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Cited by 5Medical Coding and Health InformationGlobal Cancer Incidence and ScreeningEconomic and Financial Impacts of Cancer

Documentation of individual-level social determinants of health among adults diagnosed with cancer in the United States

Open access
Hsu, Chia-Yun, Henderson, Gabriel, Slejko, Julia F., Hussain, Arif, Swain, Richard Scott, Kalesan, Bindu, Huang, Pei-Lin, Onukwugha, Eberechukwu·2025·Scientific Reports
DOI 10.1038/s41598-025-13825-3PMC PMC12350795
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Abstract

The ICD-10-CM diagnosis codes ranging from Z55 to Z65 (Z codes) capture individual-level social determinants of health (SDOH) and are typically under-coded in individuals’ health records. There is limited characterization of SDOH Z codes among adults diagnosed with cancer and information about oncologists’ perspectives on Z codes. Using pooled cross sections, we selected 35,099 adults from the IQVIA PharMetrics ® Plus for Academics data and 17,852 individuals from linked cancer registry and claims data. The prevalence of comorbidities and metastatic cancer was higher among individuals with Z codes compared to those without Z code claims. The proportion of individuals with any Z code claims ranged from 0.6 to 0.9% in the PharMetrics Plus data and from 1.4 to 2.1% in the linked data. Among 112 medical oncologists/hematologists who responded to a survey about SDOH Z codes, 54% had not heard of Z codes to document SDOH. 26% reported that lack of reimbursement makes it difficult to increase their documentation and 64% would consider using Z codes if they are tied to payer reimbursement. Individuals with Z codes had greater comorbidity and cancer burden. Additional research is needed and depends on improved coding of SDOH Z codes.

AI snapshot

Written from the paper’s full text.

TL;DR

This study characterizes documentation of ICD-10-CM Z55–Z65 social determinants of health (SDOH) Z codes among adults diagnosed with breast, colorectal, lung, or prostate cancer using private payer and SEER-Medicare claims, and surveys oncologists about Z-code use. Z-code documentation was very low and associated with greater comorbidity and metastatic disease, while many oncologists were unaware of Z codes and reimbursement was a key potential facilitator.

Key findings

  • Among 35,099 adults in IQVIA PharMetrics® Plus for Academics and 17,852 in linked SEER-Medicare data, any Z-code claims ranged from 0.6 to 0.9% and 1.4 to 2.1%, respectively; claim-level Z-code documentation ranged from 0.04 to 0.09% and 0.03 to 0.05%.
  • Individuals with Z codes had higher disease burden: female 70% vs. 58% in PharMetrics Plus and 67% vs. 53% in SEER-Medicare; metastatic cancer 19% vs. 12% and 13% vs. 8%; 3 or more comorbidities 18% vs. 10% and 47% vs. 29%.
  • Top 2019 Z codes were Z57.2 occupational exposure to dust (26%), Z60.2 problems related to living alone (14%), and Z65.8 other specified psychosocial circumstances (14%) in PharMetrics Plus; in SEER-Medicare, Z60.2 (22%), Z63.4 disappearance and death of family member (10%), and Z65.8 (9%).
  • Among 112 surveyed medical oncologists/hematologists, 54% had not heard of Z codes, 26% reported lack of reimbursement makes documentation difficult, and 64% would consider using Z codes if tied to payer reimbursement.

Methods

  • Pooled cross-sectional analysis of a 10% random sample of PharMetrics Plus 2016–2020 and SEER-Medicare 5% Cancer Files 2016–2019; included adults with breast, colorectal, lung, or prostate cancer and continuous enrollment, with Z codes (ICD-10-CM Z55–Z65) measured at individual, claim, and diagnosis levels.
  • Area-level SDOH measures from PolicyMap were linked at the state level (and county level for SEER-Medicare); descriptive statistics and chi-square/t-tests compared characteristics between individuals with and without Z codes.
  • Oncologist survey administered in 2023 to Cardinal Health Oncology Provider Extended Network summit attendees via audience response system; 112 of 124 invited physicians responded (90.3% response rate), with descriptive frequencies reported.

Limitations

  • The paper states billing codes proxy for SDOH; screening items may map to multiple Z codes, other vocabularies do not ensure complete capture, documented Z codes may be out-of-date or inaccurate, and state-level was the smallest common area, so results may change at smaller area levels.
  • The paper states excluding patients with enrollment periods shorter than 12 months may disproportionately omit individuals with higher social risk factors, likely underrepresenting those who should have a documented Z code.
  • The paper states the oncologist survey used voluntary participation and a convenience sample, so self-selection bias cannot be eliminated and responses may not generalize outside Cardinal Health’s Oncology Provider Extended Network or the broader clinician community.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1038/s41598-025-13825-3

Cites

31 of 31 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

21 of 39 cited sources checked · 11 supported · 0 flagged · accuracy not yet measured

  • Premisecitation

    ICD-10-CM Z codes (Z55-Z65) are typically under-coded in individuals' health records.

