scholarly journals Statistical Modeling of Determinants of Anemia Prevalence among Children Aged 6–59 Months in Nigeria: A Cross-Sectional Study

Anemia ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Ropo Ebenezer Ogunsakin ◽  
Bayowa Teniola Babalola ◽  
Oludare Akinyemi
Keyword(s):  
Public Health ◽  
Logistic Regression ◽  
Regression Models ◽  
Goodness Of Fit ◽  
Household Wealth ◽  
Demographic And Health Surveys ◽  
Negative Consequences ◽  
Logistic Regression Models ◽  
Childhood Anemia ◽  
Prevalence Of Anemia

Objective. Childhood anemia remains a significant public health challenge in developing countries, and it has negative consequences on the growth of the children. Therefore, it is essential to identify the determinants of childhood anemia, as these will help in formulating appropriate health policies in order to meet the United Nations MDG goal. This study aims to assess and model the determinants of the prevalence of anemia among children aged 6–59 months in Nigeria. To accomplish the aims of the study, the authors applied single-level and multilevel binary logistic regression models. Methods. To measure the relative impact of individual and household-level factors for childhood anemia among children aged 6–59 months, this study undertakes data from Nigeria Demographic and Health Surveys with both binary logistic and multilevel logistic regression models. The fit of the model was assessed by Hosmer–Lemeshow goodness-of-fit, variance inflation factor, and likelihood ratio tests. Results. The study established that about 67.01% of the children were anemic and identified sex of children, mother’s education, religion, household wealth status, total children ever born, age of children, place of residence, and region to have a statistical significant effect on the prevalence of anemia. The adjusted odds ratio (aOR) for anemia was 0.56 (95% CI = 0.50, 0.63) in children aged from 24 to 42 months and 0.40 (95% CI = 0.36, 0.45) in children aged from 43 to 59 months. Also, children who reside in certain geographical-political zones of Nigeria are associated with increased childhood anemia. Conclusion. This study has highlighted the high prevalence of childhood anemia in Nigeria and indicated the need to improve mothers’ education and regional variations. Findings from this study can help policymakers and public health institutions to map out programs targeting these regions as a measure of tackling the prevalence of anemia among the Nigerian populace.

On Graphically Checking Goodness-of-fit of Binary Logistic Regression Models

2009 ◽  
Vol 48 (03) ◽  
pp. 306-310 ◽  
Author(s):  
C. E. Minder ◽  
G. Gillmann
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Regression Models ◽  
Goodness Of Fit ◽  
Graphical Method ◽  
Logistic Models ◽  
Binary Logistic Regression ◽  
Logistic Regression Models ◽  
Logistic Analysis ◽  
Viable Solution

Summary Objectives: This paper is concerned with checking goodness-of-fit of binary logistic regression models. For the practitioners of data analysis, the broad classes of procedures for checking goodness-of-fit available in the literature are described. The challenges of model checking in the context of binary logistic regression are reviewed. As a viable solution, a simple graphical procedure for checking goodness-of-fit is proposed. Methods: The graphical procedure proposed relies on pieces of information available from any logistic analysis; the focus is on combining and presenting these in an informative way. Results: The information gained using this approach is presented with three examples. In the discussion, the proposed method is put into context and compared with other graphical procedures for checking goodness-of-fit of binary logistic models available in the literature. Conclusion: A simple graphical method can significantly improve the understanding of any logistic regression analysis and help to prevent faulty conclusions.

scholarly journals Ordinal logistic regression models: application in quality of life studies

2008 ◽  
Vol 24 (suppl 4) ◽  
pp. s581-s591 ◽  
Author(s):  
Mery Natali Silva Abreu ◽  
Arminda Lucia Siqueira ◽  
Clareci Silva Cardoso ◽  
Waleska Teixeira Caiaffa
Keyword(s):  
Quality Of Life ◽  
Logistic Regression ◽  
Regression Models ◽  
Goodness Of Fit ◽  
Ordinal Logistic Regression ◽  
Proportional Odds Model ◽  
Outcome Variable ◽  
Proportional Odds ◽  
Logistic Regression Models

Quality of life has been increasingly emphasized in public health research in recent years. Typically, the results of quality of life are measured by means of ordinal scales. In these situations, specific statistical methods are necessary because procedures such as either dichotomization or misinformation on the distribution of the outcome variable may complicate the inferential process. Ordinal logistic regression models are appropriate in many of these situations. This article presents a review of the proportional odds model, partial proportional odds model, continuation ratio model, and stereotype model. The fit, statistical inference, and comparisons between models are illustrated with data from a study on quality of life in 273 patients with schizophrenia. All tested models showed good fit, but the proportional odds or partial proportional odds models proved to be the best choice due to the nature of the data and ease of interpretation of the results. Ordinal logistic models perform differently depending on categorization of outcome, adequacy in relation to assumptions, goodness-of-fit, and parsimony.

scholarly journals Explaining withdrawal’s persistence: correlates of withdrawal use in Albania, Armenia, Jordan, and Turkey observed in a cross-sectional study

2021 ◽  
Vol 5 ◽  
pp. 92
Author(s):  
Timothee Fruhauf ◽  
Ghada Al-Attar ◽  
Amy O. Tsui
Keyword(s):  
Regression Models ◽  
Household Wealth ◽  
Cross Sectional Study ◽  
Low Fertility ◽  
Demographic And Health Surveys ◽  
Multinomial Regression ◽  
Cross Sectional ◽  
Logistic Regression Models ◽  
Western Asia ◽  
Geographical Cluster

Background: Withdrawal dominates the contraceptive method mix in a geographical cluster of countries in South-Eastern Europe and Western Asia that have, in part, reached low fertility. This study examines the socio-demographic determinants associated with withdrawal use in Armenia, Albania, Jordan and Turkey that could explain withdrawal’s persistence and inform contraceptive programs in these unique settings. Methods: Cross-sectional data on 31,569 married women 15 to 49 years were drawn from the Demographic and Health Surveys in Albania (2017-2018), Armenia (2015-2016), Jordan (2017-2018), and Turkey (2013). For each country, multinomial regression models estimating withdrawal use among all women and logistic regression models estimating withdrawal use among contraceptive users were used to evaluate the association with age, marital duration, parity, education, residence, and household wealth. Results: The socio-demographic determinants associated with withdrawal use varied by country among all women and among all contraceptive users. While these associations were not all significant for all four countries general trends included that women were more likely to use withdrawal than not use contraception, but less likely to use withdrawal than other methods with increasing parity, higher education, and greater household wealth. Measures of association are reported by country for each correlate. Conclusions: Despite the similar contraceptive mix in these four countries, no single set of factors was found to explain withdrawal’s persistence. Withdrawal’s prevalence in this geographical cluster may instead result from different balances of intertwined circumstances that include couples’ fertility decisions, access to modern contraception and availability of abortion services.

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