# The effect of occupational exposure to welding fumes on trachea, bronchus and lung cancer: A systematic review and meta-analysis from the WHO/ILO Joint Estimates of the Work-related Burden of Disease and Injury

Dana Loomis, Angel Mario Dzhambov, Natalie C. Momen, Nicholas Chartres, Alexis Descatha, Neela Guha, Seong Kyu Kang, Alberto Modenese, Rebecca L. Morgan, Seoyeon Ahn, et al.

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Dana Loomis, Angel Mario Dzhambov, Natalie C. Momen, Nicholas Chartres, Alexis Descatha, et al. The effect of occupational exposure to welding fumes on trachea, bronchus and lung cancer: A systematic review and meta-analysis from the WHO/ILO Joint Estimates of the Work-related Burden of Disease and Injury. Environment International, 2022, 170, pp.107565. 10.1016/j.envint.2022.107565. hal-03888877

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# The effect of occupational exposure to welding fumes on trachea, bronchus and lung cancer: A systematic review and meta-analysis from the WHO/ILO Joint Estimates of the Work-related Burden of Disease and Injury

Dana Loomis<sup>a,b,1</sup>, Angel M. Dzhambov<sup>c,d,1,*</sup>, Natalie C. Momen<sup>e</sup>, Nicholas Chartres<sup>f</sup>, Alexis Descatha<sup>g,h</sup>, Neela Guha<sup>1</sup>, Seong-Kyu Kang<sup>j</sup>, Alberto Modenese<sup>k</sup>, Rebecca L. Morgan<sup>l</sup>, Seoyeon Ahn<sup>m</sup>, Martha S. Martínez-Silveira<sup>n</sup>, Siyu Zhang<sup>o</sup>, Frank Pega<sup>e,*</sup>

## ARTICLE INFO

### Keywords:

- Burden of disease
- Occupational health
- Systematic review
- Meta-analysis
- Welding
- Tracheal neoplasms
- Lung neoplasms

## ABSTRACT

**Background:** The World Health Organization (WHO) and the International Labour Organization (ILO) are the producers of the WHO/ILO Joint Estimates of the Work-related Burden of Disease and Injury (WHO/ILO Joint Estimates). Welding fumes have been classified as carcinogenic to humans (Group 1) by the WHO International Agency for Research on Cancer (IARC) in IARC Monograph 118; this assessment found sufficient evidence from studies in humans that welding fumes are a cause of lung cancer. In this article, we present a systematic review and meta-analysis of parameters for estimating the number of deaths and disability-adjusted life years from trachea, bronchus, and lung cancer attributable to occupational exposure to welding fumes, to inform the development of WHO/ILO Joint Estimates on this burden of disease (if considered feasible).

**Objectives:** We aimed to systematically review and meta-analyse estimates of the effect of any (or high) occupational exposure to welding fumes, compared with no (or low) occupational exposure to welding fumes, on trachea, bronchus, and lung cancer (three outcomes: prevalence, incidence, and mortality).

**Data sources:** We developed and published a protocol, applying the Navigation Guide as an organizing systematic review framework where feasible. We searched electronic databases for potentially relevant records from published and unpublished studies, including Medline, EMBASE, Web of Science, CENTRAL and CISDOC. We also

<footnote><sup>a</sup> School of Community Health Sciences, University of Nevada, Reno, Reno, NV, the United States of America</footnote>

<footnote><sup>b</sup> Plumas County Public Health Agency, Plumas County, CA, the United States of America</footnote>

<footnote><sup>c</sup> Department of Hygiene, Faculty of Public Health, Medical University of Plovdiv, Plovdiv, Bulgaria</footnote>

<footnote><sup>d</sup> Institute for Highway Engineering and Transport Planning, Graz University of Technology, Graz, Austria</footnote>

<footnote><sup>e</sup> Department of Environment, Climate Change and Health, World Health Organization, Geneva, Switzerland</footnote>

<footnote><sup>f</sup> Program on Reproductive Health and the Environment, Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Francisco, San Francisco, the United States of America</footnote>

<footnote><sup>g</sup> AP-HP (Paris Hospital "Assistance Publique Hôpitaux de Paris"), Occupational Health Unit, University Hospital of West Suburb of Paris, Poincaré Site, Garches, France /Versailles St-Quentin Univ - Paris Saclay Univ (UVSQ), UMS 011, UMR-S 1168, France</footnote>

<footnote><sup>h</sup> Univ Angers, CHU Angers, Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR S1085, SFR ICAT, CAPTV CDC, Angers, France</footnote>

<footnote><sup>i</sup> Office of Environmental Health Hazard Assessment, California Environmental Protection Agency, Oakland, CA, the United States of America</footnote>

<footnote><sup>j</sup> Department of Occupational and Environmental Medicine, Gil Medical Center, Gachon University College of Medicine, Incheon, Republic of Korea</footnote>

<footnote><sup>k</sup> Department of Biomedical, Metabolic and Neural Sciences, University of Modena & Reggio Emilia, Modena, Italy</footnote>

<footnote><sup>l</sup> Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada</footnote>

<footnote><sup>m</sup> National Pension Research Institute, Jeonju-si, Republic of Korea</footnote>

<footnote><sup>n</sup> Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, BA, Brazil</footnote>

<footnote><sup>o</sup> National Institute for Occupational Health and Poison Control, Center for Disease Control and Prevention, Beijing, People's Republic of China</footnote>

<footnote>* Corresponding authors.</footnote>

<footnote>E-mail addresses: danaloomis@countyofplumas.com (D. Loomis), angelleloti@gmail.com (A.M. Dzhambov), momenn@who.int (N.C. Momen), nicholas.chartres@ucsf.edu (N. Chartres), alexis.descatha@inserm.fr (A. Descatha), neela@berkeley.edu (N. Guha), sk.kang@gachon.ac.kr (S.-K. Kang), alberto.modenese@unimore.it (A. Modenese), morganrl@mcmaster.ca (R.L. Morgan), ahnseoyeon@nps.or.kr (S. Ahn), martha.silveira@gmail.com (M.S. Martínez-Silveira), zhangsy@niohp.chinacdc.cn (S. Zhang), pegaf@who.int (F. Pega).</footnote>

<footnote n="1">¹ Joint first authors.</footnote>
searched grey literature databases, Internet search engines, and organizational websites; hand-searched reference lists of previous systematic reviews; and consulted additional experts.

