Global age-sex-specific fertility, mortality, healthy life expectancy (HALE), and population estimates in 204 countries and territories, 1950-2019 : a comprehensive demographic analysis for the Global Burden of Disease Study 2019

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 age-specific 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. Findings The 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 this study, in 2019, 102 had a TFR lower than 2.1, which is considered a good approximation of replacement-level fertility. All countries in sub-Saharan Africa had TFRs above replacement level in 2019 and accounted for 27.1% (95% UI 26.4-27.8) of global livebirths. Global life expectancy at birth increased from 67.2 years (95% UI 66.8-67.6) in 2000 to 73.5 years (72.8-74.3) in 2019. The total number of deaths increased from 50.7 million (49.5-51.9) in 2000 to 56.5 million (53.7-59.2) in 2019. Under-5 deaths declined from 9.6 million (9.1-10.3) in 2000 to 5.0 million (4.3-6.0) in 2019. Global population increased by 25.7%, from 6.2 billion (6.0-6.3) in 2000 to 7.7 billion (7.5-8.0) in 2019. In 2019, 34 countries had negative natural rates of increase; in 17 of these, the population declined because immigration was not sufficient to counteract the negative rate of decline. Globally, HALE increased from 58.6 years (56.1-60.8) in 2000 to 63.5 years (60.8-66.1) in 2019. HALE increased in 202 of 204 countries and territories between 2000 and 2019. Interpretation Over the past 20 years, fertility rates have been dropping steadily and life expectancy has been increasing, with few exceptions. Much of this change follows historical patterns linking social and economic determinants, such as those captured by the GBD Socio-demographic Index, with demographic outcomes. More recently, several countries have experienced a combination of low fertility and stagnating improvement in mortality rates, pushing more populations into the late stages of the demographic transition. Tracking demographic change and the emergence of new patterns will be essential for global health monitoring. Copyright (C) 2020 The Author(s). Published by Elsevier Ltd.Peer reviewe

Measuring universal health coverage based on an index of effective coverage of health services in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Background Achieving universal health coverage (UHC) involves all people receiving the health services they need, of high quality, without experiencing financial hardship. Making progress towards UHC is a policy priority for both countries and global institutions, as highlighted by the agenda of the UN Sustainable Development Goals (SDGs) and WHO's Thirteenth General Programme of Work (GPW13). Measuring effective coverage at the health-system level is important for understanding whether health services are aligned with countries' health profiles and are of sufficient quality to produce health gains for populations of all ages. Methods Based on the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we assessed UHC effective coverage for 204 countries and territories from 1990 to 2019. Drawing from a measurement framework developed through WHO's GPW13 consultation, we mapped 23 effective coverage indicators to a matrix representing health service types (eg, promotion, prevention, and treatment) and five population-age groups spanning from reproductive and newborn to older adults (≥65 years). Effective coverage indicators were based on intervention coverage or outcome-based measures such as mortality-to-incidence ratios to approximate access to quality care; outcome-based measures were transformed to values on a scale of 0–100 based on the 2·5th and 97·5th percentile of location-year values. We constructed the UHC effective coverage index by weighting each effective coverage indicator relative to its associated potential health gains, as measured by disability-adjusted life-years for each location-year and population-age group. For three tests of validity (content, known-groups, and convergent), UHC effective coverage index performance was generally better than that of other UHC service coverage indices from WHO (ie, the current metric for SDG indicator 3.8.1 on UHC service coverage), the World Bank, and GBD 2017. We quantified frontiers of UHC effective coverage performance on the basis of pooled health spending per capita, representing UHC effective coverage index levels achieved in 2019 relative to country-level government health spending, prepaid private expenditures, and development assistance for health. To assess current trajectories towards the GPW13 UHC billion target—1 billion more people benefiting from UHC by 2023—we estimated additional population equivalents with UHC effective coverage from 2018 to 2023. Findings Globally, performance on the UHC effective coverage index improved from 45·8 (95% uncertainty interval 44·2–47·5) in 1990 to 60·3 (58·7–61·9) in 2019, yet country-level UHC effective coverage in 2019 still spanned from 95 or higher in Japan and Iceland to lower than 25 in Somalia and the Central African Republic. Since 2010, sub-Saharan Africa showed accelerated gains on the UHC effective coverage index (at an average increase of 2·6% [1·9–3·3] per year up to 2019); by contrast, most other GBD super-regions had slowed rates of progress in 2010–2019 relative to 1990–2010. Many countries showed lagging performance on effective coverage indicators for non-communicable diseases relative to those for communicable diseases and maternal and child health, despite non-communicable diseases accounting for a greater proportion of potential health gains in 2019, suggesting that many health systems are not keeping pace with the rising non-communicable disease burden and associated population health needs. In 2019, the UHC effective coverage index was associated with pooled health spending per capita (r=0·79), although countries across the development spectrum had much lower UHC effective coverage than is potentially achievable relative to their health spending. Under maximum efficiency of translating health spending into UHC effective coverage performance, countries would need to reach $1398 pooled health spending per capita (US$ adjusted for purchasing power parity) in order to achieve 80 on the UHC effective coverage index. From 2018 to 2023, an estimated 388·9 million (358·6–421·3) more population equivalents would have UHC effective coverage, falling well short of the GPW13 target of 1 billion more people benefiting from UHC during this time. Current projections point to an estimated 3·1 billion (3·0–3·2) population equivalents still lacking UHC effective coverage in 2023, with nearly a third (968·1 million [903·5–1040·3]) residing in south Asia. Interpretation The present study demonstrates the utility of measuring effective coverage and its role in supporting improved health outcomes for all people—the ultimate goal of UHC and its achievement. Global ambitions to accelerate progress on UHC service coverage are increasingly unlikely unless concerted action on non-communicable diseases occurs and countries can better translate health spending into improved performance. Focusing on effective coverage and accounting for the world's evolving health needs lays the groundwork for better understanding how close—or how far—all populations are in benefiting from UHC. Funding Bill & Melinda Gates Foundation

