Alcohol consumption, wealth and health - Authors' reply

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Socioeconomic status as an effect modifier of alcohol consumption and harm: an analysis of linked cohort data

Background: Alcohol-related mortality and morbidity are higher in socioeconomically disadvantaged populations. It is unclear if elevated harm reflects differences in consumption, reverse causation or greater risk of harm following similar consumption. We investigated whether the harmful effects differed by socioeconomic status accounting for alcohol consumption and other health-related factors. Methods: Alcohol consumption (weekly units and binge drinking) data (n=50,236; 429,986 person-years of follow-up) were linked to deaths, hospitalisations and prescriptions. The primary outcome was alcohol-attributable hospitalisation/death. The relationship between alcohol attributable harm and socioeconomic status was investigated for four measures (education level, social class, household income and area-based deprivation) using Cox proportional hazards models. The potential for alcohol consumption and other risk factors mediating the social patterning was explored. Downward social selection for high-risk drinkers (reverse causation) was tested by comparing change in area deprivation over time. Findings: Low socioeconomic status was consistently associated with markedly elevated alcohol-attributable harms, including after adjustment for weekly consumption, binge drinking, body mass index and smoking. There was evidence of effect modification: for example, relative to light drinkers living in advantaged areas, the hazard ratio for excessive drinkers was 6.75 (95% CI 5.09-8.93) in advantaged and 11.06 (95% CI 8.53-14.35) in deprived areas. We found little support for downward social selection. Interpretation: Disadvantaged social groups experience greater alcohol-attributable harms compared to the advantaged for given levels of alcohol consumption, even after accounting for different drinking patterns, obesity and smoking status at the individual level

Previous psychiatric hospitalizations as risk factors for single and multiple future alcohol-related hospitalizations in patients with alcohol use disorders

Background and aims: People with alcohol use disorder (AUD) often have co-occurring psychiatric conditions. The association between psychiatric conditions and AUD relapse has not yet been fully explored. This study aimed to quantify different psychiatric comorbidities as risk factors for first and multiple AUD rehospitalizations in patients already hospitalized once for AUD. Methods: We used a nation-wide routine health-care database in Scotland, UK, between 2010 and 2019. Individuals with a first hospitalization for AUD (codes F10.0-9 in the ICD-10 codes) were checked for previous hospitalizations where the main or co-occurring cause was a psychiatric condition (any other F0-F99 code in ICD-10). The final cohort included 23 529 patients, 18 620 of whom did not have a history of any other psychiatric comorbidity. First, individuals with a history of any previous psychiatric hospitalization were grouped and compared with those without on the basis of time to AUD rehospitalization. Then, individuals with different histories of psychiatric hospitalization were compared with each other. Cox and Prentice, Williams and Peterson gap-time models were used for single and multiple AUD rehospitalizations, respectively. Results: The AUD rehospitalization rate in individuals with a previous psychiatric hospitalization was 8% higher compared with those without [hazard ratio (HR) = 1.08, 95% confidence interval (CI) = 1.01–1.14]. The difference in rehospitalization rate reduced following the first rehospitalization (HR at second rehospitalization from first: 0.95, 95% CI = 0.87–1.04 and HR at third rehospitalization from second: 0.94, 95% CI = 0.84–1.07). Mood disorders and neurotic, stress-related and somatoform disorders were associated with a 54% (HR = 1.54, 95% CI = 1.38–1.72) and 39% (HR = 1.39, 95% CI = 1.17–1.66) increase in the risk of a first AUD rehospitalization. Other conditions, such as disorders due to psychoactive substance use or schizophrenia, were associated with decreases in future AUD rehospitalization (HR = 0.89, 95% CI = 0.82–0.97 and HR = 0.82, 95% CI = 0.58–1.16, respectively). Conclusions: Patients with AUD appear to have different rates of AUD rehospitalization based on different co-occurring psychiatric conditions. Addiction-related characteristics may be more relevant risk indicators for multiple AUD readmission than psychiatric comorbidities

