What are the optimal cut-off values of BMI (and other anthropometric indices) to define overweight and obesity in Asian populations? As can be seen from Table 2-3 there have been many recent attempts in different countries including China, Singapore, Taiwan, Thailand, Indonesia, and Japan to answer this question.
Introduction
urrently many countries in Asia, including developing countries, are undergoing a
“nutrition transition” (1, 2), which is due to rapid social and economic development and urbanization These changes include demographic, nutritional and epidemiological transitions In a typical nutrition transition society both under and over nutrition co-exist
Vietnam has transitioned from malnutrition to over-nutrition due to economic improvements In HCMC, the prevalence of overweight (BMI ≥ 25 kg/m²) among adults in 2000 was 12.9%, with women (15-49 years) exhibiting a 9.7% prevalence A later survey showed higher overweight prevalence in urban areas (18%) compared to suburban (13%) and rural areas (6%) Despite these observations, no detailed assessment of obesity and overweight in HCMC adults and associated risk factors has been conducted, highlighting the need for such information to inform public health policies and prevent an obesity epidemic amidst continued economic development.
In 2002 Vietnam formulated its first national program for prevention of chronic non- communicable diseases The findings from this study provide an evidence basis for the development of the national program for prevention of chronic non-communicable diseases especially in the area of prevention of obesity and appropriate dietary recommendations for healthy living
The findings from the study should also be of use to the Nutrition Centre as the key Health Department organization responsible for planning and developing local policies and programs for obesity prevention in HCMC In particular, the results will help the Nutrition Centre plan future research and evaluations of interventions to prevent overweight, and other related chronic non-communicable diseases, and to plan communication strategies about nutrition and prevention of chronic non-communicable diseases in HCMC
This thesis aims to assess the prevalence of obesity and related risk factors in Vietnamese adults aged 20-60 years living in urban areas of HCMC Vietnam, using newly defined optimal cut-off values of some important anthropometric indices for Vietnamese population Then prevalence of obesity based on these cut-off values will be presented following by factors associated with high BMI in Vietnamese adults living in HCMC The thesis has 7 chapters and is organized as follows: Chapter 2 provides a review of the relevant literature including an assessment of the levels and trends in overweight and obesity in adults in East and Southeast Asia Chapter 3 describes the main survey methods including sampling, measurements, data collection and data analysis Chapter 4 uses ROC analysis to determine optimal cut-off values for anthropometric indices to identify populations with increased risk of diabetes or CVD risk factors Chapter 5 examines the prevalence of overweight and obesity in this urban adult population in Vietnam using traditional recently recommended cut offs for Asian populations Chapter 6 uses a multi- variable logistic model to assessment factors associated with high BMI Chapter 7 summarizes the key findings and proposes conclusion and recommendation that can be drawn from the results.
Literature review
Nutrition transition in Asia
“Nutrition transition” is a term coined to describe the changes in diet and nutritional status that take place as communities in developing countries develop economically and urbanize Nutrition transition has been reported in Asia and the Pacific as well as in the developing world Evidences of nutrition transition can be found in several countries including China, Sri Lanka, Thailand, Malaysia, South Korea (1, 5, 6) It begins with dietary change in the population from traditional diets rich in fruits and vegetables to a diet full of pre-processed foods, foods of animal origin, and foods that contain more sugar, fat and alcohol In addition, urban populations tend to do less physical activity in work and leisure due to the many convenient domestic and work related labour saving devices and mechanized transportation During last half century, there have been large shifts of populations from rural to urban areas throughout the developing world (1)
A further factor contributing to the increase in obesity and chronic diseases is the possibility of “foetal programming” Firstly, under-nutrition of the foetus during pregnancy and of young children, which are both common in developing countries, can cause metabolic changes that are required to adapt to the nutritional stress during these periods of life These metabolic changes help the foetus or young child to survive in the low energy environment Such processes themselves do not directly lead to increasing morbidity and mortality, but rather, leave these children (and later adults) susceptible to obesity, adult- onset diabetes and cardiovascular diseases when faced with a richer, more energy dense diet and reduced physical activity (1)
As a result of changes in diet and physical activity as well as foetal programming, the number of overweight and obese people has increased especially in urban environments in developing countries In these ‘nutrition transition’ countries there are new patterns of morbidity with increasing diet-related chronic diseases (coronary heart disease, diabetes, hypertension, stroke and certain cancer such as gallbladder cancer, breast cancer, colon and rectal cancer) existing concurrently with long-established problems (nutritional deficiencies and infectious diseases) As the nutrition transition develops obesity becomes a problem of both urban poor as well as rich, rather than urban rich alone A key characteristic of the nutrition transition is the existence of both under and overweight in the same population (7) This may also be true even at a family level There are approximately 3-15% Asian households that include both under and over weight individuals, typically, an underweight child and overweight non-elderly adults In other words, there are 30-60% of households where a household member is underweight but another is overweight (1)
Nutrition transition significantly impacts society with increased morbidity and mortality, resulting in substantial human and economic burdens The human costs include disability and death from chronic diseases such as cardiovascular diseases, diabetes, hypertension, stroke, and cancer The economic costs encompass healthcare expenses for treating these conditions, premature mortality costs, and lost productivity due to absenteeism or decreased productivity among affected individuals who continue working Notably, the economic implications of this growing chronic disease burden in developing countries remain largely unexplored.
