Skip to main content

Educational differences in diabetes and diabetes self-management behaviours in WHO SAGE countries



Diabetes mellitus represents a substantial global health challenge, with prevalence rising in low- and middle-income countries (LMICs). Although diabetes is known to follow a socioeconomic gradient, patterns in LMICs are unclear. This study examined associations between education and diabetes, and diabetes self-management behaviours, in six LMICs.


Cross-sectional data for 31,780 participants from China, Ghana, India, Mexico, Russia, and South Africa from the World Health Organization Study on Global AGEing and adult health (SAGE) study were used. Participants aged ≥50 years completed face-to-face interviews between 2007 and 2010. Participants self-reported diabetes diagnosis, physical activity, sedentary time, fruit and vegetable consumption, any special diet/program for diabetes, whether they were taking insulin for diabetes and number of years of education. Height, weight, waist, and hip circumference were measured. Country-specific survey-weighted log-binomial regression models were fitted to examine associations between the number of years of education and self-reported diabetes diagnosis (primary analysis). In secondary analyses, among those with a self-reported diabetes diagnosis, generalised linear regression models were fitted to examine associations between education and i) physical activity, ii) sedentary time, iii) fruit and vegetable consumption, iv) special diet for diabetes, v) taking insulin, vi) BMI, vii) waist circumference and viii) hip circumference.


There was strong evidence of an association between years of education and diabetes diagnosis in Ghana (RR = 1.09, 95% CI: 1.06–1.13) and India (RR = 1.09, 95% CI: 1.07–1.12) only. In India, greater years of education was associated with higher leisure physical activity, fruit and vegetable intake, rates following a special diet or taking insulin, but also higher mean BMI, waist and hip circumference. Relationships between education and self-management behaviours were rarely seen in the other countries.


Associations between education and diabetes, and behavioural self-management (India only) was more evident in the two least developed (Ghana and India) of the WHO SAGE countries, indicating increasing diabetes diagnosis with greater numbers of years of education. The lack of gradients elsewhere may reflect shifting risk from higher to lower educated populations. While there was some suggestion that self-management behaviours were greater with increased education in India, this was not observed in the other countries.

Peer Review reports


Diabetes mellitus represents a substantial global health challenge, with prevalence in the world’s adult population doubling from 4.7 to 8.5% between 1980 and 2014 [1]. Research has shown a considerable proportion of the world’s population with diabetes live in low-and middle-income countries (LMICs), with the highest proportion of adults under 50 years with diabetes found in low-income countries (67%) [2], where prevalence has risen most rapidly since 1980 [1]. The prevalence of diabetes is estimated to increase by 69% in LMICs and 20% in high-income countries (HICs) between 2010 and 2030 [3]. The global cost of diabetes was estimated at $1.31 trillion in 2015, with the economic burden as a percentage of Gross Domestic Product (GDP) larger in LMICs than in HICs [4].

In high-income countries, diabetes follows a socioeconomic gradient, with the most socioeconomically disadvantaged at greatest risk [5]. Socioeconomic gradients among LMICs are less clear, and of interest given that these countries shoulder a growing healthcare burden associated with diabetes [1]. Evidence suggest that socioeconomic gradients in non-communicable diseases (NCDs) might be different in LMICs compared to HICs [6, 7]. Further, NCDs risk factors often change over time, with countries following an epidemiological transition associated with increasing development [8]. The transition of countries towards a higher level of development may be associated with a reversal of the socioeconomic gradient in diabetes risk. For example, at a national level, Xu et al. [9] found that diabetes prevalence increased with increasing country-level socioeconomic status in developing countries, but decreased with increasing status in developed countries. When considering individual-level socioeconomic position, evidence shows, for example, an attenuation between 2002 and 2008 of the high concentration of diabetes cases amongst the most advantaged in South Africa [10]. However, evidence remains mixed in other LMICs [11, 12]. In one of few studies which examined socioeconomic gradients conducted in multiple countries, Tyrovolas et al. (2015) [13] found that education was more strongly associated with diabetes in some countries (e.g., Ghana, India, Poland) than others (Mexico, Russia), and suggested that level of development may moderate education-diabetes associations. However, their study was focussed on examining associations between obesity, diabetes and disability and thus their findings on socioeconomic gradients were exploratory and did not account for potential confounders of the association. Furthermore, that study also excluded any focus on behavioural self-management. Contrasting findings across LMICs is hampered by challenges obtaining comparable estimates across studies due to varying definitions of diabetes; data sources and representativeness; years of data collection; and confounders considered [14].