Comparing the performance of two-stage residual inclusion methods when using physician's prescribing preference as an instrumental variable: unmeasured confounding and noncollapsibility

Aim: The first objective is to compare the performance of two-stage residual inclusion (2SRI), two-stage least square (2SLS) with the multivariable generalized linear model (GLM) in terms of the reducing unmeasured confounding bias. The second objective is to demonstrate the ability of 2SRI and 2SPS in alleviating unmeasured confounding when noncollapsibility exists. Materials and methods: This study comprises a simulation study and an empirical example from a real-world UK population health dataset (Clinical Practice Research Datalink). The instrumental variable (IV) used is based on physicians' prescribing preferences (defined by prescribing history). Results: The percent bias of 2SRI in terms of treatment effect estimates to be lower than GLM and 2SPS and was less than 15% in most scenarios. Further, 2SRI was found to be robust to mild noncollapsibility with the percent bias less than 50%. As the level of unmeasured confounding increased, the ability to alleviate the noncollapsibility decreased. Strong IVs tended to be more robust to noncollapsibility than weak IVs. Conclusion: 2SRI tends to be less biased than GLM and 2SPS in terms of estimating treatment effect. It can be robust to noncollapsibility in the case of the mild unmeasured confounding effect

Associations of statin adherence and lipid targets with adverse outcomes in myocardial infarction survivors:a retrospective cohort study

Objectives: To examine associations between statin adherence and lipid target achievement in myocardial infarction (MI) survivors, and their associations with mortality and recurrent MIs. Design: Retrospective cohort study using linked clinical records within the National Health Service Greater Glasgow and Clyde (NHS GGC) Data Safe Haven. Setting: Routine clinical practice in the NHS GGC area between January 2009 and July 2017. Participants: Patients ≥18 years who experienced a non-fatal MI hospital admission (ICD10: I21, I22) between January 2009 and July 2014 (n=11 031), followed up from the date of MI admission until July 2017 or death, whichever occurred first. Primary and secondary outcome measures: Statin adherence was estimated using encashed prescriptions and lipid results from routine biochemistry data. Primary lipid and statin adherence targets were LDL ≤1.8 mmol/L and adherence ≥50%, and were related to all-cause death, deaths due to cardiovascular disease (CVD) (ICD10: I00–I99 as the underlying cause), and recurrent MI in unadjusted models and models adjusting for age, sex, socioeconomic deprivation and year of MI. Results: Over 4.5 years follow-up, 76% achieved LDL ≤1.8 mmol/L, and 84.5% had average adherence ≥50%. Patients with adherence <50% had an increased risk of not meeting LDL ≤1.8 mmol/L, in adjusted models (OR 2.03, 95% CI 1.78 to 2.31, p<0.0001). In univariable models, not meeting LDL ≤1.8 mmol/L was associated with increased risks of all-cause mortality (HR 1.27, 95% CI 1.16 to 1.39, p<0.0001) and CVD mortality (HR 1.29, 95% CI 1.11 to 1.51, p=0.0013). Adherence <50% was associated with increased risks of all-cause mortality (HR 1.58, 95% CI 1.44 to 1.74, p<0.0001) and CVD mortality (HR 1.60, 95% CI 1.36 to 1.88, p<0.0001). Adjustment for confounders did not abrogate these associations. Neither exposure was associated with recurrent MIs. Conclusions: Non-achievement of lipid and adherence targets are associated with increased risks of all-cause and CVD mortality. Further work is required to optimise their use to improve outcomes in clinical practice

Impact of legislation to reduce the drink-drive limit on road traffic accidents and alcohol consumption in Scotland: a natural experiment study

