Assessing Differences in Social Determinants of Health Screening Rates in a Large,Urban Safety-Net Health System

Abstract

Introduction/Objective:

Previous studies have evaluated the implementation of standardized social determinants of health (SDOH) screening within healthcare settings, however, less is known about where screening gaps may exist following initial implementation based on facility characteristics. The objective of this study is to assess differences in screening rates for SDOH at a large, urban healthcare system.

Methods:

We used electronic health record data obtained from NYC Health + Hospitals primary care sites from 2019 to 2022. We calculated the mean number of visits that were SDOH screened by visit type, facility size, and the percentages of community characteristics. We conducted 4 logistic regression models predicting the odds of screening for any SDOH and for specific SDOH needs (housing, food, and medical cost assistance) based on facility type, facility size, and the socioeconomic characteristics of the surrounding community.

Results:

Among the 3 212 650 visits included, 16.90% were SDOH screened. Across all 4 multivariate logistic regression models predicting SDOH screening, a visit had significantly lower odds of being screened if based at a midsize or small facility, if it was a telemedicine visit, or based at a facility located in a zip-code with a higher percentage of SDOH needs.

Conclusions:

Our study found important differences in SDOH screening rates at a large, NYC-based health system based on size, visit type, and community level characteristics. In particular, our findings point to barriers related to facility size and telemedicine workflow that should be addressed to increase uptake of SDOH screening within different visits and facility types.

Keywords

community health socioeconomic factors social determinants of health screening health care facilities/services

Introduction

There is growing recognition that social determinants of health (SDOH) such as housing insecurity, transportation access, and food insecurity, shape patient outcomes in powerful ways.¹ For example, previous research has documented that patients with unmet social needs have higher rates of emergency department (ED) utilization,² lower rates of medication adherence,³ and overall worsened health outcomes.⁴ Such findings signal the need to integrate efforts aimed at addressing SDOH in clinical settings.

In response, there has been a push from multiple agencies to promote SDOH screening within healthcare facilities, with the goal of connecting patients with SDOH needs to effective resources. This includes the Centers for Medicare and Medicaid Services (CMS) Accountable Health Communities (AHC) Health-Related Social Needs Screening (HRSN) tool, a standardized, 10-item tool for providers to screen for SDOH within clinical settings across 5 domains: living situation, food, transportation, utilities, and safety.⁵ Beginning in 2024, CMS requires hospitals within federal payment programs to screen for SDOH among Medicare populations using this standardized screener, and to submit 2 quality measures which report how many patients have been screened for SDOH, and how many screened positive for SDOH needs.⁵ Other standardized screeners include the National Association of Community Health Centers’ (NACHC) Protocol for Responding to and Assessing Patient Assets, Risks, and Experiences (PRAPARE) tool that was developed to make SDOH screening consistent within community health centers nationwide.⁶ Individual health systems have also developed their own SDOH screening tools, tailored to the needs of their own patient populations.^6
-9

Previous studies have evaluated the implementation of standardized SDOH screening within clinical settings, focusing on implications for staffing and workflow,⁸ barriers to adoption,⁹ and the process of connecting patients screened for SDOH with external resources.¹⁰ These studies have found that clinical settings face challenges integrating SDOH into existing workflows, including countering the idea that screening is optional or only required for certain patient populations,^8,11 and maintaining flexibility in the face of changing requirements.⁸ Other challenges include low patient literacy,^8,11 technical challenges related to the screening platform,¹¹ and overburdened staff.^7,8,10
-12 Studies also noted the importance of having a clear process for referring patients to community resources, and tracking patients’ progress after the referral is made.^7,10,12

However, less is known about where screening gaps may exist following initial implementation based on facility characteristics. For example, studies show that smaller facilities lag behind larger facilities in the adoption of evidence-based practices and implementing changes to existing workflow, which may be relevant for conducting SDOH screening.^13
-15 Similarly, healthcare organizations in safety-net communities struggle with limited funding,¹⁶ and have fewer resources available for investing in new technologies or staff training required for implementing new practices.^17,18 This study aims to fill this gap in knowledge by assessing SDOH screening rates at a large, multi-site, urban integrated public healthcare system. In particular, we examine whether differences emerge in screening rates based on facility size, visit type, and characteristics of the surrounding community. Findings from this study can identify barriers to screening on a health system level, and pinpoint organizational factors that are important to address to ensure that SDOH screening is comprehensively adopted across all clinic types.

