Adjusted indices of multiple deprivation to enable comparisons within and between constituent countries of the UK including an illustration using mortality rates

OBJECTIVES: Social determinants can have a major impact on health and as a consequence substantial inequalities are seen between and within countries. The study of inequalities between countries relies on having accurate and consistent measures of deprivation across the country borders. However, in the UK most socioeconomic deprivation measures are not comparable between countries. We give a method of adjusting the Indices of Multiple Deprivation (IMD) for use across the UK, describe the deprivation of each UK country, and show the problems introduced by naïvely using country-specific deprivation measures in a UK-wide analysis of mortality rates. SETTING/PARTICIPANTS: 42 148 geographic areas covering the population of the UK. OUTCOME MEASURES: Adjusted IMD scores based on the income and employment domains of country-specific IMD scores, adjusting for the contribution of other domains. The mortality rate among people aged under 75 years standardised to the UK age structure was compared between country-specific and UK-adjusted IMD quintiles. RESULTS: Of the constituent countries of the UK, Northern Ireland was the most deprived with 37% of the population living in areas in the most deprived fifth of the UK, followed by Wales with 22% of the population living in the most deprived fifth of the UK. England and Scotland had similar levels of deprivation. Deprivation-specific mortality rates were similar in England and Wales. Northern Ireland had lower mortality rates than England for each deprivation group, with similar differences for each group. Scotland had higher mortality rates than England for each deprivation group, with larger differences for more deprived groups. CONCLUSIONS: Analyses of between-country and within-country inequalities by socioeconomic position should use consistent measures; failing to use consistent measures may give misleading results. The published adjusted IMD scores we describe allow consistent analysis across the UK

A retrospective cohort study assessing patient characteristics and the incidence of cardiovascular disease using linked routine primary and secondary care data

This is the final version. Available from the publisher via the DOI in this record.Objectives: Data linkage combines information from several clinical data sets. The authors examined whether coding inconsistencies for cardiovascular disease between components of linked data sets result in differences in apparent population characteristics. Design: Retrospective cohort study. Setting: Routine primary care data from 40 Scottish general practitioner (GP) surgeries linked to national hospital records. Participants: 240 846 patients, aged 20 years or older, registered at a GP surgery. Outcomes: Cases of myocardial infarction, ischaemic heart disease and stroke (cerebrovascular disease) were identified from GP and hospital records. Patient characteristics and incidence rates were assessed for all three clinical outcomes, based on GP, hospital, paired GP/hospital (similar diagnoses recorded simultaneously in both data sets) or pooled GP/hospital records (diagnosis recorded in either or both data sets). Results: For all three outcomes, the authors found evidence (p<0.05) of different characteristics when using different methods of case identification. Prescribing of cardiovascular medicines for ischaemic heart disease was greatest for cases identified using paired records (p≤0.013). For all conditions, 30-day case fatality rates were higher for cases identified using hospital compared with GP or paired data, most noticeably for myocardial infarction (hospital 20%, GP 4%, p=0.001). Incidence rates were highest using pooled GP/hospital data and lowest using paired data. Conclusions: Differences exist in patient characteristics and disease incidence for cardiovascular conditions, depending on the data source. This has implications for studies using routine clinical data

The Epidemiology of Multimorbidity in Primary Care

Background: Multimorbidity places a substantial burden on patients and the healthcare system but few contemporary data are available. Aim: To describe the epidemiology of multimorbidity in adults in England and quantify associations between multimorbidity and health service utilisation. Design: Retrospective cohort study Setting: A random sample of 403,985 adult patients (≥18 years) in England who were registered with a general practice on 1 January 2012 and included in the Clinical Practice Research Datalink. Methods: We defined multimorbidity as having two or more of 36 long-term conditions recorded in patients’ medical records and quantified associations between multimorbidity and health service utilisation (GP consultations, prescriptions, and hospitalisations) over four years. Results: 27.2% of patients were multimorbid. The most prevalent conditions were hypertension (18.2%), depression or anxiety (10.3%), and chronic pain (10.1%). Prevalence of multimorbidity was higher in females than males (30% vs. 24.4% respectively) and among those with lower socioeconomic status (33.8% in the most deprived quintile vs. 24.2% in the least deprived quintile). Physical-mental comorbidity contributed a much greater proportion of overall morbidity in both younger patients and those patients with lower socioeconomic status. Multimorbidity was strongly associated with health service utilisation. Multimorbid patients accounted for 53% of GP consultations, 79% of prescriptions, and 56% percent of hospital admissions. Conclusion: Multimorbidity is common, socially patterned, and associated with increased health service utilisation. These findings support the need to improve the quality and efficiency of health services providing care to multimorbid patients at the practice and national-level.This study received no specific funding. Kirsty Rhodes was supported by the UK Medical Research Council (grant number: U105260558)

Regression discontinuity analysis for pharmacovigilance:statin example reflected trial findings showing little evidence of harm

OBJECTIVES: The study aims to explore the use of regression discontinuity analysis (RDA) to examine effects of prescription of statins on total cholesterol and adverse outcomes (type 2 diabetes, rhabdomyolysis and myopathy, myalgia and myositis, liver disease, CVD, and mortality). STUDY DESIGN AND SETTING: We conducted a prospective cohort study using the Clinical Practice Research Datalink including patients with QRISK scores of 10 to 30 in 2010 to 2013 who were last followed-up in October 2016. Comparing patients with QRISK≥20 and QRISK<20, we explored RDA assumptions, provided proof of concept analyses (total cholesterol as outcome), and investigated the effect of statins prescription on adverse outcomes. RESULT: RDA confirmed statin prescription reduced total cholesterol (Mean difference (MD) -1.33 mmol/L, 95%Confidence Interval (CI) -1.93 to -0.73). RDA provided little evidence for adverse effects on diabetes, myalgia and myositis, liver disease, CVD, or mortality. The RDA analysis findings are similar to RCT results. Findings from non-RDA analysis agree with published observational studies. CONCLUSION: RDA can be used with large routine clinical datasets to provide evidence on effects of medications which are prescribed according to a threshold. Testable RDA assumptions were satisfied, but confidence intervals were wide, partly due to the low compliance with the prescribing threshold