Introduction: Understanding the affordability of healthy eating has always been a critical question but has escalated in importance considering the global context of more recent soaring food prices and the worsening of food and nutrition security indicators since the COVID-19 pandemic. The objective of this study was to evaluate the cost and affordability of basic healthy eating in South Africa and Kenya, through the application of a Basic Healthy Food Basket (BHFB) modeling framework applied within the food intake and nutritional context of the target countries. Methods: The construction of the BHFB models was based on a number of key considerations (i.e., 'building blocks'): household demographic (size and composition), socio-economically disaggregated household income levels, minimum wage data, typical food intake patterns, nationally monitored food retail prices and official country-specific nutrient intake and food consumption guidelines. For both countries BHFB's were compiled and interpreted at a national level, as well as for a 'more plant-based' BHFB. The nutritional adequacy of the national BHFB's was evaluated. Results: Based on national demographical statistics in Kenya and South Africa, the reference family was defined as a four-member reference household consisting of 2 adults and 2 children. The detailed composition of the national BHFB's is presented, consisting of 31 food items in Kenya and 24 food items in South Africa - covering all the food groups according to nutritional guidelines. The nutritional adequacy of the various BHFB's is discussed and were generally deemed adequate for the majority of micro-nutrients - particularly the micro-nutrients of concern in the various countries. In January 2023 the cost of the national BHFB in Kenya and South Africa amounted to KSh33 800 (US$270) and R4 715 (US$ 262) respectively, - potentially excluding approximately 60% of the population from being able to afford a basic healthy food basket. A movement to 'more plant-based' BHFB's (i.e., proportionally less meat/fish/eggs and proportionally more legumes) reduced the cost of the BHFB's with 15% in Kenya and in South Africa. From a social support perspective, the analyses indicated that social support in the form of child support grants and children benefiting from school feeding programmes could result in the share of households able to afford basic healthy eating to increase by approximately 10 percentage points in the South African context. Conclusions and recommendations: The study showed that the high (and rising) cost of basic healthy eating prevents large numbers of households in South Africa and Kenya from being able to afford basic healthy eating when considering current income distribution data. The research outcomes of the BHFB methodology applied in this study to monitor and evaluate the cost and affordability of basic healthy eating, should be monitored regularly and taken into consideration to inform food and nutrition policy decisions and actions. However, from a methodological perspective several critical challenges will have to be addressed to improve the measurement accuracy of the BHFB models, including the availability of official food retail price monitoring of a wider range of food items; household demographics, household income data, food intake data and household-level food expenditure data that are more frequently updated, as well as socio-economically and geographically disaggregated data on typical food intake behavior. From the perspective of plant versus animal protein sources, the study suggested that food intake patterns with an increased focus on legumes as a source of plant-based protein could help to improve the affordability of basic healthy eating. However, it is also important to consider consumer acceptability in this context from various perspectives, including sensory acceptability, cultural acceptability, and the available time for food preparation. The study identified the critical need to evaluate the cost of basic healthy eating on a geographically more refined level. This is based on the diversity of food intake behavior in different regions within the target countries as pointed out by in-country experts. However, future research to develop an improved understanding of food intake pattern in different regions/provinces/counties will be a critical enabler, along with geographically disaggregated food retail price data and demographical information.
