Child nutrition during conflict and displacement: evidence from areas affected by the Boko Haram insurgency in Nigeria

Iacoella, Francesco; Tirivayi, Nyasha

doi:10.1016/j.puhe.2020.03.012

Cited by 16 publications

(18 citation statements)

References 8 publications

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“…Terms of trade, however, were only important in South Sudan. We did not see strong associations with forced displacement or armed con ict intensity, as documented elsewhere [30], and, critically, rainfall abnormalities (as opposed to total precipitation) were not an important predictor in any model. A recent review of 90 studies concludes that wasting is understudied relative to stunting; the review also nds that, while adequate rainfall during the growing season has been associated with less wasting, relationships with drought and armed con ict are inconclusive [31].…”

Section: Discussioncontrasting

confidence: 41%

Can We Predict the Burden of Wasting in Crisis-Affected Countries? Findings from Somalia and South Sudan

Checchi

Frison

Warsame

et al. 2022

Preprint

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BackgroundSample surveys are the mainstay of surveillance for wasting in settings affected by crises, but are burdensome and have limited geographical coverage due to insecurity and other access issues. As a possible complement to surveys, we explored a statistical approach to predict the prevalent burden of wasting malnutrition for small population strata in two crisis-affected countries, Somalia (2014-2018) and South Sudan (2015-2018).MethodsFor each country, we sourced datasets generated by humanitarian actors or other entities on insecurity, displacement, food insecurity, access to services, epidemic occurrence and other factors on the causal pathway to malnutrition. We merged these with datasets of sample household anthropometric surveys done at administrative level 3 (district, county) as part of nutritional surveillance, and, for each of several outcomes including binary and continuous indices based on either weight-for-height or middle-upper-arm circumference, fitted and evaluated the predictive performance of generalised linear models and, as an alternative, machine learning random forests.ResultsWe developed models based on 85 ground surveys in Somalia and 175 in South Sudan. Livelihood type, measles incidence, vegetation index and water price were important predictors in Somalia, and livelihood, rainfall and terms of trade (purchasing power) in South Sudan. However, both generalised linear models and random forests had low performance for both binary and continuous anthropometric outcomes.ConclusionsPredictive models had disappointing performance and are not usable for action. The range of data used and their quality probably limited our analysis. The predictive approach remains theoretically attractive, and deserves further evaluation with larger datasets across multiple settings.

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Section: Discussioncontrasting

confidence: 41%

Can We Predict the Burden of Wasting in Crisis-Affected Countries? Findings from Somalia and South Sudan

Checchi

Frison

Warsame

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Terms of trade, however, were important in South Sudan but marginal in Somalia. We saw inconsistent associations with forced displacement or armed conflict intensity, though these have been documented elsewhere [31], and, critically, rainfall abnormalities (as opposed to total precipitation) were not an important predictor in any model. A recent review of 90 studies concludes that acute malnutrition is understudied relative to chronic malnutrition (stunting); the review also finds that, while adequate rainfall during the growing season has been associated with less acute malnutrition, relationships with drought and armed conflict are inconclusive [32].…”

Section: Discussionmentioning

confidence: 45%

Can we predict the burden of acute malnutrition in crisis-affected countries? Findings from Somalia and South Sudan

et al. 2022

View full text Add to dashboard Cite

Background Sample surveys are the mainstay of surveillance for acute malnutrition in settings affected by crises but are burdensome and have limited geographical coverage due to insecurity and other access issues. As a possible complement to surveys, we explored a statistical approach to predict the prevalent burden of acute malnutrition for small population strata in two crisis-affected countries, Somalia (2014–2018) and South Sudan (2015–2018). Methods For each country, we sourced datasets generated by humanitarian actors or other entities on insecurity, displacement, food insecurity, access to services, epidemic occurrence and other factors on the causal pathway to malnutrition. We merged these with datasets of sample household anthropometric surveys done at administrative level 3 (district, county) as part of nutritional surveillance, and, for each of several outcomes including binary and continuous indices based on either weight-for-height or middle-upper-arm circumference, fitted and evaluated the predictive performance of generalised linear models and, as an alternative, machine learning random forests. Results We developed models based on 85 ground surveys in Somalia and 175 in South Sudan. Livelihood type, armed conflict intensity, measles incidence, vegetation index and water price were important predictors in Somalia, and livelihood, measles incidence, rainfall and terms of trade (purchasing power) in South Sudan. However, both generalised linear models and random forests had low performance for both binary and continuous anthropometric outcomes. Conclusions Predictive models had disappointing performance and are not usable for action. The range of data used and their quality probably limited our analysis. The predictive approach remains theoretically attractive and deserves further evaluation with larger datasets across multiple settings.

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“…The required sample size was determined using single population proportion formula n=z² p(1-p)/d², considering P=value 57% [from likelihood of acute malnutrition in conflict areas in Northern Nigeria; Lacoella and Tirivayi, 2020], confidence level 95% and within 5% precision. Accordingly, the minimum sample size (n) was found to be 377.…”

Section: Sample Size Determinationmentioning

confidence: 99%

Impact of boko haram insurgency on the nutritional status of two communities in Gulani Local Government Area, Yobe State, Nigeria

Musab¹,

Agofure²

2021

Glo Jnl Pure Appl Sci

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The Boko Haram insurgency has consistently posed a threat to the lives of residents in the North-East and national security in general. The insurgency has led to increased incidence of diseases, food insecurity, unemployment, violation of human rights, displacements and shelter problems among others. The study was designed to investigate the impact of boko haram insurgency on the nutritional status of two communities in Gulani Local Government Area, Yobe State, Nigeria. The study was a descriptive cross sectional study conducted among 391 respondents utilizing a simple random sampling technique. A questionnaire was administered to obtain information from respondents while anthropometric measurements of height and weight were carried out with the appropriate instruments. The analysed data was presented in descriptive of frequency, Tables and charts. Majority of the respondents were males 269(68.8%) and most 185(47.3%) were farmers. The mean height, weight and body mass index (BMI) were 1.61±0.87, 62.60±14.93 and 22.39±4.70 respectively. The BMI classification shows that 50.5% of the respondents had normal weight, 25.4% had overweight and 19.2% were underweight. Similarly, majority of the respondents 227(70.8%) affirmed that the insurgency has affected the availability and quantity of staple foods, affected their ability of going to farms 169(61.0%) and led to increase in food prices 305(78.0%). Meanwhile, 142(36.3%) affirmed that they harvest 10-20 quantity (bags) of grains before the insurgency, which slightly decreased to 5-10 quantity (bags) of grains as affirmed by 135 (34.5%) of the respondents during the insurgency. The study shows the boko haram insurgency continues to pose serious challenges to farming activities, which has led to increased food prices as well as poor nutritional status of residents of the affected areas. Hence adequate measures must be put in place to mitigate the impact of the insurgency on residents of the affected areas.

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Child nutrition during conflict and displacement: evidence from areas affected by the Boko Haram insurgency in Nigeria

Cited by 16 publications

References 8 publications

Can We Predict the Burden of Wasting in Crisis-Affected Countries? Findings from Somalia and South Sudan

Can We Predict the Burden of Wasting in Crisis-Affected Countries? Findings from Somalia and South Sudan

Can we predict the burden of acute malnutrition in crisis-affected countries? Findings from Somalia and South Sudan

Impact of boko haram insurgency on the nutritional status of two communities in Gulani Local Government Area, Yobe State, Nigeria

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