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This article presents global cancer statistics by world region for the year 2022 based on updated estimates from the International Agency for Research on Cancer (IARC). There were close to 20 million new cases of cancer in the year 2022 (including nonmelanoma skin cancers [NMSCs]) alongside 9.7 million deaths from cancer (including NMSC). The estimates suggest that approximately one in five men or women develop cancer in a lifetime, whereas around one in nine men and one in 12 women die from it. Lung cancer was the most frequently diagnosed cancer in 2022, responsible for almost 2.5 million new cases, or one in eight cancers worldwide (12.4% of all cancers globally), followed by cancers of the female breast (11.6%), colorectum (9.6%), prostate (7.3%), and stomach (4.9%). Lung cancer was also the leading cause of cancer death, with an estimated 1.8 million deaths (18.7%), followed by colorectal (9.3%), liver (7.8%), female breast (6.9%), and stomach (6.8%) cancers. Breast cancer and lung cancer were the most frequent cancers in women and men, respectively (both cases and deaths). Incidence rates (including NMSC) varied from four-fold to five-fold across world regions, from over 500 in Australia/New Zealand (507.9 per 100,000) to under 100 in Western Africa (97.1 per 100,000) among men, and from over 400 in Australia/New Zealand (410.5 per 100,000) to close to 100 in South-Central Asia (103.3 per 100,000) among women. The authors examine the geographic variability across 20 world regions for the 10 leading cancer types, discussing recent trends, the underlying determinants, and the prospects for global cancer prevention and control. With demographics-based predictions indicating that the number of new cases of cancer will reach 35 million by 2050, investments in prevention, including the targeting of key risk factors for cancer (including smoking, overweight and obesity, and infection), could avert millions of future cancer diagnoses and save many lives worldwide, bringing huge economic as well as societal dividends to countries over the forthcoming decades.

This article provides an update on the global cancer burden using the GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer. Worldwide, an estimated 19.3 million new cancer cases (18.1 million excluding nonmelanoma skin cancer) and almost 10.0 million cancer deaths (9.9 million excluding nonmelanoma skin cancer) occurred in 2020. Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), followed by lung (11.4%), colorectal (10.0 %), prostate (7.3%), and stomach (5.6%) cancers. Lung cancer remained the leading cause of cancer death, with an estimated 1.8 million deaths (18%), followed by colorectal (9.4%), liver (8.3%), stomach (7.7%), and female breast (6.9%) cancers. Overall incidence was from 2-fold to 3-fold higher in transitioned versus transitioning countries for both sexes, whereas mortality varied <2-fold for men and little for women. Death rates for female breast and cervical cancers, however, were considerably higher in transitioning versus transitioned countries (15.0 vs 12.8 per 100,000 and 12.4 vs 5.2 per 100,000, respectively). The global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020, with a larger increase in transitioning (64% to 95%) versus transitioned (32% to 56%) countries due to demographic changes, although this may be further exacerbated by increasing risk factors associated with globalization and a growing economy. Efforts to build a sustainable infrastructure for the dissemination of cancer prevention measures and provision of cancer care in transitioning countries is critical for global cancer control.

