31)  0.58 (0.36–0.93) 
Hispanic 0.2 (0.13–0.31)  0.64 (0.43–0.94) 
Asian American or Pacific Islander 0.37 (0.19–0.74)  1.16 (0.6–2.26) 
Other race/multiple race 0.32 (0.18–0.58)  1 (0.57–1.76) 
Postoperative hemorrhage or hematoma (PDI 08) White 16.7 (13.76–20.27) .012 Reference 
Black 23.92 (18.09–31.63)  1.43 (1.07–1.93) 
Hispanic 13.31 (10.01–17.7)  0.8 (0.59–1.07) 
Asian American or Pacific Islander 19.41 (11.48–32.78)  1.16 (0.68–1.99) 
Other race/multiple race 22.48 (15.15–33.34)  1.35 (0.9–2.02) 
Postoperative respiratory failure (PDI 09) White 110.13 (101.64–119.32) <.001 Reference 
Black 150.26 (133.44–169.16)  1.37 (1.21–1.55) 
Hispanic 147.07 (133.34–162.19)  1.34 (1.21–1.49) 
Asian American or Pacific Islander 126.32 (102.55–155.51)  1.15 (0.93–1.42) 
Other race/multiple race 158.74 (135.94–185.3)  1.45 (1.23–1.7) 
Postoperative sepsis (PDI 10) White 56.73 (52.55–61.24) <.001 Reference 
Black 87.34 (78.88–96.7)  1.54 (1.38–1.73) 
Hispanic 68.14 (61.88–75.02)  1.2 (1.08–1.34) 
Asian American or Pacific Islander 51.56 (40.09–66.28)  0.91 (0.7–1.17) 
Other race/multiple race 68.07 (57.93–79.96)  1.2 (1.02–1.42) 
Central venous catheter-related BSI (PDI 12) White 1.54 (1.35–1.75) <.001 Reference 
Black 2.16 (1.86–2.52)  1.41 (1.22–1.62) 
Hispanic 1.53 (1.31–1.79)  0.99 (0.86–1.15) 
Asian American or Pacific Islander 1.17 (0.85–1.62)  0.76 (0.55–1.05) 
Other race/multiple race 1.79 (1.43–2.25)  1.17 (0.94–1.45) 
Neonatal BSI (NQI 03) White 216.12 (199.69–233.86) <.001 Reference 
Black 245.57 (224.09–269.04)  1.14 (1.02–1.28) 
Hispanic 285.74 (258.56–315.69)  1.33 (1.18–1.51) 
Asian American or Pacific Islander 257.94 (209.55–317.15)  1.2 (0.96–1.5) 
Other race/multiple race 269.67 (232.52–312.57)  1.25 (1.06–1.48) 
PDIAdjusted Rate Per 10 000 (95% CI)PAdjusted Odds Ratio
(95% CI)
Accidental puncture or laceration (PDI 01) White 0.6 (0.49–0.75) .342 NA 
Black 0.55 (0.43–0.7)  NA 
Hispanic 0.54 (0.43–0.68)  NA 
Asian American or Pacific Islander 0.64 (0.45–0.91)  NA 
Other race/multiple race 0.68 (0.51–0.9)  NA 
Iatrogenic pneumothorax (PDI 05) White 0.32 (0.24–0.44) .047 Reference 
Black 0.19 (0.11–0.31)  0.58 (0.36–0.93) 
Hispanic 0.2 (0.13–0.31)  0.64 (0.43–0.94) 
Asian American or Pacific Islander 0.37 (0.19–0.74)  1.16 (0.6–2.26) 
Other race/multiple race 0.32 (0.18–0.58)  1 (0.57–1.76) 
Postoperative hemorrhage or hematoma (PDI 08) White 16.7 (13.76–20.27) .012 Reference 
Black 23.92 (18.09–31.63)  1.43 (1.07–1.93) 
Hispanic 13.31 (10.01–17.7)  0.8 (0.59–1.07) 
Asian American or Pacific Islander 19.41 (11.48–32.78)  1.16 (0.68–1.99) 
Other race/multiple race 22.48 (15.15–33.34)  1.35 (0.9–2.02) 
Postoperative respiratory failure (PDI 09) White 110.13 (101.64–119.32) <.001 Reference 
Black 150.26 (133.44–169.16)  1.37 (1.21–1.55) 
Hispanic 147.07 (133.34–162.19)  1.34 (1.21–1.49) 
Asian American or Pacific Islander 126.32 (102.55–155.51)  1.15 (0.93–1.42) 
Other race/multiple race 158.74 (135.94–185.3)  1.45 (1.23–1.7) 
Postoperative sepsis (PDI 10) White 56.73 (52.55–61.24) <.001 Reference 
Black 87.34 (78.88–96.7)  1.54 (1.38–1.73) 
Hispanic 68.14 (61.88–75.02)  1.2 (1.08–1.34) 
Asian American or Pacific Islander 51.56 (40.09–66.28)  0.91 (0.7–1.17) 
Other race/multiple race 68.07 (57.93–79.96)  1.2 (1.02–1.42) 
Central venous catheter-related BSI (PDI 12) White 1.54 (1.35–1.75) <.001 Reference 
Black 2.16 (1.86–2.52)  1.41 (1.22–1.62) 
Hispanic 1.53 (1.31–1.79)  0.99 (0.86–1.15) 
Asian American or Pacific Islander 1.17 (0.85–1.62)  0.76 (0.55–1.05) 
Other race/multiple race 1.79 (1.43–2.25)  1.17 (0.94–1.45) 
Neonatal BSI (NQI 03) White 216.12 (199.69–233.86) <.001 Reference 
Black 245.57 (224.09–269.04)  1.14 (1.02–1.28) 
Hispanic 285.74 (258.56–315.69)  1.33 (1.18–1.51) 
Asian American or Pacific Islander 257.94 (209.55–317.15)  1.2 (0.96–1.5) 
Other race/multiple race 269.67 (232.52–312.57)  1.25 (1.06–1.48) 

