Innovative approaches: A
byANH Academy
Academy Week Research Conference
 | Agriculture, Food Systems, Nutrition
Date and Time
From: 1 July 2020, 14:30
To: 1 July 2020, 15:30
Location
Country: United Kingdom
Open Full Event title slide

 

Intro animation:

Innovative approaches studies at ANH2020

 

Session recording:

ANH2020: Innovative approaches A

 

Speakers and presentations:

  • Session chair: Sera Young, Northwestern University
    @ProfSeraYoung @NorthwesternU
  • Brooke Colaiezzi, Tufts University
    @bcola30 @TuftsNutrition
    INDDEX24: An innovative global dietary assessment platform for increasing the availability, access, and use of dietary data
    Presentation | Slides
  • Rainier Masa, University of North Carolina at Chapel Hill
    @UNC
    Does household food insecurity mean the same thing for different groups of youth? Testing for measurement invariance
    Presentation | Slides
  • Silvia Alonso, International Livestock Research Institute
    @SilviaAlonso78 @ILRI
    Evaluation and validation of an African Food Safety Index
    Presentation | Slides
  • Winnie Sambu, University of Cape Town
    @wsambu @UCT_news
    Measuring child malnutrition in South Africa: a comparison of estimates from panel and cross-section surveys
    Presentation | Slides

Abstracts:

INDDEX24: An innovative global dietary assessment platform for increasing the availability, access, and use of dietary data

Brooke Colaiezzi1

Sarah Wafa1

Winnie Bell1

Jerome Some1

Hallie Perlick1

Cathleen Prata1

Beatrice Rogers1

Jennifer Coates1

1Friedman School of Nutrition Science and Policy, Tufts University, United States

Introduction

Individual-level quantitative dietary data are often viewed as prohibitively expensive and time-consuming to generate. Few low- and middle-income countries (LMICs) regularly collect these data. When they are collected it can take years to process and analyze the data after the survey has concluded. The length of this process is due, in large part, to the lack of available and accessible dietary data research infrastructure (e.g. electronic dietary assessment tools that circumvent the need for data entry; accessible food composition tables, portion conversions, and standardized recipes). INDDEX24 is a global dietary assessment platform that seeks to address these bottlenecks.      

Methods

Priority technical specifications for a global dietary assessment platform were drafted and reviewed by experts with extensive dietary assessment experience in LMICs. Specifications included use of the multiple pass 24-hour dietary recall method (24HR), contextual adaptability (i.e. the ability to adapt the tool for surveys conducted in different contexts), offline data collection capability, and an interviewer-administered format. Existing dietary assessment platforms and dietary assessment technologies were evaluated against the technical specifications via a structured literature review and key informant interviews. An initial version of INDDEX24 was developed collaboratively with two technology firms and tested through feasibility studies in Vietnam and Burkina Faso. Feedback was collected from users of the platform in Viet Nam and Burkina Faso. Webinars were conducted with potential users of the platform. Additional development rounds implemented feedback from these studies. The platform’s relative validity, time, and cost compared to use of paper-based 24-hour dietary recalls were then evaluated in Vietnam and Burkina Faso. The platform became available to select beta users in the fall of 2019. An official launch of the INDDEX24 platform is anticipated by the end of 2020.

Findings

INDDEX24 is a novel solution to many of the challenges of scaling up dietary data collection in LMICs. It is comprised of a mobile application (app) for dietary data collection that is linked to a web app for managing and sharing dietary data inputs (i.e. food composition data, recipes, food descriptors, and portion conversions). Users can search the web app for dietary data inputs and copy and customize them to their research context or upload their own inputs. The web app is used to build the survey food and recipe list, assign portion size estimation aids to foods and recipes, and manage food composition and portion conversion factor data. The INDDEX24 mobile app collects data using the 24HR method and can be used offline in settings without internet connectivity. It was built using CommCare. The CommCareHQ website enables INDDEX24 users to adapt the mobile app questionnaire text to their survey language and context, add survey modules, monitor data collection efforts in real time, and provides a reporting feature with summary statistics. To maximize the time and cost saving benefits of the platform, a concerted effort will be needed to populate the web app with dietary data inputs from LMICs.

Conclusion

The extensive consultative process and evidence driven design has produced an innovative platform that balances flexibility of adaptation to a range of contexts with quality control and standardization.

Does Household Food Insecurity Mean the Same Thing for Different Groups of Youth? Testing for Measurement Invariance

Rainier Masa1

Anjalee Sharma1

Zoheb Khan2

Gina Chowa1

Senzelwe Mthembu2

1University of North Carolina at Chapel Hill, United States

2Centre for Social Development in Africa, South Africa

Introduction

Validity of household food insecurity measures and its invariance or equivalence is critical for practice and research. Household food insecurity measures are used to inform program eligibility and make inferences about relationships of food insecurity with a range of outcomes. However, there is scant evidence to demonstrate measurement invariance of food insecurity measures among different groups of youth. We examined construct validity and measurement invariance of the Household Food Insecurity Access Scale (HFIAS), a common measure of food access, across gender, developmental age, time, and countries.