**Study eligibility and criteria:** We included working-age (≥15 years) workers in the formal and informal economy in any Member State of WHO and/or ILO but excluded children (<15 years) and unpaid domestic workers. We included randomized controlled trials, cohort studies, case-control studies, and other non-randomized intervention studies with an estimate of the effect of any (or high) occupational exposure to welding fumes, compared with occupational exposure to no (or low) welding fumes, on trachea, bronchus, and lung cancer (prevalence, incidence, and mortality).

**Study appraisal and synthesis methods:** At least two review authors independently screened titles and abstracts against the eligibility criteria at a first review stage and full texts of potentially eligible records at a second stage, followed by extraction of data from qualifying studies. If studies reported odds ratios, these were converted to risk ratios (RRs). We combined all RRs using random-effects meta-analysis. Two or more review authors assessed the risk of bias, quality of evidence, and strength of evidence, using the Navigation Guide tools and approaches adapted to this project. Subgroup (e.g., by WHO region and sex) and sensitivity analyses (e.g., studies judged to be of "high"/"probably high" risk of bias compared with "low"/"probably low" risk of bias) were conducted.

**Results:** Forty-one records from 40 studies (29 case control studies and 11 cohort studies) met the inclusion criteria, comprising over 1,265,512 participants (≥22,761 females) in 21 countries in three WHO regions (Region of the Americas, European Region, and Western Pacific Region). The exposure and outcome were generally assessed by job title or self-report, and medical or administrative records, respectively. Across included studies, risk of bias was overall generally probably low/low, with risk judged high or probably high for several studies in the domains for misclassification bias and confounding.

Our search identified no evidence on the outcome of having trachea, bronchus, and lung cancer (prevalence). Compared with no (or low) occupational exposure to welding fumes, any (or high) occupational exposure to welding fumes increased the risk of acquiring trachea, bronchus, and lung cancer (incidence) by an estimated 48 % (RR 1.48, 95 % confidence interval [CI] 1.29–1.70, 23 studies, 57,931 participants, I² 24 %; moderate quality of evidence). Compared with no (or low) occupational exposure to welding fumes, any (or high) occupational exposure to welding fumes increased the risk dying from trachea, bronchus, and lung cancer (mortality) by an estimated 27 % (RR 1.27, 95 % CI 1.04–1.56, 3 studies, 8,686 participants, I² 0 %; low quality of evidence). Our subgroup analyses found no evidence for difference by WHO region and sex. Sensitivity analyses supported the main analyses.

**Conclusions:** Overall, for incidence and mortality of trachea, bronchus, and lung cancer, we judged the existing body of evidence for human data as "sufficient evidence of harmfulness" and "limited evidence of harmfulness", respectively. Occupational exposure to welding fumes increased the risk of acquiring and dying from trachea, bronchus, and lung cancer. Producing estimates for the burden of trachea, bronchus, and lung cancer attributable to any (or high) occupational exposure to welding fumes appears evidence-based, and the pooled effect estimates presented in this systematic review could be used as input data for the WHO/ILO Joint Estimates.

**Protocol identifier:** https://doi.org/10.1016/j.envint.2020.106089.

# 1. Introduction

## 1.1. Background

The World Health Organization (WHO) and the International Labour Organization (ILO) produce the WHO/ILO Joint Estimates of the Work-related Burden of Disease and Injury (WHO/ILO Joint Estimates) (Pega et al., 2021a,b; World Health Organization and International Labour Organization 2021a,b). The organizations estimate the numbers of deaths and disability-adjusted life years (DALYs) that are attributable to selected occupational risk factors. The WHO/ILO Joint Estimates are based on already existing WHO and ILO methodologies for estimating the burden of disease for selected occupational risk factors (International Labour Organization 2014; World Health Organization 2016). They expand these existing methodologies with estimation of the burden of several prioritized additional pairs of occupational risk factors and health outcomes. For this purpose, population attributable fractions, the proportional reduction in burden from the health outcome achieved by a reduction of exposure to the theoretical minimum risk exposure level (Murray et al. 2004), are calculated for each additional risk factor-outcome pair. These fractions are applied to the total burden of disease envelopes for the health outcome from the WHO Global Health Estimates (World Health Organization 2017).

The WHO/ILO Joint Estimates may include a methodology for estimating, and estimates of, the burden of trachea, bronchus, and lung cancer from occupational exposure to welding fumes if feasible, as one of the additional prioritized risk factor-outcome pairs. To select parameters with the best and least biased evidence for their estimation models, WHO and ILO, supported by a large number of individual

experts, have conducted a systematic review and meta-analysis of studies on the relationship between occupational exposure to welding fumes and trachea, bronchus, and lung cancer according to protocol (Pega et al. 2020a); we present these analyses in this study record article. The organizations are also conducting or have completed several other systematic reviews and meta-analyses on other additional risk factor-outcome pairs (Descatha et al. 2018; Godderis et al. 2018; Li et al. 2018; Mandrioli et al. 2018; Hulshof et al. 2019; Paulo et al. 2019; Rugulies et al. 2019; Teixeira et al. 2019; Tenkate et al. 2019; Descatha et al. 2020; Li et al. 2020; Hulshof et al. 2021a,b; Pachito et al. 2021; Rugulies et al. 2021; Teixeira et al. 2021a,b; World Health Organization 2021; Schlünssen Under review). One of these was focused on pairs with cancer outcomes: occupational exposure to solar ultraviolet radiation and the risk of malignant skin melanoma and non-melanoma skin cancer, respectively (World Health Organization 2021). To our knowledge, these are the first systematic reviews and meta-analyses, with a pre-published protocol, conducted specifically for an occupational burden of disease study. An editorial provides an overview of this series of systematic reviews and meta-analyses from the WHO/ILO Joint Estimates and outlines its scientific, methodological, policy, editorial, and other innovations (Pega et al. 2021c). Several new systematic review methods were also developed specifically for the WHO/ILO Joint Estimates (Pega et al. 2020b; Momen et al. 2022; Pega et al. 2022a,b). The WHO/ILO joint estimation methodology and the WHO/ILO Joint Estimates are separate from these systematic reviews, and they are described in more detail and reported elsewhere (Pega et al., 2021a,b; World Health Organization and International Labour Organization 2021a,b). For example, WHO/ILO Joint Estimates have been published of the global, regional, and national burdens of ischemic heart disease
and stroke attributable to exposure to long working hours for 194 countries (Pega et al. 2021a).

