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Article ; Online: The level of utilization and associated factors of WHO recommended antenatal care visits in South Asian countries.

Al-Zubayer, Md Akib / Shanto, Hasibul Hasan / Kundu, Subarna / Sarder, Md Alamgir / Ahammed, Benojir

Dialogues in health

2024 Volume 4, Page(s) 100175

Abstract: Background: Antenatal care can play an important role in reducing the death of both mothers and children. This study was designed to find out the determinants of world health organization recommended antenatal care visits in six South Asian countries to ...

Abstract	Background: Antenatal care can play an important role in reducing the death of both mothers and children. This study was designed to find out the determinants of world health organization recommended antenatal care visits in six South Asian countries to achieve the targets for Sustainable Development Goal. Methods: This study used recent demographic and health survey data from six South Asian countries such as Afghanistan (2015), Bangladesh (2017-18), India (2015-16), Maldives (2016-17), Nepal (2016), and Pakistan (2047-18). Descriptive statistics were calculated for the distribution and prevalence of antenatal care visits. Bivariate and multivariable logistic regressions were used to investigate the influencing factors of antenatal care visits. Results: 71,862 women aged 15 to 49 years were included in this study, and 46.64% (95% Confidence Interval = 45.59 - 47.69%) had world health organization recommended antenatal care visits. In the pooled data, urban women (AOR ([Adjusted Odds Ratio]=1.48; 95% CI [Confidence Interval]=1.33-1.66), richest family (AOR=1.48; 95% CI=1.25-1.76), women's higher education (AOR=3.76; 95% CI=3.33-4.25), women's partner/husband's higher education (AOR=1.69; 95% CI=1.50-1.92), 35-49 years (AOR=1.25, 95% CI=1.11-1.42), women's age at first birth >25 years (AOR=1.51, 95% CI=1.36-1.68) and fully media exposure (AOR=2.11; 95% CI=1.74-2.56) were significantly positively associated with WHO recommended antenatal care visits. Whereas, working women (AOR=0.82; 95% CI=0.76-0.88), healthcare decision maker by their husband/others (AOR=0.71, 95% CI=0.60-0.84), ≥7 children (AOR=0.59; 95% CI=0.50-0.69), and ≥7 family members (AOR=0.82; 95% CI=0.73-0.93) had significant negative effect on antenatal care visits. In country specific analysis, overall, media exposure, secondary and above education of women, ≥25 of years age at first birth, and <4 living children were the key factors of antenatal care visits. Conclusions: This study reveals an overall scenario of the WHO-recommended antenatal care visit in South Asian countries, and significant factors related to ANC that we can concentrate onto improve accessibility to healthcare services and promote education and media exposure, especially for rural and less educated women, to increase the prevalence of WHO-recommended antenatal visits in South Asian countries In addition, evidence from this study can be used to assist the policymakers in planning and taking proper steps to increase WHO-recommended antenatal care visits by focusing on the related factors in South Asian countries.
Language	English
Publishing date	2024-02-28
Publishing country	United States
Document type	Journal Article
ISSN	2772-6533
ISSN (online)	2772-6533
DOI	10.1016/j.dialog.2024.100175
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Article ; Online: Machine learning algorithms for predicting malnutrition among under-five children in Bangladesh.

Talukder, Ashis / Ahammed, Benojir

Nutrition (Burbank, Los Angeles County, Calif.)

2020 Volume 78, Page(s) 110861

Abstract: Objective: The aim of this study was is to predict malnutrition status in under-five children in Bangladesh by using various machine learning (ML) algorithms.: Methods: For analysis purposes, the nationally representative secondary records from the ... ...

