Background: The Government Regulation PP No. 109/2012 in Indonesia establishes that tobacco products contain addictive substances thatAddictivc are hannfu! for health. his mentioned that pictorial health warnings (PH\v') should be used on every cigarette package and on any advertisement. In Surabaya, every tobacco company has been implementing this rule on cigarette billboard advertisements since January 2014. The purpose of this study is to explore public opinion about Pl-1\"\I based on PP 109/2012. Methods: ThJS study was a dcscnptivc research using questionnaires and in-depth interviews to collect dam. 111c data was collected to assess the knowledge, attitude, action of the smokers, and respondents' perception nfter \'Jewing the pictorial health warnings on the billboards. There were 500 participants selected by mulusrage random sampling to answer the questionnaires. In-depth Interviews were conducted with 20 participants selected by purposive random sampling in public places uch as public transportnuon, hotels, restaurants and malls. 111is study was conducted over two months in five areas of Surabaya city (center, south, north, west, and cast). Analysis was done by crossmbulation. Results: "Ilic results showed that PH\X' number 1-5 encouraged people to quit smoking if they were smokers and not to start smoking if they were non-smokers. Moreover, people believed the accuracy of the PH\V messages cspcoally for image number 2 ( image 2 is of a skclcton/ghos1 looking over :it the smoker) which is being used on billboards advertising cigarettes in Surabaya, and docs not seem to nlarrn or cause people distrcssFrom in-depth interviews it can be concluded that pictorial health warning number 4, which is a smoker with n bnby, could not warn people about cignreucs' impact. Instead people thought it was funny, more of n joke Conclusions: It could be concluded that PJ-1\\"f numbers 1-5, with the exception of image number 2 and 4,werc effectively used to warn the people of Surabaya both smokers and non smokers about the negative health 1mp:icts of smoking
Background: Se'i meat is one of the beef processed are made by way of smoked using wood embers of kusambi (Scheichera oleaca), se'i has been known in the city of Kupang as one of the meat food products. The processing of se'i meat require nitrite as a preservative to obtain good color and prevent the growth of microbes. According to the regulations of Food and Drug Supervisory Agency of the Republic of Indonesia number 36 of 2013, Nitrite use is permitted with a maximum limit of use of 30 mg/kg, but in reality in everyday life obtained the use of nitrites usage exceeds the threshold that has been set. This study aims to know the difference nitrite content in meat meat processing company se'i between modern and traditional in the city of Kupang. Method: The study was descriptive research laboratory tests. The Subjects in this study were se'i meat (smoked meat typical Timor) and the object of study is two meat processing company se'i (smoked meat typical Timor) traditional and cottage industry in the city of Kupang. Data analysis result of research done descriptively and are presented in table and narrative. Result: se'i derived from traditional industries and domestic industries have different nitrite content. The highest nitrite levels found in se'i meat derived from domestic industry in the amount of 110.19 mg/kg and the lowest levels found in se'i meat derived from domestic industry that is equal to 22.28 mg/kg. Conclusion: All samples se'i meat derived from traditional industries and home industries containing nitrite in meat processing se'i as a whole is the same. for traditional industry better prepared than the domestic industry in terms of completeness of workers and conditions of business premises. Suggested to the government to inform the regulations on the use of food additives and the dangers of using these foods on health, especially on nitrite preservatives.
