In: Journal of policy and practice in intellectual disabilities: official journal of the International Association for the Scientific Study of Intellectual Disabilities, Volume 12, Issue 4, p. 303-311
AbstractAn increased life expectancy of people with Down syndrome (DS) and the occurrence of early aging are now recognized. However, few data exist on functioning and disability profile of early‐aging people with DS, which takes into account how health conditions interact with environmental factors. Based on the World Health Organization's International Classification of Functioning, Disability and Health (ICF), the authors collected preliminary data describing functioning and disability about early aging from 31 adults with DS aged 45 and older residing in the greater Milan, Italy, area. ICF checklist was used to collect data. Of the 31, 18 were women (mean age = 51.1), all were unmarried, 21 lived with their family, and 23 never worked. Few problems in body functions (e.g., vision and hearing, cognitions) and body structures (e.g., ear and eye structures, cardiovascular, endocrine, and digestive systems, skin structures) were reported. ICF categories within Activity and Participation domain emphasized the facilitating effect of environmental factors on activities related to domestic life (e.g., doing housework, acquisition of goods and services, preparation of meals). Within the Environmental Factors domain there are few barriers, mainly with respect to labor services, and very strong facilitating factors, particularly related to product of technology (for personal indoor and outdoor mobility and transportation) and the support of family members. The authors note that the findings can set out premises for further study on aging people with DS and their caregivers at national and international levels.
ABSTRACTThe aim of this paper is to investigate the association between structural and cognitive aspects of social capital and self-rated health among adults aged 50 or more, living in three countries: Finland, Poland and Spain. The study, which was based on data from the European Social Survey (2008/09), was a part of the EU research project COURAGE in Europe. More specifically the paper assesses the association between social capital indicators – informal social network and general trust – and good self-rated health through single-level and joint effects analyses. The results showed that Finland was a country of high social capital, in terms of both social networks and general trust, while Spain showed low levels of general trust and Poland low levels of informal social networks. As to the association between social capital and self-rated health, high levels of general trust and high networks were found to be associated with good health among all countries' respondents. Older persons living in partnerships, with higher education, higher levels of engagement in informal networks and general trust, were found to be more likely to show good self-rated health. Our comparative analyses revealed different associations between social capital and health according to country.
Background: Pain is a common symptom, often associated with neurological and musculoskeletal conditions, and experienced especially by females and by older people. The aims of this study are to evaluate the temporal variations of pain rates among general populations for the period 1991-2015 and to project 10-year pain rates. Methods: We used the harmonized dataset of ATHLOS project, which included 660,028 valid observations in the period 1990-2015 and we applied Bayesian age-period-cohort modeling to perform projections up to 2025. The harmonized Pain variable covers the content "self-reported pain experienced at the time of the interview", with a dichotomous (yes or no) modality. Results: Pain rates were higher among females, older subjects, in recent periods, and among observations referred to cohorts of subjects born between the 20s and the 60s. The 10-year projections indicate a noteworthy increase in pain rates in both genders and particularly among subjects aged 66 or over, for whom a 10-20% increase in pain rate is foreseen; among females only, a 10-15% increase in pain rates is foreseen for those aged 36-50. Conclusions: Projected increase in pain rates will require specific interventions by health and welfare systems, as pain is responsible for limited quality of subjective well-being, reduced employment rates and hampered work performance. Worksite and lifestyle interventions will therefore be needed to limit the impact of projected higher pain rates. ; The ATHLOS project (Ageing Trajectories of Health: Longitudinal Opportunities and Synergies) has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 635316.