The establishment of appropriate programs to control epidemic diet-related chronic diseases in rapidly developing countries are major challenges for governments and policies makers in these countries A number of different strategies are worth considering when formulating policies in response to a nutrition transition These include promoting agriculture development to encourage vegetable and fruit production; developing price mechanisms to solve the micronutrient deficiencies problems by increasing fruit and vegetable intake and other food sources of micronutrients; supporting the preservation of traditional diets for example by proving training on traditional cooking methods for newly married women; using mass media and school-based programs to promote good nutrition, local foods and regular physical activity (1).
Levels and trends of obesity in East Asia and Southeast Asia
2.2.1 East Asia and Southeast Asia:
In this section, the levels and trends of obesity are presented with a focus on studies from East Asia and Southeast Asia, where the populations are similar to Vietnam There is strong evidence of problems with overweight and obesity in many countries in East Asia and Southeast Asia, and some examples are presented in Table 2-1 As can be seen from Table 2-1, currently there are many countries where over 20% of the adults are overweight (BMI
≥ 25kg/m 2 ) These numbers would be even higher if the prevalence was based on the recently proposed lower BMI cut-off values (BMI ≥ 23 and 27.5 kg/m 2 for overweight and obesity respectively) for Asian populations (8) In addition to current data, the table also presents the results of earlier surveys of overweight and obesity in these selected countries, and in many reveals trends of increasing overweight and obesity Over the 10 year period, the prevalence of overweight increased by over 5% in China, Thailand and Malaysia In China, for instance, the level of overweight in adults increased from 14.9% in 1992 to 18.6% in 2001 However, the prevalence of overweight and obesity seems to be settled at a relatively high level (over 20%) in some countries like Japan, Hong Kong, Singapore
Table 2-1 Prevalence of overweight and obesity among Asian populations
Nation Previous data Most recent data
Overweight B MI 25-29.9 Ob esity BM I ≥ 30 Year Reference Overweight B MI 25-29.9 Ob esity BM I ≥ 30 Year Reference
3 Singapore adults nationwide 26.2 (BMI ≥ 25kg/m 2 ) 1992 (12) 24.4 6.0 199
Evidence of obesity in Vietnam has been found mainly in Hanoi and Hochiminh City which are the two largest cities in the north and the south of Vietnam respectively
2.2.2.1 Hanoi (Capital), north of Vietnam
There is only one study reporting the prevalence of obesity in adults in Hanoi This study assessed the prevalence of overweight and related risk factors in retired women and housewives aged 20-59 years living in a rural district of Hanoi (22) The author found that the prevalence of overweight (BMI ≥ 25 kg/m 2 ) and obesity (BMI ≥ 30 kg/m 2 ) was 15.5% and 1.1% respectively Overweight increased with age and reached a peak at 50-59 years (19.9%) This study also indicated that central adiposity was a problem with 33.6% having a high waist to hip ratio (WHpR) (define as WHpR>0.85 for women) and 35.3% women had high waist circumference (define as ≥ 80cm)
2.2.2.2 Hochiminh City, south of Vietnam
A study about the nutritional status of children under 5 years and women of reproductive age (15-49 years) in HCMC found that the prevalence of obesity (defined as BMI ≥ 25 kg/m 2 ) in women of reproductive age was 9.7%, and that it increased with age from 6.6% in women at 20-29 years, to 7% at 30-39 years, and to 16.9%, at 40-49 years (3)
An epidemiological survey of diabetes in the population over 15 years in HCMC in 2001(23) found the overall prevalence of overweight and obesity (BMI>25 kg/m 2 ) was 12.9% This included 11.3% with a BMI between 25-30 kg/m 2 , 1.3% with BMI 30-35 kg/m 2 and 0.2% with BMI >35 kg/m 2 In this study, the prevalence of overweight was relatively low in the sub-population aged 20-29 years (5.3%) compared with 16.3% for 30-
39 years, 21.2% for 40-49 years, and 22.1% for 50-59 years The survey also found that the overweight rate was significantly higher in urban areas (18.3% for urban versus 7.6% for rural areas, p0.85 in female and >0.95 in male) was significantly higher in the high BMI group (73.5%) in comparison with the normal BMI group (34.4%)
Also this survey (23) found the overall prevalence of under-weight (BMI 25 kg/m 2 and >30 kg/m 2 for defining overweight and obesity in Western populations (Table 2-2) However, these cut-off values may not be appropriate for Asian populations Many studies in Asia have found a higher risk of diabetes mellitus, cardiovascular diseases, percent body fat compared with Western populations at a given level of BMI (8, 32) This implies lower cut-off values for BMI are needed to define overweight and obesity for Asian populations in comparison to Western populations
Many studies have attempted to find optimal BMI cut-off values to define overweight and obesity in Asia The two most common approaches to identify BMI cut offs have been comparisons of BMI with the level of body fat (8, 32), (33) and with indicators of cardiovascular diseases (34, 35) In the body fat approach, optimal BMI cut-off values have been calculated based on the assumption that the percentage of body fat in Asians at the cut-off values for overweight and obesity should be the same as the percentage of body fat in Caucasians with a BMI of 25 and 30 kg/m 2 Initially the percent body fat of the studied population has been measured using a “gold standard” body composition method Then optimal cut-off values for BMI have been calculated based on a body fat prediction equation for Caucasians (BF% =1.2 x BMI + 0.23 x age – 10.8x sex - 5.4)(32) In the cardiovascular disease approach, optimal BMI cut off values for a given population have been calculated based on individual and pooled cardiovascular risk factors including hypertension, blood glucose, and lipid profiles using different methods including using odds ratio and logistic regression(36), sensitivity and specificity analysis and population attributable risk percentage (34) and receiver operating characteristic (ROC) analysis (35)(Table 2-3)
Different optimal BMI cut-off values for defining overweight and obesity have been reported in different countries Table 2-3 In general, the optimal BMI cut-off values to define overweight are lower than those observed for Western populations They vary from
Variations in BMI cut-off points for obesity designation exist across Asian countries, ranging from 21 to 25 kg/cm2 This variability stems from diverse body compositions, ethnicities, urban development levels, socioeconomic factors, and the stage of nutrition transition within each country.
