Race, Ethnicity, Language, and the Treatment of Low-Risk Febrile Infants

Colleen K Gutman; Paul L Aronson; Nidhi V Singh; Michelle L Pickett; Kamali Bouvay; Rebecca S Green; Britta Roach; Hannah Kotler; Jessica L Chow; Emily A Hartford; Mark Hincapie; Ryan St Pierre-Hetz; Jessica Kelly; Laura Sartori; Jennifer A Hoffmann; Jacqueline B Corboy; Kelly R Bergmann; Bolanle Akinsola; Vanessa Ford; Natalie J Tedford; Theresa T Tran; Sasha Gifford; Amy D Thompson; Andrew Krack; Mary Jane Piroutek; Samantha Lucrezia; SunHee Chung; Nabila Chowdhury; Kathleen Jackson; Tabitha Cheng; Christian D Pulcini; Nirupama Kannikeswaran; Larissa L Truschel; Karen Lin; Jamie Chu; Neh D Molyneaux; Myto Duong; Leslie Dingeldein; Jerri A Rose; Carly Theiler; Sonali Bhalodkar; Emily Powers; Muhammad Waseem; Ahmed Lababidi; Xinyu Yan; Xiang-Yang Lou; Rosemarie Fernandez; K Casey Lion

doi:10.1001/jamapediatrics.2023.4890

. 2023 Nov 13;178(1):55–64. doi: 10.1001/jamapediatrics.2023.4890

Race, Ethnicity, Language, and the Treatment of Low-Risk Febrile Infants

Colleen K Gutman ^1,^43,^✉, Paul L Aronson ^2,⁴², Nidhi V Singh ³, Michelle L Pickett ⁴, Kamali Bouvay ⁵, Rebecca S Green ^6,³⁹, Britta Roach ⁷, Hannah Kotler ⁸, Jessica L Chow ^9,³⁷, Emily A Hartford ¹⁰, Mark Hincapie ^11,⁴¹, Ryan St Pierre-Hetz ¹¹, Jessica Kelly ¹², Laura Sartori ¹², Jennifer A Hoffmann ¹³, Jacqueline B Corboy ¹³, Kelly R Bergmann ¹⁴, Bolanle Akinsola ¹⁵, Vanessa Ford ¹⁵, Natalie J Tedford ¹⁶, Theresa T Tran ¹⁶, Sasha Gifford ^17,⁴⁰, Amy D Thompson ¹⁸, Andrew Krack ¹⁹, Mary Jane Piroutek ²⁰, Samantha Lucrezia ²¹, SunHee Chung ^22,⁴⁴, Nabila Chowdhury ²³, Kathleen Jackson ²⁴, Tabitha Cheng ²⁵, Christian D Pulcini ^26,⁴⁵, Nirupama Kannikeswaran ²⁷, Larissa L Truschel ²⁸, Karen Lin ²⁸, Jamie Chu ^29,³⁸, Neh D Molyneaux ²⁹, Myto Duong ³⁰, Leslie Dingeldein ³¹, Jerri A Rose ³¹, Carly Theiler ³², Sonali Bhalodkar ^2,⁴², Emily Powers ^2,⁴², Muhammad Waseem ^33,⁴⁶, Ahmed Lababidi ^1,⁴³, Xinyu Yan ³⁴, Xiang-Yang Lou ³⁴, Rosemarie Fernandez ³⁵, K Casey Lion ^21,³⁶

¹Department of Emergency Medicine, University of Florida College of Medicine, Gainesville

²Department of Pediatrics, Yale School of Medicine, New Haven, Connecticut

³Department of Pediatrics, Division of Pediatric Emergency Medicine, Baylor College of Medicine, Houston, Texas

⁴Department of Pediatrics, Medical College of Wisconsin, Milwaukee

⁵Division of Emergency Medicine, Cincinnati Children’s Hospital Medical Center and Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio

⁶Division of Emergency Medicine, Department of Pediatrics, Harvard Medical School and Boston Children’s Hospital, Boston, Massachusetts

⁷Division of Pediatric Emergency Medicine, Monroe Carell Jr. Children’s Hospital at Vanderbilt, Nashville, Tennessee

⁸Division of Emergency Medicine, The George Washington University School of Medicine and Health Sciences and Children’s National Health System, Washington, DC

⁹Division of Emergency Medicine, Children’s Hospital Los Angeles, Los Angeles, California

¹⁰Department of Pediatrics, University of Washington School of Medicine and Seattle Children’s Hospital, Seattle

¹¹Department of Pediatrics, University of Pittsburgh Medical Center and Children’s Hospital of Pittsburgh, Pittsburgh, Pennsylvania

¹²Department of Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania

¹³Department of Pediatrics, Ann & Robert H. Lurie Children’s Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, Illinois

¹⁴Department of Pediatric Emergency Medicine, Children’s Minnesota, Minneapolis, Minnesota

¹⁵Department of Pediatrics and Emergency Medicine, Children’s Healthcare of Atlanta, Emory University School of Medicine, Atlanta, Georgia

¹⁶Division of Pediatric Emergency Medicine, Department of Pediatrics, School of Medicine, University of Utah, Salt Lake City

¹⁷Ronald O. Perelman Department of Emergency Medicine/New York University Langone Health, New York, New York

¹⁸Department of Pediatrics, Nemours Children’s Hospital of Delaware, Wilmington

¹⁹Department of Pediatrics, School of Medicine, Section of Emergency Medicine, University of Colorado and Children’s Hospital Colorado, Aurora

²⁰Department of Emergency Medicine, University of California Irvine and Children’s Hospital of Orange County, Orange

²¹Department of Pediatrics, University of Louisville School of Medicine, Louisville, Kentucky

²²Department of Emergency Medicine, Oregon Health and Science University, Portland

²³Division of Pediatric Emergency Medicine, Johns Hopkins Children’s Center, Baltimore, Maryland

²⁴Division of Pediatric Emergency Medicine, Department of Pediatrics, Medical University of South Carolina, Charleston

²⁵Department of Emergency Medicine, Harbor University of California Los Angeles Medical Center and the David Geffen School of Medicine at the University of California, Los Angeles

²⁶Department of Pediatrics, University of Vermont Larner College of Medicine, Burlington

