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. Funding: Bill & Melinda Gates Foundation. © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licens

Global burden of 87 risk factors in 204 countries and territories, 1990�2019: a systematic analysis for the Global Burden of Disease Study 2019

Background: Rigorous analysis of levels and trends in exposure to leading risk factors and quantification of their effect on human health are important to identify where public health is making progress and in which cases current efforts are inadequate. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides a standardised and comprehensive assessment of the magnitude of risk factor exposure, relative risk, and attributable burden of disease. Methods: GBD 2019 estimated attributable mortality, years of life lost (YLLs), years of life lived with disability (YLDs), and disability-adjusted life-years (DALYs) for 87 risk factors and combinations of risk factors, at the global level, regionally, and for 204 countries and territories. GBD uses a hierarchical list of risk factors so that specific risk factors (eg, sodium intake), and related aggregates (eg, diet quality), are both evaluated. This method has six analytical steps. (1) We included 560 risk�outcome pairs that met criteria for convincing or probable evidence on the basis of research studies. 12 risk�outcome pairs included in GBD 2017 no longer met inclusion criteria and 47 risk�outcome pairs for risks already included in GBD 2017 were added based on new evidence. (2) Relative risks were estimated as a function of exposure based on published systematic reviews, 81 systematic reviews done for GBD 2019, and meta-regression. (3) Levels of exposure in each age-sex-location-year included in the study were estimated based on all available data sources using spatiotemporal Gaussian process regression, DisMod-MR 2.1, a Bayesian meta-regression method, or alternative methods. (4) We determined, from published trials or cohort studies, the level of exposure associated with minimum risk, called the theoretical minimum risk exposure level. (5) Attributable deaths, YLLs, YLDs, and DALYs were computed by multiplying population attributable fractions (PAFs) by the relevant outcome quantity for each age-sex-location-year. (6) PAFs and attributable burden for combinations of risk factors were estimated taking into account mediation of different risk factors through other risk factors. Across all six analytical steps, 30 652 distinct data sources were used in the analysis. Uncertainty in each step of the analysis was propagated into the final estimates of attributable burden. Exposure levels for dichotomous, polytomous, and continuous risk factors were summarised with use of the summary exposure value to facilitate comparisons over time, across location, and across risks. Because the entire time series from 1990 to 2019 has been re-estimated with use of consistent data and methods, these results supersede previously published GBD estimates of attributable burden. Findings: The largest declines in risk exposure from 2010 to 2019 were among a set of risks that are strongly linked to social and economic development, including household air pollution; unsafe water, sanitation, and handwashing; and child growth failure. Global declines also occurred for tobacco smoking and lead exposure. The largest increases in risk exposure were for ambient particulate matter pollution, drug use, high fasting plasma glucose, and high body-mass index. In 2019, the leading Level 2 risk factor globally for attributable deaths was high systolic blood pressure, which accounted for 10·8 million (95 uncertainty interval UI 9·51�12·1) deaths (19·2% 16·9�21·3 of all deaths in 2019), followed by tobacco (smoked, second-hand, and chewing), which accounted for 8·71 million (8·12�9·31) deaths (15·4% 14·6�16·2 of all deaths in 2019). The leading Level 2 risk factor for attributable DALYs globally in 2019 was child and maternal malnutrition, which largely affects health in the youngest age groups and accounted for 295 million (253�350) DALYs (11·6% 10·3�13·1 of all global DALYs that year). The risk factor burden varied considerably in 2019 between age groups and locations. Among children aged 0�9 years, the three leading detailed risk factors for attributable DALYs were all related to malnutrition. Iron deficiency was the leading risk factor for those aged 10�24 years, alcohol use for those aged 25�49 years, and high systolic blood pressure for those aged 50�74 years and 75 years and older. Interpretation: Overall, the record for reducing exposure to harmful risks over the past three decades is poor. Success with reducing smoking and lead exposure through regulatory policy might point the way for a stronger role for public policy on other risks in addition to continued efforts to provide information on risk factor harm to the general public. Funding: Bill & Melinda Gates Foundation. © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licens

Abordagens freqüentista e bayesiana para avaliação genética de bovinos da raça Canchim para características de crescimento Frequentist and bayesian approachs for genetic evaluation of Canchim beef cattle for growth traits

