Noncommunicable disease risk factors and socioeconomic inequalities – what are the links?

(1)

www.wpro.who.int

Noncommunicable disease risk factors and socioeconomic inequalities–what are the links?A multicountry analysis of noncommunicable disease surveillance data

WHO - SES COVER Final.indd 1 6/11/2010 11:42:55 AM

(2)

i Section Title

Noncommunicable disease risk factors and socioeconomic inequalities – what are the links?

A multicountry analysis of noncommunicable disease surveillance data

Report to the WHO Regional Office for the Western Pacific

(3)

WHO Library Cataloguing in Publication Data Noncommunicable disease risk factos and socioeconomic inequalities – what are the links? : a multicountry analysis of noncommunicable disease surveillance data.

1. Non-communicable diseases. 2. Risk factors. 3. Social class. 4. Socioeconomic factors.

ISBN 978 92 9061 474 6 (NLM Classification: WT 30)

© World Health Organization 2010

All rights reserved. Publications of the World Health Organization can be obtained from WHO Press, World Health Organization, 20 Avenue Appia, 1211 Geneva 27, Switzerland (tel.: +41 22 791 3264; fax: +41 22 791 4857; e-mail: bookorders@who.int).

Requests for permission to reproduce or translate WHO publications – whether for sale or for noncommercial distribution – should be addressed to WHO Press, at the above address (fax: +41 22 791 4806; e-mail: permissions@who.int). For WHO Western Pacific Regional Publications, request for permission to reproduce should be addressed to the Publications Office, World Health Organization, Regional Office for the Wes tern Pacific, P.O. Box 2932, 1000, Manila, Philippines, Fax. No. (632) 521-1036, email:

publications@wpro.who.int

The designations employed and the presentation of the material in this publication do not imply the expression of any opinion whatsoever on the part of the World Health Organization concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Dotted lines on maps represent approximate border lines for which there may not yet be full agreement.

The mention of specific companies or of certain manufacturers’ products does not imply that they are endorsed or recommended by the World Health Organization in preference to others of a similar nature that are not mentioned. Errors and omissions excepted, the names of proprietary products are distinguished by initial capital letters.

All reasonable precautions have been taken by the World Health Organization to verify the information contained in this publication. However, the published material is being distributed without warranty of any kind, either expressed or implied. The responsibility for the interpretation and use of the material lies with the reader. In no event shall the World Health Organization be liable for damages arising from its use.

Dr Lingzhi Kong, Deputy Director-General, Department of Disease Control, Ministry of Health

Professor Guansheng Ma, Associate Director, National Institute for Nutrition and Food Safety, Chinese Center for Disease Control and Prevention

Prof Chen Chunming, Senior Advisor, Chinese Center for Disease Control and Prevention Mr Zhaohui Cui, Statistician, National Institute for Nutrition and Food Safety, Chinese Center for Disease Control and Prevention

Fiji Islands

Dr Temo K Waqanivalu, National NCD Advisor, Ministry of Health Malaysia

Dr Zainal Ariffin Bin Omar, Deputy Director, Disease Control Division, Ministry of Health Dr Mohamed Ismail Bin Abd Samad, Senior Principal Assistant Director, Disease Control Division, Ministry of Health

Nauru

Hon Dr Kieren Keke, M.P. Minister for Health, Sport and Transport, Ministry of Health Ms Maree Bacigalupo, Secretary, Health and Medical Services, Ministry of Health Dr Godfrey Itine Waidubu, Director of Public Health (Acting) and Senior Medical Officer (Physician)

Philippines

Ms Frances Prescilla Cuevas, Chief, Health Program Officer, Degenerative Disease Office, Department of Health

Dr Marina Baquilod, Co-ordinator, Chronic Disease Epidemiology, Department of Health Ms Felicidad V. Velandria, Food and Nutrition Research Institute

Ms Charmaine Duante, Statistician, Food and Nutrition Research Institute Dr Dante D. Morales, Chair, Steering Committee, NNHeS, 2003 Dr Antonio L. Dans, Chair, Technical Working Committee, NNHeS, 2003 Corresponding agency:

Centre for Physical Activity and Health (CPAH) School of Public Health

University of Sydney

Level 2, Medical Foundation Building K25 94 Parramatta Road, Camperdown NSW 2050 Sydney AUSTRALIA

e-mail: cpah@health.usyd.edu.au; tel: +61 2 9036 3193; fax: +61 2 9036 3184

(4)

iii Section Titleiii Table of Contents

A multicountry analysis of noncommunicable disease surveillance data

iii Section Title

iii Table of Contents

TABLES

Table 2.1 Overview of country cut-off points for NCD risks ... 15 Table 2.2 Overview of country cut-off points for demographic and SES variables (country-provided cut-off points) ... 18 Table 3.1 Survey characteristics in participating countries ... 20 Table 3.2 Location of data analysis ... 20 Table 3.3 Survey characteristics of participating countries by

demographic and socioeconomic status for men and women ... 21 Table 3.1.1. Prevalence rates of risk factors by socioeconomic status for men and women, China ... 22 Table 3.1.2 Probability of having NCD risk factors by socioeconomic status for men and women, China ... 25 Table 3.2.1 Prevalence rates of risk factors by socioeconomic status for men and women, Fiji ... 32 Table 3.2.2 Probability of having NCD risk factors by socioeconomic status for men and women, Fiji ... 35 Table 3.3.1 Prevalence rates of risk factors by socioeconomic status for men and women, Malaysia ... 42 Table 3.3.2 Probability of having NCD risk factors by socioeconomic status for men and women, Malaysia ... 45 Table 3.4.1 Prevalence rates of risk factors by socioeconomic status for men and women, Nauru ... 51 Table 3.4.2 Probability of having NCD risk factors by socioeconomic status for men and women, Nauru ... 53 Table 3.5.1 Prevalence rates of risk factors by socioeconomic

characteristics for men and women, Philippines ... 59 Table 3.5.2 Probability of having NCD risk factors by socioeconomic status for men and women, Philippines ... 61 Table 4.2.1 Summary of Age and risk factors association across

countries by sex ... 76 Table 4.2.2 Summary of Education and risk factors association across countries by sex ... 76 Table 4.2.3 Summary of Income and risk factors association across countries by sex ... 77 Table 4.2.4 Summary of Region and risk factors association across countries by sex ... 77 Table 4.2.5 Summary of Ethnicity and risk factors association across countries by sex ... 78

