What causes the burden of stroke in Scotland? A comparative risk assessment approach linking the Scottish Health Survey to administrative health data

Background: The availability of robust evidence to inform effective public health decision making is becoming increasingly important, particularly in a time of competing health demands and limited resources. Comparative Risk Assessments (CRA) are useful in this regard as they quantify the contribution of modifiable exposures to the disease burden in a population. The aim of this study is to assess the contribution of a range of modifiable exposures to the burden of disease due to stroke, an important public health problem in Scotland. Methods: We used individual-level response data from eight waves (1995–2012) of the Scottish Health Survey linked to acute hospital discharge records from the Scottish Morbidity Record 01 (SMR01) and cause of death records from the death register. Stroke was defined using the International Classification of Disease (ICD) 9 codes 430–431, 433–4 and 436; and the ICD10 codes I60-61 and I63-64 and stroke incidence was defined as a composite of an individual’s first hospitalisation or death from stroke. A literature review identified exposures causally linked to stroke. Exposures were mapped to the layers of the Dahlgren & Whitehead model of the determinants of health and Population Attributable Fractions were calculated for each exposure deemed a significant causal risk of stroke from a Cox Proportional Hazards Regression model. Population Attributable Fractions were not summed as they may add to more than 100% due to the possibility of a person being exposed to more than one exposure simultaneously. Results: Overall, the results suggest that socioeconomic factors explain the largest proportion of incident stroke hospitalisations and deaths, after adjustment for confounding. After DAG adjustment, low education explained 38.8% (95% Confidence Interval 26.0% to 49.4%, area deprivation (as measured by the Scottish Index of Multiple Deprivation) 34.9% (95% CI 26.4 to 42.4%), occupational social class differences 30.3% (95% CI 19.4% to 39.8%), high systolic blood pressure 29.6% (95% CI 20.6% to 37.6%), smoking 25.6% (95% CI 17.9% to 32.6%) and area deprivation (as measured by the Carstairs area deprivation Index) 23.5% (95% CI 14.4% to 31.7%), of incident strokes in Scotland after adjustment. Conclusion: This study provides evidence for prioritising interventions that tackle socioeconomic inequalities as a means of achieving the greatest reduction in avoidable strokes in Scotland. Future work to disentangle the proportion of the effect of deprivation transmitted through intermediate mediators on the pathway between socioeconomic inequalities and stroke may offer additional opportunities to reduce the incidence of stroke in Scotland

How do world and European standard populations impact burden of disease studies? A case study of disability-adjusted life years (DALYs) in Scotland

Background Disability-Adjusted Life Years (DALYs) are an established method for quantifying population health needs and guiding prioritisation decisions. Global Burden of Disease (GBD) estimates aim to ensure comparability between countries and over time by using age-standardised rates (ASR) to account for differences in the age structure of different populations. Different standard populations are used for this purpose but it is not widely appreciated that the choice of standard may affect not only the resulting rates but also the rankings of causes of DALYs. We aimed to evaluate the impact of the choice of standard, using the example of Scotland. Methods DALY estimates were derived from the 2016 Scottish Burden of Disease (SBoD) study for an abridged list of 68 causes of disease/injury, representing a three-year annual average across 2014–16. Crude DALY rates were calculated using Scottish national population estimates. DALY ASRs standardised using the GBD World Standard Population (GBD WSP) were compared to those using the 2013 European Standard Population (ESP2013). Differences in ASR and in rank order within the cause list were summarised for all-cause and for each individual cause. Results The ranking of causes by DALYs were similar using crude rates or ASR (ESP2013). All-cause DALY rates using ASR (GBD WSP) were around 26% lower. Overall 58 out of 68 causes had a lower ASR using GBD WSP compared with ESP2013, with the largest falls occurring for leading causes of mortality observed in older ages. Gains in ASR were much smaller in absolute scale and largely affected causes that operated early in life. These differences were associated with a substantial change to the ranking of causes when GBD WSP was used compared with ESP2013. Conclusion Disease rankings based on DALY ASRs are strongly influenced by the choice of standard population. While GBD WSP offers international comparability, within-country analyses based on DALY ASRs should reflect local age structures. For European countries, including Scotland, ESP2013 may better guide local priority setting by avoiding large disparities occurring between crude and age-standardised results sets, which could potentially confuse non-technical audiences

