Investigating Rhythmicity in App Usage to Predict Depressive Symptoms: Protocol for Personalized Framework Development and Validation Through a Countrywide Study.

BACKGROUND: Understanding a student's depressive symptoms could facilitate significantly more precise diagnosis and treatment. However, few studies have focused on depressive symptom prediction through unobtrusive systems, and these studies are limited by small sample sizes, low performance, and the requirement for higher resources. In addition, research has not explored whether statistically significant rhythms based on different app usage behavioral markers (eg, app usage sessions) exist that could be useful in finding subtle differences to predict with higher accuracy like the models based on rhythms of physiological data. OBJECTIVE: The main objective of this study is to explore whether there exist statistically significant rhythms in resource-insensitive app usage behavioral markers and predict depressive symptoms through these marker-based rhythmic features. Another objective of this study is to understand whether there is a potential link between rhythmic features and depressive symptoms. METHODS: Through a countrywide study, we collected 2952 students' raw app usage behavioral data and responses to the 9 depressive symptoms in the 9-item Patient Health Questionnaire (PHQ-9). The behavioral data were retrieved through our developed app, which was previously used in our pilot studies in Bangladesh on different research problems. To explore whether there is a rhythm based on app usage data, we will conduct a zero-amplitude test. In addition, we will develop a cosinor model for each participant to extract rhythmic parameters (eg, acrophase). In addition, to obtain a comprehensive picture of the rhythms, we will explore nonparametric rhythmic features (eg, interdaily stability). Furthermore, we will conduct regression analysis to understand the association of rhythmic features with depressive symptoms. Finally, we will develop a personalized multitask learning (MTL) framework to predict symptoms through rhythmic features. RESULTS: After applying inclusion criteria (eg, having app usage data of at least 2 days to explore rhythmicity), we kept the data of 2902 (98.31%) students for analysis, with 24.48 million app usage events, and 7 days' app usage of 2849 (98.17%) students. The students are from all 8 divisions of Bangladesh, both public and private universities (19 different universities and 52 different departments). We are analyzing the data and will publish the findings in a peer-reviewed publication. CONCLUSIONS: Having an in-depth understanding of app usage rhythms and their connection with depressive symptoms through a countrywide study can significantly help health care professionals and researchers better understand depressed students and may create possibilities for using app usage-based rhythms for intervention. In addition, the MTL framework based on app usage rhythmic features may more accurately predict depressive symptoms due to the rhythms' capability to find subtle differences. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/51540.

Role of internet use, mobile phone, media exposure and domestic migration on reproductive health service use in Bangladeshi married adolescents and young women.

Numerous studies have identified factors that are associated with increased access to reproductive health services in lower-middle-income countries (LMICs). However, limited studies examined the influence of access to internet or a mobile phone, media exposure and domestic migration on reproductive health services use in LMICs like Bangladesh. This study investigated the role of such factors on the use of contraceptives, antenatal care (ANC) and postnatal care (PNC) by married adolescents and young women in Bangladesh and whether it was varied by area. Secondary data for 1665 married women aged 15-24 years, sourced from the 2019 Multiple Indicator Cluster Surveys, were included in both bivariate analyses and logistic regression modelling to examine the role of access to internet and/or mobile phone, media exposure and domestic migration on the outcome variables (contraceptive, ANC and PNC). All regression models were controlled for age, wealth, education and number of existing children. Among all participants, 69.8% were aged 20-24 years and 85.6% lived in rural areas. Of the total sample, 67.5% used contraceptives, 75.7% utilised ANC and 48.7% accessed PNC. Domestic migration significantly increased contraceptive use, with women who had moved locally within the last five years 1.84 times more likely to use contraception than those who had never moved (95% CI: 1.41-2.41, p<0.001). Women with internet or mobile phone access were more likely to receive ANC (aOR: 1.57, 95% CI: 1.22-2.00, p<0.001) compared to those without internet/mobile phone access. Media exposure was found to increase the likelihood of receiving ANC in urban areas. No significant influence was found on the use of PNC. Internet/mobile-based platforms are promising avenues for public health messaging regarding ANC in Bangladeshi married adolescents and young women. Further research is required into determinants of PNC service use in low-resource settings.

