Association between socio-economic status and non-communicable disease risk in young adults from Kenya, South Africa, and the United Kingdom

doi:10.21203/rs.3.rs-2113966/v1

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Association between socio-economic status and non-communicable disease risk in young adults from Kenya, South Africa, and the United Kingdom

https://doi.org/10.21203/rs.3.rs-2113966/v1

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published 13 Jan, 2023

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There is a pressing need for global health preventions to curb the escalating burden of non-communicable diseases (NCDs). Utilising multi-country study designs can improve our understanding of how socio-economic context shapes the aetiology of NCDs, and this has great potential to advance global health interventions. We examined the association between socio-economic status (SES) and NCD risk, and the potential moderating effects of smoking and alcohol intake in young adults (18-35-year-olds) from Kenya, South Africa (SA), and the United Kingdom (UK). Our study was a cross-sectional online survey that included 3000 respondents (n = 1000 per country, 50% women) conducted in April 2022. We utilised information on twelve NCDs to classify respondents as having “no condition”, “at least one condition”, and “multimorbidity” (having two or more conditions). A total household asset score was calculated and used as a proxy of SES, and subsequently categorised into quintiles (Q1-Q5; lowest-highest). Ordered logistic regression was used to test the associations between NCD risk and exposure variables. We found that a one-category increase in SES quintile was inversely associated with an increased NCD risk in the UK but was positively associated in SA and Kenya. In all countries, smoking and/or alcohol intake were positively associated with an increased NCD risk, and showed some moderating effects on the SES-NCD relationships. Specifically, in Kenya, the risk of developing NCD was more than two times higher in those in the middle (Q3) SES group (OR: 2.493; 95% CI: 1.519–4.091; p < 0.001) compared to their lowest (Q1) SES counterparts. After adjusting for smoking and alcohol, the ORs of middle (Q3) SES group changed from 2.493 to 2.241 (1.360–3.721; p = 0.002). Overall, we found that the strength and direction of SES-NCD associations differed within and between countries. This study highlights how different SES contexts shape the risk of NCDs among young adults residing in countries at different levels of economic development.

Non-communicable diseases (NCDs) such as type 2 diabetes, cardiovascular disease (CVD), and cancer are the leading cause of mortality, accounting for over 71.0% of all global deaths^1,2.

Regrettably, 77.0% of all NCD-related deaths occur in low- and middle-income countries (LMICs)². This is likely because LMICs are battling to provide effective preventative and treatment strategies to combat the rapid increase in NCDs and their prominent risk factors^3,4. Behavioural risk factors such as harmful use of alcohol and tobacco are one of the major drivers of NCDs^4–7. Given that these behavioural risk factors are often adopted earlier in life^4–7, understanding the aetiology of NCDs in young adults (18–35 years of age) provides a window period for behaviour modification for the prevention of NCDs and related deaths. This is paramount since an estimated 1.7 million deaths occur in people younger than 30 years of age^1,2.

Although the aetiology of NCDs is multi-factorial, there is growing interest in the association between socio-economic status (SES) and the risk of developing NCDs^4,8. SES includes several indicators such as affluency (i.e., owning assets such as a motor vehicle), education attained, and economy, which represent the SES of an individual or the socio-economic development of a country ^4,9,10. Individuals or communities with low SES are typically at greater risk of developing NCDs compared to their counterparts^4,9,10. However, large-scale studies often focus on the association between SES and NCD risk at a national level^4,9,10. This approach, however, fails to recognise that regardless of the country’s income status, there might be individual variations in the strength and direction of this association at different levels of SES.

It is well established that the epidemiology of NCDs differs between high-income countries (HICs) and LMICs^11–14. For instance, SES is inversely associated with NCDs such as obesity, type 2 diabetes, and hypertension in HICs ^10–12, however, this relationship tends to be positive in LMICs^13–15. Although the mechanisms underlying these discrepancies are not fully understood, several risk factors including population growth, ageing, and urbanisation and epidemiological transition have been implicated in the rising prevalence of NCDs, particularly in LMICs^3,16. Accordingly, urbanisation and the epidemiological transition are at different stages within the African region¹⁷, with South Africa (SA) being farthest along the transition¹⁷. This may partly explain why SA has reportedly higher NCD prevalence (e.g., obesity and hypertension) and associated risk factors than its counterparts^18–21. Notably, these studies were conducted in middle-age and older participants^18–21, yet a better understanding of the adverse socio-environmental factors on NCD epidemiology during early life is equally crucial for combating NCDs in the African region.

Given the global health concern around the increasing burden of NDCs and their detrimental effects on human development and public health systems, multi-country comparative analyses and collaborations are essential for the development of effective strategies to curb the rising prevalence of NCDs, especially if established earlier in life. Therefore, the study aims were to examine the association between SES and NCD incidence, and the potential moderating effects of smoking and alcohol intake in young adults (18-35-year-olds) from three countries: lower-middle-income country (Kenya); upper middle-income country (SA) and high-income country (United Kingdom (UK)).

