Table 2.

Association between socioeconomic determinants of health and cardiovascular disease, diabetes, and obesity.

Author (Year)	Socioeconomic Determinants of Health	Outcomes	Results	How Socioeconomic Determinants Were Handled	Strength of Association between SE Determinant and Outcome
Harding (2004) [25]	Socioeconomic position measured by multiple indices: access to cars, housing tenure, overcrowding, and occupational social class.	Mortality from cardiovascular diseases	After controlling for age and socioeconomic position, the hazard ratios (HR) were imprecise, and the only noteworthy findings were for the oldest age group. Age at migration and duration of residence were independently associated with more than 20% change in circulatory mortality among ages 45–54 years in 1971. A weak positive relationship was also seen for CHD mortality in the oldest age (45–54) cohort.	Adjusted	Unclear because SE determinant was adjusted concurrently with age
Mainous et al. (2006) [32]	Education: assessed as having reported an achieved qualification or not. Self-assessed spoken English language: measured as “very well”, “fairly well”, “slightly”, or “not at all.”	Undetected elevated blood pressure Previously diagnosed hypertension Previously diagnosed diabetes Undetected elevated blood glucose	Greater English language skills were significantly associated with lower prevalence of previously diagnosed hypertension among Indians, Pakistanis, and Bangladeshis. Greater English language skills were significantly associated with lower prevalence of previously diagnosed hypertension among only Indians and Pakistanis. Greater English language skills were significantly associated with lower prevalence of previously diagnosed diabetes among Indians only. It is only among the Bangladeshi ethnic group where a significant association was seen between greater language skills and lower prevalence of undetected elevated blood glucose.	Direct comparison	Significant association
Martinson, McLanahan and Brooks-Gunn (2012) [29]	Education: measured as high and low education. Income: measured as poor (family being in the bottom 30 percent of the income distribution). SES: family income and mother’s education.	Child overweight	Low socioeconomic status is associated with lower risk of child overweight among children of non-white native and foreign-born mothers. For children born to white immigrant mothers, low income and low education are associated with an increase in the risk of overweight.	Direct comparison	Significant association
Martinson, McLanahan and BrooksGunn (2015) [30]	Mother’s education: measured as ‘high’ (have completed A-levels or the vocational equivalent) and ‘low’ (completed O-levels or less) education.	Child BMI trajectory	Relative to White children aged 3 of native-born mothers, Asian children aged 3 of both native- and foreign-born mothers start out thinner but increase in weight at a faster rate that is statistically significant. Black children of native-born mothers have heavier weights at 3 years compared to black children of foreign-born mothers at age 3; however, children of foreign-born mothers increase in weight at a faster rate. These results did not significantly change after controlling for SES and other demographic variables simultaneously.	Adjusted	Unclear because SE determinant was adjusted concurrently with mother’s age at birth, parity and low birthweight status of child
Agyemang et al. (2016) [4]	Education: measured as none or elementary, lower vocational or lower secondary, intermediate vocational or intermediate/higher secondary, higher vocational or university.	Obesity (BMI ≥ 30 Kg/m2) Abdominal obesity Type 2 diabetes	The following results were adjusted for age and education simultaneously. The prevalence ratio (PR) of obesity among Ghanaian men in London was 15 times greater compared to that of Ghanaian men in rural Ghana, 15.04 (95% CI 5.98, 37.84). For women in London, the PR was 6.6 times greater, 6.63 (95% CI 5.04, 8.72). The prevalence ratio (PR) of abdominal obesity among Ghanaian men in London was 10 times greater compared to that of Ghanaian men in rural Ghana, 10.48 (95% CI 4.43, 24.77). For women in London, the PR was 2.6 times greater, 2.56 (95% CI 2.25, 2.91). The prevalence ratio (PR) of type 2 diabetes among Ghanaian men in London was 3 times greater compared to that of Ghanaian men in rural Ghana, 3.06 (95% CI 1.67, 5.6). For women in London, the PR was 1.7 times greater, 1.67 (95% CI 1.09, 2.58).	Adjusted	Unclear because results of crude associations not presented, and the SE determinant was adjusted concurrently with age
Boateng et al. (2017) [6]	Education, employment, source of income—no details on these variables provided.	10-Year CVD risk as estimated from the PCE equations for Black men and women.	An association of migration with CVD risk was observed for Ghanaian women living in London compared with those in rural Ghana (OR = 1.45; 95% CI 1.04–2.01). Adjustment for education, employment, and sources of income simultaneously did not significantly alter the risk estimate. A similar case was found for men.	Adjusted	No change in results
