Understanding Varied Attitudes Towards Muslims

The focus of this research was to determine the prevalence and type of Islamophobia in the Victorian population. Islamophobia sentiment feeds the actions of right-wing extremist attacks on Muslim communities. But it has also become widespread in Australian society, and normalised in everyday settings, such as our mainstream media. Islamophobia cannot be treated with a singular approach or mode of intervention. Our study comes at a critical time; it provides empirical evidence on the extent of the problem, as well as documenting the varied manifestations of Islamophobic sentiment, with the view to developing potential action points and policy. In November 2019 we undertook a survey of 4019 Victorians. We asked questions on their attitudes towards cultural diversity, racial equality and privilege; trust and fairness; Muslims and Islam; and other ethno-cultural groups in Australian society; their experiences of racism and discrimination; their contact with Muslims and knowledge of Islam; and their political affiliation. To our knowledge, this was the largest survey undertaken in Victoria with the purpose of solely measuring Victorians perceptions of Muslims and Islam. Based on respondents’ answers, we used latent class analysis to segment the Victorian population. Five groups were generated: Islamophobic, Islamophobic with assimilationist tendencies, Undecided, Progressive with concerns about Islam, and Progressive. We then distilled the demographic and attitudinal attributes of these groups, with the view to identifying roles and drivers to help guide policy and intervention. We tested this five group segmentation with community organisations in Victoria working in the broad areas of diversity and multiculturalism, and with a particular emphasis on Muslim and non-Muslim relations. The groupings made sense on-the-ground, and they provided a strong pathway forward for program and policy design

Regional analysis of associations between infant and young child feeding practices and diarrhoea in Indian children

Studies on the association between infant and young child feeding (IYCF) practices and diarrhoea across regional India are limited. Hence, we examined the association between IYCF practices and diarrhoea in regional India. A weighted sample of 90,596 (North = 11,200, South = 16,469, East = 23,317, West = 11,512, Central = 24,870 and North-East = 3228) from the 2015–2016 National Family Health Survey in India was examined, using multivariate logistic regressions that adjust for clustering and sampling weights. The IYCF indicators included early initiation of breastfeeding (EIBF), exclusive breastfeeding (ExcBF), predominant breastfeeding (PBF), bottle feeding (BotF), continued breastfeeding (BF) at one-year, continued BF at two years, children ever breastfed and the introduction of solid, semi-solid or soft foods (ISSSF). Diarrhoea prevalence was lower among infants who were BF within one-hour of birth and those who were exclusively breastfed. Multivariate analyses revealed that continued BF at one and two years, and infants who were introduced to complementary foods had a higher prevalence of diarrhoea. EIBF and ExcBF were protective against diarrhoea in the regions of North, East and Central India. PBF, BotF and ISSSF were risk factors for diarrhoea in Central India. Continued BF at two years was a risk factor for diarrhoea in Western India. Findings suggested that EIBF and ExcBF were protective against diarrhoea in Northern, Eastern and Central India, while PBF, BotF, continued BF at two years and ISSSF were risk factors for diarrhoea in various regions in India. Improvements in IYCF practices are likely to reduce the burden of diarrhoea-related morbidity and mortality across regions in India

Factors associated with inadequate receipt of components and non-use of antenatal care services in India : a regional analysis

