Understanding and predicting pregnancy termination in Bangladesh: A comprehensive analysis using a hybrid machine learning approach.

Reproductive health issues, including unsafe pregnancy termination, remain a significant concern for women in developing nations. This study focused on investigating and predicting pregnancy termination in Bangladesh by employing a hybrid machine learning approach. The analysis used data from the Bangladesh Demographic and Health Surveys conducted in 2011, 2014, and 2017 to 2018. Ten independent variables, encompassing factors such as age, residence, division, wealth index, working status, BMI, total number of children ever born, recent births, and number of living children, were examined for their potential associations with pregnancy termination. The dataset undergoes preprocessing, addressing missing values and balancing class distributions. To predict pregnancy termination, 8 classical machine learning models and hybrid models were used in this study. The models' performance was evaluated based on the area under the curve, precision, recall, and F1 score. The results highlighted the effectiveness of the hybrid models, particularly the Voting hybrid model (area under the curve: 91.97; precision: 84.14; recall: 83.87; F1 score: 83.84), in accurately predicting pregnancy termination. Notable predictors include age, division, and wealth index. These findings hold significance for policy interventions aiming to reduce pregnancy termination rates, emphasizing the necessity for tailored approaches that consider regional disparities and socioeconomic factors. Overall, the study demonstrates the efficacy of hybrid machine learning models in comprehending and forecasting pregnancy termination, offering valuable insights for reproductive health initiatives in Bangladesh and similar contexts.

Rural and urban differences in the prevalence and determinants of Type-2 diabetes in Bangladesh.

OBJECTIVE: To estimate the prevalence of Type 2 Diabetes (T2D) in urban and rural settings and identify the specific risk factors for each location. METHOD: We conducted this study using data from the 2017-18 Bangladesh Demographic and Health Survey (BDHS), sourced from the DHS website. The survey employed a stratified two-stage sampling method, which included 7,658 women and 7,048 men aged 18 and older who had their blood glucose levels measured. We utilized chi-square tests and ordinal logistic regression to analyze the association between various selected variables in both urban and rural settings and their relationship with diabetes and prediabetes. RESULTS: The prevalence of T2D was 10.8% in urban areas and 7.4% in rural areas, while pre-diabetes affected 31.4% and 27% of the populations in these respective settings. The study found significant factors influencing diabetes in both urban and rural regions, particularly in the 55-64 age group (Urban: AOR = 1.88, 95% CI [1.46, 2.42]; Rural: AOR = 1.87, 95% CI [1.54, 2.27]). Highly educated individuals had lower odds of T2D, while wealthier and overweight participants had higher odds in both areas. In rural regions, T2D risk was higher among caffeinated drink consumers and those not engaged in occupation-related physical activity, while these factors did not show significant influence in urban areas. Furthermore, urban participants displayed a significant association between T2D and hypertension. CONCLUSION: Our study outlines a comprehensive strategy to combat the increasing prevalence of T2D in both urban and rural areas. It includes promoting healthier diets to control BMI level, encouraging regular physical activity, early detection through health check-ups, tailored awareness campaigns, improving healthcare access in rural regions, stress management in urban areas, community involvement, healthcare professional training, policy advocacy like sugary drink taxation, research, and monitoring interventions. These measures collectively address the T2D challenge while accommodating the distinct features of urban and rural settings.

Individual- and community-level factors associated with the presence of adequate iodized salt in households in Bangladesh: a multilevel modelling approach.

