An efficient Parkinson's disease detection framework: Leveraging time-frequency representation and AlexNet convolutional neural network.

Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting the quality of life of over 10 million individuals worldwide. Early diagnosis is crucial for timely intervention and better patient outcomes. Electroencephalogram (EEG) signals are commonly used for early PD diagnosis due to their potential in monitoring disease progression. But traditional EEG-based methods lack exploration of brain regions that provide essential information about PD, and their performance falls short for real-time applications. To address these limitations, this study proposes a novel approach using a Time-Frequency Representation (TFR) based AlexNet Convolutional Neural Network (CNN) model to explore EEG channel-based analysis and identify critical brain regions efficiently diagnosing PD from EEG data. The Wavelet Scattering Transform (WST) is employed to capture distinct temporal and spectral characteristics, while AlexNet CNN is utilized to detect complex spatial patterns at different scales, accurately identifying intricate EEG patterns associated with PD. The experiment results on two real-time EEG PD datasets: San Diego dataset and the Iowa dataset demonstrate that frontal and central brain regions, including AF4 and AFz electrodes, contribute significantly to providing more representative features compared to other regions for PD detection. The proposed architecture achieves an impressive accuracy of 99.84% for the San Diego dataset and 95.79% for the Iowa dataset, outperforming existing EEG-based PD detection methods. The findings of this research will assist to create an essential technology for efficient PD diagnosis, enhancing patient care and quality of life.

Impact of prenatal maternal dietary exclusion on childhood obesity and overweight risk.

BACKGROUND: Child birthweight is a measure of fetal nutrition that is primarily determined by prenatal maternal (PM) diet. Child birthweight and child obesity/overweight risk are well established to be linked. Nevertheless, no studies have investigated the impact of PM dietary exclusion on child obesity/overweight risk or body mass index z-score (BMIz). OBJECTIVES: The study aimed to determine whether PM dietary exclusion affected the child's BMIz, obesity/overweight risk, whether child birthweight serves as a mediator of this, and whether PM use of dietary supplements can protect against this. METHODS: Waves within the years 2004-2019 from the Longitudinal Study of Australian Children, a population-based cohort study, were analyzed. The participants were aged 0 to 15 years during these waves of the study. Analysis was conducted using logistic and linear models. A total of 5,107 participants were involved in the first wave of the study. RESULTS: The PM exclusion of fish was associated with a higher risk of being underweight at age 14 or 15 years and mild-to-moderate obesity at age 6 or 7 years. The PM exclusion of egg was associated with a higher risk of being overweight at age 14 or 15 years. The exclusion of dairy was associated with more mixed effects. Mediation effects did not reach statistical significance. Moderation effects involving PM dietary supplement use, when they did occur, were associated with higher child BMIz and usually a higher risk of obesity/overweight. CONCLUSIONS: Fish and eggs are likely important parts of PM diets for preventing childhood obesity and overweight. Further studies will be needed to determine reasons for this and the apparent adverse effects of dietary supplements on overweight/obesity risk.

Investigating school absenteeism and refusal among Australian children and adolescents using Apriori association rule mining.

Identifying and determining the multitude of reasons behind school absences of students is often challenging. This study aims to uncover the hidden reasons for school absence in children and adolescents. The analysis is conducted on a national survey that includes 2967 Australian children and adolescents aged 11-17. The Apriori association rule generator of machine learning techniques and binary logistic regression are used to identify the significant predictors of school absences. Out of 2484, 83.7% (n = 2079) aged (11-17) years children and adolescents have missed school for various reasons, 42.28% (n = 879) are (11-15) years old, 24.52% (n = 609) and 16.9% (n = 420) are 16- and 17-years old adolescents respectively. A considerable proportion of adolescents, specifically 16.4% (n = 407) and 23.4% (n = 486) of 16 and 17 years old, respectively, have selected 'refused to say' as their reason for not attending school. It also highlights the negative outcomes associated with undisclosed reasons for school absence, such as bullying, excessive internet/gaming, reduced family involvement, suicide attempts, and existential hopelessness. The findings of the national survey underscore the importance of addressing these undisclosed reasons for school absence to improve the overall well-being and educational outcomes of children and adolescents.

