A systematic review of the impact of the COVID-19 pandemic on the mental health of adolescents and young people with disabilities aged 15-29 years.

BACKGROUND: The COVID-19 pandemic has exacerbated the psychological burden on young people around the world and may have disproportionately large impacts for young people with disabilities. This review aims to systematically review the quantitative evidence on the impact of the COVID-19 pandemic on the mental health of young people with disabilities and evaluate the quality of included studies. METHODS: A systematic search was conducted using 5 electronic databases. The quality of the studies was assessed using the SIGN risk of bias assessment tool. A narrative synthesis was performed to synthesize the results of included studies. RESULTS: The initial search yielded 1935 studies, of which two met the eligibility criteria, one longitudinal study and one cross-sectional study, both assessed to be of low quality. In the cross-sectional study, young people with intellectual and developmental disabilities self-reported an increase in mental health symptoms. The longitudinal study found no evidence of a change in mental health symptoms from pre-pandemic to during the pandemic among young people with autism spectrum disorder, although these individuals reported negative impacts of the COVID-19 pandemic on their emotional or mental health. CONCLUSIONS: The findings of this review provide some weak evidence of a negative impact of the COVID-19 pandemic on the mental health of young people with disabilities. Importantly, the findings highlight the lack of research in this area. More research is needed to investigate the impact of the pandemic on the mental health of young disabled people, in order for governments to develop emergency preparedness plans to safeguard the well-being of this population.

Trends in mental health inequalities for people with disability, Australia 2003 to 2020.

OBJECTIVE: Cross-sectional studies have demonstrated that people with disability have substantial inequalities in mental health compared to people without disability. However, it is not known if these inequalities have changed over time. This study compared the mental health of people with and without disability annually from 2003 to 2020 to investigate time trends in disability-related mental health inequalities. METHODS: We use annual data (2003-2020) of the Household, Income and Labour Dynamics in Australia Survey. Mental health was measured using the five-item Mental Health Inventory. For each wave, we calculated population-weighted age-standardised estimates of mean Mental Health Inventory scores for people with and without disability and calculated the mean difference in Mental Health Inventory score to determine inequalities. Analyses were stratified by age, sex and disability group (sensory or speech, physical, intellectual or learning, psychological, brain injury or stroke, other). RESULTS: From 2003 to 2020, people with disability had worse mental health than people without disability, with average Mental Health Inventory scores 9.8 to 12.1 points lower than for people without disability. For both people with and without disability, Mental Health Inventory scores decreased, indicating worsening mental health, reaching the lowest point for both groups in 2020. For some subpopulations, including young females and people with intellectual disability, brain injury or stroke, mental health inequalities worsened. CONCLUSION: This paper confirms that people with disability experience worse mental health than people without disability. We add to previous findings by demonstrating that disability-related inequalities in mental health have been sustained for a long period and are worsening in some subpopulations.

COVID-19 vaccination coverage and vaccine hesitancy among Australians with disability and long-term health conditions.

ISSUE ADDRESSED: COVID-19 vaccination is the cornerstone of managing Australia's COVID-19 pandemic and the success of the vaccination program depends on high vaccination coverage. This paper examined differences in COVID-19 vaccination coverage and vaccine hesitancy for people with disability, long-term health conditions, and carers - subgroups that were prioritised in Australia's vaccination program. METHODS: Using data from 2400 Australians who participated in two waves of the Taking the Pulse of the Nation survey in April and May 2021, we described vaccination coverage and hesitancy among people with disability, severe mental health conditions, severe long-term health conditions, frequent need for assistance with everyday activities, and carers. RESULTS: Vaccination coverage was estimated to be 8.2% in the population overall and was similar for people with disability, those with frequent need for assistance, and carers. It was higher for people with severe long-term health conditions (13.4%) and lower for people with severe mental health conditions (4.3%). Vaccine hesitancy was high overall (35.6%) and was similarly high across the priority groups, with only small differences for people with disability, severe long-term health conditions and frequent need for assistance. CONCLUSIONS: This study highlights a lack of difference in vaccination coverage for people with disability, long-term health conditions, and carers compared to the general population. So what? Sub-optimal vaccination coverage for people in the priority groups leaves many people at significant risk of serious disease or death if exposed to COVID-19, particularly in light of easing of disease-control restrictions across Australia and the emergence of new variants.

Does the effect of disability acquisition on mental health differ by employment characteristics? A longitudinal fixed-effects analysis.