Survey integrity and processes. This cross-sectional study was conducted in April 2022 using “i-Say panel” as outlined in Supplementary Figure S1. Briefly, prospective panellists or people interested in participating in survey were recruited via a multi-source recruitment process that meet the quality of Global Research Standards. Through a partnership with an American consumer credit reporting agency (TransUnion), digital fingerprinting technology known as TruValidate collected the respondents’ device information. Thereafter, a unique identifier was issued to the respondents’ device, based on its distinctive characteristics. Using various authentication tools (e.g., Multi-Factor authentication), the device underwent a fraud check and deduplication review to prevent the creation of multiple entries and panel accounts in the same survey. Additionally, a tool known as Dataguard is used, which is a cyber robot designed to detect open ends (e.g., copied and pasted text and exaggerated and/or unrealistic typing speed), data pattern recognition (algorithms to review new panellists and flag those that match the profile of known fraudsters), speeding (measures the pace of survey completion in answers per minute), and straight lining (identifies respondents who gave the same answer to all statements in a grid question). If the respondents are caught by Dataguard, they are automatically removed from the survey in real-time. After these screening stages, a total of 3000 respondents from three countries (UK, SA, and Kenya; 1000 respondents per country) were randomly recruited (Fig. 1). In each country, an equal ratio of males and females was sampled.

Survey data collected. An online survey questionnaire was administered through Ipsos, measuring socio-demographic characteristics (e.g., household assets), medical history (e.g., NCDs), and lifestyle behavioural risk factors (smoking and alcohol intake). A household asset score from a list of 22 assets was used as an indicator of affluence (furniture, mattress, bed, gas cooker, stove, refrigerator, air conditioner, washing machine, bicycle, motorbike, vehicle, generator, fan, microwave, television, computer or tablet, satellite dish, smartphone, mobile phone, flush toilet, tap water, and electricity). This score was based on standard measures used in the Demographic and Health Surveys household questionnaire (www.measuredhs.com). Data on twelve NCDs (heart attack, stroke, high cholesterol, diabetes, overweight or obesity, asthma or chronic obstructive pulmonary disease, sore joints or muscle problems, cancer, mental health condition (e.g., depression or anxiety), liver disease, chronic kidney disease, and hypertension) were collected. If respondents replied “no” to all twelve conditions, they were classified as having “No condition”. In contrast, respondents who answered “yes” to one or two or more conditions were classified as having “at least one condition” or “multimorbidity”, respectively. Current smoking status (yes or no) was measured, with smoking frequency (amount per day) recorded and categorised into four groups, based on scoring in the range 0–1; 1–5; 6–10; and 11 or more, respectively. Notably, smoking was not limited to cigarettes but also included chewing tobacco, pipe, cigars, vape, hubbly, and snuff or dagga. Furthermore, participants were asked if they consumed alcohol (yes or no), and those who replied “yes” were further asked to estimate their frequency which was also categorised into four groups, occasionally/not every week; once a week; 2–3 times a week; and every day. For this survey, only respondents between 18–35 years of age were included for participation. The study was approved by the Human Research Ethics Committee (Medical) of the University of the Witwatersrand (H21/06/36). Respondents provided informed consent before their inclusion in the study.

Statistical analysis

Where applicable, data were tested for normality using distribution plots or the Shapiro Wilk test. Data are presented as frequencies (%) or median (interquartile range). Differences between the three countries were compared using the Kruskal- Wallis test. The Chi-square test of independence was used for the categorical variables. For each country, the sum of 22 household items (a proxy of SES) was categorised into quintiles (quintile 1 (Q1) (lowest) to quintile 5 (Q5) (highest)) and used as an exposure variable in the subsequent analyses. Four ordered logistic regression analyses were performed to examine the effects of SES, smoking, and alcohol intake in driving NCDs (a 3-level categorical outcome variable, with “no condition” as the reference group). The first model tested the association between SES and NCD. The second model, which was divided into two parts, explored the effects of smoking (model 2.1) and alcohol intake (model 2.2) on NCD risk. Both smoking and alcohol intake were assessed as five-level moderator variables with non-smokers and non-drinkers as reference groups. Thereafter, the third model was performed to test the hypothesis that smoking (model 3.1), or alcohol intake (model 3.2) will reduce the odds ratios (OR) and/or significance (p value) of SES-NCD association. For these analyses, both the moderator (smoking or alcohol) and exposure (SES) variables were included in the same models. In the final model, the two moderators were included in the same model to assess the independent effect of SES on NCD outcome. Two sets of results were generated by stratifying the outputs according to country and combining the three counties into one sample (combined analysis). All models were controlled for the potential effect of age, gender, and country (in combined sample only). All data analyses were conducted using Stata® (Version 17.0, StataCorp, College Station, TX, USA). A two-tailed test was considered statistically significant if p < 0.050.

