
==== Front
Perm J
tpj
tpj
The Permanente Journal
1552-5767
1552-5775
The Permanente Press

38916447
10.7812/TPP/23.155
TPJ-23-155
Brief Report
Comparing Hospital Length of Stay Between Persons With LEP and English-Speaking Patients in a Large Rural Academic Medical Center
http://orcid.org/0009-0002-6984-1898
Verkhovsky Samuel B MIPP, MPH 1
http://orcid.org/0000-0002-9479-3288
Kong Lixi MS 2
http://orcid.org/0000-0002-7399-622X
Oliver Brant J PhD, MS, MPH, FNP-BC, PMHNP-BC 1 3 4
1 Office of Care Experience, the Value Institute, Dartmouth Health, Lebanon, NH, USA
2 Analytics Institute, Dartmouth Health, Lebanon, NH, USA
3 Chronic Health Improvement Research Program, Department of Community and Family Medicine, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
4 The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
Samuel B Verkhovsky, MIPP, MPH samuel.b.verkhovsky@hitchcock.org
2024
10 6 2024
28 3 270277
© 2024 The Authors.
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Published by The Permanente Federation LLC under the terms of the CC BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/.

Abstract

Background

Patients with limited English proficiency that are hospitalized without regular access to professional medical interpreters have a longer length of stay (LOS).1 The authors studied the difference in LOS between English-speaking patients and patients with limited English proficiency in New Hampshire’s only academic trauma medical center. The authors also examined race, ethnicity, and distance of residence from hospital.

Methods

Retrospective data were queried from EPIC, the electronic medical record system used by the authors. Queried data focused on inpatient hospitalizations between January 1, 2019, and December 31, 2021. Patient primary language was grouped into English, Spanish, and other non-English language.

Results

Spanish-speaking patients live on average 39.6 miles farther from a hospital than English-speaking patients and have a 0.34 lower case mix index. After English, Spanish is the second-most frequently spoken language. Regression analyses found language to be a significant factor in LOS, LOS variance, and case mix index.

Discussion

A 2.34-day longer LOS for Spanish-speaking patients demonstrates an important health care disparity warranting further attention.
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pmcIntroduction

Although length of stay (LOS) for inpatient hospitalization is multifactorial and influenced by various other social determinants of health, limited English proficiency (LEP) is known to be an independent driver of disparities in health care and is an important focus area for health equity.1,2 Mounting evidence suggests that when inpatients with LEP do not have regular access to professional medical interpreters, their inpatient LOS may be longer,1,3,4 quality of care is decreased, and safety outcomes are worse.5,6 In this brief report, the authors comment on differences in inpatient LOS between English-speaking patients and patients with LEP hospitalized in a large rural academic medical center in the northeastern United States. These findings contribute to a need for more research and evidence for rural populations with LEP.

Methods

The authors retrospectively queried electronic medical record data for inpatient hospitalizations from January 1, 2019, through December 31, 2021. Patients’ primary languages were grouped into 3 categories: 1) English, 2) Spanish, and 3) all other non-English languages. Primary language was determined by asking patients what their primary language was upon their first entry into the medical system when registering. LEP was defined in this study by the patients stated preferred primary language. Demographic data, including commonly used variables for social and behavioral determinants of health, were used (race, ethnicity, and primary ZIP code), and the authors also looked at throughput metrics such as LOS, geometric mean LOS, and case mix index (CMI). LOS variance was calculated as the ratio between LOS and geometric mean LOS, and the authors only looked at encounters with LOS ≤ 20 days to avoid outlier effects in smaller populations. Patient driving distance to the hospital was calculated using ZIP code data.

Data analysis

All analyses were calculated using SAS statistical software.7 The association between language and other independent variables was studied by conducting a χ2 test followed by generalized linear regression. LOS analysis was then done by fitting single-regression results first to study causal relationships between LOS and each of the other patient demographic and throughput measures. Multiple regression was then conducted, guided by results of prior single-regression analyses to investigate factors associated with significant LOS variations. The LOS distribution did not pass the Kolmogorov–Smirnov test for normality, but there was a fairly large overall sample size, which provides reasonable conditions for regression analysis.

