Metabolomic profiling reveals altered phenylalanine metabolism in Parkinson’s disease in an Egyptian cohort

Introduction: Parkinson’s disease (PD) is the most common motor neurodegenerative disease worldwide. Given the complexity of PD etiology and the different metabolic derangements correlated to the disease, metabolomics profiling of patients is a helpful tool to identify patho-mechanistic pathways for the disease development. Dopamine metabolism has been the target of several previous studies, of which some have reported lower phenylalanine and tyrosine levels in PD patients compared to controls.Methods: In this study, we have collected plasma from 27 PD patients, 18 reference controls, and 8 high-risk controls to perform a metabolomic study using liquid chromatography-electrospray ionization–tandem mass spectrometry (LC-ESI-MS/MS).Results: Our findings revealed higher intensities of trans-cinnamate, a phenylalanine metabolite, in patients compared to reference controls. Thus, we hypothesize that phenylalanine metabolism has been shifted to produce trans-cinnamate via L-phenylalanine ammonia lyase (PAL), instead of producing tyrosine, a dopamine precursor, via phenylalanine hydroxylase (PAH).Discussion: Given that these metabolites are precursors to several other metabolic pathways, the intensities of many metabolites such as dopamine, norepinephrine, and 3-hydroxyanthranilic acid, which connects phenylalanine metabolism to that of tryptophan, have been altered. Consequently, and in respect to Metabolic Control Analysis (MCA) theory, the levels of tryptophan metabolites have also been altered. Some of these metabolites are tryptamine, melatonin, and nicotinamide. Thus, we assume that these alterations could contribute to the dopaminergic, adrenergic, and serotonergic neurodegeneration that happen in the disease

Global mortality from dementia: Application of a newmethod and results from the global burden of disease study 2019

INTRODUCTION: Dementia is currently one of the leading causes of mortality globally, and mortality due to dementia will likely increase in the future along with corresponding increases in population growth and population aging. However, large inconsistencies in coding practices in vital registration systems over time and between countries complicate the estimation of global dementia mortality. METHODS: We meta-analyzed the excess risk of death in those with dementia and multiplied these estimates by the proportion of dementia deaths occurring in those with severe, end-stage disease to calculate the total number of deaths that could be attributed to dementia. RESULTS: We estimated that there were 1.62 million (95% uncertainty interval [UI]: 0.41–4.21) deaths globally due to dementia in 2019. More dementia deaths occurred in women (1.06 million [0.27–2.71]) than men (0.56 million [0.14–1.51]), largely but not entirely due to the higher life expectancy in women (age-standardized female-to-male ratio 1.19 [1.10–1.26]). Due to population aging, there was a large increase in all-age mortality rates from dementia between 1990 and 2019 (100.1% [89.1–117.5]). In 2019, deaths due to dementia ranked seventh globally in all ages and fourth among individuals 70 and older compared to deaths from other diseases estimated in the Global Burden of Disease (GBD) study. DISCUSSION: Mortality due to dementia represents a substantial global burden, and is expected to continue to grow into the future as an older, aging population expands globally

Use of multidimensional item response theory methods for dementia prevalence prediction: an example using the Health and Retirement Survey and the Aging, Demographics, and Memory Study

Background: Data sparsity is a major limitation to estimating national and global dementia burden. Surveys with full diagnostic evaluations of dementia prevalence are prohibitively resource-intensive in many settings. However, validation samples from nationally representative surveys allow for the development of algorithms for the prediction of dementia prevalence nationally. Methods: Using cognitive testing data and data on functional limitations from Wave A (2001–2003) of the ADAMS study (n = 744) and the 2000 wave of the HRS study (n = 6358) we estimated a two-dimensional item response theory model to calculate cognition and function scores for all individuals over 70. Based on diagnostic information from the formal clinical adjudication in ADAMS, we fit a logistic regression model for the classification of dementia status using cognition and function scores and applied this algorithm to the full HRS sample to calculate dementia prevalence by age and sex. Results: Our algorithm had a cross-validated predictive accuracy of 88% (86–90), and an area under the curve of 0.97 (0.97–0.98) in ADAMS. Prevalence was higher in females than males and increased over age, with a prevalence of 4% (3–4) in individuals 70–79, 11% (9–12) in individuals 80–89 years old, and 28% (22–35) in those 90 and older. Conclusions: Our model had similar or better accuracy as compared to previously reviewed algorithms for the prediction of dementia prevalence in HRS, while utilizing more flexible methods. These methods could be more easily generalized and utilized to estimate dementia prevalence in other national surveys