Methods

Study Setting and Sample

This study uses electronic health record (EHR) data obtained from 61 NYC Health + Hospitals (NYC H+H) primary care sites from 2019 to 2022, which was extracted from the Epic electronic health record system. NYC H+H is a large integrated public healthcare system consisting of eleven hospitals and 30 community health centers across New York City.¹⁹ Patients less than 18 years of age were excluded from this analysis. Since 2017, NYC H+H began encouraging clinics to screen patients at least once annually for SDOH-related needs using a standardized screener within routine primary care visits.⁷ We included in-person and telemedicine adult primary care visits for new and established patients, which were identified using visit type identifiers in Epic. The data were analyzed on a visit-level, and eligible visits that recorded a response to an SDOH screening question (ie, either yes or no) were categorized as “SDOH Screened.” Eligible visits that did not record any answer to an SDOH screening question were marked as “Unscreened.” Based on visit volume, we categorized facilities as small, medium. Lastly, sociodemographic data on the community surrounding each facility was obtained from the AHRQ Social Determinants of Health Database.²⁰ Variables represent the percent of the population within the zip code where each clinic is located that are uninsured, unemployed, receiving Supplemental Nutrition Assistance Program (SNAP) benefits, non-English speaking, have poverty status determined, and rent insecure (defined as having rent equal 50% or more of total household income) for 2021, the most recent year available.

Statistical Analysis

For each facility size category and visit type, we calculated the mean number of screened and unscreened visits across all 3 years included in our analysis, as well as the percentages of different neighborhood characteristics among visits that were screened and unscreened. We then conducted 4 multivariate logistic regression models predicting the odds of screening for any SDOH and predicting screening for specific SDOH needs (housing, food, and medical cost assistance) based on facility type, facility size, and the sociodemographic characteristics of the surrounding community. We also conducted bivariate logistic regression predicting the odds of any SDOH screening and for specific SDOH needs with the same set of predictors, results of which are reported in Appendix Table 1. Analyses were conducted with Stata SE17. This study using anonymized visit-level data was approved by the institutional review board of New York University.

Results

Table 1 contains results from our descriptive analyses. Among the 3 212 650 visits included in our analyses, 16.90% (n = 542,953) had results from an SDOH screening recorded. Only 7.67% (n = 23,656) of visits in small facilities included SDOH screening, compared with 21.89% (n = 70,887) of visits in mid-size facilities and 17.40% (n = 445,127) of visits in large facilities. Similarly, just 1.52% (n = 9,467) of telemedicine visits had an SDOH screening recorded, compared with 27.12% (n = 524,872) of office-based visits. When looking at the factors in the community where the clinics were located, for visits with an SDOH screening recorded, the mean uninsured rate (7.96%; SD: 3.58,) as well as the percentage of individuals that received SNAP benefits (26.17%; SD: 11.87,) had poverty status determined (20.33%; SD: 8.81), were rent insecure (30.52%; SD: 3.96,) and had limited English proficiency (10.99%; SD: 5.98) was higher compared with visits that did not have an SDOH screening recorded.

Table 1.

Descriptive Statistics of Visits With Reported SDOH Screening Results Within NYC Health +Hospitals Primary Care Sites for Adults≥18, 2019 to 2022.

Variable	SDOH screening recorded	No SDOH screening recorded	Total/overall
Number of visits (%)*	542 953 (16.90)	2 669 697 (83.10)	3 212 650
Number of visits (%) based at:*
Large facilities	445 127 (17.40)	2 113 058 (82.60)	2 558 185 (80.18)
Mid-size facilities	70 887 (21.89)	252 926 (78.11)	323 813 (10.15)
Small facilities	23 656 (7.67)	284 743 (92.33)	308 399 (9.67)
Number of visits (%) categorized as:*
Office visit	524 872 (27.12)	1 410,372 (72.88)	1 935 244 (75.62)
Telemedicine visit	9 467 (1.52)	614 327 (98.48)	623 794 (24.38)
% Uninsured in surrounding community (mean and SD)	7.96 (3.58)	7.08 (2.99)	7.23 (3.12)
% Receiving SNAP benefits in surrounding community (mean and SD)	26.17 (11.87)	21.89 (11.59)	22.61 (11.75)
% Limited English proficiency in surrounding community (mean and SD)	10.99 (5.98)	9.55 (6.65)	9.79 (6.57)
% Poverty insecure in surrounding community (mean and SD)	20.33 (8.81)	17.08 (7.45)	17.63 (7.80)
% Rent insecure in surrounding community (mean and SD)	30.52 (3.96)	29.58 (4.52)	29.74 (4.45)

SDOH screening recorded and No SDOH screening columns report row percentages and total/overall column reports column percentages.