Effective self-management of diabetes requires compliance with medication regimens, as well as modifying behavioural risk factors including maintaining a healthy weight, being physically active, and eating a healthy diet. Little is known about the extent to which people living with diabetes in LMICs engage in behaviours to help manage their condition, nor about socioeconomic gradients in these behaviours. If gradients follow patterns observed among HICs, this could contribute to worsening disease progression amongst those who are more socioeconomically disadvantaged if this group has poorer engagement in diabetes self-care, as shown elsewhere [15, 16].

Examining socioeconomic gradients in diabetes and diabetes self-management across multiple LMICs could highlight population segments where public health initiatives are best focused. The primary aim of this study was to examine associations between years of education and diabetes diagnosis by education in six LMICs. In secondary analyses, associations between education and diabetes self-management behaviours among those with diabetes were considered by country.



Data were from the first wave of the World Health Organization Study on Global Ageing and Adult Health (WHO SAGE) [17]. WHO SAGE involves nationally representative cohorts of adults aged ≥50 years from six LMICs (China, Ghana, India, Mexico, Russia, South Africa) undergoing rapid economic development. The United Nations Development Program’s (UNDP) Human Development Index (HDI) is a widely recognized tool for measuring and comparing development across countries. According to this index, the six LMICs examined in this study were considered and presented in all tables in order of development – Ghana (lowest ranking: 138 in 2010), India, South Africa, China, Mexico, Russia (ranked 60 in 2010) [18].

The response rate for the first wave ranged from 51% in Mexico to 93% in China. The dataset, as opposed to single country studies, enables systematic investigation of prevalence and socioeconomic gradients of diabetes and diabetes self-management behaviours, given the validated standardised measurement tools and approaches to data collection and management across countries. Although WHO SAGE collected data for a wave zero and some of the first wave participants featured in wave zero, this prior wave was part of the World Health Survey of 70 countries and, as such, used a different sampling approach to the WHO SAGE longitudinal cohort which began with wave one. Therefore, only the first wave of data was used for this analysis.

Full details of the study are provided elsewhere. In brief, multistage cluster random sampling was conducted in each country with all participants from households classified as ‘50+ year households’ invited to complete an individual face-to-face interview. Proxy respondents were identified for participants who could not complete the interview; these were included in the analysis where data on the key study variables were provided. Baseline interviews were conducted between 2007 and 2010. Person-level analysis weights were calculated for each country; these included both a sample selection and a post-stratification factor, with the most recent population estimates provided by the national statistical offices in each country.


This study uses secondary data from the WHO SAGE study which was approved by the WHO’s Ethical Review Board. Consent to use these data for this study was provided by the WHO Multi-Country Studies Data Archive.


Self-reported diabetes diagnosis was recorded during face-to-face interviews, with participants asked, “Have you ever been diagnosed with diabetes (high blood sugar)?” Those who responded ‘yes’ to this question were recorded as having diabetes. Participants were asked to exclude diabetes associated with a pregnancy.


Education was used as a marker of socioeconomic status; increases in education in LMICS are believed to be an important factor in improving health [19]. Each participant was asked if they had ever been to school and how many years of schooling (including higher education) they had completed. Those who stated they had never been to school were recoded to 0 years of education.

Diabetes self-management behaviours

Behaviours that contribute to better self-management of diabetes were considered for those who reported a diabetes diagnosis in secondary analyses.

Physical activity, defined as minutes spent in leisure-time physical activity and active transport in a typical week, and sedentary behaviour, defined as minutes spent sitting in a typical day, were recorded using the Global Physical Activity Questionnaire [20]. Fruit and vegetable consumption were both reported as the number of servings eaten on a typical day. Participants also reported if they had been following a special diet, exercise regimen, or weight control program for diabetes during the last two weeks and if they had been taking insulin or other blood sugar lowering medications during that period.

Height (cm), weight (kg), waist (cm) and hip (cm) circumferences were objectively measured by WHO SAGE interviewers. Body mass index (BMI) was calculated for each participant by dividing weight by height in metres squared (kg/m2).

Other covariates

Sex, age, ethnicity and urbanicity (urban/rural) of residence of each participant were recorded. Ethnic groups varied by country (see Table 1). Ethnicity was not considered for Mexico as ethnicity was recorded as “none” or “missing” for most participants.