Ethics review

The Dartmouth Health Institutional Review Board has reviewed this study and has determined that it qualifies as quality improvement and does not meet criteria for human participants research.

Results

The authors’ sample consisted of 54,830 inpatient hospitalizations for 38,513 unique patients (Table 1). Data included all inpatient hospital encounters with a hospital account class of “inpatient.” Psychiatric admissions and newborns were not included. Only 0.7% of the sample was excluded from analysis due to missing language data. There were 50 different languages spoken in the sample, and English was the primary language of 99.25% of the patients. Spanish was the second-most common primary language (0.23%). Approximately 1.5% of the 38,513 patients had unknown race data and 1.7% had unknown ethnicity (including those who declined to list). Overall, language was highly associated with both race and ethnicity, meaning that a majority of White patients’ primary language was English, and most of these were non-Hispanic.

Table 1: Descriptive characteristics of the study population

Characteristic	Total
(N = 38,513)	English proficient
(N = 38,216)	Limited English proficiency
(N = 297)	
Age, y
mean, (SD)	n = 38,501
56 (22.6)	n = 38,204
56 (22.6)	n = 297
48.8 (25.1)	
Driving distance (mi)
mean,(SD)	n = 38,345
77.51 (194.50)	n = 38,050
77.39 (194.34)	n = 295
94.19 (213.88)	
Sex, n (%)				
 Female	19,957	19,798 (51.81)	159 (53.54)	
 Male	18,544	18,406 (48.16)	138 (46.46)	
 Missing data	12	12 (0.03)	0 (0)	
Primary language, n (%)				
 English	38,216 (99.23)	38,216	0	
 Spanish	88 (0.23)	0	88	
 Chinese-Mandarin	24 (0.06)	0	24	
 American Sign Language	22 (0.06)	0	22	
 Russian	11 (0.03)	0	11	
 Vietnamese	9 (0.02)	0	9	
 Swahili	8 (0.02)	0	8	
 Portuguese	8 (0.02)	0	8	
 French	7 (0.02)	0	7	
 French-Canadian	7 (0.02)	0	7	
 Others	113 (0.29)	0	113	
Ethnicity, n (%)				
 Not Hispanic nor Latino	37,297	37,100 (97.08)	197 (66.33)	
 Hispanic or Latino	557	469 (1.23)	88 (29.63)	
 Declines to list/unknown or unavailable/missing data	659	647 (1.69)	12 (4.04)	
Race, n (%)				
 White	36,963	36,785 (96.26)	178 (59.93)	
 Black or African American	389	362 (0.95)	27 (9.10)	
 Asian	366	301 (0.79)	65 (21.89)	
 American Indian/Alaska Native	135	134 (0.35)	1 (0.34)	
 Native Hawaiian/other Pacific Islander	24	23 (0.06)	1 (0.34)	
 Multiracial	2	1 (0)	1 (0.34)	
 Unknown/unavailable/declines to list/missing data	634	610 (1.60)	24 (8.08)	
SD, Standard deviation.

Primary language and distance traveled to hospital

The study population had ZIP codes throughout the entire United States, however a majority were clustered in New Hampshire and Vermont (Figure) and primary language was significantly associated with driving distance from the hospital. Spanish-speaking patients lived (on average) 39.55 miles farther away from the hospital than English-speaking patients (p < 0.01), and 45.61 miles farther than other non-English language–speaking patients (p < 0.05). English and other non-English language–speaking patients did not have a statistically significant difference in driving distance from hospital.

Figure: Patient primary ZIP code distributions by primary language. Map created using © Mapbox , © OpenStreetMap .

