Perceived Patient Safety Culture among Healthcare Providers in Southern Jordanian Hospitals during COVID-19 Pandemic

Background: A good and continuous assessment of the safety culture in the area of providing services in the healthcare sector will provide an initial step towards providing safe conditions for patient care. Aims: This study aimed to evaluate the perception of patient safety culture among healthcare providers in southern Jordanian hospitals during the COVID-19 pandemic. Method: A cross-sectional study was conducted among healthcare providers (physicians, nurses, and their administrators). Between July and November 2021, data was gathered by an Arabic version of the Hospital Survey of Patient Safety Culture Questionnaire (HSPSC) from 531 healthcare providers using the convenience sample approach. In four southern Jordanian hospitals.  Results: This study found that there were some areas of strength such as: organizational learning/continuous improvement, communication openness, communication about errors, supervisor, manager, or clinical support for patient safety, and hospital management support for patient safety. The reporting-related composites (response to error and reporting patient safety events), staffing, teamwork across hospital units, and information exchange were areas for improvement. Conclusion: There was a need for advancement in the practices of patient safety culture in southern Jordanian hospitals. Reporting-related composites, staffing, handoffs, and information exchange are areas of patient safety that need quick refinement

Nurses’ attitude toward trauma-informed care in critical care units: A cross-sectional study

Objective: Trauma-informed care (TIC) is an innovative concept that recognizes the lasting impact of psychological trauma. Critical care nurses’ attitude toward TIC plays an essential role in assessing, monitoring, and addressing trauma experienced by patients. This study aimed to describe critical care nurses’ attitudes toward TIC in critical care units at Jordanian military hospitals. Methods: This descriptive-analytical cross-sectional study was performed on 315 critical care nurses at seven military hospitals (two from the north, three from the center, and two from the south) in 2022. The data collection tool was the Attitudes Related to Trauma-Informed Care-35 (ARTIC-35) scale. Multi-stage sampling approach was used to recruit critical care nurses. Statistical Package for Social Science (IBM-SPSS) V. 23 was used to analyze data. Results: In the sample of 315 nurses, gender was significantly associated with the reactions subscale (P = 0.001). Also, there were statistical differences between nurses working in the emergency department and those working in intensive care units regarding the responses and behaviors subscales, with emergency department nurses scoring higher on the scale (P = 0.003). Regarding in-service training in trauma-related care, the causes subscale was significantly different based on the number of training sessions with higher scores among nurses with higher training (P = 0.02). Conclusion: Critical care nurses had an unfavorable attitude toward TIC. Health organizations, nurse managers, universities, and other stakeholders should collaborate to achieve a national strategy to raise awareness about TIC. Much research is needed to explore nurses’ attitudes toward TIC in diverse health sectors

Critical care nurses' experiences during the Illness of family members : a qualitative study

Introduction: A loved one's hospitalization in a critical care unit is a traumatic experience for families. However, because of their status and professional competence, a family member who is also a critical care nurse has additional obstacles and often long-term consequences. Objectives: To describe the experiences of critical care nurse-family members when a loved one is admitted to a critical care unit at the Hotel-Dieu de France hospital. Methods: A qualitative path based on van Manen's hermeneutic phenomenology combining both descriptive and interpretive models were adopted. Results: The lived experience of critical care nurses in providing care for their family members admitted into the same critical care were summarized in five themes. Nurses were torn between roles, consisting of confounding roles, their registered nurse status, and watchfulness. The lived experience of critical care nurses in providing care for their family members admitted into the same critical care was summarized into specialized knowledge that included a double-edged sword of seeking information and difficulty delivering the information. Critical nurses compete for expectations, including those placed on self and family members, resulting in emotional and personal sacrifice while gaining insight into the experiences. Conclusions: Critical care nurse-family members have a unique experience compared to the rest of the family, necessitating specialized care and attention. Increased awareness among healthcare providers could be a start in the right direction

Attitudes toward COVID-19 disease and vaccination in Hungary: A comparison of nurses and health workers against non-health workers

