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Comparison of Two Clinical Case Definitions in Detecting Overweight and Obesity Among Registered Nurses in a District Specialist Hospital

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Comparison of Two Clinical Case Definitions in Detecting Overweight and Obesity Among Registered Nurses in a District Specialist Hospital Comparison of Two Clinical Case Definitions in Detecting Overweight and Obesity Among Registered Nurses in A District Specialist Hospital Members: Teh Pei Nee1 Chiew Shoen Chuen2 Sheila Gopal Krishnan3 Yap Ee Lee4 Fauziah Yusof5 Rasidah Abdul Manan5 Mathavi Santhrasegaran1 Roszimah bt Ismail6 Hazira Abdul Kadir7 1Staff Nurse, Special Care Nursery, Hospital Seri Manjung 2Pharmacist, Clinical Research Centre, Hospital Seri Manjung 3Head of Paediatric Department, Hospital Seri Manjung 4Nursing Sister, Paediatric Ward, Hospital Seri Manjung 5Staff Nurse, Paediatric Ward, Hospital Seri Manjung 6Staff Nurse, Intensive Care Unit, Hospital Seri Manjung 7Staff Nurse, Psychiatric Clinic, Hospital Seri Manjung NMRR-16-766-28807 1 AREA LAND : 1,168km² POPULATION : 247,603 ( 2015 ) 2 The Manjung District is a district in the southwestern part of Perak state, Malaysia. The district is well known for Pangkor Island, a major attraction in MANJUNG Perak and the home of the Royal Malaysian Navy (TLDM) Lumut Naval Base and dockyard. Bandar Seri Manjung is the district's principal urban center while smaller towns include Lumut , Sitiawan, Ayer Tawar, Pantai Remis and Beruas. 3 HOSPITAL SERI MANJUNG 305 beds NAVI HOSPITAL 1 HOSPITAL DESA PANGKOR 1 PRIVATE HOSPITAL 2 GOVERMENT HEALTH CLINIC 31 PRIVATE HEALTH CLINIC 72 5 Departments Units • Medical • Haemodialysis Unit • Surgical • ICU / CCU • Orthopaedic • Physiotherapy Unit • Ophthalmology • Occupational • Emergency & Traumatology Rehabilitation • Paediatrics • Sterile Equipment Supply • Obstetrics & Gynaecology Unit • Psychiatry & Mental Health • Health Education Unit • Diagnostic & Imaging • Medical Social Work Unit • Pharmacy & Supply • Counselling Psychology • Pathology Department Unit • Dietetics & Catering • Quality Unit / Innovation & CRC(Clinical Research Centre) 6 7 Health burden 8 BMI = weight (kg) height (m2) Overweight and obesity classification Category IBMI (kg/m2) ABMI (kg/m2 ) Underweight <18.50 <18.50 Normal 18.50-24.99 18.50-22.99 Overweight 25.00-29.99 23.00-27.49 Obese ≥ 30.00 ≥ 27.50 Source: WHO 2004¹, CPG on Management of Obesity 2004². 9 3) There has been some contention whether the generalisation of the IBMI (International Body Mass Index) to the Asian population will underestimate the prevalence of overweight and obesity. 4) In year 2004, WHO was recommended additional BMI cut-off points for Asian populations for public health. (≥23 kg/m2 as increased risk and ≥27.5 kg/m2 as high risk). 10 5) BMI cut-off points have been revised to suit Asian population due to: (i) high prevalence of Type 2 Diabetes Mellitus among Asian individuals with BMI < 25.0kg/m2, (ii) higher cardiovascular risk factors among Asian individuals at any BMI level, and (iii) population based association between BMI, body fat percentage and distribution. 11 Lamon-Fava S et al, 1996 Prevalence of overweight and obesity is Hossain, 2007 highest in developing countries and is Bhurosy et al , 2014 associated with increase in incidence of cardiovascular disease. Deuremberg-Yap M et The BMI recommendation for public health was al, 2001 less than IBMI classification. (Singapore) (≥23 kg/m2 as increased risk and ≥27.5 kg/m2 as high risk) Feng R N, et al 2012 The optimal BMI for men and women to predict (China) co-morbidities was less than IBMI classification. ( 24 kg/m2 ) Ren Q, et al 2016 The optimal BMI for men and women to predict (China) Hypertension was less than IBMI classification (23.53kg/m2 and 24.25kg/m2). Tanu, et al 2014 The optimal BMI to predict Hypertension was (India) ≥24.5kg/m2 (men) and ≥24.9kg/m2( women). 12 Bogossian FE et al, The prevalence of overweight and obesity among 2012 nurses and midwives were higher compared to the general population in Australia, New Zealand and UK. Miller SK et al, The prevalence of overweight, obesity and 2008 morbidly obesity among American nurses were 30%, 18.7% and 5.2% respectively. Ogunjimi LO et al, The prevalence of obesity among Nigerian nurses 2010 62.6%. 13 Malaysian National Health Increase of obesity prevalence from and Morbidity Survey 14 % (2006) to 15.1% (2011) (MNHMS) in local population aged above 18 years 2006, 2011 Malaysian National Health IBMI classification and Morbidity Survey, - overweight: 30.0% and obesity : 17.7% 2015 ABMI classification – overweight: 33.4% and obesity: 30.6%. WHO- Non Communicable • Among obese population, female Malaysians Disease Profile in Malaysia, were more affected than the male 2012 counterparts Coomarasamy JD et al, The prevalence of overweight and obesity 2014 among female nurses in Malaysia were 33.5% and 17.1% respectively. 14 This study endeavours to answer whether by using IBMI classification among Asian population would lead to a significant proportion of the overweight individuals going below the radar. Hypothesis : Are we missing a significant number of overweight nurses with associated comorbidities by using IBMI criteria? 