## 1.2. Rationale

To consider the feasibility of estimating the burden of trachea, bronchus, and lung cancer from occupational exposure to welding fumes, and to ensure that potential estimates of burden of disease are reported in adherence with the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) (Stevens et al. 2016), WHO and ILO require a systematic review and meta-analysis of studies with estimates of the relative effect of any (or high) occupational exposure to welding fumes on the prevalence of, incidence of or mortality from trachea, bronchus, and lung cancer, compared with the theoretical minimum risk exposure level of no (or low) occupational exposure to welding fumes. The theoretical minimum risk exposure level is the exposure level that would result in the lowest possible population risk, even if it is not feasible to attain this exposure level in practice (Murray et al. 2004).

In 2017, a WHO International Agency for Research on Cancer (IARC) Monograph 118 working group reported their findings on welding fumes (Guha et al. 2017; International Agency for Research on Cancer 2018). Welding fumes comprise a mix of fine solid particles, including metal oxides, silicates, and fluorides. They are released during welding, which joins metals, usually by electricity (arc welding) or by a fuel gas (gas welding). The IARC Monograph 118 details that welding fumes were classified as “carcinogenic to humans” (Guha et al. 2017; International Agency for Research on Cancer 2018). IARC based this assessment on “sufficient evidence” from the >50 epidemiologic studies on the effect of exposure to welding fumes (generally assessed indirectly through welding process or material, branch of industry, occupation, job title, job task, expert assessment or self-report) on lung cancer (International Agency for Research on Cancer 2018).

We are aware of four published meta-analyses reporting on the effect of welding fume exposure on development of lung cancer (Sjogren et al. 1994; Moulin 1997; Ambroise et al. 2006; Honaryar et al. 2019). While these meta-analyses vary in eligibility criteria of included studies, all suggested an increased risk in the development of lung cancer. The earliest meta-analysis, which only included studies that accounted for tobacco smoking and exposure to asbestos, examined stainless steel welders (assessed indirectly by the worker through self-report or by a workplace manager or the worker's spouse) and the occurrence of lung cancer (Sjogren et al. 1994). The pooled risk ratio (RR) from three case-referent (case-control) and two cohort studies included in the meta-analysis was 1.94 (95 % CI 1.28–2.93). However, the authors neither tested for nor measured heterogeneity in the meta-analysis, nor assessed the quality of the body of evidence.

A 2006 meta-analysis, an update of Moulin (1997), included population surveys, case-control studies, and industry-based cohort studies to assess the relationship between lung cancer and welding (Ambroise et al. 2006). The pooled RR for the cohort studies was 1.29 (95 % CI 1.19–1.40; $\chi^2$ 20.6, p 0.99), and that for the case-control studies was 1.27 (95 % CI 1.11–1.46; $\chi^2$ 13.0, p 0.60) when only studies without reporting bias were included in the analysis. No further assessment of the quality of the evidence was reported. The authors attempted to control for confounding by tobacco smoking, and – when crude and adjusted RRs were available – it appeared that no or only slight confounding by tobacco smoking was detected.

The most recently published meta-analysis (Honaryar et al. 2019) analysed the studies included in the IARC assessment conducted in 2017 (Guha et al. 2017; International Agency for Research on Cancer 2018). Pooled effect estimates, stratified by study design, suggested increased RRs in development of lung cancer of 1.29 (95 % CI 1.20–1.39; $I^2$ 26.4 %) across 22 cohort studies; 1.87 (1.53–2.29; $I^2$ 44.1 %) across 15 case-control studies; and 1.17 (1.04–1.38; $I^2$ 41.2 %) across eight case-control studies that accounted for confounding by tobacco smoking and exposure to asbestos.

However, to our knowledge, no systematic review has been conducted of studies with estimates of the effect of occupational exposure to welding fumes on trachea, bronchus, and lung cancer. We did not identify any systematic review protocol on the topic up to the year 2020 (PROSPERO – accessed May 14, 2020). Subsequently, we published the protocol for this systematic review in the same year (Pega et al. 2020a).

Different contexts may result in different exposures and effects of these exposures on the health outcome. Work in the informal economy, for example, may lead to different exposures and exposure effects than does work in the formal economy. The informal economy is defined as “all economic activities by workers and economic units that are – in law or in practice – not covered or insufficiently covered by formal arrangements”, but excluding “illicit activities, in particular the provision of services or the production, sale, possession or use of goods forbidden by law, including the illicit production and trafficking of drugs, the illicit manufacturing of and trafficking in firearms, trafficking in persons and money laundering, as defined in the relevant international treaties” (p4) (104th International Labour Conference 2015). Therefore, we considered the formality of the economy studied as a key contextual factor in studies included in our systematic review.

Our systematic review and meta-analysis differ from previous efforts in that it:

* Is tailored to the needs of estimation of burden of disease.

* Is based on a pre-published, peer-reviewed protocol (Pega et al. 2020a).

* Includes studies of working-age ($\ge$15 years) workers in the formal and informal economy.

* Includes a broader set of non-randomized intervention studies, such as quasi-experimental, controlled before-after studies and interrupted time series studies.

* Followed all stages of a systematic review as defined in the Navigation Guide framework (Woodruff and Sutton 2014), including assessments of the risk of bias, quality of evidence, and strength of evidence, with the Navigation Guide's tools and approaches (Lam et al. 2016a).

* Includes only occupational exposure to welding fumes (not all exposures including environmental ones).

* Updates prior review and meta-analytic evidence on lung cancer, but also includes trachea and bronchus cancer in the outcome definition.

* Includes studies published up to 30 April 2020, plus studies awaiting classification identified up to 30 April 2022.

