Emergency room resource utilization by patients with low-back pain

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  • 1 Department of Neurosurgery, Cedars-Sinai Medical Center, and
  • | 2 Department of Neurosurgery, University of Southern California, Los Angeles, California
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OBJECTIVE

The objective of this study was to determine factors associated with admission to the hospital through the emergency room (ER) for patients with a primary diagnosis of low-back pain (LBP). The authors further evaluated the impact of ER admission and patient characteristics on mortality, discharge disposition, and hospital length of stay.

METHODS

The authors conducted a retrospective analysis of patients with LBP discharged from hospitals according to the Nationwide Inpatient Sample (NIS) between 1998 and 2007. Univariate comparisons of patient characteristics according to the type of admission (ER versus non-ER) were conducted. Multivariate analysis evaluated factors associated with an ER admission, risk of mortality, and nonroutine discharge.

RESULTS

According to the NIS, approximately 183,151 patients with a primary diagnosis of LBP were discharged from US hospitals between 1998 and 2007. During this period, an average of 65% of these patients were admitted through the ER, with a significant increase from 1998 (54%) to 2005 (71%). Multivariate analysis revealed that uninsured patients (OR 2.1, 95% CI 1.7–2.6, p < 0.0001) and African American patients (OR 1.5, 95% CI 1.2–1.7, p < 0.0001) were significantly more likely to be admitted through the ER than private insurance patients or Caucasian patients, respectively. Additionally, a moderate but statistically significant increase in the likelihood of ER admission was noted for patients with more preexisting comorbidities (OR 1.1, 95% CI 1.0–1.2, p < 0.001). An 11% incremental increase in the odds of admission through the ER was observed with each year increment (OR 1.1, 95% CI 1.0–1.2, p < 0.001). Highest income patients ($45,000+) were more likely to be admitted through the ER (OR 1.3, 95% CI 1.1–1.6, p = 0.007) than the lowest income cohort. While ER admission did not impact the risk of mortality (OR 0.95, 95% CI 0.60–1.51, p = 0.84), it increased the odds of a nonroutine discharge (OR 1.39, 95% CI 1.26–1.53, p < 0.0001).

CONCLUSIONS

A significant majority of patients discharged from hospitals in the US from 1998 to 2007 with a primary diagnosis of LBP were admitted through the ER, with more patients being admitted via this route each year. These patients were less likely to be discharged directly home compared with patients with LBP who were not admitted through the ER. Uninsured and African American patients with LBP were more likely to be admitted through the ER than their counterparts, as were patients with more preexisting health problems. Interestingly, patients with LBP at the highest income levels were more likely to be admitted through hospital ERs. The findings suggest that socioeconomic factors may play a role in the utilization of ER resources by patients with LBP, which in turn appears to impact at least the short-term outcome of these patients.

ABBREVIATIONS

ER = emergency room; LBP = low-back pain; NIS = Nationwide Inpatient Sample.

OBJECTIVE

The objective of this study was to determine factors associated with admission to the hospital through the emergency room (ER) for patients with a primary diagnosis of low-back pain (LBP). The authors further evaluated the impact of ER admission and patient characteristics on mortality, discharge disposition, and hospital length of stay.

METHODS

The authors conducted a retrospective analysis of patients with LBP discharged from hospitals according to the Nationwide Inpatient Sample (NIS) between 1998 and 2007. Univariate comparisons of patient characteristics according to the type of admission (ER versus non-ER) were conducted. Multivariate analysis evaluated factors associated with an ER admission, risk of mortality, and nonroutine discharge.

RESULTS

According to the NIS, approximately 183,151 patients with a primary diagnosis of LBP were discharged from US hospitals between 1998 and 2007. During this period, an average of 65% of these patients were admitted through the ER, with a significant increase from 1998 (54%) to 2005 (71%). Multivariate analysis revealed that uninsured patients (OR 2.1, 95% CI 1.7–2.6, p < 0.0001) and African American patients (OR 1.5, 95% CI 1.2–1.7, p < 0.0001) were significantly more likely to be admitted through the ER than private insurance patients or Caucasian patients, respectively. Additionally, a moderate but statistically significant increase in the likelihood of ER admission was noted for patients with more preexisting comorbidities (OR 1.1, 95% CI 1.0–1.2, p < 0.001). An 11% incremental increase in the odds of admission through the ER was observed with each year increment (OR 1.1, 95% CI 1.0–1.2, p < 0.001). Highest income patients ($45,000+) were more likely to be admitted through the ER (OR 1.3, 95% CI 1.1–1.6, p = 0.007) than the lowest income cohort. While ER admission did not impact the risk of mortality (OR 0.95, 95% CI 0.60–1.51, p = 0.84), it increased the odds of a nonroutine discharge (OR 1.39, 95% CI 1.26–1.53, p < 0.0001).

