The social patterning of risk factors for noncommunicable diseases in five countries: evidence from the modeling the epidemiologic transition study (METS)
© The Author(s). 2016
Received: 8 January 2016
Accepted: 25 August 2016
Published: 9 September 2016
Associations between socioeconomic status (SES) and risk factors for noncommunicable diseases (NCD-RFs) may differ in populations at different stages of the epidemiological transition. We assessed the social patterning of NCD-RFs in a study including populations with different levels of socioeconomic development.
Data on SES, smoking, physical activity, body mass index, blood pressure, cholesterol and glucose were available from the Modeling the Epidemiologic Transition Study (METS), with about 500 participants aged 25–45 in each of five sites (Ghana, South Africa, Jamaica, Seychelles, United States).
The prevalence of NCD-RFs differed between these populations from five countries (e.g., lower prevalence of smoking, obesity and hypertension in rural Ghana) and by sex (e.g., higher prevalence of smoking and physical activity in men and of obesity in women in most populations). Smoking and physical activity were associated with low SES in most populations. The associations of SES with obesity, hypertension, cholesterol and elevated blood glucose differed by population, sex, and SES indicator. For example, the prevalence of elevated blood glucose tended to be associated with low education, but not with wealth, in Seychelles and USA. The association of SES with obesity and cholesterol was direct in some populations but inverse in others.
In conclusion, the distribution of NCD-RFs was socially patterned in these populations at different stages of the epidemiological transition, but associations between SES and NCD-RFs differed substantially according to risk factor, population, sex, and SES indicator. These findings emphasize the need to assess and integrate the social patterning of NCD-RFs in NCD prevention and control programs in LMICs.
KeywordsSocioeconomic status Noncommunicable diseases Low and middle income countries Smoking Physical activity Obesity Hypertension Risk factors
Surveillance of risk factors of non communicable diseases (NCD-RFs) is an essential component of programs to address non-communicable diseases (NCDs) in all countries . It is well recognized that socioeconomic status (SES) influences the risk and vulnerability of individuals to NCD-RFs and NCDs, which underlies the need to examine the prevalence of NCD-RFs along SES categories. While the social patterning of NCD-RFs is well documented in high income countries, fewer studies are available in low and middle income countries (LMICs), and findings are generally inconsistent.
One potential explanation for mixed results on the social patterning of NCD-RFs in LMICs is the fact that the direction of these associations may change along a country’s socioeconomic development [2–6]. A recent study using data from the World Health Survey showed that the prevalence of smoking was higher and consumption of fruit and vegetables lower among lower SES groups in high income as well as in LMICs . On the other hand, physical inactivity was less prevalent in populations of low SES, especially in low-income countries, and results were mixed for alcohol consumption . A recent review suggested that, in LMICs, high SES is associated with overall healthier dietary patterns but also with higher calorie, cholesterol, and saturated fat intakes . Blood concentration of total cholesterol and the prevalence of obesity, hypertension and diabetes are generally higher among individuals with high SES in low-income countries [3, 4, 9, 10], while in middle income countries they are generally higher among low SES individuals [2, 11, 12].
Although evidence is accumulating for a “social transition” of NCD-RFs from the higher to the lower SES groups along a country’s socioeconomic development [2–6], this notion has been vividly debated recently [13, 14]. The importance of the socio-economic determinants of NCDs has been frequently highlighted in recent years [15, 16]. However, few studies examined the social patterning of NCD-RFs in LMICs and direct comparison of results is often limited by the different methods used in different studies. In this study, we examine the association of education and wealth with several NCD-RFs (smoking, physical activity, alcohol abuse, obesity, hypertension, high cholesterol and high blood glucose) in young adults (25–45 years) in five populations of African descent at different stages of the epidemiological transition, drawn from Ghana, Jamaica, South Africa, Seychelles and the United States.
