Social determinants and emergency department utilization: Findings from the Veterans Health Administration

a b s t r a c t

Background: Social determinants of health (SDH) are strong predictors of morbidity and mortality but health care systems struggle to integrate documentation of SDH into health records in ways that can be used for health ser- vices research. Given the impact of social factors on health, it is important to examine the relationship with emer- gency department (ED) utilization.

Objective: To examine the association between seven indicators of SDH and ED utilization using electronic health record data from the Veterans Health Administration (VHA).

Methods: This cross-sectional analysis included data from all patients who had at least one health care visit in the Veterans Integrated Service Network region 4 from October 1, 2015 through September 30, 2016 (n = 293,872). Seven categories of adverse SDH included violence, housing instability, employment or financial problems, legal problems, social or family problems, lack of access to care or transportation, and non-specific psychosocial needs identified through structured coding in EHR. Negative binomial regression was used to examine the association of the count of adverse SDH (0-7) with the count of ED visits, adjusting for socio-demographic and health-related factors.

Results: Approximately 18% of patients visited the ED during the observation period. After adjusting for covari- ates, adverse SDH were positively associated with VHA ED utilization. Each of the SDH indicators, other than legal issues, was positively associated with increased ED utilization.

Conclusion: Even after accounting for several demographic and health-related factors, adverse SDH demonstrated strong positive associations with VHA ED utilization.


Social determinants of health (SDH) – the conditions in which peo- ple are born, grow, live, work and age – are strong predictors of morbid- ity and mortality but health care systems struggle to integrate non- medical factors into their data and Processes of care [1]. SDH are crucial for understanding biomedical outcomes and health care utilization [2]. For example, adverse SDH such as homelessness and unemployment are strongly linked to attempted suicide [3], which could lead to greater

* Corresponding author at: University of Southern California, Suzanne Dworak-Peck School of Social Work, 669 W. 34th Street, Los Angeles, CA 90089, United States of America.

E-mail address: [email protected] (J.R. Blosnich).

emergency department utilization. However, because most health care systems in the US do not adequately document SDH in electronic health records (EHR), understanding how SDH are linked to acute, high-cost care utilization, such as emergency departments, is largely unexplored. Health care providers in the ED often deliver care with just a snap- shot of patient information but make critical life-changing (if not life- saving) decisions for patients. In 2016, across the US, there were ap- proximately 145 million visits to the ED with about 9% of those visits resulting in admission. [4] Increasing utilization of the ED coupled with increasing costs [5] warrants research into factors that are associ- ated with frequent ED utilization [4,6]. Beyond demographic and phys- ical or Mental illness, limited studies show that SDH – specifically adverse SDH – are important for understanding ED utilization. For 0735-6757/

example, Behr and Diaz found that lower social support and unemploy- ment were associated with frequent ED utilization [7]. Through a community-based sample, Moss and colleagues found that homeless persons visited the ED at a rate that was 3 times higher than the US norm [8], and Doran et al. noted the concomitant needs of homeless pa- tients frequently using the ED (e.g., financial strain and food insecurity) [9]. Montgomery and colleagues found that Veterans’ use of acute care – including ED use – often predicted negative housing outcomes, such as eviction [10]. Other research shows that intimate partner violence is highly associated with greater ED use [11-14]. These previous studies, however, often examined specific adverse SDH without taking into ac- count the potential of co-occurring social factors and examining how cumulative burdens of adverse SDH relate with ED utilization.

The Veterans Health Administration (VHA) operates the nation’s single largest integrated healthcare system and has implemented two universal Screening procedures that are conducted by providers during outpatient visits to assess for military sexual trauma and housing insta- bility [15,16]. The VHA has also taken steps to integrate screening for food security [17] and intimate partner violence [18], two social factors for which national organizations recommend universal screening [19- 22]. With its early adoption of EHR and attention to SDH, the VHA has great potential to fill gaps in the literature about how co-occurring SDH are associated with health care utilization. For example, Blonigen et al. found that homelessness and legal problems were significantly as- sociated with ED utilization among veterans living with psychiatric con- ditions [23]. However, the breadth of SDH was limited in this study, and its focus on Psychiatric patients makes its applicability to the general VHA population unclear.

Based on the growing need to understand how SDH impact health care, the goal of this study was to examine the association between SDH identified in records and VHA ED utilization using VHA EHR. Specif- ically, we hypothesized a positive association between SDH and VHA ED utilization such that increasing burdens of SDH would be associated with significantly greater VHA ED utilization.


