14 research outputs found
Cereal Crops Commercialization Decisions of Smallholder Farmers: A Case Study of Horro District, Western Ethiopia
No full textThis study aimed at identifying and analysing factors affecting commercialization decisions of smallholder farmers on major cereal crops in Horro District, Oromia, Ethiopia. A multi-stage sampling approach was employed to choose the district, kebele and sample households. A total of 144 farm households were considered in this study. Focus group discussions, key informant interviews and field observations were held to generate qualitative data. This was supplemented by secondary data collected from different published and unpublished sources. Descriptive statistics and logit model were used to identify determinants of commercialization decisions of smallholder farmers and effects of explanatory variables on their decision. The results of the study indicated that 16 per cent of the sampled farm households were in commercialized category, whereas the majority (84%) fell in non-commercialized category. Access to credit service, storage methods, extension access, oxen resource endowment, farming equipment endowments and total harvest were important in affecting commercialization decisions of smallholder farmers, focusing on wheat, barley, maize and teff. To accelerate agricultural commercialization in the area, there is need to encourage market infrastructure, credit services, extension service, andimproved crops storage facilities.
Keywords: smallholder farmers, cereal crop, commercialization decision, Horro district, western Ethiopi
Cereal Crops Commercialization Decisions of Smallholder Farmers: A Case Study of Horro District, Western Ethiopia
No full textThis study aimed at identifying and analysing factors affectingcommercialization decisions of smallholder farmers on major cereal crops inHorro District, Oromia, Ethiopia. A multi-stage sampling approach wasemployed to choose the district, kebele and sample households. A total of 144farm households were considered in this study. Focus group discussions, keyinformant interviews and field observations were held to generate qualitativedata. This was supplemented by secondary data collected from differentpublished and unpublished sources. Descriptive statistics and logit modelwere used to identify determinants of commercialization decisions ofsmallholder farmers and effects of explanatory variables on their decision.The results of the study indicated that 16 per cent of the sampled farmhouseholds were in commercialized category, whereas the majority (84%)fell in non-commercialized category. Access to credit service, storagemethods, extension access, oxen resource endowment, farming equipmentendowments and total harvest were important in affecting commercializationdecisions of smallholder farmers, focusing on wheat, barley, maize and teff.To accelerate agricultural commercialization in the area, there is need toencourage market infrastructure, credit services, extension service, andimproved crops storage facilitie
Determinants of non-adherence to iron folic acid supplementation among pregnant women attending public health facilities in Bishoftu Town, Ethiopia: case–control study
No full textAbstract Background Iron folic acid supplementation is one of the most global public health interventions to alleviate iron deficiency anemia during pregnancy. Both folic acid and iron deficiencies constitute the major micronutrient deficiencies in Ethiopian women. Non-adherence to folic acid supplementation is a factor for iron deficiency anemia among pregnant women including at selected study area, Objective To identify determinants of non-adherence to iron-folic acid intake among pregnant women who attended antenatal care in Bishoftu town public health facilities, Ethiopia 2022. Methods Facility-based unmatched case–control study was conducted from April 1 to May 30, 2022. A total of 105 cases and 211 controls of pregnant women participated in the study by using systematic random sampling methods for control and consecutive sampling was used for the case. Cases were pregnant women who started ANC service and take iron/folate supplements one month before data collection and who received IFA tablets less than 4 days per week. Controls were pregnant women who started ANC service and take iron/folate supplements one month before data collection and who take IFA tablets greater or equal to 4 days per week. Data were collected by trained health workers using interviewer administered structured questionnaires, entered into Epi-data version 4.6, and exported to Statistical Package for Social Sciences version 25 for analysis. Logistic regression was used to identify determinants of the non-adherence to iron-folic acid supplementation, and the variables with p-value 16 weeks (AOR = 2.41, 95% CI:1.11, 5.25), women who received ANC visit two and three [AOR = 5.17, 95% CI (2.50, 13.02), AOR = 2.95 95% CI (1.26, 6.80)], women with inadequate knowledge (AOR = 3.5,95% CI:1.70,7.20), women who received 30 and below tablets (AOR = 7.80, 95% CI:4.01, 18.02) were the determinants of non-adherence to iron-folic acid supplementation. Conclusions Women's age, pregnancy weeks during first ANC, frequency of ANC follow-up, knowledge of women on iron-folic acid supplementation, women who received 30 and below tablets were significant predictors for non-adherence to folic acid supplementation. Thus, the town health office should work on non-adherence to folic acid supplementation during pregnancy to improve knowledge of all age groups of women during ANC visits with concerned stakeholders
Characterising acute and chronic care needs: insights from the Global Burden of Disease Study 2019
No full textChronic care manages long-term, progressive conditions, while acute care addresses short-term conditions. Chronic conditions increasingly strain health systems, which are often unprepared for these demands. This study examines the burden of conditions requiring acute versus chronic care, including sequelae. Conditions and sequelae from the Global Burden of Diseases Study 2019 were classified into acute or chronic care categories. Data were analysed by age, sex, and socio-demographic index, presenting total numbers and contributions to burden metrics such as Disability-Adjusted Life Years (DALYs), Years Lived with Disability (YLD), and Years of Life Lost (YLL). Approximately 68% of DALYs were attributed to chronic care, while 27% were due to acute care. Chronic care needs increased with age, representing 86% of YLDs and 71% of YLLs, and accounting for 93% of YLDs from sequelae. These findings highlight that chronic care needs far exceed acute care needs globally, necessitating health systems to adapt accordingly.
