Purpose
Lower socio‐economic status (SES) and psychopathy are risk factors for criminal behaviour. This study examines whether psychopathic trait scores moderate the relationship between childhood ...family and neighbourhood SES and adult arrests.
Methods
A large group of Midwest children ages 0–11 years old during 1967–1971 were interviewed as adults in 1989–1995 (N = 1144) at mean age 29. Childhood family SES was based on information collected during the interview and neighbourhood SES were based on census tract information from childhood. Psychopathic trait scores were based on information from interviews and case records. Official arrest data were used to assess criminal behaviour in adulthood.
Results
Childhood family SES, childhood neighbourhood SES, and psychopathic trait scores each independently predicted the number of adult arrests. As expected, lower childhood family SES and childhood neighbourhood SES predicted a larger number of adult arrests, and higher psychopathic trait scores were associated with a greater number of adult arrests. Childhood family SES and childhood neighbourhood SES also interacted with psychopathic trait scores to predict adult arrests. For individuals with low psychopathic trait scores, lower childhood family SES and lower childhood neighbourhood SES each predicted a higher number of adult arrests, whereas this was not the case for individuals with high psychopathic trait scores.
Conclusions
Childhood SES (family and neighbourhood) continues to affect criminal behaviour long into adulthood. But neither childhood family SES, childhood neighbourhood SES, or psychopathic traits alone explain the extent of adult arrests. For people with comparably low levels of psychopathic traits, childhood family and neighbourhood socio‐economic status continued to impact adult arrests.
Previous research has identified that both low‐ and high‐socio‐economic groups tend to be dehumanized. However, groups that have a deprived position are more willing to interiorize the negative ...perceptions that others have about them compared with affluent groups. In this project, we address the role of meta‐(de)humanization (the perceived humanity one thinks is ascribed or denied to one’s group) based on socio‐economic status differences and its influence in the perceived psychological well‐being. We conducted two studies: In Study 1 (correlational, N = 990), we analysed the relationship between socio‐economic status, meta‐dehumanization, and well‐being. Results indicated that lower socio‐economic status positively predicted more meta‐dehumanization and worse well‐being. Moreover, meta‐dehumanization mediated the relationship between socio‐economic status and well‐being. In Study 2 (experimental, N = 354), we manipulated socio‐economic status (low‐, middle‐, and high‐socio‐economic status conditions) to evaluate its influence on meta‐dehumanization and well‐being. Results indicated that individuals of low (vs. higher)‐socio‐economic status perceived more meta‐dehumanization and reported worse well‐being. Finally, a multicategorical mediational analysis indicated that low (vs. middle or high)‐socio‐economic status led to worse well‐being through higher perceived meta‐dehumanization. We discuss differences in perceived meta‐(de)humanization based on groups’ socio‐economic status and implications on the population’s well‐being.
Reported benefits of environmental citizen science include the collection of large volumes of data, knowledge and skills gained by participants, local action, and policy influence. However, it is ...unclear how diverse citizen science participants are, raising concerns about representativeness of data and whether individual, societal, and environmental benefits are evenly distributed. We surveyed 8,220 people representing a cross section of the population in Great Britain to ask whether they had participated in environmental citizen science, allowing us to examine who is and who is not participating. Using logistic regression, we examined relationships between demographic variables, and crucially the interactions between these variables, and the likelihood of participation and whether participation was repeated. Men were more likely to participate than women. People identifying as from white ethnic groups were more likely to participate than those identifying as from minority ethnic groups; participation by women from minority ethnic groups was particularly low. Participation by those from white ethnic groups declined with socio-economic status, but this was not the case for those from minority ethnic groups. Participation was highest amongst those in education (studying at school, college, or university) and lowest amongst the unemployed. We recommend citizen science practitioners carefully consider the aims of projects and thus the diversity of participants they wish to attract. We discuss potential mechanisms for widening participation, for example, engaging participants through third parties already embedded in communities and providing a variety of tasks for people with different amounts of time and types of skills to offer. Finally, we encourage practitioners to document and publish participant demographics to monitor diversity in citizen science.
In child welfare policies, as in contemporary society in general, great attention has been given to parenting roles and investing in ‘positive’ parenting practices. Several studies have suggested ...that socio‐economic factors frame parenting practices. There is broad evidence of a significant correlation between socio‐economic inequalities and child welfare intervention rates. Nevertheless, few studies have investigated parenting practices in a child welfare population. The aim of the present study was to investigate the association between socio‐economic status (SES) and parenting practices in a Norwegian child welfare population. The study was based on a cross‐sectional survey conducted in 2018–2019. The sample consisted of 256 parents (71.5% females). Linear regression analysis, adjusting for potential confounding and intermediate factors, was conducted. Lower SES was associated with higher levels of positive parenting/involvement practices (b = 0.146, CI: 0.026–0.266, P = 0.018), indicating an inverse pattern compared with the general population. When adjusting for symptoms of anxiety and depression, the association was slightly attenuated but remained statistically significant. No significant association was found between SES and inconsistent discipline/other disciplinary practices. The present study offers insights that should be useful in practice and further large‐scale studies.
