Frequent emergency department utilization and behavioral health diagnoses.

Frequent Emergency Department Utilization and Behavioral Health Diagnoses Jessica Castner ▼ Yow-Wu B. Wu ▼ Navinder Mehrok ▼ Angad Gadre ▼ Sharon Hewner

Background: There are 12 million emergency department (ED) visits each year related to behavioral health diagnoses. Frequent ED utilization among subpopulations, such as those with behavioral health diagnoses, flags the need for more accessible and effective healthcare delivery systems. Reducing frequent ED use is essential to controlling healthcare cost and poor outcomes of ED overcrowding. Objectives: The purpose of this study is to stratify individuals by overall health complexity and examine the relationship of behavioral health diagnoses (psychiatric and substance abuse) as well as frequent treat-and-release ED utilization in a cohort of Medicaid recipients. Methods: This study was a retrospective analysis of 2009 Medicaid claims from two Western New York State counties. The claims represented 56,491 individuals (18–64 years old). Individuals were stratified into four separate cohorts for analysis based on underlying disease complexity. Data were analyzed using logistic regression models. Results: The following factors significantly increased the odds of frequent treat-and-release ED use in all the four complexity cohorts: psychiatric diagnosis (ORs = 1.4–2.3), substance abuse (ORs = 2.4–3.8), and smoking (ORs = 1.7–4.0). Medicaid patients with behavioral health diagnoses show high risk of frequent treat-and-release ED use. Discussion: The results of this study show that psychiatric diagnosis, substance abuse, and smoking are associated with increased odds of frequent treat-and-release ED utilization for Medicaid recipients in all categories of underlying disease complexity. Our findings support associations reported in the literature. Key Words: comorbidity emergency health service utilization Medicaid mental health smoking substance-related disorders Nursing Research, January/February 2015, Vol 64, No 1, 3–12

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mergency departments (EDs) are a safety net for the U.S. healthcare system. Annually, there are over 136 million ED cases, resulting in over half of all hospitalizations (Centers for Disease Control and Prevention [CDC], 2010). Twelve million ED visits per year are related to behavioral health comorbidities and crises (Owens, Mutter, & Stocks, 2010). Although ED care is becoming an integral component for the care of complex, chronically ill patients, ED care is associated with an increased risk of fragmentation, duplication, increased cost, patient distress, and conflicting provider guidance (Doran, Raven, & Rosenheck, 2013; Lunsky et al., 2012; Vashi et al., 2013). In addition, ED overcrowding is associated with increased morbidity and mortality (Bernstein et al., 2009; Sun et al., 2013). Recent healthcare market changes, driven by the Patient Protection and Affordable Care Act (ACA; Patient Protection and Affordable Care Act, 42 U.S.C. § 18001 et seq., 2010) implementation, highlight the need to better understand and prevent treat-andrelease ED utilization—especially for Medicaid recipients Jessica Castner, PhD, RN, CEN, is Assistant Professor; Yow-Wu B. Wu, PhD, is Associate Professor; Navinder Mehrok, BS, is Graduate Student Programmer; Angad Gadre, BS, is Graduate Student Programmer; and Sharon Hewner, PhD, RN, is Assistant Professor, School of Nursing, University at Buffalo, New York. DOI: 10.1097/NNR.0000000000000065 Nursing Research

(Taubman, Allen, Wright, Baicker, & Finkelstein, 2014). The purpose of this study is to stratify individuals by overall health complexity and examine the relationship of behavioral health diagnoses (psychiatric and substance abuse) as well as treat-andrelease ED utilization in a cohort of Medicaid recipients.

LITERATURE REVIEW Theoretical Framework Wagner’s (1998) Chronic Care Model (CCM) summarizes key components for improving proactive healthcare delivery and patient outcomes. Broadly, the model addresses community and health system context along with an informed, activated patient and a prepared, proactive care team. Specific to the health system domain, the model includes concepts of appropriate delivery system design, decision support, and clinical information systems (Wagner, 1998). In order to understand and evaluate appropriate delivery system design, patient subpopulation risk of potentially avoidable hospitalizations and ED visits should be stratified based on multiple comorbidity and anticipated disease complexity (Victorian Government Department of Health [VGDOH], 2011, 2014). www.nursingresearchonline.com

Copyright © 2014 Wolters Kluwer Health | Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.

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Frequent Emergency Department Use

Disease complexity is the actual or potential risk of a misalignment between patient need and healthcare services based on multimorbidity and interaction of chronic conditions and individual, social, and clinical factors (Grembowski et al., 2014). Services that increase in intensity from primary prevention and usual general care to intensive care coordination must be appropriately matched with this underlying complexity (VGDOH, 2014). Increased ED utilization is theorized to result when patient complexity is not appropriately augmented with enhanced intensity in care coordination (VGDOH, 2011). Population-level surveillance of diagnoses, comorbidities, and characteristics that increase the risk of ED utilization flags the need for improvements in the existing healthcare delivery design system (California Department of Public Health, 2013; VGDOH, 2011, 2014).