## 1.3. Description of the risk factor

The definition of the risk factor, the risk factor levels, and its theoretical minimum risk exposure level are presented in Table 1. The risk factor is defined as having two levels: Any (or high) occupational exposure to welding fumes and no (or low) occupational exposure to welding fumes. The assumed theoretical minimum risk exposure level is no (or low) occupational exposure to welding fumes. If studies reported

<caption>Table 1</caption>

Definitions of the risk factor, risk factor levels, and the minimum risk exposure level.

<table><thead><tr><td>Concept</td><td>Definition</td></tr></thead><tbody><tr><td>Risk factor</td><td>Occupational exposure to welding fumes from welding any material by any welding process</td></tr><tr><td>Risk factor levels</td><td>1. Any (or high) occupational exposure to welding fumes<br>2. No (or low) occupational exposure to welding fumes</td></tr><tr><td>Theoretical minimum risk exposure level</td><td>No (or low) occupational exposure to welding fumes</td></tr></tbody></table>

Footnote: Adapted from the protocol (Pega et al. 2020a).
exposure levels differing from the standard levels we define here, then, if
possible, we converted the reported levels to the standard levels and, if
not possible, we reported analyses on these alternate exposure levels as
supplementary information in the systematic review.

1.4. *Description of the outcome*

The WHO Global Health Estimates group outcomes into standard burden of disease categories (World Health Organization 2017), based on standard codes from the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) (World Health Organization 2015). The relevant WHO Global Health Estimates category for our systematic review is: “II.A7. Trachea, bronchus, and lung cancer” (World Health Organization 2017), and this category covers ICD-10 codes “C33 Malignant neoplasm of trachea” and “C34 Malignant neoplasm of bronchus and lung”. Our systematic review covers the entire burden of disease of the relevant WHO Global Health Estimates category.

1.5. *How the risk factor may impact the outcome*

Official health estimates of the burden of disease attributable to an occupational risk factor require a sufficient level of scientific consensus that the risk factor causes the disease or other specified health outcome (Stevens et al. 2016). The above mentioned conclusion of the working group of individual experts convened by IARC in 2017 is the most recent scientific consensus that exposure to welding fumes is a sufficient cause of lung cancer in humans (Guha et al. 2017; International Agency for Research on Cancer 2018). or The working group for the IARC Monograph Volume 118 reported that, for the mechanistic data, there was

strong evidence to suggest that welding fumes are carcinogenic through chronic inflammation and immune suppression, and moderate evidence to suggest genotoxicity, induction of oxidative stress, and altered cell proliferation or death (International Agency for Research on Cancer (2018), p262–263). The working group reported “limited evidence in experimental animals for the carcinogenicity of gas metal arc stainless steel welding fumes” (International Agency for Research on Cancer (2018), p265). It judged there to be “sufficient evidence in humans for the carcinogenicity of welding fumes” and that “welding fumes cause cancer of the lung” (International Agency for Research on Cancer (2018), p265). Its overall evaluation, based on a synthesis of evidence streams of mechanistic, animal, and human studies, was that “Welding fumes are carcinogenic to humans and cause cancer of the lung (Group 1)” (International Agency for Research on Cancer (2018), p265). Therefore, welding fumes are an established risk factor for human health. The IARC hazard identification did not focus specifically on the effect of occupational exposure to welding fumes (as opposed to any exposure, including environmental ones), but this is the focus of the current systematic review and meta-analysis.

Causal diagrams are useful tools in epidemiologic research and evidence synthesis, because they provide transparent, graphical solutions for organizing the current state of knowledge about research topics (Rehfuess et al. 2013). Causal diagrams, such as directed acyclic graphs (Greenland et al. 1999) and logic models (Anderson et al. 2011), visually present complex relationships between variables and provide the framework for identifying study inclusion/exclusion criteria, guiding the literature search strategy, informing the variables for data extraction, and examining the factors that may contribute to differences between studies. The exposure and outcome of interest, as well as potential effect modifiers (variables that may modify the effect of the exposure on

<figure><img src="image_3.png" /></figure>

Footnote: Adapted from the protocol (Pega et al. 2020a).

<caption>**Fig. 1.** Logic model of the possible causal relationship between occupational exposure to welding fumes and trachea, bronchus, and lung cancer.</caption>
the outcome) and confounders (variables that are associated with and precede both the exposure and outcome), are presented on a single diagram, with arrowheads showing the directionality in the relationships.

Fig. 1 presents the logic model for our systematic review and meta-analysis of the causal relationship between occupational exposure to welding fumes (risk factor) and trachea, bronchus, and lung cancer (outcome). This is an a priori, process-orientated logic model (Rehfuess et al. 2018) that seeks to capture the complexity of the risk factor-outcome causal relationship (Anderson et al. 2011). The Tier 1: "Important confounders" are age and sex. The Tier 2: "Other potentially important confounders" are socioeconomic position, tobacco smoking and exposure to asbestos, which was commonly used as an insulating material in ships, the material covering rod electrodes, the cylinders holding acetylene gas, and the heat-protective equipment of welders and blankets to slow cooling of the weld (Fig. 1). Potential effect modifiers are: country, age, sex, industrial sector, formality of economy, tobacco smoking, exposure to asbestos, base metals welded, welding technique/ process, duration of welding tasks, and related activities (preparation, clean-up, breaks, etc.), the position of the welder, degree of ventilation of the occupational setting, and the use of personal protective equipment. Furthermore, the welders' level of experience may also influence the particles generated from welding fumes (Chang et al. 2013); increased exposure may occur for apprentice welders or welders with minimal training (Graczyk et al. 2016).

## 2. Objectives

To systematically review and meta-analyse randomized control studies, cohort studies, case-control studies, and other non-randomized intervention studies with estimates of the relative effect of any (or high) occupational exposure to welding fumes on the prevalence of, incidence of or mortality from trachea, bronchus, and lung cancer in any year among the working-age population, compared with the minimum risk exposure level of no (or low) exposure to welding fumes.