Background: Failure to use antenatal care (ANC) and inadequate receipt of components of ANC pose a significant risk for the pregnant woman and the baby. This study aimed to examine a regional analysis of factors associated with receiving no ANC and inadequate receipt of components of ANC services among Indian women. Method: Information from 173,970 women of reproductive age 15–49 years from the 2019–21 India National Family Health Survey (NFSH-5) was analysed. Logistic regression analyses that adjusted for cluster and survey weights were conducted to assess the socio-demographic and other factors associated with receiving non-use of ANC and inadequate receipt of components of ANC, respectively, in the six regions and 28 states, and 8 union territories in India. Results: Across regions in India, 7% of women reported no ANC, and the prevalence of inadequate and adequate receipt of components of ANC in all six regions ranged from 67 to 89% and 8% to 24%, respectively. Of all the 36 federated entities, the prevalence of inadequate receipt of ANC components was less than two-thirds in Tamil Nadu, Puducherry, Andaman and the Nicobar Islands, Odisha, and Gujarat. Our analyses revealed that associated factors vary by region, state, and union territories. Women from poor households reported increased odds of receiving no ANC in North, East and North-eastern regions. Women who reported no schooling in South, East and Central regions were associated with increased odds of receiving no ANC. Women from poor households in Himachal Pradesh, Bihar, Uttar Pradesh, Nagaland, Manipur, Uttar Pradesh, and Madhya Pradesh states reported significantly higher odds of inadequate components ANC than women from rich households. The receipt of inadequate components of ANC was significantly higher among women who never read magazines in Delhi, Ladakh, Karnataka, Telangana, Jharkhand, Maharashtra, Uttar Pradesh, Chhattisgarh, Arunachal Pradesh, Manipur, and Mizoram states in India. Conclusion: A better understanding of the factors associated with and incorporating them into the short- and long-term intervention strategies, including free financial support from the Indian government to encourage pregnant women from lower socioeconomic groups to use health services across all regions, states and union territories

Infant and young child feeding practices among adolescent mothers and associated factors in India

Adequate infant and young child feeding (IYCF) improve child survival and growth. Globally, about 18 million babies are born to mothers aged 18 years or less and have a higher likelihood of adverse birth outcomes in India due to insufficient knowledge of child growth. This paper examined factors associated with IYCF practices among adolescent Indian mothers. This cross-sectional study extracted data on 5148 children aged 0–23 months from the 2015–2016 India National Family Health Survey. Survey logistic regression was used to assess factors associated with IYCF among adolescent mothers. Prevalence of exclusive breastfeeding, early initiation of breastfeeding, timely introduction of complementary feeding, minimum dietary diversity, minimum meal frequency, and minimum acceptable diet rates were: 58.7%, 43.8%, 43.3%, 16.6%, 27.4% and 6.8%, respectively. Maternal education, mode of delivery, frequency of antenatal care (ANC) clinic visits, geographical region, child’s age, and household wealth were the main factors associated with breastfeeding practices while maternal education, maternal marital status, child’s age, frequency of ANC clinic visits, geographical region, and household wealth were factors associated with complementary feeding practices. IYCF practices among adolescent mothers are suboptimal except for breastfeeding. Health and nutritional support interventions should address the factors for these indicators among adolescent mothers in India

Exclusive breastfeeding rates and associated factors in 13 "Economic Community of West African States" (ECOWAS) countries

Exclusive breastfeeding (EBF) has important protective effects on child survival and also increases the growth and development of infants. This paper examined EBF rates and associated factors in 13 “Economic Community of West African States” (ECOWAS) countries. A weighted sample of 19,735 infants from the recent Demographic and Health Survey dataset in ECOWAS countries for the period of 2010–2018 was used. Survey logistic regression analyses that adjusted for clustering and sampling weights were used to determine the factors associated with EBF. In ECOWAS countries, EBF rates for infants 6 months or younger ranged from 13.0% in Côte d’Ivoire to 58.0% in Togo. EBF decreased significantly by 33% as the infant age (in months) increased. Multivariate analyses revealed that mothers with at least primary education, older mothers (35–49 years), and those who lived in rural areas were significantly more likely to engage in EBF. Mothers who made four or more antenatal visits (ANC) were significantly more likely to exclusively breastfeed their babies compared to those who had no ANC visits. Our study shows that EBF rates are still suboptimal in most ECOWAS countries. EBF policy interventions in ECOWAS countries should target mothers with no schooling and those who do not attend ANC. Higher rates of EBF are likely to decrease the burden of infant morbidity and mortality in ECOWAS countries due to non-exposure to contaminated water or other liquids

Consequences of misspecifying across-cluster time-specific residuals in multilevel latent growth curve models

