Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2025 Aug 1.
Published in final edited form as: J Nurs Scholarsh. 2024 May 13;57(1):39–46. doi: 10.1111/jnu.12980

Documentation of social determinants of health across individuals from different racial and ethnic groups in home healthcare

Mollie Hobensack 1, Danielle Scharp 2, Jiyoun Song 3, Maxim Topaz 2,4,5
PMCID: PMC12315521  NIHMSID: NIHMS2096548  PMID: 38739091

Abstract

Introduction:

Home healthcare (HHC) enables patients to receive healthcare services within their homes to manage chronic conditions and recover from illnesses. Recent research has identified disparities in HHC based on race or ethnicity. Social determinants of health (SDOH) describe the external factors influencing a patient’s health, such as access to care and social support. Individuals from racially or ethnically minoritized communities are known to be disproportionately affected by SDOH. Existing evidence suggests that SDOH are documented in clinical notes. However, no prior study has investigated the documentation of SDOH across individuals from different racial or ethnic backgrounds in the HHC setting. This study aimed to (1) describe frequencies of SDOH documented in clinical notes by race or ethnicity and (2) determine associations between race or ethnicity and SDOH documentation.

Design:

Retrospective data analysis.

Methods:

We conducted a cross-sectional secondary data analysis of 86,866 HHC episodes representing 65,693 unique patients from one large HHC agency in New York collected between January 1, 2015, and December 31, 2017. We reported the frequency of six SDOH (physical environment, social environment, housing and economic circumstances, food insecurity, access to care, and education and literacy) documented in clinical notes across individuals reported as Asian/Pacific Islander, Black, Hispanic, multi-racial, Native American, or White. We analyzed differences in SDOH documentation by race or ethnicity using logistic regression models.

Results:

Compared to patients reported as White, patients across other racial or ethnic groups had higher frequencies of SDOH documented in their clinical notes. Our results suggest that race or ethnicity is associated with SDOH documentation in HHC.

Conclusion:

As the study of SDOH in HHC continues to evolve, our results provide a foundation to evaluate social information in the HHC setting and understand how it influences the quality of care provided.

Clinical Relevance:

The results of this exploratory study can help clinicians understand the differences in SDOH across individuals from different racial and ethnic groups and serve as a foundation for future research aimed at fostering more inclusive HHC documentation practices.

Keywords: clinical notes, home health care, natural language processing, nursing informatics, social determinants of health

INTRODUCTION

Healthy People 2030 defines social determinants of health (SDOH) as broadly encompassing social factors such as economic stability, healthcare access and quality, social and community context, education access and quality, and neighborhood and built environment (Healthy People 2030, n.d.; Hood et al., 2016). Despite extensive literature confirming the impact of SDOH on health outcomes (McGilton et al., 2018; Perez et al., 2022), there remains a gap in understanding specific mechanisms by which SDOH interact and affect individual populations. Individuals from racially and ethnically minoritized groups are disproportionately affected by negative SDOH, such as disparities in income, access to care, education, utilities, environmental conditions, and insurance (Wakefield et al., 2020). Negative SDOH (e.g., no social support, cluttered environment) have been reported to exacerbate health disparities and lead to suboptimal health outcomes for racially and ethnically minoritized communities, including diabetes-related complications (Walker et al., 2014), COVID-19-related mortality (Green et al., 2021), and cardiovasular complications such as stroke, myocardial infarction, and mortality (Jilani et al., 2021).

