Noncommunicable disease risk factors and socioeconomic inequalities - what are the links? by Alexander Pascual

Noncommunicable disease risk factors and socioeconomic inequalities â&#x20AC;&#x201C; what are the links? A multicountry analysis of noncommunicable disease surveillance data

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Noncommunicable disease risk factors and socioeconomic inequalities â&#x20AC;&#x201C; what are the links? A multicountry analysis of noncommunicable disease surveillance data

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Noncommunicable disease risk factors and socioeconomic inequalities â&#x20AC;&#x201C; what are the links? A multicountry analysis of noncommunicable disease surveillance data

Section Title

Report to the WHO Regional Office for the Western Pacific

Principal authors Centre for Physical Activity and Health, School of Public Health, University of Sydney Professor Adrian Bauman, Dr Philayrath Phongsavan, Ms Stephanie Schoeppe, Ms Tien Chey Collaborating country authors 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.

People’s Republic of China 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

A multicountry analysis of noncommunicable disease surveillance data

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Background ............................................................................................................ 1 Relationships between NCD risk factors and socioeconomic status............. 2 Socioeconomic status and smoking............................................................. 3 Socioeconomic status and alcohol. ............................................................. 3 Socioeconomic status and nutrition............................................................ 4 Socioeconomic status and obesity. ............................................................. 5 Socioeconomic status and physical activity................................................. 5 Socioeconomic status and blood pressure................................................. 6 Socioeconomic status and blood lipids....................................................... 7 Socioeconomic status and diabetes............................................................. 7 Purpose of the project ...................................................................................... 8 Methodology ....................................................................................................... 11 Country participation criteria..........................................................................12 Protocol for data analysis.................................................................................13 Results .................................................................................................................. 19 China .............................................................................................................. 22 Prevalence rates of risk factors by SES measures................................... 22 â&#x20AC;&#x2030;Association between risk factors and SES measures (adjusted analyses)..................................................................................................... 25 Fiji .................................................................................................................... 32 Prevalence rates of risk factors by SES measures................................... 32 Association between risk factors and SES measures (adjusted analyses)..................................................................................................... 35 Malaysia............................................................................................................. 42 Prevalence rates of risk factors by SES measures.................................... 42 Association between risk factors and SES measures (adjusted analyses)..................................................................................................... 45 Nauru ............................................................................................................... 51 Prevalence rates of risk factors by SES status.......................................... 51 Association between risk factors and SES measures (adjusted analyses)..................................................................................................... 53 Philippines......................................................................................................... 59 Prevalence rates of risk factors by SES measures.................................... 59 Association between risk factors and SES measures (adjusted analyses)..................................................................................................... 61 Cross-country comparison of prevalence rates and associations between NCD risk factors and SES................................................................ 69 Cross-country comparison of prevalence rates............................................ 70 Cross-country comparison of of NCD risk factors by SES. ........................ 75 Synthesis and Discussion................................................................................... 79 Comment on socioeconomic factors. ........................................................... 80 Comment on NCD risk factors..................................................................... 81 Programme and policy implications................................................................ 83

SectionofTitle Table Contents

Table of Contents

Noncommunicable disease risk factors and socioeconomic inequalities â&#x20AC;&#x201C; what are the links?

Annex: Project Partners..................................................................................... 85 Endnotes................................................................................................................ 89

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

A multicountry analysis of noncommunicable disease surveillance data

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

SectionofTitle Table Contents

FIGURES

Table of Contents

Noncommunicable disease risk factors and socioeconomic inequalities â&#x20AC;&#x201C; what are the links?

Figure 3.3.2a Probability of smoking by socioeconomic status for men and women, Malaysia................................................ Figure 3.3.2b Probability of low vegetable and fruit consumption by socioeconomic status for men and women, Malaysia...... Figure 3.3.2c Probability of obesity by socioeconomic status for men and women, Malaysia...................................................... Figure 3.3.2d Probability of central obesity by socioeconomic status for men and women, Malaysia................................................ Figure 3.3.2e Probability of high blood pressure by socioeconomic status for men and women, Malaysia.................................... Figure 3.3.2f Probability of high total cholesterol by socioeconomic status for men and women, Malaysia.................................... Figure 3.3.2g Probability of elevated fasting blood glucose by socioeconomic status for men and women, Malaysia...... Figure 3.3.2h Probability of high occupational physical activity by socioeconomic status for men and women, Malaysia...... Figure 3.3.2i Probability of highly active commuting by socioeconomic status for men and women, Malaysia...... Figure 3.3.2j Probability of high leisure-time physical activity by socioeconomic status for men and women, Malaysia...... Figure 3.4.2a Probability of smoking by socioeconomic status for men and women, Nauru.......................................................... Figure 3.4.2b Probability of heavy alcohol drinking by socioeconomic status for men and women, Nauru.......... Figure 3.4.2c Probability of low vegetable and fruit consumption by socioeconomic status for men and women, Nauru.......... Figure 3.4.2d Probability of obesity by socioeconomic status for men and women, Nauru.......................................................... Figure 3.4.2e Probability of central obesity by socioeconomic status for men and women, Nauru................................................... Figure 3.4.2f Probability of high blood pressure by socioeconomic status for men and women, Nauru....................................... Figure 3.4.2g Probability of high total cholesterol by socioeconomic status for men and women, Nauru....................................... Figure 3.4.2h Probability of elevated fasting blood glucose by socioeconomic status for men and women, Nauru.......... Figure 3.4.2i Probability of high occupational physical activity by socioeconomic status for men and women, Nauru.......... Figure 3.4.2j Probability of highly active commuting by socioeconomic status for men and women, Nauru.......... Figure 3.4.2k Probability of high leisure-time physical activity by socioeconomic status for men and women, Nauru.......... Figure 3.5.2a Probability of smoking by socioeconomic status for men and women, Philippines.................................................. Figure 3.5.2b Probability of heavy alcohol drinking by socioeconomic status for men and women, Philippines............................... Figure 3.5.2c Probability of low vegetable and fruit consumption by socioeconomic status for men and women, Philippines...................................................................................

48 48 48 49 49 49 50 50 50 51 55 55 56 56 56 57 57 57 58 58 58 64 64 64

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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 socio economic 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

SectionofTitle Table Contents

A multicountry analysis of noncommunicable disease surveillance data

Noncommunicable disease risk factors and socioeconomic inequalities â&#x20AC;&#x201C; what are the links?

Acknowledgements

viii

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.

A multicountry analysis of noncommunicable disease surveillance data

Section Title Background

Background

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

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.

A multicountry analysis of noncommunicable disease surveillance data

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.

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

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 lowand 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

Section Title Background

Socioeconomic status and smoking

Noncommunicable disease risk factors and socioeconomic inequalities â&#x20AC;&#x201C; what are the links?

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 SouthEast 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 higherincome 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

A multicountry analysis of noncommunicable disease surveillance data

Socioeconomic status and obesity

Socioeconomic status and physical activity Research has examined different domains or settings for healthenhancing physical activity, with initial interest focusing on leisuretime 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

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 countryâ&#x20AC;&#x2122;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

Section Title Background

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.

Background

Noncommunicable disease risk factors and socioeconomic inequalities â&#x20AC;&#x201C; what are the links?

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 leisuretime 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 countriesa 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.

A multicountry analysis of noncommunicable disease surveillance data

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.66 Overall, 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

Socioeconomic status and blood lipids

Section Title 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

Noncommunicable disease risk factors and socioeconomic inequalities â&#x20AC;&#x201C; what are the links?

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 selfreported) 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

A multicountry analysis of noncommunicable disease surveillance data

Section Title 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.

A multicountry analysis of noncommunicable disease surveillance data

Section Title Methodology

Methodology

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

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: demographic and socioeconomic status: uu age; uu sex; and uu individual- and area-level SES measures (e.g. urban/rural residence, education, occupation, employment status, income)b; and health risk factors: uu health behaviour measures (e.g. smoking, alcohol consumption, dietary habits, physical activity); uu anthropometry measures (e.g. height, weight, waist circumference); and uu biochemical measures (e.g. fasting blood lipid levels, fasting blood glucose levels, oral glucose tolerance test (OGTT), diastolic and systolic blood pressure). nn

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

A multicountry analysis of noncommunicable disease surveillance data

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.

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

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 countriesâ&#x20AC;&#x2122; 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.

Section Title Methodology

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

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

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.

Methodology

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.

A multicountry analysis of noncommunicable disease surveillance data

China Current smoking: Having ever smoked, smoked continuously/cumulatively for six months and more, and smoking 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

Poor diet

Overweight and obese

Nauru Philippines Current smoking: Current smoking: Smoking daily or Smoked in the past month weekly at the time of preceding the survey the survey

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 (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) Consumed <400g of fresh and dry vegetables per day

Note: Cut-off points based on (1) Consumed <5 Consumed <5 Consumed <5 Consumed <5 servings servings of fruits and servings of fruits and servings of fruits and of fruits and vegetables vegetables per day vegetables per day vegetables per day per day

Fresh fruit consumption of <100g per day Acceptable BMI <24 Overweight BMI 24–27.9 Obese BMI ≥28

Acceptable BMI 18.5–24.9 Overweight BMI 25.0–29.9 Obese BMI ≥30.0

Acceptable BMI18.5–24.9 Overweight BMI 25.0–29.9 Obese BMI ≥30.0

Acceptable BMI 18.5–24.9 Overweight BMI 25.0–29.9 Obese BMI ≥30.0

Men ≥85 cm Women ≥80 cm

Note: Cut-off points based on WHO international classification (3) Men ≥110 cm Women ≥100cm

Note: Cut-off points based on WHO international classification (3) Men≥90 cm Women ≥80 cm

Note: Cut-off points based on WHO international classification (3) Men ≥110 cm Women ≥100cm

Note: China national cut-off point used (4)

Note: Pacific cut-off points (5,6,7)

Note: Cut-off points for adult Asians (8)

Note: Pacific cut-off points (5,6,7)

Note: Cut-off points based on classification for adult Asians (2)

Central obesity (waist circumference)

Fiji Malaysia Current smoking: Current smoking: Smoke either daily or Some daily or weekly yes, but not every day at the time of the survey

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)

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

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.

Risk factor Smoking

Section Title Methodology

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

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

Table 2.1 Overview of country cut-off points for NCD risks1 Risk factor China High blood Systolic blood pressure pressure ≥140mmHg and/ or diastolic blood pressure ≥90mmHg, or

Methodology

Took medication for hypertension in the last two weeks preceding the survey

High cholesterol

Note: Definition according to (1) Total cholesterol ≥5.72 mmol/L Note: China national cut-off point used (2)

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

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

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

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

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

Note: Definition according to (1)

Total cholesterol ≥5.5 mmol/L

Total cholesterol >6.5 Total cholesterol ≥5.5 mmol/L mmol/L

Note: Cut-off point considered to constitute an increased (borderline) risk for developing cardiovascular disease Fasting serum blood glucose ≥6.1mml/L

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

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

Fasting plasma blood glucose ≥7.0 mmol/L

Fasting blood glucose >125mg/dL

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

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

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

A multicountry analysis of noncommunicable disease surveillance data

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

Fiji Three domains of PA were computed:

Malaysia 2 Three domains of PA were computed using the following MET Occupational PA: Occupational PA: values: • Heavy (25% of time Respondents asked Moderate MET= 4.0 sitting or standing, duration of a typical Vigorous MET = 8.0 75% of time doing work day, then asked Cycling/walking non-mechanical farm to indicate on a 4-point MET=4.0 work, steel-making, Likert scale (‘Almost dancing, sports, always’ to ‘almost For all three domains, loading, mining, etc.). never’) hours at work respondents were • Moderate (40% per day engaged in: classified into three PA of time sitting or • sitting or standing levels according to GPAQ standing, 60% of with little walking, scoring protocol: time doing activities • physical effort like • Low PA: No activity such as driving cars, continuous walking, OR some activity electrical work, gardening, and but not moderate or operating machinery • heavy lifting or heavy vigorous. or metal incision, construction work. • Moderate PA: 3+ days etc.). of vigorous activity of • Light (75% of the at least 20 minutes Respondents then time sitting or per day OR 5+ days grouped into: standing, 25% of of moderate activity • Heavy occupational time standing active, PA (doing heavy work and/or walking of at such as office work, least 30 minutes per always and usually repairing electric day OR 5+ days of and moderate work appliances, shop always) vs. moderate/ any combination of workers, waiters walking, moderate light PA (responses or hotel workers, or vigorous activities, indicated in other chemical laboratory achieving a minimum Likert categories). workers, lecturers, of at least 600 METetc.). min/weeks. Active commuting: • High PA: activity on • Cycling/walking at least 3 days and Respondents then (always or usually) vs. accumulating at least grouped into: motorized vehicles, • Heavy occupational PA such as bus, car, taxi, 1500 MET-minutes/ week OR 7+ days of vs. moderate /light PA bilibili, boat any combination of walking, moderate Active commuting: or vigorous activities, LTPA: • Cycling/walking (for • Exercise ≥30mins or accumulating at least at least 30 minutes) more per day vs. <30 3000 MET-minutes/ vs. using motorized week mins vehicles such as bus, car or motorcycle For Occupational PA, Active commuting and Leisure-time PA (LTPA): LTPA, respondents then • Exercise ≥4 times per grouped into: week for 150 minutes • High PA vs. moderate/ in total vs. exercise low PA <4 times per week

Nauru 2 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 METmin/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

Philippines 3 Three domains of PA were computed. Occupational PA: Respondents asked typical hours of work day, and percentage (0%–100%) spent sitting/standing, doing continuous moderateintensity, 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.

For Occupational PA, Active commuting and LTPA: LTPA, respondents then • Exercise either ‘every grouped into: day’ or ‘3–5 times • High PA vs. moderate/ a week’ for 30–45 low PA minutes’

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

Section Title Methodology

China Three domains of PA were computed:

Risk factor Physical activity (PA)

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

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.

Methodology

Table 2.2 Overview of country cut-off points for demographic and SES variables (country-provided cut-off points) Variable Age Education Region (urban/rural)

Income

Ethnicity

China • 18 –35 years • 36 –49 years • 50 –65 years • Primary school • Secondary school • Professional/university • Large cities (most developed) • Medium and small cities • Rural I areas • Rural II areas • Rural III areas • Rural IV areas (most remote/rural)

Fiji • 18 –35 years • 36 –49 years • 50 –65 years • Primary • Secondary • At least tertiary • Rural • Urban

Regions were further defined into 2 categories: • urban (large, medium and small cities) • rural (rural I-IV areas) Individual income per Not available annum categorized into: • Low (< 2000 Yuan/year) • Medium (2000–9999 Yuan/year) • High (> 10000 Yuan/year) Not available

• Fijian • Indo-Fijian • Other

Malaysia • 25 –35 years • 36 –49 years • 50 –64 years • Primary • Secondary • At least tertiary • Rural • Urban

Nauru • 18 –35 years • 36 –49 years • 50 –65 years • Primary • Secondary • At least tertiary Not available

Household income per Not available month categorized into: • Low (<RM 1000) • Medium (RM 1000–3999) • High (>3999) • Malay • Chinese • Indian • Others

Not available

Philippines • 20 –35 years • 36 –49 years • 50 –65 years • Primary • Secondary • At least tertiary 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 ) Not available

A multicountry analysis of noncommunicable disease surveillance data

Section Title Results

Results

Noncommunicable disease risk factors and socioeconomic inequalities â&#x20AC;&#x201C; what are the links?

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

Results

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 3.1 Survey characteristics in participating countries Country China Fiji Malaysia Nauru Philippines

Survey Response Sampling year Survey name rate (%) Age (years) Region procedures 2002 China National Nutrition 79.1 18-65 National Stratified, multistage and Health Survey cluster random sampling 2002 Noncommunicable unknown 18-65 National Multistage cluster random Disease STEPwise Survey sampling 2006 Noncommunicable 84.6 25-64 National Stratified, two-stage cluster Disease STEPwise Survey random sampling 2004 Noncommunicable 82.3 18-65 National Random sampling 2006* Disease STEPwise Survey 2003 National Nutrition and 97.0 20-65 National Stratified, three- stage Health Survey random sampling

Sample size 142 693 6763 2572 2085 3307

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

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 3.2 Location of data analysis Country China Fiji Malaysia Nauru Philippines

Location of analysis Analysis conducted in Sydney with CPAH, during a two-week supervised visit CPAH, Sydney In-country CPAH, Sydney Analysis conducted in Sydney with CPAH, during a one-week supervised visit and also in-country (with CPAH providing distance support)

Group responsible for analysis National Institute for Nutrition and Food Safety, Chinese Center for Disease Control and Prevention CPAH Diseases Control Division Ministry of Health, Malaysia CPAH Food and Nutrition Research Institute, Philippines

Availability of country-specific report Yes No Yes No Yes

A multicountry analysis of noncommunicable disease surveillance data

Section Title 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 Men Women n (%) n (%) SEX Men Women AGE 1 2 18-35 yrs 36-49 yrs 50 yrs+ REGION Rural Urban INCOME 3 Low Medium High Very high EDUCATION 4 Low Moderate High ETHNICITY 5 Group 1 Group 2 Group 3 Group 4

Fiji (N=6763) 5 Men Women n (%) n (%)

Malaysia (N=2572) 1 3 5 Men Women n (%) n (%)

Nauru (N=2085) Men Women n (%) n (%)

Philippines (N=3307) 2 3 Men Women n (%) n (%)

63 931 (44.9) 2878 (46.3) 1044 (40.6) 925 (49.3) 1660 (52.2) 78 295 (55.1) 3343 (53.7) 1528 (59.4) 952 (50.7) 1647 (47.8) 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) 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) 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) 43 822 (68.5) 52 046 (66.5) 659 (22.9) 619 (18.5) 534 (51.1) 744 (48.7) 20 109 (31.5) 26 249 (33.5) 2219 (77.1) 2724 (81.5) 5109 (48.9) 784 (51.3)

9785 (15.6) 11 607 (15.1) 47 694 (75.9) 58 064 (75.7) 5335 (8.5) 7018 (9.2) -

389 (22.2) 442 (25.3) 416 (26.2) 406 (26.4)

349 (17.1) 384 (22.8) 425 (26.6) 482 (33.4)

483 (46.3) 723 (47.3) 467 (44.7) 618 (40.4) 94 (9.0) 187 (12.2) -

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) 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) 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) -

1063 (36.9) 1321 (39.5) 581 (55.7) 847 (55.4) 1453 (50.5) 1669 (49.9) 186 (17.8) 273 (17.9) 362 (12.6) 353 (10.5) 79 (7.6) 151 (9.9) --197 (18.9) 257 (16.8)

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. 4 Educational level for all countries: low (primary); medium (high school); high (Uuniversity). 5 Fiji ethnicity: Group 1 (Fijian); Group 2 (Indian); Group 3 (Other). Malaysia ethnicity: Group 1 (Malay); Group 2 (Chinese); Group 3 (Indian); Group 4 (Other). 1

Noncommunicable disease risk factors and socioeconomic inequalities â&#x20AC;&#x201C; what are the links?

China Prevalence rates of risk factors by SES measures Table 3.1.1. Prevalence rates of risk factors by socioeconomic status for men and women, China (N=142 693)

Results

Current smoking

AGE 18-35 yrs 36-49 yrs 50-65 yrs REGION Rural Urban INCOME <2000 Yuan/Y 2000-9999 Yuan/Y >10000 Yuan/Y EDUCATION Primary Secondary Tertiary

Hazardous drinking*

Men n (%)

Women n (%)

Men n (%)

Women n (%)

Consumed <400g/ day vegetables Men Women n (%) n (%)

10 886 (51.3) 14 704 (63.0) 11 413 (57.9)

254 (0.8) 662 (2.4) 1070 (5.1)

262 (4.0) 677 (8.8) 538 (8.0)

31 (0.4) 53 (0.6) 51 (0.7)

4780 (72.8) 5461 (71.5) 4880 (72.6)

26 014 (59.1) 10 989 (54.3)

1218 (2.3) 768 (2.9)

1006 (7.0) 471 (7.2)

94 (0.6) 41 (0.5)

17 934 (59.8) 15 820 (57.0) 2637 (49.1)

931 (2.6) 888 (2.6) 133 (1.9)

640 (6.0) 806 (7.1) 130 (5.7)

63 (0.6) 68 (0.6) 12 (0.5)

6535 (72.1) 6967 (72.1) 1355 (73.8)

7651 (76.3) 8354 (76.2) 1730 (78.3)

7536 (83.1) 6437 (66.6) 868 (47.3)

8126 (81.0) 6647 (60.7) 842 (38.1)

12 552 (61.8) 22 364 (57.4) 2087 (41.8)

1302 (3.7)

503 (7.9)

76 (0.7)

4392 (69.7)

8052 (75.0)

5225 (82.9)

8528 (79.5)

654 (1.7)

904 (7.0)

56 (0.5)

9455 (72.9)

9007 (77.4)

9015 (69.5)

6890 (59.2)

30 (0.7)

70 (4.2)

3 (0.2)

1274 (76.4)

1003 (79.1)

843 (50.5)

457 (36.0)

6082 (76.9) 6580 (75.1) 5400 (77.4)

Consumed <100g/ day fruit Men Women n (%) n (%) 4635 (70.5) 5528 (72.4) 4920 (73.2)

5194 (65.7) 5963 (68.1) 4718 (67.7)

10 217 (70.8) 11 775 (74.7) 11 461 (79.5) 12 214 (77.5) 4904 (75.3) 6287 (79.8) 3622 (55.6) 3661 (46.5)

* Hazardous drinking: >60g for men and >40g for women

The prevalence of current smoking was assessed to be higher in men than women across all age groups, and slightly higher in rural than urban regions. Current smoking was proportionately higher in the low-income group and was also higher among those with a lower educational level. Hazardous drinking was more prevalent among men across all SES measures, and also slightly higher in the lower education group. Low vegetable consumption was common among wealthy and educated men and women, and among those living in urban areas. In contrast, low fruit consumption was more common among rural residents, and among the poor and those with a low educational level.

A multicountry analysis of noncommunicable disease surveillance data

Table 3.1.1 Prevalence rates of risk factors by socioeconomic status for men and women, China (N=142 693) (Cont. 1) Obese 1 Men Women n (%) n (%)

1 2 3 4 5

High blood pressure 3 Men Women n (%) n (%)

High High fasting cholesterol 4 blood glucose 5 Men Women Men Women n (%) n (%) n (%) n (%)

2086 (5.3) 3785 (8.5) 8578 (22.0) 12 656 (28.5) 7590 (19.4) 8071 (16.3) 371 (2.6) 430 (2.7) 247 (1.7) 334 (2.0) 2142 (12.1) 2903 (12.9) 7634 (43.4) 8534 (38.2) 4643 (26.3) 4942 (19.8) 287 (4.4) 446 (5.5) 422 (6.0) 518 (5.9) 1227 (4.6) 2553 (8.1) 5288 (20.0) 8646 (27.8) 5085 (19.1) 5696 (16.7) 188 (2.1) 284 (2.8) 159 (1.8) 236 (2.3) 2331 (9.5) 3333 (11.5) 8399 (34.4) 9985 (34.7) 5747 (23.4) 5822 (18.0) 359 (3.8) 430 (3.9) 345 (3.5) 451 (3.9) 605 (13.1) 660 (11.8) 2199 (47.8) 2097 (37.5) 1197 (25.8) 1242 (18.7) 97 (5.2) 141 (6.2) 145 (6.8) 149 (5.6) 861 (4.8) 3226 (10.1) 3652 (20.5) 10 763 (34.0) 4346 (24.2) 7575 (22.8) 154 (2.6) 408 (3.8) 145 (2.3) 396 (3.6) 2874 (8.3) 3263 (10.2) 10 582 (30.8) 9736 (30.6) 7026 (20.3) 5098 (13.7) 428 (3.4) 420 (3.6) 435 (3.2) 411 (3.3) 493 (11.4) 199 (6.1) 1978 (46.1) 691 (21.3) 861 (19.9) 340 (8.4) 76 (4.2) 48 (3.5) 89 (4.5) 45 (2.9)

Obese: BMI≥28; Central obesity: ≥85cm for men and ≥80cm for women; High blood pressure SBP≥140mmHg and/or DBP≥90mmHg; High cholesterol ≥5.72 mmol/L; High fasting glucose ≥7.0mmol/L

Obesity and central obesity increased with age for both men and women, although the prevalence was higher among women, those living in urban areas, and those with higher incomes. While obesity was less common among women with tertiary education, it was more common among men with tertiary education. There were marked gender differences in the distributions of high blood pressure prevalence rates, with men more likely than women to have hypertension, regardless of educational level. At-risk cholesterol levels were more prevalent among those in the high-income group, for both men and women, and also among those in urban regions. Diabetes risk increased in prevalence with age for both men and women and was higher in urban areas and more prevalent among those in the high-income group and those with high educational attainment (for men only).