Reduced quality of life (QoL) is a known consequence of chronic disease in children, and this association may be more evident in those who are socio‐economically disadvantaged. The aims of this ...systematic review were to assess the association between socio‐economic disadvantage and QoL among children with chronic disease, and to identify the specific socio‐economic factors that are most influential. MEDLINE, Embase and PsycINFO were searched to March 2015. Observational studies that reported the association between at least one measure of social disadvantage in caregivers and at least one QoL measure in children and young people (age 2–21 years) with a debilitating non‐communicable childhood disease (asthma, chronic kidney disease, type 1 diabetes mellitus and epilepsy) were eligible. A total of 30 studies involving 6957 patients were included (asthma (six studies, n = 576), chronic kidney disease (four studies, n = 796), epilepsy (14 studies, n = 2121), type 1 diabetes mellitus (six studies, n = 3464)). A total of 22 (73%) studies reported a statistically significant association between at least one socio‐economic determinant and QoL. Parental education, occupation, marital status, income and health insurance coverage were associated with reduced QoL in children with chronic disease. The quality of the included studies varied widely and there was a high risk of reporting bias. Children with chronic disease from lower socio‐economic backgrounds experience reduced QoL compared with their wealthier counterparts. Initiatives to improve access to and usage of medical and psychological services by children and their families who are socio‐economically disadvantaged may help to mitigate the disparities and improve outcomes in children with chronic illnesses.
Summary
Up until now, differences in HbA1c levels by socio‐economic status (SES) have been identified, but not yet quantified in people with type 2 diabetes. The aim of this study was therefore to ...assess the difference in HbA1c levels between people with type 2 diabetes of different SES in a systematic review and meta‐analysis. A systematic literature search was conducted in MEDLINE, Embase, Ebsco, and the Cochrane Library until January 14, 2018. Included studies described adults with type 2 diabetes in whom the association between SES and HbA1c levels was studied. Studies were rated for methodological quality and data were synthesized quantitatively (meta‐analysis) and qualitatively (levels of evidence), stratified for type of SES variable, i.e., education, income, deprivation, and employment. Fifty‐one studies were included: 15 high, 27 moderate, and 9 of low methodological quality. Strong evidence was provided that people of low SES have higher HbA1c levels than people of high SES, for deprivation, education, and employment status. The pooled mean difference in HbA1c levels between people of low and high SES was 0.26% (95% CI, 0.09‐0.43) or 3.12 mmol/mol (95% CI, 1.21‐5.04) for education and 0.20% (95% CI, −0.05 to 0.46) or 2.36 mmol/mol (95%CI, −0.61 to 5.33) for income. In conclusion, our systematic review and meta‐analysis showed that there was an inverse association between SES and HbA1c levels in people with type 2 diabetes. Future research should focus on finding SES‐sensitive strategies to reduce HbA1c levels in people with type 2 diabetes.
Data from the nationally representative US National Health and Nutrition Examination Survey (NHANES) III cohort were used to examine the hypothesis that socio-economic status is consistently and ...negatively associated with levels of biological risk, as measured by nine biological parameters known to predict health risks (diastolic and systolic blood pressure, pulse, HDL and total cholesterol, glycosylated hemoglobin, c-reactive protein, albumin and waist–hip ratio), resulting in greater cumulative burdens of biological risk among those of lower education and/or income. As hypothesized, consistent education and income gradients were seen for biological parameters reflecting cardiovascular, metabolic and inflammatory risk: those with lower education and income exhibiting greater prevalence of high-risk values for each of nine individual biological risk factors. Significant education and income gradients were also seen for summary indices reflecting cumulative burdens of cardiovascular, metabolic and inflammatory risks as well as overall total biological risks. Multivariable cumulative logistic regression models revealed that the education and income effects were each independently and negatively associated with cumulative biological risks, and that these effects remained significant independent of age, gender, ethnicity and lifestyle factors such as smoking and physical activity. There were no significant ethnic differences in the patterns of association between socio-economic status and biological risks, but older age was associated with significantly weaker education and income gradients.