    “are typically under-coded in individuals’ health records”
    • Also stated in: § Introduction · plain
      “Z codes remain underutilized in real-world data14–16”
    Quote in the paperSubstantial claim2 of 3 sources checked
    • cites [14] Maksut, J., Hodge, C., Van, C. & Khau, M. A. R. Utilization of Z Codes for Social Determinants of Health Among Medicare Fee-For-Service Beneficiaries, 2019 (2021).

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [15] Liss, D. T. et al. Social needs identified by diagnostic codes in privately insured U.S. Adults. Am. J. Prev. Med. 63, 1007–1016. https://doi.org/10.1016/j.amepre.2022.07.009 (2022). source

      The Discussion’s Limitations section says voluntary Z-code assignment likely led to substantial underdocumentation of social needs, supporting the claim’s central point. But the study examined privately insured U.S. adults during a four-year period ending in 2019, and the authors caution that its documentation patterns may not generalize to people with other insurance or no insurance. (LLM, conf=0.82)

      Experimental — accuracy not yet measured
    • cites [16] Guo, Y. et al. International classification of diseases, tenth revision, clinical modification social determinants of health codes are poorly used in electronic health records. Medicine 99, e23818. https://doi.org/10.1097/md.0000000000023818 (2020). source
  • Premisecitation

    Expanding beyond Z codes to include SNOMED CT, LOINC®, and CPT® codes does not ensure complete capture of all SDOH factors.

    “Moreover, expanding beyond Z codes to include SNOMED CT, LOINC®, and CPT® codes does not ensure complete capture of all SDOH factors33.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [33] Arons, A., DeSilvey, S., Fichtenberg, C. & Gottlieb, L. Documenting social determinants of health-related clinical activities using standardized medical vocabularies. JAMIA Open. 2, 81–88. https://doi.org/10.1093/jamiaopen/ooy051 (2018). source
  • Premisecitation

    Available studies using Medicare and private insurance claims data reported that about 1 in 1,000 claims included Z codes.

    “Available studies using Medicare and private insurance claims data reported that about 1 in 1,000 claims included Z codes14,15.”
    Quote in the paperSubstantial claim1 of 2 sources checked
    • cites [14] Maksut, J., Hodge, C., Van, C. & Khau, M. A. R. Utilization of Z Codes for Social Determinants of Health Among Medicare Fee-For-Service Beneficiaries, 2019 (2021).

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [15] Liss, D. T. et al. Social needs identified by diagnostic codes in privately insured U.S. Adults. Am. J. Prev. Med. 63, 1007–1016. https://doi.org/10.1016/j.amepre.2022.07.009 (2022). source

      The Methods describe a study using private-insurance claims, and the Introduction discusses prior Medicare Z-code studies. But the Results report that 521,334 of 29.5 million adults (1.8%) had a Z code—a proportion of patients, not the claimed roughly 1 in 1,000 claims. Our copy does not report that claims-level figure. (LLM, conf=0.85)

      Experimental — accuracy not yet measured
  • Premisecitation

    Disparities in oncology outcomes, including differences in individual-level socioeconomic factors during the COVID-19 pandemic, have fueled an interest in the documentation of individual-level SDOH.

    “More recently, disparities in oncology outcomes, including differences in individual-level socioeconomic factors during the COVID-19 pandemic, have fueled an interest in the documentation of individual-level social determinants of health (SDOH)8,9.”
    Quote in the paperSubstantial claim1 of 2 sources checked
    • cites [8] American Association for Cancer Research. CancerDisparitiesProgressReport.org [Internet]. http://www.CancerDisparitiesProgressReport.org (2022).

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [9] I. Patel, M. et al. Racial and ethnic disparities in cancer care during the COVID-19 pandemic. JAMA Netw. Open. 5, e2222009. https://doi.org/10.1001/jamanetworkopen.2022.22009 (2022). source

      The Results (Experiences and Concerns Regarding Adverse Social and Economic Effects; page unavailable) report racial and ethnic differences in patient-reported job loss, food insecurity, and childcare loss during the pandemic. The Methods (page unavailable) show that the study collected individual-level socioeconomic information. The paper does not say that these disparities fueled interest in documenting individual-level SDOH. (LLM, conf=0.82)

      Experimental — accuracy not yet measured
  • Premisecitation

    Documented Z codes available in claims may reflect out-of-date or inaccurate information related to the current or historical relevance of the SDOH factor from the patient's perspective.