Public opinion on COVID-19 pandemic in Bangladesh: Disruption to public lives and trust in government's immunisation performance.

OBJECTIVES: The COVID-19 pandemic has stretched Bangladesh government's capability for disaster engagement. As normalcy is interrupted, people's confidence in the government in ending the crisis needs evaluation, especially considering the past vaccination successes in Bangladesh and growing worldwide vaccine hesitancy amidst the COVID-19 misinfodemic. This study assessed the level of public life disruption due to the pandemic at the micro-level and how much impact it had on people's trust in the government's capacity for successful national immunisation. METHODS: Given the infectious nature of the pandemic, the study conducted an online survey of 2291 respondents, distributed proportionally across sex and income groups. We conducted bivariate analyses and fitted generalised linear models to assess disruption to respondents' lives, and their trust in the government's immunisation ability, which were measured using multiple parameters. RESULTS: Nearly 50% of the respondents reported multifaceted disputations in their daily lives, with 90% suffering financially. Trust in the government was very low at the time of the survey as only 11.3% of respondents had faith that the government could successfully conduct a mass vaccination campaign. Rural residents and non-earning members of families found their lives to be less disrupted. Comparatively higher income families and highly educated individuals had lesser confidence in the government's inoculation capabilities. CONCLUSIONS: For the vaccine campaign to be successful, effective risk communication and timely display of data-driven decision-making efforts targeting the groups who are more sceptical of immunisation campaigns could be of significance to the Bangladesh government.

Exploring Frequency Band-Based Biomarkers of EEG Signals for Mild Cognitive Impairment Detection.

Mild Cognitive Impairment (MCI) is often considered a precursor to Alzheimer's disease (AD), with a high likelihood of progression. Accurate and timely diagnosis of MCI is essential for halting the progression of AD and other forms of dementia. Electroencephalography (EEG) is the prevalent method for identifying MCI biomarkers. Frequency band-based EEG biomarkers are crucial for identifying MCI as they capture neuronal activities and connectivity patterns linked to cognitive functions. However, traditional approaches struggle to identify precise frequency band-based biomarkers for MCI diagnosis. To address this challenge, a novel framework has been developed for identifying important frequency sub-bands within EEG signals for MCI detection. In the proposed scheme, the signals are first denoised using a stationary wavelet transformation and segmented into small time frames. Then, four frequency sub-bands are extracted from each segment, and spectrogram images are generated for each sub-band as well as for the full filtered frequency band signal segments. This process produces five different sets of images for five separate frequency bands. Afterwards, a convolutional neural network is used individually on those image sets to perform the classification task. Finally, the obtained results for the tested four sub-bands are compared with the results obtained using the full bandwidth. Our proposed framework was tested on two MCI datasets, and the results indicate that the 16-32 Hz sub-band range has the greatest impact on MCI detection, followed by 4-8 Hz. Furthermore, our framework, utilizing the full frequency band, outperformed existing state-of-the-art methods, indicating its potential for developing diagnostic tools for MCI detection.

Identifying Social Determinants and Measuring Socioeconomic Inequalities in the Use of Four Different Mental Health Services by Australian Adolescents Aged 13-17 Years: Results from a Nationwide Study.

