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Public attitudes toward climate change influence climate and energy policies and guide individual mitigation and adaptation behaviors. Over the last decade, as scientific certainty about the causes and impacts of, and solutions to the climate crisis has increased, cities, states, and regions in the United States have pursued diverse policy strategies. Yet, our understanding of how Americans’ climate views are changing remains largely limited to national trends. Here we use a large US survey dataset ( N = 27 075 ) to estimate dynamic, state-level changes in 16 climate change beliefs, risk perceptions, and policy preferences over 13 years (2008–2020). We find increases in global warming issue importance and perceived harm in every state. Policy support, however, increased in more liberal states like California and New York, but remained stable elsewhere. Year-by-year estimates of state-level climate opinions can be used to support sub-national mitigation and adaptation efforts that depend on public support and engagement.

Motivated by recent successes in model-based pre-election polling, we propose a kinetic model for opinion formation which includes voter demographics and socio-economic factors like age, sex, ethnicity, education level, income and other measurable factors like behaviour in previous elections or referenda as a key driver in the opinion formation dynamics. The model is based on Toscani’s kinetic opinion formation model (Toscani G. 2006 Kinetic models of opinion formation. Commun. Math. Sci. 4, 481–496.) and the leader–follower model of Düring et al. (Düring B. et al. 2009 Boltzmann and Fokker–Planck equations modelling opinion formation in the presence of strong leaders. Proc. R. Soc. A 465, 3687–3708.), and leads to a system of coupled Boltzmann-type equations and associated, approximate Fokker–Planck-type systems. Numerical examples using data from general elections in the UK show the effect different demographics have on the opinion formation process and the outcome of elections. This article is part of the theme issue ‘Kinetic exchange models of societies and economies’.

Background Limited evidence exists regarding the relationship between health literacy and health-related quality of life (HRQoL) in Australian patients from primary care. The objective of this study was to investigate the impact of health literacy on HRQoL in a large sample of patients without known vascular disease or diabetes and to examine whether the difference in HRQoL between low and high health literacy groups was clinically significant. Methods This was a cross-sectional study of baseline data from a cluster randomised trial. The study included 739 patients from 30 general practices across four Australian states conducted in 2012 and 2013 using the standard Short Form Health Survey (SF-12) version 2. SF-12 physical component score (PCS-12) and mental component score (MCS-12) are derived using the standard US algorithm. Health literacy was measured using the Health Literacy Management Scale (HeLMS). Multilevel regression analysis (patients at level 1 and general practices at level 2) was applied to relate PCS-12 and MCS-12 to patient reported life style risk behaviours including health literacy and demographic factors. Results Low health literacy patients were more likely to be smokers (12 % vs 6 %, P  = 0.005), do insufficient physical activity (63 % vs 47 %, P  < 0.001), be overweight (68 % vs 52 %, P  < 0.001), and have lower physical health and lower mental health with large clinically significant effect sizes of 0.56 (B (regression coefficient) = −5.4, P  < 0.001) and 0.78(B = -6.4, P  < 0.001) respectively after adjustment for confounding factors. Patients with insufficient physical activity were likely to have a lower physical health score (effect size = 0.42, B = −3.1, P  < 0.001) and lower mental health (effect size = 0.37, B = −2.6, P  < 0.001). Being overweight tended to be related to a lower PCS-12 (effect size = 0.41, B = −1.8, P  < 0.05). Less well-educated, unemployed and smoking patients with low health literacy reported worse physical health. Health literacy accounted for 45 and 70 % of the total between patient variance explained in PCS-12 and MCS-12 respectively. Conclusions Addressing health literacy related barriers to preventive care may help reduce some of the disparities in HRQoL. Recognising and tailoring health related communication to those with low health literacy may improve health outcomes including HRQoL in general practice.

