How often do physicians record the International Classification of Diseases 10thRevision “Z-Codes” for social determinants of health? A nationwide analysis using the National Ambulatory Medical Care Survey

How often do physicians record the International Classification of Diseases 10th Revision “Z-Codes” for social determinants of health? 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A nationwide analysis using the National Ambulatory Medical Care Survey Robert Wooten , Andrea McKinnond , Sobia S. Hussaini , Amber K. Brooks , Charles T. Jensen , Ethan Stonerook , Chris Gillette doi: https://doi.org/10.1101/2025.04.03.25325190 Robert Wooten 1 Department of PA Studies, Wake Forest School of Medicine MS, PA-C Roles: Assistant Professor Find this author on Google Scholar Find this author on PubMed Search for this author on this site Andrea McKinnond 1 Department of PA Studies, Wake Forest School of Medicine MMS, PA-C Roles: Assistant Professor Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sobia S. Hussaini 1 Department of PA Studies, Wake Forest School of Medicine MHA Roles: Assistant Professor Find this author on Google Scholar Find this author on PubMed Search for this author on this site Amber K. Brooks 2 Department of Anesthesiology, Wake Forest School of Medicine MD Roles: Assistant Professor Find this author on Google Scholar Find this author on PubMed Search for this author on this site Charles T. Jensen 1 Department of PA Studies, Wake Forest School of Medicine MS, MMS, PA-C Roles: Assistant Professor Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ethan Stonerook 1 Department of PA Studies, Wake Forest School of Medicine MMS, PA-C Roles: Assistant Professor Find this author on Google Scholar Find this author on PubMed Search for this author on this site Chris Gillette 3 Department of PA Studies and Department of Epidemiology and Prevention, Wake Forest School of Medicine PhD Roles: Assistant Professor Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: cgillett{at}wakehealth.edu Abstract Full Text Info/History Metrics Data/Code Preview PDF ABSTRACT Objective Racial/ethnic minoritized patients and those who lack health insurance are at the highest risk for experiencing healthcare disparities, mainly because of systemic disparities in the provision of care, termed social determinants of health (SDOH). Starting with the International Classification of Diseases 10 th revision (ICD10), physicians can record SDOH factors that may impact their patients’ health. The objective of this study was to describe the recording of SDOH ICD10 ‘Z-codes’ in non-federal physician clinic visits and identify the characteristics of those visits. Methods This was a cross-sectional analysis of the National Ambulatory Medical Care Survey (NAMCS) in 2016 and 2018, the only two years with available ‘Z-code’ data. We used Andersen’s Behavioral Model of Health Services Utilization to select variables for analysis. Visits for children and adults were included in the analysis. We used appropriate unweighted and weighted descriptive statistics which accounts for the complex survey design to analyze the data. Results There were a total of 23,118 unweighted visits representing 1,744,110,765 weighted visits for 2016 and 2018. In 2016, 0.21 per 100 visits (n=32 unweighted, n=1,876,542 weighted) that included a ‘Z-code’. In 2018, there were 0.08 per 100 visits (n=10 unweighted, n=681,948 weighted visits) that included a ‘Z-code’. Almost all (92.86%, n=39) physician Z-code recording was for patients who was of White race and the majority of visits with a ‘Z-code’ (59.52%, n=25) were paid for by private insurance. Conclusions Consistent with smaller-scale research, these results show that physicians may not be using ‘Z-codes’ with their racial ethnic minoritized patients, suggesting a potential disparity in recording SDOH ‘Z-codes’. More work is needed to understand methods to improve uptake of ‘Z-codes’. 1. Introduction Social determinants/drivers of health (SDOH) are non-medical sociocultural factors, such as poverty, job/food insecurity, racism, homelessness, reliable transportation, etc., that negatively impact peoples’ health. 1 It is well-known that Black, Hispanic/Latino, American Indian, and Alaska Native Americans are at high risk of experiencing SDOH disparities that impact health outcomes. 2 Racial and ethnic minorities in the United States (US) face longstanding health disparities which primarily stem from social hierarchies and not underlying genetic differences. One of the most well-documented examples of racial health disparities reveals that Black Americans’ life expectancy, particularly Black men’, has always been lower than White Americans. In 2018, Black Americans’ life expectancy was 3.6 years shorter than non-Hispanic White Americans’. 