ICD-10 Z-Code Health-Related Social Needs and Increased Healthcare Utilization

Social Determinants of Health and the Use of Community-Based Rehabilitation Following Stroke: Methodologic Considerations

Social determinants are nonmedical factors frequently used to study disparities in health outcomes but have not been widely explored in regard to rehabilitation service utilization. In our National Institutes of Child Health and Human Development-funded study, Access to and Effectiveness of Community-Based Rehabilitation After Stroke, we reviewed several conceptual models and frameworks for the study of social determinants to inform our work. The overall objective of this special communication is to describe our approach to identifying, selecting, and using area-level measures of social determinants to explore the relationship between social determinants and rehabilitation use. We present our methods for developing a conceptual model and a methodologic framework for the selection of social determinant measures relevant to rehabilitation use, as well as an overview of publicly available data on social determinants. We then discuss the methodologic challenges encountered and future directions for this work.

Applying an ELSI lens to real-world data and novel genomic insights for personalized mental healthcare

Improving the understanding of the complex relationship between genetic predispositions, environmental influences, and sociocultural factors in the development and progression of mental illness is crucial for optimizing treatment efficacy and addressing longstanding health disparities. This paper discusses the ethical, legal, and social implications (ELSI) of recent advancements in biomedical research, particularly in genome-wide association studies (GWAS), phenome-wide association studies (PheWAS), and genome-wide environment interaction studies (GWEIS). Despite recent scientific progresses, challenges such as inadequate study methodology (e.g., correlational studies) and lack of diversity within study samples persist. Recent discoveries of several genetic variants of diseases, could augment and improve, or even challenge, existing understanding of the onset and management of mental illness. Leveraging real-world data (RWD), including electronic health record data (EHRs) focused on social determinant of health alongside biobank data, offers further opportunities to enhance the understanding of gene-environment interactions and inform efforts for reducing disparities in mental healthcare. Increased knowledge can support timely, holistic, evidence-based, and personalized care. Addressing ELSI considerations and maximizing the use of RWD is essential for advancing ethical and inclusive psychiatric genetics research, ultimately improving patient outcomes and promoting equitable access to evidence-based treatments.

Accuracy of ICD-10 Diagnostic Codes for Identifying Housing Instability

This cohort study assesses the performance of International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) Z59 codes for identifying housing instability during health care encounters.

Social Risk Factors and Racial and Ethnic Disparities in Health Care Resource Utilization Among Medicare Advantage Beneficiaries With Psychiatric Disorders

The intersection of social risk and race and ethnicity on mental health care utilization is understudied. This study examined disparities in health care treatment, adjusting for clinical need, among 25,780 Medicare Advantage beneficiaries with a diagnosis of a psychiatric disorder. We assessed contributions to disparities from racial and ethnic differences in the composition and returns of social risk variables. Black and Hispanic beneficiaries had lower rates of mental health outpatient visits than Whites. Assessing composition, Black and Hispanic beneficiaries experienced greater financial, food, and housing insecurity than White beneficiaries, factors associated with greater mental health treatment. Assessing returns, food insecurity was associated with an exacerbation of Hispanic-White disparities. Health care systems need to address the financial, food and housing insecurity of racial and ethnic minority groups with psychiatric disorder. Accounting for racial and ethnic differences in social risk adjustment-based payment reforms has significant implications for provider reimbursement and outcomes.

Social determinants of health Z-code documentation practices in mental health settings: a scoping review

Mental health remains an urgent global priority, alongside efforts to address underlying social determinants of health (SDoH) that contribute to the onset or exacerbate mental illness. SDoH factors can be captured in the form of International Classification of Disease, Tenth Revision, Clinical Modification (ICD-10-CM), SDoH Z codes. In this scoping review, we describe current SDoH Z-code documentation practices, with a focus on mental health care contexts. Among 2 743 061 374 health care encounters noted across 12 studies in the United States, SDoH Z-code documentation rates ranged from 0.5% to 2.4%. Documentation often involved patients under 64 years of age who are publicly insured and experience comorbidities, including depression, bipolar disorder and schizophrenia, chronic pulmonary disease, and substance abuse disorders. Documentation varied across hospital types, number of beds per facility, patient race/ethnicity, and geographic region. Variation was observed regarding patient sex/gender, although SDoH Z codes were more frequently documented for males. Documentation was most observed in government, nonfederal, and private not-for-profit hospitals. From these insights, we offer policy and practice recommendations, as well as considerations for patient data privacy, security, and confidentiality, to incentivize more routine documentation of Z codes to better assist patients with complex mental health care needs.

