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The use of a multiple imputation method to investigate the trends in Histologic types of lung cancer in Songkhla province, Thailand, 1989–2013

Overview of attention for article published in BMC Cancer, July 2016
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Title
The use of a multiple imputation method to investigate the trends in Histologic types of lung cancer in Songkhla province, Thailand, 1989–2013
Published in
BMC Cancer, July 2016
DOI 10.1186/s12885-016-2441-8
Pubmed ID
Authors

Hutcha Sriplung, Seesai Yeesoonsang, Edward McNeil, Surichai Bilheem

Abstract

The incidence of lung cancer in many parts of the country as shown in cancer registry statistics is not decreasing. The incidence of adenocarcinoma (ADCA) in Songkhla is now higher than that of squamous cell carcinoma (SCC) in both sexes. The percentage of the unknown histologic type of lung cancer in Songkhla is around 30 %. The objective of this study is to estimate trends in incidence of the two major histologic types of lung cancer: SCC and ADCA, in Songkhla province of Thailand from 1989 to 2013. Age-standardized incidence rates (ASR) were used to compare and described the trends in both major types of cancers. Multinomial logistic regression models were used to impute unknown histological cancer types using a multiple imputation (MI) method to account for the high percentage of unknown histology. The multinomial predictive model for major types of lung cancer in Songkhla consisted of sex, age, year of diagnosis, and place of residence. After MI, the number of cases with both SCC and ADCA in both sexes increased by one-third of the number of cases with originally known histology. The increasing trends were observed in ADCA in both sexes while SCC in males was stable and in females was decreasing. A rapid increase in the incidence of ADCA was found while the incidence of SCC in males showed no significant change and it was declining in females. These results warrant an investigation into risk factors other than cigarette smoking. The number of cases has limited use when the age structure of the population under study is changing. Year of diagnosis was one of the predictors in the MI model.

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The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 17%
Student > Postgraduate 2 17%
Student > Master 2 17%
Student > Doctoral Student 1 8%
Professor 1 8%
Other 3 25%
Unknown 1 8%
Readers by discipline Count As %
Medicine and Dentistry 5 42%
Veterinary Science and Veterinary Medicine 1 8%
Agricultural and Biological Sciences 1 8%
Chemical Engineering 1 8%
Computer Science 1 8%
Other 1 8%
Unknown 2 17%