Inequalities in population health loss by multiple deprivation: COVID-19 and pre-pandemic all-cause disability-adjusted life years (DALYs) in Scotland

Background: COVID-19 has caused almost unprecedented change across health, education, the economy and social interaction. It is widely understood that the existing mechanisms which shape health inequalities have resulted in COVID-19 outcomes following this same, familiar, pattern. Our aim was to estimate inequalities in the population health impact of COVID-19 in Scotland, measured by disability-adjusted life years (DALYs) in 2020. Our secondary aim was to scale overall, and inequalities in, COVID-19 DALYs against the level of pre-pandemic inequalities in all-cause DALYs, derived from the Scottish Burden of Disease (SBoD) study. Methods: National deaths and daily case data were input into the European Burden of Disease Network consensus model to estimate DALYs. Total Years of Life Lost (YLL) were estimated for each area-based deprivation quintile of the Scottish population. Years Lived with Disability were proportionately distributed to deprivation quintiles, based on YLL estimates. Inequalities were measured by: the range, Relative Index of Inequality (RII), Slope Index of Inequality (SII), and attributable DALYs were estimated by using the least deprived quintile as a reference. Results: Marked inequalities were observed across several measures. The SII range was 2048 to 2289 COVID-19 DALYs per 100,000 population. The rate in the most deprived areas was around 58% higher than the mean population rate (RII = 1.16), with 40% of COVID-19 DALYs attributed to differences in area-based deprivation. Overall DALYs due to COVID-19 ranged from 7 to 20% of the annual pre-pandemic impact of inequalities in health loss combined across all causes. Conclusion: The substantial population health impact of COVID-19 in Scotland was not shared equally across areas experiencing different levels of deprivation. The extent of inequality due to COVID-19 was similar to averting all annual DALYs due to diabetes. In the wider context of population health loss, overall ill-health and mortality due to COVID-19 was, at most, a fifth of the annual population health loss due to inequalities in multiple deprivation. Implementing effective policy interventions to reduce health inequalities must be at the forefront of plans to recover and improve population health

Can changes in spending on health and social care explain the recent mortality trends in Scotland? A protocol for an observational study

Introduction: There have been steady reductions in mortality rates in the majority of high-income countries, including Scotland, since 1945. However, reductions in mortality rates have slowed down since 2012–2014 in these nations; and have reversed in some cases. Deaths among those aged 55+ explain a large amount of these changing mortality trends in Scotland. Increased pressures on health and social care services have been suggested as one factor explaining these changes. This paper outlines a protocol for the approach to testing the extent to which health and social care pressures can explain recent mortality trends in Scotland. Although a slower rate of mortality improvements have affected people of all ages, certain ages have been more negatively affected than the others. The current analyses will be run by age-band to test if the service pressure-mortality link varies across age-group. Methods and analysis: This will be an observational ecological study based on the Scottish population. The exposures of interest will be the absolute (primary outcome) and percentage (secondary outcome) change in real terms per capita spending on social and healthcare services between 2011 and 2017. The outcome of interest will be the absolute (primary outcome) and percentage (secondary outcome) change in age-standardised mortality rate between 2012 and 2018 for men and women separately. The units of analysis will be the 32 local authorities and the 14 territorial health boards. The analyses will be run for both all age-groups combined and for the following age bands: <1, 1–15, 16–44, 45–64, 65–74, 75–84 and 85+. A series of descriptive analyses will summarise the distribution of health and social care expenditure and mortality trends between 2011 and 2018. Linear regression analysis will be used to investigate the direct association between health care spending and mortality rates. Ethics and dissemination: The data used in this study will be publicly available and aggregated and will not be individually identifiable; therefore, ethical committee approval is not needed. This work will not result in the creation of a new data set. On completion, the study will be stored within the National Health Service research governance system. All of the results will be published once they have been shared with partner agencies