Adjusted for severity on the basis of AHRQ published adjustment strategy that is unique to each PDI. Bolded text highlights significant values. CI, confidence interval; NA, not applicable; NQI, Neonatal Quality Indicator.

FIGURE 1

Racial/ethnic disparities in PDIs for hospitalized US children. The left panel displays adjusted odds ratios, and the right panel shows adjusted odds ratios stratified by payer. Six PDIs where disparities were detected are displayed.

FIGURE 1

Racial/ethnic disparities in PDIs for hospitalized US children. The left panel displays adjusted odds ratios, and the right panel shows adjusted odds ratios stratified by payer. Six PDIs where disparities were detected are displayed.

Close modal

Medicaid patients had significantly greater odds of 4 of the 7 PDIs, specifically postoperative events (hemorrhage, sepsis, and respiratory failure) and central venous catheter-related BSIs (Fig 2). Adjusted rates and odds ratios for each PDI with payer analysis are included in Supplemental Table 4.

FIGURE 2

Adjusted odds ratios by payer for PDIs for hospitalized US children. Four PDIs where disparities were detected are displayed.

FIGURE 2

Adjusted odds ratios by payer for PDIs for hospitalized US children. Four PDIs where disparities were detected are displayed.

Close modal

Stratified analyses (Fig 1 and Supplemental Table 5) demonstrated that, even among privately insured children, racial and ethnic disparities persisted for certain PDIs. Hospitalized Black and Hispanic children had significantly greater odds of 3 PDIs, specifically central-venous catheter-related BSIs for both Black and Hispanic patients, and postoperative hemorrhage for Hispanic patients.

A recent study demonstrated that hospitals with higher safety grades do not consistently deliver that safe care among different patient populations, highlighting that high safety does not associate with equity.23 Given a potential disconnect between safety and equity, our study sought to identify disparities in pediatric safety events; we found disparities for hospitalized Black and/or Hispanic children in 5 of 7 nationally developed and validated safety metrics (PDIs) and disparities for Medicaid-covered patients were identified for 4 of 7 PDIs. Our findings document national racial, ethnic, and payer disparities in AHRQ PDI measures of pediatric safety. We found that Black patients and Medicaid-insured patients experienced greatest disparities in postoperative sepsis, and Hispanic patients experienced the greatest inequity in postoperative respiratory failure. In addition, Black and Hispanic patients experience inequities with BSIs.