CONCLUSIONS

A significant majority of patients discharged from hospitals in the US from 1998 to 2007 with a primary diagnosis of LBP were admitted through the ER, with more patients being admitted via this route each year. These patients were less likely to be discharged directly home compared with patients with LBP who were not admitted through the ER. Uninsured and African American patients with LBP were more likely to be admitted through the ER than their counterparts, as were patients with more preexisting health problems. Interestingly, patients with LBP at the highest income levels were more likely to be admitted through hospital ERs. The findings suggest that socioeconomic factors may play a role in the utilization of ER resources by patients with LBP, which in turn appears to impact at least the short-term outcome of these patients.

ABBREVIATIONS

ER = emergency room; LBP = low-back pain; NIS = Nationwide Inpatient Sample.

Low-back pain (LBP) is one of the most common and one of the most costly health conditions in the US.6 Approximately 70%–80% of the population will experience LBP at least once in their lifetime, with 2%–5% seeking medical attention each year.28 Five percent of American workers will miss at least 1 day of work due to LBP, which translates to an approximately $50 billion loss in productivity, and total costs in excess of $100 billion per year.20

The estimated 2.6 million annual emergency room (ER) visits for LBP-related disorders represent 2.3% of all ER visits per year in the US.11 Several recent studies acknowledge the increasing prevalence of patients with LBP presenting to the ER,11–13,19,32 with significant associated costs,19 but there remains a paucity of research on factors—socioeconomic ones in particular—contributing to this phenomenon, as well as its impact on the outcomes of these patients. A greater understanding of the demographics and socioeconomic characteristics of patients with LBP admitted to the hospital through the ER, as well as the impact of ER admission on patient outcomes, may lead to more effective methods of treatment, better utilization of resources, and a reduction in the economic and financial burden of patients with LBP on ER services.

The aim of this study was to determine factors associated with hospital admission through the ER for patients with LBP. We also investigated the effect of ER admission on the posthospital outcomes of patients with LPB.

Methods

Data Source

This study used the Nationwide Inpatient Sample (NIS) database to analyze the routes of admission and outcomes of patients with LBP admitted to US hospitals between 1998 and 2007. Data were obtained from the Healthcare Cost and Utilization Project of the Agency for Healthcare Research and Quality. The NIS contains discharge data from a sample of hospitals selected for inclusion in the database using a stratified random sampling technique. This produces a representative 20% subsample of all American nonfederal hospital discharges so that conclusions drawn from the database can be confidently generalized to the entire American medical community.

Inclusion and Exclusion Criteria for Cases Analyzed

Data were queried to identify hospital inpatient discharge cases with a primary diagnosis of LBP (ICD-9-CM diagnosis code 724.2) discharged between 1998 and 2007. Patients with spinal compression fracture (nontraumatic) as denoted by ICD-9-CM diagnosis code 733.13 were identified and such condition was adjusted in the multivariate models. Because the admission source was critical for this study, we excluded patients missing this variable; patients with missing race were also excluded.

Patient Characteristics

Patient characteristics included age, sex, race, median household income, primary payer (e.g., Medicare, Medicaid), discharge disposition (e.g., routine, transfer), and preexisting comorbidities documented according to the acceptable Elixhauser comorbidities.10 Age and decades were used as continuous variables. Race was described in detail (e.g., Caucasian, African American, Hispanic) and also coded as Caucasian versus non-Caucasian. Patients were considered non-ER admissions whenever routinely admitted or transferred from another hospital or long-term care facility; patients admitted through the ER were coded as such. We considered patients with expected primary payer of self-pay or no charges to be uninsured.

Outcomes of Interest

Assessing factors associated with the odds of being admitted through the ER was of particular interest in this study. Secondary outcomes evaluated included mortality and unfavorable discharge disposition (i.e., “nonroutine” discharge). Discharge disposition (binary outcome) was deemed “nonroutine” whenever transfer to another hospital or long-term care facility, home health care, or death occurred; otherwise, the patient was considered a routine discharge.

Statistical Analysis

Univariate chi-square and t-tests were used to evaluate associations between admission type (ER vs non-ER) and patient characteristics. Multivariate logistic and linear regression models were implemented to evaluate factors associated with an ER admission, odds of mortality, and the odds of a nonroutine discharge. Factors adjusted for in these multivariate models included a patient's age, sex, race, insurance, comorbidities, median household income, hospital bed size, and spinal compression fracture. Adjusted odds ratio (OR), 95% confidence intervals (CIs), and corresponding p values were reported. Nationwide estimates were made possible through the SAS PROC SURVEY methodology. All analyses were conducted using SAS (version 9.1 for Windows, SAS Institute Inc.).