Background: Pain is a common symptom, often associated with neurological and musculoskeletal conditions, and experienced especially by females and by older people, and with increasing trends in general populations. Different risk factors for pain have been identified, but generally from studies with limited samples and a limited number of candidate predictors. The aim of this study is to evaluate the predictors of pain from a large set of variables and respondents. Methods: We used part of the harmonized dataset of ATHLOS project, selecting studies and waves with a longitudinal course, and in which pain was absent at baseline and with no missing at follow-up. Predictors were selected based on missing distribution and univariable association with pain, and were selected from the following domains: Socio-demographic and economic characteristics, Lifestyle and health behaviours, Health status and functional limitations, Diseases, Physical measures, Cognition, personality and other psychological measures, and Social environment. Hierarchical logistic regression models were then applied to identify significant predictors. Results: A total of 13,545 subjects were included of whom 5348 (39.5%) developed pain between baseline and the average 5.2 years' follow-up. Baseline risk factors for pain were female gender (OR 1.34), engaging in vigorous exercise (OR 2.51), being obese (OR 1.36) and suffering from the loss of a close person (OR 1.88) whereas follow-up risk factors were low energy levels/fatigue (1.93), difficulties with walking (1.69), self-rated health referred as poor (OR 2.20) or average to moderate (OR 1.57) and presence of sleep problems (1.80). Conclusions: Our results showed that 39.5% of respondents developed pain over a five-year follow-up period, that there are proximal and distal risk factors for pain, and that part of them are directly modifiable. Actions aimed at improving sleep, reducing weight among obese people and treating fatigue would positively impact on pain onset, and avoiding vigorous exercise should be advised to people aged 60 or over, in particular if female or obese. ; The ATHLOS project (Ageing Trajectories of Health: Longitudinal Opportunities and Synergies) has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 635316
We investigated the relation between alcohol drinking and healthy ageing by meansof a validated health status metric, using individual data from the Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) project. For the purposes of this study, the ATHLOS harmonised dataset, which includes information from individuals aged 65+ in 38 countries, was analysed (n = 135,440). Alcohol drinking was reflected by means of three harmonised variables: alcohol drinking frequency, current and past alcohol drinker. A set of 41 self-reported health items and measured tests were used to generate a specific health metric. In the harmonised dataset, the prevalence of current drinking was 47.5% while of past drinking was 26.5%. In the pooled sample, current alcohol drinking was positively associated with better health status among older adults ((b-coef (95% CI): 1.32(0.45 to 2.19)) and past alcohol drinking was inversely related (b-coef (95% CI): −0.83 (−1.51 to −0.16)) with health status. Often alcohol consumption appeared to be beneficial only for females in all super-regions except Africa, both age group categories (65–80 years old and 80+), both age group categories, as well as among all the financial status categories (all p < 0.05). Regional analysis pictured diverse patterns in the association for current and past alcohol drinkers. Our results report the need for specific alcohol intake recommendations among older adults that will help them maintain a better health status throughout the ageing process. ; This work was supported by the five-year Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) project. The ATHLOS project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 635316. The ATHLOS project researchers are grateful for data contribution and funding in the following studies: (A) The 10/66 study (10/66): The 10/66 study is supported by theWellcome Trust (GR066133/ GR080002), the European Research Council (340755), US Alzheimer's Association, WHO, FONDACIT (Venezuela) and the Puerto Rico State Government, and the Medical Research Council (MR/K021907/1 to A.M.P.). The authors gratefully acknowledge the work of the 10/66 Dementia Research Group who provided data for this paper; (B) The Australian Longitudinal Study of Ageing (ALSA): The ALSA study was supported by grants from the South Australian Health Commission, the Australian Rotary Health Research Fund, the US National Institute on Aging (Grant No. AG 08523–02), theO ce for the Ageing (SA), Elderly Citizens Homes (SA), the National Health and Medical Research Council (NH&MRC 22922), the Premiers Science Research Fund (SA) and the Australian Research Council (DP0879152; DP130100428). The authors gratefully acknowledge the work of the project team at the Flinders Centre for Ageing Studies, Flinders University who provided data for this paper; (C) The ATTICA study: The ATTICA study is supported by research grants from the Hellenic Cardiology Society (HCS2002) and the Hellenic Atherosclerosis Society (HAS2003). The authors gratefully acknowledge the work of the project team at the Harokopio University who provided data for this paper; (D) The China Health and Retirement Longitudinal Study (CHARLS): The CHARLS study has received critical support from Peking University, the National Natural Science Foundation of China, the Behavioral and Social Research Division of the National Institute on Aging and the World Bank. The authors gratefully acknowledge the work of the project team at the Peking University who provided data for this paper; (E) Collaborative Research on Ageing (COURAGE) in Europe: The COURAGE study was supported by the