Home healthcare (HHC) is a type of post-acute care that provides services within a patient’s home, such as skilled nursing and therapies. HHC facilitates the management of chronic illnesses in the community for more than 3 million adults annually (MedPac, n.d.). Although prior research suggests that HHC aligns with patient preferences, reduces hospital length of stay (Admi et al., 2015), and lessens medical expenditures (Howard et al., 2019), emerging evidence supports disparities in HHC delivery and quality based on race or ethnicity (Chase et al., 2018; Smith et al., 2021; Song, Zolnoori, et al., 2021). For example, a large cohort study including HHC patients showed that Asian, Black, and Hispanic patients were more likely to be rehospitalized or visit the emergency department (ED) than White patients (Chase et al., 2018). Prior research has shown that Black and Hispanic patients are more likely to experience delays at the start of HHC visits White patients (Song, Zolnoori, et al., 2021). Additionally, Hispanic and American Indian patients discharged from the hospital were less likely to be discharged with HHC compared to White patients (Smith et al., 2021). This evidence emphasizes the need to further investigate racial and ethnic disparities in HHC. Given that HHC clinicians provide care within a patient’s homesetting (Hobensack et al., 2023), they are uniquely positioned to assess SDOH. A clinician’s perception of race or ethnicity, which may be reflected in their clinical documentation, could have an impact on the quality of care they provide (Sun et al., 2022). Examining HHC clinicians’ documentation can help understand how SDOH may influence health outcomes. Prior research has shown the potential of mining clinical notes for SDOH to elicit a richer understanding of the individual-level SDOH influencing a patient’s health (Chen et al., 2020). Studies conducted in the inpatient setting suggest that compared to structured data (e.g., standardized assessments using controlled vocabularies, flow sheets), clinical documentation detailing more complex SDOH is found in unstructured free-text clinical notes (Vest et al., 2017). In the HHC setting, prior research has demonstrated the ability and value of extracting SDOH from clinical notes (Hobensack et al., 2023; Song, Ojo, et al., 2021), but no prior study has reported the frequency of SDOH documented in clinical notes by race or ethnicity in the HHC setting.

In previous work, we applied natural language processing (NLP)—a subfield of artificial intelligence focused on enabling computers to understand, interpret, and generate human language—to clinical notes to extract six SDOH in the HHC setting (i.e., physical environment, social environment, housing and economic circumstances, food insecurity, access to care, and education and literacy; Hobensack et al., 2023). As an extension of this work, this study aimed to (1) describe frequencies of SDOH documented in clinical notes by race or ethnicity and (2) determine associations between race or ethnicity and SDOH documentation. Examining variations in clinicians’ documentation of SDOH across patients of diverse racial or ethnic backgrounds can facilitate a deeper understanding of potential inequities, thus fostering advancements toward the delivery of equitable care within the HHC setting.

METHODS

We conducted a cross-sectional secondary data analysis including patients who received HHC services between January 1, 2015, and December 31, 2017, from one large HHC agency in New York (VNS Health, n.d.). Data were aggregated at the HHC episode level. An HHC episode is defined as the timeframe during which a patient receives HHC services. The episode of care was chosen as the unit of analysis since different SDOH may be documented in each HHC episode. The Institutional Review Board of the participating HHC agency approved this study.

Dataset

We included structured and unstructured data from the HHC agency’s electronic health record. Structured data were defined as Outcome and Assessment Information Set (OASIS) version C2 items, which are standardized assessments completed upon HHC admission, transfer, and discharge (CMIS, 2017; O’Connor & Davitt, 2012). Unstructured data were defined as free-text clinical notes. Clinical notes were written by nurses, physical therapists, occupational therapists, and social workers. There were two types of clinical notes: (1) visit notes, which detailed the patient’s condition and treatment given during the HHC visit, and (2) care coordination notes, which detailed communication between healthcare professionals and other administrative tasks related to patient care.

Independent variable: Race or ethnicity

Race or ethnicity was obtained from the OASIS item M0140, indicating that the patient was reported as American Indian or Alaskan Native (i.e., Native American), Asian, Black, Hispanic or Latino (i.e., Hispanic), Native Hawaiian or Pacific Islander (i.e., Pacific Islander), or White. We created an Asian/Pacific Islander category that included patients reported as Asian, Native Hawaiian, or Pacific Islander (NIH, n.d.). For patients with more than one race or ethnicity selected, we created a multi-racial category.

Dependent variable: Documentation of SDOH in free-text clinical notes

In previous work (Hobensack et al., 2023), our team developed an NLP algorithm to extract SDOH from unstructured free-text clinical notes (n = 2,341,018). As part of this work, we conducted a survey with HHC experts to determine what SDOH may be related to the risk of ED visits or hospitalizations. Through this process, we identified six SDOH: physical environment (e.g., dirty, cluttered, roaches), social environment (e.g., widowed, lives alone), housing and economic circumstances (e.g., cannot afford, not covered), food insecurity (e.g., meals on wheels, food stamps), access to care (e.g., unable to go out, no glucometer), and education and literacy (e.g., translator, language line). Then, the NLP algorithm was developed to identify the six SDOH categories. SDOH were coded as binary variables (documented or not documented) for each clinical note. Full details of this study are described elsewhere (Hobensack et al., 2023).

