Background‘Adverse childhood experiences’ (ACEs) are associated with increased risk of negative outcomes in later life: ACEs have consequently become a policy priority in many countries. Despite ACEs ...being highly socially patterned, there has been very little discussion in the political discourse regarding the role of childhood socioeconomic position (SEP) in understanding and addressing them. The aim here was to undertake a systematic review of the literature on the relationship between childhood SEP and ACEs.MethodsMEDLINE, PsycINFO, ProQuest and Cochrane Library databases were searched. Inclusion criteria were: (1) measurement of SEP in childhood; (2) measurement of multiple ACEs; (3) ACEs were the outcome; and (4) statistical quantification of the relationship between childhood SEP and ACEs. Search terms included ACEs, SEP and synonyms; a second search additionally included ‘maltreatment’. Overall study quality/risk of bias was calculated using a modified version of the Hamilton Tool.ResultsIn the ACEs-based search, only 6 out of 2825 screened papers were eligible for qualitative synthesis. The second search (including maltreatment) increased numbers to: 4562 papers screened and 35 included for synthesis. Eighteen papers were deemed ‘high’ quality, five ‘medium’ and the rest ‘low’. Meaningful statistical associations were observed between childhood SEP and ACEs/maltreatment in the vast majority of studies, including all except one of those deemed to be high quality.ConclusionLower childhood SEP is associated with a greater risk of ACEs/maltreatment. With UK child poverty levels predicted to increase markedly, any policy approach that ignores the socioeconomic context to ACEs is therefore flawed.PROSPERO registration numberCRD42017064781.
Box 1 Social distancing measures Advising the whole population to self-isolate at home if they or their family have symptoms Bans on social gatherings (including mass gatherings) Stopping flights and ...public transport Closure of “non-essential” workplaces (beyond the health and social care sector, utilities, and the food chain) with continued working from home for those that can Closure of schools, colleges, and universities Prohibition of all “non-essential” population movement Limiting contact for special populations (eg, care homes, prisons) The health benefits of social distancing measures are obvious, with a slower spread of infection reducing the risk that health services will be overwhelmed. Box 2 Groups at particular risk from responses to covid-19 Older people—highest direct risk of severe covid-19, more likely to live alone, less likely to use online communications, at risk of social isolation Young people—affected by disrupted education at critical time; in longer term most at risk of poor employment and associated health outcomes in economic downturn Women—more likely to be carers, likely to lose income if need to provide childcare during school closures, potential for increase in family violence for some People of East Asian ethnicity—may be at increased risk of discrimination and harassment because the pandemic is associated with China People with mental health problems—may be at greater risk from social isolation People who use substances or in recovery—risk of relapse or withdrawal People with a disability—affected by disrupted support services People with reduced communication abilities (eg, learning disabilities, limited literacy or English language ability)—may not receive key governmental communications Homeless people—may be unable to self-isolate or affected by disrupted support services People in criminal justice system—difficulty of isolation in prison setting, loss of contact with family Undocumented migrants—may have no access to or be reluctant to engage with health services Workers on precarious contracts or self-employed—high risk of adverse effects from loss of work and no income People on low income—effects will be particularly severe as they already have poorer health and are more likely to be in insecure work without financial reserves People in institutions (care homes, special needs facilities, prisons, migrant detention centres, cruise liners)—as these institutions may act as amplifiers Table 1 Health effects of social distancing measures and actions to mitigate them Mechanism Summary of effects Summary of mitigations Economic effects In the UK, 3.5 million additional people are expected to need universal credit (which includes unemployment payments) as a result of the pandemic.3 The growth of the informal, gig economy in some countries has created a large group of people who are especially vulnerable as they do not get sick pay, are on zero hours contracts, or are self-employed.4 They can easily lose all their income, and even if this is only temporary they often lack the safety net of savings. Unemployment has large negative effects on both physical and mental health,7 with a meta-analysis reporting a 76% increase in all-cause mortality in people
The world is experiencing multiple intersecting urgent and existential crises, which have profound and inequitable implications for population health. Arguably, the design of the current, dominant ...economic system and its antecedents is the root cause of these crises, as it externalises impacts on nature, climate and population health, exacerbates inequalities, and rewards extraction, rent-seeking and social hierarchy. A 'wellbeing economy', which aims to achieve social justice within planetary boundaries, has been proposed as an alternative approach to economic design. Many governments, businesses and organisations have expressed interest or commitment to this, but not at the required scale or with the required urgency. Indeed, there is the risk now that the radicalism of a wellbeing economy approach is undermined in its delivery thus far as it has either only been adopted in rhetoric or nascent form; or implemented only as isolated components rather than as part of a comprehensive shift. We, therefore, propose a series of criteria by which judgement can be made on whether progress towards a wellbeing economy is occurring: 1) Is the economy explicitly viewed by relevant actors as serving social, health, cultural, equity and nature outcomes, rather than the reverse?; 2) Is there a comprehensive and plausible pathway to design the economy in a way that achieves these outcomes?; 3) Is there a clear commitment to transitioning away from socially and ecologically damaging economic activities and doing so in a just way?; 4) Are there clear mechanisms that extend democracy over all sectors of the economy, including economic strategy and policy design, and in ownership of economic assets?; 5) Are negative externalities between policy areas or populations assessed and avoided, and positive externalities identified and promoted?; and 6) Are all the measures of economic success focused on social, health, cultural, equity and nature outcomes? We then apply these criteria using a series of examples to show contrasts between genuine wellbeing approaches and wellbeing economy 'window dressing'.