Background Recently, the demand for care has risen, while in contrast, healthcare resources remain limited. These resources include health expenditure, the number of physicians, nurses, and hospital beds. Many studies have revealed that healthcare resources are one of the most critical factors contributing to a population’s health status. The healthcare system plays a key role in transforming these resources into health outcomes, which are widely used as indicators to measure population health and the performance of healthcare systems. Previous work has primarily investigated the relationship between health expenditure or the number of doctors and population health. However, the association between healthcare resources as a whole has yet to be widely examined. Methods This study utilized multilevel regression analysis to explore the association between healthcare resources, healthcare systems, and population health outcomes across 25 European countries. The healthcare systems in these countries are primarily categorized into two types: Beveridge-type and Bismarck-type. In addition to regression analysis, descriptive statistics were used to analyze the allocation patterns of healthcare resources. Welch’s t-test was employed to compare the performance metrics of the Beveridge-type and Bismarck-type healthcare systems, providing a statistical basis for understanding differences in their effectiveness. Results The regression analysis revealed positive correlations between health expenditure per capita, the number of physicians, and nurses, and life expectancy at birth, while the number of hospital beds showed a negative correlation. Conversely, infant mortality was negatively correlated with health expenditure per capita and the number of physicians and nurses, and positively correlated with the number of hospital beds. The models did not find statistical significance in the effects of healthcare system type (Beveridge-type or Bismarck-type) on life expectancy at birth or infant mortality rates. Additionally, Welch’s t-test indicated that the Beveridge-type healthcare system generally showed better performance outcomes compared to the Bismarck-type system. Conclusions The findings indicate that higher allocations of certain healthcare resources, such as hospital beds, are associated with poorer health outcomes, which suggests potential inefficiencies in resource utilization. Observations also show that countries using the same healthcare systems tend to have similar patterns of resource allocation, which may influence the performance of these systems. Policymakers should consider these associations when planning resource allocation and when selecting or modifying healthcare system models in their countries.

Introduction Food insecurity is one of the most serious issues, especially in developing countries, that harm many public health outcomes through increased under nutrition, mental health problem, and premature mortality. It is widespread socio-economic problem of Ethiopia, with unequal distribution among its regions, during COVID-19 and other shock event manifestations for the last three years. This study aimed to analyse country-wise and region-specific food insecurity prevalence; assess its variation among regions; and identify predictors that influenced households’ food insecurity in Ethiopia during COVID-19. Methods This study used longitudinal data from the World Bank's Ethiopia-High Frequency Phone Survey, which looked at 3,300 households' experiences of food insecurity over five rounds, yielding 13,517 observations throughout time. The non-parametric model, Kruskal–Wallis Test, was used to asses food insecurity differences across regions; while the parametric, Generalized Multilevel Binomial Regression Model, was used to identify significant predictors of households’ food insecurity experience. Results There are significant variations in food insecurity among regions of Ethiopia during COVID-19. Sumali was the region with highest food insecurity prevalence followed by Tigray, SNNP, Oromia, and Amhara where these regions were also facing another shocks, in addition to COVID-19, such a displacement and drought. Female-headed household and income loss are directly associated with likelihood of being food insecure. Dwelling in urban (coefficient = -0.3707, p  = 0.0003), being employed (coefficient = -0.1869, p  = 0.0161), benefiting assistance (coefficient = -0.3504, p  = 0.0029), and operating non-farm business during COVID-19 (coefficient = -0.4074, p  = 0.0000) were significant and negatively associated predictors of households’ food insecurity. Besides, household’s worry and financial threat due to the outbreak of pandemic were the two COVID-19 related predictors that had significant effect on household’s food insecurity. Income loss was the most determinant variable (coefficient = 0.8562, p  = 0.0000) that had largest influence on household’s likelihood of being food insecure. As time went, the decline in food insecurity was attributed to either decreased outbreak of the pandemic and/or improved households’ resilience to shocks. Conclusions Even while food insecurity is a major issue in Ethiopia, not all its regions are at equal status. Household’s food insecurity is determined by his ability to handle the problem economically, and withstand shock events like COVID-19 that subtly disrupts social and economic networks. Intervention measures taken to insure food insecurity in the country should take in to account regions’ food insecurity inequalities and their vulnerability to shock event manifestations. During shocks, boosting households’ ability to cope up with unexpected risk event can save the exacerbation of food insecurity problem.

