Abstract
Background:
Hip fractures are common in the elderly, and patients with hip fractures frequently have comorbid illnesses. Little is known about the relationship between comorbid illness and hospital costs or length of stay following the treatment of hip fracture in the United States. We hypothesized that specific individual comorbid illnesses and multiple comorbid illnesses would be directly related to the hospitalization costs and the length of stay for older patients following hip fracture.
Methods:
With use of discharge data from the 2007 Nationwide Inpatient Sample, 32,440 patients who were fifty-five years or older with an isolated, closed hip fracture were identified. Using generalized linear models, we estimated the impact of comorbidities on hospitalization costs and length of stay, controlling for patient, hospital, and procedure characteristics.
Results:
Hypertension, deficiency anemias, and fluid and electrolyte disorders were the most common comorbidities. The patients had a mean of three comorbidities. Only 4.9% of patients presented without comorbidities. The average estimated cost in our reference patient was $13,805. The comorbidity with the largest increased hospitalization cost was weight loss or malnutrition, followed by pulmonary circulation disorders. Most other comorbidities significantly increased the cost of hospitalization. Compared with internal fixation of the hip fracture, hip arthroplasty increased hospitalization costs significantly.
Conclusions:
Comorbidities significantly affect the cost of hospitalization and length of stay following hip fracture in older Americans, even while controlling for other variables.
Level of Evidence:
Prognostic Level II. See Instructions for Authors for a complete description of levels of evidence.
There are estimated to be >250,000 hip fractures annually in the United States, with the incidence continuing to increase in elderly patients1,2. Hip fractures have a major impact on health-related quality of life, and represent a major source of health-care expenditure3-6.
Patients with a hip fracture frequently present with comorbid illnesses. Among women with a hip fracture, one in four has chronic pulmonary disease or congestive heart failure, one in five has diabetes, and one in ten has cerebrovascular disease or an acute or old myocardial infarction. Men with a hip fracture have a high prevalence of chronic pulmonary disease, diabetes, cancer, acute or old myocardial infarction, peripheral vascular disease, and chronic renal failure1.
Besides the increased resource use, comorbid illnesses have a major impact on mortality from hip fracture. A 2005 study demonstrated that the presence of three or more comorbidities is the strongest preoperative risk factor for mortality in patients with hip fractures7. Other risk factors reported were respiratory disease and malignant tumor.
Previous small studies have examined the impact of comorbid illnesses on hospitalization costs for hip fracture in the elderly in Singapore and the impact of comorbidities on hospital costs for hip arthroplasty at a single center in the United States8,9. However, to our knowledge, no large-scale study has quantified the influence of comorbid illness on hospital costs and length of stay following hip fracture in the United States.
We hypothesized that specific comorbid illness and the presence of multiple comorbid illnesses would be directly related to the cost of hospitalization and the length of stay for older patients after a hip fracture. A better understanding of the impact of comorbid illnesses on inpatient costs and length of stay may advance the discussion on appropriate reimbursement for patients with hip fractures and multiple comorbidities and lead to the development of strategies to better manage comorbidities in this population.
With use of discharge data from the 2007 Nationwide Inpatient Sample (NIS), a data set produced from the Healthcare Cost and Utilization Project (HCUP) by the Agency for Healthcare Research and Quality (AHRQ), 49,212 patients who met the inclusion criteria were identified10. The data set includes discharge data from 1044 hospitals located in forty states, approximating a 20% stratified sample of U.S. community hospitals. All patients in the data set were discharged between January 1, 2007 and December 31, 2007.
Patients were selected if they were over the age of fifty-five years, had a Major Diagnostic Category of “Musculoskeletal System and Connective Tissue,” had a principal ICD-9-CM (International Classification of Diseases, Ninth Revision, Clinical Modification) diagnosis code for a closed fracture of the hip, and ICD-9-CM procedure codes indicating they had operative treatment of the fracture. ICD-9-CM diagnostic codes were queried for specific patterns of hip fracture: 820.0 (closed transcervical fracture), 820.2 (closed pertrochanteric fracture), and 820.8 (unspecified closed fracture of femoral neck). Open fractures were not included in this analysis. Procedure codes of 79.15 and 79.35 identified patients who underwent internal fixation of the fracture, and procedure codes 81.51 and 81.52 identified patients who underwent arthroplasty.