BACKGROUND: The aim of this study is to estimate the factors at both the individual and community levels related to the adequacy of iodized salt in households in Bangladesh. METHODS: For this study we utilized the 2019 Multiple Indicator Cluster Survey data. A total of 61 242 households were chosen as samples from Bangladesh. In our study of socio-economic disparities, we applied a concentration indexing method. To identify the factors associated with the adequacy of iodine in salt at both the individual and community levels, we employed multilevel logistic regression. Aside from the multilevel regression used in the study, we also applied spatial analysis. RESULTS: The results indicated that the prevalence of iodine adequacy in household salt was found to be 57.8% (95% confidence interval 57.4 to 58.2). Rural areas have a higher concentration of iodine than urban areas. According to the multilevel model, younger women (adjusted odds ratio [aOR] 0.70), Muslim women (aOR 0.89), illiterate women (aOR 0.80) and those from poor households (aOR 0.33) were found to be less likely to consume iodine in concentrated salt compared with their counterparts. Disabled women and those with low media exposure have a lower likelihood of iodine adequacy in salt compared to their reference group. Furthermore, households in urban areas exhibited higher odds of having iodine adequacy in salt compared with households in rural areas. Barisal, Chattogram, Dhaka, Khulna, Mymensingh, Rajshahi and Rangpur Divisions have lower iodine adequacy in salt compared with Sylhet Division. CONCLUSIONS: The findings reveal that religion, physical disability and exposure to media exert an equal influence on the presence of iodized salt intake. Moreover, women's age, wealth status, education level and the educational background of the household head positively contribute to the adequacy of iodine in household salt. In light of these results, policymakers are advised to prioritize efforts aimed at enhancing iodine concentration, with a particular focus on mass media advertising, especially in rural areas (excluding Sylhet Division).

Prediction of suicidal ideation with associated risk factors among university students in the southern part of Bangladesh: Machine learning approach.

BACKGROUND: The prevalence of suicidal ideation has become an urgent issue, particularly among adolescents. The primary objective of this research is to determine the prevalence of suicidal ideation among students in the southern region of Bangladesh and to predict this phenomenon using machine learning (ML) models. METHODS: The data collection process involved using a simple random sampling technique to gather information from university students located in the southern region of Bangladesh during the period spreading from April 2022 to June 2022. Upon accounting for missing values and non-response rates, the ultimate sample size was determined to be 584, with 51.5 % of participants identifying as male and 48.5 % female. RESULTS: A significant proportion of students, precisely 19.9 %, reported experiencing suicidal ideation. Most participants were female (77 %) and unmarried (78 %). Within the machine learning (ML) framework, KNN exhibited the highest accuracy score of 91.45 %. In addition, the Random Forest (RF), and Categorical Boosting (CatBoost) algorithms exhibited comparable levels of accuracy, achieving scores of 90.60 and 90.59 respectively. LIMITATIONS: Using a cross-sectional design in research limits the ability to establish causal relationships. CONCLUSION: Mental health practitioners can employ the KNN model alongside patients' medical histories to detect those who may be at a higher risk of attempting suicide. This approach enables healthcare professionals to take appropriate measures, such as counselling, encouraging regular sleep patterns, and addressing depression and anxiety, to prevent suicide attempts.

Prevalence and determinants of hypertension in South-Asian Urban Communities: findings from Demographic and Health Surveys (DHS) data of South Asian countries.

Hypertension increases risk of stroke and other cardiovascular diseases, however, its prevalence and determinants in South Asian urban communities using country representative community-based datasets is lacking. This study evaluated prevalence of hypertension and it's determinants among urban residents of three South Asian countries. Urban population data from demographic and health surveys in Bangladesh, India, and Nepal were extracted. Hypertension prevalence was defined as systolic/diastolic blood pressure ≥ 140/ 90 mmHg. Age, education, wealth, physical activity, alcohol, BMI were considered as risk factors associated with the increased risk of hypertension. We performed binary logistic regression and calculated adjusted Odds Ratios (AOR) with 95% confidence interval (CI) to assess factors related to hypertension. Hypertension prevalence was 37.4% in India, 25.1% in Bangladesh and 18.4% in Nepal. Prevalence increased with age in all settings. Females had reduced odds of hypertension in Bangladesh (AOR 0.75; CI: 0.69, 0.81) and Nepal (AOR 0.62; CI: 0.54, 0.71), but higher risk in India (AOR 2.54; CI: 2.45, 2.63). Low education, caffeine consumption, obesity was associated with higher prevalence of hypertension in all three countries. Smokers had increased odds of hypertension in India (AOR 1.11; CI: 1.06, 1.15) and Nepal (AOR 1.23; 1.02, 1.47). Overall, hypertension prevalence is high in all three countries. Modifiable socioeconomic and lifestyle factors (education, wealth index, smoking status, caffeine consumption and BMI) associated with hypertension. Comprehensive hypertension pacific and sensitive interventions (including behavioral modification treatments and timely screening and access to health care) are urgently needed to prevent and control hypertension among urban populations in South Asia.

