Browse
Submit Data
Databases
API
Help

Dataset Information

27 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

What underlies the observed hospital volume-outcome relationship?

ABSTRACT:

Background

Studies of the hospital volume-outcome relationship have highlighted that a greater volume activity improves patient outcomes. While this finding has been known for years, most studies to date have failed to delve into what underlies this relationship.

Objective

This study aimed to shed light on the basis of the hospital volume effect on patient outcomes by comparing treatment modalities for epithelial ovarian carcinoma patients.

Data

An exhaustive dataset of 355 patients in first-line treatment for Epithelial Ovarian Carcinoma (EOC) in 2012 in three regions of France was used. These regions account for 15% of the metropolitan French population.

Methods

In the presence of endogeneity induced by a reverse causality between hospital volume and patient outcomes, we used an instrumental variable approach. Hospital volume of activity was instrumented by the distance from patients' homes to their hospital, the population density, and the median net income of patient municipalities.

Results

Based on our parameter estimates, we found that the rate of complete tumor resection would increase by 15.5 percentage points with centralized care, and by 8.3 percentage points if treatment decisions were coordinated by high-volume centers compared to decentralized care.

Conclusion

As volume alone is an imperfect correlate of quality, policy-makers need to know what volume is a proxy for in order to devise volume-based policies.

SUBMITTER: Huguet M

PROVIDER: S-EPMC8760746 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

On the Hospital Volume and Outcome Relationship: Does Specialization Matter More Than Volume?

Project description:ObjectiveTo evaluate the relationship between hospital volume and outcome by focusing on alternative measures of volume that capture specialization and overall throughput of hospitals.Data sources/study settingHospital administrative data from the state of Victoria, Australia; data contain 1,798,474 admitted episodes reported by 135 public and private acute-care hospitals.Study designThis study contrasts the volume-outcome relationship using regression models with different measures of volume; two-step and single-step risk-adjustment methods are used.Data collection/extraction methodsThe sample is restricted to ischemic heart disease (IHD) patients (ICD-10 codes: I20-I25) admitted during 2001/02 to 2004/05.Principal findingsOverall hospital throughput and degree of specialization display more substantive implications for the volume-outcome relationship than conventional caseload volume measure. Two-step estimation when corrected for heteroscedasticity produces comparable results to single-step methods.ConclusionsDifferent measures of volume could lead to vastly different conclusions about the volume-outcome relationship. Hospital specialization and throughput should both be included as measures of volume to capture the notion of size, focus, and possible congestion effects.

| S-EPMC4693841 | biostudies-literature

The Hospital Compare mortality model and the volume-outcome relationship.

Project description:We ask whether Medicare's Hospital Compare random effects model correctly assesses acute myocardial infarction (AMI) hospital mortality rates when there is a volume-outcome relationship.Medicare claims on 208,157 AMI patients admitted in 3,629 acute care hospitals throughout the United States.We compared average-adjusted mortality using logistic regression with average adjusted mortality based on the Hospital Compare random effects model. We then fit random effects models with the same patient variables as in Medicare's Hospital Compare mortality model but also included terms for hospital Medicare AMI volume and another model that additionally included other hospital characteristics.Hospital Compare's average adjusted mortality significantly underestimates average observed death rates in small volume hospitals. Placing hospital volume in the Hospital Compare model significantly improved predictions.The Hospital Compare random effects model underestimates the typically poorer performance of low-volume hospitals. Placing hospital volume in the Hospital Compare model, and possibly other important hospital characteristics, appears indicated when using a random effects model to predict outcomes. Care must be taken to insure the proper method of reporting such models, especially if hospital characteristics are included in the random effects model.

| S-EPMC2965498 | biostudies-literature

Assessing the hospital volume-outcome relationship in surgery: a scoping review protocol.

