scholarly journals Impact Of Risk Adjustment For Socioeconomic Status On Medicare Advantage Plan Quality Rankings

2018 ◽  
Vol 37 (7) ◽  
pp. 1065-1072 ◽  
Author(s):  
Shayla N. M. Durfey ◽  
Amy J. H. Kind ◽  
Roee Gutman ◽  
Kristina Monteiro ◽  
William R. Buckingham ◽  
...  
Keyword(s):  
Socioeconomic Status ◽  
Risk Adjustment ◽  
Medicare Advantage ◽  
Plan Quality

scholarly journals Low Socioeconomic Status Is Associated With Increased Complication Rates: Are Risk Adjustment Models Necessary in Cervical Spine Surgery?

2019 ◽  
Vol 10 (6) ◽  
pp. 748-753 ◽  
Author(s):  
Alexander M. Lieber ◽  
Anthony J. Boniello ◽  
Yehuda E. Kerbel ◽  
Philip Petrucelli ◽  
Venkat Kavuri ◽  
...  
Keyword(s):  
Socioeconomic Status ◽  
Low Socioeconomic Status ◽  
Risk Adjustment ◽  
Hospital Costs ◽  
Income Group ◽  
Complication Rates ◽  
Cervical Spine Surgery ◽  
Spine Care ◽  
Anterior Discectomy And Fusion ◽  
Income Quartile

Study Design: Retrospective cohort study. Objectives: The objective of this study was to determine whether lower socioeconomic status was associated with increased resource utilization following anterior discectomy and fusion (ACDF). Methods: The National Inpatient Sample database was queried for patients who underwent a primary, 1- to 2-level ACDF between 2005 and 2014. Trauma, malignancy, infection, and revision surgery were excluded. The top and bottom income quartiles were compared. Demographics, medical comorbidities, length of stay, complications, and hospital cost were compared between patients of top and bottom income quartiles. Results: A total of 69 844 cases were included. The bottom income quartile had a similar mean hospital stay (2.04 vs 1.77 days, P = .412), more complications (2.45% vs 1.77%, P < .001), and a higher mortality rate (0.18% vs 0.11%, P = .016). Multivariate analysis revealed bottom income quartile was an independent risk factor for complications (odds ratio = 1.135, confidence interval = 1.02-1.26). Interestingly, the bottom income quartile experienced lower mean hospital costs ($17 041 vs $17 958, P < .001). Conclusion: Patients in the lowest income group experienced more complications even after adjusting for comorbidities. Therefore, risk adjustment models, including socioeconomic status, may be necessary to avoid potential problems with access to orthopedic spine care for this patient population.

scholarly journals New Risk-Adjustment System Was Associated With Reduced Favorable Selection In Medicare Advantage

2012 ◽  
Vol 31 (12) ◽  
pp. 2630-2640 ◽  
Author(s):  
J. Michael McWilliams ◽  
John Hsu ◽  
Joseph P. Newhouse
Keyword(s):  
Risk Adjustment ◽  
Medicare Advantage ◽  
Adjustment System

Beyond classical risk adjustment: Socioeconomic status and hospital performance in urologic oncology.

2017 ◽  
Vol 35 (6_suppl) ◽  
pp. 526-526
Author(s):  
Anobel Y. Odisho ◽  
John L. Gore ◽  
Ruth Douglas Etzioni
Keyword(s):  
Socioeconomic Status ◽  
Partial Nephrectomy ◽  
Radical Cystectomy ◽  
Risk Adjustment ◽  
Radical Nephrectomy ◽  
Health Planning ◽  
Safety Net ◽  
Safety Net Hospital ◽  
Hospital Factors ◽  
Readmission Risk

