TY - JOUR
T1 - Using ecological data to estimate a regression model for individual data
T2 - The association between arsenic in drinking water and incidence of skin cancer
AU - Guo, How Ran
AU - Lipsitz, Stuart R.
AU - Hu, Howard
AU - Monson, Richard R.
N1 - Funding Information:
This work was funded in part by Grant NSC-87-2314-B006-090 from the National Science Council, Republic of China, Grant 2-P30-ES-00002 from the National Institute of Environmental Health Sciences, and ARCO. We also thank the Taiwan National Cancer Registry Program, the Taiwan Provincial Department of Environmental Protection, and Dr. Chien-Jen Chen for their help in carrying out this study. No contacts with human study subjects were made, and no experimental animals were used in this study.
PY - 1998/11
Y1 - 1998/11
N2 - In ecologic studies, participants are studied by groups, and the exposure status of each group is usually represented by a single indicator, mostly the mean exposure. In this paper, we propose using multiple variables derived from dummy variables at the individual level to describe the exposure. An analysis of the association between arsenic in drinking water and skin cancer was used as an example. Well water arsenic levels and skin cancer incidence from 1980 to 1987 were assessed for 243 townships in Taiwan. We first analyzed the data using the mean arsenic concentration in each township as the only exposure variable. The second analysis used multiple variables to describe arsenic exposure; each variable denoted the percentage of wells with arsenic levels within a specific range in each township. Although the first approach did not identify associations between arsenic levels and skin cancer, the multiple-variable approach identifies a positive association at the highest arsenic exposure category (> 0.64 mg/L) in both men and women. Therefore, using multiple variables to describe an exposure in ecologic studies may facilitate a better description of the exposure status and thereby lead to more accurate risk assessment, especially when the dose- response relationship is not linear.
AB - In ecologic studies, participants are studied by groups, and the exposure status of each group is usually represented by a single indicator, mostly the mean exposure. In this paper, we propose using multiple variables derived from dummy variables at the individual level to describe the exposure. An analysis of the association between arsenic in drinking water and skin cancer was used as an example. Well water arsenic levels and skin cancer incidence from 1980 to 1987 were assessed for 243 townships in Taiwan. We first analyzed the data using the mean arsenic concentration in each township as the only exposure variable. The second analysis used multiple variables to describe arsenic exposure; each variable denoted the percentage of wells with arsenic levels within a specific range in each township. Although the first approach did not identify associations between arsenic levels and skin cancer, the multiple-variable approach identifies a positive association at the highest arsenic exposure category (> 0.64 mg/L) in both men and women. Therefore, using multiple variables to describe an exposure in ecologic studies may facilitate a better description of the exposure status and thereby lead to more accurate risk assessment, especially when the dose- response relationship is not linear.
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U2 - 10.1006/enrs.1998.3863
DO - 10.1006/enrs.1998.3863
M3 - Article
C2 - 9841806
AN - SCOPUS:0031775105
SN - 0013-9351
VL - 79
SP - 82
EP - 93
JO - Environmental Research
JF - Environmental Research
IS - 2
ER -