Culturally and Linguistically Diverse Gamblers of East Asian Descent in Australia: A Comprehensive Review of Current Evidence.

As a country with one of the highest per capita gambling losses per year in the world, and an evolving multicultural profile, Australia has become an important setting in which to examine the harms and benefits related to gambling. The Australian population includes people from East Asian cultural backgrounds who are a key demographic of interest for gambling operators planning to grow revenue. However, Australian gambling research has concentrated primarily on those belonging to the dominant cultural group. Most of the previous and limited number of studies to examine gambling among culturally and linguistically diverse (CALD) residents have focused on people of Chinese descent, and much of the literature is now becoming relatively old. This review examines the current evidence around cultural variations in gambling prevalence, motivations, beliefs, behaviours, and help service utilisation, focusing on gamblers with an East Asian cultural background. Numerous domains in which gambling motivations and behaviours vary across cultural groups are identified, and methodological considerations related to ethnographic gambling research are discussed. This review found that while barriers and predictors to help-seeking for CALD gamblers have been studied extensively, contemporary evidence of help service utilisation and effectiveness in Australia is lacking. Further research providing an accurate assessment of the impacts of gambling for CALD gamblers is needed to ensure that harm minimisation resources are effective for those most vulnerable to harm.

The adverse effect of ambient temperature on respiratory deaths in a high population density area: the case of Malta.

BACKGROUND: The effect of ambient temperature on respiratory mortality has been consistently observed throughout the world under different climate change scenarios. Countries experiencing greater inter-annual variability in winter temperatures (and may not be lowest winter temperatures) have greater excess winter mortality compared to countries with colder winters. This study investigates the association between temperature and respiratory deaths in Malta which has one of the highest population densities in the world with a climate that is very hot in summer and mild in winter. METHODS: Daily number of respiratory deaths (7679 deaths) and meteorological data (daily average temperature, daily average humidity) were obtained from January 1992 to December 2017. The hot and cold effects were estimated at different temperatures using distributed lag non-linear models (DLNM) with a Poisson distribution, controlling for time trend, relative humidity and holidays. The reference temperature (MMT) for the minimum response-exposure relationship was estimated and the harvesting effects of daily temperature (0-27 lag days) were investigated for daily respiratory mortality. Effects were also explored for different age groups, gender and time periods. RESULTS: Cooler temperatures (8-15 °C) were significantly related to higher respiratory mortality. At 8.9 °C (1st percentile), the overall effect of daily mean temperature was related to respiratory deaths (RR 2.24, 95%CI 1.10-4.54). These effects were also found for males (95%CI 1.06-7.77) and males across different age groups (Males Over 65 years: RR 4.85, 95%CI 2.02-11.63 vs Males between 16 and 64 years: RR 5.00, 95%CI 2.08-12.03) but not for females. Interestingly, colder temperatures were related to respiratory deaths in the earliest time period (1992-2000), however, no strong cold effect was observed for later periods (2000-2017). In contrast, no heat effect was observed during the study period and across other groups. CONCLUSIONS: The higher risk for cold-related respiratory mortality observed in this study could be due to greater inter-annual variability in winter temperatures which needs further exploration after adjusting for potential physical and socio-demographic attributes. The study provides useful evidence for policymakers to improve local warning systems, adaptation, and intervention strategies to reduce the impact of cold temperatures.

A review of the prevalence, trends, and determinants of coexisting forms of malnutrition in neonates, infants, and children.

