Estimating the causal effects of income on health: how researchers' definitions of "income" matter.

BACKGROUND: There is a well-established cross-sectional association between income and health, but estimates of the causal effects of income vary substantially. Different definitions of income may lead to substantially different empirical results, yet research is often framed as investigating "the effect of income" as if it were a single, easily definable construct. METHODS/RESULTS: The aim of this paper is to introduce a taxonomy for definitional and conceptual issues in studying individual- or household-level income for health research. We focus on (1) the definition of the income measure (earned and unearned; net, gross, and disposable; real and nominal; individual and household; relative and absolute income) and (2) the definition of the causal contrast (amount, functional form assumptions/transformations, direction, duration of change, and timing of exposure and follow-up). We illustrate the application of the taxonomy to four examples from the published literature. CONCLUSIONS: Quantified estimates of causal effects of income on health and wellbeing have crucial relevance for policymakers to anticipate the consequences of policies targeting the social determinants of health. However, much prior evidence has been limited by lack of clarity in distinguishing between different causal questions. The present framework can help researchers explicitly and precisely articulate income-related exposures and causal questions.

Loop diuretic utilisation with or without heart failure: impact on prognosis.

BACKGROUND & AIMS: Many patients are prescribed loop diuretics without a diagnostic record of heart failure. Little is known about their characteristics and prognosis. METHODS: Glasgow regional health records (2009-2016) were obtained for adults with cardiovascular disease or taking loop diuretics. Outcomes were investigated using Cox models with hazard ratios adjusted for age, sex, socioeconomic deprivation, and co-morbid disease (adjHR). RESULTS: Of 198,898 patients (median age 65 years; 55% women), 161,935 (81%) neither took loop diuretics nor had a diagnostic record of heart failure (reference group), 23,963 (12%) were taking loop diuretics but had no heart failure recorded, 7,844 (4%) had heart failure recorded and took loop diuretics and 5,156 (3%) had heart failure recorded but were not receiving loop diuretics.Five-year mortality was only slightly higher for heart failure in absence of loop diuretics (22%; adjHR: 1.2 [95% CI 1.1-1.3]), substantially higher for those taking loop diuretics with no heart failure recorded (40%; adjHR: 1.8 [95% CI 1.7-1.8]) and highest for heart failure treated with loop diuretics (52%; adjHR: 2.2 [95% CI 2.0-2.2]). CONCLUSIONS: For patients with cardiovascular disease, many are prescribed loop diuretics without a diagnosis of heart failure being recorded. Mortality is more strongly associated with loop diuretic use than with a heart failure record. The diagnosis of heart failure may be often missed, or loop diuretic use is associated with other conditions with a prognosis similar to heart failure, or inappropriate loop diuretic use increases mortality; all might be true.

Pharmacological treatments for alcohol dependence: Evidence on uptake, inequalities and comparative effectiveness from a UK population-based cohort.

INTRODUCTION: We assessed the prevalence of prescribing of certain medications for alcohol dependence and the extent of any inequalities in receiving prescriptions for individuals with such a diagnosis. Further, we compared the effectiveness of two of the most prescribed medications (acamprosate and disulfiram) for alcohol dependence and assessed whether there is inequality in prescribing either of them. METHODS: We used a nationwide dataset on prescriptions and hospitalisations in Scotland, UK (N = 19,748). We calculated the percentage of patients receiving alcohol dependence prescriptions after discharge, both overall and by socio-economic groups. Binary logistic regressions were used to assess the odds of receiving any alcohol-dependence prescription and the comparative odds of receiving acamprosate or disulfiram. Comparative effectiveness in avoiding future alcohol-related hospitalisations (N = 11,239) was assessed using Cox modelling with statistical adjustment for potential confounding. RESULTS: Upto 7% of hospitalised individuals for alcohol use disorder received prescriptions for alcohol dependence after being discharged. Least deprived socio-economic groups had relatively more individuals receiving prescriptions. Inequalities in prescribing for alcohol dependence existed, especially across sex and comorbidities: males had 12% (odds ratio [OR] 0.88, 95% confidence interval [CI] 0.81-0.96) and those with a history of mental health hospitalisations had 10% (OR 0.90, 95% CI 0.82-0.98) lower odds of receiving prescriptions after an alcohol-related hospitalisation. Prescribing disulfiram was superior to prescribing acamprosate in preventing alcohol-related hospitalisations (hazard ratio ranged between 0.60 and 0.81 across analyses). Disulfiram was relatively less likely prescribed to those from more deprived areas. DISCUSSION AND CONCLUSIONS: Inequalities in prescribing for alcohol dependence exists in Scotland with lower prescribing to men and disulfiram prescribed more to those from least deprived areas.