Table 1 Unweighted descriptive characteristics of the sample by country*

Statistical analysis

Survey weighted descriptive statistics were calculated for prevalence of diagnosis of diabetes for each country. To address the first aim, country-specific survey weighted log-binomial regression models were fitted to examine associations between education and diabetes diagnosis. Unadjusted analyses and analyses adjusting for potential confounding variables (age, sex, and urbanicity) were considered (Adjustment 1). Ethnicity was also considered to be a potential confounding variable. However, as data on ethnicity was not available for Mexico, ethnicity was only considered in sensitivity analyses in further adjusted models in all countries apart from Mexico (Adjustment 2), to assess whether this influenced observed associations.

To address the secondary aim, only those who reported a diabetes diagnosis were considered as the aim was to examine educational gradients in behaviours to help manage their condition among those living with diabetes. Since very few participants reported conducting any physical activity, physical activity outcomes were dichotomised. Country-specific survey-weighted logistic or log-binomial regression models were fitted to examine associations between the education and leisure time and transport physical activity (any: no/yes), special diet (no/yes), and insulin, or diabetes medication (no/yes). Linear regression was used to examine associations between education and sedentary time (square root transformed prior to modelling to address skewness), BMI, waist and hip circumference. Poisson regression was used to model associations between education and the number of servings of fruit per day; number of servings of vegetables per day; and the number of servings of fruit and vegetables per day. Analyses with and without adjustment for confounders were considered. Analyses were conducted using Stata version 14·2.

Missing data

A complete case analysis was conducted assuming the data were missing completely at random. A small proportion of participants ranging from 0·03% in India to 4·6% in Mexico had missing diabetes data (Supplementary Table 1). Apart from South Africa (16·8%), most countries had a small proportion of missing education data. The percentage of participants with diabetes was similar among those who did (10·7%) and did not (9·7%) have missing education data. After omitting those with missing data for all covariates considered, the sample sizes were 4152 (96·5%) for Ghana, 6505 (99·2%) for India, 3071 (80·0%) for South Africa, 12,685 (96.3%) for China, 1954 (93·8%) for Mexico and 3413 (86.6%) for Russia. Comparisons of omitted and complete case samples showed complete case participants had lower average years of schooling in all countries (apart from Mexico which had no education data among those omitted), higher average age (apart from Mexico which showed the opposite) and a lower proportion of females in Ghana and India but higher in South Africa (Supplementary Table 2). Only those with complete data for all self-management behaviours among those with diabetes were considered for the second aim, resulting in sample sizes of 137 (82·0% of those with diabetes) in Ghana, 413 (86·4%) in India, 239 (66·4%) in South Africa, 654 (77.6%) in China, 315 (76·8%) in Mexico and 187 (53·4%) in Russia. Missing data was particularly high in Russia and South Africa where many participants refused to have anthropometric measurements taken. A comparison of characteristics for participants with diabetes showed the complete case sample was generally representative of the full sample (Supplementary Table 3). Although, on average, the complete case sample tended to be older than those with missing data in all countries apart from Russia.


The weighted prevalence of diabetes diagnosis ranged from 3·8% in Ghana to 18·4% in Mexico (Fig. 1). Unweighted descriptive characteristics for the sample are presented in Table 1. There was a lot of variability in schooling between the countries with the mean years of schooling ranging from 3.6 years (SD = 4.7) in India to 10.9 years (SD = 3.9) in Russia.

Fig. 1
figure 1

Survey-weighted-prevalence of self-reported diabetes diagnosis by country* with 95% confidence intervals. *Countries are presented in order of level of development from least to most developed [17]

Socioeconomic gradients in diabetes

After accounting for confounders, a positive association between education and diabetes diagnosis was observed in Ghana (RRadj1 = 1.09; 95% CI = 1.06–1.13) and India (RRadj1 = 1.09; 95% CI = 1.07–1.12), with an estimated increase in risk of diabetes diagnosis of 9% with each additional year of education in both (Fig. 2). However, there was not strong evidence of an association between education and diabetes diagnosis for South Africa, China, Mexico, or Russia after adjustment. Estimated risk ratios are presented in Supplementary Table 4.