Project description:IntroductionEven if a positive volume-outcome correlation in surgery is mostly admitted in many surgical fields, the various ways to assess this relationship make it difficult for researchers and policymakers to use it. Our aim is therefore to provide an overview of the way hospital volume-outcome relationship was assessed. Through this overview, our goal is to identify potential gaps in the assessment of this relationship, to help researchers who want to pursue work in this field and, ultimately, to help policy makers interpret such analyses.Methods and analysisThis review will be conducted using the six stages of the scoping review method: identifying the research question, searching for relevant studies, selecting studies, data extraction, collating, summarising and reporting the results and concluding. This review will address all the key questions used to assess the volume-outcome relationship in surgery.Primary research papers investigating the hospital volume-outcome relationship from 2009 will be included. Studies only looking at surgeons' volume-outcome relationship or studies were the volume variable is not individualisable will be excluded.Both MEDLINE and Scopus will be searched along with grey literature. Two researchers will perform all the stages of the review: screen the titles and abstracts, review the full text of selected articles to determine final inclusions and extract the data. The results will be summarised quantitatively using numerical counts.Ethical considerations and disseminationReviews of published articles are considered secondary analysis and do not need ethical approval. The findings will be disseminated through multiple channels like conferences and peer-reviewed journals.

| S-EPMC7539612 | biostudies-literature

Hospital volume-outcome relationship in vulvar cancer treatment: a Japanese Gynecologic Oncology Group study.

Project description: Not available

| S-EPMC7930436 | biostudies-literature

Volume-outcome relationship in rectal cancer surgery.

Project description:IntroductionHospital centralization effect is reported to lower complications and mortality for high risk and complex surgery operations, including colorectal surgery. However, no linear relation between volume and outcome has been demonstrated. Aim of the study was to evaluate the increased surgical volume effect on early outcomes of patient undergoing laparoscopic restorative anterior rectal resection (ARR).MethodsA retrospective analysis of all consecutive patients undergoing ARR with primary anastomosis between November 2016 and December 2020 after centralization of rectal cancer cases in an academic Centre. Short-term outcomes are compared to those of patients operated in the same unit during the previous 10 years before service centralization. The primary outcome was estimated anastomotic leak rate. Mean operative time, need of conversion, postoperative use of blood transfusion, radicality, in-hospital stay, number and type of complications, readmission and reoperation rate, mortality and 1-year and stoma persistence rates were evaluated as secondary outcomes.Results86 patients were operated in the study period and outcomes compared to those of 101 patients operated during the previous ten years. Difference in volume of surgery was significant between the two periods (p 0.019) and the estimated leak rate was significantly lower in the higher volume unit (p 0.047). Mean operative time, need of conversion, postoperative use of blood transfusion and in-hospital stay (p < 0.05) were also significantly reduced in Group A.ConclusionThis study suggests that the shift toward higher volume in rectal cancer surgery is associated to decreased anastomotic leak rate. Potentiation of lower volume surgical units may yield optimal perioperative outcomes.

| S-EPMC8777490 | biostudies-literature

Hospital volume affects outcome after total knee arthroplasty.

Project description:BACKGROUND AND PURPOSE: The influence of hospital volume on the outcome of total knee joint replacement surgery is controversial. We evaluated nationwide data on the effect of hospital volume on length of stay, re-admission, revision, manipulation under anesthesia (MUA), and discharge disposition for total knee replacement (TKR) in Finland. PATIENTS AND METHODS: 59,696 TKRs for primary osteoarthritis performed between 1998 and 2010 were identified from the Finnish Hospital Discharge Register and the Finnish Arthroplasty Register. Hospitals were classified into 4 groups according to the number of primary and revision knee arthroplasties performed on an annual basis throughout the study period: 1-99 (group 1), 100-249 (group 2), 250-449 (group 3), and ? 450 (group 4). The association between hospital procedure volume and length of stay (LOS), length of uninterrupted institutional care (LUIC), re-admissions, revisions, MUA, and discharge disposition were analyzed. RESULTS: The greater the volume of the hospital, the shorter was the average LOS and LUIC. Smaller hospital volume was not unambiguously associated with increased revision, re-admission, or MUA rates. The smaller the annual hospital volume, the more often patients were discharged home. INTERPRETATION: LOS and LUIC ought to be shortened in lower-volume hospitals. There is potential for a reduction in length of stay in extended institutional care facilities.

| S-EPMC4366674 | biostudies-literature

Relationship between patient safety and hospital surgical volume.

