original article

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electronic-Perak Medical Journal Vol 2 No 1 August 2022

ORIGINAL ARTICLE

Comparison of Two Clinical Case Definitions in Detecting

Overweight and Obesity among Registered Nurses in a District Specialist Hospital

Pei-Nee Teh^1*, Shoen-Chuen Chiew², Sheila Gopal-Krishnan¹, Ee-Lee Yap³, Fauziah Yusof⁴, Rasidah Abdul-Manan¹, Mathavi Santhrasegaran¹, Roszimah Ismail⁵, Hazira Abdul Kadir⁶

1Paediatric Department, Hospital Seri Manjung, Ministry of Health MALAYSIA

2Clinical Research Centre, Hospital Seri Manjung, Ministry of Health MALAYSIA

3Medical Department, Hospital Seri Manjung, Ministry of Health MALAYSIA

4Paediatric Department, Hospital Sultanah Bahiyah, Ministry of Health MALAYSIA

5Intensive Care Unit, Hospital Seri Manjung, Ministry of Health MALAYSIA

6Blood Taking Unit, Hospital Seri Manjung, Ministry of Health MALAYSIA

i.e. (i) ≥23 kg/m² as increased risk; and (ii) ≥27.5 kg/m² as high risk⁽⁶⁾. A review has shown that Asians had higher cardiovascular risk factors than the western population regardless of BMI value⁽⁷⁾.

Malaysian National Health and Morbidity Survey (NHMS) 2015⁽⁸⁾ revealed that 30.6% of local population aged above 18 years was obese and the trend increased as compared to 26.2% in 2006 and 27.2% in 2011⁽⁹⁾. The BMI cut-off point of obesity for the above statistics was 27.5kg/m². If Malaysians follow the additional BMI cut-off points recommended by WHO expert consultants for Asian populations⁽⁶⁾ and not the International BMI (IBMI) definition to determine the prevalence of overweight and obesity, potential public health action can be taken in identifying warning signs of those chronic diseases or comorbidities.

INTRODUCTION

Obesity has become a major health crisis in both developed and less developed countries over the past few decades.

Prevalence of overweight and obesity is highest in developing countries⁽¹⁾ and is associated with increase in incidence of cardiovascular disease (CVD)⁽²⁾. In addition, evidence has shown that overweight and obesity increased the risk of Type II Diabetes Mellitus and cancer^(3,4). Obesity is also associated with hypercholesterolaemia, a major risk factor for stroke, heart disease and diabetes⁽⁵⁾.

Studies have shown that the Asian population has different body mass index (BMI) association with body fat percentage and higher health risk than Europeans⁽⁶⁾. In 2004, WHO expert consultants have recommended additional BMI cut- off points for Asian populations for public health, discompopu

Keywords: body max index, overweight, obesity, nurses, comorbidity

Citation: Teh PN, Chiew SC, Gopal- Krishnan S, et al. Comparison of two clinical case definitions in detecting overweight and obesity among registered nurses in a district specialist hospital. e-PMJ, Volume 2(1), 2022

*Correspondence to:

Pei-Nee Teh, BSc of Nursing Degree, [email protected]

Received: 16 July 2019 Accepted: 18 February 2020

Copyright: © 2022 Teh et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Competing interest: The authors have declared that no competing interest exist.

ABSTRACT

INTRODUCTION & OBJECTIVES

Generalization of International Body Mass Index classification (IBMI) to Asians may underestimate the prevalence of overweight and obesity. This study aimed to compare the prevalence of overweight and obesity using IBMI and As ian Body Mass Index (ABMI) among female nurses; compare the prevalence of cardiovascular (CV)-related co-morbidities among overweight and obese individuals using both definitions and determine the factors associated with overweight and obesity.

METHODOLOGY

A cross-sectional study was conducted among female nurses in Hospital Seri Manjung (HSM) in 2016.