Background & Aim: Hungary started to administer several COVID-19 vaccines; however, attitudes toward COVID-19 and vaccination are still poorly understood. This study aimed to explore how the attitudes toward COVID-19 disease are associated with the attitudes toward COVID-19 vaccination in Hungary and compare the attitudes of health and non-health workers toward COVID-19 disease and vaccination in Hungary. Methods & Materials: Using a descriptive, cross-sectional design, we recruited a sample of 1820 persons through an electronic survey. In addition to the sociodemographic questions, the attitudes towards COVID-19 disease and vaccination were assessed through self-developed, literature-based questionnaires. Principal component analysis, Spearman's correlation, linear regression, and the Mann-Whitney test were used in the data analysis. Results: The mean age for the study participants (n=1735) was 43.8 ± 6.2 years, and females were the majority (84.6%). Overall, participants showed good attitudes toward COVID-19 disease (mean score= 3.48, SD= 0.43) as compared to their attitudes toward COVID-19 vaccination (Mean score= 2.67, SD=  0.44). A positive correlation was found between the attitudes toward COVID-19 disease and attitudes toward vaccination in Hungary (r= 0.247, p< 0.01). Nurses and other health workers showed more positive attitudes toward COVID-19 vaccination than non-health workers. Conclusion: Hungarian decision-makers should intervene to improve the public's willingness to be vaccinated against COVID-19 or future pandemics. Health workers' knowledge and positive attitudes should be utilized in the media to encourage the general population to be vaccinated. The suggested questionnaires need to be validated for future pandemics' use

Knowledge and Practices of Menstrual Hygiene among Adolescent Schoolgirls in Jordanian Badia Region: A Field Study

&nbsp; Abstract &nbsp; Background: Normal menstrual cycle is associated with physiological and pathological changes throughout the girls&rsquo; lives. It involves physical changes in a girl&#39;s body designed to prepare her for pregnancy each month. Significant changes in a girl&rsquo;s life take place during adolescence and the onset of menstruation. Aims: The study aims to assess the level of knowledge and practices of menstrual hygiene among adolescent schoolgirls in the Jordanian Badia Region. Methods: A cross-sectional study was conducted from February to March 2022. The total number of participants was 550 from six schools for girls in the Badia region. Data was collected using a questionnaire that assessed knowledge and practices of the menstrual cycle. Descriptive statistics (mean, standard deviation, frequency,&nbsp;percentage) and multivariable logistic regression analysis were used to determine the predictors of the level of knowledge and practice of the menstrual cycle. Results: The adolescent schoolgirls have adequate level of knowledge, represented by a percentage of 65. Furthermore, the results show that the overall level of girls&rsquo; practices was at a poor level (58%), the highest score was for&rdquo;Schoolgirls should have a discussion with their mothers about menstruation and what to do during their periods&rdquo;, and the lowest score was for &ldquo;If she has to, she will have to change the pads at school&rdquo;. Moreover, age, mothers&rsquo; highest level of education, and family income were the predictors of safe knowledge and practices of the menstrual cycle. Conclusion: Overall, the adolescent schoolgirls are reasonably knowledgeable. Additionally, the girls&#39; overall level of practice was poor and menstrual hygiene knowledge and practices need to be improved. Therefore, health programs concerning knowledge and safe practices of menstrual cycles should be conducted in a school setting

Medication errors occurrence and reporting: A qualitative study of the Jordanian nurses' experiences

Background & Aim: Medication errors are a significant concern in healthcare, with effective management largely dependent on understanding its causes and reporting practices. This study aims to explore the experiences of Jordanian nurses in relation to medication error occurrence and reporting within the Jordanian context and the factors that may influence their decisions to report or not. Methods & Materials: A qualitative descriptive approach was used. 24 nurses from three different hospitals were interviewed. The hospitals included a major governmental institution, a private facility, and a university hospital, ensuring diverse healthcare settings. Data were analyzed using Braun and Clarke’s thematic analysis, and the study was reported guided by the COREQ checklist. Results: Three major themes were identified: Obsolete policies and guidelines, Adapting to an Unhealthy Environment, and Trying to adjust: creating own definition for medication errors. In our study, medication errors emerged as a pervasive issue across Jordanian hospitals, attributed to both systemic failures and individual practices. Despite existing policies, participants reported frequent medication errors due to obsolete guidelines, lack of adherence, and an environment that hinders effective medication administration. Conclusion: The study reveals the critical issues of medication errors in Jordanian hospitals due to outdated policies and challenging environments. It emphasizes the need for updated protocols and a culture supportive of error reporting. Addressing these factors is essential for improving patient safety and healthcare quality