CMI and LOS

Chi-square analyses associating language, race, and ethnicity were all significantly related (Table 2). In single-regression analyses (Table 3a and b), Spanish-speaking patients had (on average) a 34% worse (higher, more acute) CMI compared with English-speaking patients (p < 0.05), and a 49% worse CMI compared with other non-English–speaking patients (p < 0.05). Conversely, English-speaking patients had no significant difference in CMI compared with other non-English–speaking patients (p = 0.20).

Table 2: Chi-square test results

Category	Language	Ethnicity	Race	
Language	–	p < 0.01	p < 0.01	
Ethnicity	p < 0.01	–	p < 0.01	
Race	p < 0.01	p < 0.01	–	

Table 3: Length of stay and length of stay variance (length of stay ≤ = 20 days)

Independent variable	Point estimate	Standard error	p valuea	95% CI	
LOS	
Primary language			< 0.01		
 Primary language (comparing Other with English)	2.02	0.60	< 0.01	0.85, 3.19	
 Primary language (comparing Other with Spanish)	1.38	0.85	0.10	−0.28, 3.03	
Race			0.07		
 Race (comparing Asian with American Indian/Alaska Native)	−0.48	0.88	0.58	−2.21, 1.24	
 Race (comparing Black or African American with American Indian/Alaska Native)	1.38	0.87	0.11	−0.32, 3.08	
 Race (comparing Multi-racial with American Indian/Alaska Native)	0.75	5.96	0.90	−10.92, 12.42	
 Race (comparing Native Hawaiian/Other pacific with American Indian/Alaska Native)	−0.16	1.93	0.93	−3.94, 3.62	
 Race (comparing White with American Indian/Alaska Native)	0.78	0.74	0.29	−0.66, 2.23	
Ethnicity (comparing Non-Hispanic nor Latino with Hispanic or Latino)	−0.31	0.36	0.38	−1.02, 0.39	
Driving distanceb	0.0003	0.0002	0.20	−0.0002, 0.0008	
CMI	1.95	0.02	< 0.01	1.91, 1.99	
Language			< 0.01		
 Primary language (Other vs English)	0.028	0.05	0.58	−0.07, 0.13	
 Primary language (Other vs Spanish)	0.24	0.07	< 0.01	0.10, 0.38	
LOS variance (LOS ≤ 20 d)	
Language			< 0.01		
 Primary language (comparing Other with English)	0.028	0.05	0.58	−0.07, 0.13	
 Primary language (comparing Other with Spanish)	0.24	0.07	< 0.01	0.10, 0.38	
Race			< 0.01		
 Race (comparing Asian with American Indian/Alaska Native)	−0.84	0.073	0.25	−0.23, 0.06	
 Race (comparing Black or African American with American Indian/Alaska Native)	0.06	0.072	0.44	−0.085, 1.29	
 Race (comparing Multiracial with American Indian/Alaska Native)	1.54	0.484	< 0.01	0.59, 2.49	
 Race (comparing Native Hawaiian/other Pacific Islander with American Indian/Alaska Native)	0.13	0.162	0.43	−0.18, 0.44	
 Race (comparing White with American Indian/Alaska Native)	0.015	0.061	0.81	−0.10, 0.14	
Ethnicity (comparing Non-Hispanic nor Latino with Hispanic or Latino)	−0.0591	0.030	< 0.05	−0.1179, −0.0003	
Driving distance	−0.00001	0.00002	0.53	−0.00005, 0.00003	
CMI	−0.033	0.0022	< 0.01	−0.038, −0.029	
a Statistical significance was defined as p < 0.05.

b Driving distance = automobile driving distance from patient’s home residence to the hospital in miles.

CI, confidence interval; CMI, case mix index; LOS, length of stay.

Language was a significant influential factor for LOS overall (p < 0.01), as was CMI (p < 0.01); however, race was not significant. Single regression for LOS variance revealed slightly different results. Language (p < 0.01), race (p < 0.01), ethnicity (p < 0.05), and CMI (p < 0.01) were all significant, but driving distance was not.