© 2025. The Author(s)
Erratum: Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017
No full textInterpretation: By quantifying levels and trends in exposures to risk factors and the resulting disease burden, this assessment offers insight into where past policy and programme efforts might have been successful and highlights current priorities for public health action. Decreases in behavioural, environmental, and occupational risks have largely offset the effects of population growth and ageing, in relation to trends in absolute burden. Conversely, the combination of increasing metabolic risks and population ageing will probably continue to drive the increasing trends in non-communicable diseases at the global level, which presents both a public health challenge and opportunity. We see considerable spatiotemporal heterogeneity in levels of risk exposure and risk-attributable burden. Although levels of development underlie some of this heterogeneity, O/E ratios show risks for which countries are overperforming or underperforming relative to their level of development. As such, these ratios provide a benchmarking tool to help to focus local decision making. Our findings reinforce the importance of both risk exposure monitoring and epidemiological research to assess causal connections between risks and health outcomes, and they highlight the usefulness of the GBD study in synthesising data to draw comprehensive and robust conclusions that help to inform good policy and strategic health planning.Stanaway, Jeffrey D-2520fbe1e553ab7130a3e14d339cc29e-0Afshin, Ashkan-4062fbc2d605ce12060facaec6d95b23-0Gakidou, Emmanuela-f92c7e1014d29cebdcab875927db3eac-0Lim, Stephen S-2dfacd56ccc922d1b607c443c3aed8b3-0Abate, Degu-4c9c9907f2717c0bf18a360b4adc23fa-0Abate, Kalkidan Hassen-c29aa366a14f60e18d131235548e6764-0Abbafati, Cristiana-f12d1252183d734ef769098209e59c75-0Abbasi, Nooshin-fe10e2a9e12733c5a369e08cac0cc626-0Abbastabar, Hedayat-db4b6e4e6a8f2f22ec5b2ef0001c80f7-0Abd-Allah, Foad-315bbcdbb313ea1850a4c83707cf22e4-0Abdela, Jemal-ea18b0db074176a0a8843cf7b6f4b574-0Abdelalim, Ahmed-729ff719b4f2e616be01aca670300fc6-0Abdollahpour, Ibrahim-01b160c436c07eb954f4720675e6bd23-0Abdulkader, Rizwan Suliankatchi-82f3def17146f3217d67e5d420a09d2f-0Abebe, Molla-f069369cc88f0ff20184f65022e52404-0Abebe, Zegeye-dc8063f4c2383f9646ee0c6014901e04-0Abera, Semaw Ferede-28fd674f722c27f0a3d66100a57fce74-0Abil, Olifan Zewdie-62e69e9e8589dc80140e0c9c76b2a743-0Abraha, Haftom Niguse-6f45c1da5e4fdda97c3adc3da2d477a6-0Roba, Aklilu Abrham-f97be0b52463568f84c9fa9affff2463-0Abu Raddad, Laith J-87179b55a3abe08d15357d22d77b0c9f-0Abun Rmeileh, Niveen M.E-e394ad81f593042412c8643ab8a7a4f9-0Accrombessi, Manfred Mario Kokou-1b088b3416d0f6ec3a7e9c864680942d-0Acharya, Dilaram-240b7a14aa142c99753566d79c7f3b22-0Acharya, Pawan-01cb4a54739afb677b91e0c05c7bface-0Adamu, Abdu Abdullahi-e39e3857e02d33a997368b54498ac2c0-0Adane, Akilew Awoke-15c0422bbf6693d459926fee9fc4d9c1-0Adebayo, Oladimeji-4c0a9d75950ff513bd301db5f932d7d9-0Adedoyin, Rufus Adesoji-84fc0bb933bb9ff247cda9f35406ff61-0Adekanmbi, Victor T-e61819d7aa2c70a57ded8f98f24e51c4-0Ademi, Zanfina-244384081418339dd051b719eea79eb7-0Adetokunboh, Olatunji O-adc3c1a0fbe4e0fcc186e9c39f20aab6-0Adib, Mina G-5e2e4a2b955c99a7148bf2835df4da1e-0Admasie, Amha-3886ac12611046939e2700837619b6cb-0Adsuar, Jose C-baf85df62abf1a7c7551a6af0dc965f5-0Afanvi, Kossivi Agbélénko-8ab9da633de1822d6740ff2bd12e2e8a-0Afarideh, Mohsen-16092959beee378428077dd5e6d04832-0Agarwal, Gina-264b1b6b950696d5c41cdcd383a30c3e-0Aggarwal, Anju-f8be93a84e70ebfb86441e91f9451992-0Aghayan, Sargis A-58cc18e7d481a0d431f2a3c93ee71b4e-0Agrawal, Anurag-509f342b1575b999d5ec6bd3f612df26-0Agrawal, Sutapa-f12c5d24e93e2c07c6321cb7dce96069-0Ahmadi, Alireza-219a6a30c7460d2b398c05ca35d5a1e1-0Ahmadi Moghadam, M-094a65b0c8189b7125eca1b8f5afae2e-0Ahmadieh, Hamid-6f9d23d4531563d07e9d3039d16294bc-0Ahmed, Muktar Beshir-827a98e28bbeb4ad75edaadbc87a6956-0Aichour, Amani Nidhal-84ac126cb31640764728380f61311b50-0Aichour, Ibtihel-984dfc4e36d078e7665d8d90220e472f-0Aichour, Miloud Taki Eddine-8ea68860413c6b03f5c2f7f808fc3e59-0Akbari, Mohammad Esmaeil-f41dc784812f38441d1a970abcd1e76f-0Akinyemiju, Tomi F.-aa496f0e0203c38d494058c117171fd1-0Akseer, Nadia-a208855455ad6ca542b60f1c9d6f0076-0Al Aly, Ziyad-5727374838f3b814fee2209100046f1f-0Al Eyadhy, Ayman A-4e6eed03ca8f2b345b91a02928d77d95-0Al Mekhlafi, H. 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Characterising Acute and Chronic Care Needs: Insights From the Global Burden of Disease Study 2019
No full textMajeed, Azeem/0000-0002-2357-9858; Batiha, Prof. Abdul-Monim/0009-0003-8933-7637; Alvis-Guzman, Nelson/0000-0001-9458-864X; Stein, Dan/0000-0001-7218-7810; Silva, Luis Rodrigues Da/0000-0001-5264-3516; Alves Carneiro, Vera L./0000-0001-7830-0095; Fowobaje, Kayode/0000-0002-3995-160X; Douiri, Abdel/0000-0002-4354-4433; Olusanya, Bolajoko/0000-0002-3826-0583; Saif-Ur-Rahman, Km/0000-0001-8702-7094; Munblit, Daniel/0000-0001-9652-6856; Anwer, Razique/0000-0002-9223-1951; Park, Eun-Cheol/0000-0002-2306-5398; Wickramasinghe, Nuwan Darshana/0000-0001-6025-6022; Rashedi, Sina/0000-0003-0146-5611; Mohammed, Shafiu/0000-0001-5715-966X; Mossialos, Elias/0000-0001-8664-9297; Farooq, Umar/0000-0002-6421-9197; Poluru, Ramesh/0000-0002-7693-418X; Fekadu, Ginenus/0000-0002-4926-0685; Bhagavathula, Akshaya/0000-0002-0581-7808; Tanha, Amirabas/0009-0004-1729-6079; Rahman, Md. Obaidur/0000-0002-2219-3013; Iyamu, Ihoghosa/0000-0003-0271-9468; Skou, Soren T/0000-0003-4336-7059; Keskin, Cumali/0000-0003-3758-0654; Roever Mhs, Phd, Post-Doctorate, Mba (Data Science), Scientific