OBJECTIVE: Coexisting Forms of Malnutrition (CFM) refers to the presence of more than one type of nutritional disorder in an individual. Worldwide, CFM affects more than half of all malnourished children, and compared to standalone forms of malnutrition, CFM is associated with a higher risk of illness and death. This review examined published literature for assessing the prevalence, trends, and determinants of CFM in neonates, infants, and children. METHODS: A review of community-based observational studies was conducted. Seven databases, (CINAHL, Cochrane Library, EMBASE, Medline, PubMed, Scopus, and Web of Science) were used in December-2021 to retrieve literature. Google, Google Scholar and TROVE were used to search for grey literature. Key stakeholders were also contacted for unpublished documents. Studies measuring the prevalence, and/or trends, and/or determinants of CFM presenting in individuals were included. The quality of included studies was assessed using the Joanna Briggs Institute (JBI) critical appraisal tools for prevalence and longitudinal studies. RESULTS: The search retrieved 14,207 articles, of which 24 were included in this review. The prevalence of CFM varied by geographical area and specific types. In children under 5 years, the coexistence of stunting with overweight/obesity ranged from 0.8% in the United States to over 10% in Ukraine and Syria, while the prevalence of coexisting wasting with stunting ranged from 0.1% in most of the South American countries to 9.2% in Niger. A decrease in CFM prevalence was observed in all countries, except Indonesia. Studies in China and Indonesia showed a positive association between rurality of residence and coexisting stunting with overweight/obesity. Evidence for other risk and protective factors for CFM is too minimal or conflicting to be conclusive. CONCLUSION: Evidence regarding the prevalence, determinants and trends for CFM is scarce. Apart from the coexistence of stunting with overweight/obesity, the determinants of other types of CFM are unclear. CFM in any form results in an increased risk of health adversities which can be different from comparable standalone forms, thus, there is an urgent need to explore the determinants and distribution of different types of CFM.

Immediate and delayed effects of climatic factors on hospital admissions for schizophrenia in Queensland Australia: A time series analysis.

BACKGROUND: Evidence of immediate and delayed effects of climatic drivers on hospital admissions for schizophrenia is limited and inconsistent. We aimed to assess the association between climatic factors and daily hospital admissions for schizophrenia in Queensland, Australia. METHODS: Daily hospital admissions for schizophrenia from January 1, 1996 to December 31, 2015 in all private and public hospitals of Queensland were obtained from Queensland Health. The association between climatic factors and hospital admissions for schizophrenia were analysed using Generalised Linear Models with Poisson distribution (GLM) and Distributed Lag non-linear Models (DLNM) across different climatic zones. RESULTS: In South East Queensland, only daily mean temperature showed an immediate negative effect on schizophrenia admissions (RR 0.93, 95%CI 0.90-0.98, p value < 0.001). For other regions, the adverse effect of temperature on hospital admissions was not significant, however, relative humidity (North: RR 1.01, 95%CI 1.00-1.02, p = 0.05) and air pressure (North: RR 1.03, 95%CI 1.00-1.05, p = 0.04; South West: RR 1.01, 95%CI 1.00-1.02, p = 0.05) had an immediate and positive effect on hospital admissions. Moreover, climatic factors had some delayed effects on schizophrenia admissions in different regions of Queensland, i.e. temperature over 0-4 lag days (South East: RR 0.97, 95%CI 0.94-0.98, p = 0.05; South West: RR 0.96, 95%CI 0.94-0.98, p = 0.01), relative humidity over 0-7 lag days (North: RR 0.95, 95%CI 0.92-0.98, p = 0.01; Central: RR 1.02, 95%CI 1.00-1.03, p = 0.05) and rainfall over 0-21 lag days (North: RR 1.03, 95%CI 1.01-1.04, p = 0.01). Meta-analysis showed significant pooled delayed effects of temperature (0-15 days lag: RR 0.95, 95% CI 0.93-0.98, p value < 0.001), relative humidity (0-7 days: RR 0.96, 95%CI 0.92-0.99, p < 0.001); rainfall (0-21 lag days: RR 1.03, 95%CI 1.01-1.04, p < 0.001) and air pressure (0-7 days lag: RR 1.02, 95%CI 1.00-1.04, p < 0.001) on schizophrenia admissions in Queensland. DISCUSSION: As this is the largest study from Australia and also internationally to extensively examine both short term and delayed association between climatic factors and daily admissions for schizophrenia, the results of the study indicate that climate plays an important role in the sudden exacerbation of acute episodes of schizophrenia. Thus, preventive measures could be taken to reduce the severity of symptoms as well as hospital admissions due to schizophrenia during vulnerable periods.

Assessing evidence for seasonality of hospital admissions for schizophrenia in Queensland, Australia: a time series observational study.

