Dataset Information

Disability acquisition and mental health: effect modification by demographic and socioeconomic characteristics using data from an Australian longitudinal study.

ABSTRACT:

Objectives

There is evidence of a causal relationship between disability acquisition and poor mental health, but the substantial heterogeneity in the magnitude of the effect is poorly understood and may be aetiologically informative. This study aimed to identify demographic and socioeconomic factors that modify the effect of disability acquisition on mental health.

Design and setting

The Household, Income and Labour Dynamics in Australia Survey is a nationally representative longitudinal survey of Australian households that has been conducted annually since 2001. Four waves of data were included in this analysis, from 2011 to 2014.

Participants

Individuals who acquired a disability (n=387) were compared with those who remained disability-free in all four waves (n=7936).

Primary outcome measure

Mental health was measured using the mental health subscale of the Short Form 36 (SF-36) general health questionnaire, which measures symptoms of depression, anxiety and psychological well-being.

Methods

Linear regression models were fitted to estimate the effect of disability acquisition on mental health, testing for effect modification by key demographic and socioeconomic characteristics. To maximise causal inference, we used a propensity score approach with inverse probability of treatment weighting to control for confounding and multiple imputation using chained equations to assess the impact of missing data.

Results

On average, disability acquisition was associated with a 5-point decline in mental health score (estimated mean difference: -5.1, 95%?CI -7.2 to -3.0). There was strong evidence that income and relationship status modified the effect, with more detrimental effects in the lowest (-12.5, 95%?CI -18.5 to -6.5) compared with highest income quintile (-1.1, 95%?CI -4.9 to 2.7) and for people not in a relationship (-8.8, 95%?CI -12.9 to -4.8) compared with those who were (-3.7, 95%?CI -6.1 to -1.4).

Conclusions

Our results suggest that the detrimental effect of disability acquisition on mental health is substantially greater for socioeconomic disadvantaged individuals.

SUBMITTER: Aitken Z

PROVIDER: S-EPMC5623536 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Disability acquisition and mental health: effect modification by demographic and socioeconomic characteristics using data from an Australian longitudinal study.

Aitken Zoe Z Simpson Julie Anne JA Bentley Rebecca R Kavanagh Anne Marie AM

BMJ open 20170918 9

<h4>Objectives</h4>There is evidence of a causal relationship between disability acquisition and poor mental health, but the substantial heterogeneity in the magnitude of the effect is poorly understood and may be aetiologically informative. This study aimed to identify demographic and socioeconomic factors that modify the effect of disability acquisition on mental health.<h4>Design and setting</h4>The Household, Income and Labour Dynamics in Australia Survey is a nationally representative longi ...[more]

PMID: 28928189

Similar Datasets

Project description:BackgroundIn Latin America, where climate change and rapid urbanization converge, non-optimal ambient temperatures contribute to excess mortality. However, little is known about area-level characteristics that confer vulnerability to temperature-related mortality.ObjectivesExplore city-level socioeconomic and demographic characteristics associated with temperature-related mortality in Latin American cities.MethodsThe dependent variables quantify city-specific associations between temperature and mortality: heat- and cold-related excess death fractions (EDF, or percentages of total deaths attributed to cold/hot temperatures), and the relative mortality risk (RR) associated with 1 °C difference in temperature in 325 cities during 2002-2015. Random effects meta-regressions were used to investigate whether EDFs and RRs associated with heat and cold varied by city-level characteristics, including population size, population density, built-up area, age-standardized mortality rate, poverty, living conditions, educational attainment, income inequality, and residential segregation by education level.ResultsWe find limited effect modification of cold-related mortality by city-level demographic and socioeconomic characteristics and several unexpected associations for heat-related mortality. For example, cities in the highest compared to the lowest tertile of income inequality have all-age cold-related excess mortality that is, on average, 3.45 percentage points higher (95% CI: 0.33, 6.56). Higher poverty and higher segregation were also associated with higher cold EDF among those 65 and older. Large, densely populated cities, and cities with high levels of poverty and income inequality experience smaller heat EDFs compared to smaller and less densely populated cities, and cities with little poverty and income inequality.DiscussionEvidence of effect modification of cold-related mortality in Latin American cities was limited, and unexpected patterns of modification of heat-related mortality were observed. Socioeconomic deprivation may impact cold-related mortality, particularly among the elderly. The findings of higher levels of poverty and income inequality associated with lower heat-related mortality deserve further investigation given the increasing importance of urban adaptation to climate change.

Project description:BACKGROUND:While the importance of medical students' demographic characteristics in influencing the scope and location of their future practice is recognized, these data are not systematically collected in Canada. This study aimed to characterize and compare the demographics of Canadian medical students with the Canadian population. METHODS:Through an online survey, delivered in 2018, medical students at 14 English-speaking Canadian medical schools provided their age, sex, gender identity, ethnicity, educational background, and rurality of the area they grew up in. Respondents also provided information on parental income, occupation, and education as markers of socioeconomic status. Data were compared to the 2016 Canadian Census. RESULTS:A total of 1388 students responded to the survey, representing a response rate of 16.6%. Most respondents identified as women (63.1%) and were born after 1989 (82.1%). Respondents were less likely, compared to the Canadian Census population, to identify as black (1.7% vs 6.4%) (P?<?0.001) or Aboriginal (3.5% vs. 7.4%) (P?<?0.001), and have grown up in a rural area (6.4% vs. 18.7%) (P?<?0.001). Respondents had higher socioeconomic status, indicated by parental education (29.0% of respondents' parents had a master's or doctoral degree, compared to 6.6% of Canadians aged 45-64), occupation (59.7% of respondents' parents were high-level managers or professionals, compared to 19.2% of Canadians aged 45-64), and income (62.9% of respondents grew up in households with income >$100,000/year, compared to 32.4% of Canadians). Assessment of non-response bias showed that our sample was representative of all students at English-speaking Canadian medical schools with respect to age, though a higher proportion of respondents were female. Additionally, there were no differences between early and late respondents with respect to ethnicity, rurality, and parental income, occupation, and education. CONCLUSIONS:Canadian medical students have different socioeconomic characteristics compared to the Canadian population. Collecting and analyzing these characteristics can inform evidence-based admissions policies.