Tomografia computadorizada na insuficiência respiratória aguda

O objetivo do presente trabalho é revisar o uso da tomografia computadorizada (TC) na insuficiência respiratória aguda (IRA) - lesão pulmonar aguda ou acute lung injury (ALI) e síndrome da angústia respiratória aguda (SARA). Foram revisados os principais trabalhos publicados na literatura em língua inglesa e localizados por pesquisa na Medline, que estudaram o uso da TC na IRA. A TC permite, num primeiro momento, uma avaliação qualitativa da morfologia pulmonar buscando a presença de hiperdensidades difusas e/ou localizadas e de outras anormalidades concomitantes (como derrame pleural e pneumotórax). Além disso, permite avaliar os histogramas de densidade pulmonares a partir dos quais podem ser efetuados o cálculo dos volumes pulmonares totais e regionais (volume das regiões ventiladas, parcialmente ventiladas, não ventiladas e de hiperdistensão). Isso possibilita quantificar recrutamento alveolar e hiperdistensão decorrentes de estratégias ventilatórias, como por exemplo do uso de diferentes níveis de pressão expiratória final positiva (PEEP).The goal of this paper is to review the use of CT scan in acute respiratory failure (ARF), acute lung injury (ALI) and acute respiratory distress syndrome (ARDS). The most important papers studying CT in ARF, published in English language literature, were found in Medline. The use of CT in patients with ARF let allowed the authors to proceed to a qualitative evaluation of lung morphology looking for diffuse an/or localized hyperdensities, and for other concomitant abnormalities (pleural effusion, pneumothorax). It is also possible to evaluate lung density histograms and calculate the gas-tissue ratio within the lungs. Likewise, one can also calculate total and regional lung volumes (aerated, poorly-aerated, non-aerated, and overdistension volumes). Knowing those volumes it is possible to quantify alveolar recruitment and overdistension due to ventilatory strategies as the use of different positive end expiratory pressure (PEEP) levels

Divergência genética em tomate estimada por marcadores RAPD em comparação com descritores multicategóricos Genetic divergence among tomato accessions using RAPD markers and its comparison with multicategoric descriptors

A estimativa da variabilidade genética existente em um banco de germoplasma é importante não só para a conservação dos recursos genéticos, mas também para aplicações no melhoramento de plantas. O presente trabalho teve como objetivo estudar a divergência genética entre 78 acessos de uma coleção de germoplasma de tomateiro, com base em 74 marcadores RAPD e correlacionar esses resultados àqueles da caracterização morfoagronômica realizada para 27 descritores. Foi utilizado o agrupamento hierárquico UPGMA para analisar os dados, observando-se a formação de 13 grupos. Esses grupos foram correlacionados a cinco descritores (hábito de crescimento, tipo de folha, cor do fruto, número de lóculos e formato do fruto). Alguns grupos apresentaram peculiaridades, a exemplo do grupo IV, que reuniu acessos com frutos no formato de pêra; o grupo VII com acessos resistentes a murcha-bacteriana e o grupo IX, que englobou acessos com folhas do tipo batata. As análises por bootstrap revelaram poucos agrupamentos consistentes. Houve correlação positiva e altamente significativa entre as matrizes geradas pelos 27 descritores qualitativos e pelos marcadores RAPD (t = 14,02). A correlação de Mantel (r = 0,39) foi altamente significativa, porém de baixa magnitude. O baixo valor verificado para esta correlação sugere que ambas as etapas de caracterização (morfoagronômica e molecular) são importantes para um conhecimento mais amplo e melhor discriminação entre os acessos de tomate.<br>The estimation of genetic variability in a germplasm bank is important not only for the conservation of the genetic resources, but also for applications in plant breeding. The genetic divergence among 78 tomato accessions was studied, based on 74 RAPD markers. Also, a correlation between the molecular profile and 27 morphological and agronomic data was performed. Cluster analysis (UPGMA), used to study the data, resulted in 13 groups that were correlated with five descriptors (growth habit, leaf type, fruit color, locule number, and fruit shape). Some groups had particularities, such as group IV that assembled accessions with pear shape fruits; group VII, that clustered accessions with bacterial wilt resistance, and group IX, that gathered accessions with potato leaf type. Bootstrap analysis revealed few consistent clusters. The results showed a positive and significant correlation between the matrixes generated out of qualitative and molecular data (t = 14.02). Mantel's correlation was highly significant, but with a low value (r = 0.39), which suggests that for a wise use of the germplasm bank accessions, both characterization, molecular and morphoagronomic, should be carried out