Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.
ADVANCED SEARCH
Log in
Top
Cancer Causes & Control
Published in: Cancer Causes & Control 1/2007

01-02-2007 | Original Paper

An evaluation of spatial and multivariate covariance among childhood cancer histotypes in Texas (United States)

Authors: James A. Thompson, Susan E. Carozza, Li Zhu

Published in: Cancer Causes & Control | Issue 1/2007

Login to get access

Abstract

Background

Spatial modeling of rare diseases, such as childhood cancer, has been hampered by imprecise risk estimates. Recent developments in Bayesian hierarchical modeling include the ability to adjust a disease risk estimate to be fully conditional for covariance among neighboring locations and for covariance among multiple diseases within each location. This joint modeling approach is called Multivariate Intrinsic Conditional Autoregressive. The objective of this study was to evaluate the spatial and histotype covariance among childhood cancer histotypes, in Texas. Results will be valuable for selecting appropriate models to support more specific etiologic studies of environmental factors for childhood cancer.

Methods

County level standard morbidity ratios for 13 childhood cancer histotype groups were estimated using Multivariate Intrinsic Conditional Autoregressive modeling and the results compared to results from two reduced models. The two reduced models were the base model specified with zero spatial covariance and the base model specified with zero histotype covariance. The results were compared using the Deviance Information Criterion and Geographical Information System techniques were used to compare patterns of standard morbidity ratios.

Results

Including histotype covariance greatly improved the Deviance Information Criterion and including spatial covariance produced a moderate improvement. Parameter evaluation by GIS techniques showed that excluding histotype covariance resulted in marked shrinkage of the risk estimates.

Conclusions

Investigation of childhood cancer could benefit by incorporating histotype covariance into environmental modeling.
Literature
1.
Short M, Carlin BP, Bushhouse S (2002) Using hierarchical spatial models for cancer control planning in Minnesota (United States). Cancer Causes Control 13:903–916PubMedCrossRef
2.
Lawson AB, Bohning D, Biggeri A, Lesaffre E, Viel JF (1999) Disease mapping and its uses. In: Lawson A, Biggeri A, Bohning D, Lesaffre E, Viel J-F, Bertollini R (eds) Disease mapping and risk assessment for public health. Wiley, New York, NY, pp 3–13
3.
Willis A, Krewski D, Jerrett M, Goldberg MS, Burnett RT (2003) Selection of ecologic covariates in the American Cancer Society Study. J Toxicol Environ Health A 66:1563–1589PubMedCrossRef
4.
Roux AVD (2004) The study of group-level factors in epidemiology: rethinking variables, study designs, and analytical approaches. Epidemiol Rev 26:104–111CrossRef
5.
Best N, Richardson S, Thomson A (2005) A comparison of Bayesian spatial models for disease mapping. Stat Methods Med Res 14:35–59PubMedCrossRef
6.
Banerjee S, Carlin BP, Gelfand AE (2004) Overview of spatial data problems. Hierarchical modeling and analysis for spatial data. Chapman & Hall/CRC, Boca Raton, pp 1–20
7.
Spiegelhalter D, Thomas A, Best N, Lunn D (2003) WinBUGS user manual: Version 1.4. MRC Biostatistics Unit, Cambridge
8.
Carlin BP, Louis TA (2000) Bayes and empirical Bayes methods for data analysis. Chapman & Hall/CRC, New York
9.
Besag J, York JC, Mollie A (1991) Bayesian image restoration, with two applications in spatial statistics (with discussion). Ann Inst Stat Math 43:1–59CrossRef
10.
Assuncao RM, Castro MSM (2004) Multiple cancer sites incidence rates estimation using a multivariate Bayesian model. Int J Epidemiol 33:508–516PubMedCrossRef
11.
Kramarova E, Plesko I, Black RJ, Obsitnikova A (1996) Improving survival for childhood cancer in Slovakia. Int J Cancer 65:594–600PubMedCrossRef
12.
Thomas A, Best N, Lunn D (2003) GeoBUGS User Manual Version 1.2. MRC Biostatistics Unit, Cambridge
13.
Spiegelhalter DJ, Best NG, Carlin BP, van der Linde A (2002) Bayesian measures of model complexity and fit (with discussion). J R Stat Soc B 64:1–34CrossRef
14.
Reynolds P, Von Behren J, Gunier RB, Goldberg DE, Hertz A, Harnly ME (2002) Childhood cancer and agricultural pesticide use: an ecologic study in California. Environ Health Perspect 110:319–324PubMedCrossRef
15.
Reynolds P, Von Behren J, Gunier RB, Goldberg DE, Hertz A, Smith DF (2003) Childhood cancer incidence rates and hazardous air pollutants in California: an exploratory analysis. Environ Health Perspect 111:663–668PubMedCrossRef
16.
Reynolds P, Von Behren J, Gunier RB, Goldberg DE, Harnly M, Hertz A (2005) Agricultural pesticide use and childhood cancer in California. Epidemiology 16:93–100PubMedCrossRef
17.
Reynolds P, Von Behren J, Gunier R, Goldberg DE, Hertz A (2005) Agricultural pesticides and lymphoproliferative childhood cancer in California. Scand J Work Environ Health 31:46–54PubMed
18.
Morris RD, Munasinghe RL (1993) Aggregation of existing geographic regions to diminish spurious variability of disease rates. Stat Med 12:1915–1929PubMed
19.
Knox EG (2005) Childhood cancers and atmospheric carcinogens. J Epidemiol Community Health 59:101–5PubMedCrossRef
20.
Maisonet M, Correa A, Misra D, Jaakkola JJK (2004) A review of the literature on the effects of ambient air pollution on fetal growth. Environ Res 95:106–15PubMedCrossRef
21.
Sram RJ, Binkova BB, Dejmek J, Bobak M (2005) Ambient air pollution and pregnancy outcomes: a review of the literature. Environ Health Perspect 113:375–382PubMedCrossRef
22.
Buffler PA, Kwan ML, Reynolds P, Urayama KY (2005) Environmental and genetic risk factors for childhood leukemia aising the evidence. Cancer Invest 23:60–75PubMed
Metadata
Title
An evaluation of spatial and multivariate covariance among childhood cancer histotypes in Texas (United States)
Authors
James A. Thompson
Susan E. Carozza
Li Zhu
Publication date
01-02-2007
Publisher
Kluwer Academic Publishers
Published in
Cancer Causes & Control / Issue 1/2007
Print ISSN: 0957-5243
Electronic ISSN: 1573-7225
DOI
https://doi.org/10.1007/s10552-006-0085-8

Other articles of this Issue 1/2007

Cancer Causes & Control 1/2007 Go to the issue

Review

Trace elements and cancer risk: a review of the epidemiologic evidence

Original Paper

Manganese superoxide dismutase polymorphism and risk of skin cancer (United States)

OriginalPaper

Serum organochlorines and breast cancer: a case–control study among African-American women

Original Paper

Which women aren’t getting mammograms and why? (United States)

Editorial

Transient caloric restriction and cancer risk (The Netherlands)

Original Paper

Meat and dairy consumption and subsequent risk of prostate cancer in a US cohort study
Webinar | 19-02-2024 | 17:30 (CET)

Keynote webinar | Spotlight on antibody–drug conjugates in cancer

Watch now

Antibody–drug conjugates (ADCs) are novel agents that have shown promise across multiple tumor types. Explore the current landscape of ADCs in breast and lung cancer with our experts, and gain insights into the mechanism of action, key clinical trials data, existing challenges, and future directions.

Dr. Véronique Diéras
Prof. Fabrice Barlesi
Developed by: Springer Medicine
Image Credits