To isolate the effect of comorbid illness, 2546 patients with additional ICD-9-CM diagnosis codes for secondary, non-hip fractures were excluded. Other patients were excluded if the database was missing information on the patient's race (12,871 patients), sex (three), cost of hospitalization (1145), or length of stay (five). An additional 151 patients had multiple surgical procedure codes indicating they underwent both arthroplasty and internal fixation. Since these patients likely represent unusual situations in which either the patient had unsuccessful initial surgical intervention or there was a coding error, we elected to exclude them from the analysis. Finally, fifty-one patients were excluded because the date of their surgical procedure preceded their admission date, suggesting that these were patients who were rehospitalized or were transfers from outside facilities. The final analysis included 32,440 patients over the age of fifty-five years with an isolated, closed hip fracture who underwent internal fixation or hip arthroplasty.
Admission-level costs were estimated as the product of billed charges and the hospital-specific ratio of costs to charges with use of standard Medicare cost reports from the Centers for Medicare and Medicaid Services (CMS). While the ratio of costs to charges approach is certainly imperfect for estimating hospital costs, it is the most prevalent technique, and department-level ratios of costs to charges have been shown in previous research to be a reasonable approach for estimating costs within a hospital and for estimating costs across hospitals for patients within a common diagnosis-related group11.
Comorbid illnesses were assigned with use of the AHRQ comorbidity software, which identifies coexisting medical conditions that are not directly related to the principal diagnosis, or the main reason for admission, and are likely to have originated prior to the hospital stay12. Comorbidities are identified as binary (0/1) variables by the method described by Elixhauser et al., using ICD-9-CM diagnoses and the diagnosis-related group in effect on the discharge date13. The Elixhauser method has been shown to have good predictive validity for comorbidities and to have better statistical performance in identifying comorbidities than several other methods14.
Patient characteristics included age, sex, race or ethnicity, and income level in the zip code of his or her home. Estimated median household income of residents in the patient's zip code was derived from zip code-demographic data obtained from Nielsen Claritas (www.nielsen.com), which showed that, in 2007, the quartiles of median household income were from $1 to $38,999, $39,000 to $47,999, $48,000 to $62,999, and ≥$63,00010.
Hospital characteristics included teaching status, bed size (small, medium, or large), region of the United States (Midwest, Northeast, South, or West), and location (urban or rural). The definition for hospital bed size differed by region and hospital location10.
Fracture variables included the pattern of hip fracture, grouped as transcervical, pertrochanteric, or unspecified femoral neck, identified with use of ICD-9-CM codes of 820.0, 820.2, and 820.8, respectively. The type of surgical procedure performed was stratified as either internal fixation or arthroplasty, and was determined by ICD-9-CM procedure codes listed in the patient records. ICD-9-CM procedure codes of 79.15, 79.35, and 78.55 were considered to be internal fixation, while ICD-9-CM codes of 81.51 and 81.52 were used for arthroplasty stratification. The number of days awaiting surgery was calculated by finding the difference between the patient's admission date and the date of surgical procedure. Finally, we included discharge destination and inhospital mortality as variables.
Statistical Methods
The cost of hospitalization (2007 U.S. dollars) and the length of stay were fit to generalized linear models to estimate the impact of the comorbidities on costs and length of stay, controlling for patient, hospital, and fracture variables as described above. Binary variables for each comorbid condition as well as for each of the patient, hospital, and fracture variables were included in the regression model and were analyzed together, which allowed determination of the effect of each of the comorbidities individually while controlling for other comorbid conditions and other characteristics. Plots of the distribution of hospitalization costs and length of stay showed obvious skewness. Therefore, to model costs and length of stay, we used a generalized linear model, assuming gamma-distributed errors and a natural log link function. Patients were clustered within hospitals, so we tested random effects models to determine the importance of clustering. There was no difference in inference between the models, and so we elected to use the simpler generalized linear model without random effects. We report not only the regression coefficients (log dollars or log days) but also the marginal effects, which are the impact of a one-unit change in the covariate on hospitalization costs (dollars) or length of stay (days).