Confounding variables

Based on literature (Chase et al., 2018) and team domain expertise in nursing, HHC, and SDOH, we identified insurance, comorbidities, therapies the patient receives at home, risk factors for hospitalization, overall status, living arrangements, and functional status as confounders. Confounding variables were extracted from the OASIS.

Statistical analysis

We calculated the proportion of clinical notes with the six SDOH documented for each racial or ethnic group included in this study (i.e., Asian/Pacific Islander, Black, Hispanic, multi-racial, Native American, White). We employed multivariable logistic regression models adjusting for insurance, comorbidities, therapies the patient receives at home, risk factors for hospitalization, overall status, living arrangements, and functional status to estimate the odds of each SDOH documented in the clinical notes by race or ethnicity. All analyses were performed using R software version 4.2.2 (R Core Team, 2021).

RESULTS

The frequencies of SDOH documented in the clinical notes by race or ethnicity are provided in Table 1. Overall, 46,805 (54%) HHC episodes contained clinical notes with SDOH documented. Clinical notes for Hispanic (66%) and Native American (66%) patients had the most SDOH documented, while clinical notes for White (49%) patients had the least SDOH documented.

TABLE 1.

Frequencies of Any SDOH documented in clinical notes by race or ethnicity (n=86,866).

Race or ethnicity Total frequency and proportion of documentation, n (%)
Native American (n = 189) 125 (66)
Hispanic (n = 11,693) 7709 (66)
Black (n = 15,176) 9428 (62)
Asian/Pacific Islander (n = 5024) 2974 (59)
Multi-racial (n = 246) 125 (51)
White (n = 54,500) 26,444 (49)
Open in a new tab

Note: This includes all mentions of any of the six SDOH (physical environment, social environment, housing and economic circumstances, food insecurity, access to care, or education and literacy) documented.

In Table 2, we provided the frequencies of each SDOH (i.e., physical environment, social environment, housing and economic circumstances, food insecurity, access to care, education and literacy) documented by race or ethnicity. Across all groups, SDOH reflecting physical environment were documented most frequently, and SDOH reflecting food insecurity were documented least frequently. SDOH reflecting social environment were documented at a similar rate across all groups. Native American patients had SDOH reflecting access to care most frequently documented, while multi-racial patients had access to care least frequently documented. White patients had SDOH reflecting housing and economic circumstances and education and literacy least frequently documented.

TABLE 2.

Frequencies of each SDOH documented in clinical notes by race or ethnicity (n= 86,866).

Race or ethnicity Total frequency and proportion of documentation, n (%)
Asian/Pacific Islander (n = 5024)
 Physical environment 806 (16)
 Social environment 635 (13)
 Housing and economic circumstances 387 (8)
 Food insecurity 114 (2)
 Access to care 262 (5)
 Education and literacy 770 (15)
Black (n = 15,176)
 Physical environment 2775 (18)
 Social environment 2460 (16)
 Housing and economic circumstances 1749 (12)
 Food insecurity 639 (4)
 Access to care 1185 (8)
 Education and literacy 620 (4)
Hispanic (n = 11,693)
 Physical environment 2179 (19)
 Social environment 1797 (15)
 Housing and economic circumstances 1287 (11)
 Food insecurity 445 (4)
 Access to care 762 (7)
 Education and literacy 1239 (11)
Multi-racial (n = 246)
 Physical environment 35 (14)
 Social environment 34 (14)
 Housing and economic circumstances 23 (9)
 Food insecurity 10 (4)
 Access to care 11 (4)
 Education and literacy 12 (5)
Native American (n = 189)
 Physical environment 38 (20)
 Social environment 23 (12)
 Housing and economic circumstances 24 (13)
 Food insecurity 5 (3)
 Access to care 20 (11)
 Education and literacy 15 (8)
White (n = 54,500)
 Physical environment 7973 (15)
 Social environment 8567 (16)
 Housing and economic circumstances 3545 (7)
 Food insecurity 1587 (3)
 Access to care 2904 (5)
 Education and literacy 1868 (3)
Open in a new tab