Use of multidimensional item response theory methods for dementia prevalence prediction : an example using the Health and Retirement Survey and the Aging, Demographics, and Memory Study

Background Data sparsity is a major limitation to estimating national and global dementia burden. Surveys with full diagnostic evaluations of dementia prevalence are prohibitively resource-intensive in many settings. However, validation samples from nationally representative surveys allow for the development of algorithms for the prediction of dementia prevalence nationally. Methods Using cognitive testing data and data on functional limitations from Wave A (2001-2003) of the ADAMS study (n = 744) and the 2000 wave of the HRS study (n = 6358) we estimated a two-dimensional item response theory model to calculate cognition and function scores for all individuals over 70. Based on diagnostic information from the formal clinical adjudication in ADAMS, we fit a logistic regression model for the classification of dementia status using cognition and function scores and applied this algorithm to the full HRS sample to calculate dementia prevalence by age and sex. Results Our algorithm had a cross-validated predictive accuracy of 88% (86-90), and an area under the curve of 0.97 (0.97-0.98) in ADAMS. Prevalence was higher in females than males and increased over age, with a prevalence of 4% (3-4) in individuals 70-79, 11% (9-12) in individuals 80-89 years old, and 28% (22-35) in those 90 and older. Conclusions Our model had similar or better accuracy as compared to previously reviewed algorithms for the prediction of dementia prevalence in HRS, while utilizing more flexible methods. These methods could be more easily generalized and utilized to estimate dementia prevalence in other national surveys

Use of multidimensional item response theory methods for dementia prevalence prediction: an example using the Health and Retirement Survey and the Aging, Demographics, and Memory Study.

BACKGROUND: Data sparsity is a major limitation to estimating national and global dementia burden. Surveys with full diagnostic evaluations of dementia prevalence are prohibitively resource-intensive in many settings. However, validation samples from nationally representative surveys allow for the development of algorithms for the prediction of dementia prevalence nationally. METHODS: Using cognitive testing data and data on functional limitations from Wave A (2001-2003) of the ADAMS study (n = 744) and the 2000 wave of the HRS study (n = 6358) we estimated a two-dimensional item response theory model to calculate cognition and function scores for all individuals over 70. Based on diagnostic information from the formal clinical adjudication in ADAMS, we fit a logistic regression model for the classification of dementia status using cognition and function scores and applied this algorithm to the full HRS sample to calculate dementia prevalence by age and sex. RESULTS: Our algorithm had a cross-validated predictive accuracy of 88% (86-90), and an area under the curve of 0.97 (0.97-0.98) in ADAMS. Prevalence was higher in females than males and increased over age, with a prevalence of 4% (3-4) in individuals 70-79, 11% (9-12) in individuals 80-89 years old, and 28% (22-35) in those 90 and older. CONCLUSIONS: Our model had similar or better accuracy as compared to previously reviewed algorithms for the prediction of dementia prevalence in HRS, while utilizing more flexible methods. These methods could be more easily generalized and utilized to estimate dementia prevalence in other national surveys

Global, regional, and national incidence, prevalence, and mortality of HIV, 1980–2017, and forecasts to 2030, for 195 countries and territories: a systematic analysis for the Global Burden of Diseases, Injuries, and Risk Factors Study 2017