Univariate analyses on continuous characteristics were performed with use of Student t tests. To assess correlation between hospitalization costs and length of stay, Spearman correlations are presented. All analyses were performed with use of Stata statistical software (version 8.2; StataCorp, College Station, Texas).
Source of Funding
This study did not receive any external funding.
To control for the multiple factors impacting cost and length of stay after hip fracture, we included comorbidities, along with demographic, hospital, and treatment characteristics in our analyses.
Comorbidities
The prevalence of comorbidities is shown in Figure 1. Hypertension was the most common (67%), followed by deficiency anemias, fluid and electrolyte disorders, chronic pulmonary disease, uncomplicated diabetes, neurological disorders, hypothyroidism, and congestive heart failure. Patients most commonly had two or three comorbidities (23.3% and 22.8%, respectively), as shown in Figure 2. Only 4.9% of patients had no comorbidity, and only about 1% had eight or more comorbidities. The average estimated cost for a hospitalization following a hip fracture in our reference patient (no comorbidity, an age of fifty-five to sixty-four years, white, female from the Northeast with a transcervical fracture undergoing internal fixation at a medium-sized, nonteaching, urban hospital) was $13,805 (see Appendix). Several comorbidities had major impacts on the cost estimates of hospitalization. Relative to patients without comorbidities, patients experiencing weight loss or malnutrition had the largest increased estimated hospitalization costs, followed by pulmonary circulation disorders. Of the most common comorbidities, hypertension was associated with slightly decreased hospitalization cost estimates, deficiency anemia added minimally to costs, and fluid and electrolyte disorders added >$1500 to cost estimates. Diabetic patients with chronic complications had higher estimated hospitalization costs than diabetic patients without chronic complications.
Given the high prevalence of hypertension as a comorbidity in the patient population, a sensitivity analysis was performed to better evaluate the clinical impact of the so-called negative impact of hypertension on costs. The sensitivity analysis was performed by combining the group of patients with no comorbidities and the group of patients with hypertension as the only comorbidity and subjecting the cohort to the generalized linear model. In this analysis, hypertension was not a significant factor influencing cost of hospitalization (p = 0.154).
In addition to costs, we also looked at the impact of comorbidities on the length of hospitalization. The reference patient (same as in the cost model) had a length of stay of just over six days, as shown in the Appendix. The effects of comorbidities on length of stay were similar but not identical to the effects on cost. Weight loss or malnutrition had the largest impact, increasing the estimated length of stay by 2.5 days (95% confidence interval [95% CI]: 2.2 to 2.8 days; p < 0.001), while congestive heart failure increased length of stay by 1.1 days (95% CI: 1.0 to 1.2 days; p < 0.001), and pulmonary circulation disorders increased length of stay by 0.9 day (95% CI: 0.6 to 1.1 days; p < 0.001). Fluid and electrolyte disorders, paralysis, and coagulopathies also increased hospitalization significantly (p < 0.001 for all).
Demographics
General characteristics of patients over fifty-five years old with a primary hip fracture undergoing surgical repair are displayed in Table I. Most patients (79.2%) were over the age of seventy-five years, were predominantly female (72.3%), white (87.9%), and were fairly evenly divided among estimated income levels.
The age of the patient was inversely related to estimated costs in the generalized linear model, but had no impact on length of stay (see Appendix). Older age increased mortality; on univariate analysis, patients over the age of eighty-five years (2.9%; 95% CI: 2.5% to 3.2%) had nearly double the mortality of younger patients (1.4%; 95% CI: 1.2% to 1.5%; p < 0.001). There was also no correlation between age and number of comorbidities (r = 0.01, p = 0.05). There were differences in age between patients with pertrochanteric fractures and patients with transcervical fractures (81.5 versus 80.9 years; p < 0.001). Patients with transcervical fractures who underwent arthroplasty were younger than those undergoing internal fixation (p < 0.001). Of those treated with arthroplasty, patients receiving total hip arthroplasty were younger than those receiving partial hip arthroplasty (p < 0.001).