Prevalence, inequality and associated factors of overweight/obesity among Bangladeshi adolescents aged 15-19 years.

BACKGROUND: The objective of the current study was to estimate the prevalence and associated factors of overweight/obesity among Bangladeshi adolescents aged 15-19 y and to identify whether wealth-related inequality exists for overweight/obesity among Bangladeshi older adolescents. METHODS: We analyzed publicly available national representative secondary data from the 2019-2020 Bangladesh Adolescent Health and Wellbeing Survey. This cross-sectional survey was carried out among 18 249 adolescents aged 15-19 y regardless of their marital status using a two-stage stratified sampling technique (the data of 9128 eligible adolescents were included in this analysis). The WHO reference population for body mass index-for-age (1+Z score) was considered as overweight/obesity. RESULTS: We found that girls had significantly (p<0.05) higher prevalence of overweight/obesity (11.63%) than boys (8.25%); however, their biological sex as well their age were not significantly associated with higher odds of overweight/obesity. Those who were in their higher grade (grade 11 and higher) in the school and had been exposed to media were more likely (1.67 and 1.39 times, respectively) to be overweight/obesity compared with primary grade (0-5) and those who experienced no media exposure, respectively. Inequality analysis revealed that adolescents belonging to wealthy households had significantly higher rates of overweight/obesity than those in poorer households (concentration index=0.093). CONCLUSIONS: The study exhibited the multifaceted nature of overweight/obesity among Bangladeshi older teenagers, revealing that their school grade, exposure to media content and wealth-related inequality emerged as significant contributing factors. The findings underscore the urgent need for targeted interventions and public health strategies to address the escalating burden of overweight and obesity in this age group.

Jute Stick-Derived Cellulose-Based Hydrogel: Synthesis, Characterization, and Methylene Blue Removal from Aqueous Solution.

In this work, microcrystalline cellulose (MCC) was isolated from jute sticks and sodium carboxymethyl cellulose (Na-CMC) was synthesized from the isolated MCC. Na-CMC is an anionic derivative of microcrystalline cellulose. The microcrystalline cellulose-based hydrogel (MCCH) and Na-CMC-based hydrogel (Na-CMCH) were prepared by using epichlorohydrin (ECH) as a crosslinker by a chemical crosslinking method. The isolated MCC, synthesized Na-CMC, and corresponding hydrogels were characterized by Fourier transform infrared (FTIR), X-ray diffraction (XRD), scanning electronic microscopy (SEM), and energy dispersive spectroscopy (EDS) for functional groups, crystallinity, surface morphology, and composite elemental composition, respectively. Pseudo-first-order, pseudo-second-order, and Elovich models were used to investigate the adsorption kinetics. The pseudo-second-order one is favorable for both hydrogels. Freundlich, Langmuir, and Temkin adsorption isotherm models were investigated. MCCH follows the Freundlich model (R2 = 0.9967), and Na-CMCH follows the Langmuir isotherm model (R2 = 0.9974). The methylene blue (MB) dye adsorption capacities of ionic (Na-CMCH) and nonionic (MCCH) hydrogels in different contact times (up to 600 min), initial concentrations (10-50 ppm), and temperatures (298-318 K) were investigated and compared. The maximum adsorption capacity of MCCH and Na-CMCH was 23.73 and 196.46 mg/g, respectively, and the removal efficiency of MB was determined to be 26.93% for MCCH and 58.73% for Na-CMCH. The Na-CMCH efficiently removed the MB from aqueous solutions as well as spiked industrial wastewater. The Na-CMCH also remarkably efficiently reduced priority metal ions from an industrial effluent. An effort has been made to utilize inexpensive, readily available, and environmentally friendly waste materials (jute sticks) to synthesize valuable adsorbent materials.

