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

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 aim was 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 in spatial 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 than hospital case fatality which had a 99th to 1st percentile ratio of 1.39 (1.29-1.49) (women) and 1.49 (1.39-1.60) (men). The contribution of case fatality to variation in deaths across districts was largest in middle ages. Pre-hospital mortality was slightly higher in men than women in most districts and age groups, whereas hospital case fatality was higher in women in virtually all districts at ages up to and including 65-74 years; after this age, it became similar between the sexes. 3 Interpretation: Most of the variation in MI death rate is due to variation in MI event rate, with a smaller role for case fatality. The majority of variation in case fatality occurs before rather than after hospital admission. Reducing subnational variations in MI mortality requires interventions that reduce event rate and pre-hospital deaths

Inequalities in mortality from leading cancers in districts of England from 2002 to 2019: population-based high-resolution spatiotemporal analysis of vital registration data

Background: Cancers are the leading cause of death in England. Our aim was to estimate trends from 2002-2019 in mortality from leading cancers for the 314 districts in England. Methods: We used vital registration data in England from 2002 to 2019 for ten leading cancers by sex according to the total number of deaths over the study period, and a residual group of all other cancers. We used a Bayesian hierarchical model to obtain robust estimates of age- and cause-specific death rates. We applied life tables to calculate the probability of dying between birth and 80 years of age by sex, cancer cause of death, district and year. We report Spearman rank correlation between the probability of dying from a cancer and district-level poverty in 2019. Findings: In 2019, the probability of dying from a cancer ranged from 0.10 (95% credible interval 0.10-0.11) to 0.17 (0.16-0.18) for women and from 0.12 (0.12-0.13) to 0.22 (0.21-0.23) for men. The most unequal cancers were lung cancer for women (3.7-times (3.2-4.4) variation between the districts with the highest and lowest probabilities of dying) and stomach cancer for men (3.2-times (2.6-4.1)). The cancers with the least geographical variability were lymphoma and multiple myeloma (1.2-times (1.1-1.4) for women and 1.2-times (1.0-1.4) for men), and leukaemia (1.1-times (1.0-1.4) for women and 1.2-times (1.0-1.5) for men). The correlation between probability of dying from a cancer and district poverty was 0.74 (0.72-0.76) for women and 0.79 (0.78-0.81) for men. The probability of dying declined in all districts from 2002 to 2019: the reductions ranged from 6.6% (0.3-13.1%) to 30.1% (25.6-34.5%) for women and 12.8% (7.1-18.8%) to 36.7% (32.2-41.2%) for men. Interpretation: Cancers with modifiable risk factors and potential for screening for pre-cancerous lesions had heterogeneous trends and the greatest inequality. Reducing these inequalities requires addressing factors affecting both incidence and survival at the local level. Funding: Wellcome Trust, Imperial College London, UKRI (MRC), National Institute of Health Researc

Availability, access, analysis and dissemination of small area data

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

Life expectancy and risk of death in 6,791 English communities from 2002 to 2019: high-resolution spatiotemporal analysis of civil registration data

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

Changes in life expectancy and house prices in London from 2002 to 2019: Hyper-resolution spatiotemporal analysis of death registration and real estate data

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 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 60% (women) most expensive LSOAs in 2002, life expectancy increased solely independently of price change. Except for the 20% of LSOAs that had been most expensive in 2002, LSOAs with larger house price increases experienced larger growth in their population, especially among people of working ages (30–69 years), had a larger share of households who had not lived there in 2002, and improved their rankings in education, poverty and employment. Interpretation: Large gains in area life expectancy in London occurred either where house prices were already high, or in areas where house prices grew the most. In the latter group, the increases in life expectancy may be driven, in part, by changing population demographics. Funding: Wellcome Trust; UKRI (MRC); Imperial College London; National Institutes of Health Research

Threats to environmentally sensitive areas from peri-urban expansion in Mauritius

There are 60 inhabited tropical island countries covering c. 3.54 million km2 of land. Tropical islands are disproportionately rich in endemic plants, birds, molluscs, and other invertebrates (Whittaker and Fernandez-Palacios 2007) relative to similar mainland habitats (see, for example, Kier et al. 2009). The majority of global coral and seagrass diversity is located in the reefs, shoals, and lagoons of tropical islands (Spalding et al. 2001, 1997), the islands accounting for 2.4% of the global land area, but housing a much greater swathe of the Earth's biological uniqueness (Kreft et al. 2008). At the same time, the topography of most tropical islands is relatively steep, compressing their high terrestrial and coastal marine biological value into relatively small areas. This compression also reduces the average size of watersheds and shortens river main stems, in particular relative to the hydrological space of continental systems (Milliman et al. 1999). Consequently, island nations rely heavily on groundwater extraction and river impoundments to provide clean water to urban areas, particularly during periods of low rainfall