Results

General Descriptors

During the 10-year period, 183,151 patients with LBP were admitted to US hospitals. Of these, 118,962 (65.0%) were admitted through the ER (Table 1). Non-Caucasian patients with LBP had higher rates of admission through the ER compared with non-ER admissions (19.7% vs 14.3%, p < 0.0001). Fewer ER-admitted patients had private insurance (28.8% vs 33.0%, p < 0.0001), more had Medicaid insurance (10.6% vs 8.8%, p < 0.0001 compared with non-ER admissions. Additionally, ER-admitted patients had higher rates of preexisting comorbidities (score 3+: 11.6% vs 9.0%, p < 0.0001) compared with non-ER patients.

TABLE 1.

Characteristics of 183,151 patients with LBP by type of admission

CharacteristicsAll Cases% ER% Non-ERp Value
No.%
No. of cases183,15110065 (n = 118,962)35 (n = 64,189)
Average age (yrs)58.659.354.8<0.0001
Females114,56862.662.562.70.67
Race<0.0001
  Caucasian150,52182.280.385.7
  African American16,6559.110.07.4
  Hispanic13,3897.38.25.7
  Asian/Pacific Islander18361.01.10.8
  Native American7510.40.40.4
Median household income ($)<0.0001
  <24,99949872.75.75.9
  25,000–34,99924,53813.426.231.7
  35,000–44,99925,26313.829.728.6
  >45,00031,56817.338.433.7
Primary payer<0.0001
  Medicare86,47047.24845.9
  Medicaid18,1769.910.68.8
  Private insurance55,35630.228.833.0
  Self-pay77544.25.22.5
Discharge status*<0.0001
  Routine128,58170.26776.2
  Nonroutine54,25029.632.823.6
Comorbidity score<0.0001
  076,67541.939.346.6
  154,96230.030.728.8
  231,96017.518.415.6
  3+19,55410.711.69.0

Nonroutine discharge corresponds to transfers, home health care, against medical advice, and death.

A preliminary description of outcomes for ER versus non-ER admissions is provided in Table 2. More patients with LBP who were admitted through the ER were discharged nonroutinely from the hospital compared with non-ER admissions (32.9% vs 23.7%, p < 0.001). Although mortality rates (0.51% vs 0.38%, p = 0.08) tended to be higher for ER-admitted patients, this did not reach statistical significance at the univariate level. Patients admitted through the ER had shorter hospital stays compared with non-ER patients (3.6 days vs 3.7 days, p = 0.003).

TABLE 2.

Characteristics and outcomes of 183,151 patients with LBP

VariableER CasesNon-ER Casesp Value
Outcomes
  Mortality rate (%)0.510.380.08
  Nonroutine discharge rate (%)32.923.7<0.0001
  Average length of stay (days)3.63.70.003
  Total charges ($)10,94012,710<0.0001
Hospital characteristics
  Bed size (%)<0.0001
    Small13.819.4
    Medium26.625.4
    Large59.655.2
  Teaching Status (%)41.140.00.47
  Region (%)<0.0001
    Northeast30.020.7
    Midwest19.023.9
    South31.338.6
    West19.716.8

Factors Associated With an ER Admission

A patient's insurance was found to be the strongest predictor for ER admission (Fig. 1). Uninsured patients were 2.1 times more likely to be admitted through the ER than private insurance patients were (OR 2.1, 95% CI 1.7–2.6, p < 0.0001). African Americans also had increased odds of ER admission (OR 1.46, 95% CI 1.2–1.7, p < 0.0001) compared with Caucasian patients. Additionally, patients with more preexisting comorbidities had a higher likelihood of admission through the ER (OR 1.1, 95% CI 1.0–1.2, p < 0.0001). An 11% incremental increase in the likelihood of ER admission was observed with each year increment (OR 1.1, 95% CI 1.0–1.2, p < 0.0001). Interestingly, patients with the highest yearly income ($45,000+) were more likely to be admitted through the ER (OR 1.3, 95% CI 1.1–1.6, p = 0.007) compared with those at the lowest median household income level (Tables 3 and 4; $1–$24,999).

FIG. 1.
FIG. 1.

Adjusted ORs for association of patient and hospital characteristics with likelihood of ER admission in patients with LBP. *p < 0.05, **p < 0.01, ***p < 0.001.

TABLE 3.

