Write an essay on boko haram

In July , the Boko Haram uprising began in Bauchi and spread to other northern states, leaving hundreds of followers, Nigerian law enforcement officers, and civilians dead [ 10 ]. The following year, attacks in the Northeast and other parts of the country including bombings, mass shootings, and executions began to rise [ 10 ].

In May , the president declared a state of emergency in the states of Borno, Yobe, and Adamawa [ 10 ].

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People in the non-conflict states have certainly been affected by the crisis, but the entire population of Borno, Yobe, and Adamawa states are considered directly impacted by the Boko Haram insurgency [ 11 ]. The timeline also shows July as a hard date for the start of the crisis in its current violent form [ 12 ]. This serves as the demarcation between pre-intervention and post-intervention for this study. Timeline of violent deaths attributed to events involving Boko Haram in Northeast Nigeria. Data from Nigeria Watch [ 40 ]. The DHS are large, nationally-representative household surveys which are comparable across countries and time periods [ 13 ].

DHS uses a two-stage sampling methodology.

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The country is first stratified by geographic usually administrative regions crossed with urban or rural designation. In each stratum, enumeration areas determined by the most recent census are used. In the first stage, a number of primary sampling units are selected from the enumeration areas in each stratum. The household lists in these areas are updated, and a fixed number of households are selected. All household members within a specific group relevant here is all women age 15—49 in the selected households are chosen for the survey [ 14 ].

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Sampling weights are provided which are used in combination with the strata and primary sampling units to obtain weighted observations. In addition to exposure to conflict living in areas under a government-declared state of emergency , explanatory covariables include:.

Wealth as determined by an index which accounts for the assets and services available to a household [ 17 ]. For this analysis, a binary variable was created for whether the household was in the bottom two wealth quintiles poorest households or not. Number of people in household, which can have a positive effect on child health many caregivers or negative stretched resources. For this reason, this term was also squared. Number of children under five and this term squared.

Similar to the variable above, a household with many children may show pooled resources, but can also mean at least some of the children do not get the attention or resources they need [ 19 ]. Educational attainment, here as a binary variable of no formal education or some education primary, secondary, or higher.

Sex, as several studies show slight differences in malnutrition outcomes between boys and girls [ 20 , 21 ]. Birth order and the term squared as higher birth orders may be protective up to a certain limit due to the experience of the mother and the help older siblings may provide in basic childcare [ 19 ]. Double-difference also known as difference-in-difference analysis is a methodology often used to estimate the causal effects of policies or programs [ 22 ].

It is a quasi-experimental design which makes use of before and after groups, but without random assignment [ 23 ]. In DD analysis, first a time- and population-specific intervention is identified. Then the difference in outcomes after and before the intervention for those affected by the intervention are compared to the difference in outcomes after and before the intervention among those not affected by the intervention. The DD approach is most frequently used within a linear regression model with a continuous outcome variable to which covariates may be added.

In this study, the intervention is conflict and the model may be written as:. In addition to any differences in mean z-scores, it is important to know if there are any changes in the likelihood that a child will be in the most vulnerable group — moderately or severely malnourished. For this, logistic regression is preferred; however, calculating the marginal effects of an interacted term such as the DD coefficient is not straightforward in non-linear models [ 24 ]. Therefore, the user-written Stata command inteff , which computes the correct marginal effect for the interaction term as well as the standard errors and z-statistic, is used [ 24 ].

The fundamental assumption of DD analysis is that of common trends [ 25 ]. Here, common trends means that if the Boko Haram insurgency had not occurred, the difference in malnutrition prevalence in all the Northeastern states would be constant over time. If this assumption is not fulfilled, any estimation of the causal effect of the conflict will be biased. Common trends is tested by examining earlier data together with the time period of interest. Here, DHS surveys from and were examined with the and surveys. The proportions of wasting adjusted for month of interview to account for seasonal differences in the surveys were calculated [ 26 ].

This was conducted in Stata with the user-written command svypxcat [ 27 ].

The and DHS surveys were at the zone e. These earlier surveys also used a different nutrition reference standard; therefore the raw height and weight data were used to calculate the z-scores with the WHO standards using the Stata user-written command zscore06 [ 28 ].