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 & 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.

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 & 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.

Evaluating the impact of minimum unit pricing (MUP) on alcohol sales after 3 years of implementation in Scotland: A controlled interrupted time-series study.

BACKGROUND AND AIMS: On 1 May 2018, Scotland introduced minimum unit pricing (MUP), a strength-based floor price below which alcohol cannot be sold, throughout all alcoholic beverages. The legislation necessitates an evaluation of its impact across a range of outcomes that will inform whether MUP will continue beyond its sixth year. We measured the impact of MUP on per-adult alcohol sales (as a proxy for consumption) after 3 years of implementation. DESIGN, SETTING AND PARTICIPANTS: Controlled interrupted time-series regression was used to assess the impact of MUP on alcohol sales in Scotland after 3 years of implementation, with England and Wales (EW) being the control group. In adjusted analyses, we included household disposable income, on-trade alcohol sales (in off-trade analyses) and substitution between drink categories (in drink category analyses) as covariates. MEASUREMENTS: Weekly data were assessed on the volume of pure alcohol sold in Scotland and EW between January 2013 and May 2021, expressed as litres of pure alcohol per adult. The impact of MUP on total (on- and off-trade combined), off-trade and on-trade alcohol sales was assessed separately. RESULTS: The introduction of MUP in Scotland was associated with a 3.0% (95% confidence interval = 1.8-4.2%) net reduction in total alcohol sales per adult after adjustment for the best available geographical control, disposable income and substitution. This reflects a 1.1% fall in Scotland in contrast to a 2.4% increase in EW. The reduction in total alcohol sales in Scotland was driven by reduced sales of beer, spirits, cider and perry. The reduction in total sales was due to reductions in sales of alcohol through the off-trade. There was no evidence of any change in on-trade alcohol sales. CONCLUSION: Minimum unit pricing has been effective in reducing population-level alcohol sales in Scotland in the 3 years since implementation.

An observational analysis of frailty in combination with loneliness or social isolation and their association with socioeconomic deprivation, hospitalisation and mortality among UK Biobank participants.

Frailty, social isolation, and loneliness have individually been associated with adverse health outcomes. This study examines how frailty in combination with loneliness or social isolation is associated with socioeconomic deprivation and with all-cause mortality and hospitalisation rate in a middle-aged and older population. Baseline data from 461,047 UK Biobank participants (aged 37-73) were used to assess frailty (frailty phenotype), social isolation, and loneliness. Weibull models assessed the association between frailty in combination with loneliness or social isolation and all-cause mortality adjusted for age/sex/smoking/alcohol/socioeconomic-status and number of long-term conditions. Negative binomial regression models assessed hospitalisation rate. Frailty prevalence was 3.38%, loneliness 4.75% and social isolation 9.04%. Frailty was present across all ages and increased with age. Loneliness and social isolation were more common in younger participants compared to older. Co-occurrence of frailty and loneliness or social isolation was most common in participants with high socioeconomic deprivation. Frailty was associated with increased mortality and hospitalisation regardless of social isolation/loneliness. Hazard ratios for mortality were 2.47 (2.27-2.69) with social isolation and 2.17 (2.05-2.29) without social isolation, 2.14 (1.92-2.38) with loneliness and 2.16 (2.05-2.27) without loneliness. Loneliness and social isolation were associated with mortality and hospitalisation in robust participants, but this was attenuated in the context of frailty. Frailty and loneliness/social isolation affect individuals across a wide age spectrum and disproportionately co-occur in areas of high deprivation. All were associated with adverse outcomes, but the association between loneliness and social isolation and adverse outcomes was attenuated in the context of frailty. Future interventions should target people living with frailty or loneliness/social isolation, regardless of age.