    “Second, documented Z codes available in claims may reflect out-of-date or inaccurate information related to the current or historical relevance of the SDOH factor from the patient’s perspective19.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [19] Kepper, M. M. et al. The adoption of social determinants of health Documentation in clinical settings. Health Serv. Res. 58, 67–77. https://doi.org/10.1111/1475-6773.14039 (2023). source

      In Results §3.5 (page unavailable), providers worry that documented SDOH information may become outdated or incorrect, remain in the chart after a need resolves, and be disputed by the patient. This supports the concern about current relevance from the patient’s perspective, but the passage concerns EHR documentation rather than Z codes available in claims; the paper does not establish that claims-specific point. (LLM, conf=0.82)

      Experimental — accuracy not yet measured
  • May combine two claims. 20
  • Classified as probably not a substantial claim. 26
  • The quote only partly states this claim. 1

  • May combine two claims.

    Interpretationcitation

    Our findings are consistent with prior published findings that Z code documentation was correlated with greater disease burden and a higher incidence of 30-day post-operative complications following colorectal surgery.

    “Our findings are consistent with prior published findings which found that Z code documentation was correlated with greater disease burden14,21, and a higher incidence of 30-day post-operative complications following colorectal surgery (i.e., correlated with more complex health status)22.”
    Quote in the paperNot a substantial claim1 of 3 sources checked
    • cites [14] Maksut, J., Hodge, C., Van, C. & Khau, M. A. R. Utilization of Z Codes for Social Determinants of Health Among Medicare Fee-For-Service Beneficiaries, 2019 (2021).

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [21] Lee, J. S., MacLeod, K. E., Kuklina, E. V., Tong, X. & Jackson, S. L. Social determinants of health-Related Z codes and health care among patients with hypertension. AJPM Focus. 2, 100089. https://doi.org/10.1016/j.focus.2023.100089 (2023). source
    • cites [22] Pollak, Y. L. E. et al. Social determinants of health Z-codes and postoperative outcomes after colorectal surgery: A National population-based study. Am. J. Surg. 224, 1301–1307. https://doi.org/10.1016/j.amjsurg.2022.06.012 (2022). source

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Prior studies using all-payer inpatient admission and EHR data observed that individuals with Z codes were younger or had higher prevalence of comorbidities.

    “Prior studies using all-payer inpatient admission and EHR data observed that individuals with Z codes were younger or had higher prevalence of comorbidities23–25.”
    Quote in the paperNot a substantial claim1 of 3 sources checked
    • cites [23] McCormack, L. A. & Madlock-Brown, C. Social determinant of health documentation trends and their association with emergency department admissions. AMIA … Annual Symposium proceedings. AMIA Symposium 823–832 (2020).
    • cites [24] Truong, H. P. et al. Utilization of social determinants of health ICD-10 Z-Codes among hospitalized patients in the united states, 2016–2017. Med. Care. 58, 1037–1043. https://doi.org/10.1097/mlr.0000000000001418 (2020). source
    • cites [25] Bensken, W. P., Alberti, P. M., Stange, K. C., Sajatovic, M. & Koroukian, S. M. ICD-10 Z-Code Health-Related social needs and increased healthcare utilization. Am. J. Prev. Med. 62, e232–e241. https://doi.org/10.1016/j.amepre.2021.10.004 (2022). source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    A study comparing state-level characteristics based on US Census data to the use of corresponding Z codes extracted from an EHR network in Florida concluded that there is under-recording of Z codes for patients facing social problems.

    “Notably, a study comparing state-level characteristics based on US Census data to the use of corresponding Z codes extracted from an electronic health record (EHR) network in Florida concluded that there is under-recording of Z codes for patients facing social problems16.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [16] Guo, Y. et al. International classification of diseases, tenth revision, clinical modification social determinants of health codes are poorly used in electronic health records. Medicine 99, e23818. https://doi.org/10.1097/md.0000000000023818 (2020). source

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Only 3.3% and 2.6% of admissions without Z codes were related to mental disorders and substance use disorders.

    “However, only 3.3% and 2.6% of admissions without Z codes were related to those conditions24.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [24] Truong, H. P. et al. Utilization of social determinants of health ICD-10 Z-Codes among hospitalized patients in the united states, 2016–2017. Med. Care. 58, 1037–1043. https://doi.org/10.1097/mlr.0000000000001418 (2020). source

      The Results section (Patient-Level Predictors of SDOH Z-Codes; page not supplied) reports 3.3% for mental diseases and disorders among admissions without an SDOH Z-code, matching the first figure. For alcohol/drug use disorders, it reports 1.1%, not 2.6%. (LLM, conf=0.92)

      Experimental — accuracy not yet measured

  • Classified as probably not a substantial claim.