## 3. Methods

### 3.1. Developed protocol

The Navigation Guide (Woodruff and Sutton 2014) for systematic reviews in environmental and occupational health was used as our guiding methodological framework and applied wherever feasible. The Navigation Guide applies established systematic review methods from clinical medicine, including standard Cochrane methods for systematic reviews of interventions, to the field of environmental and occupational health to ensure systematic and rigorous evidence synthesis that reduces bias and maximizes transparency (Woodruff and Sutton 2014). The need for further methodological development and refinement of the relatively novel Navigation Guide has been acknowledged (Woodruff and Sutton 2014). Our systematic review used most of the Navigation Guide framework; steps 1–6 for the stream on human data were conducted; we left out steps for the stream on non-human data, opting instead for a brief narrative synthesis of that evidence (see Section 1.4).

We developed and published our protocol (Pega et al. 2020a). This protocol adheres with the preferred reporting items for systematic review and meta-analysis protocols statement (PRISMA-P) (Moher et al. 2015; Shamseer et al. 2015), with the abstract adhering with the reporting items for systematic reviews in journal and conference abstracts (PRISMA-A). Any modification of the methods stated in the protocol is reported in Section 8 in this article. Our review has been presented in concordance with the preferred reporting items for systematic review and meta-analysis statement (PRISMA) (Page et al. 2021b). The reporting of the parameters for estimating the burden of trachea, bronchus, and lung cancer that is attributable to occupational exposure to welding fumes in the systematic review adheres to the requirements of the GATHER guidelines (Stevens et al. 2016) because the

WHO/ILO Joint Estimates that may be produced consecutive to this systematic review must also adhere to these reporting guidelines.

All methods and reporting guidelines were standardised across all systematic reviews conducted for the WHO/ILO Joint Estimates (Pega et al. 2021c).

### 3.2. Searched literature

#### 3.2.1. Electronic academic databases

We searched the seven following electronic academic databases to the specified date:

1. International Clinical Trials Register Platform (inception to 15 April 2020).

2. CENTRAL (1 January 1996 to 15 April 2020).

3. Ovid Medline (1 January 1946 to 30 April 2020).

4. PubMed (1 January 1946 to 15 April 2020).

5. EMBASE (1 January 1947 to 15 April 2020).

6. Web of Science (1 January 1945 to 15 April 2020).

7. CISDOC (1 January 1901 to 31 December 2012).

The Ovid MEDLINE search strategy was presented in the protocol (Pega et al. 2020a). The full search strategies for all databases were revised by an information scientist and are presented in Appendix 1 in the Supplementary data. We performed searches in electronic databases operated in the English language using a search strategy in the English language between March and May 2020. When we neared completion of the review, we conducted a search of the Ovid MEDLINE and PubMed databases on 14 March 2022 to capture the most recent publications (e. g., publications ahead of print). Deviations from the planned search strategy are documented in Section 8.

#### 3.2.2. Electronic grey literature databases

We searched the following two electronic academic databases up to 21 April 2020:

1. OpenGrey (https://www.opengrey.eu/).

2. Grey Literature Report (https://www.nyam.org/library/collections-and-resources/grey-literature-report/).

#### 3.2.3. Internet search engines

We also searched Google (https://www.google.com) and Google Scholar (https://www.google.com/scholar) and screened the first 100 hits for potentially relevant records, as has previously been done in Cochrane Reviews (Pega et al. 2022c).

#### 3.2.4. Organizational websites

The websites of the seven following international organizations and national government departments were searched in May 2020:

1. International Labour Organization (https://www.ilo.org).

2. World Health Organization (https://www.who.int).

3. International Agency for Research on Cancer (https://www.iarc.fr).

4. European Agency for Safety and Health at Work (https://www.osha.europa.eu).

5. Eurostat (https://www.ec.europa.eu/eurostat/web/main/home).

6. China National Knowledge Infrastructure (https://www.cnki.net).

7. Finnish Institute of Occupational Health (https://www.tti.fi/en/).

8. United States National Institute of Occupational Safety and Health (NIOSH) of the United States of America, using the NIOSH data and statistics gateway (https://www.cdc.gov/niosh/data).

#### 3.2.5. Hand-searching and expert consultation

We hand-searched for potentially eligible studies in:

* Reference lists of previous systematic reviews.
* Reference lists of all included study records.

* Study records published over the past 24 months in the three peer-reviewed academic journals with the largest number of included studies.

* Study records that have cited the included studies (identified in the Web of Science citation database).

* Collections of the review authors.

Additional experts were contacted with a list of included studies, with the request to identify potentially eligible additional studies.

### 3.3. Selected studies

Study selection was carried out in Covidence systematic review software (Veritas Health Innovation, Melbourne, Australia). All study records identified in the search were downloaded, and duplicates were identified and deleted. Afterwards, at least two review authors, working in pairs, independently screened titles and abstracts (step 1), and then full texts (step 2) of potentially relevant study records. A third review author resolved any disagreements between the first two review authors. Study records were not assigned to reviewers who had been authors of that study. The study selection was documented in a flow chart in the systematic review, as per PRISMA guidelines.

### 3.4. Eligibility criteria

The population, exposure, comparator, and outcome (PECO) criteria (Morgan et al. 2018) are described below.

#### 3.4.1. Types of populations

We included studies of working-age (≥15 years) workers in the formal and informal economy. Studies of children (aged < 15 years) and unpaid domestic workers were excluded. Participants residing in any Member State of WHO and/or ILO and working in any industrial sector or occupation were included. Occupational exposure to welding fumes may potentially have further population reach (e.g., as an environmental exposure, through the release of welding fumes from the workplace into the community); the scope of our systematic reviews did not capture these populations and impacts on them. Appendix A in the Supplementary data for Pega et al. (2020a) provides a briefer overview of the PECO criteria.

#### 3.4.2. Types of exposures

We included studies of occupational exposure to welding fumes in accordance with our standard definition (Table 1). Occupational exposure to welding fumes may be measured in several ways:

* Directly with quantitative measurement (e.g., by means of technology, such as air monitoring).

* Directly by observation of the work process.

* Indirectly by proxy of occupation (or job title), such as relevant codes and/or titles of the International Standard Classification of

Occupations (ISCO) (International Labour Organization 1966; 1987; 2012) (Table 2).

* Indirectly by job task of welding.

* Indirectly by classification in a job-exposure matrix (JEM) based on expert judgment or data external to the study.

* Indirectly by judgment of scientists with subject matter expertise.

