Accuracy of Mothers' Perception of Birth Size to Predict Birth Weight Data in Bangladesh.

OBJECTIVES: The prevalence of low birth weight (LBW) is an important indicator of child health and wellbeing. However, in many countries, decisions regarding care and treatment are often based on mothers' perceptions of their children's birth size due to a lack of objective birth weight data. Additionally, birth weight data that is self-reported or recorded often encounters the issue of heaping. This study assesses the concordance between the perceived birth size and the reported or recorded birth weight. We also investigate how the presence of heaped birth weight data affects this concordance, as well as the relationship between concordance and various sociodemographic factors. METHODS: We examined 4,641 birth records reported in the 2019 Bangladesh Multiple Indicator Cluster Survey. The sensitivity-specificity analysis was performed to assess perceived birth size's ability to predict LBW, while Cohen's Kappa statistic assessed reliability. We used the kernel smoothing technique to correct heaping of birth weight data, as well as a multivariable multinomial logistic model to assess factors associated with concordance. RESULTS: Maternally-perceived birth size exhibited a low sensitivity (63.5%) and positive predictive value (52.6%) for predicting LBW, but a high specificity (90.1%) and negative predictive value (93.4%). There was 86.1% agreement between birth size and birth weight-based classifications (Kappa = 0.49, indicating moderate agreement). Smoothed birth weight data did not improve agreement (83.4%, Kappa = 0.45). Of the sociodemographic factors, early marriage was positively associated with discordance (i.e., overestimation). CONCLUSIONS: An important consideration when calculating the LBW prevalence is that maternally perceived birth size is not an optimal proxy for birth weight. Focus should be placed on encouraging institutional births and educating community health workers and young mothers about the significance of measuring and recording birth weight.

Quantification of Urbanization Using Night-Time Light Intensity in Relation to Women's Overnutrition in Bangladesh.

Urbanization is accelerating in developing countries, which are simultaneously experiencing a rise in the prevalence of overnutrition (i.e., overweight and obesity), specifically among women. Since urbanization is a dynamic process, a continuous measure may better represent it when examining its association with overnutrition. However, most previous research has used a rural-urban dichotomy-based urbanization measure. This study utilized satellite-based night-time light intensity (NTLI) data to measure urbanization and evaluate its association with body weight in reproductive-aged (15-49) women in Bangladesh. Multilevel models estimated the association between residential area NTLI and women's body mass index (BMI) or overnutrition status using data from the latest Bangladesh Demographic and Health Survey (BDHS 2017-18). Higher area-level NTLI was associated with a higher BMI and increased odds of being overweight and obese in women. Living in areas with moderate NTL intensities was not linked with women's BMI measures, whereas living in areas with high NTL intensities was associated with a higher BMI or higher odds of being overweight and obese. The predictive nature of NTLI suggests that it could be used to study the relationship between urbanization and overnutrition prevalence in Bangladesh, though more longitudinal research is needed. This research emphasizes the necessity for preventive efforts to offset the expected public health implications of urbanization.

Targeting at risk households in Bangladesh exposed to arsenic contamination: a spatial analysis.

This study seeks to identify the spatial risk pattern of households (HHs) exposed to arsenic contamination in Bangladesh by adjusting potential socio-economic, demographic factors. Data from Bangladesh Multiple Indicator Cluster Survey 2012-13 are used where hierarchical Bayesian spatial ordered logit model is implemented. The analysis shows that 25% of HH water samples were arsenic contaminated, although the majority (95%) of HHs used improved water sources. Arsenic contamination risk in the HH water was significantly associated with water source type and location, place of residence and districts. The model-based spatial prediction reveals that the north-east and south-west parts of Bangladesh have a high risk of contamination. To ensure the quality of HH water, our findings suggest that chemical test should be promoted considering the spatial risk of arsenic contaminations variations among HHs of Bangladesh. Furthermore, the study findings can effectively contribute in the planning of future interventions and programs.

Spatial risk distribution and determinants of E. coli contamination in household drinking water: a case study of Bangladesh.

The Escherichia coli (E. coli) contamination in the household (HH) drinking water is often a public health concern. Very few studies explore the associated factors and spatial risk modeling together for E. coli contamination in Bangladesh, this research gap motivates to explore this fact further by utilizing Bangladesh Multiple Indicator Cluster Survey (MICS) 2012-13 data. A Bayesian spatial ordered logit model was used to examine the associated factors and spatial risks of the E. coli contamination. The results show that 62% of HH water samples were contaminated with E. coli. After controlling for different factors, a high level of E. coli contamination was observed among HHs who had access to non-improved water sources. Moreover, no significant rural-urban difference was observed. The spatial prediction of the high-risk contamination was prominent in districts like Dhaka and Bandarban. The study findings can provide insights into the planning of policy activities in Bangladesh.