AIM: In this study, we aimed to identify the determinants of four different forms of mental health service usage (general health services, school counselling, telephone, and online services), and the number of mental health services accessed (single and multiple) by Australian adolescents aged 13-17 years. We also measured socioeconomic inequality in mental health services' usage following the concentration index approach within the same sample. SUBJECT AND METHODS: The data came from the nationwide cross-sectional survey, Young Minds Matter (YMM): the second Australian Child and Adolescent Survey of Mental Health and Wellbeing. Random effect models were used to identify the factors associated with four different mental health services and the number of services accessed. Further, the Erreygers' corrected concentration indices for binary variables were used to quantify the socioeconomic inequality in each mental health service. The four services were the general health service (GP, specialist, psychiatrist, psychologist, hospital including emergency), school services, telephone counselling and online services. RESULTS: Overall, 31.9% of the total analytical sample (n = 2268) aged 13-17 years old visited at least one service, with 21.9% accessing a single service and 10% accessing multiple services. The highest percentage of adolescents used online services (20.1%), followed by general mental health services (18.3%), while school services (2.4%) were the least used service. Age, gender, family type and family cohesion statistically significantly increased the use of general health and multiple mental health service usage (p < 0.05). Area of residence was also found to be a significant factor for online service use. The concentration indices (CIs) were -0.073 (p < 0.001) and -0.032 (p < 0.001) for health and telephone services, respectively, which implies pro-rich socio-economic inequality. CONCLUSION: Adolescents from low-income families frequently used general mental health services and telephone services compared to those who belonged to high-income families. The study concluded that if we want to increase adolescents' usage of mental health services, we need to tailor our approaches to their socioeconomic backgrounds. In addition, from a policy standpoint, a multi-sectoral strategy is needed to address the factors related to mental health services to reduce inequity in service utilisation.

Public opinion on COVID-19 vaccine prioritization in Bangladesh: Who gets the vaccine and whom do you leave out?

One of the most challenging aspects of the COVID-19 pandemic is the inability to ensure equitable distribution of vaccines to fight the pandemic. Many governments around the globe had to prioritize and perform a triage in distributing the vaccines due to the limited supply as well as a lack of financial strength to acquire a sufficient number of vaccines in time. The present study assessed the public opinion in Bangladesh regarding vaccination prioritization strategy and its associated aspects. Due to the infectious nature of the viral transmission, the study used an online survey and collected a sample of 2291 respondents, distributed proportionally across sex, and income groups. Descriptive statistics and multinomial logistic regression modelling were utilized to conduct the analyses. The results emphasized unanimous preference of prioritized vaccination leaning towards the frontline workers, the severely sick and the elderly. However, the segregation across ethnicity was noted with no major preference among sexes or religion. The results reinforce the Bangladesh government's undertaken strategy of prioritization. However, the preference rankings varied across sociodemographic factors including self-assessed COVID-19 knowledge and income tiers, among others. The findings underline the necessity of improved risk communication strategies to ensure public confidence and conformity to vaccination efforts and their effective deployment across the country.

Early detection of paediatric and adolescent obsessive-compulsive, separation anxiety and attention deficit hyperactivity disorder using machine learning algorithms.

Purpose: Mental health issues of young minds are at the threshold of all development and possibilities. Obsessive-compulsive disorder (OCD), separation anxiety disorder (SAD), and attention deficit hyperactivity disorder (ADHD) are three of the most common mental illness affecting children and adolescents. Several studies have been conducted on approaches for recognising OCD, SAD and ADHD, but their accuracy is inadequate due to limited features and participants. Therefore, the purpose of this study is to investigate the approach using machine learning (ML) algorithms with 1474 features from Australia's nationally representative mental health survey of children and adolescents. Methods: Based on the internal cross-validation (CV) score of the Tree-based Pipeline Optimization Tool (TPOTClassifier), the dataset has been examined using three of the most optimal algorithms, including Random Forest (RF), Decision Tree (DT), and Gaussian Naïve Bayes (GaussianNB). Results: GaussianNB performs well in classifying OCD with 91% accuracy, 76% precision, and 96% specificity as well as in detecting SAD with 79% accuracy, 62% precision, 91% specificity. RF outperformed all other methods in identifying ADHD with 91% accuracy, 94% precision, and 99% specificity. Conclusion: Using Streamlit and Python a web application was developed based on the findings of the analysis. The application will assist parents/guardians and school officials in detecting mental illnesses early in their children and adolescents using signs and symptoms to start the treatment at the earliest convenience.