(6)

v Section Titlev Table of Contents

v Section Title

v Table of Contents

FIGURES

Figure 3.1.2a Probability of smoking by socioeconomic status for men and women, China ... 28 Figure 3.1.2b Probability of heavy alcohol drinking by socioeconomic status for men and women, China ... 28 Figure 3.1.2c Probability of low vegetable consumption by

socioeconomic status for men and women, China ... 28 Figure 3.1.2d Probability of low fruit consumption by socioeconomic status for men and women, China ... 29 Figure 3.1.2e Probability of obesity by socioeconomic status for

men and women, China ... 29 Figure 3.1.2f Probability of central obesity by socioeconomic status for men and women, China ... 29 Figure 3.1.2g Probability of high blood pressure by socioeconomic status for men and women, China ... 30 Figure 3.1.2h Probability of high total cholesterol by socioeconomic status for men and women, China ... 30 Figure 3.1.2i Probability of elevated fasting blood glucose by

socioeconomic status for men and women, China ... 30 Figure 3.1.2j Probability of high occupational physical activity by

socioeconomic status for men and women, China ... 31 Figure 3.1.2k Probability of highly active commuting by

socioeconomic status for men and women, China ... 31 Figure 3.1.2l Probability of high leisure-time physical activity by

socioeconomic status for men and women, China ... 31 Figure 3.2.2a Probability of smoking by socioeconomic status

for men and women, Fiji ... 38 Figure 3.2.2b Probability of heavy alcohol drinking by socioeconomic status for men and women, Fiji ... 38 Figure 3.2.2c Probability of low vegetable and fruit consumption by socioeconomic status for men and women, Fiji ... 38 Figure 3.2.2d Probability of obesity by socioeconomic status

for men and women, Fiji ... 39 Figure 3.2.2e Probability of central obesity by socioeconomic status for men and women, Fiji ... 39 Figure 3.2.2f Probability of high blood pressure by socioeconomic status for men and women, Fiji ... 39 Figure 3.2.2g Probability of high total cholesterol by socioeconomic status for men and women, Fiji ... 40 Figure 3.2.2h Probability of elevated fasting blood glucose by

socioeconomic status for men and women, Fiji ... 40 Figure 3.2.2i Probability of high occupational physical activity by

socioeconomic status for men and women, Fiji ... 40 Figure 3.2.2j Probability of highly active commuting by

socioeconomic status for men and women, Fiji ... 41 Figure 3.2.2k Probability of high leisure-time physical activity by

socioeconomic status for men and women, Fiji ... 41

(7)

vi Table of Contents

Figure 3.3.2a Probability of smoking by socioeconomic status

for men and women, Malaysia ... 48 Figure 3.3.2b Probability of low vegetable and fruit consumption by socioeconomic status for men and women, Malaysia ... 48 Figure 3.3.2c Probability of obesity by socioeconomic status for

men and women, Malaysia ... 48 Figure 3.3.2d Probability of central obesity by socioeconomic status for men and women, Malaysia ... 49 Figure 3.3.2e Probability of high blood pressure by socioeconomic status for men and women, Malaysia ... 49 Figure 3.3.2f Probability of high total cholesterol by socioeconomic status for men and women, Malaysia ... 49 Figure 3.3.2g Probability of elevated fasting blood glucose by

socioeconomic status for men and women, Malaysia ... 50 Figure 3.3.2h Probability of high occupational physical activity by socioeconomic status for men and women, Malaysia ... 50 Figure 3.3.2i Probability of highly active commuting by

socioeconomic status for men and women, Malaysia ... 50 Figure 3.3.2j Probability of high leisure-time physical activity by

socioeconomic status for men and women, Malaysia ... 51 Figure 3.4.2a Probability of smoking by socioeconomic status for men and women, Nauru ... 55 Figure 3.4.2b Probability of heavy alcohol drinking by

socioeconomic status for men and women, Nauru ... 55 Figure 3.4.2c Probability of low vegetable and fruit consumption by socioeconomic status for men and women, Nauru ... 56 Figure 3.4.2d Probability of obesity by socioeconomic status for

men and women, Nauru ... 56 Figure 3.4.2e Probability of central obesity by socioeconomic status for men and women, Nauru ... 56 Figure 3.4.2f Probability of high blood pressure by socioeconomic status for men and women, Nauru ... 57 Figure 3.4.2g Probability of high total cholesterol by socioeconomic status for men and women, Nauru ... 57 Figure 3.4.2h Probability of elevated fasting blood glucose by

socioeconomic status for men and women, Nauru ... 57 Figure 3.4.2i Probability of high occupational physical activity by socioeconomic status for men and women, Nauru ... 58 Figure 3.4.2j Probability of highly active commuting by

socioeconomic status for men and women, Nauru ... 58 Figure 3.4.2k Probability of high leisure-time physical activity by

socioeconomic status for men and women, Nauru ... 58 Figure 3.5.2a Probability of smoking by socioeconomic status for men and women, Philippines ... 64 Figure 3.5.2b Probability of heavy alcohol drinking by socioeconomic status for men and women, Philippines ... 64 Figure 3.5.2c Probability of low vegetable and fruit consumption by socioeconomic status for men and women,

Philippines ... 64

(8)

vii Section Title vii Table of Contents

vii Section Title

vii Table of Contents

Figure 3.5.2d Probability of obesity by socioeconomic status for

men and women, Philippines ... 65 Figure 3.5.2e Probability of central obesity by socioeconomic status for men and women, Philippines ... 65 Figure 3.5.2f Probability of high blood pressure by socioeconomic status for men and women, Philippines ... 65 Figure 3.5.2g Probability of high total cholesterol by socioeconomic status for men and women, Philippines ... 66 Figure 3.5.2h Probability of elevated fasting blood glucose by socioeconomic status for men and women, Philippines ... 66 Figure 3.5.2i Probability of high occupational physical activity by

socioeconomic status for men and women,

Philippines ... 66 Figure 3.5.2j Probability of highly active commuting by socio-

economic status for men and women, Philippines ... 67 Figure 3.5.2k Probability of high leisure-time physical activity by

socioeconomic status for men and women,

Philippines ... 67 Figure 4.1.1 Summary of prevalence rates for smoking across

countries by age, sex and socioeconomic status ... 70 Figure 4.1.2 Summary of prevalence rates for hazardous drinking across countries by age, sex and socioeconomic

status ... 70 Figure 4.1.3 Summary of prevalence rates for poor vegetable/fruit consumption across countries by age, sex and

socioeconomic status ... 71 Figure 4.1.4 Summary of prevalence rates for obesity across

countries by age, sex and socioeconomic status ... 71 Figure 4.1.5 Summary of prevalence rates for central obesity

across countries by age, sex and socioeconomic

status ... 72 Figure 4.1.6 Summary of prevalence rates for high blood pressure across countries by age, sex and socioeconomic

status ... 72 Figure 4.1.7 Summary of prevalence rates for high cholesterol

across countries by age, sex and socioeconomic

status ... 73 Figure 4.1.8 Summary of prevalence rates for high fasting blood glucose across countries by age, sex and socio-

economic status... 73 Figure 4.1.9 Summary of prevalence rates for high levels of

occupational physical activity across countries by age, sex and socioeconomic status ... 74 Figure 4.1.10 Summary of prevalence rates for high levels of

commuting activity across countries by age, sex and socioeconomic status ... 74 Figure 4.1.11 Summary of prevalence rates for high levels of leisure- time physical activity across countries by age, sex and socioeconomic status ... 75

(9)

viii Acknowledgements

Acknowledgements

This report is a product of an international collaborative research initiative between the WHO Regional Office for the Western Pacific, the Centre for Physical Activity and Health (CPAH) and the Prevention Research Centres (PRC) at the School of Public Health, University of Sydney (Australia), and participating countries in the WHO Western Pacific Region (China, Fiji, Malaysia, Nauru and the Philippines).

