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”” [
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
The data for calculating sex differences in the association between night shift work and cancer risk.
Study | OR | LCI | UCI | Gender |
---|---|---|---|---|
Walasa WM (2018) | 0.95 | 0.57 | 1.58 | Female |
Talibov M (2018) | 1.03 | 0.98 | 1.08 | Female |
Papantoniou K (2016) | 1.21 | 0.89 | 1.65 | Female |
Wang P (2015) | 1.34 | 1.05 | 1.72 | Female |
Li WJ (2015) | 0.73 | 0.66 | 0.82 | Female |
Datta K (2014) | 1.51 | 0.27 | 8.52 | Female |
Rabstein S (2013) | 1.01 | 0.68 | 1.5 | Female |
Fritschi L (2013) | 1.02 | 0.71 | 1.45 | Female |
Menegaux F (2013) | 1.4 | 1.01 | 1.92 | Female |
Grundy A (2013) | 2.21 | 1.14 | 4.31 | Female |
Bhatti P (2013) | 1.02 | 0.74 | 1.42 | Female |
Hansen J (2012) | 2.1 | 1.3 | 3.2 | Female |
Hansen J (2012) | 2.1 | 1 | 4.5 | Female |
Lie JS (2011) | 1.3 | 0.9 | 1.8 | Female |
Lie JS (2006) | 2.21 | 1.1 | 4.45 | Female |
Pesch B (2010) | 2.48 | 0.62 | 9.99 | Female |
Hansen J (2001) | 1.5 | 1.3 | 1.7 | Female |
Truong (2014) | 1.32 | 1.02 | 1.72 | Female |
Kwon P (2015) | 0.88 | 0.69 | 1.12 | Female |
Davis S (2001) | 1.6 | 0.8 | 3.2 | Female |
Leary ES (2006) | 1.04 | 0.79 | 1.38 | Female |
Devore EE (2017) | 0.96 | 0.83 | 1.11 | Female |
Knutsson A (2013) | 2.02 | 1.03 | 3.95 | Female |
Carter BD (2014) | 1.27 | 1.03 | 1.56 | Female |
Poole EM (2010) | 0.8 | 0.51 | 1.23 | Female |
Viswanathan AN (2007) | 1.47 | 1.03 | 2.1 | Female |
Akerstedt T (2015) | 1.77 | 1.03 | 3.04 | Female |
Koppes LLJ (2014) | 0.87 | 0.72 | 1.05 | Female |
Natti J (2012) | 2.82 | 1.2 | 6.65 | Female |
Schernhammer ES (2006) | 1.79 | 1.06 | 3.01 | Female |
Pronk A (2010) | 0.8 | 0.5 | 1.2 | Female |
Schernhammer ES (2003) | 1.35 | 1.03 | 1.77 | Female |
Vistisen HT (2017) | 0.9 | 0.8 | 1.01 | Female |
Schernhammer ES (2013) | 1.28 | 1.07 | 1.53 | Female |
Gu FY (2015) | 1.08 | 0.98 | 1.19 | Female |
Lahti TA (2008) | 1.02 | 0.94 | 1.12 | Female |
Bai YS (2016) | 0.9 | 0.66 | 1.23 | Female |
Travis RC (2016) | 1 | 0.92 | 1.08 | Female |
Wegrzyn LR (2017) | 0.95 | 0.77 | 1.17 | Female |
Wegrzyn LR (2017) | 2.15 | 1.23 | 3.73 | Female |
Heckman CJ (2017) | 0.79 | 0.71 | 0.89 | Female |
Jorgensen JT (2017) | 0.91 | 0.77 | 1.08 | Female |
Talibov M (2018) | 1.03 | 0.98 | 1.09 | Male |
Tse LA (2017) | 1.76 | 1.07 | 2.89 | Male |
Papantoniou K (2015) | 1.38 | 1.05 | 1.81 | Male |
Parent M (2012) | 2.02 | 1.25 | 3.26 | Male |
Natti J (2012) | 1.78 | 0.8 | 4 | Male |
Lahti TA (2008) | 1.1 | 1.03 | 1.19 | Male |
Bai YS (2016) | 1.27 | 1.01 | 1.59 | Male |
Akerstedt T (2017) | 0.91 | 0.74 | 1.12 | Male |
Dickerman BA (2016) | 1 | 0.7 | 1.2 | Male |
Lin YS (2015) | 1.43 | 0.78 | 2.63 | Male |
Hammer GP (2015) | 0.93 | 0.73 | 1.18 | Male |
Gapstur SM (2014) | 1.08 | 0.95 | 1.22 | Male |
Kubo T (2011) | 1.79 | 0.57 | 5.68 | Male |
Behrens T (2017) | 3.08 | 1.67 | 5.69 | Male |
Kubo T (2006) | 3 | 1.2 | 7.7 | Male |
Lin YS (2013) | 0.83 | 0.43 | 1.6 | Male |
Yong M (2014) | 1.04 | 0.89 | 1.21 | Male |
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
The outcome of binary analysis.
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
In summary, we believe that the final conclusion of the paper after objective analysis is credible.
The authors declare no competing financial interests.
Supplementary Search Strategy and Supplementary Table 1.