Global Burden of Disease Cancer Collaboration IMPORTANCE Cancer and other noncommunicable diseases (NCDs) are now widely recognized as a threat to global development. The latest United Nations high-level meeting on NCDs reaffirmed this observation and also highlighted the slow progress in meeting the 2011 Political Declaration on the Prevention and Control of Noncommunicable Diseases and the third Sustainable Development Goal. Lack of situational analyses, priority setting, and budgeting have been identified as major obstacles in achieving these goals. All of these have in common that they require information on the local cancer epidemiology. The Global Burden of Disease (GBD) study is uniquely poised to provide these crucial data. OBJECTIVE To describe cancer burden for 29 cancer groups in 195 countries from 1990 through 2017 to provide data needed for cancer control planning. EVIDENCE REVIEW We used the GBD study estimation methods to describe cancer incidence, mortality, years lived with disability, years of life lost, and disability-adjusted life-years (DALYs). Results are presented at the national level as well as by Socio-demographic Index (SDI), a composite indicator of income, educational attainment, and total fertility rate. We also analyzed the influence of the epidemiological vs the demographic transition on cancer incidence. FINDINGS In 2017, there were 24.5 million incident cancer cases worldwide (16.8 million without nonmelanoma skin cancer [NMSC]) and 9.6 million cancer deaths. The majority of cancer DALYs came from years of life lost (97%), and only 3% came from years lived with disability. The odds of developing cancer were the lowest in the low SDI quintile (1 in 7) and the highest in the high SDI quintile (1 in 2) for both sexes. In 2017, the most common incident cancers in men were NMSC (4.3 million incident cases); tracheal, bronchus, and lung (TBL) cancer (1.5 million incident cases); and prostate cancer (1.3 million incident cases). The most common causes of cancer deaths and DALYs for men were TBL cancer (1.3 million deaths and 28.4 million DALYs), liver cancer (572 000 deaths and 15.2 million DALYs), and stomach cancer (542 000 deaths and 12.2 million DALYs). For women in 2017, the most common incident cancers were NMSC (3.3 million incident cases), breast cancer (1.9 million incident cases), and colorectal cancer (819 000 incident cases). The leading causes of cancer deaths and DALYs for women were breast cancer (601 000 deaths and 17.4 million DALYs), TBL cancer (596 000 deaths and 12.6 million DALYs), and colorectal cancer (414 000 deaths and 8.3 million DALYs). CONCLUSIONS AND RELEVANCE The national epidemiological profiles of cancer burden in the GBD study show large heterogeneities, which are a reflection of different exposures to risk factors, economic settings, lifestyles, and access to care and screening. The GBD study can be used by policy makers and other stakeholders to develop and improve national and local cancer control in order to achieve the global targets and improve equ...

Background Accurate and up-to-date assessment of demographic metrics is crucial for understanding a wide range of social, economic, and public health issues that affect populations worldwide. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 produced updated and comprehensive demographic assessments of the key indicators of fertility, mortality, migration, and population for 204 countries and territories and selected subnational locations from 1950 to 2019.Methods 8078 country-years of vital registration and sample registration data, 938 surveys, 349 censuses, and 238 other sources were identified and used to estimate age-specific fertility. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate age-specific fertility rates for 5-year age groups between ages 15 and 49 years. With extensions to age groups 10-14 and 50-54 years, the total fertility rate (TFR) was then aggregated using the estimated age-specific fertility between ages 10 and 54 years. 7417 sources were used for under-5 mortality estimation and 7355 for adult mortality. ST-GPR was used to synthesise data sources after correction for known biases. Adult mortality was measured as the probability of death between ages 15 and 60 years based on vital registration, sample registration, and sibling histories, and was also estimated using ST-GPR. HIV-free life tables were then estimated using estimates of under-5 and adult mortality rates using a relational model life table system created for GBD, which closely tracks observed agespecific mortality rates from complete vital registration when available. Independent estimates of HIV-specific mortality generated by an epidemiological analysis of HIV prevalence surveys and antenatal clinic serosurveillance and other sources were incorporated into the estimates in countries with large epidemics. Annual and single-year age estimates of net migration and population for each country and territory were generated using a Bayesian hierarchical cohort component model that analysed estimated age-specific fertility and mortality rates along with 1250 censuses and 747 population registry years. We classified location-years into seven categories on the basis of the natural rate of increase in population (calculated by subtracting the crude death rate from the crude birth rate) and the net migration rate. We computed healthy life expectancy (HALE) using years lived with disability (YLDs) per capita, life tables, and standard demographic methods. Uncertainty was propagated throughout the demographic estimation process, including fertility, mortality, and population, with 1000 draw-level estimates produced for each metric. FindingsThe global TFR decreased from 2•72 (95% uncertainty interval [UI] 2•66-2•79) in 2000 to 2•31 (2•17-2•46) in 2019. Global annual livebirths increased from 134•5 million (131•5-137•8) in 2000 to a peak of 139•6 million (133•0-146•9) in 2016. Global livebirths then declined to 135•3 million (127•2-144•1) in 2019. Of the 204 countries and territories included in...