The sample size required was 384. Pregnant nurses were excluded. Demography, health, work environment, diet and physical activity were collected via structured interview. Height and weight were measured. Sensitivity and specificity of both definitions in predicting CV-related co-morbidities were calculated. Factors associated with overweight and obesity were analysed via multiple logistic regression, using ABMI classification.

RESULTS

We interviewed 393 nurses. The prevalence of overweight was similar between IBMI 37.2% and ABMI 34.6%. The prevalence of obesity was higher using ABMI (43.8% vs. 26.0%). Using both definitions, the prevalence of CV-related comorbidities among overweight (IBMI 14.4% vs. ABMI 10.3%) and obese (IBMI 24.5% vs. ABMI 20.3%) nurses were similar. In predicting CV-related co-morbidities, IBMI had slightly lower sensitivity (85.2%; 95% CI: 72.3, 92.9) than ABMI (90.7%; 95% CI: 78.9, 96.5). Nurses aged between 37-41 and 42-59 years old had triple odds of being overweight or obese compared to nurses aged between 24-32 years old (OR: 3.61; 95% CI: 1.59, 8.23; p=0.002 and OR: 3.21; 95% CI: 1.54, 6.72;

p=0.002). Married nurses had higher odds of being overweight or obese (OR: 10.64; 95% CI: 2.03, 55.64;

p=0.005) than single nurses. Nurses who adhered to food pyramid less than 50% of the time had higher odds (OR: 2.38, 95% CI: 1.31, 4.33; p=0.005) than adherent nurses.

CONCLUSION

The prevalence of overweight by both classifications was similar, but obesity prevalence was higher with ABMI. The prevalence of CV-related co-morbidities among overweight and obese nurses was similar using both classifications. Increasing age, being married, and lack of adherence to food pyramid were associated with overweight and obesity.

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and obesity by IBMI are 25kg/m² as overweight and 30kg/m² as obesity; and the cut-off point of ABMI are 23 kg/m² as overweight and 27.5 kg/m² as obesity⁽¹⁹⁾.

Cardiovascular (CV)-related comorbidity is the medical problem (illness) that is related to the cardiovascular system or may increase the risk of cardiovascular disease. For instance, hypertension (HTN), hypercholesterolaemia, Type 2 Diabetes Mellitus (T2DM), ischaemic heart disease (IHD), mitral valve replacement (MVR) and so on.

METHODOLOGY

This was a cross-sectional study conducted at HSM, a 305- bedded district hospital with multi-disciplinary specialties, located at central region of Perak, Malaysia. All female nurses working in HSM were invited for participation in the study. Female nurses who were pregnant, on confinement leave, study leave, unpaid leave and those who refused for consent were excluded.

A minimum sample size of 384 was required to achieve 5%

precision in estimating the prevalence of overweight and obesity among female nurses, which was 50.6% in a previous study⁽¹³⁾ (using EpiCalc 2000 software). For determining the factors associated overweight and obesity, 300 to 500 participants were sufficient⁽²⁰⁾. A list of all registered female nurses in HSM was obtained from the nursing administrative unit. Stratified random sampling was used. The stratification was based on working schedule. The working schedule was categorized as office hour, day-evening shift and day- evening-night shift. Random number list of 427 was generated using EpiCalc 2000 software, with the anticipation of 10% non-response rate. The invitation letters for nurses’

participation were sent to sisters of all departments and units. Written consent was taken before study participation.

Their height and weight were measured for BMI calculation.

A 30-minutes interview session between the trained investigator and the participant was conducted. The participants were interviewed using a questionnaire modified from Canadian National Survey of the Work and Health of Nurses, 2005⁽²¹⁾. Author’s permission for questionnaire adaptation was obtained. Demography data, anthropometry measurements of height and weight, health status, working environment, dietary intake pattern based on Food Pyramid⁽²²⁾ and physical activities⁽²³⁾ were collected.

The participants were asked about their medical problems in health status section. The interview took place from September to October 2016. Pre-test of the questionnaire was performed on pathology and pharmacy staffs to ensure feasibility of the questionnaire. The results of the pre-test were excluded from final analysis.