526 Background: Safety-net hospitals care for more patients of lower socioeconomic status (SES) than non-safety-net hospitals and may be disproportionately punished under readmission risk adjustment models that do not incorporate (SES). We developed a readmission risk adjustment framework incorporating SES to assess impact of SES on safety-net hospital rankings for patients undergoing major surgery for urologic malignancies. Methods: Quasi-experimental design using California Office of Statewide Health Planning and Development data from 2007-2011. Subjects included all patients undergoing radical cystectomy for bladder cancer (n = 3,771), partial nephrectomy (n = 5,556), and radical nephrectomy (n = 13,136) for kidney cancer. Unadjusted hospital rankings and predicted rankings under a base model, which simulated the Medicare Hospital Readmissions Reduction Program model, were compared with predicted rankings under models incorporating socioeconomic status. Socioeconomic status was derived from a multifactorial neighborhood score at the ZIP code level calculated from US Census data. The main outcome measures were hospital rankings based on 30-day all-cause readmission rate and differences between model predicted rankings. Results: For all procedures, the addition of socioeconomic status, geographic, and hospital factors changed the overall hospital rankings significantly compared with the base model (p < 0.01), with the exception of socioeconomic status in radical cystectomy (p = 0.07) and socioeconomic status and rural factors in partial nephrectomy (p = 0.12). For radical nephrectomy and partial nephrectomy, the addition of socioeconomic status and hospital factors significantly improved the mean ranking of safety-net hospitals and improved the ratio of observed relative to expected rankings (p < 0.01). For radical cystectomy there was no significant change in rankings with the addition of socioeconomic status, rural status, or hospital factors. Conclusions: Adding socioeconomic status to existing Medicare readmission risk adjustment models leads to significant changes in hospital rankings, with a differential impact on safety-net hospitals.

scholarly journals Beyond classic risk adjustment: Socioeconomic status and hospital performance in urologic oncology surgery

Cancer ◽  
2018 ◽  
Vol 124 (16) ◽  
pp. 3372-3380 ◽  
Author(s):  
Anobel Y. Odisho ◽  
Ruth Etzioni ◽  
John L. Gore
Keyword(s):  
Socioeconomic Status ◽  
Risk Adjustment ◽  
Hospital Performance ◽  
Urologic Oncology

scholarly journals Impact of Risk Adjustment for Socioeconomic Status on Risk-adjusted Surgical Readmission Rates

2016 ◽  
Vol 263 (4) ◽  
pp. 698-704 ◽  
Author(s):  
Laurent G. Glance ◽  
Arthur L. Kellermann ◽  
Turner M. Osler ◽  
Yue Li ◽  
Wenjun Li ◽  
...  
Keyword(s):  
Socioeconomic Status ◽  
Risk Adjustment ◽  
Readmission Rates

scholarly journals Favorable Selection, Risk Adjustment, and the Medicare Advantage Program

2012 ◽  
Vol 48 (3) ◽  
pp. 1039-1056 ◽  
Author(s):  
Michael A. Morrisey ◽  
Meredith L. Kilgore ◽  
David J. Becker ◽  
Wilson Smith ◽  
Elizabeth Delzell
Keyword(s):  
Risk Adjustment ◽  
Medicare Advantage

scholarly journals The use of linked data to develop and validate measures of variation in mortality and readmissions for public reporting purposes in New South Wales, Australia

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Kim Sutherland ◽  
Sadaf Marashi-Pour ◽  
Huei-Yang Chen ◽  
Jean-Frédéric Lévesque
Keyword(s):  
Socioeconomic Status ◽  
Ischaemic Stroke ◽  
Acute Care ◽  
Risk Adjustment ◽  
Linked Data ◽  
New South ◽  
New South Wales ◽  
Case Mix ◽  
C Statistic ◽  
South Wales

