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

1. Introduction

Recent years have witnessed a rise in the number of people working late or night shifts in different employment sectors, such as healthcare, construction, transportation, and food preparation [1,2]. The rate of shift work can exceed 15% of the workforce in many countries of North America, continental Europe, and Australia [2], and the trend is increasing. Night shift workers not only have higher short-term safety risks because of decreased alertness [3] but also have greater long-term health risks, including for diabetes [4], obesity [5], cardiovascular disease [6], depression [7], and cancer [8]. In 2007, a report by the International Agency for Research on Cancer (IARC) classified night shift work involving circadian disruption as "probably carcinogenic to humans" based on sufficient evidence in animal experiment and limited evidence in humans [9]. Therefore, investigating the influence of night shift work has captured attention. Most previous original studies verified the effect of night shift work on cancer risk, but the results are controversial for different cancers. Some findings have indicated that night shift work is significantly associated with higher cancer risk [10-39] whereas other studies have provided insignificant evidence for this relationship [40-66], motivating further study.

There were several postulated causal mechanisms that explain how night shift work multiplies cancer risk. First, melatonin, a marker of circadian rhythms, has a fundamental impact on inhibiting carcinogenesis through antioxidation, regulation of immunity, free radical scavenging, and antiangiogenesis [67]. Generally, night shift workers have a substantially decreased melatonin level during the nighttime [68, 69]. Melatonin suppression has been reported in breast [69], prostate [70], lung [71], ovarian [67], and gastrointestinal [72] cancers. Second, the 24-hour circadian rhythm is generated via interacting feedback loops of the circadian genes in all cells of both the hypothalamic suprachiasmatic nucleus (SCN) and all peripheral tissues [73, 74]. Night shift work can induce a conflict between the endogenous circadian clock and the external shifted sleep period and feeding behavior, leading to a dampening of the gene expression rhythm (25% of circadian genes) and subsequent disordered expression of transcription and translation in these cells [73, 74]. These disturbances can interfere with cell proliferation, apoptosis, hormonal balancing, metabolism, DNA damage repair, and immune and neuroendocrine functions. Recent studies have uncovered that the disruptive expression of circadian genes especially increases the risk of cancers in the immune, skeletal, digestive, and reproductive systems in which cell proliferation, metabolism, and DNA damage repair are required to maintain daily function [74]. Overall, the mechanisms based on hormonal and molecular levels manifest that the influence of night shift work on cancer is systemic and is not limited to a specific organ. However, many previous meta-analyses have identified the association of night shift work with only one type of cancer, including breast [75-78], prostate [79-81], and colorectal [82] cancers, among others. Only one study [8] analyzed the relationship between night shift work and the risk of cancers in women. Accordingly, we aimed to classify the association between night shift work and the risk of multiple cancers from a comprehensive perspective.

Previous studies have revealed that the circadian timing system differs in the sexes, which is mediated by different neuroendocrine contexts, such as sex hormones and their receptors in SCN [16, 83, 84]. Compared with male sex, female sex has been associated with earlier timing and larger amplitude of melatonin and earlier timing and longer duration of sleep [85, 86]. After night shift work, women showed greater impaired performance in health and cognition compared with men. For example, accuracy, alertness, the amplitude of melatonin, and working memory deteriorate more in women [3, 86, 87], enabling us to understand why female was more susceptible to sleep and wake disturbances after shift work [3]. More intense response to shift work in women reminds us whether the effect of night shift work on cancers varies with different genders.

Consequently, we conducted a meta-analysis to investigate this sex difference. We also expanded upon previous meta-analyses by not only evaluating the association between night shift work and a specific cancer but also estimating whether there was a dose-response relationship between night shift work and the risk of multiple cancers.

2. Methods

2.1. Search Strategy. We conducted a comprehensive updated search through May 2018 using PubMed, Embase, and Web of Science databases. Two investigators searched for eligible English articles independently. 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". In addition, we manually reviewed the reference lists of articles for additional relevant studies.

2.2. Inclusion and Exclusion Criteria. Studies were included if they satisfied the following criteria: (i) the research was a case-control study, cohort study, or nested case-control study; (ii) the exposure of interest was night shift work, and the outcome of interest was the risk of any type of cancer; (iii) the study reported adjusted risk estimates (odds ratio, OR; relative risk, RR; hazard ratio, HR) with 95% confidence intervals (CIs) or provided sufficient data to allow calculation. Studies were excluded if they satisfied the following criteria: (i) the study did not provide sufficient data; (ii) the study mentioned recurrent cancer; (iii) when more than one article was based on the same study population, we only included the study with the largest number of cases.

2.3. Data Extraction. Data extraction was conducted independently by two authors for the following items: first author, publication year, study location, study design, number of cases, occupation, quality score, definition of exposure, participant sex, type of cancer, adjusted OR with 95% CI, adjusted covariates, and exposure assessment. As the prevalence of tumor is very low, we considered that ORs equaled RRs or HRs, providing similar risk estimates [88]. According to the definition of work schedule, we divided work schedules into rotating shift (working a regular shift schedule), fixed shift (permanent night work), and mixed (with no clear work schedule). ORs of the longest versus shortest exposure time were extracted from articles as the exposure indicator for statistical analysis. We also extracted dose information from ordinal categorical data ([greater than or equal to] 3 levels of the exposure category) for dose-response meta-analysis.

2.4. Quality Assessment. Two authors performed quality assessment using the Newcastle-Ottawa Quality Assessment Scale (NOS) [89]. The scale comprises a total of 9 points on the three parts of the NOS, including participant selection (0-4 points), comparability (0-2 points), and exposure or outcome assessment (0-3 points). Scores of [greater than or equal to] 7 indicate a high quality.

2.5. Statistical Analysis. All statistical analyses were performed using Stata version 12.0 (StataCorp, College Station, TX, USA). We preferentially extracted adjusted ORs from original articles to evaluate the association between night shift work and cancer risk. If there were no adjusted ORs for specific subgroup analyses, a number of cases and participants would be extracted to calculate OR. The inverse variance method was used to combine ORs. If [I.sup.2] for the heterogeneity test was [less than or equal to] 50%, a fixed effects model was adopted to pool ORs; otherwise, a random effects model was selected. To explore potential heterogeneity, we performed subgroup analyses, metaregression analyses, and sensitivity analyses. One subgroup analysis was the classification of work schedules; we used a random effects model to evaluate the effect size for cancer on different work schedules [80]. To confirm the stability of results, a sensitivity analysis was conducted by omitting one study and then recalculating the rest of studies. The leave-one-out analysis was used to examine the weight of influence of each study on pooled OR [90].

A generalized least-squares trend (GLST) model was used to estimate the overall dose-response relationship of night shift work and the risk of cancer by computing risk estimates for different ordinal levels of night shift work. There were at least three ordinal levels of the exposure category in each study. The midpoint of the upper and lower boundaries of each level was considered the average exposure. The upper boundary of the highest level was considered the same as the adjacent category if it was not provided [76]. We used a two-stage random effect model to evaluate the linearity between night shift work and the risk of cancer.

Potential publication bias was estimated with the Begg funnel plot. Furthermore, the contour-enhanced funnel plot and the trim and fill method were used together to analyze the cause of bias. All reported P values were two-sided, and statistical significance was set at P [less than or equal to] 0.05.

3. Results

3.1. Study Selection. Figure 1 illustrates the results of the literature search and the process of selection. A total of 753 articles were initially identified from PubMed, Embase, and Web of Science databases. After screening based on the title and abstract, 143 articles were selected for full-text assessment; 53 studies were eligible for the final analysis. We also retrieved four relevant articles from the reference lists. Finally, 57 studies [10-66] were included in the analysis of the association of night shift work with risk of cancer.