Sleep is fundamental to health and well-being, yet relatively little research attention has been paid to sleep quality. This paper addresses how socio-economic circumstances and gender are associated ...with sleep problems. We examine (i) socio-economic status (SES) patterning of reported sleep problems, (ii) whether SES differences in sleep problems can be explained by socio-demographic characteristics, smoking, worries, health and depression, and (iii) gender differences in sleep problems, addressing the relative contribution of SES, smoking, worries, health and depression in explaining these differences. Logistic regression is used to analyse the British
Psychiatric Morbidity Survey 2000, which interviewed 8578 men and women aged 16–74. Strong independent associations are found between sleep problems and four measures of SES: household income, educational qualifications, living in rented housing and not being in paid employment. Income differences in sleep problems were no longer significant when health and other characteristics were adjusted. The higher odds of sleep problems among the unemployed and adults with low education remained significant following adjustment. Women reported significantly more sleep problems than men, as did the divorced and widowed compared with married respondents. Gender differences in sleep problems were halved following adjustment for socio-economic characteristics, suggesting that SES inequalities play a major part in accounting for gender differences in sleep problems. Our study casts doubt on the primacy of physiological explanations underlying these gender differences. Since disadvantaged socio-economic characteristics are strongly associated with sleep problems, we conclude that disrupted sleep may be a mechanism through which low SES is linked to poor health.
Socio-economic status (SES) disparities exist in the uptake of COVID-19 vaccination; however, most studies were conducted during the initial pandemic wave when vaccination was less discretionary, ...limiting generalizability. We aimed to determine whether differences in vaccination uptake across SES strata widened after the removal of vaccination-differentiated measures prior to the rollout of the second boosters, in a nationwide cohort of older Singaporeans at higher risk of severe-COVID-19.
Retrospective population-based cohort study.
Retrospective population-based cohort study of all Singaporeans aged ≥60 years from 22nd February 2021–14th February 2023. Cox regression models controlling for demographics and comorbidities were used to estimate hazard-ratios (HRs) for the uptake of primary vaccination as well as first/second boosters, as recorded in the national vaccination registry, according to SES (housing type).
836,170 individuals were included for completion of a primary vaccine series; 784,938 individuals for completion of the first booster and 734,206 individuals for the completion of the second booster. Differences in vaccination uptake by SES strata were observed (e.g. vaccination uptake in lowest-SES 1–2 room public-housing versus highest-SES private housing: second booster, 47.6% vs. 58.1%; first booster, 93.9% vs. 98.0%). However, relative differences did not markedly widen during second booster rollout when vaccination was more discretionary (e.g. amongst those aged 60–69 years: 0.75 95% CI = 0.73–0.76 for the first booster; 0.81 95% CI = 0.79–0.84 for the second booster).
While differences in vaccination uptake across SES strata by housing type persisted during the rollout of primary vaccination and subsequent boosters in a nationwide cohort of older Singaporeans, differences did not widen substantially when vaccination was made more discretionary.
Aims
To describe type 1 diabetes incidence in Scotland between 2006 and 2019.
Methods
Repeated annual cross‐sectional studies of type 1 diabetes incidence were conducted. Incident cases were ...identified from the Scottish Care Information—Diabetes Collaboration (SCI‐DC), a population‐based register of people with diagnosed diabetes derived from primary and secondary care data. Mid‐year population estimates for Scotland were used as the denominator to calculate annual incidence with stratification by age and sex. Joinpoint regression was used to investigate whether incidence changed during the study period. Age and sex‐specific type 1 diabetes incidence over the whole time period was estimated by quintile of the Scottish Index of Multiple Deprivation (SIMD), an area‐based measure, in which Q1 and Q5 denote the most and least deprived fifths of the population, respectively, with quasi‐Poisson regression used to compare incidence for Q5 compared to Q1.
Results
The median (IQR) age of the study population of 14,564 individuals with incident type 1 diabetes was 24.1 (12.3–42.4) years, 56% were men, 23% were in Q1 and 16% were in Q5. Incidence of T1DM was higher in men than women overall (at around 22 and 17 per 100,000, respectively) and in under 15 year olds (approximately 40 per 100,000 in both sexes) than other age groups and was similar across the study period in all strata. There was an inverse association between socio‐economic status and type 1 diabetes incidence for 15–29, 30–49 and 50+ year olds incidence rate ratio (IRR) for Q5 compared to Q1; IRR (95% CI) 0.52 (0.47–0.58), 0.68 (0.61–0.76) and 0.53(0.46–0.61), respectively but not for under 15 year olds 1.02 (0.92–1.12).
Conclusion
Incidence of type 1 diabetes varies by age, sex and socio‐economic status and has remained approximately stable from 2006 to 2019 in Scotland.
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