Background : It is widely recognised that drink driving is a leading cause of road traffic accidents (RTAs). There is evidence that changing the drink-drive limit from a blood alcohol concentration of 0.08 to 0.05 g/dl is effective in reducing RTAs. Scotland changed the blood alcohol concentration limit to 0.05 g/dl on 5 December 2014. Aims : To assess whether or not the numbers and rates of RTAs and per capita alcohol consumption in Scotland were reduced because of the 2014 drink-drive legislation. To assess whether or not the 2014 change in legislation provided good value for money. Design : A natural experimental, quantitative study. The control group was England and Wales, that is, the other countries in Great Britain, where the drink-drive legislation remained unchanged. Setting : Great Britain. Participants : The entire population of Scotland, England and Wales for the period of January 2013– December 2016. Intervention : The change to drink-drive legislation in Scotland. Outcome measures : The counts and rates of RTAs; and per capita alcohol consumption. Methods : For the numbers and rates of RTAs (both traffic flow and population denominators were used), and separately for the intervention and control trial groups, negative binomial regression models were fitted to panel data sets to test for a change in outcome level after the new 2014 legislation was in place. To obtain a ‘difference-in-differences’ (DiD)-type measure of effect, an interaction term between the intervention group indicator and the binary covariate for indicating pre and post change in legislation (‘pseudo’-change for the control group) was assessed. For off- and on-trade per capita alcohol sales, and separately for the intervention and control trial group, seasonal autoregressive integrated moving average error models were fitted to the relevant time series. Results : The change to drink-drive legislation was associated with a 2% relative decrease in RTAs in Scotland [relative risk (RR) 0.98, 95% CI 0.91 to 1.04; p = 0.53]. However, the pseudo-change in legislation was associated with a 5% decrease in RTAs in England and Wales (RR 0.95, 95% CI 0.90 to 1.00; p = 0.05). For RTA rates, with traffic flow as the denominator, the DiD-type estimate indicated a 7% increase in ratesfor Scotland relative to England and Wales (unadjusted RR 1.07, 95% CI 0.98 to 1.17; p = 0.1). The change to drink-drive legislation was associated with a 0.3% relative decrease in per capita off-trade sales (–0.3%, 95% CI –1.7% to 1.1%; p = 0.71) and a 0.7% decrease in per capita on-trade sales (–0.7%, 95% CI –0.8% to –0.5%; p < 0.001).  Conclusion : The change to drink-drive legislation in Scotland in December 2014 did not have the expected effect of reducing RTAs in the country, and nor did it change alcohol drinking levels in Scotland. This main finding for RTAs was unexpected and the research has shown that a lack of enforcement is the most likely reason for legislation failure.  Future work : Investigations into how the public interpret and act on changes in drink-drive legislation would be welcome, as would research into whether or not previous change in drink-drive legislation effects on RTAs in other jurisdictions are associated with the level of enforcement that took place

Assessing the performance of physician’s prescribing preference as an instrumental variable in comparative effectiveness research with moderate and small sample sizes: a simulation study

Aim: This simulation study is to assess the utility of physician's prescribing preference (PPP) as an instrumental variable for moderate and smaller sample sizes. Materials and methods: We designed a simulation study to imitate a comparative effectiveness research under different sample sizes. We compare the performance of instrumental variable (IV) and non-IV approaches using two-stage least squares (2SLS) and ordinary least squares (OLS) methods, respectively. Further, we test the performance of different forms of proxies for PPP as an IV. Results: The percent bias of 2SLS is around approximately 20%, while the percent bias of OLS is close to 60%. The sample size is not associated with the level of bias for the PPP IV approach. Conclusion: Irrespective of sample size, the PPP IV approach leads to less biased estimates of treatment effectiveness than OLS adjusting for known confounding only. Particularly for smaller sample sizes, we recommend constructing PPP from long prescribing histories to improve statistical power

Joint modelling of longitudinal processes and time-to-event outcomes in heart failure: systematic review and exemplar examining the relationship between serum digoxin levels and mortality