Clustering of lifestyle and health behaviours in Australian adolescents and associations with obesity, self-rated health and quality of life.

OBJECTIVE: The primary aim of this study was to identify clusters of lifestyle and health behaviours and explore their associations with health outcomes in a nationally representative sample of Australian adolescents. METHODS: The study participants were 3127 adolescents aged 14-15 years who participated in the eighth wave of the birth cohort of the Longitudinal Study of Australian Children (LSAC). A latent class analysis (LCA) was performed to identify clusters based on the behaviours of physical activity, alcohol consumption, smoking, diet, eating disorders, sleep problems and weight consciousness. Multinomial logistic regression models were fitted to the following health outcome variables: obesity, self-rated general health and pediatric health-related quality of life, to investigate their associations with LCA clusters. RESULTS: Based on the prevalence of health behaviour related characteristics, LCA identified gender based distinct clusters of adolescents with certain outward characteristics. There were five clusters for male and four clusters for female participants which are named as: healthy lifestyle, temperate, mixed lifestyle, multiple risk factors, and physically inactive (male only). Adolescents in the healthy lifestyle and temperate clusters reported low and moderately active health risk behaviours, for example, low physical activity, inadequate sleep and so on, while these behaviours were prevailing higher among adolescents of other clusters. Compared to adolescents of healthy lifestyle clusters, male members of physically inactive (OR = 3.87, 95% CI: 1.12 - 13.33) or mixed lifestyle (OR = 5.57, 95% CI: 3.15 - 9.84) clusters were over three to five times more likely to have obesity; while for female adolescents, members of only multiple risk factors clusters (OR = 3.61, 95% CI: 2.00 - 6.51) were over three time more likely to have obesity compared to their counterpart of healthy lifestyle clusters. Adolescents of physically inactive (b = -9.00 for male only), mixed lifestyle (b = -2.77 for male; b = -6.72 for female) or multiple risk factors clusters (b = -6.49 for male; b = -6.59 for female) had a stronger negative association with health-related quality of life scores compared to adolescents of healthy lifestyle clusters. CONCLUSION: The study offers novel insights into latent class classification through the utilisation of different lifestyles and health-related behaviours of adolescents to identify characteristics of vulnerable groups concerning obesity, general health status and quality of life. This classification strategy may help health policy makers to target vulnerable groups and develop appropriate interventions.

A latent class analysis of the socio-demographic factors and associations with mental and behavioral disorders among Australian children and adolescents.

BACKGROUND: Previous studies have shown a relationship between socio-demographic variables and the mental health of children and adolescents. However, no research has been found on a model-based cluster analysis of socio-demographic characteristics with mental health. This study aimed to identify the cluster of the items representing the socio-demographic characteristics of Australian children and adolescents aged 11-17 years by using latent class analysis (LCA) and examining the associations with their mental health. METHODS: Children and adolescents aged 11-17 years (n = 3152) were considered from the 2013-2014 Young Minds Matter: The Second Australian Child and Adolescent Survey of Mental Health and Wellbeing. LCA was performed based on relevant socio-demographic factors from three levels. Due to the high prevalence of mental and behavioral disorders, the generalized linear model with log-link binomial family (log-binomial regression model) was used to examine the associations between identified classes, and the mental and behavioral disorders of children and adolescents. RESULTS: This study identified five classes based on various model selection criteria. Classes 1 and 4 presented the vulnerable class carrying the characteristics of "lowest socio-economic status and non-intact family structure" and "good socio-economic status and non-intact family structure" respectively. By contrast, class 5 indicated the most privileged class carrying the characteristics of "highest socio-economic status and intact family structure". Results from the log-binomial regression model (unadjusted and adjusted models) showed that children and adolescents belonging to classes 1 and 4 were about 1.60 and 1.35 times more prevalent to be suffering from mental and behavioral disorders compared to their class 5 counterparts (95% CI of PR [prevalence ratio]: 1.41-1.82 for class 1; 95% CI of PR [prevalence ratio]: 1.16-1.57 for class 4). Although children and adolescents from class 4 belong to a socio-economically advantaged group and shared the lowest class membership (only 12.7%), the class had a greater prevalence (44.1%) of mental and behavioral disorders than did class 2 ("worst education and occupational attainment and intact family structure") (35.2%) and class 3 ("average socio-economic status and intact family structure") (32.9%). CONCLUSIONS: Among the five latent classes, children and adolescents from classes 1 and 4 have a higher risk of developing mental and behavioral disorders. The findings suggest that health promotion and prevention as well as combating poverty are needed to improve mental health in particular among children and adolescents living in non-intact families and in families with a low socio-economic status.