Fig. 2
figure 2

Unadjusted and adjusted risk ratio of self-reported diabetes diagnosis by education for each WHO SAGE country. *Countries are presented in order of level of development from least to most developed [17]

Socioeconomic gradients in diabetes self-management factors among those with diagnosed diabetes

Physical activity and sedentary behaviour

Descriptive statistics for self-management factors for those with diabetes diagnosis are presented in Table 2. Among those with diabetes, the percentage of participants who conducted any leisure-time physical activity was low, ranging from 6.4% in Russia to 21.7% in China, but higher for any transport physical activity, ranging from 18.4% in South Africa to 70.1% in Ghana. Average fruit consumption was similar across all countries, ranging from 1.2 (SD = 0.8) in India to 2.1 (SD = 1.5) in Ghana. Average vegetable intake among participants with diabetes was highest in China (6.6 portions) and lowest in Mexico (1.6). Less than half (47.3%) of participants with diabetes in Mexico were consuming a special diet for diabetes and only approximately half (52.8%) of participants with diabetes in India were taking insulin or blood sugar lowering medication. Average body mass index among those with diabetes was highest in South Africa (31.4 kg/m2 [SD = 7.1]) and lowest in India (22.9 kg/m2 [SD = 4.4]).

Table 2 Unweighted sample descriptive characteristics of diabetes self-management factors by country* for those with self-reported diabetes diagnosis

There was moderate to strong evidence of positive associations between education and leisure-time physical activity in Ghana (ORadj1 = 1.15; 95% CI = 1.05–1.26) India (ORadj1 = 1.11; 95% CI = 1.03–1.19) and South Africa (ORadj1 = 1.16; 95% CI = 1.00–1.35) after adjustment, but not in the other countries (Table 3), although the association attenuates in South Africa after further adjustment for ethnicity. There was little evidence of an association between education and any transport physical activity or sedentary time for most countries after adjustment for confounders (Table 3).

Table 3 Associations between education and diabetes self-management factors among those with self-reported diabetes diagnosis by country

Fruit and vegetable intake

Although point estimates for the associations between education and fruit, vegetable, or fruit and vegetable intake combined were all in the same direction for the six countries (Table 3), suggesting increased consumption among those with higher education, estimated risk ratios were low (1.00–1.04). Furthermore, there was only strong evidence of an association between education and fruit intake in China, education and vegetable intake in India and Russia, and education and fruit and vegetable intake combined in China, India, Mexico, and Russia.

Special diet and medication

There was only weak evidence of an association between education and the likelihood of being on a special diet among those with diabetes for most countries apart from India where the rates of being on a special diet (RRadj1 = 1.06; 95% CI = 1.02–1.09) or of taking insulin or diabetes medication (RRadj1 = 1.04; 95% CI = 1.01–1.07) increased with increasing years of education. The opposite pattern for medication was observed in Russia (RRadj1 = 0.94; 95% CI = 0.91–0.98).

BMI, waist circumference and hip circumference

Associations between education and the adiposity measures (BMI, waist circumference, and hip circumference) among participants with diabetes diagnosis were inconsistent across countries (Table 3). There was only evidence of an association between education and all three outcomes in India, with higher education found to be associated with higher average BMI, waist circumference and hip circumference.


Our study provides evidence on associations between years of education and self-reported diabetes diagnosis and diabetes self-management behaviours in six LMICs undergoing rapid economic development. Results showed that while self-reported diabetes diagnosis was more prevalent amongst the most educated in Ghana and India – the least developed countries examined – there was no evidence of an association between years of education and diabetes in Russia or Mexico, or in China or South Africa after confounder adjustment.

Our study advances limited existing evidence on the relationship between socioeconomic status and diabetes in LMICs [13]. Findings are broadly consistent with those of prior reports [21,22,23] on the existence and direction of associations of education with diabetes in individual countries. For example, prior research from Chinese adults aged over 45 years found no evidence of an association between education and diabetes consistent with our findings [21], as did research from Russia [23]. Furthermore, among older adults in India, higher education was associated with higher numbers of self-reported diagnosed chronic diseases, consistent with our results [22]. In addition, a systematic review of predictors of diabetes diagnosis in Ghana highlighted secondary and tertiary level of education as a risk factor for diabetes in some prior research, in line with our findings [24]. In contrast to our findings, prior longitudinal research from Mexico showed higher education was associated with a lower probability of diabetes diagnosis when participants were asked if a doctor or medical personnel had ever told them that they had diabetes or a high blood sugar level [25]. Reasons for inconsistent findings could include variations in the populations, sampling frames or methods used to assess education and diabetes. Our study advances this work by comparing multiple countries using standardised approaches.