Adjusted OR of mortality and nonroutine discharge

CharacteristicsMortalityNonroutine Discharge
OR (95% CI)p ValueOR (95% CI)p Value
Age (decade increments)1.54 (1.35–1.77)<0.00011.68 (1.63–1.72)<0.0001
Male vs female1.68 (1.10–2.58)0.020.80 (0.74–1.72)<0.0001
Non-Caucasian vs other0.74 (0.37–1.49)0.400.82 (0.72–0.94)0.003
ER vs non-ER admission0.95 (0.60–1.51)0.841.39 (1.26–1.53)<0.0001
Insurance (ref: private)
  Medicare0.69 (0.34–1.39)0.291.71 (1.51–1.92)<0.0001
  Medicaid0.63 (0.17–2.27)0.481.42 (1.18–1.72)0.0003
  Uninsured1.77 (0.49–6.33)0.381.27 (0.97–1.67)0.08
Elixhauser comorbidity score (per unit increment)1.72 (1.46–2.03)<0.00011.37 (1.32–1.43)<0.0001
Median household income (ref: >$45,000)
  $1–$24,9992.36 (1.13–4.91)0.020.96 (0.75–1.23)0.75
  $25,000–$34,9990.56 (0.30–1.01)0.070.96 (0.86–1.10)0.52
  $35,000–$44,9990.99 (0.59–1.67)0.960.98 (0.88–1.10)0.75
Hospital bed size (ref: small)
  Medium1.05 (0.52–2.10)0.900.92 (0.80–1.10)0.27
  Large1.00 (0.53–1.88)0.990.94 (0.82–1.07)0.35
TABLE 4.

Parameter estimates and p values for multivariate analysis of in-hospital length of stay and total charges

CharacteristicsIn-Hospital Length of StayTotal Charges
Estimate (days)p ValueEstimate ($)p Value
Age (decade increments)0.20<0.0001140.78
Males−0.24<0.00013650.06
Non-Caucasians0.200.00103020<0.0001
ER admissions−0.25<0.0001−2849<0.0001
Insurance (ref: private)
  Medicare0.42<0.000112600.007
  Medicaid0.240.03−1810.54
  Uninsured−0.210.05−8510.01
Elixhauser comorbidity score (per score increment)0.65<0.00011561<0.0001
Median income (ref: >$45,000)
  $1–$24,999−0.080.39−5760<0.0001
  $25,000–$34,9990.200.0003−5409<0.0001
  $35,000–$44,9990.110.05−4676<0.0001
Hospital bed size (ref: small)
  Medium0.080.911865<0.0001
  Large0.080.173810<0.0001

Comorbidities

A detailed analysis of the established and well-studied Elixhauser comorbidities10 is documented (Fig. 2, Tables 3 and 4). Hypertension was the most commonly documented comorbidity in both ER and non-ER cohorts (38% vs 33%, p < 0.0001), respectively. Diabetes was the next most frequent comorbidity documented (ER 14% vs non-ER 13%, p < 0.0001), followed by chronic pulmonary disease (ER 14% vs non-ER 13%, p < 0.0001), and depression (ER 11% vs non-ER 12%). Fluid and electrolyte disorders (ER 9% vs non-ER 6%, p < 0.0001), hypothyroidism (ER 9% vs non-ER 7%, p < 0.0001), deficiency anemia (ER 7% vs non-ER 6%, p < 0.0001), and obesity (ER 7% vs non-ER 6%, p < 0.0001) were also documented in a significant fraction of patients.

FIG. 2.
FIG. 2.

Comorbidity rates for patients by type of admission. *p < 0.05, **p < 0.01, ***p < 0.001. ns = nonsignificant.

Outcome Analysis

Multivariate models that adjusted for a patient's age, sex, race, admission type (ER vs non-ER), insurance, comorbidities, median household income, hospital bed size, and the occurrence of a spinal decompression fracture were evaluated (Table 3). We found no association between the type of admission (ER vs non-ER) and the risk of mortality (OR 0.95, 95% CI 0.60–1.51, p = 0.84). However, older age (OR 1.54, 95% CI 1.35–1.77, p < 0.0001), being male (OR 1.68, 95% CI 1.10–2.58, p = 0.02), having higher preexisting comorbidities (OR 1.72, 95% CI 1.46–2.03, p < 0.0001), and lowest income (OR 2.36, 95% CI 1.13–4.91, p = 0.02) were significantly associated with an increased risk of mortality. Patients admitted through the ER were more likely to be discharged nonroutinely (OR 1.39, 95% CI 1.26–1.53, p < 0.0001) than the non-ER cohort. Older age patients (OR 1.68, 95% CI 1.63–1.72, p < 0.0001), with Medicare (OR 1.71, 95% CI 1.51–1.92, p < 0.0001), those with more preexisting medical conditions (OR 1.37, 95% CI 1.32–1.43, p < 0.0001), and documented as having a spinal compression fracture (OR 2.0, 95% CI 1.55–2.59, p < 0.0001) were significantly associated with an increased risk of nonroutine discharge. Male patients (OR 0.80, 95% CI 0.74–1.72, p < 0.0001) and non-Caucasians (OR 0.82, 95% CI 0.72–0.94, p = 0.003) had a significant reduction in the odds of a nonroutine discharge.

