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. Author manuscript; available in PMC: 2023 Aug 1.
Published in final edited form as: Pain Manag Nurs. 2022 Mar 6;23(4):385–390. doi: 10.1016/j.pmn.2022.01.007

Patient Race, Ethnicity, Language, and Pain Severity in Primary Care: A Retrospective Electronic Health Record Study

Maichou Lor 1,*, Theresa A Koleck 2,*
PMCID: PMC9308623  NIHMSID: NIHMS1777802  PMID: 35260338

Abstract

Background:

Patient race, ethnicity, and culture including language are intertwined and may influence patient reporting of pain severity.

Purpose:

To describe documentation of patient’s self-reported pain presence and severity by race, ethnicity, and language – specifically, Spanish, Hmong, Lao, or Khmer requiring an interpreter or English.

Design and Sample:

Retrospective, electronic health record clinical data mining study of 79,195 patient visits with documented pain scores from one primary care clinic. Hurdle regression was used to explore the effect of race, ethnicity, and language on the chances of having any pain (versus no pain) and pain severity for visits with pain scores ≥1, controlling for age, gender, and documentation of a pain diagnosis. Mann-Whitney tests were used to explore the influence of English versus non-English language on pain severity within a race or ethnicity category.

Results:

Pain scores were higher for limited English proficiency, compared to English-speaking, patients within the Asian race or Hispanic/Latino ethnicity category. Older age, female gender, pain diagnosis, Black or African American race, and Spanish or Lao language increased the chance of having any pain. These same factors, plus American Indian or Alaska Native race, contributed to higher pain severity. Asian race, in contrast, decreased the chance of reporting any pain and contributed to lesser pain severity.

Conclusions:

Race, in addition to a new area of focus, language, impacted both the chances of reporting any pain and pain severity. Additional research is needed on the impact of language barriers on pain severity reporting, documentation, and differences in pain outcomes and disparities.

Keywords: Pain, Race, Ethnic groups, Language, Minority health, Electronic health records

Introduction

Despite advances in pain and pain management, racial and ethnic minorities continue to have unaddressed pain compared to White individuals (Anderson et al., 2009; Campbell & Edwards, 2012; Ezenwa et al., 2006). In the United States (U.S.), racial minorities are defined as persons who are categorized as non-White and include African American or Black, Asian, American Indian or Alaska Native, and Native Hawaiian or other Pacific Islander (United States Census, n.d.; Shavers et al., 2010). Ethnicity is defined by the U.S. Census and the National Institutes of Health as: (1) Hispanic or Latino and (2) not Hispanic or Latino (National Institute of Health, n.d.; Ennis, Rios-Vargas, & Albert, 2011). Current research indicates that race and ethnicity often influence how providers address pain complaints and treatments (Hirsh et al., 2009; Wandner et al., 2014). For example, research has shown that healthcare providers do not assess pain in older African Americans at the same rate as older White Americans (Incayawar & Todd, 2013). A systematic review of 17 articles reported that Black and Hispanic patients were less likely than White patients to receive analgesia for acute pain in the emergency department (Lee et al., 2019). The greater prevalence of undertreated pain highlights the challenges of improving pain management for racial and ethnic minorities.

A contributing factor for pain management disparities among racial and ethnic minorities is pain severity underreporting (Mossey, 2011). Pain severity is a subjective experience measured by self-report which is, in turn, generally used to guide pain management (Oldenmenger et al., 2009). Recent epidemiological studies found that the prevalence of pain among racial and ethnic minority groups is similar to or sometimes lower than that of Whites. According to the 2012 National Health Interview Survey (NHIS), the prevalence of reported pain was 49.3%, 53.5%. and 59.7% among English-speaking Hispanic White, non-Hispanic Black, and non-Hispanic White individuals, respectively (Nahin, 2015). From the 2002–2018 NHIS, Zajacova and colleagues found that African Americans were less likely to report any pain compared to White Americans (Zajacova et al., 2021). While it is clear that there is disparity in pain reporting between individuals who report their race as White versus Black or African American, it is unclear how pain reporting differs for other racial and ethnic minority populations.