Editor, Leonardo/0000-0002-7517-5548; Oliveira, Arao/0000-0001-6408-0634; /0000-0002-6657-2170; Hankey, Graeme/0000-0002-6044-7328; Cerin, Ester/0000-0002-7599-165X; Sachdev, Perminder/0000-0002-9595-3220; Hosseinzadeh, Mehdi/0000-0003-3040-1801; Christensen, Steffan/0000-0001-9068-0641; Bhaskar, Sonu/0000-0002-9783-3628; Tonelli, Marcello/0000-0002-0846-3187; Wang, Yanzhong/0000-0002-0768-1676; Papadopoulou, Paraskevi/0000-0002-8194-7495; Chong, Yuen Yu/0000-0001-5664-2051; Sun, Jing/0000-0002-0097-2438; Maulud, Sazan Qadir/0000-0002-2399-3055; Ward, Paul/0000-0002-5559-9714; Korzh, Oleksii/0000-0001-6838-4360; Murillo-Zamora, Efren/0000-0002-1118-498X; Sharma, Saurab/0000-0002-9817-5372; Moni, Mohammad Ali/0000-0003-0756-1006; Alam, Khurshid/0000-0002-7402-7519; , Nikolaos`/0000-0002-7269-2785; Guha, Avirup/0000-0003-0253-1174; Amin, Tarek Tawfik/0000-0003-2502-110X; Altirkawi, Khalid/0000-0002-7331-4196; Gazzelloni, Federica/0000-0002-4285-611X; Bulamu, Norma/0000-0003-4822-4858; Almalki, Mohammed J./0000-0001-9201-3164; Pepito, Veincent Christian/0000-0001-5391-3784; Kivimaki, Mika/0000-0002-4699-5627; Tufa, Derara Girma/0000-0002-4905-1189; Ezzikouri, Sayeh/0000-0002-3982-6163; Al-Raddadi, Rajaa/0000-0002-4188-0169; , Pawar/0000-0002-6157-2462; Andarge, Biniyam Demisse/0000-0002-6870-9193; Cerin, Ester/0000-0002-7599-165X; Mathioudakis, Alexander G/0000-0002-4675-9616; Krishan, Kewal/0000-0001-5321-0958; Tabish, Mohammad/0000-0001-8737-4088; Lim, David/0000-0002-2837-0973; Stephens, Jacqueline/0000-0002-7278-1374; Gorini, Giuseppe/0000-0002-1413-5948; Ntsekhe, Mpiko/0000-0002-0851-7675; Biswas, Atanu/0000-0001-5696-9839; Cederroth, Christopher R./0000-0001-7267-5136; Martinez Valle, Adolfo/0000-0001-6473-0262; Maude, Richard/0000-0002-5355-0562; Peng, Minijin/0000-0002-1350-4780; Goleij, Pouya/0000-0002-2213-497X; Algammal, Abdelazeem/0000-0001-5580-1559; M. Abdel-Azeem, Ahmed/0000-0003-2897-3966; Beran, David/0000-0001-7229-3920; Postma, Maarten/0000-0002-6306-3653; Adnani, Qorinah Estiningtyas Sakilah/0000-0002-4625-4861; Ks, Shaji/0000-0002-6029-9679; Anyasodor, Anayochukwu/0000-0003-1928-7657; Shin, Jae Il/0000-0003-2326-1820; Shiri, Rahman/0000-0002-9312-3100; Shivarov, Velizar/0000-0001-5362-7999; Hanif, Asif/0000-0002-2670-6402Chronic care manages long-term, progressive conditions, while acute care addresses short-term conditions. Chronic conditions increasingly strain health systems, which are often unprepared for these demands. This study examines the burden of conditions requiring acute versus chronic care, including sequelae. Conditions and sequelae from the Global Burden of Diseases Study 2019 were classified into acute or chronic care categories. Data were analysed by age, sex, and socio-demographic index, presenting total numbers and contributions to burden metrics such as Disability-Adjusted Life Years (DALYs), Years Lived with Disability (YLD), and Years of Life Lost (YLL). Approximately 68% of DALYs were attributed to chronic care, while 27% were due to acute care. Chronic care needs increased with age, representing 86% of YLDs and 71% of YLLs, and accounting for 93% of YLDs from sequelae. These findings highlight that chronic care needs far exceed acute care needs globally, necessitating health systems to adapt accordingly. Chronic care manages long-term, progressive conditions, while acute care handles short-term ones. Here, authors show chronic conditions account for most of the global health burden, with 68% of DALYs and 93% of YLDs attributed to chronic care needs.Leona M. and Harry B. Helmsley Charitable Trust as part of the Addressing the Challenge and Constraints of Insulin Sources; Department of Science and Innovation at the National Research Foundation; Centre of Excellence in Epidemiological Modeling and Analysis, Stellenbosch University, South Africa; American University of Antigua; Shaqra University; National Institute for Health and Care Research (NIHR), Applied Research Collaboration (ARC); Department of Food Hygiene, Faculty of Veterinary Medicine, Amol University of Special Modern Technologies; University of Melbourne and Monash University; Department of Community Medicine, School of Medicine, International Medical University, Malaysia; International Centre for Casemix; Institute for Health Metrics and Evaluation (IHME) for the global health research initiatives at the University of Washington; Bernhard Nocht Institute of Tropical Medicine in Hamburg, Germany; Public Health Agency of Canada; Sir Seewoosagur Ramgoolam Medical College in Belle Rive, Mauritius; Heidelberg Institute of Global Health (HIGH), Faculty of Medicine, University Hospital, Heidelberg University in Heidelberg, Germany; Stellenbosch Institute of Advanced Study (STIAS), in Stellenbosch, South Africa; Kasturba Medical College, Mangalore and Manipal Academy of Higher Education, Manipal; Discipline of Public Health Medicine, University of KwaZulu-Natal in Durban, South Africa; CNPq [307329/2022-4]; University of Leicester Department of Population Health Sciences; Sociedad Argentina de Medicina; Global Alliance for Musculoskeletal Health; Italian Center of Precisione Medicine and Chronic Inflammation; United States Department of Veterans Affairs; Indian Institute of Technology Delhi (IIT Delhi); Ain Shams University, Faculty Of Medicine, Internal Medicine, and Adult Hematology Department in Cairo, Egypt; Institute of Health Sciences, Wallaga University; Department of Environmental Health Engineering of Isfahan University of Medical Sciences in Isfahan, Iran; Auckland University of Technology; UK National Institute of Health Research (NIHR) Academic Clinical Lectureship; Research Institute for Endocrine Sciences in Tehran, Iran; Asian Institute of Medicine, Science and