Administrative and EHR data were abstracted from VA’s Corporate Data Warehouse for all patients who had at least one health care visit in the Veterans Integrated Service Network region 4 (VISN-4) from Oc- tober 1, 2015 through September 30, 2016. VISN-4 comprises most of Pennsylvania, all of Delaware, multiple counties of southern New Jersey, eastern Ohio, northern West Virginia, and two counties in southern New York.

Identification of adverse SDH variables relied on two strategies using EHR data from inpatient and outpatient visits during the study period. One strategy involved searching for social determinants that were doc- umented during care in inpatient and outpatient visits using Interna- tional Classification of Disease (ICD-10) codes indicative of SDH; VA- specific codes indicating receipt of VA services for adverse SDH (e.g., supportive housing); and data from standardized psychosocial as- sessments conducted for patients with social work referrals (e.g., concerns about housing, social support, transportation). Specific details of this methodology and categorization are available elsewhere.

[3] The second strategy included data from universal VA screens per- formed by providers during outpatient visits to assess patients for hous- ing instability and military sexual trauma. Details about the methodology and application of VA universal screens for housing insta- bility and military sexual trauma are also detailed previously [16,24]. The social determinants that resulted from these two strategies were categorized into 7 categories: violence, housing instability, employment or financial problems, legal problems, social or family problems, lack of access to care or transportation, and non-specific psychosocial needs. [3]

married [i.e., separated, divorced, widowed, never married]). Due to small frequencies of some racial identities, race was recoded to white, black/African American, other minority (i.e., Asian, Native Hawaiian/Pa- cific Islander, American Indian/Alaska Native, multiple racial identities), and unknown. We included whether patients lived in urban or rural areas through designations created by the VHA based on the patient’s residence and travel time to VHA facilities [25]. If patients had multiple indicators of locale during the study period or were missing data on lo- cale, we recoded the patient into an “unknown” group. We also included priority enrollment group status, which is a VHA-specific categorization of patients based on an algorithm that includes several factors including assessment of disability linked to military service (i.e., service- connected disability), military service era, and gross Household income. Priority enrollment group contains eight categories that we recoded into 3 broad categories of any service-connected disability (groups 1-4), no service-connected disability but VA Pension/Medicaid- eligible (group 5), and no service-connected disability (groups 6-8) [26].

The dependent variable of ED visits was measured using the VHA’s stop code 130, indicating services obtained from the VHA ED [27]. Be- cause the main outcome was VHA ED visits, we also included several other patient-level covariates related to VHA ED utilization. We summa- rized medical comorbidity severity via the weighted Elixhauser co- morbidity index [28], which includes 30 conditions associated with mortality and Health care costs.[29]. Consistent with prior research, we categorized the score as b0, 0, 1-2, 3-4, and >=5. [28] Although the Elixhauser index includes measures of serious mental illness (i.e., Alcohol use disorder, Drug use disorder, psychosis, depression), it does not include suicidal ideation or suicide attempt (i.e., suicide mor- bidity), which are highly related to VHA ED utilization [30]. To measure suicide morbidity, we used ICD-10 codes that indicated suicidal ideation (i.e., R45.851), and for suicide attempt, we followed specific guidance outlined by the National Center for Health Statistics, including T14.91, X codes, and T codes [31]. In addition to ICD codes, we used the VHA’s unique suicide prevention Applications Network (SPAN), a dataset comprised of incidents of patients’ serious suicidal ideation and suicide attempts as noted by VHA’s Suicide Prevention Coordinators. ICD-based and SPAN-based suicide morbidity do not perfectly overlap, [32] so in- cluding both data systems maximizes capturing any suicide morbidity in VHA. Because we used suicide morbidity as a covariate, we combine suicidal ideation and attempt into a variable of any suicide morbidity. Lastly, we accounted for the total number of inpatient and outpatient visits during FY2016; because of skewness, we created an ordinal cate- gorization of 1-25, 26-50, and N50 visits.

We summarized the prevalence of all variables using frequencies and percentages. We summarized prevalence of VHA ED visits during FY 2016 in categories of 0, 1-2, 3-4, or N5, but in multivariable models, we modeled VHA ED visits as a count variable. Because of overdispersion of the dependent variable (i.e., number of VHA ED visits), we used negative binomial regression to determine the associa- tion of the count of adverse SDH with the count of VHA ED visits. Specif- ically, we first used a base model in which we regressed the count of VHA ED visits on socio-demographic and health-related factors. In a sec- ond model, we added adverse SDH as a count variable.

We also conducted seven separate negative binomial regression models for each individual adverse SDH to estimate their unique associ- ations with frequency of VHA ED visits while controlling for socio- demographic and health-related factors. We report adjusted incident rate ratios and used 99% confidence intervals because of the large sam- ple size. All analyses were conducted in Stata/MP Version 15.1. The in- stitutional review board of [institution name masked for review] approved this study.