Table 2-2 presents the traditional (Western) cut-off values for BMI recommended by WHO for comparisons between studies However, WHO has also proposed a modified BMI classification for public health and clinical use for defining overweight and obesity in Asian populations (Table 2-4) In this definition, the cut-off values for underweight and normal weight are similar to those recommended for Western populations The differences are with the cut-off values for defining overweight (> 23kg/m 2 ) and obesity (> 27.5kg/m 2 ), which are lower than those for Western populations Whenever possible, Asian countries should use both BMI categories in reporting prevalence of overweight or obesity to facilitate international comparisons
Table 2-2 Traditional cut-off values (37) for BMI
Classification BMI (kg/m 2 ) Risk of co-morbidities
Table 2-3 Suggested BMI cut-off values for defining obesity in Asian populations from different countries and the World Health Organization
Population Overweight Obesity Reference Year Analytical method
Thai 22.1 27.0 (33) 1998 Comparison with Caucasian populations using prediction equation for percentage body fat from BMI, age and sex
Comparison with Caucasian populations using prediction equation for percentage body fat from BMI, age and sex
Singaporean 21.0 27.0 (32) 2000 Comparison with Caucasian populations using prediction equation for percentage body fat from BMI, age and sex
Comparison with Caucasian populations using prediction equation for percentage body fat from BMI, age and sex
Chinese 23.0-24.3 Not reported (39) 1999 ROC analysis to examine the relationship between anthropometric indices and cardiovascular risk factors
Examined relationship between anthropometric indices and cardiovascular risk factors using OR and logistic regression
Examined relationship between anthropometric indices and cardiovascular risk factors using partial correlation and covariance analysis, and
Chinese 24.0 28.0 (34) 2002 Analysis based on sensitivity, specificity for cardiovascular risk factors and population-attributable risk percentage
ROC* analysis to examine the relationship between anthropometric indices and cardiovascular risk factors with optimal cut-off value determined by equal sensitivity and specificity
ROC* analysis to examine the relationship between anthropometric indices and cardiovascular risk factors with optimal cut-off value determined by largest sum of sensitivity and specificity
ROC* analysis to examine the relationship between anthropometric indices and cardiovascular risk factors but exact optimal cut-off value not determined
* ROC: receiver operating characteristic analysis
Table 2-4 Recommended BMI cut-off values for Asian populations from WHO (8)
Classification BMI (kg/m 2 ) Risk of co-morbidities
2.3.2 Waist circumference cut-off values for abdominal obesity
It is not only the amount of fat but also its distribution that determines the health risks associated with obesity Abdominal or viscera fat (android obesity) is strongly associated with the cardiovascular risk factors, which include type 2 diabetes, impaired glucose tolerance, hypertension and dyslipidemia (high triglyceride, low HDL cholesterol)
(16) Waist circumference is reported as a simple clinical measure that can reflect visceral fat, and thus is also an important anthropometric index in terms of body fat distribution
Similar to the situation with BMI, there are well-established cut-off values of waist circumference for defining overweight and obesity for Western populations (see Table 2-5)
However, these values do not seem to be appropriate for Asian populations, which have higher risks for diabetes mellitus and CVD at lower waist circumference cut-off values
Numerous studies have sought to establish appropriate waist circumference cut-off values for Asian populations, similar to BMI Table 2-6 outlines examples of optimal waist circumference cut-off values reported in various studies These values vary significantly, ranging from 80.5 to 90.0 cm for men and 79.5 to 85.5 cm for women.
Table 2-5 Traditional waist circumference cut off values for defining abdominal obesity
Table 2-6 Suggested waist circumference cut off values for defining abdominal obesity in
Country Waist circumference (cm) Reference
WHO recommendations ≥ 90 ≥ 80 (16) China ≥ 85 ≥ 80 (34) Japan ≥ 90 ≥ 85 (43) Taiwan ≥ 80.5 ≥ 71.5 (35) Singapore ≥ 90 ≥ 80 (44)
There are many methods of assessment of adiposity in humans ranging from the most commonly used methods like the combination of weight and height (BMI), skinfold thickness, and body circumferences to newer methods based on electrical resistance or impedance, densitometry, dual energy X-ray Absorptionometry (DEXA), magnetic resonance imaging and computer-assisted tomography Densitometry is regarded as the gold standard method for measuring body fat (45) However, due to its cost and complexity of instrumentation, densitometry is limited to use in laboratory rather than large-scale population-based studies
Combinations of weight and height (BMI), skinfold thickness and bioelectrical impedance analysis (BIA) are the most commonly used methods of measurement body composition in epidemiological studies (45-47) These methods are popular because they are simple to implement, require less time than other methods, are relatively low cost and use portable instruments BMI has been discussed above In this section, skinfold thickness and bioelectrical impedance analysis methods will be examined in detail
Skinfold thickness is the second most widely used method to determine body composition since it provides a direct measure of body fat Source of variation in skinfold thickness measurements include differences in the site of measuring of each skinfold, the manner in which the skinfold is picked up and the depth of calliper bite (45) The correlation of skinfold thickness with densitometry (which is consider as the gold standard method for body composition(45)) is similar to the correlation of BMI with densitometry, and in some reports even higher (48-50)
Skinfold thickness measures the combined thickness of two skinfolds and the underlying adipose tissue at specific measurement points Skinfold calipers, with two prongs, are commonly used to determine the thickness The prongs are applied with a predetermined force to the skinfold, and the thickness is recorded on the caliper display Various caliper models exist, with Harpenden calipers recognized for their accuracy in skinfold thickness measurement.