Abstract	Objective: The aim of this study was is to predict malnutrition status in under-five children in Bangladesh by using various machine learning (ML) algorithms. Methods: For analysis purposes, the nationally representative secondary records from the 2014 Bangladesh Demographic and Health Survey (BDHS) were used. Five well-known ML algorithms such as linear discriminant analysis (LDA), k-nearest neighbors (k-NN), support vector machines (SVM), random forest (RF), and logistic regression (LR) have been considered to accurately predict malnutrition status among children. Additionally, a systematic assessment of the algorithms was performed by using accuracy, sensitivity, specificity, and Cohen's κ statistic. Results: Based on various performance parameters, the best results were accomplished with the RF algorithm, which demonstrated an accuracy of 68.51%, a sensitivity of 94.66%, and a specificity of 69.76%. Additionally, a most extreme discriminative ability appeared by RF classification (Cohen's κ = 0.2434). Conclusion: On the basis of the findings, we can presume that the RF algorithm was moderately superior to any other ML algorithms used in this study to predict malnutrition status among under-five children in Bangladesh. Finally, the present research recommends applying RF classification with RF feature selection when the prediction of malnutrition is the core interest.
MeSH term(s)	Algorithms ; Bangladesh/epidemiology ; Child ; Humans ; Machine Learning ; Malnutrition/diagnosis ; Malnutrition/epidemiology ; Support Vector Machine
Language	English
Publishing date	2020-05-15
Publishing country	United States
Document type	Journal Article
ZDB-ID	639259-3
ISSN	1873-1244 ; 0899-9007
ISSN (online)	1873-1244
ISSN	0899-9007
DOI	10.1016/j.nut.2020.110861
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Article ; Online: Machine learning models for prediction of double and triple burdens of non-communicable diseases in Bangladesh.

Al-Zubayer, Md Akib / Alam, Khorshed / Shanto, Hasibul Hasan / Maniruzzaman, Md / Majumder, Uttam Kumar / Ahammed, Benojir

Journal of biosocial science

2024 Volume 56, Issue 3, Page(s) 426–444

Abstract: Increasing prevalence of non-communicable diseases (NCDs) has become the leading cause of death and disability in Bangladesh. Therefore, this study aimed to measure the prevalence of and risk factors for double and triple burden of NCDs (DBNCDs and ... ...

Abstract	Increasing prevalence of non-communicable diseases (NCDs) has become the leading cause of death and disability in Bangladesh. Therefore, this study aimed to measure the prevalence of and risk factors for double and triple burden of NCDs (DBNCDs and TBNCDs), considering diabetes, hypertension, and overweight and obesity as well as establish a machine learning approach for predicting DBNCDs and TBNCDs. A total of 12,151 respondents from the 2017 to 2018 Bangladesh Demographic and Health Survey were included in this analysis, where 10%, 27.4%, and 24.3% of respondents had diabetes, hypertension, and overweight and obesity, respectively. Chi-square test and multilevel logistic regression (LR) analysis were applied to select factors associated with DBNCDs and TBNCDs. Furthermore, six classifiers including decision tree (DT), LR, naïve Bayes (NB), k-nearest neighbour (KNN), random forest (RF), and extreme gradient boosting (XGBoost) with three cross-validation protocols (K2, K5, and K10) were adopted to predict the status of DBNCDs and TBNCDs. The classification accuracy (ACC) and area under the curve (AUC) were computed for each protocol and repeated 10 times to make them more robust, and then the average ACC and AUC were computed. The prevalence of DBNCDs and TBNCDs was 14.3% and 2.3%, respectively. The findings of this study revealed that DBNCDs and TBNCDs were significantly influenced by age, sex, marital status, wealth index, education and geographic region. Compared to other classifiers, the RF-based classifier provides the highest ACC and AUC for both DBNCDs (ACC = 81.06% and AUC = 0.93) and TBNCDs (ACC = 88.61% and AUC = 0.97) for the K10 protocol. A combination of considered two-step factor selections and RF-based classifier can better predict the burden of NCDs. The findings of this study suggested that decision-makers might adopt suitable decisions to control and prevent the burden of NCDs using RF classifiers.
MeSH term(s)	Humans ; Noncommunicable Diseases ; Overweight ; Bangladesh ; Bayes Theorem ; Obesity ; Hypertension ; Machine Learning ; Diabetes Mellitus
Language	English
Publishing date	2024-03-20
Publishing country	England
Document type	Journal Article
ZDB-ID	390961-x
ISSN	1469-7599 ; 0021-9320
ISSN (online)	1469-7599
ISSN	0021-9320
DOI	10.1017/S0021932024000063
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Article ; Online: A multilevel analysis of individual and community-level factors associated with childhood immunisation in Bangladesh: Evidence from a pooled cross-sectional survey.

Sarder, Md Alamgir / Lee, Ka Yiu / Keramat, Syed Afroz / Hashmi, Rubayyat / Ahammed, Benojir

Vaccine: X

2023 Volume 14, Page(s) 100285

Abstract: Introduction: Previous studies on childhood vaccinations in Bangladesh relied on single-level analyses and ignored the clustering and hierarchical structure of data collected from people living in different geographical units. This study, therefore, ... ...