Agyemang et al. (2018) [27]	Education: measured as none or elementary, lower vocational or lower secondary, intermediate vocational or intermediate/higher secondary, higher vocational or university.	Prevalence of hypertension Hypertension awareness Control	The following results were adjusted for age, education, and BMI, simultaneously. Adjusted prevalence ratio of hypertension in London compared to rural Ghana was 1.97 (95% CI: 1.58–2.45) for males and 1.51 (95% CI: 1.28–1.78) for females. Age-standardized hypertension treatment ranged from 44% in London in men, and 56% in London in women. The adjusted odds ratio of Ghanaians living in London compared to Ghanaians in rural Ghana was 2.04 (95% CI: 1.28–3.25) for males and 1.51 (95% CI: 1.16–1.95) for females as The adjusted odds ratio of Ghanaians living in London compared to Ghanaians in rural Ghana was 0.86 (0.49–1.58) for males and 0.84 (0.60–1.17) for females.	Adjusted	Unclear because results of crude associations not presented, and SE determinants were adjusted concurrently with age and BMI
Bijlholt et al. (2018) [26]	Education: none or elementary, lower vocational or lower secondary, intermediate vocational or intermediate/higher secondary, higher vocational or university.	Awareness of Type 2 Diabetes Mellitus (T2DM) Treatment of T2DM Control of T2DM	T2DM awareness was 2.7 times higher among Ghanaian migrants living in London compared to rural Ghanaians, OR = 2.7 (95% CI: 1.3–5.6). Adjustment for age, sex and level of education concurrently did not have any effect on the odds ratio; OR = 2.7 (95% CI: 1.2–6.0). T2DM treatment was 4 times higher among Ghanaians in London compared to rural Ghanaians, OR = 4.0 (95% CI: 1.9–8.3). Adjustment for age, sex and level of education concurrently slightly reduced the odds of treatment between rural Ghanaians and Ghanaian migrants in London to 3.4 (95% CI: 1.5–7.5). Control of T2DM was comparable between rural Ghanaians and Ghanaian migrants in London, OR = 0.4 (95% CI: 0.2–0.9) and this association remained after adjusting for age, sex and level of education simultaneously, OR = 0.4 (95% CI: 0.2–0.9).	Adjusted	Unclear because SE determinant was adjusted concurrently with age and sex
van Nieuwenhuizen et al. (2018) [28]	Education: none or elementary, lower vocational or lower secondary, intermediate vocational or intermediate/higher secondary, higher vocational or university.	Cardiovascular Health	Relative to rural Ghanaians, Ghanaians in London had 95% lower odds of having 6 or more components of ideal cardiovascular health (Crude OR = 0.050 (0.026–0.095; p < 0.001). After adjustment for age, gender and education level simultaneously, the odds ratio only reduced slightly, OR = 0.043 (0.021–0.087); p < 0.001 with no change in the association.	Adjusted	Unclear because SE determinant was adjusted concurrently with age and sex
Higgins, Nazroo and Brown (2019) [31]	English language proficiency (reads or speaks English). Socio-economic characteristics measured using Registrar General Social Class based on self-reported occupation; highest educational qualification; equivalised household income quintiles; area level deprivation-measured using the Index of Multiple Deprivation 2004 variable.	Obesity (continuous waist circumference)	For women, the addition of socio-economic characteristics results in notable further reductions to the waist circumference of those ethnic groups with the lowest socio-economic status (the Pakistani and Bangladeshi groups, followed by the Black Caribbean and Black African groups), relative to White women. For example, the coefficient for Bangladeshi women reduces from 4.36 cm to 3.22 cm, relative to White women. Similarly for men, the addition of the socio-economic characteristics block of variables results in notable further reductions to the waist circumference of those ethnic groups with lower socio-economic position (Black Caribbean and Bangladeshi men), relative to White men, but also increases the coefficients of those with a higher socio-economic position (Indian, Chinese and Black African men). For Pakistani men (who have a low socio-economic position) the waist circumference coefficient increases, relative to White men, when socio-economic characteristics are added to the model. When area deprivation was included in the socio-economic status block, there was a strong association between area deprivation and waist circumference for both men and women—waist circumference increases as area deprivation increases. The association is particularly strong for Men—for example, men who live in the most derived areas have a waist circumference 0.90 cm greater than those who live in the least deprived areas.	Adjusted	Significant association

CVD—cardiovascular disease; CHD—coronary heart disease; BMI—body mass index; SES—socioeconomic status; OR—odds ratio; PCE—Pooled Cohort Equations.