scholarly journals The socioeconomic distribution of alcohol-related violence in England and Wales

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0243206
Author(s):  
Lucy Bryant ◽  
Carly Lightowlers
Keyword(s):  
Risk Factors ◽  
Domestic Violence ◽  
Socioeconomic Status ◽  
Prevalence Rates ◽  
Publicly Funded ◽  
England And Wales ◽  
Socioeconomic Groups ◽  
Lower Socioeconomic ◽  
Nationally Representative ◽  
Domestic Violence Services

Inequalities in alcohol-related health harms have been repeatedly identified. However, the socioeconomic distribution of alcohol-related violence (violence committed by a person under the influence of alcohol)–and of subtypes such as alcohol-related domestic violence–remains under-examined. To examine this, data are drawn from nationally representative victimisation survey, the Crime Survey for England and Wales, from years 2013/14 to 2017/18. Socioeconomic status specific incidence and prevalence rates for alcohol-related violence (including subtypes domestic, stranger, and acquaintance violence) were created. Binomial logistic regressions were performed to test whether the likelihood of experiencing these incidents was affected by socioeconomic status when controlling for a range of pre-established risk factors associated with violence victimisation. Findings generally show lower socioeconomic groups experience higher prevalence rates of alcohol-related violence overall, and higher incidence and prevalence rates for alcohol-related domestic and acquaintance violence. Binomial logistic regression results show that the likelihood of experiencing these types of violence is affected by a person’s socioeconomic status–even when other risk factors known to be associated with violence are held constant. Along with action to address environmental and economic drivers of socioeconomic inequality, provision of publicly funded domestic violence services should be improved, and alcohol pricing and availability interventions should be investigated for their potential to disproportionately benefit lower socioeconomic groups.

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

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.

scholarly journals Leave No Child Behind: Using Data from 1.7 Million Children from 67 Developing Countries to Measure Inequality Within and Between Groups of Births and to Identify Left Behind Populations

2020 ◽  
Author(s):  
Antonio P. Ramos ◽  
Robert E. Weiss ◽  
Martin Flores
Keyword(s):  
Risk Factors ◽  
Socioeconomic Status ◽  
Mortality Risk ◽  
Rural Areas ◽  
Socioeconomic Group ◽  
Policy Makers ◽  
Socioeconomic Groups ◽  
Mortality Risks ◽  
Multiple Risk Factors ◽  
Left Behind

Background: Goal 3.2 from the Sustainable Development Goals (SDG) calls for reductions in national averages of Under-5 Mortality. However, it is well known that within countries these reductions can coexist with left behind populations that have mortality rates higher than national averages. To measure inequality in under-5 mortality and to identify left behind populations, mortality rates are often disaggregated by socioeconomic status within countries. While socioeconomic disparities are important, this approach does not quantify within group variability since births from the same socioeconomic group may have different mortality risks. This is the case because mortality risk depends on several risk factors and their interactions and births from the same socioeconomic group may have different risk factor combinations. Therefore mortality risk can be highly variable within socioeconomic groups. We develop a comprehensive approach using information from multiple risk factors simultaneously to measure inequality in mortality and to identify left behind populations. Methods: We use Demographic and Health Surveys (DHS) data on 1,691,039 births from 182 different surveys from 67 low and middle income countries, 51 of which had at least two surveys. We estimate mortality risk for each child in the data using a Bayesian hierarchical logistic regression model. We include commonly used risk factors for monitoring inequality in early life mortality for the SDG as well as their interactions. We quantify variability in mortality risk within and between socioeconomic groups and describe the highest risk sub-populations. Findings: For all countries there is more variability in mortality within socioe- conomic groups than between them. Within countries, socioeconomic membership usually explains less than 20% of the total variation in mortality risk. In contrast, country of birth explains 19% of the total variance in mortality risk. Targeting the 20% highest risk children based on our model better identifies under-5 deaths than targeting the 20% poorest. For all surveys, we report efficiency gains from 26% in Mali to 578% in Guyana. High risk births tend to be births from mothers who are in the lowest socioeconomic group, live in rural areas and/or have already experienced a prior death of a child. Interpretation: While important, differences in under-5 mortality across socioeconomic groups do not explain most of overall inequality in mortality risk because births from the same socioeconomic groups have different mortality risks. Similarly, policy makers can reach the highest risk children by targeting births based on several risk factors (socioeconomic status, residing in rural areas, having a previous death of a child and more) instead of using a single risk factor such as socioeconomic status. We suggest that researchers and policy makers monitor inequality in under-5 mortality us- ing multiple risk factors simultaneously, quantifying inequality as a function of several risk factors to identify left behind populations in need of policy interventions and to help monitor progress toward the SDG.