Socio-demographic, behavioural, and non-communicable disease characteristics in the total sample and various countries. There were significant differences in age group proportions between the three countries (p < 0.001) (Table 1). Notably, the UK had the highest percentage of young adults within the 30–35 years of age (47.7%). As expected, household SES (as a total score or country-quintile categorical variable) differed between the three countries. South Africa had the highest percentage of smokers (36.9%) and those who consume alcohol (71.5%). Six out of twelve NCDs, namely, heart attack, high cholesterol, overweight or obesity, chronic lung diseases, mental health conditions, and hypertension were significantly different between the countries (p < 0.05) (Table 1). For example, the UK had a higher mental health condition prevalence (37.7%) than both SA (26.3%) and Kenya (27.9%) (p < 0.001). In contrast, SA (15.2%) and Kenya (15.5%) had a higher hypertension incidence than the UK (9.9%) (p < 0.001). Notably, the proportion of participants that had “no condition”, “at least one condition”, or multimorbidity differed between the three countries.

Table 1

Socio-demographic, behavioural, and non-communicable disease characteristics of young adults from United Kingdom, South Africa, and Kenya
	Combined sample	United Kingdom	South Africa	Kenya	P value
Age groups (years) (n (%))
18–21	439 (14.6)	122 (12.2)	171 (17.1)	146 (14.6)	< 0.001
22–25	797 (26.6)	185 (18.5)	270 (27.0)	342 (34.2)
26–29	772 (25.7)	216 (21.6)	260 (26.0)	296 (29.6)
30–35	992 (33.1)	477 (47.7)	299 (29.9)	216 (21.6)
Household SES (sum of 22 items)	14 (11–16)	15 (13–17)	15 (13–17)	12 (9–15)	< 0.001
Household SES in quintiles (n (%))
Q1 (Lowest)	747 (24.9)	273 (27.3)	240 (24.0)	234 (23.4)	< 0.001
Q2 (Second lowest)	635 (21.2)	137 (13.7)	263 (26.3)	235 (23.5)
Q3 (Middle)	655 (21.8)	317 (31.7)	155 (15.5)	183 (18.3)
Q4 (Upper middle)	486 (16.2)	109 (10.9)	222 (22.2)	155 (15.5)
Q5 (Highest)	477 (15.9)	164 (16.4)	120 (12.0)	193 (19.3)
Behavioural factors (%)
Currently smoking (yes)	835 (27.8)	296 (29.6)	369 (36.9)	170 (17.0)	< 0.001
Smoking frequency amongst the 27.8% who reported smoking (n, %)
Less than 1 per day	166 (19.9)	52 (17.6)	57 (15.4)	57 (33.5)	< 0.001
1 to 5 per day	412 (49.3)	124 (41.9)	189 (51.2)	99 (58.2)
6 to 10 per day	162 (19.4)	64 (21.6)	90 (24.4)	8 (4.7)
11 or more per day	95 (11.4)	56 (18.9)	33 (8.9)	6 (3.5)
Data are presented as frequencies (%) and median (interquartile range).
(Continued from previous page)

	Combined sample	United Kingdom	South Africa	Kenya	P value
Alcohol intake (yes)	1851 (61.7)	627 (62.7)	715 (71.5)	509 (50.9)	< 0.001
Alcohol intake frequency among the 61.7% that reported alcohol consumption (n (%))
Occasionally/not every week	902 (48.7)	249 (39.7)	387 (54.1)	266 (52.3)	< 0.001
Once a week	477 (25.8)	198 (31.6)	149 (20.8)	130 (25.5)
2 to 3 times a week	438 (23.7)	162 (25.8)	172 (24.1)	104 (20.4)
Everyday	34 (1.8)	18 (2.9)	7 (1.0)	9 (1.8)
Non-communicable diseases (n (%))
Heart attack	133 (4.4)	15 (1.5)	58 (5.8)	60 (6.0)	< 0.001
Stroke	39 (1.3)	19 (1.9)	9 (0.9)	11 (1.1)	0.115
High blood cholesterol	275 (9.2)	71 (7.1)	112 (11.2)	92 (9.2)	0.006
Diabetes	220 (7.3)	72 (7.2)	72 (7.2)	76 (7.6)	0.927
Overweight or obesity	585 (19.5)	224 (22.4)	159 (16.0)	202 (20.2)	0.001
Asthma/Chronic lung disease	398 (13.3)	161 (16.1)	128 (12.8)	109 (10.9)	0.003
Sore joints or muscle problems	497 (16.6)	148 (14.8)	174 (17.4)	175 (17.5)	0.174
Cancer	36 (1.2)	14 (1.4)	11 (1.1)	11 (1.1)	0.781
Mental health conditions	918 (30.7)	377 (37.7)	262 (26.3)	279 (27.9)	< 0.001
Liver disease	36 (1.2)	11 (1.1)	9 (0.9)	16 (1.6)	0.337
Chronic kidney disease	36 (1.2)	12 (1.2)	12 (1.2)	12 (1.2)	1.000
Hypertension (High blood pressure)	405 (13.5)	99 (9.9)	151 (15.2)	155 (15.5)	< 0.001
Non-communicable disease category (n (%))
No condition	138 (4.6)	8 (0.8)	76 (7.6)	54 (5.4)	< 0.001
At least one condition	2404 (80.3)	843 (84.3)	777 (77.7)	784 (78.4)
Multimorbidity	450 (15.0)	149 (14.9)	147 (14.7)	162 (16.2)
Data are presented as frequencies (%) and median (interquartile range).