In the multivariate logistic regression model predicting the occurrence of any SDOH screenings (Table 2), a visit had significantly lower odds of having SDOH screener information recorded if based at a midsize facility (AOR: 0.85; 95% CI: 0.84-0.86) or small facility (AOR: 0.35; 95% CI: 0.34-0.35) compared with a large facility, or if the visit was a telemedicine visit (AOR: 0.03; 95% CI: 0.03-0.04) compared with an in-person visit. A visit had significantly higher odds of having an SDOH screener recorded if the visit was based at a facility located in a zip-code with a higher percentage of individuals who were uninsured (AOR: 1.10; 95% CI: 1.10-1.11), had limited English proficiency (AOR:1.04; 95% CI: 1.42-1.05), had an income below the poverty line (AOR: 1.20; 95% CI: 1.19-1.20), or were rent insecure (AOR: 1.04; 95% CI: 1.04-1.04). A visit was also more likely to have results from any SDOH screener recorded if the visit was based at a facility located in a zip-code with a lower percentage of individuals who receive SNAP benefits (AOR: 0.91, 95% CI: 0.91-0.91). These outcomes were nearly identical to the results of multivariate logistic models predicting the occurrence of screening for specific SDOH needs (housing, food insecurity, and medical cost assistance,) with the exception of screening for housing needs, which had significantly lower odds of occurring if based in a facility located in a zip-code with a higher number of uninsured individuals (AOR: 0.98; 95% CI: 0.98-0.99).

Table 2.

Logistic Regression Results Examining the Odds of Screening for SDOH Primary Clinics at Visits Within NYC Health +Hospitals for adults≥18, 2019 to 2022 (Odds Ratio With 95% Confidence Interval in Parentheses).

Variable	Any SDOH screening	Screened for housing needs	Screened for food insecurity	Screened for medical cost needs
Facility size
Large (base)	—	—	—	—
Mid-size	0.85 (0.84-0.86)**	0.74 (0.73-0.75)**	0.77 (0.76-0.78)**	0.84 (0.83-0.85)**
Small	0.35 (0.34-0.35)**	0.52 (0.51-0.53)**	0.36 (0.35-0.37)**	0.36 (0.36-0.37)**
Visit type
In-person (base)	—	—	—	—
Telemedicine	0.03 (0.03-0.04)**	0.03 (0.03-0.04)**	0.03 (0.03-0.03)**	0.04 (0.03-0.04)**
% Uninsured	1.10 (1.10-1.11)**	0.98 (0.98-0.99)**	1.10 (1.10-1.10)**	1.10 (1.09-1.10)**
% Receives SNAP	0.91 (0.91-0.91)**	0.93 (0.93-0.93)**	0.91 (0.91-0.92)**	0.91 (0.91-0.92)**
% With limited English proficiency	1.04 (1.42-1.05)**	1.02 (1.02-1.02)**	1.04 (1.04-1.04)**	1.04 (1.04-1.04)**
% Poverty status determined	1.20 (1.19-1.20)**	1.18 (1.18-1.19)**	1.19 (1.19-1.20)**	1.20 (1.19-1.20)**
% Rent insecure	1.04 (1.04-1.04)**	1.04 (1.04-1.04)**	1.03 (1.03-1.03)**	1.04 (1.04-1.04)**

P < .05. **P < .001.

Discussion

This paper examined differences in SDOH screening rates based on facility and visit type, and found that in-person visits, as well as visits that occurred in larger facilities, were significantly more likely to have a screening recorded. Additionally, visits occurring in a community with higher SDOH needs, in particular a higher percentage of uninsured, low income, and rent-insecure, as well as individuals with limited English proficiency, were significantly more likely to have recorded SDOH screenings.