Epidemiology of Asthma and Influence of Ethnicity

2018 ◽  
Vol 39 (01) ◽  
pp. 003-011 ◽  
Author(s):  
Theo Moraes ◽  
Malcolm Sears ◽  
Padmaja Subbarao
Keyword(s):  
Risk Factors ◽  
Health Care ◽  
Socioeconomic Status ◽  
Asthma Prevalence ◽  
Modifiable Risk Factors ◽  
Prevalence Rates ◽  
Mechanistic Studies ◽  
The Past ◽  
Health Care Interventions ◽  
Insight Into

AbstractAsthma is a heterogeneous disorder with a complex etiology. Prevalence rates for asthma have been increasing in many countries over the past few decades. While it is unclear why this increase is occurring, the variation reported in asthma prevalence and severity associated with ethnicity offers some insight into the determinants of asthma. In this chapter, we discuss the data linking asthma to ethnicity and some of the factors that may explain this association. These include socioeconomic status, environmental exposures, the host microbiome, and genetics. A better understanding of these processes may inform future mechanistic studies and identify modifiable risk factors for targeted health care interventions.

Sexual behaviour and contraceptive use among socioeconomic groups of young women in the United States

1981 ◽  
Vol 13 (1) ◽  
pp. 31-45 ◽  
Author(s):  
Kathleen Ford ◽  
Melvin Zelnik ◽  
John F. Kantner
Keyword(s):  
Socioeconomic Status ◽  
Sexual Activity ◽  
Young Women ◽  
Contraceptive Use ◽  
The United States ◽  
Group Differences ◽  
Socioeconomic Groups ◽  
First Intercourse ◽  
Lower Socioeconomic ◽  
The Relationship

SummaryAlthough much research has been devoted to understanding differences in contraceptive behaviour among socieconomic groups of married women, little is known about group differences among young unmarried women. In this paper, data from a national survey of women 15–19 years of age are used to study the relationship between socioeconomic status, sexual activity, and contraceptive use. The socioeconomic status of the young women is related to age at first intercourse, contraceptive use at first intercourse, regularity of use, and use of medical methods. The results indicate that both an earlier initiation of sexual activity and less regular use of contraceptives in all probability lead to a concentration of pregnancies in the lower socioeconomic groups.

scholarly journals Dental Treatment Needs and Related Risk Factors among School Children with Special Needs in Taiwan

2021 ◽  
Vol 11 (6) ◽  
pp. 452
Author(s):  
Szu-Yu Hsiao ◽  
Ping-Ho Chen ◽  
Shan-Shan Huang ◽  
Cheng-Wei Yen ◽  
Shun-Te Huang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Socioeconomic Status ◽  
Dental Caries ◽  
Dental Treatment ◽  
Significant Risk ◽  
World Health ◽  
Treatment Needs ◽  
Lower Socioeconomic Status ◽  
Lower Socioeconomic ◽  
Related Risk

The purpose of this study was to assess dental treatment needs (TNs) and related risk factors of children with disabilities (CD). This cross-sectional study recruited 484 CD, 6 to 12 years of age, from 10 special education schools in Taiwan. Dental status and TNs were examined and evaluated by well-trained dentists and based on the criteria set by the World Health Organization (1997). The results indicated that 61.78% required restorative dental treatment due to their dental caries. On average, each participant had 2.72 teeth that required treatment, and 6.38 surfaces required restoration. One-quarter of the participants (24.79%) required 1- or 2-surface restoration, and one out of three (36.98%) had more complex TNs (including 3 or more surfaces to be filled, pulp care, extraction, and more specialized care). The significant risk factors associated with restorative TNs among CD were those whose parents had lower socioeconomic status, frequent sweets intake, insufficient tooth-brushing ability, and poor oral health. Most of the CD had extensive unmet TNs for their caries and required complex treatment to recover the function of their teeth. Encouraging parents/caregivers to take their children for dental treatment, promoting awareness of the importance of dental hygiene, giving assistance to brushing their teeth after eating, and controlling and/or modifying sweet diet habits are necessary to reduce CD’s dental caries, especially those with lower socioeconomic status parents/caregivers.

scholarly journals Neighborhood Greenness Attenuates the Adverse Effect of PM2.5 on Cardiovascular Mortality in Neighborhoods of Lower Socioeconomic Status

2019 ◽  
Vol 16 (5) ◽  
pp. 814 ◽  
Author(s):  
Maayan Yitshak-Sade ◽  
Peter James ◽  
Itai Kloog ◽  
Jaime Hart ◽  
Joel Schwartz ◽  
...  
Keyword(s):  
Socioeconomic Status ◽  
Cardiovascular Mortality ◽  
Sociodemographic Factors ◽  
Lower Socioeconomic Status ◽  
Census Block Group ◽  
Socioeconomic Groups ◽  
Lower Socioeconomic ◽  
Level Information ◽  
Highly Correlated ◽  
Particulate Matter 2.5