The effect of SES on non-communicable diseases risk, with smoking and alcohol intake as moderators in youth from United Kingdom, South Africa, and Kenya. Table 2 shows the series of ordered regression outputs, whereby SES and behavioural risk factors (smoking and alcohol intake) were regressed on NCD category. In the UK sample, those who were in the second lowest SES quintile (Q2) were 46.7% less likely to develop NCDs than their lowest (Q1) SES counterparts. However, there were no differences in NCD risk between Q1 and upper quintile groups (i.e., Q3, Q4, and Q5) (Model 1). Furthermore, respondents that reported a smoking frequency of 11 or more cigarettes per day had a 2-fold higher of developing NCDs than their counterparts (Model 2.1). In contrast, alcohol intake was not associated with the risk of developing an NCD in the UK group (Model 2.2.). Adjustments made for smoking and alcohol intake had only minor effects on the influence of SES on NCD risk (Table 2). For example, the risk of developing NCD in those in Q2 declined from model 1 (Odds ratios (ORs) per unit increase in SES quintile level:0.533; 95% confidence interval (CI): 0.288–0.985; p = 0.045) to model 4 (0.506; 0.272–0.943; p = 0.032) after controlling for the potential behavioural effects (Table 2).

Contrary to the above findings, South Africans with a household SES score in the middle and upper SES quintiles (Q3 to Q5) were at higher risk of developing an NCD or multimorbidity compared to their lowest (Q1) SES counterparts (Table 2). Also, the regression analyses revealed that those who reported a smoking frequency of less than one per day (1.851; 1.005–3.420; p = 0.048) and consumed alcohol once a week (2.111; 1.308–3.407, p = 0.002) were and everyday (6.112; 1.355–27.568; p = 0.019) more likely to be diagnosed with an NCD or multiple NCDs compared to their non-smoking or -drinking counterparts (models 2.1 and 2.2). Notably, the effects of smoking (Model 3.1) and alcohol intake (Model 3.2) had a small impact on the relationship between SES and NCD risk (Table 2). For example, the ORs of those with the highest (Q5) SES score declined from 2.658 (1.547–4.565; p < 0.001) in model 1 to 2.448 (1.423–4.345; p = 0.001) in model 4 (Table 2).

Like SA, a one-unit increase in SES quintile group was associated with a higher risk of developing NCD in the Kenyan youth (Table 2). Specifically, Kenyans with a SES score in Q3, Q4, and Q5 had a 2-fold greater risk of developing NCDs compared to their lowest (Q1) SES counterparts. Smoking was not significantly associated with the risk of developing NCD in Kenya (e.g., Smokers -Less than 1 per day vs. Non-smokers: 1.786; 0.964–3.309; p = 0.065). However, respondents that consumed alcohol, especially those who drank “2 to 3 times a week” and “everyday” (Table 2) were at greater risk of developing NCD. Adjusting for smoking and alcohol intake decreased the relative log odds of SES in increasing NCD risk. For instance, the risk of developing NCD in those in Q5 declined from 2.796 (1.719–4.549; p < 0.001)) (model 1) to 2.611 (1.590–4.289; p < 0.001) in model 3.1 (after adjusting for smoking), 2.402 (1.452–3.974; p = 0.001) in model 3.2 (after adjusting for alcohol intake), and finally to 2.395 (1.442–3.974; p = 0.001) in model 4 after adjusting for both moderators (Table 2).

In the combined sample, those in the middle and upper (Q3, Q4, and Q5) SES groups had higher risk of developing an NCD or multimorbidity compared to their poorest (Q1) counterparts (Model 1). Models 2.1 and 2.2 revealed that those who reported smoking frequency of less than one per day and those who consumed alcohol were more likely to be diagnosed with an NCD(s) (Table 2). Subsequently, after adjusting for smoking and alcohol intake, the ORs of SES on the likelihood of being diagnosed with an NCD or NCD multimorbidity decreased (Table 2). For instance, the ORs of the highest (Q5) SES group decreased from 1.937 (1.448–2.589; p < 0.001) in model 1 to 1.892 (1.410–2.538; p < 0.001) in model 3.1 (after adjusting for smoking) to 1.782 (1.324–2.399; p < 0.001) in model 3.2 (after adjusting for alcohol intake), and finally to 1.785 (1.324–2.407; p < 0.001) in model 4 after adjusting for both moderators (Table 2).

