Detection of Heart Arrhythmia on Electrocardiogram using Artificial Neural Networks.

The electrocardiogram, also known as an electrocardiogram (ECG), is considered to be one of the most significant sources of data regarding the structure and function of the heart. In order to obtain an electrocardiogram, the contractions and relaxations of the heart are first captured in the proper recording medium. Due to the fact that irregularities in the functioning of the heart are reflected in the ECG indications, it is possible to use these indications to diagnose cardiac issues. Arrhythmia is the medical term for the abnormalities that might occur in the regular functioning of the heart (rhythm disorder). Environmental and genetic variables can both play a role in the development of arrhythmias. Arrhythmias are reflected on the ECG sign, which depicts the same region regardless of where in the heart they occur; thus, they may be seen in ECG signals. This is how arrhythmias can be detected. Due to the time limits of this study, the ECG signals of individuals who were healthy, as well as those who suffered from arrhythmias were divided into 10-minute segments. The arithmetic mean approach is one of the fundamental statistical factors. It is used to construct the feature vectors of each received wave and interval, and these vectors offer information regarding arrhythmias in accordance with the agreed-upon temporal restrictions. In order to identify the heart arrhythmias, the obtained feature vectors are fed into a classifier that is based on a multilayer perceptron neural network. In conclusion, ROC analysis and contrast matrix are utilised in order to evaluate the overall correct classification result produced by the ECG-based classifier. Because of this, it has been demonstrated that the method that was recommended has high classification accuracy when attempting to diagnose arrhythmia based on ECG indications. This research makes use of a variety of diagnostic terminologies, including ECG signal, multilayer perceptron neural network, signal processing, disease diagnosis, and arrhythmia diagnosis.

Automatic Detection of Cases of COVID-19 Pneumonia from Chest X-ray Images and Deep Learning Approaches.

Machine learning has already been used as a resource for disease detection and health care as a complementary tool to help with various daily health challenges. The advancement of deep learning techniques and a large amount of data-enabled algorithms to outperform medical teams in certain imaging tasks, such as pneumonia detection, skin cancer classification, hemorrhage detection, and arrhythmia detection. Automated diagnostics, which are enabled by images extracted from patient examinations, allow for interesting experiments to be conducted. This research differs from the related studies that were investigated in the experiment. These works are capable of binary categorization into two categories. COVID-Net, for example, was able to identify a positive case of COVID-19 or a healthy person with 93.3% accuracy. Another example is CHeXNet, which has a 95% accuracy rate in detecting cases of pneumonia or a healthy state in a patient. Experiments revealed that the current study was more effective than the previous studies in detecting a greater number of categories and with a higher percentage of accuracy. The results obtained during the model's development were not only viable but also excellent, with an accuracy of nearly 96% when analyzing a chest X-ray with three possible diagnoses in the two experiments conducted.

Deep Learning-Based Networks for Detecting Anomalies in Chest X-Rays.

X-ray images aid medical professionals in the diagnosis and detection of pathologies. They are critical, for example, in the diagnosis of pneumonia, the detection of masses, and, more recently, the detection of COVID-19-related conditions. The chest X-ray is one of the first imaging tests performed when pathology is suspected because it is one of the most accessible radiological examinations. Deep learning-based neural networks, particularly convolutional neural networks, have exploded in popularity in recent years and have become indispensable tools for image classification. Transfer learning approaches, in particular, have enabled the use of previously trained networks' knowledge, eliminating the need for large data sets and lowering the high computational costs associated with this type of network. This research focuses on using deep learning-based neural networks to detect anomalies in chest X-rays. Different convolutional network-based approaches are investigated using the ChestX-ray14 database, which contains over 100,000 X-ray images with labels relating to 14 different pathologies, and different classification objectives are evaluated. Starting with the pretrained networks VGG19, ResNet50, and Inceptionv3, networks based on transfer learning are implemented, with different schemes for the classification stage and data augmentation. Similarly, an ad hoc architecture is proposed and evaluated without transfer learning for the classification objective with more examples. The results show that transfer learning produces acceptable results in most of the tested cases, indicating that it is a viable first step for using deep networks when there are not enough labeled images, which is a common problem when working with medical images. The ad hoc network, on the other hand, demonstrated good generalization with data augmentation and an acceptable accuracy value. The findings suggest that using convolutional neural networks with and without transfer learning to design classifiers for detecting pathologies in chest X-rays is a good idea.