Abstract	Introduction: Previous studies on childhood vaccinations in Bangladesh relied on single-level analyses and ignored the clustering and hierarchical structure of data collected from people living in different geographical units. This study, therefore, aimed to investigate the association between individual and community-level factors of full childhood immunisation with an improved analytical approach. Methods: Participants were 13,752 children aged 12-59 months. Data were extracted from the Bangladesh Demographic and Health Survey (BDHS) conducted in 2007, 2011, 2014, and 2017-18. A two-level multilevel logistic regression method was used to analyse the data. Results: Approximately 87% of the children were fully immunised. In the fully adjusted model, at the individual level, mothers who had primary and above education (Adjusted odds ratio [AOR] = 1.78; 95% Confidence Interval [CI]: 1.57, 2.01), mass media exposure (AOR = 1.14; 95% CI: 1.00, 1.30), having vaccination cards (AOR = 3.65; 95% CI: 3.23, 4.14), and having at least 4 antenatal care (ANC) visits (AOR = 1.24; 95% CI: 1.06, 1.44) were strongly associated with full childhood immunisation. At community-level, rural residency (AOR = 1.25; 95% CI: 1.08, 1.44), community women's education (AOR = 1.24; 95% CI: 1.07, 1.43), and community ANC utilisation (AOR = 1.38; 95% CI: 1.19, 1.61) were significantly associated with full childhood immunisation. Conclusion: Along with individual-level factors, community-level factors have a significant effect on childhood immunisation. Policymakers should target improving community-level characteristics, such as community poverty, education levels, and the number of community-level ANC visits, to increase the national level of childhood immunisation. Public health intervention programs aiming at increasing awareness of childhood immunisation should include elements at both individual and community levels.
Language	English
Publishing date	2023-03-23
Publishing country	England
Document type	Journal Article
ISSN	2590-1362
ISSN (online)	2590-1362
DOI	10.1016/j.jvacx.2023.100285
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Article ; Online: Prevalence and socioeconomic determinants of awareness and visitation of community clinic among ever married women: evidence from Bangladesh Demographic and Health Survey, 2017-2018.

Al-Zubayer, Md Akib / Shanto, Hasibul Hasan / Kumkum, Rabeya / Alam, Sk Tasnuva / Ahammed, Benojir

BMJ open

2023 Volume 13, Issue 2, Page(s) e067823

Abstract: Objectives: Bangladesh has made remarkable progress in improving the population's health, but maternal health and healthcare facilities are still in a vulnerable situation. This study aims to investigate the prevalence and determinants of awareness and ... ...

Abstract	Objectives: Bangladesh has made remarkable progress in improving the population's health, but maternal health and healthcare facilities are still in a vulnerable situation. This study aims to investigate the prevalence and determinants of awareness and visitation of community clinics (CCs) in Bangladesh. Design: A population-based cross-sectional study. Setting: The data were collected from the most recent Bangladesh Demographic and Health Survey conducted in 2017-2018. Participants: This study's participants are 18 893 women aged 15-49 years throughout all administrative regions. Primary and secondary outcome measures: The outcomes are awareness and visitation of CCs, defined as if women are aware and visit of CCs. Materials and methods: Descriptive statistics, bivariate and multivariate binary logistics analysis were used to determine the prevalence and associated factors of awareness and visitation of CCs. Results: The prevalence of awareness and visitation to CCs were 60.26% and 15.92%, respectively. The result of the multivariate analysis revealed that higher education, division and higher number of children were significantly positively associated, whereas the richest wealth index was significantly negatively associated with both awareness and visitation to CCs. Furthermore, the urban residence was negatively and respondent involvement in currently working was positively significantly related to awareness of CCs. Moreover, male household heads and exposure to media were significantly positively related to visitation to CCs. Conclusion: The study result highlights that more than half of the women were aware of CCs however, the CCs' visit rates were comparatively low. Priority-based public health programmes for women through community health workers are urgently needed to increase the awareness and visitation of CCs.
MeSH term(s)	Child ; Female ; Male ; Humans ; Bangladesh/epidemiology ; Prevalence ; Cross-Sectional Studies ; Family Conflict ; Socioeconomic Factors ; Family Characteristics
Language	English
Publishing date	2023-02-09
Publishing country	England
Document type	Journal Article
ZDB-ID	2599832-8
ISSN	2044-6055 ; 2044-6055
ISSN (online)	2044-6055
ISSN	2044-6055
DOI	10.1136/bmjopen-2022-067823
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Article ; Online: Prevalence and socioeconomic determinants of awareness and visitation of community clinic among ever married women