Background Understanding the patterns of HIV/AIDS epidemics is crucial to tracking and monitoring the progress of prevention and control efforts in countries. We provide a comprehensive assessment of the levels and trends of HIV/AIDS incidence, prevalence, mortality, and coverage of antiretroviral therapy (ART) for 1980–2017 and forecast these estimates to 2030 for 195 countries and territories. Methods We determined a modelling strategy for each country on the basis of the availability and quality of data. For countries and territories with data from population-based seroprevalence surveys or antenatal care clinics, we estimated prevalence and incidence using an open-source version of the Estimation and Projection Package—a natural history model originally developed by the UNAIDS Reference Group on Estimates, Modelling, and Projections. For countries with cause-specific vital registration data, we corrected data for garbage coding (ie, deaths coded to an intermediate, immediate, or poorly defined cause) and HIV misclassification. We developed a process of cohort incidence bias adjustment to use information on survival and deaths recorded in vital registration to back-calculate HIV incidence. For countries without any representative data on HIV, we produced incidence estimates by pulling information from observed bias in the geographical region. We used a re-coded version of the Spectrum model (a cohort component model that uses rates of disease progression and HIV mortality on and off ART) to produce age-sex-specific incidence, prevalence, and mortality, and treatment coverage results for all countries, and forecast these measures to 2030 using Spectrum with inputs that were extended on the basis of past trends in treatment scale-up and new infections. Findings Global HIV mortality peaked in 2006 with 1·95 million deaths (95% uncertainty interval 1·87–2·04) and has since decreased to 0·95 million deaths (0·91–1·01) in 2017. New cases of HIV globally peaked in 1999 (3·16 million, 2·79–3·67) and since then have gradually decreased to 1·94 million (1·63–2·29) in 2017. These trends, along with ART scale-up, have globally resulted in increased prevalence, with 36·8 million (34·8–39·2) people living with HIV in 2017. Prevalence of HIV was highest in southern sub-Saharan Africa in 2017, and countries in the region had ART coverage ranging from 65·7% in Lesotho to 85·7% in eSwatini. Our forecasts showed that 54 countries will meet the UNAIDS target of 81% ART coverage by 2020 and 12 countries are on track to meet 90% ART coverage by 2030. Forecasted results estimate that few countries will meet the UNAIDS 2020 and 2030 mortality and incidence targets. Interpretation Despite progress in reducing HIV-related mortality over the past decade, slow decreases in incidence, combined with the current context of stagnated funding for related interventions, mean that many countries are not on track to reach the 2020 and 2030 global targets for reduction in incidence and mortality. With a growing population of people living with HIV, it will continue to be a major threat to public health for years to come. The pace of progress needs to be hastened by continuing to expand access to ART and increasing investments in proven HIV prevention initiatives that can be scaled up to have population-level impact

Global mortality from dementia : Application of a new method and results from the Global Burden of Disease Study 2019

Introduction Dementia is currently one of the leading causes of mortality globally, and mortality due to dementia will likely increase in the future along with corresponding increases in population growth and population aging. However, large inconsistencies in coding practices in vital registration systems over time and between countries complicate the estimation of global dementia mortality. Methods We meta-analyzed the excess risk of death in those with dementia and multiplied these estimates by the proportion of dementia deaths occurring in those with severe, end-stage disease to calculate the total number of deaths that could be attributed to dementia. Results We estimated that there were 1.62 million (95% uncertainty interval [UI]: 0.41-4.21) deaths globally due to dementia in 2019. More dementia deaths occurred in women (1.06 million [0.27-2.71]) than men (0.56 million [0.14-1.51]), largely but not entirely due to the higher life expectancy in women (age-standardized female-to-male ratio 1.19 [1.10-1.26]). Due to population aging, there was a large increase in all-age mortality rates from dementia between 1990 and 2019 (100.1% [89.1-117.5]). In 2019, deaths due to dementia ranked seventh globally in all ages and fourth among individuals 70 and older compared to deaths from other diseases estimated in the Global Burden of Disease (GBD) study. Discussion Mortality due to dementia represents a substantial global burden, and is expected to continue to grow into the future as an older, aging population expands globally.Peer reviewe

Mapping age- and sex-specific HIV prevalence in adults in sub-Saharan Africa, 2000–2018