Patient-level characteristics extracted from EHR included age group (18-39, 40-69, >=70), sex, ethnicity, and marital status (married vs. not


The VISN-4 patient population (n = 293,872) was largely composed of men (91.7%) and individuals who self-identified as White (79.7%); 48% were aged 70 years or older, reflecting the United States’ Veteran population at large (Table 1) [33]. The majority of the sample had no or minor medical comorbidity severity (weighted Elixhauser scores of 0 or less), and about 16% of the sample had high medical co-morbidity severity (scores of >=5) (Table 2). Most patients (43%) were categorized

Table 2

Prevalence of social determinants of health, health-related factors, and VHA emergency department visits.

Social determinants of health n (%)

Individual types of social determinants of health

Violence 9646 (3.3)

Housing instability 17,738 (6.0)

Employment/financial 10,353 (3.5)

Legal issues 4561 (1.5)

Social/familial problems 7954 (2.7)

into priority enrollment groups that indicated some level of service-

Lack access to care/transportation



connected disability. Approximately 16.4% of patients had at least one

indicator of adverse SDH in their EHR during FY2016 and almost 6%

Non-specific psychosocial needs

Count of social determinants of health



had two or more indicators. Non-specific psychosocial needs were the

most prevalent adverse SDH (6.9%) followed by housing instability (6.0%) and employment/financial problems (3.5%). Just over 1% of pa- tients had an ICD or SPAN indicator of suicide morbidity. Just over 1 in 10 patients (13%) had N25 inpatient and outpatient visits during the study period. Approximately 14.2% of patients in the sample visited the VHA ED one or two times, 2.8% visited the VHA ED three or four times, and 1.4% visited the VHA ED five times or more times during

FY2016. N0



After adjusting for socio-demographic factors, severity of medical 0






co-morbidities, suicide morbidity, and inpatient/outpatient visits, ad- >=5



verse SDH were positively associated with VHA ED utilization (see Suicide morbidity



Table 3). Patients with one adverse SDH indicator had a rate of VHA Total inpatient/outpatient visits

ED visits that was about 36% greater than patients who had no adverse 1-25



SDH indicators (aIRR = 1.36, 99% CI = 1.32-1.41). Patients with seven 26-50






that of (aIRR = 2.10, 99%CI = 1.43-3.09) patients who had no adverse




SDH indicators (Table 3). Likelihood ratio tests comparing nested




Health-related factors

























Weighted Elixhauser Co-morbidity Index score

adverse SDH indicators had a rate of VHA ED visits that was over twice

VHA Emergency Department visits

models with (model 1) and without (model 2) the SDH indicators indi- cated that model 2 was a significantly better fit to the data than model 1 (X2 = 870.8, p b 0.01). In the full model, aIRRs for adverse SDH of six or greater had among the strongest effect sizes; stronger than medical co-

3-4 8244 (2.8)

N5 4160 (1.4)

morbidity and suicide morbidity. Independent multivariable models adjusted for socio-demographic factors, severity of medical co-morbidities, suicide morbidity, and inpa- tient/outpatient visits, showed that all individual adverse SDH, except for legal issues, had significant positive associations with greater fre-

Table 1

socio-demographic characteristics among VISN4 patients (N = 293,872), FY2016.

n (%)









White 234,351 (79.7)

Black/African American



Other minority








quency of VHA ED visits (Table 4). For example, patients with an indica- tor of experiencing violence had a rate of VHA ED visits that was 1.5 times greater than patients without such an indicator (aIRR = 1.56, 99%CI = 1.48-1.64).

Although there was not an independent variable that imposed a “structural zero” value [34] for VHA ED visits, we conducted post hoc analyses using zero-inflated negative binomial regression. The esti- mates were largely similar between models (data not shown), suggest- ing that zero inflation was not an issue with the outcome of VHA ED utilization.







4. Discussion

Marital status




Not married







18-39 29,661 (10.1)









Even after accounting for several covariates strongly related to ED utilization, including suicide morbidity, medical co-morbidity, and use of non-ED healthcare, adverse SDH were among the variables that dem- onstrated a strong positive association with VHA ED utilization. These results support findings from other studies demonstrating a positive re- lationship between frequent ED use and social factors [7,8,27,35], but




the present study enhances the literature by exploring the impact of cu-




mulative experiences of adverse SDH that are detectable in EHR,


Priority enrollment group

Service connected or disabled [1-4] VA Pension/Medicaid-eligible [5]







(e.g., violence, homelessness, legal problems, financial strain, etc.).

