Geographic weighted regression analysis of hot spots of anemia and its associated factors among children aged 6–59 months in Ethiopia: A geographic weighted regression analysis and multilevel robust Poisson regression analysis

Tesema, Getayeneh Antehunegn; Tessema, Zemenu Tadesse; Angaw, Dessie Abebaw; Tamirat, Koku Sisay; Teshale, Achamyeleh Birhanu

doi:10.1371/journal.pone.0259147

Cited by 15 publications

(15 citation statements)

References 65 publications

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“…As the level of anemia status of women category increases, there is a high likelihood of having an anemic child as compared to non-anemic women. This finding is in line with studies from in Ethiopia 14 , India 41 , Southern Africa 42 . The possible reason might be mothers and their children will have similar exposure to infections and other environmental factors that affect red blood cells production and iron storage 42 .…”

Section: Discussionsupporting

confidence: 93%

“…Children born to mothers with low education level may not get adequate nutrition such as iron rich food like meat and poultry due to poor affordability of food items and lack of knowledge about iron-rich foods for children. In addition, uneducated mothers were less likely to utilize child health services which may have a significant and positive impact on child health 14 .…”

Section: Discussionmentioning

confidence: 99%

“…Previous spatial and multilevel studies using EDHS have been reported in Ethiopia using classical statistical methods and broader regional level geographic boundaries without accounting for spatial correlations 14 , 16 , 18 , 19 . Appropriate small area level estimates (zone level) are needed for effective interventions.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian spatio-temporal modelling of child anemia in Ethiopia using conditional autoregressive model

Tessema

Tesema

Ahern

et al. 2022

Sci Rep

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Anemia is a common health problem for women and under five children in low income countries. According to the WHO, anemia is considered a serious public health problem when the prevalence is greater than 40%. The prevalence of anemia among children under five in Ethiopia changes over time, and is spatially correlated because it is influenced by environmental, socio-economic and other related factors. However, to our knowledge, there is no small area level estimates of anemia among children under five in Ethiopia. Therefore, this study aimed to assess zonal level estimates of anemia using a Bayesian spatio-temporal conditional autoregressive modeling approach. The data for the study was extracted from the Ethiopian Demographic and Health Surveys (EDHS) from 2005 to 2016. A sample of 18,939 children aged 6–59 months were considered for this study. A Bayesian spatio-temporal conditional autoregressive model was implemented to identify the risk of child anemia. Smoothed relative risks along with the 95% credible interval were reported. The queen’s adjacency matrix method was used in spatial smoothing and in estimating the relative risk. The prevalence of anemia among children aged 6–59 months in Ethiopia was 54% in 2005, 44% in 2011 and 57% in 2016. This study showed that low maternal education, low socio-economic status of women, and maternal anemia at zone level were strongly associated with child anemia in Ethiopia. Therefore, enhancing education for women, improving women's socioeconomic status, and mitigating maternal anemia are crucial to reduce the prevalence of childhood anemia in Ethiopia.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionmentioning

confidence: 99%

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Bayesian spatio-temporal modelling of child anemia in Ethiopia using conditional autoregressive model

Tessema

Tesema

Ahern

et al. 2022

Sci Rep

Self Cite

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“…Arc GIS version 10.8 software was used to investigate the presence of spatial variation and map model parameters between local models. The global spatial autocorrelation (Global Moran’s I) was calculated to declare whether delayed ANC initiation was dispersed, clustered, or randomly distributed in Ethiopia [ 35 , 36 ]. Global Moran’s I is a spatial statistic used to measure spatial autocorrelation by taking the entire dataset and producing a single output value that ranges from − 1 to + 1.…”

Section: Methodsmentioning

confidence: 99%

Geographical weighted regression analysis of delayed antenatal care initiation and its factors among all reproductive-aged women in Ethiopia, 2016