Predicting the Intention and Adoption of Near Field Communication Mobile Payment.

With the increasing use of mobile devices and new technologies, electronic payments, such as near field communication (NFC) mobile payments, are gaining traction and gradually replacing the currency-based cash payment methods. Despite multiple initiatives by various parties to encourage mobile payments, adoption rates in developing countries have remained low. The purpose of this research is to explore the prime determinants of NFC mobile-payment adoption intention and to develop a model of mobile payment adoption that includes perceived risk (PR) as one of the major elements by extending the UTAUT2 theory components. An online survey was used to acquire data from 370 NFC mobile payments users for the current study. To validate the components and their correlations, structural equation modelling (SEM) was implemented. According to the findings, performance expectancy (PE), hedonic motivation (HM), social influence (SI), and facilitating conditions (FC) have substantial impacts on the consumers' intentions to adopt NFC mobile payments (INFC). Effort expectancy (EE) and PR were reported to have no considerable effects on the adoption intention. In addition, INFC is revealed to be a major mediator between the associations of the actual adoption of NFC mobile payment (ANFC) with PE, HM, and SI. The findings of the study would assist providers and marketers in better understanding of the consumers' behavior, designing effective marketing strategies to enhance the consumers' positive intentions, and achieving the mass adoption of NFC mobile payments in different environmental contexts.

Modelling the significance of social media marketing activities, brand equity and loyalty to predict consumers' willingness to pay premium price for portable tech gadgets.

In order to sustain business operations during the COVID-19 pandemic, nearly all industries have to adopt online technology and social media marketing activities (SMMAs). Globally, portable tech gadgets are rapidly expanding, but empirical studies on SMMAs in relation to portable tech gadgets in Malaysia have remained scarce. Therefore, this study examined the elements of SMMAs and their influence on brand equity in terms of brand awareness (BBA) and brand image (BBI) as well as brand loyalty (BRL) and willingness to pay premium price (WPP) among Malaysian consumers of portable tech gadgets users. Five components of SMMAs, namely entertainment (ENT), interactivity (INT), trendiness (TRE), customisation (CUS), and electronic word-of-mouth (EWOM), were examined to understand how SMMAs influence BBA, BBI, BRL, and WPP. An online survey was conducted with 1332 Malaysian youths who used social media platforms maintained by portable tech gadget brands as their marketing strategies. The gathered data were evaluated using structural equation modelling. The study's results indicated the significant and positive effects of TRE, CUS, and EWOM on BBA and BBI. INT was revealed to have no significant impact on BBA and BBI. Furthermore, BBI and BBA partially mediated the relationships of the components of SMMAs with WPP. As for the theoretical underpinning, this study used the stimulus-organism-response (S-O-R) model to connect SMMAs (as stimuli), brand equity (as organism), and BRL and WPP (as responses). This study was the first to use the S-O-R model to explore the effects of SMMAs on BRL and WPP in this sector of portable tech gadgets. The study's findings can guide portable tech gadget brands in Malaysia in redesigning and developing the most efficient strategies of SMMAs, which should be tailored to maximise revenues, even during any crisis period (such as the COVID-19 pandemic) when physical marketing activities are deemed difficult.

A Global Study on the Correlates of Gross Domestic Product (GDP) and COVID-19 Vaccine Distribution.