Technology (AIMST University), Malaysia; Tsinghua University International Cooperation Special Project of Initiative Scientific Research Program; Department of Community Medicine at Kasturba Medical College, Mangalore; Manipal Academy of Higher Education in Manipal, India; Kornhauser Research Fellowship at The University of Sydney; Global Consortium for Public Health Research, Datta Meghe Institute of Higher Education and Research; Manipal Academy of Higher Education [K43 TW010716-05s1]; Sistema Nacional de Investigacion (SNI); Panama's Secretaria Nacional de Ciencia, Tecnologia e Innovacion (SENACYT); Taipei Medical University; Lung Foundation Australia; George Institute for Global Health and University of New SouthWales; Alexander von Humboldt (AvH) Foundation; National Institute for Health Research (NIHR) Biomedical Research Center at Guy's and St Thomas' National Health Service Foundation Trust, King's College London; NIHR [NIHR202769]; Italian Ministry of Health, Ricerca Corrente 2023; Ateneo de Manila University; International Center of Medical Sciences Research (ICMSR), Islamabad [44000]; I.M. Sechenov First Moscow State Medical University (Sechenov University); Nassau University Medical Center; Datta Meghe Institute of Higher Education and Research; Data Science Research Unit at Charles Sturt University; King Abdulaziz University (DSR) in Jeddah; King Abdulaziz City for Science & Technology (KACSAT) in Saudi Arabia, Science and Technology Development Fund (STDF); US-Egypt Science & Technology Joint Fund; Academy of Scientific Research and Technology (ASRT), in Egypt; Manipal College of Health Professions, Manipal Academy of Higher Education; International Center of Medical Sciences Research (ICMSR) in Islamabad, Pakistan; Ain Shams University; Egyptian Fulbright Mission Program; School of Computational and Integrative Sciences [SCIS]; Jawaharlal Nehru University in New Delhi, India; Institute for Advanced Studies in Basic Sciences (IASBS) Research Council; Kerala University of Health Sciences in Kerala, India; Jazan University; Department of Forensic Medicine and Toxicology, Kasturba Medical College, Mangalore, Manipal Academy Of Higher Education, Manipal, India; Health Data Research UK; International Graduate Research Scholarship, University of Tasmania; Bizzell Global, LLC; Scientific Research Unit at Shaqra University, Saudi Arabia; Medical Ethics and Law Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Institute for Health Metrics and Evaluation (IHME); Christian Medical College Vellore; Saveetha Institute of Medical and Technical Sciences and SRM Institute of Science and Technology; University of Agriculture, Faisalabad; Italian Ministry of Health [2023]; Public Health Sciences Division of Fred Hutchinson Cancer Center; Department of Basic Medical Sciences, Neyshabur University of Medical Sciences in Neyshabur, IranThis research was funded by The Leona M. and Harry B. Helmsley Charitable Trust as part of the Addressing the Challenge and Constraints of Insulin Sources and Supply (ACCISS) Study. Statements and conclusions presented in this report are those of the authors alone and do not necessarily reflect the views of the Helmsley Charitable Trust. All references and conclusions are intended for educational and informative purposes and do not constitute an endorsement or recommendation from the Helmsley Charitable Trust. O O Adetokunboh acknowledges support from the Department of Science and Innovation at the National Research Foundation, the Centre of Excellence in Epidemiological Modeling and Analysis, Stellenbosch University, South Africa. J M Acuna acknowledges support from American University of Antigua. A Ahmad acknowledges support from Shaqra University. K Ahmadi acknowledges support from the National Institute for Health and Care Research (NIHR), Applied Research Collaboration (ARC), Northwest London (the views expressed are those of the author and not necessarily those of the NIHR or the Department of Health and Social Care). S Alian Samakkhah acknowledges support from the Department of Food Hygiene, Faculty of Veterinary Medicine, Amol University of Special Modern Technologies. S M Alif acknowledges support from The University of Melbourne and Monash University. S M Aljunid acknowledges support from the Department of Community Medicine, School of Medicine, International Medical University, Malaysia 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. S Almustanyir acknowledges support from the Institute for Health Metrics and Evaluation (IHME) for the global health research initiatives at the University of Washington. J H Amuasi acknowledges support from Bernhard Nocht Institute of Tropical Medicine in Hamburg, Germany. A Badawi acknowledges support from the Public Health Agency of Canada. I Banerjee acknowledges support from Sir Seewoosagur Ramgoolam Medical College in Belle Rive, Mauritius. S Barteit acknowledges support from the Heidelberg Institute of Global Health (HIGH), Faculty of Medicine, University Hospital, Heidelberg University in Heidelberg, Germany. H Benzian acknowledges support from Stellenbosch Institute of Advanced Study (STIAS), in Stellenbosch, South Africa. A N Bhat, J R Padubidi, A Shetty, B S K Shetty, and B Unnikrishnan acknowledge support from Kasturba Medical College, Mangalore and Manipal Academy of Higher Education, Manipal. M K Boachie acknowledges support from the Discipline of Public Health Medicine, University of KwaZulu-Natal in Durban, South Africa. L C Brant acknowledges support from CNPq (307329/2022-4). T Brugha acknowledges support from The University of Leicester Department of Population Health Sciences and the Leicestershire Partnership