Features of the environment may modify the effect of particulate matter ≤2.5 µm in aerodynamic diameter (PM2.5) on health. Therefore, we investigated how neighborhood sociodemographic and land-use characteristics may modify the association between PM2.5 and cardiovascular mortality. We obtained residence-level geocoded cardiovascular mortality cases from the Massachusetts Department of Public Health (n = 179,986), and PM2.5 predictions from a satellite-based model (2001–2011). We appended census block group-level information on sociodemographic factors and walkability, and calculated neighborhood greenness within a 250 m buffer surrounding each residence. We found a 2.54% (1.34%; 3.74%) increase in cardiovascular mortality associated with a 10 µg/m3 increase in two-day average PM2.5. Walkability or greenness did not modify the association. However, when stratifying by neighborhood sociodemographic characteristics, smaller PM2.5 effects were observed in greener areas only among cases who resided in neighborhoods with a higher population density and lower percentages of white residents or residents with a high school diploma. In conclusion, the PM2.5 effects on cardiovascular mortality were attenuated by higher greenness only in areas with sociodemographic features that are highly correlated with lower socioeconomic status. Previous evidence suggests health benefits linked to neighborhood greenness may be stronger among lower socioeconomic groups. Attenuation of the PM2.5–mortality relationship due to greenness may explain some of this evidence.

scholarly journals The thick skin bias in judgments about people in poverty

2020 ◽  
pp. 1-26
Author(s):  
NATHAN N. CHEEK ◽  
ELDAR SHAFIR
Keyword(s):  
Socioeconomic Status ◽  
Life Events ◽  
Representative Sample ◽  
Lower Socioeconomic Status ◽  
Negative Experiences ◽  
Lower Socioeconomic ◽  
Wide Range ◽  
Nationally Representative ◽  
Thick Skin ◽  
People’S Perceptions

Abstract We present a series of studies documenting what we call a ‘thick skin bias’ in people's perceptions of those living in poverty. Across a wide range of life events, from major to minor, people of lower socioeconomic status (SES) are systematically perceived as being less harmed by negative experiences than higher-SES people, even when this is patently false. In 18 studies, including a pre-registered survey of a nationally representative sample, we find that laypeople and professionals show the thick skin bias. We distinguish the bias from a tendency to dehumanize those in poverty and argue it cannot be attributed to the belief that the mere expectation that bad things will happen buffers people in poverty from suffering. The thick skin bias has potentially profound implications for the institutional and interpersonal neglect of those most in need of greater care and resources.

scholarly journals Inequality-Related Health and Social Factors and Their Impact on Well-Being during the COVID-19 Pandemic: Findings from a National Survey in the UK

2021 ◽  
Vol 18 (3) ◽  
pp. 1014
Author(s):  
Daniel Tzu-Hsuan Chen ◽  
Yi-Jen Wang
Keyword(s):  
Risk Factors ◽  
Social Factors ◽  
Well Being ◽  
Household Size ◽  
Subjective Well Being ◽  
Medical Conditions ◽  
Socioeconomic Groups ◽  
Lower Socioeconomic ◽  
Related Risk ◽  
The Impact

Background: Lower socioeconomic groups and disadvantaged populations across the world suffer disproportionately from the coronavirus disease 2019 (COVID-19) pandemic. This study aimed to examine the impact of health- and social-inequality–related factors on well-being in order to further distinguish each of their effects during the pandemic. Methods: A nationally-representative sample of 5077 UK respondents aged 18 years or older was recruited through an online survey panel during the COVID-19 pandemic. Their subjective well-being was measured using the 11-point Cantril Ladder of Life Scale. The impact of inequality-related health and social factors (pre-existing medical conditions, household size and occupation), as well as COVID-19–related risk factors (symptoms, confirmed infections, and social distancing behaviours) on well-being were analysed using multiple linear regression models. The associations between the COVID-19–related risk factors and well-being according to the respondents’ household size and occupation were modelled in order to test the differences by their socioeconomic profile. Results: We identified inverted V-shaped associations between household size and subjective well-being during the COVID-19 pandemic. Compared to single-person households, respondents from households of two to four persons showed better well-being (β = 0.57; CI (0.44, 0.72)), whereas living in crowded households of five persons or more was associated with decreased well-being (β = −0.48; CI (−0.71, −0.25)). Furthermore, lower-skilled occupations (elementary occupations: β = −0.31; CI (−0.58, −0.03); logistics and transport services: β = −0.37; CI (−0.74, −0.01)) and chronic medical conditions (cardiometabolic or respiratory diseases: β = −0.25; CI (−0.41, −0.1); and mental health conditions: β = −1.12; CI (−1.28, −0.96)) were factors associated with reduced well-being during the pandemic. Interactions between a positive COVID-19 diagnosis, symptoms, and crowded households were identified (β = −0.95; CI (−1.76, −0.14) and β = −4.74; CI (−9.87, −1.61), respectively). Conclusions: In a national sample, the levels of general subjective well-being during the COVID-19 pandemic and lockdowns were disproportionately distributed across different groups within society. Preventive policies should explicitly focus on reaching lower socioeconomic groups; more emphasis should be placed on the coordination of multisectoral support in order to tackle existing health and social inequalities.

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