Knowledge, attitudes, and factors determining the willingness for COVID-19 vaccination among students in Bangladesh: An online-based cross-sectional study.

Aim: The most effective way to avoid COVID-19 is through immunization against the virus that causes the disease. The primary objective of this study was to assess the extent of knowledge, attitudes, acceptability, and factors influencing COVID-19 vaccination acceptance among higher secondary and university students in Bangladesh. Subject and methods: A structured questionnaire-based online survey was conducted among 451 students residing in Khulna and Gopalganj cities from February to August, 2022. The willingness to accept the COVID-19 vaccine was compared with a few covariates using the chi-square test, and we then used binary logistic regression to identify the determinants that led Bangladeshi students to receive the COVID-19 vaccine. Results: During the study period, almost 70% of the students obtained immunizations, with 56% of men and 44% of women reporting having done so. The age range of 26-30 years had the highest percentage of students who had received vaccinations, and 83.9% of students agreed that the COVID-19 vaccine is crucial for students. The results of the binary logistic regression clearly show that gender, degree of education, and respondents' willingness, encouragement, and beliefs have a significant impact on students' eagerness to receive COVID-19 vaccination. Conclusion: The rising trend in vaccination status among Bangladeshi students is highlighted by this study. Additionally, our results eloquently demonstrate that vaccination status varies by gender, education level, willingness, encouragement, and respondent's viewpoint. The outcomes of this study are essential for health policy makers and other interested parties to successfully organize their immunization program for young adults and children at various levels.

Smoke-free status of homes and workplaces among Indian people: Evidence from Global Adult Tobacco SurveyData-2016/2017.

AIMS: This study aimed to determine the impact of correlates on tobacco control/smoke-free status of homes and workplace among Indian people. To assess the magnitude of the problem, the relationship between smoke-free status and secondhand smoke (SHS) exposure was also explored. METHODS: Data was extracted from the Global Adult Tobacco Survey Data (GATS)-2017. It was a household survey that included people aged 15 years or older and covered all 30 states and 2 Union Territories (UTs) of India. A logistic regression model was used to determine the correlates of smoke-free status of homes and workplaces. Additionally, the Pearson correlation was used to explore the relationship between smoke-free status and the proportion of participants exposed to SHS both at homes and in the workplaces. RESULTS: The overall prevalence of smoke-free status in the home and workplace was 62.8% and 51.7%, respectively. Results of multivariate analysis (Logistic regression) illustrated that indicators like tobacco smoking status, place of residence, region, education, occupation, wealth quintile, and knowledge status about children's illness were significantly associated with the respondent's intention to live in a completely smoke-free environment both at home and in the workplace in India. This study revealed that SHS exposure was significantly negatively associated with a smoke-free status. CONCLUSION: This study will help the policymakers to promote efficient policies for improving smoke-free status and to ensure a better environment both at home and in the workplace in India.

Dynamic changes in prevalence of type 2 diabetes along with associated factors in Bangladesh: Evidence from two national cross-sectional surveys (BDHS 2011 and BDHS 2017-18).

BACKGROUND AND AIMS: Prevalence of type 2 diabetes has been rising rapidly especially in low- and middle-income countries. The purpose of this study is to analyze the prevalence of type 2 diabetes in 2011 and 2017-18, and the risk factors based on the Bangladesh Demographic and Health Surveys (BDHS). METHODS: The study comprised of data from the BDHS 2011 and the BDHS 2017-18 with 7565 (50.6% female) and 12,299 respondents (56.9% female) respectively. The plasma blood glucose was classified into normal, pre-diabetes, and diabetes categories. While univariate analysis was performed to determine the data, Chi-square and gamma analyses were used for bivariate estimation of the correlation between diabetes status and other comorbidities. Multinomial and ordinal logistic regression were also performed to trace the link between diabetes and various risk factors. RESULTS: The 2011 BDHS survey indicated that nearly half of the participants (47.6%) had pre-diabetic condition, while 10.2% were diagnosed with diabetes. On the other hand, the BDHS 2017-18 demonstrated that 28.6% and 8.6% of participants had pre-diabetes and diabetes, respectively. Results of inferential statistics showed that gender (p<0.05), age, wealth status, physical activity, BMI (p<0.01), and caffeinated beverages (p<0.05), were significantly linked with diabetes status. CONCLUSION: According to our findings, older people and people with lower education are more likely to develop diabetes. BMI` Physically active, wealth status, diet and lifestyle were significant predictors of type 2 diabetes. Healthy lifestyle, physical activities, proper knowledge and awareness can reduce the risk of T2D.

