Diagnosis of obesity and clinical implications

Grant, Paul; Lipscomb, David

doi:10.1002/pdi.1313

Cited by 1 publication

(2 citation statements)

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“…16 In this study obesity is defined as a BMI equal to, or more than 30 kg/m,² which is in agreement with previous literature examining bariatric coding and ACCD definitions of obesity. 5,11,13,18 The World Health Organization (WHO) defines the BMI calculation methodology as a person's weight in kilograms divided by the square of the person's height in metres (kg/m²). 17 The study was approved by the Edith Cowan University (ECU) Human Research Ethics Committee, the WACHS Human Research Ethics Committee, the WACHS Research Governance Office and the WACHS Chief Executive.…”

Section: Methods Design and Settingmentioning

confidence: 99%

“…The high prevalence of obesity within the Australian community is well documented, with the Australian Bureau of Statistics' (ABS) National Health Survey 2017-18 revealing that 31.3% of Australians aged 18 years and over were obese, 1 and 35.6% were overweight. Consistent increases in the prevalence of obesity rates in Australia have been recorded since the ABS commenced collecting obesity data in 1995, from 18.7% in 1995, 24.4% in 2007-08 to 31.3% in 2017-18. [1][2][3] More disturbingly, Australian obesity projections predict that in 2035, 42% of the population will be obese.…”

Section: Introductionmentioning

confidence: 91%

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An audit of obesity data and concordance with diagnostic coding for patients admitted to Western Australian Country Health Service hospitals

McClean¹,

Cross²,

Reed³

2021

AJAN

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Objective: Accurate patient obesity data can be used to identify and mitigate patient manual handling risks to healthcare staff. This study investigates the accuracy of patient obesity data within the Western Australian Country Health Service (WACHS) and examines factors potentially affecting obesity data accuracy.Background: Risk of injuries to healthcare staff are increasing due to rising patient obesity. Consistent increases in the prevalence of obesity in Australia have been recorded since 1995 and Australian obesity projections predict that 42% of the population will be obese in 2035. To manage the increased risks of injuries to healthcare workers due to obese patient management, accurate healthcare data relating to patient obesity is required.Design: Researchers examined records of patients admitted to WACHS hospitals with Type II Diabetes, which has confirmed links with obesity. Manual data extraction and comparison of obesity related data within patient medical records and electronic patient admission data was conducted to determine accuracy.Results: Analysis of the patient data examination demonstrated poor recording of weight (67%), height (24%) and Body Mass Index (BMI) when weight and height measurements were recorded (10%). Poor obesity data accuracy was also determined by low sensitivity results (40%), high false negative results (60%) and a Cohen's kappa value of 0.44. Discussion:The sensitivity result demonstrates that only 40% of obese patients were coded as obese when obesity is recorded in their medical files, and the false negative result demonstrates that where obesity notations were present in medical files, 60% of these cases were incorrectly coded as 'normal weighted' . There was only moderate agreement between the occurrences of coded obesity and the recorded obese patient notations in the medical files. Conclusion:Further research is required to inform enhancements to improve obesity recording and coding accuracy, which will increase the collection of reliable obesity data that could be used to reduce obese patient handling risks to nurses and other healthcare staff.

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