A detailed description of the METS study has been previously published . In brief; 500 participants, approximately 50 % female, were enrolled in each of five study sites: rural Ghana, urban Jamaica, urban South Africa, the Seychelles and Maywood (suburb of Chicago, IL, USA). These sites represent a broad range of socio-economic development, as defined by the United Nations Human Development Index (HDI), i.e., low-middle for Ghana, middle for South Africa, high for Jamaica and the Seychelles, and very high for the US. For recruitment; a simple random sample was generated in Ghana from the population census for the rural town of Nkwantakese; a sex- and age-stratified random sample was drawn from previously enumerated areas of Khayelitsha in South Africa (a large township adjacent to the city of Cape Town); districts were randomly sampled in Kingston, Jamaica, beginning from a fixed point in each district with door-to-door recruitment; a sex- and age-stratified random sample was used in Seychelles from the national census; and all city blocks in the community were randomized and door-to-door recruitment was conducted in Maywood, USA (suburbs of Chicago). Individuals were excluded if they were HIV-positive, were pregnant or lactating, or were unable to participate in normal physical activities. METS was approved by the institutional ethics committees in the five sites involved. Written informed consent was obtained from all participants.
All measurements were performed early in the morning at outpatient clinics or testing sites, located in the communities. Smoking was assessed by an administered structured questionnaire. Current smokers refer to participants reporting smoking at least one cigarette per day. Physical activity was measured using an accelerometer (Actical, Phillips Respironics, Bend, OR, USA), with participants wearing the accelerometer at all times during 8 days (apart while bathing, showering, or swimming). Data were obtained based on six complete days of activity. Weight was measured to the nearest 0.1 kg using the same standard calibrated weighing scale at all 5 sites (Seca 770, Hamburg, Germany). Height was measured to the nearest 0.1 cm using a stadiometer (Invicta Stadiometer, Invicta, London, UK) with the participant’s head held in the Frankfort plane. We used weight and height to calculate body mass index (BMI,kg/m2). Obesity was defined as BMI ≥ 30 kg/m2.
Systolic and diastolic blood pressures were measured using an automatic digital blood pressure monitor in all sites (HEM-747Ic, Omron Healthcare, Bannockburn, IL, USA). Six measurements were made in two sets of three; the mean of the last 2 measurements for each set is considered. Hypertension was defined as systolic/diastolic BP ≥ 140/90 mmHg. Participants were asked to fast from the evening prior to the baseline clinic examination. Fasting blood samples were drawn for analysis of glucose and lipids. Serum was obtained within two hours of collection and stored at −80 °C at each study site. Analyses of lipids were conducted at the Center for Laboratory Medicine, Canton Hospital, St. Gallen, Switzerland. High cholesterol was defined as total cholesterol ≥5.2 mmol/l. Fasting plasma glucose was measured using the glucose oxidase method at each site at the time of collection. Elevated blood glucose was defined as plasma glucose ≥5.6 mmol/l in all sites. In the Ghanaian sample we did not examine elevated blood glucose as there was no certainty that participants were in the fasting state at blood collection.
Two indicators of SES were used: education and a proxy measure of wealth (referred as “wealth” in this paper). Education was assessed with the question: “Do you have any of the following degrees?” and was categorized as high (post secondary and tertiary education, corresponding to the International Standard Classification of Education (ISCED) levels 4–8), middle (secondary or vocational education, ISCED levels 2–3) and low (primary education, ISCED levels 0–1) . Wealth was calculated from several questions assessing the household’s ownership of a number of assets (iron, fridge, cable, etc.), categorized as 0/1. These items were summed and an assets ownership score was created; the score was further categorized according to site-specific tertiles.
Statistical analysis was conducted using Stata v.13.1 (Stata corp, College Station, TX, USA). With the few exceptions mentioned below, all analyses were stratified by site. Because there was no evidence of gender differences in the effect of education or wealth on most NCD-RFs (p for interaction >0.05) and given the small sample size, main analyses were sex-adjusted. Sex-stratified analyses are presented as supplements. We used least squares linear regression to calculate age-and sex-adjusted prevalence rates of NCD-RFs for each educational/wealth group in the five sites. Relative inequalities in NCD-RFs were assessed using age-and sex-adjusted logistic regressions, separately for education and wealth. In order to test whether the associations between SES and NCD-RFs differed by site, an interaction term between education or wealth and site was fitted in the different regression models described above including all sites. We further tested whether education modified the association between wealth and NCD-RFs by adding an interaction term between education and wealth in the logistic regression analyses.
Of the 2506 participants originally included in the METS study, 326 were excluded from the present analysis because of missing values for SES indicators (N = 9) or NCD-RFs (N = 326), categories not mutually exclusive. Excluded participants were more likely to be men (p < 0.001), had lower education but higher wealth (p < 0.001) and were more frequently from Jamaica or the USA (p < 0.001). There were no age differences between excluded and included participants.