A Fast and Minimal System to Identify Depression Using Smartphones: Explainable Machine Learning-Based Approach.

BACKGROUND: Existing robust, pervasive device-based systems developed in recent years to detect depression require data collected over a long period and may not be effective in cases where early detection is crucial. Additionally, due to the requirement of running systems in the background for prolonged periods, existing systems can be resource inefficient. As a result, these systems can be infeasible in low-resource settings. OBJECTIVE: Our main objective was to develop a minimalistic system to identify depression using data retrieved in the fastest possible time. Another objective was to explain the machine learning (ML) models that were best for identifying depression. METHODS: We developed a fast tool that retrieves the past 7 days' app usage data in 1 second (mean 0.31, SD 1.10 seconds). A total of 100 students from Bangladesh participated in our study, and our tool collected their app usage data and responses to the Patient Health Questionnaire-9. To identify depressed and nondepressed students, we developed a diverse set of ML models: linear, tree-based, and neural network-based models. We selected important features using the stable approach, along with 3 main types of feature selection (FS) approaches: filter, wrapper, and embedded methods. We developed and validated the models using the nested cross-validation method. Additionally, we explained the best ML models through the Shapley additive explanations (SHAP) method. RESULTS: Leveraging only the app usage data retrieved in 1 second, our light gradient boosting machine model used the important features selected by the stable FS approach and correctly identified 82.4% (n=42) of depressed students (precision=75%, F1-score=78.5%). Moreover, after comprehensive exploration, we presented a parsimonious stacking model where around 5 features selected by the all-relevant FS approach Boruta were used in each iteration of validation and showed a maximum precision of 77.4% (balanced accuracy=77.9%). Feature importance analysis suggested app usage behavioral markers containing diurnal usage patterns as being more important than aggregated data-based markers. In addition, a SHAP analysis of our best models presented behavioral markers that were related to depression. For instance, students who were not depressed spent more time on education apps on weekdays, whereas those who were depressed used a higher number of photo and video apps and also had a higher deviation in using photo and video apps over the morning, afternoon, evening, and night time periods of the weekend. CONCLUSIONS: Due to our system's fast and minimalistic nature, it may make a worthwhile contribution to identifying depression in underdeveloped and developing regions. In addition, our detailed discussion about the implication of our findings can facilitate the development of less resource-intensive systems to better understand students who are depressed and take steps for intervention.

Multilevel analysis to identify the factors associated with caesarean section in Bangladesh: evidence from a nationally representative survey.

BACKGROUND: Caesarean delivery has a significant role in reducing maternal and child death. However, unnecessary utilization has adverse health effects. This study aimed to assess the prevalence and associated factors of caesarean delivery in Bangladesh. METHODS: Data from the latest Bangladesh Multiple Indicator Cluster Survey (MICS, 2019) was used in this study. Since MICS data are hierarchical in nature, multilevel modelling was used. RESULTS: The prevalence of caesarean section (CS) was 67.4% among Bangladeshi women. Multilevel analysis suggests the age of the women, household wealth status, utilization of antenatal care (ANC) , delivery at a health facility and division were significantly associated with CS. Women who delivered in a private health facility had the highest odds for CS (odds ratio [OR] 10.35 [95% confidence interval {CI} 8.55 to 12.54]). Women 30-34 y of age had a 36% higher likelihood of CS compared with women 15-19 y of age (OR 1.36 [95% CI 1.03 to 1.79]). The odds of CS positively increased with household wealth status. Women who had at least one ANC visit had a 1.7 times higher possibility of CS (OR 1.70 [95% CI 1.26 to 2.30]). CONCLUSIONS: Policy guidelines on caesarean deliveries are urgently needed in Bangladesh to avoid unnecessary caesarean deliveries and protect mothers from the consequences.

Equity Analysis of Repeated Cross-Sectional Survey Data on Mental Health Outcomes in Saskatchewan, Canada during COVID-19 Pandemic.