Section Title Results

1072 (6.0) 979 (4.6) 3738 (21.0) 3098 (14.6) 1571 (8.8) 980 (3.5) 85 (1.3) 92 (1.2) 51 (0.8) 50 (0.6) 1637 (7.9) 2606 (10.1) 6479 (31.3) 8404 (32.8) 4080 (19.6) 4211 (16.2) 279 (3.7) 233 (2.6) 218 (2.8) 225 (2.4) 1519 (8.4) 3103 (15.4) 5995 (33.4) 9688 (48.5) 6582 (36.4) 7822 (38.8) 294 (4.4) 551 (7.7) 400 (5.7) 577 (7.6)

AGE 18-35 yrs 36-49 yrs 50-65 yrs REGION Rural Urban INCOME <2000 Yuan/Y 2000-9999 Yuan/Y >10000 Yuan/Y EDUCATION Primary Secondary Tertiary

Central obesity 2 Men Women n (%) n (%)

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

Table 3.1.1 Prevalence rates of risk factors by socioeconomic status for men and women, China (N=142 693) (Cont. 2)

Results

High leval of occupational physical activity Men Women n (%) n (%) AGE 18-35 yrs 36-49 yrs 50-65 yrs REGION Rural Urban INCOME <2000 Yuan/Y 2000-9999 Yuan/Y >10000 Yuan/Y EDUCATION Primary Secondary Tertiary

Highly active commuting (≥30mins/day) Men Women n (%) n (%)

High LTPA^ (≥ 4 times and ≥150min/ week) Men Women n (%) n (%)

10 287 (49.1) 11 804 (51.6) 8541 (44.1)

10 516 (35.5) 11 377 (42.9) 6048 (29.4)

5550 (41.3) 6083 (43.1) 5067 (50.2)

7893 (45.5) 6965 (49.1) 4564 (54.8)

360 (2.6) 501 (3.3) 1459 (11.6)

466 (2.2) 839 (4.8) 1919 (14.2)

27 956 (64.2) 2676 (13.6)

25 833 (50.3) 2108 (8.3)

12 966 (47.8) 3734 (35.5)

14 647 (51.6) 4775 (41.7)

479 (1.7) 1841 (14.2)

442 (1.3) 2782 (15.8)

6689 (69.0) 23 281 (49.3) 339 (6.6)

6075 (53.3) 21 277 (37.3) 309 (4.6)

10 155 (53.7) 5546 (36.2) 778 (27.6)

11 563 (57.3) 6403 (41.1) 1156 (34.6)

454 (2.2) 1271 (7.3) 553 (17.0)

572 (2.3) 1701 (7.7) 861 (18.9)

13 392 (66.9) 17 127 (44.7) 113 (2.3)

17 799 (51.6) 10 106 (26.6) 36 (0.8)

6800 (54.9) 9162 (40.7) 738 (26.6)

10 443 (57.5) 8210 (43.0) 769 (29.5)

374 (2.8) 1399 (5.6) 547 (18.0)

907 (3.9) 1941 (7.5) 376 (12.7)

Note: High levels of occupational physical activity, active commuting and leisure-time physical activity are protective of NCD risks. LTPA = Leisure time physical activity.

High levels of work-related physical activity were more common among men than women across all age groups. However, for both sexes, high levels of work-related physical activity were more prevalent among those with low levels of education and income and those living in rural areas. Active commuting was more common among those with low education and income, those living in rural areas and among older adults. LTPA was prevalent among the oldest age group for both men and women, those living in urban regions, and those with high levels of income and education.

A multicountry analysis of noncommunicable disease surveillance data

Association between risk factors and SES measures (adjusted analyses) This section summarizes the results of logistic regression analyses examining the independent association in China between SES and risk factors by sex, adjusting for other SES indicators.

AGE 18-35 yrs 36-49 yrs 50-65 yrs REGION Rural Urban INCOME <2000 Yuan/Y 2000-9999 Yuan/Y >10000 Yuan/Y EDUCATION Primary Secondary Tertiary 1

Consumed Hazardous drinkingâ&#x20AC;&#x2030;1 <400g/day vegetables Men Women Men Women OR (95% CI)

Consumed <100g/day fruit Men Women

OR (95% CI)

1.0 1.6 (1.5-1.7) 1.2 (1.2-1.3)

1.0 2.6 (2.3-3.1) 4.9 (4.2-5.7)

1.0 2.3 (2.0-2.7) 2.0 (1.7-2.3)

1.0 1.3 (0.8-2.0) 1.4 (0.9-2.3)

1.0 1.0 1.0 1.0 (0.9-1.0) 0.9 (0.9-1.0) 1.1 (1.0-1.2) 1.0 (1.0-1.1) 1. 0 (1.0-1.1) 1.1 (1.0-1.2)

1.0 1.1 (1.0-1.2) 1.0 (0.9-1.1)

1.0 0.9 (0.9-1.0)

1.0 1.4 (1.3-1.6)

1.0 1.1 (1.0-1.2)

1.0 0.9 (0.6-1.4)

1.0 1.3 (1.2-1.4)

1.0 1.4 (1.3-1.5)

1.0 0.5 (0.5-0.5)

1.0 0.4 (0.4-0.4)

1.0 0.9 (0.9-1.0) 0.8 (0.7-0.9)

1.0 1.0 (0.9-1.1) 0.8 (0.6-1.0)

1.0 1.3 (1.2-1.5) 1.2 (0.9-1.5)

1.0 1.0 (0.6-1.7) 1.2 (0.5-2.5)

1.0 0.9 (0.9-1.0) 0.9 (0.8-1.0)

1.0 0.9 (0.8-1.0) 0.9 (0.8-1.0)

1.0 0.5 (0.5-0.6) 0.3 (0.3-0.4)

1.0 0.9 (0.8-0.9) 0.5 (0.5-0.5)

1.0 0.6 (0.5-0.7) 0.3 (0.2-0.4)

1.0 0.9 (0.8-1.0) 0.5 (0.4-0.7)

1.0 0.7 (0.5-1.1) 0.4 (0.1-1.3)

1.0 1.2 (1.1-1.3) 1.4 (1.2-1.6)

1.0 1.1 (1.0-1.2) 1.1 (1.0-1.3)

1.0 0.6 (0.6-0.7) 0.5 (0.4-0.6)

1.0 0.5 (0.5-0.6) 0.4 (0.3-0.4)

Hazardous drinking: >60g for men and >40g for women

The odds of smoking increased with age for both men and women, and for those living in urban areas (women only). Men and women with high educational levels had lower odds of being smokers than those with only primary education. Men and women in the high-income group had higher odds of reporting hazardous drinking than those in the low-income group, but this association did not reach significance. Men with secondary and tertiary education, or those living in urban areas, were less likely to consume the recommended amount of vegetables per day. Similarly, women living in urban areas were also less likely to consume the recommended amount of vegetables per day. In contrast, those urban men and women who had higher

Current smoking Men Women

Section Title Results

Table 3.1.2 Odds ratio (OR) and 95% confidence interval (CI) for the probability of having NCD risk factors by socioeconomic status for men and women, China (N=142 693)

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

incomes and higher educational attainment were more likely to consume the recommended amount of fruit per day. Table 3.1.2 Odds ratio (OR) and 95% confidence interval (CI) for the probability of having NCD risk factors by socioeconomic status for men and women, China (N=142 693) (Cont. 1) Obese1 Men Women

Central obesity11 Men Women

High blood pressure3 Men Women

High total High fasting blood cholesterol4 glucose5 Men Women Men Women

OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI)

Results

AGE 18-35 yrs 36-49 yrs 50-65 yrs REGION Rural Urban INCOME <2000 Yuan/Y 2000-9999 Yuan/Y >10000 Yuan/Y EDUCATION Primary Secondary Tertiary 1 2 3 4 5

1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.3 (1.2-1.4) 2.3 (2.1-2.5) 1.7 (1.6-1.9) 2.9 (2.6-3.2) 2.5 (2.3-2.7) 5.2 (4.9-5.6) 2.8 (2.2-3.6) 2.2 (1.7-2.8) 3.6 (2.6-4.9) 4.0 (2.9-5.5) 1.5 (1.4-1.7) 3.8 (3.5-4.1) 2.1 (2.0-2.3) 6.7 (6.1-7.3) 5.9 (5.6-6.3) 16.7 (15.5-18.0) 3.6 (2.8-4.6) 6.8 (5.4-8.6) 8.0 (6.0-10.9) 12.9 (9.5-17.5) 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.8 (1.7-2.0) 1.4 (1.3-1.5) 1.9 (1.8-2.0) 1.3 (1.2-1.3) 1.3 (1.3-1.4) 1.1 (1.1-1.2) 1.2 (1.0-1.5) 1.6 (1.4-1.9) 2.6 (2.2-3.2) 2.3 (2.0-2.8) 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.7 (1.6-1.8) 1.3 (1.2-1.4) 1.7 (1.6-1.8) 1.3 (1.2-1.4) 1.2 (1.2-1.3) 1.1 (1.1-1.2) 1.6 (1.3-1.9) 1.2 (1.0-1.4) 1.4 (1.1-1.7) 1.3 (1.1-1.5) 1.9 (1.7-2.1) 1.2 (1.1-1.3) 1.9 (1.7-2.2) 1.3 (1.1-1.4) 1.2 (1.1-1.3) 1.1 (1.1-1.2) 1.9 (1.4-2.5) 1.4 (1.1-1.8) 1.8 (1.4-2.4) 1.2 (1.0-1.6) 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.6 (1.4-1.7) 1.2 (1.1-1.2) 1.6 (1.4-1.7) 1.0 (0.9-1.0) 1.1 (1.0-1.1) 1.0 (0.9-1.0) 1.4 (1.2-1.7) 1.2 (1.0-1.4) 1.4 (1.2-1.8) 1.1 (0.9-1.3) 1.5 (1.3-1.7) 0.6 (0.5-0.7) 1.6 (1.5-1.8) 0.5 (0.4-0.6) 0.9 (0.8-1.0) 0.6 (0.6-0.7) 1.4 (1.1-2.0) 1.2 (0.8-1.7) 1.4 (1.0-1.9) 1.0 (0.7-1.4)

Obese: BMI≥28; Central obesity: ≥85cm for men and ≥80cm for women; High blood pressure SBP≥140mmHg and/or DBP≥90mmHg; High cholesterol ≥5.72 mmol/L; High fasting glucose ≥7.0mmol/L

The odds of being obese increased with age (especially for women) and for those living in urban areas, with higher income and higher educational attainment (men only). Similar patterns of associations were observed for central obesity. Women with tertiary education were less likely to be obese or centrally obese those with primary education. The odds of having high blood pressure, high total cholesterol and diabetes increased with age and for men and women living in urban regions compared with rural regions. A similar pattern was also observed for associations with income for these risk factors. Having high educational attainment was also related to increased odds for high total cholesterol and high fasting blood glucose (men only).

A multicountry analysis of noncommunicable disease surveillance data

Table 3.1.2. Odds ratio (OR) and 95% confidence interval (CI) for the probability of having NCD risk factors by socioeconomic status for men and women, China (N=142 693) (Cont. 2)

OR (95% CI)

1.0 1.2 (1.2-1.3) 0.6 (0.6-0.7)

1.0 1.3 (1.2-1.3) 0.5 (0.5-0.5)

1.0 1.1 (1.0-1.1) 1.2 (1.2-1.3)

1.0 1.0 (1.0-1.1) 1.1 (1.1-1.2)

1.0 1.3 (1.1-1.5) 5.9 (5.2-6.7)

1.0 2.6 (2.3-2.9) 10.1 (9.0-11.4)

1.0 0.2 (0.1-0.2)

1.0 0.2 (0.2-0.2)

1.0 0.9 (0.8-0.9)

1.0 1.0 (1.0-1.1)

1.0 5.7 (5.1-6.4)

1.0 8.7 (7.8-9.7)

1.0 0.4 (0.4-0.4) 0.1 (0.1-0.1)

1.0 0.4 (0.4-0.5) 0.1 (0.1-0.1)

1.0 0.5 (0.5-0.6) 0.4 (0.4-0.5)

1.0 0.6 (0.6-0.6) 0.5 (0.5-0.6)

1.0 1.7 (1.5-1.9) 2.1 (1.8-2.4)

1.0 1.7 (1.6-1.9) 2.7 (2.4-3.1)

1.0 0.5 (0.5-0.5) 0.03 (0.03-0.04)

1.0 0.4 (0.4-0.4) 0.02 (0.02-0.03)

1.0 0.7 (0.6-0.7) 0.5 (0.4-0.5)

1.0 0.7 (0.6-0.7) 0.4 (0.4-0.5)

1.0 2.2 (1.9-2.5) 4.1 (3.5-4.9)

1.0 2.0 (1.8-2.2) 2.3 (1.9-2.7)

Note: High levels of physical activity at work, travel and leisure time are protective of NCD risks; here logistic analyses modelled the probability of having high levels of physical activity. ^LTPA = Leisure time physical activity.

Living in an urban area, having a high income and having high educational attainment reduced the likelihood of being physically active at work and engaging in active travel of 30 minutes or more each day. Being older increased the odds of engaging in active travel and participating in a high level of LTPA. In contrast, those living in urban regions, with higher education and income were less likely to engage in active travel. Those living in urban areas, with high income and high educational attainment were also more likely to report a high LTPA level.

This section provides graphical presentations of the odds ratios from logistic regression analyses examining the independent association in China between SES and risk factors by sex, adjusting for other SES indicators.

Section Title Results

AGE 18-35 yrs 36-49 yrs 50-65 yrs REGION Rural Urban INCOME <2000 Yuan/Y 2000-9999 Yuan/Y >10000 Yuan/Y EDUCATION Primary Secondary Tertiary

High LTPA^ (≥ 4 times and ≥150min/ week) Men Women

High occupational PA Men Women

High active commuting (≥30mins/day) Men Women

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

Figure 3.1.2a Odds ratio (OR) and 95% confidence interval (CI) for the probability of smoking by socioeconomic status for men and women, China (N=142 693) 10.0

4.9

Men Women

2.6

Odds ratio

1.6

1.4

1.2

1.0

1.0 0.9

0.9

0.8

0.9 0.6

0.5 0.3

0.1

36–49

Results

Urban REGION RG: Rural

2000–9999 Yuan >10 000 Yuan INCOME (per year) RG: <2000 Yuan Socioeconomic status

Secondary

Tertiary EDUCATION RG: Primary

Figure 3.1.2b Odds ratio (OR) and 95% confidence interval (CI) for the probability of heavy alcohol drinking by socioeconomic status for men and women, China (N=142 693) 10.0

Odds ratio

2.3

Men Women

2.0 1.4

1.3 1.0

1.3

1.1

1.0

1.2 1.2

0.9

0.9 0.7

0.1

36–49

50–65 AGE (years) RG: 18–35

Urban REGION RG: Rural

2000–9999 Yuan >10 000 Yuan INCOME (per year) RG: <2000 Yuan Socioeconomic status

0.5

Secondary

0.4

Tertiary EDUCATION RG: Primary

Figure 3.1.2c Odds ratio (OR) and 95% confidence interval (CI) for the probability of low vegetable consumption by socioeconomic status for men and women, China (N=142 693) 10.0

Men Women Odds ratio

50–65 AGE (years) RG: 18–35

1.0

1.0 1.0

1.3 1.4 0.9

0.9

0.1

36–49

50–65 AGE (years) RG: 18–35

Urban REGION RG: Rural

1.4

1.2 1.1 0.9

1.1

0.9 0.9

2000–9999 Yuan >10 000 Yuan INCOME (per year) RG: <2000 Yuan Socioeconomic status

Secondary

Tertiary EDUCATION RG: Primary

A multicountry analysis of noncommunicable disease surveillance data

Figure 3.1.2d Odds ratio (OR) and 95% confidence interval (CI) for the probability of low fruit consumption by socioeconomic status for men and women,China (N=142 693) 10.0

1.1 1.1

1.1

1.0 0.5

0.1

36–49

50–65 AGE (years) RG: 18–35

0.4

Urban REGION RG: Rural

0.5

0.6

0.5

0.3 0.3 2000–9999 Yuan >10 000 Yuan INCOME (per year) RG: <2000 Yuan Socioeconomic status

Secondary

0.4

Tertiary EDUCATION RG: Primary

Figure 3.1.2e Odds ratio (OR) and 95% confidence interval (CI) for the probability of obesity by socioeconomic status for men and women, China (N=142 693) 10.0

Men Women

3.8

Odds ratio

2.3

1.8

1.5

1.3

1.4

1.7

1.9 1.3

1.2

1.6

1.5

1.2

1.0 0.6

0.1

36–49

50–65 AGE (years) RG: 18–35

Urban REGION RG: Rural

2000–9999 Yuan >10 000 Yuan INCOME (per year) RG: <2000 Yuan Socioeconomic status

Secondary

Tertiary EDUCATION RG: Primary

Figure 3.1.2f Odds ratio (OR) and 95% confidence interval (CI) for the probability of central obesity by socioeconomic status for men and women, China (N=142 693) 10.0

6.7 2.9

2.1

Odds ratio

1.7

1.9 1.3

1.7

Men Women

1.9 1.3

1.3

1.0

1.6

1.6 1.0

0.5 0.1

36–49

50–65 AGE (years) RG: 18–35

Urban REGION RG: Rural

2000–9999 Yuan >10 000 Yuan INCOME (per year) RG: <2000 Yuan Socioeconomic status

Secondary

Tertiary EDUCATION RG: Primary

Section Title Results

1.0

Odds ratio

Men Women

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

Figure 3.1.2g Odds ratio (OR) and 95% confidence interval (CI) for the probability of high blood pressure by socioeconomic status for men and women,China (N=142 693) 100.0

Men Women

Odds ratio

16.7 10.0

5.9

5.2 2.5

1.3 1.1

1.0

1.2 1.1

1.1

1.0 0.9 0.6

0.1

36–49

Results

Urban REGION RG: Rural

2000–9999 Yuan >10 000 Yuan INCOME (per year) RG: <2000 Yuan Socioeconomic status

Secondary

Tertiary EDUCATION RG: Primary

Figure 3.1.2h Odds ratio (OR) and 95% confidence interval (CI) for the probability of high total cholesterol by socioeconomic status for men and women,China (N=142 693) 100.0

Odds ratio

Men Women 6.8

10.0 2.8

3.6 2.2 1.2

1.6

1.9 1.2

1.4

1.2

1.0

0.1

36–49

50–65 AGE (years) RG: 18–35

Urban REGION RG: Rural

2000–9999 Yuan >10 000 Yuan INCOME (per year) RG: <2000 Yuan Socioeconomic status

Secondary

Tertiary EDUCATION RG: Primary

Figure 3.1.2i Odds ratio (OR) and 95% confidence interval (CI) for the probability of elevated fasting blood glucose by socioeconomic status for men and women, China (N=142 693) 100.0

Odds ratio

50–65 AGE (years) RG: 18–35

10.0

8.0 3.6

Men Women

12.9

4.0 2.6 2.3

1.4 1.3

1.8

1.2

1.0

0.1

36–49

50–65 AGE (years) RG: 18–35

Urban REGION RG: Rural

2000–9999 Yuan >10 000 Yuan INCOME (per year) RG: <2000 Yuan Socioeconomic status

1.4

1.1

Secondary

1.0

Tertiary EDUCATION RG: Primary

A multicountry analysis of noncommunicable disease surveillance data

Figure 3.1.2j Odds ratio (OR) and 95% confidence interval (CI) for the probability of a high level of occupational physical activity by socioeconomic status for men and women,China (N=142 693) 10.0

Men Women

Odds ratio

1.2 1.3 1.0 0.6 0.5

0.4

0.5 0.4

0.4

0.2

0.1

0.2

0.1 0.1

50–65 AGE (years) RG: 18–35

Urban REGION RG: Rural

2000–9999 Yuan >10 000 Yuan INCOME (per year) RG: <2000 Yuan Socioeconomic status

0.02 Tertiary

Secondary EDUCATION RG: Primary

Figure 3.1.2k Odds ratio (OR) and 95% confidence interval (CI) for the probability of highly active commuting by socioeconomic status for men and women,China (N=142 693) 10.0

Odds ratio

Men Women 1.0

1.1

1.2 1.1

1.0

1.0 0.9 0.5

0.1

36–49

50–65 AGE (years) RG: 18–35

Urban REGION RG: Rural

0.6 0.4

0.5

2000–9999 Yuan >10 000 Yuan INCOME (per year) RG: <2000 Yuan Socioeconomic status

0.7

0.7 0.5 0.4

Secondary

Tertiary EDUCATION RG: Primary

Figure 3.1.2l Odds ratio (OR) and 95% confidence interval (CI) for the probability of a high level of leisure-time physical activity by socioeconomic status for men and women,China (N=142 693)

Odds ratio

100.0

10.0

5.9

Men Women

10.1 5.7

8.7

2.6

1.7 1.7

1.3

2.1 2.7

4.1 2.2

2.3

2.0

1.0

0.1

36–49

50–65 AGE (years) RG: 18–35

Urban REGION RG: Rural

2000–9999 Yuan >10 000 Yuan INCOME (per year) RG: <2000 Yuan Socioeconomic status

Secondary

Tertiary EDUCATION RG: Primary

36–49

Section Title Results

0.03 0.1

Noncommunicable disease risk factors and socioeconomic inequalities â&#x20AC;&#x201C; what are the links?