The project was supported, in part, by the WHO Regional Office for the Western Pacific.

The support provided by Dr Gauden Galea (Regional Adviser,

Noncommunicable Diseases), Dr Cherian Varghese (Technical Officer, Noncommunicable Diseases), Ms Anjana Bhushan (Technical Officer, Health in Development), Ms Ailene Trinos and Ms Sylvia Brown (Assistants in the WHO Western Pacific Regional Office), and Dr Han Tieru (WHO Representative, Malaysia) is gratefully acknowledged.

(10)

1 Section Title1 Background

Background

(11)

2 Background

For the purposes of this report, the term ‘noncommunicable disease (NCD)’ refers to the four broad categories of cardiovascular disease, cancer, chronic respiratory disease and diabetes.¹ The growing burden of NCDs affects all levels of society in rich and poor countries and is contributing to increasing proportions of the total burden of disease, especially among adults. In 2005, NCDs accounted for at least 50% of all deaths worldwide and projections indicate that, by 2015, at least 60% of deaths will be attributable to NCDs.² Socioeconomic factors have been recognized as playing a major role in the distribution of NCDs in both wealthy and poor countries.^3,4

In developed countries, evidence shows that NCDs and their risk factors initially occur in groups with the highest socioeconomic status (SES) and those living in urban areas, before the burden of disease shifts to all social groups.^5,6 Evidence from developed countries also shows that those from higher SES groups are usually the first to respond to NCD prevention campaigns, while those from lower SES groups continue to experience increasing rates of NCDs.⁷ It has been argued that the patterns observed in developed countries are likely to be replicated in developing countries, whereby the NCD burden shifts gradually to those with lower educational attainment or economic status.^{8 9,10} The double burden of disease that currently challenges many developing countries will place increased stress on already stretched clinical and prevention resources, with those from lower SES groups possibly receiving

inadequate care for both noncommunicable and communicable diseases.

Relationships between NCD risk factors and SES

An NCD risk factor refers to any characteristic or attribute of an individual which increases that person’s risk of developing an NCD. The likelihood of developing NCDs depends upon the severity and number of risk factors that individuals possess or to which they are exposed.

These risk factors can be genetic, behavioural or environmental.

This section provides a brief overview of current knowledge on the relationships between key NCD behavioural risk factors (e.g., physical inactivity, poor nutrition, smoking, alcohol consumption) and biological risk conditions (e.g., obesity, high blood pressure, blood lipids, high blood glucose levels) and measures of demographic and SES (e.g., sex, age, urban/rural residence, ethnicity, education, income). The same risk factors can affect more than one NCD condition (e.g., smoking, poor nutrition and obesity are common risk factors for heart disease and diabetes), and they are also likely to cluster with each other. For example, central adiposity is clustered with high blood pressure, high cholesterol and physical inactivity.

(12)

3 Section Title

3 Background

For the purposes of this review, relevant peer-reviewed articles on developing countries were researched in electronic databases, including Medline, CINAHL and PsycINFO. Additional papers were identified via manual searching. Additional information on associations between NCD risk factors and SES was gained by auditing various websites.

This involved a review of the ‘grey’ literature (including organizations’

websites, reports, media releases and news) to obtain a broad range of available information from developing countries.

Socioeconomic status and smoking

The burgeoning growth in tobacco use in most developing countries contrasts with the declining rates in developed countries.¹¹ This represents a potential threat to vascular health and cancer risk in low- and middle-income countries. While smokers in these countries are initially more likely to belong to higher SES groups, the trend among tobacco companies to target poor communities to take up tobacco use suggests that this pattern might change. Studies from Brazil, China, India, South Africa, Viet Nam and Central America now show an inverse relationship: the prevalence of smoking in low SES groups is higher than among high SES groups.^12,13,14

In terms of sex and smoking status, the literature generally indicates a higher prevalence of smoking among men than among women in developing countries. This finding is not limited to one particular geographical area, but is noted in Africa, Asia, the Pacific islands and South America. Given the very large populations of countries such as China and India, this translates into hundreds of millions of women smoking in these countries, despite the relatively lower prevalence rates among women. While prevalence among men is higher than that among women in most countries, and continues to rise, anecdotal evidence also indicates that smoking rates may also be rising rapidly among women, especially among affluent urban young women in China, India and Singapore.¹⁵

Socioeconomic status and alcohol

Those who consume alcohol at levels considered to be ‘hazardous’

are at increased risk of traffic injuries, violence and other unintentional injuries, engaging in unsafe sexual practices and smoking. Unlike the case with smoking, which classifies people as current smokers, ex-smokers or non-smokers, there are no agreed measures and classifications of hazardous alcohol drinking. Various studies discuss hazardous alcohol consumption in terms of binge drinking (i.e., consuming at least five units of alcohol in one sitting for men or four for women), frequent

(13)

4 Background

drunkenness, or drinking hazardously to increase health risks, as measured by frequency, amount and duration of drinking.

Despite the measurement challenges, most studies show that men are more likely to consume alcohol frequently and in larger amounts than women, and this is seen consistently across countries and cultures.16,17,18,19,20 Differences between men and women in tolerance levels for alcohol have been suggested as a factor for explaining the differences observed.²¹ Cultural influences and tolerance of heavy drinking (or lack thereof) may also be relevant.^22,23 Those from lower SES groups, those living in rural areas, or those with lower levels of education are generally more likely to use alcohol on a regular basis.24,25,26,27 A recent study examining social inequalities in alcohol consumption in 15 countries found that, while those with less education were more likely to drink heavily in most countries (Austria, the Czech Republic, France, Finland, Germany, Hungary, the Netherlands and Switzerland), in Brazil and Mexico those from better-educated groups were more likely to consume alcohol in a risky manner.²⁸ These findings confirm that patterns of social inequalities in alcohol consumption vary across countries. While the association between SES and alcohol intake has been documented in a number of South Asian, Eastern European, African and Latin American countries, populationwide data for South- East Asia and the Pacific are still limited.

Socioeconomic status and nutrition

The globalization of food trading and marketing has resulted in major shifts in food consumption patterns towards diets high in sugar, fats and salt, as well as refined foods low in dietary fibre and micronutrients.^29,30,31 Rapid urbanization and economic development are further contributing to the higher consumption of relatively protein-rich, higher-fat foods among those from higher SES groups. This pattern is observed consistently in India, Tonga and Viet Nam.^32,33,34 Specifically, data from the China Health and Nutrition Survey (CHNS) showed a three-fold increase (from 22.8% to 66.6% between 1989 and 1993) in consumption of higher-fat foods among higher-income adults.³⁵ A similar trend was also observed among lower- and middle-income households. Also in China, evidence from the 1989-1993 longitudinal data of the CHNS indicates a strong correlation between rising household income and high-fat diets.³⁶ In this study, the proportion of the population obtaining more than 30% of their energy from fat was higher in urban and higher- income households than in those in rural areas or with lower incomes.