Characteristics of the participants included in the study by site
World bank income classd
Lower middle income
Upper middle income
Upper middle income
Women, N (%)
Age, mean (SD)
Educational level, N (%)
Lower than secondary
Wealth tertiles, N (%)
Smoking, N (%)
Physically active, N (%)
Obese, N (%)
Hypertensive, N (%)
High cholesterol, N (%)
Elevated blood glucose, N (%)
Association of education and wealth with risk factors of noncommunicable diseases by site, age 25–45 years, Ghana (N = 438), South Africa (N = 484), Jamaica (N = 323), Seychelles (N = 438), United States (N = 448)
Elevated blood glucose
The association between SES indicators and NCD-RFs significantly differed by site for smoking, physical activity and elevated blood glucose (p < 0.05). Education did not seem to modify the association between wealth and NCD-RFs, with the exception of obesity in the USA sample, where the association between wealth and obesity was stronger among individuals with a higher educational level (p for interaction = 0.030).
Discussion and Conclusions
In this study, we examined the social patterning of several NCD-RFs in five populations from countries at different levels of the epidemiological transition. The distribution of NCD-RFs was substantially patterned by education and wealth, with individuals with less education or wealth generally showing a higher prevalence of several NCD-RFs. However, associations differed substantially according to NCD-RFs, population, sex, and SES indicator. Smoking and high physical activity were associated with low SES in most countries. Obesity, hypertension, cholesterol and elevated blood glucose had a different social distribution by country, sex, and SES indicator. For example, the prevalence of elevated blood glucose tended to be associated with low education, but not with low wealth, in countries at an advanced stage of the epidemiologic transition (the Seychelles and the USA). The association of SES with obesity and cholesterol tended to be direct in some countries but inverse in others. The prevalence of some NCD-RFs seemed to be higher in intermediate SES categories, especially in populations from countries at an intermediate stage of the epidemiologic transition (e.g., obesity in South Africa, Jamaica and Seychelles).
Inconsistencies in the social patterning of NCD-RFs by risk factor, population, sex, and SES indicator have been observed in virtually all multi-country studies in LMICs [3–6, 18]. Our results, based on data from five populations recruited using standardized data collection methods, support the notion that the association between SES indicators and NCD-RFs changes along the epidemiological transition, at different paces for men and women (generally more rapidly in men) and differently for different risk factors (e.g., earlier for smoking). A major implication of these results is the need to assess and integrate the social patterning of NCD-RFs in NCD surveillance and in prevention and control programs in LMICs. In particular, we emphasize the need to routinely collect data according to several socio-economic categories and to monitor the progression of the social distribution of NCDs and their risk factors over time.
A strong social patterning of smoking was observed in most sites, as expected. Remarkably, nearly all less educated men were current smokers in the USA, likely reflecting a more disadvantaged community than the general American population. In the South African sample, higher wealth seemed to be protective against smoking, while highly educated women were more likely to smoke. These results partly contrast with previous studies reporting an inverse association of income and education with current smoking in the adult South African population , and higher smoking cessation rates among highly educated women . The positive association between education and smoking that we observed among women may be explained by the relatively young age of our study population. The low prevalence of smoking in Ghana (with virtually no smoking women) and its association with education among men suggests that rural Ghana may still be at a very early stage of the smoking epidemics .
Physical activity and obesity
Social differences in physical activity were examined in a previous paper on this population . We found comparable levels of physical activity across sites, and an inverse association between SES and physical activity for both genders, with the exception of the South African sample where the middle SES groups had the highest prevalence of physical activity. The fact that high SES individuals were more likely to own a car in the five sites (and thus presumably move less) is a plausible explanation for higher physical activity levels in the lower SES groups . Moreover, low SES individuals are more likely to be employed in activity-intense jobs. Our results are consistent with previous international reports on the association between SES and physical activity in LMICs [23, 24]. However, in the USA a positive association between SES and physical activity is generally observed [25, 26], as in most high income countries, while in our study physical activity levels were higher among low SES individuals. These differences are probably related to the fact that low SES individuals in this disadvantaged population subgroup were less likely to own a car and had a longer commuting to work compared to individuals of low SES in the general population of the country .