Fiji Prevalence rates of risk factors by SES measures Table 3.2.1 Prevalence rates of risk factors by socioeconomic status for men and women, Fiji (N=6763)

Results

Current smoking Men Women n (%) n (%)

Hazardous drinking1 Men Women n (%) n (%)

Consumed <5 servings/day fruit and vegetables Men Women n (%) n (%)

Obese2 Men Women n (%) n (%)

AGE 18-35 yrs

789 (47.3)

335 (18.0)

363 (21.8)

85 (4.5)

1400 (84.0)

1514 (81.3)

168 (10.4)

376 (22.1)

36-49 yrs

424 (54.3)

153 (16.2)

53 (6.7)

28 (3.0)

617 (79.1)

732 (77.2)

182 (24.4)

357 (38.3)

50-65 yrs EDUCATION Primary

221 (51.2)

61 (11.6)

12 (2.7)

1 (0.1)

314 (72.7)

401 (75.5)

100 (23.4)

246 (46.8)

497 (62.7)

160 (15.9)

62 (7.8)

21 (2.1)

656 (82.8)

846 (83.7)

83 (10.8)

363 (36.9)

Secondary

731 (49.8)

315 (17.6)

238 (16.2)

77 (4.3)

1203 (81.9)

1408 (78.5)

269 (19.0)

517 (30.9)

Tertiary ETHNICITY Indo-Fijian

206 (33.6)

75 (13.9)

128 (20.8)

15 (2.8)

472 (77.1)

391 (72.9)

98 (16.2)

98 (19.8)

691 (47.6)

65 (3.9)

131 (9.0)

4 (0.2)

1202 (82.7)

1295 (77.6)

82 (5.8)

282 (17.7)

Fijian

597 (56.1)

386 (29.2)

240 (22.6)

86 (6.5)

837 (78.7)

1079 (81.7)

243 (23.4)

511 (41.5)

Other REGION Rural

146 (40.3)

99 (28.3)

56 (15.6)

24 (6.7)

292 (80.8)

273 (77.5)

125 (36.3)

185 (56.2)

398 (60.4)

124 (20.1)

87 (13.2)

7 (1.2)

507 (76.8)

491 (79.4)

54 (8.4)

158 (27.0)

Urban

1036 (46.7)

426 (15.6)

340 (15.3)

106 (3.9)

1825 (82.2)

2156 (79.2)

396 (18.4)

820 (31.9)

1 2

Hazardous drinking: frequency of drinking in the past 12 months and average number of drinks consumed per day; Obese: BMIâ&#x2030;Ľ30

The prevalence of current smoking was higher in men than women across all age groups, and higher among men with primary education. The proportions of current smoking were higher among ethnic Fijians and those living in rural areas. Hazardous drinking was more prevalent among young men, those of Fijian ethnicity, those with tertiary education and those living in urban settings. The prevalence of respondents reporting not eating the recommended amount of fruit and vegetables was high for both men and women, although the prevalence appeared to decrease with age and with increasing educational level. The proportion of obesity was assessed to be higher in urban settings and among older age groups.

A multicountry analysis of noncommunicable disease surveillance data

Table 3.2.1 Prevalence rates of risk factors by socioeconomic status for men and women, Fiji (N=6763) (Cont. 1)

47 (2.9)

36 (2.0)

125 (34.7)

117 (25.5)

35 (10.0)

51 (11.1)

36-49 yrs

31 (4.2)

168 (18.1)

66 (8.8)

80 (8.5)

159 (50.6)

162 (31.7)

73 (23.1)

121 (23.7)

50-65 yrs EDUCATION Primary

21 (4.8)

159 (30.3)

43 (10.1)

110 (21.1)

93 (43.3)

137 (44.3)

56 (27.1)

122 (38.7)

20 (2.5)

203 (20.7)

61 (7.8)

105 (10.6)

126 (43.4)

174 (34.3)

53 (18.2)

137 (26.8)

Secondary

31 (2.2)

220 (13.2)

60 (4.2)

103 (5.9)

164 (37.2)

199 (32.0)

71 (16.1)

128 (20.7)

Tertiary ETHNICITY Indo-Fijian

6 (1.1)

53 (10.8)

36 (6.0)

18 (3.5)

87 (54.7)

43 (29.4)

40 (27.7)

28 (19.1)

17 (1.2)

123 (7.8)

51 (3.6)

84 (5.2)

233 (46.0)

209 (31.8)

119 (24.1)

135 (20.5)

Fijian

32 (3.1)

261 (21.3)

82 (7.9)

95 (7.3)

109 (35.9)

147 (30.5)

41 (13.6)

92 (19.2)

Other REGION Rural

8 (2.3)

92 (27.8)

24 (6.7)

47 (13.8)

36 (44.1)

61 (42.8)

4 (5.0)

67 (47.0)

11 (1.6)

69 (11.8)

30 (4.6)

43 (7.1)

98 (40.8)

61 (29.3)

22 (9.3)

22 (10.9)

Urban

47 (2.2)

407 (15.9)

128 (5.9)

183 (6.9)

279 (43.0)

355 (33.2)

142 (22.3)

271 (25.2)

Central obesity: ≥110cm for men and ≥100cm for women; High blood pressure SBP≥140mmHg and/or DBP≥90mmHg; High cholesterol ≥5.5 mmol/L; High fasting glucose ≥6.1mmol/L

Central obesity increased markedly with age for women, and the prevalence was marginally higher in urban regions compared. High blood pressure was more common among those with only primary-level education, for both men and women. The prevalence rates of elevated cholesterol were highest for men with tertiary education but lowest for women of the same educational level. There was a clear trend of increasing diabetes prevalence with increasing age, with diabetes being more common among those living in urban areas. While diabetes was more prevalent among men with tertiary education, an inverse association with educational level was observed for women.

Section Title Results

149 (8.8)

High fasting blood glucose4 Men Women n (%) n (%)

6 (0.3)

High cholesterol3 Men Women n (%) n (%)

AGE 18-35 yrs

High blood pressure2 Men Women n (%) n (%)

Central obesity1 Men Women n (%) n (%)

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

Table 3.2.1 Prevalence rates of risk factors by socioeconomic status for men and women, Fiji (N=6763) (Cont. 2)

Results

High level of occupational physical activity Men Women n (%) n (%)

Highly active commuting Men Women n (%) n (%)

High LTPA1 (≥30 minutes on a typical day) Men Women n (%) n (%)

AGE 18-35 yrs

581 (35.0)

227 (12.2)

899 (53.9)

828 (44.5)

583 (35.0)

312 (16.8)

36-49 yrs

265 (34.0)

134 (14.1)

391 (50.3)

402 (42.4)

215 (27.7)

161 (17.0)

50-65 yrs EDUCATION Primary

144 (33.4)

74 (14.0)

252 (58.3)

195 (36.7)

99 (22.9)

65 (12.2)

333 (42.1)

137 (13.5)

448 (56.5)

458 (45.5)

211 (26.6)

111 (11.0)

Secondary

480 (32.7)

230 (12.9)

797 (54.4)

756 (42.2)

441 (30.0)

294 (16.4)

Tertiary ETHNICITY Indo-Fijian

176 (28.8)

67 (12.5)

292 (47.7)

207 (38.8)

241 (39.4)

132 (24.7)

477 (32.9)

197 (11.8)

631 (43.5)

609 (36.5)

365 (25.1)

247 (14.8)

Fijian

405 (38.2)

188 (14.2)

669 (63.0)

702 (53.3)

438 (41.2)

206 (15.6)

Other REGION Rural

107 (29.7)

51 (14.4)

241 (66.7)

114 (32.3)

95 (26.1)

85 (24.0)

317 (48.1)

110 (17.7)

469 (71.1)

389 (62.9)

203 (30.8)

84 (13.5)

Urban

672 (30.4)

326 (12.0)

1073 (48.4)

1035 (38.1)

694 (31.3)

454 (16.7)

Note: High levels of physical activity during work, travel and leisure time are protective of NCD risks; here logistic analyses modelled the probability of having high levels of physical activity; 1 LTPA = Leisure time physical activity.

High levels of work-related physical activity were more common among men than women across all age groups, and for those with only primary-level education, those of ethnic Fijian descent and those living in rural areas. High levels of active travel were common among those with primary education, for men and women, and among those of ethnic Fijian descent and those living in rural areas. LTPA was more prevalent among the youngest age group, for both men and women, and among those with tertiary education and those living in urban areas. LTPA was also more common among Fijian men and women classified as ‘other’.

A multicountry analysis of noncommunicable disease surveillance data

Association between risk factors and SES measures (adjusted analyses) Table 3.2.2 Odds ratio (OR) and 95% confidence interval (CI) for the probability of having NCD risk factors by socioeconomic status for men and women, Fiji (N=6763)

36-49 yrs 50-65 yrs EDUCATION Illiterate/primary

1.0

1.1 (0.9-1.3) 0.9 (0.7-1.1) 0.3 (0.2-0.5) 0.5 (0.3-0.9) 0.6 (0.5-0.8) 0.6 (0.5-0.8) 3.7 (2.9-4.8) 2.2 (1.8-2.7) 0.9 (0.7-1.1) 0.5 (0.3-0.7) 0.2 (0.1-0.5) 0.1 (0.0-0.8) 0.4 (0.3-0.5) 0.5 (0.4-0.6) 4.4 (3.1-6.1) 3.0 (2.3-3.8) 1.0

1.0

0.7 (0.5-0.9) 1.3 (0.9-1.9) 0.6 (0.3-1.2) 0.7 (0.5-0.9) 0.5 (0.4-0.6) 2.1 (1.6-2.9) 0.8 (0.6-1.0)

Tertiary ETHNICITY Indo-Fijian

0.3 (0.2-0.4)

0.5 (0.4-0.8) 1.3 (0.9-2.0) 0.2 (0.1-0.4) 0.5 (0.4-0.6) 0.3 (0.3-0.5) 1.8 (1.2-2.6) 0.5 (0.3-0.6)

Urban

1.0

0.6 (0.5-0.7)

Other REGION Rural

1.0

Secondary

Fijian

1.0

1.5 (1.3-1.8) 10.5 (8.0-13.9) 3.2 (2.4-4.2) 18.0 (6.2-52.3) 0.8 (0.7-1.0) 1.3 (1.1-1.6) 6.0 (4.5-7.9) 4.1 (3.4-4.9) 0.9 (0.7-1.1) 11.6 (8.2-16.4) 2.1 (1.4-3.1) 20.7 (6.4-66.7) 0.9 (0.7-1.2) 1.2 (0.9-1.6) 8.9 (6.4-12.3) 6.6 (5.0-8.6) 1.0

1.0

0.8 (0.6-0.9) 0.8 (0.6-1.0) 1.1 (0.8-1.6) 2.1 (0.8-5.5) 1.6 (1.3-2.0) 1.2 (1.0-1.5) 2.8 (2.0-3.9) 1.5 (1.2-1.8)

Hazardous drinking: frequency of drinking in the past 12 months and average number of drinks consumed per day; Obese: BMI≥30

Having a tertiary education and living in an urban area were related to reduced odds of being a smoker, for both men and women. Older women and men were less likely to engage in hazardous drinking, while women with tertiary education were less likely than men to engage in hazardous drinking. While living in an urban area increased the odds of not consuming the recommended amount of fruit and vegetables, having a tertiary education reduced the odds of not consuming the recommended amount of fruit and vegetables, for both men and women. The prevalence of obesity increased with age for both men and women. Men with secondary and tertiary education were more likely to be obese, while women with tertiary education were less likely to be obese than those with primary education. Ethnic Fijians and ‘others’ were more likely to be obese than those classified as Indo-Fijian. Living in an urban area also increased the odds of being obese.

Section Title Results

AGE 18-35 yrs

Current smoking Men Women OR (95% CI) OR (95% CI)

Consumed <5 servings/day fruit and Hazardous drinking1 vegetables Obese2 Men Women Men Women Men Women OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI)

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

Table 3.2.2 Odds ratio (OR) and 95% confidence interval (CI) for the probability of having NCD risk factors by socioeconomic status for men and women, Fiji (N=6763) (Cont. 1) Central obesity1 High blood pressure2 High cholesterol3 Men Women Men Women Men Women OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) AGE 18-35 yrs

Results

36-49 yrs

1.0

High fasting blood glucose4 Men Women OR (95% CI) OR (95% CI) 1.0

1.0

13.1 (5.2-32.9) 2.3 (1.8-2.9) 3.5 (2.3-5.1) 4.6 (3.0-6.9) 2.0 (1.5-2.8) 1.4 (1.0-1.8) 3.3 (2.1-5.1) 2.4 (1.6-3.5)

50-65 yrs 16.9 (6.3-45.3) 4.1 (3.1-5.5) 3.4 (2.1-5.3) 13.2 (8.5-20.3) 1.4 (1.0-2.0) 2.3 (1.7-3.3) 4.4 (2.6-7.3) 4.6 (3.0-6.9) EDUCATION Illiterate/primary 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 Secondary 1.3 (0.7-2.5) 0.7 (0.5-0.8) 0.6 (0.4-0.9) 1.0 (0.7-1.5) 0.8 (0.6-1.2) 1.1 (0.8-1.4) 1.2 (0.8-1.8) 0.8 (0.5-1.1) Tertiary ETHNICITY Indo-Fijian

0.8 (0.3-1.9) 0.6 (0.4-0.9) 0.9 (0.6-1.5) 0.8 (0.4-1.3) 1.8 (1.2-2.8) 0.9 (0.6-1.4) 2.3 (1.4-3.9) 0.7 (0.4-1.1) 1.0

1.0

Fijian

3.0 (1.6-5.4) 4.1 (3.2-5.2) 2.7 (1.9-3.9) 1.8 (1.3-2.4) 0.7 (0.5-0.9) 1.0 (0.8-1.3) 0.5 (0.3-0.7) 1.1 (0.8-1.5)

Other REGION Rural

1.6 (0.7-3.9) 4.9 (3.5-6.8) 2.0 (1.2-3.4) 2.7 (1.8-4.0) 0.8 (0.5-1.3) 1.5 (1.0-2.2) 0.1 (0.0-0.3) 3.7 (2.5-5.7)

Urban

1.6 (0.8-3.3) 1.7 (1.2-2.2) 1.6 (1.0-2.4) 1.0 (0.7-1.4) 1.1 (0.8-1.5) 1.2 (0.9-1.7) 2.6 (1.6-4.4) 3.1 (1.9-5.0)

1.0

Central obesity: ≥110cm for men and ≥100cm for women; High blood pressure SBP≥140mmHg and/or DBP≥90mmHg; High cholesterol ≥5.5 mmol/L; 4 High fasting glucose ≥6.1mmol/L 1

The prevalence of central obesity increased with age for both men and women. Women were less likely to be centrally obese the higher their educational levels. Ethnic Fijians and ‘others’ were more likely to be centrally obese, especially among women. The odds of having elevated blood pressure and cholesterol increased with age for men and women, although the risk reduced slightly for men in the oldest age group. Men with tertiary education were more likely to have high cholesterol than women. The odds of having diabetes increased with age for both men and women, and for men with tertiary education. Living in an urban area increased the likelihood of having diabetes, for both men and women.

A multicountry analysis of noncommunicable disease surveillance data

Table 3.2.2 Odds ratio (OR) and 95% confidence interval (CI) for the probability of having NCD risk factors by socioeconomic status for men and women, Fiji (N=6763) (Cont. 2)

1.0

0.9 (0.7-1.1)

1.2 (1.0-1.5)

0.8 (0.7-1.0)

0.9 (0.8-1.1)

0.8 (0.6-0.9)

1.2 (1.0-1.5)

0.8 (0.6-1.0)

1.2 (0.9-1.7)

1.2 (1.0-1.6)

0.7 (0.6-0.9)

0.6 (0.4-0.7)

0.9 (0.6-1.2)

1.0

Secondary

0.7 (0.6-0.8)

1.1 (0.8-1.4)

1.0 (0.8-1.2)

0.8 (0.7-1.0)

1.0 (0.8-1.2)

1.5 (1.2-1.9)

Tertiary ETHNICITY Indo-Fijian

0.6 (0.5-0.8)

1.1 (0.8-1.6)

0.8 (0.6-1.0)

1.5 (1.1-1.9)

2.6 (1.9-3.5)

1.0

Fijian

1.2 (1.0-1.5)

2.2 (1.8-2.6)

2.0 (1.7-2.3)

2.1 (1.8-2.5)

1.0 (0.8-1.3)

Other REGION Rural

1.0 (0.8-1.3)

1.3 (0.9-1.8)

3.2 (2.5-4.0)

0.9 (0.7-1.2)

1.0 (0.8-1.3)

1.7 (1.3-2.3)

1.0

Urban

0.5 (0.4-0.6)

0.6 (0.5-0.8)

0.4 (0.3-0.5)

1.1 (0.9-1.3)

1.0 (0.8-1.4)

36-49 yrs 50-65 yrs EDUCATION Illiterate/primary

Living in an urban area reduced the probability of engaging in a high level of work-related physical activity, for both men and women. Ethnic Fijian men and women were more likely to report active commuting, but living in an urban area reduced the odds of engaging in active community. Having a tertiary education increased the odds of participating in high LTPA, for both men and women, but only ethnic Fijian men and women classified as ‘others’ were more likely to engage in high LTPA.

This section provides the graphical presentations of the odds ratios from logistic regression analyses examining the independent association in Fiji between SES and risk factors by sex, adjusting for other SES indicators.

Section Title Results

AGE 18-35 yrs

High active commuting Men Women OR (95% CI) OR (95% CI)

High LTPA1 (≥30 minutes on a typical day) Men Women OR (95% CI) OR (95% CI)

High occupational physical activity Men Women OR (95% CI) OR (95% CI)

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

Figure 3.2.2a Odds ratio (OR) and 95% confidence interval (CI) for the probability of smoking by socioeconomic status for men and women, Fiji (N=6763) 100.0 11.6

Odds ratio

10.5 10.0

1.0

1.5

1.1 0.9

0.9

0.5 0.1

36–49

50–65 AGE (years) RG: 18–35

Results

0.9

0.6 0.7 Urban REGION RG: Rural

0.8 0.8

0.5 0.3 2000–9999 Yuan >10 000 Yuan INCOME (per year) RG: <2000 Yuan Socioeconomic status

Secondary

Tertiary EDUCATION RG: Primary

Figure 3.2.2b Odds ratio (OR) and 95% confidence interval (CI) for the probability of heavy alcohol drinking by socioeconomic status for men and women,Fiji (N=6763) 100.0

18.0

Odds ratio

10.0

3.2

20.7

Men Women

2.1

1.3

1.1

1.0 0.1 0.0

0.3

0.5

0.6 0.2

36–49

0.2

0.1

50–65

Secondary

Tertiary EDUCATION RG: Illiterate/Primary Socioeconomic status

AGE (years) RG: 18–35

Fijan

Other ETHNICITY RG: Indo-Fijan

Urban REGION RG: Rural

Figure 3.2.2c Odds ratio (OR) and 95% confidence interval (CI) for the probability of low vegetable and fruit consumption by socioeconomic status for men and women,Fiji (N=6763) 10.0

Men Women Odds ratio

Men Women

1.3

1.2

1.6

1.2

1.0 0.6 0.6 0.4 0.1

36–49

0.5

50–65 AGE (years) RG: 18–35

0.7

0.9

0.8 0.5

Secondary

0.5

0.3

Tertiary EDUCATION RG: Illiterate/Primary Socioeconomic status

Fijan

Other ETHNICITY RG: Indo-Fijan

Urban REGION RG: Rural

A multicountry analysis of noncommunicable disease surveillance data

Figure 3.2.2d Odds ratio (OR) and 95% confidence interval (CI) for the probability of obesity by socioeconomic status for men and women, Fiji (N=6763) 100.0

Odds ratio

10.0

4.4

3.7 2.2

6.0 3.0

2.1

8.9

Men Women 6.6

4.1

2.8

1.8

1.5

1.0

36–49

50–65

Secondary

Tertiary EDUCATION RG: Illiterate/Primary Socioeconomic status

AGE (years) RG: 18–35

Fijan

Other ETHNICITY RG: Indo-Fijan

Urban REGION RG: Rural

Figure 3.2.2e Odds ratio (OR) and 95% confidence interval (CI) for the probability of central obesity by socioeconomic status for men and women,Fiji (N=6763) 100.0 16.9

13.1 10.0

Men Women

Odds ratio

4.1 2.3

3.0 4.1 1.3

1.6

4.9

0.8

1.6

1.7

1.0 0.7 0.1

36–49

50–65

0.6

Secondary

Tertiary EDUCATION RG: Illiterate/Primary Socioeconomic status

AGE (years) RG: 18–35

Fijan

Other ETHNICITY RG: Indo-Fijan

Urban REGION RG: Rural

Figure 3.2.2f Odds ratio (OR) and 95% confidence interval (CI) for the probability of high blood pressure by socioeconomic status for men and women,Fiji (N=6763) 100.0

Men Women

13.2 Odds ratio

10.0

3.5

4.6

3.4

2.7 1.0

1.8

2.0 2.7

1.6 1.0

1.0 0.9

0.6 0.1

36–49

50–65 AGE (years) RG: 18–35

Secondary

0.8

Tertiary EDUCATION RG: Illiterate/Primary Socioeconomic status

Fijan

Other ETHNICITY RG: Indo-Fijan

Urban REGION RG: Rural

0.5

0.1

Section Title Results

0.8

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

Figure 3.2.2g Odds ratio (OR) and 95% confidence interval (CI) for the probability of high total cholesterol by socioeconomic status for men and women,Fiji (N=6763) 10.0 2.3

Odds ratio

2.0

1.8

1.4

1.0

1.0 0.9

0.8

0.1

36–49

50–65

Secondary

Tertiary EDUCATION RG: Illiterate/Primary Socioeconomic status

AGE (years) RG: 18–35

Results

1.1 1.2

0.8

0.7

Fijan

Other ETHNICITY RG: Indo-Fijan

Urban REGION RG: Rural

Figure 3.2.2h Odds ratio (OR) and 95% confidence interval (CI) for the probability of elevated fasting blood glucose by socioeconomic status for men and women,Fiji (N=6763) 10. 0

3. 3

4. 4 4. 6

2. 4

3. 7

2. 3

Odds ratio

1. 2

2. 6 3. 1

1. 1

1. 0 0. 8

0. 7

Men Women

0. 5

0. 1

0. 0

0. 1

36–49

50–65

Secondary

Tertiary EDUCATION RG: Illiterate/Primary Socioeconomic status

AGE (years) RG: 18–35

Fijan

Other ETHNICITY RG: Indo-Fijan

Urban REGION RG: Rural

Figure 3.2.2i Odds ratio (OR) and 95% confidence interval (CI) for the probability of a high level of occupational physical activity by socioeconomic status for men and women,Fiji (N=6763) 10.0

Men Women Odds ratio

1.5

1.1

Men Women

1.2

1.1

1.2 1.2

1.0

1.3

1.0 0.9

0.1

0.8

36–49

50–65 AGE (years) RG: 18–35

0.7

0.6

Secondary

Tertiary EDUCATION RG: Illiterate/Primary Socioeconomic status

0.5

Fijan

Other ETHNICITY RG: Indo-Fijan

0.6

Urban REGION RG: Rural

A multicountry analysis of noncommunicable disease surveillance data

Figure 3.2.2j Odds ratio (OR) and 95% confidence interval (CI) for the probability of highly active commuting by socioeconomic status for men and women,Fiji (N=6763) 10.0

Men Women

3.2 Odds ratio

2.2 2.0 1.2

1.0

1.0 0.8 0.9

0.8

0.7

0.8

0.9

0.8

36–49

50–65

Secondary

Tertiary EDUCATION RG: Illiterate/Primary Socioeconomic status

AGE (years) RG: 18–35

Fijan

Other ETHNICITY RG: Indo-Fijan

Urban REGION RG: Rural

Figure 3.2.2k Odds ratio (OR) and 95% confidence interval (CI) for the probability of a high level of leisure-time physical activity by socioeconomic status for men and women,Fiji (N=6763) 10.0

Odds ratio

2.6 1.5

1.2

1.5

1.0

2.1

1.7 1.0

1.0

Men Women 1.1 1.0

1.0 0.9

0.8 0.6 0.1

36–49

50–65 AGE (years) RG: 18–35

Secondary

Tertiary EDUCATION RG: Illiterate/Primary Socioeconomic status

Fijan

Other ETHNICITY RG: Indo-Fijan

Urban REGION RG: Rural

0.1

Section Title Results

0.4 0.4

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

Malaysia Prevalence rates of risk factors by SES measures Table 3.3.1 Prevalence rates of risk factors by socioeconomic status for men and women, Malaysia (N=2572) Current smoking Men Women n (%) n (%)

Consumed <5 Hazardous servings/day fruit drinking1 and vegetables Men Women Men Women n (%) n (%) n (%) n (%)

Obese2 Men Women N (%) n (%)

Central obesity3 Men Women n (%) n (%)

Results

AGE 25 – 35 yrs

134 (53.2)

10 (2.3)

193 (76.6) 327 (76.6) 33 (13.1)

76 (17.8)

84 (33.3) 206 (48.2)

36 – 49 yrs

206 (49.0)

19 (3.0)

308 (73.3) 481 (75.3) 49 (11.7) 143 (22.4) 168 (40.0) 385 (60.3)

50 – 64 yrs

148 (39.8)

18 (3.9)

265 (71.2) 330 (71.4) 62 (16.7)

Malay

308 (52.2)

11 (1.3)

429 (73.8) 629 (74.3) 87 (15.0) 187 (22.1) 245 (42.2) 496 (58.6)

Chinese

63 (33.9)

10 (3.7)

146 (78.5) 203 (74.4) 21 (11.3)

36 (13.2)

93 (50.0) 150 (54.9)

Indian

22 (27.8)

3 (2.0)

58 (73.4) 104 (68.9) 13 (16.5)

49 (32.5)

52 (65.8) 110 (72.8)

100 (50.5)

23 (8.9)

133 (67.2) 202 (78.6) 26 (13.1)

40 (15.6)

62 (31.3) 151 (58.8)

Rural

275 (51.5)

23 (3.1)

398 (74.5) 560 (75.4) 76 (14.2) 147 (19.8) 210 (39.3) 437 (58.7)

Urban

213 (41.8)

24 (3.1)

368 (72.2) 578 (73.6) 71 (13.9) 165 (21.0) 242 (47.5) 470 (59.9)

< RM 1000

250 (51.8)

27 (3.7)

343 (69.6) 567 (70.3) 96 (19.9) 173 (23.9) 186 (38.5) 455 (62.9)

RM 1000 - 3999

207 (44.3) 13 (0.02)

365 (77.7) 498 (80.1) 73 (15.6) 105 (17.0) 220 (47.1) 347 (56.1)

91 (19.7) 200 (53.8) 316 (68.4)

ETHNICITY

Other races REGION

INCOME

31 (33.0)

7 (0.04)

Low

188 (49.2)

26 (4.0)

272 (71.2) 503 (77.0) 56 (14.7) 134 (20.5) 173 (45.3) 422 (64.6)

Medium

270 (48.6)

18 (2.4)

418 (75.2) 569 (74.5) 73 (13.1) 154 (20.2) 233 (41.9) 430 (56.3)

30 (28.3)

3 (2.7)

> RM 3999

58 (71.6)

73 (73.7) 17 (18.1)

34 (18.2)

46 (48.9) 105 (56.1)

EDUCATION

High 1 2 3

76 (71.7)

66 (59.5) 18 (17.0)

24 (21.6)

46 (43.4)

55 (49.5)

The very low prevalence of hazardous drinking (5+ drinks for men and 4+ drinks for women) was deemed negligible for analyses; Obese: BMI≥30; Central obesity: ≥90cm for men and ≥80cm for women

Current smoking was higher among men than women, for men living in rural areas and for those with low incomes. Smoking was also more common among men and women with low educational levels. The prevalence of those who reported eating less than the recommended amount of fruit and vegetables per day was relatively high for both men and women, and across all age groups. Obesity (except for women) and central obesity increased with age,

A multicountry analysis of noncommunicable disease surveillance data

with the prevalence of central obesity higher in urban areas. While obesity was more common among those with low incomes, obesity was marginally more common among those with high education levels. In contrast, while central obesity was more prevalent among men with high incomes, central obesity was more common among women in low income groups. For education, the prevalence of central obesity was higher in the low educational group for both men and women.