Frequent ED Utilization Contrary to the common misperception that frequent treat-andrelease ED users are inappropriately replacing primary care options with ED visits, evidence suggests that frequent ED users use all services (including primary care) more frequently to meet their complex health needs (Doran et al., 2013; Doupe et al., 2012; Lunsky et al., 2012; Mian & Pong, 2012). Existing studies that compare frequent ED users to those who never or rarely use the ED show that the frequent ED users are more likely to have complex chronic conditions (Billings & Raven, 2013; Capp et al., 2013; Doran et al., 2013; Lunsky et al., 2012; Meyer, Qiu, Chen, Larkin, & Altice, 2013). When framed within the CCM, the characteristics and comorbidities of these frequent treat-andrelease ED users can be used as a needs assessment for community intervention, such as accessible and after-hours care (Wagner, 1998; Wajnberg, Hwang, Torres, & Yang, 2012; Weinick, Burns, & Mehrotra, 2010). This needs assessment can allow for insights and direction into the need for healthcare system redesign or increasing the intensity of community nursing services, like care coordination efforts (VGDOH, 2014; Wagner, 1998). Medicaid (the primary insurer for working-aged adults in poverty) recipients represent a vulnerable patient population associated with frequent ED use—even when controlling for primary care access (Billings & Raven, 2013; Capp et al., 2013; Doupe et al., 2012; Mian & Pong, 2012; New York State Department of Health, 2010). Medicaid recipients use the ED disproportionately. Although only 21% of those under the age of 65 were insured by a public health plan such as Medicaid or State Children’s Health Insurance programs in 2009, nearly one third of all ED visits were by individuals covered by these plans (CDC, 2010, 2013a). Capp and colleagues (2013) found that the minority (12%) of individual Medicaid recipients are frequent ED users (four or more annual ED visits), but this minority of Medicaid patients represented 38% of all ED visits. As Medicaid coverage is expanded under the ACA, ED utilization is increasing (Taubman et al., 2014). Medicaid patients are more likely to chronically use the ED for ambulatory care conditions than individuals with private insurance, indicating the need to evaluate

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the accessibility and effectiveness of primary care delivery systems that are available to Medicaid recipients (Johnson et al., 2012). Medicaid patients also have a high burden of substance abuse disorder diagnoses—increasing their vulnerability and complexity (Billings & Raven, 2013; Capp et al., 2013). Behavioral health diagnoses (psychiatric and substance abuse) are associated with increased patient complexity, evidenced by an increased risk of frequent ED utilization (Grembowski et al., 2014). In a multistate study by Mortensen (2010), the average annual ED visit rate for Medicaid recipients was 0.4–0.5, indicating that more than half of Medicaid recipients did not visit the ED in a year. Several studies have replicated evidence that the most frequent users of the ED experience psychiatric or substance abuse comorbidities, particularly for working-aged adults (Capp et al., 2013; CDC, 2013b; Doran et al., 2013; Minassian, Vilke, & Wilson, 2013; Pillow, Doctor, Brown, Carter, & Mulliken, 2013). Not only do those with psychiatric diagnoses and substance abuse utilize the ED more frequently overall, but they also represent the highest rates of treat-and-release ED visits in general (Capp et al., 2013) and revisit within 30 days of hospitalization (Vashi et al., 2013). Substance abuse, in particular, is correlated with the highest rates of ED use. Doupe and colleagues (2012) found that 67% of highly frequent ED users—categorized as those that visit the ED more than six times a year—had a substance abuse diagnosis. Medicaid recipients with alcohol abuse diagnoses were more likely to frequent the ED for care (Capp et al., 2013). However, studies that include both Medicaid and individuals with other insurance show mixed results when testing for relationships between substance abuse disorders and ED utilization (Walley et al., 2012). Smoking is a risk factor for several common medical reasons to visit the ED, such as respiratory infection (U.S. Department of Health and Human Services, 2014). Although smoking is technically coded as a substance abuse disorder, smoking status has often been excluded from previous studies on the impact of substance abuse and ED frequency (Capp et al., 2013; CDC, 2013b; Walley et al., 2012). No ED utilization studies were found that isolated the impact of smoking as a unique substance abuse category. To study the relationships between behavioral health diagnoses and frequent ED utilization, a category or measure of underlying complexity and multimorbidity with meaningful implications for nursing care is needed. Multiple algorithms have been developed to measure disease complexity and comorbidity for practice and research (Guralnik, 1996), but most focus on hospitalized cases to evaluate mortality (Charlson, Pompei, Ales, & MacKenzie, 1987; Grendar et al., 2012; Naessens, Leibson, Krishan, & Ballard, 1992) and hospital length of stay (Young, Kohler, & Kowalski, 1994; Zhong, Chow, & He, 2012), rather than incorporate diagnoses from community care settings to represent the population at large. However, these approaches often exclude nonhospitalized or healthy segments of the