Furthermore, an important, and understudied area of pain research is patient language. Specifically, patient language in the context of language barriers or limited English proficiency (LEP) have been identified as a major barrier to effective pain communication between patients and providers (Anderson et al., 2009). Research has shown that language barriers or having LEP contributed to delayed pain assessment, treatment, and longer length of stay in the hospital (Kyi et al., 2019; Mitchell et al., 2009). The inability of patients to communicate in their native language about their pain with their healthcare providers could contribute to the existing pain disparities in reporting and treatment. Approximately 25 million Americans age five and older have LEP in the U.S. (Migration Policy Institute, 2015). Patients with LEP often require the assistance of a professional or lay interpreter to facilitate pain communication with a clinician (Diamond et al., 2009; International Medical Interpreter Association & Education Development Center, Inc., 2007; Rose et al., 2010). Yet, it is unclear whether the presence of an interpreter contributes to better or inadequate pain reporting for patients with language barriers or LEP. Understanding the impact of interpreter services on patient pain reporting will provide information about areas of improvement for patient care.

Patient race, ethnicity, and language are interrelated and may influence patient reporting of pain severity. These factors may also influence clinician documentation and management of patient pain (Lamas et al., 2018; Ozkaynak et al., 2017). There are no studies examining the effect of patient race, ethnicity, and language in the context of reporting pain presence and severity. The availability of structured “real world” patient race, ethnicity, and pain data in the electronic health record (EHR) make the EHR an ideal data source for exploring relationships among these factors (Curtis et al., 2018; Dorflinger et al., 2014; Koleck et al., 2019). Therefore, the purpose of this EHR clinical data mining study is to describe documentation of patient’s self-reported pain presence and severity by patient race (i.e., American Indian or Alaska Native, Asian, Black or African American, and Native Hawaiian, or Other Pacific Islander, and White), ethnicity (i.e., Hispanic/Latino or not Hispanic/Latino), and language (i.e., English, Spanish, Hmong, Lao, and Khmer) at a primary care clinic.

Methods

This study was approved by the University of Wisconsin-Madison’s Institutional Review Board.

Study Design and Sample

We conducted a retrospective, EHR clinical data mining study of patient visits with pain scores documented between January 1, 2014, and December 31, 2019, from one primary care clinic within a large health academic institution in the Midwest U.S. The primary care clinic is a multi-ethnic hub that serves large populations of Southeast Asian (especially Hmong, Lao, and Cambodian), Black or African American, and Latinx patients. Eligibility criteria for this study include: (1) patient age 18 and over at the time of office visit, (2) having a pain score documented, and (3) having a preferred language of English, Spanish, Hmong, Lao, or Khmer.

Variables

Pain Severity.

Visit pain scores were documented using a self-reported, 11-point numeric scale (0–10 score).

Race and Ethnicity.

Racial categories documented in the EHR for patient visits included American Indian or Alaska Native, Asian, Black or African American, Native Hawaiian or Other Pacific Islander, and White. Ethnicity was categorized as Hispanic/Latino or not Hispanic/Latino.

Language.

Preferred language is documented in the EHR for each patient visit. Visits with a preferred language other than English required an in-person, phone, or provider interpreter, indicating that the patient had LEP. We included English, Spanish, and three Southeast Asian languages – Hmong, Lao, and Khmer– in this study based on the patient population of the clinic.

Covariates and Confounders.

Gender (i.e., male or female) and age at time of visit were obtained from the medical record for each patient visit. We also created a variable to indicate if a visit was related to pain (e.g., lower back pain). To do this, we queried the word “pain” in the primary or other diagnosis (up to seven diagnoses were documented per visit) name/annotation and created a binary yes/no pain-related visit variable.