3.2. Study Characteristics. The characteristics of the abovementioned studies are summarized in Table 1. Fifty-seven articles were included in this meta-analysis, including 21 case-control studies, 6 nested case-control studies, and 30 cohort studies. One article [10] included two cohorts, the Nurses' Health Study (NHS) and Nurses' Health Study II (NHS II). Therefore, a total of 58 studies were finally enrolled, involving 225,976 cases and 5,143,838 participants. We extracted information about sex in each article, except these articles that did not include classification by sex [12, 15, 48], to analyze the effect of night shift work on cancers in men and women separately. Several studies [20, 28, 43, 44, 53] analyzed the association between night shift work and different kinds of cancers. We also classified all kinds of cancer analyzed in the included studies into seven categories, including digestive system, hematological system, prostate, breast, reproductive system, lung, and skin cancers. A total 27 articles were from Europe, 11 from Asia, 17 from North America, and 3 from Australia. Most studies were based on a population with no specific occupation whereas other studies involved participants with a specific occupation, such as nurses, textile workers, women in the military, and pilots. According to the definition of night shift work, work schedules were classified as rotating shift (29 studies), fixed shift (9 studies), or mixed shift (27 studies). In fact, a cross section of studies described different work schedules, which we extracted simultaneously. Exposure assessment was performed using a questionnaire, interview, or databases. A total 43 studies were adjusted for more than four confounders and 15 studies for fewer than four confounders. The average NOS score was 7.2, and scores ranged from 4 to 8.

3.3. Association between Night Shift Work and the Risk of Various Cancers. The random effects model was used to pool the ORs, indicating the relationship between night shift work and risk of multiple cancers. The pooled OR was 1.15 (95% CI = 1.08-1.22, P < 0.001), with high heterogeneity ([I.sup.2] = 76.2%, P < 0.001) (shown in online Figure S1). We observed that night shift work could increase the risk of cancers both in men (OR = 1.14, 95% CI = 1.05-1.25, P = 0.003) and women (OR = 1.12, 95% CI = 1.04-1.20, P = 0.002), with high heterogeneity in men ([I.sup.2] = 78.3%, P < 0.001) and women ([I.sup.2] = 62.3%, P < 0.001) (Figure 2). In cancers that can occur in both men and women (i.e., excluding breast, prostate, and reproductive system cancers, such as ovarian, endometrial, and testis cancer), night shift work demonstrated a positive association with the risk of cancer in men (OR= 1.09, 95% CI = 1.01-1.17, P = 0.031) but not in women (OR= 1.02, 95% CI = 0.94-1.12, P = 0.637).

3.4. Subgroup Analysis and Metaregression Analysis. To explore the source of potential heterogeneity and assess the influence of specific characteristics of night shift work and cancer risk, we conducted subgroup analyses, including for shift schedule, type of cancer, study region, participant occupation, study design, exposure assessment, number of adjusted variables, and NOS score (Table 2). Among the different work schedules, rotating shift work (OR = 1.14, 95% CI = 1.04-1.24) increased cancer risk whereas fixed shift work (OR = 1.09, 95% CI = 0.90-1.31) did not. A significant relationship was observed for breast cancer (OR = 1.22, 95% CI = 1.08-1.38), prostate cancer (OR = 1.26, 95% CI = 1.05-1.52), and digestive system cancer (OR = 1.15, 95% CI = 1.01-1.32). With respect to region, studies in Europe (OR = 1.18, 95% CI = 1.10-1.28) and North America (OR = 1.16, 95% CI = 1.04-1.31) showed higher ORs than those in Asia and Australia. When stratified by study design, a positive association was revealed for case-control studies (OR = 1.28, 95% CI = 1.15-1.42) and cohort studies (OR = 1.07, 95% CI = 1.00-1.15) but not nested case-control studies. For different occupations, studies based on populations in which no specific occupation was classified showed higher risk estimates (OR = 1.17, 95% CI = 1.10-1.25). Nurses (OR= 1.17, 95% CI = 1.02-1.35) had elevated cancer risk, but participants with industrial occupations did not. The interview group, which had more comprehensive information collection, presented a higher risk estimate (OR= 1.32, 95% CI = 1.17-1.49) than studies using questionnaires and databases to collect information. Regarding NOS score, studies with high-quality scores were associated with increased risk (OR = 1.14, 95% CI = 1.081.21) and decreased heterogeneity ([I.sup.2] = 61.6%, P [less than or equal to] 0.001) whereas those with low-quality scores did not show this positive relationship and had high heterogeneity ([I.sup.2] = 86.9%, P [less than or equal to] 0.001). Additionally, increased risk was present in studies with more than four adjusted variables. Studies with fewer than four adjusted variables showed no elevated risk of cancer, with high heterogeneity ([I.sup.2] = 82.7%, P [less than or equal to] 0.001). We performed metaregression analyses to assess the potential heterogeneity sources (Table 2); however, the results showed that none of the subgroups generated the potential heterogeneity.

3.5. Sensitivity Analysis. Sensitivity analysis showed that the pooled ORs were stable and did not identify the origins of heterogeneity. After omitting 19 studies by the leave-one-out analyses, we found a stable positive relationship (OR = 1.06, 95% CI = 1.02-1.11) between night shift work and the risk of cancer, with low heterogeneity ([I.sup.2] = 29.8%). It was found that none of the individual studies could powerfully change the positive result.

3.6. Dose-Response Analysis of Night Shift Work and the Risk of Cancers. Twenty-nine studies, which involved at least three levels of night shift exposure, were included in the dose-response analysis of night shift work and cancer risk. We used the two-stage random effects model to evaluate the linearity relationship (P < 0.001). For every 5 years of night shift work, the risk of cancer increased by 3.2% (OR = 1.032, 95% CI = 1.013-1.051) (Figure 3).

3.7. Publication Bias. The Begg test showed a potential publication bias among all enrolled studies (P = 0.001). After combining the trim and fill method and contour-enhanced funnel plot, the result showed that most of the filled studies were outside the 10% line, which indicated that the previously verified bias was mostly caused by the high heterogeneity, not the publication bias (Figure 4). The filled risk estimate was still positive, as before (OR= 1.06, 95% CI= 1.01-1.11), such that the pooled OR was stable in our study.

4. Discussion

This meta-analysis, consisting of 58 studies with 225,976 cases and 5,143,838 participants, revealed a positive relationship between night shift work and the risk of cancer. Compared with people who never experience working late, the risk of cancer was found to be increased by 15% in all shift workers, by 12% in female workers and 14% in male workers. A linear dose-response relationship showed a positive gradient of cancer risk with cumulative years of night shift work; for every 5 years of night shift work, cancer risk increased by 3.2%. Yuan et al. [8] confirmed that night shift work elevates the risk of multiple cancers in women, especially breast cancer. Several meta-analyses [79-81] have verified the positive relationship between night shift work and risk of prostate cancer. We obtained the same result, i.e., that long-time night shift work was associated with a higher risk of breast cancer (OR= 1.22, 95% CI = 1.08-1.38), prostate cancer (OR = 1.26, 95% CI = 1.05-1.52), and cancers in women (OR = 1.12, 95% CI = 1.04-1.20). As far as we know, this is the first meta-analysis to comprehensively explore the effect of night shift work on multiple cancers in the whole population and separately in men and women.

Tissue-specific functions and output circadian rhythms are related to the different cell-based clock genes in periphery [83]. To exclude the tissue-specific influence, we only analyzed cancers that can occur in both men and women and found that night shift work increased cancer risk in men (OR = 1.09, 95% CI = 1.02-1.17) but not in women (OR = 1.02, 95% CI = 0.94-1.12). One meta-analysis involving colorectal cancer [82] demonstrated that night shift work could increase the risk of this type of cancer in women. However, we did not find a risk relationship for either men or women based on more studies of colorectal cancer (data not shown). Although there were considerably fewer articles on other cancers than on breast and prostate cancers, the low heterogeneity for digestive system cancer (P = 0.081, [I.sup.2] = 40.2%), hematological system cancer (P = 0.066, [I.sup.2] = 54.7%), and lung cancer (P = 0.078, [I.sup.2] = 49.5%) presented a more reliable conclusion. Previous studies have suggested that a common mechanism might be shared among hormone-dependent cancers including prostate cancer in men and breast and ovarian cancers in women [91, 92]. Melatonin has been implicated in antiproliferation effects in vivo and in vitro, and an elevated PSA level has been strongly connected with night shift work [91, 93], which could illustrate why breast and prostate cancers are more sensitive to night shift work than other common cancers.