Informing investment to reduce inequalities: a modelling approach

Background: Reducing health inequalities is an important policy objective but there is limited quantitative information about the impact of specific interventions. Objectives: To provide estimates of the impact of a range of interventions on health and health inequalities. Materials and methods: Literature reviews were conducted to identify the best evidence linking interventions to mortality and hospital admissions. We examined interventions across the determinants of health: a ‘living wage’; changes to benefits, taxation and employment; active travel; tobacco taxation; smoking cessation, alcohol brief interventions, and weight management services. A model was developed to estimate mortality and years of life lost (YLL) in intervention and comparison populations over a 20-year time period following interventions delivered only in the first year. We estimated changes in inequalities using the relative index of inequality (RII). Results: Introduction of a ‘living wage’ generated the largest beneficial health impact, with modest reductions in health inequalities. Benefits increases had modest positive impacts on health and health inequalities. Income tax increases had negative impacts on population health but reduced inequalities, while council tax increases worsened both health and health inequalities. Active travel increases had minimally positive effects on population health but widened health inequalities. Increases in employment reduced inequalities only when targeted to the most deprived groups. Tobacco taxation had modestly positive impacts on health but little impact on health inequalities. Alcohol brief interventions had modestly positive impacts on health and health inequalities only when strongly socially targeted, while smoking cessation and weight-reduction programmes had minimal impacts on health and health inequalities even when socially targeted. Conclusions: Interventions have markedly different effects on mortality, hospitalisations and inequalities. The most effective (and likely cost-effective) interventions for reducing inequalities were regulatory and tax options. Interventions focused on individual agency were much less likely to impact on inequalities, even when targeted at the most deprived communities

sgsR: a structurally guided sampling toolbox for LiDAR-based forest inventories

Establishing field inventories can be labor intensive, logistically challenging and expensive. Optimizing a sample to derive accurate forest attribute predictions is a key management-level inventory objective. Traditional sampling designs involving pre-defined, interpreted strata could result in poor selection of within-strata sampling intensities, leading to inaccurate estimates of forest structural variables. The use of airborne laser scanning (ALS) data as an applied forest inventory tool continues to improve understanding of the composition and spatial distribution of vegetation structure across forested landscapes. The increased availability of wall-to-wall ALS data is promoting the concept of structurally guided sampling (SGS), where ALS metrics are used as an auxiliary data source driving stratification and sampling within management-level forest inventories. In this manuscript, we present an open-source R package named sgsR that provides a robust toolbox for implementing various SGS approaches. The goal of this package is to provide a toolkit to facilitate better optimized allocation of sample units and sample size, as well as to assess and augment existing plot networks by accounting for current forest structural conditions. Here, we first provide justification for SGS approaches and the creation of the sgsR toolbox. We then briefly describe key functions and workflows the package offers and provide two reproducible examples. Avenues to implement SGS protocols according to auxiliary data needs are presented

How have changes in death by cause and age group contributed to the recent stalling of life expectancy gains in Scotland? Comparative decomposition analysis of mortality data, 2000–2002 to 2015–2017

Objective: Annual gains in life expectancy in Scotland were slower in recent years than in the previous two decades. This analysis investigates how deaths in different age groups and from different causes have contributed to annual average change in life expectancy across two time periods: 2000–2002 to 2012–2014 and 2012–2014 to 2015–2017. Setting Scotland. Methods: Life expectancy at birth was calculated from death and population counts, disaggregated by 5 year age group and by underlying cause of death. Arriaga’s method of life expectancy decomposition was applied to produce estimates of the contribution of different age groups and underlying causes to changes in life expectancy at birth for the two periods. Results: Annualised gains in life expectancy between 2012–2014 and 2015–2017 were markedly smaller than in the earlier period. Almost all age groups saw worsening mortality trends, which deteriorated for most cause of death groups between 2012–2014 and 2015–2017. In particular, the previously observed substantial life expectancy gains due to reductions in mortality from circulatory causes, which most benefited those aged 55–84 years, more than halved. Mortality rates for those aged 30–54 years and 90+ years worsened, due in large part to increases in drug-related deaths, and dementia and Alzheimer’s disease, respectively. Conclusion: Future research should seek to explain the changes in mortality trends for all age groups and causes. More investigation is required to establish to what extent shortcomings in the social security system and public services may be contributing to the adverse trends and preventing mitigation of the impact of other contributing factors, such as influenza outbreaks