At the same time, Popkin noted that the increased affordability of edible oil had led to increases in fat consumption among those in lower SES groups, which in part explained the nutrition transition observed across all socioeconomic groups in China.³⁷

(14)

5 Section Title

5 Background

Socioeconomic status and obesity

A seminal review by Sobal and Stunkard documented a direct association between SES and obesity among men, women and children in developing countries: obesity is more prevalent among those of higher SES than among those of lower SES.³⁸ This pattern is the opposite of that found in developed countries, especially among women, in whom a strong inverse association between SES and obesity is observed.

A more recent review of the evidence, based on 14 surveys conducted between 1982 and 2003 in lower- to middle-income countries (Brazil, Chile, China, Cuba, India, Lithuania, Peru, Russian Federation, Samoa and South Africa), however, shows a changing picture in the relationship between SES and obesity, with the burden of obesity shifting towards individuals of lower SES as a country’s gross national product increases.³⁹ This pattern persists regardless of how SES is measured, i.e., at the individual (e.g., income, education) or community level (e.g., employment level, educational level, income of the community). For example, a recent study examining data collected from 1992 to 2000 in 36 developing countries in Sub-Saharan Africa, North Africa and the Middle East, Central Asia, East and South Asia, Latin America and the Caribbean shows a higher prevalence of overweight than of underweight among young women living in rural and urban areas.⁴⁰

Socioeconomic status and physical activity

Research has examined different domains or settings for health- enhancing physical activity, with initial interest focusing on leisure- time physical activity. A global review revealed that the prevalence of inactivity in leisure time varies according to level of economic development, averaging 23% in the United States of America and Northwestern Europe (Belgium, France, Germany, Iceland, Ireland, the Netherlands and the United Kingdom), 30% in Central and Eastern Europe (Bulgaria, Hungary, Poland, Romania and Slovakia), 39% in Mediterranean countries (Greece, Italy, Portugal and Spain), 42% in the Asia-Pacific Rim (Japan, the Republic of Korea and Thailand), and 44% in other developing countries (Colombia, South Africa and Venezuela).⁴¹ A review of cross-national data from Eastern Europe (Estonia, Latvia and Lithuania) showed that lower educational level is a strong and consistent predictor of leisure-time inactivity in both men and women.⁴² In an urban Brazilian population, total physical inactivity was positively associated with age and SES.⁴³ Sobngwi and colleagues, however, found that urban populations in Cameroon spent twice as much time as rural populations in leisure activities.⁴⁴

(15)

6 Background

Other domains of analysis of physical activity include occupational (workplace) energy expenditure, active commuting or transport, and active domestic tasks and chores. Cross-national data from Pacific island countries (Fiji, Kiribati and Vanuatu) showed that levels of occupational physical activity are higher in rural than in urban populations.⁴⁵ The higher energy expenditure of rural populations is a result of their predominantly manual occupations, such as subsistence farming and fishing. Similarly, in Cameroon, urban populations were characterized by lower occupational physical activity and reduced time spent on walking and cycling for transportation, compared with rural populations.⁴⁶ In contrast, most of the physical activity reported in urban populations of Benin City, Nigeria, was attributed to occupational physical activity (including active transport time, spent walking or cycling to work) rather than leisure-time activity.⁴⁷ The study noted that senior male staff (representing higher SES) had a lower level of physical activity than junior male staff. In another cross-country comparison (of Kazakhstan and Kyrgyzstan), those in lower-status occupations reported much greater physical activity at work than those in higher-status occupations.⁴⁸ Generally, while men tend to be more active than women in terms of work, transport and leisure-time physical activity, time-use studies across the world consistently show that women spend more time in active domestic chores than men and have fewer hours of leisure- time activity.⁴⁹ Although research on energy expenditure in domestic activities points to the potential contributions of these activities to health, measuring energy expenditure attributed to such activities at the population level remains a challenge in developing countries.⁵⁰ Considering that domestic activities may be a primary source of energy expenditure for women, population-wide assessment of this domain of physical activity is required to address the information gap.

Socioeconomic status and blood pressure

In many transitional countries^a hypertension appears to be more prevalent among urban than rural populations.^51,52 For example, in a study from Viet Nam, hypertension was found to be most prevalent in the urban population and in the richest income quintile of the rural population.⁵³ The study noted a gradient of risk for hypertension in rural areas: the risk was 1.5 times higher for those in the richest quintile compared with the poorest. Thus, rapid urbanization and transition from agrarian life to wage-earning, modern city life are reported as major contributors to increases in elevated blood pressure in urban areas.^54,55,56 Studies of Caribbean, African and United States populations have also observed increased prevalence of hypertension with age.⁵⁷ Associations

a Countries moving towards a market-style economy.

(16)

7 Section Title

7 Background

of hypertension with other SES measures, including education, income and occupation, have been observed, but are inconsistent. In Jamaica, both low- and high-income groups are reported to have elevated blood pressure.⁵⁸ In Africa, both lower-income groups (because of more socioeconomic stress, lower access to facilities, and poorer diet) and higher-income groups (because of greater obesity, access to food and alcohol consumption, and less exercise) are considered to be at risk of developing hypertension.⁵⁹ An explanation for these non-linear associations is the differential effect of SES indicators on mediating risk factors such as obesity, physical activity and alcohol consumption.^60,61,62 Socioeconomic status and blood lipids

Higher lipid levels have been observed among those from middle- and upper-income or higher-SES groups. In Nigeria, in 1996, Taylor and colleagues reported mean plasma total cholesterol to be higher in the medium-income group than in the low-income group, for adults aged 20-59 years.⁶³ In a cohort of 1169 Chinese urban male workers, those with more education were found to have significantly higher low density lipoprotein (LDL) cholesterol than those with less education.⁶⁴ It is hypothesized that increased consumption of saturated dietary fat and reduced physical activity among the more privileged Chinese workers contributed to the patterns observed. Obesity may also be a contributing factor.

In contrast, Larranaga and colleagues, in 2005, found an inverse relationship between cholesterol and SES status.⁶⁵ Adults of lower SES attending primary care clinics in the Basque region of Spain were found to have abnormally higher LDL cholesterol levels compared with patients of higher SES.

Not all studies have confirmed an association between cholesterol and SES. A Hong Kong study of 2847 Chinese adults with known risk factors for glucose intolerance found no significant association between occupation or education levels and total cholesterol, in either men or women.⁶⁶Overall, the evidence to date indicates an inconsistent relationship between cholesterol levels and SES measures.