The effect of a minimum price per unit of alcohol in Scotland on alcohol-related ambulance call-outs: A controlled interrupted time-series analysis.

BACKGROUND AND AIMS: On 1 May 2018, Scotland introduced a minimum unit price (MUP) of £0.50 for alcohol, with one UK unit of alcohol being 10 ml of pure ethanol. This study measured the association between MUP and changes in the volume of alcohol-related ambulance call-outs in the overall population and in call-outs subsets (night-time call-outs and subpopulations with higher incidence of alcohol-related harm). DESIGN: An interrupted time-series (ITS) was used to measure variations in the daily volume of alcohol-related call-outs. We performed uncontrolled ITS on both the intervention and control group and a controlled ITS built on the difference between the two series. Data were from electronic patient clinical records from the Scottish Ambulance Service. SETTING AND CASES: Alcohol-related ambulance call-outs (intervention group) and total ambulance call-outs for people aged under 13 years (control group) in Scotland, from December 2017 to March 2020. MEASUREMENTS: Call-outs were deemed alcohol-related if ambulance clinicians indicated that alcohol was a 'contributing factor' in the call-out and/or a validated Scottish Ambulance Service algorithm determined that the call-out was alcohol-related. FINDINGS: No statistically significant association in the volume of call-outs was found in both the uncontrolled series [step change = 0.062, 95% confidence interval (CI) = -0.012, 0.0135 P = 0.091; slope change = -0.001, 95% CI = -0.001, 0.1 × 10-3 P = 0.139] and controlled series (step change = -0.01, 95% CI = -0.317, 0.298 P = 0.951; slope change = -0.003, 95% CI = -0.008, 0.002 P = 0.257). Similarly, no significant changes were found for the night-time series or for any population subgroups. CONCLUSIONS: There appears to be no statistically significant association between the introduction of minimum unit pricing for alcohol in Scotland and the volume of alcohol-related ambulance call-outs. This was observed overall, across subpopulations and at night-time.

Evaluating the impact of minimum unit pricing for alcohol on road traffic accidents in Scotland after 20 months: An interrupted time series study.

BACKGROUND AND AIMS: On 1 May 2018, Scotland implemented Minimum Unit Pricing (MUP) of £0.50 per unit of alcohol with the aim to lower alcohol consumption and related harms, and reduce health inequalities. We measured the impact of MUP on the most likely categories of road traffic accidents (RTAs) to be affected by drink-driving episodes (fatal and nighttime) up to 20 months after the policy implementation. Further, we checked whether any association varied by level of socio-economic deprivation. METHODS: An interrupted time series design was used to evaluate the impact of MUP on fatal and nighttime RTAs in Scotland and any effect modification across socio-economic deprivation groups. RTAs in England and Wales (E&W) were used as a comparator. Covariates representing severe weather events, bank holidays, seasonal and underlying trends were adjusted for. RESULTS: In Scotland, MUP implementation was associated with 40.5% (95% confidence interval: 15.5%, 65.4%) and 11.4% (-1.1%, 24.0%) increases in fatal and nighttime RTAs, respectively. There was no evidence of differential impacts of MUP by level of socio-economic deprivation. While we found a substantial increase in fatal RTAs associated with MUP, null effects observed in nighttime RTAs and high uncertainty in sensitivity analyses suggest caution be applied before attributing causation to this association. CONCLUSION: There is no evidence of an association between the introduction of minimum unit pricing for alcohol in Scotland and a reduction in fatal and nighttime road traffic accidents, these being outcome measure categories that are proxies of outcomes that directly relate alcohol consumption to road traffic accidents.

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.

Transportability of two heart failure trials to a disease registry using individual patient data.