Table 2

Ordered logistic regression analyses for the effect of SES on non-communicable diseases risk, with smoking and alcohol intake as moderators in young adults from United Kingdom, South Africa, and Kenya
	United Kingdom		South Africa			Kenya		Combined sample
	OR (95% CI)	P value	OR (95% CI)	P value		OR (95% CI)	P value	OR (95% CI)	P value
Model 1 (Exposure variable only)
Household SES in quintiles
Q1 (Lowest)	Ref.	Ref.	Ref.		Ref.	Ref.	Ref.	Ref.	Ref.
Q2 (Second lowest)	0.533 (0.288–0.985)	0.045	1.499 (0.969–2.317)		0.069	1.483 (0.924–2.378)	0.102	1.118 (0.844–1.481)	0.435
Q3 (Middle)	0.819 (0.529–1.269)	0.372	2.063 (1.252-3.400)		0.005	2.493 (1.519–4.091)	< 0.001	1.609 (1.227–2.109)	0.001
Q4 (Upper middle)	0.574 (0.297–1.107)	0.098	2.233 (1.416–3.520)		0.001	2.192 (1.307–3.678)	0.003	1.606 (1.196–2.159)	0.002
Q5 (Highest)	0.899 (0.533–1.517)	0.691	2.658 (1.547–4.565)		< 0.001	2.796 (1.719–4.549)	< 0.001	1.937 (1.448–2.589)	< 0.001
Model 2.1 (Smoking as a moderator variable only)
Smoking status and frequency
Non-smokers	Ref.	Ref.	Ref.		Ref.	Ref.	Ref.	Ref.	Ref.
Smokers-Less than 1 per day	1.057 (0.476–2.345)	0.891	1.851 (1.005–3.410)		0.048	1.786 (0.964–3.309)	0.065	1.598 (1.098–2.325)	0.014
Smokers-1 to 5 per/day	0.815 (0.455–1.459)	0.491	1.061 (0.706–1.595)		0.776	1.630 (0.990–2.684)	0.055	1.142 (0.868–1.504)	0.342
Smokers-6 to 10 per day	1.395 (0.712–2.734)	0.332	1.225 (0.710–2.113)		0.465	1.350 (0.243–7.487)	0.731	1.280 (0.852–1.923)	0.234
Smokers-11 or more per day	2.123 (1.115–4.044)	0.022	0.898 (0.385–2.096)		0.803	2.549 (0.482–13.479)	0.271	1.524 (0.936–2.483)	0.090
Model 2.2 (Alcohol intake as a moderator variable only)
Alcohol intake status and frequency
Non-drinkers	Ref.	Ref.	Ref.		Ref.	Ref.	Ref.	Ref.	Ref.
Drinkers-Occasionally/not every week	1.335 (0.870–2.047)	0.186	1.349 (0.925–1.968)		0.119	1.419 (0.983–2.050)	0.062	1.355 (1.082–1.697)	0.008
Drinkers-once a week	0.868 (0.523–1.439)	0.583	2.111 (1.308–3.407)		0.002	1.559 (0.973–2.497)	0.065	1.451 (1.108–1.902)	0.007
Drinkers-2 to 3 times a week	1.022 (0.606–1.721)	0.936	1.518 (0.941–2.447)		0.087	1.933 (1.162–3.214)	0.011	1.448 (1.090–1.923)	0.011
Drinkers-everyday	0.945 (0.227–3.927)	0.938	6.112 (1.355–27.568)		0.019	6.833 (1.667–28.002)	0.008	2.793 (1.306–5.974)	0.008

Data are presented as odds ratio (OR) and 95% confidence interval (CI). All models were controlled for the potential effect of age, gender, and country (in the combined sample only).

(Continued from previous page)