Summary Background Sustainable Development Goal 3.2 has targeted elimination of preventable child mortality, reduction of neonatal death to less than 12 per 1000 livebirths, and reduction of death of children younger than 5 years to less than 25 per 1000 livebirths, for each country by 2030. To understand current rates, recent trends, and potential trajectories of child mortality for the next decade, we present the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 findings for all-cause mortality and cause-specific mortality in children younger than 5 years of age, with multiple scenarios for child mortality in 2030 that include the consideration of potential effects of COVID-19, and a novel framework for quantifying optimal child survival. Methods We completed all-cause mortality and cause-specific mortality analyses from 204 countries and territories for detailed age groups separately, with aggregated mortality probabilities per 1000 livebirths computed for neonatal mortality rate (NMR) and under-5 mortality rate (U5MR). Scenarios for 2030 represent different potential trajectories, notably including potential effects of the COVID-19 pandemic and the potential impact of improvements preferentially targeting neonatal survival. Optimal child survival metrics were developed by age, sex, and cause of death across all GBD location-years. The first metric is a global optimum and is based on the lowest observed mortality, and the second is a survival potential frontier that is based on stochastic frontier analysis of observed mortality and Healthcare Access and Quality Index. Findings Global U5MR decreased from 71·2 deaths per 1000 livebirths (95% uncertainty interval [UI] 68·3–74·0) in 2000 to 37·1 (33·2–41·7) in 2019 while global NMR correspondingly declined more slowly from 28·0 deaths per 1000 live births (26·8–29·5) in 2000 to 17·9 (16·3–19·8) in 2019. In 2019, 136 (67%) of 204 countries had a U5MR at or below the SDG 3.2 threshold and 133 (65%) had an NMR at or below the SDG 3.2 threshold, and the reference scenario suggests that by 2030, 154 (75%) of all countries could meet the U5MR targets, and 139 (68%) could meet the NMR targets. Deaths of children younger than 5 years totalled 9·65 million (95% UI 9·05–10·30) in 2000 and 5·05 million (4·27–6·02) in 2019, with the neonatal fraction of these deaths increasing from 39% (3·76 million [95% UI 3·53–4·02]) in 2000 to 48% (2·42 million; 2·06–2·86) in 2019. NMR and U5MR were generally higher in males than in females, although there was no statistically significant difference at the global level. Neonatal disorders remained the leading cause of death in children younger than 5 years in 2019, followed by lower respiratory infections, diarrhoeal diseases, congenital birth defects, and malaria. The global optimum analysis suggests NMR could be reduced to as low as 0·80 (95% UI 0·71–0·86) deaths per 1000 livebirths and U5MR to 1·44 (95% UI 1·27–1·58) deaths per 1...