Discussion

The extensive utilization of ER resources by patients with LBP has been well documented. Over the course of 2 studies, Friedman found that approximately 2.6–2.7 million patients visited the ER annually with a primary complaint of LBP.12,13 Through a survey of ER physicians, Elam et al. found that LBP was a frequent cause of visits to the ER, with 59% of ER physicians encountering patients with LBP 5–20 times per month, while 35% reported treating these patients more than 20 times every month.9 These patients often visit the ER multiple times and receive repeated diagnostic testing,2 resulting in significant associated financial expenditures and only short-term pain relief for most.19 Similarly, in our analysis of a national database of patients admitted to US hospitals over nearly a decade, a total of 183,151 patients with a primary diagnosis of LBP were documented. Of these, a significant majority (65%) was admitted through the ER, with an almost 20% increase in this cohort over time, from 54% in 1998 to 71% in 2005.

Most epidemiological studies of patients with LBP have focused on issues such as various treatment methods and outcomes,7,17,22,29 as well as costs associated with these treatments,8,14,16,23,33 while only a handful have analyzed the impact of socioeconomic variables, including ethnicity and race, on the diagnosis and treatment of patients with LBP.4,5,20 To our knowledge, this is the first study to not only analyze the likelihood of visiting the ER, but to specifically focus on the interplay between socioeconomic factors and the likelihood of being admitted to the hospital through the ER, as well as the effect of this admission route on short-term outcome (i.e., discharge disposition and mortality) for patients with LBP. Identifying factors that increase the likelihood of patients with LBP utilizing ER resources can potentially lead to more targeted and preventative treatment strategies that would allow for both improved clinical outcomes as well as cost savings for an already overburdened health care system in the US. In this study, we found that African American (OR 1.5, p < 0.0001) and uninsured (OR 2.1, p < 0.0001) patients were significantly more likely to be admitted through the ER, and admission through the ER significantly increased the likelihood of a nonroutine discharge for these patients, and tended to increase the risk of death as well.

It has been previously established that the inequities in access to health care resources for a variety of conditions can result from racial and ethnic factors.1,3,27 With regard to spinal conditions in particular, race and ethnicity have been found to influence health care providers' decisions to not only administer analgesic treatment,15 but also to order advanced imaging and recommend surgery for patients with LBP,31 as well as those with scoliosis.25

Our finding that African Americans with LBP were almost twice as likely to not merely visit the ER, but to actually be admitted to the hospital via this route, may be reflective of these discrepancies in access to appropriate nonemergent health care services and treatments.

LBP is also one of the most common reasons for seeking medical care for the uninsured and poor, primarily through the ER.21,24 Friedman et al.13 found that as many as 42% of patients with LBP in the ER of 1 hospital were unemployed, many of whom were likely uninsured. This same study found that insurance status was associated with decisions to order advanced imaging and that uninsured patients in particular were less likely to undergo such studies.13 Moreover, patients with LBP who were discharged from the ER had substantial morbidity even months later, with 70% reporting functional impairment 1 week later, and nearly half still impaired after 3 months.12 That our analysis revealed uninsured patients to be twice as likely to be admitted to the hospital via the ER, then, appears intuitive as these patients not only use ER services more often, but appear to receive suboptimal long-term benefit from these services and seemingly will eventually require hospitalization.

Patients of lower socioeconomic status have been found to have an increased prevalence of LBP,20,28 perhaps as a result of the types of manual labor involved in most lower-paying occupations.28 Interestingly, in our analysis, patients with LBP in the lowest annual income groups ($1–$24,999 and $25,000–$34,999) were actually less likely to be admitted to the hospital through the ER (OR 0.76, 95% CI 0.63–0.93, p = 0.006) than those with relatively higher annual incomes (≥ $45,000). Although this finding seems counterintuitive, there may be several explanations for this phenomenon. The first explanation is that the NIS database stratifies median income into only 4 groups ($1–$24,999, $25,000–$34,999, $35,000–$44,999, and $45,000 and above). As such, the lowest income group can likely be considered to be those living below the poverty level. In a study of nonfederal hospitals that operated an ER in California in 2007, Hsia et al.18 found that, even after controlling for insurance status, lower-income patients visiting the ER were at higher risk for leaving without being seen. Although their study did not include exact income values and instead relied on census data to estimate mean income for patients within a certain zip code, the finding that the lowest-income patients were at the highest risk for leaving the ER without being seen may offer insight into our results concerning this group of patients. Our finding that, after multivariate analysis, the lowest-income patients in the NIS database were actually less likely to be admitted to the hospital through the ER may be a result of these patients actually leaving the ER without ever being seen by a physician, perhaps as a result of overcrowding of ERs in those areas that serve a high proportion of extremely low-income patients. Other studies have also documented socioeconomic status as a risk factor for “left before being seen” in US emergency departments.26,30