The government held a diphtheria sub-national immunization week in 2012 to overcome the problem of diphtheria outbreaks in East Java Province. Bangkalan District is the district with the high incidence of diphtheria in East Java Province. This study aims to analyze the determinants of diphtheria events in Bangkalan District after the diphtheria sub-national immunization week in 2012. This study uses a case-control study design. Case samples were taken from a total of 31 cases, and control samples were taken as many as 124 people spread in 25 case villages and25 control villages. The bivariate analysis a using chi-square test and simple logistic regression. Multivariate analysis using logistic regression. The results showed that based on the results of bivariate analysis, individual and household levels related to diphtheria events, namely the status of Diphtheria Pertussis Tetanus (DPT) immunization, sub-national immunization week status, age and mother's education. Multivariate analysis showed that factors related to the diphtheria incidence in Bangkalan District after the 2012 diphtheria sub-national immunization week were DPT immunization (p = 0.012; OR= 4.765), incomplete DPT immunization (p = 0.001; OR = 6.276), age 3- 7 years (p = 0.014; OR = 15.137), ages 7-15 years (p = 0.001; OR = 41.984), and are not immunized at the time of sub-national immunization week (p = 0.020; OR = 3.553). The conclusion of this study is the DPT immunization status, age and status of diphtheria sub-national immunization week were the dominant factors affecting the incidence of diphtheria in Bangkalan District.
Relationship between health, economic and tobacco has become crucial issue that requires strong, intensive and comprehensive approaches to be solved. For some countries, including Indonesia, it is a very challenging issue because of the political, economic and cultural determinants are influencing the direction and achievement of preventing and controlling tobacco health impact.
Background: The earthquake that hit the Jailolo sub-district in 2015 caused massive damage and loss. This catastrophic event affected not only impacted the local government's economy but also affected many communities, households and individuals living in these communities. Purpose: Aim of this study is to assess the economic resilience of communities in the Jailolo sub-district in response to earthquakes. Methods: This research was based on a descriptive observational study and employed a survey method to assess the economic resilience of communities in the Jailolo sub-district. The study was conducted in five villages, namely Tedeng, Payo, Saria, Matui, and Buku Maadu. The cut-off point for each indicator was classified as very high criteria (>1.05), high (0.95–1.05), moderate (0.85–0.94), low (0.74–0.84), and very low (≤0.73). Results: The proportion of community home ownership was found to be 100% (Resilience Factor Index (RFI)=1.67). The proportion of community work was 33.75% (RFI=0.68). The proportion of dual-income sources of communities in the Jailolo sub-district was 50.89% (RFI=1.02). The proportion of community income that exceeded the provincial minimum wage (PMW) was 8.71% (RFI=0.10). Based on the results of these indicators, the economic resilience of people in the Jailolo sub-district, which was obtained by considering the average RFI of each indicator, was 0.86. Conclusion: Community economic resilience in the Jailolo sub-district was found to be in the medium category. The highest and lowest resilience factors resulted from home ownership and income, respectively.
Background: Surabaya is one of the pioneers of the enactment of Smoke Free Area regulation. Regulations about Smoke Free Area ( SFA) and Smoke- Restricted Area (SRA) have validated at 2008 but started to be implemented in 2009. The aim of this study is to describe compliance of the implementation of that regulations from 2012until2014. Methods: This was a descriptive study with time series method. It saw how the implementation regulation based on the two surveys have done. The first survey conducted in 2012 with a sample size of 154 and the second survey conducted in 2014 by the number 300. The Survey samples were Categorized as Smoke Restricted Areas is a public place and Smoking Free Areas is health facilities. Cluster random sampling was used based on different areas of Surabaya (East, West, Center, North, and South). Data collection was done by an observation check list. The variable Consist of people are still found to be smoking inside the building, there are smoking room, Ashtray, cigarettes butts and cigarette sellers were found in the Smoke - Free Area. Results: The study founded that some indicators had improved in terms of the implementation of compliance regulations. But they also found an increase in violations in several indicators. Improved Implementation obtained on the indicators found room smoking increased (5.8% to 24%), showed signs ofbanned smoking (31.2% to 53%). Increasing the number of violations found in some indicator consist of founded people smoke (2 5.3 % to 41.3 % ), There was a smell of cigarette smoke (25.3% to 34.3%), found ashtrays and lighters (27.3% to 53.3 %), found cigarette butts (29.2% to 59%), there were indications of cooperation with the tobacco industry (22.1 % to 44.7%). Conclusions: The duration of the implementation of the regulations do not affect the compliance of the implementation of the regulation. The need for reinforcement in order to strengthen the implementation of regulations SFA in Surabaya.