Our findings are consistent with and complement other studies that have focused on different aspects of patient safety for hospitalized children. For example, the Global Assessment of Pediatric Patient Safety Trigger Tools revealed racial and ethnic and socioeconomic disparities in rates of triggers and adverse events for hospitalized children.14 In this work, triggers were not themselves adverse events, but required chart review to confirm whether an adverse event occurred; triggers included medication triggers (eg, vitamin K administration after warfarin or opiate-related constipation), as well as hospital-care triggers (eg, intravenous infiltration or unplanned transfers).14,33 Our work with PDIs identified disparities in a different set of safety events and complements findings from these earlier studies to highlight the broad range and depth of disparities in hospitalized children.

Our work is also consistent with findings from other studies that have focused on specific safety events, such as sepsis or central line-associated BSI (CLABSI). Although our study focused on postoperative sepsis, other studies (both single-center and national hospital cohorts) have shown racial and ethnic disparities exist in pediatric sepsis, despite advances in sepsis care that include reductions in sepsis-related mortality in US children.16,34 In a cohort of children’s hospitals, CLABSI rates were persistently higher in Black and Hispanic versus white children, despite development and implementation of central-line bundle practices known to decrease CLABSI rates.15 A recent single-center study demonstrated mitigation of disparities in CLABSI rates after quality-improvement (QI) interventions.35 This research documented that collaboration with multidisciplinary stakeholders can create effective, equity-focused QI interventions that reduce infection rates for Black patients.35 QI can thus be an efficacious tool for achieving equity. Accomplishing this goal requires thoughtfully stratifying outcomes to identify and understand disparities before launching targeted QI efforts.36 

Of note, no disparities were observed in our analysis for accidental puncture or laceration, and Black and Hispanic children had significantly lower adjusted odds of iatrogenic pneumothorax than white children. The AHRQ technical report definition for the iatrogenic pneumothorax PDI focuses on secondary diagnosis of iatrogenic pneumothorax, and excludes nontraumatic iatrogenic pneumothorax present on admission or related to chest trauma, pleural effusion, thoracic surgeries, or transpleural cardiac procedures.27 Lower rates of iatrogenic pneumothorax for Black and Hispanic adult patients have been reported by other investigators.23 Perhaps there are different drivers of disparities in procedure-related disparities, compared with infection-related disparities, which can be explored in future work. Additional research on safety events for which there are no disparities or lower rates for historically disadvantaged groups may provide valuable insights into how to improve care for patients undergoing these procedures.

Adverse patient-safety events harm children and families, resulting in millions of dollars in direct and indirect medical costs,37 and are likely attributable to a variety of different factors. Our results highlight that racial and ethnic disparities in care go beyond payer differences because stratification by insurance continued to show greater adjusted odds of safety events for certain PDIs. The reasons for these disparities, however, cannot be definitively determined in such a secondary data analysis. It is reasonable, however, to consider several plausible factors, including structural racism in the US health care system, clinician bias, insufficient cultural responsiveness, communication barriers, and/or impaired access to high-quality and timely health care.38,42 There are stark examples of structural racism in health care. Recent studies in adults showed that, relative to white patients, Black patients in the United States are more likely to receive care at institutions that have higher rates of adverse safety events and are more likely to experience adverse safety events, even when admitted to the same hospital as white patients.9,43 Another study demonstrated that hospitals who see a smaller percentage of very low birth weight infants who are Black have higher mortality rates for Black patients, and demonstrated worse overall quality of care at hospitals that service higher populations of historically disadvantaged groups.44 Studies have also shown that disproportionately minority-serving hospitals are more likely to have overcrowded emergency departments, reduced access to subspeciality services, less timely interventions, and less chance of providing hospital transfers.45,46 