Project description:ObjectivesThere is evidence that disability acquisition causes a decline in mental health, but few studies have examined the causal mechanisms through which the effect operates. This study used a novel approach to mediation analysis to quantify interventional indirect effects (IIEs) through employment and income.Design and settingWe used four waves of longitudinal data (2011-2014) from the Household, Income and Labour Dynamics in Australia Survey, a nationally representative survey of Australian households.ParticipantsWorking aged individuals who acquired a disability (n=233) were compared with those who remained disability-free in all four waves (n=5419).Primary outcome measureSelf-reported mental health was measured using the Mental Health Inventory subscale of the Short Form 36 general health questionnaire, which measures symptoms of depression, anxiety and psychological well-being.Statistical analysisWe conducted a causal mediation analysis quantifying IIEs of disability acquisition on mental health operating through two distinct mediators: employment status and income. We used multiple imputation with 50 imputed datasets to account for missing data.ResultsThe total causal effect of disability acquisition on mental health was estimated to be a 4.8-point decline in mental health score (estimated mean difference: -4.8, 95% CI -7.0 to -2.7). The IIE through employment was estimated to be a 0.5-point difference (-0.5, 95% CI -1.0 to 0.0), accounting for 10.6% of the total effect, whereas there was no evidence that income explained any of the effects.ConclusionsThis study estimated that disability-related mental health inequalities could be reduced by 10.6% if employment rates were the same for people with disability as those without disability. The results suggest that employment is implicated in the relationship between disability acquisition and mental health and that more research is needed to understand the influence of other aspects of employment and other socioeconomic characteristics.

Project description:Frequent mental distress, defined as 14 or more self-reported mentally unhealthy days in the past 30 days,* is associated with adverse health behaviors, increased use of health services, mental disorders (e.g., diagnosis of major depressive disorder), chronic diseases, and functional limitations (1). Adults with disabilities more often report depression and anxiety (2), reduced health care access (3), and health-related risk behaviors (4) than do adults without disabilities. CDC analyzed 2018 Behavioral Risk Factor Surveillance System (BRFSS) data to compare the prevalence of frequent mental distress among adults with disabilities with that among adults without disabilities and to identify factors associated with mental distress among those with disabilities. Nationwide, an estimated 17.4 million adults with disabilities reported frequent mental distress; the prevalence of reported mental distress among those with disabilities (32.9%) was 4.6 times that of those without disabilities (7.2%). Among adults with disabilities, those with both cognitive and mobility disabilities most frequently reported mental distress (55.6%). Adults with disabilities who reported adverse health-related characteristics (e.g., cigarette smoking, physical inactivity, insufficient sleep, obesity, or depressive disorders) or an unmet health care need because of cost also reported experiencing more mental distress than did those with disabilities who did not have these characteristics. Adults living below the federal poverty level reported mental distress 70% more often than did adults in higher income households. Among states, age-adjusted prevalence of mental distress among adults with disabilities ranged from 25.2% (Alaska) to 42.9% (New Hampshire). Understanding the prevalence of mental distress among adults with disabilities could help health care providers, public health professionals, and policy makers target interventions and inform programs and policies to ensure receipt of mental health screening, care, and support services to reduce mental distress among adults with disabilities.

Project description:Knowledge on the occurrence of multimorbidity is important from the viewpoint of public policies, as this condition increases the consumption of medicines as well as the utilization and expenses of health services, affecting life quality of the population. The objective of this study was to estimate prevalence of self-reported multimorbidity in Brazilian adults (?18 years old) according to socioeconomic and demographic characteristics. A descriptive study is presented herein, based on data from the National Health Survey, which was a household-based survey carried out in Brazil in 2013. Data on 60,202 adult participants over the age of 18 were included. Prevalences and its respective confidence intervals (95%) were estimated according to sex, age, education level, marital status, self-reported skin color, area of residence, occupation and federative units (states). Poisson regression models univariate and multivariate were used to evaluate the association between socioeconomic and demographic variables with multimorbidity. To observe the combinations of chronic conditions the most common groups in pairs, trios, quartets and quintets of chronic diseases were observed. The prevalence of multimorbidity was 23.6% and was higher among women, in individuals over 60 years of age, people with low educational levels, people living with partner, in urban areas and among unemployed persons. The states of the South and Southeast regions presented higher prevalence. The most common groups of chronic diseases were metabolic and musculoskeletal diseases. The results demonstrated high prevalence of multimorbidity in Brazil. The study also revealed that a considerable share of the economically active population presented two or more chronic diseases. Data of this research indicated that socioeconomic and demographic aspects must be considered during the planning of health services and development of prevention and treatment strategies for chronic diseases, and consequently, multimorbidity.

Dataset Information

Disability acquisition and mental health: effect modification by demographic and socioeconomic characteristics using data from an Australian longitudinal study.

Objectives

Design and setting

Participants

Primary outcome measure

Methods

Results

Conclusions

Publications

Disability acquisition and mental health: effect modification by demographic and socioeconomic characteristics using data from an Australian longitudinal study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets