Prevalence and factors associated with Sugar-Sweetened Beverage Consumption among adolescents and women aged 10-49 years in Kenya: a cross-sectional study

doi:10.21203/rs.3.rs-5004584/v1

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Prevalence and factors associated with Sugar-Sweetened Beverage Consumption among adolescents and women aged 10-49 years in Kenya: a cross-sectional study

https://doi.org/10.21203/rs.3.rs-5004584/v1

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Sugar sweetened beverages (SSBs) are major sources of free sugars in diet. Their intake is associated with obesity, chronic diseases, and increased risk of premature deaths. Studies indicate SSB consumption impacts females more than males but limited evidence exists on SSB intake among women especially in low- and middle-income countries. We aimed to determine the prevalence and determinants of SSB consumption among women aged 10–49 years in Kenya using secondary data from the first and second rounds of Kenya's Performance Monitoring for Accountability (PMA) nutrition surveys. Analysis was stratified by residence (rural vs. urban) and univariable and multivariable logistic regression was used to assess the determinants of SSB consumption. The prevalence was 52.4% (95% CI: 47.9, 56.8) increasing from 48.3% in 2018 to 63.6% in 2019. Prevalence was slightly higher in urban than rural areas (53.2% vs. 51.7%), those aged 20–29 years (54.1%), in union (52.9%), tertiary level of education (59.1%), richest (52.4%), and the employed (57.0%). Urban women had 1.29-fold higher odds compared to rural women. Education level was significantly associated with SSB consumption, with higher education linked to increased odds of consumption. The findings of this study highlight the need for development of policies that seek to reduce SSB consumption with the adoption of interventions that target urban residents, the educated, the rich, and overweight.

Epidemiology

Sugar sweetened beverages

women

adolescents

Sugar sweetened beverages (SSBs) are the main contributors of free sugars in diets with very low nutritional benefit¹. They include non-carbonated and carbonated soft drinks, flavored water, energy drinks, flavored milk products, ready-to-drink tea, and coffee². The health effect of SSBs consumption is a significant public health issue, especially in low and middle-income countries (LMICs)³. This is mainly because of their popularity, rapid urbanization, economic growth, and nutritional transition in LMICs⁴.

Globally, the burden of SSB consumption by 2010 contributed to 8.5 million disability-adjusted life years and 184,000 deaths, with the most significant burden being the LMICs³. SSBs consumption has been independently associated with non-communicable diseases (NCDs) such as obesity⁵, type-2 diabetes⁶, cardiovascular disease⁷, dental caries and erosion⁸, and an increased risk of premature deaths⁹. Previous studies have associated high consumption of SSBs with an unhealthy lifestyle, such as a poor-quality diet, lower intake of fruits and vegetables, and inadequate household food supply^10–13. In addition, gender, age, socioeconomic status, marital status, and educational level have been positively associated with SSB consumption^14–16.

The worldwide intake of SSBs increased by 16% from 2015 to 2018, with the largest increase in sub-Saharan Africa (SSA) from 1.93–41.4%¹⁷. In SSA, the rates of SSBs consumption differ by country and sociodemographic characteristic, with consumption ranging from 30% among Ghanaian adolescents¹⁸, 87% among Nigerian Adolescents¹⁹ and 16% among adults in South Africa²⁰. The national sugar consumption in Kenya has gradually increased between 1969 and 2009 from 35.5 g per person per day to 60.8 g²¹. This is more than twice the WHO-recommended intake of 25 g per day [22]. Data collected by PMA on the intake of SSBs, showed the proportion of adolescents and women (10–49 years) consuming SSBs increased from 47.7% in 2017 to 63.1% in 2018²². The significant increase in sugar consumption could be attributed to growth in retail, industrial, and food services²³, and increased consumption of tea and coffee²⁴.

The burden of SSB consumption contributes significantly to the development of obesity and nutrition-related NCDs, impacting national and household income²⁵. However, there is a scarcity of information regarding the consumption of SSBs in Kenya with the existing study focusing on a pooled analysis of Kenya and Burkina Faso with no specific prevalence rates and insights tailored to the Kenyan context²⁶. Furthermore, research has shown women who consume SSBs have a higher risk of cardiovascular disease and metabolic risks ²⁷. This Study aimed to determine the prevalence and factors associated with SSB consumption among adolescents and women aged 10–49 years in Kenya. The findings of these studies are critical for the development of measures tailored to prevent nutrition-related diseases in Kenya.

Data source, Study design, population, and Setting

This cross-sectional study was based in Kenya, a lower-middle income country (LMIC) with a population of over 54 million and over 12.1 million women aged 10–49 years living across 47 counties²⁸. 69% of Kenyans live in rural areas with an overall national poverty headcount rate of 38.6%²⁹.