Global pattern, trend, and cross-country inequality of early musculoskeletal disorders from 1990 to 2019, with projection from 2020 to 2050

Background: This study aims to estimate the burden, trends, forecasts, and disparities of early musculoskeletal (MSK) disorders among individuals ages 15 to 39 years. Methods: The global prevalence, years lived with disabilities (YLDs), disability-adjusted life years (DALYs), projection, and inequality were estimated for early MSK diseases, including rheumatoid arthritis (RA), osteoarthritis (OA), low back pain (LBP), neck pain (NP), gout, and other MSK diseases (OMSKDs). Findings: More adolescents and young adults were expected to develop MSK disorders by 2050. Across five age groups, the rates of prevalence, YLDs, and DALYs for RA, NP, LBP, gout, and OMSKDs sharply increased from ages 15–19 to 35–39; however, these were negligible for OA before age 30 but increased notably at ages 30–34, rising at least 6-fold by 35–39. The disease burden of gout, LBP, and OA attributable to high BMI and gout attributable to kidney dysfunction increased, while the contribution of smoking to LBP and RA and occupational ergonomic factors to LBP decreased. Between 1990 and 2019, the slope index of inequality increased for six MSK disorders, and the relative concentration index increased for gout, NP, OA, and OMSKDs but decreased for LBP and RA. Conclusions: Multilevel interventions should be initiated to prevent disease burden related to RA, NP, LBP, gout, and OMSKDs among individuals ages 15–19 and to OA among individuals ages 30–34 to tightly control high BMI and kidney dysfunction. Funding: The Global Burden of Disease study is funded by the Bill and Melinda Gates Foundation. The project is funded by the Scientific Research Fund of Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital (2022QN38). © 2024 The Author **Please note that there are multiple authors for this article therefore only the name of the first 30 including Federation University Australia affiliate “Biswajit Banik” is provided in this record*

Global burden of vision impairment due to age-related macular degeneration, 1990–2021, with forecasts to 2050: a systematic analysis for the Global Burden of Disease Study 2021