ETHICAL CONSIDERATION

Ethical approval was obtained from Medical Research and Ethics Committee (MREC) of Ministry of Health (MOH), Malaysia. All responses were confidential and respondents were allowed to refuse participation in the study. National Medical Research Register (NMRR) No.: NMRR-16-766- 28807.

Studies done locally and elsewhere have proven that overweight and obesity are major health problems and among the health professionals, the nurses are not spared from this global crisis. There are also implications that by using the IBMI classification among the Asian population may lead to a significant proportion of the overweight individuals going below the radar⁽⁶⁾.

Nurses serve as role models in health education for promoting healthy lifestyle and preventing diseases.

Unfortunately, nurses have been found to be overweight or obese in many countries. Studies done in Australia, New Zealand and UK have shown that the prevalence of obesity among nurses and midwives were higher than the general population⁽¹⁰⁾. Also, higher prevalence of overweight and obesity was found among American nurses compared to the general population⁽¹¹⁾. A total of 62.2% of Nigerian nurses were obese⁽¹²⁾. Half (50.6%) of female nurses were overweight or obese in a local study⁽¹³⁾.

These statistics indicate that nurses have higher risk for developing non-communicable diseases and will be a significant contributor to our increasing national morbidity and mortality statistics. As health educators, nurses have the responsibility to walk the talk. Healthy lifestyle and behaviour must become part and parcel of their professional lives. They must become a major mover in changing lifestyles and health related behaviours within the community. Studies conducted elsewhere have shown correlation between nurses working hours and BMI.

Evidence of association between working hour exposure and increased body weight was found in several studies^(14,15,16). Diet and physical activity contributed towards overweight and obesity⁽¹⁷⁾. There is evidence of interaction between dietary and physical activity to prevent obesity and diabetes⁽¹⁸⁾.

The remaining gap that is still present for the topic is: “Are we missing a significant number of registered nurses in Hospital Seri Manjung (HSM) who were overweight or obese with associated comorbidities but did not fulfil IBMI criteria?” This study would also be helpful in comparing sensitivity of two different clinical definitions in detecting overweight and obese nurses with cardiovascular (CV)- related co-morbidities.

This study aimed to (i) compare the prevalence of overweight and obesity based on IBMI and Asian BMI cut-off point (ABMI) among female registered nurses in HSM, (ii) compare the prevalence of CV-related comorbidities among overweight and obese individuals according to both definitions, and (iii) determine the factors associated with overweight and obesity, using BMI definition that resulted in a higher sensitivity for predicting CV-related comorbidities.

DEFINITION

Overweight and obesity are classified based on BMI. BMI is an index of weight-for-height and is calculated by the square of the height in metres (kg/m²). Both Both the IBMI and ABMI are used in this study. The cut-off point of overweight an

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Health Status Medical Problem

(without / with 1 or more comorbidities) No

HTN

Hypercholesterolaemia T2DM

IHD MVR Asthma OA

Hyperthyroidism Others

301 (76.6) 24 (6.1) 23 (5.9) 16 (4.1) 6 (1.5) 1 (0.3) 13 (3.3)

7 (1.8) 4 (1.0) 19 (4.8) Working Environment/Status

Working Schedule Office Hour Day + Evening Day + Evening + Night

87 (22.1) 14 (3.6) 292 (74.3) Frequency of Night Shift per Month

NA

1-2 times/month 3-4 times/month

More than 4 times/month 1 time every 2 months

101 (25.7) 151 (38.4) 122 (31.0) 7 (1.8) 12 (3.1) Dietary Intake & Pattern

Food Pyramid Awareness

Yes 393 (100.0)

Correct Labelling of Food Pyramid

Correct 156 (39.7)

Frequency of Food Pyramid Adherence Always

Sometimes Never

82 (20.9) 251 (63.9)

60 (15.3) Frequency of Meals & Snacks per Day

0-2 times 3 times

More than 3 times

304 (77.4) 55 (14.0)