Even after accounting for deprivation, mortality rates are higher in Scotland relative to the rest of Western Europe. Higher mortality from alcohol- and drug-related deaths (DRDs), violence and ...suicide (particularly in young adults) contribute to this 'excess' mortality. Age-period and cohort effects help explain the trends in alcohol-related deaths and suicide, respectively. This study investigated whether age, period or cohort effects might explain recent trends in DRDs in Scotland and relate to exposure to the changing political context from the 1980s.
We analysed data on DRDs from 1979 to 2013 by sex and deprivation using shaded contour plots and intrinsic estimator regression modelling to identify and quantify relative age, period and cohort effects.
The peak age for DRDs fell around 1990, especially for males as rates increased for those aged 18 to 45 years. There was evidence of a cohort effect, especially among males living in the most deprived areas; those born between 1960 and 1980 had an increased risk of DRD, highest for those born 1970 to 1975. The cohort effect started around a decade earlier in the most deprived areas compared to the rest of the population.
Age-standardised rates for DRDs among young adults rose during the 1990s in Scotland due to an increased risk of DRD for the cohort born between 1960 and 1980, especially for males living in the most deprived areas. This cohort effect is consistent with the hypothesis that exposure to the changing social, economic and political contexts of the 1980s created a delayed negative health impact.
ObjectivesThe rate of improvement in all-cause mortality rates has slowed in the UK since around 2012. While evidence suggests that UK Government ‘austerity’ policies have been largely responsible, ...it has been proposed that rising obesity may also have contributed. The aim here was to estimate this contribution for Scotland and England.MethodsWe calculated population attributable fractions (PAFs) resulting from changes in Body Mass Index (BMI) between the mid-1990s and late 2000s for all-cause mortality among 35–89-year olds in 2017–2019. We used BMI data from national surveys (the Scottish Health Survey and the Health Survey for England), and HRs from a meta-analysis of 89 European studies. PAFs were applied to mortality data for 2017–2019 (obtained from national registries), enabling comparison of observed rates, BMI-adjusted rates and projected rates. Uncertainty in the estimates is dominated by the assumptions used and biases in the underlying data, rather than random variation. A series of sensitivity analyses and bias assessments were therefore undertaken to understand the certainty of the estimates.ResultsIn Scotland, an estimated 10% (males) and 14% (females) of the difference between observed and predicted mortality rates in 2017–2019 may be attributable to previous changes in BMI. The equivalent figures for England were notably higher: 20% and 35%, respectively. The assessments of bias suggest these are more likely to be overestimates than underestimates.ConclusionsSome of the recent stalled mortality trends in Scotland and England may be associated with earlier increases in obesity. Policies to reduce the obesogenic environment, including its structural and commercial determinants, and reverse the impacts of austerity, are needed.
Abstract
Background
As Scotland strives to become a country where children flourish in their early years, it is faced with the challenge of socio-economic health inequalities, which are at risk of ...widening amidst austerity policies. The aim of this study was to explore trends in infant mortality rates (IMR) and stillbirth rates by socio-economic position (SEP) in Scotland, between 2000 and 2018, inclusive.
Methods
Data for live births, infant deaths, and stillbirths between 2000 and 2018 were obtained from National Records of Scotland. Annual IMR and stillbirth rates were calculated and visualised for all of Scotland and when stratified by SEP. Negative binomial regression models were used to estimate the association between SEP and infant mortality and stillbirth events, and to assess for break points in trends over time. The slope (SII) and relative (RII) index of inequality compared absolute and relative socio-economic inequalities in IMR and stillbirth rates before and after 2010.
Results
IMR fell from 5.7 to 3.2 deaths per 1000 live births between 2000 and 2018, with no change in trend identified. Stillbirth rates were relatively static between 2000 and 2008 but experienced accelerated reduction from 2009 onwards. When stratified by SEP, inequalities in IMR and stillbirth rates persisted throughout the study and were greatest amongst the sub-group of post-neonates. Although comparison of the SII and RII in IMR and stillbirths before and after 2010 suggested that inequalities remained stable, descriptive trends in mortality rates displayed a 3-year rise in the most deprived quintiles from 2016 onwards.