Analysis

Analysis was carried out using Stata SE 16.0. Each visit was treated as an independent observation because research has shown that patient pain severity ratings are not typically correlated from one clinical visit to the next (Goldsmith et al., 2018; Miller et al., 2017). First, we performed a detailed descriptive analysis of all data. Due to the zero-inflated distribution of pain scores (i.e., 54.5% of visits had a pain score of 0), we used nonparametric Kruskal-Wallis tests to compare visit pain scores by race, ethnicity, and language category. If the null hypothesis for the Kruskal-Wallis test was rejected, we used the dunntest package to perform Dunn’s test and generate multiple nonparametric pairwise comparisons (Dinno, 2018).

The pain score distribution also informed our selection of regression modeling technique. We used Cragg two-equation hurdle regression to explore the effect of race, ethnicity, and language on visit pain score, while controlling for age at visit, patient gender, and documentation of a pain diagnosis. Specifically, we used the churdle routine in Stata to fit a linear hurdle model (stata.com, n.d.), which combines a selection model (i.e., any pain vs. no pain) and an outcome model (i.e., pain score ≥1) with a lower limit of 0. All predictors were included in both the selection and outcome models. Female gender, no pain diagnosis, White race, not Hispanic/Latino ethnicity, and English language served as the reference groups. We used regression coefficients, odds ratios, and significance tests from the selection model to explore the impact of race, ethnicity, and language on chances of having any pain (i.e., any pain vs. no pain). Then, we used regression coefficients, conditional (i.e., controlled for other variables in the model) mean estimates (CMEs) of pain score, and significance tests from the outcome model to explore the influence of predictors on pain severity for visits with pain scores ≥1.

Finally, we used nonparametric Mann-Whitney tests to explore the influence of (1) race versus language on pain severity by comparing visit pain scores for Asian patients who speak English to Asian patients with LEP who speak Hmong, Lao, or Khmer and (2) ethnicity versus language on pain severity by comparing Hispanic/Latino patients who speak English to Hispanic/Latino patients with LEP who speak Spanish. A significance level of 0.05 was used to determine statistical significance.

Results

A total of N=79,109 patient visits with a documented pain score for n=10,558 unique patients were included in our analysis (Table 1). The median number of visits per patient was 5 (Min=1; Max=123; IQR=2–10) visits. The mean number of days between visits by patient was 171.5 ± 155.3 (median [Med]=129.1; Min=0; Max=1,676; IQR=76.41–215.29) days, supporting the decision to treat patient visits as independent observations. The average age of patients at the time of visit was 49.41 ± 16.81 years. The majority of visits were for female (62.46%), White (80.10%), not Hispanic/Latino (96.06%), and English-speaking (97.86%) patients. Out of the 21,986 visits with a pain diagnosis documented (27.79%), half (n=11,300, 51.3%) had a pain-related primary diagnosis. The top five pain-related primary diagnoses were chronic pain (n=531), chronic bilateral low back pain without sciatica (n=324), neck pain (n=284), chronic pain syndrome (n=283), and low back pain (n=282).

Table 1.

Summary of study variables by visit, N=79,109

Variables n (%) or M (SD)
Age 49.41 (16.81)
Gender
 Male 29,697 (37.54)
 Female 49,412 (62.46)
Pain diagnosis
 Yes 21,986 (27.79)
 No 57,123 (72.21)
Race
 White 62,563 (80.10)
 Black or African American 11,489 (14.71)
 Asian 3,089 (3.95)
 American Indian or Alaska Native 862 (1.10)
 Native Hawaiian or Other Pacific Islander 102 (0.13)
Ethnicity
 Hispanic/Latino 3,103 (3.94)
 Not Hispanic/Latino 75,581 (96.06)
Language
 English 77,416 (97.86)
 Spanish 498 (0.63)
 Hmong 658 (0.83)
 Lao 267 (0.34)
 Khmer 270 (0.34)
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Note. M=mean, SD=standard deviation. Sample sizes are different by variable due to missing or unreported data.