Nursing Research • January/February 2015 • Volume 64 • No. 1

population where nursing health promotion interventions may also be delivered. The COMPLEXedex was developed to categorize underlying disease complexity for nursing care management in an entire population, rather than just hospitalized patients (Hewner & Seo, 2014). The COMPLEXedex clinical algorithm divides the entire population into segments that align with anticipated nursing care intervention intensity at the primary, secondary, and tertiary levels of prevention (Hewner & Seo, 2014; Homer & Hirsch, 2006; Lynn, Straube, Bell, Jencks, & Kambic, 2007). The algorithm was developed at a regional managed care organization in 2005 to benchmark and evaluate the impact of nursing care management on health outcomes, such as hospitalization and ED visits (Hewner, 2014; Hewner, Seo, Gothard, & Johnson, 2014). Evidence from research using the algorithm shows that increasing levels of complexity are associated with increasing rates of hospitalization (Hewner & Seo, 2014). Although increasing levels of complexity are also associated with increasing rates of treatand-release ED utilization, details of this relationship have not yet been reported in the published literature (Hewner, 2014). (Details of the COMPLEXedex development are described in the Methods section of this article.) The COMPLEXedex does not yet include psychiatric (besides major depression) and substance abuse diagnoses.

Hypothesis This study examines the following hypothesis in a population of Medicaid recipients, stratified by four disease complexity segments (healthy/unclassified, at risk, chronic, and system failure): The presence of psychiatric diagnoses, substance abuse, and smoking is associated with increased likelihood of frequent treatand-release ED use, controlling for age, gender, and outpatient healthcare utilization. (Figure 1 portrays this hypothesis.)

METHODS Design The population, variables, and analyses in this study were selected to address the aforementioned gaps in the current literature. In particular, we address treat-and-release ED visits for the Medicaid population stratified by chronic disease complexity and the unique impact of smoking as a substance abuse variable. We conducted a retrospective review of 2009 Medicaid claims in two Western New York State counties. These counties were chosen as part of the urban, suburban, and rural areas surrounding for one of the poorest cities in the United States (Thomas, 2014). The institutional review board deemed this study of the deidentified data set as nonengaged—or not human subjects— research.

Sample Individuals, 18–64 years of age, with 10–12 months of enrollment during the calendar year were included. This 2009 Medicaid


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FIGURE 1. The hypothesis tested in the study. In this model, smoking is a subcategory of substance abuse. Dotted boxes indicate covariates.

cohort contained 56,491 individuals with healthcare utilization across all hospitals, clinics, and systems in the bicounty region.

Complexity Segments The individual Medicaid recipients were stratified into four separate segments based on common chronic disease complexity and comorbidity using the COMPLEXedex algorithm. The COMPLEXedex originated from a private health plan’s evaluation of nurse care coordination. The COMPLEXedex algorithm uses International Classification of Diseases, Version 9 (ICD-9) codes, grouped into standard definitions according to the Healthcare Effectiveness Data and Information Set (HEDIS). HEDIS measures are developed and selected to compare the population-level quality of care across several different settings and providers (Landon et al., 2007; National Committee for Quality Assurances, n. d.). Not all health problems or diagnoses relevant to nurses are currently selected as part of the measures. Rather, the measures are based on the overall population impact and potential to improve the performance of health delivery systems (National Committee for Quality Assurances, 2013). The measures are used in public reporting and ranking of health plan, hospital, and provider quality. A key determination for HEDIS to develop and select a measure is the potential to use resources more efficiently, such as potentially avoidable hospitalizations and ED utilization. From the HEDIS definitions, chronic diseases amenable to a nursing care coordination program were selected for the COMPLEXedex and organized into a hierarchy. This hierarchy was based on the anticipated increasing need for nursing care coordination intensity. The algorithm includes minor chronic conditions of hypertension (HTN) and lipid disorders (LD); major chronic conditions of asthma, major depression, chronic obstructive pulmonary disease (COPD); diabetes (DM); coronary


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artery disease (CAD); heart failure (HF); and chronic kidney disease (CKD). The order of the 14 hierarchical disease classifications starts with CKD as the most complex, followed by HF—with and without CAD and DM comorbidity. These are categorized into the system failure segment. Comorbid DM and CAD, CAD, DM, COPD, major depression, and asthma place the patient into the chronic segment. HTN and LD, HTN or LD is in the at-risk segment. Those who did not fall into this hierarchy were categorized into the healthy/unclassified segment. After eliminating cases with no claims, the count of cases in each complexity segment was healthy/unclassified (n = 28,427), at risk (n = 7,300), chronic (n = 18,795), and system failure (n = 1,969). It is noteworthy that the COMPLEXedex already categorizes major depression as a chronic disease that increases the risk of health service utilization. Thus, this current study excluded major depression from the additional psychiatric diagnoses.