We used secondary data from the first²² and second³⁰ rounds of Kenya's performance monitoring for accountability (PMA) nutrition surveys. The surveys used a multi-stage stratified cluster design, with stratification based on administrative regions (11 counties) and residence (rural-urban). The enumeration areas were then independently selected within each stratum, resulting in 151 enumeration areas, from which female household members 15–49 years were randomly selected for an interview. Round one included 7,267 females (99.3% response rate) while round two had 9,411 females. Data were collected by trained interviewers using standardized questionnaires in May to August 2017 for Round I and May to July 2018 for round II. Our final sample size was 9,468 (7,084 from the first round and 2384 from the second round) after dropping participants with missing observations and those below 10 years and above 49 years from round II.

Study Variables

Dependent variable

The main outcome variable, SSB consumption, was measured by asking the participant, “Ate/drank yesterday: Any sugar-sweetened beverages like sweet fruit drinks, fizzy drinks, sweet tea”, with an optional response of Yes, No, or No response. We created a binary outcome variable based on the questions and equated it to 1 if the respondents consumed any SSB and 0 otherwise [20, 26].

Independent variables

We used a conceptual framework (Supplementary Fig. 1) that was adopted and modified from the WHO social determinants of health framework to identify the individual factors associated with SSBs consumption among Kenyan women. We operationalized our explanatory variables based on previous studies on SSB consumption (Table 1).

Table 1

Operational Definitions
Variables	Operational Definition	ref
County of residence	Nairobi, Bungoma, Kericho, Kiambu, Kitui, Nandi, Nyamira, Siaya, Kakamega, West-Pokot	^30,47
Residence	Rural and Urban	^10,30,47
Age (years)	Age was recoded into four categories; 10—19, 20—29, 30—39, 40—49	^10,30,47
Marital Status	Marital status was recoded into two groups in-union (married, living with a man) and not in-union (divorced, widow, never married)	^48,49
Education level	Education level was categorized into no formal education, primary, post-primary & secondary, tertiary level.	^50,51
Employment Status	Employment status was categorized into two groups; employed and unemployed	⁵²
Wealth/income status	Five wealth quintiles (poorest, poorer, middle, richer, richest) were computed based on a wealth index generated using principal component analysis of the household assets.	^3,51,53
Pregnancy status	Pregnancy status was recorded to if respondents were pregnant or not	^14,49,54
Food Insecurity	Food insecurity was assessed using an 8-item food insecurity experience scale (FIES). Raw scores were calculated by summing item scores and categorized to mild food insecure (scores < 4), moderate food insecure (scores 4, 5 & 6), and severe food insecure (scores 7 & 8).	^55–58
Body Mass Index (BMI)	BMI was assessed using mid-upper arm circumference (MUAC) and categorized as underweight (MUAC < 25.5), normal weight (MUAC 25.4—29.9), overweight (MUAC 29.9—31.1), and obese (MUAC > 31.1).	^45,59,60
Minimum Dietary Diversity (MDD)	MDD was assessed using a dichotomous indicator of whether a woman has consumed at least 5 out of 10 defined food groups. We aggregated the 10 food groups and summed them into scores from 0 to 10. A binary outcome was generated and equated to 1 if a respondent consumed at least five of ten food groups and 0 otherwise.	⁶¹
Sugar and Savory Snack Consumption	A binary outcome (snack consumption) was generated and assigned 1 if a woman consumed either any savory and fried snack or sugary foods or jiggery and 0 otherwise.	^62,63

Statistical Analysis

The analysis was stratified by residence (rural or urban) and adjusted for cluster sampling using survey weights available in the dataset. We described the participants’ characteristics and the prevalence of SSB using frequencies and weighted percentages. The chi-square test of independence was used to assess the association between SSB consumption and participants’ characteristics, while univariable and multivariable logistic regression were used to assess the determinants of SSB consumption. Explanatory variables that were clinically significant and those with p < 0.25 in the univariable analysis were retained for inclusion in the multivariable logistic regression model. All analyses were performed using STATA software version 15 ³¹.

Participants' characteristics

Our final sample was composed of 9,468 adolescent girls and women aged 10–49 years of whom about two thirds were rural dwellers. The majority of the respondents were from Kilifi County (13.3%), had a minimum dietary diversity of less than five (69.3%), and did not consume snacks (56.9%). More than three-quarters of the respondents were unemployed and surveyed in the first round (74.8%) (Table 2).