One meta-analysis [8] analyzing the influence of night shift work on the risk of multiple cancers in women included up to 61 articles. Although light at night (LAN) [94] has been considered one of the risk factors for cancer, studies describing LAN were not included in our meta-analysis if the analysis of LAN was not connected to night shift work. We also excluded cross-sectional studies or studies only describing sleep duration. Therefore, the exposure of all 58 studies in our article was night shift work, which could decrease the clinical heterogeneity, making a more reliable result possible. Whereas the definition of night shift work differs largely among studies, we further divided work schedules into fixed shift, rotating shift, and mixed schedule, to reduce heterogeneity. Consistent with Mancio et al. [79], rotating shift workers had evidence of a higher risk of cancer whereas no association was observed in fixed shift workers. One speculation was that constant and rapid changing work times among rotating shift workers may necessitate a severe circadian disruption, causing failed adaptation, whereas fixed night shift workers had sufficient time to adapt almost completely to the shift cycle [95]. Consequently, rotating shift work resulted in a more profound effect on carcinogenesis through severe circadian disruption.

Our subgroup analyses also uncovered other meaningful results. One finding demonstrated that prostate, breast, and digestive system cancers

were connected with night shift work whereas night shift work did not raise the risk of cancers of the hematological system, reproductive system, lung, and skin. In addition, Yuan et al. [8] found that female night shift workers in Europe and North America have greater risk of cancer than women in Asia and Australia. Based on the whole population, our results were consistent with those findings and indicate that the association of cancer risk with night shift work is not largely different between men and women. The different associations might be attributed to the limitations of the study populations. Many studies from Asia were limited to industrial workers whereas most studies from Europe and North America were based on the general population. However, the contrasting results might essentially be owing to differences in ethnicity or sensitivity. More specific exploration based on ethnicity is indispensable in future research. Moreover, studies based on the general population showed a higher cancer risk than those among nurses and industrial workers, and the pooled ORs in population could be better generalized to the overall population. Cohort studies, meaning higher-quality study designs, also indicated the same positive association between night shift work and the risk of cancer. Accordingly, the higher pooled ORs in these subgroups could confirm this association more powerfully.

Through analyzing Q and [I.sup.2] values, we found a significant heterogeneity among the studies included in this article (P < 0.001, [I.sup.2] = 76.2%); therefore, we used a random effects model to decrease the heterogeneity. After subgroup analyses, we found that fixed shift work, digestive system cancer, reproductive system cancer, unclassified occupation, interview data collection, and high-quality studies were related to less heterogeneity, representing more reliable results. However, all P values in the metaregression analyses did not reflect a statistical difference, such that heterogeneity could not be explained by metaregression analysis. One-by-one-omitted sensitivity and leave-one-out analyses showed that the pooled risk estimates were stable and positive, even when 19 studies were omitted, until heterogeneity was reduced to 29.8%. Although we did not find an obvious source of heterogeneity, the specific subgroup analyses, such as a more uniform definition of work schedules, unclassified occupation based on population, more detailed interviews, and high-quality studies, could decrease the potential heterogeneity.

Theoretical biological mechanisms for the positive relationship between night shift work and cancer risk are complex. First, night shift work and LAN could disturb the normal synchrony with the day-night rhythm and sleeping and diet patterns and bring about circadian disruption, which could suppress the secretion of melatonin [80]. Melatonin plays a pivotal role in inhibiting carcinogenesis through antioxidation, regulation of the immune system, free radical scavenging, and antiangiogenesis [67]. Decreased melatonin levels might disturb its antiproliferation effects on prostate cancer cells both in vivo and in vitro [93] and induce continuous secretion of estrogen, to increase the risk of breast cancer [77]. Second, night shiftwork can reduce the exposure time to sunlight and subsequently decrease vitamin D levels [80]. Studies have supported the inverse association between circulating vitamin D levels and risk of breast [96], colorectal [97], and prostate cancer [98]. Third, from a molecular perspective, night shift work could constitute a disruption of the feedback loops of circadian genes and lead to subsequent disordered expression of transcription and translation in all cells, which could pose a threat to cell proliferation, metabolism, regulation of the immune system, and DNA damage repair, causing carcinogenesis [73, 74].

To the best of our knowledge, this meta-analysis is the first and most comprehensive of its kind to identify the association between night shift work and risk of cancer from the perspective of diverse cancers and by sex. There were several strengths in our meta-analysis. First, we enrolled a large number of articles, even using strict inclusion criteria. The massive study population could enhance statistical power and ensure more accurate risk estimation. Second, a linear dose-response analysis was used to quantify the association between accumulative years of night shift work and cancer risk. Third, we classified work schedules and found that rotating shift work could increase cancer risk whereas fixed shift work could not. The classification of work schedules could decrease clinical heterogeneity to make the results more reliable. Fourth, 34 of 57 studies were carried out among the general population, such that the pooled OR could be better extended to the entire population. Our meta-analysis also had several limitations. First, a significantly high heterogeneity was discovered. We observed significant variability in the study design, risk estimates, study population, definition of night shift work, and exposure assessment. Each of these aspects may generate heterogeneity. Even though many statistical methods were used, we still had trouble finding an obvious source of potential heterogeneity; therefore, the conclusions reached in our meta-analysis should be interpreted with caution. Second, the lack of a consistent definition of night shift work may lead to a certain degree of misclassification and result in a dilution of the pooled OR [8]. Third, given that most night shift workers tend to have lower socioeconomic status, a lower uptake of screening and response rates may result in underestimation of the pooled risk estimates. Finally, studies using interviews could actively collect more detailed information, presenting a stronger risk compared with studies using questionnaire- and database-based data collection. In addition, there is inherent recall bias when conducting interviews or questionnaires. Hence, different exposure assessment methods and studies with lower quality or a less number of adjusted variables can cause information bias.

In conclusion, our meta-analysis identified a positive relationship between night shift work and cancer risk, using a comprehensive perspective of common cancers. We revealed that the risk of cancer increases cumulatively by 3.2% for every 5 years of night shift work. Moreover, we found no difference between men and women in the association between night shift work and the risk of cancer. Overall, on the grounds that public health is adversely affected by night shift work and its prevalence is on the rise, it is indispensable to develop shift work schedules with the aim of reducing cancer risk. Our meta-analysis does not merely increase public awareness, it also supports the recommendation for regular cancer screening among night shift workers.

https://doi.org/10.1155/2018/7925219

Conflicts of Interest

The authors declare no competing financial interests.

Acknowledgments

This study was partly funded by the National Natural Science Foundation of China (Grant Nos. NSFC 81502195 and NSFC 81672512) and Medicine and Health Science Technology Development Project of Shandong Province (No. 2016WS0258). We also thank Analisa Avila, ELS, of Liwen Bianji, Edanz Group China (http://www.liwenbianji.cn/ac), for editing the English text of a draft of this manuscript.

Supplementary Materials

Figure S1: forest plots of studies describing the association between night shift work and the risk of multiple cancers. [I.sup.2]: the indicator for judging the degree of heterogeneity; OR: odds ratio; CI: confidence interval. The squares and horizontal lines represent the study-specific OR and 95% CI. The diamond represents the pooled OR and 95% CI. (Supplementary Materials)

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Wen Liu (iD), Zhonghan Zhou (iD), Dahai Dong (iD), Lijiang Sun (iD), and Guiming Zhang (iD)

Department of Urology, The Affiliated Hospital of Qingdao University, Qingdao, China

Correspondence should be addressed to Lijiang Sun; slijiang999@126.com and Guiming Zhang; zhangguiming9@126.com

Received 10 July 2018; Accepted 25 September 2018; Published 26 November 2018

Academic Editor: Fabrizia Bamonti

Caption: Figure 1: Flow chart of identification of relevant studies.

Caption: Figure 3: Dose-response relation plots between night shift work and the risk of multiple cancers. OR: odds ratio. Solid line represents the estimated OR by years, and the dotted lines represent the low and upper limits of 95% CIs separately.

Caption: Figure 4: The contour-enhanced funnel plot of studies assessing the association between night shift work and the risk of cancers after using the trim and fill method.
Table 1: Characteristics of studies included in meta-analysis.

