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Spending and Mortality in US Acute Care Hospitals
John A. Romley, PhD; Anupam B. Jena, MD, PhD; June F. O'Leary, PhD; and Dana P. Goldman, PhD
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Spending and Mortality in US Acute Care Hospitals

John A. Romley, PhD; Anupam B. Jena, MD, PhD; June F. O'Leary, PhD; and Dana P. Goldman, PhD
Across the US, adults with major medical conditions were less likely to die in hospitals with higher spending levels, even after adjusting for patient risk.
Background: Despite evidence that greater US Medicare spending is not associated with better quality of care at a regional level, recent studies suggest that greater hospital spending is associated with lower risk-adjusted mortality. Studies have been limited to older data, specific US states and conditions, and the Medicare population.


Objectives: To analyze the association between hospital spending and risk-adjusted inpatient mortality for 6 major medical conditions in US acute care hospitals.


Study Design: Retrospective cohort study of riskadjusted inpatient mortality, with hospital spending taken from the Dartmouth Atlas of Health Care. The study population included 2,635,510 patients admitted to 1201 US hospitals between 2003 and 2007.


Methods: Patient-level logistic regression models were used to estimate the effect of hospital spending on inpatient mortality, controlling for mortality risk, comorbidities, community characteristics (eg, median household income in a patient’s zip code), hospital volume and ownership, and admission year.


Results: Patients treated at hospitals in the highest spending quintile (relative to the lowest) had lower risk-adjusted inpatient mortality for acute myocardial infarction (odds ratio [OR] 0.751, 95% confidence interval [CI] 0.656-0.859), congestive heart failure (OR 0.652, 95% CI 0.560-0.759), stroke (OR 0.852, 95% CI, 0.739-0.983), and hip fracture (OR 0.691, 95% CI 0.545-0.876). Greater spending was associated with lower mortality primarily in nonteaching hospitals, hospitals with fewer than the median number of beds, and nonprofit/public hospitals.


Conclusions: Greater hospital spending is associated with lower risk-adjusted inpatient mortality for major medical conditions in the United States.


(Am J Manag Care. 2013;19(2):e46-e54)
Spending on inpatient care appears to be efficacious in reducing mortality.
 
  • This finding contrasts with evidence that US regions with higher Medicare spending do not have better quality of care.
 
  •  Inpatient spending is associated with lower mortality primarily in nonteaching hospitals, small hospitals, and nonprofit/public hospitals.
 
  •  Greater intensity of nursing care does not explain the association between spending and risk-adjusted mortality.
For more than 2 decades, research has documented significant geographic variation in the use of medical resources across the United States.1,2 Evidence indicates that higher Medicare spending is not associated with better quality measures or health outcomes at a regional level.2,3 The possibility that the United States can improve quality of care while simultaneously lowering medical care spending is central to healthcare reform.

Although greater total medical spending may not be associated with better health outcomes across regions, there may be specific instances in which greater medical spending is beneficial.4 For example, several recent studies suggest that greater hospital spending or resource use is associated with lower risk-adjusted mortality.5-10 A study of 6 California teaching hospitals demonstrated that patients with congestive heart failure (CHF) had lower mortality rates when treated at hospitals where lengths of stay and total costs were higher.5 Another study of Pennsylvania hospitals found lower postadmission mortality among patients in hospitals with more intensive end-of-life treatment (eg, mechanical ventilation, intensive care unit admission).6 An analysis of Medicare patients between 2001 and 2005 demonstrated an association between lower 30-day mortality and greater inpatient spending.7 An analysis of patients admitted with 1 of 6 acute medical conditions to California hospitals found lower risk-adjusted inpatient mortality in higher-spending hospitals.9 In Ontario, Canada, greater hospital spending was associated with lower readmission rates as well as mortality.10

Clearly, increasing the value of medical spending is central to improving US healthcare and while the importance of this goal is largely undisputed, how best to achieve it is not. Although recent work suggests that greater hospital spending is associated with lower risk-adjusted mortality, studies have been limited to the Medicare population, patients treated up to a decade or more ago,7-9 hospitals in specific states,5,6,9,10 small samples of hospitals,5 a single medical condition,5 or postsurgical patients.8

We used a national sample of hospital admissions from 2003 to 2007 to evaluate the association between hospital spending and inpatient mortality for adults with 6 major medical conditions. Our measure of hospital spending—the sum of inpatient physician visits, hospital room charges, laboratory testing, diagnostic imaging, medication, and procedures—was drawn from the Dartmouth Atlas of Health Care, which calculates hospital spending among Medicare beneficiaries in the last 2 years of life.1 In addition to patient comorbid conditions, sociodemographic characteristics, and hospital and regional factors, we used validated risk adjustment models developed by the Agency for Healthcare Research and Quality (AHRQ) to account for underlying differences in inpatient mortality risk across hospitals.9,11 We analyzed whether hospital teaching status, size, or for-profit ownership modified the association between hospital spending and inpatient mortality.

METHODS

Study Sample


We used the Nationwide Inpatient Sample (NIS) from the Healthcare Cost and Utilization Project to identify a nationally representative sample of patients admitted to US acute care hospitals between 2003 and 2007.12 The institutional review board at the RAND Corporation exempted these de-identified data from human subjects review. The NIS approximates a 20% stratified random sample of US community hospitals and provides demographic and clinical data on each patient, including age, sex, race, month/year of hospital admission, length of stay, primary and secondary diagnoses, primary and secondary procedures, discharge outcome (eg, discharge to home, in-hospital mortality), and source of payment. Diagnoses and procedures are coded according to the International Classification of Diseases, Ninth Revision (ICD-9).The number of hospitals in NIS grew from 994 in 2003 to 1044 in 2007.12

We studied 6 medical conditions: acute myocardial infarction (AMI), CHF, stroke, gastrointestinal (GI) hemorrhage, hip fracture, and pneumonia. These conditions were selected because the processes of care associated with them are considered important indicators of inpatient quality,11 the acute conditions reflect chronic conditions evaluated in the Dartmouth Atlas of Health Care, and they comprise a substantial portion of all hospital admissions in the United States (6.6% from 2003 to 2007).