Table 2

Participants' characteristics
Characteristics	Residence (n (%))		Total
	Urban n (%)	Rural n (%)	n (%)
Residence	2992(31.6)	6476(68.4)	9468
Rural
Urban
Counties of residence
Nairobi	717(24.0)	0	717(7.6)
Bungoma	250(8.4)	683(10.6)	933(9.9)
Kericho	332(11.1)	483(7.5)	815(8.6)
Kiambu	588(19.6)	271(4.2)	859(9.1)
Kilifi	310(10.4)	945(14.6)	1255(13.3)
Kitui	176(5.9)	673(10.4)	849(9.0)
Nandi	191(6.4)	598(9.2)	789(8.3)
Nyamira	138(4.6)	502(7.8)	640(6.8)
Siaya	97(3.2)	595(9.2)	692(7.3)
Kakamega	102(3,4)	752(11.6)	854(9.0)
West Pokot	91(3.0)	974(15.0)	1065(11.3)
Age (years)
10–19	705(23.6)	2302(35.6)	3007(31.8)
20–29	1407(47.0)	2264(35.0)	3671(38.8)
30–39	664(22.2)	1341(20.7)	2005(21.2)
40–49	216(7.2)	569(8.8)	785(8.3)
Marital status
In Union	1759(58.8)	3525(54.4)	5284(55.8)
Not-in-union	1233(41.2)	2951(45.6)	4184(44.2)
Level of education
No formal education	40(1.3)	732(11.3)	772(8.2)
Primary level	1239(41.4)	4026(62.2)	5265(55.6)
Post-Primary & Secondary level	1088(36.4)	1374(21.2)	2462(26.0)
Tertiary level	625(20.9)	344(5.3)	969(10.2)
Employment status
Employed	967(32.3)	981(15.2)	1948(20.6)
Unemployed	2025(67.7)	5495(84.9)	7520(79.4)
Wealth status
1. Poorest	331(11.1)	1971(30.4)	2302(24.3)
2. Poorer	385(12.9)	1934(29.9)	2319(24.5)
3. Middle	615(20.6)	1152(17.8)	1767(18.7)
4. Richer	768(25.7)	927(14.3)	1695(17.9)
5. Richest	893(29.9)	492(7.6)	1385(14.6)
Pregnancy status
Pregnant	74(2.5)	212(3.3)	286(3.0)
Not pregnant	2918(97.5)	6264(96.7)	9182(97.0)
^aMDD score
Less than 5 groups	1740(58.2)	4825(74,5)	6565(69.3)
More than 5 groups	1252(41.8)	1651(25.5)	2903(30.7)
Household Food security
Mild food insecure	1510(50.5)	1697(26.2)	3207(33.9)
Moderate food insecure	692(23.1)	1445(22.3)	2137(22.6)
Severe food insecure	790(26.4)	3334(51.5)	4124(43.6)
Snack Consumption
Non-consumer	1337(44.7)	4048(62.5)	5385(56.9)
Consumer	1655(55.3)	2428(37.5)	4083(43.1)
^bBMI
Normal	1031(34.5)	2135(22.0)	3166(33.4)
Underweight	875(29.2)	3038(46.9)	3913(41.3)
Overweight/obese	1086(36.3)	1303(20.1)	2389(25.2)
Survey year
2018			7084(74.8)
2019			2384(25.2)
^aMDD- Minimum dietary diversity \|^bBMI- Body mass index

Prevalence of SSB consumption

The prevalence of SSB consumption was 52.4 (95% CI: 47.9, 56.8) with a slightly higher prevalence in urban areas than in rural areas (53.2% vs. 51.7%). The prevalence was highest in Kericho county (65.3%), among those aged 20–29 years (54.1%), in union (52.9%), with tertiary level of education (59.1%), the richest (52.4%), and employed (57.0%). Notable variations were observed in the rural areas with the highest consumption among those aged 40–49 (54.4%), not in union (52.5), richer (55.7%), pregnant (52.0%), and with normal BMI (54.7), while it was highest in urban West Pokot (87.0%). The prevalence of SSB increased from 48.3% in 2018 to 63.6% in 2019 (Table 3).

