OBJECTIVES

Few studies have analyzed potential inequities in both pediatric patient safety events (PSEs) and adverse events (AEs) - PSEs leading to harm - nor in PSEs by event type. We sought to examine potential inequities in rates of pediatric PSEs overall, by severity, and by category based on race and ethnicity, insurance payor, and language as measured using voluntary incident reports (IRs).

METHODS

We conducted a retrospective cohort study of pediatric hospitalizations between January 19, 2012 through December 31, 2019 at a US urban, tertiary care children’s hospital. Analyzing 85 458 hospitalizations, we compared PSEs overall, by severity, and by event category by race and ethnicity, insurance payor, and language using incident rate ratios (IRRs).

RESULTS

In models controlling for covariates, we found that hospitalizations of Latinx (IRR 1.17, 95% confidence interval [CI] 1.07−1.29), non-Latinx Black/African American (IRR 1.17, 95% CI 1.01−1.34), publicly insured (IRR 1.10, 95% CI 1.02−1.20), and nonprivately/nonpublicly insured (IRR 1.12, 95% CI 1.02−1.23) children had higher rates of PSEs compared with reference groups, but the association between language and PSEs was not significant. There were similar patterns among AEs, although only the association between hospitalizations of Latinx patients and AEs was significant. Medication, fluid, or blood and lines or tubes PSEs drove many inequities.

CONCLUSIONS

We found inequities in PSEs as recorded by IRs, suggesting differences in care related to race, ethnicity, and payor. Limitations include analysis of a single center, that event categories are unique to the institution analyzed, and the voluntary nature of IRs.

Adverse events (AEs), patient safety events (PSEs) that result in harm to patients, lead to patient morbidity and mortality and increase the cost of care.1–4  Although the rate of AEs is highly variable in pediatric inpatient research, in a recent analysis of 16 hospitals in the United States, roughly half of AEs that occurred among hospitalized pediatric patients were preventable, emphasizing the need to understand best practices for preventing and mitigating both AEs and PSEs.5,6  “PSE” is a term that not only includes AEs, but also “no-harm events,” “near misses,” and “hazardous conditions,” all of which are events or circumstances that could but did not lead to patient harm.1 

Racial and ethnic differences have been described in the quality of care delivered to children throughout the hospital system from primary care to the ICU.7–10  Previous pediatric studies have shown that Black, Latinx, those using a language other than English (LOE), and those with public insurance receive lower quality of care in the hospital compared with white patients, those using English, and those with private insurance, respectively.11–14  However, there have still been relatively few studies examining inequities in pediatric patient AEs. Stockwell and colleagues showed that publicly insured children experience higher rates of preventable adverse events and Latinx children experience higher rates of all AEs, preventable AEs, and high-severity AEs.14  Other studies found no consistent evidence for racial or ethnic disparities in AEs.15  There has also been very little work examining potential racial, ethnic, payor, and language inequities among hospitalized children that analyze both AEs and PSEs. Additionally, few studies have examined PSEs by category of event.

To help understand upstream hospital conditions that can lead to AEs, we performed a retrospective analysis of a tertiary hospital voluntary incident reporting system to explore potential variation in the incidence of PSEs by race and ethnicity, language, and insurance status among hospitalized children. We explored PSEs by severity and by event type.

We conducted a retrospective cohort study of children hospitalized at an urban tertiary children’s hospital in the United States between December 19, 2012 and December 31, 2019. Children were eligible for inclusion if they were ages 0 to 17 and were not deceased at the time of the analysis. Those with missing medical record number (MRN), admission date, discharge date, sex, race and ethnicity, insurance payor, or language data were excluded from analysis. When there were 2 hospitalization encounters with overlapping dates (which could represent being transferred to different units or having procedures done during the hospitalization), we only included the encounter with the longest time period recorded (ie, the longest number of days). We extracted the following sociodemographic data from the hospital’s electronic health record, Epic: child age in years (continuous), sex (female, male), insurance payor (private, public, other), race and ethnicity (Latinx ethnicity, non-Latinx American Indian/Alaska Native, non-Latinx Asian, non-Latinx Black/African American, non-Latinx multiracial, non-Latinx Native Hawaiian/other Pacific Islander, non-Latinx other race, non-Latinx white, and unknown or declined to answer), and language (using English and using a LOE). The aforementioned sociodemographic variable categorizations were based on how the data are structured in the electronic health record with the exception of language, which we dichotomized to “English” and “not English,” including anyone using a LOE in the latter category. We also extracted admission and discharge date and time variables to calculate a length of stay in days.

We merged this hospitalization dataset with the hospital’s incident report (IR) system (RLDatix, Risk-Incident Reporting Software, 1 Yonge Street, Suite 2300, Toronto, Ontario) dataset for the same time period. Incident reporting at this facility is voluntary, self-reported, and open to all employees. A central patient safety department, patient safety subject matter experts (SMEs), and administrative and clinical leaders review IR data and then SMEs rank harm severity. Beginning July 2016, final harm severity rankings were adjudicated by a multidisciplinary team in addition to patient safety leadership.

When filing, reporters select an IR event category among 26 choices that patient safety SMEs review and recategorize when appropriate. Although the author of the IR submits a categorization of the event with the description of the incident, if an event has more than 1 issue (eg, a medication, fluid, or blood issue and an issue during major procedure), 2 IRs are submitted. SMEs review IRs for duplicates, which are removed. For the purpose of this analysis, SMEs from the IR central patient safety department - including the Patient Safety Director, Executive Medical Director of Quality and Patient Safety, and Patient Safety Program Manager – collapsed event types into the following: (1) medication, fluid, or blood issue; (2) line or tube issue; (3) fall or pressure injury issue; (4) personal rights or conduct issue; (5) diagnosis or treatment issue; (6) issue with the environment; and (7) issue during major procedure. Supplemental Table 6 displays a list of the 26 subcategories and to which of the collapsed categories they were assigned.

