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Applied Health Economics and Health Policy

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Open Access 01-08-2016 | Original Research Article

Measuring the Volume-Outcome Relation for Complex Hospital Surgery

Authors: Woohyeon Kim, Stephen Wolff, Vivian Ho

Published in: Applied Health Economics and Health Policy | Issue 4/2016

Abstract

Background

Prominent studies continue to measure the hospital volume-outcome relation using simple logistic or random-effects models. These regression models may not appropriately account for unobserved differences across hospitals (such as differences in organizational effectiveness) which could be mistaken for a volume outcome relation.

Objective

To explore alternative estimation methods for measuring the volume-outcome relation for six major cancer operations, and to determine which estimation method is most appropriate.

Methods

We analyzed patient-level hospital discharge data from three USA states and data from the American Hospital Association Annual Survey of Hospitals from 2000 to 2011. We studied six major cancer operations using three regression frameworks (logistic, fixed-effects, and random-effects) to determine the correlation between patient outcome (mortality) and hospital volume.

Results

For our data, logistic and random-effects models suggest a non-zero volume effect, whereas fixed-effects models do not. Model-specification tests support the fixed-effects or random-effects model, depending on the surgical procedure; the basic logistic model is always rejected. Esophagectomy and rectal resection do not exhibit significant volume effects, whereas colectomy, pancreatic resection, pneumonectomy, and pulmonary lobectomy do.

Conclusions

The statistical significance of the hospital volume-outcome relation depends critically on the regression model. A simple logistic model cannot control for unobserved differences across hospitals that may be mistaken for a volume effect. Even when one applies panel-data methods, one must carefully choose between fixed- and random-effects models.

Available only for authorised users

In this paper, “random-effects model” means a model whose intercept includes a random effect.

All data were obtained from the respective states’ Departments of Health.

Patients under the age of 21 years are dropped to avoid issues arising from different patterns of cancer treatment for children and adults. Transfer-out patients are dropped to avoid endogeneity issues. For each procedure, less than 0.5 % of patients were unlinked to hospital-level data.

Specifically, for each procedure and within each year, we count the number of observations whose age, sex, and state agreed (an estimate for an upper bound on the number of multiple admissions). The maximum number of duplicate patients is at most 0.10 % of a procedure’s sample; see Appendix 2 in the Supplemental Material.

Surgeon volume, used in some robustness-check specifications, is defined analogously.

The conditional likelihood function, introduced in [30], avoids the incidental-parameter problem.

See section 17.4.4 in Greene [31].

If surgeons perform all or nearly all of their procedures in a single hospital, then including both hospital and surgeon fixed effects in a regression will cause problems of multicollinearity. In this case, if identifying both effects is important, one can use a two-way nested model: e.g., a fixed term for the hospital effect and a random term for the surgeon effect. See Schielzeth et al. [32].

Because surgeons can perform procedures across multiple hospitals, an ideal setting would account for correlation of error terms at both the hospital and physician level. Stata does not currently appear to handle clustered standard errors under multi-level clustering. Due to this limitation, in our models we implement cluster-robust standard errors at the hospital level only.

Given \( \gamma = 0 \) and (2), the law of iterated expectations and the definition of correlation yield the stated result.

Before the three inclusion criteria in 3.1 were applied, the number of patients for each procedure was as follows. Colectomy: 165,729; esophagectomy: 4854; pancreatic resection: 14,357; pneumonectomy: 5065; pulmonary lobectomy: 54,620; rectal resection: 36,199.

The exact within-hospital variations for each procedure are as follows: Colectomy: 30.11 %; esophagectomy: 37.92 %; pancreatic resection: 33.88 %; pneumonectomy: 43.75 %; pulmonary lobectomy: 29.60 %; rectal resection: 37.39 %.

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Title: Measuring the Volume-Outcome Relation for Complex Hospital Surgery
Authors: Woohyeon Kim
Stephen Wolff
Vivian Ho
Publication date: 01-08-2016
Publisher: Springer International Publishing
Published in: Applied Health Economics and Health Policy / Issue 4/2016
Print ISSN: 1175-5652
Electronic ISSN: 1179-1896
DOI: https://doi.org/10.1007/s40258-016-0241-6

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Measuring the Volume-Outcome Relation for Complex Hospital Surgery

Abstract

Background

Objective

Methods

Results

Conclusions

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Background

Objective

Methods

Results

Conclusions

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