34 (8.7) Eating Outside Food

Yes 384 (97.7)

Frequency of Eating Outside Food Once a month

Once a week 2-3 times a week

More than 3 times a week Twice a month

Once in 3 months 2-3 times a year NA

48 (12.2) 66 (16.8) 116 (29.5) 145 (36.9) 7 (1.8) 1 (0.3) 1 (0.3) 9 (2.3) Interval between Last Meal & Sleeping Time

Less than 1 hour 1-2 hours 2-3 hours 3 hours and more

7 (1.8) 115 (29.3)

1 (0.3) 270 (68.7) Physical Activity

Duration of MIPA per Week in minutes, median (IQR)

150 (235) Duration of VIPA per Week in minutes,

median (IQR)

15 (60) Remarks: T2DM-Type 2 Diabetes Mellitus, HTN-Hypertension, IHD- Ischaemic Heart Disease, MVR-Mitral Valve Replacement, OA- Osteoarthritis, NA-Not Applicable

*5 missing data

STATISTICAL ANALYSIS

The data collected was entered into Statistical Package for the Social Sciences (SPSS) version 20. Demographic data was presented as descriptive statistics. Prevalence of overweight and obesity as well as prevalence of CV-related comorbidities was presented as percentages. Sensitivity and specificity of IBMI and ABMI in predicting CV-related comorbidities were calculated. The gold standard of CV- related comorbidities was diagnosis made by medical doctor(s), as self-reported by the respondents.

Factors associated with overweight and obesity were analysed using multiple logistic regression (MLogR), using the BMI classification that resulted in a higher sensitivity for predicting CV-related comorbidities. All the steps of MLogR were done and all its assumptions were met. All the variables in univariate analysis were included for initial analysis of MLogR. From this analysis, the variables with p- value of < 0.05 in model if term removed (i.e. only variables which significantly change the model if being removed were included in the final multivariate analysis). Age was categorized as it did not fulfil the assumption of “linear in logit”. The p-value of less than 0.05 was considered statistically significant.

RESULTS

A total of 393 out of 427 invited nurses, attended the interview session. The non-response rate was 7.96%. The median age of nurses was 36 (IQR: 9) years old. Majority were Malay (91.9%) and married (96.2%). The median of BMI was 26.30 (IQR: 6.50) kg/m². The nurses’ BMI ranged from 16.00 to 44.63 kg/m². Most of the nurses (76.6%) had no medical problem. The most common medical problems were HTN (6.1%), followed by hypercholesterolaemia (5.9%) and T2DM (4.1%). Other CV-related comorbidities included IHD (1.5%) and MVR (0.3%). Only 39.7% of the nurses were able to label the food pyramid correctly although all claimed to be aware of Malaysian food pyramid 2010. Adherence to the food pyramid occurred less than 50% of the time (sometimes) in almost two third of the respondents (63.9%) and 15.3% of them had never adhere to food pyramid (Table 1).

Table 1: Characteristics of Respondents (n=393)

Characteristics n (%)

Demographic Data

Age in years, median (IQR)* 36 (9) Ethnicity

Malay Chinese Indian Others

361 (91.9) 3 (0.8) 23 (5.9)

6 (1.5) Marital Status

Single Married Divorced Widow

9 (2.3) 378 (96.2)

2 (0.5) 4 (1.0) Body Mass Index (BMI)

BMI in kg/m², median (IQR) 26.30 (6.50)

BMI range in kg/m² 16.00-44.63

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Table 2: Percentage of Respondents (General) & Percentage of Respondents with Comorbidities in Each Category of BMI According to 2 Clinical Case Definitions

IBMI ABMI

Category (n, %)¹ Comorbidities in each BMI category (n, %)²

CV-related comorbidities

(n, %)³

Category (n, %)¹ Comorbidities in each BMI category (n, %)²

CV-related comorbidities

(n, %)³ Underweight

(<18.5kg/m²)