Skinfold measurements taken at several different sites can be used in equations to estimate total body fat These equations are based on studies in which skinfold thickness measurements have been validated against a “gold standard” of body fat measurement, such as densitometry Measuring skinfold thickness at several sites also helps to evaluate the pattern of distribution of body fat The Durnin-Womersley and Jackson-Pollock-Ward equations are amongst the most commonly used to estimate body fat but a number of other equations have been reported in Norton et al 1996 (51) However, these prediction equations have only been validated for Western populations and so have limited application for Asian populations due to the difference in body composition and body build Several studies have pointed out that these prediction equations may not be appropriate for Asian populations and that specific equations may be needed for different ethnic groups in Asian
(46, 52, 53) To date, there are no published body fat prediction equations that have been validated for Asian populations in general However, there are reports of body fat prediction equations that have been developed in specific populations and countries (54- 57)
The body fat prediction equations developed in Japanese populations (55, 56) seem to be the most appropriate for use in Vietnam and have been used in the analyses because they were developed in a population of similar age (19-60 years) and using the skinfold sites (triceps, subscapular and abdominal) used in our study:
For women: body density = 1.07931-0.00059 x sum of three skinfolds (mm)-0.00015 x age For men: body density = 1.09556-0.00062 x sum of three skinfolds (mm)-0.00028 x age
Obesity related risk factors
A variety of socio-economic risk factors have been reported to be associated with obesity in different countries, and examples from a selection of countries are presented in Table 2-8 and Table 2-9 Smoking status, occupation, education, level of dietary energy intake, level of physical activity, wealth status, television viewing, residence (urban versus rural) and marital status have all commonly been associated with obesity in adults For some factors, the patterns of association are reversed between Western and Asian countries For example, obesity is associated with people who have low levels of education, low economic status in Western countries, but in Asian countries, the association is reversed with more obesity in people who are successful, have high-education and high socio-economic status
Table 2-8 Socio-economic risk factors of obesity in men in different countries
Countries Risk factors of obesity Reference Australia Low education, high-skill occupation, frequent television viewing (60)
France Low education level, ex-smoker, marital status (married), high energy intake, low carbohydrate intake (61)
Taiwan Residing location (mountainous), metabolic equivalent (MET) score, high alcohol consumption (62)
Thailand Non-smoker, urban, married (19)
Marital status (married), higher education, non-smoker, low active occupation, high energy intake, high carbohydrate intake, high protein intake, high fat intake, low commuting physical activity, higher socio- economic class
Table 2-9 Obesity socio-economic risk factors in women in different countries
Countries Risk factors of obesity Reference
Australia Non-smoker, low physical activity, frequent television viewing, low education level, country of birth (Australia and New Zealand), high income,
Low education level, use of oral contraceptives, menopausal status (yes), young age at menarche (9-11 year), high energy intake, low carbohydrate intake (61)
Taiwan Location of residence (mountainous), low education level (62)
Thailand Urban, non-smoker, married (19)
China Marital status (married), higher education, low active occupation, high energy intake, low carbohydrate intake, high commuting physical activity, higher socio-economic class
Again, the diversity of Asian populations mentioned earlier is reflected in the anthropometry, lipid profile and blood pressure of populations from different Asian countries (some examples can be found in Table 2-10 and Table 2-11) Mean height varied from 167.0 to 169.8 cm for men and from 154.3 to 157.5 cm for women Similarly, mean weight varied from 67.5 to 69.2 kg for men and from 53.8 to 62.5 kg for women Gender difference was also seen for height, weight, waist circumference and blood pressure
Table 2-10 Anthropometry and CVD risk factors for men from different populations in Asia
Countries Age Height weight BMI Waist TC HDL LDL TG SBP DBP Ref China 18-69 169.5 67.5 23.5 84 5.5 1.4 3.5 1.7 125.5 77.4 (36) Malaysia 18-69 166.8 69.2 24.8 86 5.9 1.3 4.0 2.0 125.8 77.8 (36) India 18-69 169.8 70.8 24.6 88.9 5.7 1.2 3.9 2.0 124.4 77.8 (36) Hong Kong 18-65 167 67.8 24.4 83.8 5.2 1.2 3.3 1.2 127.5 77.8 (64)
Table 2-11 Anthropometry and CVD risk factors for women from different populations in Asia
Countries Age Height weight BMI Waist TC HDL LDL TG SBP DBP Reference
TC: total cholesterol (mmol/L), HDL: high density lipoprotein cholesterol (mmol/L), LDL: low density lipoprotein cholesterol (mmol/L), TG: triglyceride (mmol/L), SBP: systolic blood pressure (mmHg), DBP: diastolic blood pressure (mmHg)
Physical activity is defined as “any bodily movement produced by skeletal muscles that results in energy expenditure”(65) However, assessment and measurement of physical activity is much more difficult than its definition Physical activity can occur in multiple contexts for different purposes like transportation, occupation (paid or unpaid), household maintenance or child care tasks, and recreation (or leisure time) (66) This makes physical activity as difficult to measure as dietary intake
There are many ways of measuring physical activity These methods can be grouped into 5 categories: behavioural observation, physiological markers (like heart rate), calorimetry, motion sensors, and questionnaires (including diaries, recall questionnaires and interview) (67) Each method has its advantages and disadvantages Only the use of questionnaires for assessment of physical activity is reviewed below because this was the method used in the thesis research
Self-report is the most widely used type of physical activity measure Self-reports are defined as self-administered or interviewer-administered recall questionnaires, activity logs or diaries, and proxy reports (typically to assess young children) (68) Self-report has many advantages It can be conducted on a large number of participants with low cost Recall questionnaires do not alter the behaviour of the participants being studied It can also be used to assess all dimensions of physical activity and the patterns of behaviour Finally, self-report can be used in a wide range of ages and measurement can be adapted to fit the needs of a particular population or research question (68) However, self-report also has some disadvantages Social desirability can lead to over reporting of physical activity Recalling physical activity is a highly complex cognitive task Children and very young adults are likely to have particular memory and recall skill limitations Some terms used in the questionnaire such as “vigorous physical activity”, “moderate physical activity”,