Socioeconomic status and diabetes

Numerous studies from developed countries have consistently documented an inverse association between Type 2 diabetes and income, education and occupation, across all adult age groups.67,68,69,70

However, the evidence is mixed on the differences between men and women. Robbins and colleagues found consistent associations between poverty and diabetes among women but not among men, while studies

(17)

8 Background

by Marmot and colleagues, in 1991, and Leonetti and colleagues, in 1992, reported an association between SES and diabetes in men.^71,72,73 Larranaga and others found that the risk of developing Type 2 diabetes was higher among residents living in the most deprived areas for both men and women, although the risk was higher among women.⁷⁴ Increased prevalence of diabetes was observed in Hong Kong (China) among men and women in the lowest SES group, as defined by

educational or occupational level, after adjusting for age.⁷⁵ However, the pattern of association is different in other developing countries. Among urban populations of southern India, Mohan and colleagues reported a significant increase in the risk for diabetes with increasing income in 2001.⁷⁶ A similar observation was also reported in studies carried out in other developing countries: diabetes is more prevalent among individuals of higher SES than among those of lower SES.^77,78,79 Explanations suggest that rapid economic transition coupled with the changes from traditional to modern lifestyles seen in many countries with high economic growth, such as China and India, without corresponding changes in educational level and health awareness, have led to decreased physical activity and increased calorie and fat consumption, which in turn has contributed to the higher prevalence of risk factors among affluent populations of developing countries.

Purpose of the project

This project identified countries in the Asia Pacific Region that have population-level surveillance data on NCD risk factors. Country teams were then set up to conduct a series of country-specific (re)analyses of the data, using a standardized protocol. Specifically, the project aimed to conduct comparable cross-country analyses to examine the relationships between NCD (behavioural) risk factors and indicators of social

disadvantage and socioeconomic status. The central research question was whether the distribution of risk factors within countries is similar across socioeconomic groups between different countries, cultures and economies, or different, and in what way the differences are manifest.

Although this question has been explored in developed countries to a large extent, cross-national data from developing countries are sparse.

To collaborate in the research, countries needed to have representative population data with broadly comparable demographic and

socioeconomic measures, as well as clearly identified (usually self- reported) measures of obesity, nutrition, alcohol, smoking, physical activity, hypertension, cholesterol and other possible NCD risk factors.

The report is divided into five chapters. Chapter 1 presents a review of the evidence on the relationships between NCD risk factors and

(18)

9 Section Title

9 Background

SES in developed and developing countries. Chapter 2 outlines the methodology for conducting the (re)analysis and provides a detailed summary of the cut-off points for NCD risk factors and SES measures used by each country. Chapter 3 presents the country-specific results in terms of prevalence rates and independent associations between risk factors and SES measures. Chapter 4 presents the cross-country comparison of associations between NCD risk factors and SES

measures. Chapter 5 discusses the policy and programme implications of these results.

(19)

(20)

11 Section Title 11 Methodology

Methodology

(21)

12 Methodology

The research in this project was a collaborative initiative between the WHO Regional Office for the Western Pacific; the Centre for Physical Activity and Health (CPAH) and the Prevention Research Centres (PRC) at the School of Public Health, University of Sydney (Australia); and participating countries in the WHO Western Pacific Region (China, Fiji, Malaysia, Nauru and the Philippines).

The CPAH/PRC group managed the communications between project partners and developed the common protocol for analysis in collaboration with the country teams. The CPAH group also provided technical support for data analysis, synthesis and interpretation of results, as well as preparation of technical reports, as requested by countries.

Some participating country teams (China, Malaysia and the Philippines) carried out the country-level analysis themselves, either in-country or during a two-week supervised visit at CPAH, in a standardized way based on the common protocol for analysis. Other country teams (Fiji and Nauru) assigned CPAH to conduct their population survey analyses.

Country participation criteria

Available cross-sectional populationwide datasets from developing countries in the Asia Pacific Region were considered eligible for inclusion in the study if they:

• comprised a representative sample of the national population;

• contained information on the sampling strategy, sample size and response rate; and

• measured most of the following variables relevant for noncommunicable disease risk:

n demographic and socioeconomic status:

u age;

u sex; and

u individual- and area-level SES measures (e.g. urban/rural residence, education, occupation, employment status, income)^b; and

n health risk factors:

u health behaviour measures (e.g. smoking, alcohol consumption, dietary habits, physical activity);

u anthropometry measures (e.g. height, weight, waist circumference); and

u biochemical measures (e.g. fasting blood lipid levels, fasting blood glucose levels, oral glucose tolerance test (OGTT), diastolic and systolic blood pressure).

b These indicators were not always comparable across countries, but estimates of SES distribution within each country could be calculated

(22)

13 Section Title

13 Methodology

Protocol for data analysis

A common protocol for analysis was provided to country teams in order to standardize the country analyses and thereby enable cross-country comparisons.

For all countries, the process of data analysis was divided into seven main steps as follows:

1. Describe the sampling methodology and data collection procedures.

2. Compute response rates.

3. Compute sampling weights (where relevant).

4. Compute descriptive statistics of all NCD risk factors and SES measures to examine the distributions according to age group and sex.

5. Create meaningful cut-off points by grouping responses into either low- or high-risk for NCD using country-specific or international cut-off points. If standard cut-off points are not available,

summarize the distributions of various continuous variables (e.g., dietary intakes reported in grams) into equal groups, using either quartiles or tertiles, as appropriate. (It was noted that cut-off points for low/high risks or classification for SES measures were not always comparable, but were based on the countries’ own distributions for those variables. National cut-off points, either provided by countries or based on international classifications, were used in preference to international cut-off points suggested in the protocol. Estimates of risk factors and SES distributions within each country were then computed. Table 2.1 shows an overview of all the country-specific cut-off points used.)

6. Compute contingency tables to examine the bivariate relationships between NCD risk factors and SES measures (e.g., crude

estimates for all risk factors calculated by region, education and income), with all analyses conducted separately for men and women.

7. Conduct a series of multivariate logistic regression analyses to examine the independent associations between different NCD risk factors and SES indicators, adjusting for age and all other SES measures included in the model. Perform all analyses separately for men and women. Produce estimated odds ratios with 95%

confidence intervals.

While all estimates were computed and presented as risk behaviours (e.g., smoking, alcohol abuse, low consumptions of fruit and vegetables, hypertension, etc.), indicators of physical activity were computed and presented as protective of NCD risks (e.g., high levels of physical activity

(23)

14 Methodology

in work, travel and leisure time). Multiple logistic regression analyses also modelled the probably of having moderate to high levels of physical activity, instead of the probability of being physically inactive.

Countries conducted the analyses using either SAS® (Statistical Analysis Software), SPSS® (Statistical Package for the Social Sciences) for

Windows®, or Stata®.