Age-related macular degeneration (AMD) is a growing public health concern worldwide, as one of the leading causes of vision impairment. We aimed to estimate global, national, and region-specific prevalence and disability-adjusted life-years (DALYs) along with tobacco as a modifiable risk factor to aid public policy addressing AMD. Methods Data on AMD were extracted from the Global Burden of Disease, Injuries, and Risk Factor Study 2021 database in 204 countries and territories, 1990–2021. Vision impairment was defined and categorised by severity as follows: moderate to severe vision loss (visual acuity from <6/18 to 3/60) and blindness (visual acuity <3/60 or a visual field <10 degrees around central fixation). The burden of vision impairment attributable to AMD was subsequently estimated. These estimates were further stratified by geographical region, age, year, sex, Healthcare Access and Quality (HAQ) Index, and Socio-demographic Index (SDI) levels. Additionally, the effect of tobacco use, a modifiable risk factor, on the burden of AMD was analysed, and projections of AMD burden were estimated through to 2050. These projections also included scenario modelling to assess the potential effects of tobacco elimination. Findings Globally, the number of individuals with vision impairment due to AMD more than doubled, rising from 3·64 million (95% uncertainty inverval [UI] 3·04–4·35) in 1990 to 8·06 million (6·71–9·82) in 2021. Similarly, DALYs increased by 91% over the same period, from 0·30 million (95% UI 0·21–0·42) to 0·58 million (0·40–0·80). By contrast, age-standardised prevalence and DALY rates declined, with prevalence rates decreasing by 5·53% (99·50 per 100 000 of the population [95% UI 83·16–118·04] in 1990 to 94·00 [78·32–114·42] in 2021) and DALY rates dropping by 19·09% (8·38 [5·70–11·53] to 6·78 [4·70–9·32]). These rates showed a consistent decrease in higher SDI quintiles, reflecting the negative correlation between HAQ Index and AMD burden. A general downward trend was observed from 1990 to 2021, with the largest age-standardised reduction occurring in the low-middle SDI quintile. The global contribution of tobacco to age-standardised DALYs decreased by 20%, declining from 12·45% (95% UI 7·73–17·37) in 1990 to 9·96% (6·12–14·06) in 2021. By 2050, the number of individuals affected by AMD is projected to increase from 3·40 million males (95% UI 2·81–4·17) in 2021 to 9·02 million (5·72–14·20) and from 4·66 million females (3·88–5·65) to 12·32 million (8·88–17·08). Eliminating tobacco use could reduce these numbers to 8·17 million males (5·59–11·92) and 11·15 million females (8·58–14·48) in 2050. Interpretation While the total prevalence and DALYs due to AMD have steadily increased from 1990 to 2021, age-standardised prevalence and DALY rates have declined, probably reflecting the effect of population ageing and growth. The consistent decrease in age-standardised rates with higher SDI levels highlights the crucial role of health-care resources and public policies in mitigating AMD-related vision impairment. The downward trend observed from 1990 to 2021 might also be partially attributed to the reduced effect of tobacco as a modifiable risk factor, with declines in tobacco use seen globally and across all SDI quintiles. The burden of vision impairment due to AMD is projected to increase to about 21·34 million in 2050. However, effective tobacco regulation has the potential to substantially reduce AMD-related vision impairment, particularly in lower SDI quintiles where health-care resources are limited.Gates FoundationpublishedVersio