This study aimed to explore the association between the GDP of various countries and the progress of COVID-19 vaccinations; to explore how the global pattern holds in the continents, and investigate the spatial distribution pattern of COVID-19 vaccination progress for all countries. We have used consolidated data on COVID-19 vaccination and GDP from Our World in Data, an open-access data source. Data analysis and visualization were performed in R-Studio. There was a strong linear association between per capita income and the proportion of people vaccinated in countries with populations of one million or more. GDP per capita accounts for a 50% variation in the vaccination rate across the nations. Our assessments revealed that the global pattern holds in every continent. Rich European and North-American countries are most protected against COVID-19. Less developed African countries barely initiated a vaccination program. There is a significant disparity among Asian countries. The security of wealthier nations (vaccinated their citizens) cannot be guaranteed unless adequate vaccination covers the less affluent countries. Therefore, the global community should undertake initiatives to speed up the COVID-19 vaccination program in all countries of the world, irrespective of their wealth.

Human rights violations and associated factors of the Hijras in Bangladesh-A cross-sectional study.

BACKGROUND: Hijras in Bangladesh face considerable discrimination, stigma, and violence despite the 2013 legislation that recognized Hijras as a third gender. There is a dearth of published literature describing the extent of human rights violations among this population and their associated factors. METHODS: A questionnaire was administered to 346 study participants aged 15 years and older, living in five urban cities of Bangladesh who self-identified as Hijra, in 2019. The six human rights violation indicators (Economic, Employment, Health, Education, Social and Civic and Political Right) assessed were categorized as binary. Associations between sociodemographic characteristics and the six human rights violations were tested using univariate and multivariate logistic regression. RESULTS: Human right violations including economic, educational, political, employment, health and social/civil right violations were reported in 73.3%, 59.3%, 58.5%, 46.4%, 42.7%, and 34.4% of the participants, respectively. Economic rights violations were associated with bisexuality (Adjusted odds ratios [AOR] 3.60, 95%CI: 1.57, 8.26) and not living with family (AOR 2.71, 95%CI: 1.21, 6.09), while Hijras who earned more than 10,000 Bangladesh Taka experienced higher odds of educational (AOR 2.77, 95%CI: 1.06, 7.19) and political rights violations (AOR 4.30, 95%CI: 1.06, 7.44). Living in Dhaka city was associated with a reduced odds for economic and political rights violation while experiencing violations of one human right could lead to violation of another in the Hijra community. CONCLUSION: Human rights violations were common in Bangladesh Hijras, particularly the Bisexual Hijras. Media and educational awareness campaigns are needed to address the underlying roots of a violation. Programs focused on the families, young people and high-income earners of this community are needed in Bangladesh.

Social media addiction and emotions during the disaster recovery period-The moderating role of post-COVID timing.

BACKGROUND: Social media addiction, a recently emerged term in medical science, has attracted the attention of researchers because of its significant physical and psychological effects on its users. The issue has attracted more attention during the COVID era because negative emotions (e.g., anxiety and fear) generated from the COVID pandemic may have increased social media addiction. Therefore, the present study investigates the role of negative emotions and social media addiction (SMA) on health problems during and after the COVID lockdown. METHODS: A survey was conducted with 2926 participants aged between 25 and 45 years from all eight divisions of Bangladesh. The data collection period was between 2nd September- 13th October, 2020. Partial Least Square Structural Equation Modelling (PLS-SEM) was conducted for data analysis by controlling the respondents' working time, leisure time, gender, education, and age. RESULTS: Our study showed that social media addiction and time spent on social media impact health. Interestingly, while anxiety about COVID increased social media addition, fear about COIVD reduced social media addition. Among all considered factors, long working hours contributed most to people's health issues, and its impact on social media addiction and hours was much higher than negative emotions. Furthermore, females were less addicted to social media and faced less health challenges than males. CONCLUSION: The impacts of negative emotions generated by the COVID disaster on social media addiction and health issues should be reconsidered. Government and employers control people's working time, and stress should be a priority to solve people's social media addiction-related issues.

Trends of Stunting Prevalence and Its Associated Factors among Nigerian Children Aged 0-59 Months Residing in the Northern Nigeria, 2008-2018.