The context of this study is an interdisciplinary project-based course at a large public university in Scandinavia. The course is taught annually to 3,300 graduate students from all fields of study, and learning to collaborate is a specified learning objective. Similar courses are widespread in higher education institutions worldwide, and empirical evidence of their impacts on students’ skill development is needed. This study examined students’ collaboration skill outcomes; whether outcomes vary by gender, academic achievement, field of study, course format (accelerated and semester based); and variations in outcomes across student groups and course classes. We used a pretest-posttest design in which 89% of students answered a self-report questionnaire about collaboration skills. The results indicate that the participating students’ interdisciplinary, interpersonal, and conflict management skills improved significantly from the beginning to the end of the course (p < .001, d > 0.4). We also found that the accelerated course format positively influenced the students’ conflict management skill outcomes and that the variability in the students’ overall collaboration outcomes was related to their student group (not their course classes). Another important takeaway from our study is that the students’ gender, academic achievement, and field of study showed little impact on their collaboration skills. The non-significance of the measured individual characteristics and the significance of the student group for students’ collaboration outcomes are important reminders for teachers in higher education to guide and support both their students’ learning and group processes in project-based courses.

Study objective: This didactical essay is directed to readers disposed to approach multilevel regression analysis (MLRA) in a more conceptual than mathematical way. However, it specifically develops an epidemiological vision on multilevel analysis with particular emphasis on measures of health variation (for example, intraclass correlation). Such measures have been underused in the literature as compared with more traditional measures of association (for example, regression coefficients) in the investigation of contextual determinants of health. A link is provided, which will be comprehensible to epidemiologists, between MLRA and social epidemiological concepts, particularly between the statistical idea of clustering and the concept of contextual phenomenon. Design and participants: The study uses an example based on hypothetical data on systolic blood pressure (SBP) from 25 000 people living in 39 neighbourhoods. As the focus is on the empty MLRA model, the study does not use any independent variable but focuses mainly on SBP variance between people and between neighbourhoods. Results: The intraclass correlation (ICC = 0.08) informed of an appreciable clustering of individual SBP within the neighbourhoods, showing that 8% of the total individual differences in SBP occurred at the neighbourhood level and might be attributable to contextual neighbourhood factors or to the different composition of neighbourhoods. Conclusions: The statistical idea of clustering emerges as appropriate for quantifying “contextual phenomena” that is of central relevance in social epidemiology. Both concepts convey that people from the same neighbourhood are more similar to each other than to people from different neighbourhoods with respect to the health outcome variable.

Age is one of the most robust correlates of criminal behavior. Yet, explanations for this relationship are varied and conflicting. Developmental theories point to a multitude of sociological, psychological, and biological changes that occur during adolescence and adulthood. One prominent criminological perspective outlined by Gottfredson and Hirschi claims that age has a direct effect on crime, inexplicable from sociological and psychological variables. Despite the attention this claim has received, few direct empirical tests of it have been conducted. We use data from Pathways to Desistance, a longitudinal study of over 1,300 serious youthful offenders (85.8 % male, 40.1 % African-American, 34.3 % Hispanic, 21.0 % White), to test this claim. On average, youths were 16.5 years old at the initial interview and were followed for 7 years. We use multilevel longitudinal models to assess the extent to which the direct effects of age are reduced to statistical and substantive non-significance when constructs from a wide range of developmental and criminological theories are controlled. Unlike previous studies, we are able to control for changes across numerous realms emphasized within differing theoretical perspectives including social control (e.g., employment and marriage), procedural justice (e.g., perceptions of the legitimacy and fairness of the legal system), learning (e.g., gang membership and exposure to antisocial peers), strain (e.g., victimization and relationship breakup), psychosocial maturity (e.g., impulse control, self-regulation and moral disengagement), and rational choice (e.g., costs and rewards of crime). Assessed separately, these perspectives explain anywhere from 3 % (procedural justice) to 49 % (social learning) of the age-crime relationship. Together, changes in these constructs explain 69 % of the drop in crime from ages 15 to 25. We conclude that the relationship between age and crime in adolescence and early adulthood is largely explainable, though not entirely, attributable to multiple co-occurring developmental changes.