Background: Human immunodeficiency virus and acquired immune deficiency syndrome (HIV/AIDS) is still among the leading causes of disease burden and mortality in sub-Saharan Africa (SSA), and the world is not on track to meet targets set for ending the epidemic by the Joint United Nations Programme on HIV/AIDS (UNAIDS) and the United Nations Sustainable Development Goals (SDGs). Precise HIV burden information is critical for effective geographic and epidemiological targeting of prevention and treatment interventions. Age- and sex-specific HIV prevalence estimates are widely available at the national level, and region-wide local estimates were recently published for adults overall. We add further dimensionality to previous analyses by estimating HIV prevalence at local scales, stratified into sex-specific 5-year age groups for adults ages 15–59 years across SSA. Methods: We analyzed data from 91 seroprevalence surveys and sentinel surveillance among antenatal care clinic (ANC) attendees using model-based geostatistical methods to produce estimates of HIV prevalence across 43 countries in SSA, from years 2000 to 2018, at a 5 × 5-km resolution and presented among second administrative level (typically districts or counties) units. Results: We found substantial variation in HIV prevalence across localities, ages, and sexes that have been masked in earlier analyses. Within-country variation in prevalence in 2018 was a median 3.5 times greater across ages and sexes, compared to for all adults combined. We note large within-district prevalence differences between age groups: for men, 50% of districts displayed at least a 14-fold difference between age groups with the highest and lowest prevalence, and at least a 9-fold difference for women. Prevalence trends also varied over time; between 2000 and 2018, 70% of all districts saw a reduction in prevalence greater than five percentage points in at least one sex and age group. Meanwhile, over 30% of all districts saw at least a five percentage point prevalence increase in one or more sex and age group. Conclusions: As the HIV epidemic persists and evolves in SSA, geographic and demographic shifts in prevention and treatment efforts are necessary. These estimates offer epidemiologically informative detail to better guide more targeted interventions, vital for combating HIV in SSA. © 2022, The Author(s).Funding text 1: S Afzal acknowledges support of the Pakistan Society of Medical Infectious Diseases and King Edward Medical University to access the relevant data of HIV from various sources. T W Bärnighausen was supported by the Alexander von Humboldt Foundation through the Alexander von Humboldt Professor award, funded by the German Federal Ministry of Education and Research. F Carvalho and E Fernandes acknowledge support from Fundação para a Ciência e a Tecnologia (FCT), I.P., in the scope of the project UIDP/04378/2020 and UIDB/04378/2020 of the Research Unit on Applied Molecular Biosciences - UCIBIO and the project LA/P/0140/2020 of the Associate Laboratory Institute for Health and Bioeconomy - i4HB; FCT/MCTES (Ministério da Ciência, Tecnologia e Ensino Superior) through the project UIDB/50006/2020. K Deribe acknowledges support by the Wellcome Trust [grant number 201900/Z/16/Z] as part of his International Intermediate Fellowship. C Herteliu and A Pana are partially supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNDS-UEFISCDI, project number PN-III-P4-ID-PCCF-2016-0084. Claudiu Herteliu is partially supported by a grant of the Romanian Ministry of Research Innovation and Digitalization, MCID, project number ID-585-CTR-42-PFE-2021. Y J Kim acknowledges support by the Research Management Centre, Xiamen University Malaysia [No. XMUMRF/2020-C6/ITCM/0004]. S L Koulmane Laxminarayana acknowledges institutional support by the Manipal Academy of Higher Education. K Krishan acknowledges non-financial support from UGC Centre of Advanced Study, CAS II, Department of Anthropology, Panjab University, Chandigarh, India. M Kumar would like to acknowledge NIH/FIC K43 TW010716-04. I Landires is a member of the Sistema Nacional de Investigación (SNI), supported by the Secretaría Nacional de Ciencia, Tecnología e Innovación (SENACYT), Panama. V Nuñez-Samudio is a member of the Sistema Nacional de Investigación (SNI), which is supported by Panama’s Secretaría Nacional de Ciencia, Tecnología e Innovación (SENACYT). O O Odukoya was supported by the Fogarty International Center of the National Institutes of Health under the Award Number K43TW010704. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Z Quazi Syed acknowledges support from JNMC, Datta Meghe Institute of Medical Sciences. A I Ribeiro was supported by National Funds through FCT, under the ‘Stimulus of Scientific Employment – Individual Support’ program within the contract CEECIND/02386/2018. A M Samy acknowledges the support from a fellowship of the Egyptian Fulbright Mission program and Ain Shams University. R Shrestha acknowledges support from NIDA K01 Award: K01DA051346. N Taveira acknowledges support from FCT and Aga Khan Development Network (AKDN) - Portugal Collaborative Research Network in Portuguese speaking countries in Africa (project reference: 332821690), and by the European & Developing Countries Clinical Trials Partnership (EDCTP), UE (project reference: RIA2016MC-1615). B Unnikrishnan acknowledges support from Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal. ; Funding text 2: LBD sub-Saharan Africa HIV Prevalence Collaborators S Afzal acknowledges support of the Pakistan Society of Medical Infectious Diseases and King Edward Medical University to access the relevant data of HIV from various sources. T W Bärnighausen was supported by the Alexander von Humboldt Foundation through the Alexander von Humboldt Professor award, funded by the German Federal Ministry of Education