In adjusted multiple regression analyses (Table 4), Spanish-speaking patients (on average) had a 2.06-day longer LOS compared with English-speaking patients (p < 0.01), and other non-English–speaking patients had (on average) a 1.75-day longer LOS (p < 0.01). CMI had a significant effect on increasing LOS (p < 0.01). Spanish-speaking patients had (on average) a 0.3-day longer LOS variance compared with English-speaking patients (p < 0.01), but the difference between other non-English–speaking patients and English speakers was not significant. Asian/multiracial patients had (on average) a 1.12-day shorter LOS variance compared with White patients, and there was not a significant difference comparing patients of other races with patients who were White. Finally, CMI continued to show significant effects on LOS variance (p < 0.01).

Table 4: Length of stay and length of stay variance (multiple regression)

Dependent variable	Independent variable	Point estimate	Standard error	p value a	95% CI	
LOS	Language (Spanish vs English)	2.06	0.78	< 0.01	0.53, 3.60	
Language (Other non-English vs English)	1.75	0.55	< 0.01	0.66, 2.83	
CMI	1.94	0.02	< 0.01	1.91, 1.99	
LOS variance (LOS ≤ 20 d)	Language (comparing Spanish with English)	0.30	0.079	< 0.01	0.14, 0.45	
Language(comparing other non-English with English)	0.07	0.054	0.18	−0.034, 0.18	
Race (comparing Asian/multiracial with White)	−1.12	0.041	< 0.01	−0.20, −0.039	
Race (comparing Others with White)	0.014	0.032	0.66	−0.05, 0.077	
CMI	−0.033	0.002	< 0.01	−0.038, −0.029	
a Statistical significance was defined as p < 0.05.

CI, confidence interval; CMI, case mix index; LOS, length of stay.

Discussion

The authors found a 2.34-day increased average LOS in Spanish-speaking patients compared with English-speaking patients, and other non-English–speaking patients had a 1.36-day increased average LOS. This is a particularly interesting finding given the rural setting of this study. A majority of the patients with LEP and the interpreters available to them live at least 1.5 hours away from the hospital in the southern part of the state. This requires increased reliance on synchronous virtual online remote medical interpreters (video or telephonic). Therefore, the patient populations with LEP, when hospitalized, experience 3 barriers to equitable care: 1) they have a language barrier; 2) they live far away from the inpatient care center (transportation and access barriers); and 3) the interpreters that serve them also live far away from the hospital. Finally, although most hospitals that have patients with LEP rely on a combination of interpreter services, both in-person on-site (staff and contracted) and remote (video and telephonic),8 hospitals in rural areas have less interpreter supply and are less likely to employ on-site interpreters.

It should be noted that the authors are not able to conclude that LEP is the only factor explaining this difference in LOS in individuals with LEP, but these findings do align with other studies that show that LEP is independently associated with longer LOS,9 and that LEP is associated with health outcome disparities.2,10,11 “Limited English proficiency was associated with lower odds of speaking up, questioning decisions of practitioners, and being unafraid to ask questions when something does not seem right.”12 For example, a recent study of patients with LEP who had angina (a cardiovascular condition) found that patients with LEP “were more likely not to report having a history of cardiovascular disease.”13

The Agency for Healthcare Research and Quality made recommendations for “Improving Patient Safety Systems for Patients with Limited English Proficiency” suggesting that hospitals should prioritize using professional interpreters to avoid adverse events in patients with LEP.14 The authors’ findings suggest similarly to other recommendations, which state that patients with LEP would benefit from a professional interpreter for every inpatient admission and discharge and also during daily rounds during hospitalizations to ensure that communication is facilitated to the maximum effectiveness.15 Furthermore, the authors are also interested in investigating other potential factors that might contribute to differences in LOS in persons with LEP, including residential proximity to the hospital,16 socioeconomic factors,17 education level, employment, and health insurance status.

