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OBJECTIVE: To investigate the effect of the workplace on the socioeconomic gradient of sickness absence. DESIGN: Comparison of the relation between socioeconomic status and employee sickness absence in three different towns. SETTINGS: The towns of Raisio, Valkeakoski, and Nokia in Finland. They are equal in size and regional social deprivation indices, located in the neighbourhood of a larger city, and produce the same services to the inhabitants. SUBJECTS: All permanent local government employees from Raisio (n = 887), Valkeakoski (n = 972), and Nokia (n = 934) on the employer's registers during 1991 to 1993. MAIN OUTCOME MEASURES: Rates of short (1-3 days) and long (> 3 days) spells of sickness absence, irrespective of cause, and separately for infection, musculoskeletal disorder, and trauma. RESULTS: In blue collar male and female workers, compared with the same sex higher grade white collar workers, the age adjusted numbers of long sick leaves were 4.9 (95% CI 4.2, 5.8) and 2.8 (2.6, 3.1) times higher, respectively. The risk varied significantly between the towns, in men in relation to long sick leaves irrespective of cause and resulting from musculoskeletal disorders, and in women in relation to long leaves resulting from infection. The numbers of long sick leaves were 3.9 (95% CI 2.8, 5.4) times higher in blue collar male workers than in higher grade white collar male workers in Raisio, 4.9 (95% CI 3.8, 6.3) times higher in Valkeakoski, and 5.8 (95% CI 4.5, 7.5) times higher in Nokia. Sickness absence of blue collar employees differed most between the towns. The rates of long sick leaves in blue collar men were 1.46 times greater (95% CI 1.25, 1.72) in Valkeakoski and 1.85 times greater (95% CI 1.58, 2.16) in Nokia than in Raisio. In men, no significant differences were found between the towns as regards the numbers of long sick leaves of higher grade white collar male workers. The socioeconomic gradients differed more between the towns in men who had worked for four years or more in the same employment ...

Objectives Policies have been introduced to reduce sickness absence, but their effectiveness is largely unknown. In a natural experiment, we examined effects of legislative changes on return to work and work participation. Methods The source population consisted of up to 72164 Finnish public sector employees with a permanent job contract in 2008-2011 (before) and in 2013-2014 (after). We used employees with a continuous sickness absence of at least 30 calendar-days (n=5708-6393), 60 compensated days (n=1481-1655) and 90 compensated days (n=766-932). We examined sustainable return to work (a minimum of 28 consecutive working days) with survival analysis as well as monthly work participation after a sickness absence, and annual gain in work participation after the intervention, using trajectory analyses. Results Sustainable return to work after 60days of sickness absence occurred earlier after the legislative changes (p value 0.017), although the effect reduced towards the end of the follow-up. There were no differences in return to work after a 30 or 90days of sickness absence. The largest annual gain, postintervention versus preintervention, in monthly work participation was observed among employees with 60days of sickness absence and was 230.9 person-years/10000 employees. The corresponding annual gains among those with 30days and 90days of sickness absence were 51.8 and 39.6, respectively. Conclusions Our findings suggest that the legislative changes, obligating early notification of prolonged sickness absences as well as assessment of remaining work ability and possibilities to continue working, may enhance sustainable return to work in the short term. Other measures will be needed to enhance work participation, especially in the long term. ; Peer reviewed

Objectives: To investigate whether low perceived organisational injustice predicts heavy drinking among employees.Methods: Data from a prospective occupational cohort study, the 10-Town Study, on 15 290 Finnish public sector local government employees nested in 2432 work units, were used. Non-drinkers were excluded. Procedural, interactional and total organisational justice, heavy drinking (>= 210 g of absolute alcohol per week) and other psychosocial factors were determined by means of questionnaire in 2000-2001 (phase 1) and 2004 (phase 2). Multilevel logistic regression analyses taking into account the hierarchical structure of the data were conducted and adjustments were made for sex, age, socio-economic status, marital status, baseline heavy drinking, psychological distress and other psychosocial risk factors such as job strain and effort/reward imbalance.Results: After adjustments, participants who reported low procedural justice at phase 1 were approximately 1.2 times more likely to be heavy drinkers at phase 2 compared with their counterparts reporting high justice. Low perceived justice in interpersonal treatment and low perceived total organisational justice were associated with increased prevalence of heavy drinking only in the model adjusted for sociodemographics.Conclusions: This is the first longitudinal study to show that low procedural justice is weakly associated with an increased likelihood of heavy drinking.