Results of the logistic regression analyses are provided in Table 3. Adjusting for insurance, comorbidities, therapies, risk for hospitalization, overall status, living status, and functional status, in Model 1, Black patients were 28% more likely (adjusted odds ratio [aOR] = 1.28, 95% confidence interval (CI) [1.22–1.35]), Hispanic patients were 26% more likely (aOR = 1.25, 95% CI [1.19–1.33]), and Native American patients were 49% more likely (aOR = 1.49, 95% CI [1.04–2.14]) to have SDOH reflecting physical environment documented in the clinical notes compared to White patients. In Model 2, Asian/Pacific Islander patients were 20% less likely (aOR = 0.80, 95% CI [0.73–0.88]), and Hispanic patients were 8% less likely (aOR = 0.92, 95% CI [0.87–0.98]) to have SDOH reflecting social environment documented in the clinical notes compared to White patients. In Model 3, Asian/Pacific Islander patients were 20% more likely (aOR = 1.20, 95% CI [1.07–1.34]), Black patients were 92% more likely (aOR = 1.92, 95% CI [1.80–2.04]), Hispanic patients were 77% more likely (aOR = 1.77, 95% CI [1.65–1.89]), and Native American patients were over two times more likely (aOR = 2.12, 95% CI [1.38–3.27]) to have SDOH reflecting housing and economic circumstances documented in the clinical notes compared to White patients. In Model 4, Asian/Pacific Islander patients were 18% less likely (aOR = 1.72, 95% CI [0.67–0.99]), Black patients were 49% more likely (aOR = 1.49, 95% CI [1.35– 1.63]), and Hispanic patients were 31% more likely (aOR = 1.31, 95% CI[1.17–1.46]) to have SDOH reflecting food insecurity documented in the clinical notes compared to White patients. In Model 5, Black patients were 42% more likely (aOR = 1.42, 95% CI [1.42–1.64]), Hispanic patients were 22% more likely (aOR = 1.22, 95%CI [1.12–1.33]), and Native American patients were over two times more likely (aOR = 2.18, 95% CI [1.36–3.48]) to have SDOH reflecting access to care documented in the clinical notes compared to White patients. Finally, in Model 6, Asian/Pacific Islander patients were over four times more likely (aOR = 4.67, 95% CI [4.26– 5.13]), Black patients were 15% more likely (aOR = 1.15, 95% CI [1.05–1.26]), Hispanic patients were over three times more likely (aOR = 3.05, 95% CI [2.82–3.29]), and Native American patients were over two times more likely (aOR = 2.45, 95% CI [1.34–4.17]) to have SDOH reflecting education and literacy documented in the clinical notes compared to White patients.

TABLE 3.

Logistic regression models examining SDOH documentation by race or ethnicity.

Race or ethnicity Model 1: Physical environment Model 2: Social environment Model 3: Housing and economic circumstances Model 4: Food insecurity Model 5: Access to care Model 6: Education and literacy
Asian/Pacific Islander 1.01 (0.93-1.09) 0 80 (0.73-0.88)* 1.20 (1.07-1.34)* 0.82 (0.67-0.99)* 0.99 (0.87-1.13) 4.67 (4.26-5.13)*
Black 1.28 (1.22-1.35)* 1.02 (0.97-1.07) 1.92 (1.80-2.04)* 1.49 (1.35-1.63)* 1.42 (1.42-1.64)* 1.15 (1.05-1.26)*
Hispanic 1.26 (1.19-1.33)* 0.92 (0.87-0.98)* 1.77 (1.65-1.89)* 1.31 (1.17-1.46)* 1.22 (1.12-1.33)* 3.05 (2.82-3.29)*
Multi-racial 0.96 (0.67-1.37) 0.89 (0.62-1.29) 1.50 (0.97-2.31) 1.44 (0.76-2.72) 0.84 (0.46-1.55) 1.38 (0.77-2.48)
Native American 1.49 (1.04-2.14)* 0.79 (0.51-1.23) 2.12 (1.38-3.27)* 0.93 (0.38-2.26) 2.18 (1.36-3.48)* 2.45 (1.34-4.17)*
White Reference Reference Reference Reference Reference Reference
Open in a new tab

Note: These models were adjusted for insurance, conditions, therapies, risk for hospitalization, overall status, living status, and functional status.

*

p <0.05.

DISCUSSION

This study is the first to report the differences in SDOH documentation in clinical notes across individuals from different racial or ethnic groups in HHC. Our findings reveal similar frequencies in SDOH documentation across patients from different racial or ethnic groups, with White patients having the least number of SDOH documented. SDOH related to a patient’s physical and social environment were most frequently documented across all groups. These observations highlight the consistent documentation of SDOH related to patients’ physical and social environments among diverse backgrounds. In addition, these findings underscore the importance of addressing bias and ensuring comprehensive SDOH documentation to promote equitable healthcare outcomes for all HHC patients.