15 General Objective To compare the prevalence of overweight and obesity based on IBMI and ABMI among female registered nurses. Specific Objectives 1)To compare the prevalence of cardiovascular (CV) related co- morbidities among those who were overweight and obese according to both definitions. 2) To determine the factors associated with overweight and obesity in the study population. 16 Cross-sectional Study Hospital Seri Manjung Nurses in all departments September - October 2016 Sample size : 384 (minimum) - Stratified random sampling (working schedule) - A random number list was generated by using Epical 2000 software. - Proportions were set at 50.6% and precision at 5% (45.6- 55.6%). MREC approved 17 Inclusion criteria : All female registered nurses in HSM Exclusion criteria : Pregnant, on confinement / paid / unpaid leave, refuse to consent 18 Data collection : Demography, health, work environment, dietary, physical activity were collected via interview by trained researchers by using questionnaire. Adapted from Canadian National Survey of the Work and Health of Nurses¹², 2005 Data analysis : Prevalence of outcome was presented as % Sensitivity & specificity of both definitions in predicting CV- related co-morbidities were calculated Associating factors were analysed using multiple logistic regression MREC approved 19 Consent taking process Measuring of height and weight as well as BMI calculation Interviewing the respondent by using questionnaire 20 Nurses who fulfilled the inclusion criteria were given Respondent Information Sheet. Researchers explained to respondents about the study. Respondents were given sufficient time to understand, ask questions and consider before deciding on their participation. All respondents were asked to sign 2 sets of informed consent form. Figure 2: Information sheet & Consent taking process 21 22 Result 23 Table 1: Characteristics of Respondents* total respondents = 393 Characteristics n (%) Demographic Data Age in years, median (quartiles) 36 (32-41) Ethnicity Malay 361 (91.9%) Chinese 3 (0.8%) Indian 23 (5.9%) Others 6 (1.5 %) Marital Status Single 9 (2.3%) Married 378 (96.2%) Divorced 2 (0.5%) 4 (1.0%) Widow Body Mass Index (BMI) BMI in kg/m2, median (quartiles) 26.30 (23.63-30.13) Weight satisfaction Satisfied 118 (30.0%) Not satisfied 275 (70.0%) 24 IBMI ABMI No. & % of No. & % of Category respondents in CVD Category respondents in CVD each category1 each category1 (n, %)2 (n, %)2 Overweight Overweight 146 (37.2%) 21 (14.4%) 136 (34.6%) 14 (10.3%) (25-29.9kg/m2) (23-27.49kg/m2) Obese Obese 102 (26.0%) 25 (24.5%) 172 (43.8%) 35 (20.3%) (≥ 30kg/m2) (≥ 27.5kg/m2) 1: The denominator was total respondents (393) 2: The denominator was respondents in the particular BMI category CVD = cardiovascular disease 25 Table 3: Sensitivity and specificity of IBMI and ABMI (overweight + obesity) definitions in prediction of CV-related co-morbidities CVD (%) Sensitivity Specificity PPV NPV IBMI definition 85.2 40.4 18.5 94.5 ABMI definition 90.7 23.6 15.9 94.1 In predicting CV-related comorbidities, IBMI was slightly less sensitive [85.2% (95% CI : 72.34; 92.95)] than ABMI [90.7% (95% CI: 78.89; 96.52)] but more specific [40.4% (95% CI: 35.17; 45.85)] than ABMI [23.6% (95% CI: 19.26; 28.56)]. 26 The risk of overweight or obese will be double in 10 years time. (OR=1.83; 95%CI: 1.24; 2.70, p=0.002). Married nurses were 13 times more likely to be overweight or obese (OR=13.11; 95%CI 2.44; 70.63, p=0.003) than single nurses. Nurses who adhered to food pyramid less than 50% of the time were 2 times more likely to be overweight or obese (OR=2.41, 95%CI: 1.33; 4.35, p=0.004) compared to nurses who were adherent. 27 1a. Prevalence of Overweight & Obesity among Nurses Country % of % of overweight obesity IBMI 1. Ogunjimi LO et al, 2010 Nigeria - 62.6% 2. Kim MJ et al, 2013 Korea 18.6% 7.4% SLIM 3. Miller SK et al , 2007 U.S. 30% 23.9% 4. Coomarasamy JD et al, Malaysia 33.5% 17.1% 2014 Current study , 2016 Malaysia 37.2% 26.0% ABMI 1. Aryee PA et al , 2013 Ghana 18.2 15.5 Current study , 2016 Malaysia 34.6% 43.8% 28 1b. Comparison with GENERAL POPULATION Subjects % of overweight % of obesity IBMI 1. NHMS 2015 General 30.0% 17.7% Population 2. Current study , 2016 Nurses 37.2% 26.0% ABMI 1. NHMS 2015 General 33.4% 30.6% Population 2. Current study , 2016 Nurses 34.6% 43.8% NHMS: National Health Morbidity Survey⁷ 2015 29 Both ABMI & IBMI definitions had good sensitivity (90.7% vs 85.2%) but IBMI had much higher specificity (40.4%) than ABMI (23.6%). However, IBMI is still a good tool to be used and we need a larger scale study to support the utilization of ABMI in Malaysian population. 30 Studies Factors van Drongelen A et al¹⁴, 2011 shift work (Systematic Review) Kim MJ et al¹³, 2013 shift work Smith P et al¹⁵, 2013 shift work increasing age, male, pre- Bogossian FE et al¹º, 2012 menopause as well as menopause Ogunjimi LO et al⁹, 2010 Eating habit and being married Age, being married & compliance to Current study, 2016 food pyramid 31 The co-morbidities, food pyramid adherence and intensity of physical activities were self-reported.
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