This paper aims to understand the impact of COVID-19 on three mental health outcomes-anxiety, depression, and mental health service use. Specifically, whether the associations between social and economic variables and these outcomes are exacerbated or buffered among equity-seeking groups in Saskatchewan. We analyzed secondary datasets of Saskatchewan adults from population-based national surveys conducted by Mental Health Research Canada (MHRC) on three occasions: cycle 2 (August 2020), cycle 5 (February 2021), and cycle 7 (June 2021). We examined temporal changes in the prevalence of anxiety, depression, and service utilization. Using the responses from 577 respondents in cycle 5 dataset (as it coincides with the peak of 2nd wave), we performed multinomial logistic regression. The policy implications of the findings were explored empirically through a World Café approach with 30 service providers, service users and policy makers in the province. The prevalence of anxiety and depression remained steady but high. Mental health services were not accessed by many who need it. Participants reporting moderate or severe anxiety were more likely to be 30-49 years old, women, and immigrants who earned less than $20,000 annually. Immigrants with either college or technical education presented with a lesser risk of severe anxiety. Factors associated with moderate or severe depression were younger age (<50 years), low household income, as well as immigrants with lower levels of education. Racialized groups had a lower risk of severe depression if they were under 30 years. Students and retirees also had a lower risk of severe depression. Canadian-born residents were more likely to require mental health supports but were not accessing them, compared to immigrants. Our analysis suggests mental health outcomes and service utilization remain a problem in Saskatchewan, especially among equity-seeking groups. This study should help drive mental health service redesign towards a client-centred, integrated, and equity-driven system in Saskatchewan.

Prevalence, trends and associated factors of hypertension and diabetes mellitus in Bangladesh: Evidence from BHDS 2011 and 2017-18.

The evolving pandemic of non-communicable diseases like hypertension, diabetes mellitus are globally on the rise, and the trend is also escalating in Bangladesh. We aimed to assess the prevalence trend and associated factors of hypertension (HTN), diabetes mellitus (DM), and hypertension- diabetes mellitus combined (HDC) among Bangladeshi adults from 2011 to 2018. Two nationally representative cross-sectional data from Bangladesh Demographic and Health Survey (BDHS): 2011 and 2017-18 were utilized. According to baseline characteristics, the average annual rate of change (AARC) was applied to quantify the annual rate of increase/decrease in HTN, DM, and HDC from 2011 to 2018. The prevalence ratios of HTN, DM, and HDC were assessed through modified Poisson regression with robust error variance (PR, 95% Confidence Interval (CI)). The data were prepared in SPSS version 23 and exported to Stata version 13 for further analysis. Among 11,686 participants, the overall mean age of the study participants was 52.79 years, Standard Deviation (SD)±12.99, and 42.28% were female. From 2011-2018, HTN, DM, and HDC prevalence in Bangladesh has increased by 13, 3.2, and 3.1 percentage points, respectively. The average annual rate of increase was observed in the HTN and HDC prevalence by all socio-economic and demographic categories during 2011-2018. The prevalence of HDC among Chittagong residents was approximately double in 2018: 3.95% (2011) versus 6.59% (2018). Increased age, inactive workers, overweight adults, and adults in wealthy families were common risk factors associated with HTN, DM, and HDC in Bangladesh. The prevalence of developing HTN and HDC was significantly higher among adults aged ≥ 70 years (PR: 2.70, 95% CI: 2.42-3.00; PR: 2.97, 95% CI: 2.08-4.24, respectively). A comprehensive approach of different stakeholders is required to develop appropriate strategies, including appropriate weight management, adequate physical activity, and healthier food habits. Health agencies should take initiatives to spread awareness among people at an early age, but special attention is needed for older people and those at risk for NCDs.

Association between depressive symptoms and poor sleep quality among pregnant women in Northern Rural Bangladesh: a community-based cross-sectional study.