This Monte Carlo study evaluates, in the context of multilevel latent growth curve models, the consequences of under- and overspecifying across-cluster time-specific residuals (i.e., Θb) on the estimation of the fixed effects, their corresponding standard errors, the variances and covariances of the random effects, Type I error rates, and the statistical power of detecting fixed effects. The results show that underspecifying Θb with all elements of Θb fixed at zero results in a large underestimation of the between- and within-level random effect and standard errors of fixed effect estimates, which, in turn, leads to serious bias in significant testing. Underspecifying Θb with diagonal elements of Θb constrained to equality, or overspecifying Θb with diagonal elements of Θb constrained to equality or freely estimated and residual covariances fixed at zero also leads to bias in the estimation of the between- and within-level random effects. Implications of the compensatory relationship occurring at the covariance level are discussed

On the application of the three-step approach to growth mixture models

This series of simulation studies evaluate, in the context of applied research settings, the impact of the parameterization of the covariance structure of the growth mixture model (GMM) on the regression coefficient and standard error estimates in the 3-step method. The results show that the 1-step approach performs better than the 3-step method across the simulation studies. However, the performance of the 3-step method depends slightly or importantly on the parameterization of the GGM from the first step, on the inclusion or not of the predictor at the first step of the analysis, on the population model, and on the type (i.e., logit vs. linear) and size of the regression coefficient estimates

Power of latent growth curve models to detect piecewise linear trajectories

Latent curve models (LCMs) have been used extensively to analyze longitudinal data. However, little is known about the power of LCMs to detect nonlinear trends when they are present in the data. This simulation study was designed to investigate the Type I error rates, rates of nonconvergence, and the power of LCMs to detect piecewise linear growth and mean differences in the slopes of the 2 joined longitudinal processes represented by the piecewise model. The impact of 7 design factors was examined: number of time points, growth magnitude (slope mean), interindividual variability, sample size, position of the turning point, and the correlation of the intercept and the second slope as well between the 2 slopes. The results show that previous results based on linear LCMs cannot be fully generalized to a nonlinear model defined by 2 linear slopes. Interestingly, design factors specific to the piecewise context (position of the turning point and correlation between the 2 growth factors) had some effects on the results, but these effects remained minimal and much lower than the effects of other design factors. Similarly, observed rates of inadmissible solutions are comparable to those previously reported for linear LCMs. The major finding of this study is that a moderate sample size (N = 200) is needed to detect piecewise linear trajectories, but that much larger samples (N = 1,500) are required to achieve adequate statistical power to detect slope mean difference of small magnitude

[In Press] Measuring the potential for hateful behaviours : development and validation of the Hate Behaviours Scale (HBS)

This article introduces and reports the psychometric properties of the Hate Behaviours Scale (HBS), which assesses individual intentions to engage in a range of violent and non-violent hateful behaviours against various target groups. Three independent U.S. adult samples (total n = 3524) were used to gather data on the scale and associated covariates and constructs. The twelve-item HBS is comprised of three subscales; Discrimination, Defensive Violence and Belligerent Violence. The higher scores on the HBS were significantly associated with past behaviours such as attending a protest against the target group (β = 0.57, p < .001), having physically hit a member of the target group (β = 0.57, p < .001), and sharing an offensive joke online about the target group (β = 0.51, p < .001). This new measure provides a modifiable, easy to use, reliable and valid multi-component scale for assessing hate against different target groups. It should allow researchers to reliably assess individual levels of hate, and establish relationships with other demographic, social, situational, and psychological characteristics. The HBS provides a short and cost-effective tool to inform and evaluate counter violent extremism interventions aimed at reducing the potential for hateful behaviours

Longitudinal data analysis : the multiple indicators growth curve model approach

Longitudinal data are used in psychiatric and neurological research to address how cognitive and neural processes change during development. One statistical method used to handle longitudinal data is latent curve modeling. Latent curve modeling examines changes in an outcome over time by explicitly modeling growth and individual differences in growth over time. Recently, however, big data analyses have helped understand and treat psychiatric and neurological disorders. The analysis of big data provides interesting and important opportunities for hypothesis generation and testing, which will enhance clinical practice. The purpose of the present chapter is to promote the use of multiple indicators growth curve model in the structural equation modeling framework for hypothesis testing about changes over time in the context of big psychiatric and neurological data. This method can be used following a data reduction technique such as exploratory factor analysis