Table 3

Prevalence of SSB consumption
Characteristics	Residence (n (%))		Total
	Urban % (95% C.I)	Rural % (95% C.I)	% (95% C.I)
Residence
Overall			52.4 (47.9, 56.8)
Urban			53.2 (46.3, 60.0)
Rural			51.7 (45.9, 57.6)
Counties of residence
Nairobi	57.9 (47.9, 67.3)		57.9 (48.0, 67.2)
Bungoma	38.2 (23.0, 56.1)	33.1 (20.2, 49.2)	33.9 (22.4, 47.6)
Kericho	59.4 (43.5, 73.6)	68.0 (39.7, 87.3)	65.3 (45.8, 80.8)
Kiambu	44.6 (28.0, 62.4)	45.6 (21.7, 73.3)	45.1 (30.9, 60.1)
Kilifi	45.9 (24.2, 69.3)	38.4 (20.1, 60.7)	40.2 (24.6, 57.9)
Kitui	68.4 (46.4, 84.4)	58.1 (41.8, 72.8)	59.8 (45.7, 72.5)
Nandi	49.4 (15.4, 84.0)	61.5 (46.6, 74.5)	59.2 (44.8, 72.3)
Nyamira	42.2 (15.9, 73.8)	62.7 (49.5, 74.3)	60.6 (48.0, 71.8)
Siaya	62.3 (19.5, 91.8)	53.8 (41.1, 66.0)	55.0 (42.5, 66.9)
Kakamega	28.9 (13.7, 51.1)	56.3 (39.5, 71.8)	52.8 (38.7, 66.5)
West Pokot	87.0 (86.2, 87.7)	45.6 (27.6, 64.8)	51.0 (34.5, 67.4)
Age (years)
10–19	50.3 (41.7, 58.9)	50.2 (44.1, 56.2)	50.2 (45.2, 55.2)
20–29	56.0 (47.0, 64.6)	52.1 (45.6, 58.5)	54.1 (48.5, 59.5)
30–39	51.0 (43.3, 58.6)	52.7 (45.9, 59.3)	51.9 (46.9, 56.9)
40–49	47.6 (37.0, 58.5)	54.4 (47.1, 61.5)	52.5 (46.4, 58.4)
Marital status
In Union	55.2 (47.3, 62.9)	51.0 (44.8, 57.2)	52.9 (47.9, 57.8)
Not-in-union	50.0 (42.3, 57.8)	52.5 (46.5, 58.5)	51.6 (46.9, 56.3)
Level of education
No formal education	20.8 (13.3, 30.9)	33.4 (23.8, 44.7) *	31.3 (23.1, 41.0) *
Primary level	52.3 (44.0, 60.4)	50.8 (44.7, 56.8) *	51.2 (46.3, 56.1) *
Post-Primary & Secondary level	52.4 (43.8, 60.9)	55.7 (48.7, 62.4) *	53.9 (48.3, 59.1) *
Tertiary level	58.5 (48.7, 67.7)	60.3 (48.0, 71.4) *	59.1 (51.3, 66.3) *
Employment status
Employed	55.7 (48.1, 63.1)	58.7 (51.2, 65.8) *	57.0 (51.7, 62.2) *
Unemployed	52.1 (44.3, 59.8)	50.3 (44.4, 56.3) *	51.0 (46.3, 55.7) *
Wealth status
1. Poorest	47.3 (35.4, 59.5)	50.6 (43.4, 57.8)	50.2 (43.8, 56.7) *
2. Poorer	46.3 (34.8, 58.2)	48.9 (41.8, 55.9)	48.3 (42.3, 54.4) *
3. Middle	51.5 (41.2, 61.8)	50.3 (42.8, 57.8)	50.8 (44.7, 57.0) *
4. Richer	49.9 (42.3, 57.5)	55.7 (47.5, 63.5)	52.6 (47.0, 58.1) *
5. Richest	59.3 (50.8, 67.4)	50.1 (44.9, 71.9)	52.4 (47.9, 56.8) *
Pregnancy status
Pregnant	42.8 (25.9, 61.6)	52.0 (42.0, 61.8)	48.7 (39.5, 58.0)
Not pregnant	53.5 (46.5, 60.3)	51.7 (45.8, 57.6)	52.5 (48.0, 56.9)
^aMDD score
Less than 5 groups	53.5 (45.9, 61.0)	50.4 (44.0, 56.8)	51.5 (46.6, 56.4)
More than 5 groups	52.8 (44.5, 60.9)	55.4 (48.0, 62.5)	54.1 (48.6, 59.4)
Household Food security
Mild food insecure	55.7 (47.4, 63.6)	55.6 (46.2, 64.7) *	55.6 (49.5, 61.6) *
Moderate food insecure	52.1 (43.1, 61.0)	57.3 (49.2, 65.0) *	55.3 (49.2, 61.2) *
Severe food insecure	48.9 (39.4, 58.6)	46.2 (39.6, 53.0) *	47.0 (41.5, 52.5) *
Snack Consumption
Non-consumer	51.1 (42.7, 59.5)	45.4 (39.4, 51.5) *	47.4 (42.4, 52.4) *
Consumer	54.8 (47.5, 61.9)	60.0 (53.2, 66.5) *	57.5 (52.6, 62.3) *
^bBMI
Normal	53.9 (46.2, 61.4)	54.7 (48.3, 60.9) *	54.3 (49.5, 59.1) *
Underweight	50.2 (41.6, 58.9)	48.0 (41.8, 54.3) *	48.7 (43.6, 53.8) *
Overweight/obese	54.8 (47.0, 62.3)	54.6 (47.5, 61.4) *	54.7 (49.4, 59.8) *
Survey year
2018	50.4 (42.0, 58.7)	46.6 (39.2, 54.1)	48.3 (42.8, 53.9) *
2019	66.1 (56.8, 74.3)	62.6 (55.7, 69.1)	63.6 (58.0, 68.8) *
^aMDD- Minimum dietary diversity \|^bBMI- Body mass index \| * p < 0.05