²⁷Department of Pediatrics, Central Michigan University College of Medicine and Children’s Hospital of Michigan, Detroit

²⁸Department of Pediatrics, Division of Pediatric Emergency Medicine, Duke University School of Medicine, Durham, North Carolina

²⁹Department of Emergency Medicine, McGovern Medical School, UTHealth Houston, Houston, Texas

³⁰Division of Pediatric Emergency Medicine, Southern Illinois University, Carbondale

³¹Rainbow Babies and Children’s Hospital and Case Western Reserve University School of Medicine, Cleveland, Ohio

³²Department of Emergency Medicine, University of Iowa, Iowa City

³³Department of Pediatrics, Lincoln Medical Center, Bronx, New York

³⁴Department of Biostatistics, University of Florida College of Medicine and College of Public Health and Health Professions, Gainesville

³⁵Department of Emergency Medicine and the Center for Experiential Learning and Simulation, University of Florida College of Medicine, Gainesville

³⁶Center for Child Health, Behavior, and Development, Seattle Children’s Research Institute, Seattle, Washington

³⁷Department of Emergency Medicine, University of California, Los Angeles

³⁸Texas Children’s Pediatrics, Houston

³⁹Department of Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania

⁴⁰Department of Emergency Medicine, Weill Cornell Medical College, New York, New York

⁴¹Division of Pediatric Emergency Medicine, Nicklaus Children’s Hospital, Miami, Florida

⁴²Department of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut

⁴³Department of Pediatrics, University of Florida College of Medicine, Gainesville

⁴⁴Department of Pediatrics, Oregon Health and Science University, Portland

⁴⁵Department of Emergency Medicine, University of Vermont Larner College of Medicine, Burlington

⁴⁶Department of Emergency Medicine, Lincoln Medical Center, Bronx, New York

Accepted for Publication: August 2, 2023.

Published Online: November 13, 2023. doi:10.1001/jamapediatrics.2023.4890

^✉

Corresponding Author: Colleen K. Gutman, MD, Department of Emergency Medicine and Pediatrics, University of Florida College of Medicine, 1329 SW 16th St, Gainesville, FL 32610 (ckays21@ufl.edu).

Author Contributions: Dr Gutman had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Drs Fernandez and Lion contributed equally as senior authors.

Concept and design: Gutman, Aronson, Lou, Fernandez, Lion.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Gutman, Singh, Hartford, Waseem.

Critical review of the manuscript for important intellectual content: All authors.

Statistical analysis: Gutman, Singh, Chow, Corboy, Bhalodkar, Lababidi, Yan, Lou.

Obtained funding: Gutman.

Administrative, technical, or material support: Singh, Pickett, Hartford, St. Pierre Hetz, Lucrezia, Cheng, Pulcini, Rose, Powers.

Supervision: Gutman, Aronson, Waseem, Fernandez, Lion.

Conflict of Interest Disclosures: Drs Gutman and Lou reported grants from National Center for Advancing Translational Sciences during the conduct of the study. Dr Tedford reported grants from the American Academy of Pediatrics CATCH Program and the National Institutes of Health outside the submitted work. No other disclosures were reported.

Funding/Support: Dr Gutman was funded for this work by the National Center for Advancing Translational Science (NCATS; KL2TR001429).

Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Meeting Presentation: These data have been presented, in part, at the Translational Science 2023 Annual Conference; Washington, DC; April 19, 2023; and at the 2023 Pediatric Academic Societies Annual Conference; Washington, DC; April 30, 2023.

Data Sharing Statement: See Supplement 2.

Additional Contributions: We are grateful to the following individuals for participating in interrater reliability assessments: Theodore Heyming, MD, University of California Irvine and Children’s Hospital of Orange County; Constance McAneney, MD, University of Cincinnati College of Medicine; Carlyn Christensen-Szalanski, MD, University of Iowa; Wendi Wendt, MD, Medical College of Wisconsin; Priya Spencer, MBBS, MPH, Central Michigan University College of Medicine and Children’s Hospital of Michigan; Rachel Weigert, MD, Children’s Minnesota; David Bergamo, MD, Nemours Children’s Hospital of Delaware; Gabrielle Pyronneau, MD, New York University Langone Health; Daniela Chan, MD, Oregon Health and Science University; Ron Kaplan, MD, University of Washington; Mary Siebenaler, MD, Southern Illinois University, Carbondale; Faria Pereira, MD, Baylor College of Medicine; Martha Stevens, MD, University of Vermont Larner College of Medicine; and Amethyst Alayari, MD, University of Colorado and Children’s Hospital Colorado. We are also grateful to the following individuals for serving as faculty mentors to study authors currently in fellowship: Lise Nigrovic, MD, Harvard Medical School and Boston Children’s Hospital; Sam Zhao, MD, The George Washington University School of Medicine and Health Sciences and Children’s National Health System; Jeanine Hall, MD, Children’s Hospital Los Angeles; Kate Deanehan, MD, Johns Hopkins Children’s Center; Danielle Graff, MD, University of Louisville School of Medicine; Christopher Pruitt, MD, Medical University of South Carolina; Mohsen Saidinejad, MD, Harbor University of California Los Angeles Medical Center and the David Geffen School of Medicine at the University of California; Holly Hanson, MD, The George Washington University School of Medicine and Health Sciences and Children’s National Health System; Joanne Agnant, MD, New York University Langone Health; Robert Hickey, MD, University of Pittsburgh Medical Center and Children's Hospital of Pittsburg; and Eric Glissmeyer, MD, School of Medicine, University of Utah. We wish to acknowledge the following individuals for their administrative support of this research: Kellie Bacon, University of California Irvine and Children’s Hospital of Orange County; Shea Lammers, Children’s Minnesota; Jessica Jung, School of Medicine, University of Utah; Kelly Huynh, MStat, School of Medicine, University of Utah; and Sharon Kim, Southern Illinois University, Carbondale. Finally, we are grateful to the steering committee of the Pediatric Emergency Medicine Collaborative Research Committee for their critical review of the study protocol and their support of the conduct of the study. None of these individuals were compensated for their contributions outside of normal salary or wages.

^✉

Corresponding author.