Pelaksanaan kegiatan Pro-sehat Daerah Tertinggal (DT) Universitas Airlangga tahap II tahun 2015 dilaksanakan melalui beberapa kegiatan,terdiri dari koordinasi tingkat kabupaten, koordinasi tingkat kecamatan atau puskesmas, pengembangan Tim pro-sehat DT di puskesmas,identifikasi masalah tingkat desa, penentuan prioritas masalah dan strategi penyelesaian masalah. Dalam tata kelola kegiatan di tingkat pedesaan, peran kepala desa sangat penting karena kepala desa sangat menentukan berbagai kegiatan yang akan dilakukan di desa. Untuk itu perlu dilakukan advokasi sehingga program kesehatan dapat masuk sebagai agenda pembangunan desa. Advokasi tidak hanya pada tingkat desa, namun juga sampai pada tingkat kecamatan dan kabupaten sehingga kebijakan pembangunan kesehatan masyarakat di pedesaan akan mendapat dukungan politis dari pengampu kebijakan. Koordinasi ditingkat kabupaten, kecamatan dan desa dilakukan melalui kunjungan dan sosialisasi. Selanjutnya dilakukan kegiatan utama yaitu need assessment dengan perwakilan kecamatan, kepala desa, serta puskesmas. Kegiatan need assessment dilakukan secara kualitatif dengan berbagai metode sesuai dengan kebutuhan dan kondisi di masing-masing kabupaten, yaitu : NGT (nominal grup technique), wawancara mendalam dan Focus Group Discussion. Hasil need assessment di empat Kabupaten tertinggal di Jawa Timur (Bangkalan, Sampang, Bondowoso dan Situbondo) menunjukkan bahwa air bersih menjadi masalah utama bagi warga yang tinggal di desa-desa terpilih. Disamping air bersih, sanitasi dan akses ke pelayanan kesehatan (termasuk didalamnya ketersediaan, keberterimaan dan kualitas bidan) merupakan permasalahan kedua dan ketiga yang mendominasi di 4 kabupaten tersebut. Penyebab utama dari masalah air bersih adalah dikarenakan faktor alam dan teknologi. Faktor alam terkait dengan sumber air yang sedikit dan sulit dijangkau. Faktor teknologi disini karena permasalahan yang sudah berlangsung lama belum juga diwujudkan solusinya dengan menggunakan teknologi tepat guna, seperti pipanisasi, penjernihan air, pendeteksian sumber air.
Importance Cancer and other noncommunicable diseases (NCDs) are now widely recognized as a threat to global development. The latest United Nations high-level meeting on NCDs reaffirmed this observation and also highlighted the slow progress in meeting the 2011 Political Declaration on the Prevention and Control of Noncommunicable Diseases and the third Sustainable Development Goal. Lack of situational analyses, priority setting, and budgeting have been identified as major obstacles in achieving these goals. All of these have in common that they require information on the local cancer epidemiology. The Global Burden of Disease (GBD) study is uniquely poised to provide these crucial data. Objective To describe cancer burden for 29 cancer groups in 195 countries from 1990 through 2017 to provide data needed for cancer control planning. Evidence Review We used the GBD study estimation methods to describe cancer incidence, mortality, years lived with disability, years of life lost, and disability-adjusted life-years (DALYs). Results are presented at the national level as well as by Socio-demographic Index (SDI), a composite indicator of income, educational attainment, and total fertility rate. We also analyzed the influence of the epidemiological vs the demographic transition on cancer incidence. Findings In 2017, there were 24.5 million incident cancer cases worldwide (16.8 million without nonmelanoma skin cancer [NMSC]) and 9.6 million cancer deaths. The majority of cancer DALYs came from years of life lost (97%), and only 3% came from years lived with disability. The odds of developing cancer were the lowest in the low SDI quintile (1 in 7) and the highest in the high SDI quintile (1 in 2) for both sexes. In 2017, the most common incident cancers in men were NMSC (4.3 million incident cases); tracheal, bronchus, and lung (TBL) cancer (1.5 million incident cases); and prostate cancer (1.3 million incident cases). The most common causes of cancer deaths and DALYs for men were TBL cancer (1.3 