Prevalence and Triggering Factors of Childhood Anemia: An Application of Ordinal Logistic Regression Model.

Introduction: Anemia is indeed a significant risk factor for children's health as it affects growth retardation and has severe short and prolonged effects that follow in morbidity and death. Notwithstanding such ways to tackle anemia, the prevalence remains high in India and poses a severe public health concern. Objectives: The primary focus of this study was to find the prevalence and to determine the factors associated with the anemia of children under five years of age in India. Problem Statement. The increasing prevalence of childhood anemia and the life-threatening consequences for millions of children in India are a major concern. Knowing the relevant associated factors with childhood anemia is essential to reduce the frequency and severity level. Study design. For analysis purposes, this study utilized a cross-sectional study design. Methodology. Using the Indian Demographic and Health Survey 2015-16 data, we used chi-squared and gamma tests to find the association. Then, we utilized multinomial logistic regression and ordinal logistic regression to find the better model and the influencing factors of anemia in India. Results: In our study, we have found that children with highly educated mothers were 36.7% less likely (OR = 0.633, P ≤ 0.001, 95% CI: 0.608, 0.658) to be higher anemic than the children with not educated mother. Children with moderate and severe anemic mothers were 163.3% (OR = 2.633, P ≤ 0.001, 95% CI: 2.565, 7.704) more likely to be higher anemic than the children with not anemic mother. Not stunting children were 21.9% (OR = 0.781, P ≤ 0.001, 95% CI: 0 .764, 0.797) less likely to be higher anemic than the stunting children. Children aged 36-59 months were 73.9% (OR = 0.361, P ≤ 0.001, 95% CI: 0.353, 0.369) less likely to be higher anemic than the children aged 6-24 months. Again, the ACI value revealed that ordinal logistic regression was a better-fitted model for these data. Conclusion: and contribution. The variables such as stunting, underweight, wasting, child age, size of the child, and source of drinking water were the most critical indicators for child anemia in India. In summary, our study result indicated the major socioeconomic and demographic factors associated with childhood anemia in India, which can help the policymaker to take quick decision to reduce the severity level.

Association Between Symptoms and Severity of Disease in Hospitalised Novel Coronavirus (COVID-19) Patients: A Systematic Review and Meta-Analysis.

Background: Symptoms of the novel coronavirus disease (COVD-19) are well known, although asymptomatic cases were also reported due to this rapidly evolving viral disease. However, there has been limited research with inconsistent findings on symptoms of COVID-19 and disease severity. We aimed to evaluate the association between symptoms and severity of disease in adult patients with confirmed COVID-19 by performing a meta-analysis. Methods: We conducted this study by searching four online databases (Medline, Web of Science, EMBASE and Cochrane library) of published studies that included symptoms of COVID-19 cases and severity of the disease between January 1, 2020, and October 31, 2021. PRISMA and MOOSE guidelines were followed, and only articles published in English were selected. We performed meta-analysis using Mantel-Haenszel random-effects model. Note that we included peer-reviewed studies conducted in Wuhan and published in the English language that reported the clinical characteristics of COVID-19, particularly the symptoms of novel coronavirus patients with their prevalence and distribution of patients based on the severity of the disease. Results: Out of 255 articles identified, a total of twenty articles, including 5390 participants, met the inclusion criteria and were included. Among the participants, 2997 (55.60%) were males, and 974 (18.07%) reported severe conditions. Fever was the most commonly reported symptom in the reported COVID-19 confirmed cases (88.47%, 95% CI: 80.74-93.35%), which was followed by cough, fatigue, and less proportionally dyspnea and myalgia. Dyspnea was the only symptom, which was associated with severity of COVID-19 (OR 2.43, 95% CI: 1.52-3.89). Conclusion: Dyspnoea was found to be associated with severity of COVID-19. People with existing respiratory illnesses, such as chronic obstructive pulmonary diseases need to be careful about the onset of such symptom and should seek medical attention.

