Disease Markers

Disease Markers / 2019 / Article

Comment on “Sex Differences in the Association between Night Shift Work and the Risk of Cancers: A Meta-Analysis of 57 Articles”

  • Pengfei Sun | Minglei Bi | ... | Zhenyu Chen |
  •  Article ID 9263862 |
  •  Published 09 May 2019
  • | View Article

Response to: Comment on “Sex Differences in the Association between Night Shift Work and the Risk of Cancers: A Meta-Analysis of 57 Articles”

  • Wen Liu | Zhonghan Zhou | ... | Guiming Zhang |
  •  Article ID 4391957 |
  •  Published 07 Jul 2019

Letter to the Editor | Open Access

Volume 2019 |Article ID 4391957 | 4 pages | https://doi.org/10.1155/2019/4391957

Response to: Comment on “Sex Differences in the Association between Night Shift Work and the Risk of Cancers: A Meta-Analysis of 57 Articles”

Academic Editor: Fabrizia Bamonti
Received25 Mar 2019
Accepted28 Mar 2019
Published07 Jul 2019

First of all, I would like to thank Professor Zhenyu Chen for his “Comment on “Sex Differences in the Association between Night Shift Work and the Risk of Cancers: A Meta-Analysis of 57 Articles”” [1]. The answers to the questions raised by Professor Chen are as follows.

In this paper, we conducted searches in strict accordance with PRISMA and the Cochrane handbook. We have indeed given a retrieval strategy in the original article: the search terms were “night shift work” or “rotating shift work” or “night work” or “shift work” and “carcinoma” or “neoplasm” or “tumor” or “cancer”, see Supplementary Search Strategy.

We stated in our article that we tested heterogeneity between studies by statistic with indicating heterogeneity, and if no significant heterogeneity existed, a fixed effects model was adopted, otherwise a random effects model was used. For this question, we recalculated the data (Table 1) with a random effects model and verified that the outcomes in our article were correct [1], so we do not doubt the statistical methods in our study.


StudyORLCIUCIGender

Walasa WM (2018)0.950.571.58Female
Talibov M (2018)1.030.981.08Female
Papantoniou K (2016)1.210.891.65Female
Wang P (2015)1.341.051.72Female
Li WJ (2015)0.730.660.82Female
Datta K (2014)1.510.278.52Female
Rabstein S (2013)1.010.681.5Female
Fritschi L (2013)1.020.711.45Female
Menegaux F (2013)1.41.011.92Female
Grundy A (2013)2.211.144.31Female
Bhatti P (2013)1.020.741.42Female
Hansen J (2012)2.11.33.2Female
Hansen J (2012)2.114.5Female
Lie JS (2011)1.30.91.8Female
Lie JS (2006)2.211.14.45Female
Pesch B (2010)2.480.629.99Female
Hansen J (2001)1.51.31.7Female
Truong (2014)1.321.021.72Female
Kwon P (2015)0.880.691.12Female
Davis S (2001)1.60.83.2Female
Leary ES (2006)1.040.791.38Female
Devore EE (2017)0.960.831.11Female
Knutsson A (2013)2.021.033.95Female
Carter BD (2014)1.271.031.56Female
Poole EM (2010)0.80.511.23Female
Viswanathan AN (2007)1.471.032.1Female
Akerstedt T (2015)1.771.033.04Female
Koppes LLJ (2014)0.870.721.05Female
Natti J (2012)2.821.26.65Female
Schernhammer ES (2006)1.791.063.01Female
Pronk A (2010)0.80.51.2Female
Schernhammer ES (2003)1.351.031.77Female
Vistisen HT (2017)0.90.81.01Female
Schernhammer ES (2013)1.281.071.53Female
Gu FY (2015)1.080.981.19Female
Lahti TA (2008)1.020.941.12Female
Bai YS (2016)0.90.661.23Female
Travis RC (2016)10.921.08Female
Wegrzyn LR (2017)0.950.771.17Female
Wegrzyn LR (2017)2.151.233.73Female
Heckman CJ (2017)0.790.710.89Female
Jorgensen JT (2017)0.910.771.08Female
Talibov M (2018)1.030.981.09Male
Tse LA (2017)1.761.072.89Male
Papantoniou K (2015)1.381.051.81Male
Parent M (2012)2.021.253.26Male
Natti J (2012)1.780.84Male
Lahti TA (2008)1.11.031.19Male
Bai YS (2016)1.271.011.59Male
Akerstedt T (2017)0.910.741.12Male
Dickerman BA (2016)10.71.2Male
Lin YS (2015)1.430.782.63Male
Hammer GP (2015)0.930.731.18Male
Gapstur SM (2014)1.080.951.22Male
Kubo T (2011)1.790.575.68Male
Behrens T (2017)3.081.675.69Male
Kubo T (2006)31.27.7Male
Lin YS (2013)0.830.431.6Male
Yong M (2014)1.040.891.21Male

