Factors contributing to high-cost hospital care for patients with COPD

Abbreviations

ALC, alternate level of care; ANOVA, analysis of variance test; COPD, chronic obstructive pulmonary disease; CI, confidence interval; FEV₁, forced expiratory volume in 1 second; ICD-10, International classification of diseases codes (10th revision); ICU, intensive care unit; LAPS, laboratory-based acute physiology score; LTC, long-term care facility; OR, odds ratio; IQR, interquartile range; pCO₂, arterial partial pressure of carbon dioxide in the blood; SD, standard deviation; TOH, the Ottawa Hospital; TOHDW, the Ottawa Hospital Data Warehouse; Q1, Q2, Q3, Q4, Q5, quintiles.

Introduction

COPD is associated with significant morbidity, mortality, functional decline, and poor quality of life.¹ It is the fifth leading cause of death worldwide² and is the number one cause for hospital admission and readmission in Canada.³ It has a substantial economic burden on the health care system and society. In the United States, care for COPD is expected to cost $49 billion annually by 2020.^4,5 Hospitalizations for COPD exacerbations are the largest contributor to total costs.^3,5,6

Several studies have identified risk factors for hospitalization and readmission among patients with COPD, but the focus has been on disease-specific risk factors, such as the FEV₁, number of previous exacerbations, and use of home oxygen therapy.^7–9 While these factors are important predictors of poor prognosis and hospitalization, they are largely not modifiable with currently available therapies.¹⁰

Previous studies have also documented a significant degree of medical comorbidity and frailty in COPD patients.^11–13 It is possible that high hospitalization costs among COPD patients may be driven as much by the patient’s functional ability than COPD-specific factors alone. In order to explore this potential relationship, it is necessary to have a better understanding of individual patient characteristics and types of hospital services used by COPD patients.

The objective of this retrospective cohort study was to determine acute care costs for patients admitted with COPD exacerbation and to examine the important patient and hospital-level factors associated with high costs. This analysis can be used to better plan and organize health services to meet patient needs. We hypothesize that the sequelae of chronic diseases, including poor functional status and frailty, are major contributors to high hospital costs in this population of hospitalized COPD patients.

Materials and methods

Study design, population, and data sources

We conducted a retrospective observational cohort study using linked administrative data sources from the TOH in Ontario, Canada. TOH is a tertiary care, publicly funded health facility with 1,100 beds, servicing a catchment area of 1.1 million people.

We identified all adult patients (≥18 years of age) with a first time (index) hospital admission for COPD exacerbation between September 1, 2010, and September 30, 2014. We used a look-back window of 2 years to ensure there were no previous COPD-related hospitalizations prior to the index visit. COPD exacerbation was defined by the ICD-10 code in the “most responsible diagnosis” field on the hospital discharge abstract (J41–J44), or by a lower respiratory tract infection (J100, J110–J116, J118, J120–J122) and COPD exacerbation listed as a secondary diagnosis. Individuals who were transferred from another facility were excluded.

We collected data from the duration of each index hospitalization using TOHDW. This is a database linking information from the hospital’s most important operational systems, including pharmacy, laboratory, radiology, patient booking, case costing, and discharge abstract.

Cost measurement

From the hospital’s perspective, total costs are the sum of direct and indirect costs. They were calculated for each index hospitalization using TOH’s case-costing system, which links financial, clinical, and patient activity information stored in TOHDW. Direct costs consist of patient care costs, including diagnostic imaging, nursing, and laboratory services. Indirect costs are those associated with administering and providing support to patient care, such as health records, housekeeping, and administrative services. TOH uses a standardized case-costing methodology that was developed by the Ontario Case Costing Initiative and is based on the Canadian Institute for Health Information Management Information Systems guidelines.^14,15 The case-costing system captures both direct and indirect costs for TOH. TOH is a publicly funded hospital, and costs do not represent insurance payments.

We ranked the total hospitalization costs by quintiles to represent the highest (Q5) and lowest (Q1) cost groups. We combined the middle quintiles (Q2–Q4) to represent the mid-range hospitalization costs. Costs are presented in nominal values in Canadian dollars.

