Sources Of Variation In Costs Research Articles

Identifying Sources of Cost Variation in Patients Undergoing Major Small and Large Bowel Procedures

Identifying Sources of Cost Variation in Patients Undergoing Major Small and Large Bowel Procedures

Bayesian profiling for cost with zeros to decompose total cost into probability of cost and mean nonzero cost.

Cost of health care can vary substantially across hospitals, centers, or providers. Data from electronic health records provide information for studying patterns of cost variation and identifying high or low cost centers. Cost data often include zero values when patients receive no care, and joint two-part models have been developed for clustered cost data with zeros. Standard methods for center comparisons, sometimes called profiling, can use these methods to incorporate zero values into total cost. However, zero costs also provide opportunities to further examine sources of cost variation and outliers. For example, a hospital may have high (or low) cost due to frequency of nonzero cost, amount of nonzero cost, or a combination of those. We give methods for decomposing hospital differences in total cost with zeros into components for probability of use (i.e., of nonzero cost) and for cost of use (mean of nonzero cost). The components multiply to total cost and quantify components on the same easily interpreted multiplicative scales. The methods are based on Bayesian hierarchical models and counterfactual arguments, with Markov chain Monte Carlo estimation. We used simulated data to illustrate use, interpretation, and visualization of the methods in diverse situations, and applied the methods to 30,024 patients at 57 US Veterans Administration hospitals to characterize outlier hospitals in one year cost of inpatient care following a cardiac procedure. Twenty eight percent of patients had zero cost. These methods are useful in providing insight into cost variation and outliers for planning future studies or interventions.

Analysis of National Rates, Cost, and Sources of Cost Variation in Adult Spinal Deformity.

Several studies suggest significant variation in cost for spine surgery, but there has been little research in this area for spinal deformity. To determine the utilization, cost, and factors contributing to cost for spinal deformity surgery. The cohort comprised 55 599 adults who underwent spinal deformity fusion in the 2001 to 2013 National Inpatient Sample database. Patient variables included age, gender, insurance, median income of zip code, county population, severity of illness, mortality risk, number of comorbidities, length of stay, elective vs nonelective case. Hospital variables included bed size, wage index, hospital type (rural, urban nonteaching, urban teaching), and geographical region. The outcome was total hospital cost for deformity surgery. Statistics included univariate and multivariate regression analyses. The number of spinal deformity cases increased from 1803 in 2001 (rate: 4.16 per 100 000 adults) to 6728 in 2013 (rate: 13.9 per 100 000). Utilization of interbody fusion devices increased steadily during this time period, while bone morphogenic protein usage peaked in 2010 and declined thereafter. The mean inflation-adjusted case cost rose from $32 671 to $43 433 over the same time period. Multivariate analyses showed the following patient factors were associated with cost: age, race, insurance, severity of illness, length of stay, and elective admission (P < .01). Hospitals in the western United States and those with higher wage indices or smaller bed sizes were significantly more expensive (P < .05). The rate of adult spinal deformity surgery and the mean case cost increased from 2001 to 2013, exceeding the rate of inflation. Both patient and hospital factors are important contributors to cost variation for spinal deformity surgery.

115 Variation in Payments for Spine Surgery Episodes of Care

Abstract INTRODUCTION Spine surgery is expensive and marked by high variation across regions and providers. Bundled payments have potential to reduce unwarranted spending associated with spine surgery. We sought to quantify variation in spine surgery payments, document sources of variation, and determine the influence of patient-level, surgeon-level, and hospital-level factors on payment variation. METHODS Cross-sectional analysis of commercial and Medicare claims data from January 2012 through March 2015 in the state of Michigan. Participants were adult patients who underwent spine-based surgical procedures. We used hierarchical regression models to analyze contributions of patient-level covariates and influence of individual surgeons and hospitals. Our primary outcome was price-standardized 90-day episode payments. We used intraclass correlation coefficients measures of variability accounted for by each level of a hierarchical model to quantify sources of spending variation. RESULTS &gt;We analyzed 17,436 spine surgery episodes performed by 195 surgeons at 50 hospitals. Mean payment in highest cost quintile of episodes ($52,767; 95% CI, $52,133 to $53,401) significantly exceeded mean episode payment in lowest cost quintile ($9814; 95% CI, $9757 to $9872; P&lt;.001). Facility payments for index admission and post-discharge payments were the greatest contributors to overall variation: 39.4% and 32.5%, respectively. After accounting for patient-level covariates, the remaining hospital-level and surgeon-level effects accounted for 2.0% (95% CI, 1.1% to 3.8%) and 4.0% (95% CI, 2.9% to 5.6%) of total variation, respectively. CONCLUSION Significant variation exists in total episode payments for spine surgery, driven mostly by variation in post-discharge and facility payments. Hospital and surgeon effects account for relatively little of observed variation. Our results suggest that alternative payment models applied to spine surgery including bundled payment will not be successful if their design assumes that surgeon and hospital preferences are the primary source of variation in costs.