We extracted the following PSE data from the IR system: MRN, event date, event time, event severity, and event category. We excluded events with severity coded as “death, progression of illness” (n = 7) and “prior event” (n = 31).

We merged events by MRN and included them only if they occurred between the admission and discharge time. PSEs were designated as AEs if their severity level included any mention of “harm,” which included any level of severity between “mild harm” and “death, unexpected.”

We tabulated baseline demographic characteristics of the sample and compared rates of having at least 1 PSE and at least 1 AE during hospitalization by age, sex, race and ethnicity, payor, and preferred language using χ-squared tests. We used negative binomial regression to calculate incident rate ratios (IRRs) of each outcome per day hospitalized by race and ethnicity, payor, and preferred language using non-Latinx white, privately insured, and patients of families using English as reference groups, respectively. We controlled for length of stay in all regression models, as well as for age and sex because of associations between these variables.16,17  For each outcome, we computed both a “partially adjusted” estimate controlling for age, sex, and length of stay and including only 1 predictor of interest at a time, and a “fully adjusted” estimate including all predictor variables of interest in the model at once. We chose to run these models a priori, acknowledging that sociodemographic characteristics are often correlated and intersectional, and so relationships between these characteristics and outcomes may differ when considering individual versus several sociodemographic characteristics in aggregate. We display both models for transparency but will focus our discussion on the fully adjusted models. Additionally, we calculated IRRs of PSEs by category using similar partially and fully adjusted models.

To account for potential differences in coding occurring after the IR system went through adjudication in July 2016, we conducted a sensitivity analysis whereby we calculated the same IRR regressions but limited the sample to patients admitted after July 1, 2016. To account for patients with readmissions driving associations, we conducted another sensitivity analysis limiting the sample to patients’ first hospitalization during the study period. All analyses were performed using Stata version 15.1 (StataCorp LLC, College Station, TX). This study was approved by the University of California Institutional Review Board. We followed the Strengthening the Reporting of Observational Studies in Epidemiology reporting guidelines.

We identified 85 458 eligible patient hospitalizations during the study period out of 122 035 hospitalizations available to analyze (Supplemental Fig 1). The average length of stay was 3.9 days. Table 1 displays the sociodemographic characteristics of the sample, of which the majority of hospitalizations were for non-Latinx white patients, those with private insurance, and those whose families use English (Table 1). Nearly 4% of hospitalizations had at least 1 PSE and 1.5% had at least 1 AE, with the distribution of these events varying significantly by nearly all sociodemographic characteristics (Table 2).

TABLE 1

Characteristics of the Sample (N = 85 458)

CharacteristicN%
Age 
 0–5 27 402 32.06 
 6–12 39 116 45.77 
 13–18 18 940 22.16 
Sex 
 Female 39 910 46.70 
 Male 45 548 53.30 
Race and ethnicity 
 American Indian/Alaska Native 506 0.59 
 Asian 13 248 15.50 
 Black/African American 5705 6.68 
 Latinx 21 413 25.06 
 Multiracial 2283 2.67 
 Native Hawaiian/other Pacific Islander 753 0.88 
 Other 5393 6.31 
 Unknown or declined 4546 5.32 
 White 31 611 36.99 
Language 
 Not English 9590 11.22 
 English 75 868 88.78 
Payor 
 Public 26 327 30.81 
 Other 15 572 18.22 
 Private 43 559 50.97 
CharacteristicN%
Age 
 0–5 27 402 32.06 
 6–12 39 116 45.77 
 13–18 18 940 22.16 
Sex 
 Female 39 910 46.70 
 Male 45 548 53.30 
Race and ethnicity 
 American Indian/Alaska Native 506 0.59 
 Asian 13 248 15.50 
 Black/African American 5705 6.68 
 Latinx 21 413 25.06 
 Multiracial 2283 2.67 
 Native Hawaiian/other Pacific Islander 753 0.88 
 Other 5393 6.31 
 Unknown or declined 4546 5.32 
 White 31 611 36.99 
Language 
 Not English 9590 11.22 
 English 75 868 88.78 
Payor 
 Public 26 327 30.81 
 Other 15 572 18.22 
 Private 43 559 50.97 
TABLE 2

Distribution of Events by Characteristics (N = 85 458)

CharacteristicPatient Safety Event, %PAdverse Event, %P
Age 
 0–5 6.51 <.001 2.82 <.001 
 6–12 2.39 0.90 
 13–18 3.07 0.92 
Sex 
 Female 3.81 .485 1.43 .040 
 Male 3.91 1.60 
Race and ethnicity 
 American Indian/Alaska Native 5.14 <.001 2.96 <.001 
 Asian 2.92 1.1 
 Black/African-American 3.84 1.6 
 Latinx 5.02 2.09 
 Multiracial 4.64 1.88 
 Natural Hawaiian/other Pacific Islander 3.85 1.59 
 Other 3.45 1.22 
 Unknown or declined 3.5 1.25 
 White 3.53 1.34 
Language 
 Not English 3.71 <.001 2.13 <.001 
 English 5.04 1.45 
Payor 
 Public 4.65 <.001 1.91 <.001 
 Other 4.64 1.85 
 Private 3.11 1.17 
Total 3.86 N/A 1.52 N/A 
CharacteristicPatient Safety Event, %PAdverse Event, %P
Age 
 0–5 6.51 <.001 2.82 <.001 
 6–12 2.39 0.90 
 13–18 3.07 0.92 
Sex 
 Female 3.81 .485 1.43 .040 
 Male 3.91 1.60 
Race and ethnicity 
 American Indian/Alaska Native 5.14 <.001 2.96 <.001 
 Asian 2.92 1.1 
 Black/African-American 3.84 1.6 
 Latinx 5.02 2.09 
 Multiracial 4.64 1.88 
 Natural Hawaiian/other Pacific Islander 3.85 1.59 
 Other 3.45 1.22 
 Unknown or declined 3.5 1.25 
 White 3.53 1.34 
Language 
 Not English 3.71 <.001 2.13 <.001 
 English 5.04 1.45 
Payor 
 Public 4.65 <.001 1.91 <.001 
 Other 4.64 1.85 
 Private 3.11 1.17 
Total 3.86 N/A 1.52 N/A 

There were 4273 incident reports among the identified. Although we present both partially and fully adjusted models in Table 3, we will focus our discussion on the fully adjusted models. Compared with hospitalizations of non-Latinx white children, hospitalizations of non-Latinx Black/African American children and Latinx children had 1.17 times (95% confidence interval [CI] 1.01–1.34 and 95% CI 1.07–1.29, respectively) the rate of PSEs per hospital day. Hospitalizations of Latinx children also had a 1.17 times increased rate of AEs (95% CI 1.01–1.35).

TABLE 3

Incident Rate Ratios of Events Based on Characteristics

N = 85 458Patient Safety EventAdverse Event
CharacteristicPartially Adjusteda IRR (95% CI)PFully Adjustedb IRR (95% CI)PPartially Adjusted IRR (95% CI)PFully Adjusted IRR (95% CI)P
Race and ethnicity 
 American Indian/Alaska Native 0.71 (0.46–1.09) .116 0.68 (0.44–1.04) .075 0.86 (0.49–1.50) .600 0.82 (0.47–1.44) .489 
 Asian 0.89 (0.80–1.00) .047 0.89 (0.79–1.00) .050 0.85 (0.71–1.03) .095 0.84 (0.70–1.02) .072 
 Black/African-American 1.21 (1.06–1.39) .005 1.17 (1.01–1.34) .032 1.16 (0.93–1.43) .190 1.11 (0.88–1.39) .377 
 Latinx 1.23 (1.13–1.33) <.001 1.17 (1.07–1.29) .001 1.28 (1.12–1.45) <.001 1.17 (1.01–1.35) .038 
 Multiracial 1.02 (0.84–1.25) .817 1.01 (0.83–1.24) .898 1.05 (0.77–1.43) .758 1.04 (0.76–1.42) .811 
 Natural Hawaiian/other Pacific Islander 1.03 (0.73–1.45) .878 1.00 (0.71–1.41) .990 0.97 (0.55–1.68) .902 0.93 (0.53–1.62) .795 
 Other 1.11 (0.95–1.29) .184 1.09 (0.94–1.27) .250 1.03 (0.81–1.32) .784 1.02 (0.79–1.30) .904 
 Unknown or declined 1.33 (1.13–1.54) <.001 1.30 (1.11–1.51) .002 1.08 (0.83–1.41) .556 1.05 (0.81–1.37) .706 
 White Ref  Ref  Ref  Ref  
Payor 
 Public 1.19 (1.10–1.28) <.001 1.10 (1.02–1.20) .021 1.24 (1.10–1.39) .001 1.12 (0.98–1.28) .102 
 Other 1.18 (1.08–1.29) <.001 1.12 (1.02–1.23) .013 1.22 (1.06–1.40) .005 1.13 (0.98–1.31) .090 
 Private Ref  Ref  Ref  Ref  
Language         
 Not English 1.14 (1.03–1.25) .009 1.02 (0.91–1.13) .764 1.30 (1.12–1.50) <.001 1.14 (0.97–1.35) .106 
 English Ref  Ref  Ref  Ref  
N = 85 458Patient Safety EventAdverse Event
CharacteristicPartially Adjusteda IRR (95% CI)PFully Adjustedb IRR (95% CI)PPartially Adjusted IRR (95% CI)PFully Adjusted IRR (95% CI)P
Race and ethnicity 
 American Indian/Alaska Native 0.71 (0.46–1.09) .116 0.68 (0.44–1.04) .075 0.86 (0.49–1.50) .600 0.82 (0.47–1.44) .489 
 Asian 0.89 (0.80–1.00) .047 0.89 (0.79–1.00) .050 0.85 (0.71–1.03) .095 0.84 (0.70–1.02) .072 
 Black/African-American 1.21 (1.06–1.39) .005 1.17 (1.01–1.34) .032 1.16 (0.93–1.43) .190 1.11 (0.88–1.39) .377 
 Latinx 1.23 (1.13–1.33) <.001 1.17 (1.07–1.29) .001 1.28 (1.12–1.45) <.001 1.17 (1.01–1.35) .038 
 Multiracial 1.02 (0.84–1.25) .817 1.01 (0.83–1.24) .898 1.05 (0.77–1.43) .758 1.04 (0.76–1.42) .811 
 Natural Hawaiian/other Pacific Islander 1.03 (0.73–1.45) .878 1.00 (0.71–1.41) .990 0.97 (0.55–1.68) .902 0.93 (0.53–1.62) .795 
 Other 1.11 (0.95–1.29) .184 1.09 (0.94–1.27) .250 1.03 (0.81–1.32) .784 1.02 (0.79–1.30) .904 
 Unknown or declined 1.33 (1.13–1.54) <.001 1.30 (1.11–1.51) .002 1.08 (0.83–1.41) .556 1.05 (0.81–1.37) .706 
 White Ref  Ref  Ref  Ref  
Payor 
 Public 1.19 (1.10–1.28) <.001 1.10 (1.02–1.20) .021 1.24 (1.10–1.39) .001 1.12 (0.98–1.28) .102 
 Other 1.18 (1.08–1.29) <.001 1.12 (1.02–1.23) .013 1.22 (1.06–1.40) .005 1.13 (0.98–1.31) .090 
 Private Ref  Ref  Ref  Ref  
Language         
 Not English 1.14 (1.03–1.25) .009 1.02 (0.91–1.13) .764 1.30 (1.12–1.50) <.001 1.14 (0.97–1.35) .106 
 English Ref  Ref  Ref  Ref  

All models control for age, gender, and length of stay. Ref = reference group.

a

Partially adjusted models control for age, sex, and length of stay.

b

Fully adjusted models additionally control for race and ethnicity, payor, and language.