“leisure time” are sometimes ambiguous to participants (68)
Several different questionnaires have been developed to assess levels of physical activity and some of the most commonly used questionnaires include: Active Australia, US CDC Behavioural Risk Factor Surveillance System (BRFSS), and the European physical activity surveillance system (EUPASS) (69, 70)
Physical activity is a global health concern, but the diverse physical activity measures in use often prevent valid international comparisons of studies of this important health related behaviour The international physical activity questionnaire (IPAQ) was developed as an instrument for cross-national monitoring of physical activity and inactivity (71)
“The development of an international measure for physical activity commenced in Geneva in 1998 and was followed by extensive reliability and validity testing undertaken in 12 countries (14 sites) across 6 continents during 2000 (71) The final results suggest that these measures have acceptable measurement properties for use in many settings and in different languages” ”Worldwide use of the IPAQ instruments for monitoring and research purposes is encouraged It is strongly recommended that no changes be made to the order or wording of the questions as this will affect the psychometric properties of the instruments” (70)
There are two forms of IPAQ: the long form (5 parts with 27 questions) and the short form
(7 questions) In each questionnaire, there are telephone and self-administered versions
The IPAQ is also available in several languages other than English All IPAQ questionnaires are available at the website www.ipaq.ki.se (70)
Table 2-12 highlights the varying physical activity levels across countries using the IPAQ questionnaire The median MET-hour/week dose, a measure of energy expenditure during physical activity, exhibits significant differences Notably, the Netherlands displays a higher median value, which aligns with the country's greater prevalence of active local transportation.
Table 2-12 Physical activity level from different countries using IPAQ
Country Median of MET-hour/week Reference:
2.4.4 Assessment of diet in adults
There are three major methods used to assess dietary intake in epidemiological research: food records, food recalls and food frequency questionnaires Each of them has both advantages and disadvantages
The food record method requires subjects to meticulously record food intake, providing accurate dietary information Its advantages include immediate recording, eliminating reliance on memory However, drawbacks arise with unmotivated, untrained, or illiterate respondents and potential eating habit alterations Subjects may underrepresent intake or avoid complex dishes to simplify recording This method can be challenging for specific age groups, such as teenagers, due to self-consciousness.
In addition, the quality of the data may decrease with increasing number of days due to respondent fatigue Finally, this method may not provide enough information about the dietary intake of those nutrients with large day to day variations in the diet (for example vitamin A)
The 24-hour dietary recall method involves asking participants to recall their food and beverage intake within the past day While increasing the number of recall days enhances nutrient intake precision, it also increases costs and may be impractical for certain nutrients The optimal number of days depends on the study's purpose, with one day being sufficient for assessing group intakes and multiple days required for individual intake assessments Limitations of this method include memory lapses and challenges in reporting portion sizes, particularly among specific populations like the elderly or young children.
A food frequency questionnaire (FFQ) is an instrument in which respondents are asked to answer their usual frequency (and sometimes the portion size) of intake of a list of foods items, which represent the usual eating habits of that population The number of food items on the list depends on the purpose of the study In general it varies between 75 and
150 food items FFQs can capture the long-term eating habits of respondents including food items that vary in consumption from day to day or from season to season They are less costly than the others methods because they can be self-administered in literate populations
Weight perception, weight concern and weight control behaviour
Beside data about prevalence and socio-demographic characteristics of overweight and obesity, weight related beliefs and attitudes as well as weight control practices of that population are also important for developing weight control and prevention strategies In other words, how people think about their weight (ideal, overweight or underweight), how much they are concerned about their weight, and if they are currently trying to gain weight, lose weight etc are factors that reflect in the weight related beliefs, attitudes and weight control practices of that population Those factors will also guide the appropriate obesity prevention strategies for that population There are obvious differences among different cultures in terms beliefs and attitudes toward overweight, fast foods, physical activity etc
In Western culture, it appears that many men and women are concerned about obesity and considerable numbers of adults (15-35%) are attempting to reduce their weight (76)
Women are often more concerned about their weight and try to lose weight more than men
(examples from Australia and United States: Table 2-15 and Table 2-16) However, there are limited data about these issues in Asian cultures
Table 2-15 Weight concern and weight control behaviour among overweight (BMI>25 kg/cm 2 )
Level of concern about current weight
Not at all concerned 23.2 4.9 Not very concerned 38.1 29.0 Quite concerned 30.4 37.7 Very concerned 8.3* 28.4*
Trying to gain weight 0.6 0 Trying to avoid gaining weight 32.6 31.1
Trying to lose weight 27.6 41.0 Doing nothing to control weight 39.2* 27.9*
* Statistically significant association between level of concern and gender
Table 2-16 weight control behaviour among overweight and obese adults over 20 years in
Sex and BMI categories Number % trying to lose weight
% trying not to gain weight
% trying to lose or not to gain weight
Methodology
Study design
Cross sectional study of a representative sample of adults aged 20-60 years living in the urban areas of Hochiminh City (HCMC).