Patient and hospitalization characteristics

Data regarding ICU admissions, mortality, length of stay, use of isolation precautions for infection control, 30-day readmission rate, and location of discharge were collected during the index hospitalization. We used the modified Elixhauser comorbidity score to describe the overall comorbidity burden.^16,17 The severity of illness was measured using a validated score for the risk of death at the time of admission to hospital and the LAPS.^18,19

We determined whether the “Alternate Level of Care” (ALC) designation was used at any point during the hospitalization, as ALC status is known to drive high hospitalization costs.²⁰ ALC status is assigned to a patient during an acute hospitalization when the acute medical issues have resolved, but the patient cannot safely return to the previous dwelling.²¹ Patients with the “ALC” designation receive less frequent and acute nursing care, and remain under ALC status until a suitable discharge location is available.

Analysis

Patient and encounter-level characteristics were described using proportions, means (SDs), and medians (IQR), and were compared across cost quintiles (low Q1, middle Q2–Q4, and high Q5). We used a one-way ANOVA test and Chi-squared tests to compare the differences of means (SD) and proportions, respectively. A subgroup analysis was performed in the highest cost group (Q5) to assess clinical and encounter-level differences based upon ALC status.

Finally, we used a multivariable logistic regression model to identify factors associated with high hospital costs (costs in the Q5 group). Variables for the regression model were selected a priori based upon clinical relevance and the data availability. These variables include age >80 years, Elixhauser score, ICU admission, LAPS, use of home care upon discharge, LTC admission upon discharge, death in hospital, and ALC status during hospitalization.

All data collection and analysis were conducted using SAS, version 9.4. All P-values were considered significant at a level of <0.05. This study was approved by the Ottawa Health Science Network Research Ethics Board, who did not require individual patient consent for this study.

Results

In total, 1,894 patients met the inclusion criteria for an index COPD hospitalization between 2010 and 2014 (Table 1). The mean age (±SD) was 73.4±12.6 years, and one-third of patients were older than 80 years (33.1%, 627 patients). During the index hospitalizations, 7.8% (n=147) died and 9.0% (n=170) were admitted to the intensive care. The median length of stay was 5 (IQR 3–9) days. Over half of the patients (54.9%, n=1,040) used isolation precautions for infection control, while 11.8% (n=201) experienced a readmission to hospital in 30 days after discharge.

Table 1 Demographics, characteristics, and costs among patients with index hospitalization for COPD exacerbation between 2010 and 2014 Notes: Costs are given in Canadian dollars. aArterial partial pressure of carbon dioxide (pCO2 in mmHg). bLaboratory-based acute physiology score (LAPS) is a point score based on 14 laboratory tests obtained in the 24 h preceding hospitalization. cAlternate level of care (ALC), which is the designation used when a patient is medically stable but cannot be discharged home, and therefore must stay in hospital. dReadmission rates exclude the patients who died during index admission (30-day readmissions/eligible patients for readmission). Abbreviations: COPD, chronic obstructive pulmonary disease; ICU, intensive care unit; IQR, interquartile range; SD, standard deviation; n/a, not applicable.

Hospital costs

Over a 4-year period, our institution spent $19,776,824 on index hospitalizations, with a median cost per case of $5,121 (IQR $2,853–$10,731) (Table 1). The highest cost quintile (Q5) accounted for 62.9% ($12,449,422) of the total spending, while the lowest cost quintile (Q1) accounted for 3.1% ($609,732).

In the highest cost quintile (Q5), comorbidity scores (Elixhauser score), baseline risk of death on admission, LAPS, and pCO₂ levels were significantly greater when compared to the other quintiles (Table 1). There was also a nonsignificant trend toward more octogenarians in Q5 versus Q1 (35.6% in Q5 vs 29.6% in Q1, P=0.21). Mortality, ICU admission, use of isolation precautions, and length of stay were all significantly greater in Q5 compared to other quintiles (P<0.001).

Discharge location

The majority of patients (54.3%, n=1,029) were discharged home after their hospitalization, while 9.7% (n=184) of patients were transferred to LTC facilities upon discharge. The proportion of LTC transfers were significantly higher in Q5 compared to Q1 (20.3% in Q5 vs 5.3% in Q1, P<0.001; Table 1). Overall, 23.8% (n=451) of patients were discharged home with support services, such as physiotherapy, occupational therapy, personal care support, and palliative care.