Every year in Nigeria, malnutrition contributes to more than 33% of the deaths of children below 5 years, and these deaths mostly occur in the northern geopolitical zones (NGZs), where nearly 50% of all children below 5 years are stunted. This study examined the trends in the prevalence of stunting and its associated factors among children aged 0-23 months, 24-59 months and 0-59 months in the NGZs. The data of 33,682 recent live births in the NGZs, extracted from the Nigeria Demographic and Health Surveys from 2008 to 2018, were used to investigate the factors associated with stunting using multilevel logistic regression. Children aged 24-59 months reported the highest prevalence of stunting, with 53.3% (95% confidence interval: 52.0-54.6%). Multivariable analyses revealed four common factors that increased the odds of a child's stunting across all age subgroups: poor households, geopolitical zone (northwest or northeast), being a male and maternal height (<145 cm). Interventional strategies focused on poverty mitigation through cash transfer and educating low socioeconomic mothers on the benefits of gender-neutral supplementary feeding and the timely monitoring of the offspring of short mothers would substantially reduce stunting across all age subgroups in the NGZs.

Internet Use Impact on Physical Health during COVID-19 Lockdown in Bangladesh: A Web-Based Cross-Sectional Study.

Previous studies on internet use frequency were focused on mental health impact, with little known about the impact on physical health during the COVID-19 lockdown. This study examined the impact of internet use frequency on self-reported physical health during the COVID-19 lockdown in Bangladesh. A web-based cross-sectional study on 3242 individuals was conducted from 2 August-1 October 2020. The survey covered demographics, internet use frequency and self-reported physical health questions. Linear regression analyses were used to examine the impact of internet use frequency on physical health. 72.5%, 69.9%, 65.1% and 55.3% respondents reported headache, back pain, numbness of the fingers and neck pain, respectively. The analyses showed increased physical health impact among regular (coefficient ß = 0.52, 95% confidence interval [CI]: 0.18-0.85, p = 0.003), frequent (ß = 1.21, 95% CI: 0.88-1.54, p < 0.001) and intense (ß = 2.24, 95% CI: 1.91-2.57, p < 0.001) internet users. Other important predictors were gender, income, occupation, regions, and working status. Frequent and extensive uses of the internet were strong predictors of physical health problems, and our findings suggest the need for increased awareness about the physical health problems that can be triggered by excessive internet usage.

Dengue in Dhaka, Bangladesh: Hospital-based cross-sectional KAP assessment at Dhaka North and Dhaka South City Corporation area.

Dengue, the most important mosquito-borne viral disease of humans is a recurring global health problem. In Bangladesh, dengue outbreaks are on the increase despite the efforts of government and it is not clear what the understanding of the general Dhaka population towards dengue fever is. Knowledge, attitude and practice (KAP) studies are essential guides in public health interventions. Hence, using KAP, this study aims to assess patient-perspectives with regards to factors associated with dengue, as well as investigate the associated factors between the two corporations in Dhaka. A Hospital-based cross-sectional study of 242 fever patients from two city-corporations in Dhaka (Dhaka North City Corporations, DNCC (n = 91, 37.6%) and Dhaka South City Corporation, DSCC (n = 151, 62.4%) was conducted using pre-tested KAP items. Wilcoxon's Rank Sum was used to determine the KAP by DNCC, DSCC and both corporations and multivariate Poisson regression analyses. The two corporations were analysed separately due to the differences in income distribution, concentration of slums, hospitals and clinics. The study found that more than half of the study population were knowledgeable about dengue (mean percentage scores was 52%), possess an appropriate and acceptable attitude towards the disease (69.2%), and about two thirds of the respondents (71.4%) engaged in practices towards its prevention. After adjusting for the potential cofounders, the factors associated with KAP about dengue fever varied between DNCC and DSCC; with duration of residency and use of mosquito nets were associated with knowledge in the north while income class and age were associated with knowledge and attitude in the south. In the pooled analysis (combining both corporations), knowledge of dengue was associated with good practice towards dengue fever among the respondents. The duration of residence in Dhaka (10+ years), not using mosquito nets and length of time spent in the hospital (7+ days) due to dengue, and decreased knowledge (Adjusted coefficient (ß) = -0.01, 95%CI: -0.02, -0.01) were associated with attitude towards dengue in DNCC. On the other hand, middle-high income class, age (40+ years) and increased knowledge were associated with practice towards dengue in DSCC (ß = 0.02, 95%CI: 0.01, 0.03). Efforts to increase knowledge about dengue fever through education by the administrations of both corporations would benefit from targeting these high-risk groups for a more sustainable outcome.