Study (year)          Region     Study design    No. of    Occupation
                                                  cases

Walasa et al.        Australia   Case-control      350         NA
(2018) [40]                          study

Talibov et al.        Europe     Case-control    131,594       NA
(2018) [41]                          study

Tse et al.             Asia      Case-control      431         NA
(2017) [11]                          study

Heckman et al.         North     Cohort study     4854       Nurses
(2017) [44]           America

Papantoniou           Europe     Case-control     1626         NA
(2017) [12]                          study

Devore et al.          North     Cohort study     3014       Nurses
(2017) [45]           America

Vistisen et al.       Europe     Cohort study     1245         NA
(2017) [42]

Wegrzyn et al.         North     Cohort study     5971       Nurses
(2017) (a) [10]       America

Wegrzyn et al.         North     Cohort study     3570       Nurses
(2017) (a) [10]       America

Jorgensen et al.      Europe     Cohort study      945       Nurses
(2017) [43]

Behrens et al.        Europe     Cohort study      76          NA
(2017) [13]

Akerstedt et al.      Europe     Cohort study      454         NA
(2017) [46]

Dickerman et al.      Europe     Cohort study      602         NA
(2016) [49]

Papantoniou et al.    Europe     Case-control     1708         NA
(2016) [47]                          study

Gyarmati et al.       Europe     Case-control      374         NA
(2016) [48]                          study

Costas et al.         Europe     Case-control      321         NA
(2016) [15]                          study

Bai et al.             Asia      Cohort study     1251         NA
(2016) [16]

Travis et al.         Europe     Cohort study     4809         NA
(2016) [14]

Gu et al.              North     Cohort study     5413       Nurses
(2015) [20]           America

Wang P. et al.         Asia      Case-control      712         NA
(2015) [17]                          study

Papantoniou et al.    Europe     Case-control     1115         NA
(2015) [18]                          study

Li et al.              Asia      Nested case-     1709      Industry
(2015) [51]                      control study

Lin et al.             Asia      Cohort study      94          NA
(2015) [50]

Kwon et al.            Asia      Nested case-     1451      Industry
(2015) [52]                      control study

Akerstedt et al.      Europe     Cohort study      463         NA
(2015) [21]

Hammer et al.         Europe     Cohort study      337      Industry
(2015) [19]

Gapstur et al.         North     Cohort study     4974         NA
(2014) [55]           America

Koppes et al.        Australia   Cohort study     2531         NA
(2014) [54]

Carter et al.          North     Cohort study     1289         NA
(2014) [23]           America

Yong et al.           Europe     Cohort study    10,873     Industry
(2014) [53]

Datta et al.           Asia      Case-control      50       Industry
(2014) [56]                          study

Truong et al.         Europe     Case-control     1126         NA
(2014) [22]                          study

Knutsson et al.       Europe     Cohort study      94          NA
(2013) [26]

Rabstein et al.       Europe     Case-control      857         NA
(2013) [57]                          study

Fritschi et al.      Australia   Case-control     1205         NA
(2013) [59]                          study

Schernhammer           North     Cohort study     1455       Nurses
et al. (2013) [24]    America

Menegaux et al.                  Case-control     1232         NA
(2013) [25]                          study

Grundy et al.          North     Case-control     1134         NA
(2013) [27]           America        study

Bhatti et al.          North     Case-control     1490         NA
(2013) [60]           America        study

Lin et al.             Asia      Cohort study      127      Industry
(2013) [58]

Hansen and            Europe     Nested case-      267       Nurses
Stevens (2012)                   control study
[30]

Hansen and            Europe     Nested case-      132      Industry
Lassen (2012) [31]               control study

Parent et al.          North     Case-control     3137         NA
(2012) [28]           America        study

Natti et al.          Europe     Cohort study      99          NA
(2012) [29]

Kubo et al.            Asia      Cohort study      17       Industry
(2011) [32]

Lie et al.            Europe     Nested case-      699       Nurses
(2011) [62]                      control study

Poole et al.           North     Cohort study      718       Nurses
(2011) [61]           America

Pesch et al.          Europe     Case-control      753         NA
(2010) [64]                          study

Pronk et al.           Asia      Cohort study      349         NA
(2010) [63]

Lahti et al.          Europe     Cohort study     6307         NA
(2008) [33]

Viswanathan et al.     North     Cohort study      515       Nurses
(2007) [34]           America

Lie et al.            Europe     Nested case-      537       Nurses
(2006) [36]                      control study

O'Leary et al.         North     Case-control      487         NA
(2006) [65]           America        study

Schernhammer           North     Cohort study     1352       Nurses
et al. (2006) [35]    America

Kubo et al.            Asia      Cohort study      31       Industry
(2006) [37]

Schernhammer           North     Cohort study      602       Nurses
et al. (2003) [38]    America

Davis et al.           North     Case-control      767         NA
(2001) [66]           America        study

Hansen                Europe     Case-control     6281         NA
(2001) [39]                          study

Study (year)             Exposure        Adjusted OR
                                          (95% CI)

Walasa et al.          Night, never         0.95
(2018) [40]           vs. 7.5+ years     (0.57-1.58)

Talibov et al.       Rotating, never        1.033
(2018) [41]           vs. 20+ years     (0.984-1.084)

Tse et al.             Night, never         1.76
(2017) [11]              vs. ever        (1.07-2.89)

Heckman et al.       Rotating, never        0.794
(2017) [44]           vs. 10+ years     (0.711-0.888)

Papantoniou             Night and           1.28
(2017) [12]          rotating, never     (1.06-1.56)
                      vs. 15+ years

Devore et al.        Rotating, never        0.96
(2017) [45]           vs. 10+ years     (0.83, 1.11)

Vistisen et al.        Night, never         0.90
(2017) [42]              vs. ever        (0.80-1.01)

Wegrzyn et al.       Rotating, never        0.95
(2017) (a) [10]       vs. 30+ years      (0.77-1.17)

Wegrzyn et al.       Rotating, never        2.15
(2017) (a) [10]       vs. 20+ years      (1.23-3.73)

Jorgensen et al.     Rotating, night,       0.91
(2017) [43]           and rotating,      (0.77-1.08)
                      never vs. ever

Behrens et al.       Rotating, never        3.08
(2017) [13]           vs. 20+ years      (1.67-5.69)

Akerstedt et al.       Night, never         0.91
(2017) [46]              vs. ever        (0.74-1.12)

Dickerman et al.     Rotating, never    1.0 (0.7-1.2)
(2016) [49]              vs. ever

Papantoniou et al.      Night and           1.21
(2016) [47]          rotating, never     (0.89-1.65)
                      vs. 15+ years

Gyarmati et al.         Night and       1.1 (0.8-1.6)
(2016) [48]          rotating, never
                      vs. 20+ years

Costas et al.          Night, never         1.77
(2016) [15]           vs. 20+ years      (1.14-2.74)

Bai et al.             Night, never         1.08
(2016) [16]           vs. 20+ years      (0.90-1.29)

Travis et al.          Night, never         1.00
(2016) [14]              vs. ever        (0.92-1.08)

Gu et al.            Rotating, never        1.08
(2015) [20]           vs. 15+ years      (0.98-1.19)

Wang P. et al.         Night, never         1.34
(2015) [17]              vs. ever        (1.05-1.72)

Papantoniou et al.      Night and           1.38
(2015) [18]          rotating, never     (1.05-1.81)
                      vs. 28+ years

Li et al.              Night, never         0.73
(2015) [51]              vs. ever        (0.66-0.82)

Lin et al.           Rotating, never        1.43
(2015) [50]              vs. ever        (0.78-2.63)

Kwon et al.          Rotating, never        0.88
(2015) [52]          vs. 30.6+ years     (0.69-1.12)

Akerstedt et al.       Night, never         1.77
(2015) [21]           vs. 21+ years      (1.03-3.04)

Hammer et al.        Rotating, never        0.93
(2015) [19]              vs. ever        (0.73-1.18)

Gapstur et al.       Rotating, night,       1.08
(2014) [55]            and evening,      (0.95-1.22)
                      never vs. ever

Koppes et al.          Night, never         0.87
(2014) [54]              vs. ever        (0.72-1.05)

