Bruce Whyte

Regional alcohol consumption and alcohol-related mortality in Great Britain: novel insights using retail sales data

BackgroundRegional differences in population levels of alcohol-related harm exist across Great Britain, but these are not entirely consistent with differences in population levels of alcohol consumption. This incongruence may be due to the use of self-report surveys to estimate consumption. Survey data are subject to various biases and typically produce consumption estimates much lower than those based on objective alcohol sales data. However, sales data have never been used to estimate regional consumption within Great Britain (GB). This ecological study uses alcohol retail sales data to provide novel insights into regional alcohol consumption in GB, and to explore the relationship between alcohol consumption and alcohol-related mortality.MethodsAlcohol sales estimates derived from electronic sales, delivery records and retail outlet sampling were obtained. The volume of pure alcohol sold was used to estimate per adult consumption, by market sector and drink type, across eleven GB regions in 2010–11. Alcohol-related mortality rates were calculated for the same regions and a cross-sectional correlation analysis between consumption and mortality was performed.ResultsPer adult consumption in northern England was above the GB average and characterised by high beer sales. A high level of consumption in South West England was driven by on-trade sales of cider and spirits and off-trade wine sales. Scottish regions had substantially higher spirits sales than elsewhere in GB, particularly through the off-trade. London had the lowest per adult consumption, attributable to lower off-trade sales across most drink types. Alcohol-related mortality was generally higher in regions with higher per adult consumption. The relationship was weakened by the South West and Central Scotland regions, which had the highest consumption levels, but discordantly low and very high alcohol-related mortality rates, respectively.ConclusionsThis study provides support for the ecological relationship between alcohol-related mortality and alcohol consumption. The synthesis of knowledge from a combination of sales, survey and mortality data, as well as primary research studies, is key to ensuring that regional alcohol consumption, and its relationship with alcohol-related harms, is better understood.

Cohort Profile: The Scottish Health Surveys Cohort: linkage of study participants to routinely collected records for mortality, hospital discharge, cancer and offspring birth characteristics in three nationwide studies

Background Although life expectancy is increasing in Scotland, the nation still has the highest rates of coronary heart disease (CHD) and selected malignancies in the UK and higher rates than most countries in Western Europe. The Scottish Health Surveys (SHeSs)—conducted in 1995, 1998 and 2003––were established to provide detailed, contemporary health information on a large, representative sample of the Scottish population. By capturing a range of behavioural, biological, psychological and social characteristics, their purpose was to monitor health in order to assist in policy formulation and the development of new health initiatives across the whole of Scotland.

Has Scotland always been the ‘sick man’ of Europe? An observational study from 1855 to 2006

Background: Scotland has been dubbed ‘the sick man of Europe’ on account of its higher mortality rates compared with other western European countries. It is not clear the length of time for which Scotland has had higher mortality rates. The root causes of the higher mortality in Scotland remain elusive. Methods: Life expectancy data from the Human Mortality Database were tabulated and graphed for a selection of wealthy, mainly European countries from around 1850 onwards. Results: Scotland had a life expectancy in the mid-range of countries included in the Human Mortality Database from the mid-19th century until around 1950. After 1950, Scottish life expectancy improved at a slower rate than in comparably wealthy nations before further faltering during the last 30 years. Scottish life expectancy now lies between that of western European and eastern European nations. The USA also displays a marked faltering in its life expectancy trend after 1981. There is an inverse association between life expectancy and the Index of Economic Freedom such that greater neoliberalism is associated with a smaller increase, or a decrease, in life expectancy. Conclusion: Life expectancy in Scotland has only been relatively low since around 1950. From 1980, life expectancy in Scotland, the USA and, to a greater extent, the former USSR displays a further relative faltering. It has been suggested that Scotland suffered disproportionately from the adoption of neoliberalism across the nations of the UK, and the evidence here both supports this suggestion and highlights other countries which may have suffered similarly.

Breastfeeding is associated with reduced childhood hospitalization: evidence from a Scottish Birth Cohort (1997-2009)