Clinician bias, insufficient cultural responsiveness, and communication barriers also can contribute to disparities. Studies have shown both minoritized children and adults who present with fractures to the hospital are less likely to receive opioids and achieve optimal pain reduction,47,48 and less likely to receive appropriate imaging studies to help make accurate diagnoses.49 A recent study demonstrated that even among healthy children, being Black is strongly associated with a higher risk of postoperative complications and mortality, noting that differences in postoperative outcomes may not be explained by racial variation in preoperative comorbidity.50 In addition, research indicates that fatigue and cognitive stresses can increase implicit bias in physicians in the emergency department.39 Disparities are additionally associated with lack of cultural responsiveness, as exemplified by research on factors associated with asthma-care quality for Medicaid-insured children, which found that patients at practice sites with the lowest cultural competence scores were significantly more likely to underuse preventive asthma medications and had significantly worse parent ratings of overall quality of asthma care.51 In addition, communication barriers can lead to a wide variety of adverse outcomes for Hispanic children, including deaths.52,55 

Frameworks have been proposed on how to ensure health equity in health care systems.36,56,57 These frameworks broadly align in recommending a 3-step process for health equity in ongoing quality and safety efforts. Step 1 consists of examining quality and safety metrics by race and ethnicity, primary language, limited English proficiency, and/or proxies for socioeconomic status (insurance coverage and median household income by zip code). Findings from this current study generate data for Step 1 in these frameworks. Step 2 is to partner with wider groups of stakeholders, including community and patient/family partners affected by disparities. These partnerships should focus on qualitatively understanding potential drivers of disparities, with an emphasis on system-related factors. A deeper understanding of these factors could be grounded in safety frameworks such as the Systems Engineering Initiative for Patient Safety model of work system and patient safety, which proposes a more comprehensive definition of structure (including the physical environment, organizational culture, error reporting and analysis, and work design).58 Step 3 is to design and test equity-focused QI interventions tailored to disparities populations. Data-monitoring systems developed in Step 1 can then support iterative rapid cycles of improvement.

Our team’s next steps will include a deeper analysis of these disparities and qualitative work to understand potential drivers. On the basis of our findings, postoperative sepsis, postoperative respiratory failure, and BSIs will be the foci. We plan to conduct qualitative interviews, and, ultimately, to design and test interventions to improve health equity in these key areas of preventable harm. We will include elements from disparities frameworks, such as Flores and Ngui3 and McKay and Parente,59 to guide our next steps. The framework outlines potential individual and systemic drivers of disparities, including higher prevalence of risk factors (eg, delayed care and limited access to high-quality outpatient care), medical errors of omission or deviations from optimal practice, language barriers, insufficient data monitoring systems, and/or disparate views of what constitutes harm between patients/families and the health system.

Certain study limitations should be noted. Approximately 9% of the sample was missing race and ethnicity data, and there can also be substantial variability in how this information is collected across hospitals.60 Of note, KID database reports an aggregate race and ethnicity variable where ethnicity takes precedence over race when hospitals report separate variables. Small sample sizes precluded analyses for important groups known to be historically disadvantaged such as Native Americans. Regression models cannot produce stable risk estimates for small sample sizes. The threshold for this size varies by total sample size, covariables included, and outcome rates. A key limitation of the study findings, therefore, is that small sample sizes did not allow for meaningful analyses for Native Americans and the self-pay group. The data set did not allow for separate analyses for uninsured children. The data set also did not include variables for limited English proficiency or health literacy, known risk factors for patient-safety events.54,55 Although AHRQ risk adjustment was performed, it is possible that there was collinearity of race and ethnicity and/or payer status with covariates used as risk adjusters in the model, leading to potential overadjustment of the independent variables. This, however, would bias the findings toward the null hypothesis (no differences detected), and yet we were able to identify disparities. In addition, the KID database allows capture of up to 40 diagnostic codes for each encounter, reducing the chance of missed safety events. It is still possible, however, that events might be missed because of limitations in local coding practices and the database extraction process. Although errors in clinical coding of discharge summaries and medical billing records may exist in KID, HCUP uses strict validation criteria, minimizing this potential limitation. Lastly, the analysis was unable to assess clustering by hospital or hospital type (eg, freestanding children’s hospitals) and other confounders associated with the risk of safety events.