PURPOSE: Longitudinal studies have suggested a causal relationship between disability acquisition and mental health, but there is substantial heterogeneity in the magnitude of the effect. Previous studies have provided evidence that socioeconomic characteristics can buffer the effect but have not examined the role of employment characteristics. METHODS: We used data from 17 annual waves of the Household, Income and Labour Dynamics in Australia Survey to compare the mental health of working age individuals before and after disability acquisition, using the Mental Health Inventory, a subscale of the SF-36 health questionnaire. Linear fixed-effects regression models were used to estimate the effect of disability acquisition on mental health. We tested for effect modification by two characteristics of people's employment prior to disability acquisition: occupational skill level and contract type. Multiple imputation using chained equations was used to handle missing data. RESULTS: Disability acquisition was associated with a substantial decline in mental health score (estimated mean difference: - 4.3, 95% CI - 5.0, - 3.5). There was evidence of effect modification by occupational skill level, with the largest effects seen for those in low-skilled jobs (- 6.1, 95% CI - 7.6, - 4.5), but not for contract type. CONCLUSIONS: The findings highlight the need for social and health policies that focus on increasing employment rates, improving the sustainability of employment, and providing employment services and education and training opportunities for people who acquire a disability, particularly for people in low-skilled occupations, to reduce the mental health inequalities experienced by people with disabilities.

Does social support modify the effect of disability acquisition on mental health? A longitudinal study of Australian adults.

PURPOSE: Disability acquisition in adulthood is associated with deterioration in mental health. Social support may act as a "buffer" against poor mental health following disability acquisition. We tested the hypothesis that women and men with low social support experienced larger declines in mental health on acquisition of a disability compared to women and men with high social support. METHODS: We assessed whether social support, measured both prior and subsequent to disability acquisition, modified the association between disability acquisition and mental health using 14 annual waves of data from the Household, Income and Labour Dynamics in Australia Survey. Participants reported at least two consecutive waves of disability preceded by at least two consecutive waves without disability (2200 participants, 15,724 observations). Fixed-effects linear regression models were used to estimate average differences in mental health between waves with and without disability, for women and men separately. We tested for effect measure modification of the association by social support, including a three-way interaction between disability and social support prior and subsequent to disability acquisition. RESULTS: Though the effects of disability acquisition on mental health were much larger for women, for both women and men there was a consistent pattern of association with social support. There was evidence that social support modified the association between disability acquisition and mental health, with the largest effects for those experiencing a change from high to low social support subsequent to disability and for people with consistently low social support. CONCLUSIONS: These findings highlight the importance of developing new policy and practice strategies to improve the mental health of people with disabilities, including interventions to promote social support at the time of disability acquisition.

Does the presence and mix of destinations influence walking and physical activity?

BACKGROUND: Local destinations have previously been shown to be associated with higher levels of both physical activity and walking, but little is known about how specific destinations are related to activity. This study examined associations between types and mix of destinations and both walking frequency and physical activity. METHOD: The sample consisted of 2349 residents of 50 urban areas in metropolitan Melbourne, Australia. Using geographic information systems, seven types of destinations were examined within three network buffers (400 meters (m), 800 m and 1200 m) of respondents' homes. Multilevel logistic regression was used to estimate effects of each destination type separately, as well as destination mix (variety) on: 1) likelihood of walking for at least 10 min ≥ 4/week; 2) likelihood of being sufficiently physically active. All models were adjusted for potential confounders. RESULTS: All destination types were positively associated with walking frequency, and physical activity sufficiency at 1200 m. For the 800 m buffer: all destinations except transport stops and sports facilities were significantly associated with physical activity, while all except sports facilities were associated with walking frequency; at 400 m, café/takeaway food stores and transport stops were associated with walking frequency and physical activity sufficiency, and sports facilities were also associated with walking frequency. Strongest associations for both outcomes were observed for community resources and small food stores at both 800 m and 1200 m. For all buffer distances: greater mix was associated with greater walking frequency. Inclusion of walking in physical activity models led to attenuation of associations. CONCLUSIONS: The results of this analysis indicate that there is an association between destinations and both walking frequency and physical activity sufficiency, and that this relationship varies by destination type. It is also clear that greater mix of destinations positively predicts walking frequency and physical activity sufficiency.

How much of the effect of disability acquisition on mental health is mediated through employment and income? A causal mediation analysis quantifying interventional indirect effects using data from four waves of an Australian cohort study.

OBJECTIVES: There 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 SETTING: We 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. PARTICIPANTS: Working aged individuals who acquired a disability (n=233) were compared with those who remained disability-free in all four waves (n=5419). PRIMARY OUTCOME MEASURE: Self-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 ANALYSIS: We 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. RESULTS: The 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. CONCLUSIONS: This 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.

Do material, psychosocial and behavioural factors mediate the relationship between disability acquisition and mental health? A sequential causal mediation analysis.

BACKGROUND: There is evidence of a causal relationship between disability acquisition and poor mental health; however, the mechanism by which disability affects mental health is poorly understood. This gap in understanding limits the development of effective interventions to improve the mental health of people with disabilities. METHODS: We used four waves of data from the Household, Income and Labour Dynamics in Australia Survey (2011-14) to compare self-reported mental health between individuals who acquired any disability (n=387) and those who remained disability-free (n=7936). We tested three possible pathways from disability acquisition to mental health, examining the effect of material, psychosocial and behavioural mediators. The effect was partitioned into natural direct and indirect effects through the mediators using a sequential causal mediation analysis approach. Multiple imputation using chained equations was used to assess the impact of missing data. RESULTS: Disability acquisition was estimated to cause a five-point decline in mental health [estimated mean difference: -5.3, 95% confidence interval (CI) -6.8, -3.7]. The indirect effect through material factors was estimated to be a 1.7-point difference (-1.7, 95% CI -2.8, -0.6), explaining 32% of the total effect, with a negligible proportion of the effect explained by the addition of psychosocial characteristics (material and psychosocial: -1.7, 95% CI -3.0, -0.5) and a further 5% by behavioural factors (material-psychosocial-behavioural: -2.0, 95% CI -3.4, -0.6). CONCLUSIONS: The finding that the effect of disability acquisition on mental health operates predominantly through material rather than psychosocial and behavioural factors has important implications. The results highlight the need for better social protection, including income support, employment and education opportunities, and affordable housing for people who acquire a disability.