PMCID: PMC10644247 PMID: 37955907

Key Points

Question

Are there disparities by race, ethnicity, and language in the treatment of febrile infants at low risk of invasive bacterial infection?

Findings

In this cross-sectional analysis of 4042 infants evaluated between 2018 and 2019, language used for medical care was significantly associated with the use of at least 1 nonindicated intervention but race and ethnicity were not.

Meaning

The findings suggest that communication barriers, both actual and perceived, may drive inequity, and implementation of new febrile infant guidelines emphasizing patient-centered communication should consider language equity.

This cross-sectional study evaluates associations of race, ethnicity, and language with treatment provided for febrile infants at low risk of bacterial infection.

Abstract

Importance

Febrile infants at low risk of invasive bacterial infections are unlikely to benefit from lumbar puncture, antibiotics, or hospitalization, yet these are commonly performed. It is not known if there are differences in management by race, ethnicity, or language.

Objective

To investigate associations between race, ethnicity, and language and additional interventions (lumbar puncture, empirical antibiotics, and hospitalization) in well-appearing febrile infants at low risk of invasive bacterial infection.

Design, Setting, and Participants

This was a multicenter retrospective cross-sectional analysis of infants receiving emergency department care between January 1, 2018, and December 31, 2019. Data were analyzed from December 2022 to July 2023. Pediatric emergency departments were determined through the Pediatric Emergency Medicine Collaborative Research Committee. Well-appearing febrile infants aged 29 to 60 days at low risk of invasive bacterial infection based on blood and urine testing were included. Data were available for 9847 infants, and 4042 were included following exclusions for ill appearance, medical history, and diagnosis of a focal infectious source.

Exposures

Infant race and ethnicity (non-Hispanic Black, Hispanic, non-Hispanic White, and other race or ethnicity) and language used for medical care (English and language other than English).

Main Outcomes and Measures

The primary outcome was receipt of at least 1 of lumbar puncture, empirical antibiotics, or hospitalization. We performed bivariate and multivariable logistic regression with sum contrasts for comparisons. Individual components were assessed as secondary outcomes.

Results

Across 34 sites, 4042 infants (median [IQR] age, 45 [38-53] days; 1561 [44.4% of the 3516 without missing sex] female; 612 [15.1%] non-Hispanic Black, 1054 [26.1%] Hispanic, 1741 [43.1%] non-Hispanic White, and 352 [9.1%] other race or ethnicity; 3555 [88.0%] English and 463 [12.0%] language other than English) met inclusion criteria. The primary outcome occurred in 969 infants (24%). Race and ethnicity were not associated with the primary composite outcome. Compared to the grand mean, infants of families that use a language other than English had higher odds of the primary outcome (adjusted odds ratio [aOR]; 1.16; 95% CI, 1.01-1.33). In secondary analyses, Hispanic infants, compared to the grand mean, had lower odds of hospital admission (aOR, 0.76; 95% CI, 0.63-0.93). Compared to the grand mean, infants of families that use a language other than English had higher odds of hospital admission (aOR, 1.08; 95% CI, 1.08-1.46).

Conclusions and Relevance

Among low-risk febrile infants, language used for medical care was associated with the use of at least 1 nonindicated intervention, but race and ethnicity were not. Secondary analyses highlight the complex intersectionality of race, ethnicity, language, and health inequity. As inequitable care may be influenced by communication barriers, new guidelines that emphasize patient-centered communication may create disparities if not implemented with specific attention to equity.

Introduction

Infants with fever in the first 2 months of life are at risk of invasive bacterial infections (IBI), such as bacteremia and bacterial meningitis.¹ In young infants, IBI may not be readily detected on physical examination.² Widely used evidence-based algorithms use blood and urine test results to identify infants aged 29 to 60 days at low risk of IBI (hereafter, low-risk febrile infants).^3,4,5,6 For low-risk febrile infants, additional interventions, such as lumbar puncture, empirical antibiotics, and hospitalization, are not routinely recommended.^3,4,5,6 However, these continue to be performed frequently.^6,7 In this low-risk population, additional interventions are not associated with improved outcomes but may increase costs and the potential for harm.^6,7,8

In the fast-paced emergency department (ED) setting, clinicians are at risk of being influenced by implicit biases, which can contribute to health inequities.^9,10 Implicit biases may be important in the treatment of low-risk febrile infants. When deciding whether to perform additional interventions, national and institutional guidelines often require that clinicians determine if families have social barriers to safe discharge.^6,11,12 However, clinician evaluation of family comprehension and access to resources may be subject to implicit biases.^{9,10,13,14,15} To evaluate disparities in the care of low-risk febrile infants, we aimed to assess the association of race and ethnicity and language used for medical care with additional interventions (lumbar puncture, empirical antibiotics, and hospitalization). We hypothesized that there would be differences in the performance of additional interventions by infant race and ethnicity and by caregiver language used for medical care.

Methods

Study Design and Setting

This was a multicenter cross-sectional study of well-appearing infants aged 29 to 60 days determined to be at low risk of IBI during evaluation for fever at 1 of 34 participating EDs between January 1, 2018, and December 31, 2019. These dates reflect a period before the COVID-19 pandemic during which clinical algorithms were widely used to identify infants at low risk for IBI.^3,4,6 Data were analyzed from December 2022 to July 2023. Professional interpreting services were available during ED care at all institutions. This study was endorsed by the Pediatric Emergency Medicine Collaborative Research Committee of the American Academy of Pediatrics Section of Emergency Medicine and was approved as an exempt study by the institutional review boards at participating institutions; as such, consent was not obtained for this retrospective research. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.¹⁶

Study Population

Infants were eligible if they were 29 to 60 days old, had a fever (temperature, 38 °C or higher) in the ED or within 24 hours before ED presentation, and met low-risk criteria per institutional definition. Institutional definition for low risk was defined based on laboratory values in each institution’s guidelines for the care of febrile infants. For institutions without guidelines (n = 13 [38%]), low-risk criteria were based on white blood cell count (between 5 and 15 × 10³/μL) and urinalysis parameters (negative leukocyte esterase, negative nitrite, and <10 white blood cells per high power field) that were commonly used at that time.¹⁷ eTable 1 in Supplement 1 details each institution’s guidelines. Other exclusion criteria were documented ill appearance, using a previously used approach^18,19; presence of a complex chronic medical condition^20,21; prematurity (<37 weeks); focal bacterial infection evident on ED physical examination (eg, cellulitis or septic joint); ED diagnosis of bronchiolitis; receipt of antibiotics within 48 hours before presentation; or transfer from another hospital.