3 According to the Centers for Disease Control and Prevention (CDC), the most common causes of death for Black Americans are heart disease, cancer, Alzheimer’s disease, and stroke. 4 Black Americans’ reports of why their health outcomes are poorer compared to other racial groups is the lack of high-quality medical care where they live as well as being more likely to be uninsured. 6 This disparity in lack of access to care is more pronounced in the southern US in states that have not expanded Medicaid through the Affordable Care Act. 7 – 8 In 2016, the International Classification of Diseases, 10 th Revision (ICD10) introduced SDOH diagnosis codes, known colloquially as ‘Z-codes’, allowing organizations and providers to document SDOH factors which negatively impact their patients’ health. 9 Addressing SDOH has been shown to improve health behaviors and health outcomes so it is important for providers to recognize SDOH factors that their patients are experiencing. 10 – 11 There has yet to be a nationally-representative examination of the use of ‘Z-codes’ in Americans without Medicare. Prior research has shown that less than 1% of visits had a Z-code being recorded; however, most of the previous research has focused on state-wide analyses or of specific populations (e.g., traditional Medicare). 12 – 14 To our knowledge, there has been no investigation of SDOH factors using the National Ambulatory Medical Care Survey (NAMCS). The NAMCS was first conducted in 1973 and has been conducted each year since 1989. 15 The purpose of this study was to describe the use of ‘Z-codes’ in non-federal physician clinic visits and identify the characteristics of those visits. 2. Materials and Methods 2.1 Study Overview This study was approved by the Institutional Review Board (IRB) at the first authors’ institution. We conducted a cross-sectional analysis of the National Ambulatory Medical Care Survey (NAMCS) in 2016 and 2018, the only two years with available ‘Z-code’ data. The NAMCS is nationally representative of non-federal physician visits. All visits were included in the analysis because the ‘Z-codes’ can be recorded for all patients. The NAMCS survey uses a multistage probability sampling design that produces an unbiased national estimation. More information on the NAMCS survey and the estimation procedure can be found on the National Center for Health Statistics website. 16 2.2 Theory and Measures We used Andersen’s Behavioral Model of Health Services Utilization to guide variable selection. 17 This theory states that health care utilization is a complex interplay between predisposing factors (e.g., patient age, gender, etc.), enabling factors (e.g., insurance, geographic location, availability of clinics, etc.), and need factors (e.g., perceived health status, perceived severity of the health problem). Figure 1 presents our operationalization of Andersen’s Behavioral Model of Health Services Utilization using the available NAMCS variables. Download figure Open in new tab Figure 1: Operationalization of Andersen’s Behavioral Model of Health Services Utilization 2.3 Statistical Analyses We identified all visits in which an ICD-10 code between Z550 and Z659 were recorded by the physician in any of the five diagnosis code variables. We used appropriate unweighted and weighted descriptive statistics using PROC SURVEYFREQ and PROC SURVEYMEANS to describe the patient visits in which a physician recorded a ‘Z-code’ to account for the complex survey design. SAS v9.4 (Cary, NC) was used to analyze the data. 3. Results and Discussion 3.1 Results Table 1 presents the visit characteristics. In 2016, there were 13,165 unweighted visits (883,725,126 weighted visits), of which 32 unweighted visits (1,876,542 weighted visits, 0.21/100 visits) included a ‘Z-code’. In 2018, there were 9,953 unweighted visits (860,385,639 weighted visits), of which 10 unweighted visits (681,948 weighted visits, 0.08 per 100 visits) visits included a ‘Z-code’. In 2016, 31 of the 32 ‘Z-codes’ (96.88%) were recorded for patients whose race was identified as White only, 0 for Black patients, and 1 for Other (Asian). In 2018, only 1 of the 10 (90%) ‘Z-codes’ was recorded for a Black patient. Table 2 presents all of the ‘Z-codes’ that were recorded during 2016 and 2018. The two most common ‘Z-codes’ were for ‘Other problems related to social environment’ (n=5) and ‘Problems in relationship with spouse or partner’ (n=5). View this table: View inline View popup Table 1: Visit Characteristics with a Z-Code (unweighted n=42, weighted n=2,558,490) View this table: View inline View popup Download powerpoint Table 2: All Social Determinants of Health ICD-10 Z-Codes Used 3.2 Discussion It is well-known that non-White Americans, especially those who identify as Black, are at higher risk for SDOH factors negatively impacting their health outcomes. 