Future research should include whether the admissions from ER versus non-ER were related to type and timing of operative treatment (see Supplemental Table for a preliminary analysis). In addition, it would be interesting to understand the differences in the cost of care if the patient is admitted on an emergency basis from the ER versus non-ER admission. These questions are likely best answered by other administrative databases such as Reuters Market Scan.

Limitations of the Study

There are several limitations to this study. First, the database is solely for admissions. Our data were limited to patient admission demographics (sex, race, median household income, primary insurance payer, type of admission) and hospital characteristics (bed size, teaching status, region of the country, location broken down as rural or urban) as contained in the database. Some information was missing, and we identified the number and percentage of patients where admission data were missing. Second, because all patients with LBP in the NIS are grouped under ICD-9-CM diagnosis code 724.2, we acknowledge the inability to assess the severity of a patient's LBP, or other underlying spinal pathologies (such as spondylolisthesis, stenosis, postlaminectomy syndrome, sagittal imbalance, and others) and thereby cannot comment on the role that these factors played in the decision to admit any particular patient. Thus, the diagnosis of LBP in the NIS was established by the admitting physician. Additionally, the patient's symptomatology leading to admission is not captured by the NIS. It cannot be determined whether every patient matched the same criteria for LBP, or whether any patients had radiculopathy, myelopathy, or even abnormal imaging. As such, this study cannot comment on the appropriateness of surgical versus nonsurgical treatment or the surgical treatment used.

Conclusions

A significant majority of patients discharged from hospitals in the US from 1998 to 2007 with a primary diagnosis of LBP were admitted through the ER, with more patients being admitted via this route each year. These patients were less likely to be discharged directly home compared with patients with LBP who were not admitted through the ER. Uninsured and African American patients with LBP were more likely to be admitted through the ER than their counterparts, as were patients with more preexisting health problems. Interestingly, patients with LBP at the lowest income levels were less likely to be admitted through hospital ERs. Our findings suggest that socioeconomic factors may play a role in the utilization of ER resources by patients with LBP, which in turn appears to impact at least the short-term outcome of these patients. Moreover, patients at the lowest end of the income spectrum may be at higher risk for leaving the ER without ever being seen. More research should be directed at finding ways to improve nonemergent health care services for these populations that may potentially reduce the burden placed on ER services in the US.

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    • Export Citation
  • 14

    Gore M, , Sadosky A, , Stacey BR, , Tai KS, & Leslie D: The burden of chronic low back pain: clinical comorbidities, treatment patterns, and health care costs in usual care settings. Spine (Phila Pa 1976) 37:E668E677, 2012

    • Search Google Scholar
    • Export Citation
  • 15

    Hirsh AT, , Hollingshead NA, , Bair MJ, , Matthias MS, , Wu J, & Kroenke K: The influence of patient's sex, race and depression on clinician pain treatment decisions. Eur J Pain 17:15691579, 2013

    • Search Google Scholar
    • Export Citation
  • 16

    Hong J, , Reed C, , Novick D, & Happich M: Costs associated with treatment of chronic low back pain: an analysis of the UK General Practice Research Database. Spine (Phila Pa 1976) 38:7582, 2013

    • Search Google Scholar
    • Export Citation
  • 17

    Howard PK, & Shapiro SE: What is known about outcomes of patients with low back pain?. Adv Emerg Nurs J 35:37, 2013

  • 18

    Hsia RY, , Asch SM, , Weiss RE, , Zingmond D, , Liang LJ, & Han W, et al.: Hospital determinants of emergency department left without being seen rates. Ann Emerg Med 58:2432, 32.e132.e3, 2011

    • Search Google Scholar
    • Export Citation
  • 19

    Jorgensen DJ: Fiscal analysis of emergency admissions for chronic back pain: a pilot study from a Maine hospital. Pain Med 8:354358, 2007

    • Search Google Scholar
    • Export Citation
  • 20

    Katz JN: Lumbar disc disorders and low-back pain: socioeconomic factors and consequences. J Bone Joint Surg Am 88:Suppl 2 2124, 2006