Background The worldwide COVID-19 pandemic was caused by a newly discovered Coronavirus. The treatment methods for COVID-19 are emerging and rapidly evolving. Existing drugs, including Ivermectin and Hydroxychloroquine, offer the hope of effective treatment in early disease. In this study, we investigated and compared outcomes of Ivermectin-Doxycycline vs. Hydroxychloroquine-Azithromycin combination therapy COVID19 patients with mild to moderate disease.Methods Patients with mild to moderate COVID-19 disease, tested positive by RT PCR for SARS-CoV-2 infection at Chakoria Upazilla Health Complex, Cox's Bazar, Bangladesh, were included in this study. Patients were divided randomly into two groups: Ivermectin 200µgm/kg single dose + Doxycycline 100 mg BID for 10days in group A, and Hydroxychloroquine 400 mg 1st day, then200mg BID for 9days + Azithromycin 500 mg daily for 5 days in group B. PCR for SARS-CoV-2 was repeated in all symptomatic patients on the second day onward without symptoms, or, for those who were asymptomatic (throughout the process), on the 5th day after taking medication and repeated every two days onward if the result is positive. Time to negative PCR and time to full symptomatic recovery was measured for each group.Results All subjects in the Ivermectin-Doxycycline group (group A) reached a negative PCR for SARS-CoV-2, at a mean of 8.93days, and all reached symptomatic recovery, at a mean of 5.93days, with 55.10% symptom-free by the 5th day. In the Hydroxychloroquine-Azithromcyin group (group B), 96.36% reached a negative PCR at a mean of 6.99days and were symptoms-free at 9.33days. Group A patients had symptoms that could have been caused by the medication in 31.67% of patients, including lethargy in 14(23.3%), nausea in 11(18.3%), and occasional vertigo in 7(11.66%) of patients. In Group B, 46.43% had symptoms that could have been caused by the medication, including 13(23.21%) mild blurring of vision and headache; 22(39.2%) increased lethargy and dizziness, 10(17.85%) occasional palpitation, and 9(16.07%) nausea and vomiting.Conclusion The Ivermectin-Doxycycline combination showed a trend toward superiority to the Hydroxychloroquine-Azithromycin combination therapy in the case of patients with mild to moderate COVID19 disease, though the difference in time to becoming symptom-free and the difference in time to negative PCR was not statistically significant.

Summary Background High-resolution estimates of HIV burden across space and time provide an important tool for tracking and monitoring the progress of prevention and control efforts and assist with improving the precision and efficiency of targeting efforts. We aimed to assess HIV incidence and HIV mortality for all second-level administrative units across sub-Saharan Africa. Methods In this modelling study, we developed a framework that used the geographically specific HIV prevalence data collected in seroprevalence surveys and antenatal care clinics to train a model that estimates HIV incidence and mortality among individuals aged 15–49 years. We used a model-based geostatistical framework to estimate HIV prevalence at the second administrative level in 44 countries in sub-Saharan Africa for 2000–18 and sought data on the number of individuals on antiretroviral therapy (ART) by second-level administrative unit. We then modified the Estimation and Projection Package (EPP) to use these HIV prevalence and treatment estimates to estimate HIV incidence and mortality by second-level administrative unit. Findings The estimates suggest substantial variation in HIV incidence and mortality rates both between and within countries in sub-Saharan Africa, with 15 countries having a ten-times or greater difference in estimated HIV incidence between the second-level administrative units with the lowest and highest estimated incidence levels. Across all 44 countries in 2018, HIV incidence ranged from 2·8 (95% uncertainty interval 2·1–3·8) in Mauritania to 1585·9 (1369·4–1824·8) cases per 100 000 people in Lesotho and HIV mortality ranged from 0·8 (0·7–0·9) in Mauritania to 676·5 (513·6–888·0) deaths per 100 000 people in Lesotho. Variation in both incidence and mortality was substantially greater at the subnational level than at the national level and the highest estimated rates were accordingly higher. Among second-level administrative units, Guijá District, Gaza Province, Mozambique, had the highest estimated HIV incidence (4661·7 [2544·8–8120·3]) cases per 100 000 people in 2018 and Inhassunge District, Zambezia Province, Mozambique, had the highest estimated HIV mortality rate (1163·0 [679·0–1866·8]) deaths per 100 000 people. Further, the rate of reduction in HIV incidence and mortality from 2000 to 2018, as well as the ratio of new infections to the number of people living with HIV was highly variable. Although most second-level administrative units had declines in the number of new cases (3316 [81·1%] of 4087 units) and number of deaths (3325 [81·4%]), nearly all appeared well short of the targeted 75% reduction in new cases and deaths between 2010 and 2020. Interpretation Our estimates suggest that most second-level administrative units in sub-Saharan Africa are falling short of the targeted 75% reduction in new cases and deaths by 2020, which is further compounded by substantial within-country variability...

