A generic theory of change-based framework with core indicators for monitoring the effectiveness of large-scale food fortification programs in low- and middle-income countries.

Large-scale food fortification (LSFF) programs are widely implemented in low- and middle-income countries (LMIC) to alleviate micronutrient deficiencies. However, these programs may not achieve the desired impact due to poor design or bottlenecks in program implementation. Monitoring and evaluation (M&E) frameworks and a set of agreed indicators can help to benchmark progress and to strengthen the evidence-base of effectiveness in a standardized way. We aimed to formulate recommendations towards core indicators for evaluating the effectiveness of LSFF programs with their associated metrics, methods, and tools (IMMT). For this, we used a multi-method iterative approach, including a mapping review of the literature, semi-structured interviews with international experts, compilation of a generic Theory of Change (ToC) framework for LSFF program delivery, and selection of IMMT for M&E of LSFF programs at key stages along the ToC delivery framework. Lastly, we conducted exploratory, qualitative interviews with key informants in Nigeria to explore experiences and perceptions related to the implementation of LSFF programs in Nigeria's context, and their opinion towards the proposed set of core IMMT. The literature search resulted in 14 published and 15 grey literature documents, from which we extracted a total of 41 indicators. Based on the available literature and interviews with international experts, we mapped a ToC delivery framework and selected nine core indicators at the output, outcome and impact level for M&E of the effectiveness of LSFF programs. Key informants in Nigeria revealed that the main bottlenecks for implementation of the proposed IMMT are related to the lack of technical capacity, equipment, laboratory infrastructure, and financial resources. In conclusion, we propose a set of nine core indicators for enabling comprehensive M&E of the effectiveness of LSFF programs in LMIC. This proposed set of core indicators can be used for further evaluation, harmonization and integration in national and international protocols for M&E of LSFF programs.

Critical review of indicators, metrics, methods, and tools for monitoring and evaluation of biofortification programs at scale.

Sound monitoring and evaluation (M&E) systems are needed to inform effective biofortification program management and implementation. Despite the existence of M&E frameworks for biofortification programs, the use of indicators, metrics, methods, and tools (IMMT) are currently not harmonized, rendering the tracking of biofortification programs difficult. We aimed to compile IMMT for M&E of existing biofortification programs and recommend a sub-set of high-level indicators (HLI) for a harmonized global M&E framework. We conducted (1) a mapping review to compile IMMT for M&E biofortification programs; (2) semi-structured interviews (SSIs) with biofortification programming experts (and other relevant stakeholders) to contextualize findings from step 1; and (3) compiled a generic biofortification program Theory of Change (ToC) to use it as an analytical framework for selecting the HLI. This study revealed diversity in seed systems and crop value chains across countries and crops, resulting in differences in M&E frameworks. Yet, sufficient commonalities between implementation pathways emerged. A set of 17 HLI for tracking critical results along the biofortification implementation pathway represented in the ToC is recommended for a harmonized global M&E framework. Further research is needed to test, revise, and develop mechanisms to harmonize the M&E framework across programs, institutions, and countries.

Safety assessment of rhamnogalacturonan-enriched carrot pectin fraction: 90-Day oral toxicity study in rats and in vitro genotoxicity studies.

The dietary fibre product examined is a pectic polysaccharide extract from carrot (Daucus carota), enriched for pectin fragments comprising mainly rhamnogalacturonan-I (RG-I) (abbreviated product name cRG-I). To assess the safety of cRG-I for use as food ingredient, repeated-dose oral toxicity and in vitro genotoxicity studies were conducted. In the subchronic toxicity study (OECD test guideline 408), Wistar Hannover rats received cRG-I at dietary levels (w/w) of 0%, 2.5%, 5% and 10% for 13 weeks. cRG-I induced no adverse effects in this study. The NOAEL was 10% in the diet (equivalent to 6.9 and 7.8 g cRG-I/kg body weight/day in male and female rats, respectively). A package of three in vitro genotoxicity tests (Ames, mouse lymphoma and micronucleus assay in human peripheral blood lymphocytes) was negative for induction of point mutation and chromosome damage. An initial Ames test showed a weak positive response in Salmonella typhimurium strain (TA1537). This response was non-reproducible and attributed to microbial contamination as subsequent tests with an irradiated batch of cRG-I including a repeat Ames test were negative. cRG-I was therefore considered to be non-mutagenic.

Prediction of fruit and vegetable intake from biomarkers using individual participant data of diet-controlled intervention studies.

Fruit and vegetable consumption produces changes in several biomarkers in blood. The present study aimed to examine the dose-response curve between fruit and vegetable consumption and carotenoid (α-carotene, ß-carotene, ß-cryptoxanthin, lycopene, lutein and zeaxanthin), folate and vitamin C concentrations. Furthermore, a prediction model of fruit and vegetable intake based on these biomarkers and subject characteristics (i.e. age, sex, BMI and smoking status) was established. Data from twelve diet-controlled intervention studies were obtained to develop a prediction model for fruit and vegetable intake (including and excluding fruit and vegetable juices). The study population in the present individual participant data meta-analysis consisted of 526 men and women. Carotenoid, folate and vitamin C concentrations showed a positive relationship with fruit and vegetable intake. Measures of performance for the prediction model were calculated using cross-validation. For the prediction model of fruit, vegetable and juice intake, the root mean squared error (RMSE) was 258.0 g, the correlation between observed and predicted intake was 0.78 and the mean difference between observed and predicted intake was - 1.7 g (limits of agreement: - 466.3, 462.8 g). For the prediction of fruit and vegetable intake (excluding juices), the RMSE was 201.1 g, the correlation was 0.65 and the mean bias was 2.4 g (limits of agreement: -368.2, 373.0 g). The prediction models which include the biomarkers and subject characteristics may be used to estimate average intake at the group level and to investigate the ranking of individuals with regard to their intake of fruit and vegetables when validating questionnaires that measure intake.