Applying causal inference and Bayesian statistics to understanding vaccine safety signals using a simulation study.

Community perception of vaccine safety influences vaccine uptake. Our objective was to assess current vaccine safety monitoring by examining factors that may influence the availability of post-vaccination survey data, and thereby the specificity and sensitivity of existing signal detection methods. We used causal directed acyclic graphs (DAGs) and a Bayesian posterior predictive analysis (PPA) signal detection method to understand biological and behavioural factors which may influence signal detection. The DAGs informed the data simulated for scenarios in which these factors were varied. The influence of biological factors such as severity of adverse reactions and behavioural factors such as healthcare-seeking behaviour upon survey participation was found to drive signal detection. Where there was a low prevalence of moderate to severe reactions, false signals were detected when there was a strong influence of reaction severity on both survey participation and seeking medical attention. These findings provide implications for future vaccine safety monitoring.

Social deprivation and spatial clustering of childhood asthma in Australia.

BACKGROUND: Asthma is the most common chronic respiratory illness among children in Australia. While childhood asthma prevalence varies by region, little is known about variations at the small geographic area level. Identifying small geographic area variations in asthma is critical for highlighting hotspots for targeted interventions. This study aimed to investigate small area-level variation, spatial clustering, and sociodemographic risk factors associated with childhood asthma prevalence in Australia. METHODS: Data on self-reported (by parent/carer) asthma prevalence in children aged 0-14 years at statistical area level 2 (SA2, small geographic area) and selected sociodemographic features were extracted from the national Australian Household and Population Census 2021. A spatial cluster analysis was used to detect hotspots (i.e., areas and their neighbours with higher asthma prevalence than the entire study area average) of asthma prevalence. We also used a spatial Bayesian Poisson model to examine the relationship between sociodemographic features and asthma prevalence. All analyses were performed at the SA2 level. RESULTS: Data were analysed from 4,621,716 children aged 0-14 years from 2,321 SA2s across the whole country. Overall, children's asthma prevalence was 6.27%, ranging from 0 to 16.5%, with significant hotspots of asthma prevalence in areas of greater socioeconomic disadvantage. Socioeconomically disadvantaged areas had significantly higher asthma prevalence than advantaged areas (prevalence ratio [PR] = 1.10, 95% credible interval [CrI] 1.06-1.14). Higher asthma prevalence was observed in areas with a higher proportion of Indigenous individuals (PR = 1.13, 95% CrI 1.10-1.17). CONCLUSIONS: We identified significant geographic variation in asthma prevalence and sociodemographic predictors associated with the variation, which may help in designing targeted asthma management strategies and considerations for service enhancement for children in socially deprived areas.

A method to identify drivers of societal change likely to affect natural assets in the future, illustrated with Australia's native biodiversity.

Human society has a profound adverse effect on natural assets as human populations increase and as global climate changes. We need to envisage different futures that encompass plausible human responses to threats and change, and become more mindful of their likely impacts on natural assets. We describe a method for developing a set of future scenarios for a natural asset at national scale under ongoing human population growth and climate change. The method involves expansive consideration of potential drivers of societal change, a reduction of these to form a small set of key drivers to which contrasting settings are assigned, which we use to develop a set of different scenarios. We use Australia's native biodiversity as the focus to illustrate the method.

Potential future scenarios for Australia's native biodiversity given on-going increases in human population.

Most natural assets, including native biodiversity (our focus), are under increasing threat from direct (loss of habitat, hunting) and indirect (climate change) human actions. Most human impacts arise from increasing human populations coupled with rises in per capita resource use. The rates of change of human actions generally outpace those to which the biota can respond or adapt. If we are to maintain native biodiversity, then we must develop ways to envisage how the biota may be affected over the next several decades to guide management and policy responses. We consider the future for Australia's native biodiversity in the context of two assumptions. First, the human population in Australia will be 40million by 2050, which has been mooted by federal government agencies. Second, greenhouse gas emissions will track the highest rates considered by the Intergovernmental Panel on Climate Change. The scenarios are based on major drivers of change, which were constructed from seven key drivers of change pertinent to native biodiversity. Five scenarios deal with differing distributions of the human population driven by uncertainties in climate change and in the human responses to climate change. Other scenarios are governed largely by global change and explore different rates of resource use, unprecedented rates of technological change, capabilities and societal values. A narrative for each scenario is provided. The set of scenarios spans a wide range of possible future paths for Australia, with different implications for the future of native biodiversity.