  • 21

    Kessler TA, & Alverson E: Health concerns and learning styles of underserved and uninsured clients at a nurse managed center. J Community Health Nurs 20:8192, 2003

    • Search Google Scholar
    • Export Citation
  • 22

    Konstantinou K, , Hider SL, , Jordan JL, , Lewis M, , Dunn KM, & Hay EM: The impact of low back-related leg pain on outcomes as compared with low back pain alone: a systematic review of the literature. Clin J Pain 29:644654, 2013

    • Search Google Scholar
    • Export Citation
  • 23

    Lawrence VA, , Tugwell P, , Gafni A, , Kosuwon W, & Spitzer WO: Acute low back pain and economics of therapy: the iterative loop approach. J Clin Epidemiol 45:301311, 1992

    • Search Google Scholar
    • Export Citation
  • 24

    Mauksch LB, , Katon WJ, , Russo J, , Tucker SM, , Walker E, & Cameron J: The content of a low-income, uninsured primary care population: including the patient agenda. J Am Board Fam Pract 16:278289, 2003

    • Search Google Scholar
    • Export Citation
  • 25

    Nuño M, , Drazin DG, & Acosta FL Jr: Differences in treatments and outcomes for idiopathic scoliosis patients treated in the United States from 1998 to 2007: impact of socioeconomic variables and ethnicity. Spine J 13:116123, 2013

    • Search Google Scholar
    • Export Citation
  • 26

    Pham JC, , Ho GK, , Hill PM, , McCarthy ML, & Pronovost PJ: National study of patient, visit, and hospital characteristics associated with leaving an emergency department without being seen: predicting LWBS. Acad Emerg Med 16:949955, 2009

    • Search Google Scholar
    • Export Citation
  • 27

    Riley WJ: Health disparities: gaps in access, quality and affordability of medical care. Trans Am Clin Climatol Assoc 123:167174, 2012

    • Search Google Scholar
    • Export Citation
  • 28

    Rubin DI: Epidemiology and risk factors for spine pain. Neurol Clin 25:353371, 2007

  • 29

    Storheim K: Targeted physiotherapy treatment for low back pain based on clinical risk can improve clinical and economic outcomes when compared with current best practice. J Physiother 58:57, 2012

    • Search Google Scholar
    • Export Citation
  • 30

    Sun BC, , Binstadt ES, , Pelletier A, & Camargo CA Jr: Characteristics and temporal trends of “left before being seen” visits in US emergency departments, 1995–2002. J Emerg Med 32:211215, 2007

    • Search Google Scholar
    • Export Citation
  • 31

    Taylor BA, , Casas-Ganem J, , Vaccaro AR, , Hilibrand AS, , Hanscom BS, & Albert TJ: Differences in the work-up and treatment of conditions associated with low back pain by patient gender and ethnic background. Spine (Phila Pa 1976) 30:359364, 2005

    • Search Google Scholar
    • Export Citation
  • 32

    Waterman BR, , Belmont PJ Jr, & Schoenfeld AJ: Low back pain in the United States: incidence and risk factors for presentation in the emergency setting. Spine J 12:6370, 2012

    • Search Google Scholar
    • Export Citation
  • 33

    Zusman M: Belief reinforcement: one reason why costs for low back pain have not decreased. J Multidiscip Healthc 6:197204, 2013

Disclosures

J. C. Liu is a consultant for Medtronic.

Author Contributions

Conception and design: Acosta. Acquisition of data: Drazin. Analysis and interpretation of data: Nuño, Patil. Drafting the article: Drazin, Yan. Critically revising the article: Drazin. Reviewed submitted version of manuscript: Drazin. Study supervision: Acosta, Liu.

Supplemental Information

Online-Only Content

Supplemental material is available with the online version of the article.

Supplemental Table. http://thejns.org/doi/suppl/10.3171/2015.7.SPINE14133.

Previous Presentations

Portions of this work were presented in abstract form at the Congress of Neurological Surgeons Meeting, in Chicago, Illinois, October 2012.

  • View in gallery

    Adjusted ORs for association of patient and hospital characteristics with likelihood of ER admission in patients with LBP. *p < 0.05, **p < 0.01, ***p < 0.001.

  • View in gallery

    Comorbidity rates for patients by type of admission. *p < 0.05, **p < 0.01, ***p < 0.001. ns = nonsignificant.