Psychological Impact of COVID-19 Pandemic in Bangladesh: Analysis of a Cross-Sectional Survey.

The COVID-19 pandemic has generated fear, panic, distress, anxiety, and depression among many people in Bangladesh. In this cross-sectional study, we examined factors associated with different levels of psychological impact as a result of COVID-19 in Bangladesh. From April 1 to 30, 2020, we used a self-administered online questionnaire to collect data from 10,609 respondents. Using the Impact of Event Scale-Revised to assess the psychological impact of the COVID-19 pandemic on respondents, we categorized the levels of impact as normal, mild, moderate, or severe. Ordinal logistic regression was used to examine the associated factors. The prevalence of mild, moderate, and severe psychological impact was 10.2%, 4.8%, and 45.5%, respectively. Multivariate analysis revealed that the odds of reporting normal vs mild, moderate, or severe psychological impact were 5.9 times higher for people living in the Chittagong Division, 1.7 times higher for women with lower education levels, 3.0 times higher among those who were divorced or separated, 1.8 times higher for those working full time, and 2.4 times higher for those living in shared apartments. The odds of reporting a psychological impact were also higher among people who did not enforce protective measures inside the home, those in self-quarantine, those who did not wear face masks, and those who did not comply with World Health Organization precautionary measures. Increased psychological health risks due to COVID-19 were significantly higher among people who experienced chills, headache, cough, breathing difficulties, dizziness, and sore throat before data collection. Our results showed that 1 in 2 respondents experienced a significant psychological impact as a result of the COVID-19 pandemic. Public health researchers should consider these factors when targeting interventions that would have a protective effect on the individual's psychological health during a pandemic or future disease outbreak.

Prevalence and Factors Associated with Mental Health Impact of COVID-19 Pandemic in Bangladesh: A Survey-Based Cross-Sectional Study.

Background: Feelings of isolation, insecurity, and instability triggered by COVID-19 could have a long-term impact on the mental health status of individuals. Objectives: The aim of this study was to examine the prevalence of mental health symptoms (anxiety, depression, and stress) in Bangladesh and the factors associated with these symptoms during the COVID-19 pandemic. Methods: From 1 to 30 April 2020, we used a validated self-administered questionnaire to conduct a cross-sectional study on 10,609 participants through an online survey platform. We assessed mental health status using the Depression, Anxiety, and Stress Scale (DASS-21). The total depression, anxiety, and stress subscale scores were divided into normal, mild, moderate, severe, and multinomial logistic regression was used to examine associated factors. Findings: The prevalence of depressive symptoms was 15%, 34%, and 15% for mild, moderate, and severe depressive symptoms, respectively. The prevalence of anxiety symptoms was 59% for severe anxiety symptoms, 14% for moderate anxiety symptoms, and 14% for mild anxiety symptoms, while the prevalence for stress levels were 16% for severe stress level, 22% for moderate stress level, and 13% for mild stress level. Multivariate analyses revealed that the most consistent factors associated with mild, moderate, and severe of the three mental health subscales (depression, anxiety, and stress) were respondents who lived in Dhaka and Rangpur division, females, those who self-quarantined in the previous seven days before the survey, and those respondents who experienced chills, breathing difficulty, dizziness, and sore throat. Conclusion: Our results showed that about 64%, 87%, and 61% of the respondents in Bangladesh reported high levels of depression, anxiety, and stress, respectively. There is a need for mental health support targeting women and those who self-quarantined or lived in Dhaka and Rangpur during the pandemic.

Factors Associated with the Perception of Risk and Knowledge of Contracting the SARS-Cov-2 among Adults in Bangladesh: Analysis of Online Surveys.