Male patients had slightly higher hospitalization costs than those in the highest economic quartiles, and males and nonwhite patients had slightly longer hospital stays, although those from higher economic quartiles did not have a longer length of stay. Black patients (+$1980; 95% CI: $1490 to $2470; p < 0.001) and Hispanic patients (+$1353; 95% CI: $914 to $1791; p < 0.001) were the demographic characteristics that were most strongly associated with estimated costs.
Hospital Characteristics (Table I)
Patients were most commonly treated at large, nonteaching, urban hospitals, and the majority of the patients included were from the southern United States, although this may be a result of which hospitals participated in reporting. Smaller hospitals, rural hospitals, and those in the western United States had slightly higher costs (see Appendix). Smaller hospitals, teaching hospitals, and hospitals outside of the Northeast had shorter lengths of stay (see Appendix).
Treatment
Of the 9359 transcervical hip fractures, 7127 were treated with arthroplasty and 2232 had internal fixation. Of the 16,299 pertrochanteric fractures, 15,644 underwent internal fixation and 655 received an arthroplasty. The locations of 6782 fractures of the femoral neck were unspecified; 1377 of them were treated with internal fixation and 5405 received arthroplasty. Regardless of fracture pattern, patients undergoing arthroplasty were similar to those undergoing internal fixation in terms of both number of comorbidities present (p = 0.6) and age (p = 0.07), but they had slightly longer hospital stays (6.4 days [95% CI: 6.4 to 6.5] versus 6.2 days [95% CI: 6.1 to 6.2]; p < 0.001) on univariate analysis.
Both fracture pattern and treatment rendered impacted costs, as shown in the Appendix. Regardless of diagnosis, patients undergoing arthroplasty had increased costs compared with those undergoing internal fixation (p < 0.01). In univariate analysis, regardless of diagnosis, the length of stay was slightly longer for patients undergoing arthroplasty (6.4 days; 95% CI: 6.4 to 6.5 days) than for those undergoing internal fixation (6.2 days; 95% CI: 6.1 to 6.2 days; p < 0.001). On multivariate analysis, the costs associated with pertrochanteric fractures treated with internal fixation (+$2626; 95% CI: +$2297 to +$2955) were higher than the reference of transcervical fractures treated with internal fixation (p < 0.001), and the costs associated with pertrochanteric fractures treated with arthroplasty (+$7469) were more than those for transcervical fractures treated with arthroplasty (+$5060) (p < 0.001). A delay to surgery of three days or more also greatly increased cost estimates (+$5764; 95% CI: $5435 to $6093; p < 0.001).
Discharge destination played a small role in both estimated hospital costs and length of stay. Relative to patients sent to a skilled nursing facility, those discharged to inpatient rehabilitation facilities spent slightly less time in the hospital (p < 0.001) and had slightly lower costs (p < 0.001). However, compared with patients sent to a skilled nursing facility, the patients who died while in the same hospital admission as the hip fracture had more comorbidities (3.9 [95% CI: 3.7 to 4.0] versus 2.9 [95% CI: 2.92 to 2.95]; p < 0.001), more procedures performed (3.7 [95% CI: 3.5 to 3.8] versus 1.9 [95% CI: 1.89 to 1.92]; p < 0.001), and longer length of stay (9.7 [95% CI: 8.9 to 10.4] versus 6.2 days [95% CI: 6.2 to 6.3]; p < 0.001) in univariate analysis. Similar to patients who died, those who were discharged to hospice had more comorbidities (3.7 [95% CI: 3.5 to 4.0] versus 2.9 [95% CI: 2.91 to 2.95]; p < 0.001) and longer hospital stays than those discharged to other sites after their hospitalization (p < 0.001). In rural hospitals, patients who died had a shorter length of stay (7.0 [95% CI: 5.5 to 8.5 days] versus 10.2 days [95% CI: 9.4 to 11.0 days]; p = 0.001) and lower estimated costs ($17,769 [95% CI: $15,631 to $19,906] versus $26,876 [95% CI: $24,452 to $29,299]; p = 0.001) than those discharged to other sites after their hospitalization, on univariate analysis. In the generalized linear model, disposition to hospice and patient mortality were both independently associated with increased costs and length of stay regardless of the types of comorbidities that were present.
