Predictors of admission and readmission to hospital for major depression: A community cohort study of 52,990 individuals.

Background Our current knowledge about predictors of admission and re-admission to hospital as a result of major depressive disorder (MDD) is limited. Here we present a descriptive analysis of factors which are associated with MDD hospitalisations within a large population cohort. Methods We linked participants of the Scottish Health Survey (SHS) to historical and prospective hospital admission data. We combined information from the SHS baseline interview and historical hospitalisations to define a range of exposure variables. The main outcomes of interest were: (1) first time admission for MDD occurring after the SHS interview; and (2) readmission for MDD. We used Cox regression to determine the association between each predictor and each outcome, after adjusting for age, gender and deprivation quintile. Results 52,990 adult SHS participants were included. During a median follow-up of 4.5 years per participant, we observed 530 first-time admissions for MDD. A relatively wide range of factors – encompassing social, individual health status, and lifestyle-related exposures – were associated with this outcome (p&#60;0.05). Among the 530 participants exhibiting a de novo admission for MDD during follow-up, 118 were later re-admitted. Only older age (over 70) and a prior non-depression related psychiatric admission were associated with readmission for MDD. Limtations MDD was defined using records of International Classification of Disease hospital discharge codes rather than formal diagnostic assessments. Conclusion These findings have implications for mental health service organisation and delivery and should stimulate future research on predictive factors for admission and readmission in MDD.</p

Hospital expenditure at the end-of-life: what are the impacts of health status and health risks?

Background: It is important for health policy and expenditure projections to understand the relationship between age, death and expenditure on health care (HC). Research has shown that older age groups incur lower hospital costs than previously anticipated and that remaining time to death (TTD) was a much stronger indicator for expenditure than age. How health behaviour or risk factors impact on HC utilisation and costs at the end of life is relatively unknown. Smoking and Body Mass Index (BMI) have featured most prominently and mixed findings exist as to the exact nature of this association.&lt;p&gt;&lt;/p&gt; Methods: This paper considers the relationship between TTD, age and expenditure for inpatient care in the last 12 quarters of life; and introduces measures of health status and risks. A longitudinal dataset covering 35 years is utilised, including baseline survey data linked to hospital and death records. The effect of age, TTD and health indicators on expenditure for inpatient care is estimated using a two-part model.&lt;p&gt;&lt;/p&gt; Results: As individuals approach death costs increase. This effect is highly significant (p&lt;0.01) from the last until the 8th quarter before death and influenced by age. Statistically significant effects on costs were found for: smoking status, systolic blood pressure and lung function (FEV1). On average, smokers incurred lower quarterly costs in their last 12 quarters of life than non-smokers (~7%). Participants’ BMI at baseline did show a negative association with probability of HC utilisation however this effect disappeared when costs were estimated.&lt;p&gt;&lt;/p&gt; Conclusions: Health risk measures obtained at baseline provide a good indication of individuals’ probability of needing medical attention later in life and incurring costs, despite the small size of the effect. Utilising a linked dataset, where such measures are available can add substantially to our ability to explain the relationship between TTD and costs.&lt;p&gt;&lt;/p&gt

Cost-effectiveness analysis in R using a multi-state modelling survival analysis framework: a tutorial

This tutorial provides a step-by-step guide to performing cost-effectiveness analysis using a multi-state modelling approach. Alongside the tutorial we provide easy-to-use functions in the statistics package R. We argue this multi-state modelling approach using a package such as R has advantages over approaches where models are built in a spreadsheet package. In particular, using a syntax-based approach means there is a written record of what was done and the calculations are transparent. Reproducing the analysis is straightforward as the syntax just needs to be run again. The approach can be thought of as an alternative way to build a Markov decision analytic model, which also has the option to use a state-arrival extended approach if the Markov property does not hold. In the state-arrival extended multi-state model a covariate that represents patients’ history is included allowing the Markov property to be tested. We illustrate the building of multi-state survival models, making predictions from the models and assessing fits. We then proceed to perform a cost-effectiveness analysis including deterministic and probabilistic sensitivity analyses. Finally, we show how to create two common methods of visualising the results, namely cost-effectiveness planes and cost-effectiveness acceptability curves. The analysis is implemented entirely within R. It is based on adaptions to functions in the existing R package mstate, to accommodate parametric multi-state modelling which facilitates extrapolation of survival curves

Do patients who die from an alcohol-related condition ‘drift’ into areas of greater deprivation? Alcohol-related mortality and health selection theory in Scotland