Table 2 shows the disproportionate share of the total hospital costs across the type of discharge location after hospitalization. Only 7.8% of patients died during the index hospitalization for COPD, accounting for 18% of the total budget. Similarly, 9.7% of patients who were discharged to LTC accounted for 17% of the total budget.

Table 2 Costs by hospital discharge location after index hospitalization for COPD between 2010 and 2014 Notes: All costs are in Canadian dollars. aSupport services refer to home care, which could include any of physiotherapy, occupational therapy, personal care, and palliative care at home. Abbreviations: COPD, chronic obstructive pulmonary disease; IQR, interquartile range.

ALC status

Overall, 6.5% (n=123) of patients were assigned the ALC designation during their hospitalization. However, 81% of these patients (n=100) fell into the top cost quintile (Q5). The remaining 23 patients were distributed as follows: 19 patients in Q4, 3 patients in Q3, and 1 patient in Q2 (Table 1).

ALC patients in the top cost quintile (Q5) were older than non-ALC patients, with a mean age of 78.3 years versus 72.2 years (P<0.001), and consisted of more octogenarians (52% vs 30%, P<0.001) (Table 3). In this cost quintile, the baseline risk of death at the time of admission was significantly higher for patients in the ALC group compared to those in the non-ALC group (16% vs 11%, P=0.002). Despite this, only a small proportion of ALC patients required ICU admission (9.0%, 9/100), and they had a lower mortality rate (P<0.001) compared with non-ALC patients. The overall length of stay was significantly higher in the ALC patient group (mean 41 days vs 17 days; P<0.001). Figure 1 demonstrates the contribution of ALC days to the total length of stay among cost quintiles.

Table 3 Characteristics by ALC status during index hospitalization for COPD exacerbation in the top cost quintile (Q5, n=379 patients), 2010–2014 Note: All costs are in Canadian dollars. Abbreviations: ALC, alternate level of care; COPD, chronic obstructive pulmonary disease; ICU, intensive care unit; IQR, interquartile range; SD, standard deviation.

Figure 1 Relative contribution of hospital days spent under ALC status, acute care, and in ICU, to the mean total length of stay in hospital among middle (Q2–Q4) and top (Q5) cost quintiles. Abbreviations: ALC, alternate level of care; ICU, intensive care unit.

ALC patients had a significantly higher median cost per case ($29,423 vs $22,166, P<0.001), which appeared driven by use of allied health professional services. Allied health service costs were significantly higher in the ALC group compared to the non-ALC patient group ($2,397 vs $872), P<0.001 (Table 3).

Factors associated with highest costs

Multivariable logistic regression demonstrated that discharge location (LTC or home care) and ALC designation during hospitalization were significantly associated with high costs during hospital stay (Table 4). Furthermore, hospitalization in which ICU admission or death occurred was also significantly associated with high costs. Age >80 years and comorbidity scores were not significantly associated with high costs. The model demonstrated strong discrimination (c-statistic =0.871).

Table 4 Adjusted odds ratios for factors contributing to high-cost hospitalizations (Q5) for patients hospitalized with COPD between 2010 and 2014 Notes: aContinuous variables in the regression model. The other variables were treated as categorical variables. Abbreviations: ALC, alternate level of care; CI, confidence interval; COPD, chronic obstructive pulmonary disease; LAPS, laboratory-based acute physiology score; ICU, intensive care unit.

Discussion

Our study highlights two COPD patient populations that contributed to high costs during hospitalization. First, patients with acute severe illness who require ICU admission and those who die in the hospital setting are significant contributors to expensive care. Second, patients who are not able to cope well at home, such as those requiring ALC designation in hospital and assisted living arrangements upon discharge, also contribute to high costs. Interestingly, the ALC-designated patients and those requiring ICU admission appear to be distinct populations with little overlap. This suggests that early recognition and intervention for the frail and functionally limited population may have a significant impact on hospital costs.

The ALC designation during hospitalization is a surrogate measure for functional status, as it is used when a patient cannot return to their previous dwelling because more support is required.²² We found that the top cost quintile had significantly more ALC-designated patients, and they incurred higher allied health professional costs than the non-ALC patients ($2,000 per case vs $900 per case in non-ALC patients). Interestingly, 78% of ALC-designated patients in the top cost quintile were discharged to either LTC facilities or home with home care services. Our findings are echoed in other studies that found that ALC patients awaiting LTC placement have more functional impairments and complex health needs, which result in long lengths of hospital stay.^22,23 If hospital costs are to be saved in this group of patients, early identification of patients with chronic disease and poor functional status in the community setting could help initiate early introduction of support services and arrangement for assisted living facilities.