    Premisecitation

    Z codes offer clinicians a standardized set of measures to document social, economic, and environmental issues faced by patients.

    “These Z codes offer clinicians a standardized set of measures to document social, economic, and environmental issues faced by patients11.”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [11] Weeks, W. B., Cao, S. Y., Lester, C. M., Weinstein, J. N. & Morden, N. E. Use of Z-Codes to record social determinants of health among Fee-for-service medicare beneficiaries in 2017. J. Gen. Intern. Med. 35, 952–955. https://doi.org/10.1007/s11606-019-05199-w (2020). source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Agarwal et al. documented a prevalence of Z code use of 7%.

    “Agarwal et al. documented a higher prevalence of Z codes use (7% vs. <4% in our paper)31.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [31] Agarwal, A. R., Prichett, L., Jain, A. & Srikumaran, U. Assessment of use of ICD-9 and ICD-10 codes for social determinants of health in the US, 2011–2021. JAMA Netw. Open. 6, e2312538–e2312538. https://doi.org/10.1001/jamanetworkopen.2023.12538 (2023). source

      The Results report that 6.8% of patients had at least one adverse SDOH code in 2021, which rounds to 7%. But this is a 2021 rate for the study’s SDOH codes, not a reported 7% prevalence of Z-code use across the full study period; the Results give 5.6% for 2011–2021 overall. No page number is available in our copy. (LLM, conf=0.88)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    An analysis of 2016 and 2017 National Inpatient Sample showed that 44% and 13% of admissions with Z codes were related to mental disorders and substance use disorders.

    “An analysis of 2016 and 2017 National Inpatient Sample (NIS) developed by Healthcare Cost and Utilization Project showed that 44% and 13% of admissions with Z codes were related to mental disorders and substance use disorders24,26.”
    Quote in the paperNot a substantial claim1 of 2 sources checked
    • cites [24] Truong, H. P. et al. Utilization of social determinants of health ICD-10 Z-Codes among hospitalized patients in the united states, 2016–2017. Med. Care. 58, 1037–1043. https://doi.org/10.1097/mlr.0000000000001418 (2020). source

      The Methods (Data) confirm use of the 2016–2017 National Inpatient Sample. The Results (Patient-Level Predictors of SDOH Z-Codes) report that 44.0% of hospitalizations with an SDOH Z-code were in the mental-disorders category, but 9.6%, not 13%, were in the alcohol/drug-use category. (LLM, conf=0.91)

      Experimental — accuracy not yet measured
    • cites [26] Agency for Healthcare Research and Quality. Healthcare Cost and Utilization Project. http://www.hcup-us.ahrq.gov/nisoverview.jsp (2022).

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Healthcare providers in endocrinology and key stakeholders acknowledged the impact of SDOH on patients' health but reported barriers at multiple levels when documenting SDOH.

    “Kepper et al. interviewed healthcare providers in endocrinology and key stakeholders, who acknowledged the impact of SDOH on patients’ health. However, they reported barriers at multiple levels when documenting SDOH19.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [19] Kepper, M. M. et al. The adoption of social determinants of health Documentation in clinical settings. Health Serv. Res. 58, 67–77. https://doi.org/10.1111/1475-6773.14039 (2023). source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Another study found that 1.8% of 29.5 million adults enrolled in private insurance had documented SDOH Z codes between 2016 and 2019.

    “Similarly, another study found that 1.8% of 29.5 million adults enrolled in private insurance had documented SDOH Z codes between 2016 and 201915.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [15] Liss, D. T. et al. Social needs identified by diagnostic codes in privately insured U.S. Adults. Am. J. Prev. Med. 63, 1007–1016. https://doi.org/10.1016/j.amepre.2022.07.009 (2022). source

  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Premisecitation

    Understanding the demographic and clinical characteristics of cancer patients with and without Z code claims will help generate appropriate hypotheses regarding the relationship between SDOH and outcomes, and enhance data collection strategies.

    “Understanding the demographic and clinical characteristics of cancer patients with and without Z code claims will help generate appropriate hypotheses regarding the relationship between SDOH and outcomes, and enhance data collection strategies19.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [19] Kepper, M. M. et al. The adoption of social determinants of health Documentation in clinical settings. Health Serv. Res. 58, 67–77. https://doi.org/10.1111/1475-6773.14039 (2023). source

      Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

      Experimental — accuracy not yet measured

  • May combine two claims.