a systematic analysis for the Global Burden of Disease Study 2021

Funding Information: Research reported in this publication was supported by the Bill & Melinda Gates Foundation (OPP1152504); Queensland Department of Health, Australia; UK Department of Health and Social Care; the Norwegian Institute of Public Health; St Jude Children's Research Hospital; and the New Zealand Ministry of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funders. Data for this research was provided by MEASURE Evaluation, funded by the US Agency for International Development (USAID). Views expressed do not necessarily reflect those of USAID, the US Government, or MEASURE Evaluation. This study uses a dataset provided by European Centre for Disease Prevention and Control (ECDC) based on data provided by WHO and Ministries of Health from the affected countries. The views and opinions of the authors expressed herein do not necessarily state or reflect those of the ECDC. 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. Health Behaviour in School-Aged Children (HBSC) is an international study carried out in collaboration with WHO/EURO. The international coordinator of the 1997\u201398, 2001\u201302, 2005\u201306, and 2009\u201310 surveys was Candace Currie and the Data Bank Manager for the 1997\u201398 survey was Bente Wold, whereas for the following survey Oddrun Samda was the databank manager. A list of principal investigators in each country can be found at http://www.hbsc.org. Parts of this material are based on data and information provided by the Canadian institute for Health Information. However, the analyses, conclusions, opinions and statements expressed herein are those of the author and not those of the Canadian Institute for Health information. The data reported here have been supplied by the US Renal Data System (USRDS). 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. The data used in this paper come from the 2009\u201310 Ghana Socioeconomic Panel Study Survey which is a nationally representative survey of over 5,000 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 the Economic Growth Center. At the same time, ISSER and the EGC are not responsible for the estimations reported by the analyst(s). The harmonised dataset was downloaded from the Global Dietary Database (GDD) website ( https://www.globaldietarydatabase.org/). The Canadian Community Health Survey - Nutrition (CCHS-Nutrition), 2015 is available online ( https://www.globaldietarydatabase.org/management/microdata-surveys/650). The harmonisation of the original dataset was performed by GDD. The data was adapted from Statistics Canada, Canadian Community Health Survey: Public Use Microdata File, 2015/2016 (Statistics Canada. CCHS-Nutrition, 2015); this does not constitute an endorsement by Statistics Canada of this product. The data is used under the terms of the Statistics Canada Open Licence (Statistics Canada. Statistics Canada Open Licence. https://www.statcan.gc.ca/eng/reference/licence). The Health and Retirement Study (HRS) is sponsored by the National Institute on Aging (grant number NIA U01AG009740) and is conducted by the University of Michigan. The Palestinian Central Bureau of Statistics granted the researchers access to relevant data in accordance with license no. 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. The results and conclusions are mine and not those of Eurostat, the European Commission, or any of the national statistical authorities whose data have been used. This manuscript is based on data collected and shared by the International Vaccine Institute (IVI) from an original study it conducted with support from the Bill & Melinda Gates Foundation. This paper uses data from SHARE Waves 1, 2, 3 (SHARELIFE), 4, 5 and 6 (dois: 10.6103/SHARE.w1.611,10.6103/SHARE.w2.611, 10.6103/SHARE.w3.611, 10.6103/SHARE.w4.611, 10.6103/SHARE.w5.611, 10.6103/SHARE.w6.611), see B\u00F6rsch-Supan et al. (2013) for methodological details. The SHARE data collection has been primarily funded by the European Commission through FP5 (QLK6-CT-2001-00360), FP6 (SHARE-I3: RII-CT-2006-062193, COMPARE: CIT5-CT-2005-028857, SHARELIFE: CIT4-CT-2006- 028812) and FP7 (SHARE-PREP: N\u00B0211909, SHARE-LEAP: N\u00B0227822, SHARE M4: N\u00B0261982). 