Background: Health selection has been proposed to explain the patterning of alcohol-related mortality by area deprivation. This study investigated whether persons who die from alcohol-related conditions are more likely to experience social drift than those who die from other causes. Methods: Deaths recorded in Scotland (2013, &gt;21 years) were coded as ‘alcohol-related’ or ‘other’ and by deprivation decile of residence at death. Acute hospital admissions data from 1996 to 2012 were used to provide premortality deprivation data. χ² tests estimated the difference between observed and expected alcohol-related deaths by first Scottish Index of Multiple Deprivation (SIMD) decile and type of death. Logistic regression models were fitted using type of death as the outcome of interest and change in SIMD decile as the exposure of interest. Results: Of 47 012 deaths, 1458 were alcohol-related. Upward and downward mobility was observed for both types of death. An estimated 31 more deaths than expected were classified ‘alcohol-related’ among cases whose deprivation score decreased, while 204 more deaths than expected were classified ‘alcohol-related’ among cases whose initial deprivation ranking was in the four most deprived deciles. Becoming more deprived and first deprivation category were both associated with increased odds of type of death being alcohol-related after adjusting for confounders. Conclusion: This study suggests that health selection appears to contribute less to the deprivation gradient in alcohol-related mortality in Scotland than an individual’s initial area deprivation category

Evaluating the impact of the Alcohol Act on off-trade alcohol sales: a natural experiment in Scotland

&lt;b&gt;Background and aims&lt;/b&gt; A ban on multi-buy discounts of off-trade alcohol was introduced as part of the Alcohol Act in Scotland in October 2011. The aim of this study was to assess the impact of this legislation on alcohol sales, which provide the best indicator of population consumption.&lt;p&gt;&lt;/p&gt; &lt;b&gt;Design Setting and Participants&lt;/b&gt; Interrupted time-series regression was used to assess the impact of the Alcohol Act on alcohol sales among off-trade retailers in Scotland. Models accounted for underlying seasonal and secular trends and were adjusted for disposable income, alcohol prices and substitution effects. Data for off-trade retailers in England and Wales combined (EW) provided a control group.&lt;p&gt;&lt;/p&gt; &lt;b&gt;Measurements&lt;/b&gt; Weekly data on the volume of pure alcohol sold by off-trade retailers in Scotland and EW between January 2009 and September 2012.&lt;p&gt;&lt;/p&gt; &lt;b&gt;Findings&lt;/b&gt; The introduction of the legislation was associated with a 2.6% (95% CI -5.3 to 0.2%, P = 0.07) decrease in off-trade alcohol sales in Scotland, but not in EW (-0.5%, -4.6 to 3.9%, P = 0.83). A statistically significant reduction was observed in Scotland when EW sales were adjusted for in the analysis (-1.7%, -3.1 to -0.3%, P = 0.02). The decline in Scotland was driven by reduced off-trade sales of wine (-4.0%, -5.4 to -2.6%, P &#60; 0.001) and pre-mixed beverages (-8.5%, -12.7 to -4.1%, P &#60; 0.001). There were no associated changes in other drink types in Scotland, or in sales of any drink type in EW.&lt;p&gt;&lt;/p&gt; &lt;b&gt;Conclusions&lt;/b&gt; The introduction of the Alcohol Act in Scotland in 2011 was associated with a decrease in total off-trade alcohol sales in Scotland, largely driven by reduced off-trade wine sales

Synthetic control methodology as a tool for evaluating population-level health interventions

Background: Many public health interventions cannot be evaluated using randomised controlled trials so they rely on the assessment of observational data. Techniques for evaluating public health interventions using observational data include interrupted time series analysis, panel data regression-based approaches, regression discontinuity and instrumental variable approaches. The inclusion of a counterfactual improves causal inference for approaches based on time series analysis, but the selection of a suitable counterfactual or control area can be problematic. The synthetic control method builds a counterfactual using a weighted combination of potential control units. Methods: We explain the synthetic control method, summarise its use in health research to date, set out its advantages, assumptions and limitations and describe its implementation through a case study of life expectancy following German reunification. Results: Advantages of the synthetic control method are that it offers an approach suitable when there is a small number of treated units and control units and it does not rely on parallel preimplementation trends like difference in difference methods. The credibility of the result relies on achieving a good preimplementation fit for the outcome of interest between treated unit and synthetic control. If a good preimplementation fit is established over an extended period of time, a discrepancy in the outcome variable following the intervention can be interpreted as an intervention effect. It is critical that the synthetic control is built from a pool of potential controls that are similar to the treated unit. There is currently no consensus on what constitutes a ‘good fit’ or how to judge similarity. Traditional statistical inference is not appropriate with this approach, although alternatives are available. From our review, we noted that the synthetic control method has been underused in public health. Conclusions: Synthetic control methods are a valuable addition to the range of approaches for evaluating public health interventions when randomisation is impractical. They deserve to be more widely applied, ideally in combination with other methods so that the dependence of findings on particular assumptions can be assessed

Effects of vildagliptin on ventricular function in patients with type 2 diabetes mellitus and heart failure: a randomized placebo-controlled trial