European Community's Seventh Framework Programme (FP7/2007–2013) under grant agreement number 223071 (COURAGE in Europe). Data from Spain were also collected with support from the Instituto de Salud Carlos III-FIS research grants number PS09/00295, PS09/01845, PI12/01490, PI13/00059, PI16/00218 and PI16/01073; the Spanish Ministry of Science and Innovation ACI-Promociona (ACI2009–1010); the European Regional Development Fund (ERDF) 'AWay to Build Europe' grant numbers PI12/01490 and PI13/00059; and by the Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III. Data from Poland were collected with support from the Polish Ministry for Science and Higher Education grant for an international co-financed project (number 1277/7PR/UE/2009/7, 2009–2012) and Jagiellonian University Medical College grant for project COURAGE-POLFUS (K/ZDS/005241). The authors gratefully acknowledge the work of COURAGE researchers who provided data for this paper; (F) The Seniors-ENRICA: The Seniors-ENRICA cohort was funded by an unconditional grant from Sanofi-Aventis, the Ministry of Health of Spain, FIS grant 12/1166 (State Secretary for R+D and FEDER-FSE) and the Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III. The authors gratefully acknowledge the work of the project team at the Universidad Autónoma de Madrid who provided data for this paper; (G) The English Longitudinal Study of Ageing (ELSA): ELSA is supported by the U.S. National Institute of Aging, the National Centre for Social Research, the University College London (UCL) and the Institute for Fiscal Studies. The authors gratefully acknowledge the UK Data Service and UCL who provided data for this paper; (H) The Health, Alcohol and Psychosocial factors In Eastern Europe (HAPIEE) study: The HAPIEE study was supported by theWellcome Trust (grant numbers WT064947, WT081081), the US National Institute of Aging (grant number 1RO1AG23522) and the MacArthur Foundation Initiative on Social Upheaval and Health. The authors gratefully acknowledge the work of the project teams at University College London, the National Institute of Public Health in Prague, the Jagiellonian University Medical College in Krakow and the Kaunas University of Medicine who provided data for this paper; (I) The Health 2000/2011 study: The authors gratefully acknowledge the National Institute for Health and Welfare in Finland who provided data for this paper; (J) Health and Retirement Study (HRS): The HRS study is supported by the National Institute on Aging (grant number NIA U01AG009740) and the Social Security Administration, and is conducted by the University of Michigan. The authors gratefully acknowledge the University of Michigan who provided data for this paper; (K) The Japanese Study of Aging and Retirement (JSTAR): The JSTAR is conducted by the Research Institute of Economy, Trade and Industry (RIETI), the Hitotsubashi University, and the University of Tokyo. The authors gratefully acknowledge the RIETI who provided data for this paper; (L) The Korean Longitudinal Study of Ageing (KLOSA): The KLOSA study is funded by the Korea Employment Information Service (KEIS) and was supported by the Korea Labor Institute's KLOSA Team. The authors gratefully acknowledge the KEIS who provided data for this paper; (M) The Mexican Health and Aging Study (MHAS): The MHAS study is partly sponsored by the National Institutes of Health/National Institute on Aging (grant number NIH R01AG018016) and the INEGI in Mexico. The authors gratefully acknowledge the MHAS team who provided data for this paper retrieved from www.MHASweb.org: (N) The Study on Global Ageing and Adult Health (SAGE): The SAGE study is funded by the U.S. National Institute on Aging and has received financial support through Interagency Agreements (OGHA 04034785; YA1323-08-CN-0020; Y1-AG-1005–01) and Grants (R01-AG034479; IR21-AG034263-0182). The authors gratefully acknowledge theWorld Health Organizationwho provided data for this paper; (O) The Survey of Health, Ageing and Retirement in Europe (SHARE): The SHARE study is funded by the European Commission through FP5 (QLK6-CT-2001–00360), FP6 (SHARE-I3: RII-CT-2006–062193, COMPARE: CIT5-CT-2005–028857, SHARELIFE: CIT4-CT-2006–028812) and FP7 (SHARE-PREP: N 211909, SHARE-LEAP: N 227822, SHARE M4: N 261982). Additional funding from the German Ministry of Education and Research, the Max Planck Society for the Advancement of Science, the U.S. National Institute on Aging (U01_AG09740-13S2, P01_AG005842, P01_AG08291, P30_AG12815, R21_AG025169, Y1-AG-4553–01, IAG_BSR06-11, OGHA_04-064, HHSN271201300071C) and from various national funding sources is gratefully acknowledged (see www.share-project.org); (P) The Irish Longitudinal study on Ageing (TILDA): The authors gratefully acknowledge the Trinity College Dublin and the Irish Social Science Data Archive (www.ucd.ie/issda) who provided data for this paper; (Q) The Uppsala Birth Cohort Multigenerational Study (UBCOS): The UBCoS study has received funding from the Swedish Research Council for Health, Working Life and Welfare (FORTE; 2006–1518 and 2013–1084) and from the Swedish Research Council (VR; 2013–5104 and 2013–5474). The authors gratefully acknowledge the Centre for Health Equity Studies at the Stockholm University and Karolinska Institutet's team who provided data for this paper. Additionally, Stefanos Tyrovolas was supported by the Foundation for Education and European Culture, the Miguel Servet programme (reference CP18/00006), and the Fondos Europeos de Desarrollo Regional. Also, Alberto Raggi is supported by a grant from the Italian Ministry of Health (Ricerca Corrente, Fondazione Istituto Neurologico C. Besta, Linea 4—Outcome Research: dagli Indicatori alle Raccomandazioni Cliniche. The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The authors had access to the data in the study and had final responsibility for the decision to submit for publication.
