Complete childhood vaccination and associated factors among children aged 12–23 months in Dabat demographic and health survey site, Ethiopia, 2022

Gelagay, Abebaw Addis; Worku, Abebaw Gebeyehu; Bashah, Debrework Tesgera; Tebeje, Nigusie Birhan; Gebrie, Mignote Hailu; Yeshita, Hedija Yenus; Cherkose, Endeshaw Adimasu; Ayana, Birhanu Abera; Lakew, Ayenew Molla; Bitew, Desalegn Anmut; Asmamaw, Desale Bihonegn; Negash, Wubshet Debebe; Belachew, Tadele Biresaw; Fentie, Elsa Awoke

doi:10.1186/s12889-023-15681-0

Cited by 6 publications

(2 citation statements)

References 34 publications

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“…This association might be a result of the fact that mothers who do not make full use of ANC services miss out on opportunities to get information about the importance and timing of child vaccinations. In addition, mothers who decide not to receive antenatal care or give birth in a healthcare facility might not give the same attention to childhood vaccinations as their peers from similar socioeconomic backgrounds [ 89 – 91 ].…”

Section: Discussionmentioning

confidence: 99%

Spatial distribution and determinants of measles vaccination dropout among under-five children in Ethiopia: A spatial and multilevel analysis of 2019 Ethiopian demographic and health survey

Zegeye,

Mekonen,

Tekeba

et al. 2024

PLoS ONE

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Background Each year, vaccine-preventable diseases cost the lives of 8.8 million under-five children. Although vaccination prevents 1–2 million childhood deaths worldwide, measles vaccination dropouts are not well studied in developing countries, particularly in Ethiopia. Therefore, this study aims to assess the spatial distribution of the measles vaccination dropout and its determinants among under-five children in Ethiopia. Methods Data from Ethiopian Demographic and Health Survey 2019 was used for data analysis. The study used a total of 5,753 children. Spatial autocorrelations was used to determine the spatial dependency of measles vaccination dropout. Ordinary interpolation was employed to forecast measles vaccination dropout. Factors associated with measles vaccination dropout were declared significant at p-values <0.05. The data were interpreted using the confidence interval and adjusted odds ratio. A model with the lowest deviance and highest logliklihood ratio was selected as the best-fit model. Results In Ethiopia, one in three under-five children had measles vaccination dropouts. Factors such as birth interval (AOR = 1.87, 95% CI: 1.30, 2.70), unmarried marital status women (AOR = 3.98, 95% CI: 1.08, 8.45), ≤1 number of under-five children (AOR = 3.86, 95% CI: 2.56, 5.81), rural place of residence (AOR = 2.43, 95% CI: 2.29, 3.11), low community-level ANC utilization (AOR = 3.20, 95% CI: 2.53, 3.56), and residing in Benishangul Gumuz (AOR = 1.80, 95% CI: 1.061, 3.06) had higher odds of measles vaccination dropout. Conclusions Measles vaccination dropout rates in Ethiopia among under-five children were high compared to the maximum tolerable vaccination dropout level of 10% by the WHO. Both individual and community-level variables were determinants of measles vaccination dropout. The ministry of health in Ethiopia should give attention to those mothers of under-five children who reported underutilization of ANC services and rural residences while designing policies and strategies in areas of high spatial clustering of vaccine dropout in Ethiopia.

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Section: Discussionmentioning

confidence: 99%

Spatial distribution and determinants of measles vaccination dropout among under-five children in Ethiopia: A spatial and multilevel analysis of 2019 Ethiopian demographic and health survey

Zegeye,

Mekonen,

Tekeba

et al. 2024

PLoS ONE

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“…In terms of Household Water Treatment (HWT) practices, the pooled proportion of HWT practice in Ethiopia was found to be 21%, indicating a significantly low level of practice (Desye et al, 2023). Improving child health outcomes in Ethiopia requires targeted actions to address inequities, poverty, and barriers to healthcare access (Atalell et al, 2022;Defar et al, 2023;Gelagay et al, 2023;Kusheta et al, 2023;Tamene et al, 2023a).…”

Section: Introductionmentioning

confidence: 99%

Impact of Water, Sanitation, and Hygiene Components on Under-5 Children's Mortality in Ethiopia: A Comparative Analysis of Count Regression Models