BACKGROUND: Adequate good quality of sleep is essential for physical fitness during pregnancy as well as being a depressive symptoms-free mind. However, there is little evidence of the relationship between depressive symptoms and poor sleep quality among pregnant women in Bangladesh. This study aimed to find the association between depressive symptoms and poor sleep quality among pregnant women in northern rural Bangladesh. METHODS: A community-based cross-sectional study was carried out from May 2021 to June 2021 among 481 pregnant women tested positive in the pregnancy test of Jaldhaka and Dimla Upazila of Nilphamari district, Rangpur Division. Data were collected with a structured questionnaire including socio-demographic conditions, sleep quality, and depressive symptoms, comprising the Pittsburgh Sleep Quality Index (PSQI) and the Patient Health Questionnaire- 9 (PHQ-9). RESULTS: 8.94% of the women had depressive symptoms, whereas 38.88% of the participants were bad sleepers. However, women who had depressive symptoms [Adjusted odds ratio (AOR) = 2.55; 95% CI 1.33-4.9] and educational qualifications above 10 years [AOR = 0.60; 95% CI: 0.39-0.92] were associated with poor sleep quality. CONCLUSIONS: A higher percentage of pregnant women had poor sleep quality, whereas depressive symptoms and academic background of the participants were significantly associated with poor sleep quality. Ensuring adequate sleep time and better quality could be helpful to prevent depressive symptoms.

Trend of COVID-19 spreads and status of household handwashing practice and its determinants in Bangladesh - situation analysis using national representative data.

The objective of the study was to assess the prevalence and factors associated with household (HH) handwashing practice in Bangladesh and draw a trend of COVID-19 spreads and compare that with the countrywide HH handwashing practice. The study is based on the two national representative publicly available datasets (MICS 2019, and confirmed cases of COVID-19). Of 61,209 (weighted) HH, the overall prevalence of HH handwashing was found 56.3%, and the prevalence was significantly varied across the socio-economic status of the HH. Map comparison suggested that the gradual increasing trend of COVID-19 cases in areas where HH handwashing practice is low. The northern part of Bangladesh had the highest handwashing practice, whereas it had less effected by COVID-19 cases. However, central Bangladesh had the hardest hit by COVID-19 cases, and it had around 50% handwashing practice coverage. Large-scale observational study is necessary to establish the causality.

Mapping the prevalence and socioeconomic predictors of low birth weight among Bangladeshi newborns: evidence from the 2019 Multiple Indicator Cluster Survey.

BACKGROUND: Low birth weight (LBW) remains a global concern for childhood morbidity and mortality. This study examined the socioeconomic factors associated with LBW among Bangladeshi newborns and drew a district-level prevalence map. METHODS: Data were extracted from the 2019 Multiple Indicator Cluster Survey for Bangladesh. Multivariate logistic regression was used to identify the predictors of LBW. RESULTS: The prevalence of LBW among Bangladeshi newborns was found to be 14.5%. Overall, the Eastern and South-Eastern regions had a higher burden of LBW. Mothers' educational status, mode of delivery, wealth index quintile of the household and area were identified as independent predictors of newborns' LBW. Mothers who completed primary and secondary education grades had a 1.6- and 1.3-fold higher possibility of having an LBW baby compared with those who completed higher secondary or higher educational grades (adjusted OR=1.62 and 1.32, 95% CI 1.21 to 2.18 and 1.06 to 1.65, respectively). Children belonging to the poorest households and residing in urban areas had a 1.4-fold higher likelihood of being LBW (p<0.05). CONCLUSIONS: This study indicates that LBW is still highly prevalent in Bangladesh. Immediate public health action is required in the highly prevalent regions identified in this study.

Trends and predictors of early initiation, exclusive and continued breast-feeding in Bangladesh (2004-2018): a multilevel analysis of demographic and health survey data.

