Estimating quality-adjusted life expectancy (QALE) for local authorities in Great Britain and its association with indicators of the inclusive economy: a cross-sectional study.

OBJECTIVES: Quantifying area-level inequalities in population health can help to inform policy responses. We describe an approach for estimating quality-adjusted life expectancy (QALE), a comprehensive health expectancy measure, for local authorities (LAs) in Great Britain (GB). To identify potential factors accounting for LA-level QALE inequalities, we examined the association between inclusive economy indicators and QALE. SETTING: 361/363 LAs in GB (lower tier/district level) within the period 2018-2020. DATA AND METHODS: We estimated life tables for LAs using official statistics and utility scores from an area-level linkage of the Understanding Society survey. Using the Sullivan method, we estimated QALE at birth in years with corresponding 80% CIs. To examine the association between inclusive economy indicators and QALE, we used an open access data set operationalising the inclusive economy, created by the System Science in Public Health and Health Economics Research consortium. RESULTS: Population-weighted QALE estimates across LAs in GB were lowest in Scotland (females/males: 65.1 years/64.9 years) and Wales (65.0 years/65.2 years), while they were highest in England (67.5 years/67.6 years). The range across LAs for females was from 56.3 years (80% CI 45.6 to 67.1) in Mansfield to 77.7 years (80% CI 65.11 to 90.2) in Runnymede. QALE for males ranged from 57.5 years (80% CI 40.2 to 74.7) in Merthyr Tydfil to 77.2 years (80% CI 65.4 to 89.1) in Runnymede. Indicators of the inclusive economy accounted for more than half of the variation in QALE at the LA level (adjusted R2 females/males: 50%/57%). Although more inclusivity was generally associated with higher levels of QALE at the LA level, this association was not consistent across all 13 inclusive economy indicators. CONCLUSIONS: QALE can be estimated for LAs in GB, enabling further research into area-level health inequalities. The associations we identified between inclusive economy indicators and QALE highlight potential policy priorities for improving population health and reducing health inequalities.

Systems science methods in public health: what can they contribute to our understanding of and response to the cost-of-living crisis?

BACKGROUND: Many complex public health evidence gaps cannot be fully resolved using only conventional public health methods. We aim to familiarise public health researchers with selected systems science methods that may contribute to a better understanding of complex phenomena and lead to more impactful interventions. As a case study, we choose the current cost-of-living crisis, which affects disposable income as a key structural determinant of health. METHODS: We first outline the potential role of systems science methods for public health research more generally, then provide an overview of the complexity of the cost-of-living crisis as a specific case study. We propose how four systems science methods (soft systems, microsimulation, agent-based and system dynamics models) could be applied to provide more in-depth understanding. For each method, we illustrate its unique knowledge contributions, and set out one or more options for studies that could help inform policy and practice responses. RESULTS: Due to its fundamental impact on the determinants of health, while limiting resources for population-level interventions, the cost-of-living crisis presents a complex public health challenge. When confronted with complexity, non-linearity, feedback loops and adaptation processes, systems methods allow a deeper understanding and forecasting of the interactions and spill-over effects common with real-world interventions and policies. CONCLUSIONS: Systems science methods provide a rich methodological toolbox that complements our traditional public health methods. This toolbox may be particularly useful in early stages of the current cost-of-living crisis: for understanding the situation, developing solutions and sandboxing potential responses to improve population health.

Incidence and risk factors of anaesthesia-related perioperative cardiac arrest: A 6-year observational study from a tertiary care university hospital.

BACKGROUND: In recent decades, the incidences of anaesthesia-related perioperative mortality and adverse outcomes have decreased drastically. However, to date, data on perioperative cardiac arrest and risk factors of perioperative cardiac arrest from European countries are scarce. OBJECTIVES: To determine the incidences of perioperative cardiac arrest and rates of anaesthesia-related and anaesthesia-contributory cardiac arrest. Identification of pre-existing risk factors leading to perioperative cardiac arrest. DESIGN: Retrospective cohort study. SETTING: Department of Anaesthesiology and Intensive Care Medicine, University Hospital of Cologne, Germany. INTERVENTIONS: Perioperative critical incident reports between 2007 and 2012 were screened, and reports on cardiac arrest within 24 h postoperatively were identified. Cardiac arrests were classified as 'anaesthesia-related', 'anaesthesia-contributory' or 'anaesthesia-unrelated' by two reviewers independently. Univariate and multi-variate logistic regression analysis was used to identify risk factors associated with perioperative cardiac arrest. RESULTS: Analysis of 318 critical incidents from 169 500 anaesthetics revealed 99 perioperative cardiac arrests. This is an overall incidence of perioperative cardiac arrest of 5.8/10 000 anaesthetics [95% confidence interval (CI), 4.7 to 7.0]. The rate of anaesthesia-related cardiac arrest was 0.7/10 000 (95% CI, 0.3 to 1.1), and the rate of anaesthesia-contributory cardiac arrest was 1.7/10 000 (95% CI, 1.1 to 2.3). Most cardiac arrests related to anaesthesia were due to respiratory events. From the multi-variate analysis, American Society of Anesthesiologists physical status grade at least 3 [P = 0.007, odds ratio (OR) 2.59 (95% CI, 1.29 to 5.19)], emergency surgery [P < 0.001, OR 4.00 (95% CI, 2.15 to 7.54)] and pre-existing cardiomyopathy [P < 0.001, OR 17.48 (95% CI, 6.18 to 51.51)] emerged as predictors of cardiac arrest. CONCLUSION: These first available European data on perioperative cardiac arrest from a large unselected cohort indicate that the overall perioperative incidence of cardiac arrest at our institution was slightly lower than published in the literature, whereas rates of anaesthesia-related and anaesthesia-contributory cardiac arrest were comparable. Most cardiac arrests related to anaesthesia were due to respiratory events. American Society of Anesthesiologists physical status grade at least 3, emergency surgery and pre-existing cardiomyopathy appear to be relevant risk factors for cardiac arrest.