Development of a Social Risk Score in the Electronic Health Record to Identify Social Needs Among Underserved Populations: Retrospective Study

BACKGROUND

Patients with unmet social needs and social determinants of health (SDOH) challenges continue to face a disproportionate risk of increased prevalence of disease, health care use, higher health care costs, and worse outcomes. Some existing predictive models have used the available data on social needs and SDOH challenges to predict health-related social needs or the need for various social service referrals. Despite these one-off efforts, the work to date suggests that many technical and organizational challenges must be surmounted before SDOH-integrated solutions can be implemented on an ongoing, wide-scale basis within most US-based health care organizations.

OBJECTIVE

We aimed to retrieve available information in the electronic health record (EHR) relevant to the identification of persons with social needs and to develop a social risk score for use within clinical practice to better identify patients at risk of having future social needs.

METHODS

We conducted a retrospective study using EHR data (2016-2021) and data from the US Census American Community Survey. We developed a prospective model using current year-1 risk factors to predict future year-2 outcomes within four 2-year cohorts. Predictors of interest included demographics, previous health care use, comorbidity, previously identified social needs, and neighborhood characteristics as reflected by the area deprivation index. The outcome variable was a binary indicator reflecting the likelihood of the presence of a patient with social needs. We applied a generalized estimating equation approach, adjusting for patient-level risk factors, the possible effect of geographically clustered data, and the effect of multiple visits for each patient.

RESULTS

The study population of 1,852,228 patients included middle-aged (mean age range 53.76-55.95 years), White (range 324,279/510,770, 63.49% to 290,688/488,666, 64.79%), and female (range 314,741/510,770, 61.62% to 278,488/448,666, 62.07%) patients from neighborhoods with high socioeconomic status (mean area deprivation index percentile range 28.76-30.31). Between 8.28% (37,137/448,666) and 11.55% (52,037/450,426) of patients across the study cohorts had at least 1 social need documented in their EHR, with safety issues and economic challenges (ie, financial resource strain, employment, and food insecurity) being the most common documented social needs (87,152/1,852,228, 4.71% and 58,242/1,852,228, 3.14% of overall patients, respectively). The model had an area under the curve of 0.702 (95% CI 0.699-0.705) in predicting prospective social needs in the overall study population. Previous social needs (odds ratio 3.285, 95% CI 3.237-3.335) and emergency department visits (odds ratio 1.659, 95% CI 1.634-1.684) were the strongest predictors of future social needs.

CONCLUSIONS

Our model provides an opportunity to make use of available EHR data to help identify patients with high social needs. Our proposed social risk score could help identify the subset of patients who would most benefit from further social needs screening and data collection to avoid potentially more burdensome primary data collection on all patients in a target population of interest.

Diagnosing social ills: Theorising social determinants of health as a diagnostic category

Medicine, as an institution and discipline, has embraced social determinants of health as a key influence on clinical practice and care. Beyond simply acknowledging their importance, most recent versions of the International Classification of Diseases explicitly codify social determinants as a viable diagnostic category. This diagnostic shift is noteworthy in the United States, where 'Z-codes' were introduced to facilitate the documentation of illiteracy, unemployment, poverty and other social factors impacting health. Z-codes hold promise in addressing patients' social needs, but there are likely consequences to medicalising social determinants. In turn, this article provides a critical appraisal of Z-codes, focussing on the role of diagnoses as both constructive and counterproductive sources of legitimacy, knowledge and responsibility in our collective understanding of health. Diagnosis codes for social determinants are powerful bureaucratic tools for framing and responding to psychosocial risks commensurate with biophysiological symptoms; however, they potentially reinforce beliefs about the centrality of individuals for addressing poor health at the population level. I contend that Z-codes demonstrate the limited capacity of diagnoses to capture the complex individual and social aetiology of health, and that sociology benefits from looking further 'upstream' to identify the structural forces constraining the scope and utility of diagnoses.

Surgeon Understanding and Perceptions of Social Determinants of Health

INTRODUCTION

Social determinants of health impact surgical outcomes. Characterization of surgeon understanding of social determinants of health is necessary prior to implementation of interventions to address patient needs. The study objective was to explore understanding, perceived importance, and practices regarding social determinants of health among surgeons.