Data Collection

At each institution, infants were identified by age and a previously used multipronged approach designed to maximize sensitivity in identifying potential individuals for inclusion.²² With this sequential approach, age-eligible infants were identified through the following parameters: temperature of 38 °C or higher while in the ED or International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) code for fever (P81.9, R50.9, R50.81) or urine testing or complete blood cell count and/or blood culture obtained in the ED. Investigators at each institution then performed manual electronic health record review to confirm eligibility.

Investigators at each institution were trained and provided with a detailed manual of operations with strict definitions for data collection. The data collection tool was developed and revised with input from experienced febrile infant and health disparities investigators and based on pilot testing at 3 participating institutions. When abstracting clinician-documented data, we used a hierarchical approach, using data from the most senior clinician possible.²³ The trained investigators abstracted data on included infants through manual electronic health record review into a central REDCap database hosted at the University of Florida.^24,25

Study Measures

The primary outcome was the receipt of at least 1 additional intervention: lumbar puncture, empirical antibiotics, or hospitalization. To address the limitations of a composite primary outcome, the individual components were assessed as secondary outcomes. We additionally assessed prevalence of IBI, defined as the isolation of a pathogenic organism in blood or cerebrospinal fluid cultures obtained at the index visit; prevalence of urinary tract infection, defined as the growth of at least 50 000 colony-forming units/mL of a pathogenic organism or at least 10 000 colony-forming units/mL if the specimen was obtained by suprapubic catheterization^26,27; and readmission within 72 hours of the index ED visit.

We assessed race and ethnicity as a predictor using a 4-level variable (non-Hispanic Black, hereafter referred to as Black; Hispanic; non-Hispanic White, hereafter referred to as White and other race or ethnicity).^28,29,30,31 Race and ethnicity were collected as separate variables using 2020 US Census Bureau categories.³² Any infant with Hispanic ethnicity was considered Hispanic, regardless of race or if race was missing.³³ Race and ethnicity were considered missing if ethnicity was missing or if race was missing with non-Hispanic or other ethnicity. We assessed language as a dichotomous predictor variable, with use of a language other than English defined based on the language documented in the electronic health record or by documentation of interpreter use in the ED. A priori–defined potential confounders included patient-level variables (ie, age, documented primary care physician, insurance type [none, public, or private], day of week of ED arrival [Sunday through Thursday or Friday through Saturday], and time of day of ED arrival [4 pm through 4 am or 4 am through 4 pm]) and ED-level variables (presence of a local clinical guideline for febrile infants, US Census region,³⁴ proportion of ED visits by families who use English [≥80% vs <80%], and proportion of ED visits by patients who are White [≥30% vs <30%]²⁹).

Data Reliability

After an initial data entry period, we assessed interrater reliability by selecting a random sample of 15 infants at each institution for partial data abstraction by a second reviewer blinded to initial data collection. The second reviewer was provided with the study definitions for data collection and abstracted dichotomous responses (yes or no) for each of 3 variables: ill appearing, ED diagnosis of bronchiolitis, and English as the language used for medical care. All discrepancies were reviewed by the primary study investigator (C.K.G.) and discussed with the institution’s primary investigator. If the κ statistic was less than 0.8,³⁵ the site investigator received targeted training before resuming data abstraction.

Statistical Analysis

We calculated descriptive statistics to characterize institutions and patients. Using complete case analysis with inverse probability weighting to account for missing race, ethnicity, and language data (eMethods in Supplement 1), we calculated unadjusted and adjusted odds ratios (aORs) and 95% CIs with bivariate and multivariable logistic regression to assess the association between race and ethnicity and language, with the primary outcome of receiving at least 1 additional intervention. Both predictor variables were included in the multivariable models, in which we also controlled for the above listed a priori–identified potential confounders with the inclusion of intercepts of hospital-level clustering as random effects. We used sum contrasts for comparisons to avoid centering our analyses White and English language users as the presumed normative reference groups. Instead, within each predictor variable, comparisons are made to the grand mean. This allows each subgroup to be compared to the same reference (the grand mean), rather than presenting data with 1 subgroup assumed to represent the normal experience; importantly, the direction and magnitude of findings are maintained.^36,37 The same methods were used for secondary outcomes.

To evaluate the intersectionality of race, ethnicity, and language, we conducted secondary exploratory analyses of the association between language and the performance of additional interventions, stratified by race and ethnicity. To ensure that our cohort represented infants most likely to be considered low risk by the treating clinician, we conducted sensitivity analyses that excluded all infants with an abnormal inflammatory marker defined by the step by step³ and Pediatric Emergency Care Applied Research Network (PECARN)⁵ algorithms. To assess our approach to missing predictors, we conducted sensitivity analyses in which we only included infants from the 18 sites with less than 5% missing race, ethnicity, and language data and included missing as a category in the race and ethnicity predictor variable. Assuming 30% of White infants with English as the language used by their caregiver in the care setting received the primary outcome of additional interventions, we determined a priori that 1000 infants would need to be included to detect a modest difference in the primary outcome (aOR<0.8 or >1.2).⁷ All analyses were performed using R Statistical Software version 4.2.1 (R Foundation) with the tidyverse (version 1.3.1) and lme4 (version 1.31) packages.

Results

Across 34 sites, 4042 infants (41.0%) met inclusion criteria (Figure). Tables 1 and 2 show the characteristics of the study sample by race and ethnicity and by language, respectively. One-fourth of infants had at least 1 additional intervention performed (range, 1 of 21 [4.8%] to 6 of 9 [66.7%] by site), including 684 of 3492 infants (21.1%) at sites with institutional febrile infant guidelines and 285 of 793 infants (35.9%) at sites without institutional febrile infant guidelines. Twenty-two infants (0.5%) were diagnosed with IBI (eTable 2 in Supplement 1) and 21 (0.5%) were diagnosed with an isolated urinary tract infection. A total of 392 infants (9.7%) had a repeat ED visit within 72 hours of the index visit, 196 of whom (4.8% of the total cohort) were admitted to the hospital at the repeat visit.

