Japan's 2011 Fukushima Dai-ichi Nuclear Power Plant Incident displaced more than 150 thousand people due to a release of radioactive material, highlighting the challenges facing us regarding radiation dose assessment and effective countermeasures (especially evacuation) when responding to a major incident. The overall aim of my PhD project was to inform the design, preparation, and delivery of measures to manage health risks of a major nuclear disaster, by adding evidence to fill knowledge gaps using data collected following the Fukushima nuclear incident. The emergency phase of the incident (i.e. within the first few hours/days) has been assessed elsewhere, so my focus was the post-emergency phase (i.e. in the days/months/years following the incident). To meet this aim, and building on a review of the relevant literature, I identified key research questions linked to the radiological and non-radiological health protection aspects in the context of the Fukushima incident. I was primarily interested in understanding 1) the potential challenges concerning the assessment of levels of external radiation exposure (the dominant exposure pathway) in children in the post-emergency phase following the Fukushima incident, and 2) the potential benefits and health risks of the post-incident evacuation in adults. The study areas are Minamisoma City and Soma City in Fukushima Prefecture, Japan, located 14–38 and 35–50 km from the Fukushima nuclear plant, respectively. Various primary and secondary datasets were used in my PhD project, including: measured levels of external radiation exposure; data on post-incident daily behaviour patterns; air dose rate data; measured levels of internal/body radio-contamination; early incident response patterns; nursing home data with evacuation records; and public health check-up data. A variety of statistical approaches to analyse these data were applied. Key findings concerning the evaluation of external radiation exposure in children included: (a) an average three-fold disagreement between the measured dose and that estimated from Japan's government dose reconstruction model, with the model overestimating it; (b) air dose rates at places where children spend most of the day being the significant predictors of the dose; and (c) poor compliance with radiation dosimeters among children (only 7.3% used the dosimeters properly). With respect to the potential benefits and health risks of early evacuation of adults, findings included: (d) limited effectiveness of early evacuation on reducing internal exposure risk; (e) a substantial mortality impact of initial evacuation (vs. non-evacuation); and (f) increased risk of long-term hyperlipidemia associated with evacuation. Overall, these findings indicate that the latitude for improving the evaluation of external radiation exposure in children in the post-emergency phase of a major nuclear incident is wide; dose reconstruction models should be developed that ensure a more realistic representation of external exposure risk factors, and factors influencing the proper use of direct measurements (using an individual radiation dosimeter) must be better understood. In addition, there is scope for reducing post-incident evacuation-related health risks; disaster planning must emphasise well-planned evacuation, with satisfactory arrangements for appropriate transportation, places to evacuate to safely, and include the long-term strategies for mitigating the evacuation-related health risks. The evidence presented in this PhD thesis will help the government, local authorities, health planners, and disaster coordinators to develop better optimized radiological and non-radiological protection policies to minimise health risks of future nuclear incidents. ; Open Access
BACKGROUND: Vaccination is one of the effective ways to develop immunity against potential life-threatening diseases in children in early age. This study is focused on analysing the age-appropriate vaccination coverage at national and subnational levels and identify the factors associated with age-appropriate coverage in Nepal. METHODS: 460 children aged 12–36 months were included in the study. The data was obtained from Nepal Demographic and Health Survey (NDHS) 2016–17. Age-appropriate coverage of Bacillus Calmette-Guerin vaccine (BCG), oral polio vaccine (OPV) doses 1–3, pentavalent vaccine (PE) doses 1–3, and first dose of measles, mumps, and rubella vaccine (MMR) were estimated using Kaplan Meier method. Multilevel logistic regression with random intercept was used to identify the factors associated with age-appropriate vaccination. RESULTS: The crude coverage of the vaccines included in the study ranged from 91.5% (95% CI, 88.5–93.7) for PE3 to 97.8% (95.8–98.7) for BCG. Although the crude coverage of all the vaccines was above 90%, the age-appropriate coverage was significantly low, ranging from 41.5% (36.5–46.6) for PE3 to 73.9% (69.2–78.1) for PE1. Furthermore, high disparity in timely vaccination coverage was observed at regional level. Compared to the age-appropriate vaccination coverage in other provinces, Province 2 had the lowest coverage of all, followed by that in Province 6. The timeliness of vaccination was significantly associated with subnational regions i.e., provinces and the season of childbirth. CONCLUSION: Although the immunization program in Nepal has achieved the target of 90% crude coverage of all the childhood vaccines, the age-appropriate coverage is significantly low which undermines the effectiveness of the vaccines administered. Thus, along with crude coverage, timeliness of the vaccines administered should be taken into consideration and thoroughly monitored at national and subnational levels. Provincial government should formulate tailored strategies to ensure the timely ...