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‫گرمبريدی‬ ‫غالمرضب‬ ، 2 ‫ببطبی‬ ‫عسیساهلل‬ ، 2 ‫علیپًر‬ ‫داييد‬ ، 1 ‫شُببز‬ ‫محمد‬ ، 3 ، ‫ببببزادٌ‬ ‫تًحید‬ 4 * 1 ‫اسؿذ،‬ ‫وبسؿٌبع‬ ‫ػالهت‬ ‫استمبء‬ ٍ ‫ثْذاؿت‬ ‫آهَصؽ‬ ‫گشٍُ‬ ، ‫ایشاى.‬ ‫تْشاى،‬ ‫تْشاى،‬ ‫پضؿىي‬ ‫علَم‬ ‫داًـگبُ‬ ‫ثْذاؿت،‬ ‫داًـىذُ‬ 2 ‫تخللي،‬ ‫دوتشاي‬ ‫داًـگبُ‬ ‫ثْذاؿت،‬ ‫داًـىذُ‬ ‫ػالهت،‬ ‫استمبء‬ ٍ ‫ثْذاؿت‬ ‫آهَصؽ‬ ‫گشٍُ‬ ‫علَم‬ ‫پضؿىي‬ ‫ایشاى.‬ ‫تْشاى،‬ ‫تْشاى،‬ 3 ‫اسؿذ‬ ‫وبسؿٌبع‬ ، ‫گشٍُ‬ ‫اپیذهیَلَطي،‬ ‫ایشا‬ ‫تْشاى،‬ ‫ثْـتي،‬ ‫ؿْیذ‬ ‫پضؿىي‬ ‫علَم‬ ‫داًـگبُ‬ ‫ثْذاؿت،‬ ‫داًـىذُ‬ ‫ى.‬ 4 ‫تخللي‬ ‫دوتشاي‬ ‫ایشاى.‬ ‫تجشیض،‬ ‫تجشیض،‬ ‫پضؿىي‬ ‫علَم‬ ‫داًـگبُ‬ ‫تغزیِ،‬ ٍ ‫ثْذاؿت‬ ‫داًـىذُ‬ ‫داًـجَیي،‬ ‫تحمیمبت‬ ‫وویتِ‬ ، ‫همبلِ:‬ ‫دسیبفت‬ ‫تبسیخ‬ 4 / 11 / 1393 ‫همبلِ:‬ ‫پزیشؽ‬ ‫تبسیخ‬ 23 / 12 / 1393 ‫چكیدٌ‬ ‫َدف‬ ‫ي‬ ‫سببقٍ‬ : ‫ؿبیع‬ ‫اص‬ ‫یىي‬ ‫هبلت‬ ‫تت‬ ‫ثیوبسي‬ ‫تشیي‬ ‫اً‬ ‫هـتشن‬ ‫ّبي‬ ‫هتي‬ ‫آهتَصؽ‬ ‫اػتت.‬ ‫حیَاى‬ ٍ ‫ؼبى‬ ‫ایتي‬ ‫اص‬ ‫پیـتگیشي‬ ‫دس‬ ‫تَاًتذ‬ ‫هلبلعتِ‬ ‫ایتي‬ ‫اًجتبم‬ ‫اص‬ ‫ّذف‬ ‫ثبؿذ.