* Indirectly by self-report by a worker or their workplace manager or spouse.

Studies using any of the preceding methods to identify occupational exposure to welding fumes were eligible for inclusion. However, studies of workers whose jobs may include occasional or infrequent welding, such as plumbers, pipefitters or vehicle repairers, were excluded from this exposure definition, but could be considered in subsequent updates. Studies using industrial sector as a proxy, which may be measured using the codes of the International Standard Industrial Classification of All Economic Activities (United Nations 2008), were also excluded, because we judged measurements of industrial sector unable to identify workers occupationally exposed to welding fumes. Similarly, studies that combined occupation as a welder into broad groups with other occupations or industrial sectors were also ineligible, as these groupings lack specificity for occupational exposure to welding fumes (International Agency for Research on Cancer 2018).

If a study presented both direct and indirect measurements, and/or objective and subjective measurements, then we prioritized direct and objective measurements. We included studies with measures from any data source, including registry data.

#### 3.4.3. Types of comparators

The included comparator was participants exposed to the theoretical minimum risk exposure level of no (or low) occupational exposure to welding fumes (Table 1). As the aim of the systematic review and meta-analysis is risk quantification (rather than hazard identification), and we are therefore pursuing the most accurate risk estimates, we excluded all other comparators, including comparators that may have included welders, such as the general population (see also text on standardized rate ratios and odds ratios [ORs] in below Section 3.4.6).

#### 3.4.4. Types of outcomes

We included studies that defined trachea, bronchus, and lung cancer in accordance with our standard definition of this outcome (see Section 1.3). We included studies that classified these cancers using the relevant diagnostic codes in ICD-10 (see above), ICD-9 (i.e., “162 Malignant neoplasm of trachea, bronchus, and lung”) or other versions of the ICD. Studies were also included if they measured the outcome with methods that we judged to approximate the ICD-10 criteria (e.g., where an ICD code was not reported, it was inferred from the information on the cancer site reported in the study record).

The following measurements of trachea, bronchus, and lung cancer were regarded as eligible:

(i) Diagnosis by a physician with imaging.

<caption>**Table 2**</caption>

International Standard Classification of Occupation (ISCO) codes and titles of occupations classified as occupationally exposed to welding fumes.

<table><thead><tr><td>ISCO revision</td><td>Code</td><td>Title</td></tr></thead><tbody><tr><td rowspan="7">ISCO-68 (International Labour Organization 1966)</td><td>87200</td><td>Welders</td></tr><tr><td>87210</td><td>Gas & electric welders (general)</td></tr><tr><td>87215</td><td>Gas welders</td></tr><tr><td>87220</td><td>Electric arc welders (hand)</td></tr><tr><td>87225</td><td>Electric arc welders (machine)</td></tr><tr><td>87230</td><td>Thermite arc welders</td></tr><tr><td>87235</td><td>Resistance welders</td></tr><tr><td>ISCO-88 (International Labour Organization 1987)</td><td>7212</td><td>Welders and flame cutters</td></tr><tr><td>ISCO-08 (International Labour Organization 2012)</td><td>7212</td><td>Welders and flame cutters</td></tr></tbody></table>

Footnotes: ISCO-68 codes adopted from Kendzia et al. (2013).
(ii) Hospital discharge records.

(iii) Other relevant administrative data (e.g., records of sickness absence or disability).

(iv) Registry data for diagnosis of and/or treatment for an eligible trachea, bronchus, and lung cancer.

(v) Medically certified cause of death.

(vi) Self-reported diagnosis.

All other measures were excluded from this systematic review.

Objective and subjective measures of the outcome were eligible. If a study presented both objective and subjective measurements, then the objective ones were selected.

### 3.4.5. Types of studies

We included studies that investigated the effect of occupational exposure to welding fumes on trachea, bronchus, and lung cancer, for any study year(s), and over any period. Eligible study designs were randomized controlled trials (including parallel-group, cluster, cross-over and factorial trials), cohort studies (both prospective and retrospective), case-control studies, and other non-randomized intervention studies (including quasi-randomized controlled trials, controlled before-after studies, and interrupted time series studies). We included a broader set of observational study designs than is commonly included, because an augmented Cochrane Review of complex interventions identified valuable additional studies using such a broader set of study designs (Arditi et al. 2016). As we have an interest in quantifying risk and not in qualitative assessment of hazard (Barroga and Kojima 2013), we excluded all other study designs (e.g., uncontrolled before-and-after, cross-sectional, qualitative, modelling, case, and non-original studies).

Study records published in any year and any language were included. The search was conducted using English language terms, so that records published in any language that presented essential information (i.e., title and abstract) in English were included. If a record was written in a language other than those spoken by the authors of this review, then the record was translated into English. Published and unpublished studies were included. Studies conducted using unethical practices were excluded (e.g., randomized controlled trials that deliberately exposed humans to a known risk factor to human health).

### 3.4.6. Types of effect measures

We included measures of the effect of any (or high) occupational exposure to welding fumes on the risk of having, acquiring or dying from cancer of the trachea, bronchus or lung, compared with the theoretical minimum risk exposure level (i.e., no or low, such occupational exposure). Included were relative effect measures, namely RR's and OR's for prevalence measures, and hazard ratios for incidence measures (e.g., acquired or died from a trachea, bronchus, and lung cancer). Measures of absolute effects (e.g., mean differences in risks or odds) were converted into relative effect measures, but if conversion was impossible, they were excluded. To ensure comparability of effect estimates and facilitate meta-analysis, if a study presented an OR, then we converted it into a RR, if possible, using the guidance provided in Cochrane's handbook for systematic reviews of interventions (Deeks et al. 2019; Higgins et al. 2021; Schünemann et al. 2021). As described by Hogue et al. (1983), this can be done by using the baseline risk. As there is some debate over the point at which to convert ORs into RR's, (Xiao et al. 2020; Doi et al. 2022a; Doi et al. 2022b; Xiao et al. 2022) for the main meta-analysis per outcome, we also meta-analysed the ORs first, and then converted the resultant pooled OR into a RR. To be comprehensive, we report these alternative analyses in an appendix.