Furthering our understanding of disparities in pediatric safety events is a critical, foundational step to ultimately testing and implementing interventions to improve patient safety for historically disadvantaged children and achieve health equity for all children. Disparities in hospital patient-safety events were identified for US Black and Hispanic children, including postoperative hemorrhage and respiratory failure, sepsis, and BSIs. This large, population-based analysis provides the best data to date regarding where future work can be directed to prioritize efforts to better understand these disparities. Future work is needed to identify modifiable factors and design targeted interventions for patients experiencing disparities in pediatric safety events.

Drs Parikh, Hall, and Kaiser conceptualized and designed the study, analyzed and interpreted data, drafted the initial manuscript, and reviewed and revised the manuscript; Drs Tieder, Dixon, Ward, Hinds, Goyal, Rangel, and Flores analyzed and interpreted data, and edited, reviewed, and revised the manuscript; and all authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

COMPANION PAPER: A companion to this article can be found online at www.pediatrics.org/cgi/doi/10.1542/peds.2023-064741.

Abbreviations
AHRQ

Agency for Healthcare Research and Quality

BSI

blood stream infection

CLABSI

central line-associated blood stream infection

HCUP

Healthcare Cost and Utilization Project

KID

Kids’ Inpatient Database

PDI

pediatric quality indicator

QI

quality improvement

1
Miller
MR
,
Elixhauser
A
,
Zhan
C
.
Patient safety events during pediatric hospitalizations
.
Pediatrics
.
2003
;
111
(
6 Pt 1
):
1358
1366
2
Miller
MR
,
Zhan
C
.
Pediatric patient safety in hospitals: a national picture in 2000
.
Pediatrics
.
2004
;
113
(
6
):
1741
1746
3
Flores
G
,
Ngui
E
.
Racial/ethnic disparities and patient safety
.
Pediatr Clin North Am
.
2006
;
53
(
6
):
1197
1215
4
Berdahl
T
,
Owens
PL
,
Dougherty
D
,
McCormick
MC
,
Pylypchuk
Y
,
Simpson
LA
.
Annual report on health care for children and youth in the United States: racial/ethnic and socioeconomic disparities in children’s health care quality
.
Acad Pediatr
.
2010
;
10
(
2
):
95
118
5
Institute of Medicine (US) Committee on Understanding and Eliminating Racial and Ethnic Disparities in Health Care
. Unequal treatment: confronting racial and ethnic disparities in health care.
Smedley
BD
,
Stith
AY
,
Nelson
AR
, eds.
Washington, DC
:
National Academies Press (US)
:
2003
6
Coffey
RM
,
Andrews
RM
,
Moy
E
.
Racial, ethnic, and socioeconomic disparities in estimates of AHRQ patient safety indicators
.
Med Care
.
2005
;
43
(
3
Suppl
):
I48
I57
7
Romano
PS
,
Geppert
JJ
,
Davies
S
,
Miller
MR
,
Elixhauser
A
,
McDonald
KM
.
A national profile of patient safety in US hospitals
.
Health Aff (Millwood)
.
2003
;
22
(
2
):
154
166
8
Bakullari
A
,
Metersky
ML
,
Wang
Y
, et al
.
Racial and ethnic disparities in health care-associated infections in the United States, 2009–2011
.
Infect Control Hosp Epidemiol
.
2014
;
35
(
Suppl 3
):
S10
S16
9
Gangopadhyaya
A
.
Urban Institute
.
Do Black and white patients experience similar rates of adverse safety events at the same hospital?
Available at: https://www.urban.org/sites/default/files/publication/104559/do-black-and-white-patients-experience-similar-rates-of-adverse-safety-events-at-the-same-hospital_0.pdf. Accessed July 21, 2023