Through a comprehensive analysis of Italy's estimates from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017, we aimed to understand the patterns of health loss and response of the health-care system, and offer evidence-based policy indications in light of the demographic transition and government health spending in the country.
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. FUNDING:Bill & Melinda Gates Foundation.
Publisher's version (útgefin grein) ; 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. ; Lucas Guimaraes Abreu acknowledges support from Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior -Brasil (Capes) -Finance Code 001, Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq) and Fundacao de Amparo a Pesquisa do Estado de Minas Gerais (FAPEMIG). Olatunji O Adetokunboh acknowledges South African Department of Science & Innovation, and National Research Foundation. Anurag Agrawal acknowledges support from the Wellcome Trust DBT India Alliance Senior Fellowship IA/CPHS/14/1/501489. Rufus Olusola Akinyemi acknowledges Grant U01HG010273 from the National Institutes of Health (NIH) as part of the H3Africa Consortium. Rufus Olusola Akinyemi is further supported by the FLAIR fellowship funded by the UK Royal Society and the African Academy of Sciences. Syed Mohamed Aljunid acknowledges the Department of Health Policy and Management, Faculty of Public Health, Kuwait University and International Centre for Casemix and Clinical Coding, Faculty of Medicine, National University of Malaysia for the approval and support to participate in this research project. Marcel Ausloos, Claudiu Herteliu, and Adrian Pana acknowledge partial support by a grant of the Romanian National Authority for Scientific Research and Innovation, CNDSUEFISCDI, project number PN-III-P4-ID-PCCF-2016-0084. Till Winfried Barnighausen acknowledges support from the Alexander von Humboldt Foundation through the Alexander von Humboldt Professor award, funded by the German Federal Ministry of Education and Research. Juan J Carrero was supported by the Swedish Research Council (2019-01059). Felix Carvalho acknowledges UID/MULTI/04378/2019 and UID/QUI/50006/2019 support with funding from FCT/MCTES through national funds. Vera Marisa Costa acknowledges support from grant (SFRH/BHD/110001/2015), received by Portuguese national funds through Fundacao para a Ciencia e a Tecnologia (FCT), IP, under the Norma TransitA3ria DL57/2016/CP1334/CT0006. Jan-Walter De Neve acknowledges support from the Alexander von Humboldt Foundation. Kebede Deribe acknowledges support by Wellcome Trust grant number 201900/Z/16/Z as part of his International Intermediate Fellowship. Claudiu Herteliu acknowledges partial support by a grant co-funded by European Fund for Regional Development through Operational Program for Competitiveness, Project ID P_40_382. Praveen Hoogar acknowledges the Centre for Bio Cultural Studies (CBiCS), Manipal Academy of Higher Education(MAHE), Manipal and Centre for Holistic Development and Research (CHDR), Kalghatgi. Bing-Fang Hwang acknowledges support from China Medical University (CMU108-MF-95), Taichung, Taiwan. Mihajlo Jakovljevic acknowledges the Serbian part of this GBD contribution was co-funded through the Grant OI175014 of the Ministry of Education Science and Technological Development of the Republic of Serbia. Aruna M Kamath acknowledges funding from the National Institutes of Health T32 grant (T32GM086270). Srinivasa Vittal Katikireddi acknowledges funding from the Medical Research Council (MC_UU_12017/13 & MC_UU_12017/15), Scottish Government Chief Scientist Office (SPHSU13 & SPHSU15) and an NRS Senior Clinical Fellowship (SCAF/15/02). Yun Jin Kim acknowledges support from the Research Management Centre, Xiamen University Malaysia (XMUMRF/2018-C2/ITCM/0001). Kewal Krishan acknowledges support from the DST PURSE grant and UGC Center of Advanced Study (CAS II) awarded to the Department of Anthropology, Panjab University, Chandigarh, India. Manasi Kumar acknowledges support from K43 TW010716 Fogarty International Center/NIMH. Ben Lacey acknowledges support from the NIHR Oxford Biomedical Research Centre and the BHF Centre of Research Excellence, Oxford. Ivan Landires is a member of the Sistema Nacional de InvestigaciA3n (SNI), which is supported by the Secretaria Nacional de Ciencia Tecnologia e Innovacion (SENACYT), Panama. Jeffrey V Lazarus acknowledges support by a Spanish Ministry of Science, Innovation and Universities Miguel Servet grant (Instituto de Salud Carlos III/ESF, European Union [CP18/00074]). Peter T N Memiah acknowledges CODESRIA; HISTP. Subas Neupane acknowledges partial support from the Competitive State Research Financing of the Expert Responsibility area of Tampere University Hospital. Shuhei Nomura acknowledges support from the Ministry of Education, Culture, Sports, Science, and Technology of Japan (18K10082). Alberto Ortiz acknowledges support by ISCIII PI19/00815, DTS18/00032, ISCIII-RETIC REDinREN RD016/0009 Fondos FEDER, FRIAT, Comunidad de Madrid B2017/BMD-3686 CIFRA2-CM. These funding sources had