METHODS

Surgical residents and attending surgeons at a single academic medical center completed surveys regarding social determinants of health. We conducted semi-structured interviews to further explore understanding and perceived importance. A conceptual framework from the World Health Organization (WHO) Commission on Social Determinants of Health informed the thematic analysis.

RESULTS

Survey response rate was 47.9% (n = 69, 44 residents [63.8%], 25 attendings [36.2%]). Respondents primarily reported good (n = 29, 42.0%) understanding of social determinants of health and perceived this understanding to be very important (n = 42, 60.9%). Documentation occurred seldom (n = 35, 50.7%), and referrals occurred seldom (n = 26, 37.7%) or never (n = 20, 29.0%). Residents reported a higher rate of prior training than attendings (95.5% versus 56.0%, P < 0.001). Ten interviews were conducted (six residents, four attendings). Residents demonstrated greater understanding of socioeconomic positions and hierarchies shaped by structural mechanisms than attendings. Both residents and attendings demonstrated understanding of intermediary determinants of health status and linked social determinants to impacting patients' health and well-being. Specific knowledge gaps were identified regarding underlying structural mechanisms including the social, economic, and political context that influence an individual's socioeconomic position.

CONCLUSIONS

Self-reported understanding and importance of social determinants of health among surgeons were high. Interviews revealed gaps in understanding that may contribute to limited practices.

Five years of Hospital at Home adoption in Catalonia: impact, challenges, and proposals for quality assurance

BACKGROUND

Hospital at home (HaH) was increasingly implemented in Catalonia (7.7 M citizens, Spain) achieving regional adoption within the 2011-2015 Health Plan. This study aimed to assess population-wide HaH outcomes over five years (2015-2019) in a consolidated regional program and provide context-independent recommendations for continuous quality improvement of the service.

METHODS

A mixed-methods approach was adopted, combining population-based retrospective analyses of registry information with qualitative research. HaH (admission avoidance modality) was compared with a conventional hospitalization group using propensity score matching techniques. We evaluated the 12-month period before the admission, the hospitalization, and use of healthcare resources at 30 days after discharge. A panel of experts discussed the results and provided recommendations for monitoring HaH services.

RESULTS

The adoption of HaH steadily increased from 5,185 episodes/year in 2015 to 8,086 episodes/year in 2019 (total episodes 31,901; mean age 73 (SD 17) years; 79% high-risk patients. Mortality rates were similar between HaH and conventional hospitalization within the episode [76 (0.31%) vs. 112 (0.45%)] and at 30-days after discharge [973(3.94%) vs. 1112(3.24%)]. Likewise, the rates of hospital re-admissions at 30 days after discharge were also similar between groups: 2,00 (8.08%) vs. 1,63 (6.58%)] or ER visits [4,11 (16.62%) vs. 3,97 (16.03%). The 27 hospitals assessed showed high variability in patients' age, multimorbidity, severity of episodes, recurrences, and length of stay of HaH episodes. Recommendations aiming at enhancing service delivery were produced.

CONCLUSIONS

Besides confirming safety and value generation of HaH for selected patients, we found that this service is delivered in a case-mix of different scenarios, encouraging hospital-profiled monitoring of the service.

Association Between ICD-10 Codes for Social Needs and Subsequent Emergency and Inpatient Use

BACKGROUND

International Classification of Diseases, 10th revision Z codes capture social needs related to health care encounters and may identify elevated risk of acute care use.

OBJECTIVES

To examine associations between Z code assignment and subsequent acute care use and explore associations between social need category and acute care use.

RESEARCH DESIGN

Retrospective cohort study.

SUBJECTS

Adults continuously enrolled in a commercial or Medicare Advantage plan for ≥15 months (12-month baseline, 3-48 month follow-up).

OUTCOMES

All-cause emergency department (ED) visits and inpatient admissions during study follow-up.