Most evidence on seasonal admission patterns for schizophrenia derives from the Northern Hemisphere with results from the Southern Hemisphere less documented. This study examines seasonal patterns in hospital admissions due to schizophrenia in Queensland, Australia, a large area that has a range of different climatic features. Daily hospital admissions data for people with the primary diagnosis of schizophrenia were collected from Queensland Health Department for the period from January 1996 to December 2015. A generalised linear regression model with Quasi-Poisson distribution was used to assess seasonal admission patterns across different climatic regions. The evidence for seasonality was also explored in subgroups that had different socio-demographic characteristics or history of prior hospitalisation for psychiatric disorders. Overall, a significant winter pattern (RR 1.05, 95%CI 1.01-1.13) was found with a peak in August (RR 1.08, 95%CI 1.03-1.17) in temperate Southeast Queensland. However, the hot humid North and Far North Queensland showed a peak in October (RR 1.10, 95%CI 1.02-1.22). Males (RR 1.11, 95%CI 1.07-1.14), people aged 40-59 years old (RR 1.10, 95%CI 1.05-1.15) and those who had never married (RR 1.09, 95%CI 1.06-1.12), were Australian by birth (RR 1.07, 95%CI 1.04-1.10) or were unemployed (RR 1.13, 95%CI 1.09-1.18) had significantly higher risk for hospital admissions, particularly during the winter months. The seasonal admission pattern for schizophrenia did not change significantly according to admission status and history of outpatient or community psychiatric treatment. The study found some evidence for seasonality of hospital admissions for schizophrenia that differed from northern tropical to southern temperate regions of Queensland.

Seasonality and schizophrenia: a comprehensive overview of the seasonal pattern of hospital admissions and potential drivers.

Schizophrenia is a severe neuropsychiatric disorder with heterogeneous aetiology mostly affecting younger people and causing immense disability. Seasonal patterns may be observed in schizophrenia hospital admissions with possible association with changing climatic parameters and socio-demographic characteristics. This study critically reviewed studies that have assessed seasonal variations of hospital admissions for schizophrenia and/or explored an association with climate parameters and/or other potential factors. Following PRISMA guidelines, a systematic literature search was conducted using electronic databases (e.g. MEDLINE, Science Direct, PsycINFO, Pub Med) from inception to February 29, 2020. Thirty five papers were identified, of which only six (17.1%) examined evidence for a seasonal pattern or monthly excess of hospital admissions and the remaining twenty nine (82.9%) assessed climatic and socio-demographic attributes relating to the seasonal pattern or increased hospitalisation for schizophrenia. While most studies reported a summer peak in hospital admission rates, other studies reported a winter peak. Most of the evidence indicated that higher temperatures (> 28 °C) were positively correlated with schizophrenia admission rates. The individual effects of other climatic parameters (e.g. relative humidity, rainfall, atmospheric pressure, sunlight) were less frequently assessed. Males, people of 21-60 years old, and those married were more vulnerable to climatic variability specifically to higher temperatures. Further studies using large sample sizes, analysis of a wide range of interacting environmental variables and sophisticated statistical approaches are needed to better understand the underlying mechanisms involved. This will also provide more reliable statistical evidence that will help in the prevention and better management of cases.

Identifying the factors influencing Saudi Arabian nurses' turnover.

AIMS: This study identifies and analyses the risk factors contributing to nursing turnover in Saudi Arabia and identifies practical solutions to decrease turnover and encourage nurses to stay in their jobs. BACKGROUND: Saudi Arabia has a unique nursing profile, as the majority of the nursing workforce are expatriates. The Saudi health care system relies on contracted expatriate nurses to provide most of the direct patient health care. For nurses from other countries, Saudi Arabia can be a challenging place to work due to a range of factors including personal, policy and organisational variables. There is a high turnover of expatriate nurses, and this has been long-standing problem for the Saudi Arabian health care system. METHOD: A cross-sectional survey design among nurses in Saudi Arabia including 502 nurses, of whom 83.7% are female. Structural equation modelling is used to examine the relationships between the study variables. Confirmatory factor analysis is used to create and validate the measurement models for variables. RESULTS: The analysis of the survey data identifies that Filipino nurses are more likely to intend to leave their current position than other expatriates, including Malaysian, Pakistani, Indian or local Saudi nurses. Many expatriates identify discrimination as an important contributing factor for their intention to leave, citing that the national salary remuneration for nurses should be based on competency and delivery of care. Furthermore, several independent variables are found to be significant predictors of anticipated turnover, including discrimination; social support from immediate supervisor; organisational commitment; and autonomy. CONCLUSIONS: This study provides the most comprehensive information available to date about the factors that influence nurses' desire to leave their current job and provides evidence for better health workforce planning in Saudi Arabia. This study strongly indicates that the main factor related to turnover is the unfair and unequal salaries paid to nurses of different nationalities in Saudi Arabia. IMPLICATIONS FOR NURSING MANAGEMENT: The findings relating to both Saudi and foreign nurse employment could be helpful to policymakers and the Ministry of Health in Saudi Arabia.