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    Gore M, , Sadosky A, , Stacey BR, , Tai KS, & Leslie D: The burden of chronic low back pain: clinical comorbidities, treatment patterns, and health care costs in usual care settings. Spine (Phila Pa 1976) 37:E668E677, 2012

    • Search Google Scholar
    • Export Citation
  • 15

    Hirsh AT, , Hollingshead NA, , Bair MJ, , Matthias MS, , Wu J, & Kroenke K: The influence of patient's sex, race and depression on clinician pain treatment decisions. Eur J Pain 17:15691579, 2013

    • Search Google Scholar
    • Export Citation
  • 16

    Hong J, , Reed C, , Novick D, & Happich M: Costs associated with treatment of chronic low back pain: an analysis of the UK General Practice Research Database. Spine (Phila Pa 1976) 38:7582, 2013

    • Search Google Scholar
    • Export Citation
  • 17

    Howard PK, & Shapiro SE: What is known about outcomes of patients with low back pain?. Adv Emerg Nurs J 35:37, 2013

  • 18

    Hsia RY, , Asch SM, , Weiss RE, , Zingmond D, , Liang LJ, & Han W, et al.: Hospital determinants of emergency department left without being seen rates. Ann Emerg Med 58:2432, 32.e132.e3, 2011

    • Search Google Scholar
    • Export Citation
  • 19

    Jorgensen DJ: Fiscal analysis of emergency admissions for chronic back pain: a pilot study from a Maine hospital. Pain Med 8:354358, 2007

    • Search Google Scholar
    • Export Citation
  • 20

    Katz JN: Lumbar disc disorders and low-back pain: socioeconomic factors and consequences. J Bone Joint Surg Am 88:Suppl 2 2124, 2006

  • 21

    Kessler TA, & Alverson E: Health concerns and learning styles of underserved and uninsured clients at a nurse managed center. J Community Health Nurs 20:8192, 2003

    • Search Google Scholar
    • Export Citation
  • 22

    Konstantinou K, , Hider SL, , Jordan JL, , Lewis M, , Dunn KM, & Hay EM: The impact of low back-related leg pain on outcomes as compared with low back pain alone: a systematic review of the literature. Clin J Pain 29:644654, 2013

    • Search Google Scholar
    • Export Citation
  • 23

    Lawrence VA, , Tugwell P, , Gafni A, , Kosuwon W, & Spitzer WO: Acute low back pain and economics of therapy: the iterative loop approach. J Clin Epidemiol 45:301311, 1992

    • Search Google Scholar
    • Export Citation
  • 24

    Mauksch LB, , Katon WJ, , Russo J, , Tucker SM, , Walker E, & Cameron J: The content of a low-income, uninsured primary care population: including the patient agenda. J Am Board Fam Pract 16:278289, 2003

    • Search Google Scholar
    • Export Citation
  • 25

    Nuño M, , Drazin DG, & Acosta FL Jr: Differences in treatments and outcomes for idiopathic scoliosis patients treated in the United States from 1998 to 2007: impact of socioeconomic variables and ethnicity. Spine J 13:116123, 2013

    • Search Google Scholar
    • Export Citation
  • 26

    Pham JC, , Ho GK, , Hill PM, , McCarthy ML, & Pronovost PJ: National study of patient, visit, and hospital characteristics associated with leaving an emergency department without being seen: predicting LWBS. Acad Emerg Med 16:949955, 2009

    • Search Google Scholar
    • Export Citation
  • 27

    Riley WJ: Health disparities: gaps in access, quality and affordability of medical care. Trans Am Clin Climatol Assoc 123:167174, 2012

    • Search Google Scholar
    • Export Citation
  • 28

    Rubin DI: Epidemiology and risk factors for spine pain. Neurol Clin 25:353371, 2007

  • 29

    Storheim K: Targeted physiotherapy treatment for low back pain based on clinical risk can improve clinical and economic outcomes when compared with current best practice. J Physiother 58:57, 2012

    • Search Google Scholar
    • Export Citation
  • 30

    Sun BC, , Binstadt ES, , Pelletier A, & Camargo CA Jr: Characteristics and temporal trends of “left before being seen” visits in US emergency departments, 1995–2002. J Emerg Med 32:211215, 2007

    • Search Google Scholar
    • Export Citation
  • 31

    Taylor BA, , Casas-Ganem J, , Vaccaro AR, , Hilibrand AS, , Hanscom BS, & Albert TJ: Differences in the work-up and treatment of conditions associated with low back pain by patient gender and ethnic background. Spine (Phila Pa 1976) 30:359364, 2005

    • Search Google Scholar
    • Export Citation
  • 32

    Waterman BR, , Belmont PJ Jr, & Schoenfeld AJ: Low back pain in the United States: incidence and risk factors for presentation in the emergency setting. Spine J 12:6370, 2012

    • Search Google Scholar
    • Export Citation
  • 33

    Zusman M: Belief reinforcement: one reason why costs for low back pain have not decreased. J Multidiscip Healthc 6:197204, 2013

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