Compared with hospitalizations of children with private insurance, hospitalizations for those with public and other insurance had 1.10 (95% CI 1.02–1.20) and 1.12 (95% CI 1.02–1.23) the rate of PSEs per hospital day, respectively. Although hospitalizations for those with public and other insurance had increased rates of AEs compared with hospitalizations of those with private insurance, the results in the fully adjusted models were not significant. Compared with those whose families use English, hospitalizations of children whose families use a LOE had a 1.02 (95% CI 0.91–1.13, P = .764) and 1.14 (95% CI 1.97–1.35, P = 0. 106) increased rate of PSEs and AEs, respectively, but these results were not significant in fully adjusted models (Table 3). Results of the sensitivity analysis of hospitalizations after July 2016 (Supplemental Table 7) and limiting to only 1 hospitalization per child (Table 4) showed similar patterns.

TABLE 4

Incident Rate Ratios of Events Based on Characteristics, Limiting to First Hospitalization of Study Period

N = 46 031Patient Safety EventAdverse Event
CharacteristicPartially Adjusteda IRR (95% CI)PFully Adjustedb IRR (95% CI)PPartially Adjusted IRR (95% CI)PFully Adjusted IRR (95% CI)P
Race and ethnicity 
 American Indian/Alaska Native 0.77 (0.43–1.38) .379 0.70 (0.39–1.27) .239 0.93 (0.45–1.96) .856 0.86 (0.41–1.81) .694 
 Asian 0.90 (0.78–1.04) .160 0.90 (0.78–1.04) .155 0.84 (0.67–1.05) .130 0.82 (0.65–1.04) .101 
 Black/African-American 1.35 (1.15–1.59) <.001 1.24 (1.04–1.47) .014 1.30 (1.01–1.68) .041 1.22 (0.94–1.59) .139 
 Latinx 1.36 (1.22–1.50) <.001 1.23 (1.10–1.39) .001 1.48 (1.26–1.74) <.001 1.30 (1.08–1.55) .005 
 Multiracial 1.16 (0.90–1.50) .246 1.14 (0.88–1.47) .325 1.00 (0.66–1.51) .983 0.98 (0.65–1.49) .932 
 Natural Hawaiian/other Pacific Islander 1.04 (0.68–1.60) .866 0.99 (0.64–1.53) .792 1.17 (0.61–2.25) .629 1.12 (0.58–2.16) .737 
 Other 1.14 (0.96–1.37) .141 1.10 (0.92–1.32) .292 1.06 (0.80–1.42) .675 1.02 (0.77–1.37) .875 
 Unknown or declined 1.42 (1.19–1.68) <.001 1.36 (1.15–1.62) <.001 1.24 (0.93–1.64) .139 1.18 (0.89–1.57) .249 
 White Ref  Ref  Ref  Ref  
Payor 
 Public 1.36 (1.24–1.47) <.001 1.23 (1.11–1.37) <.001 1.38 (1.19–1.60) <.001 1.18 (1.00–1.39) .045 
 Other 1.32 (1.19–1.47) <.001 1.24 (1.11–1.39) <.001 1.40 (1.18–1.65) <.001 1.25 (1.05–1.49) .010 
 Private Ref  Ref  Ref  Ref  
Language         
 Not English 1.23 (1.09–1.39) .001 1.04 (0.91–1.20) .551 1.48 (1.24–1.77) <.001 1.22 (1.00–1.49) .053 
 English Ref  Ref  Ref  Ref  
N = 46 031Patient Safety EventAdverse Event
CharacteristicPartially Adjusteda IRR (95% CI)PFully Adjustedb IRR (95% CI)PPartially Adjusted IRR (95% CI)PFully Adjusted IRR (95% CI)P
Race and ethnicity 
 American Indian/Alaska Native 0.77 (0.43–1.38) .379 0.70 (0.39–1.27) .239 0.93 (0.45–1.96) .856 0.86 (0.41–1.81) .694 
 Asian 0.90 (0.78–1.04) .160 0.90 (0.78–1.04) .155 0.84 (0.67–1.05) .130 0.82 (0.65–1.04) .101 
 Black/African-American 1.35 (1.15–1.59) <.001 1.24 (1.04–1.47) .014 1.30 (1.01–1.68) .041 1.22 (0.94–1.59) .139 
 Latinx 1.36 (1.22–1.50) <.001 1.23 (1.10–1.39) .001 1.48 (1.26–1.74) <.001 1.30 (1.08–1.55) .005 
 Multiracial 1.16 (0.90–1.50) .246 1.14 (0.88–1.47) .325 1.00 (0.66–1.51) .983 0.98 (0.65–1.49) .932 
 Natural Hawaiian/other Pacific Islander 1.04 (0.68–1.60) .866 0.99 (0.64–1.53) .792 1.17 (0.61–2.25) .629 1.12 (0.58–2.16) .737 
 Other 1.14 (0.96–1.37) .141 1.10 (0.92–1.32) .292 1.06 (0.80–1.42) .675 1.02 (0.77–1.37) .875 
 Unknown or declined 1.42 (1.19–1.68) <.001 1.36 (1.15–1.62) <.001 1.24 (0.93–1.64) .139 1.18 (0.89–1.57) .249 
 White Ref  Ref  Ref  Ref  
Payor 
 Public 1.36 (1.24–1.47) <.001 1.23 (1.11–1.37) <.001 1.38 (1.19–1.60) <.001 1.18 (1.00–1.39) .045 
 Other 1.32 (1.19–1.47) <.001 1.24 (1.11–1.39) <.001 1.40 (1.18–1.65) <.001 1.25 (1.05–1.49) .010 
 Private Ref  Ref  Ref  Ref  
Language         
 Not English 1.23 (1.09–1.39) .001 1.04 (0.91–1.20) .551 1.48 (1.24–1.77) <.001 1.22 (1.00–1.49) .053 
 English Ref  Ref  Ref  Ref  

All models control for age, gender, and length of stay. Ref = reference group.

a

Partially adjusted models control for age, sex, and length of stay.

b

Fully adjusted models additionally control for race and ethnicity, payor, and language.

