Health utility after emergency medical admission: a cross-sectional survey.
OBJECTIVE
Health utility combines health related quality of life and mortality to produce a generic outcome measure reflecting both morbidity and mortality. It has not been widely used as an outcome measure in evaluations of emergency care and little is known about the feasibility of measurement, typical values obtained or baseline factors that predict health utility. We aimed to measure health utility after emergency medical admission, to compare health utility to age, gender and regional population norms, and identify independent predictors of health utility.


METHODS
We selected 5760 patients across three hospitals who were admitted to hospital by ambulance as a medical emergency. The EQ-5D questionnaire was mailed to all who were still alive 30 days after admission. Health utility was estimated by applying tariff values to the EQ-5D responses or imputing a value of zero for those who had died. Multivariable analysis was used to identify independent predictors of health utility at 30 days.


RESULTS
Responses were received from 2488 (47.7%) patients, while 541 (9.4%) had died. Most respondents reported some or severe problems with each aspect of health. Mean health utility was 0.49 (standard deviation 0.35) in survivors and 0.45 (0.36) including non-survivors. Some 75% had health utility below their expected value (mean loss 0.32, 95% confidence interval 0.31 to 0.33) and 11% had health utility below zero (worse than death). On multivariable modelling, reduced health utility was associated with increased age and lower GCS, varied according to ICD10 code and was lower among females, patients with recent hospital admission, steroid therapy, or history of chronic respiratory disease, malignancy, diabetes or epilepsy.


CONCLUSIONS
Health utility can be measured after emergency medical admission, although responder bias may be significant. Health utility after emergency medical admission is poor compared to population norms. We have identified independent predictors or health utility that need to be measured and taken into account in non-randomized evaluations of emergency care.
School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK. s.goodacre@sheffield.ac.uk
Steve W
SW
Goodacre
Richard W
RW
Wilson
Mike
M
Bradburn
Martina
M
Santarelli
Jon P
JP
Nicholl
D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D002423:Cause of Death; D016638:Critical Illness / Q000401:mortality* / Q000628:therapy; D003430:Cross-Sectional Studies; D004632:Emergency Medical Services / Q000706:statistics & numerical data*; D004638:Emergency Treatment / Q000401:mortality* / Q000706:statistics & numerical data; D005260:Female; D006305:Health Status Indicators; D017052:Hospital Mortality / Q000639:trends*; D006760:Hospitalization / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D011788:Quality of Life; D018570:Risk Assessment; D012737:Sex Factors; D011795:Surveys and Questionnaires; D017741:Survivors; D006113:United Kingdom; D055815:Young Adult