Project description:To examine the relationship between hospital volume and in-hospital adverse events.Patient safety indicator (PSI) was used to identify hospital-acquired adverse events in the Nationwide Inpatient Sample database in abdominal aortic aneurysm, coronary artery bypass graft, and Roux-en-Y gastric bypass from 2005 to 2008.In this observational study, volume thresholds were defined by mean year-specific terciles. PSI risk-adjusted rates were analyzed by volume tercile for each procedure.Overall, hospital volume was inversely related to preventable adverse events. High-volume hospitals had significantly lower risk-adjusted PSI rates compared to lower volume hospitals (p < .05).These data support the relationship between hospital volume and quality health care delivery in select surgical cases. This study highlights differences between hospital volume and risk-adjusted PSI rates for three common surgical procedures and highlights areas of focus for future studies to identify pathways to reduce hospital-acquired events.

| S-EPMC3419887 | biostudies-other

The Volume-Outcome Relationship Revisited: Practice Indeed Makes Perfect.

Project description:OBJECTIVE:To examine the causal effect of a hospital's experience with treating hip fractures (volume) on patient treatment outcomes. DATA SOURCES:We use a full sample of administrative data from German hospitals for 2007. The data provide detailed information on patients and hospitals. We also reference the hospitals' addresses and the zip codes of patients' place of residence. STUDY DESIGN:We apply an instrumental variable approach to address endogeneity concerns due to reverse causality and unobserved patient heterogeneity. As instruments for case volume, we use the number of potential patients and number of other hospitals in the region surrounding each hospital. PRINCIPAL FINDINGS:Our results indicate that after applying an instrumental variables (IV) regression of volume on outcome, volume significantly increases quality. CONCLUSIONS:We provide evidence for the practice-makes-perfect hypothesis by showing that volume is a driving factor for quality.

| S-EPMC5785314 | biostudies-literature

The volume and outcome relationship for thyroidectomy in England.

Project description:PurposeThe delivery of surgical care in England has seen a momentum towards centralisation within larger volume hospitals and surgical teams. The aim of this study was to investigate outcomes in England in relationship to hospital and surgeon annual volumes for total thyroidectomy.MethodsData were extracted from the Hospital Episodes Statistics (HES) database for England. A 6-year period (April 2012-March 2018 inclusive) for all adult admissions for thyroidectomy was used in the analysis. The primary outcome measure used was a length of hospital stay greater than 2 days or an emergency readmission within 30 days following surgery. This was used as a proxy for surgical complications. A multilevel modelling strategy was used to adjust for hierarchy and potentially confounding.ResultsData for 22,823 total thyroidectomies across 144 hospital trusts were used for analysis. For total thyroidectomy, larger volume surgeons had reduced levels of post-surgical complications; length of stay > 2 and > 4 days; emergency readmission at 30 days; and hypoparathyroidism, vocal cord palsy, stridor, and tracheostomy at 1-year post-surgery. Larger hospital volume was associated with lower levels of emergency readmission at 30 days and hypoparathyroidism at 1 year.ConclusionsThere is significant correlation between surgeon volume and clinical outcome for total thyroidectomy. The relationship was approximately linear, and a low-volume threshold could not be defined.

| S-EPMC8188540 | biostudies-literature

Measuring the Volume-Outcome Relation for Complex Hospital Surgery.

Project description:BackgroundProminent 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.ObjectiveTo explore alternative estimation methods for measuring the volume-outcome relation for six major cancer operations, and to determine which estimation method is most appropriate.MethodsWe 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.ResultsFor 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.ConclusionsThe 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.

| S-EPMC4937076 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data