Visit pain scores by race, ethnicity, and language

Pain scores by race, ethnicity, and language category are presented in Table 2. Pain scores differed by race (p=0.0001) and language (p=0.0001), but not ethnicity (p=0.0992). Specifically, visits for Black or African American patients had higher pain scores than all other race categories (all p<0.0001). Similarly, visits for American Indian or Alaska Native patients had higher pain scores than White (p=0.0055), Asian (p=0.0001), and Native Hawaiian or Other Pacific Islander (p=0.0277) patients. While visits for White patients had statistically significantly higher pain scores than visits for Asian patients (p=0.0016), the mean (2.15 vs. 2.09, respectively) and median (both 0) pain scores were not clinically meaningfully different. In terms of language, visits conducted in Spanish or Lao had higher pain scores than visits in English (both p<0.0001), Hmong (both p<0.0001), and Khmer (p=0.0019 and p=0.0045, respectively).

Table 2.

Pain scores by race, ethnicity, and language

Variables M (SD), Med (Q1-Q3) p-value*
Race
 White 2.15 (2.88), 0 (0–4) 0.0001
 Black or African American 3.26 (3.54), 2 (0–7)
 Asian 2.09 (2.98), 0 (0–4)
 American Indian or Alaska Native 2.55 (3.26), 0 (0–5)
 Native Hawaiian or Other Pacific Islander 1.74 (2.51), 0 (0–3)
Ethnicity
 Hispanic/Latino 2.24 (2.97), 0 (0–5) 0.0992
 Not Hispanic/Latino 2.32 (3.02), 0 (0–5)
Language
 English 2.31 (3.02), 0 (0–5) 0.0001
 Spanish 3.07 (3.20), 2 (0–6)
 Hmong 2.28 (3.16), 0 (0–5)
 Lao 3.19 (3.38), 2 (0–7)
 Khmer 2.39 (3.00), 0 (0–5)
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Note. M=mean, SD=standard deviation, Med=median, Q1=quartile 1, Q3=quartile 3;

*

p-value from Kruskal-Wallis test

Influence of race, ethnicity, and language on chances of reporting any pain at a visit

Based on the hurdle regression coefficients from the selection model, increasing patient age (b=0.0008; OR=1.00, 95CI=1.00–1.00; p=0.004), having a pain diagnosis (b=1.1329; OR=3.10, 95CI=3.04–3.17; p<0.001), being Black or African American race (b=0.1370; OR=1.15, 95CI=1.12–1.18; p<0.001), speaking the Spanish language (b=0.1534; OR=1.17, 95CI=1.00–1.35; p=0.045), and speaking the Lao language (b=0.3115; OR=1.37, 95CI=1.15–1.62; p<0.001) increased the chances of having any pain (i.e., any pain vs. no pain). Visits conducted in Khmer were not statistically significant for increasing the chances of reporting any pain (b=0.1662; OR=1.18, 95CI=1.00–1.40; p=0.052). In contrast, male gender (b=−0.1238; OR=0.88, 95CI=0.87–0.90; p<0.001) and Asian race (b=−0.1680; OR=0.85, 95CI=0.80–0.90; p<0.001) decreased the chances of reporting any pain. While not statistically significant, being of Hispanic/Latino ethnicity (b=−0.0450; OR=0.96, 95CI=0.90–1.01; p=0.122) also trended towards decreasing the chances of reporting any pain.