Study population and setting
The reference population of this study is Vietnamese adults aged 20-60 years of both gender living in urban areas of Southern provinces and cities of Vietnam The source population was Vietnamese adults of both gender aged 20-60 years living in urban areas of HCMC
HCMC with its population of over 5 millions people is the largest and economically most advanced city in Vietnam The City is divided into four administrative levels; districts, wards, quarters and hamlets In total it has 22 districts with 303 wards, and on average, there are 15 wards per district The districts have been classified into urban, sub- urban and rural districts There are 13 urban districts with 182 wards, four sub-urban districts with 56 wards and four rural districts with 65 wards In each ward, on average, there are four quarters and in each quarter, there are a variable number of hamlets Information about the population size is available from the 1999 Vietnam National Census down to the ward level but not for quarters and hamlets The exact number and name of all wards are available but the exact number of quarters and hamlets in each ward is only available from the local administration in each ward administration (78) In summary a sampling frame of districts and wards is available for cluster sample surveys of the population.
Sampling design
Because the primary aim of the study was to assess “the prevalence of obesity in Vietnamese adults living in urban areas of HCMC, Vietnam”, the sample size calculation used the formula for estimating single proportions:
Z α = coefficient of statistical significant - alpha Δ = the width of the confidence interval p = expected proportion of individuals in the sample with obesity
Assuming the level of statistical significance of alpha= 0.05, i.e Z α = 1.96
The value of p used was 0.138, which was based on the results of a previous study reporting the prevalence of obesity in Vietnamese adults 15 -60 years of age living in urban area of HCMC (23) Δ = 0.025 (the required precision of estimate ± 2.5% of the true value)
Because of the sampling design, the sample size was expanded to account for the effects of clustering using a design effect of 2 This design effect was chosen based on a previous study of obesity in HCMC (23) Thus the final sample size was estimated to be 1462, which was rounded up to 1,500 subjects
The secondary aim of this study was to investigate the association between obese and non-obese groups in terms of risk factors and obesity co-morbidities In terms of investigating the differences of some of these characteristics between obese and non-obese groups, we have:
The predicted proportion of obesity is 13.8% (from previous study (23)) So the predicted obesity and non-obesity subjects among 1500 subjects are 207 and 1293 respectively This means the expected ratio of non-case (non-obesity) and case (obesity) is 1293/207 = 6.2 /1
With an assumed alpha value of 0.05 and power value of 0.8, design effect of 2 and sample size of 1500, we expect that we could estimate the difference in any characteristic between obese and non obese groups within ± 15% of the true value
The study used a multistage cluster sampling strategy with the following stages:
• Stage 1: Randomly selected 30 wards (clusters) from 182 wards in 13 urban districts of HCMC using the proportionate to the population size (PPS) method based on the data from 1999 Vietnam National Census
• Stage 2: Simple random sampling was used to select one quarter in each of the selected wards from stage one with an assumption of approximately equal population in each quarter
• Stage 3: Simple random sampling was used to select one hamlet from each of the selected quarters from stage two with an assumption of approximately equal population in each hamlet
• Stage 4: A list of all households in each hamlet was prepared by the local health workers Fifty households were randomly selected from this list Each hamlet usually contained approximately 50 houses If there were less than 50 houses, then more households were identified from a neighbouring hamlet in that quarter The lists of all adults aged 20-60 years in each selected household were prepared by community health workers Finally, one adult was selected from each household using simple random sampling
To recruit participants, 30 wards were chosen, and their local health workers were invited to training on survey methods These health workers compiled lists of 50 households and their adult members (aged 20-60) in each hamlet Researchers selected one adult from each household, and the lists were given back to the health workers They visited each household to explain the study and obtain informed consent from the selected adults.
If selected subjects needed time to consider their participation, they were contacted again by the local health workers three days later for their response The visits of local health workers occurred at least one week before the scheduled survey day, this provided sufficient time for the participants to arrange their activities to allow them to attend the clinic organised by the survey team if they agreed to participate in the study
If a selected adult declined to participate in the study, the local health workers were responsible for selecting a replacement subject of the same gender and age resident in the same hamlet This task was completed under the supervision of the primary investigator
Of the 317 subjects who declined to participate, a comparative analysis against non-substituted subjects revealed no statistically significant differences in educational level, economic status, or occupation This absence of bias ensures the representativeness of the 1,480 participating subjects in the survey sample.