Relationship Between Costs and Length of Stay
Costs and length of service were moderately correlated (r = 0.59, p < 0.001) as would be expected. The correlation between cost and length of stay was stronger for urban hospitals (r = 0.61, p < 0.001) than for rural hospitals (r = 0.51, p < 0.001). Regardless of which comorbidities were present, there was a weak correlation between the number of comorbidities and the estimated hospitalization costs (r = 0.22, p < 0.001), as shown in Figure 3, and between the number of comorbidities and length of stay, as shown in Figure 4 (r = 0.23, p < 0.001). However, as the number of comorbidities increased, the variability in estimated costs increased with the increasing number of comorbidities. There was also a weak correlation between the number of comorbidities and the number of procedures performed (r = 0.18, p < 0.001).
This analysis of the impact of comorbidities on hospitalization costs following hip fracture demonstrates an important relationship between both the identity of the comorbidity as well as the overall burden of comorbidities on acute hospitalization costs. These findings also demonstrate a relationship between comorbid illness and increased length of hospital stay.
Comorbid illnesses are associated with increased resource use among patients in similar diagnosis-related groups15. However, reimbursements for common procedures such as internal fixation account for a patient categorized as having a major comorbid condition, a comorbid condition, or no comorbid condition. Likewise, reimbursement for hemiarthroplasty accounts for a patient categorized as either with or without a major comorbid condition. Therefore, even if a patient has multiple major comorbid conditions, reimbursement only accounts for a single, unspecified comorbidity and does not account for the increased resource use among some patient groups.
It is not surprising that the weight loss or malnutrition comorbidity, identified by the Elixhauser method, was a major factor increasing the cost of hospitalization. Previous studies of hospitalized patients have demonstrated that malnutrition is an independent risk factor for higher complication rates and increased mortality, length of hospital stay, and costs16. Likewise, it is well documented that health-care expenditures increase greatly in the last month of life17, likely because of the increased number of procedures and length of stay.
A previous study of seventy patients from Singapore found dementia, which was present in 9.3% of the study group, to be the comorbidity that most increased costs18. The Singapore study did not include weight loss as a comorbid illness in their model. The prevalence of neurological disorders, including dementia, in our data set was nearly double (18.1%) the prevalence seen in the Singapore study, and it was not associated with elevated costs in our model, although psychoses (3.1% of our study population) added slightly more than $700 to the estimated cost of hospitalization. Increasing comanagement by geriatricians and orthopaedic surgeons, for example, has been shown to decrease time to surgery, length of stay, and postoperative infections, all of which would be expected to decrease total costs19.The Singapore study also found that diabetes, hypertension, and osteoarthritis did not significantly add to total costs18. Although we found a significant increase in cost with diabetes, the effect was not significant in uncomplicated cases. The weak correlation between the number of comorbidities and cost or length of stay may be explained by the increased variability seen in costs and length of stay as the number of comorbidities increased. A patient with multiple comorbidities that are not major drivers of cost, such as hypertension and uncomplicated diabetes, would be expected to have lower costs than patients presenting with weight loss and metastatic cancer, for example, so the identity of the comorbidities is likely more important than the number of comorbid conditions.
Other factors, in addition to patient comorbidities, are also drivers of cost. One study demonstrated that cost is significantly related to days spent awaiting surgery, preoperative sepsis, operative complications, and cerebrovascular accidents18, which agrees with our finding that a delay to surgery greatly increases costs. Regardless of the fracture pattern, the choice of operative procedure plays a significant role in the cost of hospitalization. Higher costs for arthroplasties are likely due to increased costs of the implants, but may also be due to longer hospital stays and the longer duration of the surgery itself relative to internal fixation20.
While it is possible that race is a marker for socioeconomic factors or specific comorbid conditions that increase cost, a sensitivity analysis excluding race from the multivariate models did not change our results and race failed to show significant collinearity with socioeconomic covariates or comorbidities. In other, nonorthopaedic studies, race has also been found to impact hospitalization costs following thyroidectomy and gastric bypass surgeries21,22.