Our results suggest that race or ethnicity is associated with SDOH documentation in HHC. Notably, Asian/Pacific Islander patients predominantly had SDOH documentation pertaining to education and literacy rather than the social environment. Literature supports this finding and has reported on the emphasis placed on familial support during the aging process in Eastern culture, which may be why social environment SDOH was not as commonly documented (Shimkhada et al., 2022; Wang et al., 2020). Individuals reported as Native American had more documentation related to housing and economic circumstances and access care (Jaramillo et al., 2022) compared to other racial groups. This is consistent with prior literature reporting that Native Americans have disproportionately higher uninsurance rates and are more likely to live in regions with limited access to healthcare (Jaramillo et al., 2022; Willging et al., 2018) likely stemming from historical displacement and relocation (Smallwood et al., 2021).

Previous research has discussed the challenges in statistically interpreting racial groups collapsed into broader categories (Ross et al., 2020; Schwabish & Feng, 2021). While this study found that Native American patients were statistically more likely to have SDOH documented compared to White patients, a limitation of this finding is small sample size (n = 189). Despite smaller sample sizes for certain racial or ethnic groups, we opted to avoid combining groups. This decision enabled a more detailed examination of the variations in SDOH documentation among individuals from diverse racial or ethnic backgrounds who are historically collapsed. However, smaller sample sizes limit generalizability and can lead to statistical challenges (Wang, 2022). Future researchers are encouraged to purposefully sample historically collapsed groups to deepen understanding of potential health disparities and opportunities to provide better quality care.

The results of this study suggest that there are statistically significant differences in SDOH documented across individuals from different racial or ethnic groups in the HHC setting. Although our analyses accounted for a range of confounding variables, we acknowledge there may be potential lurking variables that were not captured in our dataset. These unmeasured factors could influence the relationship between race or ethnicity and SDOH documentation. For instance, cultural nuances in communication styles, socioeconomic factors beyond those documented, or undocumented health behaviors may play a critical role. Recognizing the limitation of our findings underscores the complexity of interpreting associations within retrospective healthcare data and the necessity for cautious inference.

Compared to White patients, patients from racially or ethnically minoritized groups were more likely to have SDOH documented.

Prior studies have reported on differences in documentation based on race, but fewer studies have investigated this in HHC (Sun et al., 2022). Healthcare providers may have implicit biases influencing their perception and documentation (Barcelona et al., 2023; Himmelstein et al., 2022). The potential impact of the documenter’s race, cultural background, and implicit biases on SDOH documentation warrants further investigation. Although not the primary focus of our study, understanding how these factors influence documentation practices could provide deeper insights into systemic biases and documentation differences. Our findings highlight the need for future research to explore the underlying mechanisms driving the observed associations between race or ethnicity and SDOH documentation. Specifically, qualitative studies examining how a documenter’s perceptions, practices, and demographics impact documentation, could yield valuable insights.

Although race has often served as a method for classifying people, it is a social construct rooted more in societal beliefs and biases than in tangible genetic differences (Helms et al., 2005). In healthcare and various other fields, these racial and ethnic labels have sometimes been misused to generalize about individuals (Dordunoo et al., 2022). We recognize that every individual is a unique combination of experiences, health conditions, environmental influences, and inherited characteristics (Wilson et al., 2019). Therefore, there is a need to study if and how implicit biases influence SDOH documentation, especially as technologies begin to focus on generating risk scores using electronically recorded data (Bompelli et al., 2021). Such research can inform training programs to address these biases.

The evolution of social documentation has rapidly evolved in the past decade. Since the version of OASIS utilized in this study (version C2), there have been updates to the tool, which include the separation of race from ethnicity into distinct variables, the inclusion of additional racial groups, and the introduction of a new variable to address health literacy, language, and transportation (Corridor Group, 2022; HealthRev Partners, n.d.). With the addition of these new variables, future studies should examine how racial or ethic bias can be specifically connected to iatrogenic outcomes in the HHC setting (Ray, 2022). Acknowledging the limitations of our dataset, this study serves as a foundation for future research aimed at exploring these complexities to foster equitable care in the HHC setting.