Limitations

Observed differences in LOS between patients with LEP and English-speaking patients may be influenced by socioeconomic factors that were not included in these analyses, including education and income level.18 Additionally, effect modifying effects may be present that were not accounted for in this analysis. “Although LEP is an independent determinant of health outcomes among adults and children, it can overlap with other disadvantageous social determinants of health, exacerbating disparities in health care access and health outcomes.”2 There was also variation in the authors’ hospital in how clinicians documented in the electronic medical record whether an interpreter was assigned or used for admitted patients, a problem experienced in many health systems. This introduces error in appropriately tracking and monitoring patient language needs and use of interpreter services. Effective December 2023, the health system has begun implementation of mandatory electronic health record documentation of interpreter services needs and use across the health system, which may strengthen the authors’ ability to study the population with LEP in much greater detail in the future.

Additionally, the relative size of the authors’ population with LEP compared with English speakers, especially in a cross-sectional sample, should be noted and may contribute to a failure to detect some associations. Given the study population’s characteristics and these limitations, the authors entertained alternative analytic methods (matching and unadjusted comparisons using t-tests or χ2 analyses). The authors decided against these because they were able to adjust for at least some known covariates, including social determinants of health (socioeconomic factors that may influence a person’s access to health care or their health care outcomes), in their regression analysis, and the sample size precluded matched analyses. Finally, and perhaps most importantly, the authors’ cross-sectional study design is vulnerable to confounding and prevents them from establishing causative relationships. Although the authors were able to show associations between LEP status and outcomes (such as LOS), this study cannot confirm if LEP status was a causative factor to those outcomes or the degree to which it may have been causative. The authors can only identify significant associations between LOS and LEP status. Given these limitations, the study findings should be interpreted as initial assessments from which future longitudinal studies can build upon.

Implications for learning health systems and population health

The ideal application of learning health system (LHS) approaches are when “science, informatics, incentives, and culture are aligned for continuous improvement and innovation, with best practices seamlessly embedded in the delivery process, [with] patients and families active participants in all elements, and new knowledge captured as an integral by‐product of the delivery experience.”19 When this ideal is approached or achieved, LHS approaches can accelerate and amplify the capability of health systems to monitor, improve, and study health services outcomes for populations. However, LHS approaches require appropriate data and analyses to inform them, and without purposeful stratification to recognized underrepresented populations and challenged geographic contexts (such as rurality), they can be rendered blind to the needs of these populations and limited in their capability to identify and execute on opportunities for improvement. In this study, the authors noted disparities seen in rural patients with LEP served by a rural academic medical center. Rural communities may face unique challenges to access to care and to interpreter services for LEP settings that rural LHS approaches must recognize, include in improvement initiatives, and investigate further.

Acknowledgments

We would like to express gratitude to Dr Lou Hart, MD, for his time in critically reviewing the authors' manuscript prior to journal submission.

Author Contributions: Samuel Verkhovsky, MIPP, MPH, was the principal investigator and lead author. Lixi Kong, MS, conducted the data analyses, prepared the manuscript tables, and participated in manuscript revisions. Brant J Oliver, PhD, MS, MPH, FNP-BC, PMHNP-BC, advised on methodology, reviewed the manuscript, and participated in revisions. This manuscript submission has been reviewed and approved by all participating authors.

Conflicts of Interest: None declared

Funding: None declared

Data-Sharing Statement: Data are available upon request. Readers may contact the corresponding author to request underlying data.
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References

