Excess Number of In-hospital Deaths Associated with Fractured Neck of Femur
- 1 Basic Facts
- 2 Brief overview of the measure
- 3 Application and interpretation of the measure
- 4 Calculation of the measure
- 5 Appraisal of the measure
- 6 Other items
- 7 Owner details
Stage of development
Potential or current usage
This measure is used as the basis of calculating an unequivocal measure of harm associated with in-hospital falls at a national level. It is part of the Quality and Safety Marker set and should be used alongside its related process and outcome markers
Brief overview of the measure
This measure was selected to provide baseline information about the excess number of in-hospital deaths associated with in hospital fractured neck of femur, enabling the calculation of changes over time in this number.
Rationale for selection
Fractured neck of femur in older people is associated with increased mortality, loss of independence and likelihood of longer hospital stays.
Type of measure
Domain(s) of quality
Application and interpretation of the measure
Stated intent of the measure
To measure the number of excess in-hospital deaths associated with in hospital fractured neck of femur in order to assess the impact of healthcare quality and safety initiatives in New Zealand.
Caveats - Considerations
The caveats of this measure are the relatively small number of fractured necks of femur, and reliance on NMDS data. There are too few events to present meaningful data at the local level using standard approaches. However, the use of statistical control charts, with a Poisson distribution, is being explored for presenting local-level data. As we are reliant on NMDS data we are dependent on good recording. While there is a general view that not all in hospital falls are recorded, there is greater confidence about consistent recording of in-hospital FNOFs. As part of testing this has been compared with those reported under the Serious and Sentinel Event reporting programme and there is a reasonable degree of
Links to other measures
This measure is part of the Quality and Safety Marker for falls set and should be used alongside its related process and outcome markers
Level of health care delivery/setting
This measure is focused on falls inside inpatient healthcare facilities. It excludes aged care facilities
This measure focuses on the total population.
Stratification by vulnerable populations
In order to adjust for risks associated with in hospital fall, the calculation of the excess number of in-hospital deaths and length of stay requires the stratification of data by age, sex, admission type and diagnostic related group.
Possible sources of bias or confounding
There can be wide variation in hospital stay outcomes by admission type, the type of healthcare received, and by patient factors such as co-morbidities. These are referred to as risks, and need to be adjusted for when assessing postoperative harm. Statistical methods such as regression modeling are commonly used to risk-adjust estimates of adverse healthcare outcomes. However, these methods can produce results that are negatively influenced by small numerator counts, lack of clinical data, and misclassification of the adverse outcomes associated with healthcare. An approach more recently used is to match individual cases and controls by these risk adjustment variables. In the analyses described here, confounding has been controlled for by grouping (stratifying) observed and expected data by these risk adjustment variables and then matching these observed and expected grouped data to estimate the excess number of in-hospital deaths. Regression modeling maybe used in future analyses once larger count data are available.
Calculation of the measure
Output of calculation
The output of this calculation is the excess number of in-hospital deaths associated with in hospital falls with a fractured neck of femur. The calculation of this measure is described below.
This measure uses the numerator data assembled to numbers of in hospital falls with a fractured neck of femur (see the specifications for numbers of in hospital falls with a fractured neck of femur).
This was calculated by using the formula E = o – e where E is the excess number of in-hospital deaths associated with in-hospital fall with FNOF, o is the observed number of in-hospital deaths in the surgical patient group with in-hospital fall with FNOF, and e is the number of in-hospital deaths for the in-hospital fall with FNOF group. A number of steps were taken to calculate the excess number of in-hospital deaths. First, discharge data for the in-hospital fall with FNOF group (i.e. numerator data) were grouped by age, sex, admission type and diagnostic related group (DRG) variables. Then the number of events whose type of discharge was death was counted as well as the total number of discharges for each stratified group. Second, discharge data for non-in-hospital fall with FNOF group (i.e. denominator data) were grouped by age, sex, admission type and DRG variables. The proportion was calculated of those with a death discharge in each stratified group compared to the total number in each stratified group. Third, the data for the in-hospital fall with FNOF group were matched by age, sex, admission type and DRG to those surgical patients without in-hospital fall with FNOF. Then the total number of discharges for the in-hospital fall with FNOF group for each combination of age, sex, admission type and DRG was multiplied by the proportion of those who died in the group without in-hospital fall with FNOF. This calculation gave the number of in-hospital deaths expected for the in-hospital fall with FNOF group. Fourth, the excess number of in-hospital deaths was calculated as the difference between the total number of observed in-hospital deaths and the total number of expected in-hospital deaths.
12 months – set for financial year in baseline
National minimum dataset (NMDS), available from the Ministry of Health.
Method of extraction
NMDS data were imported into Microsoft Access 2010. Queries were developed separately for extracting denominator data and numerator data. Calculations were undertaken in Microsoft Excel 2010. This approach could be adapted to be undertaken more easily through SAS.
Appraisal of the measure
Availability of evidence to support application of the measure
Measure is formulated on and underpinned by an evidence based clinical practice guideline.
Evidence of feasibility and reliability of implementation
Reliability - The measure has been demonstrated to be reliable (i.e. free from random error)., Interpretation - The measure allows unambiguous interpretation of better or worse performance., Data extraction - Data collection specifications for the measure are well defined., Data sources - Required data elements for the measure can be obtained from existing data sources., IT software - Existing IT software is sufficient for data collection., Validity - The measure has been demonstrated to be valid (i.e. it measures what it purports to).
Date of entry to library
Owner (Organisation name)
Health Quality and Safety Commission
Owner (Email contact)
Creator (Organisation name)
Health Quality and Safety Commission