RESULTS

There were 352,280 patients with any assigned Z codes and 704,560 sampled controls with no Z codes. Among patients with commercial plans, Z code assignment was associated with a 26% higher rate of ED visits [adjusted incidence rate ratio (aIRR) 1.26, 95% CI: 1.25-1.27] and 42% higher rate of inpatient admissions (aIRR 1.42, 95% CI: 1.39-1.44) during follow-up. Among patients with Medicare Advantage plans, Z code assignment was associated with 42% (aIRR 1.42, 95% CI: 1.40-1.43) and 28% (aIRR 1.28, 95% CI: 1.26-1.30) higher rates of ED visits and inpatient admissions, respectively. Within the Z code group, relative to community/social codes, socioeconomic Z codes were associated with higher rates of inpatient admissions (commercial: aIRR 1.10, 95% CI: 1.06-1.14; Medicare Advantage: aIRR 1.24, 95% CI 1.20-1.27), and environmental Z codes were associated with lower rates of both primary outcomes.

CONCLUSIONS

Z code assignment was independently associated with higher subsequent emergency and inpatient utilization. Findings suggest Z codes' potential utility for risk prediction and efforts targeting avoidable utilization.

Learning in real world practice: Identifying implementation strategies to integrate health-related social needs screening within a large health system

Introduction

Health systems have many incentives to screen patients for health-related social needs (HRSNs) due to growing evidence that social determinants of health impact outcomes and a new regulatory context that requires health equity measures. This study describes the experience of one large urban health system in scaling HRSN screening by implementing improvement strategies over five years, from 2018 to 2023.

Methods

In 2018, the health system adapted a 10-item HRSN screening tool from a widely used, validated instrument. Implementation strategies aimed to foster screening were retrospectively reviewed and categorized according to the Expert Recommendations for Implementing Change (ERIC) study. Statistical process control methods were utilized to determine whether implementation strategies contributed to improvements in HRSN screening activities.

Results

There were 280,757 HRSN screens administered across 311 clinical teams in the health system between April 2018 and March 2023. Implementation strategies linked to increased screening included integrating screening within an online patient portal (ERIC strategy: involve patients/consumers and family members), expansion to discrete clinical teams (ERIC strategy: change service sites), providing data feedback loops (ERIC strategy: facilitate relay of clinical data to providers), and deploying Community Health Workers to address HRSNs (ERIC strategy: create new clinical teams).

Conclusion

Implementation strategies designed to promote efficiency, foster universal screening, link patients to resources, and provide clinical teams with an easy-to-integrate tool appear to have the greatest impact on HRSN screening uptake. Sustained increases in screening demonstrate the cumulative effects of implementation strategies and the health system's commitment toward universal screening.

Disparities in adherence and emergency department utilization among people with epilepsy: A machine learning approach

PURPOSE

We used a machine learning approach to identify the combinations of factors that contribute to lower adherence and high emergency department (ED) utilization.

METHODS

Using Medicaid claims, we identified adherence to anti-seizure medications and the number of ED visits for people with epilepsy in a 2-year follow up period. We used three years of baseline data to identify demographics, disease severity and management, comorbidities, and county-level social factors. Using Classification and Regression Tree (CART) and random forest analyses we identified combinations of baseline factors that predicted lower adherence and ED visits. We further stratified these models by race and ethnicity.

RESULTS

From 52,175 people with epilepsy, the CART model identified developmental disabilities, age, race and ethnicity, and utilization as top predictors of adherence. When stratified by race and ethnicity, there was variation in the combinations of comorbidities including developmental disabilities, hypertension, and psychiatric comorbidities. Our CART model for ED utilization included a primary split among those with previous injuries, followed by anxiety and mood disorders, headache, back problems, and urinary tract infections. When stratified by race and ethnicity we saw that for Black individuals headache was a top predictor of future ED utilization although this did not appear in other racial and ethnic groups.

CONCLUSIONS

ASM adherence differed by race and ethnicity, with different combinations of comorbidities predicting lower adherence across racial and ethnic groups. While there were not differences in ED use across races and ethnicity, we observed different combinations of comorbidities that predicted high ED utilization.

Social Determinants of Health-Related Z Codes and Health Care Among Patients With Hypertension

Introduction

Tracking social needs can provide information on barriers to controlling hypertension and the need for wraparound services. No recent studies have examined ICD-10-CM social determinants of health-related Z codes (Z55-Z65) to indicate social needs with a focus on patients with hypertension.

Methods

Three cohorts were identified with a diagnosis of hypertension during 2016-2017 and continuously enrolled in fee-for-service insurance through June 2021: (1) commercial, age 18-64 years (n=1,024,012); (2) private insurance to supplement Medicare (Medicare Supplement), age ≥65 years (n=296,340); and (3) Medicaid, age ≥18 years (n=146,484). Both the proportion of patients and healthcare encounters or visits with social determinants of health-related Z code were summarized annually. Patient and visit characteristics were summarized for 2019.