AGE 25 – 35 yrs 36 – 49 yrs 50 – 64 yrs ETHNICITY Malay Chinese Indian Other races REGION Rural Urban INCOME < RM 1000 RM 1000 -3999 > RM 3999 EDUCATION Low Medium High 1 2 3

High cholesterol2 Men Women n (%) n (%)

High fasting blood glucose3 Men Women n (%) n (%)

32 (12.7) 107 (25.5) 161 (43.3)

2 (7.5) 133 (20.8) 188 (40.7)

19 (7.5) 56 (13.3) 66 (17.7)

22 (5.2) 65 (10.2) 105 (22.7)

10 (4.0) 37 (8.8) 50 (13.4)

19 (4.4) 65 (10.2) 78 (16.9)

199 (34.3) 69 (31.7) 23 (29.1) 73 (36.9)

257 (30.3) 81 (29.7) 35 (23.2) 76 (29.6)

364 (62.7) 101 (54.3) 50 (63.3) 95 (48.0)

531 (62.7) 150 (54.9) 86 (57.0) 133 (51.8)

68 (11.7) 10 (5.4) 21 (26.6) 23 (11.6)

115 (13.6) 31 (11.4) 32 (21.2) 31 (12.1)

175 (32.8) 125 (24.5)

200 (26.7) 154 (19.6)

78 (14.6) 63 (12.4)

106 (14.2) 86 (11.0)

54 (10.1) 43 (8.4)

91 (12.2) 71 (9.1)

142 (29.4) 129 (27.6) 29 (30.9)

185 (25.6) 125 (20.2) 43 (23.0)

72 (14.9) 47 (10.1) 11 (11.7)

106 (14.7) 52 (8.4) 34 (18.2)

58 (12.0) 38 (8.1) 5 (5.3)

89 (12.3) 54 (8.7) 19 (10.2)

144 (37.7) 134 (24.1) 22 (20.8)

48 (31.9) 131 (17.1) 14 (12.6)

59 (15.4) 67 (12.1) 24 (22.6)

110 (16.8) 73 (9.6) 9 (8.1)

48 (12.6) 41 (7.4) 8 (7.5)

98 (15.0) 59 (7.7) 5 (4.5)

High blood pressure SBP≥140mmHg and/or DBP≥90mmHg; High cholesterol >6.5 mmol/L; High fasting glucose ≥7.0mmol/L

The prevalence of elevated blood pressure increased with age for both men and women, and was more prevalent among those with lower educational levels, in both men and women. The prevalence of elevated cholesterol and high fasting blood sugar increased with age for both men and women, and was more common in rural than urban areas.

High blood pressure1 Men Women n (%) n (%)

Section Title Results

Table 3.3.1 Prevalence rates of risk factors by socioeconomic status for men and women, Malaysia (N=2572) (Cont. 1)

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

High fasting blood glucose was most prevalent among those in the lowest income group and those with low educational levels, for both men and women.

Table 3.3.1 Prevalence rates of risk factors by socioeconomic status for men and women, Malaysia (N=2572) (Cont. 2)

Results

High level of occupational physical activity Men Women n (%) n (%) AGE 25 – 35 yrs 36 – 49 yrs 50 – 64 yrs ETHNICITY Malay Chinese Indian Other races REGION Rural Urban INCOME < RM 1000 RM 1000 - 3999 > RM 3999 EDUCATION Low Medium High

Highly active commuting Men Women n (%) n (%)

High LTPA1 Men Women n (%) n (%)

70 (27.8) 130 (31.0) 100 (26.9)

78 (18.3) 118 (18.5) 75 (16.2)

45 (17.9) 61 (14.5) 49 (13.2)

64 (15.0) 92 (14.4) 52 (11.3)

40 (15.9) 54 (12.9) 32 (8.6)

43 (10.1) 68 (10.6) 34 (7.4)

173 (29.8) 55 (29.6) 20 (25.3) 52 (26.3)

154 (18.2) 50 (18.3) 26 (17.2) 41 (16.0)

93 (16.0) 28 (15.1) 9 (11.4) 25 (12.6)

120 (14.2) 43 (15.8) 14 (9.3) 31 (12.1)

76 (13.1) 18 (9.7) 3 (3.8) 29 (14.6)

78 (9.2) 29 (10.6) 15 (9.9) 23 (8.9)

180 (33.7) 120 (23.5)

146 (19.7) 125 (15.9)

86 (16.1) 69 (13.5)

106 (14.3) 102 (13.0)

64 (12.0) 62 (12.2)

68 (9.2) 77 (9.8)

154 (31.2) 128 (27.2) 18 (22.2)

132 (16.4) 127 (20.4) 12 (12.1)

75 (15.2) 68 (14.5) 12 (14.8)

114 (14.1) 79 (12.7) 15 (15.2)

61 (12.4) 54 (11.5) 11 (13.6)

83 (10.3) 51 (8.2) 11 (11.1)

128 (33.5) 149 (26.8) 23 (21.7)

116 (17.8) 138 (18.1) 17 (15.3)

60 (15.7) 83 (14.9) 12 (11.3)

90 (13.8) 97 (12.7) 21 (18.9)

37 (9.7) 71 (12.8) 18 (17.0)

63 (9.6) 69 (9.0) 13 (11.7)

High levels of physical activity at work were most prevalent among those aged 36-49 years, but generally declined with increasing age for both men and women. Highly active commuting was more common in the youngest age group, but generally declined by age 50-64 years for both men and women. The prevalence of a high level of LTPA was more common among those with high levels of education and income (>RM 3999).

A multicountry analysis of noncommunicable disease surveillance data

Association between risk factors and SES measures (adjusted analyses) Table 3.3.2 Odds ratio (OR) and 95% confidence interval (CI) for the probability of having NCD risk factors by socioeconomic status for men and women, Malaysia (N=2572)

Malay Chinese Indian Other races REGION Rural Urban INCOME < RM 1000 RM 1000 - 3999 > RM 3999 EDUCATION Low Medium High 1 2

1.0 1.4 (0.6 - 3.1) 1.8 (0.7- 4.3)

1.0 0.8 (0.6 - 1.2) 0.7 (0.5 - 1.1)

1.0 0.8 (0.6 - 1.1) 0.7 (0.5 - 0.9)

1.0 0.9 (0.6 - 1.4) 1.3 (0.8 - 2.1)

1.0 1.3 (1.0 - 1.8) 1.2 (0.8- 1.7)

Section Title Results

AGE 25 – 35 yrs 36 – 49 yrs 50 – 64 yrs ETHNICITY

1.0 1.0 0.5 (0.4 - 0.7) 3.0 (1.2 - 7.4) 0.4 (0.2 - 0.7) 1.3 (0.4 - 4.8) 0.8 (0.6 - 1.2) 7.2 (3.4 - 15.2)

1.0 1.2 (0.8 - 1.9) 1.0 (0.6 - 1.7) 0.7 (0.5 - 1.0)

1.0 1.0 (0.7 - 1.3) 0.8 (0.5 - 1.2) 1.2 (0.9 - 1.7)

1.0 0.7 (0.4 - 1.2) 1.1 (0.6 - 2.2) 0.9 (0.5 - 1.4)

1.0 0.5 (0.4 - 0.8) 1.6 (1.1 - 2.4) 0.6 (0.4 - 0.9)

Consumed <5 servings/day fruit and Current smoking Hazardous drinking1 vegetables Obese2 Men Women Men Women Men Women Men Women OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) 1.0 0.7 (0.5 - 1.0) 0.5 (0.3 - 0.7)

1.0 0.8 (0.6 - 1.0)

1.0 1.2 (0.6 - 2.2)

1.0 0.7 (0.5 - 1.0)

1.0 0.9 (0.7 - 1.2)

1.0 1.0 (0.7 - 1.4)

1.0 1.1 (0.9 - 1.5)

1.0 0.9 (0.7 - 1.3) 0.8 (0.5 - 1.5)

1.0 0.6 (0.3 - 1.3) 0.2 (0.0 - 2.0)

1.0 1.5 (1.1 - 2.2) 1.2 (0.7 - 2.2)

1.0 2.1 (1.6 - 2.7) 2.0 (1.2 - 3.5)

1.0 1.0 (0.7 - 1.5) 1.0 (0.5 - 2.1)

1.0 0.7 (0.5 - 0.9) 0.8 (0.5 - 1.5)

1.0 0.8 (0.6 - 1.1) 0.4 (0.2 - 0.7)

1.0 1.0 (0.5 - 1.9) 1.7 (0.5 - 6.6)

1.0 1.0 (0.7 - 1.4) 0.8 (0.5 - 1.4)

1.0 0.7 (0.5 - 0.9) 0.3 (0.2 - 0.5)

1.0 1.0 (0.6 - 1.5) 1.3 (0.7 - 2.5)

1.0 1.1 (0.8 - 1.4) 1.2 (0.7 - 2.0)

The very low prevalence of drinking hazardous (5+ drinks for men and 4+ drinks for women) was deemed negligible for analyses; Obese: BMI≥30

Older men with high educational levels and of Chinese or Indian descent were less likely to be smokers. There were no significant associations between smoking, region and income. Compared with ethnic Malays, women of Chinese and ‘other’ descent were more likely to be smokers. The probability of consuming less than the recommended amounts of fruits and vegetables per day decreased with increasing age for both men and women. Women with high incomes were more likely to not meet the recommended dietary guidelines, while those in the medium and high education groups were more likely to consume the

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

recommended amounts of fruits and vegetables per day than those in the low education group. Women of Chinese descent were less likely to be obese than ethnic Malay women, while women of Indian descent were more likely to be obese.

Table 3.3.2 Odds ratio (OR) and 95% confidence interval (CI) for the probability of having NCD risk factors by socioeconomic status for men and women, Malaysia (N=2572) (Cont. 1)

Results

Central obesity1 High blood pressure2 High cholesterol3 Men Women Men Women Men Women OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI)

AGE 25 – 35 yrs 36 – 49 yrs 50 – 64 yrs ETHNICITY Malay Chinese Indian Other races REGION Rural Urban INCOME < RM 1000 RM 1000 - RM 3999 > RM 3999 EDUCATION Low Medium High 1 2 3 4

High fasting blood glucose4 Men Women OR (95% CI) OR (95% CI)

1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.3 (0.9- 1.8) 1.6 (1.2 - 2.0) 2.3 (1.5 - 3.52) 2.9 (1.9 - 4.5) 1.8 (1.1 - 3.2) 2.0 (1.2 - 3.3) 2.1 (1.0 - 4.4) 2.2 (1.3 - 3.7) 2.4 (1.7- 3.4) 2.2 (1.6 - 2.3) 4.7 (3.0 - 7.3) 7.0 (4.5 - 10.8) 2.6 (1.5 - 4.6) 4.7 (2.8 - 7.9) 3.4 (1.6 - 7.1) 3.5 (2.0 - 6.2) 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.2 (0.9 - 1.7) 0.8 (0.6 - 1.1) 1.3 (0.9 - 1.9) 1.1 (0.8 - 1.6) 0.2 (0.1 - 0.5) 0.7 (0.4 - 1.0) 0.4 (0.2 - 0.9) 0.7 (0.4 - 1.1) 2.5 (1.5 - 4.2) 1.9 (1.3 - 2.8) 0.7 (0.4 - 1.2) 0.7 (0.4 - 1.1) 0.6 (0.3 - 1.3) 0.3 (0.2 - 0.7) 2.5 (1.3 - 4.7) 1.9 (1.2 - 3.2) 0.7 (0.5 - 1.0) 1.0 (0.8 - 1.4) 1.1 (0.6 - 1.6) 0.9 (0.6 - 1.3) 0.5 (0.3 - 0.9) 0.7 (0.4 - 1.0) 1.1 (0.6 - 1.9) 0.9 (0.6 - 1.5) 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.2 (0.9 - 1.6) 1.1 (0.9 - 1.4) 0.7 (0.5 - 0.9) 0.7 (0.5 - 0.9) 1.0 (0.7 - 1.4) 0.9 (0.7 - 1.3) 1.0 (0.6 - 1.5) 0.8 (0.5 - 1.1) 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.4 (1.0 - 1.9) 0.9 (0.7 - 1.1) 1.4 (1.0 - 1.9) 1.0 (0.8 - 1.4) 0.9 (0.6 - 1.4) 0.7 (0.5 - 1.0) 0.8 (0.5 - 1.4) 1.0 (0.7 - 1.4) 1.3 (0.7 - 2.2) 0.9 (0.6 - 1.5) 1.3 (0.7 - 2.3) 1.8 (1.1 - 3.1) 0.4 (0.2 - 1.2) 1.3 (0.7 - 2.5) 0.8 (0.3 - 2.1) 0.9 (0.4 - 2.1) 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.9 (0.7 - 1.3) 0.9 (0.7 - 1.2) 0.7 (0.5 – 1.0) 0.7 (0.5 – 1.0) 0.9 (0.6 - 1.4) 0.9 (0.6 - 1.2) 0.8 (0.5 - 1.3) 0.7 (0.5 - 1.0) 0.9 (0.5 - 1.5) 0.7 (0.5 - 1.1) 0.6 (0.4 - 1.1) 0.5 (0.3 - 0.9) 1.6 (0.8 - 3.3) 0.8 (0.4 - 1.8) 0.9 (0.4 - 2.2) 0.4 (0.2 - 1.2)

Central obesity: ≥90cm for men and ≥80cm for women; High blood pressure SBP≥140mmHg and/or DBP≥90mmHg; High cholesterol >6.5 mmol/L; High fasting glucose ≥7.0mmol/L

Older men were more likely to report central obesity than younger men, while women and men of Indian descent were more likely to be centrally obese than ethnic Malays. A clear trend in significant associations between increasing age and high blood pressure, high cholesterol and high fasting blood sugar was observed. Indians of both sexes were significantly more likely to have high fasting blood glucose than Malays.

A multicountry analysis of noncommunicable disease surveillance data

AGE 25 – 35 yrs 36 – 49 yrs 50 – 64 yrs ETHNICITY Malay Chinese Indian Other races REGION Rural Urban INCOME < RM 1000 RM 1000 - RM 3999 > RM 3999 EDUCATION Low Medium High

High active commuting Men Women OR (95% CI) OR (95% CI)

High LTPA 1 Men Women OR (95% CI) OR (95% CI)

1.0 1.0 (0.7-1.5) 0.8 (0.5-1.1)

1.0 1.0 (0.7-1.4) 0.9 (0.6-1.2)

1.0 0.7 (0.4-1.1) 0.6 (0.4-1.0)

1.0 0.9 (0.6-1.3) 0.6 (0.4-1.0)

1.0 0.9 (0.5-1.3) 0.6 (0.03-1.0)

1.0 1.0 (0.6-1.5) 0.6 (0.4-1.0)

1.0 1.1 (0.8-1.6) 0.9 (0.5-1.6) 0.7 (0.5-1.1)

1.0 1.1 (0.6-1.5) 1.1 (0.7-1.7) 0.8 (0.6-1.3)

1.0 0.9 (0.6-1.5) 0.7 (0.3-1.5) 0.7 (0.4-1.1)

1.0 1.2 (0.8-1.8) 0.6 (0.3-1.0) 0.8 (0.5-1.2)

1.0 0.7 (0.4-1.3) 0.2 (0.1-0.8) 1.1 (0.7-1.8)

1.0 1.2 (0.8-1.9) 1.0 (0.5-1.8) 0.9 (0.5-1.5)

1.0 0.6 (0.5-0.8)

1.0 0.7 (0.5-0.9)

1.0 0.8 (0.6-1.2)

1.0 0.9 (0.7-1.2)

1.0 1.1 (0.7-1.6)

1.0 1.1 (0.8-1.5)

1.0 1.0 (0.7-1.4) 0.8 (0.4-1.5)

1.0 1.4 (1.0-1.8) 0.8 (0.4-1.5)

1.0 1.0 (0.7-1.5) 1.2 (0.6-2.6)

1.0 0.8 (0.6-1.1) 0.9 (0.5-1.7)

1.0 0.8 (0.5-1.3) 0.9 (0.4-2.0)

1.0 0.7 (0.5-1.0) 0.9 (0.4-1.9)

1.0 0.7 (0.5-1.0) 0.6 (0.3-1.0)

1.0 0.9 (0.7-1.3) 0.8 (0.5-1.5)

1.0 0.8 (0.5-1.2) 0.6 (0.3-1.2)

1.0 0.7 (0.6-1.1) 1.3 (0.7-2.3)

1.0 1.2 (0.8-2.0) 1.9 (0.9-3.9)

1.0 0.8 (0.6-1.2) 1.1 (0.5-2.2)

Living in urban areas significantly reduced the probability of men and women engaging in high levels of work-related physical activity. Male respondents of Indian descent were less likely to engage in high levels of LTPA than Malays.

This section provides the graphical presentations of the odds ratios from logistic regression analyses examining the independent association in Malaysia between SES and risk factors by sex, adjusting for other SES indicators.

High occupational physical activity Men Women OR (95% CI) OR (95% CI)

Section Title Results

Table 3.3.2 Odds ratio (OR) and 95% confidence interval (CI) for the probability of having NCD risk factors by socioeconomic status for men and women, Malaysia (N=2572) (Cont. 2)

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

Figure 3.3.2a Odds ratio (OR) and 95% confidence interval (CI) for the probability of smoking by socioeconomic status for men and women, Malaysia (N=2572) 100.0

Odds ratio

10.0

Women 1.7

1.3 1.2

1.0

1.0 0.7

0.0

36–49

0.8

0.5

0.1

50–64

0.9

0.8

0.6

0.4

Chinese

AGE (years) RG: 25–35

Results

Indian ETHNICITY RG: Malay

Other

Urban

REGION RG: Rural Socioeconomic status

0.8

0.4

0.2

RM 1000 > RM 3999 –3999 INCOME RG: <RM 1000

Medium

High

EDUCATION RG: Low

Figure 3.3.2b Odds ratio (OR) and 95% confidence interval (CI) for the probability of low vegetable and fruit consumption by socioeconomic status for men and women,Malaysia (N=2572) 10.0

Odds ratio

1.2 1.0

1.5

1.2

1.0

2.1

Men

2.0

Women

1.2 1.0

1.0 0.8 0.8

0.7 0.7

0.8

0.7

0.9

0.7

0.8 0.3

0.1

36–49

50–64

Chinese

AGE (years) RG: 25–35

Indian ETHNICITY RG: Malay

Other

Urban

REGION RG: Rural Socioeconomic status

RM 1000 > RM 3999 –3999 INCOME RG: <RM 1000

Medium

High

EDUCATION RG: Low

Figure 3.3.2c Odds ratio (OR) and 95% confidence interval (CI) for the probability of obesity by socioeconomic status for men and women, Malaysia (N=2572) 10.0

Men Odds ratio

3.0

1.8

1.4

Men

7.2

1.3

1.1 1.6

1.2

1.0 1.1

1.3

1.0

Women

1.0 1.1

1.2

1.0 0.9

0.9

0.7 0.5 0.1

36–49

50–64

AGE (years) RG: 25–35

Chinese

Indian ETHNICITY RG: Malay

0.7

0.6

Other

Urban

REGION RG: Rural Socioeconomic status

0.8

RM 1000 > RM 3999 –3999 INCOME RG: <RM 1000

Medium EDUCATION RG: Low

High

A multicountry analysis of noncommunicable disease surveillance data

Figure 3.3.2d Odds ratio (OR) and 95% confidence interval (CI) for the probability of central obesity by socioeconomic status for men and women,Malaysia (N=2572) 10.0

1.3 1.6

2.5 2.2

Men 1.9

1.2

1.0

1.2 1.1

Women

1.3

1.4

1.0

0.1

36–49

50–64

Chinese

AGE (years) RG: 25–35

0.9

0.9 0.9

RM 1000 > RM 3999 –3999 INCOME RG: <RM 1000

Medium

0.9

0.7

Indian ETHNICITY RG: Malay

Other

Urban

REGION RG: Rural Socioeconomic status

0.9 0.7

High

EDUCATION RG: Low

Figure 3.3.2e Odds ratio (OR) and 95% confidence interval (CI) for the probability of high blood pressure by socioeconomic status for men and women,Malaysia (N=2572) 100.0

