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      Association between short birth spacing and child malnutrition in Bangladesh: a propensity score matching approach

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          Abstract

          Objectives

          This study aimed to explore the effects of short birth spacing (SBS), which is defined as a period of less than 33 months between two successive births, on multiple concurrent forms of child malnutrition (MCFCM) and at least one form of child malnutrition (ALOFCM) using propensity score matching (PSM).

          Methods

          This study used data extracted from the 2017-18 Bangladesh Demographic and Health Survey. PSM with four different distance functions, including logistic regression, classification and regression tree, single hidden layer neural network and random forest, were performed to evaluate the effects of SBS on MCFCM and ALOFCM. We also explored how the effects were modified in different subsamples, including women’s empowerment, education and economic status (women’s 3E index)–constructed based on women’s decision-making autonomy, education level, and wealth index, and age at marriage, and place of residence.

          Results

          The prevalence of SBS was 22.16% among the 4652 complete cases. The matched samples of size 2062 generated by PSM showed higher odds of MCFCM (adjusted OR (AOR)=1.25, 95% CI=1.02 to 1.56, p=0.038) and ALOFCM (AOR=1.20, 95% CI=1.01 to 1.42, p=0.045) for the SBS children compared with their counterparts. In the subsample of women with 3E index≥50% coverage, the SBS children showed higher odds of MCFCM (AOR: 1.43, 95% CI=1.03 to 2.00, p=0.041] and ALOFCM (AOR: 1.33, 95% CI=1.02 to 1.74, p=0.036). Higher odds of MCFCM (AOR=1.27, 95% CI=1.02 to 1.58, p=0.036) and ALOFCM (AOR=1.23, 95% CI=1.02 to 1.51, p=0.032) for SBS children than normal children were also evident for the subsample of mothers married at age≤18 years.

          Conclusion

          SBS was significantly associated with child malnutrition, and the effect was modified by factors such as women’s autonomy and age at marriage.

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          Most cited references56

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          An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies

          The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The propensity score allows one to design and analyze an observational (nonrandomized) study so that it mimics some of the particular characteristics of a randomized controlled trial. In particular, the propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects. I describe 4 different propensity score methods: matching on the propensity score, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score. I describe balance diagnostics for examining whether the propensity score model has been adequately specified. Furthermore, I discuss differences between regression-based methods and propensity score-based methods for the analysis of observational data. I describe different causal average treatment effects and their relationship with propensity score analyses.
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            Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies.

            (2004)
            A WHO expert consultation addressed the debate about interpretation of recommended body-mass index (BMI) cut-off points for determining overweight and obesity in Asian populations, and considered whether population-specific cut-off points for BMI are necessary. They reviewed scientific evidence that suggests that Asian populations have different associations between BMI, percentage of body fat, and health risks than do European populations. The consultation concluded that the proportion of Asian people with a high risk of type 2 diabetes and cardiovascular disease is substantial at BMIs lower than the existing WHO cut-off point for overweight (> or =25 kg/m2). However, available data do not necessarily indicate a clear BMI cut-off point for all Asians for overweight or obesity. The cut-off point for observed risk varies from 22 kg/m2 to 25 kg/m2 in different Asian populations; for high risk it varies from 26 kg/m2 to 31 kg/m2. No attempt was made, therefore, to redefine cut-off points for each population separately. The consultation also agreed that the WHO BMI cut-off points should be retained as international classifications. The consultation identified further potential public health action points (23.0, 27.5, 32.5, and 37.5 kg/m2) along the continuum of BMI, and proposed methods by which countries could make decisions about the definitions of increased risk for their population.
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              The central role of the propensity score in observational studies for causal effects

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                Author and article information

                Journal
                BMJ Paediatr Open
                BMJ Paediatr Open
                bmjpo
                bmjpo
                BMJ Paediatrics Open
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2399-9772
                2024
                18 March 2024
                : 8
                : 1
                : e002240
                Affiliations
                [1 ]departmentDepartment of Statistics , Ringgold_421979Comilla University , Cumilla, Bangladesh
                [2 ]departmentDepartment of Biostatistics & Data Science , University of Kansas Medical Center , Kansas City, KS, USA
                [3 ]departmentDepartment of Statistics , Ringgold_95324University of Dhaka , Dhaka, Bangladesh
                [4 ]departmentDepartment of Mathematics , Texas A&M University-Commerce , Commerce, Texas, USA
                [5 ]departmentInstitute of Statistical Research and Training , Ringgold_95324University of Dhaka , Dhaka, Bangladesh
                [6 ]departmentDepartment of Mathematics and Statistics , Cleveland State University , Cleveland, OH, USA
                [7 ]departmentDepartment of Economics , Ringgold_54493University of Chittagong , Chittagong, Bangladesh
                [8 ]departmentDepartment of Pharmacy, School of Pharmaceutical Sciences , Ringgold_185960State University of Bangladesh , Dhaka, Bangladesh
                Author notes
                [Correspondence to ] Md. Jamal Hossain; jamal.du.p48@ 123456gmail.com ; Foyez Ahmmed; foyez.sbi@ 123456gmail.com
                Author information
                http://orcid.org/0000-0001-9706-207X
                Article
                bmjpo-2023-002240
                10.1136/bmjpo-2023-002240
                10953308
                38499349
                8fa1022c-fc37-4f24-ac4f-f767866a5dbf
                © Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 23 August 2023
                : 04 March 2024
                Categories
                Nutrition
                1506
                Original research
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                child abuse,epidemiology
                child abuse, epidemiology

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