Carter et al.        Rotating, night,       1.27
(2014) [23]            and evening,      (1.03-1.56)
                      never vs. ever

Yong et al.          Rotating, never        1.04
(2014) [53]              vs. ever        (0.89-1.21)

Datta et al.           Night, never         1.51
(2014) [56]              vs. ever        (0.27-8.52)

Truong et al.          Night, never         1.32
(2014) [22]              vs. ever        (1.02-1.72)

Knutsson et al.        Night, never         2.02
(2013) [26]              vs. ever        (1.03-3.95)

Rabstein et al.        Night, never         1.01
(2013) [57]              vs. ever        (0.68-1.5)

Fritschi et al.        Night, never         1.02
(2013) [59]           vs. 20+ years      (0.71-1.45)

Schernhammer         Rotating, never        1.28
et al. (2013) [24]    vs. 15+ years      (1.07-1.53)

Menegaux et al.        Night, never         1.40
(2013) [25]           vs. 4.5+ years     (1.01-1.92)

Grundy et al.          Mixed, never         2.21
(2013) [27]           vs. 30+ years      (1.14-4.31)

Bhatti et al.          Night, never         1.02
(2013) [60]            vs. 7+ years      (0.74-1.42)

Lin et al.           Rotating, never        0.83
(2013) [58]              vs. ever        (0.43-1.60)

Hansen and              Night and       2.1 (1.3-3.2)
Stevens (2012)        evening, never
[30]                  vs. 20+ years

Hansen and            Evening, never    2.1 (1.0-4.5)
Lassen (2012) [31]    vs. 15+ years

Parent et al.          Night, never         2.016
(2012) [28]           vs. 10+ years     (1.246-3.261)

Natti et al.           Night, never         2.148
(2012) [29]              vs. ever       (1.178-3.917)

Kubo et al.          Rotating, never        1.79
(2011) [32]              vs. ever        (0.57-5.68)

Lie et al.             Night, never          1.3
(2011) [62]           vs. 12+ years       (0.9-1.8)

Poole et al.         Rotating, never         0.8
(2011) [61]           vs. 20+ years      (0.51-1.23)

Pesch et al.           Night, never         2.48
(2010) [64]           vs. 20+ years      (0.62-9.99)

Pronk et al.           Mixed, never          0.8
(2010) [63]           vs. 17+ years       (0.5-1.2)

Lahti et al.         Rotating, never        1.07
(2008) [33]              vs. ever        (1.01-1.13)

Viswanathan et al.   Rotating, never        1.47
(2007) [34]           vs. 20+ years      (1.03-2.10)

Lie et al.           Rotating, never        2.21
(2006) [36]           vs. 30+ years      (1.10-4.45)

O'Leary et al.         Mixed, never         1.04
(2006) [65]              vs. ever        (0.79-1.38)

Schernhammer         Rotating, never        1.79
et al. (2006) [35]    vs. 20+ years      (1.06-3.01)

Kubo et al.          Rotating, never         3.0
(2006) [37]              vs. ever         (1.2-7.7)

Schernhammer         Rotating, never        1.35
et al. (2003) [38]    vs. 15+ years      (1.03-1.77)

Davis et al.           Mixed, never          1.6
(2001) [66]            vs. 3+ years       (0.8-3.2)

Hansen                 Night, never          1.5
(2001) [39]           vs. 0.6+ years      (1.3-1.7)

Study (year)         Type of cancer

Walasa et al.          Colorectal
(2018) [40]              cancer

Talibov et al.        Hematological
(2018) [41]           system cancer

Tse et al.           Prostate cancer
(2017) [11]

Heckman et al.         Skin cancer
(2017) [44]

Papantoniou            Colorectal
(2017) [12]              cancer

Devore et al.          Colorectal
(2017) [45]              adenoma

Vistisen et al.       Breast cancer
(2017) [42]

Wegrzyn et al.        Breast cancer
(2017) (a) [10]

Wegrzyn et al.        Breast cancer
(2017) (a) [10]

Jorgensen et al.      Unclassified
(2017) [43]              cancer

Behrens et al.       Prostate cancer
(2017) [13]

Akerstedt et al.     Prostate cancer
(2017) [46]

Dickerman et al.     Prostate cancer
(2016) [49]

Papantoniou et al.    Breast cancer
(2016) [47]

Gyarmati et al.      Stomach cancer
(2016) [48]

Costas et al.            Chronic
(2016) [15]            lymphocytic
                        leukemia

Bai et al.            Unclassified
(2016) [16]              cancer

Travis et al.         Breast cancer
(2016) [14]

Gu et al.             Unclassified
(2015) [20]              cancer

Wang P. et al.        Breast cancer
(2015) [17]

Papantoniou et al.   Prostate cancer
(2015) [18]

Li et al.             Breast cancer
(2015) [51]

Lin et al.            Biliary tract
(2015) [50]              cancer

Kwon et al.            Lung cancer
(2015) [52]

Akerstedt et al.      Breast cancer
(2015) [21]

Hammer et al.        Prostate cancer
(2015) [19]

Gapstur et al.       Prostate cancer
(2014) [55]

Koppes et al.         Breast cancer
(2014) [54]

Carter et al.        Ovarian cancer
(2014) [23]

Yong et al.           Unclassified
(2014) [53]              cancer

Datta et al.          Breast cancer
(2014) [56]

Truong et al.         Breast cancer
(2014) [22]

Knutsson et al.       Breast cancer
(2013) [26]

Rabstein et al.       Breast cancer
(2013) [57]

Fritschi et al.       Breast cancer
(2013) [59]

Schernhammer           Lung cancer
et al. (2013) [24]

Menegaux et al.       Breast cancer
(2013) [25]

Grundy et al.         Breast cancer
(2013) [27]

Bhatti et al.        Ovarian cancer
(2013) [60]

Lin et al.             Pancreatic
(2013) [58]              cancer

Hansen and            Breast cancer
Stevens (2012)
[30]

Hansen and            Breast cancer
Lassen (2012) [31]

Parent et al.         Unclassified
(2012) [28]              cancer

Natti et al.          Unclassified
(2012) [29]              cancer

Kubo et al.          Prostate cancer
(2011) [32]

Lie et al.            Breast cancer
(2011) [62]

Poole et al.         Ovarian cancer
(2011) [61]

Pesch et al.          Breast cancer
(2010) [64]

Pronk et al.          Breast cancer
(2010) [63]

Lahti et al.           Non-Hodgkin
(2008) [33]             lymphoma

Viswanathan et al.     Endometrial
(2007) [34]              cancer

Lie et al.            Breast cancer
(2006) [36]

O'Leary et al.        Breast cancer
(2006) [65]

Schernhammer          Breast cancer
et al. (2006) [35]

Kubo et al.          Prostate cancer
(2006) [37]

Schernhammer           Colorectal
et al. (2003) [38]       cancer

Davis et al.          Breast cancer
(2001) [66]

Hansen                Breast cancer
(2001) [39]

Study (year)                    Adjusted item

Walasa et al.            Age group, education level,
(2018) [40]            socioeconomic status, lifetime
                       cigarette smoking, and alcohol
                             intake 10 years ago

Talibov et al.               Cumulative benzene,
(2018) [41]                     formaldehyde,
                           and ionizing radiation

Tse et al.            Age at interview, marital status,
(2017) [11]              unemployment status, family
                          prostate cancer history,
                       consumption of deep fried food,
                           consumption of pickled
                        vegetable, green tea drinking
                              habits, and cbpai

Heckman et al.          Years of shift work, hours of
(2017) [44]             sleep, sleep adequacy, sleepy
                      days per week, snoring, restless
                      legs syndrome, family history of
                        melanoma, hours spent in sun,
                         number of severe sunburns,
                        sunburn severity, artificial
                       tanning frequency, annual uv at
                       residence, moles on lower legs,
                            natural hair color in
                        adolescence, marital status,
                       financial status, BMI, physical
                          activity, smoking status,
                             menopausal status,
                        postmenopausal hormones, oral
                       contraceptive use, and healthy
                                eating index

