Exploring Undernourishment: Part 2 — Literature Review
A Visual Data Exploration Research Project to Better Understand the Nuances of Our Global Nutrition
Contents
This is Part 2 of an 8-Part research project aiming to better understand the nuances of our global nutrition. It explores this topic through the utilisation of data visualisation and data science techniques. It is complimented by a Web App: ExploringUndernourishment, which is freely available to the public.
Part 1 — Introduction and Overview
Part 2 — Literature Review ← Selected page
Part 3 — Data Exploration
Part 4 — Research Area 1: General Trend
Part 5 — Research Area 2: Most Successful Countries
Part 6 — Research Area 3: Surprising Trends
Part 7 — Research Area 4: Most Influential Indicator
Part 8 — Recommendations and Conclusions
Literature Review
From the United Nations
The UN has conducted an extensive amount of work in the area of food security and nutrition around the globe. In their flagship publication The State of the World (FAO 2019), the FAO thoroughly analyse and discuss the current state of food security, plus the affect that economic slowdown has on food security. The researchers have identified that after a decade of decreased world hunger, in recent years the prevalence of undernourishment has actually risen for a number of countries, which is directly attributable to economic sustainability and viability in the nations. The scope of analysis and level of monitoring have been expanded for the FAO in recent years in order to better capture the nuances of the SDG Goal (to End World Hunger), and has facilitated the ability to provide better guidance on how to address the challenges of food security. The United Nations is continuing to invest time and resources to tackle this important world problem, and has announced more research project to address this topic; including the Enhanced Parametric Approach Including In-Depth Thematic Analysis of Underlying Factors and Drivers Behind Food Security and Nutrition Trends (FAO 2020d).
In 2014, FAO defined the Prevalence of Undernourishment score as ‘the probability that a randomly selected individual from the reference population is found to consume less than his/her calorie requirement for an active and healthy life’ (FAO 2014, p. 4). The mathematical equation for the PoU is given in Equation 0, and the graphical representation given in Figure 0.
The FAO also defines that the parameters needed for the calculation of the PoU indicator are:
- The mean level of dietary energy consumption (DEC);
- A cut-off point defined as the Minimum Dietary Energy Requirement (MDER);
- The coefficient of variation (CV) as a parameter accounting for inequality in food consumption; and
- A skewness (SK) parameter accounting for asymmetry in the distribution.
Therefore, when viewed from purely an analytical perspective, it can be seen that by addressing the parameters which influence the Prevalence of Undernourishment metric, this will in turn decrease the Prevalence of Undernourishment score. This is quite logical.
From Academia
Beyond the UN, there is a substantial amount of research on the topic of eradicating Global Hunger and Undernourishment. Fontell & Luchsinger (2011) discuss the lack of food and poor quality of sustenance has flow-on effects such as limited and stunted development of children, and leads to diminished physical and cognitive abilities in adults. Aiming to address this, Mughal & Fontan-Sers (2020) have examined the ability for South-Asian nations to curb — and in some instances, stall — relative increases in extreme hunger and food insecurity, through the means of substantially increasing their production of cereal and grain products. Moreover, Harris-Fry et al. (2015) identified that the problem cannot be solved by high-level governmental policies, because confounded by the low-level socio-economic complexities such as education/literacy, household size, discretionary wealth, and environmental factors (drought, etc). Paradoxically, Harris-Fry and colleagues only found a very marginal improvement in undernourishment when a household had a vegetable garden, due to influences such as seasonal crop yields and food-prices only lowering when food security is high (which is an effect of the market supply and demand phenomenon). Therefore, from an academic perspective, this issue is continuing to be worked on and better understood, as the different facets and complexities is quite vexing from and within academia.
From Data Science
The object of solving world hunger (specifically, undernourishment) provides a unique and complicated opportunity for Data Scientists. Specifically, it is the challenge around understanding which factors (or variables) influence the target outcome, or if there are some other unknown parameters which influence undernourishment in a substantive manner. There have been some attempts to solve such a multi-dimensional problem (eg: Mbolanyi et al 2017; Abafita & Kim 2014), with analysis focussing on feature-selection techniques such as Principal Component Analysis and Partial Dependency Analysis. This avenue of analysis is an important and interesting venture to undertake, and should be used in conjunction with some machine-learning variable influence techniques (such as Random Forest variable importance results) in order to thoroughly understand the data. However, considering the work already undertaken in this area, it is unlikely that anything more than creating more awareness of the nuances of the data, and its inherent complexities, will result from this analysis.
References
FAO 2014, Refinements to the FAO Methodology for estimating the Prevalence of Undernourishment Indicator, viewed 17 May 2020, <http://www.fao.org/3/a-i4046e.pdf>.
FAO 2019, The State of Food Security and Nutrition in the World: Safeguarding Against Economic Slowdowns and Downturns, viewed 16 May 2020, <http://www.fao.org/3/ca5162en/ca5162en.pdf>.
FAO 2020d, Enhanced Parametric Approach Including In-Depth Thematic Analysis of Underlying Factors and Drivers Behind Food Security and Nutrition Trends, viewed 16 May 2020, <https://unstats.un.org/sdgs/metadata/files/Metadata-02-01-01.pdf>.
Fontell & Luchsinger 2011, ‘Sustainable efforts to eradicate Global hunger, undernourishment and malnutrition’, Journal of Global Business Issues, vol. 5, no. 2, pp. 79–83, ProQuest central database.
Mughal & Fontan-Sers 2020, ‘Cereal production, undernourishment, and food insecurity in South Asia, Review of Development Economics, vol. 24, no. 2, pp. 524–45, Wiley Online Library, <https://doi-org.ezproxy.lib.uts.edu.au/10.1111/rode.12659>.
Harris-Fry et al. 2015, ‘Socio-economic determinants of household food security and womens dietary diversity in rural Bangladesh: a cross-sectional study’, Journal of Health, Population and Nutrition, vol. 33, ISSN: 16060997, DOI: 10.1186/s41043–015–0022–0.
Mbolanyi et al. 2017, ‘Determinants of household food security in a rangeland area of Uganda’, African Journal of Rural Development, vol. 2, no. 2, pp. 213–23, ISSN: 2415–2838, DOI: 10.22004/ag.econ.262839.
Abafita & Kim 2014, ‘Determinants of Household Food Security in Rural Ethiopia: An Empirical Analysis’, Journal of Rural Development, vol. 37, no. 2, pp. 129–57, DOI: 10.22004/ag.econ.196613.
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