no role in the writing of the manuscript or the decision to submit it for publication. George C Patton acknowledges support from a National Health & Medical Research Council Fellowship. Marina Pinheiro acknowledges support from FCT for funding through program DL 57/2016 -Norma transitA3ria. Alberto Raggi, David Sattin, and Silvia Schiavolin acknowledge support by a grant from the Italian Ministry of Health (Ricerca Corrente, Fondazione Istituto Neurologico C Besta, Linea 4 -Outcome Research: dagli Indicatori alle Raccomandazioni Cliniche). Daniel Cury Ribeiro acknowledges support from the Sir Charles Hercus Health Research Fellowship -Health Research Council of New Zealand (18/111). Perminder S Sachdev acknowledges funding from the NHMRC Australia. Abdallah M Samy acknowledges support from a fellowship from the Egyptian Fulbright Mission Program. Milena M Santric-Milicevic acknowledges support from the Ministry of Education, Science and Technological Development of the Republic of Serbia (Contract No. 175087). Rodrigo Sarmiento-Suarez acknowledges institutional support from University of Applied and Environmental Sciences in Bogota, Colombia, and Carlos III Institute of Health in Madrid, Spain. Maria Ines Schmidt acknowledges grants from the Foundation for the Support of Research of the State of Rio Grande do Sul (IATS and PrInt) and the Brazilian Ministry of Health. Sheikh Mohammed Shariful Islam acknowledges a fellowship from the National Heart Foundation of Australia and Deakin University. Aziz Sheikh acknowledges support from Health Data Research UK. Kenji Shibuya acknowledges Japan Ministry of Education, Culture, Sports, Science and Technology. Joan B Soriano acknowledges support by Centro de Investigacion en Red de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III (ISCIII), Madrid, Spain. Rafael Tabares-Seisdedos acknowledges partial support from grant PI17/00719 from ISCIII-FEDER. Santosh Kumar Tadakamadla acknowledges support from the National Health and Medical Research Council Early Career Fellowship, Australia. Marcello Tonelli acknowledges the David Freeze Chair in Health Services Research at the University of Calgary, AB, Canada. ; "Peer Reviewed"
Publisher's version (útgefin grein) ; Background In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution. Findings Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990-2010 time period, with the greatest annualised rate of decline occurring in the 0-9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10-24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10-24 years were also in the top ten in the 25-49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain (fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the top-ranked causes of DALYs in both the 50-74-year and 75-years-and-older age groups. Since 1990, there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries. In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI. Interpretation As disability becomes an increasingly large component of disease burden and a larger component of health expenditure, greater research and development investment is needed to identify new, more effective intervention strategies. With a rapidly ageing global population, the demands on health services to deal with disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of universal and more geographically specific influences on health reinforces the need for regular reporting on population health in detail and by underlying cause to help decision makers to identify success stories of disease control to emulate, as well as opportunities to improve. Copyright (C) 2020 The Author(s). Published by Elsevier Ltd. ; Research reported in this publication was supported by the Bill & Melinda Gates Foundation; the University of Melbourne; Queensland Department of Health, Australia; the National Health and Medical Research Council, Australia; Public Health England; the Norwegian Institute of Public Health; St Jude Children's Research Hospital; the Cardiovascular Medical Research and Education Fund; the National Institute on Ageing of the National Institutes of Health (award P30AG047845); and the National Institute of Mental Health of the National Institutes of Health (award R01MH110163). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funders. The authors alone are responsible for the views expressed in this Article and they do not necessarily represent the views, decisions, or policies of the institutions with which they are affiliated, the National Health Service (NHS), the National Institute for Health Research (NIHR), the UK Department of Health and Social Care, or Public Health England; the United States Agency for International Development (USAID), the US Government, or MEASURE Evaluation; or the European Centre for Disease Prevention and Control (ECDC). This research used data from the Chile National Health Survey 2003, 2009-10, and 2016-17. The authors are grateful to the Ministry of Health, the survey copyright owner, for allowing them to have the database. All results of the study are those of the authors and