Copyright © 2021 The Author(s). Drug target Mendelian randomization (MR) studies use DNA sequence variants in or near a gene encoding a drug target, that alter the target's expression or function, as a tool to anticipate the effect of drug action on the same target. Here we apply MR to prioritize drug targets for their causal relevance for coronary heart disease (CHD). The targets are further prioritized using independent replication, co-localization, protein expression profiles and data from the British National Formulary and clinicaltrials.gov. Out of the 341 drug targets identified through their association with blood lipids (HDL-C, LDL-C and triglycerides), we robustly prioritize 30 targets that might elicit beneficial effects in the prevention or treatment of CHD, including NPC1L1 and PCSK9, the targets of drugs used in CHD prevention. We discuss how this approach can be generalized to other targets, disease biomarkers and endpoints to help prioritize and validate targets during the drug development process. ; The authors are grateful to the studies and consortia that provided summary association results and to the participants of the biobanks and research cohorts. This research has been conducted using the UK Biobank Resource under Application Number 12113. UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government, and the Northwest Regional Development Agency. It has also had funding from the Welsh Assembly Government and the British Heart Foundation. M.G.M. is supported by a BHF Fellowship FS/17/70/33482. A.F.S. is supported by BHF grant PG/18/5033837 and the UCL BHF Research Accelerator AA/18/6/34223. C.F. and A.F.S. received additional support from the National Institute for Health Research University College London Hospitals Biomedical Research Centre. A.D.H. is an NIHR Senior Investigator. We further acknowledge support from the Rosetrees Trust. The UCLEB Consortium is supported by a British Heart Foundation Program Grant (RG/10/12/28456). M.K. was supported by grants from the Wellcome Trust, UK (221854/Z/20/Z), the UK Medical Research Council (R024227 and S011676), the National Institute on Aging, NIH (R01AG056477 and RF1AG062553), and the Academy of Finland (311492). AH receives support from the British Heart Foundation, the Economic and Social Research Council (ESRC), the Horizon 2020 Framework Program of the European Union, the National Institute on Aging, the National Institute for Health Research University College London Hospitals Biomedical Research Centre, the UK Medical Research Council and works in a unit that receives support from the UK Medical Research Council. A.G. is funded by the Member States of EMBL. P.C. is supported by the Thailand Research Fund (MRG6280088). D.A.L. is supported by a British Heart Foundation Chair (CH/F/20/90003) and British Heart Foundation grant (AA/18/7/34219), is a National Institute of Health Research Senior Investigator (NF-0616-10102) and works in a Unit that receives support from the University of Bristol and UK Medical Research Council (MC_UU_00011/6). This work was funded in part by the UKRI and NIHR through the Multimorbidity Mechanism and Therapeutics Research Collaborative (MR/V033867/1).

The preliminary meta-analysis of RCT data were presented at BPS 2018 by NH. The preprint version of this paper has been deposited on medrxiv: https://doi.org/10.1101/2020.09.07.20189571. ; Copyright © 2021 The Author(s). Development of cholesteryl ester transfer protein (CETP) inhibitors for coronary heart disease (CHD) has yet to deliver licensed medicines. To distinguish compound from drug target failure, we compared evidence from clinical trials and drug target Mendelian randomization of CETP protein concentration, comparing this to Mendelian randomization of proprotein convertase subtilisin/kexin type 9 (PCSK9). We show that previous failures of CETP inhibitors are likely compound related, as illustrated by significant degrees of between-compound heterogeneity in effects on lipids, blood pressure, and clinical outcomes observed in trials. On-target CETP inhibition, assessed through Mendelian randomization, is expected to reduce the risk of CHD, heart failure, diabetes, and chronic kidney disease, while increasing the risk of age-related macular degeneration. In contrast, lower PCSK9 concentration is anticipated to decrease the risk of CHD, heart failure, atrial fibrillation, chronic kidney disease, multiple sclerosis, and stroke, while potentially increasing the risk of Alzheimer's disease and asthma. Due to distinct effects on lipoprotein metabolite profiles, joint inhibition of CETP and PCSK9 may provide added benefit. In conclusion, we provide genetic evidence that CETP is an effective target for CHD prevention but with a potential on-target adverse effect on age-related macular degeneration. ; This research has been conducted using the UK Biobank Resource under Application Number 12113. The authors are grateful to UK Biobank participants. We gratefully acknowledge the support of UCLEB and CHARGE. Funding and role of funding sources: A.F.S. is supported by BHF grant PG/18/5033837 and the UCL BHF Research Accelerator AA/18/6/34223. C.F. and A.F.S. received additional support from the National Institute for Health Research University College London Hospitals Biomedical Research Centre. M.G.M. is supported by a BHF Fellowship FS/17/70/33482. A.D.H. is an NIHR Senior Investigator. This work was supported by the UKRI/NIHR Strategic Priorities Award in Multimorbidity Research (MR/V033867/1). This work was additionally supported by a grant [R01 LM010098] from the National Institutes of Health (USA). We further acknowledge support from the Rosetrees and Stoneygate Trust. The UCLEB Consortium is supported by a British Heart Foundation Program Grant (RG/10/12/28456). T.R.G. receives support from the UK Medical Research Council (MC_UU_00011/4). D.O.M.K. is supported by the Dutch Science Organization (ZonMW-VENI Grant 916.14.023). A D.H. receives support from the UK Medical Research (MC_UU_12019/1). M.K. is supported by the Wellcome Trust (221854/Z/20/Z), the UK Medical Research Council (MR/S011676/1, MR/R024227/1), National Institute on Aging (NIH), US (R01AG062553), and the Academy of Finland (311492). D.A.L. is supported by a Bristol BHF Accelerator Award (AA/18/7/34219) and BHF Chair (CH/F/20/90003) and works in a unit that receives support from the University of Bristol and the UK Medical Research Council (MC_UU_00011/6). D.A.L. is a National Institute of Health Research Senior Investigator (NF-0616-10102). N.F. is supported by the National Institutes of Health (R01-MD012765, R01-DK117445, R21- HL140385). P.C. is supported by the Thailand Research Fund (MRG6280088. UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government, and the Northwest Regional Development Agency. It has also had funding from the Welsh Assembly Government and the British Heart Foundation. Infrastructure for the CHARGE Consortium is supported in part by the National Heart, Lung, and Blood Institute grant R01HL105756.