The positive association between education and self-reported diabetes diagnosis in Ghana and India – the two least developed countries included in this study – may reflect the greater accessibility of desirable westernised practices, such as vehicle ownership and sedentary lifestyles, and increased fast food, soft drink and alcohol consumption, amongst those of a higher educational status [26,27,28]. These groups may have greater means of engaging in these behaviours which place them at increased risk of overweight and diabetes. That these associations were not observed in the remaining countries studied may be attributable to their more advanced levels of development. Economic development in countries results in resolution of food shortage problems, even among the poor; increasing availability of energy-dense foods including chain brand fast food outlets; and increased automation and reduced manual labour in workplaces and communities [29]. Such changes plausibly play a role in the rise of NCDs like diabetes through their impact on population diet and physical activity levels. Against the backdrop of these changes, the shifting distribution of diabetes risk away from the most advantaged is consistent with explanations stemming from diffusion of innovations theory [30, 31], whereby higher educated individuals tend to be the first to become aware of new knowledge (such as disease risk factors), and quicker to adapt behaviours to reduce risk.

It is importantly to acknowledge that the WHO SAGE study relied on self-report of diabetes diagnosis. Therefore, it is possible that differences observed between countries may be due to under-reporting of diabetes across the different contexts. It is probable that there is a relationship between education and the likelihood of seeking care and receiving a diagnosis for diabetes. For example, research from South Africa found socioeconomic inequalities in diabetes, measured using wealth indictors, with higher levels of self-reported diabetes diagnosis among the wealthier who have greater access to health care [32]. Furthermore, the prevalence of diabetes diagnosis reported appeared high in some contexts leading to questions about the representativeness of all samples. While the estimated prevalence was within the 95% confidence intervals for 2010 estimates from a pooled analysis of 751 population-based studies for almost all of the countries [33], the estimated prevalence of almost 20% in Mexico was found to be high compared to the estimated 10.4% (95% CI: 5.9–14.8%) in this study, although some data sources used in that analysis spanned a greater age distribution than in WHO SAGE, with adults as young as 20 years included.

Acknowledging the cross-sectional design and challenges with self-reported diabetes diagnosis, the findings according to country level of development are consistent with other evidence of shifting socioeconomic gradients in NCDs as countries transition to higher income [10, 29, 34, 35]. While we cannot determine trends from the present study, should the countries examined here follow these patterns, without intervention the risk of diabetes may soon shift to the most disadvantaged, with the more developed countries here possibly in the midst of this shift, and Ghana and India (depending on future rates of development) potentially the last to experience this gradient reversal.

Diabetes self-management is important since it can help patients enhance diabetes control and significantly reduce their likelihood of long-term complications [36]. However, data regarding diabetes self-management in LMICs are sparse. While rates of engagement in self-management behaviours varied greatly across countries and behaviours, in many cases these were less than optimal. A limited number of previous studies have examined medication compliance or other individual behaviours amongst people with diabetes in these contexts [37,38,39], but to our knowledge this study is the first to systematically investigate the engagement in behaviours that aid in diabetes self-management across multiple LMICs, and the distribution of these behaviours according to education among people with diabetes. Findings from WHO SAGE showed that among people self-reporting diabetes diagnosis, higher education was associated with more leisure-time physical activity in Ghana and India only. The opportunity to engage in leisure-time physical activity opportunities may be more restricted amongst the least educated in these countries. Other studies in LMICs have shown similar associations [26, 40]. We found no evidence of educational gradients in leisure-time physical activity in other countries. This may be attributable to relatively low proportions of people with diabetes reporting any leisure-time physical activity (from only ~ 6·0% in Mexico and South Africa, to 21·5% in China), resulting in insufficient variability to detect strong gradients. We found no evidence of associations between education and either transport-related activity or sedentary behaviours among people with diabetes in any country. Technological progress across all countries may have resulted in widespread access to both automated work systems and sedentary pursuits across educational groups [41, 42]. The somewhat mixed nature of observed associations of education level with these and the remaining behavioural factors examined is in line with findings of a systematic review of the socioeconomic gradients in NCD behavioural risk factors in LMICs [40]. That review concluded that despite some evidence of socioeconomic gradients, heterogeneity between measures limited the certainty of their findings, noting that studies of the distribution of behavioural risk factors in these countries are scarce and produce inconsistent results.