Conclusion
Whilst Scotland has experienced downward trends in IMR and stillbirth rates between 2000 and 2018, the persistence of socio-economic inequalities and suggestion that mortality rates amongst the most deprived groups may be worsening warrants further action to improve maternal health and strengthen support for families with young children.
Increasingly Burden of Disease (BOD) measures are being used to influence policy decisions because they summarise the complete effects of morbidity and mortality in an equitable manner. An important ...element of producing non-fatal BOD estimates are severity distributions. The Global Burden of Disease (GBD) study use the same severity distributions across countries due to a lack of available country-specific data. In the Scottish BOD (SBOD) study we developed national severity distributions for cancer types. The main aim of this study was to consider the extent to which the use of worldwide severity distributions in BOD studies are influencing cross-country comparisons, by comparing weighted-average disability weights (DW) based on GBD severity distributions with nationally derived severity distributions in Scotland for cancer types.
We obtained individual records from the Scottish Cancer Registry for 21 cancer types and linked these to registered deaths. We estimated prevalent cancer cases for 2016 and assigned each case to sequelae using GBD 2016 study definitions. We compared the impact of using severity distributions based on GBD 2016, a Scotland-wide distribution, and distributions specific to deprivation strata in Scotland, on the weighted-average DW for each cancer type.
The relative difference in point estimates of weighted-average DW based on GBD 2016 worldwide severity distributions compared with Scottish national severity distributions resulted in overestimates in the majority of cancers (17 out of 21 cancer types). The largest overestimates were for gallbladder and biliary tract cancer (70.8%), oesophageal cancer (31.6%) and pancreatic cancer (31.2%). Furthermore, the use of weighted-average DW based on Scottish national severity distributions rather than sub-national Scottish severity distributions stratified by deprivation quintile overestimated weighted-average DW in the least deprived areas (16 out of 18 cancer types), and underestimated in the most deprived areas (16 out of 18 cancer types).
Our findings illustrate a bias in point estimates of weighted-average DW created using worldwide severity distributions. This bias would have led to the misrepresentation of non-fatal estimates of the burden of individual cancers, and underestimated the scale of socioeconomic inequality in this non-fatal burden. This highlights the importance of not interpreting non-fatal estimates of burden of disease too precisely, especially for sub-national estimates and those comparing populations when relying on data inputs from other countries. It is essential to ensure that any estimates are based upon country-specific data as far as possible.
Previous UK and European research has highlighted important variations in mortality between populations after adjustment for key determinants such as poverty and deprivation. The aim here was to ...establish whether similar populations could be identified in the US, and to examine changes over time. We employed Poisson regression models to compare county-level mortality with national rates between 1968 and 2016, adjusting for poverty, education, race (a proxy for exposure to racism), population change and deindustrialisation. Results are presented by means of population-weighted cartograms, and highlight widening spatial inequalities in mortality over time, including an urban to rural, and south-westward, shift in areas with the highest levels of such unexplained ‘excess’ mortality. There is a need to understand the causes of the excess in affected communities, given that it persists after adjustment for such a broad range of important health determinants.
•For the first time, trends in ‘excess’ (adjusted) mortality in the US are analysed.•Population-weighted cartograms are used to present and explore county-level trends.•Maps highlight widening spatial inequalities over time in this form of mortality.•We identify urban and rural changes in excess mortality over c.50 years (1968–2016).•Policy-relevant analyses identify areas with highest levels of excess mortality.
Background:
COVID-19 is responsible for increasing deaths globally. As most people dying with COVID-19 are older with underlying long-term conditions (LTCs), some speculate that YLL are low. We aim ...to estimate YLL attributable to COVID-19, before and after adjustment for number/type of LTCs, using the limited data available early in the pandemic.
Methods:
We first estimated YLL from COVID-19 using WHO life tables, based on published age/sex data from COVID-19 deaths in Italy. We then used aggregate data on number/type of LTCs in a Bayesian model to estimate likely combinations of LTCs among people dying with COVID-19. We used routine UK healthcare data from Scotland and Wales to estimate life expectancy based on age/sex/these combinations of LTCs using Gompertz models from which we then estimate YLL.
Results:
Using the standard WHO life tables, YLL per COVID-19 death was 14 for men and 12 for women. After adjustment for number and type of LTCs, the mean YLL was slightly lower, but remained high (11.6 and 9.4 years for men and women, respectively). The number and type of LTCs led to wide variability in the estimated YLL at a given age (e.g. at ≥80 years, YLL was >10 years for people with 0 LTCs, and <3 years for people with ≥6).
Conclusions:
Deaths from COVID-19 represent a substantial burden in terms of per-person YLL, more than a decade, even after adjusting for the typical number and type of LTCs found in people dying of COVID-19. The extent of multimorbidity heavily influences the estimated YLL at a given age. More comprehensive and standardised collection of data (including LTC type, severity, and potential confounders such as socioeconomic-deprivation and care-home status) is needed to optimise YLL estimates for specific populations, and to understand the global burden of COVID-19, and guide policy-making and interventions.
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