and Research. F Carvalho and E Fernandes acknowledge support from Fundação para a Ciência e a Tecnologia (FCT), I.P., in the scope of the project UIDP/04378/2020 and UIDB/04378/2020 of the Research Unit on Applied Molecular Biosciences - UCIBIO and the project LA/P/0140/2020 of the Associate Laboratory Institute for Health and Bioeconomy - i4HB; FCT/MCTES (Ministério da Ciência, Tecnologia e Ensino Superior) through the project UIDB/50006/2020. K Deribe acknowledges support by the Wellcome Trust [grant number 201900/Z/16/Z] as part of his International Intermediate Fellowship. C Herteliu and A Pana are partially supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNDS-UEFISCDI, project number PN-III-P4-ID-PCCF-2016-0084. Claudiu Herteliu is partially supported by a grant of the Romanian Ministry of Research Innovation and Digitalization, MCID, project number ID-585-CTR-42-PFE-2021. Y J Kim acknowledges support by the Research Management Centre, Xiamen University Malaysia [No. XMUMRF/2020-C6/ITCM/0004]. S L Koulmane Laxminarayana acknowledges institutional support by the Manipal Academy of Higher Education. K Krishan acknowledges non-financial support from UGC Centre of Advanced Study, CAS II, Department of Anthropology, Panjab University, Chandigarh, India. M Kumar would like to acknowledge NIH/FIC K43 TW010716-04. I Landires is a member of the Sistema Nacional de Investigación (SNI), supported by the Secretaría Nacional de Ciencia, Tecnología e Innovación (SENACYT), Panama. V Nuñez-Samudio is a member of the Sistema Nacional de Investigación (SNI), which is supported by Panama’s Secretaría Nacional de Ciencia, Tecnología e Innovación (SENACYT). O O Odukoya was supported by the Fogarty International Center of the National Institutes of Health under the Award Number K43TW010704. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Z Quazi Syed acknowledges support from JNMC, Datta Meghe Institute of Medical Sciences. A I Ribeiro was supported by National Funds through FCT, under the ‘Stimulus of Scientific Employment – Individual Support’ program within the contract CEECIND/02386/2018. A M Samy acknowledges the support from a fellowship of the Egyptian Fulbright Mission program and Ain Shams University. R Shrestha acknowledges support from NIDA K01 Award: K01DA051346. N Taveira acknowledges support from FCT and Aga Khan Development Network (AKDN) - Portugal Collaborative Research Network in Portuguese speaking countries in Africa (project reference: 332821690), and by the European & Developing Countries Clinical Trials Partnership (EDCTP), UE (project reference: RIA2016MC-1615). B Unnikrishnan acknowledges support from Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal.; Funding text 3: This work was primarily supported by grant OPP1132415 from the Bill & Melinda Gates Foundation. The funder of the study had no role in study design, data collection, data analysis, data interpretation, writing of the report, or decision to publish. The corresponding authors had full access to all the data in the study and had final responsibility for the decision to submit for publication. ; Funding text 4: S Afzal reports leadership or fiduciary role in other board, society, committee or advocacy group, unpaid, with the Pakistan society of Community Medicine & Public Health, the Pakistan Association of Medical Editors, and the Pakistan Society of Medical Infectious Diseases, all outside the submitted work. R Ancuceanu reports 5 payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing, or educational events from Avvie, Sandoz, and B Braun, all outside the submitted work. T W Bärnighausen reports research grants from the European Union (Horizon 2020 and EIT Health), German Research Foundation (DFG), US National Institutes of Health, German Ministry of Education and Research, Alexander von Humboldt Foundation, Else-Kröner-Fresenius-Foundation, Wellcome Trust, Bill & Melinda Gates Foundation, KfW, UNAIDS, and WHO; consulting fees from KfW on the OSCAR initiative in Vietnam; participation on a Data Safety Monitoring Board or Advisory Board with the NIH-funded study “Healthy Options” (PIs: Smith Fawzi, Kaaya), Chair, Data Safety and Monitoring Board (DSMB), German National Committee on the “Future of Public Health Research and Education,” Chair of the scientific advisory board to the EDCTP Evaluation, Member of the UNAIDS Evaluation Expert Advisory Committee, National Institutes of Health Study Section Member on Population and Public Health Approaches to HIV/AIDS (PPAH), US National Academies of Sciences, Engineering, and Medicine’s Committee for the “Evaluation of Human Resources for Health in the Republic of Rwanda under the President’s Emergency Plan for AIDS Relief (PEPFAR),” University of Pennsylvania (UPenn) Population Aging Research Center (PARC) External Advisory Board Member; leadership or fiduciary role in other board, society, committee or advocacy group, paid or unpaid, as co-chair of the Global Health Hub Germany (which was initiated by the German Ministry of Health); all outside the submitted work. J das Neves reports grants or contracts from Ref. 13605 – Programa GÉNESE, Gilead Portugal (PGG/002/2016 – Programa GÉNESE, Gilead Portugal) outside the submitted work. L Dwyer-Lindgren reports support for the present manuscript from the Bill & Melinda Gates Foundation through grant OPP1132415. I Filip reports other financial or non-financial interests from Avicenna Medical and Clinical Research Institute, outside the submitted work. E Haeuser reports support for the present manuscript from the Bill & Melinda Gates Foundation through grant OPP1132415. C Herteliu reports grants from Romanian Ministry of Research Innovation and Digitalization, MCID, for project number ID-585-CTR-42-PFE-2021 (Jan 2022-Jun 2023) “Enhancing institutional performance through development of infrastructure and