Because of sample size limitations, we could not analyze inequities in PSEs related to the environment. Analyses of all other categories of PSEs used by the patient safety SMEs at our organization are summarized in Table 5 and revealed similar patterns to pooled evidence, although the findings were not always significant. However, when examining lines or tubes events in fully adjusted models, inequities worsened among hospitalizations of non-Latinx Black/African American (IRR 1.48, 95% CI 1.14–1.91, P < .003), Latinx (IRR 1.44, 95% CI 1.20–1.72, P < .001), publicly insured (IRR 1.21, 95% CI 1.03–1.42, P = .021), and other nonprivately insured (IRR 1.21, 95% CI 1.01–1.44, P = .040) patients (Table 5).

TABLE 5

Incident Rate Ratios of Events Based on Characteristics, by Specific Event Types

N = 85 458Medication, Fluid, or Blood (N = 787)Lines or Tubes (N = 596)Falls or Pressure Injury (N = 388)
CharacteristicPartially Adjusteda IRR (95% CI)PFully Adjustedb IRR (95% CI)PPartially Adjusteda IRR (95% CI)PFully Adjustedb IRR (95% CI)PPartially Adjusted IRR (95% CI)PFully Adjustedb IRR (95% CI)P
Race and ethnicity 
 American Indian/Alaska Nativec 0.84 (0.48–1.47) .551 0.81 (0.47–1.42) .465 0.82 (0.40–1.68) .594 0.76 (0.37–1.56) .455 0.56 (0.23–1.39) .212 0.52 (0.21–1.31) .166 
 Asian 0.82 (0.68–0.99) .037 0.83 (0.69–1.00) .690 0.91 (0.72–1.14) .408 0.90 (0.71–1.14) .380 0.62 (0.46–0.83) .001 0.61 (0.46–0.83) .001 
 Black/African–American 1.08 (0.86–1.35) .503 1.04 (0.83–1.30) .752 1.59 (1.24–2.03) <.001 1.48 (1.14–1.91) .003 1.24 (0.93–1.66) .157 1.14 (0.84–1.55) .396 
 Latinx 1.23 (1.09–1.40) .001 1.24 (1.07–1.43) .004 1.59 (1.36–1.86) <.001 1.44 (1.20–1.72) <.001 1.03 (0.85–1.25) .742 0.95 (0.77–1.81) .658 
 Multiracial 0.90 (0.65–1.25) .518 0.89 (0.64–1.24) .489 1.10 (0.75–1.61) .615 1.08 (0.74–1.58) .690 0.99 (0.63–1.55) .951 0.97 (0.61–1.52) .885 
 Natural Hawaiian/other Pacific Islander 1.25 (0.77–2.03) .370 1.22 (0.75–1.35) .622 1.28 (0.70–2.32) .425 1.20 (0.65–2.19) .562 0.89 (0.39–2.02) .776 0.85 (0.37–1.94) .703 
 Other 1.07 (0.84–1.36) .595 1.06 (0.83–1.35) .622 1.00 (0.73–1.38) .987 0.98 (0.71–1.34) .879 1.07 (0.76–1.49) .713 1.04 (0.74–1.46) .818 
 Unknown or declined 1.32 (1.04–1.70) .024 1.31 (1.03–1.66) .030 0.85 (0.58–1.22) .373 0.81 (0.56–1.18) .276 1.04 (0.72–1.51) .827 1.02 (0.70–1.48) .931 
 White Ref  Ref  Ref  Ref  Ref  Ref  
Payor 
 Public 1.17 (1.04–1.32) .010 1.10 (0.97–1.25) .153 1.45 (1.25–1.68) <.001 1.21 (1.03–1.42) .021 1.24 (1.04–1.48) .015 1.20 (0.99–1.45) .068 
 Other 1.14 (0.99–1.31) .063 1.08 (0.94–1.25) .264 1.34 (1.13–1.59) .001 1.21 (1.01–1.44) .040 1.12 (0.91–1.38) .279 1.08 (0.87–1.34) .464 
 Private Ref  Ref  Ref  Ref  Ref  Ref  
Language 
 Not English 1.03 (0.88–1.20) .730 0.89 (0.75–1.05) .172 1.39 (1.17–1.66) <.001 1.09 (0.89–1.33) .397 1.07 (0.86–1.34) .559 1.04 (0.81–1.34) .760 
 English Ref  Ref  Ref  Ref  Ref  Ref  
N = 85 458Medication, Fluid, or Blood (N = 787)Lines or Tubes (N = 596)Falls or Pressure Injury (N = 388)
CharacteristicPartially Adjusteda IRR (95% CI)PFully Adjustedb IRR (95% CI)PPartially Adjusteda IRR (95% CI)PFully Adjustedb IRR (95% CI)PPartially Adjusted IRR (95% CI)PFully Adjustedb IRR (95% CI)P
Race and ethnicity 
 American Indian/Alaska Nativec 0.84 (0.48–1.47) .551 0.81 (0.47–1.42) .465 0.82 (0.40–1.68) .594 0.76 (0.37–1.56) .455 0.56 (0.23–1.39) .212 0.52 (0.21–1.31) .166 
 Asian 0.82 (0.68–0.99) .037 0.83 (0.69–1.00) .690 0.91 (0.72–1.14) .408 0.90 (0.71–1.14) .380 0.62 (0.46–0.83) .001 0.61 (0.46–0.83) .001 
 Black/African–American 1.08 (0.86–1.35) .503 1.04 (0.83–1.30) .752 1.59 (1.24–2.03) <.001 1.48 (1.14–1.91) .003 1.24 (0.93–1.66) .157 1.14 (0.84–1.55) .396 
 Latinx 1.23 (1.09–1.40) .001 1.24 (1.07–1.43) .004 1.59 (1.36–1.86) <.001 1.44 (1.20–1.72) <.001 1.03 (0.85–1.25) .742 0.95 (0.77–1.81) .658 
 Multiracial 0.90 (0.65–1.25) .518 0.89 (0.64–1.24) .489 1.10 (0.75–1.61) .615 1.08 (0.74–1.58) .690 0.99 (0.63–1.55) .951 0.97 (0.61–1.52) .885 
 Natural Hawaiian/other Pacific Islander 1.25 (0.77–2.03) .370 1.22 (0.75–1.35) .622 1.28 (0.70–2.32) .425 1.20 (0.65–2.19) .562 0.89 (0.39–2.02) .776 0.85 (0.37–1.94) .703 
 Other 1.07 (0.84–1.36) .595 1.06 (0.83–1.35) .622 1.00 (0.73–1.38) .987 0.98 (0.71–1.34) .879 1.07 (0.76–1.49) .713 1.04 (0.74–1.46) .818 
 Unknown or declined 1.32 (1.04–1.70) .024 1.31 (1.03–1.66) .030 0.85 (0.58–1.22) .373 0.81 (0.56–1.18) .276 1.04 (0.72–1.51) .827 1.02 (0.70–1.48) .931 
 White Ref  Ref  Ref  Ref  Ref  Ref  
Payor 
 Public 1.17 (1.04–1.32) .010 1.10 (0.97–1.25) .153 1.45 (1.25–1.68) <.001 1.21 (1.03–1.42) .021 1.24 (1.04–1.48) .015 1.20 (0.99–1.45) .068 
 Other 1.14 (0.99–1.31) .063 1.08 (0.94–1.25) .264 1.34 (1.13–1.59) .001 1.21 (1.01–1.44) .040 1.12 (0.91–1.38) .279 1.08 (0.87–1.34) .464 
 Private Ref  Ref  Ref  Ref  Ref  Ref  
Language 
 Not English 1.03 (0.88–1.20) .730 0.89 (0.75–1.05) .172 1.39 (1.17–1.66) <.001 1.09 (0.89–1.33) .397 1.07 (0.86–1.34) .559 1.04 (0.81–1.34) .760 
 English Ref  Ref  Ref  Ref  Ref  Ref  
TABLE 5