Papantoniou            Age, centre, educational level,
(2017) [12]          sex, history of colorectal cancer,
                         BMI, smoking, leisure time
                         physical activity, alcohol
                          consumption, total energy
                        intake in grams/day, all red
                           meat consumption, sleep
                         duration, bisphosphonates,
                                 and NSAIDs

Devore et al.          Age, time period of first lower
(2017) [45]                 endoscopy, reason for
                        endoscopy, family history of
                        cancer, height, BMI, physical
                         activity, smoking, alcohol
                         intake, menopausal status,
                        menopausal hormone use, oral
                       contraceptive use, multivitamin
                         use, total calcium intake,
                         vitamin d intake, red meat
                           intake, NSAIDs use, and
                          predicted vitamin D score

Vistisen et al.       Calendar year, age, age at birth
(2017) [42]           of first child, number of births,
                       family history of breast cancer
                           or ovarian cancer, oral
                           contraception, hormone
                       replacement therapy, other sex
                        hormones, medication related
                         to alcoholism, mammography
                          screening attendance, and
                      highest family educational level

Wegrzyn et al.          Age, height, BMI, BMI at age
(2017) (a) [10]       18, adolescent body size, age at
                      menarche, age at first birth and
                      parity combined, breast feeding,
                        type of menopause and age at
                             menopause, combined
                         menopausal hormone therapy,
                         duration of estrogen alone
                         menopausal hormone therapy,
                          duration of estrogen and
                           progesterone menopausal
                        hormone therapy, first-degree
                      family history of breast cancer,
                      history of benign breast disease,
                        alcohol consumption, physical
                            activity, and current
                              mammography use.

Wegrzyn et al.          Age, height, BMI, BMI at age
(2017) (a) [10]       18, adolescent body size, age at
                      menarche, age at first birth and
                      parity combined, breast feeding,
                        type of menopause and age at
                             menopause, combined
                         menopausal hormone therapy,
                         duration of estrogen alone
                         menopausal hormone therapy,
                          duration of estrogen and
                           progesterone menopausal
                        hormone therapy, first-degree
                      family history of breast cancer,
                          history of benign breast
                        disease, alcohol consumption,
                           physical activity, and
                              mammography use.

Jorgensen et al.          Age, smoking, pack-years,
(2017) [43]            physical activity, BMI, alcohol
                       consumption, diet (vegetables,
                            fruit, and fatty meat
                          consumption), preexisting
                           diseases (hypertension,
                          diabetes, and myocardial
                     infarction), self-reported health,
                         stressful work environment,
                           marital status, female
                        reproductive factors (birth,
                         use of hormone therapy, and
                            oral contraceptives)

Behrens et al.          Age, smoking, family history
(2017) [13]             of prostate cancer, level of
                            school education, and
                              equivalent income

Akerstedt et al.        Age, education level, tobacco
(2017) [46]               consumption, BMI, having
                        children, coffee consumption,
                             and previous cancer

Dickerman et al.        Age, education, BMI, physical
(2016) [49]            activity, social class, smoking
                        status, alcohol use, snoring,
                                and zygosity

Papantoniou et al.     Age, centre, educational level,
(2016) [47]              parity, menopausal status,
                      family history of breast cancer,
                          BMI, smoking status, oral
                         contraceptive use, leisure
                           time physical activity,
                            alcohol consumption,
                             and sleep duration

Gyarmati et al.         Age, sex, educational level,
(2016) [48]            centre, BMI, cigarette smoking
                      status, family history, physical
                        activity level, total energy
                         intake, grams of red meat,
                          grams of vegetables, and
                             grams of fruit and
                             alcohol consumption

Costas et al.           Region, age, sex, worked on a
(2016) [15]                farm, family history of
                          hematologic malignancies,
                          BMI, tobacco consumption
                      (never, past, and current), sleep
                           problems, and education

Bai et al.               Age, BMI, family history of
(2016) [16]             cancer, alcohol drinking and
                          smoking status, number of
                        children, menopausal status,
                        hormone replacement therapy,
                          and contraception status

Travis et al.           Socioeconomic status, parity
(2016) [14]             and age at first birth, BMI,
                          alcohol intake, strenuous
                      physical activity, family history
                          of breast cancer, age at
                        menarche, oral contraceptive
                         use, smoking, living with a
                        partner, and hormone therapy

Gu et al.                 Age, alcohol consumption,
(2015) [20]            physical exercise, multivitamin
                         use, menopausal status and
                         postmenopausal hormone use,
                          physical exam in the past
                       2 years, healthy eating score,
                         smoking status, pack-years,
                        BMI, and husband's education

Wang P. et al.           Age, education, BMI, age at
(2015) [17]             menarche, menopausal status,
                      parity, physical activity, breast
                         feeding, and family history
                                  of cancer

Papantoniou et al.     Age, centre, educational level,
(2015) [18]              family history of prostate
                       cancer, physical activity over
                      the past decade, smoking status,
                        past sun exposure, and daily
                              meat consumption

Li et al.                  Age at the beginning of
(2015) [51]                       follow-up

Lin et al.                  Age, BMI, history of
(2015) [50]              cholelithiasis, history of
                        diabetes, cigarette smoking,
                         alcohol drinking, perceived
                           stress, and sleep time

Kwon et al.              Adjusted for age, smoking,
(2015) [52]                 parity, and endotoxin

Akerstedt et al.        Age, education level, tobacco
(2015) [21]               consumption, BMI, having
                        children, coffee consumption,
                          previous cancer, and use
                            of hormones including
                             oral contraceptives

Hammer et al.            Age and professional status
(2015) [19]

Gapstur et al.           Age, race, education, BMI,
(2014) [55]            smoking status, family history
                      of prostate cancer, and painful/
                             frequent urination

Koppes et al.             Night work, age, origin,
(2014) [54]                children in household,
                         education, occupation, job
                       tenure (years), and contractual
                                working hours

Carter et al.          Oral contraceptive use, age at
(2014) [23]                menarche and menopause,
                           tubal ligation, parity,
                        postmenopausal estrogen use,
                      race, family history of cancers,
                          exercise, BMI, and height

Yong et al.               Age, job level, cigarette
(2014) [53]                smoking, and employment
                           duration in categories

Datta et al.                        None
(2014) [56]

Truong et al.          Age, study area, parity, age at
(2014) [22]           first full-term pregnancy, age at
                         menarche, family history of
                        breast cancer, current use of
                        hormonal replacement therapy,
                          BMI, tobacco, and alcohol

Knutsson et al.          Height, weight, waist, hip
(2013) [26]              circumference, educational
                         level, number of children,
                         smoking, menopausal status,
                           oral contraceptive use,
                             hormones other than
                       contraceptives, alcohol intake,
                         educational level, BMI, and
                               waist-hip ratio

Rabstein et al.       Age, adjusted for family history
(2013) [57]               of breast cancer, hormone
                       replacement use, and number of
                                 mammograms

Fritschi et al.        Light at night, phase shift and
(2013) [59]           sleep disruption, poor diet, lack
                       of physical activity and little
                           time outdoors, and age

Schernhammer          Age, smoking status, age at start
et al. (2013) [24]      of smoking, cigarettes smoked
                        per day among current smoker,
                       time since quitting among past
                      smokers, fruit intake, vegetable
                       intake, BMI, and environmental
                              smoking exposures

Menegaux et al.        Age, study area, parity, age at
(2013) [25]           first full-term pregnancy, age at
                         menarche, family history of
                       breast cancer, current hormonal
                          replacement therapy, BMI,
                            tobacco, and alcohol

Grundy et al.                  Age and centre
(2013) [27]

Bhatti et al.             Age at reference, county,
(2013) [60]           reference year, duration of oral
                        contraceptive use, number of
                       full-term pregnancies, and BMI

Lin et al.             Age, BMI, history of diabetes,
(2013) [58]              alcohol drinking, cigarette
                         smoking, perceived stress,
                               and sleep time

Hansen and                Adjusted for age, weight
Stevens (2012)           regularity, use of hormone
[30]                     replacement therapy, age at
                       menarche, menstrual regularity,
                       menopausal status, age at birth
                      of first child, breast cancer in
                         mother or sister, and total
                            duration of lactation