Objective To evaluate the risk of childhood hospitalization associated with infant feeding patterns at 6-8 weeks of age in Scotland. Study design A retrospective population level study based on the linkage of birth, death, maternity, infant health, child health surveillance, and admission records for children born as single births in Scotland between 1997 and 2009 (n = 502 948) followed up to March 2012. Descriptive analyses, Kaplan Meier tests, and Cox regression were used to quantify the association between the mode of infant feeding and risk of childhood hospitalization for respiratory, gastrointestinal, and urinary tract infections, and other common childhood ailments during the study period. Results Within the first 6 months of life, there was a greater hazard ratio (HR) of hospitalization for common childhood illnesses among formula-fed infants (HR 1.40; 95% CI 1.35-1.45) and mixed-fed infants (HR 1.18; 95% CI 1.11-1.25) compared with infants exclusively breastfed after adjustment for parental, maternal, and infant health characteristics. Within the first year of life and beyond, a greater relative risk of hospitalization was observed among formula-fed infants for a range of individual illnesses reported in childhood including gastrointestinal, respiratory, and urinary tract infections, otitis media, fever, asthma, diabetes, and dental caries. Conclusions Using linked administrative data, we found greater risks of hospitalization in early childhood for a range of common childhood illnesses among Scottish infants who were not exclusively breastfed at 6-8 weeks of age.

Alcohol-related mortality in deprived UK cities: worrying trends in young women challenge recent national downward trends

Background Glasgow, the largest city in Scotland, has high levels of deprivation and a poor-health profile compared with other parts of Europe, which cannot be fully explained by the high levels of deprivation. The ‘excess’ premature mortality in Glasgow is now largely attributable to deaths from alcohol, drugs, suicide and violence. Methods Alcohol-related mortality in Glasgow from 1980 to 2011 was examined relative to the equally deprived UK cities of Manchester and Liverpool with the aim of identifying differences across the cities, with respect to gender, age and birth cohort, that could help explain the ‘excess’ mortality in Glasgow. Results In the 1980s, alcohol-related mortality in Glasgow was three times higher than in Manchester and Liverpool. Alcohol-related mortality increased in all three cities over the subsequent three decades, but a sharp rise in deaths in the early 1990s was unique to Glasgow. The increase in numbers of deaths in Glasgow was greater than in Manchester and Liverpool, but there was little difference in the pattern of alcohol-related deaths, by sex or birth cohort that could explain the excess mortality in Glasgow. The recent modest decrease in alcohol-related mortality was largely experienced by all birth cohorts, with the notable exception of the younger cohort (born between 1970 and 1979): women in this cohort across all three cities experienced disproportionate increases in alcohol-related mortality. Conclusions It is imperative that this early warning sign in young women in the UK is acted on if deaths from alcohol are to reduce in the long term.

Epidemiology of hospitalization due to alcohol-related harm: Evidence from a Scottish cohort study

OBJECTIVE To examine a broad range of risk factors and their association with alcohol-related hospital admissions in a Scottish general population. DESIGN Observational record-linkage study in Scotland from 1998 to 2008 involving 8305 respondents aged 16-74 years who participated in the 1998 Scottish Health Survey. Outcome was defined as first-time hospital admission with at least one alcohol-related diagnosis. METHODS Cox proportional hazards modelling was applied to estimate the hazard ratio (HR) of first-time hospitalization with an alcohol-related condition associated with a range of behavioural, social and biological risk factors. FINDINGS In total, 287 (3.4%) respondents experienced at least one alcohol-related hospitalization during the observation period. Moderate to excessive drinking was the strongest predictor of subsequent admission to hospital with an alcohol-related diagnosis, with clear evidence of a dose - response relationship. Moderate and heavy smoking were also significant predictors of subsequent admission to hospital with an alcohol-related problem. Social factors - such as being in receipt of income-related benefits [HR 1.68, 95% confidence interval (CI) 1.25-2.28]; being retired or economically inactive; and being separated, divorced or widowed (HR 2.34, 95% CI 1.70-3.22) - were also significant predictors of alcohol-related hospitalization. CONCLUSIONS Moderate and higher levels of weekly alcohol consumption, moderate to heavy smoking, economic circumstances and marital status are the main risk factors for alcohol-related hospitalization in the Scottish population. These findings add to the evidence that population-based strategies are needed to limit alcohol-related morbidity.

Informing the ‘early years’ agenda in Scotland: understanding infant feeding patterns using linked datasets

Background Providing infants with the ‘best possible start in life’ is a priority for the Scottish Government. This is reflected in policy and health promotion strategies to increase breast feeding, which gives the best source of nutrients for healthy infant growth and development. However, the rate of breast feeding in Scotland remains one of the lowest in Europe. Information is needed to provide a better understanding of infant feeding and its impact on child health. This paper describes the development of a unique population-wide resource created to explore infant feeding and child health in Scotland. Methods Descriptive and multivariate analyses of linked routine/administrative maternal and infant health records for 731 595 infants born in Scotland between 1997 and 2009. Results A linked dataset was created containing a wide range of background, parental, maternal, birth and health service characteristics for a representative sample of infants born in Scotland over the study period. There was high coverage and completeness of infant feeding and other demographic, maternal and infant records. The results confirmed the importance of an enabling environment—cultural, family, health service and other maternal and infant health-related factors—in increasing the likelihood to breast feed. Conclusions Using the linked dataset, it was possible to investigate the determinants of breast feeding for a representative sample of Scottish infants born between 1997 and 2009. The linked dataset is an important resource that has potential uses in research, policy design and targeting intervention programmes.