ABSTRACTObjectivesTo investigate variation across 78 New South Wales public hospitals, in mortality in the 30 days following admission and in returns to acute care (readmissions) in the 30 days following discharge for acute myocardial infarction, ischaemic stroke, heart failure, pneumonia, hip fracture surgery. ApproachLinked data were used to (1) construct an analytic unit – an index period of care that comprised concatenated acute, contiguous hospitalisations with the principal diagnosis of interest; (2) to capture outcomes both within the index hospital and following discharge, wherever they occurred; (3) to enhance risk adjustment with one year look back for relevant comorbidities; (4) to assess fair attribution of outcomes. A risk-standardised mortality ratio (RSMR) and a risk standardised readmission ratio (RSRR) were calculated as the ratio of the observed to the expected number events at a given hospital, by developing and validating condition specific system-level prediction models. Funnel plots identified outliers. For the RSRR, the competing risk of death was considered. ResultsFor both outcome indicators, sensitivity was enhanced by the use of linked data (33%-100% more deaths; 23%–32% more returns to acute care or readmissions). For mortality, RSMRs that only capture deaths in hospital, as opposed to deaths within 30 days of admission, were shown to be biased and change the outlier status of about 20% of hospitals. Including socioeconomic status in risk adjustment models altered the outlier status of about 10% of hospitals on the cusp of statistical significance but did not significantly alter the RSMRs. For returns to acute care, sensitivity analyses that included socioeconomic status in the models found there was no significant improvement in discriminatory power. For example, in the case of ischaemic stroke, the c-statistic for the model without inclusion of SES was 0.593 (0.578-0.610); inclusion of SES resulted in a c-statistic of 0.600 (0.583-0.616). There were some changes in hospital-level results but there was no clear evidence of a systematic effect on results. ConclusionThe risk-standardised ratio method, based on linked data, compares a hospital’s results given its case mix with an average New South Wales hospital with the same case mix. Ratio-based indicators have been reported publicly and have proven to be a valuable screening tool to identify hospitals where further investigation may be required locally.

scholarly journals Does Risk Adjustment Reduce Vaccination in the Elderly? Evidence From Medicare Advantage

2018 ◽  
Vol 77 (2) ◽  
pp. 176-186
Author(s):  
Brett Lissenden ◽  
Rajesh Balkrishnan
Keyword(s):  
Influenza Vaccination ◽  
Risk Adjustment ◽  
Chronic Conditions ◽  
The Elderly ◽  
Positive Influence ◽  
Medicare Advantage ◽  
Vaccination Rates ◽  
Adjustment Policy ◽  
Percentage Points ◽  
Risk Adjustment Model

To combat risk selection, it is becoming increasingly common for payments to insurers (and providers) to adjust for patients’ chronic conditions. A possible unintended negative consequence is to reduce insurers’ (and providers’) incentives to prevent chronic conditions. This study examined the effect of Medicare’s risk adjustment for payments to Medicare Advantage plans, first introduced in 2004, on pneumonia and influenza vaccination for the elderly. The analysis used the 2000 through 2010 waves of the Medicare Current Beneficiary Survey and a difference-in-differences approach. Presumably by decreasing Medicare Advantage plans’ positive influence on vaccination, Medicare’s risk adjustment policy was estimated to have reduced pneumonia vaccination rates by 2.9 percentage points (4%, p = .039) and to have possibly reduced influenza vaccination rates by 2.2 percentage points (3%, p = .096). The results clarify an argument against including vaccine-preventable conditions, like pneumonia, in a risk adjustment model.

scholarly journals Assessment of the Medicare Advantage Risk Adjustment Model for Measuring Veterans Affairs Hospital Performance

2018 ◽  
Vol 1 (8) ◽  
pp. e185993 ◽  
Author(s):  
Todd H. Wagner ◽  
Peter Almenoff ◽  
Joseph Francis ◽  
Josephine Jacobs ◽  
Christine Pal Chee
Keyword(s):  
Risk Adjustment ◽  
Hospital Performance ◽  
Veterans Affairs ◽  
Medicare Advantage ◽  
Adjustment Model ◽  
Risk Adjustment Model
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