transdisciplinary research ecosystem within socio-economic domain – PERFECTIS,” from Romanian National Authority for Scientific Research and Innovation, CNDS-UEFISCDI, for project number PN-III-P4-ID-PCCF-2016-0084 (Oct 2018-Sep 2022) “Understanding and modelling time-space patterns of psychology-related inequalities and polarization,” and project number PN-III-P2-2.1-SOL-2020-2-0351 (Jun 2020-Oct 2020) “Approaches within public health management in the context of COVID-19 pandemic,” and from the Ministry of Labour and Social Justice, Romania for project number “Agenda for skills Romania 2020-2025”; all outside the submitted work. J J Jozwiak reports payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing, or educational events from Teva, Amgen, Synexus, Boehringer Ingelheim, Zentiva, and Sanofi as personal fees, all outside the submitted work. J Khubchandani reports other financial interests from Teva Pharmaceuticals, all outside the submitted work. K Krishnan reports other non-financial support from UGC Centre of Advanced Study, CAS II, Department of Anthropology, Panjab University, Chandigarh, India, outside the submitted work. H J Larson reports grants or contracts from the MacArthur Foundation and Merck to London School of Hygeine and Tropical Medicine, and from the Vaccine Confidence Fund to the University of Washington; payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing, or educational events from Center for Strategic and International Studies as payment to LSHTM for co-chairing HighLevel Panel and from GSK as personal payment for developing training sessions and lectures; leadership or fiduciary role in other board, society, committee or advocacy group, pair, with the ApiJect Advisory Board; all outside the submitted work. O O Odukoya reports support for the present manuscript from the Fogarty International Center of the National Institutes of Health under the Award Number K43TW010704. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. A Pans reports grants from Romanian National Authority for Scientific Research and Innovation, CNDS-UEFISCDI, for project number PN-III-P4-ID-PCCF-2016-0084 (Oct 2018-Sep 2022) “Understanding and modelling time-space patterns of psychology-related inequalities and polarization,” and project number PN-III-P2-2.1-SOL-2020-2-0351 (Jun 2020-Oct 2020) “Approaches within public health management in the context of COVID-19 pandemic,” outside the submitted work. S R Pandi-Perumal reports royalties from Springer for editing services; stock or stock options in Somnogen Canada Inc as the President and Chief Executive Officer; all outside the submitted work. A Radfar reports other financial or non-financial interests from Avicenna Medical and Clinical Research Institute, outside the submitted work. A I Ribeiro reports grants or contracts from National Funds through FCT, under the ‘Stimulus of Scientific Employment – Individual Support’ program within the contract CEECIND/02386/2018, outside the submitted work. J M Ross reports support for the present manuscript from the Bill & Melinda Gates Foundation through grant OPP1132415; grants or contracts from National Institutes of Health and Firland Foundation as payments to their institution; consulting fees from United States Agency for International Development as personal payments, and from KNCV Tuberculosis Foundation as payments to their institution; all outside the submitted work. E Rubagotti reports payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing, or educational events from the Greenwich China Office and Unviersity Prince Mohammad VI, Morocco, all outside the submitted work. B Sartorius reports grants or contracts from DHSC – GRAM Project; Leadership or fiduciary role in other board, society, committee or advocacy group, paid or unpaid, as a member of the GBD Scientific Council and a Member of WHO RGHS; all outside the submitted work. J A Singh reports consulting fees from Crealta/Horizon, Medisys, Fidia, PK Med, Two labs Inc, Adept Field Solutions, Clinical Care options, Clearview healthcare partners, Putnam associates, Focus forward, Navigant consulting, Spherix, MedIQ, Jupiter Life Science LLC, UBM LLC, Trio Health, Medscape, WebMD, and Practice Point communications, and the National Institutes of Health and the American College of Rheumatology; payment or honoraria for participating in the speakers bureau for Simply Speaking; support for attending meetings and/or travel from the steering committee of OMERACT, to attend their meeting every 2 years; participation on a Data Safety Monitoring Board or Advisory Board as an unpaid member of the FDA Arthritis Advisory Committee; leadership or fiduciary role in other board, society, committee or advocacy group, paid or unpaid, as a member of the steering committee of OMERACT, an international organization that develops measures for clinical trials and receives arm’s length funding from 12 pharmaceutical companies, with the Veterans Affairs Rheumatology Field Advisory Committee as Chair, and with the UAB Cochrane Musculoskeletal Group Satellite Center on Network Meta-analysis as a director and editor; stock or stock options in TPT Global Tech, Vaxart pharmaceuticals, Atyu Biopharma, Adaptimmune Therapeutics, GeoVax Labs, Pieris Pharmaceuticals, Enzolytics Inc, Series Therapeutics, Tonix Pharmaceuticals, and Charlotte’s Web Holdings Inc. and previously owned stock options in Amarin, Viking, and Moderna pharmaceuticals; all outside the submitted work. N Taveira reports grants or contracts from FCT and Aga Khan Development Network (AKDN) – Portugal Collaborative Research Network in Portuguese speaking countries in Africa (Project reference: 332821690) and from European & Developing Countries Clinical Trials Partnership (EDCTP), UE (Project reference: RIA2016MC-1615), as payments made to their institution, all outside the submitted work