BACKGROUND: Equations to estimate glomerular filtration rate (GFR) are routinely used to assess kidney function. Current equations have limited precision and systematically underestimate measured GFR at higher values. OBJECTIVE: To develop a new estimating equation for GFR: the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. DESIGN: Cross-sectional analysis with separate pooled data sets for equation development and validation and a representative sample of the U.S. population for prevalence estimates. SETTING: Research studies and clinical populations ("studies") with measured GFR and NHANES (National Health and Nutrition Examination Survey), 1999 to 2006. PARTICIPANTS: 8254 participants in 10 studies (equation development data set) and 3896 participants in 16 studies (validation data set). Prevalence estimates were based on 16,032 participants in NHANES. MEASUREMENTS: GFR, measured as the clearance of exogenous filtration markers (iothalamate in the development data set; iothalamate and other markers in the validation data set), and linear regression to estimate the logarithm of measured GFR from standardized creatinine levels, sex, race, and age. RESULTS: In the validation data set, the CKD-EPI equation performed better than the Modification of Diet in Renal Disease Study equation, especially at higher GFR (P < 0.001 for all subsequent comparisons), with less bias (median difference between measured and estimated GFR, 2.5 vs. 5.5 mL/min per 1.73 m(2)), improved precision (interquartile range [IQR] of the differences, 16.6 vs. 18.3 mL/min per 1.73 m(2)), and greater accuracy (percentage of estimated GFR within 30% of measured GFR, 84.1% vs. 80.6%). In NHANES, the median estimated GFR was 94.5 mL/min per 1.73 m(2) (IQR, 79.7 to 108.1) vs. 85.0 (IQR, 72.9 to 98.5) mL/min per 1.73 m(2), and the prevalence of chronic kidney disease was 11.5% (95% CI, 10.6% to 12.4%) versus 13.1% (CI, 12.1% to 14.0%). LIMITATION: The sample contained a limited number of elderly people and racial and ethnic minorities with measured GFR. CONCLUSION: The CKD-EPI creatinine equation is more accurate than the Modification of Diet in Renal Disease Study equation and could replace it for routine clinical use. PRIMARY FUNDING SOURCE: National Institute of Diabetes and Digestive and Kidney Diseases.

As part of a World Health Organization-led effort to update the empirical evidence base for the leishmaniases, national experts provided leishmaniasis case data for the last 5 years and information regarding treatment and control in their respective countries and a comprehensive literature review was conducted covering publications on leishmaniasis in 98 countries and three territories (see 'Leishmaniasis Country Profiles Text S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S11, S12, S13, S14, S15, S16, S17, S18, S19, S20, S21, S22, S23, S24, S25, S26, S27, S28, S29, S30, S31, S32, S33, S34, S35, S36, S37, S38, S39, S40, S41, S42, S43, S44, S45, S46, S47, S48, S49, S50, S51, S52, S53, S54, S55, S56, S57, S58, S59, S60, S61, S62, S63, S64, S65, S66, S67, S68, S69, S70, S71, S72, S73, S74, S75, S76, S77, S78, S79, S80, S81, S82, S83, S84, S85, S86, S87, S88, S89, S90, S91, S92, S93, S94, S95, S96, S97, S98, S99, S100, S101'). Additional information was collated during meetings conducted at WHO regional level between 2007 and 2011. Two questionnaires regarding epidemiology and drug access were completed by experts and national program managers. Visceral and cutaneous leishmaniasis incidence ranges were estimated by country and epidemiological region based on reported incidence, underreporting rates if available, and the judgment of national and international experts. Based on these estimates, approximately 0.2 to 0.4 cases and 0.7 to 1.2 million VL and CL cases, respectively, occur each year. More than 90% of global VL cases occur in six countries: India, Bangladesh, Sudan, South Sudan, Ethiopia and Brazil. Cutaneous leishmaniasis is more widely distributed, with about one-third of cases occurring in each of three epidemiological regions, the Americas, the Mediterranean basin, and western Asia from the Middle East to Central Asia. The ten countries with the highest estimated case counts, Afghanistan, Algeria, Colombia, Brazil, Iran, Syria, Ethiopia, North Sudan, Costa Rica and Peru, together account for 70 to 75% of global estimated CL incidence. Mortality data were extremely sparse and generally represent hospital-based deaths only. Using an overall case-fatality rate of 10%, we reach a tentative estimate of 20,000 to 40,000 leishmaniasis deaths per year. Although the information is very poor in a number of countries, this is the first in-depth exercise to better estimate the real impact of leishmaniasis. These data should help to define control strategies and reinforce leishmaniasis advocacy.