Md Akib Al-Zubayer / Hasibul Hasan Shanto / Rabeya Kumkum / Sk Tasnuva Alam / Benojir Ahammed

BMJ Open, Vol 13, Iss

evidence from Bangladesh Demographic and Health Survey, 2017–2018

2023 Volume 2

Abstract: Objectives Bangladesh has made remarkable progress in improving the population’s health, but maternal health and healthcare facilities are still in a vulnerable situation. This study aims to investigate the prevalence and determinants of awareness and ... ...

Abstract	Objectives Bangladesh has made remarkable progress in improving the population’s health, but maternal health and healthcare facilities are still in a vulnerable situation. This study aims to investigate the prevalence and determinants of awareness and visitation of community clinics (CCs) in Bangladesh.Design A population-based cross-sectional study.Setting The data were collected from the most recent Bangladesh Demographic and Health Survey conducted in 2017–2018.Participants This study’s participants are 18 893 women aged 15–49 years throughout all administrative regions.Primary and secondary outcome measures The outcomes are awareness and visitation of CCs, defined as if women are aware and visit of CCs.Materials and methods Descriptive statistics, bivariate and multivariate binary logistics analysis were used to determine the prevalence and associated factors of awareness and visitation of CCs.Results The prevalence of awareness and visitation to CCs were 60.26% and 15.92%, respectively. The result of the multivariate analysis revealed that higher education, division and higher number of children were significantly positively associated, whereas the richest wealth index was significantly negatively associated with both awareness and visitation to CCs. Furthermore, the urban residence was negatively and respondent involvement in currently working was positively significantly related to awareness of CCs. Moreover, male household heads and exposure to media were significantly positively related to visitation to CCs.Conclusion The study result highlights that more than half of the women were aware of CCs however, the CCs’ visit rates were comparatively low. Priority-based public health programmes for women through community health workers are urgently needed to increase the awareness and visitation of CCs.
Keywords	Medicine ; R
Subject code	360
Language	English
Publishing date	2023-02-01T00:00:00Z
Publisher	BMJ Publishing Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Maternal Healthcare Services Utilisation and Its Associated Risk Factors: A Pooled Study of 37 Low- and Middle-Income Countries.

Shanto, Hasibul Hasan / Al-Zubayer, Md Akib / Ahammed, Benojir / Sarder, Md Alamgir / Keramat, Syed Afroz / Hashmi, Rubayyat / Haque, Rezwanul / Alam, Khorshed

International journal of public health

2023 Volume 68, Page(s) 1606288

Abstract: Objectives: ...

Abstract	Objectives:
MeSH term(s)	Pregnancy ; Child ; Female ; Humans ; Maternal Health Services ; Developing Countries ; Prenatal Care ; Facilities and Services Utilization ; Patient Acceptance of Health Care ; Delivery of Health Care
Language	English
Publishing date	2023-10-23
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2274130-6
ISSN	1661-8564 ; 1661-8556
ISSN (online)	1661-8564
ISSN	1661-8556
DOI	10.3389/ijph.2023.1606288
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Article ; Online: Prevalence and associated factors of underweight and overweight/obesity among reproductive-aged women: A pooled analysis of data from South Asian countries (Bangladesh, Maldives, Nepal and Pakistan).

Ferdausi, Farzana / Al-Zubayer, Md Akib / Keramat, Syed Afroz / Ahammed, Benojir

Diabetes & metabolic syndrome

2022 Volume 16, Issue 3, Page(s) 102428

Abstract: Background and aims: Underweight and overweight/obesity is a critical public health problem among women in South Asian countries. This study aimed to find the prevalence of underweight and overweight/obesity and discover its associated factors among ... ...