Table 1. Infant Characteristics, Evaluation, and Treatment, and Outcomes by Race and Ethnicity.

	No. (%)
	Non-Hispanic Black (n = 612)	Hispanic (n = 1054)	Non-Hispanic White (n = 1741)	Other race or ethnicity (n = 369)^a	Missing race or ethnicity (n = 266)^b
Infant characteristics
Age, median (IQR), d	46 (38-53)	45 (38-53)	45 (38-52)	45 (27-53)	45 (37-52)
Sex^c
Female	245 (43.2)	401 (45.9)	672 (44.0)	124 (41.8)	119 (47.4)
Male	322 (56.8)	473 (54.1)	855 (56.0)	173 (58.2)	132 (52.6)
Uses a language other than English	9 (1.5)	364 (34.5)	10 (0.6)	62 (16.8)	18 (6.8)
Insurance type
Private	131 (21.4)	160 (15.2)	1185 (68.1)	175 (47.4)	120 (45.1)
Public	448 (73.2)	856 (81.2)	463 (26.6)	170 (46.1)	122 (45.9)
None	20 (3.3)	31 (2.9)	36 (2.1)	12 (3.3)	9 (3.4)
Unknown	13 (2.1)	7 (0.7)	57 (3.3)	12 (3.3)	15 (5.6)
Documented as having a PCP	547 (89.4)	901 (85.5)	1630 (93.6)	332 (90.0)	246 (92.5)
Febrile in the ED
Yes	364 (59.5)	635 (60.2)	927 (53.2)	234 (63.4)	158 (59.4)
No, objective fever at home	248 (40.5)	419 (39.8)	814 (46.8)	135 (36.6)	108 (40.6)
Immunizations ≤72 h before ED visit	21 (3.4)	23 (2.2)	30 (1.7)	14 (3.8)	8 (3.0)
ED evaluation and treatment
Partial evaluation^d	100 (16.3)	139 (13.2)	197 (11.3)	52 (14.1)	30 (11.3)
Abnormal inflammatory markers	20 (3.3)	27 (2.6)	39 (2.2)	12 (3.3)	7 (2.6)
No. (%) based on step by step³ or PECARN⁵ algorithm^e	133 (21.7)	182 (17.3)	296 (17.0)	83 (22.5)	50 (18.8)
Chest radiography obtained in ED	93 (15.2)	149 (14.1)	171 (9.8)	47 (12.7)	32 (12.0)
Respiratory virus testing in the ED
Yes and positive result	104 (17.0)	208 (19.7)	341 (19.6)	83 (22.5)	53 (19.9)
Yes and negative result	138 (22.5)	302 (28.7)	438 (25.2)	97 (26.3)	60 (22.6)
No, not tested	372 (60.5)	544 (51.6)	962 (55.3)	189 (51.2)	155 (57.5)
Infant outcomes
Invasive bacterial infection^f	8 (1.3)	4 (0.4)	5 (0.3)	2 (0.5)	3 (1.1)
Urinary tract infection^g	5 (0.8)	7 (0.7)	5 (0.3)	2 (0.5)	2 (0.7)
Viral meningitis	8 (1.3)	15 (1.4)	35 (2.0)	8 (2.2)	4 (1.5)
Any revisit within 72 h after ED visit	68 (11.1)	111 (10.5)	157 (9.0)	40 (10.8)	16 (6.0)
Revisit with admission within 72 h after ED visit	31 (5.1)	42 (4.0)	87 (5.0)	24 (6.5)	12 (4.5)

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Abbreviations: ED, emergency department; PCP, primary care physician; PECARN, Pediatric Emergency Care Applied Research Network.

^{^a}

Includes 351 non-Hispanic infants with the following races: American Indian or Alaska Native (7), Asian (188), multiple races (36), Native Hawaiian or Other Pacific Islander (18), and other, not specified (119). Also includes 1 infant documented as having other ethnicity and other race.

^{^b}

Includes 76 non-Hispanic infants with missing race; 94 missing both ethnicity and race; 4 with other ethnicity and missing race; and 109 with missing ethnicity and the following races: American Indian or Alaska Native (1), Asian (5), Black (9), multiple races (1), other, not specified (35), and White (59).

^{^c}

Sample size of 3516 (567 non-Hispanic Black; 874 Hispanic; 1527 non-Hispanic White; 297 other, not specified; and 249 missing) excludes 526 infants (45 non-Hispanic Black, 180 Hispanic, 214 non-Hispanic White, 72 other, and 15 missing) with missing data on sex.

^{^d}

Defined as any infant without complete blood cell count or urinalysis during the ED visit.

^{^e}

Included in the study cohort if the abnormal value was not incorporated into the institution’s guideline or standardized study criteria.

^{^f}

Bacteremia or meningitis as defined by growth of a pathogenic organism in the blood or cerebrospinal fluid.

^{^g}

Growth of at least 50 000 colony-forming units/mL of a pathogenic organism in the urine (or at least 10 000/mL if specimen was obtained via suprapubic catheterization) in the absence of bacteremia or meningitis.

Table 2. Infant Characteristics, Evaluation, and Treatment and Outcomes by Language Used for Medical Care.