Measures The outcome variable for individuals in this study included three ED use categories: low, moderate, and frequent ED utilization. The independent variables included psychiatric diagnoses, substance abuse, smoking, age, gender, and number of non-ED outpatient visits. The operational definition and rationale for each measure are described below. Outcome: ED Use The main outcome variable for this study was an indicator for low, moderate, or frequent ED use. We tabulated only rate for the treat-and-release ED visits by creating a count of these visits, divided by the number of months enrolled in Medicaid. The unit of analysis was individual recipients. There is no standard definition for frequent ED use, but three to four visits per year serve as the general cutoff to define frequent ED users (Billings & Raven, 2013; Capp et al., 2013; Locker, Baston, Mason, & Nicholl, 2007). Thus, we defined frequent ED use as a rate of three or more treat-and-release (outpatient) ED visits per 12-member months. The referent group of low ED users was defined as a rate of zero to one ED outpatient visit per 12-member months. The moderate ED users were defined as a rate of 1.01–2.99 outpatient ED visits per 12-member months. Independent variables There were three main independent or explanatory variables in this study: psychiatric diagnoses, substance abuse, and smoking. The psychiatric diagnoses, substance abuse, and smoking variables were represented as binary indicators. The first five ICD-9 codes were analyzed for all inpatient, outpatient, and emergency encounters for each recipient. We modified a standardized crosswalk of ICD-9 codes (APS Healthcare, 2007). The first explanatory variable denoted psychiatric diagnoses— excluding major depression. Major depression was included as a recognized chronic illness in the complexity stratification.


Specific psychiatric diagnosis codes were within the following ICD-9 coding ranges, with major depression codes excluded: 290.00–290.90, 293.00–293.84, 293.90–298.90, 300.00–302.90, 306.00–316.00, 995.50–995.85, V61.11, V61.12, V71.09. A value of 1 was assigned if any provider submitted the corresponding mental health diagnosis claim for the individual over the study period year, whereas a value of 0 was assigned to represent no recent history of psychiatric diagnosis claim. Qualitatively, psychiatric diagnoses included anxiety, cognitive (e.g., amnesia), mood (e.g., bipolar, unspecified mood, or depressive disorders excluding major depression), personality, psychotic, and other mental health disorders. The second explanatory variable was substance abuse. Specific substance abuse codes were within the following ICD-9 coding ranges: 291.00–292.9 and 303.00–305.93. A value of 1 was assigned if any provider submitted the corresponding substance abuse codes. If no corresponding ICD-9 codes were present in any of the claims for the individual for the year, a value of 0 was assigned to represent no recent history of substance abuse. Qualitatively, the substance abuse flag represents claims related to the intoxication, use, abuse, withdrawal, and remission from substances, such as alcohol, unspecified or mixed drugs, cocaine, opioids, hallucinogenics, sedative/hypnotics, amphetamines, and cannabis. The third explanatory variable represented smoking status. Smoking status has been often excluded from previous, similar studies using a substance abuse variable (Capp et al., 2013; CDC, 2013b; Walley et al., 2012). Conceptually, it is expected that smoking would not alter decision-making and thinking processes as other commonly abused substances. However, because of the well-known deleterious impacts smoking has on physical health, we included smoking in our study as a potential predictor of treat-and-release ED utilization. Smoking was also added as a separate variable because it represented the most prevalent substance abuse code (between 15% and 27% of each cohort). The specific ICD-9 code used to represent smoking was 305.10. A value of 1 was assigned if any provider submitted this ICD-9 code for the individual during the study year; otherwise, a value of 0 was designated. Covariates Demographic variables (gender and age) and outpatient healthcare utilization were included as covariates.

ANALYSIS Data were screened and cleaned before analysis. The unit of analysis was the individual Medicaid recipient. Descriptive statistics for all variables were calculated. We fitted a multinomial logistic regression model to evaluate the relationship of the explanatory variables to the frequency of ED use. Multinomial logistic regression is appropriate when the dependent variable has three levels—as in this study—with low, moderate, and frequent treat-and-release ED use (Tabachnick & Fidell, 2007).




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TABLE 1. Characteristics of the Groups Healthy/unclassified (n = 28,427) Variable Age (years) Outpatient/clinic visits (number) Psychiatric diagnosis Substance abuse Smoking (yes) Female (yes) ED visits—outpatient only Low (0–1) Moderate (1.01–2.99) Frequent (>3)

At risk (n = 7,300)

M

SD

Chronic (n = 18,795)

M

SD

System failure (n = 1,969)

M

SD

M

SD

33.9 3.4 n 3,177 1,742 4,240 19,241

11.6 3.9 % 11.2 6.1 14.9 67.7

45.1 5.4 n 1,271 518 1,417 4,172

10.9 4.6 % 17.4 7.1 19.4 57.2

43.0 7.2 n 5,595 2,256 5,021 12,860

12.2 6.1 % 29.8 12.0 26.7 68.4

50.1 8.7 n 320 169 295 1,082

10.3 7.6 % 16.3 8.6 15.0 55.0

20,893 4,684 2,850

73.5 16.5 10.0

5,473 1,047 780

75.0 14.3 10.7

11,879 3,309 3,607

63.2 17.6 19.2

1,210 313 446

61.5 15.9 22.7

Note. ED = emergency department; SD = standard deviation.