OBJECTIVE: To quantify the association between long working hours and alcohol use. DESIGN: Systematic review and meta-analysis of published studies and unpublished individual participant data. DATA SOURCES: A systematic search of PubMed and Embase databases in April 2014 for published studies, supplemented with manual searches. Unpublished individual participant data were obtained from 27 additional studies. REVIEW METHODS: The search strategy was designed to retrieve cross sectional and prospective studies of the association between long working hours and alcohol use. Summary estimates were obtained with random effects meta-analysis. Sources of heterogeneity were examined with meta-regression. RESULTS: Cross sectional analysis was based on 61 studies representing 333 693 participants from 14 countries. Prospective analysis was based on 20 studies representing 100 602 participants from nine countries. The pooled maximum adjusted odds ratio for the association between long working hours and alcohol use was 1.11 (95% confidence interval 1.05 to 1.18) in the cross sectional analysis of published and unpublished data. Odds ratio of new onset risky alcohol use was 1.12 (1.04 to 1.20) in the analysis of prospective published and unpublished data. In the 18 studies with individual participant data it was possible to assess the European Union Working Time Directive, which recommends an upper limit of 48 hours a week. Odds ratios of new onset risky alcohol use for those working 49-54 hours and ≥55 hours a week were 1.13 (1.02 to 1.26; adjusted difference in incidence 0.8 percentage points) and 1.12 (1.01 to 1.25; adjusted difference in incidence 0.7 percentage points), respectively, compared with working standard 35-40 hours (incidence of new onset risky alcohol use 6.2%). There was no difference in these associations between men and women or by age or socioeconomic groups, geographical regions, sample type (population based v occupational cohort), prevalence of risky alcohol use in the cohort, or sample attrition rate. CONCLUSIONS: Individuals whose working hours exceed standard recommendations are more likely to increase their alcohol use to levels that pose a health risk.

International audience ; The Dementias Platform UK Data Portal is a data repository facilitating access to data for 3 370 929 individuals in 42 cohorts. The Data Portal is an end-to-end data management solution providing a secure, fully auditable, remote access environment for the analysis of cohort data. All projects utilising the data are by default collaborations with the cohort research teams generating the data. The Data Portal uses UK Secure eResearch Platform infrastructure to provide three core utilities: data discovery, access, and analysis. These are delivered using a 7 layered architecture comprising: data ingestion, data curation, platform interoperability, data discovery, access brokerage, data analysis and knowledge preservation. Automated, streamlined, and standardised procedures reduce the administrative burden for all stakeholders, particularly for requests involving multiple independent datasets, where a single request may be forwarded to multiple data controllers. Researchers are provided with their own secure 'lab' using VMware which is accessed using two factor authentication. Over the last 2 years, 160 project proposals involving 579 individual cohort data access requests were received. These were received from 268 applicants spanning 72 institutions (56 academic, 13 commercial, 3 government) in 16 countries with 84 requests involving multiple cohorts. Projects are varied including multi-modal, machine learning, and Mendelian randomisation analyses. Data access is usually free at point of use although a small number of cohorts require a data access fee.

International audience ; The Dementias Platform UK Data Portal is a data repository facilitating access to data for 3 370 929 individuals in 42 cohorts. The Data Portal is an end-to-end data management solution providing a secure, fully auditable, remote access environment for the analysis of cohort data. All projects utilising the data are by default collaborations with the cohort research teams generating the data. The Data Portal uses UK Secure eResearch Platform infrastructure to provide three core utilities: data discovery, access, and analysis. These are delivered using a 7 layered architecture comprising: data ingestion, data curation, platform interoperability, data discovery, access brokerage, data analysis and knowledge preservation. Automated, streamlined, and standardised procedures reduce the administrative burden for all stakeholders, particularly for requests involving multiple independent datasets, where a single request may be forwarded to multiple data controllers. Researchers are provided with their own secure 'lab' using VMware which is accessed using two factor authentication. Over the last 2 years, 160 project proposals involving 579 individual cohort data access requests were received. These were received from 268 applicants spanning 72 institutions (56 academic, 13 commercial, 3 government) in 16 countries with 84 requests involving multiple cohorts. Projects are varied including multi-modal, machine learning, and Mendelian randomisation analyses. Data access is usually free at point of use although a small number of cohorts require a data access fee.

The Dementias Platform UK Data Portal is a data repository facilitating access to data for 3 370 929 individuals in 42 cohorts. The Data Portal is an end-to-end data management solution providing a secure, fully auditable, remote access environment for the analysis of cohort data. All projects utilising the data are by default collaborations with the cohort research teams generating the data. The Data Portal uses UK Secure eResearch Platform infrastructure to provide three core utilities: data discovery, access, and analysis. These are delivered using a 7 layered architecture comprising: data ingestion, data curation, platform interoperability, data discovery, access brokerage, data analysis and knowledge preservation. Automated, streamlined, and standardised procedures reduce the administrative burden for all stakeholders, particularly for requests involving multiple independent datasets, where a single request may be forwarded to multiple data controllers. Researchers are provided with their own secure 'lab' using VMware which is accessed using two factor authentication. Over the last 2 years, 160 project proposals involving 579 individual cohort data access requests were received. These were received from 268 applicants spanning 72 institutions (56 academic, 13 commercial, 3 government) in 16 countries with 84 requests involving multiple cohorts. Projects are varied including multi-modal, machine learning, and Mendelian randomisation analyses. Data access is usually free at point of use although a small number of cohorts require a data access fee.