This study investigated the perception and awareness of risk among adult participants in Bangladesh about Coronavirus Disease 2019 (COVID-19). During the lockdown era in Bangladesh at two different time points, from 26-31 March 2020 (early lockdown) and 11-16 May 2020 (late lockdown), two self-administered online surveys were conducted on 1005 respondents (322 and 683 participants, respectively) via social media. To examine risk perception and knowledge-related factors towards COVID-19, univariate and multiple linear regression models were employed. Scores of mean knowledge (8.4 vs. 8.1, p = 0.022) and perception of risk (11.2 vs. 10.6, p < 0.001) differed significantly between early and late lockdown. There was a significant decrease in perceived risk scores for contracting SARS-Cov-2 [ß = -0.85, 95%CI: -1.31, -0.39], while knowledge about SARS-Cov-2 decreased insignificantly [ß = -0.22, 95%CI: -0.46, 0.03] in late lockdown compared with early lockdown period. Self-quarantine was a common factor linked to increased perceived risks and knowledge of SARS-Cov-2 during the lockdown period. Any effort to increase public awareness and comprehension of SARS-Cov-2 in Bangladesh will then offer preference to males, who did not practice self-quarantine and are less worried about the propagation of this kind of virus.

The impact of antenatal care, iron-folic acid supplementation and tetanus toxoid vaccination during pregnancy on child mortality in Bangladesh.

BACKGROUND: Appropriate antenatal care (ANC) is an important preventive public health intervention to ensure women's and newborn health outcomes. The study aimed to investigate the impact of ANC, iron-folic acid (IFA) supplementation and tetanus toxoid (TT) vaccination during pregnancy on child mortality in Bangladesh. METHOD: A cross-sectional study of three datasets from the Bangladesh Demographic and Health Surveys for the years 2004, 2007 and 2011 were pooled and used for the analyses. A total weighted sample of 16,721 maternal responses (5,364 for 2004; 4,872 for 2007 and 6,485 for 2011) was used. Multivariate logistic models that adjusted for cluster and sampling weights were used to examine the impact of ANC, IFA supplementation and TT vaccination during pregnancy on the death of a child aged 0-28 days (neonatal), 1-11 months (post-neonatal) and 12-59 months (child). RESULTS: Multivariable analyses revealed that the odds of postnatal and under-5 mortality was lower in mothers who had ANC [Odds Ratio (OR) = 0.60, 95% confidence interval (95% CI): 0.43-0.85], IFA supplementation [OR = 0.66, 95% CI: (0.45-0.98)] and ≥2 TT vaccinations (OR = 0.43, 95% CI: 0.49-0.78) for post-natal mortality; and for under-5 mortality, any form of ANC (OR = 0.69, 95% CI: 0.51-0.93), IFA supplementation (OR = 0.67, 95% CI: 0.48-0.94) and ≥2 TT vaccinations (OR = 0.50, 95% CI: 0.36-0.69). When combined, TT vaccination with IFA supplementation, and TT vaccination without IFA supplementation were protective across all groups. CONCLUSION: The study found that ANC, IFA supplementation, and TT vaccination during pregnancy reduced the likelihood of child mortality in Bangladesh. The findings suggest that considerable gains in improving child survival could be achieved through ensuring universal coverage of ANC, promoting TT vaccination during pregnancy and IFA supplementation among pregnant women in Bangladesh.

Predictors of stillbirths in Bangladesh: evidence from the 2004-2014 nation-wide household surveys.

BACKGROUND: Globally, stillbirth remains a significant public health issue, particularly in developing countries such as Bangladesh. OBJECTIVE: This study aimed to investigate the potential predictors of stillbirths in Bangladesh over a ten-year period. METHODS: The Bangladesh Demographic and Health Surveys data for the years 2004, 2007, 2011 and 2014 (n = 29,094) were used for the study to investigate the predictors of stillbirths. Stillbirth was examined against a set of community, socio-economic and child characteristics, using a multivariable logistic regression model that adjusted for cluster and sampling variability. RESULTS: The pooled rate of stillbirth in Bangladesh was 28 in 1000 births (95% CI: 22, 34). Stillbirth rates were higher in rural compared to urban areas in Bangladesh. Mothers who had a secondary or higher level of education (OR = 0.59, 95%CI: 0.43-0.82, P = 0.002) and those with primary education (OR = 0.66, 95%CI: 0.55-0.80, P < 0.001) were less likely to experience stillbirths compared to mothers with no education. Mothers with more than two children were significantly less likely to have stillbirths compared to mothers with one child. Those from poor households reported increased odds of stillbirth compared to those from rich households. CONCLUSION: Our analysis indicated that no maternal education, primiparity and poor household were predictors of stillbirths in Bangladesh. A collaborative effort is needed to reduce stillbirth rates among these high-risk groups in Bangladesh, with the socio-economic and health-related Sustainable Development Goals providing a critical vehicle for the co-ordination of this work.