Determinants of SSB consumption

Univariable analysis showed that urban women had reduced odds of SSB consumption compared to rural residents; however, urban women in West Pokot had about five times higher odds (OR: 4.86; 95% CI: 3.23–7.31) of SSB consumption compared to urban women in Nairobi County. Age was not significantly associated with SSB consumption, but younger (10–19 years) women in urban areas had higher odds of SSB consumption, while the opposite was true among women in rural regions. Higher education and wealth status increased the odds of consumption. Significant factors included household food security, snack consumption, BMI, and survey year, with snack consumers and those from 2019 showing higher odds (OR: 1.5 and 1.87, respectively). In multivariable regression, urban women had 1.29-fold higher odds of SSB consumption than rural women. Urban women in West Pokot and rural women in Kericho had higher odds than women in Nairobi and Kilifi, respectively. Educational level was significantly associated with SSB consumption, with a gradient increase in the odds of SSB consumption and educational level across rural and urban women. (Table 4)

Table 4

Factors associated with SSB consumption:
Characteristics	univariable			Multivariable
	Urban cOR (95% C.I)	Rural cOR (95% C.I)	Total cOR (95% C.I)	Urban aOR (95% C.I)	Rural aOR (95% C.I)	Total aOR (95% C.I)
Residence
Rural			Ref			Ref
Urban			0.94 (0.7, 1.5)			1.3 (0.8, 2.0)
Counties of residence
Nairobi	Ref		Ref	Ref		Ref
Bungoma	0.5 (0.2, 1.0)	0.8 (0.3, 2.5)	0.4 (0.2, 0.8)	0.5 (0.2, 1.0)	0.7 (0.2, 2.3)	0.4 (0.2, 0.8)*
Kericho	1.1 (0.5, 2.3)	3.4 (0.8,15.0)	1.4 (0.6, 3.4)	1.0 (0.4, 2.2)	3.1 (0.8, 12.9)	1.3 (0.5, 3.0)
Kiambu	0.6 (0.4, 1.3)	1.4 (0.3, 6.0)	0.6 (0.3, 1.2)	0.6 (0.2, 1.5)	1.1 (0.3, 5.2)	0.6 (0.3, 1.2)
Kilifi	0.6 (0.2, 1.8)	Ref	0.5 (0.2, 1.1)	0.6 (0.2, 1.6)	Ref	0.5 (0.2, 1.1)
Kitui	1.6 (0.6, 4.3)	2.2 (0.7, 6.8)	1.1 (0.5, 2.2)	1.7 (0.5, 6.1)	2.2 (0.8, 6.6)	1.1 (0.5, 2.4)
Nandi	0.7 (0.6, 4.3)	2.6 (0.7, 7.6)	1.1 (0.5, 2.1)	0.7 (0.1, 4.3)	2.5 (0.8, 7.5)	1.0 (0.4, 2.4)
Nyamira	0.5 (0.1, 4.0)	2.7 (0.9, 7.8)	1.1 (0.6, 2.1)	0.5 (0.1, 1.7)	2.3 (0.8, 6.2)	1.0 (0.5, 2.0)
Siaya	1.2 (0.2, 8.5)	1.9 (0.7, 5.3)	0.9 (0.5, 1.7)	1.3 (0.2, 8.8)	1.6 (0.6, 4.4)	0.8 (0.4, 1.7)
Kakamega	0.3 (0.1, 0.9)	2.1 (0.7, 6.4)	0.8 (0.4, 1.6)	0.3 (0.1, 0.7)	1.6 (0.5, 4.9)	0.7 (0.3, 1.4)
West Pokot	4.9 (3.2, 7.3)	1.3 (0.4, 4.5)	0.8 (0.3, 1.7)	4.6 (2.9, 7.2)	1.6 (0.5, 4.7)	0.9 (0.4, 2.1)
Age (years)
10–19	Ref	Ref	Ref	Ref	Ref	Ref
20–29	1.3 (0.9, 1.8)	1.1 (0.9, 1.3)	1.2 (0.9, 1.4)	0.9 (0.7, 1.4)	1.2 (1.0, 1.4)	1.1 (0.9, 1.3)
