Estimates of the worldwide incidence and mortality from 27 major cancers and for all cancers combined for 2012 are now available in the GLOBOCAN series of the International Agency for Research on Cancer. We review the sources and methods used in compiling the national cancer incidence and mortality estimates, and briefly describe the key results by cancer site and in 20 large “areas” of the world. Overall, there were 14.1 million new cases and 8.2 million deaths in 2012. The most commonly diagnosed cancers were lung (1.82 million), breast (1.67 million), and colorectal (1.36 million); the most common causes of cancer death were lung cancer (1.6 million deaths), liver cancer (745,000 deaths), and stomach cancer (723,000 deaths).

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Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012.

抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010.

This paper is a re-make of Chapters 1-3 of the Interim Report World Agriculture: towards 2030/2050 (FAO, 2006). In addition, this new paper includes a Chapter 4 on production factors (land, water, yields, fertilizers). Revised and more recent data have been used as basis for the new projections, as follows: (a) updated historical data from the Food Balance Sheets 1961-2007 as of June 2010; (b) undernourishment estimates from The State of Food Insecurity in the World 2010 (SOFI) and related new parameters (CVs, minimum daily energy requirements) are used in the projections; (c) new population data and projections from the UN World Population Prospects - Revision of 2008; (d) new GDP data and projections from the World Bank; (e) a new base year of 2005/2007 (the previous edition used the base year 1999/2001); (f) updated estimates of land resources from the new evaluation of the Global Agro-ecological Zones (GAEZ) study of FAO and IIASA. Estimates of land under forest and in protected areas from the GAEZ are taken into account and excluded from the estimates of land areas suitable for crop production into which agriculture could expand in the future; (g) updated estimates of existing irrigation, renewable water resources and potentials for irrigation expansion; and (h) changes in the text as required by the new historical data and projections. Like the interim report, this re-make does not include projections for the Fisheries and Forestry sectors. Calories from fish are, however, included, in the food consumption projections, along with those from other commodities (e.g. spices) not analysed individually. The projections presented reflect the magnitudes and trajectories we estimate the major food and agriculture variables may assume in the future; they are not meant to reflect how these variables may be required to evolve in the future in order to achieve some normative objective, e.g. ensure food security for all, eliminate undernourishment or reduce it to any given desired level, or avoid food overconsumption leading to obesity and related NonCommunicable Diseases.

World agriculture towards 2030/2050: the 2012 revision

The ‘Great Acceleration’ graphs, originally published in 2004 to show socio-economic and Earth System trends from 1750 to 2000, have now been updated to 2010. In the graphs of socio-economic trends, where the data permit, the activity of the wealthy (OECD) countries, those countries with emerging economies, and the rest of the world have now been differentiated. The dominant feature of the socio-economic trends is that the economic activity of the human enterprise continues to grow at a rapid rate. However, the differentiated graphs clearly show that strong equity issues are masked by considering global aggregates only. Most of the population growth since 1950 has been in the non-OECD world but the world’s economy (GDP), and hence consumption, is still strongly dominated by the OECD world. The Earth System indicators, in general, continued their long-term, post-industrial rise, although a few, such as atmospheric methane concentration and stratospheric ozone loss, showed a slowing or apparent stabilisation over the past decade. The post-1950 acceleration in the Earth System indicators remains clear. Only beyond the mid-20th century is there clear evidence for fundamental shifts in the state and functioning of the Earth System that are beyond the range of variability of the Holocene and driven by human activities. Thus, of all the candidates for a start date for the Anthropocene, the beginning of the Great Acceleration is by far the most convincing from an Earth System science perspective.

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The trajectory of the Anthropocene: The Great Acceleration

The United Nations (UN) recently released population projections based on data until 2012 and a Bayesian probabilistic methodology. Analysis of these data reveals that, contrary to previous literature, the world population is unlikely to stop growing this century. There is an 80% probability that world population, now 7.2 billion people, will increase to between 9.6 billion and 12.3 billion in 2100. This uncertainty is much smaller than the range from the traditional UN high and low variants. Much of the increase is expected to happen in Africa, in part due to higher fertility rates and a recent slowdown in the pace of fertility decline. Also, the ratio of working-age people to older people is likely to decline substantially in all countries, even those that currently have young populations.