Methods

Sample included 6,098 youth aged 15 to 24 years old from Ghana (70%) and South Africa (30%). We examined construct validity by gender, developmental age, time and country. After confirming validity, we explored measurement invariance by sequentially testing baseline, configural, metric, and scalar invariances across gender, developmental age, time and countries. We evaluated model fit using model chi-square, comparative fit index (CFI), Tucker-Lewis index (TLI), and standardized root mean square residual (SRMR). All analyses were conducted using Mplus 8.3 (Muthén & Muthén, 1998-2017).

Findings

Results demonstrated construct validity of the unidimensional HFIAS when used with Ghanaian and South African youth. Results also indicated configural, metric and scalar invariance across different groups of youth. HFIAS remained invariant across gender (male or female), developmental age (middle adolescence or young adulthood), countries (Ghana or South Africa) and across time (baseline, after six months, after one year, and after two years). All models showed good fit, which included CFI and TLI values of ≥ 0.95 and SRMR values of < 0.08. Ghanaian and South African youth’s responses to HFIAS were consistent with its intended meaning. HFIAS was also invariant. First, the unidimensional structure of HFIAS applied to all groups (configural) . Second, individual HFIAS items were equally salient to the construct of food insecurity for all groups across gender, developmental age, countries and time (metric). Third, members of different groups (e.g., men or women) who experienced the same true levels of inadequate food access chose the same response option (scalar). In other words, HFIAS scores are comparable across gender, developmental age, time and countries. In turn, these results suggest that HFIAS means the same thing for different groups of youth.

Conclusion

Given the widespread use of HFIAS to determine program eligibility, assess level of need, and examine empirical associations, it is imperative to establish its measurement invariance or equivalence. Once invariance is tested and confirmed, researchers, practitioners and decision makers can be more confident about the validity of their findings and decisions. This increase in confidence is attributed to establishing that all items comprising HFIAS mean the same thing for different groups of youth across gender, developmental age, time and countries.

References

Muthén, L.K. and Muthén, B.O. (1998-2017). Mplus user’s guide. Eighth edition. Los Angeles, CA: Muthén & Muthén.

Evaluation and validation of an African Food Safety Index

Silvia Alonso1

Winta Sintayehu2

Abraham Getachew2

Wezi Chunga2

Florence Mutua3

Kebede Amenu4

Filipe De Souza1

Peace Mutuwa2

Ibrahim Gariba2

Yohannes Zelalem2

Ian Dohoo5

Delia Grace6

Amare Ayalew2

1International Livestock Research Institute, Ethiopia

2Partnership for Aflatoxin Control in Africa, Ethiopia

3International Livestock Research Institute, Tanzania

4College of Veterinary Medicine and Agriculture, Addis Ababa University, Ethiopia

5University of Prince Edward Island, Canada

6International Livestock Research Institute, Kenya

Introduction

The African Union (AU) launched in 2015 the Comprehensive Africa Agriculture Development Programme (CAADP) Biannual Review (BR) to monitor progress on agricultural development in the continent1. The CAADP BR encompassed 43 indicators, 7 of which tracked nutrition, but none captured food safety. In 2019, following a series of consultations at various AU fora led by the Partnership for Aflatoxin Control in Africa (PACA), and with advice from BR Experts Tasks Force, an “African food safety index” (AFSI) was included. We conducted an evaluation and validation of the AFSI to assess its robustness and appropriateness for monitoring food safety progress. 

Methods

The index was developed by a multi-disciplinary team of experts and validated by AU country member states. AFSI is composed of 3 sub-indexes capturing three distinct elements of food safety: (i) Food safety systems (i.e. policies and institutions), (ii) public health impact related to foodborne diseases and (iii) trade related food safety infractions2. The data submitted by countries was obtained from CAADP and analyzed to evaluate the robustness of the index and its indicators. Item-response theory was used to analyze if the indicators under the food systems index represented an underlying scale of the robustness of a country’ food safety system. The association and predictability between this and the trade and public health indexes was also analyzed. An online survey was administered to all the CADDP and/Codex Focal points in each of the 55 AU member states to gather their views on the availability and accessibility of data, and their feedback on the use of the AFSI tool. Finally, in-country visits were conducted in 9 countries, representing AU member states and different degrees of participation in AFSI reporting. The visits included interviews with relevant ministries and AFSI key personnel, and a group consultation with food safety stakeholders.

Findings

Out of the 55 AU member states, fifty countries submitted data relevant to AFSI, with varying degree of completeness. Countries that failed to report also failed to report all other BR indicators. The combination of food systems sub-index indicators did not represent an underlying scale of the adequacy of food systems in a country, and while they captured well the in-country presence of food safety policies, they did not appropriately capture the level of operability of such policies. The in-country visits showed that CAADP mandates were in general clear, although countries that failed to report in AFSI were found to have a limited understanding on the BR processes, rather than a specific problem with the food safety index. Countries that submitted in all AFSI sub-indexes showed a good level of coordination with relevant ministries and specifically with the Codex Alimentarius Focal Units in the country. While the index’s indicators appeared clear to those in charge of gathering the data, countries reported a varying degree of data accessibility, and most of them reported concerns on the representativity and reliability of the surveillance data, both public health and trade.