Machine Learning Approaches for Predicting Hypertension and Its Associated Factors Using Population-Level Data From Three South Asian Countries.

Background: Hypertension is the most common modifiable risk factor for cardiovascular diseases in South Asia. Machine learning (ML) models have been shown to outperform clinical risk predictions compared to statistical methods, but studies using ML to predict hypertension at the population level are lacking. This study used ML approaches in a dataset of three South Asian countries to predict hypertension and its associated factors and compared the model's performances. Methods: We conducted a retrospective study using ML analyses to detect hypertension using population-based surveys. We created a single dataset by harmonizing individual-level data from the most recent nationally representative Demographic and Health Survey in Bangladesh, Nepal, and India. The variables included blood pressure (BP), sociodemographic and economic factors, height, weight, hemoglobin, and random blood glucose. Hypertension was defined based on JNC-7 criteria. We applied six common ML-based classifiers: decision tree (DT), random forest (RF), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), logistic regression (LR), and linear discriminant analysis (LDA) to predict hypertension and its risk factors. Results: Of the 8,18,603 participants, 82,748 (10.11%) had hypertension. ML models showed that significant factors for hypertension were age and BMI. Ever measured BP, education, taking medicine to lower BP, and doctor's perception of high BP was also significant but comparatively lower than age and BMI. XGBoost, GBM, LR, and LDA showed the highest accuracy score of 90%, RF and DT achieved 89 and 83%, respectively, to predict hypertension. DT achieved the precision value of 91%, and the rest performed with 90%. XGBoost, GBM, LR, and LDA achieved a recall value of 100%, RF scored 99%, and DT scored 90%. In F1-score, XGBoost, GBM, LR, and LDA scored 95%, while RF scored 94%, and DT scored 90%. All the algorithms performed with good and small log loss values <6%. Conclusion: ML models performed well to predict hypertension and its associated factors in South Asians. When employed on an open-source platform, these models are scalable to millions of people and might help individuals self-screen for hypertension at an early stage. Future studies incorporating biochemical markers are needed to improve the ML algorithms and evaluate them in real life.

Correction: Prevalence of unintended pregnancy and its associated factors: Evidence from six south Asian countries.

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

Machine Learning Approaches to Identify Patient Comorbidities and Symptoms That Increased Risk of Mortality in COVID-19.

Providing appropriate care for people suffering from COVID-19, the disease caused by the pandemic SARS-CoV-2 virus, is a significant global challenge. Many individuals who become infected may have pre-existing conditions that may interact with COVID-19 to increase symptom severity and mortality risk. COVID-19 patient comorbidities are likely to be informative regarding the individual risk of severe illness and mortality. Determining the degree to which comorbidities are associated with severe symptoms and mortality would thus greatly assist in COVID-19 care planning and provision. To assess this we performed a meta-analysis of published global literature, and machine learning predictive analysis using an aggregated COVID-19 global dataset. Our meta-analysis suggested that chronic obstructive pulmonary disease (COPD), cerebrovascular disease (CEVD), cardiovascular disease (CVD), type 2 diabetes, malignancy, and hypertension as most significantly associated with COVID-19 severity in the current published literature. Machine learning classification using novel aggregated cohort data similarly found COPD, CVD, CKD, type 2 diabetes, malignancy, and hypertension, as well as asthma, as the most significant features for classifying those deceased versus those who survived COVID-19. While age and gender were the most significant predictors of mortality, in terms of symptom-comorbidity combinations, it was observed that Pneumonia-Hypertension, Pneumonia-Diabetes, and Acute Respiratory Distress Syndrome (ARDS)-Hypertension showed the most significant associations with COVID-19 mortality. These results highlight the patient cohorts most likely to be at risk of COVID-19-related severe morbidity and mortality, which have implications for prioritization of hospital resources.

