Khatab, Khaled

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Sheffield Hallam University Research Archive

Prevalence of child welfare in Egypt using Bayesian Geospatial ModellingKHATAB, Khaled <http://orcid.org/0000-0002-8755-3964>Available from Sheffield Hallam University Research Archive (SHURA) at:http://shura.shu.ac.uk/23955/This document is the author deposited version.  You are advised to consult the publisher's version if you wish to cite from it.Published versionKHATAB, Khaled (2018). Prevalence of child welfare in Egypt using Bayesian Geospatial Modelling. In: Geospatial Child Health and Epidemiology Seminar, 20th June 2018. (Unpublished) Copyright and re-use policySee http://shura.shu.ac.uk/information.htmlSheffield Hallam University Research Archivehttp://shura.shu.ac.ukAssociate Risk Factors with Child Welfare including Child Labour and Violence against Child issues in Egypt using Bayesian Geospatial Modelling with Multiple Imputation.Khaled KhatabSheffield Hallam University, UK.Geospatial Child Health and Epidemiology Seminar20th June, 2018Outline • Child Health and Epidemiology  Research Group-Sheffield.• Associate Risk Factors with Child Welfare including Child Labour and Violence against Child issues in Egypt using Bayesian Geospatial Modelling with Multiple Imputation.2Part IChild Health and Epidemiology  Research Group-Sheffield3Aim:• The overall goal of this research group is to develop and deploy new and improved evidence on the causes and determinants of neonatal and child morbidity and mortality, on intervention handling, and on the effectiveness of interventions to enlighten and impact the research priorities in this area nationally and internationally. • This includes estimating specific distribution of the main causes of under-5 deaths, as well as maternal death and of the proportion of these deaths that can be prevented, and saving life by developing precise interventions, and enhancing modelling methodology along with the newly predications of efficacy and effectiveness of child survival intervention.Child Health and Epidemiology  Research Group-Sheffield4Child Health and Epidemiology Group-SheffieldThis group covers the following areas:• Child Mortality and Morbidity.• Child & Adolescent Mental Health.• Child labour and Environmental influence.• Interventions & Evidence.01/02/20195Associate Risk Factors with Child Welfare including Child Labour and Violence against Child issues in Egypt using Bayesian Geospatial Modelling with Multiple Imputation.Part IIObjectives • Determine the distribution of children who work among various provinces/governorates and different business sectors .• To investigate the associations between different types of outcomes (economic activities) and the socioeconomic and demographic indicators. This is because little or no research has extensively modelled these associated risk factors with the child labour.• Furthermore, little is known about geographical affects of the child labour in Egypt.7Outcome Variables• Type of work and the number of hours per weekThis study, however, has used the ILO classifications which are classified the number of hours worked per week into three groups as follows: A) Less= 15hrs a week; B) between 16 and less than 45hrs a week; and C) over 45hrs a week.• The economic activities were classified into three groups as well:• A) Non-Hazardous wage work• B) Hazardous wage work• C) Household work8Risk Factors• We considered the following socio-demographic factors and the associated risk factors of the child labour as explanatory variables: child’s age (5-17years), sex, household size, place of residence, wealth index, mother education, father education, parental survivorship and violent discipline approaches against children. • Wealth index were used as proxies for the socio-economic position of the household because EDHS does not collect information on household income and expenditure. Egypt comprises of 27 governorates, which were categorised by EDHS into 7 areas namely: Urban governorates, Lower Egypt urban, Lower Egypt rural, Upper Egypt urban, Upper Egypt rural and Frontier governorates. • However, in spatial analysis, we have used 27 governorates to investigate the spatial effects in the prevalence of overlap economic activities of children at the state level. This was achieved using a geo-additive semi-parametric multinomial model.9Statistical Methods • To estimate model parameters, we applied the fully integrated Bayesian approach. Though the estimation method with this model is difficult, the estimated posterior odds ratios (OR) that were produced could be understood as similar to those of normal logistic models. The analysis was carried out using version 2.1 of the BayesX software package, which certifies Bayesian inference based on Markov chain Monte Carlo (MCMC) simulation techniques (See Khatab, et al, 2017, PLOSONE).