NHS Trust, United Kingdom. L A Camera acknowledges support from the Sociedad Argentina de Medicina. M Cross acknowledges support from the Global Alliance for Musculoskeletal Health. G Damiani acknowledges support from the Italian Center of Precisione Medicine and Chronic Inflammation. R P Dellavalle acknowledges support from the United States Department of Veterans Affairs, which in no way endorses the content of the manuscript. S Dey acknowledges support from the Indian Institute of Technology Delhi (IIT Delhi) for the Institute Chair Fellowship. G M T ElGohary acknowledges support from the Ain Shams University, Faculty Of Medicine, Internal Medicine, and Adult Hematology Department in Cairo, Egypt. WEtafa acknowledges support from the Institute of Health Sciences, Wallaga University. A Fatehizadeh acknowledges support from the Department of Environmental Health Engineering of Isfahan University of Medical Sciences in Isfahan, Iran. V L Feigin acknowledges support from Auckland University of Technology. J C Glasbey acknowledges support from a UK National Institute of Health Research (NIHR) Academic Clinical Lectureship. A Halimi acknowledges support from the Research Institute for Endocrine Sciences in Tehran, Iran. N E Ismail acknowledges institutional support from Asian Institute of Medicine, Science and Technology (AIMST University), Malaysia. J S Ji acknowledges support from Tsinghua University International Cooperation Special Project of Initiative Scientific Research Program. N Joseph acknowledges support from the Department of Community Medicine at Kasturba Medical College, Mangalore, and Manipal Academy of Higher Education in Manipal, India. H Kandel acknowledges support from the Kornhauser Research Fellowship at The University of Sydney. M N Khatib acknowledges support from the Global Consortium for Public Health Research, Datta Meghe Institute of Higher Education and Research. S L Koulmane Laxminarayana acknowledges support from Manipal Academy of Higher Education. M Kumar acknowledges support from K43 TW010716-05s1. I Landires acknowledges support from the Sistema Nacional de Investigacion (SNI) which is supported by Panama's Secretaria Nacional de Ciencia, Tecnologia e Innovacion (SENACYT). K Latief acknowledges support from Taipei Medical University during the conduct of this review. G Liu acknowledges support from the Lung Foundation Australia. M A Mahmoud acknowledges support from Taibah University to participate in this research project. P Maulik acknowledges support from the George Institute for Global Health and University of New SouthWales. S Mohammed acknowledges support from the Alexander von Humboldt (AvH) Foundation. M Molokhia acknowledges support from the National Institute for Health Research (NIHR) Biomedical Research Center at Guy's and St Thomas' National Health Service Foundation Trust, King's College London, and NIHR grant NIHR202769. A R Pathan acknowledges support from Author Gates Publications. P Pedersini acknowledges support from the Italian Ministry of Health, Ricerca Corrente 2023. V C Pepito acknowledges support from Ateneo de Manila University. Z Z Piracha acknowledges support from the International Center of Medical Sciences Research (ICMSR), Islamabad (44000) Pakistan. R V Polibin acknowledges support from the Department of Epidemiology and Evidence-Based Medicine, F. Erismann Institute of Public Health, I.M. Sechenov First Moscow State Medical University (Sechenov University). I Qattea acknowledges support from the Nassau University Medical Center. Z Quazi Syed acknowledges support from Datta Meghe Institute of Higher Education and Research. A Rahman acknowledges support from the Data Science Research Unit at Charles Sturt University. E M M Redwan acknowledges support from King Abdulaziz University (DSR) in Jeddah, and King Abdulaziz City for Science & Technology (KACSAT) in Saudi Arabia, Science and Technology Development Fund (STDF), and US-Egypt Science & Technology Joint Fund, and The Academy of Scientific Research and Technology (ASRT), in Egypt. B Reshmi acknowledges support from Manipal College of Health Professions, Manipal Academy of Higher Education. U Saeed acknowledges support from the International Center of Medical Sciences Research (ICMSR) in Islamabad, Pakistan. A M Samy acknowledges support from Ain Shams University and the Egyptian Fulbright Mission Program. R K Saroj acknowledges support from the School of Computational and Integrative Sciences (SC&IS), Jawaharlal Nehru University in New Delhi, India. HR Shahsavari acknowledges support from the Institute for Advanced Studies in Basic Sciences (IASBS) Research Council. K S Shaji acknowledges support from Kerala University of Health Sciences in Kerala, India. M Shanawaz acknowledges support from Jazan University. P H Shetty acknowledges support from the Department of Forensic Medicine and Toxicology, Kasturba Medical College, Mangalore, Manipal Academy Of Higher Education, Manipal, India. A Sheikh acknowledges support from Health Data Research UK. A Singh acknowledges support from the International Graduate Research Scholarship, University of Tasmania. D A Sleet acknowledges support from Bizzell Global, LLC. MTabish acknowledges support from the Scientific Research Unit at Shaqra University, Saudi Arabia. MTaheri acknowledges support from the Medical Ethics and Law Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran. G W Tesema acknowledges support from the Institute for Health Metrics and Evaluation (IHME). Nih Thomas acknowledges support from the Christian Medical College Vellore. M R Tovani-Palone acknowledges support from the Saveetha Institute of Medical and Technical Sciences and SRM Institute of Science and Technology. Sai Ullah acknowledges support from the University of Agriculture, Faisalabad. J H Villafane acknowledges support and funding from the Italian Ministry of Health, Ricerca Corrente 2023. H Xiao acknowledges support from the Public Health Sciences Division of Fred Hutchinson Cancer Center. S Yaghoubi acknowledges support from the Department of Basic Medical Sciences, Neyshabur University of Medical Sciences in Neyshabur, Iran
Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017
No full textAbstract: Background The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 comparative risk assessment (CRA) is a comprehensive approach to risk factor quantification that offers a useful tool for synthesising evidence on risks and risk outcome associations. With each annual GBD study, we update the GBD CRA to incorporate improved methods, new risks and risk outcome pairs, and new data on risk exposure levels and risk outcome associations. Methods We used the CRA framework developed for previous iterations of GBD to estimate levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs), by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or groups of risks from 1990 to 2017. This study included 476 risk outcome pairs that met the GBD study criteria for convincing or probable evidence of causation. We extracted relative risk and exposure estimates from 46 749 randomised controlled trials, cohort studies, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. Using the counterfactual scenario of theoretical minimum risk exposure level (TMREL), we estimated the portion of deaths and DALYs that could be attributed to a given risk. We explored the relationship between development and risk exposure by modelling the relationship between the Socio-demographic Index (SDI) and risk-weighted exposure prevalence and estimated expected levels of exposure and risk-attributable burden by SDI. Finally, we explored temporal changes in risk-attributable DALYs by decomposing those changes into six main component drivers of change as follows: (1) population growth; (2) changes in population age structures; (3) changes in exposure to environmental and occupational risks; (4) changes in exposure to behavioural risks; (5) changes in exposure to metabolic risks; and (6) changes due to all other factors, approximated as the risk-deleted death and DALY rates, where the risk-deleted rate is the rate that would be observed had we reduced the exposure levels to the TMREL for all risk factors included in GBD 2017. Findings In 2017,34.1 million (95% uncertainty interval [UI] 33.3-35.0) deaths and 121 billion (144-1.28) DALYs were attributable to GBD risk factors. Globally, 61.0% (59.6-62.4) of deaths and 48.3% (46.3-50.2) of DALYs were attributed to the GBD 2017 risk factors. When ranked by risk-attributable DALYs, high systolic blood pressure (SBP) was the leading risk factor, accounting for 10.4 million (9.39-11.5) deaths and 218 million (198-237) DALYs, followed by smoking (7.10 million [6.83-7.37] deaths and 182 million [173-193] DALYs), high fasting plasma glucose (6.53 million [5.23-8.23] deaths and 171 million [144-201] DALYs), high body-mass index (BMI; 4.72 million [2.99-6.70] deaths and 148 million [98.6-202] DALYs), and short gestation for birthweight (1.43 million [1.36-1.51] deaths and 139 million [131-147] DALYs). In total, risk-attributable DALYs declined by 4.9% (3.3-6.5) between 2007 and 2017. In the absence of demographic changes (ie, population growth and ageing), changes in risk exposure and risk-deleted DALYs would have led to a 23.5% decline in DALYs during that period. Conversely, in the absence of changes in risk exposure and risk-deleted DALYs, demographic changes would have led to an 18.6% increase in DALYs during that period. The ratios of observed risk exposure levels to exposure levels expected based on SDI (O/E ratios) increased globally for unsafe drinking water and household air pollution between 1990 and 2017. This result suggests that development is occurring more rapidly than are changes in the underlying risk structure in a population. Conversely, nearly universal declines in O/E ratios for smoking and alcohol use indicate that, for a given SDI, exposure to these risks is declining. In 2017, the leading Level 4 risk factor for age-standardised DALY rates was high SBP in four super-regions: central Europe, eastern Europe, and central Asia; north Africa and Middle East; south Asia; and southeast Asia, east Asia, and Oceania. The leading risk factor in the high-income super-region was smoking, in Latin America and Caribbean was high BMI, and in sub-Saharan Africa was unsafe sex. O/E ratios for unsafe sex in sub-Saharan Africa were notably high, and those for alcohol use in north Africa and the Middle East were notably low. Interpretation By quantifying levels and trends in exposures to risk factors and the resulting disease burden, this assessment offers insight into where past policy and programme efforts might have been successful and highlights current priorities for public health action. Decreases in behavioural, environmental, and occupational risks have largely offset the effects of population growth and ageing, in relation to trends in absolute burden. Conversely, the combination of increasing metabolic risks and population ageing will probably continue to drive the increasing trends in non-communicable diseases at the global level, which presents both a public health challenge and opportunity. We see considerable spatiotemporal heterogeneity in levels of risk exposure and risk-attributable burden. Although levels of development underlie some of this heterogeneity, O/E ratios show risks for which countries are overperforming or underperforming relative to their level of development. As such, these ratios provide a benchmarking tool to help to focus local decision making. Our findings reinforce the importance of both risk exposure monitoring and epidemiological research to assess causal connections between risks and health outcomes, and they highlight the usefulness of the GBD study in synthesising data to draw comprehensive and robust conclusions that help to inform good policy and strategic health planning. Copyright (C) 2018 The Author(s). Published by Elsevier Ltd