    Premisecitation

    Individuals who identify with a racial or ethnic minority experience disparities in cancer outcomes, including later stage diagnosis, lower likelihood of receiving recommended treatment, lower likelihood of receiving high-quality diagnostic evaluation, lower likelihood of a provider referral for mammography, lower likelihood of timely follow-up after an abnormal screening test, and higher cancerm

    “individuals who identify with a racial or ethnic minority experience: (1) later stage diagnosis; (2) lower likelihood of receiving recommended treatment; (3) lower likelihood of receiving high-quality diagnostic evaluation; (4) lower likelihood of a provider referral for mammography; (5) lower likelihood of a timely follow-up after an abnormal screening test; or (6) higher cancer mortality2.”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [2] Alcaraz, K. I. et al. Understanding and addressing social determinants to advance cancer health equity in the united states: A blueprint for practice, research, and policy. Cancer J. Clin. 70, 31–46. https://doi.org/10.3322/caac.21586 (2020). source

      The Economic environment section reports later-stage diagnosis and higher cancer mortality among socioeconomically disadvantaged people, while Improve access to high-quality care describes inequitable access to timely, high-quality diagnosis and treatment among disadvantaged populations. These passages do not establish the six listed disparities specifically for racial or ethnic minority individuals. The paper also does not document lower rates of recommended treatment, mammography referral, or timely follow-up after an abnormal screening test for that group. (LLM, conf=0.82)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Awareness of the impacts of SDOH on health outcomes is evident from the oncology literature, largely due to the persistent disparities, substantial barriers to treatment, and the financial toxicity commonly associated with cancer care.

    “Awareness of the impacts of SDOH on health outcomes is evident from the oncology literature, largely due to the persistent disparities, substantial barriers to treatment, and the financial toxicity commonly associated with cancer care1,2,8,9,18.”
    Quote in the paperNot a substantial claim4 of 5 sources checked
    • cites [1] Siegel, R. L., Giaquinto, A. N., Jemal, A. & Cancer statistics CA Cancer J. Clin. 74, 12–49. https://doi.org/10.3322/caac.21820 (2024). source

      The “Cancer disparities by race and ethnicity” section links structural racism and poverty to worse cancer outcomes and describes unequal access to screening and recommended treatment. The Conclusion calls for expanded access to high-quality care. These passages support the disparities and treatment-barriers portions of the claim, but the passages found do not discuss financial toxicity associated with cancer care. (LLM, conf=0.85)

      Experimental — accuracy not yet measured
    • cites [2] Alcaraz, K. I. et al. Understanding and addressing social determinants to advance cancer health equity in the united states: A blueprint for practice, research, and policy. Cancer J. Clin. 70, 31–46. https://doi.org/10.3322/caac.21586 (2020). source
    • cites [8] American Association for Cancer Research. CancerDisparitiesProgressReport.org [Internet]. http://www.CancerDisparitiesProgressReport.org (2022).

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [9] I. Patel, M. et al. Racial and ethnic disparities in cancer care during the COVID-19 pandemic. JAMA Netw. Open. 5, e2222009. https://doi.org/10.1001/jamanetworkopen.2022.22009 (2022). source
    • cites [18] Altice, C. K., Banegas, M. P., Tucker-Seeley, R. D. & Yabroff, K. R. Financial hardships experienced by cancer survivors: A systematic review. J. Natl. Cancer Inst. 109. https://doi.org/10.1093/jnci/djw205 (2017). source

  • May combine two claims.

    Findingown data

    The annual number of individuals diagnosed with cancer ranged from 11,107 to 16,381 in PharMetrics Plus and from 4,437 to 4,636 in SEER-Medicare.

    “The annual number of individuals diagnosed with cancer ranged from 11,107 to 16,381 in the PharMetrics Plus data and ranged from 4,437 to 4,636 in the SEER-Medicare data (Appendix C).”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    25% of surveyed oncologists ranked lack of evidence demonstrating the impact of Z codes documentation on patient outcomes as a top future priority.

    “a lack of evidence demonstrating the impact of Z codes documentation on patient outcomes (25%)”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    The proportion of individuals with any Z code claims ranged from 0.6 to 0.9% in the PharMetrics Plus data and from 1.4 to 2.1% in the linked data.

    “The proportion of individuals with any Z code claims ranged from 0.6 to 0.9% in the PharMetrics Plus data and from 1.4 to 2.1% in the linked data.”
    • Also stated in: § Z code measures in claims data · plain
      “The proportion of individuals with any Z code claims ranged from 0.6 to 0.9% in the PharMetrics Plus data from 2016 to 2020 and from 1.4 to 2.1% in the SEER-Medicare data from 2016 to 2019”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    We identified 35,099 individuals with cancer in the IQVIA PharMetrics® Plus for Academics database and 17,852 in the linked SEER-Medicare 5% Cancer Files.