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

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.

Mental Health Following Acquisition of Disability in Adulthood--The Impact of Wealth.

BACKGROUND: Acquisition of a disability in adulthood has been associated with a reduction in mental health. We tested the hypothesis that low wealth prior to disability acquisition is associated with a greater deterioration in mental health than for people with high wealth. METHODS: We assess whether level of wealth prior to disability acquisition modifies this association using 12 waves of data (2001-2012) from the Household, Income and Labour Dynamics in Australia survey--a population-based cohort study of working-age Australians. Eligible participants reported at least two consecutive waves of disability preceded by at least two consecutive waves without disability (1977 participants, 13,518 observations). Fixed-effects linear regression was conducted with a product term between wealth prior to disability (in tertiles) and disability acquisition with the mental health component score of the SF-36 as the outcome. RESULTS: In models adjusted for time-varying confounders, there was evidence of negative effect measure modification by prior wealth of the association between disability acquisition and mental health (interaction term for lowest wealth tertile: -2.2 points, 95% CI -3.1 points, -1.2, p<0.001); low wealth was associated with a greater decline in mental health following disability acquisition (-3.3 points, 95% CI -4.0, -2.5) than high wealth (-1.1 points, 95% CI -1.7, -0.5). CONCLUSION: The findings suggest that low wealth prior to disability acquisition in adulthood results in a greater deterioration in mental health than among those with high wealth.

Local environments as determinants of walking in Melbourne, Australia.

Structural changes to neighbourhoods that promote walking are likely to benefit everyone in a community and result in long-term improvements in the population's physical activity and health. We consider time spent walking in relation to objectively measured features of people's local environments (functionality, safety, destinations and aesthetics) in Melbourne, Australia. We used multilevel ordered logistic regression analysis to examine variations in time spent walking amongst 2334 residents of 49 small areas (Census Collection Districts - CCDs). Features on each street segment within a 400 m radius of a randomly selected household within each CCD were measured, supplemented by geographic information system data. Models were adjusted for socio-demographic and socio-economic characteristics of individual and socio-economic characteristics of areas. We found that increasing track length, having paths located closer to roads, fewer buildings with driveways, a greater presence and variety of destinations and views of shops, light industry, offices and/or schools (commercial views) were associated with more time spent walking in local environments in adjusted models. As such, each of the four features of local environments considered (design, safety, destinations and aesthetics) were associated on at least one measure with walking. Characteristics of areas reflecting urbanisation (destinations, commercial views, path location and driveways) were associated with increased time spent walking. This study provides important evidence on how urban design can be utilised to improve physical activity.

Area disadvantage, individual socio-economic position, and premature cancer mortality in Australia 1998 to 2000: a multilevel analysis.

OBJECTIVE: To examine associations between area and individual socio-economic characteristics and premature cancer mortality using multilevel analysis. METHODS: We modeled cancer mortality among 25-64-year-old men and women (n = 16,340) between 1998 and 2000 in Australia. Socio-economic characteristics of Statistical Local Areas (n = 1,317) were measured using an Index of Relative Socio-economic Disadvantage (quintiles), and individual socio-economic position was measured by occupation (professionals, white and blue collar). RESULTS: After adjustment for within-area variation in age and occupation, the probability of premature cancer mortality was highest in the most disadvantaged areas for all-cancer mortality for men (RR 1.48 95% CI 1.35-1.63) and women (RR 1.30 95% CI 1.18-1.43) and for lung cancer mortality for men (1.91 95% CI 1.63-2.25) and women (1.51 95% CI 1.04-2.18). Men in blue collar occupations had a higher rate of cancer mortality (RR 1.57 95% CI 1.50-1.65) and lung cancer mortality (RR 2.31 95 % CI 2.09-2.56), whereas men in white collar occupations had a lower all-cancer mortality rate (RR 0.78 95% CI 0.72-0.85). Compared with professionals, women in white collar occupations had an all-cancer mortality rate that was lower (RR 0.85 95% CI 0.80-0.90). When deaths from breast cancer were excluded, women in blue collar occupations had a significantly higher all-cancer mortality rate than professionals (RR 1.12 95% CI 1.02-1.22). CONCLUSIONS: Area disadvantage and individual socio-economic position were independently associated with premature cancer mortality, suggesting that interventions to reduce inequalities should focus on places and people.