LIMITATIONS

Limitations to consider within this study relate to how the variables race and ethnicity were reported within one variable in the OASIS Version C2. In addition, while we purposefully chose not to collapse races into broader categories to align with our research question, this limits our ability to generalize to other samples. We also acknowledge that the data used in this study were collected several years ago, and documentation policies have evolved; future studies should further explore the validity of these findings in other HHC datasets. We recognize that race is a social construct, and that race or ethnicity alone should not be used to assume care needs. We used NLP to measure SDOH variables in clinical notes. We encourage additional NLP methodologies to explore how to best measure SDOH in HHC clinical notes. This study did not explore how many notes were documented or the average note length by race or ethnicity; we encourage future studies to examine this to better understand the proportion of SDOH documentation across each patient’s HHC episode. A limitation in our results is that our measurement of SDOH is based on what clinicians document in clinical notes and, therefore, may contain certain biases related to the documenter’s perception. Future studies should incorporate clinician characteristics in their analysis. Since the clinician fills out the race or ethnicity variable in OASIS, there is a limitation in knowing the validity of data collected. Exploring communication and patient involvement while completing the OASIS would aid in understanding the validity of SDOH, race, and ethnicity documented.

CONCLUSION

This is the first study conducted in HHC to explore the differences in SDOH clinical note documentation across individuals from different racial or ethnic groups. Compared to White patients, Asian/Pacific Islander, Black, Hispanic, and Native Americans had higher frequencies of SDOH documented. In addition, our results suggest that race or ethnicity is associated with SDOH documentation in HHC. This study serves as a foundation for future research aimed at fostering more inclusive HHC documentation practices.

CLINICAL RESOURCES.

ACKNOWLEDGMENTS

This was conducted with researchers from Columbia University School of Nursing and University of Pennsylvania. I started this work as a PhD candidate at Columbia University School of Nursing and completed this project in my new role at Icahn School of Medicine at Mount Sinai. A special thanks to Dr. Kenrick Cato for prompting this manuscript. The funding of this study was supported by the training grant National Institute for Nursing Research (T32NR007969—M.H., D.S.), Agency for Healthcare Research and Quality (R01 HS027742), and the Jonas Scholarship (MH).

Funding information

National Institute for Nursing Research, Grant/Award Number: T32NR007969; Agency for Healthcare Research and Quality, Grant/Award Number: R01 HS027742; Jonas Philanthropies

Footnotes

CONFLICT OF INTEREST STATEMENT

The authors of this manuscript have nothing to disclose.