    “We identified 35,099 individuals with cancer in the IQVIA PharMetrics® Plus for Academics database (PharMetrics Plus data) and 17,852 in the linked Surveillance, Epidemiology, and End Results-Medicare 5% Cancer Files (SEER-Medicare data) (Appendix A).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    The proportion of claims with Z code documentation among the study population ranged from 0.04 to 0.09% from 2016 to 2020 in PharMetrics Plus and from 0.03 to 0.05% in SEER-Medicare.

    “The proportion of claims with Z code documentation among the study population ranged from 0.04 to 0.09% from 2016 to 2020 in the PharMetrics Plus data and from 0.03 to 0.05% in the SEER-Medicare data (Appendix D).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Z63 increased from 19% in 2016 to 42% in 2020 in PharMetrics Plus, representing the most common Z code category.

    “In contrast, ‘Z63: Other problems related to primary support group’ increased from 19% in 2016 to 42% in 2020, representing the most common Z code category (Table 3).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingnone given

    Financial incentives, a greater connection to value-based healthcare, and evidence regarding the benefits of documenting Z codes were key motivators for survey respondents to increase Z code documentation in clinical practice.

    “Financial incentives, a greater connection to value-based healthcare, and evidence regarding the benefits of documenting Z codes were key motivators for survey respondents to increase Z code documentation in clinical practice.”
    Quote in the paperNot a substantial claim

  • The quote only partly states this claim.

    Findingown data

    The most prevalent Z code category in PharMetrics Plus in 2016 was Z60, representing 41% among all Z-code categories.

    “the most prevalent Z code category in the PharMetrics Plus in 2016 was ‘Z60: Problems related to social environment’, which represented 41% among all Z-code categories.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    In the SEER-Medicare population, the distribution of "fast food restaurant" using the state-level variable showed statistically significant differences between individuals with and without Z code documentation, whereas there was no difference using the county-level measure.

    “For example, distribution of “fast food restaurant” using the state-level variable showed statistically significant differences between individuals with and without Z codes documentation whereas there was no difference using the county-level measure.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Among individuals with metastasis, the proportion with any Z code claims ranged from 1.1 to 1.6% in PharMetrics Plus and from 1.2 to 4.2% in SEER-Medicare.

    “Among individuals with metastasis, the proportion of individuals with any Z code claims ranged from 1.1 to 1.6% in the PharMetrics Plus data and from 1.2 to 4.2% in the SEER-Medicare data (Fig. 2).”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    The STROBE checklist for cross-sectional studies was utilized and completed for this study.

    “The STROBE (Strengthening the reporting of observational studies in epidemiology) checklist for cross sectional studies was utilized and completed for this study.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    PolicyMap aggregates area-level data from a range of authoritative public and private sources, including the US Census Bureau, Census County Business Patterns, the Department of Health and Human Services' Agency for Healthcare Research and Quality, the Federal Financial Institutions Examination Council, and the Office of Community Planning and Development, among others.

    “PolicyMap aggregates area-level data from a range of authoritative public and private sources, including the US Census Bureau, Census County Business Patterns, the Department of Health and Human Services’ Agency for Healthcare Research and Quality, the Federal Financial Institutions Examination Council, and the Office of Community Planning and Development, among others36.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [36] PolicyMap. Data Dictionary. https://www.policymap.com/data/dictionary (2025).

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    The American Hospital Association and CMS endorse the use of Z codes in all healthcare settings to document individual-level SDOH and develop targeted strategies to support patients' social needs.

    “The American Hospital Association and Centers for Medicare & Medicaid Services (CMS) endorse the use of Z codes in all healthcare settings to document individual-level SDOH and develop targeted strategies to support patients’ social needs12,13.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [12] American Hospital Association. ICD-10-CM Coding for Social Determinants of Health. https://www.aha.org/system/files/2018-04/value-initiative-icd-10-code-social-determinants-of-health.pdf (2022).

      Not checked in the free run.

    • cites [13] Centers for Medicare & Medicaid Services. Using Z Codes: The Social Determinants of Health Data Journey to Better Outcomes. https://www.cms.gov/files/document/zcodes-infographic.pdf (2022).

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Interpretationreasoning

    The responses received may not generalize to oncologists outside of Cardinal Health's Oncology Provider Extended Network and may not reflect the perspectives of the broader clinician community.

    “The responses received may not generalize to oncologists outside of Cardinal Health’s Oncology Provider Extended Network and may not reflect the perspectives of the broader clinician community.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    The survey response rate was 90.3%: 112 respondents out of 124 physicians who were invited.