Risk factors for under-5 mortality: evidence from Bangladesh Demographic and Health Survey, 2004-2011.

OBJECTIVE: The aim of this study was to identify factors associated with mortality in children under 5 years of age using a nationally representative sample of singleton births for the period of 2004-2011. DESIGN, SETTING AND PARTICIPANTS: Pooled 2004, 2007 and 2011 cross-sectional data sets of the Bangladesh Demographic and Health Surveys were analysed. The surveys used a stratified two-stage cluster sample of 16,722 singleton live-born infants of the most recent birth of a mother within a 3-year period. MAIN OUTCOME MEASURES: Outcome measures were neonatal mortality (0-30 days), postneonatal mortality (1-11 months), infant mortality (0-11 months), child mortality (1-4 years) and under-5 mortality (0-4 years). RESULTS: Survival information for 16,722 singleton live-born infants and 522 deaths of children <5 years of age included: 310 neonatal deaths, 154 postneonatal deaths, 464 infant deaths, 58 child deaths and 522 under-5 deaths. Multiple variable analysis showed that, over a 7-year period, mortality reduced significantly by 48% for postneonatal deaths, 33% for infant deaths and 29% for under-5 deaths, but there was no significant reduction in neonatal deaths (adjusted OR (AOR) = 0.79, 95% CI 0.59 to 1.06) or child deaths (AOR = 1.00, 95% CI 0.51 to 1.94). The odds of neonatal, postneonatal, infant, child and under-5 deaths decreased significantly among mothers who used contraceptive and mothers who had other children aged 3 years or older. The risk of neonatal, postneonatal, infant, child and under-5 deaths was significantly higher in mothers who reported a previous death of a sibling. CONCLUSIONS: Our study suggests that family planning is needed to further reduce the overall rate of under-5 deaths in Bangladesh. To reduce childhood mortality, public health interventions that focus on child spacing and contraceptive use by mothers may be most effective.

The Association between Chronic Arsenic Exposure and Hypertension: A Meta-Analysis.

Background. There is inconclusive evidence from cross-sectional and cohort studies that arsenic exposure is a risk factor involved in the development of hypertension. Methods. A database search, using several keywords, was conducted to identify relevant studies. Separate odds ratio estimates for arsenic exposure with concentration only and arsenic exposure with duration, including biomarker, were extracted from studies that met all inclusion criteria. The extracted odds ratios (OR) comparing the highest exposure categories with the lowest in each study were pooled using the random effects methods of meta-analysis. Heterogeneity of odds ratios in the included studies were analyzed using I(2) statistics. Results. Eight studies were analyzed. Using the exposure as arsenic concentration in the drinking water, the OR estimate was 1.9 (95% CI: 1.2-3.0), with the I(2) = 92%, while using the exposure as concentration and duration, the OR estimate was 1.4 (95% CI: 0.95-2.0) with the I(2) = 80%. Meta-regression was done and the quality of exposure measurement was found to be significantly associated with the effect measure. For a one unit increase in the score from exposure assessment, the odds ratio decreased by 6%. No publication bias was evident. The only major weaknesses of this study were heterogeneity across studies and small sample size. Conclusions. The study findings provide limited evidence for a relationship between arsenic and hypertension. In summary, the relationship between arsenic exposure and hypertension is still inconclusive and needs further validation through prospective cohort studies.