Impacts of a New Supermarket on Dietary Behavior and the Local Foodscape in Kisumu, Kenya: Protocol for a Mixed Methods, Natural Experimental Study

Socioeconomic inequalities in food purchasing practices and expenditure patterns: Results from a cross-sectional household survey in western Kenya

Introduction

Socioeconomic inequalities contribute to poor health. Inequitable access to diverse and healthy foods can be a risk factor for non-communicable diseases, especially in individuals of low socioeconomic status. We examined the extent of socioeconomic inequalities in food purchasing practices, expenditure, and consumption in a resource-poor setting in Kenya.

Methods

We conducted a secondary analysis of baseline cross-sectional data from a natural experimental study with a sample size of 512 individuals from 376 households in western Kenya. Data were collected on household food sources, expenditure and food consumption. Household socioeconomic status (SES) was assessed using the multiple correspondence analysis (MCA) model. Concentration indices (Ci) and multivariable linear regression models were used to establish socioeconomic inequalities.

Results

About half (47.9%) of individuals achieved a minimum level of dietary diversity with the majority coming from wealthier households. The two most consumed food groups were grains and roots (97.5%, n = 499) and dark green leafy vegetables (73.8%, n = 378), but these did not vary by SES. The consumption of dark green leafy vegetables was similar across wealth quantiles (Ci = 0.014, p = 0.314). Overall, the wealthier households spent significantly more money on food purchases with a median of USD 50 (IQR = 60) in a month compared to the poorest who spent a median of USD 40 (IQR = 40). Of all the sources of food, the highest amount was spent at open-air markets median of USD 20 (IQR = 30) and the expenditure did not vary significantly by SES (Ci = 0.4, p = 0.684). The higher the socioeconomic status the higher the total amount spent on food purchases. In multivariable regression analysis, household SES was a significant determinant of food expenditure [Adjusted coefficient = 6.09 (95%confidence interval CI = 2.19, 9.99)].

Conclusion

Wealthier households spent more money on food compared to the poorest households, especially on buying food at supermarkets. Individuals from the poorest households were dominant in eating grains and roots and less likely to consume a variety of food groups, including pulses, dairy, eggs and fruits, and vegetables. Individuals from the poorest households were also less likely to achieve adequate dietary diversity. Deliberate policies on diet and nutrition are required to address socioeconomic inequalities in food purchasing practices.

Foodscapes, finance, and faith: Multi-sectoral stakeholder perspectives on the local population health and wellbeing in an urbanizing area in Kenya

Introduction

Rapid urbanization (growth of cities) can upset the local population's health and wellbeing by creating obesogenic environments which increase the burden of non-communicable diseases (NCDs). It is important to understand how stakeholders perceive the impact of urbanizing interventions (such as the construction of a new hypermarket) on the health and wellbeing of local populations. Because low- and middle-income countries (LMICs) lack the reliable infrastructure to mitigate the effects of obesogenic environments, so engaging stakeholders who influence dietary habits is one population-level strategy for reducing the burden of NCDs caused by newly built developments.

Methods

We conducted key informant interviews with 36 stakeholders (25 regulatory and 11 local community stakeholders) from Kisumu and Homa Bay Counties of Western Kenya in June 2019. We collected stakeholders' perspectives on the impacts of a new Mall and supermarket in Kisumu, and existing supermarkets in Homa Bay on the health and wellbeing of local populations.

Results

Through thematic discourse analysis, we noted that some stakeholders thought supermarkets enabled access to unhealthy food items despite these outlets being also reliable food sources for discerning shoppers. Others linked the changing physical environment to both an increase in pollution and different types of diseases. Stakeholders were unsure if the pricing and convenience of supermarkets would stop local populations from buying from their usual small-scale food vendors. The key finding of this study was that engaging relevant stakeholders as part of population health impact assessments of new developments in cities are important as it directs focus on health equity and prevention in instances of resource constraints. The findings highlight, also, that community members have a strong awareness of the potential for interventions that would improve the health and wellbeing of local populations.

Navigating the local foodscape: qualitative investigation of food retail and dietary preferences in Kisumu and Homa Bay Counties, western Kenya

Introduction

Non-communicable diseases have risen markedly over the last decade. A phenomenon that was mainly endemic in high-income countries has now visibly encroached on low and middle-income settings. A major contributor to this is a shift towards unhealthy dietary behavior. This study aimed to examine the complex interplay between people’s characteristics and the environment to understand how these influenced food choices and practices in Western Kenya.

