Rich and ever richer? Differential returns across socioeconomic groups

Abstract Background Despite reduced sanctions and more permissive attitudes toward cannabis use in the USA and Europe, the prevalences of adolescent cannabis use have remained rather stable in the twenty-first century. However, whether trends in adolescent cannabis use differ between socioeconomic groups is not known. The aim of this study was to examine trends in cannabis use according to socioeconomic status among Finnish adolescents from 2000 to 2015. Methods A population-based school survey was conducted biennially among 14–16-year-old Finns between 2000 and 2015 (n = 761,278). Distributions for any and frequent cannabis use over time according to socioeconomic adversities were calculated using crosstabs and chi-square test. Associations between any and frequent cannabis use, time, and socioeconomic adversities were studied using binomial logistic regression results shown by odds ratios with 95% confidence intervals. Results At the overall level, the prevalences of lifetime and frequent cannabis use varied only slightly between 2000 and 2015. Cannabis use was associated with socioeconomic adversities (parental unemployment in the past year, low parental education, and not living with both parents). The differences in any and frequent cannabis use between socioeconomic groups increased significantly over the study period. Conclusions Although the overall changes in the prevalence of adolescent cannabis use were modest, cannabis use increased markedly among adolescents with the most socioeconomic adversities. Socioeconomic adversities should be considered in the prevention of adolescent cannabis use.

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Differential Patterns of Social Media Use Associated with Loneliness and Health Outcomes in Selected Socioeconomic Groups

Journal of Technology in Behavioral Science ◽

10.1007/s41347-021-00208-4 ◽

2021 ◽

Author(s):

Pedram Gharani ◽

Sumanta Ray ◽

Meghana Aruru ◽

Saumyadipta Pyne

Keyword(s):

Social Media ◽

Health Outcomes ◽

Media Use ◽

Social Media Use ◽

Socioeconomic Groups

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The effects of non-pharmaceutical interventions on SARS-CoV-2 transmission in different socioeconomic populations in Kuwait: a modeling study

BMC Public Health ◽

10.1186/s12889-021-10984-6 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Fatima Khadadah ◽

Abdullah A. Al-Shammari ◽

Ahmad Alhashemi ◽

Dari Alhuwail ◽

Bader Al-Saif ◽

...

Keyword(s):

Disease Transmission ◽

Reproduction Number ◽

Transmission Model ◽

Socioeconomic Groups ◽

Before And After ◽

Cross Transmission ◽

Community Transmission ◽

The Impact ◽

Pharmaceutical Interventions ◽

The Basic Reproduction Number

Abstract Background Aggressive non-pharmaceutical interventions (NPIs) may reduce transmission of SARS-CoV-2. The extent to which these interventions are successful in stopping the spread have not been characterized in countries with distinct socioeconomic groups. We compared the effects of a partial lockdown on disease transmission among Kuwaitis (P1) and non-Kuwaitis (P2) living in Kuwait. Methods We fit a modified metapopulation SEIR transmission model to reported cases stratified by two groups to estimate the impact of a partial lockdown on the effective reproduction number ($$ {\mathcal{R}}_e $$ R e ). We estimated the basic reproduction number ($$ {\mathcal{R}}_0 $$ R 0 ) for the transmission in each group and simulated the potential trajectories of an outbreak from the first recorded case of community transmission until 12 days after the partial lockdown. We estimated $$ {\mathcal{R}}_e $$ R e values of both groups before and after the partial curfew, simulated the effect of these values on the epidemic curves and explored a range of cross-transmission scenarios. Results We estimate $$ {\mathcal{R}}_e $$ R e at 1·08 (95% CI: 1·00–1·26) for P1 and 2·36 (2·03–2·71) for P2. On March 22nd, $$ {\mathcal{R}}_e $$ R e for P1 and P2 are estimated at 1·19 (1·04–1·34) and 1·75 (1·26–2·11) respectively. After the partial curfew had taken effect, $$ {\mathcal{R}}_e $$ R e for P1 dropped modestly to 1·05 (0·82–1·26) but almost doubled for P2 to 2·89 (2·30–3·70). Our simulated epidemic trajectories show that the partial curfew measure greatly reduced and delayed the height of the peak in P1, yet significantly elevated and hastened the peak in P2. Modest cross-transmission between P1 and P2 greatly elevated the height of the peak in P1 and brought it forward in time closer to the peak of P2. Conclusion Our results indicate and quantify how the same lockdown intervention can accentuate disease transmission in some subpopulations while potentially controlling it in others. Any such control may further become compromised in the presence of cross-transmission between subpopulations. Future interventions and policies need to be sensitive to socioeconomic and health disparities.

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Physical inactivity and health inequality during coronavirus: a novel opportunity or total lockdown?