2 We found, using a nationally-representative dataset of non-federal physician clinic visits in the United States, that more than 9 out of every 10 visits that included a SDOH ICD10 codes were used for White patients with private health insurance. Previous research has shown that identifying and addressing SDOH factors can improve health behaviors and outcomes. 10 – 11 The fact that physicians are not currently documenting SDOH for non-White Americans is indicative of a potential racial and ethnic health disparity. The ‘Z-codes’ use were low for everyone across both years, however, this is extremely problematic for non-White Americans since they are some of the most affected by SDOH factors. Additionally, those who have no insurance or who are on Medicare and/or Medicaid are also more likely to experience SDOH factors. The rates of ‘Z-code’ usage in our study are similar but lower than other studies. 12 – 14 The Centers for Medicare and Medicaid study that examined ‘Z-codes’ recorded for non-elderly patients were different from our study, with the exception for spousal relationship problems, than patients who were on fee-for-service Medicare. 14 In this study and in the Medicare study, women accounted for the majority of visits in which ‘Z-codes’ were recorded. 14 In fact, the percentage of women who had a ‘Z-code’ recorded in the Medicare study (54.6%) was remarkably similar to our present study (54.76%). 14 This may be attributed to ‘benevolent sexism’ (the belief that a particular gender is frail and in need of protection from harm) which typically impacts women. 18 . Another possibility is that physician judgement and heuristics in the American culture leads to an increase in ‘Z-code’ usage for women patients. 18 Future research should investigate whether these factors or others impact physician’s’ recording of ‘Z-codes’ in certain populations. It is also notable that there were no visits in which PAs and NPs were included that a ‘Z-code’ was recorded. We also found that allopathic physicians were responsible for almost all of the ‘Z-code’ recording nationally (95.24%). This indicates not only a potential racial/ethnic health disparity, but also a professional behavior disparity. Future research needs to identify potential reasons for these discrepancies so that more physicians, PAs, and NPs can document SDOH factors for their patients. While the results of our study are interesting, there are limitations that affect generalizability and internal validity. First, NAMCS only collects 5 variables where the physician can document a diagnosis code, so it is possible that the physician may have recorded a ‘Z-code’ if the patient had more than 5 diagnoses. The study published by Medicare looked at all diagnoses, which may account for the differences in our findings compared to that earlier publication. 14 Second, diagnosis coding may be impacted by the physicians’ clinic, which is not possible to identify in the NAMCS datasets. Another potential source of bias is the physicians themselves: physicians may wish to avoid perceived bias by not the ‘Z-codes’ for patients who are socio-demographically at-risk of SDOH factors. We also did not compare visits that included a ‘Z-code’ with visits that did not include a ‘Z-code’ to identify potential correlates for ‘Z-code’ recording. Future research should compare and examine these visits to identify potentially modifiable features to increase the rate of ‘Z-code’ reporting. 3.3 Conclusion Improving SDOH documentation can ensure that physicians are aware of and can help mitigate the effect of these factors on Americans’ health. This study provides potential evidence of a potentially unintended healthcare disparity in that American physicians only recorded 1 SDOH ‘Z-code’ for a demographically at-risk patient while recording almost 90% of ‘Z-codes’ for White patients who have private insurance. 3.4 Implications The results suggest that physicians may not be using ‘Z-codes’ with their racial/ethnic minoritized patients. This may propagate health disparities by not identifying preventable SDOH factors that are negatively impacting their patients’ health. Future research needs to investigate whether this is a true disparity or a limitation of the NAMCS datasets. Data Availability All data produced in the present study are available upon reasonable request to the authors References 1. ↵ Social determinants of health . World Health Organization website . https://www.who.int/health-topics/social-determinants-of-health#tab=tab_1 . Accessed July 29, 2022 . 2. ↵ Noonan AS , Velasco-Mondragon HE , Wagner FA . Improving the health of African Americans in an overdue opportunity for social justice . Public Health Rev . 2016 ; 37 : Article 12. 3. ↵ Schwandt H , Currie J , Bar M , et al. Inequality in mortality between Black and White Americans by age, place, and cause and in comparison to Europe, 1990 to 2018 . Proc Natl Acad Sci U S A . 