Automatic stratification of prostate tumour aggressiveness using multiparametric MRI: a horizontal comparison of texture features.

Background: Previous studies have identified apparent diffusion coefficient (ADC) from diffusion-weighted imaging (DWI) can stratify prostate cancer into high- and low-grade disease (HG and LG, respectively). In this study, we consider the improvement of incorporating texture features (TFs) from T2-weighted (T2w) multiparametric magnetic resonance imaging (mpMRI) relative to mpMRI alone to predict HG and LG disease. Material and methods: In vivo mpMRI was acquired from 30 patients prior to radical prostatectomy. Sequences included T2w imaging, DWI and dynamic contrast enhanced (DCE) MRI. In vivo mpMRI data were co-registered with 'ground truth' histology. Tumours were delineated on the histology with Gleason scores (GSs) and classed as HG if GS ≥ 4 + 3, or LG if GS ≤ 3 + 4. Texture features based on three statistical families, namely the grey-level co-occurrence matrix (GLCM), grey-level run length matrix (GLRLM) and the grey-level size zone matrix (GLSZM), were computed from T2w images. Logistic regression models were trained using different feature subsets to classify each lesion as either HG or LG. To avoid overfitting, fivefold cross validation was applied on feature selection, model training and performance evaluation. Performance of all models generated was evaluated using the area under the curve (AUC) method. Results: Consistent with previous studies, ADC was found to discriminate between HG and LG with an AUC of 0.76. Of the three statistical TF families, GLCM (plus select mpMRI features including ADC) scored the highest AUC (0.84) with GLRLM plus mpMRI similarly performing well (AUC = 0.82). When all TFs were considered in combination, an AUC of 0.91 (95% confidence interval 0.87-0.95) was achieved. Conclusions: Incorporating T2w TFs significantly improved model performance for classifying prostate tumour aggressiveness. This result, however, requires further validation in a larger patient cohort.

Short - term effects of temperature on hospital admissions for acute myocardial infarction: A comparison between two neighboring climate zones in Vietnam.

BACKGROUND: Vietnam is one of the countries most affected by climate change, but few studies have focused on the population health effects of climate variation. Extreme heatwaves and cold spells might exacerbate underlying chronic conditions and precipitate hospitalization or early death. This study examined the short-term effects of ambient temperature extremes on hospital admissions (HAs) due to acute myocardial infarction (AMI) between different climate zones in the Central Coast region of Vietnam. METHODS: Information from medical records of all 3328 cases of AMI HAs (with hospital records cross-checked by clinicians) was collected from three hospitals in the South-Central Coast region (tropical savanna climate) and North-Central Coast region (tropical monsoon climate) for the period 2008-2015. Meteorological data were obtained from the National Hydro-Meteorological and Environment Network Centre. We used distributed lag non-linear models to assess the association between daily average temperature and AMI HAs, accounting for long-term trend and other meteorological variables. RESULTS: We found a negative and significant association between AMI HAs and temperature in the North-Central Coast region while conversely there was a positive and significant association in the South-Central Coast region. In the North-Central Coast region, the risk of AMI HAs increased by 11% (Relative risk (RR): 1.11, 95% CI: 0.91-1.35, p > 0.05) at moderately low temperatures (10th percentile of temperature range - 18.5 °C) and increased by 25% (RR: 1.25, 95% CI: 1.02-1.55, p < 0.05) at extremely low temperatures (5th percentile of temperature range - 16.8 °C). In the South-Central Coast region, the risk of AMI HAs increased by 18% (RR: 1.18, 95% CI: 0.95-1.47, p > 0.05) and 36% (RR: 1.36, 95% CI: 1.06-1.73, p < 0.05) at moderately high temperatures (90th percentile of temperature range -29.5 °C) and extreme high temperatures (95th percentile of temperature range - 29.9 °C), respectively. CONCLUSIONS: Risk of AMI is associated with extremely high and extremely low temperature in Vietnam and the risk varies in relation to the local regional climate. Public health preparedness and multi-level interventions should attempt to reduce people's exposure in periods of disadvantageous temperatures.

Sociodemographic, climatic variability and lower respiratory tract infections: a systematic literature review.