The prevalence of obesity was higher in women than men, as commonly found in African American populations [28, 29]. Studies assessing social differences in obesity among African American women have yielded conflicting results [30, 31]. In our study, lower education was associated with higher prevalence of obesity in countries at an intermediate stage of the epidemiologic transition (South Africa and the Seychelles), but the reverse was observed in the Ghanaian and the USA’s sample. Rural Ghana, from which our sample was drawn, is probably at an early stage of the epidemiologic transition. Results for the USA sample are consistent with findings for physical activity and maybe explained by the peculiarities of this particular population, sampled in the Chicago suburbs, as a negative association between education and obesity is generally found in the USA . Previous studies have suggested that education and material resources interact in shaping the distribution of obesity in LMIC settings [6, 11]. In our study, education did not modify the association between wealth and obesity in African countries, although we had limited power to detect significant interactions. In this US population from a Chicago neighbourhood, high education seemed to confer an even greater obesity risk to individuals in the lowest wealth tertile. Inconsistencies with results from other studies may be related to the possibility that the American population from a Chicago suburb that participated in this study is not representative of all African-Americans. In rural Ghana, women with high SES had a higher prevalence of obesity (particularly those in the highest wealth tertile), in contrast with a previous study that reported a positive association between SES and obesity in men and an inverse association in women . These divergences suggest that social influences on NCD-RFs may differ in urban and rural areas in LMICs. In South Africa and Jamaica, obesity rates were higher in the middle SES groups, consistent with intermediary stages of secular shifts in the social patterning of obesity.
Hypertension, high cholesterol and elevated blood glucose
Hypertension was strongly socially patterned in the USA, participants with low SES having a significantly higher prevalence relative to those with high SES. This pattern has been frequently observed in high income countries [34–36]. Differences in treatment rates or access to health care may partly explain this association, along with trends towards higher salt intake, lower potassium intake, greater BMI, and higher alcohol intake in low SES individuals [36, 37]. Social differences in hypertension were less pronounced in other sites in our study, as expected given that a mix of positive and negative gradients has been found across studies in sub-Saharan Africa [38–42].
In countries at a later stage of the epidemiologic transition (USA and Seychelles), high blood cholesterol and glucose were generally more prevalent among low SES groups, as reported in other studies [35, 43]. Poorer nutrition and higher BMI are potential mediators of these associations; although in the USA the prevalence of obesity tended to be higher among higher SES groups. In countries at intermediate/early stages of the epidemiologic transition, individuals with a low wealth tended to have lower prevalence of high cholesterol and elevated glucose, consistent with higher lipid levels generally found among people from middle or higher SES groups in many transitional countries . Larger consumption of a diet rich in saturated fat and reduced physical activity among the wealthier persons may explain these associations in LMICs.
Strengths and limitations
This is one of the few studies to examine the association of two indicators of SES with several NCD-RFs in LMICs, using validated questionnaires and standardized measurements in samples of several populations at different levels of the epidemiological transition. The relatively young age of the participants also allows assessing the social determinants of NCD-RFs before NCDs have developed, which limits interference of treatments (and their social distribution). This study also has several limitations. First, the sample per site was fairly small, limiting the analysis of gender-specific associations and interactions. Subsequently, several associations showed large effect sizes but non-significant p values, likely related to insufficient statistical power. However, given the scarcity of data on the SES- NCD-RF associations in LMICs, this study is useful for informing public health strategies. Second, the study populations represent specific communities in most study sites, which are not representative of their respective national populations. The specificities of some of these communities may explain some inconsistencies between our results and those of previous studies, especially in the USA. Third, our wealth variable was constructed using a simple method assigning equal weights to all owned assets within and between countries to account, while some assets may have more values than others. Finally, although our cross sectional data on the social distribution of NCD-RFs in countries at different level of socioeconomic development may inform on future trends, only repeated surveys allow monitoring the actual social transition of risk factors over time in each population. Further sufficiently powered studies, allowing analyses by sex, age and SES status, should assess trends in the social patterning of NCD-RFs in a range of countries of vastly different socio-economic development, using repeated independent cross-sectional surveys.