Influence of race, ethnicity, and language on pain severity in visits with pain scores ≥1

For visits where a patient reported a pain score ≥1, increasing age (b=0.0051) and having a pain diagnosis (b=0.5595) were associated with higher pain scores, whereas male gender (b=0.3899) was associated with lower pain scores (all p<0.001). In relation to race, Black or African American (CME=3.11, 95CI=3.05–3.17; p<0.001) and American Indian or Alaska Native (CME=2.49, 95CI=2.28–2.70; p=0.003) race contributed to higher pain scores, while Asian race (CME=2.01, 95CI=1.89–2.14; p=0.012) contributed to lower pain scores when compared to White (CME=2.17, 95CI=2.15–2.20). Native Hawaiian or Other Pacific Islander (CME=1.90, 95CI=1.43–2.38) race also contributed to lower pain scores; however, this finding did not reach statistical significance (p=0.268). For language, visits conducted in Spanish (CME=2.80, 95CI=2.46–3.14; p=0.004) or Lao (CME=3.26, 95CI=2.85–3.67; p<0.001) contributed to higher pain scores compared to English (CME=2.31, 95CI=2.29–2.33). This trend extended to Hmong (CME=2.55, 95CI=2.28–2.82; p=0.076) and Khmer (CME=2.53, 95CI=2.17–2.89; p=0.229) language. Pain severity for visits in patients with Hispanic/Latino ethnicity (CME=2.26, 95CI=2.14–2.38) and not Hispanic/Latino ethnicity (CME=2.32, 95CI=2.29–2.34) were similar (p=0.378).

Influence of race or ethnicity versus language on pain severity

In 98% (n=1,174 out of 1,192) of visits conducted in Hmong, Lao, or Khmer, the patient identified as Asian race. Another n=1,915 visits were categorized as English language, Asian race. In 96% (n=480 out of 498) of visits conducted in Spanish, the patient identified as Hispanic/Latino ethnicity. An additional n=2,608 visits were categorized as English language, Hispanic/Latino ethnicity.

Pain scores were higher (p<.0001) for visits for Asian patients conducted in Hmong, Lao, or Khmer (x¯= 2.53 ± 3.20; Med=0, IQR=0–5) compared to visits for Asian patients conducted in English (x¯=1.82 ± 2.79; Med=0, IQR=0–4). Likewise, pain scores were higher (p<0.0001) for visits for Hispanic/Latino patients conducted in Spanish (x¯=2.98 ± 3.20; Med=2, IQR=0–6) compared to visits for Hispanic/Latino patients conducted in English (x¯=2.11 ± 2.91; Med=0, IQR=0–4).

Discussion

To our knowledge, this study is the first to examine the effect of patient race, ethnicity, and language in reported pain severity in the primary care setting. Overall, we found that patient race and language impacted both the chances of reporting any pain and reported pain severity when pain scores were ≥1. Specifically, pain scores were higher for patients with LEP compared to English-speaking patients within a race (i.e., Asian) or ethnicity (i.e., Hispanic/Latino) category. Furthermore, we found that increasing age, being female, having a pain diagnosis, being Black or African American race, and having the visit conducted in Spanish or Lao language were associated with higher levels of any pain reported (i.e., any pain vs. no pain). These same factors, plus American Indian or Alaska Native race, contributed to greater pain severity (i.e., higher pain scores) in visits where the patient reported a pain score ≥1. Asian race, in contrast, decreased the chance of reporting any pain and contributed to lesser pain severity in visits where the patient reported a pain score ≥1.

Our results of Asian race contributing to decreased chances of reporting any pain and to lower pain scores in visits where pain was reported is contrary to recent studies that found higher levels of pain in Asian Americans (Ahn et al., 2017; Chan et al., 2013; Dhingra et al., 2011). The additional language-specific analyses that we performed within the Asian race category may inform this apparent discrepancy with the previous literature. Interestingly, we found that pain scores were higher for visits for Asian patients conducted in Hmong, Lao, or Khmer compared to visits for Asian patients conducted in English. This finding represents a new and unique contribution to the literature as there are only a few studies on Southeast Asian languages, specifically Hmong (Koleck & Lor, 2021; Lor et al., 2020; Lor et al., 2021), in the context of pain. The higher pain scores reported for these Asian cultural subgroups compared to others identifying as Asian race, as well as the higher pain scores reported by Spanish-speaking Hispanic/Latino patients compared to English-speaking Hispanic/Latino patients, highlight the importance of studying the impact of cultural subgroup, LEP, and use of interpreters on pain severity reporting and documentation as well as differences in pain outcomes.