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World population stabilization unlikely this century

World Population Prospects, the 2010 Revision: Estimation and projection methodology

Projections of countries’ future populations, broken down by age and sex, are widely used for planning and research. They are mostly done deterministically, but there is a widespread need for probabilistic projections. We propose a Bayesian method for probabilistic population projections for all countries. The total fertility rate and female and male life expectancies at birth are projected probabilistically using Bayesian hierarchical models estimated via Markov chain Monte Carlo using United Nations population data for all countries. These are then converted to age-specific rates and combined with a cohort component projection model. This yields probabilistic projections of any population quantity of interest. The method is illustrated for five countries of different demographic stages, continents and sizes. The method is validated by an out of sample experiment in which data from 1950–1990 are used for estimation, and applied to predict 1990–2010. The method appears reasonably accurate and well calibrated for this period. The results suggest that the current United Nations high and low variants greatly underestimate uncertainty about the number of oldest old from about 2050 and that they underestimate uncertainty for high fertility countries and overstate uncertainty for countries that have completed the demographic transition and whose fertility has started to recover towards replacement level, mostly in Europe. The results also indicate that the potential support ratio (persons aged 20–64 per person aged 65+) will almost certainly decline dramatically in most countries over the coming decades.

https://www.pnas.org/content/pnas/109/35/13915.full.pdf

Bayesian probabilistic population projections for all countries

We describe a Bayesian projection model to produce country-specific projections of the total fertility rate (TFR) for all countries. The model decomposes the evolution of TFR into three phases: pre-transition high fertility, the fertility transition, and post-transition low fertility. The model for the fertility decline builds on the United Nations Population Division’s current deterministic projection methodology, which assumes that fertility will eventually fall below replacement level. It models the decline in TFR as the sum of two logistic functions that depend on the current TFR level, and a random term. A Bayesian hierarchical model is used to project future TFR based on both the country’s TFR history and the pattern of all countries. It is estimated from United Nations estimates of past TFR in all countries using a Markov chain Monte Carlo algorithm. The post-transition low fertility phase is modeled using an autoregressive model, in which long-term TFR projections converge toward and oscillate around replacement level. The method is evaluated using out-of-sample projections for the period since 1980 and the period since 1995, and is found to be well calibrated.

https://www.stat.washington.edu/raftery/Research/PDF/Alkema2011Demography.pdf

Probabilistic projections of the total fertility rate for all countries.

OVER THE PAST quarter century the HIV virus has spread to all corners of the globe resulting in one of the deadliest epidemics of modern times. In 2007 a total of 2.1 million men women and children died of AIDS. The death toll will remain high in the future because 33.2 million individuals are currently infected and about 2.5 million new HIV infections occur each year (UNAIDS 2007). Most of these currently and newly infected individuals are likely to die of AIDS eventually despite the increasing availability of treatment. In response to this unprecedented health threat a massive global effort has been mounted to reduce infections through prevention programs and to extend the lives of infected individuals with antiretroviral therapy (ART). Prevention efforts are partly responsible for a major turning point in the epidemic: after a period of rapid spread the epidemic appears to have stabilized in most countries and the proportion of adults infected with HIV is no longer increasing (Shelton et al. 2006; UNAIDS 2007). In a number of countries the proportion infected is even declining. The first part of this article reviews the highly diverse regional and country patterns of HIV epidemics and discusses possible causes of the geographic variation in epidemic sizes. We describe past trends in HIV epidemics and offer explanations for the recent stabilization and/or decline of the epidemic. Next we examine projections of the future course of the epidemic estimate the peak years of regional epidemics and briefly assess the potential future impact of new prevention technologies. We conclude with a summary of the past and projected future impact of the HIV/AIDS epidemic on key demographic variables including the crude death rate the population age structure and population growth. (authors)

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Gerhard K. Heilig

Papers

World population stabilization unlikely this century

World Population Prospects, the 2010 Revision: Estimation and projection methodology

Bayesian probabilistic population projections for all countries

Probabilistic projections of the total fertility rate for all countries.

Has the HIV epidemic peaked