Small Area Variations in Hospitalization Rates
How Much You See Depends on How You Look
Bibliographic Data
| ID | 9103505 |
|---|---|
| Authors | Michael Shwartz (0000-0003-4840-1682, corresponding author), Arlene S Ash (0000-0002-8448-0253, Boston University), Jennifer Anderson (0000-0002-0713-6897, Boston University), Lisa I Lezzoni, Susan M C Payne, Susan Payne (0000-0001-9907-3477, Boston University), Joseph D Restuccia (corresponding author) |
| Year | 1994 |
| Volume | 32 |
| Issue | 3 |
| Pages | 189-201 |
| Publication date | 1994-03-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Medical Care (JOURNAL) |
| Journal identifiers | ISSN: 0025-7079 • E-ISSN: 1537-1948 |
| Publisher | Ovid Technologies (Wolters Kluwer Health) (PUBLISHER) |
| DOI | 10.1097/00005650-199403000-00001 |
| PMID | 8145597 |
| OpenAlex | W2002315992 |
| Language | EN |
| Citations received | 4 |
| References cited | 9 |
This research investigates the degree that estimates of the magnitude of small area variations in hospitalization rates depend on both the estimation method and the number of years of data used. Hospital discharge abstracts for patients 65 and older from acute care hospitals in Massachusetts from 1982 to 1987 were analyzed. The SCV statistic, the approach used in many current small area variation studies, and empirical Bayes (EB), an approach that adjusts more fully for the effect of random variation, were compared. EB estimates based on 3 years of data were best able to predict future area-specific hospitalization rates. Compared to EB estimates using 3 years of data, the SCV statistic with 1 year of data overestimated the median amount of systematic variation by over 70% for the 68 conditions studied; with 3 years of data, the SCV overestimated the median by 55%. Regardless of method, the same conditions were identified as relatively more variable and the same geographic areas were found to have higher than expected hospitalization rates. The magnitude of differences in hospitalization rates depends on how the data are analyzed and how many years of data are used. Hospitalization rates across small geographic areas may vary substantially less than reported previously
Bayes' theorem · Bayesian probability · Estimation · Magnitude (astronomy) · Statistic · Statistics · Demography · Geriatric Care and Nursing Homes · Global Health Care Issues · Healthcare Policy and Management · Mathematics · Medicine
Variations in the Utilization of Coronary Angiography for Elderly Patients with an Acute Myocardial Infarction
Estimating a Composite Measure of Hospital Quality From the Hospital Compare Database
Does More “Appropriateness” Explain Higher Rates of Cardiac Procedures Among Patients Hospitalized With Coronary Heart Disease
Bringing Responsibility for Small Area Variations in Hospitalization Rates Back to the Hospital
Exploratory data analysis
Statistical Methods for Rates and Proportions
Variations in Medical Care among Small Areas
Small-Area Variations in the Use of Common Surgical Procedures
Hysterectomy
Hospitalization of medicaid children
Surgical Rate Variations
Variation in Hospital Admissions Among Small Areas
Professional uncertainty and the problem of supplier-induced demand
| Unique citing works | 4 |
|---|---|
| Citations per year | 0,13 |
| Citation span | 1995 - 2011 (17) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 3 |