Background: Joint modelling combines two or more statistical models to reduce bias and increase efficiency. As the use of joint modelling increases it is important to understand how and why it is being applied to heart failure research. Methods: A systematic review of major medical databases of studies which used joint modelling within heart failure alongside an exemplar; joint modelling repeat measurements of serum digoxin with all-cause mortality using data from the Effect of Digoxin on Mortality and Morbidity in Patients with Heart Failure (DIG) trial. Results: Overall, 28 studies were included that used joint models, 25 (89%) used data from cohort studies, the remaining 3 (11%) using data from clinical trials. 21 (75%) of the studies used biomarkers and the remaining studies used imaging parameters and functional parameters. The exemplar findings show that a per unit increase of square root serum digoxin is associated with the hazard of all-cause mortality increasing by 1.77 (1.34–2.33) times when adjusting for clinically relevant covariates. Conclusion: Recently, there has been a rise in publications of joint modelling being applied to heart failure. Where appropriate, joint models should be preferred over traditional models allowing for the inclusion of repeated measures while accounting for the biological nature of biomarkers and measurement error

Temporal trends and risk factors for readmission for infections, gastrointestinal and immobility complications after an incident hospitalisation for stroke in Scotland between 1997 and 2005

Background: Improvements in stroke management have led to increases in the numbers of stroke survivors over the last decade and there has been a corresponding increase of hospital readmissions after an initial stroke hospitalisation. The aim of this study was to examine the one year risk of having a readmission due to infective, gastrointestinal or immobility (IGI) complications and to identify temporal trends and any risk factors.&lt;p&gt;&lt;/p&gt; Methods: Using a cohort of first hospitalised for stroke patients who were discharged alive, time to first event (readmission for IGI complications or death) within 1 year was analysed in a competing risks framework using cumulative incidence methods. Regression on the cumulative incidence function was used to model the risks of having an outcome using the covariates age, sex, socioeconomic status, comorbidity, discharge destination and length of hospital stay.&lt;p&gt;&lt;/p&gt; Results: There were a total of 51,182 patients discharged alive after an incident stroke hospitalisation in Scotland between 1997–2005, and 7,747 (15.1%) were readmitted for IGI complications within a year of the discharge. Comparing incident stroke hospitalisations in 2005 with 1997, the adjusted risk of IGI readmission did not increase (HR = 1.00 95% CI (0.90, 1.11). However, there was a higher risk of IGI readmission with increasing levels of deprivation (most deprived fifth vs. least deprived fifth HR = 1.16 (1.08, 1.26).&lt;p&gt;&lt;/p&gt; Conclusions: Approximately 15 in 100 patients discharged alive after an incident hospitalisation for stroke in Scotland between 1997 and 2005 went on to have an IGI readmission within one year. The proportion of readmissions did not change over the study period but those living in deprived areas had an increased risk

A critical reflection on the use of improvement science approaches in public health

Objective: ‘Improvement science’ is used to describe specific quality improvement methods (including tests of change and statistical process control). The approach is spreading from clinical settings to population-wide interventions and is being extended from supporting the adoption of proven interventions to making generalisable claims about new interventions. The objective of this narrative review is to evaluate the strengths and risks of current improvement science practice, particularly in relation to how they might be used in population health. Methods: A purposive sampling of published studies to identify how improvement science methods are being used and for what purpose. The setting was Scotland and studies that focused on health and wellbeing outcomes. Results: We have identified a range of improvement science approaches which provide practitioners with accessible tools to assess small-scale changes in policy and practice. The strengths of such approaches are that they facilitate consistent implementation of interventions already known to be effective and motivate and empower staff to make local improvements. However, we also identified a number of potential risks. In particular, their use to assess the effectiveness of new interventions often seems to pay insufficient attention to random variation, measurement bias, confounding and ethical issues. Conclusions: The use of current improvement science methods to generate evidence of effectiveness for population-wide interventions is problematic and risks unjustified claims of effectiveness, inefficient resource use and harm to those not offered alternative effective interventions. Newer methodological approaches offer alternatives and should be more widely considered