Global, regional, and national age-sex-specific mortality and life expectancy, 1950–2017: a systematic analysis for the Global Burden of Disease Study 2017
No full text© 2018 The Author(s). Background: Assessments of age-specifc mortality and life expectancy have been done by the UN Population Division, Department of Economics and Social Afairs (UNPOP), the United States Census Bureau, WHO, and as part of previous iterations of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD). Previous iterations of the GBD used population estimates from UNPOP, which were not derived in a way that was internally consistent with the estimates of the numbers of deaths in the GBD. The present iteration of the GBD, GBD 2017, improves on previous assessments and provides timely estimates of the mortality experience of populations globally. Methods: The GBD uses all available data to produce estimates of mortality rates between 1950 and 2017 for 23 age groups, both sexes, and 918 locations, including 195 countries and territories and subnational locations for 16 countries. Data used include vital registration systems, sample registration systems, household surveys (complete birth histories, summary birth histories, sibling histories), censuses (summary birth histories, household deaths), and Demographic Surveillance Sites. In total, this analysis used 8259 data sources. Estimates of the probability of death between birth and the age of 5 years and between ages 15 and 60 years are generated and then input into a model life table system to produce complete life tables for all locations and years. Fatal discontinuities and mortality due to HIV/AIDS are analysed separately and then incorporated into the estimation. We analyse the relationship between age-specifc mortality and development status using the Socio-demographic Index, a composite measure based on fertility under the age of 25 years, education, and income. There are four main methodological improvements in GBD 2017 compared with GBD 2016: 622 additional data sources have been incorporated; new estimates of population, generated by the GBD study, are used; statistical methods used in diferent components of the analysis have been further standardised and improved; and the analysis has been extended backwards in time by two decades to start in 1950. Findings: Globally, 18·7% (95% uncertainty interval 18·4-19·0) of deaths were registered in 1950 and that proportion has been steadily increasing since, with 58·8% (58·2-59·3) of all deaths being registered in 2015. At the global level, between 1950 and 2017, life expectancy increased from 48·1 years (46·5-49·6) to 70·5 years (70·1-70·8) for men and from 52·9 years (51·7-54·0) to 75·6 years (75·3-75·9) for women. Despite this overall progress, there remains substantial variation in life expectancy at birth in 2017, which ranges from 49·1 years (46·5-51·7) for men in the Central African Republic to 87·6 years (86·9-88·1) among women in Singapore. The greatest progress across age groups was for children younger than 5 years; under-5 mortality dropped from 216·0 deaths (196·3-238·1) per 1000 livebirths in 1950 to 38·9 deaths (35·6-42·83) per 1000 livebirths in 2017, with huge reductions across countries. Nevertheless, there were still 5·4 million (5·2-5·6) deaths among children younger than 5 years in the world in 2017. Progress has been less pronounced and more variable for adults, especially for adult males, who had stagnant or increasing mortality rates in several countries. The gap between male and female life expectancy between 1950 and 2017, while relatively stable at the global level, shows distinctive patterns across super-regions and has consistently been the largest in central Europe, eastern Europe, and central Asia, and smallest in south Asia. Performance was also variable across countries and time in observed mortality rates compared with those expected on the basis of development. Interpretation: This analysis of age-sex-specifc mortality shows that there are remarkably complex patterns in population mortality across countries. The fndings of this study highlight global successes, such as the large decline in under-5 mortality, which refects signifcant local, national, and global commitment and investment over several decades. However, they also bring attention to mortality patterns that are a cause for concern, particularly among adult men and, to a lesser extent, women, whose mortality rates have stagnated in many countries over the time period of this study, and in some cases are increasing
Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017
No full textBackground:
The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 comparative risk assessment (CRA) is a comprehensive approach to risk factor quantification that offers a useful tool for synthesising evidence on risks and risk–outcome associations. With each annual GBD study, we update the GBD CRA to incorporate improved methods, new risks and risk–outcome pairs, and new data on risk exposure levels and risk–outcome associations.