    “The survey response rate was 90.3%: 112 respondents out of 124 physicians who were invited.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductreasoning

    The study protocol was reviewed by the University of Maryland, Baltimore (UMB) Institutional Review Board (HP-00098764) and determined to be exempt under 45 CFR 46.104(d).

    “This study protocol was reviewed by the University of Maryland, Baltimore (UMB) Institutional Review Board (HP-00098764) and determined to be exempt under 45 CFR 46.104(d).”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The PolicyMap database includes over 50,000 gold-standard public indicators such as economic, housing, transportation, and quality of life data for 2009 to 2020.

    “The PolicyMap database includes over 50,000 gold-standard public indicators such as economic, housing, transportation, and quality of life data for 2009 to 202035.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [35] PolicyMap. https://www.policymap.com (2021).

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductreasoning

    All study procedures were completed in accordance with the applicable UMB Human Research Protections Program and IRB guidelines and regulations.

    “All study procedures were completed in accordance with the applicable UMB Human Research Protections Program and IRB guidelines and regulations.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductreasoning

    The UMB Institutional Review Board (IRB) approved this study as secondary research for which informed consent was not required.

    “The UMB Institutional Review Board (IRB) approved this study as secondary research for which informed consent was not required.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Interpretationreasoning

    Underreporting of individual-level SDOH hampers attempts to contextualize public health surveillance data at the individual level or leverage big-data analytics for public health surveillance monitoring.

    “Underreporting of individual-level SDOH hampers attempts to contextualize public health surveillance data at the individual level or leverage big-data analytics for public health surveillance monitoring.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductreasoning

    Limiting each oncologist or hematologist to one Summit during a nine-month duration avoided duplicate data collection from the same individuals and ensured the inclusion of a broader range of physician perspectives.

    “Each oncologist or hematologist was limited to participating in one Summit during a nine-month duration. This approach avoided duplicate data collection from the same individuals and ensured the inclusion of a broader range of physician perspectives.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    CMS reported that Z codes were utilized by 1.31-1.59% of the 30.5 million Medicare beneficiaries between 2016 and 2019.

    “CMS reported that Z codes were utilized by 1.31–1.59% of the 30.5 million Medicare beneficiaries between 2016 and 201914.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [14] Maksut, J., Hodge, C., Van, C. & Khau, M. A. R. Utilization of Z Codes for Social Determinants of Health Among Medicare Fee-For-Service Beneficiaries, 2019 (2021).

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    A study involving convenience samples of patients from federal health centers found that 92% of patients with SDOH-related issues were not initially coded with Z codes by nurse practitioners and patient providers.

    “Similarly, a study involving convenience samples of patients from federal health centers found that 92% of patients with SDOH-related issues were not initially coded with Z codes by nurse practitioners and patient providers17.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [17] Coppa, D., Winchester, S. B., McAlvin, E., Roberts, M. B. & Maestri, X. Use of social determinants of health codes in home-based primary care. J. Am. Association Nurse Practitioners. 34, 42–49. https://doi.org/10.1097/jxx.0000000000000586 (2021). source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    One study identified less favorable social determinants and health outcomes data for Asian-American populations compared to Non-Hispanic White populations at the Census tract level but not at the city level.

    “One study identified less favorable social determinants and health outcomes data for Asian-American populations compared to Non-Hispanic White populations at the Census tract level but not at the city level34.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [34] Spoer, B. R. et al. Neighborhood-level Asian American populations, social determinants of health, and health outcomes in 500 US cities. Ethn. Dis. 31, 433–444. https://doi.org/10.18865/ed.31.3.433 (2021). source

  • May combine two claims.

    Study design and conductreasoning

    The use of a minimum continuous enrollment requirement improves our ability to observe claims but may reduce our ability to observe individuals who are more likely to experience SDOH factors.

    “The use of a minimum continuous enrollment requirement improves our ability to observe claims but may reduce our ability to observe individuals who are more likely to experience SDOH factors.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The top three Z codes based on the Medicare report in 2019 were Z59.0 (22%), Z63.4 (12%), and Z60.2 (12%).

    “By comparison, the top three Z codes based on the Medicare report in 2019 were ‘Z59.0 Homelessness’ (22%), Z63.4 Disappearance and death of family member (12%), and ‘Z60.2 Problems related to living alone’ (12%)14.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [14] Maksut, J., Hodge, C., Van, C. & Khau, M. A. R. Utilization of Z Codes for Social Determinants of Health Among Medicare Fee-For-Service Beneficiaries, 2019 (2021).

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductreasoning

    This study met the criteria for waiver of informed consent, and the UMB IRB granted approval to waive the need for informed consent.