If a study reported an eligible effect estimate without measure of uncertainty (e.g., 95% CI or standard deviation), but did report another statistic that could be used to back-calculate (or estimate) an eligible measure of uncertainty (e.g., a p value from a regression analysis), then we calculated the measure of uncertainty. That is, for the effect estimate reported in the study by Steenland et al. (1986), we calculated its 95%

CI from the p value reported, using the method outlined by Altman and Bland (2011). If we entered a study with such a back-calculated measure of uncertainty in a meta-analysis, we conducted a sensitivity analysis of the meta-analysis with the effect estimate removed and used this analysis to assess if the study made a substantive difference to the meta-analysis. We report the methods and input data for such calculations in an appendix.

To ensure an unexposed comparison group, studies that included a general population comparator group were excluded from this systematic review, as the general population may include persons potentially occupationally exposed to welding fumes. Therefore, standardized RR's, for example or standardized OR's, where the rates or odds of prevalence, incidence or mortality among the exposed population were compared with the rates or odds amongst the general population, were excluded from the systematic review (see also Section 3.4.3).

If a study presented estimates for the effect from two or more alternative models that had been adjusted for different variables, then we systematically prioritized the estimate from the model that we considered best adjusted, applying the lists of potential effect modifiers and confounders identified in our logic model (Fig. 1). We generally prioritized estimates from models adjusted for more potential confounders over those from models adjusted for fewer. For example, if a study presented estimates from a crude, unadjusted model (Model A), a model adjusted for one potential confounder (Model B), and a model adjusted for two potential confounders (Model C), then we prioritized the estimate from Model C. However, we also considered the potential for over-adjustment in models that included non-confounders as covariates. We prioritized estimates from models unadjusted for mediators over those from models that adjusted for mediators, because adjustment for mediators can introduce bias. For example, if Model A had been adjusted for two confounders and Model B had been adjusted for the same two confounders and a potential mediator (e.g., biomarkers of exposure to welding fumes), then we chose the estimate from Model A. We prioritized estimates from models that could adjust for time-varying confounders that were at the same time also mediators, such as marginal structural models (Pega et al. 2016), over estimates from models that could only adjust for time-varying confounders, such as fixed-effects models (Gunasekara et al. 2014), over estimates from models that could not adjust for time-varying confounding. If a study presented effect estimates from two or more potentially eligible models, we provide an explanation as to why we prioritized the model we selected.

If adjustment for one or both Tier 1 confounders was somewhat unclear (due to unclear reporting), but we reasonably assumed it to have occurred, we did include this study in the meta-analysis, but conducted a sensitivity analysis without this study to check the impact of the study on the meta-analysis (see Section 3.9).

For case-control studies matched by Tier 1 confounding variables (i. e., age and sex), we applied the following eligibility criteria, as applied in a previous systematic review for the WHO/ILO Joint Estimates (World Health Organization 2021). As Pearce (2016) has pointed out, "Matching in a case-control study does not control for confounding by the matching factors" (p.1), so it may be necessary to control for the matching factors in the analysis. Matching, without controlling for the matching factors may create an association with the matching factor, even if no such association existed before matching (Pearce 2016). Therefore, if a case-control study matched by one or both Tier 1 confounders, but did not adjust for these matching variables (e.g., in a regression analysis), we included this study in the systematic review, but excluded it from the meta-analysis (as done previously (World Health Organization 2021)). As stated by Pearce (2016), a matched (conditional) analysis is not always required, and standard (unconditional) analysis may be valid and appropriate; therefore, we included effect estimates regardless of conditionality of analysis.

For the Kendzia et al. (2013) individual participant data analysis, we referred to the original study record(s) of an included study to systematically identify the best effect estimate(s) for this included study (i.e.,
the one(s) reported in the original study record(s) or the recalculated one(s) reported in the study record of the Kendzia analysis).

### 3.5. Data extraction and data items

We used the standard data extraction sheet that WHO and ILO have developed for their series of systematic reviews for the WHO/ILO Joint Estimates. The data extraction sheet was trialled until data extractors reached convergence and agreement. At least two review authors independently extracted data on study characteristics (including study authors, study year, study country, participants, exposure, and outcome), study design (including study type, comparator, epidemiological model(s) used, and effect estimate measure), and risk of bias (including source population representation, blinding, exposure assessment, outcome assessment, confounding, incomplete outcome data, selective outcome reporting, conflict of interest including statements of declarations of interest and funding sources, and other sources of bias). A third review author resolved conflicts in data extraction. Data were entered into and managed with Excel (Microsoft, Redmond, United States of America (USA)).

### 3.6. Requested missing data

We did not request missing data (see Section 8).

### 3.7. Assessed risk of bias

Standard risk of bias tools do not exist for systematic reviews for hazard identification or those for risk assessment in occupational and environmental health (Rooney et al. 2016). The five such tools developed specifically for occupational and environmental health are for either or both hazard identification and risk assessment, and they differ substantially in the types of studies (randomized, observational, and/or simulation studies) and data (e.g., human, animal and/or in vitro) they seek to assess (Rooney et al. 2016). However, all five tools, including the Navigation Guide, assess risk of bias in human studies similarly (Rooney et al. 2016).

Consistent with using the Navigation Guide as our organizing framework, we used its risk of bias tool, which builds on the standard risk of bias assessment methods of Cochrane (Higgins et al. 2021) and the US Agency for Healthcare Research and Quality (Viswanathan et al. 2008), and has been successfully applied in several systematic reviews (Koustas et al. 2014; Lam et al. 2014; Vesterinen et al. 2015; Johnson et al. 2016; Lam et al. 2016a,b; Lam et al. 2017; Lam et al. 2021). To adhere with the latest methods in the Navigation Guide, we used updates from a version published in the protocol for a recent systematic review (Lam et al. 2016b; Lam et al. 2021).

We assessed risk of bias on the individual study level and across the body of evidence for each outcome. To judge the risk of bias in each domain, we applied a priori instructions (Li et al. 2018), adapted from the Navigation Guide systematic review (Lam et al. 2016b; Lam et al. 2021), and further described in our protocol (Pega et al. 2020a). The assessment was conducted along the Navigation Guide risk of bias domains, including consideration of source population representation, blinding, exposure assessment, outcome assessment, confounding, incomplete outcome data, selective outcome reporting, conflict of interest, and other sources of bias.