Risk conditions relating to anthropometric or physiological factors (elevated blood pressure, elevated blood glucose, abnormal blood lipids, overweight/obesity) and behavioural factors (tobacco use, alcohol consumption, physical inactivity and unhealthy diet) were selected (Table 2.1) because, combined, they have the greatest impact on contributing to NCDs. Also, to produce comparable analyses across all countries, only broadly comparable risk factors available in the datasets were considered for inclusion in the study.

(24)

15 Section Title

15 Methodology

Table 2.1 Overview of country cut-off points for NCD risks ¹

Risk factor China Fiji Malaysia Nauru Philippines

Smoking Current smoking:

Having ever smoked, smoked continuously/cumulatively for six months and more, and smoking in the past month preceding the survey

Current smoking:

Smoke either daily or yes, but not every day

Current smoking:

Some daily or weekly at the time of the survey

Current smoking:

Smoking daily or weekly at the time of the survey

Current smoking:

Smoked in the past month preceding the survey

Hazardous drinking Hazardous drinking (measuring pure alcohol in grams) defined according to level of risk.

Men: Low risk: 1–40g Medium risk: 41–60g High risk: >60g Women: Low risk: 1–20g Medium risk:

21–40g High risk: >40g Note: Cut-off points based on (1)

Hazardous drinking based on frequency of drinking in past 12 months and average number of drinks consumed per day

At-risk drinking referred to as drinking five or more standard drinks per day (for men) and four or more standard drinks (for women)

Hazardous drinking based on frequency of drinking in past 12 months and average number of drinks consumed per day

Hazardous drinking (measuring pure alcohol in grams) defined according to level of risk.

Men: Low risk: 1–40g Medium risk: 41–60g High risk: >60g Women: Low risk: 1–20g Medium risk:

21–40g High risk: >40g Note: Cut-off points based on (1)

Poor diet Consumed <400g of fresh and dry vegetables per day Fresh fruit consumption of

<100g per day

Consumed <5 servings of fruits and vegetables per day

Overweight and

obese Acceptable BMI <24 Overweight BMI 24–27.9 Obese BMI ≥28 Note: Cut-off points based on classification for adult Asians (2)

Acceptable BMI 18.5–24.9 Overweight BMI 25.0–29.9 Obese BMI ≥30.0 Note: Cut-off points based on WHO international classification (3)

Acceptable BMI18.5–24.9 Overweight BMI 25.0–29.9 Obese BMI ≥30.0 Note: Cut-off points based on WHO international classification (3)

Acceptable BMI 18.5–24.9 Overweight BMI 25.0–29.9 Obese BMI ≥30.0 Note: Cut-off points based on WHO international classification (3)

Central obesity (waist circumference)

Men ≥85 cm Women ≥80 cm Note: China national cut-off point used (4)

Men ≥110 cm Women ≥100cm Note: Pacific cut-off points (5,6,7)

Men≥90 cm Women ≥80 cm Note: Cut-off points for adult Asians (8)

Men ≥110 cm Women ≥100cm Note: Pacific cut-off points (5,6,7)

Men ≥102 cm Women ≥88 cm Note: Cut-off points based on WHO international classification (9)

1 Country-provided cut-off points used to describe low-/high-risk groups.

Sources:(1) English DR et al. The quantification of drug caused morbidity and mortality in Australia. Canberra,Department of Human Services and Health, 1995. (2) WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and interventions strategies. Lancet, 2004, 363, 9403: 157-163. (3) Obesity – Preventing and managing the global epidemic. Report of a WHO Consultation. Geneva, World Health Organization, 2000 (WHO Technical Report Series 894). (4) Cooperative Meta-analysis Group of China Obesity Task Force. Predictive values of body mass index and waist circumference to risk factors of related diseases in Chinese adult population.

Chinese journal of epidemiology, 2002, 23(1):5-10. (5) Craig C et al. Identifying cut-points in anthropometric indexes for predicting previously undiagnosed diabetes and cardiovascular risk factors in the Tongan population. Obesity research and clinical practice, 2007, 1:17-25. (6) Swinburn BA et al. Body size and composition in Polynesians. International journal of obesity, 1999, 23:1178-1183. (7) Personal communication, Egger G. (8) World Health Organization/International Obesity Task Force . The Asia-Pacific perspective: redefining obesity and its treatments. Sydney, Health Communications Australia,2000. (9) Obesity: Preventing and managing the global epidemic. Report of a WHO Consultation on Obesity.

Health Geneva,World Health Organization, 1998.

(25)

16 Methodology

Table 2.1 Overview of country cut-off points for NCD risks¹

Risk factor China Fiji Malaysia Nauru Philippines

High blood

pressure Systolic blood pressure

≥140mmHg and/

or diastolic blood pressure ≥90mmHg, or

Took medication for hypertension in the last two weeks preceding the survey Note: Definition according to (1)

Systolic blood pressure ≥140mmHg and/or diastolic blood pressure ≥90mmHg

Note: Definition according to (1)

Systolic blood pressure

≥140mmHg and/or diastolic blood pressure

≥ 90mmHg

≥90mmHg

High cholesterol Total cholesterol

≥5.72 mmol/L Note: China national cut-off point used (2)

Total cholesterol

≥5.5 mmol/L Note: Cut-off point considered to constitute an increased (borderline) risk for developing cardiovascular disease

Total cholesterol >6.5 mmol/L

Note: Cut-off point considered to constitute a high risk for developing cardiovascular diseases

Total cholesterol ≥5.5 mmol/L

Note: Cut-off point considered to constitute an increased (borderline) risk for developing cardiovascular diseases

Total cholesterol >239 mg/dL (>6.13 mmol/L) Note: Cut-off point according to (3)

High fasting blood

glucose Individuals who reported a previous medical diagnosis of diabetes and were receiving treatment during the survey were classified as diabetic.

Fasting blood glucose

≥7.0 mmol/L, or Oral glucose tolerance test (OGTT) ≥ 11.1mmol/L, or Note: Cut-off point based on (4) WHO classification of diabetes mellitus

Fasting serum blood glucose ≥6.1mml/L

Note: Cut-off point based on (4) WHO classification of diabetes mellitus

Fasting plasma blood glucose ≥7.0 mmol/L

Fasting blood glucose

>125mg/dL

1 Country-provided cut-off points used to describe low-/high-risk groups

Sources: (1) WHO)/International Society of Hypertension . Guidelines for the management of hypertension. Journal of hypertension, 2003, 21:1983-1992. (2) Committee Dyslipidemia Force. Control of dyslipidemia. Chinese journal of cardiology, 1997, 25(3):169-172. (3) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). Executive summary of the Third Report of the National Cholesterol Education Program (NCEP). Journal of tha American Medical association, 2001 285:2486-97. (4) Definition, diagnosis and classification of diabetes mellitus and its complications. Report of a WHO Consultation. Part 1. Geneva, World Health Organization, 1999.