Our study also found that patients in the highest cost quintile had a higher mortality rate compared to those in the lower cost quintiles. Given that one in three patients in the top cost quintile were >80 years, had a high baseline risk of death on admission (11%), a larger degree of physiologic derangement (LAPS), and need for ICU, the high mortality rates are not unexpected. Our results are consistent with other work, demonstrating that high end-of-life care costs are largely driven by the hospitalization cost in the last 4 months of life.^24,25 In Ontario, Canada, $4.7 billion was spent on health care at the end of life in 1 year, representing 10% of the total health care budget.²⁴

In this study, we did not examine the appropriateness of hospitalization and ICU admission at the end of life or the causes of death in hospital; however, previous work has suggested that a proportion of hospitalizations and intensive care at the end of life are potentially avoidable.^26,27 Kronman et al showed that more primary care visits in the 12 months prior to death were associated with lower costs, less in-hospital death, fewer preventable hospitalizations, and fewer hospital days at the end of life.²⁶ One might presume that this could be due to better discussion of advanced directives and goals of care. Finally, Zhang et al demonstrated that early discussions on goals of care with patients reduced the high health care costs at the end of life in the cancer population.²⁸ These data suggest that the high health care spending due to hospitalization at the end of life can be reduced, while improving the quality of patient care for patients with chronic disease.

Finally, a large focus has been placed on readmissions as the cause for increased cost because the current perception is that hospital readmissions are a marker for poor quality of care.²⁹ While the readmission rate was higher in our highest cost group (12.6% vs 9.5%, after exclusion of deaths), previous literature has cautioned using readmission rates as an indicator for quality of care, as it is estimated that only 25% of readmissions are avoidable.^30,31 These results underscore the need for additional work to understand whether early identification of the frail patient with chronic disease, better community-based health services for frail patients, and emphasis on advanced care planning will have a favorable impact on health care costs and improved quality of care for patients with COPD.

Based on our review of the literature, to date, this has been the first cohort study that captures indirect and direct hospital costs using a case-costing system in a population of patients admitted for acute exacerbations of COPD. The identification of discharge locations, ALC status, and cost of allied health professional assessment in hospital are helpful surrogates for a patient’s functional status and have not been previously evaluated in this population.

The main limitation of this study lies in the accuracy of COPD diagnosis on the medical record and the subsequent ICD-10 coding for COPD in our administrative database. We were not able to reference the coded diagnosis against actual pulmonary function testing values. Furthermore, it is possible that our cohort contained some patients who did not have a first-time COPD-related hospitalization. We used a 2-year look-back window at our institution to ensure no previous COPD-related hospitalizations, however, patients may have been hospitalized elsewhere for COPD exacerbation during this period. It is possible that this phenomenon affected the breakdown of costs. Regarding costs, we did not adjust for inflation between the years included in this study. However, with a low inflation rate in Canada, it is unlikely that the lack of adjustment for inflation would have significantly impacted the cumulative costs. Finally, this study was conducted at a single, academic, publicly funded hospital in Canada, which may affect the external validity of our findings.

In summary, findings from this study lend support to our hypothesis that the sequelae of chronic disease, including poor functional status and frailty, are major contributors to high hospital costs in this population of hospitalized COPD patients. Focusing on interventions that identify frail patients with declining functional ability in the community setting may reduce costs for the health system. Specifically, functional assessments at home, better and earlier discussions of advanced care directives, and reorganization of hospital care based on frailty and functional status may improve the quality of care and reduce costs for patients with COPD and other chronic medical diseases.

Acknowledgment

The authors are grateful to J Diotte for review of the manuscript and assistance with preparation for submission.

Author contributions

SM had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All authors contributed to the concept and study design. JM performed the initial data extraction and analysis from TOHDW. JM and SM conducted additional analysis. SM, JM, PER, DK, KT, and AJF reviewed the analysis and contributed to critical review of the manuscript. All authors contributed toward data analysis, drafting and critically revising the paper and agree to be accountable for all aspects of the work.

Disclosure

The authors report no conflicts of interest in this work.

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