Wealth and Sedentary Time Are Associated With Dietary Patterns Among Preadolescents in Nairobi City, Kenya

Adolescents' dietary patterns, their drivers and association with double burden of malnutrition in adolescents: a cross-sectional study in Kenya's urban slums

BACKGROUND

The double burden of malnutrition (DBM) during adolescence is associated with growth and developmental impairment and risk of non-communicable diseases. There is limited evidence on adolescent's dietary patterns (DPs), and how they contribute to DBM in urban low income contexts in sub Saharan Africa. This study assessed DPs of adolescents, their drivers and association with DBM in Kenya's urban slums.

METHODS

Anthropometric, socio-demographic and dietary-intake data were collected through a cross-sectional survey of 621 adolescents from three major urban slums in Nairobi, Kenya. DPs were derived using principal component analysis. Multinomial-logistic-regression was used to assess the association between the DPs, individual and environmental factors and DBM.

RESULTS

Two DPs were identified: traditional DP (whole grains/cereals, rice, fruits, legumes/nuts, and water) and transitioning DP (refined cereals (maize/wheat), vegetables, meat, tea/coffee, sweet ultra-processed/deep fried snacks). Adolescents from Mathare (the largest slum) were more likely to adhere to the traditional DP (RRR = 3.43; 95% CI 1.85-6.37). Cultural background (Luo) had a positive association (RRR = 4.28; 95% CI 1.97-9.32), while longer residency in the slum (> 10 years) had a negative association (RRR = 0.47; 95% CI 0.25-0.90) with transitioning DP. The transitioning DP had a positive (non-linear) association with overweight/obesity in girls (RRR = 2.79; 95% CI 1.16-6.71). The DPs were not associated with thinness or stunting.

CONCLUSION

The DPs indicate various stages of nutrition transition of adolescent diets, which are influenced by cultural background, neighbourhood and duration of stay in the slum. Transitioning DP may expose adolescents to the risks of overweight/obesity in the long-term.

Profile of Children with Undernutrition Admitted in Two Secondary-Level Hospitals in Maputo City, Mozambique

Mozambique has one of the highest child undernutrition rates in Sub-Saharan Africa. The aim of this study was to characterize the profile of children from 1 to 14 years old hospitalized for undernutrition and to explore associated risk factors. Clinical, demographic, socioeconomic, and environmental data were collected. Anthropometric measurements and stool samples were collected from a child and their caretaker. The wealth index was determined using Principal Components Analysis. A total of 449 children and their caretakers were enrolled. The children had a median age of 1.0 year [IQR: 1.0-2.0], and 53.9% (242/449) were male. Most were admitted with severe undernutrition (35.7%, 159/449 kwashiorkor and 82.0%, 368/449 with -3SD Z-score indexes). The most common co-morbidities were HIV (30.0%, 120/400), diarrhea (20.0%; 80/400), and anemia (12.5%; 50/400). Among the caretakers, 9.5% (39/409) were underweight, 10.1% (40/397) were overweight, and 14.1% (56/397) were obese. Intestinal parasites were found in 24.8% (90/363) children and in 38.5% (77/200) caretakers. The majority of children (60.7%, 85/140) came from low- to middle-wealth households. Most were severely undernourished, suggesting that they seek medical care too late. The finding of overweight/obese caretakers in combination with undernourished children confirms that Mozambique is facing a double burden of malnutrition.

Tensorial Principal Component Analysis in Detecting Temporal Trajectories of Purchase Patterns in Loyalty Card Data: Retrospective Cohort Study

BACKGROUND

Loyalty card data automatically collected by retailers provide an excellent source for evaluating health-related purchase behavior of customers. The data comprise information on every grocery purchase, including expenditures on product groups and the time of purchase for each customer. Such data where customers have an expenditure value for every product group for each time can be formulated as 3D tensorial data.

OBJECTIVE

This study aimed to use the modern tensorial principal component analysis (PCA) method to uncover the characteristics of health-related purchase patterns from loyalty card data. Another aim was to identify card holders with distinct purchase patterns. We also considered the interpretation, advantages, and challenges of tensorial PCA compared with standard PCA.

METHODS

Loyalty card program members from the largest retailer in Finland were invited to participate in this study. Our LoCard data consist of the purchases of 7251 card holders who consented to the use of their data from the year 2016. The purchases were reclassified into 55 product groups and aggregated across 52 weeks. The data were then analyzed using tensorial PCA, allowing us to effectively reduce the time and product group-wise dimensions simultaneously. The augmentation method was used for selecting the suitable number of principal components for the analysis.

RESULTS

Using tensorial PCA, we were able to systematically search for typical food purchasing patterns across time and product groups as well as detect different purchasing behaviors across groups of card holders. For example, we identified customers who purchased large amounts of meat products and separated them further into groups based on time profiles, that is, customers whose purchases of meat remained stable, increased, or decreased throughout the year or varied between seasons of the year.

CONCLUSIONS

Using tensorial PCA, we can effectively examine customers' purchasing behavior in more detail than with traditional methods because it can handle time and product group dimensions simultaneously. When interpreting the results, both time and product dimensions must be considered. In further analyses, these time and product groups can be directly associated with additional consumer characteristics such as socioeconomic and demographic predictors of dietary patterns. In addition, they can be linked to external factors that impact grocery purchases such as inflation and unexpected pandemics. This enables us to identify what types of people have specific purchasing patterns, which can help in the development of ways in which consumers can be steered toward making healthier food choices.