Objectives: This study sought to examine the safety of the dipeptidyl peptidase-4 inhibitor, vildagliptin, in patients with heart failure and reduced ejection fraction. Background: Many patients with type 2 diabetes mellitus have heart failure and it is important to know about the safety of new treatments for diabetes in these individuals. Methods: Patients 18 to 85 years of age with type 2 diabetes and heart failure (New York Heart Association functional class I to III and left ventricular ejection fraction [LVEF] &lt;0.40) were randomized to 52 weeks treatment with vildagliptin 50 mg twice daily (50 mg once daily if treated with a sulfonylurea) or matching placebo. The primary endpoint was between-treatment change from baseline in echocardiographic LVEF using a noninferiority margin of −3.5%. Results: A total of 254 patients were randomly assigned to vildagliptin (n = 128) or placebo (n = 126). Baseline LVEF was 30.6 ± 6.8% in the vildagliptin group and 29.6 ± 7.7% in the placebo group. The adjusted mean change in LVEF was 4.95 ± 1.25% in vildagliptin treated patients and 4.33 ± 1.23% in placebo treated patients, a difference of 0.62 (95% confidence interval [CI]: −2.21 to 3.44; p = 0.667). This difference met the predefined noninferiority margin of −3.5%. Left ventricular end-diastolic and end-systolic volumes increased more in the vildagliptin group by 17.1 ml (95% CI: 4.6 to 29.5 ml; p = 0.007) and 9.4 ml (95% CI: −0.49 to 19.4 ml; p = 0.062), respectively. Decrease in hemoglobin A1c from baseline to 16 weeks, the main secondary endpoint, was greater in the vildagliptin group: −0.62% (95% CI: −0.93 to −0.30%; p &lt; 0.001; −6.8 mmol/mol; 95% CI: −10.2 to −3.3 mmol/mol). Conclusions: Compared with placebo, vildagliptin had no major effect on LVEF but did lead to an increase in left ventricular volumes, the cause and clinical significance of which is unknown. More evidence is needed regarding the safety of dipeptidyl peptidase-4 inhibitors in patients with heart failure and left ventricular systolic dysfunction. (Effect of Vildagliptin on Left Ventricular Function in Patients With Type 2 Diabetes and Congestive Heart Failure; NCT00894868

Do age, period or cohort effects explain circulatory disease mortality trends, Scotland 1974-2015?

Objective: We aimed to explore whether age, period or cohort effects explain the trends and inequalities in ischaemic heart disease (IHD) and cerebrovascular disease (CeVD) mortality in Scotland. Methods: We analysed IHD and CeVD deaths for 1974–2015 by sex, age and area deprivation, visually explored the data using heatmaps and dotplots and built regression models. Results: CeVD mortality improved steadily over time while IHD mortality improved more rapidly from the late 1980s. Age effects were evident; both outcomes showed an exponential relationship with age for all except males for IHD in the 1980s and 1990s. The mortality profiles by age became older, although improvement was slower for those aged &lt;50 years for IHD, especially for males, and faster for CeVD in females aged &lt;65 years. Rates were higher, and inequalities greater, among males, especially for IHD. For IHD, increased risk for males over females reduced with age (incidence rate ratio for 41–50 year old males=4.28 (95% CI 4.12 to 4.44) and 1.17 (95% CI 1.16 to 1.18) for 71–80 year olds). Inequalities in IHD mortality by area deprivation persisted over time, increasing from around 10% to around 25% higher risk in the most deprived areas between 1974 and 1986 before declining in absolute terms from around 2000. Inequalities for CeVD increased after the late 1980s. Conclusions: IHD and CeVD mortality in Scotland exhibit age but not recent distinct period or cohort effects. The improvements in mortality rates have been more sustained for CeVD and inequalities greater for IHD

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

Evidence synthesis for constructing directed acyclic graphs (ESC-DAGs): a novel and systematic method for building directed acyclic graphs

Background: Directed acyclic graphs (DAGs) are popular tools for identifying appropriate adjustment strategies for epidemiological analysis. However, a lack of direction on how to build them is problematic. As a solution, we propose using a combination of evidence synthesis strategies and causal inference principles to integrate the DAG-building exercise within the review stages of research projects. We demonstrate this idea by introducing a novel protocol: ‘Evidence Synthesis for Constructing Directed Acyclic Graphs’ (ESC-DAGs)’.\ud Methods: ESC-DAGs operates on empirical studies identified by a literature search, ideally a novel systematic review or review of systematic reviews. It involves three key stages: (i) the conclusions of each study are ‘mapped’ into a DAG; (ii) the causal structures in these DAGs are systematically assessed using several causal inference principles and are corrected accordingly; (iii) the resulting DAGs are then synthesised into one or more ‘integrated DAGs’. This demonstration article didactically applies ESC-DAGs to the literature on parental influences on offspring alcohol use during adolescence. Conclusions: ESC-DAGs is a practical, systematic and transparent approach for developing DAGs from background knowledge. These DAGs can then direct primary data analysis and DAG-based sensitivity analysis. ESC-DAGs has a modular design to allow researchers who are experienced DAG users to both use and improve upon the approach. It is also accessible to researchers with limited experience of DAGs or evidence synthesis