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

Early marriage is a form of violation of child rights to grow and develop. The Sustainable Development Goals had included early marriage in target 5.3, aiming to eliminate by 2030. This study examines the socio-demographic factors associated with women's early marriage in Bangladesh, Ghana, and Iraq using information extracted from 2019, 2017-2018, and 2018 Multiple Indicator Cluster Surveys (MICSs) of Bangladesh, Ghana, and Iraq, respectively. The chi-square test examined the association between socio-demographic factors and early marriage separately in all three countries. In logistic regression, key factors were primarily evaluated for determining effects on early marriage separately in all three countries. The mean age of the mother at first marriage was found to be 16.86, 20.23, and 20.05 years in Bangladesh, Ghana, and Iraq successively. According to surveys conducted in Bangladesh, Ghana, and Iraq, education levels of household heads and women, wealth status, mass media, number of household members, and residence were significant factors linked to early marriage. The odds of getting married early were significantly higher among women with no formal education and primary education than women with secondary or higher education in all three countries. In terms of economic status, a negative association was found between wealth status and early marriage in both Bangladesh and Ghana. Based on the findings, the study recommended that government take the necessary steps to reduce child marriage in all three countries by raising women's education and campaigning women by media to harmful effects of early marriage, particularly women from low-income families.

Does worriedness among the rural adults promote COVID-19 related awareness in Bangladesh?

People living in urban areas are usually more aware of their health issues due to the availability and accessibility of health care facilities. Several studies have illustrated anxiousness, attitudes, and perceptions among urban people during COVID-19. This research attempted to assess how worriedness among rural adults may promote COVID-19 related awareness in Bangladesh. A cross-sectional online survey of 311 respondents aged 18 or greater was conducted through Facebook focusing only on the people living in rural areas. The survey included a consent form and requested demographic as well as pandemic related information in a three-section questionnaire from the respondents. We used the chi-square test statistic for bivariate analysis and the binary logistic regression model along with some tools to validate the model to analyze the impact of worriedness on awareness. The bivariate result showed a significant association among regular hand washing ( p = . 007 ) , knowledge about the proper amount of time for washing one's hands effectively ( p = . 004 ) , rules of social distancing ( p = . 00 ) , and education level ( p = . 046 ) with our outcome variable worriedness. From our binary logistic regression model fitting, it emerged that the females ( p = .032, OR = .729) who regularly wash their hands (0R = .393, p = .023), know the rules of social distancing for "yes" (0R = 14.525, p < .01), and "no" groups (0R = 5.518, p < .01), and age groups (18-27, 28 to 37, 38 to 47) were more worried. Results from our modeling justify an accuracy of 73.08%, a sensitivity of 93.71%, and a specificity of 29.33% with Cohen's kappa statistic = .2716, suggesting a fair model fitting. This study shows that the current COVID-19 situation created awareness among females and adults aged between 18 to 47 years in rural Bangladesh.

Factors associated with age of mother at first birth in Albania: application of quantile regression model.

The objective of this study was to explore the risk factors that can determine the age of mother at first birth in Albania. The necessary information was extracted from a nationally representative sample survey, Albania Demographic and Health Survey, 2017-18 dataset. To identify the possible risk factors of the age of mother at first birth, we applied quantile regression model. The mean age of mother at first birth was found to be 22.38 years with standard deviation of 3.56 years. The minimum and maximum age was reported 15 and 45 years, respectively. From the result of quantile regression, respondent's current age, education level and partner's higher education level were found positive impact on age of the mother at first birth. However, rural area, partner's age and smoking status found negative impact on age of the mother at first birth. This study suggests that women's education should be more prioritized because it can interfere with the idea of early marriage. Awareness can also be raised by social activities since the improvement of social conditions and reduction of social deprivation can increase the age of mother at first birth.

Prevalence of unintended pregnancy and its associated factors: Evidence from six south Asian countries.