Effect of Collaborative Care on Cost Variation in an Intensive Care Unit

Improving the cost-effectiveness of health care requires an understanding of the genesis of health care costs and in particular the sources of cost variation. Little is known about how multiple physicians, caring collaboratively for patients, contribute to costs. To explore the effect of collaborative care by physicians on variation in discretionary costs in an intensive care unit (ICU) by determining the contributions of the attending intensivists and ICU fellows. Prospective, observational study using a multivariable model of median discretionary costs for the first day in the ICU, adjusting for confounding variables. Analysis included 3514 patients who spent more than 2 hours in the ICU on the initial day. Impact of the physicians was assessed via variables representing the specific intensivist and ICU fellow responsible on the first ICU day and allowing for interaction terms. On the initial day, patients spent a median of 10.6 hours (interquartile range, 6.3-16.5) in the ICU, with median discretionary costs of $1343 (interquartile range, $788-2208). There was large variation in adjusted costs attributable to both the intensivists ($359; 95% CI, $244-$474) and the fellows ($756; 95% CI, $550-$965). The interaction terms were not significant (P = .12-.79). In an ICU care model with intensivists and subspecialty fellows, both types of physicians contributed significantly to the observed variation in discretionary costs. However, even in the presence of a hierarchical arrangement of clinical responsibilities, the influences on costs of the 2 types of physicians were independent.

Dairy barn layout and construction: Effects on initial building costs

On small dairy farms, high investment costs and lack of investment capital may delay the modernising of facilities. The aim of this study was to investigate the importance of economics of scale in building costs of barns compared to other sources of variation in costs. The study includes 44 farms with a mean herd size of 49.5 ± 15.1 cows, built between year 1999 and 2006 and with a mean total area in the barns of 896 ± 454 m 2 . Building cost data were obtained from farmers and merged with construction, mechanisation and layout data from the same barns. Construction costs decreased up to approximately 1250 m 2 while mechanisation costs and total building costs decreased up to approximately 1000 m 2 . A further increase in building area had only limited effect on the building costs per m 2 . Models including explanatory variables showed that milking and service area was significantly more expensive than other areas. AMS-barns were all together not significantly more expensive than other barns, since the increased mechanisation cost is offset by a lower requirement for milking area. Farmers remodelling their barns were able to realise a modernised building for a certain herd size for a lower cost compared to a completely new building. The use of their own effort varied considerably between projects. In many cases, farmers would be able to find alternative income sources with a higher hourly rate than the value of their own effort suggested by the model.

Economic grand rounds: Prevalence and cost of treating mental disorders among elderly recipients of Medicare services.

Economic grand rounds: Prevalence and cost of treating mental disorders among elderly recipients of Medicare services.

Sources of Hospital Cost Variation by Urban-Rural Location

Under current law, Medicare's Prospective Payment System (PPS) recognizes a 25% differential in standardized average costs per admission between rural and urban areas. Using Medicare Part A and B claims data from four states across the country, this paper employs descriptive and multivariate techniques to explain the underlying sources of cost variation. DRG mix and within-DRG severity differences were found to contribute only 10-15% to the overall urban-rural variation compared to about one third for wage differences. Differences in procedure intensity for basically similar patients explained another 25-50% of the urban-rural difference depending on illness. Evidence was also found for systematic cost and procedure differences between core city versus suburban hospitals. If the urban-rural rate differential were eliminated, the impact on urban hospitals could have major effects on the intensive way in which physicians treat patients. Whether this would have demonstrable outcome effects is unknown, but initial severity differences alone do not justify the observed procedural differences.

Sources of Hospital Cost Variation by Urban-Rural Location

Under current law, Medicare's Prospective Payment System (PPS) recognizes a 25% differential in standardized average costs per admission between rural and urban areas. Using Medicare Part A and B claims data from four states across the country, this paper employs descriptive and multivariate techniques to explain the underlying sources of cost variation. DRG mix and within-DRG severity differences were found to contribute only 10–15% to the overall urban-rural variation compared to about one third for wage differences. Differences in procedure intensity for basically similar patients explained another 25–50% of the urban-rural difference depending on illness. Evidence was also found for systematic cost and procedure differences between core city versus suburban hospitals. If the urban-rural rate differential were eliminated, the impact on urban hospitals could have major effects on the intensive way in which physicians treat patients. Whether this would have demonstrable outcome effects is unknown, but initial severity differences alone do not justify the observed procedural differences.