in no way committed to the Ministry. The Costa Rican Longevity and Healthy Aging Study project is a longitudinal study by the University of Costa Rica's Centro Centroamericano de Poblacion and Instituto de Investigaciones en Salud, in collaboration with the University of California at Berkeley. The original pre-1945 cohort was funded by the Wellcome Trust (grant 072406), and the 1945-55 Retirement Cohort was funded by the US National Institute on Aging (grant R01AG031716). The principal investigators are Luis Rosero-Bixby and William H Dow and co-principal investigators are Xinia Fernandez and Gilbert Brenes. The accuracy of the authors' statistical analysis and the findings they report are not the responsibility of ECDC. ECDC is not responsible for conclusions or opinions drawn from the data provided. ECDC is not responsible for the correctness of the data and for data management, data merging and data collation after provision of the data. ECDC shall not be held liable for improper or incorrect use of the data. The Health Behaviour in School-Aged Children (HBSC) study is an international study carried out in collaboration with WHO/EURO. The international coordinator of the 1997-98, 2001-02, 2005-06, and 2009-10 surveys was Candace Currie and the databank manager for the 1997-98 survey was Bente Wold, whereas for the following surveys Oddrun Samdal was the databank manager. A list of principal investigators in each country can be found on the HBSC website. Data used in this paper come from the 2009-10 Ghana Socioeconomic Panel Study Survey, which is a nationally representative survey of more than 5000 households in Ghana. The survey is a joint effort undertaken by the Institute of Statistical, Social and Economic Research (ISSER) at the University of Ghana and the Economic Growth Centre (EGC) at Yale University. It was funded by EGC. ISSER and the EGC are not responsible for the estimations reported by the analysts. The Palestinian Central Bureau of Statistics granted the researchers access to relevant data in accordance with license number SLN2014-3-170, after subjecting data to processing aiming to preserve the confidentiality of individual data in accordance with the General Statistics Law, 2000. The researchers are solely responsible for the conclusions and inferences drawn upon available data. Data for this research was provided by MEASURE Evaluation, funded by USAID. The authors thank the Russia Longitudinal Monitoring Survey, conducted by the National Research University Higher School of Economics and ZAO Demoscope together with Carolina Population Center, University of North Carolina at Chapel Hill and the Institute of Sociology, Russia Academy of Sciences for making data available. This paper uses data from the Bhutan 2014 STEPS survey, implemented by the Ministry of Health with the support of WHO; the Kuwait 2006 and 2014 STEPS surveys, implemented by the Ministry of Health with the support of WHO; the Libya 2009 STEPS survey, implemented by the Secretariat of Health and Environment with the support of WHO; the Malawi 2009 STEPS survey, implemented by Ministry of Health with the support of WHO; and the Moldova 2013 STEPS survey, implemented by the Ministry of Health, the National Bureau of Statistics, and the National Center of Public Health with the support of WHO. This paper uses data from Survey of Health, Ageing and Retirement in Europe (SHARE) Waves 1 (DOI:10.6103/SHARE. w1.700), 2 (10.6103/SHARE.w2.700), 3 (10.6103/SHARE.w3.700), 4 (10.6103/SHARE.w4.700), 5 (10.6103/SHARE.w5.700), 6 (10.6103/SHARE.w6.700), and 7 (10.6103/SHARE.w7.700); see Borsch-Supan and colleagues (2013) for methodological details. The SHARE data collection has been funded by the European Commission through FP5 (QLK6-CT-2001-00360), FP6 (SHARE-I3: RII-CT-2006-062193, COMPARE: CIT5-CT-2005-028857, SHARELIFE: CIT4-CT-2006-028812), FP7 (SHARE-PREP: GA N degrees 211909, SHARE-LEAP: GA N degrees 227822, SHARE M4: GA N degrees 261982) and Horizon 2020 (SHARE-DEV3: GA N degrees 676536, SERISS: GA N degrees 654221) and by DG Employment, Social Affairs & Inclusion. Additional funding from the German Ministry of Education and Research, the Max Planck Society for the Advancement of Science, the US National Institute on Aging (U01_AG09740-13S2, P01_AG005842, P01_AG08291, P30_AG12815, R21_AG025169, Y1-AG-4553-01, IAG_BSR06-11, OGHA_04-064, HHSN271201300071C), and from various national funding sources is gratefully acknowledged. This study has been realised using the data collected by the Swiss Household Panel, which is based at the Swiss Centre of Expertise in the Social Sciences. The project is financed by the Swiss National Science Foundation. The United States Aging, Demographics, and Memory Study is a supplement to the Health and Retirement Study (HRS), which is sponsored by the National Institute of Aging (grant number NIA U01AG009740). It was conducted jointly by Duke University and the University of Michigan. The HRS is sponsored by the National Institute on Aging (grant number NIA U01AG009740) and is conducted by the University of Michigan. This