MétaCan
Menu
Back to cohort
Record W2097819223 · doi:10.1016/s0140-6736(15)00195-6

Changes in health in England, with analysis by English regions and areas of deprivation, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013

2015· article· en· W2097819223 on OpenAlexaffabout
John Newton, Adam Briggs, Christopher J L Murray, Daniel Dicker, Kyle J Foreman, Haidong Wang, Mohsen Naghavi, Mohammad H. Forouzanfar, Summer Lockett Ohno, Ryan M Barber, Theo Vos, Jeffrey D Stanaway, Jürgen C Schmidt, Andrew Hughes, Derek F J Fay, Russell Ecob, C. Gresser, Martin McKee, Harry Rutter, Ibrahim Abubakar, Raghib Ali, H Ross Anderson, Amitava Banerjee, Derrick Bennett, Eduardo Bernabé, Kamaldeep Bhui, S. M. Biryukov, Rupert Bourne, Carol Brayne, Nigel Bruce, Traolach Brugha, Michael Burch, Simon Capewell, Daniel Casey, Rajiv Chowdhury, Matthew M Coates, Cyrus Cooper, Julia Critchley, Paul I. Dargan, Mukesh Dherani, Paul Elliott, Majid Ezzati, Kevin Fenton, Maya Fraser, Thomas Fürst, Felix Greaves, Mark Green, David Gunnell, B. M. Hannigan, Roderick J. Hay, Simon I Hay, Harry Hemingway, Heidi J. Larson, Raimundas Lunevičius, Ronan A Lyons, Wagner Marcenes, Amanda J. Mason‐Jones, Fiona E. Matthews, Henrik Møller, Michele E Murdoch, Charles R. Newton, Neil Pearce, Frédéric B. Piel, Daniel Pope, Kazem Rahimi, Alina Rodriguez, Peter Scarborough, Austin E Schumacher, Ivy Shiue, Liam Smeeth, Alison Tedstone, Jonathan Valabhji, Hywel C Williams, Charles Wolfe, Anthony D. Woolf, Adrian Davis

Bibliographic record

VenueThe Lancet · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsPopulation Health Research Institute
FundersMedical Research CouncilPublic Health EnglandWellcome TrustBritish Heart FoundationInstitute for Health Metrics and EvaluationNational Institute for Health and Care ResearchBill and Melinda Gates Foundation
KeywordsEuropean unionHealth Survey for EnglandYears of potential life lostMedicineDemographyGeographyResidenceBurden of diseaseInequalitySocial deprivationDiseaseEnvironmental healthLife expectancyPopulationEconomic growthSociology

Abstract

fetched live from OpenAlex

BACKGROUND: In the Global Burden of Disease Study 2013 (GBD 2013), knowledge about health and its determinants has been integrated into a comparable framework to inform health policy. Outputs of this analysis are relevant to current policy questions in England and elsewhere, particularly on health inequalities. We use GBD 2013 data on mortality and causes of death, and disease and injury incidence and prevalence to analyse the burden of disease and injury in England as a whole, in English regions, and within each English region by deprivation quintile. We also assess disease and injury burden in England attributable to potentially preventable risk factors. England and the English regions are compared with the remaining constituent countries of the UK and with comparable countries in the European Union (EU) and beyond. METHODS: We extracted data from the GBD 2013 to compare mortality, causes of death, years of life lost (YLLs), years lived with a disability (YLDs), and disability-adjusted life-years (DALYs) in England, the UK, and 18 other countries (the first 15 EU members [apart from the UK] and Australia, Canada, Norway, and the USA [EU15+]). We extended elements of the analysis to English regions, and subregional areas defined by deprivation quintile (deprivation areas). We used data split by the nine English regions (corresponding to the European boundaries of the Nomenclature for Territorial Statistics level 1 [NUTS 1] regions), and by quintile groups within each English region according to deprivation, thereby making 45 regional deprivation areas. Deprivation quintiles were defined by area of residence ranked at national level by Index of Multiple Deprivation score, 2010. Burden due to various risk factors is described for England using new GBD methodology to estimate independent and overlapping attributable risk for five tiers of behavioural, metabolic, and environmental risk factors. We present results for 306 causes and 2337 sequelae, and 79 risks or risk clusters. FINDINGS: Between 1990 and 2013, life expectancy from birth in England increased by 5·4 years (95% uncertainty interval 5·0-5·8) from 75·9 years (75·9-76·0) to 81·3 years (80·9-81·7); gains were greater for men than for women. Rates of age-standardised YLLs reduced by 41·1% (38·3-43·6), whereas DALYs were reduced by 23·8% (20·9-27·1), and YLDs by 1·4% (0·1-2·8). For these measures, England ranked better than the UK and the EU15+ means. Between 1990 and 2013, the range in life expectancy among 45 regional deprivation areas remained 8·2 years for men and decreased from 7·2 years in 1990 to 6·9 years in 2013 for women. In 2013, the leading cause of YLLs was ischaemic heart disease, and the leading cause of DALYs was low back and neck pain. Known risk factors accounted for 39·6% (37·7-41·7) of DALYs; leading behavioural risk factors were suboptimal diet (10·8% [9·1-12·7]) and tobacco (10·7% [9·4-12·0]). INTERPRETATION: Health in England is improving although substantial opportunities exist for further reductions in the burden of preventable disease. The gap in mortality rates between men and women has reduced, but marked health inequalities between the least deprived and most deprived areas remain. Declines in mortality have not been matched by similar declines in morbidity, resulting in people living longer with diseases. Health policies must therefore address the causes of ill health as well as those of premature mortality. Systematic action locally and nationally is needed to reduce risk exposures, support healthy behaviours, alleviate the severity of chronic disabling disorders, and mitigate the effects of socioeconomic deprivation. FUNDING: Bill & Melinda Gates Foundation and Public Health England.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.124
Threshold uncertainty score0.247

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.010
Bibliometrics0.0110.016
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.050
GPT teacher head0.343
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations361
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueThe LancetSame topicHealth disparities and outcomesFrench-language works237,207