Strengths of this study include the large, multi-country sample, and standardised sampling and measurement approaches applied systematically across all countries to ensure comparability and the ability to adjust for key potential confounders. The sample with diabetes was recruited from the population, and not only limited to those attending clinics or known to medical or community workers. Limitations include the self-reported measure of diabetes, which may have resulted in exclusion of unknown or undiagnosed cases and an underestimation of prevalence. This may be particularly pronounced in low socioeconomic status groups who have less access to healthcare and could lead to false positive associations of socioeconomic position with disease [7]. We could not distinguish Type 1 and Type 2 diabetes. Furthermore, medication and insulin prescription and use may vary depending on unmeasured factors, such as disease severity. Aside from taking insulin/medication or being on a special diet, we also could not examine whether individuals engaging in behavioural factors did so specifically in order to help manage their diabetes.


Efforts to manage diabetes in low-resource countries are hindered by a lack of rigorous data on risk groups and behaviours. Our findings help address this gap and have significance for governments, policy makers and health workers. They provide robust data on the distribution of diabetes and its behavioural self-management in LMICs that can assist in identifying priority groups for targeted education and behavioural support for diabetes control. While diabetes remains a disease disproportionately affecting the higher educated in least developed LMICs, this is not the case in countries undergoing rapid economic development, where no such associations were apparent. In the context of limited existing data, and considering typical trends by which increasing development shifts the socioeconomic gradients in diabetes risk towards the poor, our findings highlight the urgent need for enhanced awareness and improvement of disease protective factors, particularly amongst those with low education in these countries.

Availability of data and materials

Data for this study were from the World Health Organization Study on global AGEing and adult health (SAGE). The de-identified data and meta-data are freely available by request through the WHO SAGE website:



Low and middle income country


High income country


Gross domestic product


Non-communicable disease


World Health Organization Study on Global Ageing and Adult Health


United Nations Development Program


Human Development Index


Body Mass Index


Standard Deviation


Confidence Interval


Risk ratio


Odds ratio


Sustainable Development Goal


  1. World Health Organization. Global report on diabetes. Geneva: World Health Organization; 2016.

    Google Scholar 

  2. Guariguata L, Whiting DR, Hambleton I, Beagley J, Linnenkamp U, Shaw JE. Global estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes Res Clin Pract. 2014;103(2):137–49.

    CAS  Article  PubMed  Google Scholar 

  3. Shaw JE, Sicree RA, Zimmet PZ. Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Res Clin Pract. 2010;87(1):4–14.

    CAS  Article  PubMed  Google Scholar 

  4. Bommer C, Heesemann E, Sagalova V, Manne-Goehler J, Atun R, Barnighausen T, et al. The global economic burden of diabetes in adults aged 20-79 years: a cost-of-illness study. Lancet Diabetes Endocrinol. 2017;5(6):423–30.

    Article  PubMed  Google Scholar 

  5. Agardh E, Allebeck P, Hallqvist J, Moradi T, Sidorchuk A. Type 2 diabetes incidence and socio-economic position: a systematic review and meta-analysis. Int J Epidemiol. 2011;40(3):804–18.

    Article  PubMed  Google Scholar 

  6. Vellakkal S, Subramanian SV, Millett C, Basu S, Stuckler D, Ebrahim S. Socioeconomic inequalities in non-communicable diseases prevalence in India: disparities between self-reported diagnoses and standardized measures. PLoS One. 2013;8(7):e68219.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  7. Vellakkal S, Millett C, Basu S, Khan Z, Aitsi-Selmi A, Stuckler D, et al. Are estimates of socioeconomic inequalities in chronic disease artefactually narrowed by self-reported measures of prevalence in low-income and middle-income countries? Findings from the WHO-SAGE survey. J Epidemiol Community Health. 2015;69(3):218–25.

    Article  PubMed  Google Scholar 

  8. Fleischer NL, Diez Roux AV, Alazraqui M, Spinelli H, De Maio F. Socioeconomic gradients in chronic disease risk factors in middle-income countries: evidence of effect modification by urbanicity in Argentina. Am J Public Health. 2011;101(2):294–301.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Xu Z, Yu D, Yin X, Zheng F, Li H. Socioeconomic status is associated with global diabetes prevalence. Oncotarget. 2017;8(27):44434–9.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Ataguba JE, Akazili J, McIntyre D. Socioeconomic-related health inequality in South Africa: evidence from general household surveys. Int J Equity Health. 2011;10(1):48.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Tagoe HA. Household burden of chronic diseases in Ghana. Ghana Medical Journal. 2012;46(2 Suppl):54–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Zimmer Z, Kwong J. Socioeconomic status and health among older adults in rural and urban China. Journal of Aging and Health. 2004;16(1):44–70.