30–39	1.0 (0.7, 1.6)	1.1 (0.9, 1.3)	1.1 (0.9, 1.3)	0.7 (0.4, 1.1)	1.2 (0.9, 1.6)	0.9 (0.7, 1.2)
40–49	0.9 (0.6, 1.3)	1.2 (1.0, 1.5)	1.1 (0.9, 1.3)	0.5 (0.3, 0.8)	1.2 (0.9, 1.6)	0.9 (0.7, 1.1)
Marital status
In Union	Ref	Ref	Ref	Ref	Ref	Ref
Not-in-union	0.8 (0.6, 1.2)	1.1 (0.9, 1.2)	0.9 (0.8, 1.1)	0.7 (0.5, 0.9)	1.2 (0.9, 1.4)	0.9 (0.8, 1.1)
Level of education
No formal education	Ref	Ref	Ref	Ref	Ref	Ref
Primary level	4.2 (2.4, 7.3)	2.1 (1.3, 3.2)	2.3 (1.6, 3.4)	5.4 (2.9, 10.2)	1.8 (1.2, 2.6)	2.2 (1.6, 3.1)
Post-Primary & Secondary level	4.2 (2.4, 7.5)	2.5 (1.5, 4.2)	2.6 (1.6, 4.1)	4.7 (2.5, 8.8)	1.9 (1.3, 3.0)	2.21 (1.5,3.2)
Tertiary level	5.4 (2.9, 10.0)	3.0 (1.5, 5.9)	3.2 (1.9, 5.3)	5.1 (2.6, 10.3)	1.9 (1.2, 3.2)	2.4 (1.5, 3.8)
Employment status
Employed	Ref	Ref	Ref	Ref	Ref	Ref
Unemployed	0.9 (0.6, 1.2)	0.7 (0.6, 0.9)	0.8 (0.7, 0.9)	0.8 (0.6, 1.2)	0.8 (0.6, 0.9)	0.8 (0.7, 1.0)
Wealth status
1- Poorest	Ref	Ref	Ref	Ref	Ref	Ref
2- Poorer	1.0 (0.6, 1.6)	0.9 (0.7, 1.3)	0.9 (0.7, 1.2)	1.0 (0.6, 1.5)	1 (0.8, 1.3)	1.0 (0.8, 1.2)
3- Middle	1.2 (0.7, 2.0)	1.0 (0.7, 1.4)	1.0 (0.8, 1.4)	1.2 (0.8, 1.7)	1.1 (0.8, 1.5)	1.1 (0.9, 1.4)
4- Richer	1.1 (0.7, 1.9)	1.2 (0.9, 1.8)	1.1 (0.8, 1.5)	1.1 (0.7, 1.8)	1.2 (0.9, 1.7)	1.2 (0.9, 1.5)
5- Richest	1.6 (0.9, 2.9)	1.4 (0.8, 2.5)	1.4 (1.0, 2.1)	1.6 (0.9, 2.7)	1.2 (0.6, 2.4)	1.4 (0.9, 2.2)
Pregnancy status
Pregnant	Ref	Ref	Ref	Ref	Ref	Ref
Not pregnant	1.5 (0.8, 3.2)	1.0 (0.7, 1.4)	1.2 (0.8, 1.6)	1.9 (0.9, 3.8)	1.0 (0.7, 1.4)	0.9 (0.8, 1.12)
^aMDD score
Less than 5 groups	Ref	Ref	Ref	Ref	Ref	Ref
More than 5 groups	1.0 (0.7, 1.3)	1.2 (0.9, 1.6)	1.1 (0.9, 1.4)	0.8 (0.6, 1.2)	0.8 (0.6, 1.0)	1.4 (1.2, 1.7)
Household Food security
Mild food insecure	Ref	Ref	Ref	Ref	Ref	Ref
Moderate food insecure	0.9 (0.6, 1.3)	1.1 (0.7, 1.6)	1.0 (0.8, 1.3)	0.9 (0.6, 1.4)	1.3 (0.2, 0.9)	1.1 (0.9, 1.5)
Severe food insecure	0.8 (0.5, 1.2)	0.7 (0.5, 1.0)	0.7 (0.5, 0.9)	0.9 (0.6, 1.4)	1.0 (0.8, 0.7)	0.9 (0.7, 1.2)
Snack Consumption
Non-consumer	Ref	Ref	Ref	Ref	Ref	Ref
Consumer	1.2 (0.9, 1.6)	1.8 (1.5, 2.3)	1.5 (1.3, 1.8)	1.1 (0.8, 1.5)	1.7 (1.4, 2.1)	1.4 (1.2, 1.7)
^bBMI
Normal	Ref	Ref	Ref	Ref	Ref	Ref
Underweight	0.9 (0.6, 1.2)	0.8 (0.7, 0.9)	0.8 (0.7, 0.9)	0.9 (0.6, 1.3)	0.8 (0.7, 0.9)	0.8 (0.7, 0.9)
Overweight/obese	1.0 (0.8, 1.3)	1.0 (0.8, 1.2)	1.0 (0.9, 1.2)	1.1 (0.9, 1.4)	0.9 (0.7, 1.0)	0.9 (0.8, 1.1)
Survey year
2018	Ref	Ref	Ref	Ref	Ref	Ref
2019	1.9 (1.2, 3.2)	1.9 (1.3, 2.8)	1.9 (1.4, 2.5)	2.4 (1.5, 3.9)	1.8 (1.2, 2.6)	2.0 (1.5, 2.7)
^aMDD- Minimum dietary diversity \|^bBMI- Body mass index \| Ref- References \| * p < 0.05 \| ** p < 0.01