This article provides a status report on the global burden of cancer worldwide using the GLOBOCAN 2018 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer, with a focus on geographic variability across 20 world regions. There will be an estimated 18.1 million new cancer cases (17.0 million excluding nonmelanoma skin cancer) and 9.6 million cancer deaths (9.5 million excluding nonmelanoma skin cancer) in 2018. In both sexes combined, lung cancer is the most commonly diagnosed cancer (11.6% of the total cases) and the leading cause of cancer death (18.4% of the total cancer deaths), closely followed by female breast cancer (11.6%), prostate cancer (7.1%), and colorectal cancer (6.1%) for incidence and colorectal cancer (9.2%), stomach cancer (8.2%), and liver cancer (8.2%) for mortality. Lung cancer is the most frequent cancer and the leading cause of cancer death among males, followed by prostate and colorectal cancer (for incidence) and liver and stomach cancer (for mortality). Among females, breast cancer is the most commonly diagnosed cancer and the leading cause of cancer death, followed by colorectal and lung cancer (for incidence), and vice versa (for mortality); cervical cancer ranks fourth for both incidence and mortality. The most frequently diagnosed cancer and the leading cause of cancer death, however, substantially vary across countries and within each country depending on the degree of economic development and associated social and life style factors. It is noteworthy that high-quality cancer registry data, the basis for planning and implementing evidence-based cancer control programs, are not available in most low- and middle-income countries. The Global Initiative for Cancer Registry Development is an international partnership that supports better estimation, as well as the collection and use of local data, to prioritize and evaluate national cancer control efforts. CA: A Cancer Journal for Clinicians 2018;0:1-31. © 2018 American Cancer Society.

BACKGROUND: Serum creatinine concentration is widely used as an index of renal function, but this concentration is affected by factors other than glomerular filtration rate (GFR). OBJECTIVE: To develop an equation to predict GFR from serum creatinine concentration and other factors. DESIGN: Cross-sectional study of GFR, creatinine clearance, serum creatinine concentration, and demographic and clinical characteristics in patients with chronic renal disease. PATIENTS: 1628 patients enrolled in the baseline period of the Modification of Diet in Renal Disease (MDRD) Study, of whom 1070 were randomly selected as the training sample; the remaining 558 patients constituted the validation sample. METHODS: The prediction equation was developed by stepwise regression applied to the training sample. The equation was then tested and compared with other prediction equations in the validation sample. RESULTS: To simplify prediction of GFR, the equation included only demographic and serum variables. Independent factors associated with a lower GFR included a higher serum creatinine concentration, older age, female sex, nonblack ethnicity, higher serum urea nitrogen levels, and lower serum albumin levels (P < 0.001 for all factors). The multiple regression model explained 90.3% of the variance in the logarithm of GFR in the validation sample. Measured creatinine clearance overestimated GFR by 19%, and creatinine clearance predicted by the Cockcroft-Gault formula overestimated GFR by 16%. After adjustment for this overestimation, the percentage of variance of the logarithm of GFR predicted by measured creatinine clearance or the Cockcroft-Gault formula was 86.6% and 84.2%, respectively. CONCLUSION: The equation developed from the MDRD Study provided a more accurate estimate of GFR in our study group than measured creatinine clearance or other commonly used equations.