The strong association between adverse SDH and VHA ED utilization may be due to the greater accessibility of the VHA ED compared to pri-

Non-service connected [6-8]



mary care services. Research in non-VA settings shows limited accessi-




bility to primary care is a common explanation for seeking care in the

Note: Percentages may not sum to 100% due to missing data.

ED, regardless of insurance status or acuity [36-39], and this may be

Table 3

Association of the count of social determinants of health with frequency of VHA emer- gency department (ED) visits, VISN-4 FY2016.

Model 1a Model 2a

(n = 293,426) (n = 293,426) aIRR (99%CI) aIRR (99%CI)

Age group










Marital status






Not married








Hispanic ethnicity





No Yes


Ref 1.31?


Ref 1.31?



Black/African American







Other minority











Male Female


Ref 1.13?


Ref 1.12?











Priority enrollment group





Non-service connected [6-8]

VA Pension/Medicaid-eligible [5]







Service connected or disabled [1-4]






Elixhauser Co-morbidity Index, weighted























Suicide morbidity

Total inpatient/outpatient visits














Number of social determinant of health







– – Ref


– – 1.36?



– – 1.37?



– – 1.40?



– – 1.52?



– – 1.71?



– – 2.22?



– – 2.10?


Likelihood ratio test indicated that model 2 was a significantly better fit to the data than model 1 (X2 = 870.8, p b 0.01).

a Estimated with negative binomial regression with VHA emergency department utili- zation outcome as a count.

* p b 0.01.

true in VA facilities as well. Similarly, patients may be using the ED as a point of entry to health care services if they are in a crisis and need help quickly. Adverse SDH such as lack of transportation, unemployment, and housing instability could make a visit to a primary care office less feasible than presenting at an ED. Consequently, efforts to improve ac- cess to primary care [40], may have greater benefit to patients experiencing multiple adverse SDH needs and significantly reduce their ED utilization. However, further research is needed about how health systems can collect data about adverse SDH to identify these vul- nerable patient populations for nuanced, health equity-focused evalua- tion of interventions around access to appropriate care.

Furthermore, if the ED is a specific setting in which to identify ad- verse SDH (and subsequently address them), incorporation of social

Table 4

Associations of individual social determinants of health with odds of greater frequency of VHA emergency department (ED) visits.

Type of adverse social determinant of healtha






Housing instability






Legal issues



Social/familial problems



Lack access to care/transportation



Non-specific psychosocial needs



a Models adjusted for age group, marital status, race, ethnicity, sex, locale, priority en- rollment group, medical comorbidity severity, suicide morbidity, and total number of in- patient/outpatient visits.s

b Estimated with truncated negative binomial regression with VHA emergency de- partment utilization outcome as a count.

* p b 0.01.

work is a key element. For example, within the VHA, social workers are an integral part of the Homeless Patient Aligned Care Teams (H- PACTs). H-PACTs offer an interprofessional, team-based approach that incorporates primary care, behavioral health, and homeless program- ming in a single setting [41]. O’Toole and colleagues showed a 19% re- duction in ED use after H-PACT enrollment [42]. A health care system in Indiana, which supports a similar program around housing, also re- ported a decrease in hospitalizations and ED visits among an 11-year panel of patients [43].

The demands of social work are well-documented, and social work shortages may result in demands outpacing the workforce [44]. Auer- bach and colleagues found cost effectiveness of staffing social workers in the ER as it relates to unnecessary inpatient admissions [45]. Their study also concluded that patients experiencing SDH deficits had a greater likelihood of being admitted. Similarly, Gordon analyzed the cost-benefit of staffing social workers in large, moderate, and small EDs, finding economic benefits to health care systems as related to preventing inpatient admissions for social reasons [46]. In an effort to review models for social work professional practice in ED settings, Bell and colleagues conducted a scoping review of 37 articles, finding three key social work practice areas, including mental health, crisis interven- tion (including suicide risk), and chronic disease [47]. While their find- ings do not specifically name SDH, it is important to note that the National Association of Social Workers Code of Ethics requires all li- censed social workers to, “…enhance human well-being and help meet the basic human needs of all people, with particular attention to the needs and empowerment of people who are vulnerable, oppressed, and living in poverty” [48]. Thus, learning how to most effectively incor- porate social workers in ED settings could provide opportunities to im- prove both patient care and completeness of ERH documentation of SDH.