    Article  PubMed  Google Scholar 

  13. Tyrovolas S, Koyanagi A, Garin N, Olaya B, Ayuso-Mateos JL, Miret M, et al. Diabetes mellitus and its association with central obesity and disability among older adults: a global perspective. Exp Gerontol. 2015;64:70–7.

    CAS  Article  PubMed  Google Scholar 

  14. Danaei G, Finucane MM, Lu Y, Singh GM, Cowan MJ, Paciorek CJ, et al. National, regional, and global trends in fasting plasma glucose and diabetes prevalence since 1980: systematic analysis of health examination surveys and epidemiological studies with 370 country-years and 2.7 million participants. Lancet. 2011;378(9785):31–40.

    CAS  Article  Google Scholar 

  15. Nelson LA, Ackerman MT, Greevy RA Jr, Wallston KA, Mayberry LS. Beyond race disparities: accounting for socioeconomic status in diabetes self-care. Am J Prev Med. 2019;57(1):111–6.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Wilkinson A, Whitehead L, Ritchie L. Factors influencing the ability to self-manage diabetes for adults living with type 1 or 2 diabetes. Int J Nurs Stud. 2014;51(1):111–22.

    Article  PubMed  Google Scholar 

  17. Kowal P, Chatterji S, Naidoo N, Biritwum R, Fan W, Lopez Ridaura R, et al. Data resource profile: the World Health Organization study on global AGEing and adult health (SAGE). Int J Epidemiol. 2012;41(6):1639–49.

    Article  PubMed  PubMed Central  Google Scholar 

  18. United Nations Development Program. Human Development Reports, 2019.

  19. Howe LD, Galobardes B, Matijasevich A, Gordon D, Johnston D, Onwujekwe O, et al. Measuring socio-economic position for epidemiological studies in low- and middle-income countries: a methods of measurement in epidemiology paper. Int J Epidemiol. 2012;41(3):871–86.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Armstrong T, Bull F. Development of the World Health Organization global physical activity questionnaire (GPAQ). J Public Health. 2006;14(2):66–70.

    Article  Google Scholar 

  21. Zhao Y, Crimmins EM, Hu P, Shen Y, Smith JP, Strauss J, et al. Prevalence, diagnosis, and management of diabetes mellitus among older Chinese: results from the China health and retirement longitudinal study. International Journal of Public Health. 2016;61(3):347–56.

    Article  PubMed  Google Scholar 

  22. Arokiasamy P, Uttamacharya KJ. multi-morbidity, functional limitations, and self-rated health among older adults in India: cross-sectional analysis. SAGE Open. 2015;5(1):1–10.

    Article  Google Scholar 

  23. Bikbov MM, Fayzrakhmanov RR, Kazakbaeva GM, Zainullin RM, Arslangareeva II, Gilmanshin TR, et al. Prevalence, awareness and control of diabetes in Russia: the Ural eye and medical study on adults aged 40+ years. PLoS One. 2019;14(4):e0215636.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  24. Asamoah-Boaheng M, Sarfo-Kantanka O, Tuffour AB, Eghan B, Mbanya JC. Prevalence and risk factors for diabetes mellitus among adults in Ghana: a systematic review and meta-analysis. Int Health. 2018;11(2):83–92.

    Article  Google Scholar 

  25. Gonzalez-Gonzalez C, Tysinger B, Goldman DP, Wong R. Projecting diabetes prevalence among Mexicans aged 50 years and older: the future elderly model-Mexico (FEM-Mexico). BMJ Open. 2017;7(10):e017330.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Hosseinpoor AR, Bergen N, Kunst A, Harper S, Guthold R, Rekve D, et al. 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(1):912.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Blakely T, Hales S, Kieft C, Wilson N, Woodward A. The global distribution of risk factors by poverty level. Bull World Health Organ 2005;83(2):118–126, DOI: /S0042-96862005000200012.

  28. Ciapponi A. Systematic review of the link between tobacco and poverty. Geneva; 2011.

  29. Dinsa GD, Goryakin Y, Fumagalli E, Suhrcke M. Obesity and socioeconomic status in developing countries: a systematic review. Obes Rev. 2012;13(11):1067–79.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  30. Rogers EM. Diffusion of innovations. New York: Free Press of Glencoe; 1964.

    Google Scholar 

  31. Haider M, Kreps GL. Forty years of diffusion of innovations: utility and value in public health. J Health Commun. 2004;9(Suppl 1):3–11.

