Objective To investigate the effect of opiate substitution treatment at the beginning and end of treatment and according to duration of treatment.Design Prospective cohort study.Setting UK General Practice Research DatabaseParticipants Primary care patients with a diagnosis of substance misuse prescribed methadone or buprenorphine during 1990-2005. 5577 patients with 267 003 prescriptions for opiate substitution treatment followed-up (17 732 years) until one year after the expiry of their last prescription, the date of death before this time had elapsed, or the date of transfer away from the practice.Main outcome measures Mortality rates and rate ratios comparing periods in and out of treatment adjusted for sex, age, calendar year, and comorbidity; standardised mortality ratios comparing opiate users’ mortality with general population mortality rates.Results Crude mortality rates were 0.7 per 100 person years on opiate substitution treatment and 1.3 per 100 person years off treatment; standardised mortality ratios were 5.3 (95% confidence interval 4.0 to 6.8) on treatment and 10.9 (9.0 to 13.1) off treatment. Men using opiates had approximately twice the risk of death of women (morality rate ratio 2.0, 1.4 to 2.9). In the first two weeks of opiate substitution treatment the crude mortality rate was 1.7 per 100 person years: 3.1 (1.5 to 6.6) times higher (after adjustment for sex, age group, calendar period, and comorbidity) than the rate during the rest of time on treatment. The crude mortality rate was 4.8 per 100 person years in weeks 1-2 after treatment stopped, 4.3 in weeks 3-4, and 0.95 during the rest of time off treatment: 9 (5.4 to 14.9), 8 (4.7 to 13.7), and 1.9 (1.3 to 2.8) times higher than the baseline risk of mortality during treatment. Opiate substitution treatment has a greater than 85% chance of reducing overall mortality among opiate users if the average duration approaches or exceeds 12 months.Conclusions Clinicians and patients should be aware of the increased mortality risk at the start of opiate substitution treatment and immediately after stopping treatment. Further research is needed to investigate the effect of average duration of opiate substitution treatment on drug related mortality.

Missing data are ubiquitous in medical research. Although there is increasing guidance on how to handle missing data, practice is changing slowly and misapprehensions abound, particularly in observational research. Importantly, the lack of transparency around methodological decisions is threatening the validity and reproducibility of modern research. We present a practical framework for handling and reporting the analysis of incomplete data in observational studies, which we illustrate using a case study from the Avon Longitudinal Study of Parents and Children. The framework consists of three steps: 1) Develop an analysis plan specifying the analysis model and how missing data are going to be addressed. An important consideration is whether a complete records' analysis is likely to be valid, whether multiple imputation or an alternative approach is likely to offer benefits and whether a sensitivity analysis regarding the missingness mechanism is required; 2) Examine the data, checking the methods outlined in the analysis plan are appropriate, and conduct the preplanned analysis; and 3) Report the results, including a description of the missing data, details on how the missing data were addressed, and the results from all analyses, interpreted in light of the missing data and the clinical relevance. This framework seeks to support researchers in thinking systematically about missing data and transparently reporting the potential effect on the study results, therefore increasing the confidence in and reproducibility of research findings.

BackgroundThe robustness of epidemiological research using routinely collected primary care electronic data to support policy and practice for common mental disorders (CMD) anxiety and depression would be greatly enhanced by appropriate validation of diagnostic codes and algorithms for data extraction. We aimed to create a robust research platform for CMD using population-based, routinely collected primary care electronic data.MethodsWe developed a set of Read code lists (diagnosis, symptoms, treatments) for the identification of anxiety and depression in the General Practice Database (GPD) within the Secure Anonymised Information Linkage Databank at Swansea University, and assessed 12 algorithms for Read codes to define cases according to various criteria. Annual incidence rates were calculated per 1000 person years at risk (PYAR) to assess recording practice for these CMD between January 1st 2000 and December 31st 2009. We anonymously linked the 2799 MHI-5 Caerphilly Health and Social Needs Survey (CHSNS) respondents aged 18 to 74 years to their routinely collected GP data in SAIL. We estimated the sensitivity, specificity and positive predictive value of the various algorithms using the MHI-5 as the gold standard.ResultsThe incidence of combined depression/anxiety diagnoses remained stable over the ten-year period in a population of over 500,000 but symptoms increased from 6.5 to 20.7 per 1000 PYAR. A ‘historical’ GP diagnosis for depression/anxiety currently treated plus a current diagnosis (treated or untreated) resulted in a specificity of 0.96, sensitivity 0.29 and PPV 0.76. Adding current symptom codes improved sensitivity (0.32) with a marginal effect on specificity (0.95) and PPV (0.74).ConclusionsWe have developed an algorithm with a high specificity and PPV of detecting cases of anxiety and depression from routine GP data that incorporates symptom codes to reflect GP coding behaviour. We have demonstrated that using diagnosis and current treatment alone to identify cases for depression and anxiety using routinely collected primary care data will miss a number of true cases given changes in GP recording behaviour. The Read code lists plus the developed algorithms will be applicable to other routinely collected primary care datasets, creating a platform for future e-cohort research into these conditions.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-016-0274-7) contains supplementary material, which is available to authorized users.

BackgroundThere is limited and conflicting evidence for associations between use of screen-based technology and anxiety and depression in young people. We examined associations between screen time measured at 16 years and anxiety and depression at 18.MethodsParticipants (n = 14,665; complete cases n = 1869) were from the Avon Longitudinal Study of Parents and Children, a UK-based prospective cohort study. We assessed associations between various types of screen time (watching television, using a computer, and texting, all measured via questionnaire at 16y), both on weekdays and at weekends, and anxiety and depression (measured via the Revised Clinical Interview Schedule at 18y). Using ordinal logistic regression, we adjusted for multiple confounders, particularly focussing on activities that might have been replaced by screen time (for example exercising or playing outdoors).ResultsMore time spent using a computer on weekdays was associated with a small increased risk of anxiety (OR for 1–2 h = 1.17, 95% CI: 1.01 to 1.35; OR for 3+ hours = 1.30, 95% CI: 1.10 to 1.55, both compared to < 1 h, p for linear trend = 0.003). We found a similar association between computer use at weekends and anxiety (OR for 1–2 h = 1.17, 95% CI: 0.94 to 1.46; OR for 3+ hours = 1.28, 95% CI: 1.03 to 1.48, p for linear trend = 0.03). Greater time spent using a computer on weekend days only was associated with a small increased risk in depression (OR for 1–2 h = 1.12, 95% CI: 0.93 to 1.35; OR for 3+ hours = 1.35, 95% CI: 1.10 to 1.65, p for linear trend = 0.003). Adjusting for time spent alone attenuated effects for anxiety but not depression. There was little evidence for associations with texting or watching television.ConclusionsWe found associations between increased screen time, particularly computer use, and a small increased risk of anxiety and depression. Time spent alone was found to attenuate some associations, and further research should explore this.Electronic supplementary materialThe online version of this article (10.1186/s12889-018-6321-9) contains supplementary material, which is available to authorized users.