Currently, the clinic’s EHR system only has classifications for racial groups (e.g., Asian, Black or African American) and not subgroups. Although we were able to distinguish the subgroups by using the language preference indicator, it is unclear whether the higher pain scores are due to language, culture, or a combination of both. Ideally, we would have compared pain scores by English-proficiency within a cultural subgroup (e.g., culturally Hmong patients who are English proficient versus culturally Hmong patients with LEP). Future research could investigate the correlation of language and culture in pain severity scores.

We observed higher chances of reporting any pain and greater pain severity in visits conducted in Spanish or Lao compared to English – additional evidence that patient language and/or culture plays a role in pain experience. While not statistically significant, visits conducted in Hmong and Khmer had higher reported pain scores as well. All visits in our study with a preferred language other than English had an in-person, phone, or provider interpreter to facilitate communication. Medical interpreters may have substantially helped patients verbalize their pain complaints to clinicians (Torres et al., 2017). Research has shown that when there is language concordance between patients and providers, patients are able to accurately report symptoms (Lor & Martinez, 2020).

Alternatively, communication challenges may prompt patients to report higher pain scores because patients perceive that reporting high pain scores leads to concerns being taken seriously by the clinician and receiving pain treatments (Catananti & Gambassi, 2010). Torres and colleagues found that due to language barriers, some Hispanic patients expressed that they felt providers were either not taking reported pain seriously or not making an effort to understand the patients’ limited English (Torres et al., 2017). The relationship has been reported, more broadly, in patients who do not have a trusting relationship with their provider (Losin, Andereson, & Wager, 2017). Hence, it is possible that this phenomenon is reflected in the Spanish-speaking patients with LEP who needed an interpreter in our study.

Access to care and acculturation level may also contribute to differences in pain experience. Although there is a scant amount of literature on Lao patients and healthcare, evidence shows that Lao patients have reduced access to healthcare (Saenphansiri et al., 2017). Moreover, acculturation level was associated with help-seeking attitudes (Thikeo et al., 2015). Lao patients who need an interpreter may have poor access to health care and be less acculturated; consequently, when these patients do seek care, their pain may be more severe or their source of pain may not be treated properly.

We also found that American Indian or Alaska Native race contributed to greater pain severity (Gore et al., 2005). However, pain research in the Native American community suggests that numeric pain scales, like the 11-point numeric pain scale used by the clinic for the majority of patient visits, may not accurately capture pain severity for Native American patients (Burhansstipanov & Hollow, 2001). Burhansstipanov and Hollow found that when Native American patients were asked to rate their pain on a linear numeric scale, some chose a “favorite or sacred” number instead of the number that correctly indicated their pain level (Burhansstipanov & Hollow, 2001). More research is needed to better understand which type of pain scale can accurately capture Native American patients’ pain severity (Burhansstipanov & Hollow, 2001).

Our findings related to the influence of sex, age, and race on patient ratings of pain severity are consistent with the literature (Bartley & Fillingim, 2013; Chan et al., 2011; Farrar et al., 2010; Lautenbacher et al., 2017; Strong et al., 2009). Previous work has shown that women are more likely to report moderate to severe pain compared to men (Bartley & Fillingim, 2013; Naylor et al., 2019; Solheim et al., 2017). Older adults report having a reduced pain sensitivity compared to young adults (Lautenbacher et al., 2017). Research has further shown that racial and ethnic differences in pain burden differ by age (Lautenbacher et al., 2017). For race, our findings that Black or African American race increases both the chance of reporting any pain and the pain severity compared to White race are well documented (Lavin & Park, 2014). A possible explanation for this finding could be a higher prevalence of certain chronic diseases. For example, Baker and colleagues reported that heart disease, musculoskeletal conditions, and microvascular disease were associated with increased pain (Baker et al., 2017).