	United Kingdom		South Africa		Kenya		Combined sample
	OR (95% CI)	P value	OR (95% CI)	P value	OR (95% CI)	P value	OR (95% CI)	P value
Model 3.1 (Both SES and smoking were included in the same model)
Household SES in quintiles
Q1 (Lowest)	Ref.	Ref.	Ref.	Ref.	Ref.	Ref.	Ref.	Ref.
Q2 (Second lowest)	0.522 (0.282–0.968)	0.039	1.484 (0.957–2.301)	0.078	1.431 (0.890–2.299)	0.139	1.105 (0.834–1.464)	0.486
Q3 (Middle)	0.810 (0.521–1.259)	0.349	2.013 (1.219–3.325)	0.006	2.376 (1.444–3.911)	0.001	1.596 (1.217–2.093)	0.001
Q4 (Upper middle)	0.579 (0.299–1.120)	0.104	2.241 (1.418–3.539)	0.001	2.055 (1.218–3.467)	0.007	1.587 (1.180–2.135)	0.002
Q5 (Highest)	0.890 (0.524–1.512)	0.668	2.630 (1.520–4.549)	0.001	2.611 (1.590–4.289)	< 0.001	1.892 (1.410–2.538)	< 0.001
Smoking status and frequency
Non-smokers	Ref.	Ref.	Ref.	Ref.	Ref.	Ref.	Ref.	Ref.
Smokers-Less than 1 per day	1.041 (0.467–2.321)	0.921	1.750 (0.948–3.231)	0.074	1.592 (0.852–2.972)	0.144	1.554 (1.067–2.263)	0.022
Smokers-1 to 5 per day	0.710 (0.442–1.446)	0.460	0.971 (0.644–1.463)	0.890	1.359 (0.818–2.258)	0.236	1.048 (0.793–1.384)	0.743
Smokers-6 to 10 per day	1.373 (0.696–2.709)	0.360	1.106 (0.634–1.928)	0.723	1.007 (0.176–5.771)	0.994	1.210 (0.802–1.825)	0.363
Smokers-11 or more per day	2.132 (1.115–4.077)	0.022	0.814 (0.347–1.912)	0.637	2.078 (0.390–11.060)	0.391	1.453 (0.890–2.371)	0.135
Model 3.2 (Both SES and alcohol intake were included in the same model)
Household SES in quintiles
Q1 (Lowest)	Ref.	Ref.	Ref.	Ref.	Ref.	Ref.	Ref.	Ref.
Q2 (Second lowest)	0.512 (0.276–0.950)	0.034	1.464 (0.946–2.267)	0.087	1.352 (0.837–2.182)	0.217	1.074 (0.810–1.425)	0.618
Q3 (Middle)	0.808 (0.520–1.256)	0.344	1.995 (1.203–3.311)	0.007	2.270 (1.368–3.765)	0.002	1.534 (1.167–2.016)	0.002
Q4 (Upper middle)	0.557 (0.287–1.079)	0.083	2.177 (1.375–3.447)	0.001	2.024 (1.196–3.423)	0.009	1.537 (1.141–2.070)	0.005
Q5 (Highest)	0.891 (0.521–1.523)	0.673	2.445 (1.405–4.257)	0.002	2.402 (1.452–3.974)	0.001	1.782 (1.324–2.399)	< 0.001
Alcohol intake status and frequency
Non-drinkers	Ref.	Ref.	Ref.	Ref.	Ref.	Ref.	Ref.	Ref.
Drinkers-Occasionally/not every week	1.412 (0.915–2.180)	0.119	1.257 (0.860–1.836)	0.237	1.241 (0.853–1.805)	0.259	1.278 (1.018–1.603)	0.034
Drinkers-once a week	0.892 (0.534–1.491)	0.664	1.985 (1.228–3.208)	0.005	1.320 (0.816–2.135)	0.258	1.343 (1.022–1.764)	0.034
Drinkers-2 to 3 times a week	1.032 (0.606–1.761)	0.906	1.263 (0.776–2.055)	0.347	1.589 (0.943–2.677)	0.082	1.277 (0.956–1.705)	0.098
Drinkers-everyday	1.001 (0.239–4.181)	0.999	4.574 (0.994–21.045)	0.051	5.509 (1.321–22.973)	0.019	2.491 (1.159–5.354)	0.019

Data are presented as odds ratio (OR) and 95% confidence interval (CI). All models were controlled for the potential effect of age, gender, and country (in combined sample only).

(Continued from previous page)

	United Kingdom		South Africa		Kenya		Combined sample
	OR (95% CI)	P value	OR (95% CI)	P value	OR (95% CI)	P value	OR (95% CI)	P value
Model 4 (SES with both moderators (smoking and alcohol intake) in the same model)
Household SES in quintiles
Q1 (Lowest)	Ref.	Ref.	Ref.	Ref.	Ref.	Ref.	Ref.	Ref.
Q2 (Second lowest)	0.506 (0.272–0.943)	0.032	1.468 (0.945–2.278)	0.087	1.337 (0.828–2.160)	0.235	1.071 (0.807–1.421)	0.633
Q3 (Middle)	0.804 (0.515–1.256)	0.338	1.964 (1.182–3.263)	0.009	2.241 (1.350–3.721)	0.002	1.535 (1.168–2.018)	0.002
Q4 (Upper middle)	0.564 (0.291–1.095)	0.091	2.213 (1.397–3.508)	0.001	1.983 (1.169–3.364)	0.011	1.539 (1.142–2.074)	0.005
Q5 (Highest)	0.884 (0.515–1.519)	0.657	2.488 (1.424–4.345)	0.001	2.395 (1.442–3.974)	0.001	1.785 (1.324–2.407)	< 0.001
Smoking status and frequency
Non-smokers	Ref.	Ref.	Ref.	Ref.	Ref.	Ref.	Ref.	Ref.
Smokers-Less than 1 per day	1.051 (0.466–2.371)	0.904	1.597 (0.854–2.987)	0.143	1.481 (0.781–2.809)	0.229	1.440 (0.982–2.109)	0.062
Smokers-1 to 5 per/day	0.833 (0.454–1.527)	0.554	0.856 (0.559–1.310)	0.474	1.062 (0.605–1.862)	0.835	0.962 (0.721–1.284)	0.794
Smokers-6 to 10 per day	1.440 (0.719–2.881)	0.303	0.984 (0.555–1.7444)	0.956	0.654 (0.111–3.841)	0.638	1.117 (0.735–1.697)	0.603
Smokers-11 or more per day	2.196 (1.136–4.246)	0.019	0.703 (0.298–1.655)	0.420	1.430 (0.250–8.168)	0.688	1.323 (0.804–2.175)	0.270
Alcohol intake status and frequency
Non-drinkers	Ref.	Ref.	Ref.	Ref.	Ref.	Ref.	Ref.	Ref.
Drinkers-Occasionally/not every week	1.393 (0.898–2.159)	0.139	1.245 (0.845–1.835)	0.267	1.185 (0.807–1.741)	0.386	1.242 (0.986–1.565)	0.065
Drinkers-once a week	0.865 (0.509–1.469)	0.592	2.029 (1.237–3.327)	0.005	1.273 (0.781–2.0273)	0.333	1.311 (0.991–1.734)	0.058
Drinkers-2 to 3 times a week	1.005 (0.581–1.741)	0.984	1.287 (0.774–2.141)	0.330	1.551 (0.877–2.744)	0.132	1.241 (0.918–1.677)	0.161
Drinkers-everyday	0.810 (0.189–3.466)	0.776	4.759 (1.003–22.542)	0.050	5.413 (1.205–24.305)	0.028	2.322 (1.064–5.066)	0.034