OBJECTIVES: Randomized controlled trials are the gold-standard for determining therapeutic efficacy, but are often unrepresentative of real-world settings. Statistical transportation methods (hereafter transportation) can partially account for these differences, improving trial applicability without breaking randomization. We transported treatment effects from two heart failure (HF) trials to a HF registry. STUDY DESIGN AND SETTING: Individual-patient-level data from two trials (Carvedilol or Metoprolol European Trial (COMET), comparing carvedilol and metoprolol, and digitalis investigation group trial (DIG), comparing digoxin and placebo) and a Scottish HF registry were obtained. The primary end point for both trials was all-cause mortality; composite outcomes were all-cause mortality or hospitalization for COMET and HF-related death or hospitalization for DIG. We performed transportation using regression-based and inverse odds of sampling weights (IOSW) approaches. RESULTS: Registry patients were older, had poorer renal function and received higher-doses of loop-diuretics than trial participants. For each trial, point estimates were similar for the original and IOSW (e.g., DIG composite outcome: OR 0.75 (0.69, 0.82) vs. 0.73 (0.64, 0.83)). Treatment effect estimates were also similar when examining high-risk (0.64 (0.46, 0.89)) and low-risk registry patients (0.73 (0.61, 0.86)). Similar results were obtained using regression-based transportation. CONCLUSION: Regression-based or IOSW approaches can be used to transport trial effect estimates to patients administrative/registry data, with only moderate reductions in precision.

Treatment effect modification due to comorbidity: Individual participant data meta-analyses of 120 randomised controlled trials.

BACKGROUND: People with comorbidities are underrepresented in clinical trials. Empirical estimates of treatment effect modification by comorbidity are lacking, leading to uncertainty in treatment recommendations. We aimed to produce estimates of treatment effect modification by comorbidity using individual participant data (IPD). METHODS AND FINDINGS: We obtained IPD for 120 industry-sponsored phase 3/4 trials across 22 index conditions (n = 128,331). Trials had to be registered between 1990 and 2017 and have recruited ≥300 people. Included trials were multicentre and international. For each index condition, we analysed the outcome most frequently reported in the included trials. We performed a two-stage IPD meta-analysis to estimate modification of treatment effect by comorbidity. First, for each trial, we modelled the interaction between comorbidity and treatment arm adjusted for age and sex. Second, for each treatment within each index condition, we meta-analysed the comorbidity-treatment interaction terms from each trial. We estimated the effect of comorbidity measured in 3 ways: (i) the number of comorbidities (in addition to the index condition); (ii) presence or absence of the 6 commonest comorbid diseases for each index condition; and (iii) using continuous markers of underlying conditions (e.g., estimated glomerular filtration rate (eGFR)). Treatment effects were modelled on the usual scale for the type of outcome (absolute scale for numerical outcomes, relative scale for binary outcomes). Mean age in the trials ranged from 37.1 (allergic rhinitis trials) to 73.0 (dementia trials) and percentage of male participants range from 4.4% (osteoporosis trials) to 100% (benign prostatic hypertrophy trials). The percentage of participants with 3 or more comorbidities ranged from 2.3% (allergic rhinitis trials) to 57% (systemic lupus erythematosus trials). We found no evidence of modification of treatment efficacy by comorbidity, for any of the 3 measures of comorbidity. This was the case for 20 conditions for which the outcome variable was continuous (e.g., change in glycosylated haemoglobin in diabetes) and for 3 conditions in which the outcomes were discrete events (e.g., number of headaches in migraine). Although all were null, estimates of treatment effect modification were more precise in some cases (e.g., sodium-glucose co-transporter-2 (SGLT2) inhibitors for type 2 diabetes-interaction term for comorbidity count 0.004, 95% CI -0.01 to 0.02) while for others credible intervals were wide (e.g., corticosteroids for asthma-interaction term -0.22, 95% CI -1.07 to 0.54). The main limitation is that these trials were not designed or powered to assess variation in treatment effect by comorbidity, and relatively few trial participants had >3 comorbidities. CONCLUSIONS: Assessments of treatment effect modification rarely consider comorbidity. Our findings demonstrate that for trials included in this analysis, there was no empirical evidence of treatment effect modification by comorbidity. The standard assumption used in evidence syntheses is that efficacy is constant across subgroups, although this is often criticised. Our findings suggest that for modest levels of comorbidities, this assumption is reasonable. Thus, trial efficacy findings can be combined with data on natural history and competing risks to assess the likely overall benefit of treatments in the context of comorbidity.

