Spatial distribution and multilevel analysis of the ideal number of children among Ethiopian women.

BACKGROUND: Ideal number of children (INC) is the number of children that a woman or man would have if they could go back to the time when they did not have any children and could choose accurately the number of children to have in their total life. Despite numerous studies on the prevalence and associated factors of the ideal number of children, there is a lack of studies that incorporated spatial and multilevel analysis. Thus, this study was aimed at the spatial and multilevel analysis of an ideal number of children and associated factors. METHODS: The study design was a cross-sectional study in which the data was obtained from Ethiopian Demographic and Health Survey (EDHS) in 2016. About 13,961 women ages 15-49 who fulfill the inclusion criterion were considered. A negative binomial regression model that incorporates spatial and multilevel analysis was employed. RESULTS: About 33 and 12.8% of the women had four and six ideal numbers of children respectively. The highest INC per woman was recorded in Oromia region 5055 (36.1%) and the lowest in Harare 35(0.2%). The INC per woman is high in rural 10,726 (76.6%) areas as compared to urban areas 3277(23.4%). The ideal number of children was spatially clustered (Global Moran's I = 0.1439, p < .00043). Significant hotspot clusters were found in the Somali region such as in Afder, Shabelle, Korahe, and Doolo zone. CONCLUSION: The spatial analysis revealed a significant clustering of the ideal number of children in the Ethiopia zone. Specifically, higher INC was observed in the Somali region, specifically in the Afder, Shabelle, Korahe, and Doolo zones. Among the various factors considered, women's age, region, place of residence, women's education level, contraception use, religion, marital status, family size, and age at first birth year were identified as significant predictors of the ideal number of children. These findings indicate that these factors play a crucial role in shaping reproductive preferences and decisions among women in the study population. Based on these findings, responsible bodies should prioritize targeted interventions and policies in high-risk regions to address women's specific reproductive needs.

Statistical models for longitudinal zero-inflated count data: application to seizure attacks.

BACKGROUND: Chronic non-communicable diseases:- such as epilepsy, are increasingly recognized as public health problems in developing and African countries. This study aimed at finding determinants of the number of epileptic seizure attacks using different count data modeling techniques. METHODS: Four common fixed-effects Poisson family models were reviewed to analyze the count data with a high proportion of zeros in longitudinal outcome, i.e., the number of seizure attacks in epilepsy patients. This is because, in addition to the problem of extra zeros, the correlation between measurements upon the same patient at different occasions needs to be taken into consideration. RESULTS: The investigation remarkably identified some important factors associated with epileptic seizure attacks. As people grow old, the number of seizure attacks increased and male patients had more seizures than their female counterparts. In general, a patient's age, sex, monthly income, family history of epilepsy andservice satisfaction were some of the significant factors responsible for the frequency of seizure attacks (P value<0.05). CONCLUSION: This study suggests that zero-inflated negative binomial is the best model for predicting and describing the number of seizure attacks as well as identifying the potential risk factors. Addressing these risk factors will definitely contain the progression of seizure attack.