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 (see www.share-project.org). This paper uses data from the Algeria - Setif and Mostaganem 2003 STEPS survey, implemented by Ministry of Health, Population and Hospital Reform (Algeria) with the support of WHO. This paper uses data from the Algeria 2016-2017 STEPS survey, implemented by Ministry of Health (Algeria) with the support of WHO. This paper uses data from the American Samoa 2004 STEPS survey, implemented by Department of Health (American Samoa) and Monash University (Australia) with the support of WHO. This paper uses data from the Armenia 2016 STEPS survey, implemented by Ministry of Health (Botswana) with the support of WHO. This paper uses data from the Azerbaijan 2017 STEPS survey, implemented by Ministry of Health (Azerbaijan) with the support of WHO. This paper uses data from the Bangladesh 2018 STEPS survey, implemented by Ministry of Health and Family Welfare (Bangladesh) with the support of WHO. This paper uses data from the Barbados 2007 STEPS survey, implemented by Ministry of Health (Barbados) with the support of WHO. This paper uses data from the Belarus 2016-2017 STEPS survey, implemented by Republican Scientific and Practical Center of Medical Technologies, Informatization, Management and Economics of Public Health (Belarus) with the support of WHO. This paper uses data from the Benin - Littoral 2007 STEPS survey, the Benin 2008 STEPS survey, and the Benin 2015 STEPS survey, implemented by Ministry of Health (Benin) with the support of WHO. This paper uses data from the Bhutan - Thimphu 2007 STEPS survey, implemented by Ministry of Health (Bhutan) with the support of WHO. This paper uses data from the Bhutan 2014 STEPS survey, implemented by Ministry of Health (Bhutan) with the support of the World Health Organization. This paper uses data from the Botswana 2014 STEPS survey, implemented by Ministry of Health (Armenia), National Institute of Health with the support of WHO. This paper uses data from the Brunei 2015-2016 STEPS survey, implemented by Ministry of Health (Brunei) with the support of WHO. This paper uses data from the Cambodia 2010 STEPS survey, implemented by Ministry of Health (Cambodia) with the support of WHO. This paper uses data from the Cameroon 2003 STEPS survey, implemented by Health of Populations in Transition (HoPiT) Research Group (Cameroon) and Ministry of Public Health (Cameroon) with the support of WHO. This paper uses data from the Cape Verde 2007 STEPS survey, implemented by Ministry of Health, National Statistics Office with the support of WHO. This paper uses data from the Central African Republic - Bangui 2010 STEPS survey and Central African Republic - Bangui and Ombella M'Poko 2016 STEPS survey, implemented by Ministry of Health and Population (Central African Republic) with the support of WHO. This paper uses data from the Comoros 2011 STEPS survey, implemented by Ministry of Health (Comoros) with the support of WHO. This paper uses data from the Congo - Brazzaville 2004 STEPS survey, implemented by Ministry of Health, Population and Hospital Reform (Algeria) with the support of WHO. This paper uses data from the Cook Islands 2003\u20132004 survey and Cook Islands 2013\u20132015 STEPS survey, implemented by Ministry of Health (Cook Islands) with the support of WHO. This paper uses data from the Eritrea 2010 STEPS survey, implemented by Ministry of Health (Eritrea) with the support of WHO. This paper uses data from the Fiji 2002 STEPS survey, implemented by Fiji School of Medicine, Menzies Center for Population Health Research, University of Tasmania (Australia), Ministry of Health (Fiji) with the support of WHO. This paper uses data from the Fiji 2011 STEPS survey, implemented by Ministry of Health (Fiji) with the support of WHO. This paper uses data from the Georgia 2016 STEPS survey, implemented by National Center for Disease Control and Public Health (Georgia) with the support of WHO. This paper uses data from the Ghana - Greater Accra Region 2006 STEPS survey, implemented by Ghana Health Service with the support of WHO. This paper uses data from the Guniea 2009 STEPS survey, implemented by Ministry of Public Health and Hygiene (Guinea) with the support of WHO. This paper uses data from the Guyana 2016 STEPS survey, implemented by Ministry of Health (Guyana) with the support of WHO. This paper uses data from the Iraq 2015 STEPS survey, implemented by Ministry of Health (Iraq) with the support of WHO. This paper uses data from the Kenya 2015 STEPS survey, implemented by Kenya National Bureau of Statistics, Ministry of Health (Kenya) with the support of WHO. This paper uses data from the Kiribati 2004\u20132006 STEPS survey and the Kiribati 2016 survey, implemented by Ministry of Health and Medical Services (Kiribati) with the support of WHO. This paper uses data from the Kuwait 2006 STEPS survey and the Kuwait 2014 STEPS survey, implemented by Ministry of Health (Kuwait) with the support of WHO. This paper uses data from the Kyrgyzstan 2013 STEPS survey, implemented by Ministry of Health (Kyrgyzstan) with the support of WHO. This paper uses data from the Laos 2013 STEPS survey, implemented by Ministry of Health (Laos) with the support of WHO. This paper uses data from the Lebanon 2016-2017 STEPS survey, implemented by Ministry of Public Health (Lebanon) with the support of WHO. This paper uses data from the Lesotho 2012 STEPS survey, implemented by Ministry of Health and Social Welfare (Lesotho) with the support of WHO. This paper uses data from the Liberia 2011 STEPS survey, implemented by Ministry of Health and Social Welfare (Liberia) with the support of WHO. This paper uses data from the Libya 2009 STEPS