Frailty in randomised controlled trials for dementia or mild cognitive impairment measured via the frailty index: prevalence and prediction of serious adverse events and attrition.

BACKGROUND: Frailty and dementia have a bidirectional relationship. However, frailty is rarely reported in clinical trials for dementia and mild cognitive impairment (MCI) which limits assessment of trial applicability. This study aimed to use a frailty index (FI)-a cumulative deficit model of frailty-to measure frailty using individual participant data (IPD) from clinical trials for MCI and dementia. Moreover, the study aimed to quantify the prevalence of frailty and its association with serious adverse events (SAEs) and trial attrition. METHODS: We analysed IPD from dementia (n = 1) and MCI (n = 2) trials. An FI comprising physical deficits was created for each trial using baseline IPD. Poisson and logistic regression were used to examine associations with SAEs and attrition, respectively. Estimates were pooled in random effects meta-analysis. Analyses were repeated using an FI incorporating cognitive as well as physical deficits, and results compared. RESULTS: Frailty could be estimated in all trial participants. The mean physical FI was 0.14 (SD 0.06) and 0.14 (SD 0.06) in the MCI trials and 0.24 (SD 0.08) in the dementia trial. Frailty prevalence (FI > 0.24) was 6.9%/7.6% in MCI trials and 48.6% in the dementia trial. After including cognitive deficits, the prevalence was similar in MCI (6.1% and 6.7%) but higher in dementia (75.4%). The 99th percentile of FI (0.31 and 0.30 in MCI, 0.44 in dementia) was lower than in most general population studies. Frailty was associated with SAEs: physical FI IRR = 1.60 [1.40, 1.82]; physical/cognitive FI IRR = 1.64 [1.42, 1.88]. In a meta-analysis of all three trials, the estimated association between frailty and trial attrition included the null (physical FI OR = 1.17 [0.92, 1.48]; physical/cognitive FI OR = 1.16 [0.92, 1.46]), although higher frailty index values were associated with attrition in the dementia trial. CONCLUSION: Measuring frailty from baseline IPD in dementia and MCI trials is feasible. Those living with more severe frailty may be under-represented. Frailty is associated with SAEs. Including only physical deficits may underestimate frailty in dementia. Frailty can and should be measured in future and existing trials for dementia and MCI, and efforts should be made to facilitate inclusion of people living with frailty.

The Effect of Minimum Unit Pricing for Alcohol on Prescriptions for Treatment of Alcohol Dependence: A Controlled Interrupted Time Series Analysis.

In 2018, Scotland introduced a minimum unit price (MUP) for alcohol to reduce alcohol-related harms. We aimed to study the association between MUP introduction and the volume of prescriptions to treat alcohol dependence, and volume of new patients receiving such prescriptions. We also examined whether effects varied across different socio-economic groups. A controlled interrupted time series was used to examine variations of our two outcomes. The same prescriptions in England and prescriptions for methadone in Scotland were used as controls. There was no evidence of an association between MUP implementation and the volume of prescriptions for alcohol dependence (immediate change: 2.74%, 95% CI: -0.068 0.014; slope change: 0% 95%CI: -0.001 0.000). A small, significant increase in slope in number of new patients receiving prescriptions was observed (0.2% 95%CI: 0.001 0.003). However, no significant results were confirmed after robustness checks. We found also no variation across different socioeconomic groups. Supplementary Information: The online version contains supplementary material available at 10.1007/s11469-023-01070-6.

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.