Separate analyses were conducted on each complexity segment of healthy/unclassified, at risk, chronic, and system failure.

RESULTS Table 1 and Figure 2 depict the characteristics of each of the cohorts. Among the 56,491 individual Medicaid recipients, the following were categorized as frequent ED users in each cohort: healthy/unclassified (10.0%), at risk (10.7%), chronic (19.2%), and system failure (22.7%). Thus, the proportion of frequent ED users increased with each category of increased overall health complexity. The majority of each cohort was categorized as low ED users (61.5–73.5%). Psychiatric diagnoses (11.2–29.8%), substance abuse (6.1–12.0%), and smoking (14.9–26.7%) were present in an increasing proportion of the cohort from the healthy/unclassified to chronic categories. The mean ages ranged from 34 years in the healthy/ unclassified cohort to 50 years in the system failure segment. Most of the Medicaid recipients were female (55.0–68.4%), with the highest ratio of female to male gender in the chronic cohort. The mean number of outpatient visits for 2009 increased, as expected, in each increasingly complex disease cohort from three annual visits in the healthy/unclassified group to nine annual visits in the system failure group. Diagnoses for each treat-and-release ED visit were heterogeneous, with each category representing less than 6% of overall claims (Table 2). The most common diagnosis claims included chest pain, asthma, infections (urinary and respiratory), abdominal pain, and back pain. Although alcohol abuse/ intoxication, anxiety, and depressive disorder were among the most common diagnoses for the chronic and system failure cohorts, medical conditions remained the most common overall reason for treat-and-release ED visits. Table 3 and Figure 3 depict the results from hypothesis test: The presence of psychiatric diagnoses, substance abuse, and smoking is associated with increased likelihood of frequent treatand-release ED use, controlling for age, gender, and outpatient

healthcare utilization. The overall model was significant in all four cohorts. Overall, the estimated percentage of variance in treat-and-release ED use explained by the independent variables tested in this study was 10% for healthy/unclassified, 9% for at-risk, 13% for chronic, and 8% for system failure cohorts. The odds of frequent treat-and-release ED use, compared to low treat-and-release ED use, for the healthy/unclassified cohort were increased over fourfold by smoking—nearly three times by substance abuse and doubled by psychiatric diagnosis. In the at-risk cohort, the odds of frequent ED use were increased fourfold by substance abuse—nearly three times by smoking and doubled by psychiatric diagnosis. The odds of frequent ED use for the chronic cohort were increased three times by substance abuse—2.5 times by smoking and doubled by psychiatric diagnosis. Finally, the odds of frequent ED use for the system failure cohort were increased 3.5 times by substance abuse—nearly three times by psychiatric diagnosis and doubled by smoking. In all four disease complexity cohorts, increasing age was associated with only slightly decreased odds of frequent treatand-release ED use, whereas female gender was related to an increase in the odds of frequent treat-and-release ED use (except the at-risk cohort where no relationship with gender was found).

FIGURE 2. Percentage of each group with behavioral health diagnoses and frequent treat-and-release emergency department use.


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TABLE 2. Most Common Diagnoses for Emergency Department Visits by Group Healthy/unclassified (n = 21,627) Rank 1 2 3 4 5 6 7 8 9 10

Code

n

UTI Acute URI Acute pharyngitis Dental disorder Viral infectiona Lumbago Acute bronchitis Ankle sprain Abdominal pain Headache

543 503 483 379 366 366 348 315 309 304

At risk (n = 3,854)

Chronic (n = 36,995)

n

Code

Code

Chest pain 135 Chest pain Lumbago 88 Asthma Chest pain, other 74 Lumbago Acute bronchitis 70 Chest pain, other Acute URI 68 Abdominal pain Hypertension 67 Acute URI UTI 66 Anxiety Ankle sprain 65 UTI Headache 63 Headache Backache 55 Acute bronchitis

System failure (n = 3,609)

n

Code

n

999 701 644 642 636 595 592 587 531 527

Chest pain Sickle cell crisis CHF Chest pain, other Alcohol abuse Abdominal pain Shortness of breath Abnormal glucose Limb pain Epilepsy

190 99 94 81 78 70 56 51 50 49

Note. CHF = congestive heart failure; URI = upper respiratory infection; UTI = urinary tract infection. aUnspecified.

Similar, but less pronounced, findings were observed when comparing moderate to low treat-and-release ED users. The following explanatory variables and covariates are listed in descending order, based on the magnitude of the odds ratio.

The number of outpatient visits was associated with only a slight increase in odds of frequent treat-and-release use for the healthy/ unclassified, at-risk, and chronic cohorts, with no relationship for the system failure cohort.

