Frances Darlington-Pollock

While much of the ethnic and racial segregation literature concentrates on residential neighbourhoods, a growing body of research demonstrates how levels of inter-ethnic mixing vary between home and other spaces, including workplace environments. Using alternative population bases from the 2011 Census for England and Wales, we explore how levels of ethnic segregation and diversity compare for working-age people in residential (‘nighttime’), workday (‘daytime’) and workplace (‘employment’) environments. Analyses of Middle Layer Super Output Areas reveal that a larger number of these zones had low residential ethnic diversity than had low workday or workplace diversity. We also find higher levels of residential segregation than workplace segregation for all ethnic groups, including the White British (majority) and minoritised groups. Notable differences in segregation between residential and workplace spaces for the Pakistani and Bangladeshi ethnic groups, for example, demonstrate that more commonly applied measures of residential segregation fail to capture the extent of inter-ethnic mixing across spaces, and at different times of the day. Commuting data are analysed to explore the role of daily mobilities in shaping these patterns. The findings nuance understandings of levels of ethnic mixing, and emphasise the need for segregation analyses across multiple spatio-temporal contexts.

In the originally published version of this manuscript, the first note had been set as the second line of text in the Introduction: “Here, we are referring to period life expectancy rather than cohort life expectancy. Period life expectancy is the number of years a newborn might be expected to live, on average, assuming the current death rates do not change. It does not consider future changes in mortality rates. Period life expectancies are used to compare trends over time, and the UK and other national life tables are period life tables for this reason.” In box 2, the first sentence in the second paragraph, “Record migration indicators on death certificates e.g. country of birth” has been changed to “Record clearer indicators of migration indicators on death certificates e.g. country of birth instead of place of birth.” In the fourth sentence after Box 2, “... and relies on the 10-yearly...

Improvements in life expectancy at birth in the UK had stalled prior to 2020 and have fallen during the COVID-19 pandemic. The stagnation took place at a time of relatively high net migration, yet we know that migrants to Australia, the USA and some Nordic countries have positively impacted national life expectancy trends, outperforming native-born populations in terms of life expectancy. It is important to ascertain whether migrants have contributed positively to life expectancy in the UK, concealing worsening trends in the UK-born population, or whether relying on national life expectancy calculations alone may have masked excess mortality in migrant populations. We need a better understanding of the role and contribution of migrant populations to national life expectancy trends in the UK.

Background: Policy responses to the Global Financial Crisis emphasized wide-ranging fiscal austerity measures, many of which have been found to negatively impact health outcomes. This paper investigates change in patterns of mortality at local authority level in England (2010–11 to 2017–18) and the relation with fiscal austerity measures.Methods: Data from official local authority administrative records are used to quantify the gap between observed deaths and what was anticipated in the 2010-based subnational population projections. Regression analyses are used to explore the relation between excess deaths, austerity and wider process of population change at local authority level.Results: We estimate 231 707 total excess deaths, the majority of which occurred since 2014–15 (89%) across the majority of local authorities (91%). Austerity is positively associated with excess deaths. For working age adults, there is a clear gradient to the impact of austerity, whereas for older adults, the impact is more uniform.Conclusions: Fiscal austerity policies contributed to an excess of deaths for older people and widened social inequalities for younger populations. These results call for an end to all austerity measures and require further research into areas with the highest total excess deaths as a priority following the COVID-19 pandemic.

There exists a large body of research focused on migration distance, where migration is either the outcome of interest or used as an input variable to model something else. However, there is little consistency in the distance thresholds used: these are often arbitrary, based on administrative boundaries or constrained by definitions available in the dataset. This causes problems with comparison across studies, and in some cases where migration distance is poorly defined could lead to issues with interpretation of results. Using Binary Logistic Regression and drawing on data from the 2011 Census Sample of Anonymised Records for England and Wales, we demonstrate that the odds of migrating vary when considering a range of population characteristics across 16 distance thresholds. We argue that the choice of distance cut‐offs needs to be population and context specific and that decisions about these cut‐offs should be made carefully as part of the study design.

Background: Little is known about how people with dementia and/or their family carers access health and social care services after a diagnosis. The aim of this study was to explore potential inequalities in care pathways for people with young-onset and late on-set dementia (YOD/LOD), including their family carers, with coronavirus disease 2019 (COVID-19) occurring throughout the course of the study and enabling a comparison between pre-pandemic and COVID-19 times. Methods: People with YOD and LOD with their family carers were recruited via local support groups in the North West Coast region of England. Semi-structured interviews explored the experiences of people with YOD and LOD and family carers on their access to both health and social care services and community-based services. Transcripts were coded by two researchers and analysed using thematic analysis. Fifteen interviews were conducted with seven people with YOD or LOD and 14 family carers between January and March 2020. Some interviews were conducted only with the person with dementia, because they did not have a family carer, and others were conducted only with the family carer, because the person with dementia was in the severe stages of the condition. Results: Four themes emerged from the interviews: (1) Getting the ball rolling: the process of diagnosis; (2) Balancing the support needs of people with dementia and carers; (3) Barriers to accessing support; and (4) Facilitators to accessing support. Inequities existed for both YOD and LOD, with emerging evidence of unequal experiences in accessing care at the beginning of the COVID-19 pandemic. Discussion: People with YOD and LOD and their carers require better support in accessing services after a diagnosis. Greater understanding of the pathways through which inequalities materialise are needed, especially those that might have been disrupted or exacerbated by the COVID-19 pandemic.

COVID-19 is unique in that it is the first global pandemic occurring amidst a crowded information environment that has facilitated the proliferation of misinformation on social media. Dangerous misleading narratives have the potential to disrupt ‘official’ information sharing at major government announcements. Using an interrupted time-series design, we test the impact of the announcement of the first UK lockdown (8–8.30 p.m. 23 March 2020) on short-term trends of misinformation on Twitter. We utilise a novel dataset of all COVID-19-related social media posts on Twitter from the UK 48 hours before and 48 hours after the announcement (n = 2,531,888). We find that while the number of tweets increased immediately post announcement, there was no evidence of an increase in misinformation-related tweets. We found an increase in COVID-19-related bot activity post-announcement. Topic modelling of misinformation tweets revealed four distinct clusters: ‘government and policy’, ‘symptoms’, ‘pushing back against misinformation’ and ‘cures and treatments’.