BMJ Open Sport & Exercise Medicine ◽

10.1136/bmjsem-2020-000903 ◽

2020 ◽

Vol 6 (1) ◽

pp. e000903

Author(s):

Natalie F Shur ◽

David Johns ◽

Stefan Kluzek ◽

Nicholas Peirce

Keyword(s):

Public Health ◽

Health Inequality ◽

Physical Inactivity ◽

The Elderly ◽

Restricted Movement ◽

Socioeconomic Groups ◽

Lower Socioeconomic ◽

The World ◽

Additional Burden ◽

Public Health Promotion

Government-restricted movement during the coronavirus pandemic in various countries around the world has led to rapid and fundamental changes in our health behaviour. As well as being at a higher risk of contracting and being hospitalised with COVID-19, the elderly, those with chronic disease and lower socioeconomic groups are also disproportionately affected by restriction of movement, further widening the physical activity health inequality. In this viewpoint we discuss the physiological sequelae of physical inactivity, and the additional burden of ageing and inflammation. We provide recommendations for public health promotion and interventions to try to mitigate the detrimental effects of physical inactivity and rebalance the health inequality.

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Rethinking urban heat stress: Assessing risk and adaptation options across socioeconomic groups in Bonn, Germany

Urban Climate ◽

10.1016/j.uclim.2021.100857 ◽

2021 ◽

Vol 37 ◽

pp. 100857

Author(s):

Simone Sandholz ◽

Dominic Sett ◽

Angelica Greco ◽

Mia Wannewitz ◽

Matthias Garschagen

Keyword(s):

Heat Stress ◽

Socioeconomic Groups ◽

Urban Heat ◽

Adaptation Options

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Prevalence of Mental Disorders by Socioeconomic Status in Australia: A Cross-Sectional Epidemiological Study

American Journal of Health Promotion ◽

10.1177/0890117120968656 ◽

2020 ◽

pp. 089011712096865

Author(s):

Rubayyat Hashmi ◽

Khorshed Alam ◽

Jeff Gow ◽

Sonja March

Keyword(s):

Mental Health ◽

Risk Factors ◽

Socioeconomic Status ◽

Mental Disorders ◽

National Health Survey ◽

Cross Sectional ◽

Lower Odds ◽

Socioeconomic Groups ◽

Income Groups ◽

Weighted Prevalence

Purpose: To present the prevalence of 3 broad categories of mental disorder (anxiety-related, affective and other disorders) by socioeconomic status and examine the associated socioeconomic risk factors of mental disorders in Australia. Design: A population-based, cross-sectional national health survey on mental health and its risk factors across Australia. Setting: National Health Survey (NHS), 2017-2018 conducted by the Australian Bureau of Statistics (ABS) Participants: Under aged: 4,945 persons, Adult: 16,370 persons and total: 21,315 persons Measures: Patient-reported mental disorder outcomes Analysis: Weighted prevalence rates by socioeconomic status (equivalised household income, education qualifications, Socio-Economic Index for Areas (SEIFA) scores, labor force status and industry sector where the adult respondent had their main job) were estimated using cross-tabulation. Logistic regression utilizing subsamples of underage and adult age groups were analyzed to test the association between socioeconomic status and mental disorders. Results: Anxiety-related disorders were the most common type of disorders with a weighted prevalence rate of 20.04% (95% CI: 18.49-21.69) for the poorest, 13.85% (95% CI: 12.48-15.35) for the richest and 16.34% (95% CI: 15.7-17) overall. The weighted prevalence rate for mood/affective disorders were 20.19% (95% CI: 18.63-21.84) for the poorest, 9.96% (95% CI: 8.79-11.27) for the richest, and 13.57% (95% CI: 12.99-14.17) overall. Other mental disorders prevalence were for the poorest: 9.07% (95% CI: 7.91-10.39), the richest: 3.83% (95% CI: 3.14-4.66), and overall: 5.93% (95% CI: 5.53-6.36). These patterns are also reflected if all mental disorders were aggregated with the poorest: 30.97% (95% CI: 29.15-32.86), the richest: 19.59% (95% CI: 18.02-21.26), and overall: 23.93% (95% CI: 23.19-24.69). The underage logistic regression model showed significant lower odds for the middle (AOR: 0.75, 95% CI: 0.53 -1.04, p < 0.1), rich (AOR: 0.71, 95% CI: 0.5-0.99, p < 0.05) and richest (AOR: 0.6, 95% CI: 0.41-0.89, p < 0.01) income groups. Similarly, in the adult logistic model, there were significant lower odds for middle (AOR: 0.84, 95% CI: 0.72-0.98, p < 0.05), rich (AOR: 0.73, 95% CI: 0.62-0.86, p < 0.01) and richest (AOR: 0.76, 95% CI: 0.63-0.91, p < 0.01) income groups. Conclusion: The prevalence of mental disorders in Australia varied significantly across socioeconomic groups. Knowledge of different mental health needs in different socioeconomic groups can assist in framing evidence-based health promotion and improve the targeting of health resource allocation strategies.

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