In the COVID-19 era, movement restrictions are crucial to slow virus transmission and have been implemented in most parts of the world, including Japan. To find new insights on human mobility and movement restrictions encouraged (but not forced) by the emergency declaration in Japan, we analyzed mobility data at 35 major stations and downtown areas in Japan—each defined as an area overlaid by several 125-meter grids—from September 1, 2019 to March 19, 2021. Data on the total number of unique individuals per hour passing through each area were obtained from Yahoo Japan Corporation (i.e., more than 13,500 data points for each area). We examined the temporal trend in the ratio of the rolling seven-day daily average of the total population to a baseline on January 16, 2020, by ten-year age groups in five time frames. We demonstrated that the degree and trend of mobility decline after the declaration of a state of emergency varies across age groups and even at the subregional level. We demonstrated that monitoring dynamic geographic and temporal mobility information stratified by detailed population characteristics can help guide not only exit strategies from an ongoing emergency declaration, but also initial response strategies before the next possible resurgence. Combining such detailed data with data on vaccination coverage and COVID-19 incidence (including the status of the health care delivery system) can help governments and local authorities develop community-specific mobility restriction policies. This could include strengthening incentives to stay home and raising awareness of cognitive errors that weaken people's resolve to refrain from nonessential movement. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11524-021-00566-7.
In: Bulletin of the World Health Organization: the international journal of public health = Bulletin de l'Organisation Mondiale de la Santé, Band 98, Heft 8, S. 518-529
BACKGROUND: Attainment of universal health coverage is a global health priority. The Myanmar Government has committed to attainment of universal health coverage by 2030, but progress so far has not been assessed. We aimed to estimate national and subnational health service coverage and financial risk protection. METHODS: We used nationally representative data from the Myanmar Demographic and Health Survey (2016) and the Integrated Household Living Condition Assessment (2010) to examine 26 health service indicators and explored the incidence of catastrophic health payment and impoverishment caused by out-of-pocket payments. We used logistic regression models of inequalities in, and risk factors for, indicators of universal health coverage. FINDINGS: Nationally, the coverage of health service indicators ranged from 18·4% (95% CI 14·9-21·9) to 96·2% (95·9-96·5). Coverage of most health services indicators was below the universal health coverage target of 80%. 14·6% (95% CI 13·9-15·3) of households that used health services faced catastrophic health-care payments. 2·0% (95% CI 1·7-2·3) of non-poor households became poor because of out-of-pocket payments for health. Health service coverage and financial risk protection varied substantially by region. Although the richest quintiles had better access to health services than the poorest quintiles, they also had a higher incidence of financial catastrophe as a result of payments for health care. Of the indicators included in the study, coverage of adequate sanitation, no indoor use of solid fuels, at least four antenatal care visits, postnatal care for mothers, skilled birth attendance, and institutional delivery were the most inequitable by wealth quintile. INTERPRETATION: Attainment of universal health coverage in Myanmar in the immediate future will be very challenging as a result of the low health service coverage, high financial risk, and inequalities in access to care. Health service coverage and financial risk protection for vulnerable, disadvantaged populations should be prioritised. FUNDING: Japanese Ministry of Health, Labour and Welfare, Ministry of Education, Culture, Sports, Science and Technology of Japan.