Association of maternal physical and mental health characteristics with the hazard of having any medical condition or disability in Australian children: A 15-year birth cohort study.

Any long-term medical condition or disability among children is a significant health issue. This study measured the incidence rate of any medical condition or disability among children from a nationally representative birth cohort, then used the random effect parametric survival regression model to assess whether the hazard of any medical condition or disability in children is associated with maternal physical and mental health characteristics (obesity, general health status, having a medical condition, stressful life events or mental illness). The study followed up 5019 children from the Longitudinal Study of Australian Children, assessing their time-to-event data from birth (2004) to 14 or 15 years of age (2018). The hazard rate of any medical condition or disability was 26.11 per 1000 person-years for all the children and 29.29 for the males-a noticeable gender difference. It was the highest (hazard rate: 62.90) among the children when their mothers had a medical condition, while the hazard rate was 22.40 per 1000 person-years among the children whose mothers had no medical conditions. The parametric panel regression results also suggested that the children of mothers with a medical condition during the 15-year study period were more likely to have a medical condition or disability (hazard ratio [HR]: 2.61, 95% confidence interval [CI]: 2.24-3.02) compared to the children of mothers with none. Similar trends were observed among children of mothers who had fair or poor general health (HR: 1.48, 95% CI: 1.15-1.91), obesity (HR: 1.40, 95% CI: 1.18-1.66) or experienced stressful life events (HR: 1.23, 95% CI: 1.06-1.43) over time compared to those whose mothers did not. These findings suggest that additional healthcare interventions targeting mothers with medical conditions, obesity, poor general health, or mental illness would help minimise the risk of medical conditions and disabilities among children.

The Healthcare Cost Burden of Asthma in Children: A Longitudinal Population-Based Study.

OBJECTIVES: This study aimed to investigate the extent of healthcare cost increase at population level due to childhood asthma. We aimed to investigate the cross-sectional relationship between asthma and healthcare costs among children aged 2 to 18 years and, in longitudinal analyses, whether costs increase with an increase in the duration of asthma prevalence. METHODS: Study participants are 4175 and 4482 children of birth and kindergarten cohorts from the nationally representative Longitudinal Study of Australian Children for whom the linked Medicare cost data are available. The children were followed in all waves from the year 2004 to 2018. Generalized linear models were used to estimate the excess healthcare costs associated with asthma. The sum of Medicare Benefits Schedule and Pharmaceutical Benefits Scheme costs constitutes the total healthcare costs. RESULTS: Total excess healthcare costs associated with asthma among the 2- to 18-year-old children were A$4316 per child. At the population level, the estimated total excess Medicare costs associated with current asthma treatment among 2- to 18-year-old children were, on average, A$190.6 million per year (2018 population and price). Compared with the non-asthmatic children, peers with persistent asthma morbidity and treatment requirements had excess costs up to A$20 727 for the B cohort children until 14 years of age, whereas excess costs for the K cohort children were A$19 571 until 18 years of age. CONCLUSIONS: Asthma in children imposes a significant financial burden on the public health system. Higher excess healthcare costs of all asthmatic children than the costs of nonasthmatic children provide further economic justification for promoting preventive efforts at early ages.

