Eilidh Fletcher

Background Health inequalities in Scotland are well documented, including the contribution of different causes to inequalities in mortality. Our aim was to estimate inequalities within a burden of disease framework, accounting for both premature mortality and the effects of morbidity, to understand the contribution of specific diseases to health inequalities prior to the COVID-19 pandemic. Methods Disability Adjusted Life Years (DALYs) for 70 individual causes of disease and injury were sourced from the Scottish Burden of Disease Study. Area-level deprivation was measured using the Scottish Index of Multiple Deprivation. Inequalities were measured by the range, Relative Index of Inequality, Slope Index of Inequality, and attributable DALYs were estimated by using the least deprived decile as a reference. Results The overall disease burden was double that in the most deprived areas (50,305 vs 20,955 DALYS per 100, 000), largely driven by inequalities in premature mortality. The rate in the most deprived decile was around 48% higher than the mean population rate (RII = 0.96), with 35% of DALYs attributed to differences in area-based deprivation. Many leading causes of disease burden in 2019 – heart disease, drug use disorders, lung cancer and COPD – were also the leading drivers of absolute and relative inequalities in the disease burden. Conclusion Our study evidences the extent of the stark levels of absolute and relative inequality prior to the COVID-19 pandemic. Given pre-pandemic stalling of mortality trend improvements and widening health inequalities, and the exacerbation of these caused by COVID-19, urgent policy attention is required to address this.

Urinary tract infections (UTIs) are common. Antibiotic treatment is usually empirical, with the risk of under-treatment of resistant infections. To characterize risk factors for antibiotic-resistant community urine isolates using routine record-linked health data. Within the NHS Scotland Infection Intelligence Platform, national surveillance patient-level data on community urine isolates (January 2012–June 2015) were linked to hospital activity and community prescribing data. Associations between age, gender, comorbidity, care home residence, previous hospitalizations, antibiotic exposure and resistant (any antibiotic) or MDR (≥1 antibiotic from ≥3 categories) urinary isolates were quantified using multivariable logistic regression. Of 40984 isolates, 28% were susceptible, 45% were resistant and 27% were MDR. Exposure to ≥ 4 different antibiotics in the prior 6 months increased MDR risk (OR 6.81, 95% CI 5.73–8.11). MDR was associated with ≥29 DDD cumulative exposure, in the prior 6 months, for any antibiotic (OR 6.54, 95% CI 5.88–7.27), nitrofurantoin (OR 8.56, 95% CI 6.56–11.18) and trimethoprim (OR 14.61, 95% CI 10.53–20.27). Associations persisted for 10–12 months for nitrofurantoin (OR 2.31, 95% CI 1.93–2.76) and trimethoprim (OR 1.81, 95% CI 1.57–2.09). Increasing age, comorbidity, previous hospitalization and care home residence were independently associated with MDR. For resistant isolates the factors were the same, but with weaker associations. To our knowledge, we have demonstrated, using national capability at scale, the risk of MDR in community urine isolates for the first time and quantified the cumulative and sustained impact of antibiotic exposure. These data will inform the development of decision support tools for UTI treatment.

We investigate parameter heterogeneity in breast cancer 1‐year cumulative hospital costs across five European countries as part of the EuroHOPE project. The paper aims to explore whether conditional mean effects provide a suitable representation of the national variation in hospital costs. A cohort of patients with a primary diagnosis of invasive breast cancer (ICD‐9 codes 174 and ICD‐10 C50 codes) is derived using routinely collected individual breast cancer data from Finland, the metropolitan area of Turin (Italy), Norway, Scotland and Sweden. Conditional mean effects are estimated by ordinary least squares for each country, and quantile regressions are used to explore heterogeneity across the conditional quantile distribution. Point estimates based on conditional mean effects provide a good approximation of treatment response for some key demographic and diagnostic specific variables (e.g. age and ICD‐10 diagnosis) across the conditional quantile distribution. For many policy variables of interest, however, there is considerable evidence of parameter heterogeneity that is concealed if decisions are based solely on conditional mean results. The use of quantile regression methods reinforce the need to consider beyond an average effect given the greater recognition that breast cancer is a complex disease reflecting patient heterogeneity. © 2015 The Authors. Health Economics Published by John Wiley & Sons Ltd.