10
Cheng
TL
,
Emmanuel
MA
,
Levy
DJ
,
Jenkins
RR
.
Child health disparities: what can a clinician do?
Pediatrics
.
2015
;
136
(
5
):
961
968
11
Cheng
TL
,
Dreyer
BP
,
Jenkins
RR
.
Introduction: child health disparities and health literacy
.
Pediatrics
.
2009
;
124
(
Suppl 3
):
S161
S162
12
Flores
G
;
Committee On Pediatric Research
.
Technical report–racial and ethnic disparities in the health and health care of children
.
Pediatrics
.
2010
;
125
(
4
):
e979
e1020
13
Braveman
P
,
Barclay
C
.
Health disparities beginning in childhood: a life-course perspective
.
Pediatrics
.
2009
;
124
(
Suppl 3
):
S163
S175
14
Stockwell
DC
,
Landrigan
CP
,
Toomey
SL
, et al
.
GAPPS Study Group
.
Racial, ethnic, and socioeconomic disparities in patient safety events for hospitalized children
.
Hosp Pediatr
.
2019
;
9
(
1
):
1
5
15
Willer
BL
,
Tobias
JD
,
Suttle
ML
,
Nafiu
OO
,
Mpody
C
.
Trends of racial/ethnic disparities in pediatric central line-associated bloodstream infections
.
Pediatrics
.
2022
;
150
(
3
):
e2021054955
16
Li
E
,
Ng
AP
,
Williamson
CG
,
Tran
Z
,
Federman
MD
,
Benharash
P
.
Assessment of racial and ethnic disparities in outcomes of pediatric hospitalizations for sepsis across the United States
.
JAMA Pediatr
.
2023
;
177
(
2
):
206
208
17
Flores
G
,
Olson
L
,
Tomany-Korman
SC
.
Racial and ethnic disparities in early childhood health and health care
.
Pediatrics
.
2005
;
115
(
2
):
e183
e193
18
Shone
LP
,
Dick
AW
,
Klein
JD
,
Zwanziger
J
,
Szilagyi
PG
.
Reduction in racial and ethnic disparities after enrollment in the State Children’s Health Insurance Program
.
Pediatrics
.
2005
;
115
(
6
):
e697
e705
19
Flores
G
,
Tomany-Korman
SC
.
Racial and ethnic disparities in medical and dental health, access to care, and use of services in US children
.
Pediatrics
.
2008
;
121
(
2
):
e286
e298
20
Lau
M
,
Lin
H
,
Flores
G
.
Racial/ethnic disparities in health and health care among US adolescents
.
Health Serv Res
.
2012
;
47
(
5
):
2031
2059
21
Flores
G
,
Lin
H
.
Trends in racial/ethnic disparities in medical and oral health, access to care, and use of services in US children: has anything changed over the years?
Int J Equity Health
.
2013
;
12
:
10
22
Austin
JM
,
D’Andrea
G
,
Birkmeyer
JD
, et al
.
Safety in numbers: the development of Leapfrog’s composite patient safety score for U.S. hospitals
.
J Patient Saf
.
2014
;
10
(
1
):
64
71
23
Gangopadhyaya
APA
,
Austin
M
,
Camione
A
,
Danforth
M
.
The Leapfrog Group and Urban Institute
.
Racial, ethnic, and payer disparities in adverse safety events: are there differences across Leapfrog hospital safety grades?
Available at: https://www.leapfroggroup.org/racial-ethnic-and-payer-disparities-adverse-safety-events-are-there-differences-across-leapfrog. Accessed July 21, 2023
24
Rinke
ML
,
Zimmer
KP
,
Lehmann
CU
, et al
.
Patient safety rounds in a pediatric tertiary care center
.
Jt Comm J Qual Patient Saf
.
2008
;
34
(
1
):
5
12
25
Parikh
K
,
Hochberg
E
,
Cheng
JJ
, et al
.
Apparent cause analysis: a safety tool
.
Pediatrics
.
2020
;
145
(
5
):
e20191819
26
McDonald
KM
,
Davies
SM
,
Haberland
CA
,
Geppert
JJ
,
Ku
A
,
Romano
PS
.
Preliminary assessment of pediatric health care quality and patient safety in the United States using readily available administrative data
.
Pediatrics
.
2008
;
122
(
2
):
e416
e425
27
Agency for Healthcare Research and Quality