Pneumonia is the leading cause of mortality and morbidity in developing countries, particularly for children and elderly. The main objective of this review paper is to review the epidemiological evidence about the effects of sociodemographic and climatic variability on pneumonia and other lower respiratory tract infections. A detailed literature search was conducted in PubMed and Scopus following PRISMA guidelines. The articles, which considered the effect of only climatic or both climatic and sociodemographic factors on pneumonia and other lower respiratory tract infections, included in this review. A total thirty-four relevant articles were reviewed. Of 34 studies, only 14 articles (41%) examined the joint effects of sociodemographic and climate factors on pneumonia and other lower respiratory infections while most of them (59%) assessed climate factors separately. Among these fourteen, only three articles (8.8%) considered detailed sociodemographic factors. All of the reviewed articles suggested different degrees of positive or negative relationship of temperature with pneumonia or other lower respiratory tract infections. Fifteen (44%) articles suggested an association with relative humidity and 13 (38%) with rainfall. Only 3 articles (8.8%) found a relationship with wind speed. Three articles (8.8%) considered other risk factors such as particulate matter 2.5 (PM2.5) and particulate matter 10 (PM10). One study among the reviewed articles used spatial analysis methods but this study did not examine the joint effects. Among the reviewed articles, 18 (53%) articles used different time series models, one article (3%) used spatiotemporal time series model, 8 (23%) studies used other models and rest 7 (21%) studies used simple descriptive analysis. A total of 18 studies (53%) were conducted in Asia, most of them in China. There were 6 studies (17%) in Europe and 8 studies (23%) in America (South, North and Central). In Africa and Oceania, only one study was found for each region. The joint effect of climate and sociodemographic factors on pneumonia and other lower respiratory tract infections remain to be determined and further research is highly recommended for future prevention of this important and common disease.

Voxel-wise prostate cell density prediction using multiparametric magnetic resonance imaging and machine learning.

BACKGROUND: There are currently no methods to estimate cell density in the prostate. This study aimed to develop predictive models to estimate prostate cell density from multiparametric magnetic resonance imaging (mpMRI) data at a voxel level using machine learning techniques. MATERIAL AND METHODS: In vivo mpMRI data were collected from 30 patients before radical prostatectomy. Sequences included T2-weighted imaging, diffusion-weighted imaging and dynamic contrast-enhanced imaging. Ground truth cell density maps were computed from histology and co-registered with mpMRI. Feature extraction and selection were performed on mpMRI data. Final models were fitted using three regression algorithms including multivariate adaptive regression spline (MARS), polynomial regression (PR) and generalised additive model (GAM). Model parameters were optimised using leave-one-out cross-validation on the training data and model performance was evaluated on test data using root mean square error (RMSE) measurements. RESULTS: Predictive models to estimate voxel-wise prostate cell density were successfully trained and tested using the three algorithms. The best model (GAM) achieved a RMSE of 1.06 (± 0.06) × 103 cells/mm2 and a relative deviation of 13.3 ± 0.8%. CONCLUSION: Prostate cell density can be quantitatively estimated non-invasively from mpMRI data using high-quality co-registered data at a voxel level. These cell density predictions could be used for tissue classification, treatment response evaluation and personalised radiotherapy.

Subgroups in language trajectories from 4 to 11 years: the nature and predictors of stable, improving and decreasing language trajectory groups.

BACKGROUND: Little is known about the nature, range and prevalence of different subgroups in language trajectories extant in a population from 4 to 11 years. This hinders strategic targeting and design of interventions, particularly targeting those whose difficulties will likely persist. METHODS: Children's language abilities from 4 to 11 years were investigated in a specialist language longitudinal community cohort (N = 1,910). Longitudinal trajectory latent class modelling was used to characterise trajectories and identify subgroups. Multinomial logistic regression was used to identify predictors associated with the language trajectories children followed. RESULTS: Three language trajectory groups were identified: 'stable' (94% of participants), 'low-decreasing' (4%) and 'low-improving' (2%). A range of child and family factors were identified that were associated with following either the low-improving or low-increasing language trajectory; many of them shared. The low-improving group was associated with mostly environmental risks: non-English-speaking background, social disadvantage and few children's books in the home. The low-decreasing group was associated with mainly biological risks: low birth weight, socioemotional problems, lower family literacy and learning disability. CONCLUSIONS: By 4 years, services can be confident that most children with low language will remain low to 11 years. Using rigid cut-points in language ability to target interventions is not recommended due to continued individual variability in language development. Service delivery models should incorporate monitoring over time, targeting according to language abilities and associated risks and delivery of a continuum of interventions across the continuum of need.