Silvia Stringhini is supported by an Ambizione Grant (n° PZ00P3_147998) from the Swiss National Science Foundation (SNSF) and by the European Commission and the Swiss State Secretariat for Education, Research and Innovation SERI (Horizon 2020 grant n° 633666). METS is funded in part by the National Institutes of Health (1R01DK80763). The funding bodies had no role in the design of the study, in collection, analysis, and interpretation of data and in writing the manuscript.
Availability of data and materials
Data can be made available through the authors.
SS and PB conceived the study, SS analyzed the data and wrote the first and successive drafts of the manuscript, PB contributed to the writing of the manuscript, TEF, JPR, EVL, BV, WR, WK, NL, LT, LRD, DS, RAD, AL, PB collected the data. All authors critically revised the manuscript for important intellectual content and contributed to the interpretation of results. All authors have given final approval of the version to be published.
The authors declare that they have no competing interests.
Ethics approval and consent to participate
The METS protocol was approved by the Institutional Review Board of Loyola University Chicago, IL, USA; the Health Sciences Institutional Review Board of the University of Wisconsin, Madison, WI, USA; the Research Ethics Committee of the University of Cape Town, South Africa; the Committee on Human Research Publication and Ethics of Kwame Nkrumah University of Science and Technology, Kumasi, Ghana; the Board for Ethics and Clinical Research of the University of Lausanne, Switzerland (the primary institution for the Seychelles site’s director) and the Research and Ethics Committee of Ministry of Health of Seychelles.; and the Ethics Committee of the University of the West Indies, Kingston, Jamaica. We obtained written informed consent from all study participants.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- World Health Organisation. Global status report on noncommunicable diseases 2014. Geneva: WHO; 2014.Google Scholar
- Stringhini S, Viswanathan B, Gedeon J, Paccaud F, Bovet P. The social transition of risk factors for cardiovascular disease in the African region: Evidence from three cross-sectional surveys in the Seychelles. Int J Cardiol. 2013;168(2):1201–6.View ArticlePubMedGoogle Scholar
- Pampel FC, Denney JT, Krueger PM. Obesity, SES, and economic development: a test of the reversal hypothesis. Soc Sci Med. 2012;74(7):1073–81.View ArticlePubMedPubMed CentralGoogle Scholar
- McLaren L. Socioeconomic status and obesity. Epidemiol Rev. 2007;29:29–48.View ArticlePubMedGoogle Scholar
- Monteiro CA, Moura EC, Conde WL, Popkin BM. Socioeconomic status and obesity in adult populations of developing countries: a review. Bull World Health Organ. 2004;82(12):940–6.PubMedGoogle Scholar
- Aitsi-Selmi A, Chandola T, Friel S, Nouraei R, Shipley MJ, Marmot MG. Interaction between education and household wealth on the risk of obesity in women in Egypt. PLoS One. 2012;7(6):e39507.View ArticlePubMedPubMed CentralGoogle Scholar
- Hosseinpoor AR, Bergen N, Kunst A, Harper S, Guthold R, Rekve D, d’Espaignet ET, Naidoo N, Chatterji S. Socioeconomic inequalities in risk factors for non communicable diseases in low-income and middle-income countries: results from the World Health Survey. BMC Public Health. 2012;12:912.View ArticlePubMedPubMed CentralGoogle Scholar
- Mayen AL, Marques-Vidal P, Paccaud F, Bovet P, Stringhini S. Socioeconomic determinants of dietary patterns in low- and middle-income countries: a systematic review. Am J Clin Nutr. 2014;100(6):1520–31.View ArticlePubMedGoogle Scholar
- Monteiro CA, Conde WL, Lu B, Popkin BM. Obesity and inequities in health in the developing world. Int J Obes Relat Metab Disord. 2004;28(9):1181–6.View ArticlePubMedGoogle Scholar