.
Technical specifications for pediatric quality indicators
. Available at: https://qualityindicators.ahrq.gov/measures/PDI_TechSpec. Accessed September 14 2023
28
Flanagin
A
,
Frey
T
,
Christiansen
SL
.
AMA Manual of Style Committee
.
Updated guidance on the reporting of race and ethnicity in medical and science journals
.
JAMA
.
2021
;
326
(
7
):
621
627
29
US Census
.
Improved race and ethnicity measures reveal US population is much more multiracial
. Available at: https://www.census.gov/library/stories/2021/08/improved-race-ethnicity-measures-reveal-united-states-population-much-more-multiracial.html. Accessed April 11 2023
30
Agency for Healthcare Research and Quality
;
HCUP
.
Healthcare Cost and Utilization Project user support
. Available at: www.hcup-us.ahrq.gov/kidoverview.jsp. Accessed April 11 2023
31
Rogers
LO
,
Heard-Garris
N
.
Documenting racial disparities or disrupting racism: a call to center systems of power, privilege, and oppression in psychological and pediatric research
.
JAMA Pediatr
.
2023
;
177
(
2
):
113
114
32
Ioannidis
JPA
,
Powe
NR
,
Yancy
C
.
Recalibrating the use of race in medical research
.
JAMA
.
2021
;
325
(
7
):
623
624
33
Stockwell
DC
,
Bisarya
H
,
Classen
DC
, et al
.
A trigger tool to detect harm in pediatric inpatient settings
.
Pediatrics
.
2015
;
135
(
6
):
1036
1042
34
Raman
J
,
Johnson
TJ
,
Hayes
K
,
Balamuth
F
.
Racial differences in sepsis recognition in the emergency department
.
Pediatrics
.
2019
;
144
(
4
):
e20190348
35
McGrath
CL
,
Bettinger
B
,
Stimpson
M
, et al
.
Identifying and mitigating disparities in central line-associated bloodstream infections in minoritized racial, ethnic, and language groups
.
JAMA Pediatr
.
2023
;
177
(
7
):
700
709
36
Lion
KC
,
Faro
EZ
,
Coker
TR
.
All quality improvement is health equity work: designing improvement to reduce disparities
.
Pediatrics
.
2022
;
149
(
Suppl 3
):
e2020045948E
37
Myers
C
,
Stockwell
DC
.
The high cost of harm
.
Mayo Clin Proc
.
2022
;
97
(
2
):
205
207
38
Flores
G
,
Abreu
M
,
Barone
CP
,
Bachur
R
,
Lin
H
.
Errors of medical interpretation and their potential clinical consequences: a comparison of professional versus ad hoc versus no interpreters
.
Ann Emerg Med
.
2012
;
60
(
5
):
545
553
39
Johnson
TJ
,
Hickey
RW
,
Switzer
GE
, et al
.
The impact of cognitive stressors in the emergency department on physician implicit racial bias
.
Acad Emerg Med
.
2016
;
23
(
3
):
297
305
40
Shen
MJ
,
Peterson
EB
,
Costas-Muñiz
R
, et al
.
The effects of race and racial concordance on patient–physician communication: a systematic review of the literature
.
J Racial Ethn Health Disparities
.
2018
;
5
(
1
):
117
140
41
Parikh
K
,
Berry
J
,
Hall
M
, et al
.
Racial and ethnic differences in pediatric readmissions for common chronic conditions
.
J Pediatr
.
2017
;
186
:
158
164.e1
42
Parikh
K
,
Hall
M
,
Kaiser
SV
, et al
.
Development of a health disparities index: proof of concept with chest radiography in asthma
.
J Pediatr
.
2021
;
238
:
290
295.e1
43
Gangopadhyaya
A
.
Urban Institute and Robert Wood Johnson Foundation
.
Black patients are more likely than white patients to be in hospitals with worse patient safety conditions
. Available at: https://www.rwjf.org/en/insights/our-research/2021/03/black-patients-are-more-likely-than-white-patients-to-be-in-hospitals-with-worse-patient-safety-conditions.html. Accessed July 21, 2023
44