8 (2.0) 0 (0.0) 0 (0.0) Underweight

(<18.5kg/m²)

8 (2.0) 0 (0.0) 0 (0.0)

Normal

(18.5-24.9 kg/m²)

137 (34.9) 20 (14.6) 8 (5.8) Normal

(18.5-22.9 kg/m²)

77 (19.6) 12 (15.6) 5 (6.5) Overweight

(25-29.9 kg/m²)

146 (37.2) 33 (22.6) 21 (14.4) Overweight (23-27.49 kg/m²)

136 (34.6) 25 (18.4) 14 (10.3) Obese

(≥ 30kg/m²)

102 (26.0) 39 (38.2) 25 (24.5) Obese (≥ 27.5kg/m²)

172 (43.8) 55 (32.0) 35 (20.3) 1: The denominator was total respondents (393)

2: The denominator was respondents in the particular BMI category

3: The denominator was respondents in the particular BMI category, CV-related comorbidities: cardiovascular-related comorbidities

Table 3b: Calculation of Sensitivity and Specificity of ABMI Definitions in Predicting CV-Related Co-Morbidities

ABMI**

DISCUSSION

Prevalence of Overweight & Obesity among Nurses

Using IBMI definition, the highest prevalence of obesity was found among Nigerian nurses⁽¹²⁾ followed by Malaysian nurses (current study), U.S. nurses⁽¹¹⁾ and Malaysian nurses in another study⁽¹³⁾. Lowest prevalence of obesity was found among Korean nurses despite of using 23kg/m² and 25kg/m² as cut-off point for overweight and obesity⁽¹⁵⁾. On the other hand, the highest prevalence of overweight was found among Malaysian nurses (current study), followed by Malaysian nurses in another study⁽¹³⁾, U.S. nurses⁽¹¹⁾ and Korean nurses⁽¹⁵⁾. The study done among Nigerian population did not calculate the prevalence of overweight⁽¹²⁾.

Among studies which presented mean (SD) of BMI, Nigerian nurses⁽¹²⁾ had the highest BMI, followed by U.S.⁽¹¹⁾ and Malaysian nurses⁽¹³⁾. Korean nurses had the lowest BMI⁽¹⁵⁾. U.S. nurses⁽¹¹⁾ had similar median and lowest BMI as compared with current study. However, the highest BMI among U.S. nurses⁽¹¹⁾ was much higher than our study.

Prevalence of Overweight & Obesity in General Population A national study⁽²⁴⁾ revealed that the prevalence of obesity among Malaysian was 11.7%. This study utilized IBMI definition.

NHMS 2015 reported that the prevalence of overweight and obesity was 30.0% and 17.7% using IBMI definition⁽⁸⁾. The same survey reported that the prevalence of overweight and obesity was 33.4% and 30.6% using the definition in Malaysian Clinical Practice Guidelines on Management of Obesity (2004); i.e. BMI cut-off point in ABMI definition. In comparison to NHMS 2015, the prevalence of overweight and obesity was higher in current study using both definitions. The postulated reason was difference in age range between current study and NHMS 2015.

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1.63 times more likely to be overweight and obese compared to nurses who had been working on shift duty for 0.08-3.00 years⁽¹⁵⁾.

A Canadian study supported the above findings by revealing that nurses who worked during night time or on rotational shift basis had higher BMI compared to nurses who worked during day time⁽¹⁴⁾. Analysis done by a study conducted among working nurses and midwifes in Australia, New Zealand and UK showed that increasing age, male, primiparous and menopause significantly predicted overweight or obesity among the study population⁽¹⁰⁾.

STRENGTHS AND LIMITATIONS

This study was the first study conducted in Perak state looking at overweight and obesity among female nurses in government hospital setting. This study compared the sensitivity of both IBMI and ABMI in predicting CV-related comorbidities. The generalisability of this study results was limited as it was conducted at a single district hospital.