Determinants of maternal morbidity during pregnancy in urban Bangladesh.

BACKGROUND: Maternal morbidities especially life-threatening pregnancy complications are major health concerns in developing countries. The main aim is to investigate the prevalence of maternal morbidity during pregnancy and its determinants among women from urban areas of Bangladesh. METHODS: The secondary data were used and extracted from the latest Bangladesh Urban Health Survey (BUHS) 2013. Several statistical models: Poisson, negative binomial (NB) and mixed Poisson were adapted and compared to explore the best model for investigating potential determinants of maternal morbidity. Pearson chi-square statistic was used for the detection of overdispersion in the data. Results Overall 13.5% of the urban women in Bangladesh suffered from at least two pregnancy complications. The study detected the overdispersion existing in the maternal morbidity count data and found the NB regression as the best choice for analyzing the data because of its smallest Akaike information criterion. Administrative division (Rangpur: p = 0.003, incidence rate ratio, IRR = 1.34, 95% confidence interval, CI: 1.11 to 1.63; Sylhet: p = 0.006, incidence rate ratio, IRR = 1.42, 95% CI: 1.11 to 1.82), unwanted pregnancy (p<0.001, IRR = 1.25, 95% CI: 1.11 to 1.40), place of delivery (p<0.001, IRR = 1.68, 95% CI: 1.53 to 1.86) and wealth index (Poor: p<0.001, IRR = 1.34, 95% CI: 1.19 to 1.50; Middle: p = 0.003, IRR = 1.21, 95% CI: 1.08 to 1.36) were found to be statistically significant determinants for maternal morbidity during pregnancy among the urban women in Bangladesh. CONCLUSIONS: Urban women in Bangladesh with an unwanted pregnancy, from the poor/middle-income group; and living in Rangpur and Sylhet divisional cities have a higher risk of maternal morbidity during pregnancy. Study findings may help the government and relevant authorities to take necessary steps for reducing maternal morbidity and mortality due to pregnancy-related complications.

An assessment of random forest technique using simulation study: illustration with infant mortality in Bangladesh.

We aimed to assess different machine learning techniques for predicting infant mortality (<1 year) in Bangladesh. The decision tree (DT), random forest (RF), support vector machine (SVM) and logistic regression (LR) approaches were evaluated through accuracy, sensitivity, specificity, precision, F1-score, receiver operating characteristics curve and k-fold cross-validation via simulations. The Boruta algorithm and chi-square ( χ 2 ) test were used for features selection of infant mortality. Overall, the RF technique (Boruta: accuracy = 0.8890, sensitivity = 0.0480, specificity = 0.9789, precision = 0.1960, F1-score = 0.0771, AUC = 0.6590; χ 2 : accuracy = 0.8856, sensitivity = 0.0536, specificity = 0.9745, precision = 0.1837, F1-score = 0.0828, AUC = 0.6480) showed higher predictive performance for infant mortality compared to other approaches. Age at first marriage and birth, body mass index (BMI), birth interval, place of residence, religion, administrative division, parents education, occupation of mother, media-exposure, wealth index, gender of child, birth order, children ever born, toilet facility and cooking fuel were potential determinants of infant mortality in Bangladesh. Study findings may help women, stakeholders and policy-makers to take necessary steps for reducing infant mortality by creating awareness, expanding educational programs at community levels and public health interventions.

Determinants of children ever born among ever-married women in Bangladesh: evidence from the Demographic and Health Survey 2017-2018.