BACKGROUND: Epidemiological studies show that high circulating cystatin C is associated with risk of cardiovascular disease (CVD), independent of creatinine-based renal function measurements. It is unclear whether this relationship is causal, arises from residual confounding, and/or is a consequence of reverse causation. OBJECTIVES: The aim of this study was to use Mendelian randomization to investigate whether cystatin C is causally related to CVD in the general population. METHODS: We incorporated participant data from 16 prospective cohorts (n = 76,481) with 37,126 measures of cystatin C and added genetic data from 43 studies (n = 252,216) with 63,292 CVD events. We used the common variant rs911119 in CST3 as an instrumental variable to investigate the causal role of cystatin C in CVD, including coronary heart disease, ischemic stroke, and heart failure. RESULTS: Cystatin C concentrations were associated with CVD risk after adjusting for age, sex, and traditional risk factors (relative risk: 1.82 per doubling of cystatin C; 95% confidence interval [CI]: 1.56 to 2.13; p = 2.12 × 10(-14)). The minor allele of rs911119 was associated with decreased serum cystatin C (6.13% per allele; 95% CI: 5.75 to 6.50; p = 5.95 × 10(-211)), explaining 2.8% of the observed variation in cystatin C. Mendelian randomization analysis did not provide evidence for a causal role of cystatin C, with a causal relative risk for CVD of 1.00 per doubling cystatin C (95% CI: 0.82 to 1.22; p = 0.994), which was statistically different from the observational estimate (p = 1.6 × 10(-5)). A causal effect of cystatin C was not detected for any individual component of CVD. CONCLUSIONS: Mendelian randomization analyses did not support a causal role of cystatin C in the etiology of CVD. As such, therapeutics targeted at lowering circulating cystatin C are unlikely to be effective in preventing CVD. ; The individual study sponsor(s) had no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication. Dr. Isgum is supported by research grants from Pie Medical Imaging, 3Mensio Medical Imaging B.V., the NWO and Foundation for Technological Sciences under Project 12726, The Netherlands Organization for Health Research and Development, and the Dutch Cancer Society. Dr. Arpegård has received funding through the Stockholm County Council (combined clinical residency and PhD training program). Dr. Amouyel has received personal fees from Servier, Hoffman Laroche, Total, Genoscreen, Alzprotect, Fondation Plan Alzheimer, and Takeda outside of the submitted work; and has shares in Genoscreen. Dr. Morris is a Wellcome Trust Senior Fellow in Basic Biomedical Science under grant number WT098017. Dr. Worrall has received compensation for his role as deputy editor of the Journal of Neurology; and has received National Institutes of Health funding through the National Institute of Neurological Disorders and Stroke (U-01 NS069208) and National Human Genome Research Institute (U-01 HG005160). Dr. Samani is supported by the British Heart Foundation (BHF); and is a National Institute for Health Research Senior Investigator. Dr. Nelson is supported by the BHF. Dr. Franco works in ErasmusAGE, a center for aging research across the life course funded by Nestlé Nutrition (Nestec Ltd.), Metagenics Inc., and AXA; Nestlé Nutrition (Nestec Ltd.), Metagenics Inc., and AXA had no role in design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript. Dr. Patel is supported by a BHF Intermediate Fellowship. Dr. Koenig has received funds through NGFNplus, project number 01GS0834; has received research grants from Abbott, Roche Diagnostics, Beckmann, and Singulex; has received honorarium for lectures from AstraZeneca, Novartis, Merck Sharp & Dohme, Amgen, and Actavis; and has served as a consultant for Novartis, Pfizer, The Medicines Company, Amgen, AstraZeneca, Merck Sharp & Dohme, and GlaxoSmithKline. Dr. Jukema is an Established Clinical Investigator of the Netherlands Heart Foundation (grant 2001 D 032). Dr. Svensson has received a grant from the Swedish Society of Medicine (SLS-412071). Dr. Kivimaki has received funding through the Medical Research Council (K013351), Economic and Social Research Council, and National Institutes of Health (HL36310). Dr. Dehghan is supported by a Netherlands Organization for Scientific Research (NWO) grant (VENI, 916.12.154) and the EUR Fellowship; and has received consultancy and research support from Metagenics Inc. (outside the scope of this work). Dr. Ingelsson is supported by grants from Göran Gustafsson Foundation, Swedish Heart-Lung Foundation (20140422), Knut and Alice Wallenberg Foundation (Knut och Alice Wallenbergs Stiftelse), European Research Council (ERC-StG-335395), Swedish Diabetes Foundation (Diabetesfonden; grant no. 2013-024), and the Swedish Research Council (VR; grant no. 2012-1397). Dr. de Bakker is an employee of Vertex Pharmaceuticals. Dr. Ärnlöv was funded by the Swedish Research Council (2012-1727, 2012-2215), Swedish Heart-Lung Foundation, Thuréus Foundation, the Marianne and Marcus Wallenberg Foundation, Dalarna University, and Uppsala University. Dr. Asselbergs is supported by a Dekker scholarship-Junior Staff Member 2014T001–Netherlands Heart Foundation and UCL Hospitals National Institute for Health Research Biomedical Research Centre. The research leading to these results has received funding from the European Union Seventh Framework Programme FP7/2007-2013 under grant agreement n° HEALTH-F2-2013-601456 (CVgenes-at-target). All other authors have reported that they have no relationships relevant to the contents of this paper to disclose. ; Peer-reviewed ; Publisher Version