Table 3-1 Comparison of the characteristics of the substituted versus non-substituted subjects
Retired, house wife, student, don’t know 24.6 24.2 24.3
Measurements
Anthropometric measurements were obtained from all participants using standardized anthropometric measurement techniques adapted from the “Anthropometric standardization reference manual” (59)
Height was measured using a Microtoise tape suspended from a wall and was recorded to the nearest 0.1cm
Weight was measured by Tanita electronic scale (Tanita body fat monitor, BF 571, Tanita Corporation, Japan) and was recorded to the nearest 100g
Waist and hip circumference were measured by non-stretch tape, and recorded to the nearest 0.1cm
Percent body fat was measured for all participants using the Tanita body fat monitor
BF 571 (Tanita Corporation, Japan) with the built-in body fat analyser based on a leg-to-leg contact electrode bioelectrical impedance analysis (BIA) system
Skinfold thicknesses were measured in a sub-sample of participants (600/1488 persons) Four sites were chosen: triceps, subscapular, abdominal and thigh skinfold thickness At each site, two repeated measurements were taken, and if the results of these measurements are more than 1mm or 10% (in obese subjects) different, a third observation was obtained (and it these cases the average of three readings will be used in analysis) Skinfold thickness was measured by Harpenden calliper (H.E Morse Co British Indicators, Ltd) Standardized methods for measuring skinfold thickness were adapted from
3.4.3 Level of concern about body weight and weight control practices
The level of concern about body weight and weight control practices was measured in all participants by interview with a structured pre-coded questionnaire The questionnaire was adapted from a questionnaire previously developed (although not validated) and used in surveys of Australian adults (76) Three items were adapted and included into our questionnaire In the first item, people were asked what they thought about their current weight (is it ideal, under or overweight) The degree of concern about their weight was asked in the second item Finally, in the third item, people were asked if they were either trying to gain, lose weight or doing nothing Details of these items can be found in the Annex
The short form of the internationally validated physical activity questionnaire (International Physical Activity Questionnaires (IPAC)) was used in this study (70) Weekly frequency and duration of four main physical activities (vigorous physical activity, moderate physical activity, walking and sitting) were asked A copy of the questionnaire is found in the Annex Apart from translation to Vietnamese, there were no changes made in the content of this questionnaire compared with the origin validated questionnaire
Discussion of this physical activity questionnaire can be found in the section
“assessment of physical activity” in Chapter 2 (Literature review) Discuss about analysis of the physical activity questionnaire can be found in analysis section of this chapter
Dietary intake has been measured in all participants with a food frequency questionnaire (FFQ) specifically developed and validated for Vietnamese adult populations living in HCMC (75) For detailed of discussion of this questionnaire, see the section
“assessment of diet in adults” in Chapter 2 (Literature review)
The frequency and portion size of the 149 food items in this FFQ were obtained by interview with the help of visual aids (real size picture book of portion sizes of different foods) For each food, the frequency of consumption was recorded using one of nine options (never, ≤ 1 time/month, 2-3 times/month, 1-2 times/week, 3-4 times/week, 5-6 times/ week, 1 time/day, 2-3 times/day, 4-5 times/day and ≥ 6 times/day) and the portion size with one of three options (0.5, 1 and 1.5 standard portion) It took about 20-30 minutes to complete the FFQ interview for each subject The interviews were conducted by medical doctors of the Community Nutrition Department of the Nutrition Centre HCMC who have had considerable experience with nutritional assessment instruments like 24h recalls and FFQs However, these interviewers were trained on the use of the FFQ prior to survey Some interviewers actually participated in the validation study of this FFQ in year 2002
A copy of FFQ can be found in the Annex
All participants were asked to fast overnight before the survey day A venous blood sample of four millilitres was collected from a vein on the participants’ forearm by trained and experienced laboratory technicians from the Nutrition Centre HCMC Serum biochemical indicators including fasting blood glucose, lipid profile (triglyceride, total cholesterol, high density cholesterol, low density cholesterol) were measured using a photometer technique (Fully Automated Bio Chemistry Analyzer, Hitachi 717, Japan) by Medic Medical Centre Laboratory HCMC
Standard internationally recommended procedures were used for the handling and processing of blood specimens and for the transportation of specimens to the Medic Medical Centre Laboratory HCMC
Systolic and diastolic blood pressure was measured in all participants by trained nurses using appropriate sized cuffs for Matsuyoshi mercurial sphygmomanometers (MY
605 P-Japan) Blood pressure was measured using the auscultatory method with the subject in the sitting position after allowing participants to appropriately rest The systolic blood pressure was determined by the onset of the ‘tapping’ Korotkoff sounds (K1) The fifth Korotkoff sound (K5), or the disappearance of Korotkoff sound, was used to define diastolic blood pressure.
Data collection
The anthropometrists were nurses who were trained for 15 days before the survey to take the anthropometric measurements of weight, height, waist circumference, hip circumference and skinfold thickness using standardized methods (based on Anthropometric standardisation reference manual (59))
The interviewers were medical doctors from the Nutrition Centre HCMC who had been trained for 2 days about interviewing techniques and the use of the survey questionnaires before the study
The questionnaire used in this study was piloted on 20 adult subjects, who were recruited from nutrition clinics at the Nutrition Centre HCMC about two months before the study, to assess the language and feasibility of the instruments Following the pilot study, some of the questions were reworded, but this experience conform feasibility of using the questionnaire in the main study
The survey clinics were held at local health stations for one morning in each of the 30 selected clusters (ward) during two months from March to April of 2004 At each location, the clinic run for 5 hours each day from 7am to 12 am Survey teams had 7 interviewers, 1 medical lab technician, 3 anthropometrists, 1 secretary, 2 local health workers and 1 team leader The clinics were prepared by local health workers who had been trained about this in advance All equipment and supplies for the survey were brought to the clinic every day by the survey team including the Tanita electronic scale, the tape measures, sphygmomanometers, Harpenden calliper, Microtoise tapes, centrifuge and laboratory supplies There were 4 desks that each of the subjects had to go including the administration desk, the anthropometric desk, the blood sample taken desk and interview desk Administration desk was the first and the last desk that all subjects visited The other the desks were visited by respondents according to their availability Each of the subjects spent approximately 1.5-2 hours at the clinic to complete the procedures Local health station staffs were responsible for administration and for door to door visits to remind subjects to come to the clinic Breakfast and drinks for subjects were provided by the survey.