Continued

N = 85 458Rights or Conduct (N = 70)Diagnosis or treatment (N = 642)Major Procedure (N = 143)
CharacteristicPartially Adjusted IRR (95% CI)PFully Adjusteda IRR (95% CI)PPartially Adjusted IRR (95% CI)PFully Adjusteda IRR (95% CI)PPartially Adjusted IRR (95% CI)PFully Adjusteda IRR (95% CI)P
Race and ethnicity 
 American Indian/Alaska Native — — 0.79 (0.41–1.53) .488 0.77 (0.40–1.50) .439 0.35 (0.04–2.73) .317 0.37 (0.05–2.93) .348 
 Asian 0.69 (0.37–1.31) .262 0.65 (0.34–1.25) .197 1.06 (0.87–1.30) .540 1.06 (0.86–1.29) .598 1.14 (0.76–1.71) .529 1.13 (0.75–1.70) .565 
 Black/African-American 1.04 (0.51–2.14) .905 0.90 (0.43–1.90) .791 1.16 (0.91–1.50) .234 1.15 (0.88–1.49) .299 0.95 (0.53–1.68) .849 0.98 (0.54–1.76) .939 
 Latinx 1.29 (0.87–1.93) .211 0.99 (0.62–1.58) .963 1.16 (0.99–1.35) .063 1.09 (0.92–1.29) .339 0.95 (0.68–1.33) .757 0.98 (0.68–1.47) .990 
 Multiracial 1.24 (0.49–3.13) .651 1.19 (0.47–3.02) .715 1.24 (0.88–1.73) .213 1.23 (0.88–1.73) .222 1.42 (0.72–2.81) .316 1.42 (0.72–2.82) .310 
 Natural Hawaiian/other Pacific Islander 0.81 (0.11–5.89) .834 0.73 (0.10–5.36) .759 1.03 (0.55–1.91) .931 1.00 (0.54–1.87) .995 1.39 (0.42–4.59) .589 1.44 (0.44–4.74) .550 
 Other 1.48 (0.76–2.87) .246 1.40 (0.72–2.73) .321 0.99 (0.74–1.32) .935 0.98 (0.73–1.31) .881 1.18 (0.66–2.08) .580 1.19 (0.67–2.11) .552 
 Unknown and declined 1.05 (0.45–2.46) .912 0.42 (0.41–2.28) .938 1.32 (1.00–1.75) .049 1.30 (0.98–1.72) .068 0.93 (0.47–1.83) .837 0.96 (0.49–1.89) .904 
 White Ref  Ref  Ref  Ref  Ref  Ref  
Payor 
 Public 1.55 (1.05–2.27) .026 1.44 (0.94–2.20) .091 1.11 (0.96–1.27) .149 1.05 (0.90–1.22) .524 0.88 (0.65–1.18) .383 0.90 (0.65–1.25) .535 
 Other 1.46 (0.94–2.27) .094 1.37 (0.86–2.16) .181 1.15 (0.98–1.35) .087 1.11 (0.94–1.31) .204 0.76 (0.53–1.10) .147 0.79 (0.54–1.14) .209 
 Private Ref  Ref  Ref  Ref  Ref  Ref  
Language 
 Not English 1.57 (1.03–2.39) .034 1.39 (0.85–2.25) .189 1.18 (0.99–1.40) .062 1.12 (0.92–1.36) .253 0.92 (0.62–1.38) .689 0.99 (0.63–1.55) .957 
 English Ref  Ref  Ref  Ref  Ref  Ref  
N = 85 458Rights or Conduct (N = 70)Diagnosis or treatment (N = 642)Major Procedure (N = 143)
CharacteristicPartially Adjusted IRR (95% CI)PFully Adjusteda IRR (95% CI)PPartially Adjusted IRR (95% CI)PFully Adjusteda IRR (95% CI)PPartially Adjusted IRR (95% CI)PFully Adjusteda IRR (95% CI)P
Race and ethnicity 
 American Indian/Alaska Native — — 0.79 (0.41–1.53) .488 0.77 (0.40–1.50) .439 0.35 (0.04–2.73) .317 0.37 (0.05–2.93) .348 
 Asian 0.69 (0.37–1.31) .262 0.65 (0.34–1.25) .197 1.06 (0.87–1.30) .540 1.06 (0.86–1.29) .598 1.14 (0.76–1.71) .529 1.13 (0.75–1.70) .565 
 Black/African-American 1.04 (0.51–2.14) .905 0.90 (0.43–1.90) .791 1.16 (0.91–1.50) .234 1.15 (0.88–1.49) .299 0.95 (0.53–1.68) .849 0.98 (0.54–1.76) .939 
 Latinx 1.29 (0.87–1.93) .211 0.99 (0.62–1.58) .963 1.16 (0.99–1.35) .063 1.09 (0.92–1.29) .339 0.95 (0.68–1.33) .757 0.98 (0.68–1.47) .990 
 Multiracial 1.24 (0.49–3.13) .651 1.19 (0.47–3.02) .715 1.24 (0.88–1.73) .213 1.23 (0.88–1.73) .222 1.42 (0.72–2.81) .316 1.42 (0.72–2.82) .310 