DATA AVAILABILITY STATEMENT

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

REFERENCES

  1. Admi H, Shadmi E, Baruch H, & Zisberg A (2015). From research to reality: Minimizing the effects of hospitalization on older adults. Rambam Maimonides Medical Journal, 6(2), e0017. 10.5041/RMMJ.10201 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Barcelona V, Scharp D, Idnay BR, Moen H, Goffman D, Cato K, & Topaz M (2023). A qualitative analysis of stigmatizing language in birth admission clinical notes. Nursing Inquiry, 30(3), e12557. 10.1111/NIN.12557 [DOI] [PubMed] [Google Scholar]
  3. Bompelli A, Wang Y, Wan R, Singh E, Zhou Y, Xu L, Oniani D, Kshatriya BSA, Balls-Berry JE, & Zhang R (2021). Social and behavioral determinants of health in the era of artificial intelligence with electronic health records: A scoping review. Health Data Science, 2021.9759016 10.34133/2021/9759016 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Chase JAD, Russell D, Huang L, Hanlon A, O’Connor M, & Bowles KH (2018). Relationships between race/ethnicity and health care utilization among older post-acute home health care patients. Journal of Applied Gerontology, 39(2), 201–213. 10.1177/0733464818758453 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Chen M, Tan X, & Padman R (2020). Social determinants of health in electronic health records and their impact on analysis and risk prediction: A systematic review. Journal of the American Medical Informatics Association, 27, 1764–1773. 10.1093/jamia/ocaa143 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. CMIS. (2017). Outcome and assessment information set OASIS-C2 guidance manual. [Google Scholar]
  7. Corridor Group. (2022). Preparing for OASIS-E: Social Determinants of Health. https://corridorgroup.com/blog/social-determinants-of-health-supporting-your-patients-health-literacy/ [Google Scholar]
  8. Dordunoo D, Abernethy P, Kayuni J, McConkey S, & Aviles-G ML (2022). Dismantling “race” in health research. The Canadian Journal of Nursing Research, 54(3), 239–245. 10.1177/08445621221074849 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Green H, Fernandez R, & MacPhail C (2021). The social determinants of health and health outcomes among adults during the COVID-19 pandemic: A systematic review. Public Health Nursing, 38(6), 942–952. 10.1111/PHN.12959 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. HealthRev Partners. (n.d.). The 5-key social determinants of health explained for home care. https://healthrevpartners.com/sdoh-home-health-assessments/
  11. Healthy People 2030. (n.d.). Social determinants of health. Retrieved July 31, 2021, from https://health.gov/healthypeople/objectives-and-data/social-determinants-health
  12. Helms JE, Jernigan M, & Mascher J (2005). The meaning of race in psychology and how to change it: A methodological perspective. American Psychologist, 60(1), 27–36. 10.1037/0003-066X.60.1.27 [DOI] [PubMed] [Google Scholar]
  13. Himmelstein G, Bates D, & Zhou L (2022). Examination of stigmatizing language in the electronic health record. JAMA Network Open, 5(1), e2144967. 10.1001/JAMANETWORKOPEN.2021.44967 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Hobensack M, Song J, Oh S, Evans L, Davoudi A, Bowles KH, McDonald M, Barron Y, Sridharan S, Wallace A, & Topaz M (2023). Social risk factors are associated with risk for hospitalization in home healthcare: A natural language processing study. Journal of the American Medical Directors Association,24(12), 1874–1880.e4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Hood CM, Gennuso KP, Swain GR, & Catlin BB (2016). County health rankings: Relationships between determinant factors and health outcomes. American Journal of Preventive Medicine, 50(2), 129–135. 10.1016/j.amepre.2015.08.024 [DOI] [PubMed] [Google Scholar]
  16. Howard J, Kent T, Stuck AR, Crowly C, & Zeng F (2019). Improved cost and utilization among Medicare beneficiaries dispositioned from the ED to receive home health care compared with inpatient hospitalization. The American Journal of Accountable Care, 7(1), 10–16 [Google Scholar]
  17. Jaramillo ET, Haozous E, & Willging CE (2022). The community as the unit of healing: Conceptualizing social determinants of health and well-being for older American Indian adults. The Gerontologist, 62(5), 732–741. 10.1093/GERONT/GNAC018 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Jilani MH, Javed Z, Yahya T, Valero-Elizondo J, Khan SU, Kash B, Blankstein R, Virani SS, Blaha MJ, Dubey P, Hyder AA, Vahidy FS, Cainzos-Achirica M, & Nasir K (2021). Social determinants of health and cardiovascular disease: Current state and future directions towards healthcare equity. Current Atherosclerosis Reports, 23(9), 1–11. 10.1007/S11883-021-00949-W/METRICS [DOI] [PubMed] [Google Scholar]
  19. McGilton KS, Vellani S, Yeung L, Chishtie J, Commisso E, Ploeg J, Andrew MK, Ayala AP, Gray M, Morgan D, Chow AF, Parrott E, Stephens D, Hale L, Keatings M, Walker J, Wodchis WP, Dubé V, McElhaney J, & Puts M (2018). Identifying and understanding the health and social care needs of older adults with multiple chronic conditions and their caregivers: A scoping review. BMC Geriatrics, 18(1), 231. 10.1186/S12877-018-0925-X [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. MedPac. (n.d.). http://www.medpac.gov/docs/default-source/reports/mar20_medpac_ch9_sec.pdf
  21. NIH.2024. (n.d.). Asian American Pacific Islander Health Scientific Interest Group. National Institutes of Health. https://oir.nih.gov/sigs/asian-american-pacific-islander-health-scientific-interest-group