paper uses data from Add Health, a program project designed by J Richard Udry, Peter S Bearman, and Kathleen Mullan Harris, and funded by a grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 17 other agencies. Special acknowledgment is due to Ronald R Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website. No direct support was received from grant P01-HD31921 for this analysis. The data reported here have been supplied by the United States Renal Data System. The interpretation and reporting of these data are the responsibility of the authors and in no way should be seen as an official policy or interpretation of the US Government. Collection of data for the Mozambique National Survey on the Causes of Death 2007-08 was made possible by USAID under the terms of cooperative agreement GPO-A-00-08-000_D3-00. This manuscript is based on data collected and shared by the International Vaccine Institute (IVI) from an original study IVI conducted. L G Abreu acknowledges support from Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (Brazil; finance code 001) and Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq, a Brazilian funding agency). I N Ackerman was supported by a Victorian Health and Medical Research Fellowship awarded by the Victorian Government. O O Adetokunboh acknowledges the South African Department of Science and Innovation and the National Research Foundation. A Agrawal acknowledges the Wellcome Trust DBT India Alliance Senior Fellowship. S M Aljunid acknowledges the Department of Health Policy and Management, Faculty of Public Health, Kuwait University and International Centre for Casemix and Clinical Coding, Faculty of Medicine, National University of Malaysia for the approval and support to participate in this research project. M Ausloos, C Herteliu, and A Pana acknowledge partial support by a grant of the Romanian National Authority for Scientific Research and Innovation, CNDS-UEFISCDI, project number PN-III-P4-ID-PCCF-2016-0084. A Badawi is supported by the Public Health Agency of Canada. D A Bennett was supported by the NIHR Oxford Biomedical Research Centre. R Bourne acknowledges the Brien Holden Vision Institute, University of Heidelberg, Sightsavers, Fred Hollows Foundation, and Thea Foundation. G B Britton and I Moreno Velasquez were supported by the Sistema Nacional de Investigacion, SNI-SENACYT, Panama. R Buchbinder was supported by an Australian National Health and Medical Research Council (NHMRC) Senior Principal Research Fellowship. J J Carrero was supported by the Swedish Research Council (2019-01059). F Carvalho acknowledges UID/MULTI/04378/2019 and UID/QUI/50006/2019 support with funding from FCT/MCTES through national funds. A R Chang was supported by National Institutes of Health/National Institute of Diabetes and Digestive and Kidney Diseases grant K23 DK106515. V M Costa acknowledges the grant SFRH/BHD/110001/2015, received by Portuguese national funds through Fundacao para a Ciencia e Tecnologia, IP, under the Norma Transitaria DL57/2016/CP1334/CT0006. A Douiri acknowledges support and funding from the National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care South London at King's College Hospital NHS Foundation Trust and the Royal College of Physicians, and support from the NIHR Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust and King's College London. B B Duncan acknowledges grants from the Foundation for the Support of Research of the State of Rio Grande do Sul (IATS and PrInt) and the Brazilian Ministry of Health. H E Erskine is the recipient of an Australian NHMRC Early Career Fellowship grant (APP1137969). A J Ferrari was supported by a NHMRC Early Career Fellowship grant (APP1121516). H E Erskine and A J Ferrari are employed by and A M Mantilla-Herrera and D F Santomauro affiliated with the Queensland Centre for Mental Health Research, which receives core funding from the Queensland Department of Health. M L Ferreira holds an NHMRC Research Fellowship. C Flohr was supported by the NIHR Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust. M Freitas acknowledges financial support from the EU (European Regional Development Fund [FEDER] funds through COMPETE POCI-01-0145-FEDER-029248) and National Funds (Fundacao para a Ciencia e Tecnologia) through project PTDC/NAN-MAT/29248/2017. A L S Guimaraes acknowledges support from CNPq. C Herteliu was partially supported by a grant co-funded by FEDER through Operational Competitiveness Program (project ID P_40_382). P Hoogar acknowledges Centre for Bio Cultural Studies, Directorate of Research, Manipal Academy of Higher Education and Centre for Holistic Development and Research, Kalaghatagi. F N Hugo acknowledges the Visiting Professorship, PRINT Program, CAPES Foundation, Brazil. B-F Hwang was supported by China Medical University (CMU107-Z-04), Taichung, Taiwan. S M S Islam was funded by a National Heart Foundation Senior Research Fellowship and supported by Deakin University. R Q Ivers was supported by a research fellowship from the National Health and Medical Research Council of Australia. M Jakovljevic