Publications
5 results found
Bennett J, Rashid T, Zolfaghari A, et al., 2023, Changes in life expectancy and house prices in London from 2002 to 2019: Hyper-resolution spatiotemporal analysis of death registration and real estate data, The Lancet Regional Health Europe, Vol: 27, Pages: 1-13, ISSN: 2666-7762
Background:London has outperformed smaller towns and rural areas in terms of life expectancy increase. Our aim was to investigate life expectancy change at very-small-area level, and its relationship with house prices and their change.Methods:We performed a hyper-resolution spatiotemporal analysis from 2002 to 2019 for 4835 London Lower-layer Super Output Areas (LSOAs). We used population and death counts in a Bayesian hierarchical model to estimate age- and sex-specific death rates for each LSOA, converted to life expectancy at birth using life table methods. We used data from the Land Registry via the real estate website Rightmove (www.rightmove.co.uk), with information on property size, type and land tenure in a hierarchical model to estimate house prices at LSOA level. We used linear regressions to summarise how much life expectancy changed in relation to the combination of house prices in 2002 and their change from 2002 to 2019. We calculated the correlation between change in price and change in sociodemographic characteristics of the resident population of LSOAs and population turnover.Findings:In 134 (2.8%) of London's LSOAs for women and 32 (0.7%) for men, life expectancy may have declined from 2002 to 2019, with a posterior probability of a decline >80% in 41 (0.8%, women) and 14 (0.3%, men) LSOAs. The life expectancy increase in other LSOAs ranged from <2 years in 537 (11.1%) LSOAs for women and 214 (4.4%) for men to >10 years in 220 (4.6%) for women and 211 (4.4%) for men. The 2.5th-97.5th-percentile life expectancy difference across LSOAs increased from 11.1 (10.7–11.5) years in 2002 to 19.1 (18.4–19.7) years for women in 2019, and from 11.6 (11.3–12.0) years to 17.2 (16.7–17.8) years for men. In the 20% (men) and 30% (women) of LSOAs where house prices had been lowest in 2002, mainly in east and outer west London, life expectancy increased only in proportion to the rise in house prices. In contrast, in the 30% (men) and
Asaria P, Bennett J, Elliott P, et al., 2022, Contributions of event rates, pre-hospital deaths and hospital case fatality to variations in myocardial infarction mortality in 326 districts in England: spatial analysis of linked hospitalisation and mortality data, The Lancet Public Health, Vol: 7, Pages: e813-e824, ISSN: 2468-2667
Background: Myocardial infarction (MI) mortality varies substantially within high-income countries. There is limited guidance on what interventions – primary and secondary prevention and/or improving care pathways and quality – can reduce and equalise MI mortality. Our aimwas to understand the contribution of incidence (event rate), pre-hospital deaths and hospital case-fatality, to how MI mortality varies within England.Methods: We used linked data on hospitalisation and deaths from 2015-2018 with geographical identifiers to estimate MI death and event rates, pre-hospital deaths and hospital case fatality for men and women aged 45 years and older in 326 districts in England. Data were analysed in a Bayesian spatial model that accounted for similarities and differences inspatial patterns of fatal and non-fatal MI. Results: The 99th to 1st percentile ratio of age-standardised MI death rate was 2.63 (95% credible interval 2.45-2.83) in women and 2.56 (2.37-2.76) in men across districts, with death rate highest in north of England. The main contributor to this variation was MI event rate, with a 99th to 1st percentile ratio of 2.55 (2.39-2.72) (women) and 2.17 (2.08-2.27) (men) across districts. Pre-hospital mortality was greater than hospital case fatality in every district. Prehospital mortality had a 99th to 1st percentile ratio 1.60 (1.50-1.70) in women and 1.75 (1.66-1.86) in men across districts and made a greater contribution to case-fatality variation thanhospital case fatality which had a 99th to 1st percentile ratio of 1.39 (1.29-1.49) (women) and1.49 (1.39-1.60) (men). The contribution of case fatality to variation in deaths across districtswas largest in middle ages. Pre-hospital mortality was slightly higher in men than women inmost districts and age groups, whereas hospital case fatality was higher in women in virtuallyall districts at ages up to and including 65-74 years; after this age, it became similar betweenthe sexes.3Interpretation: Mos
Rashid T, Bennett J, Paciorek C, et al., 2021, Life expectancy and risk of death in 6,791 English communities from 2002 to 2019: high-resolution spatiotemporal analysis of civil registration data, The Lancet Public Health, Vol: 6, Pages: e805-e816, ISSN: 2468-2667
Background: There is limited data with high spatial granularity on how mortality and longevity have changed in English communities. We estimated trends from 2002 to 2019 in life expectancy and probabilities of death at different ages for all 6,791 English middle-layer super output areas (MSOAs).Methods: We used de-identified data for all deaths in England from 2002 to 2019 with information on age, sex and MSOA of residence, and population counts by age, sex and MSOA. We used a Bayesian hierarchical model to obtain estimates of age-specific death rates by sharing information across age groups, MSOAs and years. We used life table methods to calculate life expectancy at birth and probabilities of death in different ages by sex and MSOA.Results: In 2002-2006 and 2006-2010, the vast majority of MSOAs experienced a life expectancy increase for both sexes. In 2010-2014, female life expectancy decreased in 351 (5%) of MSOAs. By 2014-2019, the number of MSOAs with declining life expectancy was 1,270 (19%) for women and 784 (12%) for men. The life expectancy increase from 2002 to 2019 was smaller where life expectancy had been lower in 2002, mostly northern urban MSOAs, and larger where life expectancy had been higher in 2002, mostly MSOAs in and around London. As a result of these trends, the gap between the 1st and 99th percentiles of MSOA life expectancy for women increased from 10.7 (95% credible interval 10.4-10.9) in 2002 to reach 14.2 (13.9-14.5) years in 2019, and from 11.5 (11.3-11.7) years to 13.6 (13.4-13.9) years for men. Interpretation: In many English communities, life expectancy declined in the years prior to the Covid-19 pandemic. To ensure that this trend does not continue there is a need for pro-equity economic and social policies, and greater investment on public health and healthcare.
Hodgson S, Fecht D, Gulliver J, et al., 2020, Availability, access, analysis and dissemination of small area data, International Journal of Epidemiology, Vol: 49, Pages: i4-i14, ISSN: 1464-3685
In this era of ‘big data’, there is growing recognition of the value of environmental, health, social and demographic data for research. Open government data initiatives are growing in number and in terms of content. Remote sensing data are finding widespread use in environmental research, including in low- and middle-income settings. While our ability to study environment and health associations across countries and continents grows, data protection rules and greater patient control over the use of their data present new challenges to using health data in research. Innovative tools that circumvent the need for the physical sharing of data by supporting non-disclosive sharing of information, or that permit spatial analysis without researchers needing access to underlying patient data can be used to support analyses while protecting data confidentiality. User-friendly visualisations, allowing small area data to be seen and understood by non-expert audiences are revolutionising public and researcher interactions with data. The UK Small Area Health Statistics Unit’s Environment and Health Atlas for England and Wales, and the US National Environmental Public Health Tracking Network offer good examples. Open data facilitates user-generated outputs, and ‘mash-ups’, and user generated inputs from social media, mobile devices, and wearable tech are new data streams which will find utility in future studies, and bring novel dimensions with respect to ethical use of small area data.
Piel FBJ, Brandon P, Hima D, et al., 2018, The challenge of opt-outs from NHS data: a small-area perspective, Journal of Public Health, Vol: 40, Pages: e594-e600, ISSN: 1741-3842
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