Classifying patients by their characteristics and clinical presentations; the use of latent class analysis.

In this article, we introduce the general statistical analysis approach known as latent class analysis and discuss some of the issues associated with this type of analysis in practice. Two recent examples from the respiratory health literature are used to highlight the types of research questions that have been addressed using this approach.

Survival analysis of time-to-event data in respiratory health research studies.

This article provides a review of techniques for the analysis of survival data arising from respiratory health studies. Popular techniques such as the Kaplan-Meier survival plot and the Cox proportional hazards model are presented and illustrated using data from a lung cancer study. Advanced issues are also discussed, including parametric proportional hazards models, accelerated failure time models, time-varying explanatory variables, simultaneous analysis of multiple types of outcome events and the restricted mean survival time, a novel measure of the effect of treatment.

Assessing the relationship of maternal short stature with coexisting forms of malnutrition among neonates, infants, and young children of Pakistan.

Evidence from previous studies suggests a strong association between pediatric undernutrition and maternal stature. However, there's a scarcity of evidence regarding the relationship between maternal stature and pediatric coexisting forms of malnutrition (CFM). This study examined the prevalence and trends of CFM at the individual, household, and community levels, using data from the Demographic & Health Surveys (DHS) of Pakistan. Furthermore, this study assessed the association between pediatric CFM and short maternal stature while adjusting for multiple covariates. A panel cross-sectional analysis was conducted using data from the 2012-2013 and 2017-2018 Pakistan Demographic & Health Survey (PDHS). We included data from 6194 mother-child dyads aged 15-49 years and 0-59 months, respectively, while excluding data from pregnant mothers and dyads with incomplete anthropometric variables and anthropometric outliers. Across the two survey periods, our findings reveal a significant decline in pediatric malnutrition, including CFM, alongside a concurrent increase in maternal overweight/obesity. Three out of four households had either a malnourished mother, and/or a malnourished child, and/or both. Our study demonstrates that short maternal stature increased the odds of various forms of pediatric undernutrition by two-to-threefolds (p < .041), but we did not find an association with wasting, overweight/obesity, and nutritional paradox. This underscores the heightened vulnerability of children born to short-stature mothers to various forms of pediatric undernutrition. Addressing the high prevalence of pediatric undernutrition among children of short-stature mothers necessitates a comprehensive approach that considers an individual's nutritional status throughout their entire life cycle.

Robust clustering of COVID-19 cases across U.S. counties using mixtures of asymmetric time series models with time varying and freely indexed covariates.

In this paper, we develop a mixture of autoregressive (MoAR) process model with time varying and freely indexed covariates under the flexible class of two-piece distributions using the scale mixtures of normal (TP-SMN) family. This novel family of time series (TP-SMN-MoAR) models was used to examine flexible and robust clustering of reported cases of Covid-19 across 313 counties in the U.S. The TP-SMN distributions allow for symmetrical/ asymmetrical distributions as well as heavy-tailed distributions providing for flexibility to handle outliers and complex data. Developing a suitable hierarchical representation of the TP-SMN family enabled the construction of a pseudo-likelihood function to derive the maximum pseudo-likelihood estimates via an EM-type algorithm.

Association of Infant Feeding Indicators and Infant Feeding Practices with Coexisting Forms of Malnutrition in Children under Six Months of Age.