Methods

This study used semi-structured guides to conduct in-depth interviews and focus group discussions with both male and female members of the community, across various socioeconomic groups, from Kisumu and Homa Bay Counties to further understand their perspectives on the influences of dietary behavior. Voice data was captured using digital voice recorders, transcribed verbatim, and translated to English. Data analysis adopted an exploratory and inductive analysis approach. Coded responses were analyzed using NVIVO 12 PRO software.

Results

Intrapersonal levels of influence included: Age, the nutritional value of food, occupation, perceived satiety of some foods as opposed to others, religion, and medical reasons. The majority of the participants mentioned location as the main source of influence at the community level reflected by the regional staple foodscape. Others include seasonality of produce, social pressure, and availability of food in the market. Pricing of food and distance to food markets was mentioned as the major macro-level influence. This was followed by an increase in population and road infrastructure.

Conclusion

This study demonstrated that understanding dietary preferences are complex. Future interventions should not only consider intrapersonal and interpersonal influences when aiming to promote healthy eating among communities but also need to target the community and macro environments. This means that nutrition promotion strategies should focus on multiple levels of influence that broaden options for interventions. However, government interventions in addressing food access, affordability, and marketing remain essential to any significant change.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-13580-4.

Comparison of household socioeconomic status classification methods and effects on risk estimation: lessons from a natural experimental study, Kisumu, Western Kenya

Introduction

Low household socioeconomic status is associated with unhealthy behaviours including poor diet and adverse health outcomes. Different methods leading to variations in SES classification has the potential to generate spurious research findings or misinform policy. In low and middle-income countries, there are additional complexities in defining household SES, a need for fieldwork to be conducted efficiently, and a dearth of information on how classification could impact estimation of disease risk.

Methods

Using cross-sectional data from 200 households in Kisumu County, Western Kenya, we compared three approaches of classifying households into low, middle, or high SES: fieldworkers (FWs), Community Health Volunteers (CHVs), and a Multiple Correspondence Analysis econometric model (MCA). We estimated the sensitivity, specificity, inter-rater reliability and misclassification of the three methods using MCA as a comparator. We applied an unadjusted generalized linear model to determine prevalence ratios to assess the association of household SES status with a self-reported diagnosis of diabetes or hypertension for one household member.

Results

Compared with MCA, FWs successfully classified 21.7% (95%CI = 14.4%-31.4%) of low SES households, 32.8% (95%CI = 23.2–44.3) of middle SES households, and no high SES households. CHVs successfully classified 22.5% (95%CI = 14.5%-33.1%) of low SES households, 32.8% (95%CI = 23.2%-44.3%) of middle SES households, and no high SES households. The level of agreement in SES classification was similar between FWs and CHVs but poor compared to MCA, particularly for high SES. None of the three methods differed in estimating the risk of hypertension or diabetes.

Conclusions

FW and CHV assessments are community-driven methods for SES classification. Compared to MCA, these approaches appeared biased towards low or middle SES households and not sensitive to high household SES. The three methods did not differ in risk estimation for diabetes and hypertension. A mix of approaches and further evaluation to refine SES classification methodology is recommended.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12939-022-01652-1.

Cross-Sectional Association of Food Source with Food Insecurity, Dietary Diversity and Body Mass Index in Western Kenya

The triple burden of malnutrition in many low- and middle-income countries (LMICs) is partly a result of changing food environments and a shift from traditional diets to high-calorie Western-style diets. Exploring the relationship between food sources and food- and nutrition-related outcomes is important to understanding how changes in food environments may affect nutrition in LMICs. This study examined associations of household food source with household food insecurity, individual dietary diversity and individual body mass index in Western Kenya. Interview-administered questionnaire and anthropometric data from 493 adults living in 376 randomly-selected households were collected in 2019. Adjusted regression analyses were used to assess the association of food source with measures of food insecurity, dietary diversity and body mass index. Notably, participants that reported rearing domesticated animals for consumption (‘own livestock’) had lower odds of moderate or severe household food insecurity (odds ratio (OR) = 0.29 (95% CI: 0.09, 0.96)) and those that reported buying food from supermarkets had lower odds of moderate or severe household food insecurity (borderline significant, OR = 0.37 (95% CI: 0.14, 1.00)), increased dietary diversity scores (Poisson coefficient = 0.17 (95% CI: 0.10, 0.24)) and higher odds of achieving minimum dietary diversity (OR = 2.84 (95% CI: 1.79, 4.49)). Our findings provide insight into the relationship between food environments, dietary patterns and nutrition in Kenya, and suggest that interventions that influence household food source may impact the malnutrition burden in this context.