This is the final version. Available on open access from Nature Research via the DOI in this record. ; Data availability: GWAS summary statistics for FG/FI analyses presented in this manuscript are deposited on https://www.magicinvestigators.org/downloads/ and will be also be available through the NHGRI-EBI GWAS Catalog https://www.ebi.ac.uk/gwas/downloads/summary-statistics. Raw files for RNA-seq mRNA expression in islet donors have been deposited in NCBI GEO database with the accession code GSE50398. Summary-level GWAS results for genetic correlation analysis with glycemic traits were downloaded from the LDHub database (http://ldsc.broadinstitute.org/ldhub/). Islets from 89 cadaver donors were provided by the Nordic Islet Transplantation Programme (http://www.medscinet.com/nordicislets/). The dexseq_count python script for RNA sequencing analysis in human pancreatic islets was downloaded from http://www-huber.embl.de/pub/DEXSeq/analysis/scripts/. Raw files for RNA-seq mRNA expression in islet donors have been deposited in NCBI GEO database with the accession code GSE50398. ; Differences between sexes contribute to variation in the levels of fasting glucose and insulin. Epidemiological studies established a higher prevalence of impaired fasting glucose in men and impaired glucose tolerance in women, however, the genetic component underlying this phenomenon is not established. We assess sex-dimorphic (73,089/50,404 women and 67,506/47,806 men) and sex-combined (151,188/105,056 individuals) fasting glucose/fasting insulin genetic effects via genome-wide association study meta-analyses in individuals of European descent without diabetes. Here we report sex dimorphism in allelic effects on fasting insulin at IRS1 and ZNF12 loci, the latter showing higher RNA expression in whole blood in women compared to men. We also observe sex-homogeneous effects on fasting glucose at seven novel loci. Fasting insulin in women shows stronger genetic correlations than in men with waist-to-hip ratio and anorexia nervosa. Furthermore, waist-to-hip ratio is causally related to insulin resistance in women, but not in men. These results position dissection of metabolic and glycemic health sex dimorphism as a steppingstone for understanding differences in genetic effects between women and men in related phenotypes. ; Academy of Finland ; ADA ; Biotechnology and Biological Sciences Research Council (BBSRC) ; BHF ; Clinical Translational Science Institute ; Croatian Ministry of Science ; Directorate C - Public Health and Risk Assessment, Health & Consumer Protection ; Dutch Kidney Foundation ; Estonian Research Council ; European Research Council (ERC) ; European Regional Development Fund (ERDF) ; European Union Horizon 2020 ; Federal Ministry of Education and Research (BMBF), Germany ; Finnish Funding Agency for Technology and Innovation ; German Research Foundation ; Greek General Secretary of Research and Technology ; Icelandic National Bioethics Committee ; IFB Adiposity Diseases ; IngaBritt och Arne Lundberg's Research Foundation ; Italian Ministry of Health ; Knut & Alice Wallenberg foundation ; Kuopio University Hospital from Ministry of Health and Social Affairs ; Affymetrix, Inc ; Lundberg Foundation ; Medical Research Council (MRC) ; Mid-Atlantic Nutrition Obesity Research Center ; Ministry of Education and Culture of Finland ; MRC-GSK pilot programme ; NHLBI ; NIA ; NIH ; Nordic Centre of Excellence on Systems biology in controlled dietary interventions and cohort studies, SYSDIET ; Novo Nordisk Foundation ; NWO/ZonMW ; Spinozapremie ; Rutgers University Cell and DNA Repository ; Stockholm County Council ; Swedish Foundation for Strategic Research ; Swedish Heart-Lung Foundation ; Swedish Research Council ; Swiss National Science Foundation ; TEKES ; Torsten och Ragnar Söderbergs Stiftelser ; Wellcome Trust ; Yrjö Jahnsson Foundation ; Note that the full list of funders and grant numbers is available in the online article and in the PDF in this record