There are some limitations to be acknowledged in this study. First, the data used in this study was obtained from one primary clinic in the Midwest U.S. Interpretation of the results may not be generalizable to other locations, settings (such as pain clinics), or cultural subgroups and should be interpreted with context. Second, the majority of visits were categorized as White race, not Hispanic/Latino ethnicity, and English language. Estimates based on small number of visits (e.g., Khmer language) may be volatile. It will be important to replicate this work in additional clinics with larger samples sizes and compare results for consistency. Third, because this was a retrospective EHR study, we were limited to data collected as part of clinical care. Patient bilingual status, socioeconomic status, immigrant status, use of different words to express pain, and acculturation information were not available. These variables could help to further contextualize our findings. Additionally, pain scores documented in the EHR do not take into account the possible mismatch between the pain number provided by the patient and the actual experience (e.g., a patient may overreport pain to obtain medication or a patient with a stoic attitude may underreport pain). Lastly, we studied pain in general (rather than focusing on a specific type of pain or chronicity). It is possible that certain chronic conditions are associated with higher ratings of pain and these chronic conditions are more prevalent in particular racial and ethnic minority groups. We also did not account for psychosocial factors (e.g., depression, anxiety, stress) in our analysis. Studies have shown that psychosocial factors are associated with an increase in clinical pain and experimental pain sensitivity (Meints & Edwards, 2018; Thompson et al., 2018). Future studies could examine how pain type/chronicity and psychosocial factors influence pain severity within the context of patient race, ethnicity, language, and cultural subgroup.

New Implications for Clinical Practice

While we caution against over interpretation of findings from this observational analysis, our results suggest new implications for nursing practices surrounding pain assessment, especially for patients with LEP. We found that pain scores were higher for patients with LEP compared to English-speaking patients identifying as the same race (i.e., Asian) or ethnicity (i.e., Hispanic/Latino). Patients with LEP may have lower health literacy and acculturation levels compared to patients who have their visits in English (Chen, Li, & Kreps, 2021). It is important that nurses assess patient cultural norms concerning pain and use interpreter or translation services to address language barriers among LEP patients. Nurses should also consider alternative pain assessments to evaluate pain in addition to scores. Traditional numeric, visual analog, and verbal rating scales may be culturally and/or linguistically inappropriate pain assessment tools (Narayan, 2010). A possible alternative, or complement to numeric pain score, could be evaluation of patient level of function as there is evidence of the association between pain severity and function (Kaye, Baluch, & Scott, 2010).

Conclusion

The findings from this study add to the growing literature on cultural (i.e., racial and ethnic) differences in pain by extending to a new area of focus – patient language in pain severity reporting. We found that the chances of reporting any pain and pain severity scores in visits with pain scores ≥1 differ by race and language. Of particular interest, reported pain scores were higher for LEP patients compared to English-speaking patients within a race (i.e., Asian) or ethnicity (i.e., Hispanic/Latino) category. These study findings illuminate the need for more pain research on cultural subgroup differences in race, ethnicity, and language. Additionally, the differences in pain severity reporting among such patient characteristics underscore the complexity of health disparities in pain management and suggest exploration of underlying mechanisms such as patient psychosocial factors, attitudes, and behaviors to determine priorities for development of pain interventions to reduce pain disparities.

Acknowledgements

The project described was supported by the Clinical and Translational Science Award (CTSA) program through the NIH National Center for Advancing Translational Sciences (NCATS) grant UL1TR002373 and the NIH National Institute of Nursing Research (NINR) grant R00NR017651 and K23NR019289. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The authors would like to thank Clark Xu for his support in data extraction and Dr. Roger Brown for his consultation on the data analysis approach and interpretation.

Footnotes

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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