Evaluating the impact of alcohol minimum unit pricing on deaths and hospitalisations in Scotland: a controlled interrupted time series study.

BACKGROUND: Since May 1, 2018, every alcoholic drink sold in Scotland has had minimum unit pricing (MUP) of £0·50 per unit. Previous studies have indicated that the introduction of this policy reduced alcohol sales by 3%. We aimed to assess whether this has led to reductions in alcohol-attributable deaths and hospitalisations. METHODS: Study outcomes, wholly attributable to alcohol consumption, were defined using routinely collected data on deaths and hospitalisations. Controlled interrupted time series regression was used to assess the legislation's impact in Scotland, and any effect modification across demographic and socioeconomic deprivation groups. The pre-intervention time series ran from Jan 1, 2012, to April 30, 2018, and for 32 months after the policy was implemented (until Dec 31, 2020). Data from England, a part of the UK where the intervention was not implemented, were used to form a control group. FINDINGS: MUP in Scotland was associated with a significant 13·4% reduction (95% CI -18·4 to -8·3; p=0·0004) in deaths wholly attributable to alcohol consumption. Hospitalisations wholly attributable to alcohol consumption decreased by 4·1% (-8·3 to 0·3; p=0·064). Effects were driven by significant improvements in chronic outcomes, particularly alcoholic liver disease. Furthermore, MUP legislation was associated with a reduction in deaths and hospitalisations wholly attributable to alcohol consumption in the four most socioeconomically deprived deciles in Scotland. INTERPRETATION: The implementation of MUP legislation was associated with significant reductions in deaths, and reductions in hospitalisations, wholly attributable to alcohol consumption. The greatest improvements were in the four most socioeconomically deprived deciles, indicating that the policy is positively tackling deprivation-based inequalities in alcohol-attributable health harm. FUNDING: Scottish Government.

Systematic review and meta-analysis of prevalence, trajectories, and clinical outcomes for frailty in COPD.

This systematic review synthesised measurement and prevalence of frailty in COPD and associations between frailty and adverse health outcomes. We searched Medline, Embase and Web of Science (1 January 2001-8 September 2021) for observational studies in adults with COPD assessing frailty prevalence, trajectories, or association with health-related outcomes. We performed narrative synthesis and random-effects meta-analyses. We found 53 eligible studies using 11 different frailty measures. Most common were frailty phenotype (n = 32), frailty index (n = 5) and Kihon checklist (n = 4). Prevalence estimates varied by frailty definitions, setting, and age (2.6-80.9%). Frailty was associated with mortality (5/7 studies), COPD exacerbation (7/11), hospitalisation (3/4), airflow obstruction (11/14), dyspnoea (15/16), COPD severity (10/12), poorer quality of life (3/4) and disability (1/1). In conclusion, frailty is a common among people with COPD and associated with increased risk of adverse outcomes. Proactive identification of frailty may aid risk stratification and identify candidates for targeted intervention.

Prevalence, measurement, and implications of frailty in stroke survivors: An analysis of three global aging cohorts.

BACKGROUND: Our understanding of the relationship between frailty and stroke, beyond the acute phase of stroke, is limited. We aimed to estimate the prevalence of frailty in stroke survivors using differing methods of assessment and describe relationships with stroke outcomes. METHODS: We used data from three international population surveys (American Health and Retirement Survey/English Longitudinal Study of Ageing/Survey for Health and Retirement in Europe) of aging. Frailty status was assessed using the Fried frailty phenotype, a 40-item frailty index (FI) and the clinical frailty scale (CFS). We created estimates of frailty prevalence and assessed association of frailty with outcomes of mortality/hospital admission/recurrent stroke at 2 years follow-up using logistic regression models adjusted for age/sex. Additional analyses explored effects of adding cognitive measures to frailty assessments and of missing grip strength data. FINDINGS: Across 9617 stroke survivors, using the frailty phenotype, 23.8% (n = 2094) identified as frail; with CFS, 30.1% (n = 2906) were moderately or severely frail; using FI, 22.7% (n = 2147) had moderate frailty and 31.9% (n = 3021) had severe frailty. Frailty was associated with increased risk of mortality/hospitalization/recurrent stroke using all three measures. Adding cognitive variables to the FI produced minimal difference in prevalence of frailty. People with physical frailty (phenotype or CFS) plus cognitive impairment had a greater risk of mortality than people with an equivalent level of frailty but no cognitive impairment. Excluding people unable to provide grip strength underestimated frailty prevalence. INTERPRETATION: Frailty is common in stroke and associated with poor outcomes, regardless of measure used. Adding cognitive variables to frailty phenotype/CFS measures identified those at greater risk of poor outcomes. Physical and cognitive impairments in stroke survivors do not preclude frailty assessment.