Funding for this study was provided by the Aase and Ejner Danielsens Foundation; Academy of Finland (41071, 77299, 102318, 110413, 117787, 121584, 123885, 124243, 124282, 126925, 129378, 134309, 286284); Accare Center for Child and Adolescent Psychiatry; Action on Hearing Loss (G51); Agence Nationale de la 359 Recherche; Agency for Health Care Policy Research (HS06516); ALF/LUA research grant in Gothenburg; ALFEDIAM; ALK-Abello´ A/S; Althingi; American Heart Association (13POST16500011); Amgen; Andrea and Charles Bronfman Philanthropies; Ardix Medical; Arthritis Research UK; Association Diabe`te Risque Vasculaire; Australian National Health and Medical Research Council (241944, 339462, 389875, 389891, 389892, 389927, 389938, 442915, 442981, 496739, 552485, 552498); Avera Institute; Bayer Diagnostics; Becton Dickinson; BHF (RG/14/5/30893); Boston Obesity Nutrition Research Center (DK46200), Bristol-Myers Squibb; British Heart Foundation (RG/10/12/ 28456, RG2008/08, RG2008/014, SP/04/002); Medical Research Council of Canada; Canadian Institutes for Health Research (FRCN-CCT-83028); Cancer Research UK; Cardionics; Cavadis B.V., Center for Medical Systems Biology; Center of Excellence in Genomics; CFI; CIHR; City of Kuopio; CNAMTS; Cohortes Sante´ TGIR; Contrat de Projets E´tat-Re´gion; Croatian Science Foundation (8875); Danish Agency for Science, Technology and Innovation; Danish Council for Independent Research (DFF-1333- 00124, DFF-1331-00730B); County Council of Dalarna; Dalarna University; Danish Council for Strategic Research; Danish Diabetes Academy; Danish Medical Research Council; Department of Health, UK; Development Fund from the University of Tartu (SP1GVARENG); Diabetes Hilfs- und Forschungsfonds Deutschland; Diabetes UK; Diabetes Research and Wellness Foundation Fellowship; Donald W. Reynolds Foundation; Dr Robert Pfleger-Stiftung; Dutch Brain Foundation; Dutch Diabetes Research Foundation; Dutch Inter University Cardiology Institute; Dutch Kidney Foundation (E033); Dutch Ministry of Justice; the DynaHEALTH action No. 633595, Economic Structure Enhancing Fund of the Dutch Government; Else Kro¨ner-Fresenius-Stiftung (2012_A147, P48/08//A11/08); Emil Aaltonen Foundation; Erasmus University Medical Center Rotterdam; Erasmus MC and Erasmus University Rotterdam; the Municipality of Rotterdam; Estonian Government (IUT20-60, IUT24-6); Estonian Research Roadmap through the Estonian Ministry of Education and Research (3.2.0304.11-0312); European Research Council (ERC Starting Grant and 323195:SZ-245 50371-GLUCOSEGENESFP7-IDEAS-ERC); European Regional Development Fund; European Science Foundation (EU/QLRT-2001-01254); European Commission (018947, 018996, 201668, 223004, 230374, 279143, 284167, 305739, BBMRI-LPC-313010, HEALTH-2011.2.4.2-2-EUMASCARA, HEALTH-2011-278913, HEALTH-2011-294713-EPLORE, HEALTH-F2- 2008-201865-GEFOS, HEALTH-F2-2013-601456, HEALTH-F4-2007-201413, HEALTH-F4-2007-201550-HYPERGENES, HEALTH-F7-305507 HOMAGE, IMI/ 115006, LSHG-CT-2006-018947, LSHG-CT-2006-01947, LSHM-CT-2004-005272, LSHM-CT-2006-037697, LSHM-CT-2007-037273, QLG1-CT-2002-00896, QLG2-CT2002-01254); Faculty of Biology and Medicine of Lausanne; Federal Ministry of Education and Research (01ZZ0103, 01ZZ0403, 01ZZ9603, 03IS2061A, 03ZIK012); Federal State of Mecklenburg-West Pomerania; Fe´de´ration Franc¸aise de Cardiologie; Finnish Cultural Foundation; Finnish Diabetes Association; Finnish Foundation of Cardiovascular Research; Finnish Heart Association; Fondation Leducq; Food Standards Agency; Foundation for Strategic Research; French Ministry of Research; FRSQ; Genetic Association Information Network (GAIN) of the Foundation for the NIH; German Federal Ministry of Education and Research (BMBF, 01ER1206, 01ER1507); GlaxoSmithKline; Greek General Secretary of Research and Technology; Go¨teborg Medical Society; Health and Safety Executive; Healthcare NHS Trust; Healthway; Western Australia; Heart Foundation of Northern Sweden; Helmholtz Zentrum Mu¨nchen—German Research Center for Environmental Health; Hjartavernd; Ingrid Thurings Foundation; INSERM; InterOmics (PB05 MIUR-CNR); INTERREG IV Oberrhein Program (A28); Interuniversity Cardiology Institute of the Netherlands (ICIN, 09.001); Italian Ministry of Health (ICS110.1/RF97.71); Italian Ministry of Economy and Finance (FaReBio di Qualita`); Marianne and Marcus Wallenberg Foundation; the Ministry of Health, Welfare and Sports, the Netherlands; J.D.E. and Catherine T, MacArthur Foundation Research Networks on Successful Midlife Development and Socioeconomic Status and Health; Juho Vainio Foundation; Juvenile Diabetes Research Foundation International; KfH Stiftung Pra¨ventivmedizin e.V.; King's College London; Knut and Alice Wallenberg Foundation; Kuopio University Hospital; Kuopio, Tampere and Turku University Hospital Medical Funds (X51001); La Fondation de France; Leenaards Foundation; Lilly; LMUinnovativ; Lundberg Foundation; Magnus Bergvall Foundation; MDEIE; Medical Research Council UK (G0000934, G0601966, G0700931, MC_U106179471, MC_UU_12019/1); MEKOS Laboratories; Merck Sante´; Ministry for Health, Welfare and Sports, The Netherlands; Ministry of Cultural Affairs of Mecklenburg-West Pomerania; Ministry of Economic Affairs, The Netherlands; Ministry of Education and Culture of Finland (627;2004-2011); Ministry of Education, Culture and Science, The Netherlands; Ministry of Science, Education and Sport in the Republic of Croatia (108-1080315-0302); MRC centre for Causal Analyses in Translational Epidemiology; MRC Human Genetics Unit; MRC-GlaxoSmithKline pilot programme (G0701863); MSD Stipend Diabetes; National Institute for Health Research; Netherlands Brain