Conclusion

The immediate implementation of AFSI within the biannual review and the level of participation to their reporting shows an encouraging and unprecedented commitment to Food safety in Africa. While the index is an all-around comprehensive Food Safety assessment, it should be improved to capture operability of systems rather than existence of procedures. The findings advocate for a strengthening of national health surveillance systems that will generate the data for monitoring of food safety public health impact, as well as improved food safety inspection and management systems at borders for both imports and exports. More standardization across countries enhances AU-wide benchmarking.

References

This study is undertaken with the financial assistance of the Technical Center for Rural Cooperation (CTA) within the framework of the project entitled Building Capacity for Institutionalizing Food Safety Tracking in the African Union Member States implemented by the African Union through the Partnership for Aflatoxin Control in Africa. The views expressed herein are those of the authors and can therefore in no way be taken to reflect the official opinion of CTA or African Union.

The CAADP Biannual Review – Measuring Progress and Keeping Accountability in Agriculture. African Union Commission – Department for Rural Economy and Agriculture (AUC – DREA) and African Union Development Agency (NEPAD). https://www.nepad.org/publication/caadp-biennial-review-measuring-progre...

Prioritizing Food Safety in Africa. African Union Commission. https://au.int/sites/default/files/documents/33005-doc-prioritizing_food...

Measuring child malnutrition in South Africa: a comparison of estimates from panel and cross-section surveys

Winnie Sambu1

1School of Economics, University of Cape Town

Introduction

Despite holding middle-income country status, South Africa has high levels of poverty, and a significant proportion of the population is affected by malnutrition. Over 40% of children under 5 years are Vitamin A deficient while a quarter of under 5-year-olds are reportedly stunted (Hall et al., 2019). Through household surveys, the country regularly collects data on a range of indicators including socio-economic and health status. A panel study conducted between 2008 and 2017, and a 2016 cross-sectional health survey collected anthropometric data, provide an opportunity to assess trends and dynamics of child malnutrition and compare estimates from the two surveys.

Methods

In this paper, we use data from five waves of the National Income Dynamics Study (NIDS) a panel study conducted between 2008-2017 and the 2016 South Africa Demographic and Health Survey (SADHS) to examine trends and dynamics of malnutrition (stunting and BMI-for-age) in the country. We also analyse the comparability of estimates from the two household surveys, given that the SADHS (2016) was conducted in between waves 4 and 5 of NIDS (2014/15 and 2017). We apply bivariate and multiple regression models to the two surveys, analysing the extent and distribution of malnutrition and their trajectories over time. We investigate potential differences within the NIDS panel, and compare the cross-sectional estimates to those of a balanced panel. We also examine variations across waves 4 and 5 of NIDS and the SADHS, and explore possible contributors to observed differences in the estimates. We employ inverse probability weighting to deal with missing data problems that are common with anthropometric data and which are likely to create bias in the estimates. Logistic regression models are used to model the predictors of stunting and overweight, using both cross-sectional and panel data, and to examine potential contributors to the discrepancies in the data.

Findings

We find significant differences in malnutrition estimates within NIDS panel and when comparing NIDS waves 4/5 to the SADHS, particularly when we focus on stunting. These differences persist even after correcting for missing data problem, which mainly affects earlier waves of NIDS and the SADHS. Cross-sectional analysis (NIDS) shows variations in stunting levels across the 5 waves; 19% in 2008, 25% (2010/11), 22% (2012), and 14% (2014/15 and 2017). Estimates from a balanced panel show more plausible declines in malnutrition over time, suggesting catch-up growth. Comparing NIDS (2014/15 and 2017) and SADHS (2016), and focusing on the under-5-year olds, we find significant differences in estimates from the two surveys with the SADHS reporting 27% stunted in 2016, compared to 21% observed in NIDS. Logistic regression models on stunting predictors show significant variations in coefficients of the correlates, particularly on parental co-residence, geographical disaggregation, and household socio-economic status. Given that other population characteristics are fairly similar across the two surveys, and after controlling for observable characteristics, we argue that there are unobserved measurement issues that affect estimates from these surveys. These issues appear to mainly affect height data, as our BMI-for-age estimates do not show the discrepancies observed in stunting estimates.

Conclusion

Our analysis brings into question the reliability of malnutrition estimates from national household surveys. We show that national surveys conducted fairly close to each other can produce stunting estimates that differ significantly, making it difficult to have an accurate picture of the state of nutritional outcomes in South Africa. Significant data quality issues, including high levels of missing data, and unobserved measurement errors are likely to be contributing to these variations. Therefore, improvements in anthropometric data collection is paramount to realising high quality data that can be used to monitor malnutrition levels and design suitable interventions for combatting malnutrition.

References

Hall, K., Sambu, W., Almeleh, C., Mabaso, K., Giese, S., Proudlock, P. 2019. South African Early Childhood Review 2019. Cape Town: Children’s Institute, University of Cape Town and Ilifa Labantwana. Available: http://ilifalabantwana.co.za/wp-content/uploads/2016/05/SA-ECD-Review-20....

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