Commentary: long-term monitoring of health inequalities in Scotland--a response to Frank and Haw.

Frank and Haw (2011) devised a set of criteria that can be used to evaluate the utility of frameworks for monitoring health inequalities. They argued that a high-quality monitoring framework should ensure the completeness and accuracy of reporting, that the measures used should be reversible and sensitive to intervention, that the measure should be statistically appropriate, and that there should be no reverse causation between the proposed outcome measures and the markers of socioeconomic status. They applied these to the Scottish Governments long-term monitoring framework for health inequalities (Scottish Government 2011) to highlight the potential pitfalls for policymakers. While we welcome their description of the Scottish Governments measures as “state of the art” and recognize that there is always room for improvement, we disagree with some aspects of their appraisal of the Scottish monitoring framework, as well as the criteria they proposed. We contend that their application of these criteria to the Scottish example reveals some of the limitations of their approach. First, Frank and Haw suggested that some of the outcome measures in the framework are not reversible or sensitive to policy change, citing as evidence the slow changes in inequalities in most of the Scottish indicators. We believe that this is an overly narrow view of the capacity of government to influence population health inequalities. Lack of change in Scotland (or any other country) is not evidence that inequalities are insensitive to policy. Moreover, it is clear that health inequalities are strongly influenced by socioeconomic policy and that change can occur rapidly (Beckfield and Krieger 2009; Mackenbach et al. 2003). Health inequalities in the United Kingdom rose rapidly during the 1980s and 1990s (Shaw et al. 2003), leaving Scotland with some populations whose mortality rates increased in absolute terms (Norman et al. 2011). Indeed, there is evidence of rather dramatic changes in inequality over time and around the world resulting from a mix of government policies (including those influencing the social determinants of health), specific health improvement interventions, and health care (Beckfield and Krieger 2009). Second, Frank and Haw attribute the insensitivity of inequalities indicators (especially coronary heart disease, all-cause mortality, and healthy life expectancy) to their undue dependence on later-life mortality or cumulative life-course experience. It is clear, however, that inequalities in mortality in Scotland are very high among young adults (Leyland 2004; Leyland et al. 2007; Norman et al. 2011). Furthermore, mortality inequalities worldwide show marked temporal and geographical variation, suggesting that socioeconomic determinants of health have a profound impact on these outcomes (Beckfield and Krieger 2009). Macintyre reviewed the evidence for policies likely to reduce health inequalities (Macintyre 2007), and we contend that there is further scope in Scotland and elsewhere for cross-sectoral policy informed by evidence. The relative stability in recent years of inequality measures is as likely to be due to policy failure as to a lack of amenability. We therefore argue that these measures are an appropriate part of a framework aimed at “long-term” monitoring, although that does not preclude the inclusion of other, short-term measures. Frank and Haw are similarly concerned that the Warwick-Edinburgh Mental Well-Being Scale (WEMWBS) is insensitive to change. Given that the WEMWBS is relatively new (Tennant et al. 2007), it seems somewhat unfair to dismiss it so quickly. It is equally possible that there have been no true population-level changes in this outcome over the relatively brief time period of interest, which might be expected given the trends in well-being witnessed in other rich countries (Lane 2001). However, the WEMWBS score was sensitive enough to detect changes in response to a parenting intervention (Lindsay et al. 2008), and it also shows inequalities between social groups (Scottish Government 2011). Although Frank and Haw regard these differences as small, the difference between the most and the least deprived groups is about 10 percent of the total achieved by the least deprived group. Furthermore, as with all such composite survey-derived measures, the scale is largely arbitrary. We view this measure as promising, particularly given the striking lack of suitable alternatives. We accept Frank and Haws concern that trends in low birth weight (LBW) reflect not only changes in the health outcome (and influences such as poverty, smoking, and nutrition) but also changes in clinical practices (e.g., inducing delivery early owing to the risk of stillbirth) and the increased survival of premature babies who previously would have been counted as neonatal deaths. A simple change here would be to report in addition on inequalities in birth weight adjusted for gestational age, with a comment on the potential for changes in clinical practice to have an influence. But it is worth noting that prematurity is also closely related to infant mortality and morbidity—and LBW is a reasonable marker of this (Smith et al. 2010). Frank and Haw criticized the alcohol-related mortality measure in relation to its statistical appropriateness. They noted the nonlinear pattern across socioeconomic groups and suggested that segmented (spline) regression (Young 2010) may be preferable to the linear regression method conventionally used to calculate the slope index of inequality (SII). In our view, the spline approach