Howell
EA
,
Hebert
P
,
Chatterjee
S
,
Kleinman
LC
,
Chassin
MR
.
Black/white differences in very low birth weight neonatal mortality rates among New York City hospitals
.
Pediatrics
.
2008
;
121
(
3
):
e407
e415
45
Ly
DP
,
Lopez
L
,
Isaac
T
,
Jha
AK
.
How do black-serving hospitals perform on patient safety indicators? Implications for national public reporting and pay-for-performance
.
Med Care
.
2010
;
48
(
12
):
1133
1137
46
Peltan
ID
,
Bledsoe
JR
,
Oniki
TA
, et al
.
Emergency department crowding is associated with delayed antibiotics for sepsis
.
Ann Emerg Med
.
2019
;
73
(
4
):
345
355
47
Goyal
MK
,
Johnson
TJ
,
Chamberlain
JM
, et al
.
Pediatric Emergency Care Applied Research Network
.
Racial and ethnic differences in emergency department pain management of children with fractures
.
Pediatrics
.
2020
;
145
(
5
):
e20193370
48
Ng
B
,
Dimsdale
JE
,
Shragg
GP
,
Deutsch
R
.
Ethnic differences in analgesic consumption for postoperative pain
.
Psychosom Med
.
1996
;
58
(
2
):
125
129
49
Marin
JR
,
Rodean
J
,
Hall
M
, et al
.
Racial and ethnic differences in emergency department diagnostic imaging at US children’s hospitals, 2016–2019
.
JAMA Netw Open
.
2021
;
4
(
1
):
e2033710
50
Nafiu
OO
,
Mpody
C
,
Kim
SS
,
Uffman
JC
,
Tobias
JD
.
Race, postoperative complications, and death in apparently healthy children
.
Pediatrics
.
2020
;
146
(
2
):
e20194113
51
Lieu
TA
,
Finkelstein
JA
,
Lozano
P
, et al
.
Cultural competence policies and other predictors of asthma care quality for Medicaid-insured children
.
Pediatrics
.
2004
;
114
(
1
):
e102
e110
52
Flores
G
.
Families facing language barriers in healthcare: when will policy catch up with the demographics and evidence?
J Pediatr
.
2014
;
164
(
6
):
1261
1264
53
Flores
G
.
The impact of medical interpreter services on the quality of health care: a systematic review
.
Med Care Res Rev
.
2005
;
62
(
3
):
255
299
54
Khan
A
,
Spector
ND
,
Baird
JD
, et al
.
Patient safety after implementation of a coproduced family centered communication program: multicenter before and after intervention study
.
BMJ
.
2018
;
363
:
k4764
55
Khan
A
,
Yin
HS
,
Brach
C
, et al
.
Patient and Family Centered I-PASS Health Literacy Subcommittee
.
Association between parent comfort with English and adverse events among hospitalized children
.
JAMA Pediatr
.
2020
;
174
(
12
):
e203215
56
Kilbourne
AM
,
Switzer
G
,
Hyman
K
,
Crowley-Matoka
M
,
Fine
MJ
.
Advancing health disparities research within the health care system: a conceptual framework
.
Am J Public Health
.
2006
;
96
(
12
):
2113
2121
57
Chin
MH
.
Advancing health equity in patient safety: a reckoning, challenge and opportunity
. [Published online ahead of print December 29, 2020]
BMJ Qual Saf
.
2020
;
bmjqs-2020-012599
58
Carayon
P
,
Schoofs Hundt
A
,
Karsh
BT
, et al
.
Work system design for patient safety: the SEIPS model
.
Qual Saf Health Care
.
2006
;
15
(
Suppl 1
):
i50
i58
59
McKay
S
,
Parente
V
.
Health disparities in the hospitalized child
.
Hosp Pediatr
.
2019
;
9
(
5
):
317
325
60
Cowden
JD
,
Flores
G
,
Chow
T
, et al
.
Variability in collection and use of race/ethnicity and language data in 93 pediatric hospitals
.
J Racial Ethn Health Disparities
.
2020
;
7
(
5
):
928
936

Competing Interests

CONFLICT OF INTEREST DISCLOSURES: The authors have indicated they have no conflicts of interest relevant to this article to disclose.

Supplementary data