Results

In 2020, the highest annual documentation of social determinants of health-related Z codes was among Medicaid beneficiaries (3.02%, 0.46% commercial, 0.42% Medicare Supplement); documentation was higher among inpatient than among outpatient visits for all insurance types. Z63 (related to primary support group) was more common among commercial and Medicare Supplement beneficiaries, and Z59 (housing and economic circumstances) was more common among Medicaid beneficiaries. The 2019 total unadjusted medical expenditures were 1.85, 1.78, and 1.61 times higher for those with social determinants of health-related Z code than for those without commercial, Medicare Supplement, and Medicaid, respectively. Patients with social determinants of health-related Z code also had higher proportions of diagnosed chronic conditions. Among Medicaid beneficiaries, differences in the presence of social determinants of health-related Z code by race or ethnicity were observed.

Conclusions

Although currently underreported, social determinants of health-related Z codes provide an opportunity to integrate social and medical data and may help decision makers understand the need for additional services among individuals with hypertension.

Association of Unmet Social Needs With Level of Capability in People With Persistent Knee Pain

BACKGROUND

Musculoskeletal providers are increasingly recognizing the importance of social factors and their association with health outcomes as they aim to develop more comprehensive models of care delivery. Such factors may account for some of the unexplained variation between pathophysiology and level of pain intensity and incapability experienced by people with common conditions, such as persistent nontraumatic knee pain secondary to osteoarthritis (OA). Although the association of one's social position (for example, income, employment, or education) with levels of pain and capability are often assessed in OA research, the relationship between aspects of social context (or unmet social needs) and such symptomatic and functional outcomes in persistent knee pain are less clear.

QUESTIONS/PURPOSES

(1) Are unmet social needs associated with the level of capability in patients experiencing persistently painful nontraumatic knee conditions, accounting for sociodemographic factors? (2) Do unmet health-related social needs correlate with self-reported quality of life?

METHODS

We performed a prospective, cross-sectional study between January 2021 and August 2021 at a university academic medical center providing comprehensive care for patients with persistent lower extremity joint pain secondary to nontraumatic conditions such as age-related knee OA. A final 125 patients were included (mean age 62 ± 10 years, 65% [81 of 125] women, 47% [59 of 125] identifying as White race, 36% [45 of 125] as Hispanic or Latino, and 48% [60 of 125] with safety-net insurance or Medicaid). We measured patient-reported outcomes of knee capability (Knee injury and Osteoarthritis Outcome Score for Joint Replacement), quality of life (Patient-Reported Outcome Measure Information System [PROMIS] Global Physical Health and PROMIS Global Mental Health), and unmet social needs (Accountable Health Communities Health-Related Social Needs Survey, accounting for insufficiencies related to housing, food, transportation, utilities, and interpersonal violence), as well as demographic factors.

RESULTS

After controlling for demographic factors such as insurance status, education attained, and household income, we found that reduced knee-specific capability was moderately associated with experiencing unmet social needs (including food insecurity, housing instability, transportation needs, utility needs, or interpersonal safety) (standardized beta regression coefficient [β] = -4.8 [95% confidence interval -7.9 to -1.7]; p = 0.002 and substantially associated with unemployment (β = -13 [95% CI -23 to -3.8]; p = 0.006); better knee-specific capability was substantially associated with having Medicare insurance (β = 12 [95% CI 0.78 to 23]; p = 0.04). After accounting for factors such as insurance status, education attained, and household income, we found that older age was associated with better general mental health (β = 0.20 [95% CI 0.0031 to 0.39]; p = 0.047) and with better physical health (β = 0.004 [95% CI 0.0001 to 0.008]; p = 0.04), but effect sizes were small to negligible, respectively.

CONCLUSION

There is an association of unmet social needs with level of capability and unemployment in patients with persistent nontraumatic knee pain. This finding signals a need for comprehensive care delivery for patients with persistent knee pain that screens for and responds to potentially modifiable social risk factors, including those based on one's social circumstances and context, to achieve better outcomes.

LEVEL OF EVIDENCE

Level II, prognostic study.