TABLE 3. Multinomial Logistic Regression: Emergency Department Use Frequency Frequent usea Group Healthyb,c

Predictor

Psychiatric diagnosis (yes) Substance abuse (yes) Smoking (yes) Age (years) Gender (female) Outpatient visits (number) Constant Psychiatric diagnosis (yes) At riskd Substance abuse (yes) Smoking (yes) Age (years) Gender (female) Outpatient visits (number) Constant Psychiatric diagnosis (yes) Chronice Substance abuse (yes) Smoking (yes) Age (years) Gender (female) Outpatient visits (number) Constant System failuref Psychiatric diagnosis (yes) Substance abuse (yes) Smoking (yes) Age (years) Gender (female) Outpatient visits (number) Constant

B 0.74*** 0.99*** 1.49*** −0.04*** 0.29*** 0.02** −2.31*** 0.62*** 1.39*** 1.03*** −0.04*** 0.12 0.03** −1.78*** 0.81*** 1.16*** 0.91*** −0.03*** 0.22*** 0.02*** −1.61*** 0.98*** 1.26*** 0.68*** −0.02** 0.30* 0.01 −1.30***

(SE) OR 95% CI (0.07) (0.09) (0.06) (0.003) (0.06) (0.006) (0.11) (0.12) (0.14) (0.11) (0.01) (0.11) (0.01) (0.22) (0.05) (0.06) (0.05) (0.01) (0.06) (0.01) (0.10) (0.16) (0.21) (0.17) (0.01) (0.14) (0.01) (0.34)

2.1 2.7 4.4 1.0 1.3 1.0 1.9 4.0 2.8 1.0 1.1 1.0 2.3 3.2 2.5 1.0 1.2 1.0 2.7 3.5 2.0 1.0 1.3 1.0

Moderate usea

B

(SE) OR 95% CI

[1.8, 2.4] 0.37*** (0.05) [2.3, 3.2] 0.57*** (0.06) [3.9, 5.0] 1.03*** (0.04) [1.0, 1.0] −0.03*** (0.002) [1.2, 1.5] 0.02 (0.04) [1.0, 1.0] −0.01 (0.004) −1.05*** (0.06) [1.5, 2.3] 0.19* (0.09) [3.0, 5.3] 0.84*** (0.12) [2.3, 3.5] 0.67*** (0.08) [1.0, 1.0] −0.02*** (0.003) [0.9, 1.4] 0.20** (0.07) [1.0, 1.0] 0.01 (0.01) −1.06*** (.15) [2.0, 2.5] 0.31*** (0.04) [2.8, 3.6] 0.56*** (0.06) [2.3, 2.7] 0.56*** (0.04) [1.0, 1.0] −0.02*** (0.01) [1.1, 1.4] 0.13** (0.04) [1.0, 1.0] 0.01*** (0.003) −0.80*** (0.08) [1.9, 3.7] 0.48** (0.15) [2.3, 5.3] 0.80*** (0.21) [1.4, 2.8] 0.22 (0.16) [1.0, 1.0] −0.01 (0.01) [1.0, 1.8] 0.15 (0.12) [1.0, 1.0] 0.01 (0.01) −1.04*** (0.29)

Model summary x2

R2

1.5 1.8 2.8 1.0 1.0 1.0

[1.3, 1.6] 2,039.3*** .10 [1.6, 2.0] [2.6, 3.0] [1.0, 1.0] [1.0, 1.0] [1.0, 1.0]

1.2 2.3 2.0 1.0 1.2 1.0

[1.0, 1.4] [1.8, 2.9] [1.7, 2.3] [1.0, 1.0] [1.1, 1.4] [1.0, 1.0]

1.4 1.8 1.8 1.0 1.1 1.0

[1.3, 1.5] 2,018.9*** .13 [1.6, 2.0] [1.6, 1.9] [1.0, 1.0] [1.1, 1.2] [1.0, 1.0]

1.6 2.2 1.3 1.0 1.2 1.0

[1.2, 2.1] [1.5, 3.3] [0.9, 1.7] [1.0, 1.0] [.93, 1.5] [1.0, 1.0]

476.5*** .09

137.9*** .08

Note. CI = confidence interval; OR = odds ratio; R2 = Nagelkerke R2; SE = standard error. Values for ORs and CI upper and lower limits rounded to the nearest tenth; consequently, the table shows some instances of significant coefficients occurring with odds ratios and upper or lower limits equal to 1. Degrees of freedom for w2 tests = 12. *p < .05. **p < .01. ***p < .001. aThe reference category is low emergency department use. bHealthy or unclassified. cn = 28,427. dn = 7,300. en = 18,795. fn = 1,969.



FIGURE 3. Adjusted odds ratios for frequent treat-and-release emergency department use.

For the healthy/unclassified cohort, smoking, substance abuse, psychiatric diagnosis, and younger age all increased the odds of moderate treat-and-release ED use. For the at-risk cohort, substance abuse, smoking, psychiatric diagnosis, younger age, and female gender were associated with increased odds of moderate treat-and-release ED use. In the chronic cohort, smoking, substance abuse, psychiatric diagnosis, younger age, increasing outpatient visits, and female gender were all associated with moderate treat-and-release ED use. Finally, in the system failure cohort, only substance abuse and psychiatric diagnosis were associated with increased odds of belonging to the moderate treat-and-release ED use group.