Men Odds ratio

10.0

2.3 2.9

4.7

Women

7.0 1.3 1.1

1.4 1.0

1.1

1.3

1.8

1.0 0.9

0.7 0.7 0.1

36–49

50–64

Chinese

AGE (years) RG: 25–35

Indian ETHNICITY RG: Malay

Other

0.7 0.7

0.7 0.7 Urban

REGION RG: Rural Socioeconomic status

RM 1000 > RM 3999 –3999 INCOME RG: <RM 1000

0.6

Medium

0.5

High

EDUCATION RG: Low

Figure 3.3.2f Odds ratio (OR) and 95% confidence interval (CI) for the probability of high total cholesterol by socioeconomic status for men and women,Malaysia (N=2572) 10.0

4.7

Odds ratio

1.8 2.0

Men

2.6

Women 1.6

1.3 1.0

1.0 0.9 0.7

0.1

0.2

36–49

50–64

AGE (years) RG: 25–35

Chinese

0.6

0.5

0.7

ETHNICITY RG: Malay

Other

0.9 0.9

0.7

0.8 0.4

0.3

Indian

0.9

Urban

REGION RG: Rural Socioeconomic status

RM 1000 > RM 3999 –3999 INCOME RG: <RM 1000

Medium EDUCATION RG: Low

High

Section Title Results

0.8

Odds ratio

2.4

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

Figure 3.3.2g Odds ratio (OR) and 95% confidence interval (CI) for the probability of elevated fasting blood glucose by socioeconomic status for men and women,Malaysia (N=2572) 10.0

Odds ratio

2.1 2.2

3.4 3.5

2.5

Men

Women

1.9 1.1

1.0

1.0 0.9 0.7

0.8

0.8 0.8

0.9

0.8 0.7

0.9 0.4

0.4

0.1

36–49

Chinese

AGE (years) RG: 25–35

Results

Indian ETHNICITY RG: Malay

Other

Urban

REGION RG: Rural Socioeconomic status

RM 1000 > RM 3999 –3999 INCOME RG: <RM 1000

Medium

High

EDUCATION RG: Low

Figure 3.3.2h Odds ratio (OR) and 95% confidence interval (CI) for the probability of a high level of occupational physical activity by socioeconomic status for men and women,Malaysia (N=2572) 10.0

Odds ratio

Men 1.1

1.1 1.1

1.0 1.0

1.0

Women

1.4

1.0 0.8 0.9

0.1

36–49

50–64

0.9

Chinese

AGE (years) RG: 25–35

Indian ETHNICITY RG: Malay

0.7 0.8

Other

0.6 0.7

Urban

REGION RG: Rural Socioeconomic status

0.8

RM 1000 > RM 3999 –3999 INCOME RG: <RM 1000

0.7

0.9

0.8 0.6

Medium

High

EDUCATION RG: Low

Figure 3.3.2i Odds ratio (OR) and 95% confidence interval (CI) for the probability of highly active commuting by socioeconomic status for men and women,Malaysia (N=2572) 10.0

Men

Women

1.2

Odds ratio

50–64

1.3

1.0

1.0 0.9

1.2

0.6 0.6

0.7

0.1

0.9

36–49

50–64

AGE (years) RG: 25–35

0.7 0.6

Chinese

Indian ETHNICITY RG: Malay

0.7

0.8

Other

0.8 0.9

0.8

0.9

0.8 0.7 0.6

Urban

REGION RG: Rural Socioeconomic status

RM 1000 > RM 3999 –3999 INCOME RG: <RM 1000

Medium EDUCATION RG: Low

High

A multicountry analysis of noncommunicable disease surveillance data

Figure 3.3.2j Odds ratio (OR) and 95% confidence interval (CI) for the probability of a high level of leisure-time physical activity by socioeconomic status for men and women,Malaysia (N=2572) 10.0

Men 1.2

1.0

1.1

Women

1.9

1.2

1.1 1.1

1.1

1.0 0.9

0.6

0.1

0.0

0.8 0.7

0.9

0.7

0.9 0.9

0.8

0.2

36–49

50–64

Chinese

AGE (years) RG: 25–35

Indian

Urban

REGION RG: Rural Socioeconomic status

RM 1000 > RM 3999 –3999 INCOME RG: <RM 1000

Medium

High

EDUCATION RG: Low

ETHNICITY RG: Malay

Other

Nauru Prevalence rates of risk factors by SES status Taking into account the nature of the Nauruan population, in which ethnic Nauruans comprise over 75% of the total (Nauru Census 2002), only two measures of SES—age and educational attainment—were collected in Nauru as part of the NCD STEPS Survey. An income-based measure of socioeconomic position was not considered, given the economic and financial crisis facing Nauru during the survey period. Table 3.4.1 Prevalence rates of risk factors by socioeconomic status for men and women, Nauru (N=2085) Current smoking Men Women n (%) n (%)

Hazardous drinking1 Men Women n (%) n (%)

Consumed <5 servings/day fruit and vegetables Men Women n (%) n (%)

Obese2 Men Women n (%) n (%)

AGE 18-35 yrs

285 (51.0)

304 (56.0)

113 (20.2)

68 (12.4)

504 (91.7)

483 (89.9)

342 (61.2)

303 (59.5)

36-49 yrs

121 (46.6)

183 (60.7)

52 (19.9)

34 (11.4)

224 (89.2)

264 (88.4)

193 (74.9)

250 (84.0)

50-65 yrs

47 (43.6)

59 (54.7)

18 (16.5)

9 (8.1)

101 (94.8)

92 (85.9)

67 (62.9)

80 (73.8)

EDUCATION Low (≤6 yrs)

27 (56.0)

23 (63.6)

6 (12.3)

3 (6.8)

45 (94.9)

31 (87.3)

29 (59.8)

26 (73.1)

Medium (7-12yrs)

394 (48.4)

490 (57.6)

162 (19.9)

101 (11.8)

724 (90.8)

751 (88.8)

533 (65.7)

560 (68.6)

High (≥13yrs)

22 (47.3)

21 (46.0)

12 (25.0)

6 (13.8)

45 (95.9)

40 (90.3)

32 (68.8)

35 (78.7)

Hazardous drinking: frequency of drinking in past 12 months and average number of drinks consumed per day; 2 Obese: BMI≥30 1

Section Title Results

Odds ratio

1.0

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

Table 3.4.1 Prevalence rates of risk factors by socioeconomic status for men and women, Nauru (N=2085) (Cont. 1)

Results

The prevalence of current smoking was generally higher in women than men across all age groups. Smoking was also more prevalent among those with low educational levels. Hazardous alcohol consumption was common among the younger age groups for both men and women, and among men with high educational levels. The prevalence of those who reported eating less than the recommended amounts of fruits and vegetables per day in the past year was very high across all age and education groups, and across both sexes. The prevalence of obesity was assessed to be very high for both men and women by age 36-49 years, with obesity common across all educational levels.

Central obesity2 Men Women n (%) n (%)

High blood pressure3 Men Women n (%) n (%)

High cholesterol4 Men Women n (%) n (%)

High fasting blood glucose51 Men Women n (%) n (%)

AGE 18-35 yrs

111 (19.8)

163 (31.9)

32 (5.8)

8 (1.5)

44 (7.8)

61 (11.3)

25 (4.5)

24 (4.4)

36-49 yrs

108 (42.2)

168 (56.3)

53 (20.6)

33 (11.0)

47 (18.0)

62 (20.4)

56 (21.7)

71 (23.6)

50-65 yrs

32 (29.4)

61 (56.4)

29 (26.5)

22 (20.4)

23 (21.1)

46 (42.7)

33 (30.9)

39 (35.8)

EDUCATION Low (≤6 yrs)

15 (31.7)

18 (48.9)

6 (13.3)

3 (6.8)

3 (6.6)

5 (13.3)

5 (11.2)

4 (9.8)

Medium (7-12yrs)

217 (26.7)

342 (41.8)

99 (12.1)

57 (6.7)

100 (12.3)

151 (17.8)

99 (12.1)

116 (13.6)

High (≥13yrs)

14 (31.5)

24 (53.7)

8 (18.1)

1 (1.5)

8 (18.1)

9 (18.5)

7 (14.0)

8 (17.4)

Estimates based on 2006 survey of n=504; Central obesity: ≥110cm for men and ≥100cm for women; High blood pressure SBP≥140mmHg and/or DBP≥90mmHg; 4 High cholesterol ≥5.5 mmol/L; 5 High fasting glucose ≥7.0mmol/L 1

2 3

The prevalence of central obesity increased with age, although this declined markedly for older men. The prevalence of high blood pressure, elevated cholesterol and high fasting blood sugar increased sharply with increasing age. Diabetes was most prevalent among those in the high education group.

A multicountry analysis of noncommunicable disease surveillance data

Table 3.4.1 Prevalence rates of risk factors by socioeconomic status for men and women, Nauru (N=2085) (Cont. 2) High active commuting Men Women n (%) n (%)

High LTPA1 Men Women n (%) n (%)

AGE 18-35 yrs 36-49 yrs 50-65 yrs

133 (23.9) 55 (21.1) 17 (16.0)

74 (13.6) 40 (13.4) 13 (11.9)

34 (6.1) 10 (3.7) 3 (2.7)

20 (3.6) 11 (3.8) 1 (1.1)

84 (15.0) 17 (6.7) 3 (3.2)

11 (2.0) 8 (2.7) 1 (1.2)

EDUCATION Low (≤6 yrs) Medium (7-12yrs) High (≥13yrs)

9 (19.0) 183 (22.5) 8 (17.8)

5 (13.9) 117 (13.7) 2 (5.0)

2 (3.5) 42 (5.2) 3 (6.5)

1 (1.6) 32 (3.7) - (- )

6 (12.4) 95 (11.6) 3 (5.6)

1 (1.6) 19 (2.2) 1 (1.3)

Note: High levels of physical activity during work, travel and leisure time are protective of NCD risks; 1 LTPA = Leisure time physical activity.

High levels of work-related physical activity were more common among younger men and women and among those with low or medium educational levels. The prevalence of a high level of active commuting was generally low, with the activity being more prevalent in the youngest age group. High levels of LTPA were more common among young men and those in the low or medium education groups (men only).

Association between risk factors and SES measures (adjusted analyses) Table 3.4.2 Odds ratio (OR) and 95% confidence interval (CI) for the probability of having NCD risk factors by socioeconomic status for men and women, Nauru (N=2085) Current smoking Men Women OR (95% CI) OR (95% CI) AGE 18-35 yrs

Consumed <5 servings/day fruit and Hazardous drinking1 vegetables Obese2 Men Women Men Women Men Women OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI)

1.0

36-49 yrs

0.8 (0.7-1.1)

1.2 (1.0-1.5)

0.9 (0.7-1.3)

0.6 (0.4-1.0)

0.7 (0.5-1.1)

0.8 (0.6-1.2)

1.9 (1.5-2.5)

3.7 (2.8-4.9)

50-65 yrs

0.8 (0.6-1.1)

1.0 (0.7-1.4)

0.7 (0.4-1.1)

0.7 (0.3-1.6)

1.7 (0.8-3.4)

0.7 (0.4-1.1)

1.1 (0.8-1.5)

2.0 (1.4-2.8)

EDUCATION Low (≤6 yrs)

1.0

Medium (7-12yrs)

0.7 (0.5-1.2)

0.8 (0.5-1.3)

0.7 (0.3-1.6)

1.9 (0.5-6.9)

0.6 (0.2-1.5)

1.1 (0.5-2.5)

1.3 (0.8-2.0)

0.8 (0.5-1.5)

High (≥13yrs)

0.7 (0.4-1.4)

0.5 (0.3-1.0)

0.9 (0.3-2.8) 3.4 (0.7-17.7) 1.2 (0.3-5.6)

1.4 (0.5-4.2)

1.4 (0.7-2.7)

1.3 (0.6-3.0)

Note: Odd ratios 95% confidence intervals adjusted for finite population correction; 1 Hazardous drinking: frequency of drinking in past 12 months and average number of drinks consumed per day; 2 Obese: BMI≥30

Section Title Results

High occupational physical activity Men Women n (%) n (%)

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

There were no significant associations with age or education and smoking, hazardous alcohol consumption, inadequate fruit and vegetable consumption, obesity or central obesity, for either men or women.

Table 3.4.2 Odds ratio (OR) and 95% confidence interval (CI) for the probability of having NCD risk factors by socioeconomic status for men and women, Nauru (N=2085) (Cont. 1)

Results

Central obesity 2 High blood pressure 3 High cholesterol 4 Men Women Men Women Men Women OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI)

High fasting blood glucose 5 1 Men Women OR (95% CI) OR (95% CI)

AGE 18-35 yrs 36-49 yrs 50-65 yrs

1.0 3.0 (2.3-3.9) 1.7 (1.2-2.5)

1.0 2.8 (2.2-3.5) 2.7 (1.9-3.8)

1.0 4.2 (2.9-6.0) 6.1 (3.9-9.4)

1.0 8.2 (4.5-15.1) 17.7 (9.2-34.2)

1.0 2.5 (1.8-3.6) 3.2 (2.1-4.9)

1.0 2.1 (1.6-2.8) 6.1 (4.2-8.8)

1.0 6.0 (2.7-13.6) 8.8 (3.1-25.2)

1.0 4.1 (1.9-8.7) 5.4 (2.0-14.8)

EDUCATION Low (≤6 yrs) Medium (7-12yrs) High (≥13yrs)

1.0 0.7 (0.5-1.2) 0.9 (0.4-1.7)

1.0 0.8 (0.5-1.3) 1.2 (0.6-2.3)

1.0 0.9 (0.5-1.9) 1.2 (0.5-2.8)

1.0 1.1 (0.4-3.1) 0.2 (0.0-1.3)

1.0 2.1 (0.8-5.1) 2.7 (0.9-7.9)

1.0 1.5 (0.7-3.3) 1.3 (0.5-3.4)

1.0 0.6 (0.1-2.6) 0.6 (0.1-3.7)

1.0 0.3 (0.1-1.3) 0.3 (0.0-1.6)

Note: Odd ratios 95% confidence intervals adjusted for finite population correction; 1 Estimates based on 2006 survey of n=504; 2 Central obesity: ≥110cm for men and ≥100cm for women; 3 High blood pressure SBP≥140mmHg and/or DBP≥90mmHg; 4 High cholesterol ≥5.5 mmol/L; 5 High fasting glucose ≥7.0mmol/L

There were significant associations between high blood pressure, high cholesterol and diabetes and age, for both men and women. No significant relationships, however, were noted between these risk factors and educational level.

Table 3.4.2 Odds ratio (OR) and 95% confidence interval (CI) for the probability of having NCD risk factors by socioeconomic status for men and women, Nauru (N=2085) (Cont. 2) High level of occupational physical activity Men Women OR (95% CI) OR (95% CI)

Highly active commuting Men Women OR (95% CI) OR (95% CI)

High LTPA^ Men Women OR (95% CI) OR (95% CI)

AGE 18-35 yrs 36-49 yrs 50-65 yrs

1.0 0.8 (0.6-1.2) 0.6 (0.3-1.1)

1.00 1.0 (0.6-1.5) 0.9 (0.5-1.7)

1.00 0.6 (0.3-1.2) 0.4 (0.1-1.5)

1.0 1.1 (0.5-2.3) 0.3 (0.1-2.1)

1.0 0.4 (0.3-0.7) 0.2 (0.1-0.6)

1.0 1.3 (0.5-3.3) 0.6 (0.1-3.9)

EDUCATION Low (≤6 yrs) Medium (7-12yrs) High (≥13yrs)

1.0 1.2 (0.6-2.6) 1.0 (0.3-2.8)

1.0 1.0 (0.4-2.6) 0.3 (0.1-1.7)

1.0 1.5 (0.3-7.4) 2.2 (0.3-15.3)

1.0 2.3 (0.2-29.3) <0.001 (<0.001->999)

1.0 0.9 (0.4-2.2) 0.5 (0.1-2.2)

1.0 1.3 (0.1-17.4) 0.8 (0.02-29.2)

Note: Odd ratios 95% confidence intervals adjusted for finite population correction; Note: High levels of physical activity at work, travel and leisure time are protective of NCD risks; here logistic analyses modelled the probability of having high levels of PA; 1 LTPA = Leisure time physical activity.

A multicountry analysis of noncommunicable disease surveillance data

There were no significant associations or trends between age and education for all domains of physical activity.

Figure 3.4.2a Odds ratio (OR) and 95% confidence interval (CI) for the probability of smoking by socioeconomic status for men and women, Nauru (N=2085) 10.0

1.2

Women

1.0

Odds ratio

Men

1.0 0.8

0.1

0.8

36–49

0.7

50–65

0.8

0.7

Medium (7–12 years)

0.5

High (=13 years)

EDUCATION RG: Low (=6 years)

AGE (years) RG: 18–35

Socioeconomic status

Figure 3.4.2b Odds ratio (OR) and 95% confidence interval (CI) for the probability of heavy alcohol drinking by socioeconomic status for men and women,Nauru (N=2085) 100.0

Men

Women

Odds ratio

10.0 3.4 1.9 1.0 0.9

0.7

0.6 0.1

Section Title Results

This section provides the graphical presentations of the odds ratios from logistic regression analyses examining the independent association in Nauru between SES and risk factors by sex, adjusting for other SES indicators.

36–49

50–65

0.9

0.7

Medium (7–12 years)

AGE (years) RG: 18–35

EDUCATION RG: Low (=6 years) Socioeconomic status

High (=13 years)

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

Figure 3.4.2c Odds ratio (OR) and 95% confidence interval (CI) for the probability of low vegetable and fruit consumption by socioeconomic status for men and women,Nauru (N=2085) 10.0

Men

Odds ratio

1.7

1.2

1.1

1.4

Women

1.0 0.8

0.7

0.7 0.6

0.1

36–49

50–65 AGE (years) RG: 18–35

Results

High (=13 years)

EDUCATION RG: Low (=6 years) Socioeconomic status

Figure 3.4.2d Odds ratio (OR) and 95% confidence interval (CI) for the probability of obesity by socioeconomic status for men and women, Nauru (N=2085) 10.0

Men

3.7 2.0

Odds ratio

1.9

1.4

1.3

1.1

Women 1.3

1.0 0.8 0.1

36–49

50–65

Medium (7–12 years)

AGE (years) RG: 18–35

High (=13 years)

EDUCATION RG: Low (=6 years) Socioeconomic status

Figure 3.4.2e Odds ratio (OR) and 95% confidence interval (CI) for the probability of central obesity by socioeconomic status for men and women,Nauru (N=2085) 10.0 3.0 Odds ratio

Medium (7–12 years)

Men

2.7

2.8

1.7

Women 1.2

1.0 0.7 0.1

36–49

50–65

0.8

0.9

Medium (7–12 years)

AGE (years) RG: 18–35

EDUCATION RG: Low (=6 years) Socioeconomic status

High (=13 years)

A multicountry analysis of noncommunicable disease surveillance data

Figure 3.4.2f Odds ratio (OR) and 95% confidence interval (CI) for the probability of high blood pressure by socioeconomic status for men and women,Nauru (N=2085) 100.0 17.7 8.2 Odds ratio

10.0

Men

Women

6.1

4.2

1.1

1.2

1.0

36–49

50–65

Medium (7–12 years)

AGE (years) RG: 18–35

0.2 High (=13 years)

EDUCATION RG: Low (=6 years) Socioeconomic status

Figure 3.4.2g Odds ratio (OR) and 95% confidence interval (CI) for the probability of high total cholesterol by socioeconomic status for men and women,Nauru (N=2085) 100.0

Men Odds ratio

10.0 2.5

6.1

3.2

2.1

Women

2.7 1.3

1.5

1.0

0.1

36–49

50–65

Medium (7–12 years)

AGE (years) RG: 18–35

High (=13 years)

EDUCATION RG: Low (=6 years) Socioeconomic status

Figure 3.4.2h Odds ratio (OR) and 95% confidence interval (CI) for the probability of elevated fasting blood glucose by socioeconomic status for men and women,Nauru (N=2085) 100.0

Odds ratio

10.0

8.8

6.0

Men

5.4

4.1

Women

1.0 0.3

0.1 0.0

0.6 36–49

50–65

0.6

0.3

Medium (7–12 years)

AGE (years) RG: 18–35

EDUCATION RG: Low (=6 years) Socioeconomic status

High (=13 years)

0.1

Section Title Results

0.9

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

Figure 3.4.2i Odds ratio (OR) and 95% confidence interval (CI) for the probability of a high level of occupational physical activity by socioeconomic status for men and women,Nauru (N=2085) 10.0

Men 1.2

1.0

Odds ratio

1.0

1.0 0.8

0.9 0.6

0.1

36–49

0.3

50–65

Medium (7–12 years)

AGE (years) RG: 18–35

Results

High (=13 years)

EDUCATION RG: Low (=6 years) Socioeconomic status

Figure 3.4.2j Odds ratio (OR) and 95% confidence interval (CI) for the probability of highly active commuting by socioeconomic status for men and women,Nauru (N=2085) 100.0

Men

Women

1.5

Odds ratio

10.0

2.3

1.1

2.2

1.0 0.4

0.6 0.1

36–49

0.3 50–65

Medium (7–12 years)

AGE (years) RG: 18–35

High (=13 years)

EDUCATION RG: Low (=6 years) Socioeconomic status

Figure 3.4.2k Odds ratio (OR) and 95% confidence interval (CI) for the probability of a high level of leisure-time physical activity by socioeconomic status for men and women,Nauru (N=2085) 100.0

Men 10.0 Odds ratio

Women

1.3 1.3

1.0 0.1 0.0

0.5

0.9

0.4 0.2 36–49

0.8

0.6 50–65

Medium (7–12 years)

AGE (years) RG: 18–35

EDUCATION RG: Low (=6 years) Socioeconomic status

High (=13 years)

A multicountry analysis of noncommunicable disease surveillance data

Philippines Prevalence rates of risk factors by SES measures Table 3.5.1 Prevalence rates of risk factors by socioeconomic characteristics for men and women, Philippines (N=3307)

Secondary Tertiary + INCOME ≤ PhP 53 064 PhP 53 065-92 192 PhP 92 193-173 387 PhP ≥173 388 1 2

nn nn

Obese2 Men Women n (%) n (%)

367 (56.2) 38 (7.3) 253 (59.6) 48 (11.3) 316 (57.4) 145 (17.5)

4 (1.1) 2 (0.8) 5 (1.4)

0 (0.0) 1 (1.0) 1 (0.3)

334 (58.2) 210 (55.4) 315 (57.4)

256 (58.7) 223 (60.0) 400 (60.3)

18 (2.7) 17 (4.0) 17 (4.1)

26 (5.3) 28 (7.3) 42 (9.6)

15 (2.2) 47 (10.1) 17 (4.1) 76 (19.1) 20 (4.3) 172 (31.3)

281 (68.3) 91 (19.5)

4 (1.2)

0 (0.0)

236 (62.3)

216 (66.7)

9 (2.0)

19 (7.2)

4 (1.2) 53 (14.7)

385 (58.3) 93 (10.3) 262 (50.8) 45 (8.2)

6 (1.2) 1 (0.8)

2 (0.7) 0 (0.0)

342 (55.9) 274 (56.6)

426 (57.7) 229 (59.3)

23 (3.8) 20 (3.6)

54 (8.5) 23 (4.7)

27 (3.9) 149 (19.0) 21 (3.3) 90 (17.1)

238 (64.1) 64 (14.5) 272 (63.8) 57 (11.0) 232 (56.5) 50 (8.0) 189 (45.9) 58 (11.1)

5 (1.6) 1 (0.4) 3 (0.8) 2 (1.5)

0 (0.0) 1 (1.2) 0 (0.0) 1 (0.2)

187 (55.0) 238 (58.1) 241 (60.8) 190 (55.1)

197 (53.6) 193 (56.7) 250 (64.4) 236 (55.1)

10 (2.6) 5 (1.3) 16 (3.8) 21 (5.4)

14 (4.7) 19 (6.7) 28 (7.4) 35 (7.9)

6 (2.1) 33 (9.3) 4 (0.7) 51 (14.6) 15 (3.7) 88 (17.5) 27 (5.8) 122 (24.8)

Hazardous drinking: >60g for men and >40g for women; Obese: BMI≥30; # Central obesity: ≥102cm for men and ≥88cm for women

The prevalence of current smoking was higher among men than women across all age groups. For women, the prevalence increased with age. Smoking was more common among those with only primary education than those with secondary and tertiary education, for both men and women. Smoking was also more common among those in the lowest income group. Hazardous alcohol consumption was not a problem across all age, education and income groups. At least 55% of adults across all age groups reported eating less than the recommended amounts of fruits and vegetables per day. A higher proportion of men and women with only primary education reported eating less than the recommended amounts of fruits and vegetables than those with secondary or at least tertiary education. Respondents in the second highest income group reported the highest proportion not meeting the recommended daily fruit and vegetable consumption. Across both sexes, obesity and central obesity increased with age, although only marginal differences were noted in the prevalence across educational levels. The prevalence of obesity and central obesity increased with increasing income, especially among women.

