Age-specific factors related to institutional delivery in Nigeria: Insights from the 2011 Multiple Indicator Cluster Survey

2017 ◽  
Vol 58 (9) ◽  
pp. 1001-1016
Author(s):  
Comfort Z. Olorunsaiye ◽  
Hannah M. Degge ◽  
Sunday J. Lengmang
Keyword(s):  
Institutional Delivery ◽  
Cluster Survey ◽  
Multiple Indicator Cluster Survey ◽  
Multiple Indicator ◽  
Specific Factors

scholarly journals Predictors of wealth-related inequality in institutional delivery: a decomposition analysis using Nepal Multiple Indicator Cluster Survey (MICS) 2019

2021 ◽  
Author(s):  
Umesh Prasad Bhusal
Keyword(s):  
Institutional Delivery ◽  
Relative Concentration ◽  
National Level ◽  
Household Head ◽  
Decomposition Analysis ◽  
Cluster Survey ◽  
Related Inequality ◽  
Multiple Indicator Cluster Survey ◽  
Multiple Indicator ◽  
Status Of Women

Abstract Background Inequality in maternal healthcare use is a major concern for low-and middle-income countries (LMICs). Maternal health indicators at the national level have markedly improved in the last couple of decades in Nepal. However, the progress is not uniform across different population sub-groups. This study aims to identify the determinants of institutional delivery, measure the wealth-related inequality, and examine the key components that explain the inequality. Methods Most recent nationally representative Multiple Indicator Cluster Survey (MICS) 2019 was used to extract data about married women (15–49 years) with a live birth within two years preceding the survey. Logistic regression models were employed to assess the association of independent variables with the institutional delivery. The concentration curves (CC) and concentration indexe (CIX) were used to analyze the inequality in institutional delivery. Wealth index scores were used as a socio-economic variable to rank households. Decomposition was performed to identify the determinants that explain socio-economic inequality. Results The socio-economic status of households to which women belong was a significant predictor of institutional delivery, along with age, parity, four or more ANC visits, education status of women, area of residence, sex of household head, religious belief, and province. The concentration curve was below the line of equality and the relative concentration index (CIX) was 0.097 (p < 0.001), meaning the institutional delivery was disproportionately higher among women from wealthy groups. The decomposition analysis showed the following variables as the most significant contributor to the inequality: wealth status of women (53.2%), education of women (17 %), residence (8.64 %) and ANC visit (6.84 %). Conclusions The pro-poor strategies are urgent to reduce the existing inequality between wealthy and poorer women. The strategies should focus on raising the education level of women especially from the rural and relatively backward province (Province 2). Increasing antenatal care (ANC) coverage through out-reach campaigns is likely to increase facility-based delivery and decrease inequality. Monitoring of healthcare indicators at different sub-population level (for example wealth, residence, province) is key to ensure equitable improvement in health status and achieve universal health coverage (UHC).

scholarly journals Predictors of wealth-related inequality in institutional delivery: a decomposition analysis using Nepal multiple Indicator cluster survey (MICS) 2019

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Umesh Prasad Bhusal
Keyword(s):  
Concentration Index ◽  
Institutional Delivery ◽  
Economic Inequality ◽  
Decomposition Analysis ◽  
Concentration Curve ◽  
Cluster Survey ◽  
Related Inequality ◽  
Multiple Indicator Cluster Survey ◽  
Multiple Indicator ◽  
Status Of Women

Abstract Background Inequality in maternal healthcare use is a major concern for low-and middle-income countries (LMICs). Maternal health indicators at the national level have markedly improved in the last couple of decades in Nepal. However, the progress is not uniform across different population sub-groups. This study aims to identify the determinants of institutional delivery, measure wealth-related inequality, and examine the key components that explain the inequality. Methods Most recent nationally representative Multiple Indicator Cluster Survey (MICS) 2019 was used to extract data about married women (15-49 years) with a live birth within two years preceding the survey. Logistic regression models were employed to assess the association of independent variables with the institutional delivery. The concentration curve (CC) and concentration index (CIX) were used to analyze the inequality in institutional delivery. Wealth index scores were used as a socio-economic variable to rank households. Decomposition was performed to identify the determinants that explain socio-economic inequality. Results The socio-economic status of households to which women belong was a significant predictor of institutional delivery, along with age, parity, four or more ANC visits, education status of women, area of residence, sex of household head, religious belief, and province. The concentration curve was below the line of equality and the relative concentration index (CIX) was 0.097 (p < 0.001), meaning the institutional delivery was disproportionately higher among women from wealthy groups. The decomposition analysis showed the following variables as the most significant contributor to the inequality: wealth status of women (53.20%), education of women (17.02%), residence (8.64%) and ANC visit (6.84%). Conclusions To reduce the existing socio-economic inequality in institutional delivery, health policies and strategies should focus more on poorest and poor quintiles of the population. The strategies should also focus on raising the education level of women especially from the rural and relatively backward province (Province 2). Increasing antenatal care (ANC) coverage through outreach campaigns is likely to increase facility-based delivery and decrease inequality. Monitoring of healthcare indicators at different sub-population levels (for example wealth, residence, province) is key to ensure equitable improvement in health status and achieve universal health coverage (UHC).