OBJECTIVE: To investigate the prevalence of the number of children ever born (CEB) and its associated determinants among women aged 15-49 years in Bangladesh. STUDY DESIGN AND SETTING: We used clustered data extracted from the last two Bangladesh Demographic and Health Surveys (BDHS 2014 and BDHS 2017-2018). A two-stage stratified sampling was used in both surveys. Mixed logistic regression modelling approach for binary responses was adapted to accommodate clustering effects via the generalised linear mixed model framework. PARTICIPANTS: The study is based on 15 924 ever-married women in BDHS 2017-2018 (14 119 in BDHS 2014) of Bangladesh. RESULTS: As per the latest BDHS 2017-2018, 42.1% of reproductive women had three or more children. Age at first marriage (p<0.001, OR 0.74, 95% CI 0.666 to 0.825), age at first birth (p<0.001, OR0.54, 95% CI 0.480 to 0.607), place of residence (p<0.001, OR 0.79, 95% CI 0.712 to 0.872), exposure of media (p<0.001, OR 0.71, 95% CI 0.647 to 0.768), religion (p<0.001, OR 1.47, 95% CI 1.277 to 1.690), husband's desire more child (p<0.001, OR 1.60, 95% CI 1.428 to 1.784), women empowerment (p<0.001, OR 1.19, 95% CI 1.075 to 1.3) and wealth index (p<0.001, OR1.61, 95% CI 0.435 to 1.796) were found to be statistically significant determinants of the number of CEB among ever-married women. The number of CEB among women was negatively associated with their own educational status (p<0.001) and husbands level of education (p<0.001). CONCLUSION: The CEB appears to be higher among women who were married before 18 years, Muslim, illiterate, living in rural areas, had first birth before 20 years, non-exposure of media and husband's desire for more children.

Association of infant and child health characteristics with the hazard of any medical condition or disability in Australian children.

BACKGROUND: The incidence of any medical condition (e.g., sight, hearing, and speech problems, blackouts, chronic pain etc.) or disability (e.g., limited use of arms or fingers, legs, and feet, or other physical long-term health condition limiting everyday activities etc.) have been increasing among Australian children in recent decades. OBJECTIVES: This study assessed whether infant or child health characteristics might be predictors of subsequent medical conditions or disabilities in children in the first 15 years of life. METHODS: Using time to event data of 5107 children, obtained from the Birth cohort of the Longitudinal Study of Australian Children, the study estimated the incidence of any medical condition or disability using the survival analysis technique. This study followed up the children from birth to 14 or 15 years of age (2004-2018) and assessed the association of infant and child health characteristics (birthweight, gestational age, use of intensive care unit or ventilator during their neonatal age and obesity) with hazard of any medical condition or disability using the random effect parametric survival regression model. The infant characteristics were measured in the Wave 1 while the children were aged 0/1 year and obesity characteristics were measured longitudinally over all the waves up to 14/15 years of age. RESULTS: The hazard rate of any medical condition or disability for all participants was 26.13 per 1000 person-years among children in Australia. This hazard incidence rate was higher among low birthweight (39.07) children compared to the children of normal birthweight (24.89) children. The hazard rate also higher among obese (34.37) children compared to the normal weight children (24.82) and among those who had received after-birth ventilation or intensive care unit emergency services (36.87) compared to those who have not received these services (24.20). The parametric panel regression model also suggests that children with low birthweight were 1.43 times (Hazard Ratio: 1.43, 95% Confidence Interval: 1.05-1.94) more likely to have any medical condition or disability than children with normal birthweight. The time to event analyses also revealed that being recipient of after-birth emergencies (HR: 1.47, 95% CI: 1.23-1.75), being male children (HR: 1.30, 95% CI: 1.14-1.48) or being obese (HR: 1.38, 95% CI: 1.07-1.79) significantly increased the likelihood of the incidence of a medical condition or disability among children. The regression model was adjusted for socio-demographic characteristics of children and mothers.. CONCLUSIONS: The study findings suggest that infants with low birth weight, hospital emergency service use and children with obesity would benefit from additional health care monitoring to minimize the risk of any medical condition or disability.

Differences in levels of E. coli contamination of point of use drinking water in Bangladesh.