survey, implemented by Secretariat of Health and Environment (Libya) with the support of WHO. This paper uses data from the Malawi 2009 STEPS survey and Malawi 2017 STEPS survey, implemented by Ministry of Health (Malawi) with the support of WHO. This paper uses data from the Mali 2007 STEPS survey, implemented by Ministry of Health (Mali) with the support of WHO. This paper uses data from the Marshall Islands 2002 STEPS survey and the Marshall Islands 2017-2018 STEPS survey, implemented by Ministry of Health (Marshall Islands) with the support of WHO. This paper uses data from the Mauritania- Nouakchott 2006 STEPS survey, implemented by Ministry of Health (Mauritania) with the support of WHO. This paper uses data from the Micronesia - Chuuk 2006 STEPS survey, implemented by Ministry of Health (Palestine) with the support of WHO. This paper uses data from the Micronesia - Chuuk 2016 STEPS survey, implemented by Chuuk Department of Health Services (Micronesia), Department of Health and Social Affairs (Micronesia) with the support of WHO. This paper uses data from the Micronesia - Pohnpei 2002 STEPS survey, implemented by Centre for Physical Activity and Health, University of Sydney (Australia), Department of Health and Social Affairs (Micronesia), Fiji School of Medicine, Micronesia Human Resources Development Center, Pohnpei State Department of Health Services with the support of WHO. This paper uses data from the Micronesia - Pohnpei 2008 STEPS survey, implemented by FSM Department of Health and Social Affairs, Pohnpei State Department of Health Services with the support of WHO. This paper uses data from the Micronesia - Yap 2009 STEPS survey, implemented by Ministry of Health and Social Affairs (Micronesia) with the support of WHO. This paper uses data from the Micronesia- Kosrae 2009 STEPS survey, implemented by FSM Department of Health and Social Affairs with the support of WHO. This paper uses data from the Moldova 2013 STEPS survey, implemented by Ministry of Health (Moldova) with the support of WHO. This paper uses data from the Mongolia 2005 STEPS survey, the Mongolia 2009 STEPS survey, and the Mongolia 2013 STEPS survey, implemented by Ministry of Health (Mongolia) with the support of WHO. This paper uses data from the Morocco 2017 STEPS survey, implemented by Ministry of Health (Morocco) with the support of WHO. This paper uses data from the Mozambique 2005 STEPS survey, implemented by Ministry of Health (Mozambique) with the support of WHO. This paper uses data from the Myanmar 2014 STEPS survey, implemented by Ministry of Health (Myanmar) with the support of WHO. This paper uses data from the Nauru 2004 STEPS survey and the Nauru 2015\u20132016 STEPS survey, implemented by Ministry of Health (Nauru) with the support of WHO. This paper uses data from the Niger 2007 STEPS survey, implemented by Ministry of Health (Niger) with the support of WHO. This paper uses data from the Palau 2011-2013 STEPS survey and the Palau 2016 STEPS survey, implemented by Ministry of Health (Palau) with the support of WHO. This paper uses data from the Palestine 2010-2011 STEPS survey, implemented by Chuuk Department of Health Services (Micronesia), Department of Health and Social Affairs (Micronesia) with the support of WHO. This paper uses data from the Qatar 2012 STEPS survey, implemented by Supreme Council of Health (Qatar) with the support of WHO. This paper uses data from the Rwanda 2012-2013 STEPS survey, implemented by Ministry of Health (Rwanda) with the support of WHO. This paper uses data from the Samoa 2002 STEPS survey and the Samoa 2013 STEPS survey, implemented by Ministry of Health (Samoa) with the support of WHO. This paper uses data from the Sao Tome and Principe 2008 STEPS survey, implemented by Ministry of Health (Sao Tome and Principe) with the support of WHO. This paper uses data from the Seychelles 2004 STEPS survey, implemented by Ministry of Health (Seychelles) with the support of WHO. This paper uses data from the Solomon Islands 2005\u20132006 STEPS survey and the Solomon Islands 2015 STEPS survey, implemented by Ministry of Health and Medical Services (Solomon Islands) with the support of WHO. This paper uses data from the Sri Lanka 2014\u20132015 STEPS survey, implemented by Ministry of Health (Sri Lanka) with the support of WHO. This paper uses data from the Sudan 2016 STEPS survey, implemented by Ministry of Health (Sudan) with the support of WHO. This paper uses data from the Swaziland 2007 STEPS survey and the Swaziland 2014 STEPS survey, implemented by Ministry of Health (Swaziland) with the support of WHO. This paper uses data from the Tajikistan 2016 STEPS survey, implemented by Ministry of Health (Tajikistan) with the support of WHO. This paper uses data from the Tanzania - Zanzibar 2011 STEPS survey, implemented by Ministry of Health (Zanzibar) with the support of WHO. This paper uses data from the Tanzania 2012 STEPS survey, implemented by National Institute for Medical Research (Tanzania) with