Though it was stated in the questionnaire that the medical problems reported were as per doctors’ diagnosis, verification of medical problems was not done by the investigators. There might be under-reporting or incorrect medical problems being reported by the participants. Food pyramid adherence and duration of physical activities were self-reported too. Furthermore, there was lack of privacy during interview despite of an enclosed area being set up for the interview. Several interview sessions were conducted concurrently at certain time point.

FUTURE RESEARCH

A larger scale study is needed to support ABMI utilization among Malaysians.

CONCLUSION

The prevalence of overweight by both classifications was similar, but the prevalence of obesity was higher with ABMI.

The prevalence of CV-related comorbidities among overweight and obese nurses was similar using both classifications. Increasing age, being married and lower adherence to food pyramid significantly contributed towards overweight and obesity.

ACKNOWLEDGEMENT

We would like to thank: -

(i) The Director General of Health Malaysia for his permission to publish this article,

(ii) Hospital Seri Manjung Director, Chief Matron, Matrons, Sisters, Clinical Research Centre (CRC) unit staffs, Special Care Nursery attendants and security guards who supported this research,

(iii) all respondents (female nurses),

(iv) Pathology and Pharmacy departments staffs involved in answering the pre-test questionnaire as well as (v) Administrative and CRC unit staffs involved in

interviewer training session.

risk factor were identified in a Malaysia study as 23.5- 25.5kg/m² (male) and 24.9-27.4kg/m² (female)⁽²⁹⁾. A study conducted in Northern China identified the optimal BMI cut- off points for metabolic syndrome as 24kg/m² regardless of gender; hypertension as 24.05 kg/m² (male) and 24.40 kg/m² (female)⁽²⁵⁾. Similarly, another study conducted at China identified the optimal BMI cut-off points for hypertension as 23.53kg/m² (male) and 24.25kg/m² (female)⁽²⁶⁾. The optimal BMI cut-off points for hypertension were slightly higher in a study conducted in India, i.e. 24.5kg/m² (male) and 24.9kg/m² (female)⁽²⁷⁾.

Factors associated with overweight and obesity

By using ABMI definition, increasing age; being married; and lower adherence to food pyramid significantly predicted overweight and obesity in current study.

(i) In General Population

A Norwegian study reported that there was an increase of 15.3% and 7.8% of overweight and obese male (10.9% and 7.2% for female) after 11 years⁽³⁰⁾. Lower education level was significantly associated with higher BMI in women.

Higher income level significantly predicted higher BMI value in men⁽³⁰⁾. A Germany study reported that high blood pressure, diabetes, low education level, male and ex-smoker significantly contributed towards higher BMI⁽³¹⁾. Physical activity level increment of at least 1.8, with reduced dietary fat content to 20-25 energy-% in sedentary adults and to 25- 35% in more physically active adults were suggested in preventing obesity and diabetes⁽¹⁸⁾.

In NHMS 2015 (ABMI definition), highest overweight prevalence was found among adults between 50 to 54 years, other Bumiputras, married, with primary education and retirees. There was a significantly higher overweight prevalence among urban as compared to rural population, and men versus women. Highest obesity prevalence was found among Indians, married adults, with secondary education and government/semi-government employees.

Women had significantly higher obesity prevalence compared to men⁽⁸⁾.

(ii) Among nurses

Similar to this study findings, the indiscriminate eating habit and being married were found to be the significant risk factors of obesity among nurses in a Nigerian study⁽¹²⁾. A systematic review showed that shift work among nurses was associated with weight gain but high-quality studies inclusive of all the potential confounding factors should be conducted⁽¹⁶⁾. However, in current study, shift work and number of shifts per month did not significantly predict overweight and obesity.

In Korea, nurses who had been working on shift duty for 6.83-38.00 years were 1.24 times more likely to be overweight and 1.32 times more likely to be obese compared to nurses who did not work on shift duty⁽¹⁵⁾. The BMI cut-off point for overweight was ≥23 kg/m² and obese was ≥25kg/m². This study also elicited that nurses who had been working on shift duty for 6.83-38.00 years were onetimes

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