IARC scientific publications最新文献_第4页

An overview of cancer survival in Africa, Asia, the Caribbean and Central America: the case for investment in cancer health services. 非洲、亚洲、加勒比和中美洲癌症生存概况:投资于癌症保健服务的理由。

IARC scientific publications Pub Date : 2011-01-01

R Sankaranarayanan, R Swaminathan, K Jayant, H Brenner

{"title":"An overview of cancer survival in Africa, Asia, the Caribbean and Central America: the case for investment in cancer health services.","authors":"R Sankaranarayanan, R Swaminathan, K Jayant, H Brenner","doi":"","DOIUrl":"","url":null,"abstract":"Population-based cancer survival data, a key indicator for monitoring progress against cancer, are reported from 27 population-based cancer registries in 14 countries in Africa, Asia, the Caribbean and Central America. In China, Singapore, the Republic of Korea, and Turkey, the 5-year age-standardized relative survival ranged from 76-82% for breast, 63-79% for cervical, 71-78% for bladder, and 44-60% for large-bowel cancer. Survival did not exceed 22% for any cancer site in The Gambia, or 13% for any cancer site except breast (46%) in Uganda. For localized cancers of the breast, large bowel, larynx, ovary, urinary bladder and for regional diseases at all sites, higher survival rates were observed in countries with more rather than less developed health services. Inter- and intra-country variations in survival imply that the levels of development of health services and their efficiency to provide early diagnosis, treatment and clinical follow-up care have a profound impact on survival from cancer. These are reliable baseline summary estimates to evaluate improvements in cancer control and emphasise the need for urgent investment to improve awareness, population-based cancer registration, early detection programmes, health-services infrastructure, and human resources in these countries in the future.","PeriodicalId":13149,"journal":{"name":"IARC scientific publications","volume":" 162","pages":"257-91"},"PeriodicalIF":0.0,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29937513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stastistical methods for cancer survival analysis. 肿瘤生存分析的统计学方法。

IARC scientific publications Pub Date : 2011-01-01

R Swaminathan, H Brenner

{"title":"Stastistical methods for cancer survival analysis.","authors":"R Swaminathan, H Brenner","doi":"","DOIUrl":"","url":null,"abstract":"Adequate and complete follow-up is a prerequisite for the conduct of any survival study. Passive follow-up relies on routine availability of mortality data through unique data linkage possibilities, while active follow-up supplements mortality ascertainment, for which there are a variety of methods. Cox proportional-hazard model was employed to test whether censoring was random in presence of loss to follow-up. Absolute survival probability was estimated by the actuarial method following semi-complete approach for all registries, and the period approach was also used wherever possible. Expected survival probability for registries was estimated from the respective country-, age- and sex-specific abridged life tables. Relative survival, as the ratio of absolute to expected survival, was calculated to exclude the effect arising from different background mortalities. To account for the differences in the age structure of the cancer cases, relative survival was adjusted for age and reported as age-standardized relative survival. Estimated incident cancer cases from less-developed countries together for every classified cancer site served as the standard population. Weights were assigned to individual patients, depending on their age, and standardization was carried out using weighted individual data. Analyses were done using the publicly available macros in SAS software.","PeriodicalId":13149,"journal":{"name":"IARC scientific publications","volume":" 162","pages":"7-13"},"PeriodicalIF":0.0,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29937794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cancer survival in Hong Kong SAR, China, 1996-2001. 1996-2001年中国香港特别行政区的癌症存活率。

IARC scientific publications Pub Date : 2011-01-01

S C Law, O W Mang

引用次数: 0

Cancer survival in Tianjin, China, 1991-1999. 1991-1999年中国天津的癌症生存率。

IARC scientific publications Pub Date : 2011-01-01

H Xishan, K Chen, H Min, D Shufen, W Jifang

引用次数: 0

Measurement error in biomarkers: sources, assessment, and impact on studies. 生物标记物的测量误差:来源、评估和对研究的影响。

IARC scientific publications Pub Date : 2011-01-01

Emily White

{"title":"Measurement error in biomarkers: sources, assessment, and impact on studies.","authors":"Emily White","doi":"","DOIUrl":"","url":null,"abstract":"Measurement error in a biomarker refers to the error of a biomarker measure applied in a specific way to a specific population, versus the true (etiologic) exposure. In epidemiologic studies, this error includes not only laboratory error, but also errors (variations) introduced during specimen collection and storage, and due to day-to-day, month-to-month, and year-to-year within-subject variability of the biomarker. Validity and reliability studies that aim to assess the degree of biomarker error for use of a specific biomarker in epidemiologic studies must be properly designed to measure all of these sources of error. Validity studies compare the biomarker to be used in an epidemiologic study to a perfect measure in a group of subjects. The parameters used to quantify the error in a binary marker are sensitivity and specificity. For continuous biomarkers, the parameters used are bias (the mean difference between the biomarker and the true exposure) and the validity coefficient (correlation of the biomarker with the true exposure). Often a perfect measure of the exposure is not available, so reliability (repeatability) studies are conducted. These are analysed using kappa for binary biomarkers and the intraclass correlation coefficient for continuous biomarkers. Equations are given which use these parameters from validity or reliability studies to estimate the impact of nondifferential biomarker measurement error on the risk ratio in an epidemiologic study that will use the biomarker. Under nondifferential error, the attenuation of the risk ratio is towards the null and is often quite substantial, even for reasonably accurate biomarker measures. Differential biomarker error between cases and controls can bias the risk ratio in any direction and completely invalidate an epidemiologic study.","PeriodicalId":13149,"journal":{"name":"IARC scientific publications","volume":" 163","pages":"143-61"},"PeriodicalIF":0.0,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30921804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Physical/chemical/immunologic analytical methods. 物理/化学/免疫分析方法。