Section Title Results

Central obesity# Men Women n (%) n (%)

AGE 20-35 yrs 36-49 yrs 50-65 yrs EDUCATION Primary

Consumed <5 Hazardous servings/day fruit Current smoking drinking1 and vegetables Men Women Men Women Men Women n (%) n (%) n (%) n (%) n (%) n (%)

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

Table 3.5.1 Prevalence rates of risk factors by socioeconomic characteristics for men and women, Philippines (N=3307) (Cont.)

Results

High blood pressure2 Men Women n (%) n (%) AGE 20-35 yrs 36-49 yrs 50-65 yrs EDUCATION Primary Secondary Tertiary + INCOME ≤ PhP 53 064 PhP 53 065-92 192 PhP 92 193-173 387 PhP ≥173 388

High cholesterol3 Men Women n (%) n (%)

High level of High fasting occupational blood physical Highly active glucose4 activity commuting Men Women Men Women Men Women n (%) n (%) n (%) n (%) n (%) n (%)

High LTPA1 Men Women n (%) n (%)

88 (13.9) 24 (4.4) 28 (4.0) 21 (4.3) 6 (1.0) 4 (1.1) 126 (34.1) 38 (20.0) 51 (8.7) 29 (6.2) 53 (9.1) 18 (3.7) 127 (28.3) 82 (18.9) 30 (6.9) 43 (10.7) 17 (4.7) 11 (3.5) 80 (32.6) 236 (14.3) 35 (9.3) 17 (4.3) 41 (10.9) 21 (5.5) 251 (42.2) 306 (41.2) 43 (9.3) 153 (26.5) 32 (7.9) 48 (8.5) 92 (29.7) 43 (18.1) 50 (9.7) 44 (6.4) 60 (11.3) 32 (5.0) 122 (21.6) 144 (28.5) 14 (3.1) 60 (14.6) 6 (1.5) 14 (3.0) 83 (34.5) 23 (18.1) 50 (13.5) 17 (4.9) 32 (8.0) 19 (5.4) 211 (24.7) 201 (18.9) 43 (5.7) 97 (10.0) 24 (3.0) 37 (3.8) 125 (32.2) 49 (16.0) 59 (9.8) 44 (5.9) 62 (10.1) 40 (5.2) 132 (21.8) 64 (10.1) 43 (6.8) 57 (9.7) 25 (4.2) 11 (2.5) 94 (32.4) 32 (21.7) 28 (5.8) 23 (5.4) 51 (11.0) 15 (3.4) 82 (16.6) 87 (15.8) 116 (18.3) 99 (19.1) 132 (28.1) 104 (13.0) 133 (27.0) 121 (20.0)

14 (2.7) 34 (6.7) 5 (0.9) 9 (2.6) 72 (29.7) 20 (4.6) 50 (10.5) 7 (1.3) 15 (3.9) 80 (33.3) 28 (7.4) 56 (8.8) 12 (2.2) 18 (3.3) 85 (38.7) 38 (7.6) 75 (14.4) 31 (7.9) 21 (3.3) 64 (29.1)

13 (11.8) 21 (14.9) 25 (18.0) 43 (21.0)

53 (16.4) 42 (10.7) 21 (5.5) 18 (4.8)

27 (8.5) 21 (5.9) 18 (4.5) 23 (5.1)

26 (7.2) 8 (2.5) 39 (9.6) 7 (2.0) 43 (11.8) 21 (5.3) 40 (11.0) 29 (6.4)

Note: High levels of physical activity at work, travel and leisure time are protective of NCD risks; here logistic analyses modelled the probability of having high levels of PA; 1 LTPA = Leisure time physical activity; 2 High blood pressure SBP≥140mmHg and/or DBP≥90mmHg; 3 High cholesterol >239mg/dL (>6.13mmol/L; 4 High fasting glucose .125mg/dL7.0mmol/L

For hypertension, elevated cholesterol and diabetes, the prevalence increased with age for both men and women, with elevated blood pressure and cholesterol more common among women with only primary education than those with tertiary education. For men, elevated cholesterol and fasting blood sugar were more common among those with tertiary education. The prevalence of hypertension, high cholesterol and diabetes was also higher among those in the highest income group than those in the lowest income group. A high level of work-related physical activity was common among men and among the younger age group for both men and women. This activity was also more prevalent among women with tertiary education, while for men it was higher among those with primary education. Highly active commuting was most prevalent among the lowest income group for both men and women. The prevalence of high LTPA was higher among men than women, and among those in the highest income group.

A multicountry analysis of noncommunicable disease surveillance data

Association between risk factors and SES measures (adjusted analyses)

AGE 20-35 yrs 36-49 yrs 50-65 yrs EDUCATION Primary Secondary Tertiary + INCOME ≤ PhP 53 064 PhP 53 065-92 192 PhP 92 193-173 387 PhP ≥173 388 1 2 3 4

1.0 1.1 (0.8-1.5) 1.0 (0.8-1.3) 1.0 0.7 (0.5-0.9) 0.6 (0.4-0.8) 1.0 1.1 (0.8-1.5) 0.9 (0.6-1.3) 0.6 (0.4-0.8)

1.0 1.7 (1.0-2.8) 2.5 (1.6-4.1) 1.0 0.6 (0.4-0.9) 0.6 (0.3-0.9) 1.0 0.7 (0.4-1.2) 0.6 (0.3-1.0) 0.8 (0.5-1.4)

1.0 0.7 (0.1-4.3) 1.2 (0.2-8.6) 1.0 0.9 (0.2-4.3) 0.8 (0.2-3.2) 1.0 0.3 (0.03-2.3) 0.6 (0.1-3.1) 1.1 (0.2-6.2)

N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A

1.0 1.0 1.0 0.8 (0.6-1.1) 1.0 (0.8-1.4) 1.6 (0.8-3.1) 0.9 (0.7-1.2) 1.1 (0.8-1.5) 1.6 (0.7-3.4) 1.0 1.0 1.0 0.7 (0.5-1.0) 0.7 (0.5-1.0) 1.7 (0.6-4.5) 0.7 (0.5-1.0) 0.8 (0.5-1.1) 1.6 (0.6-4.4) 1.0 1.0 1.0 1.3 (0.9-1.8) 0.8 (0.5-1.2) 0.4 (0.1-1.4) 1.5 (1.0-2.1) 1.1 (0.7-1.6) 1.3 (0.5-3.3) 1.2 (0.8-1.7) 0.7 (0.5-1.1) 1.8 (0.7-4.5)

0R cannot be computed due to the very small sample size; Hazardous drinking: >60g for men and >40g for women; Obese: BMI≥30; Central obesity: ≥102cm for men and ≥88cm for women

Older women were more likely to smoke than their younger counterparts. Those with secondary and tertiary education were less likely to smoke than those with only primary education. Similarly, those with higher incomes (eg., PhP ≥173 388 for men) were less likely to smoke. There were no significant associations between hazardous alcohol consumption and age, education level and income quartile among men. Men with secondary and tertiary education were less likely to consume less than five servings of fruit and vegetables per day. Compared with the youngest age group, older men and women were more likely to become centrally obese. Men with secondary and tertiary education were more than twice as likely to become centrally obese than those with only primary education, while women with higher incomes were significantly more

1.0 1.3 (0.8-2.4) 1.7 (0.9-3.2) 1.0 1.2 (0.6-2.4) 0.7 (0.3-1.6) 1.0 1.4 (0.6-3.5) 1.7 (0.8-3.6) 1.7 (0.8-3.8)

Central obesity4 Men Women OR OR (95% CI) (95% CI) 1.0 2.0 (0.9-4.1) 2.1 (1.0-4.4) 1.0 2.1 (1.2-3.5) 2.4 (1.3-4.3) 1.0 0.3 (0.1-1.2) 1.4 (0.5-4.2) 2.1 (0.7-6.8)

1.0 2.2 (1.4-3.3) 4.3 (2.9-6.5) 1.0 1.0 (0.7-1.5) 1.1 (0.7-1.6) 1.0 1.6 (0.9-2.8) 2.0 (1.1-3.5) 2.7 (1.6-4.8)

Current smoking Men Women OR OR (95% CI) (95% CI)

Consumed <5 Hazardous servings/day fruit drinking2 and vegetables Obese3 Men Women Men Women Men Women 1 OR OR OR OR OR OR (95% CI) (95% CI) (95% CI) (95% CI) (95% CI) (95% CI)

Section Title Results

Table 3.5.2 Odds ratio (OR) and 95% confidence interval (CI) for the probability of having NCD risk factors by socioeconomic status for men and women, Philippines (N=3307)

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

likely to become centrally obese than those in the lowest income quartile.

Results

Table 3.5.2 Odds ratio (OR) and 95% confidence interval (CI) for the probability of having NCD risk factors by socioeconomic status for men and women, Philippines (N=3307) (Cont. 1)

AGE 20-35 yrs 36-49 yrs 50-65 yrs EDUCATION Primary Secondary Tertiary + INCOME ≤ PhP 53 064 PhP 53 065-92 192 PhP 92 193-173 387 PhP ≥173 388 1 2 3

High blood pressure1 Men Women OR (95% CI) OR (95% CI)

High cholesterol2 Men Women OR (95% CI) OR (95% CI)

1.0 2.4 (1.8-3.4) 4.6 (3.2-6.6) 1.0 1.2 (0.8-1.8) 1.1 (0.7-1.6) 1.0 1.0 (0.7-1.6) 1.9 (1.3-2.9) 1.6 (1.1-2.5)

1.0 1.7 (0.9-3.3) 2.8 (1.4-5.5) 1.0 1.8 (0.8-4.0) 2.2 (1.0-4.7) 1.0 1.4 (0.6-3.7) 2.3 (1.0-5.4) 2.1 (0.9-4.8)

1.0 4.8 (3.0-7.8) 13.2 (8.2-21.3) 1.0 0.8 (0.5-1.2) 0.6 (0.4-0.9) 1.0 1.3 (0.8-2.1) 0.8 (0.6-1.4) 1.4 (0.9-2.2)

1.0 2.2 (1.6-3.0) 5.4 (3.9-7.4) 1.0 0.8 (0.5-1.3) 1.1 (0.6-2.1) 1.0 1.3 (0.9-1.9) 1.7 (1.1-2.6) 2.1 (1.4-3.1)

High fasting blood glucose3 Men Women OR (95% CI) OR (95% CI) 1.0 5.1 (1.8-14.8) 9.6 (3.4-27.2) 1.0 1.6 (0.5-4.8) 2.4 (0.8-7.1) 1.0 1.1 (0.3-4.5) 2.0 (0.6-6.7) 7.0 (2.4-20.6)

1.0 3.4 (1.0-11.4) 9.4 (3.1-28.8) 1.0 1.9 (0.8-4.8) 1.7 (0.5-5.5) 1.0 1.3 (0.3-5.3) 1.4 (0.4-5.1) 1.2 (0.3-4.6)

High blood pressure SBP≥140mmHg and/or DBP≥90mmHg; High cholesterol >239mg/dL (>6.13mmol/L; High fasting glucose .125mg/dL7.0mmol/L

There were significant associations between the probability of having hypertension, elevated cholesterol and high fasting blood glucose and increasing age, for both men and women. A significant association between high blood pressure and increasing income was also noted for men. For education, men with tertiary education were twice as likely to be at increased risk of having high cholesterol than those with only primary education. Positive associations were also observed between elevated cholesterol and income levels for women. For elevated fasting blood glucose, men in the highest income group had significantly increased odds of having diabetes compared with those in the lowest income group.

A multicountry analysis of noncommunicable disease surveillance data

Table 3.5.2 Odds ratio (OR) and 95% confidence interval (CI) for the probability of having NCD risk factors by socioeconomic status for men and women, Philippines (N=3307) (Cont. 2) High LTPA1 Men Women OR (95% CI) OR (95% CI)

1.0 0.9 (0.7-1.3) 0.8 (0.6-1.17)

1.0 0.7 (0.4-1.3) 1.0 (0.5-1.7)

1.0 1.1 (0.7-1.7) 1.1 (0.7-1.8)

1.0 0.7 (0.4-1.4) 1.2 (0.7-2.0)

1.0 1.0 (0.6-1.8) 0.7 (0.3-1.6)

1.0 1.4 (0.7-3.0) 1.2 (0.6-2.4)

1.0 0.8 (0.6-1.2) 0.8 (0.6-1.2)

1.0 0.7 (0.4-1.3) 1.0 (0.5-2.1)

1.0 0.9 (0.6-1.5) 0.6 (0.4-1.0)

1.0 1.4 (0.8-2.7) 1.4 (0.6-2.9)

1.0 1.2 (0.7-2.0) 1.2 (0.7-2.1)

1.0 0.8 (0.3-1.7) 0.5 (0.2-1.3)

1.0 1.2 (0.8-1.8) 1.6 (1.1-2.5) 1.1 (0.7-1.7)

1.0 1.4 (0.5-1.9) 1.8 (0.7-4.8) 2.2 (0.9-5.4)

1.0 1.1 (0.7-1.7) 1.1 (0.7-1.8) 1.1 (0.7-1.7)

1.0 1.0 (0.6-1.8) 0.7 (0.3-1.6) 1.0 (0.6-1.8)

1.0 1.3 (0.7-2.3) 1.5 (0.9-2.7) 1.4 (0.8-2.8)

1.0 0.8 (0.3-2.3) 2.4 (0.9-6.0) 2.9 (1.2-7.2)

Women in the highest income group were more likely to engage in high levels of LTPA than those in the lowest income group.

This section provides the graphical presentations of the odds ratios from logistic regression analyses examining the independent association in the Philippines between SES and risk factors by sex, adjusting for other SES indicators.

Section Title Results

AGE 20-35 yrs 36-49 yrs 50-65 yrs EDUCATION Primary Secondary Tertiary + INCOME ≤ PhP 53 064 PhP 53 065-92 192 PhP 92 193-173 387 PhP ≥173 388

Highly active commuting Men Women OR (95% CI) OR (95% CI)

High level of occupational physical activity Men Women OR (95% CI) OR (95% CI)

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

Figure 3.5.2a Odds ratio (OR) and 95% confidence interval (CI) for the probability of smoking by socioeconomic status for men and women, Philippines (N=3307) 10.0

Odds ratio

Men

2.5

1.7 1.1

1.1

1.0

1.0 0.7 0.6 0.1

36–49

50–65

0.9 0.6

Medium

AGE (years) RG: 20–35

Results

0.7

0.6 High

EDUCATION RG: Low Socioeconomic status

PhP 53 065 – 92 192

0.6

0.6 PhP 92 193 – 173 387 INCOME RG: PhP=53 064

0.8

PhP = 173 388

Figure 3.5.2b Odds ratio (OR) and 95% confidence interval (CI) for the probability of heavy alcohol drinking by socioeconomic status for men and women,Philippines (N=3307) 100.0

Men

Women

Odds ratio

10.0 1.2

1.1

1.0

0.9

0.7

0.8

0.0

0.6

0.3

0.1

36–49

50–65

Medium

AGE (years) RG: 20–35

High EDUCATION RG: Low Socioeconomic status

PhP 53 065 – 92 192

PhP 92 193 – 173 387 INCOME RG: PhP=53 064

PhP = 173 388

Figure 3.5.2c Odds ratio (OR) and 95% confidence interval (CI) for the probability of low vegetable and fruit consumption by socioeconomic status for men and women,Philippines (N=3307) 10.0

Men Odds ratio

Women

1.5

1.3

1.1

1.0

1.1

Women 1.2

1.0 0.9

0.8

0.1

36–49

50–65 AGE (years) RG: 20–35

0.7 0.7

Medium

0.7 0.8

High EDUCATION RG: Low Socioeconomic status

0.7

0.8

PhP 53 065 – 92 192

PhP 92 193 – 173 387 INCOME RG: PhP=53 064

PhP = 173 388

A multicountry analysis of noncommunicable disease surveillance data

Figure 3.5.2d Odds ratio (OR) and 95% confidence interval (CI) for the probability of obesity by socioeconomic status for men and women, Philippines (N=3307) 10.0

Odds ratio

Men 1.6 1.7

1.6 1.3

1.7

1.2

1.6

1.4

1.3

Women 1.8 1.7

1.7

1.0 0.7

36–49

50–65

Medium

AGE (years) RG: 20–35

High EDUCATION RG: Low Socioeconomic status

PhP 53 065 – 92 192

PhP 92 193 – 173 387 INCOME RG: PhP=53 064

PhP = 173 388

Figure 3.5.2e Odds ratio (OR) and 95% confidence interval (CI) for the probability of central obesity by socioeconomic status for men and women,Philippines (N=3307) 10.0

Men

4.3 2.1

Odds ratio

2.0 2.2

2.4

2.1

1.1

1.0

1.6

1.4

Women 2.1

2.0

2.7

0.3 0.1

36–49

50–65

Medium

AGE (years) RG: 20–35

High EDUCATION RG: Low Socioeconomic status

PhP 53 065 – 92 192

PhP 92 193 – 173 387 INCOME RG: PhP=53 064

PhP = 173 388

Figure 3.5.2f Odds ratio (OR) and 95% confidence interval (CI) for the probability of high blood pressure by socioeconomic status for men and women,Philippines (N=3307) 100.0

Men

Women

13.2 4.8

Odds ratio

10.0

4.6

2.4 1.2

1.1

1.0

1.3

1.9

1.6 1.4

1.0 0.8 0.1

36–49

50–65 AGE (years) RG: 20–35

Medium

0.8

0.6 High EDUCATION RG: Low Socioeconomic status

PhP 53 065 – 92 192

PhP 92 193 – 173 387 INCOME RG: PhP=53 064

PhP = 173 388

0.1

Section Title Results

0.4

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

Figure 3.5.2g Odds ratio (OR) and 95% confidence interval (CI) for the probability of high total cholesterol by socioeconomic status for men and women,Philippines (N=3307) 10.0

2.8 5.4

Odds ratio

1.7 2.2

2.2

1.8

1.1

Men

2.3

1.4

Women 2.1

1.7

1.3

2.1

1.0 0.8 0.1

36–49

Medium

AGE (years) RG: 20–35

Results

High EDUCATION RG: Low Socioeconomic status

PhP 53 065 – 92 192

PhP 92 193 – 173 387 INCOME RG: PhP=53 064

PhP = 173 388

Figure 3.5.2h Odds ratio (OR) and 95% confidence interval (CI) for the probability of elevated fasting blood glucose by socioeconomic status for men and women,Philippines (N=3307) 100.0

Odds ratio

10.0

Men

9.4

9.6

Women 7.0

5.1 3.4 1.6 1.9

2.4

1.7

1.1 1.3

2.0 1.4

PhP 53 065 – 92 192

PhP 92 193 – 173 387 INCOME RG: PhP=53 064

1.2

1.0

0.1

36–49

50–65

Medium

AGE (years) RG: 20–35

High EDUCATION RG: Low Socioeconomic status

PhP = 173 388

Figure 3.5.2i Odds ratio (OR) and 95% confidence interval (CI) for the probability of a high level of occupational physical activity by socioeconomic status for men and women,Philippines (N=3307) 10.0

Odds ratio

50–65

Men 1.0

1.0

1.4

1.6

Women 2.2

1.8 1.1

1.0 0.9

0.8 0.7

0.1

0.8

1.2

0.8 0.7

36–49

50–65 AGE (years) RG: 20–35

Medium

High EDUCATION RG: Low Socioeconomic status

PhP 53 065 – 92 192

PhP 92 193 – 173 387 INCOME RG: PhP=53 064

PhP = 173 388

A multicountry analysis of noncommunicable disease surveillance data

Figure 3.5.2j Odds ratio (OR) and 95% confidence interval (CI) for the probability of highly active commuting by socioeconomic status for men and women,Philippines (N=3307)

Odds ratio

10.0

Men 1.4

1.4

1.2

1.1

Women

1.1

1.0

1.1

1.1 1.0

1.0 0.9 0.6

36–49

50–65

Medium

AGE (years) RG: 20–35

High EDUCATION RG: Low Socioeconomic status

PhP 53 065 – 92 192

PhP 92 193 – 173 387 INCOME RG: PhP=53 064

PhP = 173 388

Figure 3.5.2k Odds ratio (OR) and 95% confidence interval (CI) for the probability of a high level of leisure-time physical activity by socioeconomic status for men and women,Philippines (N=3307) 100.0

Men Odds ratio

10.0

Women 2.9

2.4 1.0

1.4

1.2

1.3

1.2

1.5

1.4

1.0

0.1

0.7 36–49

50–65 AGE (years) RG: 20–35

0.8 Medium

0.5 High EDUCATION RG: Low Socioeconomic status

0.8 PhP 53 065 – 92 192

PhP 92 193 – 173 387 INCOME RG: PhP=53 064

PhP = 173 388

Section Title Results

0.1

0.7

A multicountry analysis of noncommunicable disease surveillance data

Section Title comparisons Cross-country

Cross-country comparisons of prevalence rates and associations between NCD risk factors and SES

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

Cross-country comparison of prevalence rates of NCD risk factors by SES

Figure 4.1.1 Summary of prevalence rates for smoking across countries by age, sex and socioeconomic status

Malay Chinese Indian Other

LEGEND 52.96–68.30 40.06–52.95 7.49–40.05 0.02–7.48

Prevalence Rates (%) of Smoking Range: 0.02–68.3

AGE

Illiterate/Primary Secondary Tertiary

Low Medium High

EDUCATION

INCOME

<RM 1000 RM 1000–3999 >RM 3999

18–35 36–49 50–65 Low ( 6 years) Medium (7–12 years) High ( 13 years)

Men Women

PHILIPPINES

Men Women

20–35 36–49 50–65

AGE

Indo-Fijan Fijan Other

NAURU

EDUCATION

AGE REGION

Rural Urban

EDUCATION

Rural Urban

Men Women

ETHNICITY

18–35 36–49 50–65

MALAYSIA 25–35 36–49 50–64

AGE

Primary Secondary Tertiary

Men Women

INCOME

FIJI

ETHNICITY REGION

<2000 Yuan 2000–9999 Yuan >10 000 Yuan

Men Women

EDUCATION

AGE REGION

Rural Urban

EDUCATION

18–35 36–49 50–65

INCOME

CHINA

PhP 53 064 PhP 53 065–92 192 Php 92 193–173 387 PhP 173 388 Primary Secondary Tertiary

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

Malay Chinese Indian Other

8.29–25.00 3.46–8.28 0.69–3.45 0.00–0.68 No data

AGE

Low Medium High

EDUCATION

Illiterate/Primary Secondary Tertiary

Low ( 6 years) Medium (7–12 years) High ( 13 years)

Men Women

PHILIPPINES

Men Women

AGE

<RM 1000 RM 1000–3999 >RM 3999

18–35 36–49 50–65

INCOME

Indo-Fijan Fijan Other

Rural Urban

NAURU

EDUCATION

AGE REGION

Rural Urban

INCOME

Men Women

25–35 36–49 50–64

18–35 36–49 50–65

LEGEND Prevalence Rates (%) of Alcohol Drinking Range: 0.00–25.0

MALAYSIA

EDUCATION

Primary Secondary Tertiary

Men Women

ETHNICITY

<2000 Yuan 2000–9999 Yuan >10 000 Yuan

FIJI AGE

REGION

Rural Urban

Men Women

EDUCATION ETHNICITY REGION

AGE

18–35 36–49 50–65

INCOME

CHINA

EDUCATION

Cross-country comparisons

This section presents maps and cross-country comparisons of the prevalence rates of NCD risk factors. SES-specific prevalence rates have been colour-coded according to quartiles.

