Evidence map›Paper›PMID 35180859›Full record

ArticleBMC health services research2022

Hospital performance comparison of inpatient fall rates; the impact of risk adjusting for patient-related factors: a multicentre cross-sectional survey.

Niklaus S Bernet, Irma Hj Everink, Jos Mga Schols, Ruud Jg Halfens, Dirk Richter, Sabine Hahn

Abstract readMulticenter Study
In one paragraph

Article in BMC health services research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Niklaus S BernetSchool of Health Professions, Applied Research & Development in Nursing, Bern University of Applied Sciences, Murtenstrasse 10, 3008, Bern, Switzerland. niklausstefan.bernet@bfh.ch.
Irma Hj EverinkDepartment of Health Services Research, Care and Public Health Research Institute, Maastricht University, PO BOX 616, MD, 6200, Maastricht, The Netherlands.
Jos Mga ScholsDepartment of Health Services Research, Care and Public Health Research Institute, Maastricht University, PO BOX 616, MD, 6200, Maastricht, The Netherlands.
Ruud Jg HalfensDepartment of Health Services Research, Care and Public Health Research Institute, Maastricht University, PO BOX 616, MD, 6200, Maastricht, The Netherlands.
Dirk RichterSchool of Health Professions, Applied Research & Development in Nursing, Bern University of Applied Sciences, Murtenstrasse 10, 3008, Bern, Switzerland.
Sabine HahnSchool of Health Professions, Applied Research & Development in Nursing, Bern University of Applied Sciences, Murtenstrasse 10, 3008, Bern, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundComparing inpatient fall rates can serve as a benchmark for quality improvement. To improve the comparability of performance between hospitals, adjustments for patient-related fall risk factors that are not modifiable by care are recommended. Thereafter, the remaining variability in risk-adjusted fall rates can be attributed to differences in quality of care provided by a hospital. Research on risk-adjusted fall rates and their impact on hospital comparisons is currently sparse. Therefore, the aims of this study were to develop an inpatient fall risk adjustment model based on patient-related fall risk factors, and to analyse the impact of applying this model on comparisons of inpatient fall rates in acute care hospitals in Switzerland.

methodsData on inpatient falls in Swiss acute care hospitals were collected on one day in 2017, 2018 and 2019, as part of an annual multicentre cross-sectional survey. After excluding maternity and outpatient wards, all inpatients older than 18 years were included. Two-level logistic regression models were used to construct unadjusted and risk-adjusted caterpillar plots to compare inter-hospital variability in inpatient fall rates.

resultsOne hundred thirty eight hospitals and 35,998 patients were included in the analysis. Risk adjustment showed that the following factors were associated with a higher risk of falling: increasing care dependency (to a great extent care dependent, odds ratio 3.43, 95% confidence interval 2.78-4.23), a fall in the last 12 months (OR 2.14, CI 1.89-2.42), the intake of sedative and or psychotropic medications (OR 1.74, CI 1.54-1.98), mental and behavioural disorders (OR 1.55, CI 1.36-1.77) and higher age (OR 1.01, CI 1.01-1.02). With odds ratios between 1.26 and 0.67, eight further ICD-10 diagnosis groups were included. Female sex (OR 0.78, CI 0.70-0.88) and postoperative patients (OR 0.83, CI 0.73-0.95) were associated with a lower risk of falling. Unadjusted caterpillar plots identified 20 low- and 3 high-performing hospitals. After risk adjustment, 2 low-performing hospitals remained.

conclusionsRisk adjustment of inpatient fall rates could reduce misclassification of hospital performance and enables a fairer basis for decision-making and quality improvement measures. Patient-related fall risk factors such as care dependency, history of falls and cognitive impairment should be routinely assessed.

Indexed as

HospitalsInpatientsCross-Sectional StudiesFemaleHumansPregnancyRisk AdjustmentRisk FactorsAccidental FallsBenchmarkingCross-Sectional StudiesHospitalsInpatientsQuality ImprovementRisk AdjustmentRisk Factors

Identifiers

PMID35180859
PMCPMC8857794

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.