A Population Health Impact Pyramid for Health Care

Policy Points To meaningfully impact population health and health equity, health care organizations must take a multipronged approach that ranges from education to advocacy, recognizing that more impactful efforts are often more complex or resource intensive. Given that population health is advanced in communities and not doctors' offices, health care organizations must use their advocacy voices in service of population health policy, not just health care policy. Foundational to all population health and health equity efforts are authentic community partnerships and a commitment to demonstrating health care organizations are worthy of their communities' trust.

Predicting Social Determinants of Health in Patient Navigation: Case Study

BACKGROUND

Patient navigation (PN) programs have demonstrated efficacy in improving health outcomes for marginalized populations across a range of clinical contexts by addressing barriers to health care, including social determinants of health (SDoHs). However, it can be challenging for navigators to identify SDoHs by asking patients directly because of many factors, including patients' reluctance to disclose information, communication barriers, and the variable resources and experience levels of patient navigators. Navigators could benefit from strategies that augment their ability to gather SDoH data. Machine learning can be leveraged as one of these strategies to identify SDoH-related barriers. This could further improve health outcomes, particularly in underserved populations.

OBJECTIVE

In this formative study, we explored novel machine learning-based approaches to predict SDoHs in 2 Chicago area PN studies. In the first approach, we applied machine learning to data that include comments and interaction details between patients and navigators, whereas the second approach augmented patients' demographic information. This paper presents the results of these experiments and provides recommendations for data collection and the application of machine learning techniques more generally to the problem of predicting SDoHs.

METHODS

We conducted 2 experiments to explore the feasibility of using machine learning to predict patients' SDoHs using data collected from PN research. The machine learning algorithms were trained on data collected from 2 Chicago area PN studies. In the first experiment, we compared several machine learning algorithms (logistic regression, random forest, support vector machine, artificial neural network, and Gaussian naive Bayes) to predict SDoHs from both patient demographics and navigator's encounter data over time. In the second experiment, we used multiclass classification with augmented information, such as transportation time to a hospital, to predict multiple SDoHs for each patient.

RESULTS

In the first experiment, the random forest classifier achieved the highest accuracy among the classifiers tested. The overall accuracy to predict SDoHs was 71.3%. In the second experiment, multiclass classification effectively predicted a few patients' SDoHs based purely on demographic and augmented data. The best accuracy of these predictions overall was 73%. However, both experiments yielded high variability in individual SDoH predictions and correlations that become salient among SDoHs.

CONCLUSIONS

To our knowledge, this study is the first approach to applying PN encounter data and multiclass learning algorithms to predict SDoHs. The experiments discussed yielded valuable lessons, including the awareness of model limitations and bias, planning for standardization of data sources and measurement, and the need to identify and anticipate the intersectionality and clustering of SDoHs. Although our focus was on predicting patients' SDoHs, machine learning can have a broad range of applications in the field of PN, from tailoring intervention delivery (eg, supporting PN decision-making) to informing resource allocation for measurement, and PN supervision.

A Pharmacy Liaison-Patient Navigation Intervention to Reduce Inpatient and Emergency Department Utilization Among Primary Care Patients in a Medicaid Accountable Care Organization: A Nonrandomized Controlled Trial

IMPORTANCE

Patients with unmet health-related social needs are at high risk for preventable health care utilization. Prior interventions to identify health-related social needs and provide navigation services with community resources have not taken place in pharmacy settings.

OBJECTIVE

To evaluate an enhancement of pharmacy care to reduce hospital admissions and emergency department (ED) visits among primary care patients in a Medicaid accountable care organization (ACO).

DESIGN, SETTING, AND PARTICIPANTS

This nonrandomized controlled trial was conducted from May 1, 2019, through March 4, 2021, with 1 year of follow-up. Study allocation was determined by odd or even medical record number. The study was performed at a general internal medicine practice at a large safety-net hospital in Boston, Massachusetts. Patients who qualified for the hospital's pharmacy care program (aged 18-64 years and within the third to tenth percentile for health care utilization and cost among Medicaid ACO membership) who attended a visit with a primary care clinician were eligible. Of 770 eligible patients, 577 were approached, 127 declined, and 86 could not be contacted.

INTERVENTIONS

Patients in the control group received usual pharmacy care focused on medication adherence. Patients in the intervention group received enhanced pharmacy care with an additional focus on identification of and intervention for health-related social needs. The intervention took place for 1 year.

MAIN OUTCOMES AND MEASURES

The primary outcome was inpatient hospital admissions and ED visits (composite outcome) in the 12 months after enrollment during the intervention period.