ASSOCIATION OF SOCIO-DEMOGRAPHIC FACTORS WITH DIARRHEA IN CHILDREN LESS THAN FIVE YEARS: A SECONDARY ANALYSIS OF MULTIPLE INDICATOR CLUSTER SURVEY SINDH 2014

1970 ◽  
Vol 7 (2) ◽  
pp. 85-89
Author(s):  
Muhammad Irfan ◽  
Syed Mustansir Hussain Zaidi ◽  
Hira Fatima Waseem
Keyword(s):  
Rural Areas ◽  
Demographic Factors ◽  
Wealth Index ◽  
Household Wealth ◽  
Cluster Survey ◽  
Multiple Indicator Cluster Survey ◽  
Increased Risk ◽  
Multiple Indicator ◽  
Mother Education ◽  
Socio Demographic Factors

Background: Diarrhea founds to be the major cause of morbidity and mortality in children less than five years. Various factors are associated with diarrhea but socio-demographic factors are the main key elements, which associated with diarrhea. Methods: This study was examined association of socio-demographic factors with diarrhea in children less than five years of age of Sindh, Pakistan, using data from the Multiple Indicator Cluster Survey (MICS) conducted from January 2014 to August 2014. Data were collected for 18,108 children in whom 16,449 children had complete data of demographic variables being included in the analysis. Bivariate analysis was done using Pearson's Chi square test and multivariate analysis being done using binary logistic regression. Results: We found increased risk of diarrhea among children lives in rural areas while household wealth index quintile was also associated with diarrhea. Children in the poor, middle and fourth wealth index quintiles being at increased risk of diarrhea compared to children in the richest wealth index quintile. The highest risk of diarrhea was found for the child having mother with no education as well as children aged 12-23 months. Conclusion: Age of child, mother education and wealth index found significant with diarrhea while Male children, child aged 12-23 months, child with no mother education, child from rural areas and child from poor households found with high risk of diarrhea.

scholarly journals Sociodemographic factors and early marriage among women in Bangladesh, Ghana and Iraq: An illustration from Multiple Indicator Cluster Survey

Heliyon ◽  
2021 ◽  
Vol 7 (5) ◽  
pp. e07111
Author(s):  
Ahmed Abdus Saleh Saleheen ◽  
Sharmin Afrin ◽  
Samia Kabir ◽  
Md. Jakaria Habib ◽  
Maliha Afroj Zinnia ◽  
...  
Keyword(s):  
Sociodemographic Factors ◽  
Early Marriage ◽  
Cluster Survey ◽  
Multiple Indicator Cluster Survey ◽  
Multiple Indicator

scholarly journals Correlates of child functional difficulties status in Ghana: A further analysis of the 2017/18 multiple indicator cluster survey

Heliyon ◽  
2020 ◽  
Vol 6 (12) ◽  
pp. e05727
Author(s):  
Nutifafa Eugene Yaw Dey ◽  
Emmanuel Dziwornu ◽  
Kwabena Frimpong-Manso ◽  
Henry Ofori Duah ◽  
Pascal Agbadi
Keyword(s):  
Cluster Survey ◽  
Functional Difficulties ◽  
Multiple Indicator Cluster Survey ◽  
Multiple Indicator

scholarly journals The Role of Sociodemographic and Psychosocial Variables in Early Childhood Development: A Secondary Data Analysis of the 2014 Multiple Indicator Cluster Survey in the Dominican Republic

2021 ◽  
Author(s):  
Laura V. Sánchez-Vincitore ◽  
Arachu Castro
Keyword(s):  
Early Childhood ◽  
Dominican Republic ◽  
Early Childhood Development ◽  
Childhood Development ◽  
Developmental Potential ◽  
Cluster Survey ◽  
Multiple Indicator Cluster Survey ◽  
Psychosocial Model ◽  
Multiple Indicator ◽  
At Home

AbstractIntroductionThe association between sociodemographic factors, such as poverty, lack of maternal schooling, and being male at birth, and childhood developmental delay and poor educational outcomes has been established in the Dominican Republic. However, moderating factors present or introduced in families to buffer the effects of such factors on childhood development are still unknown.MethodsWe conducted a secondary analysis of the 2014 Multiple Indicator Cluster Survey for the Dominican Republic, a national household survey focused on maternal and child health and development. The first aim of our study was to determine if a sociodemographic model predicted early childhood development. The second aim was to determine if a psychosocial model (including family childrearing practices, discipline, and early childhood stimulation) predicted early childhood development above and beyond the sociodemographic model.ResultsWe found that both models predicted childhood development significantly, but that the psychosocial model explained 5% more variance than the sociodemographic model. The most relevant sociodemographic predictors were socioeconomic position and mother’s education, which uniquely explained 21% and 17% of the early childhood development variance, respectively. The most salient psychosocial predictors of early childhood development were: 1) attendance to an early childhood education program, which uniquely explained 15.0% of the variance; 2) negative discipline, which uniquely explained 12.4% (negative impact); 3) the number of children’s books at home, which uniquely explained 12.0%; and 4) stimulating activities at home, which uniquely explained 5%.ConclusionThese results have multiple implications for social programs that aim to improve children’s developmental potential in contexts of poverty. Although the results show a protective effect of psychosocial factors, sustainable and large-scale intervention should not be limited to just buffering effects, but to actually solve the underlying problem which is that poverty prevents children from reaching their developmental potential.

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