OBJECTIVES: This study aimed to evaluate the internal cesium (Cs) level among pregnant women in Minamisoma City (the area straddling the evacuation zones) over a 5-year period after Japan's 2011 Fukushima nuclear accident and assess the status and transition of their food-acquiring preferences during this period. DESIGN: A retrospective observational study of a screening along with a questionnaire survey. SETTING: This study was conducted in an obstetrics and gynaecology service in Minamisoma City in Fukushima, Japan. PARTICIPANTS: Participants included pregnant women who applied for the voluntary internal radiation exposure screening programme. PRIMARY AND SECONDARY OUTCOME MEASURES: Internal radiation exposure was evaluated using the whole-body counter (WBC) in the screening programme. Data from a food acquisition preference questionnaire administered to the participants were analysed and compared across the 5-year period after adjusting for covariates. RESULTS: Overall, 804 screening programmes were conducted with 579 participants during the study period. All participants had internal contamination levels below the detection limit of the WBC unit (220 and 250 Bq/body for Cs-134 and Cs-137, respectively). Based on the most conservative assumption, their maximum annual effective doses by Cs-134 and Cs-137 together were estimated at 16 µSv/year. Contrary to limited internal contamination risks and counter-dose initiatives by the government, a considerable number of pregnant women were still concerned about consuming potentially contaminated local food products when purchasing them at supermarkets between 2012 (78.4%) and 2015 (75.0%). CONCLUSIONS: Health effects from post-accident internal radiation exposure were likely to be insignificant in pregnant women. National/local action is urgently needed to promote scientific understanding in pregnant women regarding limited internal exposure risks from local food products in the market. However, few mothers chose to participate in the internal radiation exposure ...
Background: The World Health Organization declared the novel coronavirus outbreak (COVID-19) to be a pandemic on March 11, 2020. Large-scale monitoring for capturing the current epidemiological situation of COVID-19 in Japan would improve preparation for and prevention of a massive outbreak. Methods: A chatbot-based healthcare system named COOPERA (COvid-19: Operation for Personalized Empowerment to Render smart prevention And care seeking) was developed using the LINE app to evaluate the current Japanese epidemiological situation. LINE users could participate in the system either though a QR code page in the prefecture's website, or a banner at the top of the LINE app screen. COOPERA asked participants questions regarding personal information, preventive actions, and non-specific symptoms related to COVID-19 and their duration. We calculated daily cross correlation functions between the reported number of infected cases confirmed by PCR and the symptom-positive group captured by COOPERA. Results: We analyzed 206,218 participants from three prefectures reported between March 5 and 30, 2020. The mean (standard deviation) age of participants was 44.2 (13.2). No symptoms were reported by 96.93% of participants, but there was a significantly positive correlation between the reported number of COVID-19 cases and self-reported fevers, suggesting that massive monitoring of fever might help to estimate the scale of the COVID-19 epidemic in real time. Conclusions: COOPERA is the first real-time system being used to monitor trends in COVID-19 in Japan, and provides useful insights to assist political decisions to tackle the epidemic.
OBJECTIVE: On 7 April 2020, the Japanese government declared a state of emergency in response to the novel coronavirus outbreak. To estimate the impact of the declaration on regional cities with low numbers of COVID-19 cases, large-scale surveillance to capture the current epidemiological situation of COVID-19 was urgently conducted in this study. DESIGN: Cohort study. SETTING: Social networking service (SNS)-based online survey conducted in five prefectures of Japan: Tottori, Kagawa, Shimane, Tokushima and Okayama. PARTICIPANTS: 127 121 participants from the five prefectures surveyed between 24 March and 5 May 2020. INTERVENTIONS: An SNS-based healthcare system named COOPERA (COvid-19: Operation for Personalized Empowerment to Render smart prevention And care seeking) was launched. It asks questions regarding postcode, personal information, preventive actions, and current and past symptoms related to COVID-19. PRIMARY AND SECONDARY OUTCOME MEASURES: Empirical Bayes estimates of age-sex-standardised incidence rate (EBSIR) of symptoms and the spatial correlation between the number of those who reported having symptoms and the number of COVID-19 cases were examined to identify the geographical distribution of symptoms in the five prefectures. RESULTS: 97.8% of participants had no subjective symptoms. We identified several geographical clusters of fever with significant spatial correlation (r=0.67) with the number of confirmed COVID-19 cases, especially in the urban centres of prefectural capital cities. CONCLUSIONS: Given that there are still several high-risk areas measured by EBSIR, careful discussion on which areas should be reopened at the end of the state of emergency is urgently required using real-time SNS system to monitor the nationwide epidemic.