	No. (%)
	Language other than English (n = 463)^a	English (n = 3555)
Infant characteristics
Age, median (IQR), d	44 (37-53)	45 (38-53)
Sex^b	174 (42.0)	1377 (44.7)
Female	174 (42.0)	1377 (44.7)
Male	240 (58.0)	1701 (55.3)
Race and ethnicity
Non-Hispanic Black	9 (1.9)	603 (17.0)
Hispanic	364 (78.6)	685 (19.3)
Non-Hispanic White	10 (2.2)	1718 (48.3)
Other race or ethnicity	62 (13.4)	307 (8.6)
Missing/unknown	18 (3.0)	242 (6.8)
Insurance type
Private	38 (8.2)	1723 (48.5)
Public	402 (86.8)	1646 (46.3)
None	17 (3.7)	90 (2.5)
Unknown	6 (1.3)	96 (2.7)
Documented as having a PCP	402 (86.8)	3233 (90.9)
Febrile in the ED
Yes	301 (65.0)	2002 (56.3)
No, objective fever at home	162 (35.0)	1553 (43.7)
Immunizations ≤72 h before ED visit	6 (1.3)	90 (2.5)
ED evaluation and management
Partial evaluation^c	42 (9.1)	476 (13.4)
Abnormal inflammatory markers^d
Based on step by step algorithm³	13 (2.8)	92 (2.6)
Based on PECARN algorithm⁵	90 (19.4)	650 (18.3)
Chest radiography obtained in ED	59 (12.7)	433 (12.2)
Respiratory virus testing in the ED
Yes and positive result	103 (22.2)	681 (19.2)
Yes and negative result	126 (27.2)	900 (25.3)
No, not tested	234 (50.5)	1974 (55.5)
Infant outcomes
Invasive bacterial infection^e	1 (0.2)	20 (0.6)
Urinary tract infection^f	2 (0.4)	19 (0.5)
Viral meningitis	8 (1.7)	62 (1.7)
Any revisit within 72 h after ED visit	45 (9.7)	343 (9.6)
Revisit with admission within 72 h after ed visit	18 (3.8)	175 (4.9)

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Abbreviations: ED, emergency department; PCP, primary care physician; PECARN, Pediatric Emergency Care Applied Research Network.

^{^a}

Includes the following languages: Spanish (370), Arabic (16), Mandarin (8), Burmese (6), Karen (6), Korean (1), Vietnamese (5), Farsi (4), Hmong (4), Japanese (3), Nepali (3), Somali (3), Cantonese (2), French (2), Portuguese (2), Swahili (2), Amharic (1), Armenian (1), Bambara (1), Bengali (1), Chatino (1), Hebrew (1), Hindi (1), Khmer (1), Pashto (1), Russian (1), Tagalog (1), and Uzbeck (1). Also includes 4 infants identified as having parents who use languages other than English with no specific language documented and 11 infants with documentation of professional interpretation and English language. Twenty-four infants who were missing language data are not included in this table.

^{^b}

Sample size of 3492 (414 Language other than English and 3078 English) excludes 526 infants (49 language other than English and 477 English) with missing data on sex.

^{^c}

Defined as any infant without complete blood cell count or urinalysis during the ED visit.

^{^d}

Included in the study cohort if the abnormal value was not incorporated into the institution’s guideline or standardized study criteria.

^{^e}

Bacteremia or meningitis as defined by growth of a pathogenic organism in the blood or cerebrospinal fluid.

^{^f}

Race and Ethnicity

Overall, 612 (15.1%) of included infants were Black (range, 0 of 29 and 0 of 58 [0%] to 13 of 32 [40.6%] by site), 1054 (26.1%) were Hispanic (range, 0 of 29 [0%] to 22 of 27 [82.1%]), 1741 (43.1%) were White (range, 0 of 9 and 0 of 27 [0%] to 23 of 29 [79.3%]), 352 (8.7%) were of other race or ethnicity (range, 0 of 29, 0 of 21, and 0 of 9 [0%] to 11 of 34 [32.4%]), and 283 (7.0%) had missing race or ethnicity (range, 0 of 19, 0 of 93, 0 of 11, and 0 of 21 [0%] to 10 of 29 [34.5%]). There was no association between race and ethnicity and the primary outcome or the receipt of either lumbar puncture or empirical antibiotics (Table 3). Compared to the grand mean, Hispanic infants had lower odds of hospital admission at the index visit (aOR, 0.76; 95% CI, 0.63-0.93).

Table 3. Results of Bivariate and Multivariable Logistic Regression Analyses.

Outcome/predictor	No. (%)	Unadjusted odds ratio (95% CI)	Adjusted odds ratio (95% CI)^a
Any intervention^b
Race and ethnicity
Non-Hispanic Black	142 (23.2)	0.98 (0.83-1.14)	1.09 (0.90-1.32)
Hispanic	253 (24.0)	1.02 (0.89-1.16)	0.85 (0.71-1.02)
Non-Hispanic White	403 (23.1)	0.95 (0.84-1.07)	1.01 (0.86-1.18)
Other race or ethnicity	87 (24.7)	1.06 (0.87-1.07)	1.07 (0.86-1.32)
Missing	81 (30.5	NA^c	NA^c
Language
Language other than English	129 (27.9)	1.17 (1.04-1.30)	1.16 (1.01-1.33)
English	826 (23.2)	0.86 (0.77-0.96)	0.86 (0.75-0.99)
Missing	14 (58.3)	NA^c	NA^c
Lumbar puncture
Race and ethnicity
Non-Hispanic Black	84 (13.7)	0.86 (0.70-1.03)	0.90 (0.71-1.14)
Hispanic	177 (16.8)	1.08 (0.93-1.27)	1.04 (0.85-1.29)
Non-Hispanic White	287 (16.5)	1.04 (0.90-1.19)	1.01 (0.84-1.22)
Other race or ethnicity	57 (16.2)	1.04 (0.83-1.30)	1.05 (0.82-1.35)
Missing	62 (23.3)	NA^c	NA^c
Language
Language other than English	84 (18.1)	1.12 (0.98-1.26)	1.06 (0.90-1.24)
English	574 (16.1)	0.90 (0.79-1.02)	0.94 (0.81-1.11)
Missing	11 (45.8)	NA^c	NA^c
Empirical antibiotics
Race and ethnicity
Non-Hispanic Black	77 (12.6)	0.92 (0.75-1.12)	1.06 (0.84-1.34)
Hispanic	166 (15.7)	1.19 (1.01-1.40)	0.97 (0.79-1.20)
Non-Hispanic White	210 (12.1)	0.87 (0.75-1.01)	1.00 (0.83-1.21)
Other race or ethnicity	50 (14.2)	1.05 (0.82-1.33)	0.97 (0.75-1.24)
Missing	51 (19.2)	NA^c	NA^c
Language
Language other than English	82 (17.7)	1.24 (1.08-1.41)	1.09 (0.93-1.28)
English	471 (13.2)	0.81 (0.71-0.92)	0.92 (0.78-1.07)
Missing	3 (12.5)	NA^c	NA^c
Hospital admission
Race and ethnicity
Non-Hispanic Black	111 (18.1)	1.06 (0.88-1.26)	1.14 (0.93-1.40)
Hispanic	178 (16.9)	0.97 (0.83-1.12)	0.76 (0.63-0.93)
Non-Hispanic White	267 (15.3)	0.84 (0.74-0.97)	0.99 (0.83-1.18)
Other race or ethnicity	69 (19.6)	1.16 (0.94-1.43)	1.16 (0.92-1.46)
Missing	55 (20.7)	NA^c	NA^c
Language
Language other than English	99 (21.4)	1.23 (1.09-1.39)	1.25 (1.08-1.46)
English	574 (16.1)	0.81 (0.72-0.92)	0.80 (0.69-0.93)
Missing	9 (37.5)	NA^c	NA^c

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Abbreviation: NA, not applicable.