Hansen and                Adjusted for age, hormone
Lassen (2012) [31]     replacement therapy, number of
                        childbirths, age at menarche,
                       years of education, occasional
                          sunbathing frequency, and
                           tobacco smoking status

Parent et al.                       None
(2012) [28]

Natti et al.              Age, longstanding illness
(2012) [29]                   (among men), and
                               smoking status

Kubo et al.               Age, BMI, alcohol intake,
(2011) [32]                smoking, exercise, and
                               marital status

Lie et al.            Age, period of diagnosis, parity,
(2011) [62]            family history of breast cancer
                          in mother or sister, and
                            frequency of alcohol
                             consumption at time
                                of diagnosis

Poole et al.                Age, duration of oral
(2011) [61]            contraceptive use, parity, BMI,
                       smoking status, tubal ligation
                         history, menopausal status,
                      family history of ovarian cancer
                          (yes/no), and duration of
                                breastfeeding

Pesch et al.           Age in 5-year groups, adjusted
(2010) [64]             for family history of breast
                         cancer, hormone replacement
                             use, and number of
                                 mammograms

Pronk et al.           Age, education, family history
(2010) [63]              of breast cancer, number of
                      pregnancies, age at first birth,
                            and physical activity

Lahti et al.               Age, social class, and
(2008) [33]                     cohort period

Viswanathan et al.      Age, age at menarche, age at
(2007) [34]             menopause, parity, BMI, oral
                         contraceptive use, use and
                         duration of postmenopausal
                           hormones, hypertension,
                            diabetes, and smoking

Lie et al.               Total employment time as a
(2006) [36]                   nurse and parity

O'Leary et al.         Age at reference date, parity,
(2006) [65]            family history, education, and
                      history of benign breast disease

Schernhammer                Age, age at menarche,
et al. (2006) [35]        menopausal status, age at
                       menopause, age at first birth,
                            BMI, current alcohol
                              consumption, oral
                             contraceptive use,
                         postmenopausal hormone use,
                        smoking status, benign breast
                      disease, family history of breast
                        cancer, and physical activity

Kubo et al.            Age, study area, family history
(2006) [37]               of prostate cancer, BMI,
                       smoking, alcohol drinking, job
                      type, physical activity at work,
                        workplace, perceived stress,
                           educational level, and
                               marriage status

Schernhammer             Age in years, smoking, BMI,
et al. (2003) [38]     physical activity in quintiles,
                       regular aspirin use, colorectal
                        cancer in parent or sibling,
                       screening endoscopy during the
                        study period, consumption of
                        beef, pork, or lamb as a main
                          dish, alcohol consumption
                        status, total caloric intake
                            in quintiles, use of
                          postmenopausal hormones,
                        menopausal status, and height
                             in seven categories

Davis et al.          Parity, family history of breast
(2001) [66]            cancer (mother or sister), oral
                          contraceptive use (ever),
                            and recent (<5 years)
                         discontinued use of hormone
                             replacement therapy

Hansen               Age, social class, age at birth of
(2001) [39]           first child, age at birth of last
                        child, and number of children

Study (year)           Exposure
                      assessment
                        method

Walasa et al.        Questionnaire
(2018) [40]

Talibov et al.       Questionnaire
(2018) [41]

Tse et al.             Interview
(2017) [11]

Heckman et al.       Questionnaire
(2017) [44]

Papantoniou          Questionnaire
(2017) [12]

Devore et al.        Questionnaire
(2017) [45]

Vistisen et al.        Database
(2017) [42]

Wegrzyn et al.       Questionnaire
(2017) (a) [10]

Wegrzyn et al.       Questionnaire
(2017) (a) [10]

Jorgensen et al.     Questionnaire
(2017) [43]

Behrens et al.         Interview
(2017) [13]

Akerstedt et al.       Interview
(2017) [46]

Dickerman et al.     Questionnaire
(2016) [49]

Papantoniou et al.     Interview
(2016) [47]

Gyarmati et al.        Interview
(2016) [48]

Costas et al.          Interview
(2016) [15]

Bai et al.           Questionnaire
(2016) [16]

Travis et al.        Questionnaire
(2016) [14]

Gu et al.            Questionnaire
(2015) [20]

Wang P. et al.         Interview
(2015) [17]

Papantoniou et al.     Interview
(2015) [18]

Li et al.            Questionnaire
(2015) [51]

Lin et al.           Questionnaire
(2015) [50]

Kwon et al.            Database
(2015) [52]

Akerstedt et al.       Interview
(2015) [21]

Hammer et al.          Database
(2015) [19]

Gapstur et al.       Questionnaire
(2014) [55]

Koppes et al.          Interview
(2014) [54]

Carter et al.        Questionnaire
(2014) [23]

Yong et al.          Questionnaire
(2014) [53]

Datta et al.           Interview
(2014) [56]

Truong et al.          Interview
(2014) [22]

Knutsson et al.      Questionnaire
(2013) [26]

Rabstein et al.        Interview
(2013) [57]

Fritschi et al.      Questionnaire
(2013) [59]

Schernhammer         Questionnaire
et al. (2013) [24]

Menegaux et al.        Interview
(2013) [25]

Grundy et al.        Questionnaire
(2013) [27]

Bhatti et al.          Interview
(2013) [60]

Lin et al.           Questionnaire
(2013) [58]

Hansen and             Interview
Stevens (2012)
[30]

Hansen and           Questionnaire
Lassen (2012) [31]

Parent et al.          Interview
(2012) [28]

Natti et al.           Interview
(2012) [29]

Kubo et al.            Database
(2011) [32]

Lie et al.             Interview
(2011) [62]

Poole et al.         Questionnaire
(2011) [61]

Pesch et al.           Interview
(2010) [64]

Pronk et al.           Interview
(2010) [63]

Lahti et al.           Database
(2008) [33]

Viswanathan et al.   Questionnaire
(2007) [34]

Lie et al.             Database
(2006) [36]

O'Leary et al.         Interview
(2006) [65]

Schernhammer         Questionnaire
et al. (2006) [35]

Kubo et al.          Questionnaire
(2006) [37]

Schernhammer         Questionnaire
et al. (2003) [38]

Davis et al.           Interview
(2001) [66]

Hansen                 Interview
(2001) [39]

Abbreviations: BMI: body mass index; NSAIDs: nonsteroidal anti-
inflammatory drugs; NA: not available. (a) This study included two
prospective cohorts (NHS and NHS2).

Table 2: The results of subgroup analyses and metaregression analyses
on the association between night shift work and the risk of cancers.

Subgroup                        No. of    Weight (%)
                                studies

Shift schedule (a)
  Rotating shift                  29        46.97
  Fixed shift                      9        11.19
  Mixed shift                     27        41.84
Type of cancer (b)
  Digestive system cancer         11        15.72
  Hematological system cancer      5         9.12
  Prostate cancer                 11        16.10
  Breast cancer                   37        39.62
  Reproductive system cancer       6         7.99
  Lung cancer                      5         7.53
  Skin cancer                      3         3.92
Region
  Australia                        3         5.03
  Europe                          27        48.92
  Asia                            11        14.29
  North America                   17        31.75
Occupation
  Unclassified occupation         35        61.45
  Industry                         9        12.09
  Nurses                          14        26.46
Study design
  Case-control study              21        32.23
  Nested case-control study        6         9.08
  Cohort study                    31        58.69
Exposure assessment
  Questionnaire                   28        53.99
  Interview                       24        34.52
  Database                         6         9.06
Number of adjusted variables
  [less than or equal to] 4       15        22.84
  >4                              43        77.16
Study score
  Low quality                     17        25.20
  High quality                    41        74.80

Subgroup                          OR (95% CI)      [I.sup.2]

