Literature DB >> 8163382

The effect of alternative case-mix adjustments on mortality differences between municipal and voluntary hospitals in New York City.

M F Shapiro1, R E Park, J Keesey, R H Brook.   

Abstract

OBJECTIVE: This study investigated how mortality differences between groups of municipal versus voluntary hospitals are affected by case-mix adjustment methods. DATA SOURCES AND STUDY
SETTING: We sampled about 10,000 random admissions from administrative data for patients hospitalized with each of six conditions in hospitals in New York City during 1984-1987. STUDY
DESIGN: We developed logistic regression models adjusting for age and gender, for principal diagnosis, for "limited other diagnoses" (secondary diagnoses that were very unlikely to result from care received), for "full other diagnoses" (all secondary diagnoses irrespective of whether they might have been due to care received), for previous diagnoses, and for other variables. PRINCIPAL
FINDINGS: For five of the six conditions, when the limited other diagnoses adjustment was used there was higher mortality in the municipal hospitals (p < .05), with 3.3 additional deaths/100 admissions for myocardial infarction, 1.2 for pneumonia, 8.3 for stroke, 2.8 for head trauma, and 0.8 for hip repair. However, when the full other diagnoses adjustment was used, differences remained significant only for stroke (4.3 additional deaths/100 admissions) and head trauma (1.3) (p < .05).
CONCLUSIONS: Estimates of mortality differences between New York City municipal and voluntary hospitals are substantially affected by which secondary diagnoses are used in case-mix adjustment. Judgments of quality should not be based on administrative data unless models can be developed that validly capture level of sickness at admission.

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Mesh:

Year:  1994        PMID: 8163382      PMCID: PMC1069990     

Source DB:  PubMed          Journal:  Health Serv Res        ISSN: 0017-9124            Impact factor:   3.402


  17 in total

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5.  Interpreting hospital mortality data. The role of clinical risk adjustment.

Authors:  S F Jencks; J Daley; D Draper; N Thomas; G Lenhart; J Walker
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6.  Accurate prediction of the outcome of pediatric intensive care. A new quantitative method.

Authors:  M M Pollack; U E Ruttimann; P R Getson
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7.  Does practice make perfect? Part I: The relation between hospital volume and outcomes for selected diagnostic categories.

Authors:  A B Flood; W R Scott; W Ewy
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8.  Flaws in mortality data. The hazards of ignoring comorbid disease.

Authors:  S Greenfield; H U Aronow; R M Elashoff; D Watanabe
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9.  Coding of acute myocardial infarction. Clinical and policy implications.

Authors:  L I Iezzoni; S Burnside; L Sickles; M A Moskowitz; E Sawitz; P A Levine
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10.  Outcomes of surgery among the Medicare aged: surgical volume and mortality.

Authors:  G Riley; J Lubitz
Journal:  Health Care Financ Rev       Date:  1985
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  7 in total

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2.  Using severity measures to predict the likelihood of death for pneumonia inpatients.

Authors:  L I Iezzoni; M Shwartz; A S Ash; Y D Mackiernan
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Review 3.  How severity measures rate hospitalized patients.

Authors:  J S Hughes; L I Iezzoni; J Daley; L Greenberg
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4.  Judging hospitals by severity-adjusted mortality rates: the influence of the severity-adjustment method.

Authors:  L I Iezzoni; A S Ash; M Shwartz; J Daley; J S Hughes; Y D Mackiernan
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5.  How much better can we predict dialysis patient survival using clinical data?

Authors:  D E Mesler; S Byrne-Logan; E P McCarthy; A S Ash; M A Moskowitz
Journal:  Health Serv Res       Date:  1999-04       Impact factor: 3.402

6.  Comparing patient outcomes across payer types: implications for using hospital discharge records to assess quality.

Authors:  Daniel D Maeng; Grant R Martsolf
Journal:  Health Serv Res       Date:  2011-06-20       Impact factor: 3.402

7.  Hospital competition, managed care, and mortality after hospitalization for medical conditions in California.

Authors:  Jeannette Rogowski; Arvind K Jain; José J Escarce
Journal:  Health Serv Res       Date:  2007-04       Impact factor: 3.402

  7 in total

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