DISCUSSION The results of our study show that psychiatric diagnoses, substance abuse, and smoking are associated with increased odds of more frequent treat-and-release ED utilization for Medicaid recipients in all categories of underlying disease complexity. The proportion of frequent treat-and-release ED users increased with each category of increased overall health complexity, which is consistent with other studies (Doupe et al., 2012; Meyer et al., 2013; Walley et al., 2012). Our study adds to nursing science by identifying several behavioral health risk factors for frequent treat-and-release ED utilization. When interpreted in light of the CCM, the findings indicate a misalignment between patient needs and services, highlighting the need for health system redesign (Wagner, 1998). By stratifying the population by complexity (using common chronic multimorbidities in the COMPLEXedex) and isolating smoking as a unique substance abuse variable, we provide a richer analytic strategy to guide, prioritize, and support ongoing nursing practice interventions. Greater understanding of the nuanced magnitude and odds ratio within each complexity group highlights the priority need to enhance smoking


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cessation efforts, substance abuse prevention and treatment, and psychiatric care efforts in target complexity-based subpopulations. In addition, this study will be used to guide refinement of the COMPLEXedex to include smoking, substance abuse, and a broader range of psychiatric diagnoses for future work in the Medicaid population. Most of the ED diagnoses in this study were medical, and psychiatric complaints were not the primary reason for visiting the ED. This finding is similar to the Capp and colleagues (2013) study of 27,169 ED visits where abdominal pain, back problems, or chest pain were also the most common diagnoses. In patients who utilized the ED 18 or more times per year, Capp and colleagues (2013) also found that sickle cell crises were a common reason for ED care. Our study isolated sickle cell crisis as a common treat-and-release ED diagnosis—especially for those in the system failure cohort (those with congestive heart failure and/or chronic kidney disease)—demonstrating the targeted need for intervention strategies to increase the community-based effectiveness and accessibility of care for those with sickle cell disease and system failure. Overall, the Medicaid cohort in our study followed similar patterns of higher ED use associated with mental health and substance abuse diagnoses when compared to other studies and populations (Billings & Raven, 2013; Doran et al., 2013; Minassian et al., 2013). By analyzing each cohort, separated by disease complexity, we were able to gain insights into population treat-and-release ED use with special meaning for unique settings and providers who treat the corresponding individuals. Psychiatric health diagnoses impacted the frequency of treatand-release ED use. The association was most pronounced in the system failure segment (those with underlying congestive heart failure or chronic kidney disease), with more than doubled odds for frequent treat-and-release ED use. A previous study in a national Veteran’s Affairs (VA) population showed that the strongest association with frequent ED use was in those with schizophrenia, anxiety, bipolar disorder, and personality disorder (Doran et al., 2013). In contrast, Capp and colleagues (2013) found high prevalence of psychiatric disorders across all categories of frequent ED use in Medicaid recipients. Using our unique analysis approach (classifying depressive disorders as a chronic illness and stratifying by underlying complexity), our findings support the notion that psychiatric diagnoses are associated with increased vulnerability to frequent treat-and-release ED use. Redesigned healthcare delivery systems are indicated to support and prioritize nursing interventions, such as intense case management and care coordination, especially for complex patients with heart failure and/or chronic kidney disease and comorbid psychiatric diagnoses.

Psychiatric health diagnoses impacted the frequency of treat-and-release ED use.


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Substance abuse was associated with an increased risk of frequent treat-and-release ED utilization across all cohorts of disease complexity. Substance abuse showed an even stronger association to frequent treat-and-release ED utilization than psychiatric diagnosis at all levels of underlying disease complexity, with the most profound impact for the at-risk cohort (those with hypertension or lipid disorder). Capp and colleagues (2013) found that alcohol abuse was related to increasing frequency of ED utilization whereas cocaine/crack, heroin/opioid, and PCP abuse was not related to ED utilization. Doran and colleagues (2013) found an association in both drug and alcohol abuse to increasing ED utilization. Further study is needed to elucidate differences across complexity categories in multisite studies in prescription drug, illegal drug, and alcohol abuse. Redesigned healthcare delivery systems are indicated to support and prioritize nursing interventions and services to assess, prevent, and treat substance abuse. Our findings indicate that these system redesign changes are a priority in the community and primary care setting where individuals in the at-risk cohort (those with hypertension and/or lipid disorders) are often managed.