Estimating global injuries morbidity and mortality : methods and data used in the Global Burden of Disease 2017 study

Background: While there is a long history of measuring death and disability from injuries, modern research methods must account for the wide spectrum of disability that can occur in an injury, and must provide estimates with sufficient demographic, geographical and temporal detail to be useful for policy makers. The Global Burden of Disease (GBD) 2017 study used methods to provide highly detailed estimates of global injury burden that meet these criteria. Methods: In this study, we report and discuss the methods used in GBD 2017 for injury morbidity and mortality burden estimation. In summary, these methods included estimating cause-specific mortality for every cause of injury, and then estimating incidence for every cause of injury. Non-fatal disability for each cause is then calculated based on the probabilities of suffering from different types of bodily injury experienced. Results: GBD 2017 produced morbidity and mortality estimates for 38 causes of injury. Estimates were produced in terms of incidence, prevalence, years lived with disability, cause-specific mortality, years of life lost and disability-adjusted life-years for a 28-year period for 22 age groups, 195 countries and both sexes. Conclusions: GBD 2017 demonstrated a complex and sophisticated series of analytical steps using the largest known database of morbidity and mortality data on injuries. GBD 2017 results should be used to help inform injury prevention policy making and resource allocation. We also identify important avenues for improving injury burden estimation in the future