Abbreviations: OR: odds ratio; LCI: lower confidence interval; UCI: upper confidence interval.

Due to the length of the article, the specific process of binary analysis was not presented. The binary analysis of dose-response relationship was performed before applying a generalized least-squares trend (GLST) model. The original data is shown in Supplementary Table 1. ORs and 95% CIs (the highest dose group compared with the reference dose group) were extracted to conduct binary analysis. The result of binary analysis was statistically significant (OR: 1.26; 95% CI: 1.13-1.40) (Figure 1), indicating that there was a positive association between night shift work and cancer. Therefore, the next step was to explore the dose-response relationship between night shift work and cancer. In addition, there are some meta-analysis articles which also analyzed dose-response relationship between night shift work and different cancer [25]. However, they did not mention the step of binary analysis in statistical methods, so we do not think that whether or not to mention binary analysis is the reason for questioning the dose-response relationship in our study.

We have analyzed the cause of publication bias and heterogeneity in our paper. First, as we have discussed in this paper, the contour-enhanced funnel plot and the trim and fill method were used together to analyze the cause of publication bias. The result showed that most of the filled studies were outside the 10% line, which indicated that the previously verified bias might be caused by heterogeneity, not the publication bias. Second, in the process of meta-analysis, a random effects model was used to minimize the influence of heterogeneity. Third, subgroup analyses and metaregression analyses were performed to assess the potential heterogeneity sources. Many subgroups, such as fixed shift, digestive system cancer, hematological system cancer, reproductive system cancer, and lung cancer, could decrease the value of and explain part of the heterogeneity (). As we have pointed out in the discussion, we attribute the remnant heterogeneity to inconsistent definition of work schedules, unclassified occupation based on population, ethnicity, and intrinsic defect of retrospective design; thus, further prospective study in a large-scale population should be performed to explore the relationship between night shift work and cancer. Fourth, leave-one-out analyses indicated a stable positive relationship between night shift work and the risk of cancer when the value of decreases to 29.8%. Therefore, we think that the conclusion of our study is credible and the closest to the truth so far.

In summary, we believe that the final conclusion of the paper after objective analysis is credible.

Conflicts of Interest

The authors declare no competing financial interests.

Supplementary Materials

Supplementary Search Strategy and Supplementary Table 1. (Supplementary Materials)

References

  1. P. Sun, M. Bi, Y. Su, and Z. Chen, “Comment on “Sex differences in the association between night shift work and the risk of cancers: a meta-analysis of 57 articles”,” Disease Markers, vol. 2019, Article ID 9263862, 1 page, 2019. View at: Publisher Site | Google Scholar
  2. Y. Gan, L. Li, L. Zhang et al., “Association between shift work and risk of prostate cancer: a systematic review and meta-analysis of observational studies,” Carcinogenesis, vol. 39, no. 2, pp. 87–97, 2018. View at: Publisher Site | Google Scholar
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Copyright © 2019 Wen Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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