presents several problems of its own. Departures from linearity are a matter of degree, and statistical techniques dependent on hypothesis testing (Sergeant and Firth 2006) are more likely to detect minor nonlinearity in large data sets. Spline methods also may give undue influence to outlying values in the extreme quantiles. It is not clear that an SII calculated using a spline approach is comparable between periods and areas that show varying degrees of linearity in inequalities. The important point is, of course, that all measures of inequality are imperfect summaries. We are not persuaded, either, that the alcohol-related mortality measure is so nonlinear as to make the measures invalid or that Frank and Haws proposed alternative avoids these problems. Frank and Haw suggested, too, that another issue in relation to statistical appropriateness is heterogeneity of outcomes; that is, some outcomes capture a mixture of conflicting trends that conceal real, but divergent, changes (Frank and Haw 2011). This is a justifiable concern in relation to the “all-cancer” indicator, since it encompasses a range of outcomes with varying determinants, preventability, and treatment. Furthermore, screening for cancers is likely to increase incidence and overdiagnosis (Gotzsche and Nielsen 2011). Accordingly, there is merit in presenting trends in inequalities for specific cancers, but this would be dependent on having sufficient statistical power, which is clearly an issue for smaller countries like Scotland. The concern expressed about the potential impact of out-of-hospital deaths on inequalities in hospital admissions for myocardial infarction is one that we accept and that could be resolved by including these deaths. Frank and Haw criticized the alcohol-related mortality indicator because of its susceptibility to reverse causation. While this is theoretically possible, previous reviews argued that this was a minor cause of health inequalities (Macintyre 1997). Most longitudinal studies that tested this “health selection” theory concluded that the concentration of ill health (including for alcohol-related deaths) in lower social groups is largely explained by premorbid social status rather than downward social movement (Davey Smith et al. 1998; Macintyre 1997). Furthermore, the Scottish parliament recently voted to introduce a minimum unit price for alcohol, an intervention whose impact on inequalities in alcohol-related mortality will be important to monitor. In summary, the four broad criteria laid out by Frank and Haw seem reasonable at first sight. In practice, however, none of them has a straightforward application in the example they chose to test. Complete and accurate statistics (e.g., with individual measures of socioeconomic status) are ideal but often not available. We agree that measures should be statistically appropriate, but the statistical approach that Frank and Haw suggested seems to create as many problems as it would solve. We agree, too, that measures should be reversible and sensitive to intervention, but reversibility is not easily assessed, and a lack of change over time should not be interpreted as irreversibility. The apparent implication that the lack of reduction in health inequalities in many high-income countries in recent years indicates an inability to reduce health inequalities in the future seems unduly pessimistic. In relation to the last criterion, reverse causation is important in principle, but evidence of reverse causation needs to be carefully assessed. We have tried to make the case here that this plays only a very small part in explaining health inequalities in high-income countries. Consequently, including this criterion in high-income countries is unhelpful and unjustified in suggesting that part of the inequality in the outcome might not be unfair. Overall, Frank and Haws critique of the Scottish Governments long-term monitoring framework is unjustified. In our view, this framework provides a valid, robust, and sensible approach to measuring progress, given the limitations of the data that are currently available or likely to be so in the near future.

O1-1.4 The patterning of deprivation and its effects on health outcomes in three post industrial cities in Britain

Scotland has some of the worst reported health in the developed world. In comparison to England and Wales it has higher mortality rates, as well as higher incidence and prevalence of heart disease, many cancers (especially lung cancer) and deaths from suicides, accidents and alcohol. Scotland also has some of the most deprived neighbourhoods in the UK, with most being concentrated in Glasgow. The link between poor health and neighbourhood deprivation is well documented but research has also shown that neighbourhood deprivation does not explain the higher levels of mortality in Glasgow compared to similarly the deprived post industrial cities of Liverpool and Manchester. The distribution or patterning of deprived neighbourhoods in Glasgow may in part be an explanation for differences between health outcomes in Glasgow and similar deprived post industrial cities. Using a combination of mortality, deprivation and contextual data at a neighbourhood level this study examines: the extent to which the distribution or patterning of deprived neighbourhoods is associated with differing neighbourhood health outcomes; whether any variance in the patterning of deprived neighbourhoods in the three cities can be detected; and more specifically, whether the difference in patterning of deprived neighbourhoods is in part an explanation for the poorer health experienced in Glasgow compared to Liverpool and Manchester?