RESULTS

Among 364 allocated patients (mean [SD] age, 50.1 [10.1] years; 216 women [59.3%]), 35 were Hispanic of any race (9.6%) and 214 were non-Hispanic Black (58.8%). All participants were included in the intention-to-treat analysis. In analyses controlling for baseline hospital admissions and ED visits the year prior to enrollment, the enhanced pharmacy care group was not associated with the odds of having any hospital admission or ED visit (adjusted odds ratio, 0.62 [95% CI, 0.23-1.62]; P = .32) among all patients and was not associated with the visit rates among those with any visit (adjusted rate ratio, 0.93 [95% CI, 0.71-1.22]; P = .62) relative to the usual pharmacy care group in the year following enrollment.

CONCLUSIONS AND RELEVANCE

The findings of this nonrandomized controlled trial suggest that inpatient and ED utilization among Medicaid ACO members at a safety-net hospital was not significantly different between groups at 1-year follow-up.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT03919084.

Social Needs Identified by Diagnostic Codes in Privately Insured U.S. Adults

INTRODUCTION

The relationships between healthcare use and social needs are not fully understood. In 2015, International Classification of Diseases, Tenth Revision coding introduced voluntary Z codes for social needs‒related healthcare encounters. This study evaluated early national patterns of Z codes in privately insured adults.

METHODS

In 2021, the authors conducted a case-control analysis of national commercial health payer claims from 2016 to 2019. Among adults with ≥6 months of continuous enrollment and ≥1 medical claims, patients with any assigned Z codes were defined as cases. Controls were selected through stratified random sampling. Z codes were organized under 3 categories: socioeconomic, community/social, and environmental.

RESULTS

Of 29.5 million adults, 521,334 patients (1.8%) had any assigned Z codes. Among all the Z codes, 53.5% identified community/social issues, 30.3% identified environmental issues, and 16.2% identified socioeconomic issues. Among socioeconomic Z codes, housing needs were frequently identified, but needs for food, utility bills, and transportation were very rarely identified. In multivariable regression analysis, females had higher odds of Z code assignment than males. Depression and chronic pulmonary disease were the 2 common comorbidities (≥5% prevalence in cases and controls) that were highly associated with Z code assignment. Less common comorbidities strongly associated with Z code assignment were drug abuse, alcohol abuse, psychoses, and AIDS/HIV.

CONCLUSIONS

In this national study of privately insured patients, many Z codes identified healthcare encounters caused by social stressors, whereas few identified food- or transportation-related causes. Depression and chronic pulmonary disease were highly associated with Z code assignment.

Access to Healthcare and the Utilization of Sexually Transmitted Infections Among Homeless Medicaid Patients 15 to 44 Years of Age

Homelessness poses a direct threat to public health in the US as many individuals face debilitating health outcomes and barriers to adequate health care. Access to STI care for the homeless Medicaid population of USA has not been well-studied using administrative claims data. Our study aims to compare health services utilization, STI screening and diagnoses among people experiencing homelessness (PEH) vs. those who are non-PEH using ICD10 codes. We used 2019 MarketScan Medicaid claims data to analyze men and women aged 15-44 years with a diagnosis code for PEH (Z59.0), non-PEH (without Z59.0) and assessed their emergency department and outpatient visits and STI/HIV diagnoses and screening rates. We identified 5135 PEH men and 3571 PEH women among 1.3 million men and 2.1 million women in the 2019 US Medicaid database. PEH patients were more likely to have ED visits (94.80% vs 33.04%) and ≥ 20 outpatient clinic visits (60.29% vs 16.16%) than non-PEH patients in 2019. Higher diagnoses were observed for syphilis 1.57% (CI 1.32-1.86) vs 0.11% (CI 0.11-0.11), HIV 3.93% (CI 3.53-4.36) vs 0.41% (CI 0.41-0.42), chlamydia 1.94% (CI 1.66-2.25) vs 0.85% (CI 0.84-0.86) and gonorrhea 1.26% (CI 1.04-1.52) vs. 0.33% (CI 0.33-0.34) (p < 0.0001) among PEH compared to non-PEH. Among PEH, higher STI/HIV diagnoses rates indicate an increase in STI burden and suboptimal STI testing indicates an underutilization of STI services despite having a higher percentage of health care visits compared to non-PEH patients. Focused STI/HIV interventions are needed to address health care needs of PEH patients.