Abdominal obesity, measured using the waist-to-hip ratio, is an emerging public health concern in India, yet national averages mask important subnational variation relevant for local policy and prevention planning. We sought to quantify district-level variation in abdominal obesity and to examine its individual- and contextual-level determinants across India. Using nationally representative data from the National Family Health Survey 2019–2021, we analysed 664,646 women aged 15–49 years and 96,010 men aged 15–54 years across 707 districts and 30,112 communities using multilevel logistic regression. Abdominal obesity was defined using WHO-anthropometric cut-offs for WHR (women: >0.85; men: >0.90). National prevalence was high, affecting 56.6% (95% confidence interval [CI]: 56.41–56.74) of women and 48.9% (95% CI: 48.31–49.46) of men, with higher levels among older adults, wealthier groups, and urban residents. Marked inter-district heterogeneity was observed, with clusters of high prevalence in northern and eastern India and distinct sex-specific spatial patterns. Variance partitioning indicated that individual-level factors accounted for about 67% of total variation among women and 70% among men, while community-, district-, and state-level factors together explained the remaining variation. These findings demonstrate the value of small-area estimates for identifying high-burden populations and informing geographically targeted public health planning.

Introduction Handwashing is fundamentally an inexpensive means of reducing the spread of communicable diseases. In developing countries, many people die due to infectious diseases that could be prevented by proper hand hygiene. The recent coronavirus (COVID-19) pandemic is a threat to people who are living in resource-limited countries including sub-Saharan Africa (SSA). Effective hand hygiene requires sufficient water from reliable sources, preferably accessible on premises, and access to handwashing facility (water and or soap) that enable hygiene behaviors. Therefore, this study aims to determine the prevalence of limited handwashing facility and its associated factors in sub-Saharan Africa. Methods Data from the Demographic and Health Surveys (DHS) were used, which have been conducted in 29 sub-Saharan African countries since January 1, 2010. A two-stage stratified random cluster sampling strategy was used to collect the data. This study comprised a total of 237,983 weighted samples. The mixed effect logistic regression model with a cluster-level random intercept was fitted. Meta-analysis and sub-group analysis were performed to establish the pooled prevalence. Results The pooled prevalence of limited handwashing facility was found to be 66.16% (95% CI; 59.67%—72.65%). Based on the final model, household head with age group between 35 and 60 [AOR = 0.89, 95% CI; 0.86—0.91], households with mobile type of hand washing facility [AOR = 1.73, 95% CI; 1.70—1.77], unimproved sanitation facility [AOR = 1.58, 95% CI; 1.55—1.62], water access more than 30 min round trip [AOR = 1.16, 95% CI; 1.13—1.19], urban residential area [AOR = 2.08, 95% CI; 2.04—2.13], low media exposure [AOR = 1.47, 95% CI; 1.31—1.66], low educational level [AOR = 1.30, 95% CI; 1.14—1.48], low income level [AOR = 2.41, 95% CI; 2.33—2.49] as well as lower middle-income level [AOR = 2.10, 95% CI; 2.14—2.17] and households who had more than three children [AOR = 1.25, 95% CI; 1.20—1.31] were associated with having limited handwashing facility. Conclusion and recommendation The pooled coverage of limited handwashing facility was high in sub-Saharan Africa. Raising awareness of the community and promoting access to handwashing materials particularly in poorer and rural areas will reduce its coverage.