The rapid spread of COVID-19 renewed the focus on how health systems across the globe are financed, especially during public health emergencies. Development assistance is an important source of health financing in many low-income countries, yet little is known about how much of this funding was disbursed for COVID-19. We aimed to put development assistance for health for COVID-19 in the context of broader trends in global health financing, and to estimate total health spending from 1995 to 2050 and development assistance for COVID-19 in 2020.
In: Micah , A E , Cogswell , I E , Cunningham , B , Ezoe , S , Harle , A C , Maddison , E R , McCracken , D , Nomura , S , Simpson , K E , Stutzman , H N , Tsakalos , G , Wallace , L E , Zhao , Y , Zende , R R , Abbafati , C , Abdelmasseh , M , Abedi , A , Abegaz , K H , Abhilash , E S , Abolhassani , H , Abrigo , M R M , Adhikari , T B , Afzal , S , Ahinkorah , B O , Ahmadi , S , Ahmed , H , Ahmed , M B , Rashid , T A , Ajami , M , Aji , B , Akalu , Y , Akunna , C J , Al Hamad , H , Alam , K , Alanezi , F M , Alanzi , T M , Alemayehu , Y , Alhassan , R K , Alinia , C , Aljunid , S M , Almustanyir , S A , Alvis-Guzman , N , Alvis-Zakzuk , N J , Amini , S , Amini-Rarani , M , Amu , H , Ancuceanu , R , Andrei , C L , Andrei , T , Angell , B & Global Burden Dis Hlth Financing 2021 , ' Tracking development assistance for health and for COVID-19 : a review of development assistance, government, out-of-pocket, and other private spending on health for 204 countries and territories, 1990-2050 ' , Lancet , vol. 398 , no. 10308 , pp. 1317-1343 . https://doi.org/10.1016/S0140-6736(21)01258-7
Background: The rapid spread of COVID-19 renewed the focus on how health systems across the globe are financed, especially during public health emergencies. Development assistance is an important source of health financing in many low-income countries, yet little is known about how much of this funding was disbursed for COVID-19. We aimed to put development assistance for health for COVID-19 in the context of broader trends in global health financing, and to estimate total health spending from 1995 to 2050 and development assistance for COVID-19 in 2020. Methods: We estimated domestic health spending and development assistance for health to generate total health-sector spending estimates for 204 countries and territories. We leveraged data from the WHO Global Health Expenditure Database to produce estimates of domestic health spending. To generate estimates for development assistance for health, we relied on project-level disbursement data from the major international development agencies' online databases and annual financial statements and reports for information on income sources. To adjust our estimates for 2020 to include disbursements related to COVID-19, we extracted project data on commitments and disbursements from a broader set of databases (because not all of the data sources used to estimate the historical series extend to 2020), including the UN Office of Humanitarian Assistance Financial Tracking Service and the International Aid Transparency Initiative. We reported all the historic and future spending estimates in inflation-adjusted 2020 US$, 2020 US$ per capita, purchasing-power parity-adjusted US$ per capita, and as a proportion of gross domestic product. We used various models to generate future health spending to 2050. Findings: In 2019, health spending globally reached $8·8 trillion (95% uncertainty interval [UI] 8·7–8·8) or $1132 (1119–1143) per person. Spending on health varied within and across income groups and geographical regions. Of this total, $40·4 billion (0·5%, 95% UI 0·5–0·5) was ...
Background The rapid spread of COVID-19 renewed the focus on how health systems across the globe are financed, especially during public health emergencies. Development assistance is an important source of health financing in many low-income countries, yet little is known about how much of this funding was disbursed for COVID-19. We aimed to put development assistance for health for COVID-19 in the context of broader trends in global health financing, and to estimate total health spending from 1995 to 2050 and development assistance for COVID-19 in 2020.
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"