acknowledges the Serbian part of this GBD-related contribution was co-funded through Grant OI175014 of the Ministry of Education Science and Technological Development of the Republic of Serbia. P Jeemon was supported by a Clinical and Public Health intermediate fellowship (grant number IA/CPHI/14/1/501497) from the Wellcome Trust-Department of Biotechnology, India Alliance (2015-20). O John is a recipient of UIPA scholarship from University of New South Wales, Sydney. S V Katikireddi acknowledges funding from a NRS Senior Clinical Fellowship (SCAF/15/02), the Medical Research Council (MC_UU_12017/13, MC_UU_12017/15), and the Scottish Government Chief Scientist Office (SPHSU13, SPHSU15). C Kieling is a CNPq researcher and a UK Academy of Medical Sciences Newton Advanced Fellow. Y J Kim was supported by Research Management Office, Xiamen University Malaysia (XMUMRF/2018-C2/ITCM/00010). K Krishan is supported by UGC Centre of Advanced Study awarded to the Department of Anthropology, Panjab University, Chandigarh, India. M Kumar was supported by K43 TW 010716 FIC/NIMH. B Lacey acknowledges support from the NIHR Oxford Biomedical Research Centre and the BHF Centre of Research Excellence, Oxford. J V Lazarus was supported by a Spanish Ministry of Science, Innovation and Universities Miguel Servet grant (Instituto de Salud Carlos III [ISCIII]/ESF, the EU [CP18/00074]). K J Looker thanks the NIHR Health Protection Research Unit in Evaluation of Interventions at the University of Bristol, in partnership with Public Health England, for research support. S Lorkowski was funded by the German Federal Ministry of Education and Research (nutriCARD, grant agreement number 01EA1808A). R A Lyons is supported by Health Data Research UK (HDR-9006), which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, NIHR (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation, and Wellcome Trust. J J McGrath is supported by the Danish National Research Foundation (Niels Bohr Professorship), and the Queensland Health Department (via West Moreton HHS). P T N Memiah acknowledges support from CODESRIA. U O Mueller gratefully acknowledges funding by the German National Cohort Study BMBF grant number 01ER1801D. S Nomura acknowledges the Ministry of Education, Culture, Sports, Science, and Technology of Japan (18K10082). A Ortiz was supported by ISCIII PI19/00815, DTS18/00032, ISCIII-RETIC REDinREN RD016/0009 Fondos FEDER, FRIAT, Comunidad de Madrid B2017/BMD-3686 CIFRA2-CM. These funding sources had no role in the writing of the manuscript or the decision to submit it for publication. S B Patten was supported by the Cuthbertson & Fischer Chair in Pediatric Mental Health at the University of Calgary. G C Patton was supported by an aNHMRC Senior Principal Research Fellowship. M R Phillips was supported in part by the National Natural Science Foundation of China (NSFC, number 81371502 and 81761128031). A Raggi, D Sattin, and S Schiavolin were supported by grants from the Italian Ministry of Health (Ricerca Corrente, Fondazione Istituto Neurologico C Besta, Linea 4-Outcome Research: dagli Indicatori alle Raccomandazioni Cliniche). P Rathi and B Unnikrishnan acknowledge Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal. A L P Ribeiro was supported by Brazilian National Research Council, CNPq, and the Minas Gerais State Research Agency, FAPEMIG. D C Ribeiro was supported by The Sir Charles Hercus Health Research Fellowship (#18/111) Health Research Council of New Zealand. D Ribeiro acknowledges financial support from the EU (FEDER funds through the Operational Competitiveness Program; POCI-01-0145-FEDER-029253). P S Sachdev acknowledges funding from the NHMRC of Australia Program Grant. A M Samy was supported by a fellowship from the Egyptian Fulbright Mission Program. M M Santric-Milicevic acknowledges the Ministry of Education, Science and Technological Development of the Republic of Serbia (contract number 175087). R Sarmiento-Suarez received institutional support from Applied and Environmental Sciences University (Bogota, Colombia) and ISCIII (Madrid, Spain). A E Schutte received support from the South African National Research Foundation SARChI Initiative (GUN 86895) and Medical Research Council. S T S Skou is currently funded by a grant from Region Zealand (Exercise First) and a grant from the European Research Council under the EU's Horizon 2020 research and innovation program (grant agreement number 801790). J B Soriano is funded by Centro de Investigacion en Red de Enfermedades Respiratorias, ISCIII. R Tabares-Seisdedos was supported in part by the national grant PI17/00719 from ISCIII-FEDER. N Taveira was partially supported by the European & Developing Countries Clinical Trials Partnership, the EU (LIFE project, reference RIA2016MC-1615). S Tyrovolas was supported by the Foundation for Education and European Culture, the Sara Borrell postdoctoral programme (reference number CD15/00019 from ISCIII-FEDER). S B Zaman received a scholarship from the Australian Government research training programme in support of his academic career. ; "Peer Reviewed"