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Mean WHZ increased in the non-conflict states by 0. The conflict states are more urbanized, mostly due to the presence of Maiduguri. There is no statistical test for common trends analysis, but visualization such as in Fig.

Nigeria’s Battle With Boko Haram

Here, wasting trends are similar before the start of the conflict and then diverge after the start of the conflict, thus fulfilling the common trends requirement. Percentages are adjusted for month of household interview. Dashed green line is the time point between the and surveys where the trends diverge. Model 1 is without covariates and Model 2 has all environmental, household, and child covariates.

The results suggest that, ceteris paribus , if children who were exposed to the Boko Haram insurgency had not been exposed, their mean WHZ would be 0. More variance can be explained with the addition of covariates to Model 1. On average, z-scores increase if the interview was conducted in the rainy season compared to the dry season. Altitude has a small, but significant effect with z-scores decreasing slightly for every meter gain in elevation. Wealth was marginally significant, but the other household variables were not.

Children whose mothers had no formal education had a mean WHZ 0. Children of Muslim mothers had a mean WHZ 0. Z-scores decreased slightly for every additional month of age and on average, boys had slightly lower z-scores than girls. The weight-for-height models have several significant coefficients, but low r-squared terms, indicating that the data have high variability, but that the explanatory covariables still provide information about the outcome. Thus, while these models have little predictive precision, the objective of the analysis — to determine whether there are before-after effects due to conflict — is accomplished.

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The results suggest that, ceteris paribus , if children who were exposed to the Boko Haram insurgency had not been exposed, the likelihood of wasting would decrease by 13 percentage points. Graphic representation of the marginal effects for the interaction term double-difference for wasting, Model 3 with select covariates. Left panel is the interaction effect and right panel is z-statistics for individual children. This study aimed to estimate the effect of the Boko Haram insurgency in Northeast Nigeria on childhood wasting. Visualization of common trends from through showed that wasting is plausibly linked to the insurgency, thus justifying the use of a double-difference methodology.

The differences in outcomes among two groups — those in conflict and non-conflict areas and in two periods — before and after the start of the conflict — were compared using household data from the Demographic and Health Surveys. Results suggest that if children who were affected by the Boko Haram insurgency in Northeast Nigeria had not experienced the conflict, they would have a mean WHZ 0. However, for at-risk children and those already suffering from malnutrition, this increase would mean better short-term health, increased resistance to other illnesses, and reduced risk of mortality.

This analysis also estimates with a high degree of significance that the likelihood of childhood wasting would have been 13 percentage points lower in the absence of conflict in Northeast Nigeria. This would reduce the proportion of children at risk of dying from malnutrition and co-morbidities and would help ensure a healthier and more productive adult population in the future.

How the conflict affected child nutrition is not fully known, but news reports from around the time of the survey give a sense of violence and lawlessness in the conflict areas. There are reports of indiscriminate executions of ordinary people, suicide attacks, and the destruction of schools and entire towns [ 31 ]. Men were fleeing forced conscription by Boko Haram and civilian groups formed to fight the insurgents [ 31 ]. Reports from the Famine Early Warning Systems Network illustrate growing food insecurity during this time [ 32 ].

Security measures such as checkpoints reduced population movements and increased transportation costs [ 32 ]. Traders and their customers were concerned about their safety in markets, which reduced food stocks and overall market functionality [ 32 ].

The Menace of Boko Haram and Fundamentalism in Nigeria | Black

This marked the start of a worsening humanitarian crisis characterized by fear, displacement, disrupted services such as health care, schools, and maintenance of infrastructure, and limited access by local and international humanitarian assistance organizations, especially to more remote areas [ 33 ]. This led to a nutrition crisis as food insecurity increased and childhood illnesses exacerbated by malnutrition often went untreated [ 33 ]. The significant covariates in the regression models also offer insight into what factors contributed to child health in this setting.

The rainy season was more protective for mean z-scores, while the dry season was more protective for likelihood of wasting. This could be due to different seasonal dynamics affecting the whole child population as opposed to the most vulnerable. A healthy child may gain weight if food security improves in the rainy season, e.