The early initiation of breast-feeding (EIBF) within 1 h of birth, exclusive breast-feeding (EBF) to 6 months and continued breast-feeding (CBF) to 2 years are key infant and young child feeding guidelines promoted globally for optimal child health and development. Using publicly available national survey data from the five most recent, consecutive Bangladesh Demographic and Health Surveys (2004, 2007, 2011, 2014 and 2017-2018), we assessed the trends in these key breast-feeding indicators. Multiple multilevel logistic regression models were built to assess socio-demographic predictors of breast-feeding using the latest 2017-2018 data set. Both EIBF and EBF have increased significantly between 2004 and 2017-2018, from 26 % to 60 % and 36 % to 68 %, respectively, and CBF decreased from 94 % to 85 %. Caesarean section delivery conferred lower EIBF practice (OR = 0·34, 95 % CI 0·27, 0·42) compared with vaginal delivery. Women who were currently working had 32 % lower odds of EBF (OR = 0·68, 95 % CI 0·48, 0·95). Compared with delivery at home, women who delivered in a health facility had 81 % higher odds of EBF (OR = 1·81, 95 % CI 1·25, 2·34). Larger family size (≥5) also predicted EBF (OR = 1·70, 95 % CI 1·21, 2·40). Rural residency was associated with 2·39 (95 % CI 1·32, 4·31) times of higher odds of CBF. Regional variation was also predictive of the various breast-feeding indicators. Although Bangladesh currently exceeds the 2019 global prevalence rates for these three breast-feeding indicators, efforts should be made to continue improving EIBF and EBF and to prevent future decreases in CBF.

Factors associated with women's approval on intimate partner violence in Bangladesh: A cross-sectional analysis of latest demographic and health survey 2017-18.

BACKGROUND: In Bangladesh, intimate partner violence (IPV) is high among women faced by their husband. Regrettably, IPV is often justified by the women themselves, resulting in poor social and health outcomes among them. In this paper, we explored the factors that influence the approval of IPV among the women by their husband in Bangladesh. METHOD: The present used the data from the most recent Bangladesh Demographic and Health Survey (BDHS) 2017-18. The BDHS 2017-18 followed a two-stage stratified random sampling techniques and the present analysis was carried out among a weighted sample of 20,127 women. Multivariate logistic regression was used to identify the demographic and socio-economic factors associated with the approval of IPV among the women. RESULTS: Overall, one in five women (20.5%) approved at least one form of violence by their husband. In adjusted analysis, women who completed secondary education were 57% less likely [AOR = 0.43, 95% CI: 0.34-0.54] to approve IPV compared to those who had no formal education. Similarly, women who belonged to Hinduism and other religious group were 42% less likely [AOR = 0.58, 95% CI: 0.45-0.74] to approve IPV compared to those who practiced Islam. Moreover, women who were exposed to television, participated in the decision on household purchase and moving outside home had 14%, 19%, and 21% less chance for approving IPV by the husband for at least one reason (p < 0.05). CONCLUSION: This study highlighted that many women in Bangladesh approve violence by their husband which could be a major obstacle to the reduction of violence from society. Behavior change intervention should be implemented, particularly targeting the less-educated Muslim women to increase the awareness on IPV in Bangladesh.

Mapping and situation analysis of basic WASH facilities at households in Bangladesh: Evidence from a nationally representative survey.

