RT Journal Article
SR Electronic
T1 The Impact of Sociodemographic Factors and Emergency Admissions on the Place of Death of Gynecological Cancer Patients in England: An Analysis of a National Mortality–Hospital Episode Statistics–Linked Data Set
JF International Journal of Gynecologic Cancer
JO Int J Gynecol Cancer
FD BMJ Publishing Group Ltd
SP 1714
OP 1721
DO 10.1097/IGC.0000000000001373
VO 28
IS 9
A1 Sonali Kaushik
A1 Luke Hounsome
A1 Catherine Blinman
A1 Robert Gornall
A1 Julia Verne
YR 2018
UL http://ijgc.bmj.com/content/28/9/1714.abstract
AB Objective The aim of this study was to develop a predictive model for risk of death in hospital for gynecological cancer patients specifically examining the impact of sociodemographic factors and emergency admissions to inform patient choice in place of death.Methods The model was based on data from 71,269 women with gynecological cancer as underlying cause of death in England, January 1, 2000, to July 1, 2012, in a national Hospital Episode Statistics–Office for National Statistics database. Two thousand eight hundred eight deaths were used for validation of the model. Logistic regression identified independent predictors of a hospital death: adjusting for year of death, age group, income deprivation quintile, Strategic Health Authority, gynecological cancer site, and number of elective and emergency hospital admissions and respective total durations of stay.Results Forty-three percent of deaths from gynecological cancer occurred in hospital. The variables significantly predicting death in hospital were less recent year of death (odds ratio [OR], 0.93; P < 0.001), increasing age (OR, 1.17; P < 0.001), increasing deprivation (OR, 1.06; P < 0. 001), increasing frequency and length of elective and emergency admissions (P < 0.001). The model correctly identified 73% of hospital deaths with a sensitivity of 75% and a specificity of 72%. The areas under the receiver operating curve were 0.78 for the predictive model and 0.71 for the validation data set. Each subsequent emergency admission in the last month of life increased the odds of death in hospital by 2.4 times (OR, 2.38; P < 0.001). Hospital deaths were significantly lower in all other regions compared with London. The model predicted a 16% reduction of deaths in hospital if 50% of emergency hospital admissions in the last month of life could be avoided by better community care.Conclusions Our findings could enable identification of patients at risk of dying in hospital to ensure greater patient choice for place of death.