We created diagnosis-specific patient samples for each hospital from ICD-9 discharge codes, according to the criteria of the AHRQ Inpatient Quality Indicators, version 4.2.13,14 Patients admitted with AMI included transfers from other hospitals. We excluded all NIS observations where any of the analysis variables was missing (0.41%).

In addition to patient data present in NIS, we linked each discharge record to community-level data from the 2000 US Census, similar to prior research.3 Hospital zip code was used unless missing; then, county and state were used. Specific variables included median annual household income, average annual Social Security income, and the percentage of the population who were living below poverty level, were employed, had less than a high school education, were Hispanic, were single, lived in an urban area, were elderly, were institutionalized elderly, and were noninstitutionalized elderly with various disabilities.

Hospital Spending and Other Characteristics

Hospital spending was drawn from the Dartmouth Atlas of Health Care, which was based on inpatient spending during the last 2 years of life among Medicare fee-for-service beneficiaries with 1 of 9 chronic diseases (including, eg, coronary artery disease and CHF), who died from January 1, 2003 to December 31, 2007.15,16 Patients were assigned to the hospital at which most inpatient care was received. This measure included reimbursements to both hospitals and physicians and was adjusted for age, sex, race and primary chronic diagnosis, but did not vary by condition.15,17 Because patients at the end of life may be similarly ill, this measure may be more closely related to a hospital’s overall approach to spending and care rather than to patients’ severity of illness.3,10,15 In addition, end-of-life spending is strongly correlated with forward-looking measures of costs5,18 and also is positively correlated with total charges from NIS for the conditions we studied.

Additional hospital characteristics included teaching status, quartile of discharge volume by condition, size, and for-profit ownership. Teaching status was defined by the presence of an American Medical Association–approved residency, membership in the Council of Teaching Hospitals, or a ratio of full-time equivalent house staff to beds of 0.25 or higher.14 Hospital size was a binary variable definedby whether a hospital had more than the median number of general medical and surgical beds in the sample (96 beds). For-profit ownership was also a binary variable. Teaching status and discharge volume were taken from NIS, while size and ownership were taken from American Hospital Association annual surveys.19

Hospitals included in the NIS samples from 2003 to 2007 were matched on Medicare provider number to hospitals in the Dartmouth Atlas of Health Care for which spending was reported. This resulted in a final sample of 1201 hospitals.

Statistical Analysis

Following prior work,9 for each diagnosis, we estimated logistic models in which patients were the unit of analysis: Deathi,h = b0 + b1 x pred mortalityi + b2 x spendingh + b3 x patient factorsi + b4 x hospital factorsh + b5 x year indicators + b6 x hospital referral region indicators + b7 x pred mortalityi x pred mortality hospital referral region,

where Deathi,h is an indicator for death of patient i in hospital h, pred mortalityi is the AHRQ patient-level predicted mortality risk (discussed below), and spendingh is hospital spending in hospital h from 2003 to 2007. Hospital spending (spending h) was divided into quintiles.3 Individual observations were weighted with discharge sample weights from NIS.

For patients admitted with each disease, we accounted for underlying comorbidities affecting inpatient mortality by applying risk adjustments from the AHRQ Inpatient Quality Indicators.13 AHRQ risk parameters are estimated from national discharge data and can be applied to patient comorbidities in other discharge data to predict diagnosis-specific inpatient mortality for a given patient. For each patient in the 6 admitting diagnoses we considered, we applied these risk parameters to obtain patient-level predicted mortality risk (pred mortalityi) based on a patient’s age, sex (interacted with age), and relevant diagnoses and procedure codes. Our model also adjusted for other patient factors (patient factorsi) including the number of Charlson-Deyo comorbid conditions3,20 and sociodemographic factors drawn from the US Census. Hospital factors were described earlier; indicator variables were included for the years in which patients were treated.

Recent evidence indicates that hospitals in higher spending regions may bill insurers for a larger number of diagnoses for a given patient than hospitals in lower-spending regions.21,22 Because NIS is based on recorded diagnoses, patients admitted to higher-spending hospitals may appear at higher mortality risk than similar patients admitted to lowerspending hospitals. To account for differences in diagnostic intensity, we included indicators for hospital referral region in our models and interacted predicted patient mortality risk with average risk within each hospital referral region. This specification allowed the effect of measured patient-level risk on mortality to be smaller in magnitude in areas with high diagnosed risk.

In addition to the baseline analysis, we explored whether the association between spending and mortality was affected by incorporating nursing intensity, measured as the share of full-time-equivalent nursing employees who were registered nurses and the number of nurses per 1000 adjusted patient days. These variables were constructed from the American Hospital Association surveys and included as independent variables in the mortality model.

Effect Modification

Following prior work,9 we analyzed whether teaching status and hospital size modified the association between hospital spending and inpatient mortality. For-profit ownership was also analyzed. To do so, we added interactions between the hospital characteristic and spending quintiles to the baseline analysis.

We reported odds ratios comparing risk-adjusted inpatient mortality in hospitals in the top-spending quintile compared with the lowest spending quintile by hospital teaching status, size, and ownership.

Sensitivity Analysis

 
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