^{^a}

Model includes race, ethnicity, language, age, day of week, time of day, documentation of primary care physician, insurance type, presence of an institutional guideline, US Census region, proportion of total visits that are non-Hispanic White, and proportion of total visits that use English, with random effects to account for clustering by site and inverse probability weighting to account for missingness in the predictor variables.

^{^b}

At least 1 of lumbar puncture, empirical antibiotics, and hospital admission.

^{^c}

Complete case analyses excluded infants with missing predictor variables.

Language Used for Medical Care

A total of 463 infants (12%) were from families who use a language other than English (range, 0 of 29, 0 of 58, 0 of 32, and 0 of 53 [0%] to 17 of 34 [50.0%]). Compared to the grand mean, children of families that use a language other than English had higher odds of receiving at least 1 additional intervention (aOR, 1.16; 95% CI, 1.01-1.33), and children of families that use English had lower odds (aOR, 0.86; 95% CI, 0.75-0.99). There was no association between language and lumbar puncture or language and empirical antibiotics. Children of families that use a language other than English had higher odds of hospitalization at the index visit compared to the grand mean (aOR, 1.25; 95% CI, 1.08-1.46). In the exploratory analyses stratified by race and ethnicity, these findings were maintained among Hispanic infants (eTable 3 in Supplement 1).

Sensitivity Analyses

The results were unchanged in sensitivity analyses that excluded infants with abnormal inflammatory markers as defined by the step by step algorithm³ (n = 105) and as defined by the PECARN algorithm⁵ (n = 744). Results were also unchanged when infants with missing race and ethnicity were included in analyses, with a fifth category for missing added to the race and ethnicity predictor variable. When limiting the analysis to the 2352 infants from sites with less than 5% missing race and ethnicity data, Hispanic infants had decreased odds of receiving at least 1 additional intervention compared to the grand mean (aOR, 0.77; 95% CI, 0.61-0.97); there continued to be no association between all other race and ethnicity categories and the primary outcome, and there was no change in the findings related to language used for medical care.

Discussion

In this multicenter cross-sectional analysis of well-appearing febrile infants at low risk of IBI, there was no overall association between infant race and ethnicity and the use of at least 1 additional of the included interventions. However, having a caregiver who used a language other than English for medical care was associated with the use of at least 1 additional intervention contrary to evidence-based recommendations.

The language inequity identified in this study makes clear the important intersection of communication and health disparities in pediatric emergency care. There are numerous ways in which language may influence medical decision-making. Implicit biases may impact clinicians’ assessment of caregiver literacy, emotional response, access to resources, and trustworthiness.^38,39 Additionally, family-centered communication relies on robust bidirectional communication between caregivers and clinicians. Although professional interpreting improves communication with and comprehension for families who use language other than English, it remains underused due to both individual and institutional barriers.^40,41,42,43 Even with professional interpreting, clinicians may not feel confident in the quality of communication. Clinicians may perform additional interventions, and in particular, pursue hospitalization, out of a concern that language barriers decrease the family’s comprehension of discharge instructions and ability to access follow-up care. Our findings suggest that clinicians may act cautiously by providing what they perceive to be the safest option for infants who have elevated risk for adverse outcomes due to language barriers experienced throughout the health care system. However, as a result, infants of families who use language other than English may instead be inequitably exposed to unnecessary potential harm, including higher costs, risk of nosocomial infections, caregiver stress, and disruption of breastfeeding.^44,45

Our findings highlight the importance of considering language used for medical care when assessing equity. Language used for medical care is rarely available in large databases; studies that seek to assess language-related disparities are often limited to single-center investigations.^{46,47,48,49,50,51} The inclusion of language underscores the complex intersectionality of race, ethnicity, and language. For example, infants from families in our study who use a language other than English—most of whom were Hispanic and used Spanish—were more likely to be hospitalized; however, Hispanic infants—most of whose families used English—were less likely to be hospitalized. These findings, which are even more prominent in subgroup analyses, highlight the distinct effects that race and ethnicity (a proxy for racism) and language used for medical care (a proxy for communication barriers and language bias) may have on outcomes, an understanding of which is critical for effective future intervention.

There are numerous examples of disparities in medical management based on race and ethnicity,^{28,29,30,31,52,53,54} so it is notable that we found no overall association between infant race and ethnicity and at least 1 additional intervention. However, although disparities were not present overall, this may mask disparities at the individual hospital level. Further, the results of our secondary analyses, in which we found that Hispanic infants were less likely to be hospitalized, indicate that racial biases may remain in the decision-making process. The directionality of the finding suggests that Hispanic infants were more likely to receive evidence-based care, which is consistent with literature demonstrating that low-value interventions (such as unnecessary hospitalization) are more commonly performed in White children.⁵⁴ However, as Black and Hispanic children have higher rates of disease complications and death in the health care system,⁵⁵ the suggestion of racial disparities in care remains alarming. Any systematic difference in the way care is provided suggests bias may play a role and can also affect unmeasured aspects of care.⁵⁶ Given the limitations of assigning import to these secondary outcomes in the setting of multiple testing, more research is needed to understand the full extent of these findings.