Breastmilk is the only recommended source of nutrition for infants below six months of age. However, a significant proportion of children are either on supplemental breastfeeding (SBF) or weaned due to the early introduction of solid/semi-solid/soft food and liquids (SSF) before six months of age. There is good evidence that exclusive breastfeeding (EBF) in infants below six months of age protects them from preventable illnesses, including malnutrition. The relationship between infant feeding practices and coexisting forms of malnutrition (CFMs) has not yet been explored. This study examined the association of different feeding indicators (continuation of breastfeeding, predominant feeding, and SSF) and feeding practices (EBF, SBF, and complete weaning) with CFM in infants aged below six months in Pakistan. National and regional datasets for Pakistan from the last ten years were retrieved from the Demographic Health Surveys (DHS) and UNICEF data repositories. In Pakistan, 34.5% of infants have some form of malnutrition. Among malnourished infants, 44.7% (~15.4% of the total sample) had a CFM. Continuation of breastfeeding was observed in more than 85% of infants, but less than a quarter were on EBF, and the rest were either SBF (65.4%) or weaned infants (13.7%). Compared to EBF, complete weaning increased the odds of coexistence of underweight with wasting, and underweight with both wasting and stunting by 1.96 (1.12-3.47) and 2.25 (1.16-4.36), respectively. Overall, breastfed children had lower odds of various forms of CFM (compared to non-breastfed), except for the coexistence of stunting with overweight/obesity. Continuation of any breastfeeding protected infants in Pakistan from various types of CFM during the first six months of life.

A Bayesian approach on the two-piece scale mixtures of normal homoscedastic nonlinear regression models.

In this application note paper, we propose and examine the performance of a Bayesian approach for a homoscedastic nonlinear regression (NLR) model assuming errors with two-piece scale mixtures of normal (TP-SMN) distributions. The TP-SMN is a large family of distributions, covering both symmetrical/ asymmetrical distributions as well as light/heavy tailed distributions, and provides an alternative to another well-known family of distributions, called scale mixtures of skew-normal distributions. The proposed family and Bayesian approach provides considerable flexibility and advantages for NLR modelling in different practical settings. We examine the performance of the approach using simulated and real data.

Catalytic behaviour of the Cu(i)/L/TEMPO system for aerobic oxidation of alcohols - a kinetic and predictive model.

Here, we disclose a new copper(i)-Schiff base complex series for selective oxidation of primary alcohols to aldehydes under benign conditions. The catalytic protocol involves 2,2,6,6-tetramethylpiperidine-N-oxyl (TEMPO), N-methylimidazole (NMI), ambient air, acetonitrile, and room temperature. This system provides a straightforward and rapid pathway to a series of Schiff bases, particularly, the copper(i) complexes bearing the substituted (furan-2-yl)imine bases N-(4-fluorophenyl)-1-(furan-2-yl)methanimine (L2) and N-(2-fluoro-4-nitrophenyl)-1-(furan-2-yl)methanimine (L4) have shown excellent yields. Both benzylic and aliphatic alcohols were converted to aldehydes selectively with 99% yield (in 1-2 h) and 96% yield (in 16 h). The mechanistic studies via kinetic analysis of all components demonstrate that the ligand type plays a key role in reaction rate. The basicity of the ligand increases the electron density of the metal center, which leads to higher oxidation reactivity. The Hammett plot shows that the key step does not involve H-abstraction. Additionally, a generalized additive model (GAM, including random effect) showed that it was possible to correlate reaction composition with catalytic activity, ligand structure, and substrate behavior. This can be developed in the form of a predictive model bearing in mind numerous reactions to be performed or in order to produce a massive data-set of this type of oxidation reaction. The predictive model will act as a useful tool towards understanding the key steps in catalytic oxidation through dimensional optimization while reducing the screening of statistically poor active catalysis.

Mapping the Morbidity Risk Associated with Coal Mining in Queensland, Australia.

The populations in the vicinity of surface coal mining activities have a higher risk of morbidity due to diseases, such as cardiovascular, respiratory and hypertensive diseases, as well as cancer and diabetes mellitus. Despite the large and historical volume of coal production in Queensland, the main Australian coal mining state, there is little research on the association of coal mining exposures with morbidity in non-occupational populations in this region. This study explored the association of coal production (Gross Raw Output-GRO) with hospitalisations due to six disease groups in Queensland using a Bayesian spatial hierarchical analysis and considering the spatial distribution of the Local Government Areas (LGAs). There is a positive association of GRO with hospitalisations due to circulatory diseases (1.022, 99% CI: 1.002-1.043) and respiratory diseases (1.031, 95% CI: 1.001-1.062) for the whole of Queensland. A higher risk of circulatory, respiratory and chronic lower respiratory diseases is found in LGAs in northwest and central Queensland; and a higher risk of hypertensive diseases, diabetes mellitus and lung cancer is found in LGAs in north, west, and north and southeast Queensland, respectively. These findings can be used to support public health strategies to protect communities at risk. Further research is needed to identify the causal links between coal mining and morbidity in non-occupational populations in Queensland.