Methods:
We used the CRA framework developed for previous iterations of GBD to estimate levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs), by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or groups of risks from 1990 to 2017. This study included 476 risk–outcome pairs that met the GBD study criteria for convincing or probable evidence of causation. We extracted relative risk and exposure estimates from 46 749 randomised controlled trials, cohort studies, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. Using the counterfactual scenario of theoretical minimum risk exposure level (TMREL), we estimated the portion of deaths and DALYs that could be attributed to a given risk. We explored the relationship between development and risk exposure by modelling the relationship between the Socio-demographic Index (SDI) and risk-weighted exposure prevalence and estimated expected levels of exposure and risk-attributable burden by SDI. Finally, we explored temporal changes in risk-attributable DALYs by decomposing those changes into six main component drivers of change as follows: (1) population growth; (2) changes in population age structures; (3) changes in exposure to environmental and occupational risks; (4) changes in exposure to behavioural risks; (5) changes in exposure to metabolic risks; and (6) changes due to all other factors, approximated as the risk-deleted death and DALY rates, where the risk-deleted rate is the rate that would be observed had we reduced the exposure levels to the TMREL for all risk factors included in GBD 2017.
Findings:
In 2017, 34·1 million (95% uncertainty interval [UI] 33·3–35·0) deaths and 1·21 billion (1·14–1·28) DALYs were attributable to GBD risk factors. Globally, 61·0% (59·6–62·4) of deaths and 48·3% (46·3–50·2) of DALYs were attributed to the GBD 2017 risk factors. When ranked by risk-attributable DALYs, high systolic blood pressure (SBP) was the leading risk factor, accounting for 10·4 million (9·39–11·5) deaths and 218 million (198–237) DALYs, followed by smoking (7·10 million [6·83–7·37] deaths and 182 million [173–193] DALYs), high fasting plasma glucose (6·53 million [5·23–8·23] deaths and 171 million [144–201] DALYs), high body-mass index (BMI; 4·72 million [2·99–6·70] deaths and 148 million [98·6–202] DALYs), and short gestation for birthweight (1·43 million [1·36–1·51] deaths and 139 million [131–147] DALYs). In total, risk-attributable DALYs declined by 4·9% (3·3–6·5) between 2007 and 2017. In the absence of demographic changes (ie, population growth and ageing), changes in risk exposure and risk-deleted DALYs would have led to a 23·5% decline in DALYs during that period. Conversely, in the absence of changes in risk exposure and risk-deleted DALYs, demographic changes would have led to an 18·6% increase in DALYs during that period. The ratios of observed risk exposure levels to exposure levels expected based on SDI (O/E ratios) increased globally for unsafe drinking water and household air pollution between 1990 and 2017. This result suggests that development is occurring more rapidly than are changes in the underlying risk structure in a population. Conversely, nearly universal declines in O/E ratios for smoking and alcohol use indicate that, for a given SDI, exposure to these risks is declining. In 2017, the leading Level 4 risk factor for age-standardised DALY rates was high SBP in four super-regions: central Europe, eastern Europe, and central Asia; north Africa and Middle East; south Asia; and southeast Asia, east Asia, and Oceania. The leading risk factor in the high-income super-region was smoking, in Latin America and Caribbean was high BMI, and in sub-Saharan Africa was unsafe sex. O/E ratios for unsafe sex in sub-Saharan Africa were notably high, and those for alcohol use in north Africa and the Middle East were notably low.
Interpretation:
By quantifying levels and trends in exposures to risk factors and the resulting disease burden, this assessment offers insight into where past policy and programme efforts might have been successful and highlights current priorities for public health action. Decreases in behavioural, environmental, and occupational risks have largely offset the effects of population growth and ageing, in relation to trends in absolute burden. Conversely, the combination of increasing metabolic risks and population ageing will probably continue to drive the increasing trends in non-communicable diseases at the global level, which presents both a public health challenge and opportunity. We see considerable spatiotemporal heterogeneity in levels of risk exposure and risk-attributable burden. Although levels of development underlie some of this heterogeneity, O/E ratios show risks for which countries are overperforming or underperforming relative to their level of development. As such, these ratios provide a benchmarking tool to help to focus local decision making. Our findings reinforce the importance of both risk exposure monitoring and epidemiological research to assess causal connections between risks and health outcomes, and they highlight the usefulness of the GBD study in synthesising data to draw comprehensive and robust conclusions that help to inform good policy and strategic health planning
Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990-2017: A systematic analysis for the Global Burden of Disease Study 2017 [Elektronisk resurs]
No full textBackground The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 comparative risk assessment (CRA) is a comprehensive approach to risk factor quantification that offers a useful tool for synthesising evidence on risks and risk-outcome associations. With each annual GBD study, we update the GBD CRA to incorporate improved methods, new risks and risk-outcome pairs, and new data on risk exposure levels and risk- outcome associations. Methods We used the CRA framework developed for previous iterations of GBD to estimate levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs), by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or groups of risks from 1990 to 2017. This study included 476 risk-outcome pairs that met the GBD study criteria for convincing or probable evidence of causation. We extracted relative risk and exposure estimates from 46 749 randomised controlled trials, cohort studies, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. Using the counterfactual scenario of theoretical minimum risk exposure level (TMREL), we estimated the portion of deaths and DALYs that could be attributed to a given risk. We explored the relationship between development and risk exposure by modelling the relationship between the Socio-demographic Index (SDI) and risk-weighted exposure prevalence and estimated expected levels of exposure and risk-attributable burden by SDI. Finally, we explored temporal changes in risk-attributable DALYs by decomposing those changes into six main component drivers of change as follows: (1) population growth; (2) changes in population age structures; (3) changes in exposure to environmental and occupational risks; (4) changes in exposure to behavioural risks; (5) changes in exposure to metabolic risks; and (6) changes due to all other factors, approximated as the risk-deleted death and DALY rates, where the risk-deleted rate is the rate that would be observed had we reduced the exposure levels to the TMREL for all risk factors included in GBD 2017.AuthorOverflow(1041
