Assessment of the Socio-Demographic and Economic Risk Factors of Childhood Diseases and Mortality in Yamaltu Deba Local Government Area, Gombe State, Nigeria

Abstract

The study Assessed major childhood diseases and child mortality in Yamaltu/Deba Local Government Area of Gombe State. It also analyzed the complex socio-economic and cultural factors that influence the distribution of diseases and mortality in the study area. Data on childhood diseases and mortality were collected from hospital visits of children aged 0-5 years at Deba General Hospital during the 10-year period from 2007-2016. Five percent of the records were sampled. Data on the demographic, socio-economic and cultural factors of respondents were collected with the aid of questionnaires, Focus Group Discussions (FGDs) and In-Depth Interviews (IDIs) to explain perceptions and attitudes regarding the major diseases and child mortality in the study area. The sample size was determined from one-third of the households in the study area. One hundred respondents were selected from 21 settlements in the 11 wards of the study area, using a systematic sampling technique. Descriptive and Inferential Statistics (regression analysis, Pearson’s correlation and ANOVA) were used in analyzing the results. The results show malaria, diarrhoea and other fevers as the major childhood diseases; with variation in childhood mortality as influenced by the demographic, socio-economic, and cultural characteristics of parents. The regression analysis with an R-value of 0.860 indicates a very high degree of correlation among the variables. It indicates that 70% of the diseases caused can be explained or accounted for by income, religion, occupation and education. The regression analyses Coefficients indicate that Religion and Education significantly predict “Disease”. The result further indicates that “Religion” which yielded a Beta (β) value of .862, t-value of 3.102, and a p-value of .002, and “Education” which yielded a Beta (β) value of .760, t-value of 2.058, and a p-value of .040 were significant. Similarly, regression analysis for mortality with an R-value of 0.536 indicates a high degree of correlation among the variables in the model. This shows that 41.8% of the mortality causes can be explained or accounted for by income, religion, occupation and education. The result of the analysis also shows that healthcare facilities in the area are not efficiently located. The study recommended that Programmes and policies aimed at addressing the health needs and economic empowerment of the population should be put in place in the study area.