Substance abuse was associated with an increased risk of frequent treat-andrelease ED utilization across all cohorts of disease complexity. The profound impact of smoking has significant research and clinical applications. The relationship between smoking and treat-and-release ED utilization frequency had the most profound impact (over four times greater odds) in the healthy/unclassified (over four times greater odds) group. In the healthy/unclassified, at-risk, and chronic segments, smoking showed a stronger association to frequent treat-and-release ED use than psychiatric diagnosis. Sequelae from smoking often seen in the ED include respiratory infection, obstructive lung disease exacerbation, cardiovascular events, autoimmune exacerbation, ectopic pregnancy, poorer general health, and mortality (U.S. Department of Health and Human Services, 2014). Continued primary and secondary prevention for smoking behaviors is essential. Nursing interventions to prevent smoking or support smoking cessation remain critical to the health of populations at all levels of complexity—particularly for those who fall into the otherwise healthy/unclassified category. It is plausible that smoking has a causal impact on ED utilization as subclinical or prediagnosed conditions compel smokers to the ED for health complaints, infections, and complications. However, it is also plausible that individuals who are currently smokers are also overrepresenting individuals with other social determinants of health that


impact ED utilization, such as occupation, family support, or self-efficacy. Our study indicated a small but statistically significant relationship that increasing ages between 18 and 64 was associated with decreased treat-and-release ED use. This is consistent with the CDC (2010) population ED visit rate estimates, which were 45 visits per 100 people for ages 24–44 and drops to 35 visits per 100 people for ages 45–65. A similar finding was reported in the VA population (Doran et al., 2013). This may be because of the increased connection to a primary care provider for routine preventative services in older working-aged adult populations. Alternatively, this may be influenced by a survival bias (sicker individuals do not survive to be included in the older age populations). Female gender was also associated with an increased risk of frequent treat-and-release ED utilization for the healthy/ unclassified, chronic, and system failure cohorts. The relationship was most pronounced for the system failure segment, with female being 1.3 times more likely to be frequent treatand-release ED users than their male counterparts. Our gender findings are opposite of both the Doran and colleagues (2013) VA study and the Capp and colleagues (2013) Medicaid study. It is plausible that regional culture, biological (such as genderrelated anemia prevalence), and social gender differences (such as likelihood to seek early care) may impact these trends. Finally, increased outpatient utilization was associated with increased treat-and-release ED utilization in all cohorts except one: The system failure group where there was no relationship. Our study offers continued corroborating evidence to dispel the myth that frequent ED use is inappropriate. Our finding was consistent with previous studies, supporting the fact that frequent ED users are complex patients who utilize all health services more frequently (Billings & Raven, 2013; Capp et al., 2013; Doran et al., 2013). Because patients are frequently referred to the ED by their primary care providers after hours (Wajnberg et al., 2012; Weinick et al., 2010), ongoing efforts to redesign the healthcare system through enhanced care coordination, improved after-hours and urgent primary care access, and enhanced effectiveness of existing care are indicated. The data used for this study were from 2009, prior to the implementation of the ACA (Taubman et al., 2014). This allowed for a baseline needs assessment during a period of relative stability before sweeping changes, such as services and income eligibility requirements, were made to the Medicaid program. Ongoing research is needed to continue to examine the impact of behavioral health diagnoses on ED utilization during and after ACA implementation. Future study is warranted to use and refine disease complexity stratification. Additional social variables known to influence ED use should be included, such as health literacy, homelessness, social support, or medication use (Schumacher et al., 2013). Additional diagnoses and more detailed analysis of different types of substance abuse or psychiatric diagnoses



should be studied. The findings from our study may be used to justify the ongoing need for targeted nursing interventions for individuals who smoke, those who use or abuse substances, and those with psychiatric diagnoses to reduce frequent treatand-release ED use.

Limitations The following limitations should be considered in interpreting our results and designing future study: The research design was a retrospective review of claims records. These records were created for billing, rather than research purposes, and are subject to misclassification of diagnoses. Our study only included individuals with at least one claim in the study year and do not represent individuals who had no interaction with the healthcare system. Our findings cannot be generalized to nonMedicaid, older adult, or pediatric patients, because our study only included working-aged adults (ages 18–64) insured by Medicaid. The cohort for this study was selected for one year in two New York State counties and may have limited generalizability to cohorts that do not have similar characteristics and contexts.

CONCLUSIONS Medicaid recipients with behavioral health diagnoses have complex needs. They have an increased risk of utilizing outpatient ED services frequently. ED utilization increases cost, fragmentation, and duplication—with less than ideal provider– patient relationship continuity. The magnitude of the risk of frequent treat-and-release ED use and association with behavioral health diagnoses varies across groups with different underlying health complexity. Ongoing interventions and programs are needed for Medicaid patients with behavioral health challenges—especially substance abuse—to enhance both community and ED care and capacity to support ideal healthcare utilization. Intervention and comparative effectiveness research is needed in this population to address smoking, substance abuse, and psychiatric diagnosis at all levels of underlying health complexity. Accepted for publication August 8, 2014. The authors acknowledge that this study was supported by the Patricia H. Garman Behavioral Health Nursing Endowment Fund. The authors thank Mr. Walter Gibson (Data Analyst), the University at Buffalo Center for Nursing Research, and Ms. Jin Young Seo (PhD Student, WHNP-BC, RN) for assistance in manuscript preparation. The authors have no conflicts of interest to report. Corresponding author: Jessica Castner, PhD, RN, CEN, School of Nursing, University at Buffalo, 212 Wende Hall, 3435 Main Street, Buffalo, NY 14214 (e-mail: [email protected]).

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