 Natural Hawaiian/other Pacific Islander 0.81 (0.11–5.89) .834 0.73 (0.10–5.36) .759 1.03 (0.55–1.91) .931 1.00 (0.54–1.87) .995 1.39 (0.42–4.59) .589 1.44 (0.44–4.74) .550 
 Other 1.48 (0.76–2.87) .246 1.40 (0.72–2.73) .321 0.99 (0.74–1.32) .935 0.98 (0.73–1.31) .881 1.18 (0.66–2.08) .580 1.19 (0.67–2.11) .552 
 Unknown and declined 1.05 (0.45–2.46) .912 0.42 (0.41–2.28) .938 1.32 (1.00–1.75) .049 1.30 (0.98–1.72) .068 0.93 (0.47–1.83) .837 0.96 (0.49–1.89) .904 
 White Ref  Ref  Ref  Ref  Ref  Ref  
Payor 
 Public 1.55 (1.05–2.27) .026 1.44 (0.94–2.20) .091 1.11 (0.96–1.27) .149 1.05 (0.90–1.22) .524 0.88 (0.65–1.18) .383 0.90 (0.65–1.25) .535 
 Other 1.46 (0.94–2.27) .094 1.37 (0.86–2.16) .181 1.15 (0.98–1.35) .087 1.11 (0.94–1.31) .204 0.76 (0.53–1.10) .147 0.79 (0.54–1.14) .209 
 Private Ref  Ref  Ref  Ref  Ref  Ref  
Language 
 Not English 1.57 (1.03–2.39) .034 1.39 (0.85–2.25) .189 1.18 (0.99–1.40) .062 1.12 (0.92–1.36) .253 0.92 (0.62–1.38) .689 0.99 (0.63–1.55) .957 
 English Ref  Ref  Ref  Ref  Ref  Ref  
a

Partially adjusted models control for age, sex, and length of stay.

b

Fully adjusted models additionally control for race and ethnicity, payor, and language.

c

No rights or conduct events were recorded for American Indian/Alaska Native patients.

We found a higher rate of PSEs among hospitalizations of non-Latinx Black/African American, Latinx, and nonprivately insured children compared with hospitalizations of non-Latinx white children and privately insured children, respectively, as reported using a voluntary IR system. The racial and ethnic and insurance inequities we found appear to be driven by PSEs related to lines or tubes events, with medication, fluid, or blood events also contributing to ethnic inequities in fully adjusted models. This is one of the first studies of which we are aware to investigate inequities in incident reports by race, ethnicity, language, and insurance by the type of PSE event in the pediatric inpatient setting.

These general inequities unfortunately mirror many other studies that demonstrate inequitable outcomes by race, ethnicity, and payor, and add to the increasing number of studies documenting inequity related to care provided within the healthcare setting. Our work is similar to prior work by Stockwell and colleagues using the Global Assessment of Pediatric Patient Safety Trigger Tool demonstrating that Latinx children experienced higher rates of AEs than non-Latinx white children.14  Both of our studies also did not find significant differences in the incidence of AEs between non-Latinx Black/African American and non-Latinx white children. However, we did find that hospitalizations of non-Latinx Black/African American versus non-Latinx white children were more likely to experience PSEs. This highlights the importance of analyzing both PSEs and AEs, as findings may differ. With larger sample sizes, our studies may have found significant inequities, as both studies found higher rates of AEs in non-Latinx Black/African American versus non-Latinx white patients.