Comorbidity and health-related quality of life in people with a chronic medical condition in randomised clinical trials: An individual participant data meta-analysis.

BACKGROUND: Health-related quality of life metrics evaluate treatments in ways that matter to patients, so are often included in randomised clinical trials (hereafter trials). Multimorbidity, where individuals have 2 or more conditions, is negatively associated with quality of life. However, whether multimorbidity predicts change over time or modifies treatment effects for quality of life is unknown. Therefore, clinicians and guideline developers are uncertain about the applicability of trial findings to people with multimorbidity. We examined whether comorbidity count (higher counts indicating greater multimorbidity) (i) is associated with quality of life at baseline; (ii) predicts change in quality of life over time; and/or (iii) modifies treatment effects on quality of life. METHODS AND FINDINGS: Included trials were registered on the United States trials registry for selected index medical conditions and drug classes, phase 2/3, 3 or 4, had ≥300 participants, a nonrestrictive upper age limit, and were available on 1 of 2 trial repositories on 21 November 2016 and 18 May 2018, respectively. Of 124 meeting these criteria, 56 trials (33,421 participants, 16 index conditions, and 23 drug classes) collected a generic quality of life outcome measure (35 EuroQol-5 dimension (EQ-5D), 31 36-item short form survey (SF-36) with 10 collecting both). Blinding and completeness of follow up were examined for each trial. Using trials where individual participant data (IPD) was available from 2 repositories, a comorbidity count was calculated from medical history and/or prescriptions data. Linear regressions were fitted for the association between comorbidity count and (i) quality of life at baseline; (ii) change in quality of life during trial follow up; and (iii) treatment effects on quality of life. These results were then combined in Bayesian linear models. Posterior samples were summarised via the mean, 2.5th and 97.5th percentiles as credible intervals (95% CI) and via the proportion with values less than 0 as the probability (PBayes) of a negative association. All results are in standardised units (obtained by dividing the EQ-5D/SF-36 estimates by published population standard deviations). Per additional comorbidity, adjusting for age and sex, across all index conditions and treatment comparisons, comorbidity count was associated with lower quality of life at baseline and with a decline in quality of life over time (EQ-5D -0.02 [95% CI -0.03 to -0.01], PBayes > 0.999). Associations were similar, but with wider 95% CIs crossing the null for SF-36-PCS and SF-36-MCS (-0.05 [-0.10 to 0.01], PBayes = 0.956 and -0.05 [-0.10 to 0.01], PBayes = 0.966, respectively). Importantly, there was no evidence of any interaction between comorbidity count and treatment efficacy for either EQ-5D or SF-36 (EQ-5D -0.0035 [95% CI -0.0153 to -0.0065], PBayes = 0.746; SF-36-MCS (-0.0111 [95% CI -0.0647 to 0.0416], PBayes = 0.70 and SF-36-PCS -0.0092 [95% CI -0.0758 to 0.0476], PBayes = 0.631. CONCLUSIONS: Treatment effects on quality of life did not differ by multimorbidity (measured via a comorbidity count) at baseline-for the medical conditions studied, types and severity of comorbidities and level of quality of life at baseline, suggesting that evidence from clinical trials is likely to be applicable to settings with (at least modestly) higher levels of comorbidity. TRIAL REGISTRATION: A prespecified protocol was registered on PROSPERO (CRD42018048202).