    “This study met these criteria, and the UMB IRB granted approval to waive the need for informed consent.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    By utilizing both PharMetrics Plus and SEER-Medicare datasets, the study can develop results that generalize to the adult population diagnosed with cancer and produce comparisons between the private payer and Medicare population.

    “By utilizing both datasets, we can develop results that generalize to the adult population diagnosed with cancer and produce comparisons between the private payer and Medicare population.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: PharMetrics Plus is representative of the commercially insured US national population for patients under 65 years of age.
40 of 40

  • The paper relies on this without support.

    There is limited characterization of SDOH Z codes among adults diagnosed with cancer and limited information about oncologists’ perspectives on Z codes.

    “There is limited characterization of SDOH Z codes among adults diagnosed with cancer and information about oncologists’ perspectives on Z codes.”
    “There is limited information about the oncologist’s perspective regarding Z codes”

  • The paper relies on this without support.

    The extent of individual-level SDOH documentation specifically for cancer patients has not been examined, including information about the top-ranked Z codes among individuals diagnosed with cancer.

    “The extent of individual-level SDOH documentation specifically for cancer patients has not been examined, including information about the top-ranked Z codes among individuals diagnosed with cancer.”

  • The paper relies on this without support.

    The availability of Z codes will not necessarily guarantee their adoption by healthcare providers.

    “The availability of Z codes will not necessarily guarantee their adoption by healthcare providers.”

  • The paper relies on this without support.

    Oncologists' buy-in will be needed to advance federal and state agency objectives to increase SDOH documentation.

    “their buy-in will be needed to advance federal and state agency objectives to increase SDOH documentation.”

  • The paper relies on this without support.

    It is possible to collect individual-level measures in administrative claims data using billing codes.

    “it is possible to collect individual-level measures in administrative claims data using billing codes.”

  • The paper relies on this without support.

    Advances in cancer treatments have not closed the gap in incidence, screening, treatment, and outcomes.

    “Advances in cancer treatments have not closed the gap in incidence, screening, treatment, and outcomes.”

  • The paper relies on this without support.

    PharMetrics Plus is representative of the commercially insured US national population for patients under 65 years of age.

    “It is representative of the commercially insured US national population for patients under 65 years of age.”

  • The paper relies on this without support.

    Oncologists are primarily responsible for coding diagnoses in the claims, including both disease-related diagnoses and relevant Z codes.

    “Oncologists are primarily responsible for coding diagnoses in the claims, including both disease-related diagnoses and relevant Z codes.”

  • The paper relies on this without support.

    A diagnosis of metastatic cancer identifies individuals with more severe disease.

    “A diagnosis of metastatic cancer identified individuals with more severe disease.”
9 of 9

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • SEER-Medicare Z63 counts do not add to the reported total

    Major

    The four annual Z63 counts shown for SEER-Medicare sum to 119.58, not 152. The 2019 count of 30.58 and the percentage of 63 in the following row appear to have been interchanged, changing which category appears most prevalent that year.

    Reported 152 · recomputed 119.58

  • PharMetrics sample is not primarily under 65

    Minor

    The discussion describes the PharMetrics Plus sample as primarily under 65, whereas Table 1 places only 41% of that sample in the 18-64 age group.

2 of 2

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Related

Authors
Hsu, C.-Y., Henderson, G., Slejko, J. F., Hussain, A., Swain, R. S., Kalesan, B., Huang, P.-L., & Onukwugha, E.
Affiliations
  • University of Maryland, Baltimore (US)
  • Cardinal Health (United States) (US)
Corresponding author
Onukwugha, Eberechukwu
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2025-08-13
Volume
15
Issue
1
Pages
29646-29646
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-025-13825-3
PMC
PMC12350795
OpenAlex
W4413116009
Cited by
5
References
31
Citations by year
2026: 5
FWCI
3.73
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • PubMed Central (submittedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
University of Maryand School of Pharmacy
Topics
  • Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
  • Health Sciences › Medicine › Oncology › Global Cancer Incidence and Screening
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Economic and Financial Impacts of Cancer
Keywords
social determinants of health, Z codes, ICD-10-CM, cancer diagnosis, undercoding, oncologists' perspectives, comorbidities, metastatic cancer, cancer burden, reimbursement, clinical documentation, health records
MeSH terms
Adult, Aged, Cross-Sectional Studies, Documentation, Female, Humans, Male, Middle Aged, Neoplasms, Registries, United States, Comorbidity, International Classification of Diseases, Social Determinants of Health

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-27.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-27.

Cited sources are checked by gpt-6-sol.

Statistics and arithmetic are recomputed, not judged.

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