    Article  PubMed  Google Scholar 

  32. Mutyambizi C, Booysen F, Stokes A, Pavlova M, Groot W. Lifestyle and socio-economic inequalities in diabetes prevalence in South Africa: a decomposition analysis. PLoS One. 2019;14(1):e0211208.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  33. Zhou B, Lu Y, Hajifathalian K, Bentham J, Di Cesare M, Danaei G, et al. Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4.4 million participants. Lancet. 2016;387(10027):1513–30.

    Article  Google Scholar 

  34. 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):940–946, DOI: /S0042-96862004001200011.

  35. 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.

    Article  PubMed  Google Scholar 

  36. Heisler M, Smith DM, Hayward RA, Krein SL, Kerr EA. How well do patients' assessments of their diabetes self-management correlate with actual glycemic control and receipt of recommended diabetes services? Diabetes Care. 2003;26(3):738–43.

    Article  PubMed  Google Scholar 

  37. Nagpal J, Bhartia A. Quality of diabetes care in the middle- and high-income group populace: the Delhi diabetes community (DEDICOM) survey. Diabetes Care. 2006;29(11):2341–8.

    Article  PubMed  Google Scholar 

  38. Rafique G, Shaikh F. Identifying needs and barriers to diabetes education in patients with diabetes. Journal of Pakistan Medical Association. 2006;56(8):347–52.

    Google Scholar 

  39. Venkataraman K, Kannan AT, Mohan V. Challenges in diabetes management with particular reference to India. International Journal of Diabetes in Developing Countries. 2009;29(3):103–9.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Allen L, Williams J, Townsend N, Mikkelsen B, Roberts N, Foster C, et al. Socioeconomic status and non-communicable disease behavioural risk factors in low-income and lower-middle-income countries: a systematic review. Lancet Glob Health. 2017;5(3):e277–e89.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Chastin SFM, DeCraemer M, Oppert J-M, Cardon G. Dynamics of sedentary behaviours and systems-based approach: future challenges and opportunities in the life course epidemiology of sedentary behaviours. In: Leitzmann MF, Jochem C, Schmid D, editors. Sedentary behaviour epidemiology. Cham: Springer International Publishing; 2018. p. 595–616.

    Chapter  Google Scholar 

  42. Mendenhall E, Kohrt BA, Norris SA, Ndetei D, Prabhakaran D. Non-communicable disease syndemics: poverty, depression, and diabetes among low-income populations. Lancet. 2017;389(10072):951–63.

    Article  PubMed  PubMed Central  Google Scholar 

Download references


The authors are also grateful to Steve Bowe, Cadeyrn Gaskin and Liliana Orellana from the Biostatistics Unit at Deakin University who provided valuable discussions about variable derivations.


SAGE is supported by the Division of Behavioral and Social Research at the National Institute on Aging, US National Institutes of Health (NIA BSR), through Interagency Agreements (OGHA 04034785; YA1323–08-CN-0020; Y1-AG-1005 − 01) with WHO, and Research Project Grants R01AG034479 and R21AG034263. Data from WHO SAGE is freely available for researchers to apply to use. No direct funding was received to support the work undertaken in this research article. SI is supported by a senior research fellowship by the Institute for Physical Activity and Nutrition, Deakin University and a postdoctoral fellowship by the National Heart Foundation of Australia.

Author information

Authors and Affiliations



KB conceived the study undertaken in this paper and co-led the writing of the manuscript with KEL. KB and KEL designed the analysis with input from all other co-authors (DC, LT, SI, RM). KEL undertook the analysis. All authors (KEL, DC, LT, SI, RM, KB) contributed to the interpretation of the findings and drafting of the manuscript. All authors have read and approved the final manuscript.

Corresponding authors

Correspondence to Karen E. Lamb or Kylie Ball.

Ethics declarations

Ethics approval and consent to participate

This study uses secondary data from the WHO SAGE study which was approved by the WHO’s Ethical Review Board. Consent to use these data for this study was provided by the WHO Multi-Country Studies Data Archive.

Consent for publication

Not applicable.

Competing interests

Dr. Lamb, Professor Crawford, Associate Professor Thornton, Dr. Islam, Professor Ralph Maddison, and Professor Ball have no conflicts of interest to disclose.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Lamb, K.E., Crawford, D., Thornton, L.E. et al. Educational differences in diabetes and diabetes self-management behaviours in WHO SAGE countries. BMC Public Health 21, 2108 (2021).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI:


  • Diabetes mellitus
  • Global Health
  • Developing countries
  • Cohort
  • Adult