• Due to a significant proportion of missing data that affected few variables, a multiple imputation was used in this stage.10Prevalence and sectoral distribution of child labour • Results showed that at least 6.7% of the children in age group from 5-14 are working, while in the age group from 6-14, there are 88.1% attending school. However, 6.3% engaging in child labour while they still attending school.• It showed also that only 10.8% completed the primary school. 11Sectoral distribution of child labourChildren Age Percent Working (%) Population 5-14yrs 6.7(993,417)Attending Work and school (%) 6-14 88.1Combining Work and school (%) 7-14 6.3Primary Completion Rate (%) 10.8T1: UNESCO Institute for Statistics, 2015 12PREVALENCE AND SECTORAL DISTRIBUTION OF CHILD LABOURSector % of Children workAgriculture:Production of cottonCaring of livestockFishing activities 52.2%Industry :Quarrying limestoneMaking bricksWorking in carpentryConstructionWorking in aluminium factories16.5%Services:Domestic WorkRepainting automobilesstreet work including selling goods, collecting garbage, and sweeping30.4%T2: Sectoral distribution of child labour, EDHS 201413PREVALENCE AND SECTORAL DISTRIBUTION OF CHILD LABOUR30%17%53%0%Working Children by sector, Ages 5-14ServicesindustryAgriculure 01/02/201914School Attendance 93799277Primary school Secondary schoolMale FemalePercent of primary (6-11) and secondary (12-17) age children currently attending school15Many children age 1-14 were disciplined during the month before the survey using nonviolent methods such as:• Explaining to the child their behavior is wrong (85%) • Taking away privileges, forbidding something the child likes, or not allowing the child to leave the house (46%)• Giving the child something else to do (38%)Nonviolent Methods of Child Discipline16Many children were also disciplined with violent methods including:• Psychological aggression – shouting, yelling, screaming (88%) and calling child dumb, lazy, or stupid (63%)• Physical punishment – shaking (46%), hitting the child on the hand, arm, or leg (55%), spanking on the bottom with bare hand (37%), and hitting child with belt or other hard object (23%)• Severe physical punishment – hitting or slapping child on face, head, or ears (41%) or beating the child over and over as hard as possible (11%)Violent Methods of Child Discipline17Child Discipline by Age3349 48418996 95911-2 3-4 5-9 10-14Any severe physical punishment Any violent discipline methodPercentage of children age 1-14 years18Child Discipline by Mother’s Education48 51 513793 93 94 93No education Some primary Primarycomp./somesecondarySecondary comp./higherAny severe physical punishment Any violent discipline methodPercentage of children age 1-14 years19Associate Risk Factors with Child Welfare including Child Labour and Violence against Child issues in Egypt  using Bayesian GeospatialSpatial effects of Hazordous Wage Work among Children in Egypt B. between16-45 hours a weekA:less than 15hrs a week20Spatial effectsA: less than 15hrs a week B. 16- 45 hours a weekSpatial effects of Household work among Children in Egypt21Spatial effectsA: less than 15hrs a weekB. 16- 45 hours a weekSpatial effects of Non Hazourd Wage Work among Children in Egypt22Results • Most of factors were significantly associated with the tree types of child labour except father education.• Non Hazourd Wage Work: The following factors were significantly associated with Non Hazourd Work: sex of child (p=0.01), place of residence (p<0.001),household size (p<0.001), wealth index (p<0.001), Mother education (p<0.001), physiological aggression (p<0.001), physical punishment (p=0.02).• Hazordous Wage Work: The following factors were significantly associated with this outcome variable: place of residence (p<0.001), wealth index (p<0.001), Mother education (p<0.001) and the three type of violence approaches (p<0.001).• Household work : All the factors were significantly associated with the household work (p<0.001).23Results• An increase in household size increases the chance of a child engaged in child labour.• When the father is missing from the household, girls are more engaged in child labour. However, When the mother is missing, both boys and girls tend to work more.• Children in the age group of 11-15 are engaged more in house work while children above 15 are more engaged in hazard work. • Lower and Upper Egypt are more affected by child labour. 01/02/201924Conclusion• The maps could be used for targeting regional development in the future. It is obvious that the Nile Delta, Upper Egypt, and south-eastern Egypt have high rates of child labour and are more affected. • Therefore, more attention is needed in those areas. These areas are more likely to have higher poverty compared with other areas.25

Prevalence of child welfare in Egypt using Bayesian Geospatial Modelling

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Prevalence of child welfare in Egypt using Bayesian Geospatial Modelling

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