Foundation (F2013(1)-28); Netherlands CardioVascular Research Initiative (CVON2011-19); Netherlands Genomics Initiative (050-060-810); Netherlands Heart Foundation (2001 D 032, NHS2010B280); Netherlands Organization for Scientific Research (NWO) and Netherlands Organisation for Health Research and Development (ZonMW) (56-464- 14192, 60-60600-97-118, 100-001-004, 261-98-710, 400-05-717, 480-04-004, 480-05-003, 481-08-013, 904-61-090, 904-61-193, 911-11-025, 985-10-002, Addiction-31160008, BBMRI–NL 184.021.007, GB-MaGW 452-04-314, GB-MaGW 452-06-004, GB-MaGW 480-01-006, GB-MaGW 480-07-001, GB-MW 940-38-011, Middelgroot-911-09-032, NBIC/BioAssist/RK 2008.024, Spinozapremie 175.010.2003.005, 175.010.2007.006); NATURE COMMUNICATIONS | DOI:10.1038/ncomms14977 ARTICLE NATURE COMMUNICATIONS | 8:14977 | DOI:10.1038/ncomms14977 | www.nature.com/naturecommunications 13 Neuroscience Campus Amsterdam; NHS Foundation Trust; National Institutes of Health (1RC2MH089951, 1Z01HG000024, 24152, 263MD9164, 263MD821336, 2R01LM010098, 32100-2, 32122, 32108, 5K99HL130580-02, AA07535, AA10248, AA11998, AA13320, AA13321, AA13326, AA14041, AA17688, AG13196, CA047988, DA12854, DK56350, DK063491, DK078150, DK091718, DK100383, DK078616, ES10126, HG004790, HHSN268200625226C, HHSN268200800007C, HHSN268201200036C, HHSN268201500001I, HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, HHSN271201100004C, HL043851, HL45670, HL080467, HL085144, HL087660, HL054457, HL119443, HL118305, HL071981, HL034594, HL126024, HL130114, KL2TR001109, MH66206, MH081802, N01AG12100, N01HC55015, N01HC55016, N01C55018, N01HC55019, N01HC55020, N01HC55021, N01HC55022, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, N01HC95159, N01HC95160, N01HC95161, N01HC95162, N01HC95163, N01HC95164, N01HC95165, N01HC95166, N01HC95167, N01HC95168, N01HC95169, N01HG65403, N01WH22110, N02HL6-4278, N01-HC-25195, P01CA33619, R01HD057194, R01HD057194, R01AG023629, R01CA63, R01D004215701A, R01DK075787, R01DK062370, R01DK072193, R01DK075787, R01DK089256, R01HL53353, R01HL59367, R01HL086694, R01HL087641, R01HL087652, R01HL103612, R01HL105756, R01HL117078, R01HL120393, R03 AG046389, R37CA54281, RC2AG036495, RC4AG039029, RPPG040710371, RR20649, TW008288, TW05596, U01AG009740, U01CA98758, U01CA136792, U01DK062418, U01HG004402, U01HG004802, U01HG007376, U01HL080295, UL1RR025005, UL1TR000040, UL1TR000124, UL1TR001079, 2T32HL007055-36, T32GM074905, HG002651, HL084729, N01-HC25195, UM1CA182913); NIH, National Institute on Aging (Intramural funding, NO1-AG-1-2109); Northern Netherlands Collaboration of Provinces; Novartis Pharma; Novo Nordisk; Novo Nordisk Foundation; Nutricia Research Foundation (2016-T1); ONIVINS; Parnassia Bavo group; Pierre Fabre; Province of Groningen; Pa¨ivikki and Sakari Sohlberg Foundation; Påhlssons Foundation; Paavo Nurmi Foundation; Radboud Medical Center Nijmegen; Research Centre for Prevention and Health, the Capital Region of Denmark; the Research Institute for Diseases in the Elderly; Research into Ageing; Robert Dawson Evans Endowment of the Department of Medicine at Boston University School of Medicine and Boston Medical Center; Roche; Royal Society; Russian Foundation for Basic Research (NWO-RFBR 047.017.043); Rutgers University Cell and DNA Repository (NIMH U24 MH068457-06); Sanofi-Aventis; Scottish Government Health Directorates, Chief Scientist Office (CZD/16/6); Siemens Healthcare; Social Insurance Institution of Finland (4/26/2010); Social Ministry of the Federal State of Mecklenburg-West Pomerania; Socie´te´ Francophone du 358 Diabe`te; State of Bavaria; Stiftelsen fo¨r Gamla Tja¨narinnor; Stockholm County Council (560183, 592229); Strategic Cardiovascular and Diabetes Programmes of Karolinska Institutet and Stockholm County Council; Stroke Association; Swedish Diabetes Association; Swedish Diabetes Foundation (2013-024); Swedish Foundation for Strategic Research; Swedish Heart-Lung Foundation (20120197, 20150711); Swedish Research Council (0593, 8691, 2012-1397, 2012-1727, and 2012-2215); Swedish Society for Medical Research; Swiss Institute of Bioinformatics; Swiss National Science Foundation (3100AO-116323/1, 31003A-143914, 33CSCO-122661, 33CS30-139468, 33CS30-148401, 51RTP0_151019); Tampere Tuberculosis Foundation; Technology Foundation STW (11679); The Fonds voor Wetenschappelijk Onderzoek Vlaanderen, Ministry of the Flemish Community (G.0880.13, G.0881.13); The Great Wine Estates of the Margaret River Region of Western Australia; Timber Merchant Vilhelm Bangs Foundation; Topcon; Tore Nilsson Foundation; Torsten and Ragnar So¨derberg's Foundation; United States – Israel Binational Science Foundation (Grant 2011036), Umeå University; University Hospital of Regensburg; University of Groningen; University Medical Center Groningen; University of Michigan; University of Utrecht; Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX) (b2011036); Velux Foundation; VU University's Institute for Health and Care Research; Va¨stra Go¨taland Foundation; Wellcome Trust (068545, 076113, 079895, 084723, 088869, WT064890, WT086596, WT098017, WT090532, WT098051, 098381); Wissenschaftsoffensive TMO; Yrjo¨ Jahnsson Foundation; and Åke Wiberg Foundation