With SDOH screening, as well as screening for specific social needs, we found size to be a significant predictor, with large and midsize facilities significantly more likely to conduct screenings compared with small facilities. This is consistent with previous literature showing that smaller practices may encounter additional barriers related to the implementation of evidence-based practices and new technologies such as lack of resources and a lower diversity of staff roles.^13
-15 However, the facilities included in our analysis are affiliated with a large healthcare system, which provides support to overcome many of the challenges to implementing SDOH screening faced by smaller, standalone primary care clinics, such as resource constraints, integrated EHR systems, staff training, and standardized screening tools.^6,12,21 As such, our results indicate that facility size itself may be a key determinant in the successful adoption of new practices, though additional research is needed to examine the mechanism underlying this finding.

The finding that SDOH screenings are more likely to be conducted within in-person primary care consults compared with telemedicine visits is consistent with the literature surrounding screening barriers and the importance of the patient-provider relationships. In particular, previous studies have demonstrated that providers find SDOH screenings easier to conduct when they have established relationships with patients, and that patients are more comfortable discussing their SDOH needs with providers whom they trust.^22,23 There is a great deal of evidence reporting concerns around privacy, communication barriers, and lower-levels of intimacy between patients and providers within telemedicine visits, which may negatively impact the ability for both patients and providers to successfully complete an SDOH screening.²² Additionally, prior research has documented that there are fewer resources available within primary care visits that occur via telemedicine, including the use of translator services, which may impact rates of SDOH screening for certain patient populations.^24
-26 Given that a lack of resources, specifically a lack of sufficient qualified staff, has been cited as a barrier to SDOH screening more generally, strategies that aim to increase staff uptake in screening within both telemedicine and in-person visits should emphasize education and training;²⁷ however, health system leaders must consider staff burnout, and consider additional strategies, such as training multiple staff types (social worker, nurse, and case managers) to perform SDOH screening, to ensure that responsibility is shared across care teams.²⁷

In terms of the community characteristics, we found that in general, communities with a greater burden of SDOH needs also had higher rates of SDOH screening, with the exception of communities with a higher percentage of SNAP recipients. Although SNAP has been successful at reducing the impact of food insecurity on US families nationwide, barriers to SNAP enrollment remain, many of which are particularly relevant to minority and immigrant populations.^28,29 These include logistical barriers such as lacking necessary documents or not knowing how to apply,^28,29 language barriers, as well as fears of repercussions, such as child removal or deportation.^28,29 Additionally, many vulnerable populations, including undocumented immigrants, are not eligible to receive SNAP benefits, and families comprised of noncitizen immigrants are significantly less likely to enroll.^28,29 As such, because SNAP eligibility requirements exclude a large portion of people who may actually need nutrition assistance, the measure of SNAP enrollment is an imperfect measure that may not be indicative of community need.

This analysis is subject to 5 main limitations. First, we rely on SDOH screening data obtained through the EHR, which does not include information obtained through physician notes, claims data (such as physician recorded Z-codes) or undocumented conversations. Second, our analysis is conducted at the visit level, and while we examine visit, facility, and community characteristics that contribute to SDOH screening rates, our analysis does not capture differences in screening rates that are driven by patient or staff provider level characteristics. Third, we do not assess how time factors may affect the outcomes in our analysis. Relatedly, because the optimal interval for SDOH screening is unknown, screening for SDOH may not be appropriate at every visit. Finally, the catchment areas of the primary care clinics included in our study are variable, and patients may not live in the same zip code as the clinic; as such, area prevalence of different neighborhood factors may not be directly correlating to patients’ needs.

Despite these limitations, our study adds to the growing literature on SDOH screening within healthcare institutions by examining the facility and visit level factors that affect screening rates. Our results are particularly salient given recent policy attention devoted to increasing rates of SDOH screening. For example, the Joint Commission (JC) recently released new SDOH screening guidelines which emphasize the importance of screening for health-related social needs within all JC-accredited organizations.³⁰ Given that many organizations currently lack capacity to screen all patients, the JC guidelines require their organizations to conduct screenings within a representative sample of their patient population, with the eventual goal of screening all patients. In particular, the current guidelines encourage organizations to conduct screenings on the most high-risk patients, as well as to screen for the needs that are most relevant to the populations they serve.³⁰ Our results support the implementation of these policies by identifying factors which affect screening rates in different visit settings.