BACKGROUND: Ensuring water, sanitation, and hygiene (WASH) facilities for households remains a major public health concern in low- and middle-income countries (LMICs). This study investigated the current situation of basic WASH facilities for households in Bangladesh and drew a national coverage map. METHODS: We analyzed the publicly available nationally representative 2019 Multiple Indicator Cluster Survey (MICS) dataset that was carried out by the Bangladesh Bureau of Statistics (BBS) with support from the United Nations Children's Emergency Fund (UNICEF). A total of 61,209 households (weighted) were included in the analysis. Both bivariate and multivariate analyses were employed to examine the relationships between independent variables (socio-demographic and economic status) and their distributions over outcome variables (basic water, sanitation, and hygiene). Further, the spatial distribution of WASH facilities at the household level was depicted. RESULTS: Coverage of access to basic water facilities at the household level was 99.5% (95% CI 99.4% to 99.6%), sanitation 60.7% (95% CI 60.0% to 61.5%), and hygiene 56.3% (95% CI 55.6% to 57.0%). However, coverage of combined access to all three components was 40.2% (95% CI 39.4% to 40.9%). Among all 64 administrative districts of Bangladesh, we found comparatively lower coverage of WASH facilities in the South and South-East regions and relatively higher in the households of the North and North-Western regions. An adjusted regression model revealed that richest households [AOR = 29.64, 95% CI 26.31 to 33.39], households in the rural areas [AOR = 1.64, 95% CI 1.50 to 1.79], household heads with higher educational attainment [AOR = 2.28, 95% CI 2.09 to 2.49], and households with 5+ family members [AOR = 1.64, 95% CI 1.56 to 1.71] had the higher likelihood to have basic WASH facilities. CONCLUSION: Less than half of the Bangladeshi households had access to all three major WASH components (basic water, sanitation, and hygiene facilities); however, variation exists at the individual parameter of basic water, sanitation, and hygiene facilities. A comprehensive WASH approach may reduce the gap and improve the quality of WASH facilities in Bangladesh.

Factors associated with the utilization of reproductive health services among the Bangladeshi married women: Analysis of national representative MICS 2019 data.

OBJECTIVES: We calculated the prevalence of reproductive health services utilization among the currently married non-pregnant women in Bangladesh and investigated its association with demographics and socio-economic factors. METHODS: Publicly available secondary country representative 2019 Multiple Indicator Cluster Survey (MICS) dataset was analyzed. A two-stage stratified random sampling technique was followed, and all administrative districts (N=64) of Bangladesh were covered in the MICS 2019. Contraceptive use, antenatal care and skilled birth attendant were considered as utilization of reproductive health services and both crude and adjusted Odd Ratio (OR) were reported using a total of 8917 (weighted) data. RESULTS: In Bangladesh, 36.9% of married non-pregnant women utilized all three reproductive health services. However, at disaggregated level, contraceptive use (66.9%), antenatal care (83%) and skilled birth attendant (59.3%) were found 2-3 folds higher then cumulative prevalence. Increased likelihood of utilization of reproductive health services (either combined or at individual services) was observed among those women who had higher education [AOR= 2.63, 95% CI 1.99 to 3.47], belong to wealthy families [AOR= 2.46, 95% CI 1.94 to 3.12], residing in urban areas [AOR= 1.33, 95% CI 1.14 to 1.54], having a smaller number of children [AOR= 1.53, 95% CI 1.25 to 1.87], and exposure to media [AOR= 1.44, 95% CI 1.26 to 1.63]. CONCLUSION: Around one-third of Bangladeshi married women received combined contraceptive use, antenatal care and skilled birth attendant reproductive health services; however, variation exist at the individual service level. Targeted public health campaign focusing on women education and media advertisement may increase the utilization.

SSG-LUGIA: Single Sequence based Genome Level Unsupervised Genomic Island Prediction Algorithm.