AIM: Unintended pregnancy is a significant public health concern in South Asian countries because of its negative association with the socioeconomic and health outcomes for both children and mothers. The present study aimed to explore the prevalence of unintended pregnancy and explore its determinants among women of reproductive age in six South Asian countries. METHODS: Nationwide latest demography and health survey data from six South Asian countries, including Bangladesh (2014), Pakistan (2017-2018), Nepal (2016), Afghanistan (2015), Maldives (2016-2017) and India (2015-2016) were pooled for the present study. Multivariate analysis was performed to explore the association between unintended pregnancy and its associated factors. RESULTS: Amongst the total women (n = 41,689), overall, 19.1% pregnancies were reported as unintended (ranging from 11.9% in India to 28.4% in Bangladesh). The logistic regression model showed that younger women (15-19 years) had 1.42 times higher chance of unintended pregnancies. The odds of unintended pregnancies was 1.24 times higher for poorest women and 1.19 times higher for poorer women. Further, urban women (aOR = 0.70, 95% CI = 0.50-0.80), women having no children (aOR = 0.10, 95% CI = 0.09-0.12), smaller (≤4) family (aOR = 0.72, 95% CI = 0.67-0.78), those who intent to use contraceptive (aOR = 0.72, 95% CI = 0.60-0.86), currently living with partner (aOR = 0.90, 95% CI = 0.81-0.99), first cohabitation in teenage (≤19 years) (aOR = 0.85, 95% CI = 0.78-0.92) were less likely to report unintended pregnancies. CONCLUSIONS: This study has showed that women's age, wealth index, place of residence, number of children, family size, the intention of contraceptive use, living with a partner, and first cohabitation age are essential determinants of unintended pregnancy. These factors should be considered when trying to reduce unintended pregnancy in six South Asian countries. However, there is a need to improve health education, counselling, skills-building, sex education, modern contraceptive use and its access in this region. Intervention programs regarding reproductive health and policies are warranted to reduce rates of unintended pregnancy in South Asian countries.

Mental Health of Physicians During COVID-19 Outbreak in Bangladesh: A Web-Based Cross-Sectional Survey.

There have been numerous studies about the health implication of COVID-19 on patients, but little attention has been paid to the impacts of the pandemic on physicians. Our paper attends to this gap by exploring the mental health of physicians in Bangladesh during the COVID-19 pandemic. This is particularly important since the mental health of physicians impacts not only on themselves, but also their professional performance and hence the care of patients. This study examined physicians' mental health outcomes by evaluating the prevalence and associated potential risk factors of anxiety and depression. Using a web-based cross-sectional survey, we collected data from 114 physicians. Seven-item Generalized Anxiety Disorder (GAD-7) scale and Nine-item Patient Health Questionnaire (PHQ-9) were used to measure the anxiety and depression, respectively. Multivariate logistic regression models were used to explore the potential risk factors related to anxiety and depression. The prevalence of anxiety and depression were 32.5 and 34.2%, respectively. Findings revealed that marital status, work per day and current job location were the main risk factors for anxiety while sex, age, and marital status were the main risk factors for depression. Our results highlight the need to implement policies and strategies for positively impacting the mental health of physicians during and after the COVID-19 pandemic.

Assessing the true association between hypertension status and stature of individuals in Bangladesh: propensity score analysis.

Inconsistent evidence is found in the literature regarding the association between individuals' stature and hypertension status. In this study, an attempt has been made to investigate the true association between height and occurrence of hypertension. For analysis purpose, this study considers Bangladesh Demographic and Health Survey (BDHS) 2011 data obtained from an observational study. By dividing height (tall/normal/short) based on 25th and 75th percentile points separately for females and males, a binary logistic regression model was fitted to the weighted data, where weights were calculated from propensity scores (PS). From the PS-based weighted data, we did not find any significant association between height and hypertension (p > 0.05). Besides the respondent's height, logistic regression analyses of a balanced data set have revolved around some well-known factors that are associated with the occurrence of hypertension: gender of the respondent, higher wealth index status, as well as overweight. This study also found higher odds of occurring hypertension among the residents of Khulna and Rangpur divisions, whereas lower likelihood of hypertension is reported for the individual living in Chittagong and Sylhet districts. The findings of this paper indicate that human stature is not a risk factor for hypertension. Apart from height, this study uncovers some important risk factors for developing hypertension. By considering these factors, awareness should be raised among male, wealthier, and overweighted individuals in Bangladesh. However, why the prevalence of hypertension is higher in Khulna and Rangpur, as well as lower in Chittagong and Sylhet, demands further research.