Inclusion of SDH data in EHR systems is an unfolding area in the US. Despite national recommendations for EHR to collect social and behav- ioral determinants of health data [49], currently no standardized, widely-adopted method exists for collecting SDH data at the point-of- care level in a health care setting [50]. For example, although primary care may be one setting to learn about patients’ SDH [51], if patients with multiple SDH needs are more likely to utilize ED services than pa- tients without SDH needs, the ED may be a better environment in which to gather data about patients’ SDH [52,53]. For instance, the VA’s univer- sal housing instability screen is only administered in outpatient settings. Although the high-volume, dynamic nature of the ED may pose barriers to data collection, studies show efficacy both in ED-based data collection

[54] and intervention efforts [55]. Further research is needed to examine

applicability with different patient populations, locale (urban vs. rural health care facilities), engaging community and social service agencies in referral and care planning, and implementation trials to scale.

Concomitant to system change, medical education to facilitate ear- lier and more rigorous training around SDH may help to prepare ED health care providers. National accrediting bodies, such as the Liaison Committee on Medical Education and the Accreditation Council for Graduate Medical Education, require that curricula include instruction on SDH at the medical school, residency program, and fellowship pro- gram levels; however, there is no guidance for best practices [56,57]. Moffett and colleagues recently integrated a novel SDH curriculum into a mandatory four-week emergency medicine clerkship for fourth- year medical students, finding high acceptability among trainees [58]. As medical education efforts around SDH spread and health care sys- tems increasingly integrate SDH into care delivery [59], longer-range as- sessment (e.g., post-matriculation follow-up with trainees in addition to immediate post-training follow-up) and creative educational methods (e.g., increasing the number of SDH-focused training eligible for con- tinuing medical education) may be tested for increasing health care pro- fessionals’ capacity around integrating patients’ SDH into care and treatment planning [60,61].

We note several limitations to this study. Reliance on ICD-10 coding indicative of SDH, particularly within the VHA system, likely resulted in underestimation because coding relies on provider input of patient self- report. Furthermore, ICD-10 codes are generally used for billing pur- poses. Because the VHA system is federally subsidized, it is unknown whether the use of ICD-10 codes in VHA EHR may differ from how non-federal health care systEMS use ICD-10 codes. The cross-sectional analyses examined prevalence over one year and cannot establish spe- cific causal links between SDH and VHA ED utilization. For example, it is possible that a visit to the ED may result in referral to social work (e.g. use of the H-PACT) or subsequent clinic visit and by a PACT/PCP, which may increase EHR documentation of SDH. Due to the unique de- mographic composition of the VHA patient population and the limited geographic focus of this study, the results may not generalize to non- VHA patient populations or other regions within VHA. The acuity of VHA ED visits (e.g., Emergency Severe Index data, New York University ED Classification data) was not assessed within the scope of this study, however other studies [62,63] suggest acuity data would be useful in fu- ture research. Because data are only from VHA, we were unable to ac- count for patients use of non-VHA ED utilization, thus potentially missing ED visits among the patient population. Lastly, although the in- clusion criteria for this study required patients have at least one inpa- tient or outpatient visit during the observation period, primary care utilization was not measured, which may contribute to the variability in VHA ED utilization.

5. Conclusion

Expanding our understanding of SDH in the health care setting and their relevance to patterns of care utilization could help decrease health care costs. Equally important, collecting SDH data offers health care pro- viders a more holistic portrait of the patient, which could potentially im- prove Disease prevention, treatment, and management. Further research could examine the associations of SDH with other utilization patterns (e.g., urgent care, outpatient visits, etc.), the effectiveness of re- ferral services that address these determinants, and enhancement of so- cial work in the ED setting.

Author contributions

CID and JRB conceptualized the study and conducted analyses. CID managed drafting the manuscript and contributions from all authors. All authors reviewed drafts.

CRediT authorship contribution statement

Camille I. Davis:Writing – original draft, Writing – review & editing, Methodology, Formal analysis.Ann Elizabeth Montgomery:Writing –

review & editing.Melissa E. Dichter:Writing – review & editing.Laura

D. Taylor:Writing – review & editing.John R. Blosnich:Conceptualiza- tion, Writing – review & editing, Formal analysis, Funding acquisition, Supervision.

Declaration of competing interest

The authors have no conflicts of interest to disclose.


The authors thank Bryan Ketterer, MS and John Cashy, PhD for their assistance in data management. This work was supported by a Veterans Integrated Service Network Region 4 Competitive Pilot Project Fund to JRB. This work was also partially supported by a VA Health Services Re- search and Development Career Development Award to JRB (CDA-14- 408). The views or opinions expressed in this work are those of the au- thors and do not necessarily reflect those of the funders, institutions, the Department of Veterans Affairs, or The United States Government.


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