We also found that hospitalizations of publicly insured patients were more likely to experience PSEs than hospitalizations of patients with private insurance. This is similar to Stockwell and colleagues findings when examining preventable AEs (although not all AEs), although our findings examining insurance payor and AEs were not statistically significant.14  In contrast to our study, they did not find differences between privately insured patients and those with other insurance. This discrepancy merits further investigation. Since this insurance category is heterogenous, the relationship between other insurance and PSEs or AEs could vary substantially by institution depending on the composition of self-pay patients versus uninsured patients.

Although we found a higher rate of PSEs among hospitalizations of children whose families use a LOE compared with those whose families use English, these results were not significant in our fully adjusted model. This is in contrast to prior work that has found a strong association between language preference and adverse events.18  Cohen and colleagues found a similar nonsignificant association between families using a LOE and medical errors that became significant when looking at the subpopulation of families that use Spanish.12  Taken together, our work and prior work suggests the importance of continuing to investigate associations between language and PSEs and what might be driving the presence and strength of these associations in different settings.

Although our study adds to a growing literature of inequities in PSEs among hospitalized children, the underlying mechanisms and causes are still not entirely clear. Researchers have theorized associations may be shaped by the interplay between several factors, including individual, clinician, and health system characteristics and behaviors, as well as environmental factors and the overarching influence of health policy.19,20  It is important to acknowledge that part of the explanation likely includes racism and discrimination on either the clinician or health system level. For example, prior work has demonstrated implicit bias among clinicians based on race, ethnicity, and socioeconomic status can influence health outcomes.21–23  On the health system level, policies may systematically put certain populations of patients at a higher risk of PSEs. For example, rounding during business hours may preclude families without access to paid sick leave – more likely to be Latinx families and low-income families - from attending rounds and being able to voice any safety concerns.24 

The explanation for why some PSE categories showed significant inequities is also not clear, although it could be partially explained by the sample size of each category. The highest number of PSEs occurred in the medication, fluid, or blood and lines or tubes categories, which demonstrated several inequities. The fact that inequities varied by PSE category suggests that examining PSEs by event type locally may help tailor quality and safety interventions to reduce these inequities.

No matter the drivers of our findings, this work underscores the need to investigate, track, and intervene upon inequities in PSEs in real time. At our institution, we have begun stratifying quality improvement metrics by race and ethnicity to identify inequities to direct interventions. These quality dashboards should be expanded to include insurance status and language. By investigating PSE inequities frequently and consistently, healthcare systems can identify PSE outcomes in need of inequity reduction interventions as well as track effectiveness of interventions to reduce the inequities. Although there is a paucity of literature about evidence-based interventions to reduce inequities by race, ethnicity, insurance payer, and language in the hospital setting, several studies have shown promising results related to addressing potential causes of these inequities. For example, Cheston and colleagues demonstrated that implementing a protocol can increase the presence of in-person interpreters on family-centered rounds.25  Frameworks with an emphasis on health equity and reducing inequities, such as the National Institute on Minority Health and Health Disparities Research Framework, can help guide the necessary future work needed to help develop, study, and implement interventions to address inequities in PSEs.26  Future research should center the patient and family experience to help identify potential causes of these inequities and potential solutions for both specific PSE categories and PSEs as a whole.

Our study limitations include that it was conducted at a single academic teaching institution, so our findings may not be generalizable to all pediatric hospitals. Additionally, using IR systems to identify PSEs are subject to reporting bias, underestimating the true incidence of events and the extent of harm,27,28  which can lead to inaccurate event rates and incomplete data. As all PSE and AE detection methods have advantages and disadvantages,29–32  our investigation of inequities in safety events should be explored and replicated using other patient safety measurement tools, such as trigger tools, malpractice claims, chart review, and risk management reports to paint a more comprehensive picture of safety events and potential inequities. Finally, it is important to note that voluntary incident reporting is subject to implicit and explicit bias33  and prior work has found that voluntary incident reporting systems likely underestimate PSEs in vulnerable populations.34  Researchers have theorized on why this occurs, hypothesizing that factors such as clinician bias may lead to safety events being more visible to staff when they occur in white patients, and that patient populations without the social resources to advocate for themselves may lead to decreased recognition and reporting of PSEs,34,35  which could have led to underestimates of inequities in our study.

We found inequities in the rate of PSEs and AEs among hospitalized children, with Latinx, non-Latinx Black/African American, and non-privately insured patients faring worse, and many of these inequities being driven by medication, fluid, or blood and lines or tubes events. Although more research is needed to identify mechanisms of these inequities, including the contribution of interpersonal and institutional racism, these alarming findings suggest that there may be differences in care related to race, ethnicity, and payor. Although the evidence-base for inpatient interventions to reduce inequities in PSEs is not robust, healthcare systems must work toward both identifying and addressing these inequities through policies and interventions aimed at eliminating harm.

We would like to thank Glenn Rosenbluth, MD and Kyle Blanchard, DPT for providing information used in the drafting of this manuscript.

Drs Karvonen, Stotts, and Bekmezian, and Ms Porter conceptualized and designed the study and drafted the initial manuscript; Dr Neuhaus conducted the analyses; Dr Pantell conceptualized and designed the study, drafted the initial manuscript, and conducted the analyses; and all authors reviewed and revised the manuscript and approve the final manuscript as submitted.

COMPANION PAPER: A companion to this article can be found online at https://www.hosppeds.org/cgi/doi/10.1542/hpeds.2024-008042.

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Competing Interests

CONFLICT OF INTEREST DISCLOSURES: The authors have no conflicts of interest relevant to this article to disclose. The content is solely the responsibility of the authors and does not represent the views of Agency for Healthcare Research and Quality, the National Center for Advancing Translational Sciences, or the National Institutes of Health.

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