20–35 36–49 50–65 PhP 53 064 PhP 53 065–92 192 Php 92 193–173 387 PhP 173 388 Primary Secondary Tertiary

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

(*China: Consumed<400g of fresh and dry vegetable per day)

Range: 53.6–95.9

Men Women

PHILIPPINES 20–35 36–49 50–65 PhP 53 064 PhP 53 065–92 192 Php 92 193–173 387 PhP 173 388 Primary Secondary Tertiary

Men Women

79.01–95.90 75.06–79.00 70.91–75.05 53.60–70.90

Prevalence Rates (%) of Poor Diet (<5 vegetable/fruit /servings/day)

AGE

Malay Chinese Indian Other

EDUCATION

Low Medium High

Low ( 6 years) Medium (7–12 years) High ( 13 years)

AGE

<RM 1000 RM 1000–3999 >RM 3999

18–35 36–49 50–65

INCOME

Rural Urban

NAURU

EDUCATION

AGE

LEGEND

REGION

Illiterate/Primary Secondary Tertiary

25–35 36–49 50–64

INCOME

Indo-Fijan Fijan Other

Men Women

EDUCATION

Rural Urban

MALAYSIA

ETHNICITY

AGE

Men Women 18–35 36–49 50–65

EDUCATION

Primary Secondary Tertiary

FIJI

REGION

<2000 Yuan 2000–9999 Yuan >10 000 Yuan

Men Women

ETHNICITY

AGE REGION

Rural Urban

EDUCATION

18–35 36–49 50–65

INCOME

CHINA

Figure 4.1.4 Summary of prevalence rates for obesity across countries by age, sex and socioeconomic status

Range: 1.3–84.0

22.34–84.00 14.46–22.83 8.16–14.45 1.30–8.15

AGE

Malay Chinese Indian Other

EDUCATION

Low Medium High

Low ( 6 years) Medium (7–12 years) High ( 13 years)

Men Women

PHILIPPINES

Men Women

AGE

(*China: BMI 28.0)

<RM 1000 RM 1000–3999 >RM 3999

18–35 36–49 50–65

20–35 36–49 50–65

INCOME

Prevalence Rates (%) of Obesity (BMI 30.0)

Rural Urban

NAURU

PhP 53 064 PhP 53 065–92 192 Php 92 193–173 387 PhP 173 388

EDUCATION

LEGEND

AGE

Illiterate/Primary Secondary Tertiary

25–35 36–49 50–64

REGION

Indo-Fijan Fijan Other

Men Women

INCOME

Rural Urban

MALAYSIA

EDUCATION

AGE

18–35 36–49 50–65

REGION

Men Women

ETHNICITY

Primary Secondary Tertiary

FIJI

ETHNICITY

<2000 Yuan 2000–9999 Yuan >10 000 Yuan

Men Women

EDUCATION

AGE REGION

Rural Urban

EDUCATION

18–35 36–49 50–65

INCOME

CHINA

Primary Secondary Tertiary

Cross-countrycompari Section Title sonscomparisons ofprevalenceratesandassociationsbetweenNCDriskfactorsandSES Cross-country

A multicountry analysis of noncommunicable disease surveillance data

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

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

Men Women

PHILIPPINES

Men Women

46.86–72.80 30.71–46.85 12.16–30.70 0.30–12.15

Prevalence Rates (%) of Central Obesity (waist circumference (cm)) Range: 0.3–72.8

AGE

Malay Chinese Indian Other

EDUCATION

Low Medium High

Low ( 6 years) Medium (7–12 years) High ( 13 years)

AGE

<RM 1000 RM 1000–3999 >RM 3999

18–35 36–49 50–65

20–35 36–49 50–65

INCOME

Rural Urban

NAURU

PhP 53 064 PhP 53 065–92 192 Php 92 193–173 387 PhP 173 388

EDUCATION

AGE

LEGEND

REGION

Illiterate/Primary Secondary Tertiary

25–35 36–49 50–64

INCOME

Indo-Fijan Fijan Other

Men Women

EDUCATION

Rural Urban

MALAYSIA

ETHNICITY

AGE

Men Women 18–35 36–49 50–65

EDUCATION

Primary Secondary Tertiary

FIJI

REGION

INCOME

<2000 Yuan 2000–9999 Yuan >10 000 Yuan

Men Women

ETHNICITY

AGE REGION

Rural Urban

EDUCATION

18–35 36–49 50–65

Primary Secondary Tertiary

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

AGE

Malay Chinese Indian Other

EDUCATION

Low Medium High

Low ( 6 years) Medium (7–12 years) High ( 13 years)

Men Women

PHILIPPINES

Men Women

AGE

25.76–43.30 19.11–25.75 8.81–19.10 1.50–8.80

<RM 1000 RM 1000–3999 >RM 3999

18–35 36–49 50–65

20–35 36–49 50–65

INCOME

Prevalence Rates (%) of High Blood Pressure (Systolic Bp 140 and/or Diastolic BP 90) Range: 1.5–43.3

Rural Urban

NAURU

PhP 53 064 PhP 53 065–92 192 Php 92 193–173 387 PhP 173 388

EDUCATION

LEGEND

AGE

Illiterate/Primary Secondary Tertiary

REGION

Indo-Fijan Fijan Other

25–35 36–49 50–64

INCOME

Rural Urban

Men Women

EDUCATION

AGE

18–35 36–49 50–65

MALAYSIA

ETHNICITY

Primary Secondary Tertiary

Men Women

REGION

<2000 Yuan 2000–9999 Yuan >10 000 Yuan

FIJI

ETHNICITY

REGION

Rural Urban

Men Women

EDUCATION

AGE

18–35 36–49 50–65

INCOME

CHINA

EDUCATION

Cross-country comparisons

CHINA

Primary Secondary Tertiary

Figure 4.1.7 Summary of prevalence rates for high cholesterol across countries by age, sex and socio-economic status

25.76–43.30 19.11–25.75 8.81–19.10 1.50–8.80

Prevalence Rates (%) of High Blood Pressure (Systolic Bp 140 and/or Diastolic BP 90) Range: 1.5–43.3

AGE

Men Women

PHILIPPINES

Men Women

20–35 36–49 50–65 PhP 53 064 PhP 53 065–92 192 Php 92 193–173 387 PhP 173 388 Primary Secondary Tertiary

Malay Chinese Indian Other

EDUCATION

Low Medium High

Low ( 6 years) Medium (7–12 years) High ( 13 years)

AGE

<RM 1000 RM 1000–3999 >RM 3999

18–35 36–49 50–65

INCOME

Rural Urban

NAURU

EDUCATION

AGE

LEGEND

REGION

Illiterate/Primary Secondary Tertiary

25–35 36–49 50–64

INCOME

Indo-Fijan Fijan Other

Men Women

EDUCATION

Rural Urban

MALAYSIA

ETHNICITY

AGE

Men Women 18–35 36–49 50–65

EDUCATION

Primary Secondary Tertiary

FIJI

REGION

<2000 Yuan 2000–9999 Yuan >10 000 Yuan

Men Women

ETHNICITY

AGE REGION

Rural Urban

EDUCATION

18–35 36–49 50–65

INCOME

CHINA

Figure 4.1.8 Summary of prevalence rates for high fasting blood glucose across countries by age, sex and socioeconomic status

(Cut-off value ranges from 5.5–6.5 mmol/L)

Range: 1.2–63.3 31.41–63.30 12.36–31.40 5.84–12.35 1.20–5.83

ETHNICITY

EDUCATION Malay Low Chinese Medium Indian High Other ETHNICITY Malay Chinese Indian Other

AGE EDUCATION

18–35 Men Women 36–49 AGE 50–65 18–35 36–49 Low ( 50–65 6 years) Medium (7–12 years) EDUCATION High ( 13 years) Low ( 6 years) Medium (7–12 years) High ( 13 years)

PHILIPPINES

Men Women

AGE

25–35 Men Women AGE 36 –49 25–35 50–64 36–49 Rural 50–64 Urban REGION <RMRural 1000 Urban RM 1000–3999 >RM 3999 ETHNICITY <RM 1000 Low RM 1000–3999 Medium >RM 3999 High

INCOME

REGION

LEGEND Prevalence Rates (%) of High Total Cholesterol (mmol/L)

EDUCATION

INCOME

EDUCATION

REGION

ETHNICITY

EDUCATION

AGE

REGION INCOME EDUCATION

AGE

18–35 Men Women AGE AGE 36 –49 36 –49 18–35 18–35 50–65 50–65 36–49 36–49 Rural Rural 50–65 50–65 Urban Urban REGION REGION Rural <2000Rural Yuan Indo-Fijan Urban 2000–9999Urban Yuan Fijan >10 000 Yuan Other INCOME ETHNICITY <2000 Yuan Indo-Fijan Illiterate/Primary 2000–9999Primary Yuan Fijan Secondary Secondary >10 000 Yuan Other Tertiary Tertiary EDUCATION EDUCATION Primary Illiterate/Primary Secondary LEGEND Secondary Tertiary Tertiary Prevalence Rates (%) of High Fasting Blood Glucose (mmol/L) 13.61–47.00 (Cut-off value ranges from 6.1–7.0 mmol/L; Philippines:>125mg/dL) 8.26–13.60 Range: 0.60–47.0 3.66–8.25 0.60–3.65

MALAYSIA NAURUNAURU MALAYSIA Men Women Men Women

20–35 36–49 50–65

PHILIPPINES PhP AGE 53 064 Men Women PhP 53 065–92 192 20–35 Php 92 193–173– 387 36 49 PhP 50–65 173 388

EDUCATION

Men FIJI Women 18–35 Men Women

AGE

CHINA CHINA FIJI Men Women

Primary INCOME PhP Secondary 53 064 PhP 53 065–92Tertiary 192 Php 92 193–173 387 PhP 173 388 EDUCATION Primary Secondary Tertiary

Cross-countrycompari Section Title sonscomparisons ofprevalenceratesandassociationsbetweenNCDriskfactorsandSES Cross-country

A multicountry analysis of noncommunicable disease surveillance data

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

Figure 4.1.9 Summaryo f prevalence rates for high levels of occupational physical activity across countries by age,s ex and socioeconomic status

25–35 36–49 50–64

AGE

18–35 36–49 50–65

Rural Urban

EDUCATION

Indo-Fijan Fijan Other

NAURU

AGE

Rural Urban

Men Women

REGION

18–35 36–49 50–65

MALAYSIA

Low ( 6 years) Medium (7–12 years) High ( 13 years)

INCOME

AGE

INCOME

<2000 Yuan 2000–9999 Yuan >10 000 Yuan

Men Women

REGION

Rural Urban

FIJI

ETHNICITY

AGE

18–35 36–49 50–65

Men Women

<RM 1000 RM 1000–3999 >RM 3999

Men Women

PHILIPPINES

32.86–69.00 21.96–32.85 15.46–21.95 0.80–15.45

Prevalence Rates (%) of Heavy Occupational Physical Activity Range: 0.8–69.0

AGE

Malay Chinese Indian Other

INCOME

LEGEND

Low Medium High

20–35 36–49 50–65 PhP 53 064 PhP 53 065–92 192 Php 92 193–173 387 PhP 173 388

EDUCATION

Illiterate/Primary Secondary Tertiary

EDUCATION

Primary Secondary Tertiary

ETHNICITY

EDUCATION

Men Women

Primary Secondary Tertiary

Figure 4.1.10 Summary of prevalence rates for high levels of commuting activity across countries by age, sex and socioeconomic status

25–35 36–49 50–64

AGE

Illiterate/Primary Secondary Tertiary

NAURU 18–35 36–49 50–65

Rural Urban

EDUCATION

Primary Secondary Tertiary

Men Women

AGE

<2000 Yuan 2000–9999 Yuan >10 000 Yuan

MALAYSIA

REGION

AGE REGION

Rural Urban

EDUCATION

Indo-Fijan Fijan Other

AGE

Rural Urban

18–35 36–49 50–65

REGION

18–35 36–49 50–65

ETHNICITY

Men Women

Low ( 6 years) Medium (7–12 years) High ( 13 years)

INCOME

FIJI

INCOME

Men Women

EDUCATION

CHINA

<RM 1000 RM 1000–3999 >RM 3999

Men Women

PHILIPPINES AGE

Malay Chinese Indian Other

INCOME

43.01–71.10 15.21–43.00 9.31–15.20 1.10–9.30 Not Applicable

Low Medium High

20–35 36–49 50–65 PhP 53 064 PhP 53 065–92 192 Php 92 193–173 387 PhP 173 388

EDUCATION

LEGEND Prevalence Rates (%) of High Active Commuting Range: 1.1–71.1

EDUCATION

Men Women

ETHNICITY

Cross-country comparisons

CHINA

Primary Secondary Tertiary

Malay Chinese Indian Other

14.51–41.20 10.46–14.50 5.54–10.45 1.20–5.53

AGE

Low Medium High

EDUCATION

Illiterate/Primary Secondary Tertiary

Low ( 6 years) Medium (7–12 years) High ( 13 years)

Men Women

PHILIPPINES

Men Women

AGE

<RM 1000 RM 1000–3999 >RM 3999

18–35 36–49 50–65

INCOME

Indo-Fijan Fijan Other

Rural Urban

NAURU

EDUCATION

AGE REGION

Rural Urban

INCOME

Men Women

25–35 36–49 50–64

18–35 36–49 50–65

LEGEND Prevalence Rates (%) of High Leisure Time Physical Activity (LTPA) Range: 1.2–41.2

MALAYSIA

EDUCATION

Primary Secondary Tertiary

Men Women

ETHNICITY

INCOME

<2000 Yuan 2000–9999 Yuan >10 000 Yuan

FIJI AGE

REGION

Rural Urban

Men Women

EDUCATION ETHNICITY REGION

AGE

18–35 36–49 50–65

EDUCATION

CHINA

20–35 36–49 50–65 PhP 53 064 PhP 53 065–92 192 Php 92 193–173 387 PhP 173 388 Primary Secondary Tertiary

Figure 4.1.11 Summary of prevalence rates for high levels of leisure-time physical activity (LTPA) across countries by age, sex and socioeconomic status

Cross-countrycompari Section Title sonscomparisons ofprevalenceratesandassociationsbetweenNCDriskfactorsandSES Cross-country

A multicountry analysis of noncommunicable disease surveillance data

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

Cross-country comparison of associations between NCD risk factors and SES

Table 4.2.1 Summary of AGE (referent group = 18-35 years or 25-35 years for Malaysia) and risk factors association1 across countries by sex

Current smoking Hazardous drinking Consumption of <100g/day fruit Consumption of <400g/day vegetables Consumption of <5 servings/day fruit and vegs. Obesity Central obesity High blood pressure High cholesterol High fasting blood glucose High level of occupational PA Highly active commuting High LTPA

Cross-country comparisons

This section presents the cross-country comparison of associations between NCD risk factors and SES.

China Men Women + ++ ++ 0 0 0 0 0 + ++ ++ ++ ++ 0 + ++

++ ++ ++ ++ ++ 0 + ++

Fiji Men Women 0 -- --- --- --

Malaysia Men Women 0 0

Nauru Men Women 0 0 0 0

Philippines Men Women 0 ++ 0 N/A

-- -++ ++ ++ 0 ++ 0 0 --

0 0 ++ ++ ++ ++ 0 0 0

0 0 0 ++ ++ ++ 0 0 0

-- -++ ++ ++ ++ ++ 0 -0

-0 ++ ++ ++ ++ 0 0 0

0 ++ ++ ++ ++ ++ 0 0 0

0 0 ++ ++ ++ ++ 0 0 0

Summary of evidence of association:

0 + ++

No significant association between risk factor and age Significant moderate risk association (risk increases with increasing age) (AOR 1.0-1.5) Significant strong risk association (risk increases with increasing age) (AOR >1.5)

--- -N/A

Significant moderate protective association (risk decreases with increasing age) (AOR 0.7 - 0.9) Significant strong protective association (risk decreases with increasing age) (AOR <0.7) No data Not applicable

Table 4.2.2 Summary of EDUCATION (referent group = Low education/Primary school) and risk factors association1 across countries by sex

China Men Women -- --- --- -0 -- --- -+ 0 + ++ 0 + + -- --- -++

0 -- --- -0 0 -- --- -++

Fiji Men Women -- --- -0 -- --- -++ 0 0 0 ++ -- -0 +

-- --- --- -0 0 0 0 0 ++

Malaysia Men Women 0 0

0 0 0 0 0 0 0 0 0

-- -0 0 0 0 0 0 0 0

Nauru Men Women 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0

Philippines Men Women -- --- -0 N/A 0 0 ++ 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0

Summary of evidence of association:

0 + ++

No significant association between risk factor and education Significant moderate risk association (risk increases with increasing education) (AOR 1.0-1.5) Significant strong risk association (risk increases with increasing education) (AOR >1.5)

--- -N/A

Significant moderate protective association (risk decreases with increasing education) (AOR 0.7 - 0.9) Significant strong protective association (risk decreases with increasing education) (AOR <0.7) No data Not applicable

Current smoking Hazardous drinking Consumption of <100g/day fruit Consumption of <400g/day vegetables Consumption of <5 servings/day fruitand vegs. Obesity Central obesity High blood pressure High cholesterol High fasting blood glucose High level of occupational PA Highly active commuting High LTPA 1

China Men Women -0 + 0 -- --- ---++ ++ + ++ ++ -- --- -++

Men

Fiji Women

Malaysia Men Women 0 0

0 0 0 0 0 0 0 0 0

+ + + + + -- --- -++

Nauru Men Women

++ 0 0 0 0 0 0 0 0

Philippines Men Women 0 0 0 N/A

0 0 0 ++ 0 0 0 0 0

0 0 ++ 0 ++ 0 0 0 0

Summary of evidence of association:

0 + ++

No significant association between risk factor and income Significant moderate risk association (risk increases with increasing income) (AOR 1.0-1.5) Significant strong risk association (risk increases with increasing income) (AOR >1.5)

--- -N/A

Significant moderate protective association (risk decreases with increasing income) (AOR 0.7 - 0.9) Significant strong protective association (risk decreases with increasing income) (AOR <0.7) No data Not applicable

Table 4.2.4 Summary of REGION (referent group = Rural) and risk factors association1 across countries by sex

China Men Women 0 + 0 0 -- --- -+ + ++ ++ + + ++ -- --++

+ + + ++ ++ -- -0 ++

Men -0

Fiji Women -0

++ ++ 0 ++ 0 ++ -- --- -0

0 + ++ 0 0 ++ -- --- -0

Malaysia Men Women 0 0

0 0 0 0 0 0 -- -0 0

Nauru Men Women

Philippines Men Women

0 0 0 0 0 0 -0 0

Summary of evidence of association:

0 + ++

No significant association between risk factor and region Significant moderate risk association (risk increases with urbanity) (AOR 1.0-1.5) Significant strong risk association (risk increases with urbanity) (AOR >1.5)

--- -N/A

Significant moderate protective association (risk decreases with urbanity) (AOR 0.7 - 0.9) Significant strong protective association (risk decreases with urbanity) (AOR <0.7) No data Not applicable

Table 4.2.3 Summary of INCOME (referent group = Low income) and risk factors association1 across countries by sex

Cross-countrycompari Section Title sonscomparisons ofprevalenceratesandassociationsbetweenNCDriskfactorsandSES Cross-country

A multicountry analysis of noncommunicable disease surveillance data

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

Table 4.2.5 Summary of ETHNICITY (referent group = Indo-Fijian; referent group = Malay) and risk factors association1 across countries by sex China Men Women

Cross-country comparisons

Fiji2 Men Women 0 ++ ++ ++

0 ++ 0 ++ 0 -- -0 ++ 0

0 ++ ++ ++ 0 ++ 0 0 ++

Malaysia3 Men Women -- -0

0 0 0 0 0 0 0 0 0

Nauru Men Women

0 0 0 0 0 0 0 0 0

Fiji ethnic groups: Indo-Fijian, Fijian, Other; 3 Malaysia ethnic groups: Malay, Chinese, Indian, Other races. Summary of evidence of association:

0 + ++

No significant association between risk factor and ethnicity Significant moderate risk association (AOR 1.0-1.5) Significant strong risk association (AOR >1.5)

--- -N/A

Significant moderate protective association (AOR 0.7 - 0.9) Significant strong protective association (AOR <0.7) No data Not applicable

Philippines Men Women

A multicountry analysis of noncommunicable disease surveillance data

Section Title Synthesis and discussion

Synthesis and discussion

Noncommunicable disease risk factors and socioeconomic inequalities â&#x20AC;&#x201C; what are the links?

While there is a substantial body of research in developed countries on the socioeconomic determinants of NCD risk factors, very little research has been conducted in developing countries.80,81,82 Since the relationships observed in developing countries may not hold in developing countries, carrying out to research to examine the distribution of NCD risk factors by socioeconomic status in developing countries is critical. This information is essential for targeting public health programmes and policies to reduce health inequalities relating to NCDs. This study examines relationships between various measures of SES and NCD risk factors among adults in low- and middle-income countries in the WHO Western Pacific Region. It includes two Pacific island countries of low- to lower-middle-income economies, one very large lower-middle-income economy (i.e., transitional economy) and two medium-sized lower- to upper-middle-income economies in South East Asiac. Given the varying levels of economic development and sociopolitical conditions, it was hypothesized that each country has a unique pattern of associations between SES measures and risk factors, necessitating analyses, such as those carried out in this study, prior to identifying populations at risk within countries. The central research question of this international collaborative project was whether the pattern of associations between demographic and SES variables and NCD risk factors were similar or different across participating countries. The correlates with social and economic factors were first explored by country and then summarized by risk factor (Chapters 3 and 4). This section synthesizes and discusses some important findings and their implications for public health programmes in tackling the emerging NCD epidemic in the five countries studied.

Comment on socioeconomic factors With the exception of Naurud, an important and over-arching finding from this study is that the inverse relationship between risk factors and socioeconomic measures may be commonly observed across all the countries under investigation. In general, most associations are in the expected direction. For example, smoking is higher among disadvantaged groups and, to a certain extent, hazardous drinking is also more prevalent in these groups. However, a substantial number of associations are also discordant. Countries vary in their population distributions of the same risk conditions, sometimes for obvious reasons, such as age or c Income groups as classified by the World Bank: low income (US$905 gross national income per capita); lower middle-income (US$ 905US$ 3595); upper middle-income (US$ 3596-US$ 11 155); high income (US$ 11 156 or more). d Nauru was the only country for which the analysis revealed no relationships between risk factors and educational attainment for men and women. This could be due to the similar distributions of prevalence and pattern of risks across all levels of educational attainment in the country.

Ethnicity was measured in Fiji and Malaysia only. It is a complex variable to analyse, with inconsistent associations, as it has attributes of social and cultural differences, religious laws and customs. It was used as a covariate in multivariate analyses, but data and patterns were not sought in detail. However, it did show variation in Fiji for smoking, hazardous smoking and biological risk factors. Differentials in NCD risk factors according to urban or rural residence were measured in China, Fiji and Malaysia only. Viewed as an independent risk issue, urban residents are often more likely than rural people to develop diabetes, obesity, hypertension and high cholesterol in China and Fiji. In Malaysia, however, the rates of hypertension, high cholesterol and diabetes among men and women are higher in rural regions, compared with urban residents. These findings are contrary to those from 20 years ago, when there were distinct differences between rural and urban regions, with cholesterol and hypertension rates being higher in urban than rural areas.84 The current analyses also show a flattening of rural-urban differentials, especially in Fiji, where differences in hypertension and cholesterol are much smaller or absent.