Phylogenies are extremely useful tools, not only for establishing genealogical relationships among a group of organisms or their parts (e.g. genes), but also for a variety of research once the phylogenies are estimated. In a recent review, Pagel (1999) eloquently outline a number of uses for phylogenetic information from discovery of drug resistance to reconstructing the common ancestor to all of life. Phylogenies have been used to predict future trends in infectious disease ( Bush et al. 1999 ) and have even been offered as evidence in a court of law ( Vogel 1997). Yet phylogenies are only as useful as they are accurate. Estimating genealogical relationships among genes at the population level presents a number of difficulties to traditional methods of phylogeny reconstruction. These traditional methods such as parsimony, neighbour-joining, and maximum-likelihood make assumptions that are invalid at the population level. For example, these methods assume ancestral haplotypes are no longer in the population, yet coalescent theory predicts that ancestral haplotypes will be the most frequent sequences sampled in a population level study ( Watterson & Guess 1977; Donnelly & Tavaré 1986; Crandall & Templeton 1993). Traditional methods require reasonably large numbers of variable characters to accurately reconstruct relationships ( Huelsenbeck & Hillis 1993) and population level studies typically lack such variation. Also, recombination is a real possibility among sequences at the population level and traditional methods assume recombination does not occur. The failure to incorporate the possibility of recombination in phylogeny reconstruction can lead to grave errors in the resulting estimated phylogeny. The combination of these effects can lead parsimony methods to infer a cumbersome amount of most parsimonious trees at the population level with no resolution among the set (e.g. over one billion trees for a set of human mitochondrial DNA (mtDNA), Excoffier & Smouse 1994). These effects can also lead neighbour-joining and traditional maximum-likelihood methods to be over confident in the resulting relationships ( Bandelt et al. 1995 ). Therefore, an alternative approach is needed to provide accurate estimates of gene genealogies at the population level that take into account these population level phenomena not addressed by traditional methods. Multiple groups have looked to network representations for population level genealogical information ( Bandelt & Dress 1992; Templeton et al. 1992 ; Excoffier & Smouse 1994; Fitch 1997). Networks allow one to naturally incorporate the often-times nonbifurcating genealogical information associated with population level divergences. The method of Templeton et al. (1992) (TCS) has been used extensively with restriction site and nucleotide sequence data to infer population level genealogies when divergences are low ( Georgiadis et al. 1994 ; Routman et al. 1994 ; Gerber & Templeton 1996; Hedin 1997; Schaal et al. 1998 ; Viláet al. 1999 , Gómez-Zurita et al. 2000). TCS has been used with traditional methods to estimate relationships among organisms that span a wide range of divergence ( Crandall & Fitzpatrick 1996; Benabib et al. 1997 ). The approach has also been used extensively with a nested analysis procedure to partition population structure from population history ( Templeton et al. 1995 ; Templeton 1998) and explore the phylogeographic history of a diversity of organisms (e.g. Johnson & Jordon 2000; Turner et al. 2000 ). In this note, we announce the availability of a new software package, TCS, to estimate genealogical relationships among sequences using the method of Templeton et al. (1992) . The TCS software opens nucleotide sequence files in either nexus ( Maddison et al. 1997 ) or phylip ( Felsenstein 1991) sequential format. Sequences should not be collapsed into haplotypes as frequency data can be incorporated into the output. The program collapses sequences into haplotypes and calculates the frequencies of the haplotypes in the sample. These frequencies are used to estimate haplotype outgroup probabilities, which correlate with haplotype age ( Donnelly & Tavaré 1986; Castelloe & Templeton 1994). An absolute distance matrix is then calculated for all pairwise comparisons of haplotypes. The probability of parsimony [as defined in Templeton et al. (1992) , equations 6, 7, and 8] is calculated for pairwise differences until the probability exceeds 0.95. The number of mutational differences associated with the probability just before this 95% cut-off is then the maximum number of mutational connections between pairs of sequences justified by the ‘parsimony’ criterion. These justified connections are then made resulting in a 95% set of plausible solutions. The program outputs the sequences, the pairwise absolute distance matrix, probabilities of parsimony for mutational steps just beyond the 95% cut-off, a test listing of connections made and missing intermediates generated, and a graph output file containing the resulting network ( Fig. 1). This graph output file can be opened in the freeware VGJ 1.0.3 ( http://www.eng.auburn.edu/department/cse/research/graphdrawing/graphdrawing.html; distributed under the terms of the GNU General Public License, Version 2), which is packaged with the TCS algorithm. The program can handle a reasonable number of sequences. For example, an HTLV data set with 69 haplotypes of length 725 bp took over one hour to run in a Macintosh G3. Memory requirements are low, and the program will run with less than 1 MB RAM. The TCS software package, including executables for Mac and PC, documentation, and Java source code, is distributed freely and is available at our website, along with a host of other programs for population genetic and phylogenetic analyses: http://bioag.byu.edu/zoology/crandalllab/programs.htm. TCS Java interface. The maximum number of steps connecting parsimoniously two haplotypes is indicated. Gaps can be treated as a 5th state or as missing data. The graph can be edited and arranged using different algorithms. By double-clicking over a haplotype, some information is displayed, such as sequences included in the haplotype and outgroup weights. The haplotype with the highest outgroup probability is displayed as a square, while other haplotypes are displayed as ovals. The size of the square or oval corresponds to the haplotype frequency. This work was supported by the Alfred P. Sloan Foundation, a Shannon Award from the National Institutes of Health, and NIH R01-HD34350.

