Standardized mortality rates are routinely used as measures of hospital performance and quality. Such metrics may, however, be biased if hospital admission thresholds differ and patient severity is not fully measured.
To examine whether comparisons of hospital mortality rates suffer from selection bias due to variations in hospital admission rates, using the example of variations by day of the week.
12,900,687 emergency department attendances and 3,418,446 unplanned admissions to all acute non-specialist hospitals of the National Health Service in England between 1 April 2013 and 28 February 2014.
Population-based retrospective cohort study. Mortality within 30 days of attendance is modelled as a function of weekend or weekday attendance and hospital-level predictors of admission rates using patient-level risk-adjusted probit and bivariate Heckman selection models. Robustness is supported by the use of different hospital-level predictors.
When examining only the admitted population, patients admitted to hospital at weekends have a 0.206 percentage point higher risk of death within 30 days compared to patients admitted during the week. However, patients attending emergency departments at weekends have a 1.390 percentage point lower probability of being admitted to hospital. Once this selection bias is accounted for, the weekend effect in mortality is reduced by two-thirds to a 0.068 percentage point increase in the risk of death.
Comparisons of standardized hospital mortality rates following unplanned admissions can be biased by variations in emergency department admission rates, leading to incorrect conclusions about quality. The use of mortality as a performance measure could therefore lead to misleading comparisons if admission rates vary and illness severity is not fully controlled for. Accounting for sample selection bias and dependence between admission and mortality rates is vital if accurate comparisons of hospital performance are to be made.

UNASSIGNED: WHO uses anthropometric classification scheme of childhood acute and chronic malnutrition based on low body mass index (BMI) (\'wasting\') and height for age (\'stunting\'), respectively. The goal of this study was to describe a novel two-axis nutritional classification scheme to (1) characterise nutritional profiles in children undergoing abdominal surgery and (2) characterise relationships between preoperative nutritional status and postoperative morbidity.
UNASSIGNED: This was a retrospective observational cohort study.
UNASSIGNED: The setting was 50 hospitals caring for children in North America that participated in the American College of Surgeons National Surgical Quality Improvement Program Paediatric from 2011 to 2013.
UNASSIGNED: Children >28 days who underwent major abdominal operations were identified.
UNASSIGNED: The cohort of children was divided into five nutritional profile groups based on both BMI and height for age Z-scores: (1) underweight/short, (2) underweight/tall, (3) overweight/short, (4) overweight/tall and (5) non-outliers (controls).
UNASSIGNED: Multiple variable logistic regressions were used to quantify the association between 30-day morbidity and nutritional profile groups while adjusting for procedure case mix, age and American Society of Anaesthesiologists class.
UNASSIGNED: A total of 39 520 cases distributed as follows: underweight/short (656, 2.2%); underweight/tall (252, 0.8%); overweight/short (733, 2.4%) and overweight/tall (1534, 5.1%). Regression analyses revealed increased adjusted odds of composite morbidity (35%) and reintervention events (75%) in the underweight/short group, while overweight/short patients had increased adjusted odds of composite morbidity and healthcare-associated infections (43%), and reintervention events (79%) compared with controls.
UNASSIGNED: Stratification of preoperative nutritional status using a scheme incorporating both BMI and height for age is feasible. Further research is needed to validate this nutritional risk classification scheme for other surgical procedures in children.