the support of WHO. This paper uses data from the Timor-Leste 2014 STEPS survey, implemented by Ministry of Health (Timor-Leste) with the support of WHO. This paper uses data from the Togo 2010\u20132011 STEPS survey, implemented by Ministry of Health (Togo) with the support of WHO. This paper uses data from the Tokelau 2005 STEPS survey, implemented by Tokelau Department of Health, Fiji School of Medicine with the support of WHO. This paper uses data from the Tonga 2004 STEPS survey and the Tonga 2011\u20132012 STEPS survey, implemented by Ministry of Health (Tonga) with the support of WHO. This paper uses data from the Tuvalu 2015 STEPS survey, implemented by Ministry of Health (Tuvalu), with the support of WHO. This paper uses data from the Uganda 2014 STEPS survey, implemented by Ministry of Health (Uganda) with the support of WHO. This paper uses data from the Uruguay 2006 STEPS survey and the Uruguay 2013-2014 STEPS survey, implemented by Ministry of Health (Uruguay) with the support of WHO. This paper uses data from the Vanuatu 2011 STEPS survey, implemented by Ministry of Health (Vanuatu) with the support of WHO. This paper uses data from the Viet Nam 2009 STEPS survey and the Viet Nam 2015 STEPS survey, implemented by Ministry of Health (Viet Nam) with the support of WHO. This paper uses data from the Virgin Islands, British 2009 STEPS survey, implemented by Ministry of Health and Social Development (British Virgin Islands) with the support of WHO. This paper uses data from the Zambia - Lusaka 2008 STEPS survey, implemented by Ministry of Health (Zambia) with the support of WHO. This paper uses data from the Zambia 2017 STEPS survey, implemented by Ministry of Health (Zambia) with the support of WHO. This research used data from the Chile National Health Survey 2003, 2009\u201310, and 2016\u201317. The authors are grateful to the Ministry of Health, survey copyright owner, for allowing them to have the database. All results of the study are those of the author and in no way committed to the Ministry. This research used information from the Health Surveys for epidemiological surveillance of the Undersecretary of Public Health. The authors thank the Ministry of Health of Chile, having allowed them to have access to the database. All the results obtained from the study or research are the responsibility of the authors and in no way compromise that institution. This research 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. Persons interested in obtaining data files from Add Health should contact Add Health, Carolina Population Center, 123 W. Franklin Street, Chapel Hill, NC 27516-2524, USA ( [email protected]). No direct support was received from grant P01-HD31921 for this analysis. This study has been realised using the data collected by the Swiss Household Panel (SHP), which is based at the Swiss Centre of Expertise in the Social Sciences FORS. The project is financed by the Swiss National Science Foundation. We thank the Russia Longitudinal Monitoring Survey, RLMS-HSE, 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 RAS for making these data available. Editorial note: The Lancet Group takes a neutral position with respect to territorial claims in published maps and institutional affiliations. Publisher Copyright: © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licenseBackground: Detailed, comprehensive, and timely reporting on population health by underlying causes of disability and premature death is crucial to understanding and responding to complex patterns of disease and injury burden over time and across age groups, sexes, and locations. The availability of disease burden estimates can promote evidence-based interventions that enable public health researchers, policy makers, and other professionals to implement strategies that can mitigate diseases. It can also facilitate more rigorous monitoring of progress towards national and international health targets, such as the Sustainable Development Goals. For three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has filled that need. A global network of collaborators contributed to the production of GBD 2021 by providing, reviewing, and analysing all available data. GBD estimates are updated routinely with additional data and refined analytical methods. GBD 2021 presents, for the first time, estimates of health loss due to the COVID-19 pandemic. Methods: The GBD 2021 disease and injury burden analysis estimated years lived with disability (YLDs), years of life lost (YLLs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries using 100 983 data sources. Data were extracted from vital registration systems, verbal autopsies, censuses, household surveys, disease-specific registries, health service contact data, and other sources. YLDs were calculated by multiplying cause-age-sex-location-year-specific prevalence of sequelae by their respective disability weights, for each disease and injury. YLLs were calculated by multiplying cause-age-se