We focused on assessing the prevalence of SSB consumption among women aged 10–49 years in Kenya while identifying the demographic, socioeconomic, and health behavior factors associated with it. Based on the findings, we estimated the prevalence of SSB among women to be 52.7%.We noted a significant increase in SSB consumption, from 48.3% in 2018 to 63.6% in 2019. This level is concerning because of the adverse health outcomes associated with SSB consumption. Notably, the prevalence is similar that of adolescents in Western Kenya (53.8%)³², and that of adolescents in 53 LMICs (54.3%)³³, but higher than that of Ghanaian adolescents (34.9%)¹⁸, adults in South Africa (16.0%)²⁰, and pregnant women in Nigeria (25%)¹⁴. This high consumption of SSBs, especially in LMICs, could be attributed to their affordability and accessibility, partly because of the increase in consumer income and the lack of action taken by policymakers to affect the price of SSBs³⁴. The government of Kenya imposes an excise tax on soft drinks, including those with and without sugar, but it is not specific to SSBs, making them affordable. These findings highlight the need for policies that aim to reduce the consumption of SSBs, including imposing taxes.

Urban areas, particularly in LMICs, are undergoing a nutritional transition, shifting from traditional diets to processed foods with high sugar levels, including SSBs³⁵. Similar to studies by^20,36, our findings established that SSB consumption was higher among urban residents, with a 29% increase in odds compared with rural residents. However, our findings are lower than those of sub-Saharan Africa, where the prevalence of SSB consumption is twice as high in urban areas than in rural areas⁴. This shift is driven by the advertising of unhealthy foods, including SSBs, particularly in urban areas³⁷. SSBs are the most advertised items in urban areas³⁸ and coupled by the boosted dissemination, including sales among street vendors, it increases accessibility to urban people.

There was a non-significant inverse association between age and SSB consumption. Similar patterns have been established in previous studies^14,15, which showed a decreased pattern of SSB consumption with an increase in age. Stratification by residence revealed that rural women aged 40–49 years had the lowest odds of SSB consumption. The higher consumption of SSBs among the younger respondents in the present study may be related to their higher exposure to advertising, taste preference, and popularity. In addition, younger people tend to eat more outside the home and at fast-food or chain restaurants, which increases the risk of SSB consumption.

Socioeconomic status is known to shape food choices with employment, guaranteeing the high purchasing power and affordability of SSBs³⁹. Our findings showed that the prevalence of SSB consumption was higher among the employed and the richest groups. Employed individuals are likely to indulge in out-of-home eating habits, which increases the likelihood of SSB consumption ¹⁴. Additionally, the trend of high SSB consumption among richer women could be influenced by lifestyle choices, as high-income individuals decide to consume unhealthy foods despite being able to afford healthier options. Implementing workplace wellness programs that promote healthy beverage choices and discourage the consumption of SSBs such as healthy beverage options in workplace cafeterias could reduce SSBs intake. Increasing taxes on beverages and reducing food marketing and meal recommendations could reduce SSBs consumption among high-income women.

