Analysis of individual-level and community-level effects on childhood undernutrition in Malawi

Ntenda, Peter Austin Morton; Chuang, Ying-Chun

doi:10.1016/j.pedneo.2017.11.019

Cited by 76 publications

(117 citation statements)

References 31 publications

(57 reference statements)

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“…This finding is consistent with previous studies. 13,23,24 Male children are believed to be more physically active and to expand more energy that should have been channeled to increase growth. 20 This study further revealed that childhood stunting was inversely related to the maternal education level, which was in line with findings in previous studies.…”

Section: Discussionmentioning

confidence: 99%

“…20 This study further revealed that childhood stunting was inversely related to the maternal education level, which was in line with findings in previous studies. 13,14,[23][24][25] Maternal education affects child nutrition via multiple pathways. Higher educational levels, for example, can be associated with higher knowledge.…”

Section: Discussionmentioning

confidence: 99%

“…9 Many previous studies emphasized the importance of socio-economic, demographic, household, environmental factors, parental characteristics, child health and feeding practice factors, and geographical locations on childhood nutrition status. [10][11][12][13] Prior studies in Indonesia on factors associated with stunting indicated that maternal education, 14,15 poverty, 15 and water and sanitation, 16 were associated with stunting.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Determinants of Stunted Children in Indonesia: A Multilevel Analysis at the Individual, Household, and Community Levels

Wicaksono¹,

Harsanti²

2020

Kesmas: National Public Health Journal

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This study aimed to examine the risk factors of childhood undernutrition in Indonesia. Determinants of childhood stunting were examined by using the 2013Indonesia Basic Health Research Survey dataset. A total of 76,165 children aged under 5 years were included in this study. The analysis used multivariatemultilevel logistic regression to determine adjusted odds ratios (aORs). The prevalence of stunting in the sample population was 36.7%. The odds of stunting increased significantly among the under-five boys, children living in slum area, and the increase of household member (aOR = 1.11, 95 %CI: 1.06–1.15; 1.09, 95%CI: 1.04–1.15; and 1.03, 95%CI: 1.02–1.04 respectively). The odds of stunting decreased significantly among children whose parents more educated (aOR = 0.87, 95 %CI: 0.83–0.91 and 0.87, 95%CI: 0.83–0.9, respectively), who live in urban area, in a province with higher Gross Domestic Product (GDP) per capita, and in a province with higher ratio of professional health worker per 1,000 population aged 0-4 years (aOR = 0.85, 95%CI: 0.81–0.89; 0.89; 95%CI: 0.79–1.00; and 0.99; 95%CI: 0.99–1.00, respectively). The study found that stunting was resulted from a complex interaction of factors, not only at the individual level, but also at household and community levels. The study findings indicate that interventions should implement multi-level approaches to address various factors from the community to the individual level.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Determinants of Stunted Children in Indonesia: A Multilevel Analysis at the Individual, Household, and Community Levels

Wicaksono¹,

Harsanti²

2020

Kesmas: National Public Health Journal

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“…Habyarimana () and Makoka () developed SLR model of child malnutrition and found child's sex and birth order and mother's education and body mass index (BMI) as potential factors of child malnutrition. Using GEE logistic model, Ntenda and Chuang () showed that children with a diarrhoeal episode in the last 2 weeks, having a poorly educated mothers, belonging to a low socio‐economic household, and living in communities with low female educational attainment are more likely to be malnourished in Malawi. Islam et al () also used GEE model to show that household with poor asset index, being a male child, and child age are the risk factors of stunting for 12‐ to 24‐month children.…”

Section: Introductionmentioning

confidence: 99%

On selection of an appropriate logistic model to determine the risk factors of childhood stunting in Bangladesh