Conclusion

Our study found important differences in SDOH screening rates at a large, urban public health system based on size, visit type, and community level characteristics. In particular, our findings point to barriers related to facility size and telemedicine workflow that should be addressed to increase uptake of SDOH screening within different visits and facility types. Policymakers and health system executives wishing to improve SDOH screenings rates within their organizations should focus on developing solutions to these barriers, for example through developing a telemedicine specific SDOH screening workflow. Future research should focus on assessing patient and staff-level differences in screening rates, including a wider variety of data sources (ie, Z-codes) to examine facility and visit level differences, and further investigating barriers to SDOH screeners in small facilities affiliated with larger organizations.

Supplemental Material

sj-docx-1-jpc-10.1177_21501319231207713 – Supplemental material for Assessing Differences in Social Determinants of Health Screening Rates in a Large, Urban Safety-Net Health System

Supplemental material, sj-docx-1-jpc-10.1177_21501319231207713 for Assessing Differences in Social Determinants of Health Screening Rates in a Large, Urban Safety-Net Health System by Zoe Lindenfeld, Kevin Chen, Supriya Kapur and Ji Eun Chang in Journal of Primary Care & Community Health

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work is supported by a grant from the Novartis Foundation.

Ethical Approval

This study using anonymized visit-level data was approved by the institutional review board of New York University, Washington Square Campus.

ORCID iD

Zoe Lindenfeld

Supplemental Material

Supplemental material for this article is available online.

References

Adler

Glymour

Fielding

Addressing social determinants of health and health inequalities. Jama. 2016;316(16):1641-1642.

Doran

Rahai

McCormack

, et al. Substance use and homelessness among emergency department patients. Drug Alcohol Depend. 2018;188:328-333. doi:10.1016/j.drugalcdep.2018.04.021

Wilder

Kulie

Jensen

, et al. The impact of social determinants of health on medication adherence: a systematic review and meta-analysis. J Gen Intern Med. 2021;36:1359-1370.

Walker

Smalls

Campbell

Strom Williams

Egede

LE.

Impact of social determinants of health on outcomes for type 2 diabetes: a systematic review. Endocrine. 2014;47(1):29-48. doi:10.1007/s12020-014-0195-0

Centers for Medicare & Medicaid Services. FY 2023 IPPS proposed rule home page. CMS. 2023. Accessed July 8, 2023. https://www.cms.gov/medicare/acute-inpatient-pps/fy-2023-ipps-proposed-rule-home-page

LaForge

Gold

Cottrell

, et al. How 6 organizations developed tools and processes for social determinants of health screening in primary care. J Ambulatory Care Manage. 2018;41(1):2-14. doi:10.1097/JAC.0000000000000221

Calvo-Friedman

Clapp

Kumar

Adams

Gogia

Davis

Scaling primary care social needs screening and referrals in New York City’s safety-net health system. NEJM Catalyst. June 21, 2023. Accessed July 9, 2023. https://catalyst.nejm.org/doi/full/10.1056/CAT.22.0371?casa_token=JpVp_exRXHkAAAAA%3AbVbljbLRQq8egPW9RFAxjpffU22teDXakY3pOImvTL0hoQi0XIDfg79aXNfKyD6qqeQbrIqhVBWpxSVe

Berry

Paul

Massar

Marcello

Krauskopf

Social needs screening and referral program at a large US public hospital system, 2017. Am J Public Health. 2020;110(S2):S211-S214. doi:10.2105/AJPH.2020.305642

Buitron

la Vega

Losi

Sprague Martinez

, et al. Implementing an EHR-based screening and referral system to address social determinants of health in primary care. Med Care. 2019;57:S133. doi:10.1097/MLR.0000000000001029

10.

Thomas-Henkel

Schulman

Screening for social determinants of health in populations with complex needs: implementation considerations. Cent Health Care Strateg. 2017 October.

11.

Meyer

Lerner

Phillips

Zumwalt

Universal screening of social determinants of health at a large US academic medical center, 2018. Am J Public Health. 2020;110(S2): S219-S221. doi:10.2105/AJPH.2020.305747

12.

Herrera

Brochier

Pellicer

Garg

Drainoni

ML.

Implementing social determinants of health screening at community health centers: clinician and staff perspectives. J Prim Care Community Health. 2019;10:2150132719887260.

13.