This study aimed to quantify the inequalities and identify the associated factors of the UN sustainable development goal (SDG) targets in relation to safe drinking water. The concentration of the gut bacterium Escherichia coli in drinking water at the point of use (POU) and other information were extracted from the latest wave of the nationally representative Bangladesh Multiple Indicator Cluster Survey (MICS 2019). Bivariate and multivariable multinomial logistic regression models were used to identify potential predictors of contamination, whereas, classification trees were used to determine specific combinations of background characteristics with significantly higher rates of contamination. A higher risk of contamination from drinking water was observed for households categorized as middle or low wealth who collected water from sources with higher concentrations of E. coli. Treatment of drinking water significantly reduced the risk of higher levels of contamination, whereas owning a pet was significantly associated with recontamination. Regional differences in the concentrations of E. coli present in drinking water were also observed. Interventions in relation to water sources should emphasize reducing the level of E. coli contamination. Our results may help in developing effective policies for reducing diarrheal diseases by reducing water contamination risks.

The gap between perceived mental health needs and actual service utilization in Australian adolescents.

Despite being highly prevalent, adolescent mental health problems are undertreated. To better understand the mental health treatment gap, we assessed the prevalence and correlates of help-seeking, including perceived need for care and access to that care. Data were drawn from Young Minds Matter (YMM) survey-the second Australian child and adolescents survey of mental health and wellbeing. Parent-reported data and self-reported child data were combined into one dataset to analyse 2464 Australian adolescents aged 13-17 years. We employed bivariate and multivariate logistic regression models to assess the correlation between independent variables (professionally assessed with mental disorders only, self-reported self-harm/suicidality only and both) and their distribution over outcome variables (perceived need and service use). Mental disorders include depression, anxiety, ADHD and conduct disorder. Our study revealed 15.0%, 4.6% and 7.7% had professionally assessed with mental disorders only, self-reported self-harm/suicidality only and both, respectively. Overall, 47.4% and 27.5% of adolescents respectively perceived need for care and used services in the past-12-months. While among those only who perceived the need, only 53% of adolescents used any services. Professionally assessed with mental disorders only, self-reported self-harm/suicidality only and both were associated with higher likelihood of perceived need and service use (p < 0.001 for all). However, adolescents who self-reported self-harm/suicidality only were not found to be significantly associated with service use among those who perceived the need for care. Adolescents who perceived the need for mental health care but did not seek care represent a treatment gap. Our results suggest the importance of reducing the wide treatment gap that exists between need and care.

Depression and anxiety have a larger impact on bullied girls than on boys to experience self-harm and suicidality: A mediation analysis.

BACKGROUND: The mechanism underlying the correlation between bullying victimization, self-harm and suicidality by gender are not well understood. This study, therefore, aimed to investigate whether the mediating effect of mental disorder (depression and anxiety) on the association between bullying victimization, and self-harm and suicidality vary across boys and girls. METHODS: Overall, 2522 Australian adolescents aged 12-17-year-olds were analyzed from a nationally representative cross-sectional survey: Young Minds Matter. A series of logistic regressions were employed using Baron and Kenny's approach to test the mediating effect of each mental disorder on the relationship between bullying victimization, and self-harm and suicidality across gender. Further, the Sobel test was used to estimate the indirect effect. RESULTS: Out of 784 (31.1%) bullied victims, 53.2% were girls and 46.8% were boys. A higher proportion of girls compared to boys experienced depression, anxiety, self-harm and suicidality (p < 0.001 for all). The relationships between bullying victimization, and self-harm and suicidality were mediated by depression (p < 0.05) in both boys and girls. While anxiety disorder mediated the association only in girls (p < 0.05). LIMITATIONS: Cross-sectional study design does not imply causality. Self-reported data about self-harm and suicidality may be susceptible to social desirability bias. CONCLUSION: Girls were more affected by bullying, self-harm and suicidality than boys. Depression mediated the correlation between bullying, and self-harm and suicidality in both boys and girls. While anxiety influenced only bullied girls to experience self-harm and suicidality. These findings warrant the need for gender-specific prevention programs to combat bullying and subsequently self-harm and suicidality in adolescents.