Hostname: page-component-77c89778f8-5wvtr Total loading time: 0 Render date: 2024-07-17T11:07:41.087Z Has data issue: false hasContentIssue false

Hurdle Poisson Regression Model for Identifying Factors Related to Noncompliance and Waiting Time for Confirmatory Diagnosis in Colorectal Cancer Screening

Published online by Cambridge University Press:  01 March 2019

Hsiao-Hsuan Jen
Affiliation:
Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
Tsung-Hsi Wang
Affiliation:
Ministry of Health and Welfare, Taiwan Department of Public Health & Medical Humanities, Institute of Public Health, National Yangming University, Taipei, Taiwan
Han-Mo Chiu
Affiliation:
Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
Szu-Min Peng
Affiliation:
Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
Chen-Yang Hsu
Affiliation:
Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
Sherry Yueh-Hsia Chiu
Affiliation:
Department of Health Care Management, College of Management, Chang Gung University, Taoyuan, Taiwan Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan
Sam Li-Sheng Chen
Affiliation:
School of Oral Hygiene, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan
Amy Ming-Fang Yen
Affiliation:
School of Oral Hygiene, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan
Yi-Chia Lee
Affiliation:
Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
Hsiu-Hsi Chen
Affiliation:
Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
Jean Ching-Yuan Fann*
Affiliation:
Department of Health Industry Management, Kainan University, Taoyuan, Taiwan
*
Author for correspondence: Jean Ching-Yuan Fann, E-mail: jeanfann@mail.knu.edu.tw

Abstract

Objectives

Population-based colorectal cancer (CRC) screening programs that use a fecal immunochemical test (FIT) are often faced with a noncompliance issue and its subsequent waiting time (WT) for those FIT positives complying with confirmatory diagnosis. We aimed to identify factors associated with both of the correlated problems in the same model.

Methods

A total of 294,469 subjects, either with positive FIT test results or having a family history, collected from 2004 to 2013 were enrolled for analysis. We applied a hurdle Poisson regression model to accommodate the hurdle of compliance and also its related WT for undergoing colonoscopy while assessing factors responsible for the mixture of the two outcomes.

Results

The effect on compliance and WT varied with contextual factors, such as geographic areas, type of screening units, and level of urbanization. The hurdle score, representing the risk score in association with noncompliance, and the WT score, reflecting the rate of taking colonoscopy, were used to classify subjects into each of three groups representing the degree of compliance and the level of health awareness.

Conclusion

Our model was not only successfully applied to evaluating factors associated with the compliance and the WT distribution, but also developed into a useful assessment model for stratifying the risk and predicting whether and when screenees comply with the procedure of receiving confirmatory diagnosis given contextual factors and individual characteristics.

Type
Method
Copyright
Copyright © Cambridge University Press 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

#

Hsiao-Hsuan Jen and Tsung-Hsi Wang are equal contributors.

References

1.Cuzick, J, Edwards, R, Segnan, N (1997) Adjusting for non-compliance and contamination in randomized clinical trials. Stat Med 16, 10171029.Google Scholar
2.Duffy, SW, Cuzick, J, Tabar, L, et al. (2002) Correcting for non-compliance bias in case-control studies to evaluate cancer screening programs. Appl Stat 51, 234243.Google Scholar
3.Giorgi Rossi, P, Federici, A, Bartolozzi, F, Farchi, S, Borgia, P, Guasticchi, G (2005) Understanding non-compliance to colorectal cancer screening: A case control study, nested in a randomised trial (ISRCTN83029072). BMC Public Health; 5, 139.Google Scholar
4.Siddiqui, AA, Patel, A, Huerta, S (2006) Determinants of compliance with colonoscopy in patients with adenomatous colon polyps in a veteran population. Aliment Pharmacol Ther 24, 16231630.Google Scholar
5.Yu, D, Hopman, WM, Paterson, WG (2008) Wait time for endoscopic evaluation at a Canadian tertiary care centre: Comparison with Canadian Association of Gastroenterology targets. Can J Gastroenterol 22, 621626.Google Scholar
6.Denters, MJ, Deutekom, M, Bossuyt, PM, Fockens, P, Dekker, E (2013) Patient burden of colonoscopy after positive fecal immunochemical testing for colorectal cancer screening. Endoscopy 45, 342349.Google Scholar
7.Mullahy, J (1986) Specification and testing of some modified count data models. J Econom 33, 341365.Google Scholar
8.Winkelmann, R (2004) Health care reform and the number of doctor visits—An econometric analysis. J Appl Econom 19, 455472.Google Scholar
9.Deb, P, Norton, EC (2018) Modeling health care expenditures and use. Ann Rev Public Health 39, 489505.Google Scholar
10.Langsetmo, L, Kats, AM, Cawthon, PM, et al. (2017) The association between objectively measured physical activity and subsequent health care utilization in older men. J Gerontol A Biol Sci Med Sci doi:10.1093/gerona/glx191.Google Scholar
11.Chiu, HM, Chen, LS, Yen, MF, et al. (2015) Effectiveness of fecal immunochemical testing in reducing colorectal cancer mortality from the One Million Taiwanese Screening Program. Cancer 121, 32213229.Google Scholar
12.Neelon, B, Ghosh, P, Loebs, PF (2013) A spatial Poisson hurdle model for exploring geographic variation in emergency department visits. J R Stat Soc Ser A Stat Soc 176, 38941310.1111/j.1467-985X.2012.01039.xGoogle Scholar
13.Brown, ER, Ibrahim, JG (2003) Bayesian approaches to joint cure-rate and longitudinal models with applications to cancer vaccine trials. Biometrics 59, 686693.Google Scholar
14.Cheung, YB (2002) Zero-inflated models for regression analysis of count data: A study of growth and development. Stat Med 21, 14611469.Google Scholar
15.Conway, RW, Maxwell, WL (1962) A queuing model with state dependent service rates. J Ind Eng 12, 132136.Google Scholar
16.Consul, PC, Famoye, F (1992) Generalized Poisson regression model. Commun Stat Theory Methods 21, 89109.Google Scholar
Supplementary material: File

Jen et al. supplementary material

Jen et al. supplementary material 1

Download Jen et al. supplementary material(File)
File 272.9 KB