Data are presented as odds ratio (OR) and 95% confidence interval (CI). All models were controlled for the potential effect of age, gender, and country (in the combined sample only).

Our main findings showed that the strength and direction of the SES-NCD association at different levels of SES varies within and between countries. We showed that a high SES was associated with a reduced risk of developing NCD or NCD multimorbidity in UK youth. In contrast, SES was associated with an increased NCD risk in SA and Kenya. Furthermore, we reported a high incidence of smoking and alcohol use in all countries, with SA having the highest levels. Additionally, we showed that smoking and/or alcohol intake was positively associated with increased NCD risk in all countries. Subsequently, smoking and/or alcohol consumption had moderating effects on the SES-NCD relationships as evidenced by the decrease in the ORs of SES, especially in young adults from SA and Kenya. However, the SES-NCD associations remained strong and significant even after adjusting for smoking and/or alcohol intake. This implies that SES is a more prominent risk factor for developing NCDs when compared to smoking and/or alcohol intake, particularly in African countries such as SA and Kenya.

The findings that high SES was positively associated with NCD risk in SA and Kenya is in accordance with several reports from these two countries^13,14,22, as well as, from other LMICs such as India and Indonesia^23,24. However, they contradict those reported from Iran, in which a low SES was associated with NCD multimorbidity²⁵. The inconsistent findings might be explained by differences in the number; type of NCDs; and methods used (i.e., self-reports, measured, or both) in the calculation of “NCD score” or NCD multimorbidity. Other confounding factors could include differences in age groups of respondents tested between the studies, 18-35-year-olds (in our study) vs 20-70-year-olds participants as in the Iranian study²⁵. Nonetheless, the positive association between SES-NCD risk in LMICs seems to be common and has been attributed to the rapid urbanisation and epidemiological transition, and their effects on lifestyle factors^13,14,22. This notion is also supported by the findings that NCDs such as obesity, diabetes, and hypertension are prevalent in affluent groups, especially in countries undergoing urbanisation and epidemiological transition including SA and Kenya^{13–15, 22}. Accordingly, we noted that the SES-NCD relationship was stronger in Kenya (a lower middle-income country in the early stages of transition) than in SA (an upper middle-income country that is farthest along the transition). This corroborates the findings that rapid urbanisation and epidemiological transition are accompanied by changes in SES and lifestyle factors, including diet, smoking, and alcohol intake^13,14,22. For instance, high consumption of unhealthy diets has been reported in high SES individuals compared to their low SES counterparts^13,22.

It has also been proposed that the positive association between SES and NCD risk in LMICs that we reported might be explained by the fact that those with high SES typically have higher education levels, thus have better health literacy and presumably more access to healthcare services²⁴. Therefore, they are more likely to be diagnosed with NCDs earlier in life than their low SES counterparts. Indeed, studies conducted in SA and Kenya have shown that the distribution and the use of healthcare facilities, which include the majority of public and all private health sectors favour those with high SES ^26,27.

The inverse association between SES and NCD risk that we reported in the UK sample corroborates other findings reported in the UK and other HICs^10,12,28,29, in which lower SES groups have unfavourable health outcomes including a greater risk of developing NCDs (i.e., cancer and CVD) and dying prematurely compared to their high SES counterparts. The exact mechanisms underlying the health inequalities in the UK are not fully understood but are believed to be driven by inequalities in universal health coverage, education, and employment opportunities^12,28.