2021 ; 118 ( 40 : e2104684118 . DOI: 10.1073/pnas.2104684118 . OpenUrl Abstract / FREE Full Text 4. ↵ Leading causes of death-females-non-Hispanic Black-United States , 2018 . Centers for Disease Control and Prevention website . https://www.cdc.gov/women/lcod/2018/nonhispanic-black/index.htm . Published March 3, 2022. Accessed February 17, 2023 . 5. Leading causes of death-males-non-Hispanic Black-United States , 2018 . Centers for Disease Control and Prevention website . https://www.cdc.gov/minorityhealth/lcod/men/2018/nonhispanic-black/index.htm . Published March 2, 2022. Accessed February 17, 2023 . 6. ↵ Funk C. Black Americans’ views about health disparities, experiences with health care . Pew Research Center website . https://www.pewresearch.org/science/2022/04/07/black-americans-views-about-health-disparities-experiences-with-health-care/#:~:text=A%20majority%20of%20Black%20adults,it%20is%20not%20a%20reason. Published April 7, 2022. Accessed February 17, 2023 . 7. ↵ Health insurance coverage and access to care among Black Americans: recent trends and key challenges . United States Department of Health and Human Services website . https://aspe.hhs.gov/sites/default/files/documents/08307d793263d5069fdd6504385e22f8/black-americans-coverages-access-ib.pdf . Published February 22, 2022. Accessed February 17, 2023 . 8. ↵ Garfield R , Orgera K , Damico A. The coverage gap: uninsured poor adults in states that do not expand Medicaid . Kaiser Family Foundation website . https://www.kff.org/medicaid/issue-brief/the-coverage-gap-uninsured-poor-adults-in-states-that-do-not-expand-medicaid/ . Published January 21, 2021. Accessed February 17, 2023 . 9. ↵ Using Z Codes : The Social Determinants of Health Data Journey to Better Outcomes. Centers for Medicare and Medicaid Services website . https://www.cms.gov/files/document/zcodes-infographic.pdf . Accessed July 29, 2022 . 10. ↵ Nickel S , von dem Knesebeck O. Do multiple community-based interventions on health promotion tackle health inequalities? Int J Equity Health . 2020 ; 19 ( 1 ): Article 157. DOI: 10.1186/s12939-020-01271-8 . OpenUrl CrossRef 11. ↵ Ware KS , Chidume T , Chou C. Social determinants of health and preventable emergency department patient encounters among adults with hypertension . Public Health Nurs . 2023 ; 40 ( 1 ): 171 – 4 . OpenUrl PubMed 12. ↵ Bensken WP , Alberti PM , Stange KC , Sajatovic M , Koroukian SM . ICD-10 Z-code health-related social needs and increased healthcare utilization . Am J Prev Med . 2022 ; 62 ( 4 ): e232 – e241 . OpenUrl CrossRef PubMed 13. Sullivan GA , Gely Y , Donaldson A , et al. Use of International Classification of Disease, 10th Revision, Clinical Modification Z Codes to identify social determinants of health among surgical patients . JAMA Surg . 2022 ; e221211 . DOI: 10.1001/jamasurg.2022.1211 . OpenUrl CrossRef 14. ↵ Maksut JL , Hodge C , Van CD , Razmi A , Khau MT . Utilization of Z codes for social determinants of health among Medicare fee-for-service beneficiaries , 2019 . Centers for Medicare and Medicaid Services website . https://www.cms.gov/files/document/z-codes-data-highlight.pdf . Published September 2021. Accessed August 15, 2022 . 15. ↵ About the Ambulatory Health Care Surveys . Centers for Disease Control and Prevention website . https://www.cdc.gov/nchs/ahcd/about_ahcd.htm . Published December 30, 2021. Accessed January 19, 2023 . 16. ↵ NAMCS Estimation Procedures . Centers for Disease Control and Prevention website . https://www.cdc.gov/nchs/ahcd/ahcd_estimation_procedures.htm . Published November 6, 2015. Accessed February 17, 2023 . 17. ↵ Babitsch B , Gohl D , von Lergerke T. Re-revisiting Andersen’s Behavioral Model of Health Services Use: a systematic review of studies from 1998-2011 . Psychosoc Med . 2012 ; 9 : Doc11 . DOI: 10.3205/psm000089 . OpenUrl CrossRef PubMed 18. ↵ Travis CV , Howerton DM , Szymanski DM . Risk, uncertainty, and gender stereotypes in healthcare decisions . Women Ther . 2012 ; 35 ( 3-4 ): 207 – 20 . OpenUrl View the discussion thread. Back to top Previous Next Posted April 04, 2025. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. 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A nationwide analysis using the National Ambulatory Medical Care Survey Robert Wooten , Andrea McKinnond , Sobia S. Hussaini , Amber K. Brooks , Charles T. Jensen , Ethan Stonerook , Chris Gillette medRxiv 2025.04.03.25325190; doi: https://doi.org/10.1101/2025.04.03.25325190 Share This Article: Copy Citation Tools How often do physicians record the International Classification of Diseases 10 th Revision “Z-Codes” for social determinants of health? A nationwide analysis using the National Ambulatory Medical Care Survey Robert Wooten , Andrea McKinnond , Sobia S. Hussaini , Amber K. Brooks , Charles T. 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