- Mendez MA, Cooper R, Wilks R, Luke A, Forrester T. Income, education, and blood pressure in adults in Jamaica, a middle-income developing country. Int J Epidemiol. 2003;32(3):400–8.View ArticlePubMedGoogle Scholar
- Aitsi-Selmi A, Bell R, Shipley MJ, Marmot MG. Education modifies the association of wealth with obesity in women in middle-income but not low-income countries: an interaction study using seven national datasets, 2005–2010. PLoS One. 2014;9(3):e90403.View ArticlePubMedPubMed CentralGoogle Scholar
- Wang A, Stronks K, Arah OA. Global educational disparities in the associations between body mass index and diabetes mellitus in 49 low-income and middle-income countries. J Epidemiol Community Health. 2014;68(8):705–11.View ArticlePubMedGoogle Scholar
- Subramanian S, Corsi DJ, Subramanyam MA, Davey Smith G. Jumping the gun: the problematic discourse on socioeconomic status and cardiovascular health in India. Int J Epidemiol. 2013;42(5):1410–26.View ArticlePubMedGoogle Scholar
- Stringhini S, Bovet P. Commentary: The social transition of cardiovascular disease in low- and middle-income countries: wait and see is not an option. Int J Epidemiol. 2013;42(5):1429–31.View ArticlePubMedGoogle Scholar
- World Health Organisation. Global status report on noncommunicable diseases 2010. In: WHO, editor. Geneva: WHO; 2010.Google Scholar
- World Health Organisation. Rio Political Declaration on Social Determinants of Health. Rio de Janeiro: WHO; 2011.Google Scholar
- UNESCO IfS. International Standard Classification of Education. 2012 edn: UNESCO; 2011.Google Scholar
- Hosseinpoor AR, Parker LA, Tursan d’Espaignet E, Chatterji S. Social determinants of smoking in low- and middle-income countries: results from the World Health Survey. PLoS One. 2011;6(5):e20331.View ArticlePubMedPubMed CentralGoogle Scholar
- Peer N, Bradshaw D, Laubscher R, Steyn K. Trends in adult tobacco use from two South African Demographic and Health Surveys conducted in 1998 and 2003. S Afr Med J. 2009;99(10):744–9.PubMedGoogle Scholar
- Ayo-Yusuf OA, Szymanski B. Factors associated with smoking cessation in South Africa. S Afr Med J. 2010;100(3):175–9.View ArticlePubMedGoogle Scholar
- Lopez AD, Collishaw NE, Piha T. A descriptive model of the cigarette epidemic in developed countries. Tob Control. 1994;3(3):242–7.View ArticlePubMed CentralGoogle Scholar
- Shoham D, Dugas LR, Bovet P, Forrester T, Lambert EV, Plange-Rhule J, Schoeller DA, Brage S, Ekelund U, Durazo-Arvizu RA et al. Association of car ownership and physical activity across the spectrum of human development: Modeling the Epidemiologic Transition Study (METS). BMC Public Health. 2015. (In press).Google Scholar
- Bosdriesz JR, Witvliet MI, Visscher TL, Kunst AE. The influence of the macro-environment on physical activity: a multilevel analysis of 38 countries worldwide. Int J Behav Nutr Phys Act. 2012;9:110.View ArticlePubMedPubMed CentralGoogle Scholar
- Held C, Iqbal R, Lear SA, Rosengren A, Islam S, Mathew J, Yusuf S. Physical activity levels, ownership of goods promoting sedentary behaviour and risk of myocardial infarction: results of the INTERHEART study. Eur Heart J. 2012;33(4):452–66.View ArticlePubMedGoogle Scholar
- Lantz PM, House JS, Lepkowski JM, Williams DR, Mero RP, Chen J. Socioeconomic factors, health behaviors, and mortality: results from a nationally representative prospective study of US adults. J Am Med Assoc. 1998;279(21):1703–8.View ArticleGoogle Scholar
- McNeill LH, Kreuter MW, Subramanian SV. Social environment and physical activity: a review of concepts and evidence. Soc Sci Med. 2006;63(4):1011–22.View ArticlePubMedGoogle Scholar
- Tate NH, Dillaway HE, Yarandi HN, Jones LM, Wilson FL. An Examination of Eating Behaviors, Physical Activity, and Obesity in African American Adolescents: Gender, Socioeconomic Status, and Residential Status Differences. J Pediatr Health Care. 2015.Google Scholar