Shift schedule (a)
  Rotating shift                1.14 (1.04-1.24)     68.7%
  Fixed shift                   1.09 (0.90-1.31)     51.1%
  Mixed shift                   1.20 (0.82-1.77)     80.7%
Type of cancer (b)
  Digestive system cancer       1.15 (1.01-1.32)     40.2%
  Hematological system cancer   1.08 (0.99-1.17)     54.7%
  Prostate cancer               1.26 (1.05-1.52)     73.2%
  Breast cancer                 1.22 (1.08-1.38)     81.2%
  Reproductive system cancer    1.06 (0.85-1.32)     49.5%
  Lung cancer                   1.08 (0.87-1.35)     53.4%
  Skin cancer                   0.93 (0.50-1.74)     74.9%
Region
  Australia                     0.91 (0.77-1.06)     0.0%
  Europe                        1.18 (1.10-1.28)     75.1%
  Asia                          1.11 (0.88-1.39)     78.3%
  North America                 1.16 (1.04-1.31)     76.1%
Occupation
  Unclassified occupation       1.17 (1.10-1.25)     69.7%
  Industry                      1.00 (0.81-1.24)     72.8%
  Nurses                        1.17 (1.02-1.35)     80.6%
Study design
  Case-control study            1.28 (1.15-1.42)     66.5%
  Nested case-control study     1.30 (0.89-1.90)     88.0%
  Cohort study                  1.07 (1.00-1.15)     70.9%
Exposure assessment
  Questionnaire                 1.08 (1.00-1.17)     77.1%
  Interview                     1.32 (1.17-1.49)     66.3%
  Database                      1.00 (0.85-1.18)     77.1%
Number of adjusted variables
  [less than or equal to] 4     1.13 (0.99-1.28)     82.7%
  >4                            1.16 (1.08-1.24)     72.8%
Study score
  Low quality                   1.16 (0.98-1.37)     86.9%
  High quality                  1.14 (1.08-1.21)     61.6%

Subgroup                            P for         P * for
                                heterogeneity   interaction

Shift schedule (a)                                 0.570
  Rotating shift                   <0.001
  Fixed shift                       0.037
  Mixed shift                      <0.001
Type of cancer (b)                                 0.298
  Digestive system cancer           0.081
  Hematological system cancer       0.066
  Prostate cancer                  <0.001
  Breast cancer                    <0.001
  Reproductive system cancer        0.078
  Lung cancer                       0.073
  Skin cancer                       0.019
Region                                             0.298
  Australia                         0.728
  Europe                           <0.001
  Asia                             <0.001
  North America                    <0.001
Occupation                                         0.795
  Unclassified occupation          <0.001
  Industry                         <0.001
  Nurses                           <0.001
Study design                                       0.845
  Case-control study               <0.001
  Nested case-control study        <0.001
  Cohort study                     <0.001
Exposure assessment                                0.075
  Questionnaire                    <0.001
  Interview                        <0.001
  Database                          0.004
Number of adjusted variables                       0.926
  [less than or equal to] 4        <0.001
  >4                               <0.001
Study score                                        0.585
  Low quality                      <0.001
  High quality                     <0.001

(a,b) Five studies report their studies including different kinds of
cancer; nine articles report their studies including different types
of shift schedules. * P values for metaregression.

Figure 2: Forest plots of studies describing the association between
night shift work and the risk of multiple cancers in women (a) and men
(b) separately. [I.sup.2]: the indicator for judging the degree of
heterogeneity; OR: odds ratio; CI: confidence interval. The squares
and horizontal lines represent the study-specific OR and 95% CI. The
diamond represents the pooled OR and 95% CI.

(a)

Study ID                                     OR (95% CI)      Weight %

Female
Walasa WM(2018)                           0.95 (0.57, 1.58)     0.88
Talibov M(2018)                           1.03 (0.98, 1.08)     3.43
Papantoniou K(2016)                       1.21 (0.89, 1.65)     1.68
Wang P(2015)                              1.34 (1.05, 1.72)     2.07
Li WJ(2015)                               0.73 (0.66, 0.82)     3.10
Datta K(2014)                             1.51 (0.27, 8.52)     0.10
Rabstein S(2013)                          1.01 (0.68, 1.50)     1.25
Fritschi L(2013)                          1.02 (0.71, 1.45)     1.42
Menegaux F(2013)                          1.40 (1.01, 1.92)     1.61
Grundy A(2013)                            2.21 (1.14, 4.31)     0.58
Bhatti P(2013)                            1.02 (0.74, 1.42)     1.58
Hansen J(2012)                            2.10 (1.30, 3.20)     1.05
Hansen J(2012)                            2.10 (1.00, 4.50)     0.47
Lie JS(2011)                              1.30 (0.90, 1.80)     1.47
Lie JS(2006)                              2.21 (1.10, 4.45)     0.53
Pesch B(2010)                             2.48 (0.62, 9.99)     0.15
Hansen J(2001)                            1.50 (1.30, 1.70)     2.91
Truong(2014)                              1.32 (1.02, 1.72)     1.97
Kwon P(2015)                              0.88 (0.69, 1.12)     2.10
Davis S(2001)                             1.60 (0.80, 3.20)     0.54
O'Leary ES(2006)                          1.04 (0.79, 1.38)     1.85
Devore EE(2017)                           0.96 (0.83, 1.11)     2.83
Knutsson A(2013)                          2.02 (1.03, 3.95)     0.57
Carter BD(2014)                           1.27 (1.03, 1.56)     2.35
Poole EM(2010)                            0.80 (0.51, 1.23)     1.09
Viswanathan AN(2007)                      1.47 (1.03, 2.10)     1.43
Akerstedt T(2015)                         1.77 (1.03, 3.04)     0.80
Koppes LLJ(2014)                          0.87 (0.72, 1.05)     2.49
Natti J(2012)                             2.82 (1.20, 6.65)     0.37
Schernhammer ES(2006)                     1.79 (1.06, 3.01)     0.85
PronkA(2010)                              0.80 (0.50, 1.20)     1.10
Schernhammer ES(2003)                     1.35 (1.03, 1.77)     1.91
Vistisen HT(2017)                         0.90 (0.80, 1.01)     3.04
Schernhammer ES(2013)                     1.28 (1.07, 1.53)     2.57
Gu FY(2015)                               1.08 (0.98, 1.19)     3.17
Lahti TA(2008)                            1.02 (0.94, 1.12)     3.23
Bai YS(2016)                              0.90 (0.66, 1.23)     1.66
Travis RC(2016)                           1.00 (0.92, 1.08)     3.28
Wegrzyn LR(2017)                          0.95 (0.77, 1.17)     2.34
Wegrzyn LR(2017)                          2.15 (1.23, 3.73)     0.77
Heckman CJ(2017)                          0.79 (0.71, 0.89)     3.08
Jorgensen JT(2017)                        0.91 (0.77, 1.08)     2.64
Subtotal (I-squared = 78.3%, p = 0.000)   1.12 (1.04, 1.20)    72.31

(b)

Study ID                                     OR (95% CI)      Weight %
Male
Talibov M(2018)                           1.03 (0.98, 1.09)     3.41
Tse LA(2017)                              1.76 (1.07, 2.89)     0.91
Papantoniou K(2015)                       1.38 (1.05, 1.81)     1.90
Parent M(2012)                            2.02 (1.25, 3.26)     0.96
Natti J(2012)                             1.78 (0.80, 4.00)     0.41
Lahti TA(2008)                            1.10 (1.03, 1.19)     3.32
Bai YS(2016)                              1.27 (1.01, 1.59)     2.21
Akerstedt T(2017)                         0.91 (0.74, 1.12)     2.35
Dickerman BA(2016)                        1.00 (0.70, 1.20)     1.91
Lin YS(2015)                              1.43 (0.78, 2.63)     0.67
Hammer GP(2015)                           0.93 (0.73, 1.18)     2.11
Gapstur SM(2014)                          1.08 (0.95, 1.22)     2.98
Kubo T(2011)                              1.79 (0.57, 5.68)     0.22
Behrens T(2017)                           3.08 (1.67, 5.69)     0.66
Kubo T(2006)                              3.00 (1.20, 7.70)     0.32
Lin YS(2013)                              0.83 (0.43, 1.60)     0.59
Yong M(2014)                              1.04 (0.89, 1.21)     2.77
Subtotal (I-squared = 62.3%, p = 0.000)   1.14 (1.05, 1.25)    27.69
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Author:Liu, Wen; Zhou, Zhonghan; Dong, Dahai; Sun, Lijiang; Zhang, Guiming
Publication:Disease Markers
Date:Jan 1, 2018
Words:13357
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