Comment on NCD risk factors For smoking, rates increase with age in China and among Filipino women and Malaysian men, while smoking rates are inversely related to education in China and Fiji and among Filipino men and women. That is, more educated men and women are less likely to smoke compared

culture. On other occasions, however, the differences, although noted, cannot be explained. For example, the relationship between a risk factor and education may be different from the relationship between the same risk factor and income. An example of this phenomenon is that the risk of hypertension is higher among Chinese women in higher income groups, compared with lower income Chinese women. However, the relationship with education is reversed, with more educated Chinese women at lower risk of hypertension compared with those in the lower education categories. This pattern also occurs in Fiji and the Philippines in the analyses, but is somewhat counter-intuitive. In the published literature from developed countries, the risk relationships by educational attainment usually parallel those seen by income categories. As noted by Pearson, education and income might be a â&#x20AC;&#x2DC;double-edged swordâ&#x20AC;&#x2122; of benefit and risk in developing countries.83 While improved economic resources and education are important for combating infectious diseases, these same socioeconomic factors are also associated with the adoption of health-compromising practices, including consumption of high fat/ high salt foods and adoption of sedentary work activities and modes of transportation.

Section Title Synthesis and discussion

A multicountry analysis of noncommunicable disease surveillance data

Noncommunicable disease risk factors and socioeconomic inequalities â&#x20AC;&#x201C; what are the links?

Synthesis and discussion

with those in the low education categories. As regards income, smoking rates are inversely related to wealth among Chinese men, and are not associated in other countries. Urban residents are more likely to smoke among Chinese women, less likely in Fiji compared with rural residents, and unrelated in Malaysia and among Chinese men. Other factors associated with increased hazardous alcohol use include age (showing a direct relationship in Chinese men and an inverse relationship among men and women in Fiji), consistent gender patterns (men having higher rates than women), and varying associations with education (showing an inverse relationship in Chinese men and Fijian women). Low fruit and vegetable consumption shows surprising associations. Although low fruit and vegetable consumption is less common among educated Fijians and Malaysians (women only) and wealthy and educated Chinese, it shows an inconsistent relationship with rural residence in China. Low fruit consumption is less common among rural Chinese, but low vegetable consumption is more common in this group, compared with urban residents. This pattern suggests that fruits and vegetables are not consistently available or affordable in rural areas, especially in China. Obesity appears to increase with age for all groups, except for Filipino and Nauruan men. It should be noted that the absence of a relationship between obesity and age in Nauruan men reflects the similar distributions of (high) prevalence of obesity across age groups in the country. Central obesity shows an even more consistent increase with age. Obesity risk increases in high-education and high-income groups in Chinese men, and with education only among Fijian men. The pattern is similar for central obesity. However, central obesity shows the opposite pattern among Chinese women, where the risk of central obesity is lower in the most educated group. Patterns of physical activity differ across its various domains. Active transport increases with age in China, but shows no age relationship elsewhere. High levels of occupational physical activity do not differ by age, after adjustment for other SES factors. Leisure-time physical activity increases with age in China, but decreases or shows no age relationship elsewhere. Work and transport physical activity are inversely related to education and income in China. Urban residents in China also engage in a higher level of physical activity during leisure time than their rural counterparts. Hypertension and cholesterol generally increase with age, but show only intermittent relationships with education and income. Hypertension is lower among educated Chinese women, and higher among Filipino

A multicountry analysis of noncommunicable disease surveillance data

Improved household SES and the increased urbanization and industrialization observed in China, Malaysia and the Philippines and, to a certain extent, Fiji and Nauru, coupled with increased access to nontraditional foods low in nutritional values and the adoption of sedentary lifestyles, have led to decreased physical activity and increased obesity. These health-related factors have manifested in increased metabolic risks, such as for diabetes. Consistent with previous studies,85,86 these analyses show that the influences of socioenvironmental changes in developing countries are more likely to affect the educated first, with wealthy and urban populations showing a worse NCD risk factor profile than their uneducated and poorer counterparts. However, the analyses also reveal complex and diverse patterns of association between socioeconomic determinants and NCD risk factors across the five countries. It is anticipated that the higher burden of NCDs will shift to those with lower educational attainment and socioeconomic status as the educated and the wealthy begin to recognize the risks of engaging in unhealthy practices.87,88 Complex yet targeted public health solutions will be required for both spectrums of the population to avoid escalating tobacco use, poor dietary habits and sedentary behaviours in the wake of economic development. While cultural and political differences may be expected to influence how policies and programmes are formulated and implemented, it is recommend that public health actions in the five countries should consider including the following key issues: nn

Individual-level programmes, aimed at affluent populations, to prevent the decline in energy expenditure that plays a major role in overweight and obesity. Environmental policies and programmes, aimed at whole populations, to preserve and support active lifestyles, including commuting that incorporates some physical exertion. Policies and programmes that support local food production and consumption, as well as distribution, to facilitate local access to affordable fruits and vegetables for all population groups, in both rural and urban settings (including in urban poor neighborhoods or slums).

Programme and policy implications

Section Title Synthesis and discussion

women, but lower among educated Filipino women. Cholesterol is higher among educated Chinese men and wealthy Filipino women. Diabetes prevalence is higher among more educated Chinese and Fijian men, and among more affluent Chinese men and women and Filipino men (when those in the highest income group are compared with those in the lowest income group).

Noncommunicable disease risk factors and socioeconomic inequalities â&#x20AC;&#x201C; what are the links?

Synthesis and discussion

Tobacco control strategies that focus particularly on the disadvantaged and the poor. Anti-smoking efforts aimed specifically at women. The relatively lower rates of smoking in this population group in all five countries, which are increasing rapidly in some countries, suggest that preventive actions are critical to avert the elevated risk facing this segment of the population.

Countries in the Western Pacific Region face the challenge of pursuing continued economic growth while preventing the adoption of health-compromising practices associated with modernization and industralization. To this end, the results of the present study point to the need for context- and country-specific consideration of social and economic factors in planning risk-factor prevention and control programmes.

A multicountry analysis of noncommunicable disease surveillance data

Section Project Annex: Title Partners

Annex: Project Partners

Noncommunicable disease risk factors and socioeconomic inequalities â&#x20AC;&#x201C; what are the links?

Annex: Project partners

Annex: Project Partners

China Dr Lingzhi Kong Deputy Director-General Department of Disease Control Ministry of Health 1 Nanlu, Xizhimenwai, Beijing 100044, China Tel: 8610 68792368 Email: konglingzhi-ncd@sina.com; Klz1953@yahoo.com Professor Guansheng Ma Associate Director National Institute for Nutrition and Food Safety Chinese Center for Disease Control and Prevention 29 Nan Wei Road, Beijing China 100050 Tel: 8610 83132572; Fax: 8610 83132021 Email: mags@chinacdc.net.cn Professor Dr Chen Chunming Senior Advisor Chinese Centre for Disease Control Beijing, China Email: chencm@ilsichina-fp.org; chencm@public.bta.net.cn Mr Zhaohui Cui (Statistician) National Institute for Nutrition and Food Safety Chinese Center for Disease Control and Prevention 29 Nanwei Road, Xuanwu District, Beijing, China Tel: 86-10-83132335; Fax: 86-10-83132021 Email: cuizhaohui2008@yahoo.com.cn Dr Yanwei Wu World Health Organization â&#x20AC;&#x201C; WPRO Email: wuy@chn.wpro.who.int Dataset: 2002 China National Nutrition and Health Survey Fiji Dr Temo K Waqanivalu Nutrition and Physical Activity Officer World Health Organisation - South Pacific Office Level 4 Provident Plaza One, Downtown Boulevard 33 Ellery Street, PO Box 113, Suva, Fiji Tel: 679-3304 600 Ext. 127; Fax: 679-3300 462 or 3311 530 Email: waqanivalut@sp.wpro.who.int

Level 6, Block E10, Parcel E, Pusat Pentadbiran Kerajaan Persekutuan 62590, Putrajaya, Malaysia Tel: (603) 8883 4145; Fax: (603) 8888 6277 Email: zainal@dph.gov.my Dr Mohamed Ismail Bin Abdul Samad Senior Principal Assistant Director Disease Control Division Ministry of Health Level 6, Block E10, Parcel E, Pusat Pentadbiran Kerajaan Persekutuan 62590, Putrajaya, Malaysia Tel: (603) 8883 4119 Fax: (603) 8888 6277 Email: ismail@dph.gov.my Dr Han Tieru WHO Representative in Malaysia Email: tieruh@maa.wpro.who.int Dataset: 2006 STEPwise survey Nauru Hon Dr Kieren Keke M.P. Minister for Health, Sport and transport Ministry of Health Republic of Nauru Email: keke@cenpac.net.nr Ms Maree Bacigalupo Secretary, Health and Medical Services Ministry of Health Republic of Nauru Tel: 674 4443892 Email: mbaci@cenpac.net.nr Dr Godfrey Itine Waidubu Primary Health Care Physician and Advisor Health Education and Promotion Unit, Majuro Hospital PO Box 16, Republic of Marshall Islands Email: godwaid@cenpac.net.nr Dataset: 2004 STEPwise survey Philippines

Malaysia

Ms Frances Prescilla Cuevas Chief, Health Program Officer Degenerative Disease Office, Department of Health Tel: (632) 743-8301 loc. 1750/ 1752/ 1753; Fax: 732-2493 Email: prescydoh@forpresident.com; prescyncd@yahoo.com

Dr Zainal Ariffin Omar Deputy Director Disease Control Division Ministry of Health

Dr Marina Baquilod Coordinator, Chronic Disease Epidemiology, Department of Health Tel: 063 2 7322493 Email: mabaquilod@yahoo.com

Dataset: 2002 STEPwise survey

A multicountry analysis of noncommunicable disease surveillance data

Dr Dante D. Morales Chair, Steering Committee NNHeS, 2003 Email: dantedmorales@yahoo.com Dr Antonio L. Dans Chair, Technical Working Committee, NNHeS, 2003 Email: tdans@zpdee.com Ms Joy Sanchez Email: joyts@yahoo.com Dataset: 2003 National Nutrition and Health Survey WHO Regional Office for the Western Pacific Dr Gauden Galea NCD Regional Adviser Ms Anjana Bhushan Technical Officer, Health in Development Ms Ailene delos Trinos Ms Sylvia Brown NCD Assistants

Professor Adrian Bauman Director Centre for Physical Activity and Health (CPAH) School of Public Health University of Sydney Level 2, Medical Foundation Building, K25 University of Sydney NSW 2006 AUSTRALIA Tel: +61 2 9036 3247 Fax: +61 2 9036 3184 Email: adrianb@health.usyd.edu.au Dr Philayrath Phongsavan Centre for Physical Activity and Health (CPAH) School of Public Health University of Sydney Level 2, Medical Foundation Building, K25 University of Sydney NSW 2006 AUSTRALIA Tel: +61 2 9036 3248 Fax: +61 2 9036 3184 Email: php@health.usyd.edu.au Ms Stephanie Schoeppe Centre for Physical Activity and Health (CPAH) School of Public Health University of Sydney Level 2, Medical Foundation Building, K25 University of Sydney NSW 2006 AUSTRALIA Tel: +61 2 9036 3192 Fax: +61 2 9036 3184 Email: stephanie.schoeppe@gmail.com

Section Project Annex: Title Partners

Ms Charmaine Duante Statistician Food and Nutrition Research Institute (FNRI) DOST Cmpd., Gen. Santos Ave., Bicutan, Taguig City-1631, Philippines Email: caduante@yahoo.com

Centre for Physical Activity and Health (CPAH) Sydney University, Australia

Ms Felicidad V. Velandria Food and Nutrition Research Institute (FNRI) DOST Cmpd., Gen. Santos Ave., Bicutan, Taguig City-1631, Philippines Tel: 063 2 839842 Email: fvvelandria@yahoo.com

A multicountry analysis of noncommunicable disease surveillance data

Section Title Endnotes

Endnotes

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

Endnotes Preventing chronic diseases: a vital investment. Geneva, World Health Organization , 2005. 2 Ibid 3 Marmot M, Wilkinson R, eds. Social determinants of health. Oxford, Oxford University Press, 1999. 4 Daar AS, et al. Grand challenges in chronic noncommunicable diseases. Nature, 2007, 450:494-496. 5 Mendez MA, Monteiro CA, Popkin BM. Overweight exceeds underweight among women in most developing countries. American journal of clinical nutrition, 2005, 81:714 –21. 6 Kim S, Symons M, Popkin BM. Contrasting socioeconomic profiles related to healthier lifestyles in China and the United States. American journal of epidemiology, 2004, 159:184191. 7 Reddy SK, and Yusuf S. Emerging epidemic of cardiovascular disease in developing countries. Circulation, 1998, 97:596-601. 8 Gupta R, Gupta VP, Ahluwalia NS. Educational status, coronary heart disease and coronary risk factor prevalence in rural population in India. British medical journal, 1994, 307:1332-1336. 9 Pais P, et al. Risk factors for acute myocardial infarction in Indians: a case-control study. Lancet, 1996, 348:358-363. 10 Op cit. Ref 7. 11 Mackay J. and Eriksen, M. Tobacco atlas. Geneva, World Health Organization , 2002. [http://www.who.int/tobacco/statistics/ tobacco_atlas/en/] 12 Op cit. Ref 8. 13 Op cit. Ref 9. 14 Narayan VKM, et al. Prevalence and patterns of smoking in Delhi: cross sectional study. British medical journal, 1996, 312:1576-1579. 15 Gender, health and tobacco. Factsheet. Geneva, World Health Organization , 2003. [http://www.who.int/entity/gender/ documents/Gender_Tobacco_2.pdf.] 16 Wilsnack SC, Wilsnack RW. International gender and alcohol research: recent findings and future directions. Alcohol research and health, 2002, 26:245-250. 17 Cockerham WC, et al. Health lifestyles in central Asia: the case of Kazakhstan and Kyrgzstan. Social science and medicine, 2004, 59:1409–1421. 18 Hao W, Chen H, Su Z. China: alcohol today. Addiction, 2005, 100:737–741. 19 Omoluabi PF. A review of the incidence of nonprescriptive psychoactive substance use/misuse in Nigeria. International journal of addictions, 1995, 30;445-458.

Endnotes

Parry CDH, et al. Alcohol use in South Africa: findings from the first Demographic and Health Survey (1998). Journal of studies on alcohol, 2005, 66:91-97. 21 Mumenthaler MS, et al. Gender differences in moderate drinking effects. Alcohol research and health, 1999, 23: 55-64. 22 Benegal V. India: alcohol and public health. Addiction, 2005, 100:1051-1056. 23 Op cit. Ref 18. 24 Neufeld KJ, et al. Regular use of alcohol and tobacco in India and its association with age, gender, and poverty. Drug and alcohol dependence, 2005, 77:283–291. 25 Medina-Moraa ME, Borgesb G, Villatoro J. The measurement of drinking patterns and consequences in Mexico. Journal of substance abuse, 2000, 12:183-196. 26 Obot, IS. The measurement of drinking patterns and alcohol problems in Nigeria. Journal of substance abuse, 2000, 12:169181. 27 Op cit. Ref 20. 28 Bloomfield K, et al. Social inequalities in alcohol consumption and alcohol-related problems in the study countries of the EU concerted action ‘Gender, culture and alcohol problems: a multi-national study’. Alcohol and alcoholism, 2006, 41:i26– i36. 29 Thang NM, Popkin BM. Patterns of food consumption in Viet Nam: effects on socioeconomic groups during an era of economic growth. European journal of clinical nutrition, 2004, 58:145-153. 30 Popkin BM, Gordon-Larsen, P. The nutrition transition: worldwide obesity dynamics and their determinants. International journal of obesity, 2004, 28:S2–S9. 31 Albala C, et al. Nutrition transition in Chile: determinants and consequences. Public health nutrition, 2002, 5(1A):123– 128. 32 Shetty PS. Nutrition transition in India. Public health nutrition, 2002, 5(1A):175–182. 33 Evans M, et al. Diet, health and the nutrition transition: some impacts of economic and socio-economic factors on food consumption patterns in the Kingdom of Tonga. Public health dialog, 2002, 9:309-315. 34 Dua S, et al. Rapid income growth adversely affects diet quality in China - particularly for the poor! Social science and medicine, 2004, 59:1505–1515. 35 Popkin BM. Nutrition in transition: The changing global nutrition challenge. Asia Pacific journal of clinical nutrition, 2001a, 10(Suppl.): S13–S18. 20

STEPS approach. Bulletin of the World Health Organization, 2006, 84:305-313. 54 Op cit. Ref 52. 55 Kusuma YS, Babu BV, Naidu JM. Prevalence of hypertension in some cross-cultural populations of Visakhapatnam District, South India. Ethnicity and disease, 2004, 14:250-259. 56 Op cit. Ref 51. 57 Cooper R, et al. The prevalence of hypertension in seven populations of West African Origin. American journal of public health, 1997, 87:160-168. 58 Mendez M, et al. Income, education, and blood pressure in adults in Jamaica, a middle-income developing country. International journal of epidemiology, 2003, 32:400-408. 59 Op cit. Ref 51. 60 Gulliford M. Commentary: epidemiological transition and socioeconomic inequalities in blood pressure in Jamaica. International journal of epidemiology, 2003, 32:408-409. 61 Op cit. Ref 58. 62 Bell AC, Adair LS, Popkin BM. Understanding the role of mediating risk factors and proxy effects in the association between socio-economic status and untreated hypertension. Social science and medicine, 2004, 59:275-283. 63 Taylor OG, et al. Profile of some risk factors for coronary heart disease in a developing country: Nigeria. African journal of medicine, 1996, 25:341-346. 64 Siegrist J, et al. Socioeconomic differences in cardiovascular risk factors in China. International journal of epidemiology, 1990, 19:905-910. 65 Larrañaga I, et al, and the Sentinel Practice Network of the Basque Country. Socio-economic inequalities in the prevalence of Type 2 diabetes, cardiovascular risk factors and chronic diabetic complications in the Basque Country, Spain. Diabetic medicine. 2005, 22:1047–1053. 66 Ko GTK, et al. A low socio-economic status is an additional risk factor for glucose intolerance in high risk Hong Kong Chinese. European journal of epidemiology, 2001, 17:289-295. 67 Marmot MG, et al. Health inequalities among British civil servants: the Whitehall II study. Lancet, 1991, 337:1387–93. 68 Robbins JM, et al. Socioeconomic status and type 2 diabetes in African Americans and non-Hispanic white women and men: evidence from the Third National Health and Nutrition Examination Survey. American journal of public health, 2001, 91:76–83. 69 Evans JMM, et al. Socio-economic status, obesity and prevalence of type 1 and type 2 diabetes mellitus. Diabetic medicine, 2000, 17:478-480. 70 Connolly VM, et al. Diabetes prevalence and socioeconomic status: a population-based study showing increased

Guo X, et al. Structural change in the impact of income on food consumption in China, 1989-1993. Economic development and cultural change, 2000, 48(4):737-760. 37 Popkin BM. The nutrition transition and obesity in the developing world. Journal of nutrition, 200b1, 131: 871S–873S. 38 Sobal J, Stunkard AJ. Socioeconomic status and obesity: a review of the literature. Psychological bulletin, 1989, 105:260275. 39 Monterio CA, et al. Socioeconomic status and obesity in adult populations of developing countries: a review. Bulletin of the World Health Organization, 2004, 82: 940-946. 40 Op cit. Ref 5. 41 Haas A, et al. Leisure-time physical activity in university students from 23 countries: associations with health beliefs, risk awareness, and national economic development. Preventive medicine, 2004, 39:182–190. 42 Pomerleau J, et al. Physical inactivity in the Baltic countries. Preventive medicine, 2000, 31:665–672. 43 Hallal PC, et al. Physical inactivity: prevalence and associated variables in Brazilian adults. Medicine and science in sports and exercise, 2003, 35:1894-900. 44 Sobngwi E, et al. Physical activity and its relationship with obesity, hypertension and diabetes in urban and rural Cameroon. International ernational journal of obesity and related metabolic disorders, 2002, 26:1009-16. 45 Taylor R, et al. Dietary intake, exercise, obesity and noncommunicable disease in rural and urban populations of three Pacific island countries. Journal of the American College of Nutrition, 1992, 11: 283-293. 46 Op cit. Ref 44. 47 Forrest KYZ, et al. Physical activity and cardiovascular risk factors in a developing population. Medicine and science in sports and exercise, 2000, 33:1598-1604. 48 Op cit. Ref 17. 49 Brunnich G, et al. Three case studies of time use survey application in lower and middle-income countries. Paris, Institute of Political Studies of Paris (Sciences-Po), 2005. [http://www. levy.org/undp-levy-conference/papers/paper_Vacarr.pdf] 50 Weller I, Corey P. The impact of excluding non-leisure energy expenditure on the relation between physical activity and mortality in women. Epidemiology, 1998, 9:632–635. 51 Opie LH, Seedat YK. Hypertension in Sub-Saharan African populations. Circulation, 2005, 112:3562-3568. 52 Kaufman JS, et al for the ICSHIP Investigators. Lifestyle, education, and prevalence of hypertension in populations of African origin. Annals of epidemiology, 1997,7:22-27. 53 Ng N, et al. Preventable risk factors for noncommunicable diseases in rural Indonesia: prevalence study using WHO 36

Section Title Endnotes

A multicountry analysis of noncommunicable disease surveillance data

Endnotes

Noncommunicable disease risk factors and socioeconomic inequalities â&#x20AC;&#x201C; what are the links?

prevalence of Type 2 diabetes mellitus in deprived areas. Journal of epidemiology and community health, 2000, 54:173177. 71 Op cit. Ref 68. 72 Op cit. Ref 67. 73 Leonetti DL, et al. Educational attainment and the risk of non-insulin-dependent diabetes or coronary heart disease in Japanese-American men. Ethnicity and disease, 1992, 2:326â&#x20AC;&#x201C;36. 74 Op cit. Ref 65. 75 Op cit. Ref 66. 76 Mohan V, et al. Intra-urban differencesin the prevalence of the metabolic syndrome in southern India - the Chennai Urban Population Study (CUPS No. 4). Diabetic medicine, 2001, 18:280-287. 77 Pan X, et al. Prevalence of diabetes and its risk factors in China, 1994. Diabetes care, 1997, 20: 1664-1669. 78 Sayeed MA, et al. Effect of socioeconomic risk factors on the difference in prevalence of diabetes between rural and urban population in Bangladesh. Diabetes care, 1997, 20: 551-555. 79 Singh RB, et al. Prevalence of type 2 diabetes mellitus and

risk of hypertension and coronary artery disease in rural and urban population with low rates of obesity. International journal of cardiology, 1998, 66: 65-72. 80 Op cit. Ref 3. 81 Noncommunicable disease and poverty: the need for pro-poor strategies in the Western Pacific Region: a review. Geneva, World Health Organization, 2007. 82 Blakeley T, et al. The global distribution of risk factors by poverty level. Bulletin of the World Health Organization, 2005, 83(2):118-126. 83 Pearson TA. Education and income: double-edged swords in the epidemiologic transition of cardiovascular disease. Ethnicity and disease, 2003, 13:158-163. 84 Trowell HC, Burkitt DP. Western diseases: their emergence and prevention. London, Edward Arnold, 1981. 85 Op cit. Ref 5. 86 Op cit. Ref 6. 87 Op cit. Ref 7. 88 Op cit. Ref 83.