Background: Achieving universal health coverage (UHC) involves all people receiving the health services they need, of high quality, without experiencing financial hardship. Making progress towards UHC is a policy priority for both countries and global institutions, as highlighted by the agenda of the UN Sustainable Development Goals (SDGs) and WHO's Thirteenth General Programme of Work (GPW13). Measuring effective coverage at the health-system level is important for understanding whether health services are aligned with countries' health profiles and are of sufficient quality to produce health gains for populations of all ages. Methods: Based on the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we assessed UHC effective coverage for 204 countries and territories from 1990 to 2019. Drawing from a measurement framework developed through WHO's GPW13 consultation, we mapped 23 effective coverage indicators to a matrix representing health service types (eg, promotion, prevention, and treatment) and five population-age groups spanning from reproductive and newborn to older adults (≥65 years). Effective coverage indicators were based on intervention coverage or outcome-based measures such as mortality-to-incidence ratios to approximate access to quality care; outcome-based measures were transformed to values on a scale of 0–100 based on the 2·5th and 97·5th percentile of location-year values. We constructed the UHC effective coverage index by weighting each effective coverage indicator relative to its associated potential health gains, as measured by disability-adjusted life-years for each location-year and population-age group. For three tests of validity (content, known-groups, and convergent), UHC effective coverage index performance was generally better than that of other UHC service coverage indices from WHO (ie, the current metric for SDG indicator 3.8.1 on UHC service coverage), the World Bank, and GBD 2017. We quantified frontiers of UHC effective coverage performance on the basis of pooled health spending per capita, representing UHC effective coverage index levels achieved in 2019 relative to country-level government health spending, prepaid private expenditures, and development assistance for health. To assess current trajectories towards the GPW13 UHC billion target—1 billion more people benefiting from UHC by 2023—we estimated additional population equivalents with UHC effective coverage from 2018 to 2023. Findings: Globally, performance on the UHC effective coverage index improved from 45·8 (95% uncertainty interval 44·2–47·5) in 1990 to 60·3 (58·7–61·9) in 2019, yet country-level UHC effective coverage in 2019 still spanned from 95 or higher in Japan and Iceland to lower than 25 in Somalia and the Central African Republic. Since 2010, sub-Saharan Africa showed accelerated gains on the UHC effective coverage index (at an average increase of 2·6% [1·9–3·3] per year up to 2019); by contrast, most other GBD super-regions had slowed rates of progress in 2010–2019 relative to 1990–2010. Many countries showed lagging performance on effective coverage indicators for non-communicable diseases relative to those for communicable diseases and maternal and child health, despite non-communicable diseases accounting for a greater proportion of potential health gains in 2019, suggesting that many health systems are not keeping pace with the rising non-communicable disease burden and associated population health needs. In 2019, the UHC effective coverage index was associated with pooled health spending per capita (r=0·79), although countries across the development spectrum had much lower UHC effective coverage than is potentially achievable relative to their health spending. Under maximum efficiency of translating health spending into UHC effective coverage performance, countries would need to reach $1398 pooled health spending per capita (US$ adjusted for purchasing power parity) in order to achieve 80 on the UHC effective coverage index. From 2018 to 2023, an estimated 388·9 million (358·6–421·3) more population equivalents would have UHC effective coverage, falling well short of the GPW13 target of 1 billion more people benefiting from UHC during this time. Current projections point to an estimated 3·1 billion (3·0–3·2) population equivalents still lacking UHC effective coverage in 2023, with nearly a third (968·1 million [903·5–1040·3]) residing in south Asia. Interpretation: The present study demonstrates the utility of measuring effective coverage and its role in supporting improved health outcomes for all people—the ultimate goal of UHC and its achievement. Global ambitions to accelerate progress on UHC service coverage are increasingly unlikely unless concerted action on non-communicable diseases occurs and countries can better translate health spending into improved performance. Focusing on effective coverage and accounting for the world's evolving health needs lays the groundwork for better understanding how close—or how far—all populations are in benefiting from UHC.