IARC scientific publications Pub Date : 2011-01-01

Jia-Sheng Wang, John D Groopman

引用次数: 0

Loss-adjusted hospital and population-based survival of cancer patients. 癌症患者损失调整后的医院和人群生存率。

IARC scientific publications Pub Date : 2011-01-01

B Ganesh, R Swaminathan, A Mathew, R Sankaranarayanan, M Hakama

{"title":"Loss-adjusted hospital and population-based survival of cancer patients.","authors":"B Ganesh, R Swaminathan, A Mathew, R Sankaranarayanan, M Hakama","doi":"","DOIUrl":"","url":null,"abstract":"This chapter presents formulae that methodologically adjust for losses, and gives examples describing magnitude of bias in survival estimates without such adjustment. Loss-adjusted survival is estimated under the assumption that survival of patients Lost to follow-up is the same as that for patients with known follow-up time and similar characteristics of different prognostic factors at first entry. The observed number of Losses to follow-up is then relocated into expected numbers of death and survivors on this basis. Standard methods, such as the actuarial one, are then applied with the sum of observed and expected outcome events. A total of 336 hospital series of treated new breast cancer cases from Mumbai with 24% lost to follow-up revealed a substantial bias of 7 per cent units for 3-year survival estimated with (54%) and without (61%) loss-adjustment. Stepwise adjustment of losses established that increasing the number of prognostic factors explained the bias better. Population-based series comprising 13 371 cases of top ranking cancers from Chennai, with loss to follow-up ranging from 7-24%, revealed negligible bias, ranging from 0-2% in 5-year survival by the loss-adjusted approach for different cancers. Data source seems to affect the need for loss-adjustment, and the loss-adjusted approach is recommended when hospital-based cancer registry data of a low- or medium-resource country are used to evaluate the outcome of cancer patients.","PeriodicalId":13149,"journal":{"name":"IARC scientific publications","volume":" 162","pages":"15-21"},"PeriodicalIF":0.0,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29937796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cancer survival in Seoul, Republic of Korea, 1993-1997. 1993-1997年大韩民国首尔的癌症存活率。

IARC scientific publications Pub Date : 2011-01-01

Y O Ahn, M H Shin

引用次数: 0

Neurodegenerative diseases. 神经退行性疾病。

IARC scientific publications Pub Date : 2011-01-01

Harvey Checkoway, Jessica I Lundin, Samir N Kelada

{"title":"Neurodegenerative diseases.","authors":"Harvey Checkoway, Jessica I Lundin, Samir N Kelada","doi":"","DOIUrl":"","url":null,"abstract":"Degenerative diseases of the nervous system impose substantial medical and public health burdens on populations throughout the world. Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS) are three of the major neurodegenerative diseases. The prevalence and incidence of these diseases rise dramatically with age; thus the number of cases is expected to increase for the foreseeable future as life spans in many countries continue to increase. Causal contributions from genetic and environmental factors are, with some exceptions, poorly understood. Nonetheless, molecular epidemiology approaches have proven valuable for improving disease diagnoses, characterizing disease prognostic factors, identifying high-risk genes for familial neurodegenerative diseases, investigating common genetic variants that may predict susceptibility for the non-familial forms of these diseases, and for quantifying environmental exposures. Incorporation of molecular techniques, including genomics, proteomics, and measurements of environmental toxicant body burdens into epidemiologic research, offer considerable promise for enhancing progress on characterizing pathogenesis mechanisms and identifying specific risk factors, especially for the non-familial forms of these diseases. In this chapter, brief overviews are provided of the epidemiologic features of PD, AD, and ALS, as well as illustrative examples in which molecular epidemiologic approaches have advanced knowledge on underlying disease mechanisms and risk factors that might lead to improved medical management and ultimately disease prevention. The chapter concludes with some recommendations for future molecular epidemiology research.","PeriodicalId":13149,"journal":{"name":"IARC scientific publications","volume":" 163","pages":"407-19"},"PeriodicalIF":0.0,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30922741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Infectious diseases. 传染病。

IARC scientific publications Pub Date : 2011-01-01

Betsy Foxman

引用次数: 0