Estimates of the worldwide incidence and mortality from 36 cancers and for all cancers combined for the year 2018 are now available in the GLOBOCAN 2018 database, compiled and disseminated by the International Agency for Research on Cancer (IARC). This paper reviews the sources and methods used in compiling the cancer statistics in 185 countries. The validity of the national estimates depends upon the representativeness of the source information, and to take into account possible sources of bias, uncertainty intervals are now provided for the estimated sex- and site-specific all-ages number of new cancer cases and cancer deaths. We briefly describe the key results globally and by world region. There were an estimated 18.1 million (95% UI: 17.5-18.7 million) new cases of cancer (17 million excluding non-melanoma skin cancer) and 9.6 million (95% UI: 9.3-9.8 million) deaths from cancer (9.5 million excluding non-melanoma skin cancer) worldwide in 2018.

Estimates of the worldwide incidence and mortality from 27 cancers in 2008 have been prepared for 182 countries as part of the GLOBOCAN series published by the International Agency for Research on Cancer. In this article, we present the results for 20 world regions, summarizing the global patterns for the eight most common cancers. Overall, an estimated 12.7 million new cancer cases and 7.6 million cancer deaths occur in 2008, with 56% of new cancer cases and 63% of the cancer deaths occurring in the less developed regions of the world. The most commonly diagnosed cancers worldwide are lung (1.61 million, 12.7% of the total), breast (1.38 million, 10.9%) and colorectal cancers (1.23 million, 9.7%). The most common causes of cancer death are lung cancer (1.38 million, 18.2% of the total), stomach cancer (738,000 deaths, 9.7%) and liver cancer (696,000 deaths, 9.2%). Cancer is neither rare anywhere in the world, nor mainly confined to high-resource countries. Striking differences in the patterns of cancer from region to region are observed.

Traditional econometric models assume a constant one-period forecast variance. To generalize this implausible assumption, a new class of stochastic processes called autoregressive conditional heteroscedastic (ARCH) processes are introduced in this paper. These are mean zero, serially uncorrelated processes with nonconstant variances conditional on the past, but constant unconditional variances. For such processes, the recent past gives information about the one-period forecast variance. A regression model is then introduced with disturbances following an ARCH process. Maximum likelihood estimators are described and a simple scoring iteration formulated. Ordinary least squares maintains its optimality properties in this set-up, but maximum likelihood is more efficient. The relative efficiency is calculated and can be infinite. To test whether the disturbances follow an ARCH process, the Lagrange multiplier procedure is employed. The test is based simply on the autocorrelation of the squared OLS residuals. This model is used to estimate the means and variances of inflation in the U.K. The ARCH effect is found to be significant and the estimated variances increase substantially during the chaotic seventies.

BACKGROUND: Usually the researchers performing meta-analysis of continuous outcomes from clinical trials need their mean value and the variance (or standard deviation) in order to pool data. However, sometimes the published reports of clinical trials only report the median, range and the size of the trial. METHODS: In this article we use simple and elementary inequalities and approximations in order to estimate the mean and the variance for such trials. Our estimation is distribution-free, i.e., it makes no assumption on the distribution of the underlying data. RESULTS: We found two simple formulas that estimate the mean using the values of the median (m), low and high end of the range (a and b, respectively), and n (the sample size). Using simulations, we show that median can be used to estimate mean when the sample size is larger than 25. For smaller samples our new formula, devised in this paper, should be used. We also estimated the variance of an unknown sample using the median, low and high end of the range, and the sample size. Our estimate is performing as the best estimate in our simulations for very small samples (n < or = 15). For moderately sized samples (15 < n < or = 70), our simulations show that the formula range/4 is the best estimator for the standard deviation (variance). For large samples (n > 70), the formula range/6 gives the best estimator for the standard deviation (variance). We also include an illustrative example of the potential value of our method using reports from the Cochrane review on the role of erythropoietin in anemia due to malignancy. CONCLUSION: Using these formulas, we hope to help meta-analysts use clinical trials in their analysis even when not all of the information is available and/or reported.