‬ ‫هؤثش‬ ‫ثیوبسي‬ ‫اسصیتبثي‬ ‫ت‬ ‫ت‬ ْ ‫ج‬ ‫ي‬ ‫ت‬ ‫ت‬ ‫ؿ‬ ‫ا‬ ‫ذ‬ ْ ‫ث‬ ‫د‬ ‫ب‬ ‫ت‬ ‫م‬ ‫ت‬ ‫ع‬ ‫ا‬ ‫ا‬ ‫ذ‬ ‫ت‬ ‫ه‬ ‫ش‬ ‫ت‬ ‫ث‬ ‫ي‬ ‫ت‬ ٌ ‫ت‬ ‫ج‬ ‫ه‬ ‫ي‬ ‫ت‬ ‫ؿ‬ ‫ص‬ َ ‫ه‬ ‫آ‬ ِ ‫ت‬ ‫ل‬ ‫ل‬ ‫ا‬ ‫ذ‬ ‫ه‬ ‫ي‬ ‫ت‬ ‫ـ‬ ‫خ‬ ‫ث‬ ‫ش‬ ‫ث‬ ‫ا‬ ‫هبلت‬ ‫تت‬ ‫پشللش‬ ‫سفتبسّبي‬ ‫ثشاثش‬ ‫دس‬ ‫داهذاساى‬ ‫تَاًوٌذػبصي‬ ‫ثَد‬ ‫چبساٍیوبق‬ ‫ؿْشػتبى‬ ‫دس‬ . ‫ي‬ ‫مًاد‬ ‫ريش‬ ‫َب‬ : ‫ایي‬ ‫پظٍّ‬ ‫ؾ‬ ‫ًیوِ‬ ‫تجشثي‬ ‫ػبا‬ ‫دس‬ 1393 ‫ؿتذ.‬ ‫اجتشا‬ ‫ؿتشلي‬ ‫آرسثبیجبى‬ ‫چبساٍیوبق‬ ‫دس‬ ‫تعتذاد‬ 200 ‫سٍؽ‬ ‫ثتب‬ ‫داهتذاس‬ ‫ًوًَِ‬ ‫طجمِ‬ ‫تلبدفي‬ ‫گیشي‬ ‫دادُ‬ ‫ؿذًذ.‬ ‫اًتخبة‬ ‫هلبلعِ‬ ‫ایي‬ ‫دس‬ ‫ؿشوت‬ ‫جْت‬ ‫اي‬ ‫ػبلتِ‬ ‫هحمك‬ ‫پشػـٌبهِ‬ ‫اص‬ ‫اػتفبدُ‬ ‫ثب‬ ‫ّب‬ ‫ؿبهل‬ ‫وِ‬ ‫اي‬ ‫ػبصُ‬ ٍ ‫آگبّي‬ ‫ؿٌبلتي،‬ ‫جوعیت‬ ‫اطالعبت‬ ‫ثْذاؿ‬ ‫اعتمبد‬ ‫هذا‬ ‫ّبي‬ ‫جوع‬ ‫ثَد،‬ ‫تي‬ ‫گتشٍُ‬ ‫ثتشاي‬ ‫آهَصؿتي‬ ‫جلؼتبت‬ ‫ػپغ‬ ‫گشدیذ.‬ ‫آٍسي‬ ‫ؿذ.‬ ‫ثشگضاس‬ ٍ ‫طشاحي‬ ‫هذاللِ‬ 3 ‫گشٍُ‬ ‫دٍ‬ ‫ّش‬ ‫اص‬ ‫اطالعبت‬ ‫هذاللِ،‬ ‫اجشاي‬ ‫اص‬ ‫پغ‬ ‫هبُ‬ ‫جوع‬ ‫آصهتَى‬ ‫اص‬ ‫اػتتفبدُ‬ ‫ثتب‬ ٍ ‫آٍسي‬ ‫آهتبسي‬ ‫ّتبي‬ ‫هي‬ ‫تجضیِ‬ ‫ٍیلىبوؼَى‬ ٍ ‫ٍیتٌي‬ ‫گشدیذ‬ ‫ٍتحلیل‬ . ‫َب‬ ‫یبفتٍ‬ : ‫هیبًگیي‬ ‫ًظش‬ ‫اص‬ ‫گشٍُ‬ ‫دٍ‬ ‫هذاللِ،‬ ‫اص‬ ‫لجل‬ ‫ًوشات‬ ‫آگبّي،‬ ‫ػبصُ‬ ‫هٌتبفع‬ ‫هَاًتع،‬ ‫ؿذت،‬ ‫(حؼبػیت،‬ ‫ثْذاؿتي‬ ‫اعتمبد‬ ‫هذا‬ ‫ّبي‬ ‫دسن‬ ‫پیـگیشي‬ ‫سفتبسّبي‬ ٍ ‫لَدوبسآهذي)‬ ٍ ‫ؿذُ‬ ‫هعٌي‬ ‫تفبٍت‬ ‫هبلت،‬ ‫تت‬ ‫اص‬ ‫وٌٌذُ‬ ‫دس‬ ‫آهَصؿتي،‬ ‫هذاللتِ‬ ‫اص‬ ‫ثعتذ‬ ‫اهتب‬ ‫ًذاؿتٌذ،‬ ‫داسي‬ ‫هعٌي‬ ‫التالف‬ ‫هذاللِ‬ ‫گشٍُ‬ ‫گشدیذ‬ ‫هـبّذُ‬ ‫وٌتشا‬ ‫گشٍُ‬ ‫ثِ‬ ‫ًؼجت‬ ‫داسي‬ ( 05 / 0 P< .) ‫وتیج‬ ٍ ‫گیری:‬ ‫ثشًبهِ‬ ‫دس‬ ‫تأثیشگزاس‬ ‫افشاد‬ ‫هـبسوت‬ ‫تئَسي‬ ‫اص‬ ‫اػتفبدُ‬ ‫ثب‬ ‫ّوشاُ‬ ‫هذالالتي‬ ‫ّبي‬ ‫هي‬ ‫آهَصؿي‬ ‫ّبي‬ ‫ثیـتشي‬ ‫اثشثخـي‬ ‫تَاًذ‬ ‫دس‬ ‫پشللش‬ ‫سفتبسّبي‬ ‫اكالح‬ ‫دس‬ ‫افشاد‬ ‫ثشًبهِ‬ ‫چٌیي‬ ‫لزا‬ ‫ثبؿذ،‬ ‫داؿتِ‬ ‫گشدد‬ ‫اجشا‬ ‫ٍػیعي‬ ‫ػلح‬ ‫دس‬ ‫ثبیذ‬ ‫ّبیي‬ . ‫كلیدی‬ ‫ياشگبن‬ : ‫هذا‬ ‫ثْذاؿتي،‬ ‫اعتمبد‬ ‫هذا‬ ‫هبلت،‬ ‫تت‬ ‫چبساٍیوبق‬ ‫آهَصؿي،‬ ‫للِ‬ ‫مقذمه‬ ‫ؿبیع‬ ‫اص‬ ‫یىي‬ ‫هبلت‬ ‫تت‬ ‫ثیوبسي‬ ‫تشیي‬ ‫ثتیي‬ ‫هـتشن‬ ‫ؿبیع‬ ‫ّبي‬ ( ‫اػتت‬ ‫تبى‬ ‫جْت‬ ‫ػشاػتش‬ ‫دس‬ ‫حیتَاى‬ ٍ ‫اًؼتبى‬ 1 ‫تي‬ ‫ایت‬ ‫ؿتیَ‬ .) ‫ثیوبسي‬ ‫ایي‬ ‫اػت.‬ ‫هتفبٍت‬ ‫دیگش‬ ‫وـَس‬ ‫ثِ‬ ‫وـَسي‬ ‫اص‬ ‫ثیوبسي‬ ‫جٌتَثي،‬ ‫(اسٍپتبي‬ ‫هذیتشاًتِ‬ ‫اطشاف‬ ‫وـَسّبي‬ ‫دس‬ ‫ثخلَف‬ ‫لبٍ‬ ‫تب)،‬...

No abstract