  22. O’Connor M, & Davitt JK (2012). The Outcome and Assessment Information Set (OASIS): A review of validity and reliability. Home Health Care Services Quarterly, 31(4), 267–301. 10.1080/01621424.2012.703908 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Perez FP, Perez CA, & Chumbiauca MN (2022). Insights into the social determinants of health in older adults. Journal of Biomedical Science and Engineering, 15(11), 261–268. 10.4236/JBISE.2022.1511023 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. R Core Team. (2021). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. [Google Scholar]
  25. Ray K (2022). Clinicians’ racial biases as pathways to Iatrogenic harms for Black People. AMA Journal of Ethics, 24(8), E768–E772. 10.1001/AMAJETHICS.2022.768 [DOI] [PubMed] [Google Scholar]
  26. Ross PT, Hart-Johnson T, Santen SA, & Zaidi NLB (2020). Considerations for using race and ethnicity as quantitative variables in medical education research. Perspectives on Medical Education, 9(5), 318–323. 10.1007/S40037-020-00602-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Schwabish J, & Feng A (2021). Combining racial groups in data analysis can mask important differences in communities. Urban Institute. https://www.urban.org/urban-wire/combining-racial-groups-data-analysis-can-mask-important-differences-communities [Google Scholar]
  28. Shimkhada R, Tse HW, & Ponce NA (2022). Life satisfaction and social and emotional support among Asian American older adults. The Journal of the American Board of Family Medicine, 35(1), 203–205. 10.3122/JABFM.2022.01.210232 [DOI] [PubMed] [Google Scholar]
  29. Smallwood R, Woods C, Power T, & Usher K (2021). Understanding the impact of historical trauma due to colonization on the health and well-being of indigenous young peoples: A systematic scoping review. Journal of Transcultural Nursing, 32(1), 59–68. 10.1177/1043659620935955/ASSET/IMAGES/LARGE/10.1177_1043659620935955-FIG2.JPEG [DOI] [PubMed] [Google Scholar]
  30. Smith JM, Jarrín OF, Lin H, Tsui J, Dharamdasani T, & Thomas-Hawkins C (2021). Racial disparities in post-acute home health care referral and utilization among older adults with diabetes. International Journal of Environmental Research and Public Health, 18(6), 1–14. 10.3390/IJERPH18063196 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Song J, Ojo M, Bowles KH, McDonald MV, Cato KD, Rossetti SC, Adams V, Chae S, Hobensack M, Kennedy E, Tark A, Kang M-J, Woo K, Barron Y, Sridharan S, & Topaz M (2021). Detecting language associated with home healthcare Patient’s risk for hospitalization and emergency department visit. Nursing Research, 71, 285–294. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Song J, Zolnoori M, McDonald MV, Barrón Y, Cato K, Sockolow P, Sridharan S, Onorato N, Bowles KH, & Topaz M (2021). Factors associated with timing of the start-of-care nursing visits in home health care. Journal of the American Medical Directors Association, 22(11), 2358–2365.e3. 10.1016/J.JAMDA.2021.03.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Sun M, Oliwa T, Peek ME, & Tung EL (2022). Negative patient descriptors: Documenting racial bias in the electronic health. Record, 41(2), 203–211. 10.1377/HLTHAFF.2021.01423 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Vest JR, Grannis SJ, Haut DP, Halverson PK, & Menachemi N (2017). Using structured and unstructured data to identify patients’ need for services that address the social determinants of health. International Journal of Medical Informatics, 107, 101–106. 10.1016/J.IJMEDINF.2017.09.008 [DOI] [PubMed] [Google Scholar]
  35. VNS Health. (n.d.). About VNS health. https://www.vnshealth.org/about/
  36. National Academies of Sciences, Engineering, and Medicine; National Academy of MedicineCommittee on the Future of Nursing 2020- 2030 In Flaubert JL, Le Menestrel S, Williams DR, & Wakefield MK (Eds.) (2020).The future of nursing 2020-2030: Charting a path to achieve health equity. National Academies Press (US). [PubMed] [Google Scholar]
  37. Walker RJ, Smalls BL, Campbell JA, Strom Williams JL, & Egede LE (2014). Impact of social determinants of health on outcomes for type 2 diabetes: A systematic review. Endocrine, 47(1), 29–48. 10.1007/S12020-014-0195-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Wang HL (2022). Race and ethnicity in U.S. may be redefined by 2024. NPR. https://www.npr.org/2022/06/15/1105104863/racial-ethnic-categories-omb-directive-15 [Google Scholar]
  39. Wang L, Yang L, Di X, & Dai X (2020). Family support, multidimensional health, and living satisfaction among the elderly: A case from Shaanxi Province, China. International Journal of Environmental Research and Public Health, 17(22), 1–18. 10.3390/IJERPH17228434 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Willging CE, Sommerfeld DH, Jaramillo ET, Lujan E, Bly RS, Debenport EK, Verney SP, & Lujan R (2018). Improving native American elder access to and use of health care through effective health system navigation. BMC Health Services Research, 18(1), 1–16. 10.1186/S12913-018-3182-Y/TABLES/2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Wilson Y, White A, Jefferson A, & Danis M (2019). Intersectionality in clinical medicine: The need for a conceptual framework. The American Journal of Bioethics, 19(2), 8–19. 10.1080/15265161.2018.1557275 [DOI] [PubMed] [Google Scholar]

Associated Data

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

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

RESOURCES