OBJECTIVE: To estimate the prevalence of diabetes and the number of people with diabetes who are > or =20 years of age in all countries of the world for three points in time, i.e., the years 1995, 2000, and 2025, and to calculate additional parameters, such as sex ratio, urban-rural ratio, and the age structure of the diabetic population. RESEARCH DESIGN AND METHODS: Age-specific diabetes prevalence estimates were applied to United Nations population estimates and projections for the number of adults aged > or =20 years in all countries of the world. For developing countries, urban and rural populations were considered separately RESULTS: Prevalence of diabetes in adults worldwide was estimated to be 4.0% in 1995 and to rise to 5.4% by the year 2025. It is higher in developed than in developing countries. The number of adults with diabetes in the world will rise from 135 million in 1995 to 300 million in the year 2025. The major part of this numerical increase will occur in developing countries. There will be a 42% increase, from 51 to 72 million, in the developed countries and a 170% increase, from 84 to 228 million, in the developing countries. Thus, by the year 2025, >75% of people with diabetes will reside in developing countries, as compared with 62% in 1995. The countries with the largest number of people with diabetes are, and will be in the year 2025, India, China, and the U.S. In developing countries, the majority of people with diabetes are in the age range of 45-64 years. In the developed countries, the majority of people with diabetes are aged > or =65 years. This pattern will be accentuated by the year 2025. There are more women than men with diabetes, especially in developed countries. In the future, diabetes will be increasingly concentrated in urban areas. CONCLUSIONS: This report supports earlier predictions of the epidemic nature of diabetes in the world during the first quarter of the 21st century. It also provides a provisional picture of the characteristics of the epidemic. Worldwide surveillance of diabetes is a necessary first step toward its prevention and control, which is now recognized as an urgent priority.

This paper develops the statistical theory for testing and estimating multiple change points in regression models. The rate of convergence and limiting distribution for the estimated parameters are obtained. Several test statistics are proposed to determine the existence as well as the number of change points. A partial structural change model is considered. The authors study both fixed and shrinking magnitudes of shifts. In addition, the models allow for serially correlated disturbances (mixingales). An estimation strategy for which the location of the breaks need not be simultaneously determined is discussed. Instead, the authors' method successively estimates each break point.

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Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%. IQ-TREE is freely available at http://www.cibiv.at/software/iqtree.

Both the magnitude and the urgency of the task of assessing global biodiversity require that we make the most of what we know through the use of estimation and extrapolation. Likewise, future biodiversity inventories need to be designed around the use of effective sampling and estimation procedures, especially for 'hyperdiverse' groups of terrestrial organisms, such as arthropods, nematodes, fungi, and microorganisms. The challenge of estimating patterns of species richness from samples can be separated into (i) the problem of estimating local species richness, and (ii) the problem of estimating the distinctness, or complementarity, of species assemblages. These concepts apply on a wide range of spatial, temporal, and functional scales. Local richness can be estimated by extrapolating species accumulation curves, fitting parametric distributions of relative abundance, or using non-parametric techniques based on the distribution of individuals among species or of species among samples. We present several of these methods and examine their effectiveness for an example data set. We present a simple measure of complementarity, with some biogeographic examples, and outline the difficult problem of estimating complementarity from samples. Finally, we discuss the importance of using 'reference' sites (or sub-sites) to assess the true richness and composition of species assemblages, to measure ecologically significant ratios between unrelated taxa, to measure taxon/sub-taxon (hierarchical) ratios, and to 'calibrate' standardized sampling methods. This information can then be applied to the rapid, approximate assessment of species richness and faunal or floral composition at 'comparative' sites.