Global, regional, and national sex-specific burden and control of the HIV epidemic, 1990-2019, for 204 countries and territories: the Global Burden of Diseases Study 2019

Background: The sustainable development goals (SDGs) aim to end HIV/AIDS as a public health threat by 2030. Understanding the current state of the HIV epidemic and its change over time is essential to this effort. This study assesses the current sex-specific HIV burden in 204 countries and territories and measures progress in the control of the epidemic. Methods: To estimate age-specific and sex-specific trends in 48 of 204 countries, we extended the Estimation and Projection Package Age-Sex Model to also implement the spectrum paediatric model. We used this model in cases where age and sex specific HIV-seroprevalence surveys and antenatal care-clinic sentinel surveillance data were available. For the remaining 156 of 204 locations, we developed a cohort-incidence bias adjustment to derive incidence as a function of cause-of-death data from vital registration systems. The incidence was input to a custom Spectrum model. To assess progress, we measured the percentage change in incident cases and deaths between 2010 and 2019 (threshold >75% decline), the ratio of incident cases to number of people living with HIV (incidence-to-prevalence ratio threshold <0·03), and the ratio of incident cases to deaths (incidence-to-mortality ratio threshold <1·0). Findings: In 2019, there were 36·8 million (95% uncertainty interval [UI] 35·1–38·9) people living with HIV worldwide. There were 0·84 males (95% UI 0·78–0·91) per female living with HIV in 2019, 0·99 male infections (0·91–1·10) for every female infection, and 1·02 male deaths (0·95–1·10) per female death. Global progress in incident cases and deaths between 2010 and 2019 was driven by sub-Saharan Africa (with a 28·52% decrease in incident cases, 95% UI 19·58–35·43, and a 39·66% decrease in deaths, 36·49–42·36). Elsewhere, the incidence remained stable or increased, whereas deaths generally decreased. In 2019, the global incidence-to-prevalence ratio was 0·05 (95% UI 0·05–0·06) and the global incidence-to-mortality ratio was 1·94 (1·76–2·12). No regions met suggested thresholds for progress. Interpretation: Sub-Saharan Africa had both the highest HIV burden and the greatest progress between 1990 and 2019. The number of incident cases and deaths in males and females approached parity in 2019, although there remained more females with HIV than males with HIV. Globally, the HIV epidemic is far from the UNAIDS benchmarks on progress metrics. Funding: The Bill & Melinda Gates Foundation, the National Institute of Mental Health of the US National Institutes of Health (NIH), and the National Institute on Aging of the NIH