Socio‐economic inequalities in amenable mortality rates are increasing across Europe, which is an affront to universal healthcare systems where the numbers of, and inequalities in, amenable deaths should be minimal and declining over time. However, the fundamental causes theory proposes that inequalities in health will be largest across preventable causes, where unequally distributed resources can be used to gain an advantage. Information on individual‐level inequalities that may better reflect the fundamental causes remains limited. We used the Scottish Longitudinal Study, with follow‐up to 2010 to examine trends in amenable mortality by a range of socio‐economic position measures. Large inequalities were found for all measures of socio‐economic position and were lowest for educational attainment, higher for social class and highest for social connection. To reduce inequalities, amenable mortality needs to be interpreted both as an indicator of healthcare quality and as a reflection of the unequal distribution of socio‐economic resources.

Background understanding care-home outbreaks of COVID-19 is a key public health priority in the ongoing pandemic to help protect vulnerable residents. Objective to describe all outbreaks of COVID-19 infection in Scottish care-homes for older people between 01/03/2020 to 31/03/2020, with follow-up to 30/06/2020. Design and setting national linked data cohort analysis of Scottish care-homes for older people. Methods data linkage was used to identify outbreaks of COVID-19 in care-homes. Care-home characteristics associated with the presence of an outbreak were examined using logistic regression. Size of outbreaks was modelled using negative binomial regression. Results 334 (41%) Scottish care-homes for older people experienced an outbreak, with heterogeneity in outbreak size (1–63 cases; median = 6) and duration (1–94 days; median = 31.5 days). Four distinct patterns of outbreak were identified: ‘Typical’ (38% of outbreaks, mean 11.2 cases and 48 days duration), Severe (11%, mean 29.7 cases and 60 days), Contained (37%, mean 3.5 cases and 13 days), and Late-onset (14%, mean 5.4 cases and 17 days). Risk of a COVID-19 outbreak increased with increasing care-home size (for ≥90 beds vs <20, adjusted OR = 55.4, 95%CI 15.0–251.7) and rising community prevalence (OR = 1.2 [1.0–1.4] per 100 cases/100,000 population increase). No routinely available care-home characteristic was associated with outbreak size. Conclusions reducing community prevalence of COVID-19 infection is essential to protect those living in care-homes. More systematic national data collection to understand care-home residents and the homes in which they live is a priority in ensuring we can respond more effectively in future.

Background Multimorbidity prevalence rates vary considerably depending on the conditions considered in the morbidity count, but there is no standardised approach to the number or selection of conditions to include. Methods and findings We conducted a cross-sectional study using English primary care data for 1,168,260 participants who were all people alive and permanently registered with 149 included general practices. Outcome measures of the study were prevalence estimates of multimorbidity (defined as ≥2 conditions) when varying the number and selection of conditions considered for 80 conditions. Included conditions featured in ≥1 of the 9 published lists of conditions examined in the study and/or phenotyping algorithms in the Health Data Research UK (HDR-UK) Phenotype Library. First, multimorbidity prevalence was calculated when considering the individually most common 2 conditions, 3 conditions, etc., up to 80 conditions. Second, prevalence was calculated using 9 condition-lists from published studies. Analyses were stratified by dependent variables age, socioeconomic position, and sex. Prevalence when only the 2 commonest conditions were considered was 4.6% (95% CI [4.6, 4.6] p < 0.001), rising to 29.5% (95% CI [29.5, 29.6] p < 0.001) considering the 10 commonest, 35.2% (95% CI [35.1, 35.3] p < 0.001) considering the 20 commonest, and 40.5% (95% CI [40.4, 40.6] p < 0.001) when considering all 80 conditions. The threshold number of conditions at which multimorbidity prevalence was >99% of that measured when considering all 80 conditions was 52 for the whole population but was lower in older people (29 in >80 years) and higher in younger people (71 in 0- to 9-year-olds). Nine published condition-lists were examined; these were either recommended for measuring multimorbidity, used in previous highly cited studies of multimorbidity prevalence, or widely applied measures of “comorbidity.” Multimorbidity prevalence using these lists varied from 11.1% to 36.4%. A limitation of the study is that conditions were not always replicated using the same ascertainment rules as previous studies to improve comparability across condition-lists, but this highlights further variability in prevalence estimates across studies. Conclusions In this study, we observed that varying the number and selection of conditions results in very large differences in multimorbidity prevalence, and different numbers of conditions are needed to reach ceiling rates of multimorbidity prevalence in certain groups of people. These findings imply that there is a need for a standardised approach to defining multimorbidity, and to facilitate this, researchers can use existing condition-lists associated with highest multimorbidity prevalence.