Burns

Goldsmith

Sen

. Horizontal and vertical integration of physicians: a tale of two tails. In: Goes

Savage

Friedman

, eds. Annual Review of Health Care Management: Revisiting The Evolution of Health Systems Organization. Vol 15. Advances in Health Care Management. Emerald Group Publishing Limited; 2014:39-117. doi:10.1108/S1474-8231(2013)0000015009

14.

Rittenhouse

Casalino

Shortell

, et al. Small and medium-size physician practices use few patient-centered medical home processes. Health Aff (Millwood). 2011;30(8): 1575-1584. doi:10.1377/hlthaff.2010.1210

15.

O’Malley

Tynan

Cohen

Kemper

Davis

MM.

Coordination of care by primary care practices: strategies, lessons and implications. Res Brief. 2009;12:1-16.

16.

Shah

Chernew

Beaulieu

Supporting health care delivery in low-income areas during COVID-19. Health Affairs Forefront. 2020. doi:10.1377/forefront.20200715.332672

17.

Payán

Sloane

Illum

, et al. Catalyzing implementation of evidence-based interventions in safety net settings: a clinical–community partnership in South Los Angeles. Health Promot Pract. 2017;18(4):586-597. doi:10.1177/1524839917705418

18.

Mejia

Zoorob

Gonzalez

Mosqueda

Levine

Key informants’ perspectives on implementing a comprehensive lung cancer screening program in a safety net healthcare system: leadership, successes, and barriers. J Cancer Educ. 2022;37(4):1144-1151. doi:10.1007/s13187-020-01931-x

19.

NYC Health + Hospitals. Locations. 2023. Accessed April 13, 2023. https://www.nychealthandhospitals.org/locations/

20.

Agency for Healthcare Research and Quality. Social determinants of health database. Published 2023. Accessed April 13, 2023. https://www.ahrq.gov/sdoh/data-analytics/sdoh-data.html

21.

Bleacher

Lyon

Mims

Cebuhar

Begum

. The feasibility of screening for social determinants of health: seven lessons learned. AAFP. Published 2019. Accessed July 9, 2023. https://www.aafp.org/pubs/fpm/issues/2019/0900/p13.html

22.

Bernhardt

King

Telehealth and food insecurity screenings: challenges and lessons learned. Mhealth. 2022;8:10. doi:10.21037/mhealth-21-31

23.

De Marchis

Torres

Fichtenberg

Gottlieb

LM.

Identifying food insecurity in health care settings: a systematic scoping review of the evidence. Fam Community Health. 2019;42(1):20. doi:10.1097/FCH.0000000000000208

24.

Batsis

McClure

Weintraub

, et al. Barriers and facilitators in implementing a pilot, pragmatic, telemedicine-delivered healthy lifestyle program for obesity management in a rural, academic obesity clinic. Implement Sci Commun. 2020;1(1):83. doi:10.1186/s43058-020-00075-9

25.

Chang

Lindenfeld

Albert

, et al. Telephone vs. Video visits during COVID-19: safety-net provider perspectives. J Am Board Fam Med. 2021;34(6):1103-1114. doi:10.3122/jabfm.2021.06.210186

26.

McCann

Telecommunication with translators: are their voices heard?

Family Practice. Oxford Academic. Published 2022. Accessed July 9, 2023. https://academic.oup.com/fampra/article-abstract/39/4/783/6356811

27.

Kostelanetz

Pettapiece-Phillips

Weems

, et al. Health care professionals’ perspectives on universal screening of social determinants of health: a mixed-methods study. Popul Health Manag. 2022;25(3):367-374. doi:10.1089/pop.2021.0176

28.

Pinard

Bertmann

FMW

Byker Shanks

, et al. What factors influence SNAP participation? Literature reflecting enrollment in food assistance programs from a social and behavioral science perspective. J Hunger Environ Nutr. 2017; 12(2):151-168. doi:10.1080/19320248.2016.1146194

29.

Pelto

Ocampo

Garduño-Ortega

, et al. The nutrition benefits participation gap: barriers to uptake of SNAP and WIC among latinx american immigrant families. J Community Health. 2020;45(3):488-491. doi:10.1007/s10900-019-00765-z

30.

The Joint Commission. New requirements to reduce health care disparities. Published 2022. Accessed April 13, 2023. https://www.jointcommission.org/-/media/tjc/documents/standards/r3-reports/r3_disparities_july2022-6-20-2022.pdf

Supplementary Material

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