Abstract	Background and aims: Underweight and overweight/obesity is a critical public health problem among women in South Asian countries. This study aimed to find the prevalence of underweight and overweight/obesity and discover its associated factors among women of reproductive age in four South Asian countries. Methods: Population-representative cross-sectional latest Demographic and Health Survey data from four South Asian countries, considering Bangladesh (2017-18), Maldives (2016-17), Nepal (2016), and Pakistan (2017-18), were pooled for this study. To investigate the factors related with underweight and overweight/obesity in women, a multivariate multinomial logistic regression model was deployed. Results: The overall prevalence of underweight and overweight/obesity among reproductive-age women in four South Asian countries was 11.8% and 36.3%, respectively. According to adjusted multivariate multinomial logistic regression analysis, women who lived in Pakistan, were older, had a better education, were from the wealthiest home, were currently in union and had media exposure had a considerably decreased probability of being underweight. In contrast, families with a large number of members had a considerably increased risk of becoming underweight. Additionally, women from the Maldives, older age, secondary education, a higher number of children, women from the richest household, currently in the union, the family had media exposure, and pregnant women have been found significantly positively associated with overweight/obesity. However, Nepalese women, large family members, rural residence, and work involvement were significantly negatively associated with overweight/obesity. Conclusion: The problem of being underweight and overweight/obesity still exists in South Asian countries. Focusing on women's age, education, wealth index, and media exposure, different public health intervention approaches are imperative to reduce unhealthy weight conditions.
MeSH term(s)	Adult ; Bangladesh/epidemiology ; Child ; Cross-Sectional Studies ; Female ; Health Surveys ; Humans ; Nepal/epidemiology ; Obesity/epidemiology ; Overweight/epidemiology ; Pakistan/epidemiology ; Pregnancy ; Prevalence ; Risk Factors ; Socioeconomic Factors ; Thinness/epidemiology
Language	English
Publishing date	2022-02-18
Publishing country	Netherlands
Document type	Journal Article ; Meta-Analysis
ZDB-ID	2273766-2
ISSN	1878-0334 ; 1871-4021
ISSN (online)	1878-0334
ISSN	1871-4021
DOI	10.1016/j.dsx.2022.102428
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Article ; Online: Correction: Prevalence of unintended pregnancy and its associated factors: Evidence from six south Asian countries.

Sarder, Md Alamgir / Islam, Sheikh Mohammed Shariful / Maniruzzaman, Md / Talukder, Ashis / Ahammed, Benojir

PloS one

2021 Volume 16, Issue 10, Page(s) e0259360

Abstract: This corrects the article DOI: 10.1371/journal.pone.0245923.]. ...

Abstract	[This corrects the article DOI: 10.1371/journal.pone.0245923.].
Language	English
Publishing date	2021-10-26
Publishing country	United States
Document type	Published Erratum
ISSN	1932-6203
ISSN (online)	1932-6203
DOI	10.1371/journal.pone.0259360
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Article: Machine learning algorithms for predicting malnutrition among under-five children in Bangladesh

Talukder, Ashis / Ahammed, Benojir

Nutrition. 2020 Oct., v. 78

2020

Abstract: The aim of this study was is to predict malnutrition status in under-five children in Bangladesh by using various machine learning (ML) algorithms.For analysis purposes, the nationally representative secondary records from the 2014 Bangladesh Demographic ...

Abstract	The aim of this study was is to predict malnutrition status in under-five children in Bangladesh by using various machine learning (ML) algorithms.For analysis purposes, the nationally representative secondary records from the 2014 Bangladesh Demographic and Health Survey (BDHS) were used. Five well-known ML algorithms such as linear discriminant analysis (LDA), k-nearest neighbors (k-NN), support vector machines (SVM), random forest (RF), and logistic regression (LR) have been considered to accurately predict malnutrition status among children. Additionally, a systematic assessment of the algorithms was performed by using accuracy, sensitivity, specificity, and Cohen's κ statistic.Based on various performance parameters, the best results were accomplished with the RF algorithm, which demonstrated an accuracy of 68.51%, a sensitivity of 94.66%, and a specificity of 69.76%. Additionally, a most extreme discriminative ability appeared by RF classification (Cohen's κ = 0.2434).On the basis of the findings, we can presume that the RF algorithm was moderately superior to any other ML algorithms used in this study to predict malnutrition status among under-five children in Bangladesh. Finally, the present research recommends applying RF classification with RF feature selection when the prediction of malnutrition is the core interest.
Keywords	discriminant analysis ; health surveys ; malnutrition ; nutrition ; prediction ; regression analysis ; Bangladesh
Language	English
Dates of publication	2020-10
Publishing place	Elsevier Inc.
Document type	Article
Note	NAL-AP-2-clean
ZDB-ID	639259-3
ISSN	1873-1244 ; 0899-9007
ISSN (online)	1873-1244
ISSN	0899-9007
DOI	10.1016/j.nut.2020.110861
Database	NAL-Catalogue (AGRICOLA)

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