The findings that smoking and/or alcohol intake are associated with an increased NCD risk are in accordance with previous literature^6,7,18,30. However, smoking and/or alcohol intake are declining in HICs yet are rapidly increasing in LMICs like SA and Kenya^6,7,18,30. This may explain the alarming higher levels of smoking and alcohol use in SA and Kenya, when compared to the UK, more especially in SA. In SA and other African countries there are lower and weaker tobacco and alcohol polices in comparison to HICs^31,32. Without aggressive policies and interventions targeting NCD risk factors, we foresee that populations in SA and Kenya will continue to suffer from the burden of NCDs.

Living with an NCD, especially with multiple NCDs has negative implications on the individual, household, and economy^23,24,33. These include catastrophic health expenditure or out-of-pocket expenditure for the patient and their household due to long-term health care^23,24,33. This may create a vicious cycle of poverty, especially if the patients and their household are already in poverty. NCDs are also associated with increased use of healthcare services as evidenced by high number of outpatient and inpatient visits in people with NCDs^24,26. This poses a major threat to the healthcare systems, particularly in SA and Kenya, where there is still a high demand for healthcare services due to the existing burden of infectious diseases such as tuberculosis (TB), human immunodeficiency virus infection and acquired immunodeficiency syndrome (HIV/AIDS), and malaria^34,35.

There is a need for a robust global multi-approach to reduce modifiable risk factors and subsequently lower the risk of NCD prevalence, particularly in LMICs. This can be achieved by implementing early preventative strategies to promote healthy lifestyle behaviours, increase knowledge and awareness about NCDs, and their associated risk factors. African countries, especially SA needs stricter and more innovative NCD risk factor policies than the current ones³², which will target smoking and alcohol intake behaviours in young adults. Such policies need to be multilevel and should consider broader societal issues such as lack of employment and recreational facilities that drive youth to engage in these unhealthy behaviours. With the increasing prevalence of NCDs and related deaths continuously overtaking infectious diseases^1–4, local and international funding from the government and related organisations need to start prioritising NCDs over infectious diseases so that more resources are allocated in managing NCDs^36,37. Additionally, primary healthcare and management clinical guidelines in LMICs need to reflect the trends in global health, thus need to shift from single disease (infectious disease) models and focus on multiple diseases or comorbidities to tackle the burden of NCDs^38–40. Furthermore, the UK government needs effective universal healthcare coverage to reduce health inequalities.

This study was based on cross-sectional data which cannot show a causal relation between SES and NCD risk. Furthermore, NCD status or NCD multimorbidity was based on self-report, which might have resulted in recall bias. However, this is the only feasible method/approach for online surveys. Our study was limited to young adults, aged 18 to 35 years, which is not representative of the general population. Nonetheless, understanding the epidemiology of NCDs in young adults has the potential to decrease the risk of developing NCDs and related deaths via targeted interventions of NCD risk factors. Despite these limitations, our study used a large sample of participants from three countries with different levels of economic development, which enabled us to provide a detailed analysis of the impact of SES (in quintiles) and its association with NCD risk.

In conclusion, we showed that the strength and direction of SES-NCD associations differed within and between countries. Our findings highlight the urgent need for preparing healthcare systems and implementing tailored interventions and policies for addressing the burden of NCDs and associated risk factors, especially in LMICs. Furthermore, it is recommended that the UK government adopt an effective prevention strategy such as a tailored NCD healthcare insurance package to protect individuals with low SES from the detrimental effects of NCDs.

Acknowledgements

SAN and this research are supported by the DSI-NRF Centre of Excellence (CoE) in Human Development at the University of the Witwatersrand, Johannesburg, South Africa. The content is solely the responsibility of the authors and does not reflect the views of the DSI-NRF CoE in Human Development.

Author contributions

Conception and design of research: A.M., and S.A.N. Performed statistical analyses and wrote the first draft of manuscript: A.M. Supervised the research and the writing of the manuscript: S.A.N. Critically reviewed and commented on the manuscript: A.C., W.P., S.N.D., and S.A.N. Approved the final version of the manuscript: A.M., A.C., W.P., S.N.D., and S.A.N. A.M. is the guarantor of this work and, as such, has full access to all the data generated in the framework of the study and takes responsibility for their integrity and the accuracy of their analysis.

Conflict of Interest

The authors declare that there are no conflicts of interest.

Ethics declaration

The study was in accordance with the relevant institutional guidelines. Ethical approval was obtained from the University of the Witwatersrand’s Human Research Ethics Committee (Medical) with the following ethical clearance number: H21/06/36. Participants provided informed consent before their inclusion in the study.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request and will soon be available on the website (https://www.wits.ac.za/coe-human/research/).

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No competing interests reported.

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Additional file 1: The international market research and public opinion polling company (IPSOS) “i-Say panel” used for the recruitment of study respondents.

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Association between socio-economic status and non-communicable disease risk in young adults from Kenya, South Africa, and the United Kingdom

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