Data analysis
Anthropometric indices were calculated using the following equations:
BMI Waist to height ratio = waist circumference (cm) /height (cm)
Waits to hip ratio = waist circumference (cm) /hip circumference (cm)
Body density * (women) = 1.07931-0.00059 x sum of three skinfolds (mm)-0.00015 x age Body density *(men) = 1.09556-0.00062 x sum of three skinfolds (mm)-0.00028 x age (* Sum of three skinfolds: triceps, subscapular, abdominal, reference (55, 56))
Percent body fat = (494 / body density) – 450 (51)
Household assets were used to construct a household wealth index as an indicator of economic status Ownership of a list of household assets was gathered by interview and included household vehicles, entertainment appliances and household appliances A wealth index was constructed using methods recommended by the World Bank Poverty Network and UNICEF, and described by Filmer & Pritchett 1998(79) The method uses the principal components statistical procedure to determine the weights for a wealth index based on information collected about household assets and facilities In our survey this information was collected in the questionnaire from interviews with subjects, and the list of the items used to construct this index can be found in questionnaire in the Annex As noted by Filmer
& Pritchett 1998 (79), the index is based on the assumption that long-term household wealth is the major contributor to variation in the asset variables used to construct the wealth index
Principal components analysis extracts from a “large number of variables those few orthogonal linear combinations of the variables that best capture the common information The first principal component is the linear index of variables, with the largest amount of information common to all of the variables.” (79)
The first principal component output was used as a scoring factor to weight each of the assets or facilities in the wealth index The wealth index for each household (Wj) was calculated using the following formula:
Wj = f1 x (a j1- a1) / (s1) + + fN x (ajN- aN) / (sN)
Where f1 is the “scoring factor” for the first asset as estimated by the first principal component, aj1 is the j th household’s value for the first asset and a1 and s1 are the mean and standard deviation of the first asset variable over all households The score for each asset is summed over the N assets used to construct the index
To ensure data quality, physical activity data was cleansed according to guidelines set by the International Physical Activity Questionnaire Any time variables for walking, moderate, or vigorous activity exceeding 120 minutes were truncated to 120 minutes to normalize their distribution The volume of each physical activity was calculated in MET-minutes/week (where MET represents multiples of resting metabolic rate) using specific formulae.
Walking MET-minutes/week = 3.3 * walking minutes * walking ‘days’
Moderate MET-minutes/week = 4.0 * moderate-intensity activity minutes * moderate days
Vigorous MET-minutes/week = 8.0 * vigorous-intensity activity minutes * vigorous-intensity days
A combined total physical activity MET-min/week was computed as the sum of Walking + Moderate + Vigorous MET-min/week scores
Finally, the MET-minutes/week score was classified into quintiles from lowest, second, middle, fourth and highest to rank the level of activity of subjects
For each participant, energy and nutrient intakes for each food item were calculated using the following formula:
[reported daily frequency of consumption] * [reported portion size for each food item] * [standard portion size in grams of that food item] * [nutrients per 100 gram] / 100
Data on standard portion size and nutrients per 100 grams were obtained from the Vietnam National Food Composition Tables and the Vietnam Nutrients and Composite Foods Database Software (Eiyokun-Nutrition Centre HCMC) (80) Energy and nutrient intake for each food item was then added up to obtain the total intake score per day for each subject
Data on wealth index, energy intake, and level of physical activity were divided into five equal groups (quintiles)
Data entry screens were prepared to check for duplicate identification numbers and provide range checks for each variable (using Epi-Info) Data were entered into computer files using standardized procedures, checked and corrected by the researchers Data were analysed using statistical package STATA version 8.2 (2003; Stata Corporation, College Station, TX, USA) and adjusted for the cluster sampling design using the “svy” commands in STATA Statistical significance was taken as p ≤ 0.05
Distributions for all study variables were checked by using histogram, box plot and examining the means and median of each continuous variable For categorical variables, expected value of each cell in each table were checked to see if there were any values less than 5 (if this was a case, then non-parametric test were used)
Age and sex-specific prevalence of overweight and obesity defined by BMI, waist circumference were calculated Prevalence was adjusted (age and sex direct standardization) based on the age distribution in HCMC from the 1999 Vietnam National Census
Two-graph receiver operator characteristic (TG-ROC) curve analysis determined the optimal cut-off values for body mass index (BMI), waist circumference, waist-to-height ratio, and waist-to-hip ratio These cut-offs were established based on the detection of obesity-related cardiovascular risk factors, including blood pressure, lipid profile, and fasting blood glucose levels.
Univariate analyses and logistic regression models were used to examine the association of overweight (defined as BMI ≥ 23 kg/m 2 ) with socio-demographic characteristics (age-group, occupation, economic status, education) and behavioural risk factors (dietary intake, physical activity, cigarette smoking, and weight control behaviour) Model building began with univariate analysis of each variable Variables whose univariate test had p-values 140 or diastolic blood pressure of >90 mmHg
• High triglyceride: triglyceride ≥ 4.52 mmol/L (400 mg/dl)
• High blood glucose: fasting blood glucose ≥ 7 mmol/L (126 mg/dl)
• High LDL cholesterol: LDL cholesterol ≥ 4.12 mmol/L (160 mg/dl)
• High total cholesterol: total cholesterol ≥ 6.18 mmol/L (240 mg/dl)
• Low HDL cholesterol: HDL cholesterol