Open AccessDevelopment of a Web-Based System for Exploring Cancer Risk With Long-term Use of Drugs: Logistic Regression Approach
Author(s) -
Hsuan-Chia Yang,
Mohaimenul Islam,
PhungAnh Nguyen,
Ching-Huan Wang,
Tahmisrin Poly,
Chih-Wei Huang,
Yu-Chuan Li
Publication year - 2021
Publication title -
jmir public health and surveillance
Language(s) - English
Resource type - Journals
ISSN - 2369-2960
DOI - 10.2196/21401
Subject(s) - medicine , confounding , odds ratio , cancer , logistic regression , epidemiology , case control study , cohort , population , cohort study , nested case control study , conditional logistic regression , environmental health
Background Existing epidemiological evidence regarding the association between the long-term use of drugs and cancer risk remains controversial. Objective We aimed to have a comprehensive view of the cancer risk of the long-term use of drugs. Methods A nationwide population-based, nested, case-control study was conducted within the National Health Insurance Research Database sample cohort of 1999 to 2013 in Taiwan. We identified cases in adults aged 20 years and older who were receiving treatment for at least two months before the index date. We randomly selected control patients from the patients without a cancer diagnosis during the 15 years (1999-2013) of the study period. Case and control patients were matched 1:4 based on age, sex, and visit date. Conditional logistic regression was used to estimate the association between drug exposure and cancer risk by adjusting potential confounders such as drugs and comorbidities. Results There were 79,245 cancer cases and 316,980 matched controls included in this study. Of the 45,368 associations, there were 2419, 1302, 662, and 366 associations found statistically significant at a level of P <.05, P <.01, P <.001, and P <.0001, respectively. Benzodiazepine derivatives were associated with an increased risk of brain cancer (adjusted odds ratio [AOR] 1.379, 95% CI 1.138-1.670; P =.001). Statins were associated with a reduced risk of liver cancer (AOR 0.470, 95% CI 0.426-0.517; P <.0001) and gastric cancer (AOR 0.781, 95% CI 0.678-0.900; P <.001). Our web-based system, which collected comprehensive data of associations, contained 2 domains: (1) the drug and cancer association page and (2) the overview page. Conclusions Our web-based system provides an overview of comprehensive quantified data of drug-cancer associations. With all the quantified data visualized, the system is expected to facilitate further research on cancer risk and prevention, potentially serving as a stepping-stone to consulting and exploring associations between the long-term use of drugs and cancer risk.