The coefficient for cost was negative with increasing age, although the difference was not significant on univariate analysis. Given the lack of correlation between age and number of comorbidities, age itself does not appear to have a major impact on the cost of hospitalization following hip fracture; however, following discharge, older patients may have longer rehabilitation times, which could increase costs following the acute hospitalization23. It is possible that lower costs in older patients are due to implantation of less expensive components for similar fracture patterns, which is supported by our finding that older patients with transcervical fractures were more likely to receive partial rather than total hip arthroplasty. However, other information about type of implant or implant costs was unavailable in the data set and was not evaluated.
This study has numerous strengths. First, it is a large, representative national sample from a single year. We were able to account for regional variations in costs as well as socioeconomic stratifications and hospital characteristics. By restricting the population to a single time period, the diagnostic evaluation and operative techniques are unlikely to have changed and are more likely to be consistent across the population. Further, the coding practices are unlikely to have changed during the study period, increasing the validity of using discharge diagnosis codes for the identification of fractures, procedures, and comorbidities.
There are some limitations to this study. There is the possibility of incomplete or inaccurate coding of diagnoses and comorbidities, especially if doing so would be unlikely to affect reimbursement. However, it is likely that hospital coders made efforts to document comorbidities that would have the greatest impact on reimbursement and acuity indices. The administrative nature of the data set is also a limitation, in that it does not include laboratory values or allow measurement of the degree of disability caused by comorbid illnesses. Although the status according to the American Society of Anesthesiologists classification might account for the severity of disability, and has been shown to be associated with hospital costs in some orthopaedic populations, it was not available in the NIS data set24.
Comorbid illnesses also increase resource use following discharge from the primary inpatient admission, which likely heightens the disparity between the overall cost of hip fractures in healthy patients and their counterparts with comorbid illness3,5,8,25,26. Although comorbid conditions could contribute to readmissions and complications following discharge, costs associated with these events were not assessed in this study. Higher costs from the acute hospital stay are associated with longer length of stay, so hospitals that discharge patients to rehabilitation beds earlier appear to have lower acute care costs and lengths of stay in the data set; therefore, our analysis likely underestimates the true costs associated with hip fracture. Discharge destination played only a small role in the acute costs of hospitalization, but we did not evaluate the different costs of disposition following discharge, which are major drivers of the full cost of hip fractures.
Our statistical model presents the marginal effect of each of the comorbidities while controlling for other comorbid conditions and variables, but does not present incremental effects of additional comorbidities. Although patients who underwent arthroplasty and internal fixation were similar in terms of age and number of comorbidities, it is possible that differences between these groups led to slightly different costs. We attempted to account for these differences by including the procedure type coupled with the diagnosis in the analysis. Finally, although the ratio of costs to charges method for estimating costs is the most widely used and is widely accepted, it is possible that actual costs of hospitalization may vary somewhat from the estimates produced by using this method.
Our results suggest that the presence of specific comorbid conditions and, to a lesser degree, the number of comorbid conditions impact hospitalization costs for older Americans following hip fracture. Accounting methods in the current reimbursement system may not adequately reflect the burden of patients with the most complex hip fractures. While future research should address whether innovative approaches to managing specific comorbidities in this population can shorten length of stay and reduce hospitalization costs, there should also be discussion about appropriate reimbursement for the management of patients presenting with hip fractures and multiple comorbid conditions.
Tables showing log linear regression and marginal effects demonstrating the impact of comorbidities as well as demographic, hospital, and diagnosis and/or treatment characteristics on the estimated cost of hospitalization and on the length of hospitalization are available with the online version of this article as a data supplement at jbjs.org.
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Disclosure: None of the authors received payments or services, either directly or indirectly (i.e., via his or her institution), from a third party in support of any aspect of this work. One or more of the authors, or his or her institution, has had a financial relationship, in the thirty-six months prior to submission of this work, with an entity in the biomedical arena that could be perceived to influence or have the potential to influence what is written in this work. No author has had any other relationships, or has engaged in any other activities, that could be perceived to influence or have the potential to influence what is written in this work. The complete Disclosures of Potential Conflicts of Interest submitted by authors are always provided with the online version of the article.