BACKGROUND: Genomic Islands (GIs) are clusters of genes that are mobilized through horizontal gene transfer. GIs play a pivotal role in bacterial evolution as a mechanism of diversification and adaptation to different niches. Therefore, identification and characterization of GIs in bacterial genomes is important for understanding bacterial evolution. However, quantifying GIs is inherently difficult, and the existing methods suffer from low prediction accuracy and precision-recall trade-off. Moreover, several of them are supervised in nature, and thus, their applications to newly sequenced genomes are riddled with their dependency on the functional annotation of existing genomes. RESULTS: We present SSG-LUGIA, a completely automated and unsupervised approach for identifying GIs and horizontally transferred genes. SSG-LUGIA is a novel method based on unsupervised anomaly detection technique, accompanied by further refinement using cues from signal processing literature. SSG-LUGIA leverages the atypical compositional biases of the alien genes to localize GIs in prokaryotic genomes. SSG-LUGIA was assessed on a large benchmark dataset `IslandPick' and on a set of 15 well-studied genomes in the literature and followed by a thorough analysis on the well-understood Salmonella typhi CT18 genome. Furthermore, the efficacy of SSG-LUGIA in identifying horizontally transferred genes was evaluated on two additional bacterial genomes, namely, those of Corynebacterium diphtheria NCTC13129 and Pseudomonas aeruginosa LESB58. SSG-LUGIA was examined on draft genomes and was demonstrated to be efficient as an ensemble method. CONCLUSIONS: Our results indicate that SSG-LUGIA achieved superior performance in comparison to frequently used existing methods. Importantly, it yielded a better trade-off between precision and recall than the existing methods. Its nondependency on the functional annotation of genomes makes it suitable for analyzing newly sequenced, yet uncharacterized genomes. Thus, our study is a significant advance in identification of GIs and horizontally transferred genes. SSG-LUGIA is available as an open source software at https://nibtehaz.github.io/SSG-LUGIA/.

Prevalence and correlates of total sleep time among the older adults during COVID-19 pandemic in Bangladesh.

Purpose: The present study was aimed to identify inappropriate sleep duration and its correlates among the Bangladeshi older adults during the COVID-19 pandemic. Material and methods: This cross-sectional study was carried out among 1030 older adults aged 60 years and above in Bangladesh. Information was collected through telephone interviews using a pretested semi-structures questionnaire installed in SurveyCTO mobile app. Sleep duration was defined as total sleep time (TST) in last 24 h including day and nighttime sleep. TST was further categorized into shorter (<7 h), recommended (7-8 h), and longer sleep (>8 h) according to 2015 National Sleep Foundation guideline. The multinomial logistic regression model identified the factors associated with sleep duration. Results: Mean TST was 7.9 h (SD=1.62). Of the total participants, 28.2% had longer and 17.8% shorter sleep duration. In the regression model, participants' age of ≥80 years (OR: 3.36, 1.46-7.73), monthly family income of <5,000 Bangladeshi Taka (OR: 3.50, 1.79-6.82), difficulty in getting medicine during COVID-19 (OR: 1.72, 1.05-2.82), lack of communication during the pandemic (OR: 2.20, 1.43-3.40) and receiving COVID-19 related information from friends/family/neighbours (OR: 1.83, 1.11-3.01) were significantly associated with shorter TST. On the other hand, monthly family income of < 5,000 Bangladeshi Taka (OR: 2.00, 1.13-3.53), difficulty in getting medicine during COVID-19 pandemic (OR: 2.01, 1.33-3.03) and receiving COVID-19 related information from radio/TV (OR: 2.09, 1.22-3.59) were associated with longer TST. Conclusions: The study findings suggest implementing sleep management program for older adults in Bangladesh, particularly during emergencies like COVID-19.

Factors associated with knowledge and use of the emergency contraceptive pill among ever-married women of reproductive age in Bangladesh: findings from a nationwide cross-sectional survey.

OBJECTIVES: The study aimed to assess the prevalence and factors associated with knowledge and use of the emergency contraceptive pill (ECP) among ever-married women in Bangladesh. METHODS: The study was based on a secondary dataset of the 2014 Bangladesh Demographic and Health Survey. Complete (weighted) data of 17,592 women aged 15-49 years were analysed. RESULTS: The prevalence of having knowledge and use of the ECP among ever-married women in Bangladesh was 13.6% and 1.8%, respectively. Administrative region and type of residence (urban or rural), household wealth index, educational level (of both the woman and her spouse), spouse's occupation, number of living children, weight, current use of contraception and a history of pregnancy termination were positively associated with knowledge and use of the ECP. CONCLUSION: A large proportion of Bangladeshi women of reproductive age had a lack of knowledge and use of the ECP. Nationwide reproductive health education programmes may improve the situation.