1. The Joint Commission . Quick safety 13: Overcoming the challenges of providing care to LEP patients. Accessed 3 July 2023. https://www.jointcommission.org/resources/news-and-multimedia/newsletters/newsletters/quick-safety/quick-safety--issue-13-overcoming-the-challenges-of-providing-care-to-lep-patients/overcoming-the-challenges-of-providing-care-to-lep-patients/
2. Espninoza J , Derrington S . How should clinicians respond to language barriers that exacerbate health inequity? AMA J Ethics. 2021;23 (2 ):E109–E116. 10.1001/amajethics.2021.109 33635190
3. John-Baptiste A , Naglie G , Tomlinson G , et al. The effect of English language proficiency on length of stay and in-hospital mortality. J Gen Intern Med. 2004;19 (3 ):221–228. 10.1111/j.1525-1497.2004.21205.x 15009776
4. Lindholm M , Hargraves JL , Ferguson WJ , Reed G . Professional language interpretation and inpatient length of stay and readmission rates. J Gen Intern Med. 2012;27 (10 ):1294–1299. 10.1007/s11606-012-2041-5 22528618
5. 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. 10.1177/1077558705275416 15894705
6. Lion KC , Rafton SA , Shafii J , et al. Association between language, serious adverse events, and length of stay among hospitalized children. Hosp Pediatr. 2013;3 (3 ):219–225. 10.1542/hpeds.2012-0091 24313090
7. SAS – Statistical Software Package: https://www.sas.com/en_us/software/stat.html
8. Centers for Medicare and Medicaid Services . Providing language services to diverse populations: Lessons from the field. 2022.
9. Manuel SP , Nguyen K , Karliner LS , Ward DT , Fernandez A . Association of English language proficiency with hospitalization cost, length of stay, disposition location, and readmission following total joint arthroplasty. JAMA Netw Open. 2022;5 (3 ):e221842. 10.1001/jamanetworkopen.2022.1842 35267037
10. Kim EJ , Kim T , Paasche-Orlow MK , Rose AJ , Hanchate AD . Disparities in hypertension associated with limited English proficiency. J Gen Intern Med. 2017;32 (6 ):632–639. 10.1007/s11606-017-3999-9 28160188
11. Cohen AL , Rivara F , Marcuse EK , McPhillips H , Davis R . Are language barriers associated with serious medical events in hospitalized pediatric patients? Pediatrics. 2005;116 (3 ):575–579. 10.1542/peds.2005-0521 16140695
12. Khan A , Parente V , Baird JD , et al. Association of patient and family reports of hospital safety climate with language proficiency in the US. JAMA Pediatr. 2022;176 (8 ):776–786. 10.1001/jamapediatrics.2022.1831 35696195
13. Herbert BM , Johnson AE , Paasche-Orlow MK , Brooks MM , Magnani JW . Disparities in reporting a history of cardiovascular disease among adults with limited English proficiency and angina. JAMA Netw Open. 2021;4 (12 ):e2138780. 10.1001/jamanetworkopen.2021.38780 34905003
14. Agency for Healthcare Research and Quality (AHRQ) . Improving patient safety systems for patients with limited English proficiency - A guide for hospitals. Rockville, MD; 2012.
15. Karliner LS , Pérez-Stable EJ , Gregorich SE . Convenient access to professional interpreters in the hospital decreases readmission rates and estimated hospital expenditures for patients with limited English proficiency. Med Care. 2017;55 (3 ):199–206. 10.1097/MLR.0000000000000643 27579909
16. Kelly C , Hulme C , Farragher T , Clarke G . Are differences in travel time or distance to healthcare for adults in global north countries associated with an impact on health outcomes? A systematic review. BMJ Open. 2016;6 (11 ):e013059. 10.1136/bmjopen-2016-013059
17. Moore L , Cisse B , Batomen Kuimi BL , et al. Impact of socio-economic status on hospital length of stay following injury: A multicenter cohort study. BMC Health Serv Res. 2015;15 :285. 10.1186/s12913-015-0949-2 26204932
18. Siddique SM , Tipton K , Leas B , et al. Interventions to reduce hospital length of stay in high-risk populations: A systematic review. JAMA Netw Open. 2021;4 (9 ):e2125846. 10.1001/jamanetworkopen.2021.25846 34542615
19. Smith M , Saunders R , Stuckhardt L , McGinnis JM , eds . Committee on the Learning Health Care System in America; Institute of Medicine. In: Best Care at Lower Cost: The Path to Continuously Learning Health Care in America. Washington, DC: The National Academies Press; 2013.