Our overall findings may suggest that applying objective risk stratification algorithms reduces the effect of implicit racial biases.⁵⁷ However, deviations from evidence-based recommendations were frequent, with one-fourth of infants receiving at least 1 additional intervention. As such, we cannot assume that an imperfectly applied clinical algorithm is responsible for our findings. This has potential implications for the implementation of the 2021 American Academy of Pediatrics guidelines for febrile infants.²⁷ First, there is a clear need to develop strategies to enhance guideline adherence. More research is needed to understand and address the drivers of care variation, but in the meantime, clinical groups can invest in monitoring quality metrics stratified by race, ethnicity, and language and engaging in equity-focused quality improvement efforts to address disparities identified locally.^58,59 Second, the new American Academy of Pediatrics guidelines take an important step in advocating for a patient-centered approach, highlighting opportunities for shared decision-making. Paradoxically, this could exacerbate disparities if there is bias in when and how clinicians engage in shared decision-making. To promote and maintain equitable clinical care, the new guidelines must be implemented with an intentional focus on equity, including through the use of strategies to communicate effectively across language barriers and to reduce bias when clinicians decide when and with whom to conduct shared decision-making.^60,61

As evidence of the need to develop strategies to enhance guideline adherence, a substantial proportion of the low-risk febrile infants in this sample underwent additional interventions, yet IBI was rare. A disproportionate number of infants with IBI were Black, but it would be inappropriate to draw conclusions about an association between race and ethnicity and infection risk. Race and ethnicity are social constructs, serving as proxies for implicit bias and racism in the medical decision-making process.⁶² Any detected difference in this retrospective analysis might be due to racial inequity in clinician assessment or documentation of ill appearance. Given the multilevel harm that results from inappropriately embedding race and ethnicity into clinical decision-making, it is critical that race and ethnicity are interpreted as social constructs and that these findings not be construed to suggest a higher innate susceptibility to infection.^63,64,65

Limitations

Our study has limitations inherent to the retrospective design. We followed best practices for conducting an electronic health record review, although site investigators were not blinded to the study question.^66,67 Our ability to exclude ill-appearing infants through detailed medical record review is a relative strength over studies that rely on administrative databases, yet it is limited by recall and outcome biases by the documenting physician. We used local guidelines to define low risk but applied standardized criteria for those sites without guidelines. As such, some included infants may not have been considered low risk by their treating clinician. However, our findings were unchanged in a planned sensitivity analysis that excluded infants with elevated inflammatory markers. We were also limited by the accuracy and availability of race, ethnicity, and language data, with 7% of our sample missing race and ethnicity data. The validity of race and ethnicity data remains an important limitation to health disparities research and there is a need for systematic improvements in how this is collected and documented.

Conclusions

In this sample of low-risk febrile infants, language used for medical care, but not race and ethnicity, was associated with the use of at least 1 nonindicated intervention. These findings suggest that care may be provided inequitably based on actual and perceived communication barriers. It is essential that clinicians maintain an intentional focus on equity in the care of this population, particularly when deciding when and with whom to conduct shared decision-making. Interventions to enhance effective bidirectional communication across language barriers should be a critical component of any efforts to implement guidelines that promote shared decision-making in clinical care.

Supplement 1.

eMethods. Missingness model

eTable 1. Characteristics and definition of “low-risk” at participating institutions

eTable 2. Details of infants diagnosed with invasive bacterial infection

eTable 3. Association between language and non-indicated interventions, stratified by race and ethnicity subgroup

Click here for additional data file.^{(141.4KB, pdf)}

Supplement 2.

Data sharing statement

Click here for additional data file.^{(13.2KB, pdf)}

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement 1.

eMethods. Missingness model

eTable 1. Characteristics and definition of “low-risk” at participating institutions

eTable 2. Details of infants diagnosed with invasive bacterial infection

eTable 3. Association between language and non-indicated interventions, stratified by race and ethnicity subgroup

Click here for additional data file.^{(141.4KB, pdf)}

Supplement 2.

Data sharing statement

Click here for additional data file.^{(13.2KB, pdf)}

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PERMALINK

Race, Ethnicity, Language, and the Treatment of Low-Risk Febrile Infants

Colleen K Gutman, MD

Paul L Aronson, MD, MHS

Nidhi V Singh, MD

Michelle L Pickett, MD, MS

Kamali Bouvay, MD

Rebecca S Green, MD

Britta Roach, DO

Hannah Kotler, MD

Jessica L Chow, MD, MPH, MS

Emily A Hartford, MD, MPH

Mark Hincapie, MD

Ryan St Pierre-Hetz, MD

Jessica Kelly, MD

Laura Sartori, MD, MPH

Jennifer A Hoffmann, MD, MS

Jacqueline B Corboy, MD, MS

Kelly R Bergmann, DO, MS

Bolanle Akinsola, MD

Vanessa Ford, MD

Natalie J Tedford, MD

Theresa T Tran, MD

Sasha Gifford, MD

Amy D Thompson, MD

Andrew Krack, MD, MS

Mary Jane Piroutek, MD

Samantha Lucrezia, MD

SunHee Chung, MD

Nabila Chowdhury, MD

Kathleen Jackson, MD

Tabitha Cheng, MD

Christian D Pulcini, MD, MEd, MPH

Nirupama Kannikeswaran, MBBS

Larissa L Truschel, MD, MPH

Karen Lin, MD

Jamie Chu, MD

Neh D Molyneaux, MD, MPH

Myto Duong, MD

Leslie Dingeldein, MD

Jerri A Rose, MD

Carly Theiler, MD

Sonali Bhalodkar, MD, MPH

Emily Powers, MD

Muhammad Waseem, MD, MS

Ahmed Lababidi, MD

Xinyu Yan, MS

Xiang-Yang Lou, PhD

Rosemarie Fernandez, MD

K Casey Lion, MD, MPH

Key Points

Question

Findings

Meaning

Abstract

Importance

Objective

Design, Setting, and Participants

Exposures

Main Outcomes and Measures

Results

Conclusions and Relevance

Introduction

Methods

Study Design and Setting

Study Population

Data Collection

Study Measures

Data Reliability

Statistical Analysis

Results

Figure. Inclusion and Exclusion Flow Diagram.

Table 1. Infant Characteristics, Evaluation, and Treatment, and Outcomes by Race and Ethnicity.

Table 2. Infant Characteristics, Evaluation, and Treatment and Outcomes by Language Used for Medical Care.

Race and Ethnicity

Table 3. Results of Bivariate and Multivariable Logistic Regression Analyses.

Language Used for Medical Care

Sensitivity Analyses

Discussion

Limitations