Este trabalho foi realizado com os objetivos de: a) comparar os componentes de (co)variância obtidos por meio dos métodos da Máxima Verossimilhança Restrita (REML) e da inferência bayesiana (IB); b) realizar a avaliação genética do peso à desmama (P240) e aos 18 meses de idade (P550) de bovinos da raça Canchim, padronizados ou não para 240 e 550 dias de idade, respectivamente, utilizando-se a metodologia dos modelos mistos e a obtenção dos componentes de (co)variância por REML ou IB; e c) verificar a semelhança entre os animais selecionados considerando-se a avaliação genética realizada com os pesos reais ou padronizados e por meio de abordagens freqüentista ou bayesiana. Foram obtidos os componentes de (co)variância, herdabilidade e correlação genética para P240 e P550. Os valores genéticos obtidos foram utilizados para simular um processo de seleção em que 10% dos touros e 50% das vacas com os maiores valores genéticos aditivos diretos teriam chance de reproduzir. Os componentes de (co)variância e os parâmetros genéticos estimados por REML, na maioria dos casos, foram inferiores às médias a posteriori obtidas por IB. Ocorreram diferenças quanto aos animais selecionados, provavelmente em decorrência das diferenças entre os componentes de (co)variância e dos parâmetros genéticos obtidos. Adotando-se a IB, a inclusão da idade do animal no momento da pesagem como covariável no modelo estatístico não provocou grande alteração dos touros e vacas selecionados.<br>This study aimed to: a) to compare the covariance components obtained by Restricted Maximum Likelihood (REML) and by bayesian inference (BI); b) to run genetic evaluations for weights of Canchim cattle measured at weaning (W240) and at eighteen months of age (W550), adjusted or not to 240 and 550 days of age, respectively, using the mixed model methodology with covariance components obtained by REML or by BI; and c) to compare selection decisions from genetic evaluations using observed or adjusted weights and by REML or BI. Covariance components, heritabilities and genetic correlation for W240 and W550 were estimated and the predicted breeding values were used to select 10% and 50% of the best bulls and cows, respectively. The covariance components obtained by REML were smaller than the a posteriori means obtained by BI. Selected animals from both procedures were not the same, probably because the covariance components and genetic parameters were different. The inclusion of age of animal at weighing as a covariate in the statistical model fitted by BI did not change the selected bulls and cows

Fatores ambientais e genéticos sobre o crescimento ao ano e ao sobreano de bovinos Nelore, criados no Nordeste do Brasil

Os dados de crescimento relativos a 2004 animais da raça zebuína Nelore, criados nos Estados do Ceará e do Piauí, foram usados para estudar a influência de fatores ambientais e estimar a herdabilidade das características peso aos 365 (ano) e 550 dias (sobreano) de idade. Os componentes de variância foram estimados pelo método da Máxima Verossimilhança Restrita. Na análise da variância foi usado um modelo que incluiu os efeitos fixos de sexo, ano, estação e rebanho de nascimento, a idade da mãe como covariável e o efeito aleatório de touro dentro de rebanho. Os efeitos fixos foram significativos para todas as características avaliadas. As médias ajustadas para esses efeitos foram: 186,16 ± 2,74 kg e 244,06 ± 5,23 kg, para os pesos aos 365 e 550 dias de idade, respectivamente. A idade da mãe ao parto influenciou somente o peso aos 365 dias, com peso máximo de 187,29 kg e idade em torno de nove anos. As estimativas de herdabilidade foram iguais a 0,56 ± 0,09 e 0,64 ± 0,12 para os pesos aos 365 e 550 dias, respectivamente.Data growth records relative to 2004 Nellore breed calves, from Ceará and Piauí states, were used to study the influence of environmental effects and to estimate the heritability of the weight traits at the 365 days (yearling) and 550 days (post-yearling) of age. The variance components were estimated by the restricted maximum likelihood method. In the analysis of variance a model that included the fixed effects of sex, year and season and herd of birth, and the age of dam as covariant and the random effect of sire within the herd, was used. The fixed effects of classification were significant for all evaluated traits. Least square means were 186.16 ± 2.74 kg and 244.06 ± 5.23 kg, for yearling and post-yearling weight respectively. Age of dam at calving influenced only the yearling weights, with estimated maximum weight of 187.29 kg and ages around nine years. Heritability estimates were .56 ± .09 and .64 ± .12, for yearling and post-yearling weight, respectively