(26)

17 Section Title

17 Methodology

Table 2.1 Overview of country cut-off points for NCD risks¹

Risk factor China Fiji Malaysia ² Nauru ² Philippines ³

Physical activity

(PA) Three domains of PA

were computed:

Occupational PA:

• Heavy (25% of time sitting or standing, 75% of time doing non-mechanical farm work, steel-making, dancing, sports, loading, mining, etc.).

• Moderate (40%

of time sitting or standing, 60% of time doing activities such as driving cars, electrical work, operating machinery or metal incision, etc.).

• Light (75% of the time sitting or standing, 25% of time standing active, such as office work, repairing electric appliances, shop workers, waiters or hotel workers, chemical laboratory workers, lecturers, etc.).

Respondents then grouped into:

• Heavy occupational PA vs. moderate /light PA Active commuting:

• Cycling/walking (for at least 30 minutes) vs. using motorized vehicles such as bus, car or motorcycle Leisure-time PA (LTPA):

• Exercise ≥4 times per week for 150 minutes in total vs. exercise

<4 times per week

Three domains of PA were computed:

Respondents asked duration of a typical work day, then asked to indicate on a 4-point Likert scale (‘Almost always’ to ‘almost never’) hours at work per day engaged in:

• sitting or standing with little walking,

• physical effort like continuous walking, gardening, and

• heavy lifting or heavy construction work.

Respondents then grouped into:

• Heavy occupational PA (doing heavy work always and usually and moderate work always) vs. moderate/

light PA (responses indicated in other Likert categories).

Active commuting:

• Cycling/walking (always or usually) vs.

motorized vehicles, such as bus, car, taxi, bilibili, boat LTPA:

• Exercise ≥30mins or more per day vs. <30 mins

Three domains of PA were computed using the following MET values:

Moderate MET= 4.0 Vigorous MET = 8.0 Cycling/walking MET=4.0

For all three domains, respondents were classified into three PA levels according to GPAQ scoring protocol:

• Low PA: No activity OR some activity but not moderate or vigorous.

• Moderate PA: 3+ days of vigorous activity of at least 20 minutes per day OR 5+ days of moderate activity and/or walking of at least 30 minutes per day OR 5+ days of any combination of walking, moderate or vigorous activities, achieving a minimum of at least 600 MET- min/weeks.

• High PA: activity on at least 3 days and accumulating at least 1500 MET-minutes/

week OR 7+ days of any combination of walking, moderate or vigorous activities, accumulating at least 3000 MET-minutes/

week

For Occupational PA, Active commuting and LTPA, respondents then grouped into:

• High PA vs. moderate/

low PA

Three domains of PA were computed using the following MET values:

Moderate MET= 4.0 Vigorous MET = 8.0 Cycling/walking MET=4.0

For all three domains, respondents classified into 3 PA levels according to GPAQ scoring protocol:

• Low PA: No activity OR some activity but moderate or vigorous

• Moderate PA: 3+ days of vigorous activity of at least 20 minutes per day OR 5+ days of moderate activity and/or walking of at least 30 minutes per day OR 5+ days of any combination of walking, moderate or vigorous activities, achieving a minimum of at least 600 MET- min/weeks,

• High PA: activity on at least 3 days and accumulating at least 1500 MET-minutes/

week OR 7+ days of any combination of walking, moderate or vigorous activities, accumulating at least 3000 MET-minutes/

week

For Occupational PA, Active commuting and LTPA, respondents then grouped into:

• High PA vs. moderate/

low PA

Three domains of PA were computed.

Respondents asked typical hours of work day, and percentage (0%–100%) spent sitting/standing, doing continuous moderate- intensity, and doing vigorous-intensity (heavy lifting/construction) work.

Responses translated into hours spent at each intensity level, assuming an eight-hour work day. To reflect greater intensity, the number of hours spent doing vigorous activities were weighted by 2.

The total numbers of hours spent were then summed and categorized into quartiles.

• Individuals in the highest quartile were classified as engaging in high PA vs. the rest.

Active commuting:

• Responses indicated hours spent cycling/

walking to places.

Responses then summed and quartiles computed. The highest quartile defined as active transport.

LTPA:

• Exercise either ‘every day’ or ‘3–5 times a week’ for 30–45 minutes’

1 Country-provided cut-off points used to describe low-/high-risk groups; ²Global Physical Activity Questionnaire – GPAQ; ³ Modified Global Physical Activity Questionnaire - GPAQ.

(27)

18 Methodology

To ensure comparable analysis across countries, only broadly comparable sociodemographic measures available in all the datasets were considered. While multiple measures of socioeconomic status were used, occupation-based measures of socioeconomic position were not included, as it was thought that these would not yield comparable data, given the variety in occupations across countries. Table 2.2 summarizes the demographic and SES variables selected for the study.

Table 2.2 Overview of country cut-off points for demographic and SES variables (country-provided cut-off points)

Variable China Fiji Malaysia Nauru Philippines

Age • 18 –35 years

• 36 –49 years

• 50 –65 years

• 18 –35 years

• 36 –49 years

• 50 –65 years

• 25 –35 years

• 36 –49 years

• 50 –64 years

• 18 –35 years

• 36 –49 years

• 50 –65 years

• 20 –35 years

• 36 –49 years

• 50 –65 years Education • Primary school

• Secondary school

• Professional/university

• Primary

• Secondary

• At least tertiary

• Primary

• Secondary

• Primary

• Secondary

• Primary

• Secondary

• At least tertiary Region (urban/rural) • Large cities (most

developed)

• Medium and small cities

• Rural I areas

• Rural II areas

• Rural III areas

• Rural IV areas (most remote/rural) Regions were further defined into 2 categories:

• urban (large, medium and small cities)

• rural (rural I-IV areas)

• Rural

• Urban • Rural

• Urban Not available Not available

Income Individual income per annum categorized into:

• Low

(< 2000 Yuan/year)

• Medium

(2000–9999 Yuan/year)

• High

(> 10000 Yuan/year)

Not available Household income per month categorized into:

• Low (<RM 1000)

• Medium (RM 1000–3999)

• High (>3999)

Not available Annual household income categorized into:

• Low (PhP ≤53 064)

• Medium

(PhP 53 065–92 192)

• High

(PhP 92 193–173 387)

• Very high (PhP ≥173 388 ) Ethnicity Not available • Fijian

• Indo-Fijian

• Other

• Malay

• Chinese

• Indian

• Others

Not available Not available

(28)

19 Section Title 19 Results

Results

(29)

20 Results

The results of the cross-national comparisons are presented here. The characteristics of the survey samples are described in Table 3.1.

Table 3.1 summarizes the profiles of five surveys conducted in the period between 2002 and 2006 by the collaborating countries. All studies reported on national surveys involving adults with a minimum entry age of 18 years. The collaborating countries comprised lower- to middle-income economies, China being the largest and the Pacific island country of the Republic of Nauru the smallest. The sample size ranged from 2085 (Nauru) to 142 693 (China). All the surveys were based on either the nutrition and health survey or the NCD STEPwise survey framework. All the surveys included men and women.