Chafamo,

Muniswamy,

Punyavathi

2024

Preprint

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Objectives To investigates the effect of WASH components on under 5 children mortality and Compare the performance of count advanced count regression models. Design In this study, data on Water, Sanitation, and Hygiene Components on Under-5 Children's Mortality in Ethiopia participating in the Ethiopian Demographic and Health survey conducted at 2019. The cross-sectional study design was used to study the Demographic and Health data from 11 different regions of Ethiopia. R programming has been used to analyse descriptive and inferential statistics. The baseline Poisson regression model exhibited overdispersion, leading to the exploration of various count regression models. The Negative Binomial regression model emerged as the best fit for the data, displaying low Akaike Information Criterion (AI) values. Setting Women of reproductive Age 15-49 with Under-Five children were interviewed. Participants A total of 5753 women of reproductive Age 15-49 with Under-Five children were interviewed. Primary Outcome Measure The number of under-5 children death, that is count variable. Results The study reveals Significant factors influencing under 5 children's mortality were identified, including rural residence, inadequate toilet facilities, reliance on unsafe water sources, household access to radios, and household composition. Conclusion Children living in rural areas faced a significantly higher risk of mortality compared to urban counterparts. Furthermore, households lacking proper sanitation facilities or using unsafe water sources exhibited elevated mortality rates among under-5-year-olds. Conversely, households with radios showed a lower risk of under-5-year-old child deaths. The number of children in a household under the age of five and female-headed households were associated with lower mortality rates.

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Machine learning algorithms’ application to predict childhood vaccination among children aged 12–23 months in Ethiopia: Evidence 2016 Ethiopian Demographic and Health Survey dataset

Demsash,

Chereka,

Walle

et al. 2023

PLoS ONE

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Introduction Childhood vaccination is a cost-effective public health intervention to reduce child mortality and morbidity. But, vaccination coverage remains low, and previous similar studies have not focused on machine learning algorithms to predict childhood vaccination. Therefore, knowledge extraction, association rule formulation, and discovering insights from hidden patterns in vaccination data are limited. Therefore, this study aimed to predict childhood vaccination among children aged 12–23 months using the best machine learning algorithm. Methods A cross-sectional study design with a two-stage sampling technique was used. A total of 1617 samples of living children aged 12–23 months were used from the 2016 Ethiopian Demographic and Health Survey dataset. The data was pre-processed, and 70% and 30% of the observations were used for training, and evaluating the model, respectively. Eight machine learning algorithms were included for consideration of model building and comparison. All the included algorithms were evaluated using confusion matrix elements. The synthetic minority oversampling technique was used for imbalanced data management. Informational gain value was used to select important attributes to predict childhood vaccination. The If/ then logical association was used to generate rules based on relationships among attributes, and Weka version 3.8.6 software was used to perform all the prediction analyses. Results PART was the first best machine learning algorithm to predict childhood vaccination with 95.53% accuracy. J48, multilayer perceptron, and random forest models were the consecutively best machine learning algorithms to predict childhood vaccination with 89.24%, 87.20%, and 82.37% accuracy, respectively. ANC visits, institutional delivery, health facility visits, higher education, and being rich were the top five attributes to predict childhood vaccination. A total of seven rules were generated that could jointly determine the magnitude of childhood vaccination. Of these, if wealth status = 3 (Rich), adequate ANC visits = 1 (yes), and residency = 2 (Urban), then the probability of childhood vaccination would be 86.73%. Conclusions The PART, J48, multilayer perceptron, and random forest algorithms were important algorithms for predicting childhood vaccination. The findings would provide insight into childhood vaccination and serve as a framework for further studies. Strengthening mothers’ ANC visits, institutional delivery, improving maternal education, and creating income opportunities for mothers could be important interventions to enhance childhood vaccination.

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Complete childhood vaccination and associated factors among children aged 12–23 months in Dabat demographic and health survey site, Ethiopia, 2022

Cited by 6 publications

References 34 publications

Spatial distribution and determinants of measles vaccination dropout among under-five children in Ethiopia: A spatial and multilevel analysis of 2019 Ethiopian demographic and health survey

Spatial distribution and determinants of measles vaccination dropout among under-five children in Ethiopia: A spatial and multilevel analysis of 2019 Ethiopian demographic and health survey

Impact of Water, Sanitation, and Hygiene Components on Under-5 Children's Mortality in Ethiopia: A Comparative Analysis of Count Regression Models

Machine learning algorithms’ application to predict childhood vaccination among children aged 12–23 months in Ethiopia: Evidence 2016 Ethiopian Demographic and Health Survey dataset

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