Tareke

Gashu

Endehabtu

2022

Contracept Reprod Med

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Background Delayed antenatal care is when the first visit is carried out after 12 gestational weeks. Despite the fact that many studies have been conducted on antenatal care initiation, little attention has been paid to its spatial pattern. Therefore, this study examine geographical weighted regression analysis of delayed antenatal care initiation and its factors among all reproductive-aged women in Ethiopia. Objective To assess geographical weighted regression analysis of delayed antenatal care initiation and its factors among all reproductive-aged women in Ethiopia, 2016. Methods This study was grounded on the 2016 Ethiopian Demographic Health Survey. It incorporated extracted sample size of 4740 (weighted) reproductive-aged women. ArcGIS version 10.8 and SaTScan™ version 9.7 software were employed to investigate geographic information. To distinguish factors associated with hotspot areas, local and global models were fitted. Result the geographic pattern of Delayed antenatal care initiation was clustered (Moran’s I = 0.38, p < 0.001). Kuldorff’s spatial scan statistics discovered three significant clusters. The most likely cluster (LLR = 66.13, p < 0.001) was situated at the zones of SNNP and Oromia regions. In the local model, being uneducated, being poor wealth, having an unwanted pregnancy, and having higher birth order were factors associated with spatial variation of delayed antenatal care. Conclusion The spatial pattern of delayed antenatal care in Ethiopia is clustered. Maternal education, wealth status, pregnancy desirability, and birth order were predictor variables of spatial variation of delayed antenatal care. Therefore, designing a hotspot area-based interventional plan could help to improve early ANC initiation.

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“…OLS is a global statistical model that is used to test and explain the relationship between the dependent and independent variables [ 28 ]. The OLS was used as a diagnostic tool as well as to select the appropriate predictors (in terms of their relationship with delayed breastfeeding initiation) for the Geographic Weighted Regression (GWR) model [ 29 ]. The Koenker Bp technique was used to see if the model could be used to do a spatially weighted regression analysis.…”

Section: Spatial Regressionmentioning

confidence: 99%

Modeling spatial determinants of initiation of breastfeeding in Ethiopia: A geographically weighted regression analysis

et al. 2022

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Background The World Health Organization (WHO) encourages breastfeeding to begin within the first hour after birth in order to save children’s lives. In Ethiopia, different studies are done on the prevalence and determinants of breastfeeding initiation, up to our knowledge, the spatial distribution and the spatial determinants of breast feeding initiation over time are not investigated. Therefore, the objectives of this study were to assess spatial variation and its spatial determinant of delayed initiation of breastfeeding in Ethiopia using Geographically Weighted Regression (GWR). Methods A cross-sectional study was undertaken using the nationally representative 2016 Ethiopian Demographic and Health Survey (EDHS) dataset. Global Moran’s I statistic was used to measure whether delayed breastfeeding initiation was dispersed, clustered, or randomly distributed in study area. Ordinary Least Squares (OLS) regression was used to identify factors explaining the geographic variation in delayed breastfeeding initiation. Besides, spatial variability of relationships between dependent and selected predictors was investigated using geographically weighted regression. Result A total weighted sample of 4169 children of aged 0 to 23 months was included in this study. Delayed initiation of breastfeeding was spatially varies across the country with a global Moran’s I value of 0.158 at (p-value<0.01). The hotspot (high risk) areas were identified in the Amhara, Afar, and Tigray regions. Orthodox religion, poor wealth index, caesarian section, baby postnatal checkup, and small size of a child at birth were spatially significant factors for delayed breastfeeding initiation in Ethiopia. Conclusion In Ethiopia initiation of breastfeeding varies geographically across region. A significant hotspot was identified in the Amhara, Afar, and Tigray regions. The GWR analysis revealed that orthodox religion, poor wealth index, caesarian section, baby postnatal checkup, and small birth weight were spatially significant factors.

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Geographic weighted regression analysis of hot spots of anemia and its associated factors among children aged 6–59 months in Ethiopia: A geographic weighted regression analysis and multilevel robust Poisson regression analysis

Cited by 15 publications

References 65 publications

Bayesian spatio-temporal modelling of child anemia in Ethiopia using conditional autoregressive model

Bayesian spatio-temporal modelling of child anemia in Ethiopia using conditional autoregressive model

Geographical weighted regression analysis of delayed antenatal care initiation and its factors among all reproductive-aged women in Ethiopia, 2016

Modeling spatial determinants of initiation of breastfeeding in Ethiopia: A geographically weighted regression analysis

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