Table 3.2 outlines the analytical approach used by the various study countries. Two countries (China and the Philippines) carried out most of the analyses during a one- or two-week supervised visit to CPAH, two countries (Fiji and Nauru) agreed to CPAH carrying out the analyses, while Malaysia did all the analyses in-country, using the analytical protocol provided.

Table 3.1 Survey characteristics in participating countries Country Survey

year Survey name Response

rate (%) Age (years) Region Sampling

procedures Sample size China 2002 China National Nutrition

and Health Survey 79.1 18-65 National Stratified, multistage

cluster random sampling 142 693

Fiji 2002 Noncommunicable

Disease STEPwise Survey unknown 18-65 National Multistage cluster random

sampling 6763

Malaysia 2006 Noncommunicable

Disease STEPwise Survey 84.6 25-64 National Stratified, two-stage cluster

random sampling 2572

Nauru 2004

2006* Noncommunicable

Disease STEPwise Survey 82.3 18-65 National Random sampling 2085

Philippines 2003 National Nutrition and

Health Survey 97.0 20-65 National Stratified, three- stage

random sampling 3307

* Estimates for fasting blood glucose based on 2006 survey of n=504

Table 3.2 Location of data analysis

Country Location of analysis Group responsible for analysis

Availability of country-specific

report China Analysis conducted in Sydney with CPAH, during a

two-week supervised visit National Institute for Nutrition and Food Safety,

Chinese Center for Disease Control and Prevention Yes

Fiji CPAH, Sydney CPAH No

Malaysia In-country Diseases Control Division

Ministry of Health, Malaysia Yes

Nauru CPAH, Sydney CPAH No

Philippines Analysis conducted in Sydney with CPAH, during a one-week supervised visit and also in-country (with CPAH providing distance support)

Food and Nutrition Research Institute, Philippines Yes

(30)

21 Section Title 21 Results

21 Section Title

21 Results

Table 3.3 summarizes the survey samples of participating countries according to demographic and socioeconomic status, for men and women. The samples across countries had comparable proportions of men and women, with women respondents slightly outnumbering men in every country except the Philippines. While most countries surveyed participants aged 18 years and older, Malaysia included respondents aged 25 years and older and the Philippines covered respondents aged 20 years and older. Comparisons of age distribution also showed the samples of three countries to be slightly younger. All the survey samples tended to have moderately educated respondents. Three countries collected data on rural/urban distribution, the China sample being predominantly rural, the Fiji sample predominantly urban, and the Malaysia samples evenly distributed across urban and rural areas. Three countries obtained data on income. In Malaysia and China, the majority of survey participants reported low and moderate incomes, respectively.

In the Philippines sample, income was equally distributed across groups.

Table 3.3 Survey characteristics of participating countries by demographic and socioeconomic status, for men and women China

(N=142 693) ³ Fiji

(N=6763)⁵ Malaysia

(N=2572)^{1 3 5} Nauru

(N=2085) Philippines

(N=3307) ^{2 3} n (%)Men Women

n (%) Men

n (%) Women

n (%) Men

n (%) Women

n (%) Men

n (%) Women

n (%) Men

n (%) Women

n (%) SEX

Men 63 931 (44.9) - 2878 (46.3) - 1044 (40.6) - 925 (49.3) - 1660 (52.2) -

Women - 78 295 (55.1) - 3343 (53.7) - 1528 (59.4) - 952 (50.7) - 1647 (47.8)

AGE ^{1 2}

18-35 yrs 21 138 (33.1) 30 225 (38.6) 1666 (57.9) 1863 (55.7) 252 (24.1) 427 (27.9) 558 (60.4) 543 (57.0) 643 (54.1) 501 (46.5) 36-49 yrs 23 205 (36.3) 27 090 (34.6) 780 (27.1) 948 (28.4) 420 (40.2) 639 (41.8) 259 (28.0) 301 (31.6) 422 (29.3) 406 (30.0) 50 yrs+ 19 588 (30.6) 20 980 (26.8) 432 (15.0) 531 (15.9) 372 (35.6) 462 (30.2) 108 (11.6) 108 (11.4) 595 (16.5) 740 (23.5) REGION

Rural 43 822 (68.5) 52 046 (66.5) 659 (22.9) 619 (18.5) 534 (51.1) 744 (48.7) - - - -

Urban 20 109 (31.5) 26 249 (33.5) 2219 (77.1) 2724 (81.5) 5109 (48.9) 784 (51.3) - - - -

INCOME³

Low 9785 (15.6) 11 607 (15.1) - - 483 (46.3) 723 (47.3) - - 389 (22.2) 349 (17.1)

Medium 47 694 (75.9) 58 064 (75.7) - - 467 (44.7) 618 (40.4) - - 442 (25.3) 384 (22.8)

High 5335 (8.5) 7018 (9.2) - - 94 (9.0) 187 (12.2) - - 416 (26.2) 425 (26.6)

Very high - - - 406 (26.4) 482 (33.4)

EDUCATION ⁴

Low 20 204 (31.6) 35 096 (44.8) 792 (27.6) 1010 (30.2) 382 (36.6) 653 (42.7) 48 (5.3) 37 (3.9) 439 (20.7) 371 (14.4) Moderate 38 787 (60.7) 38 757 (49.5) 1469 (51.1) 1793 (53.7) 556 (53.3) 764 (50.0) 814 (89.6) 851 (91.1) 684 (42.1) 810 (50.7) High 4940 (7.7) 4442 (5.7) 613 (21.3) 537 (16.1) 106 (10.2) 111 (7.3) 46 (5.1) 46 (5.0) 519 (37.2) 451 (35.0) ETHNICITY ⁵

Group 1 - - 1063 (36.9) 1321 (39.5) 581 (55.7) 847 (55.4) - - - -

Group 2 - 1453 (50.5) 1669 (49.9) 186 (17.8) 273 (17.9) - - - -

Group 3 - - 362 (12.6) 353 (10.5) 79 (7.6) 151 (9.9) - - - -

Group 4 - - -- -- 197 (18.9) 257 (16.8) - - - -

1 Malaysia age groups: 25-35yrs; 36-49yrs; 50-65yrs. ² Philippines age groups: 20-35yrs; 36-49yrs; 50-65yrs. ³ China income per annum: Low <2000 Yuan; Medium 2000-9999 Yuan; High >10000 Yuan. Malaysia income per month: Low

<1000 RM; Medium 1000-3999 RM; High >3999 RM. Philippines income per year: Low ≤53 064 PhP; Medium 53 065-92 192 PhP; High 92 193-173 387 PhP; Very high ≥173 388 PhP. ⁴Educational level for all countries: low (primary);

medium (high school); high (Uuniversity). ⁵Fiji ethnicity: Group 1 (Fijian); Group 2 (Indian); Group 3 (Other). Malaysia ethnicity: Group 1 (Malay); Group 2 (Chinese); Group 3 (Indian); Group 4 (Other).