- Flegal KM, Carroll MD, Kit BK, Ogden CL. Prevalence of obesity and trends in the distribution of body mass index among US adults, 1999–2010. JAMA. 2012;307(5):491–7.View ArticlePubMedGoogle Scholar
- Obesity: Situation and trends. [http://www.who.int/gho/ncd/risk_factors/obesity_text/en/]
- Greenlund KJ, Liu K, Dyer AR, Kiefe CI, Burke GL, Yunis C. Body mass index in young adults: Associations with parental body size and education in the CARDIA Study. Am J Public Health. 1996;86(4):480–5.View ArticlePubMedPubMed CentralGoogle Scholar
- Coogan PE, Wise LA, Cozier YC, Palmer JR, Rosenberg L. Lifecourse educational status in relation to weight gain in African American women. Ethn Dis. 2012;22(2):198–206.PubMedPubMed CentralGoogle Scholar
- Drewnowski A, Specter SE. Poverty and obesity: the role of energy density and energy costs. Am J Clin Nutr. 2004;79(1):6–16.PubMedGoogle Scholar
- Addo J, Smeeth L, Leon DA. Obesity in urban civil servants in Ghana: association with pre-adult wealth and adult socio-economic status. Public Health. 2009;123(5):365–70.View ArticlePubMedGoogle Scholar
- Boykin S, Diez-Roux AV, Carnethon M, Shrager S, Ni H, Whitt-Glover M. Racial/ethnic heterogeneity in the socioeconomic patterning of CVD risk factors: in the United States: the multi-ethnic study of atherosclerosis. J Health Care Poor Underserved. 2011;22(1):111–27.PubMedPubMed CentralGoogle Scholar
- Kanjilal S, Gregg EW, Cheng YJ, Zhang P, Nelson DE, Mensah G, Beckles GL. Socioeconomic status and trends in disparities in 4 major risk factors for cardiovascular disease among US adults, 1971–2002. Arch Intern Med. 2006;166(21):2348–55.View ArticlePubMedGoogle Scholar
- Colhoun HM, Hemingway H, Poulter NR. Socio-economic status and blood pressure: an overview analysis. J Hum Hypertens. 1998;12(2):91–110.View ArticlePubMedGoogle Scholar
- Stamler R, Shipley M, Elliott P, Dyer A, Sans S, Stamler J. Higher blood pressure in adults with less education. Some explanations from INTERSALT. Hypertension. 1992;19(3):237–41.View ArticlePubMedGoogle Scholar
- Ploubidis GB, Mathenge W, De Stavola B, Grundy E, Foster A, Kuper H. Socioeconomic position and later life prevalence of hypertension, diabetes and visual impairment in Nakuru Kenya. Int J Public Health. 2013;58(1):133–41.View ArticlePubMedGoogle Scholar
- Norman R, Bradshaw D, Steyn K. Chronic diseases, risk factors and lifestyle based on the, South Africa Adult Demographic and Health Survey. In: Bradshaw D, Steyn K, editors. Poverty and Chronic Diseases in South Africa :Technical Report 2001. Tygerberg: Tygerberg: Medical Research Council of South Africa - Burden of Disease Research Unit 2001; 2001.Google Scholar
- Lang T, Pariente P, Salem G, Tap D. Social, professional conditions and arterial hypertension: an epidemiological study in Dakar. Senegal J Hypertens. 1988;6(4):271–6.PubMedGoogle Scholar
- Poulter NR, Khaw K, Hopwood BE, Mugambi M, Peart WS, Sever PS. Determinants of blood pressure changes due to urbanization: a longitudinal study. J Hypertens Suppl. 1985;3(3):S375–7.PubMedGoogle Scholar
- Bovet P, Ross AG, Gervasoni JP, Mkamba M, Mtasiwa DM, Lengeler C, Whiting D, Paccaud F. Distribution of blood pressure, body mass index and smoking habits in the urban population of Dar es Salaam, Tanzania, and associations with socioeconomic status. Int J Epidemiol. 2002;31(1):240–7.View ArticlePubMedGoogle Scholar
- Krishnan S, Cozier YC, Rosenberg L, Palmer JR. Socioeconomic status and incidence of type 2 diabetes: results from the Black Women’s Health Study. Am J Epidemiol. 2010;171(5):564–70.View ArticlePubMedPubMed CentralGoogle Scholar
- Bauman A, Phongsavan P, Schoeppe S, Chey T. Noncommunicable disease risk factors and socioeconomic inequalities–what are the links? A multicountry analysis of noncommunicable disease surveillance data. In: World Health Organization, editor. Report to the WHO Regional Office for the Western Pacific. Geneva: World Health Organization; 2010.Google Scholar