Bhowmik

Das

2018

Maternal & Child Nutrition

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Stunting is the core measure of child health inequalities as it reveals multiple dimensions of child health and development status. The main focus of this study is to show the procedure of selecting the most appropriate logistic regression model for stunting by developing and comparing several plausible models, which ultimately helps to identify the predictors of childhood stunting in Bangladesh. This study utilizes child anthropometric data collected in the 2014 Bangladesh Demographic and Health Survey. Valid height-for-age anthropometric indices were available for a total of 6,931 children aged 0-59 months, of which about 36% were stunted. Ordinary logistic, survey logistic, marginal logistic, and random intercept logistic regression models were developed assuming independence, sampling design, cluster effect, and hierarchy of the data. Based on a number of model selection criteria, random intercept logistic model is found the most appropriate for the studied children. A number of child, mother, household, regional, and community-level variables were included in the model specification. The factors that increased the odds of stunting are children older than 11 months, short birth interval, recent morbidity of children, lower maternal education, young maternity, lower maternal body mass index, poor household wealth, urban residential place, and living in Sylhet division. Findings of this study recommend to utilize an appropriate logistic model considering the issues relevant to the data, particularly sampling design and clustering for determining the risk factors of childhood stunting in Bangladesh.

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“…The community characteristics were not derived directly from the respondents but derived by aggregating relevant individual response at the cluster level and dividing the resulting distribution into two or three equal parts. This method has been widely used in deriving community characteristics from the Demographic and Health Survey (DHS) data [43][44]. Table 1 provides more information on the research variables.…”

Section: Explanatory Variablesmentioning

confidence: 99%

Analysis of individual-level and community-level effects on advanced maternal age pregnancy in Nigeria

Solanke

Oduyoye

Oni

2020

Preprint

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Background: Advanced Maternal Age (AMA) pregnancy refers to pregnancy in women aged 35 years or older at the time of delivery. Existing studies have been more concerned with the obstetrics implication of AMA pregnancy. The associated social factors such as the individual and community level factors have remained largely unexplored particularly in population-based studies. This study examines the individual and community level factors associated with AMA pregnancy in Nigeria.Methods: Data were extracted from the 2018 Nigeria Demographic and Health Survey. A weighted sample of 13,105 women was analysed. The outcome variable was AMA pregnancy. The explanatory variables were individual characteristics (age, marital status, media exposure, current contraceptive use, period spent schooling, female autonomy, remarriage and income group) and community characteristics (proportion who delayed marriage in community, community level of female higher education, proportion of female in professional occupation in community, proportion of divorced or separated women in community, proportion of women in commuter marriage, type of community and geo-political zone of residence). Three mixed-effects multilevel logistic regression models were estimated. Results: Findings reveal 10.9% prevalence of AMA pregnancy in Nigeria. While the odds of AMA pregnancy were lower among women in very advanced (AOR=0.592, p<0.001; 95% CI: 0.509-0.688) and extremely advanced (AOR=0.290, p<0.001; 95% CI: 0.239-0.353) (AOR=1.794, p<0.001; 95% CI: 1.580-2.008) age groups, the odds were higher among women who had high exposure to mass media (AOR=1.794, p<0.001; 95% CI: 1.580-2.008), long schooling period (AOR=2.680, p<0.001; 95% CI: 2.058-3.488), remarried (AOR=2.269, p<0.001; 95% CI: 1.915-2.688) and from richest households (AOR=1.271, p<0.05; 95% CI: 1.098-1.472). The values of the Intra-Class Correlation (ICC) across the three fitted models reveal significant effects of the community characteristics on the odds of AMA pregnancy.Conclusion: Individual and community characteristics have important effects on AMA pregnancy. Health planners and authorities in the country should include AMA pregnancy as one of the priority areas of women’s health and safe motherhood strategies in addition to raising awareness of the obstetrics implication of AMA pregnancy through community-based public health education programme.

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Analysis of individual-level and community-level effects on childhood undernutrition in Malawi

Cited by 76 publications

References 31 publications

Determinants of Stunted Children in Indonesia: A Multilevel Analysis at the Individual, Household, and Community Levels

Determinants of Stunted Children in Indonesia: A Multilevel Analysis at the Individual, Household, and Community Levels

On selection of an appropriate logistic model to determine the risk factors of childhood stunting in Bangladesh

Analysis of individual-level and community-level effects on advanced maternal age pregnancy in Nigeria

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