million deaths and 28.4 million DALYs), liver cancer (572 000 deaths and 15.2 million DALYs), and stomach cancer (542 000 deaths and 12.2 million DALYs). For women in 2017, the most common incident cancers were NMSC (3.3 million incident cases), breast cancer (1.9 million incident cases), and colorectal cancer (819 000 incident cases). The leading causes of cancer deaths and DALYs for women were breast cancer (601 000 deaths and 17.4 million DALYs), TBL cancer (596 000 deaths and 12.6 million DALYs), and colorectal cancer (414 000 deaths and 8.3 million DALYs). Conclusions and Relevance The national epidemiological profiles of cancer burden in the GBD study show large heterogeneities, which are a reflection of different exposures to risk factors, economic settings, lifestyles, and access to care and screening. The GBD study can be used by policy makers and other stakeholders to develop and improve national and local cancer control in order to achieve the global targets and improve equity in cancer
Importance: Cancer and other noncommunicable diseases (NCDs) are now widely recognized as a threat to global development. The latest United Nations high-level meeting on NCDs reaffirmed this observation and also highlighted the slow progress in meeting the 2011 Political Declaration on the Prevention and Control of Noncommunicable Diseases and the third Sustainable Development Goal. Lack of situational analyses, priority setting, and budgeting have been identified as major obstacles in achieving these goals. All of these have in common that they require information on the local cancer epidemiology. The Global Burden of Disease (GBD) study is uniquely poised to provide these crucial data. Objective: To describe cancer burden for 29 cancer groups in 195 countries from 1990 through 2017 to provide data needed for cancer control planning. Evidence Review: We used the GBD study estimation methods to describe cancer incidence, mortality, years lived with disability, years of life lost, and disability-adjusted life-years (DALYs). Results are presented at the national level as well as by Socio-demographic Index (SDI), a composite indicator of income, educational attainment, and total fertility rate. We also analyzed the influence of the epidemiological vs the demographic transition on cancer incidence. Findings: In 2017, there were 24.5 million incident cancer cases worldwide (16.8 million without nonmelanoma skin cancer [NMSC]) and 9.6 million cancer deaths. The majority of cancer DALYs came from years of life lost (97%), and only 3% came from years lived with disability. The odds of developing cancer were the lowest in the low SDI quintile (1 in 7) and the highest in the high SDI quintile (1 in 2) for both sexes. In 2017, the most common incident cancers in men were NMSC (4.3 million incident cases); tracheal, bronchus, and lung (TBL) cancer (1.5 million incident cases); and prostate cancer (1.3 million incident cases). The most common causes of cancer deaths and DALYs for men were TBL cancer (1.3 million deaths and 28.4 million DALYs), liver cancer (572000 deaths and 15.2 million DALYs), and stomach cancer (542000 deaths and 12.2 million DALYs). For women in 2017, the most common incident cancers were NMSC (3.3 million incident cases), breast cancer (1.9 million incident cases), and colorectal cancer (819000 incident cases). The leading causes of cancer deaths and DALYs for women were breast cancer (601000 deaths and 17.4 million DALYs), TBL cancer (596000 deaths and 12.6 million DALYs), and colorectal cancer (414000 deaths and 8.3 million DALYs). Conclusions and Relevance: The national epidemiological profiles of cancer burden in the GBD study show large heterogeneities, which are a reflection of different exposures to risk factors, economic settings, lifestyles, and access to care and screening. The GBD study can be used by policy makers and other stakeholders to develop and improve national and local cancer control in order to achieve the global targets and improve equity in cancer care.
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"