R Holubkov1, V L Holt, F A Connell, J P LoGerfo. 1. Department of Biostatistics, University of Washington, School of Public Health and Community Medicine, Seattle, USA.
Abstract
OBJECTIVE: To develop an effective, concise presentation of hospital-specific birth event and delivery-related complication rates, including significant deviations from expected rates calculated using risk-adjusted peer hospital data, for distribution to all Washington State hospitals with delivery services. DATA SOURCES: Complete data for calendar year 1993, including inpatient discharge records for mothers and newborns, birth certificates, and infant death records, for 74 hospitals from Washington state source files. STUDY DESIGN: Institutions were classified into four peer groups based on presence of neonatal intensive care units, number of births, and rural/urban location. Twenty-three clinical indicators of procedure (e.g., cesarean section) and complication rates were analyzed and presented. METHODS: For each indicator, observed and expected rates (adjusted within peer group for categorized baseline risk factors) were calculated and presented by institution. Effective graphic and numeric techniques for presenting significant deviations from expected rates were developed. Results were calculated in terms of numbers of events as well as rates. Approaches applicable to institutions with small numbers of deliveries were selected. PRINCIPAL FINDINGS: Exact confidence intervals (C.I.s) for event rates were superior to binomial or Poisson approximations for small hospitals. For calculating expected rates, indirect adjustment was used due to small numbers within risk factor categories. For all indicators, observed and expected rates along with 95 percent C.I.s for the true rate were presented graphically by institution for each peer group. Transforming C.I.s into "statistically acceptable ranges" allowed hospital personnel to assess their performance in terms of actual numbers of events as well as rates. CONCLUSIONS: Readily available statistical methods and straightforward descriptive approaches allow accurate presentation of outcomes for both large and small institutions.
OBJECTIVE: To develop an effective, concise presentation of hospital-specific birth event and delivery-related complication rates, including significant deviations from expected rates calculated using risk-adjusted peer hospital data, for distribution to all Washington State hospitals with delivery services. DATA SOURCES: Complete data for calendar year 1993, including inpatient discharge records for mothers and newborns, birth certificates, and infant death records, for 74 hospitals from Washington state source files. STUDY DESIGN: Institutions were classified into four peer groups based on presence of neonatal intensive care units, number of births, and rural/urban location. Twenty-three clinical indicators of procedure (e.g., cesarean section) and complication rates were analyzed and presented. METHODS: For each indicator, observed and expected rates (adjusted within peer group for categorized baseline risk factors) were calculated and presented by institution. Effective graphic and numeric techniques for presenting significant deviations from expected rates were developed. Results were calculated in terms of numbers of events as well as rates. Approaches applicable to institutions with small numbers of deliveries were selected. PRINCIPAL FINDINGS: Exact confidence intervals (C.I.s) for event rates were superior to binomial or Poisson approximations for small hospitals. For calculating expected rates, indirect adjustment was used due to small numbers within risk factor categories. For all indicators, observed and expected rates along with 95 percent C.I.s for the true rate were presented graphically by institution for each peer group. Transforming C.I.s into "statistically acceptable ranges" allowed hospital personnel to assess their performance in terms of actual numbers of events as well as rates. CONCLUSIONS: Readily available statistical methods and straightforward descriptive approaches allow accurate presentation of outcomes for both large and small institutions.
Authors: Margaret M Byrne; Christina N Daw; Harlan A Nelson; Tracy H Urech; Kenneth Pietz; Laura A Petersen Journal: Health Serv Res Date: 2008-11-04 Impact factor: 3.402