Non-married residents had reduced odds of consuming SSB. Similarly,⁴⁰ established a higher prevalence among married participants while⁴¹ found the contrary. Couples may influence each other's habits into SSB consumption. Moreover, SSBs are integrated more into family meals in Kenya with consumption, primarily tea, included in almost four out of five eating episodes²⁴. Therefore, there are recommendations for a lower-sugar breakfast by reducing sugar in tea.

The prevalence and risk of SSB consumption significantly increased with an increase in education among women in both rural and urban residencies. However, urban women had higher odds of SSB consumption than did rural women. Other studies have reported similar findings⁴² whereas other studies in varied settings have contrasted the same findings^15,42. It is known that people with a lower level of education consume unhealthy diets including SSBs. However, the notion that education level does not influence SSB consumption was identified in our study, implying that higher health literacy does not guarantee healthier beverage consumption. Therefore, interventions need to be tailored to target different educational levels and demographics to promote the benefits of reducing SSB consumption.

Similar to what⁴⁰ found, the consumption of snacks in our study was associated with increased odds of SSB consumption. These findings could be linked to the fact that incremental daily calorie intake from high-energy foods is associated with diet beverage consumption⁴³. Additionally, the pairing of SSBs with fast food is likely driven by availability at times of purchase, promotions, as well as pricing and ‘packaging’ of SSBs with food.

We found a direct relationship between SSBs consumption and obesity, with obese women having increased odds of SSBs consumption. This association has been well established in other studies, where SSB consumption was positively associated with obesity^44–46. SSBs contain large amounts of refined sugars, resulting in a high glycemic load and poor satiating properties, which contribute to excessive weight gain ⁴⁶. Therefore, it is important that public health policies aim to reduce the consumption of SSBs and encourage other healthy alternatives.

Food cravings induced by pregnancy and other associated changes in hormones could increase the need for SSB intake in women. Consumption of SSB was higher among pregnant women in rural areas than among non-pregnant women. Similar findings have been reported by¹⁴, in which pregnant women had a higher consumption of SSBs. Pregnant women typically need to consume additional calories during pregnancy. However, the observed increase in SSBs consumption during pregnancy is concerning and the high levels of consumption among pregnant women could be attributed to a lack of awareness of the harmful effects of SSBs during pregnancy. Interventions to reduce consumption should target non-gravid women for health education and adopt a healthy lifestyle during antenatal care¹⁴.

To our knowledge, this is the first study to investigate SSBs among women and adolescents using a representative national sample, thus filling a critical research gap in Kenya. This study could inform public health policy regarding sugar consumption as it examined a broader range of factors and a larger sample size compared with previous studies. However, our study did not evaluate the types of SSBs consumed, the frequency of SSB consumption, or the servings of sugar consumed in SSBs. SSB intake was also assessed based on recall; therefore, it was not representative of usual SSB intake.

Overall, slightly more than half of Kenyan adolescents and women aged 10–49 years consume sugar-sweetened beverages. These findings were accompanied by an increasing trend in consumption, which was associated with sociodemographic and socioeconomic factors. The findings of this study highlight the need for the development of policies that seek to reduce SSB consumption by adopting interventions that target urban residents who are educated, rich, and overweight.

Data availability

Data that supports the findings reported was accessed upon approval of the request submitted to the PMA and was used as per the data agreement. Access to the datasets can be requested through Kenya Nutrition Round 1 SQ Survey | PMA DataLab and Kenya Nutrition Round 2 HQFCQ Survey | PMA DataLab for the first and second rounds respectively.

Competing interests

The author(s) declare no competing interests.

Ethics

The study used publicly available data and did not involve any contact with the participants. Therefore, no further ethical approval was required since the PMA surveys had received ethical approval prior to data collection.

Author Contribution

CA, JO, PK, LN, EW: Conceptualization and supervision: NC, SO, RW- data analysis, Writing- original draft preparation: CA, JO, EW- validation.: NC, SO, RW: Writing- reviewing and editing

Acknowledgement

We acknowledge the Performance Measurement for Accountability for granting access to the PMA survey datasets used in this study.

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The authors declare no competing interests.

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Prevalence and factors associated with Sugar-Sweetened Beverage Consumption among adolescents and women aged 10-49 years in Kenya: a cross-sectional study

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Abstract

Introduction

Methods

Data source, Study design, population, and Setting

Study Variables

Dependent variable

Independent variables

Statistical Analysis

Results

Participants' characteristics

Prevalence of SSB consumption

Determinants of SSB consumption

Discussion

Conclusion

Abbreviations

Declarations

Data availability

Competing interests

Ethics

Author Contribution

Acknowledgement

References

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