All risk of bias assessors jointly trialled the application of the risk of bias criteria until they had synchronized their understanding and application of these criteria. Two or more study authors independently assessed the risk of bias for each study by outcome. Where individual assessments differed, a third author resolved the conflict. For each included study, we reported the risk of bias assessment by domains in a standard “Risk of bias” table (Higgins et al. 2021). For the entire body of evidence, we presented the study-level risk of bias ratings for each individual study by domains in a “Risk of bias summary” figure (Higgins et al. 2021).

During the systematic review, we identified the need to further develop our criteria for assessments of risk of bias due to exposure misclassification. We agreed that for studies that based exposure assignment on an occupation or a job title of “welder” (or similar) alone, we would rate risk of bias in this domain to be relatively higher than for studies that assigned exposure based on a job task of “welding” or employed a welding-specific questionnaire or more complex job exposure matrix. We present the fully updated risk of bias assessment criteria in Appendix 2. This supersedes the criteria presented in the protocol (see Appendix C in Pega et al. (2020a)).

### 3.8. Synthesised evidence (including conducted meta-analysis)

We conducted meta-analyses separately for estimates of the effect on prevalence, incidence, and mortality. If we found two or more studies with an eligible effect estimate, two or more review authors independently investigated the clinical heterogeneity (Deeks et al. 2011) of the studies in terms of participants (including country, sex, age, and industrial sector or occupation), level of risk factor exposure, comparator, and outcomes, following our protocol (Pega et al. 2020a). Differences by country could include or be expanded to include differences by country group (e.g., WHO region or World Bank income group). If the effect estimates differed considerably by WHO region, sex, and/or age or a combination of these, then we synthesised evidence for the relevant populations defined by these variables or combination thereof. If we found effect estimates to be clinically homogeneous across WHO regions, sex, and/or age groups, then we combined studies from all these populations into one pooled effect estimate that would be applied across all combinations of WHO regions, sexes, and age groups in the WHO/ILO Joint Estimate.

If we judged two or more studies for the relevant combination of WHO region, sex, and age group or combination thereof, to be sufficiently clinically homogeneous to potentially be combined using quantitative meta-analysis, then we tested the statistical heterogeneity of the studies using the I² statistic (Figueroa 2014). If two or more clinically homogeneous studies were found to be sufficiently homogeneous statistically to be combined in a meta-analysis, we pooled the effect estimates of these studies in a quantitative meta-analysis, using the inverse variance method with a random effects model to account for cross-study heterogeneity (Figueroa 2014). We prepared the data for entry using Excel and conducted the meta-analysis in RevMan version 5.4.1 (Nordic Cochrane Centre, Copenhagen, Denmark). We input RR (log-transformed) and their standard errors into RevMan with a precision of two decimal places. Standard errors were calculated from the lower confidence limits, which sometimes results in small discrepancies between the upper confidence limit reported in the original study record and that displayed in the forest plot of the relevant meta-analysis.

If a study reported two or more estimates of the effect of different levels of exposure, as compared with no (or low) exposure (reference group), the estimates associated with these comparisons were not independent, as the study participants in the reference group were shared across the comparisons. Therefore, these estimates could not be included in the same meta-analysis as if they came from separate studies. In such cases, we computed a composite (average) study-level effect estimate for the comparison of each exposure level versus the reference group, by taking within-study correlation into consideration as suggested in the Cochrane handbook for systematic reviews of interventions (McKenzie and Brennan 2021). We followed the principles outlined by (Borenstein et al. 2009). We then entered this pooled effect estimate for this study in the meta-analysis and reported more detailed methodological information and data inputs in an appendix.

We neither quantitatively combined data from studies with different designs (e.g., did not combine cohort studies with case-controls studies), nor unadjusted effect estimates with adjusted ones. We only combined studies that we judged to have a minimum acceptable level of
adjustment for confounders (i.e., a study must have adjusted for at least one of the two Tier 1 confounders of age or sex). In instances where two or more studies of the same data source (e.g., the same study cohort) were eligible for inclusion in the meta-analysis, we prioritized in this order: i) the study with the most informative assessment of exposure to welding fumes; ii) the study with the longest follow-up period; and iii) the study with the most complete control of relevant potential confounders. If our pre-specified rules for selecting a study's result did not allow us to uniquely identify-one for inclusion, we randomly selected one study. If quantitative synthesis was not feasible, we synthesised the study findings narratively and identified the estimates that we judged to be the highest quality evidence available.

### 3.9. Conducted subgroup and sensitivity analyses

We conducted the following subgroup analyses for the main meta-analysis:

* WHO region (six categories: African Region, Region of the Americas, Eastern Mediterranean Region, European Region, South-East Asian Region, and Western Pacific Region).

* Sex (three categories: female, male, and other).

* Occupation (ISCO codes).

* Cancer site (four categories: trachea, bronchus, lung cancer, and two or more of these sites).

* Publication year of the study (four categories: 1980 s, 1990 s, 2000 s, and 2010 s).

We found insufficient data to conduct our planned subgroup analyses by:

* Age group (13 categories: 15–19, 20–24, 25–29, ..., 90–94, and ≥ 95 years).

* Socio-economic status (e.g., education or income level).

* Industrial sector (ISIC codes).

* Formality of the economy (two categories: informal economy, and formal economy).

We conducted the following sensitivity analyses:

* Studies judged to be of “high”/“probably high” risk of bias in any domain, compared with “low”/“probably low” risk of bias in all domains.

* Studies judged to be of “high”/“probably high” risk of bias from exposure misclassification, compared with “low”/“probably low” risk of bias in this domain.

* Studies judged to be of “high”/“probably high” risk of bias from conflict of interest, compared with “low”/“probably low” risk of bias in this domain.

* Studies judged to be of “high”/“probably high” risk of confounding, compared with “low”/“probably low” risk of confounding.

* Studies with documented or approximated ICD-10 diagnostic codes (e.g., as recorded in administrative health records), compared with studies without ICD-10 codes (e.g., self-reports).

* Studies with adjustment for tobacco smoking and/or exposure to asbestos, compared with studies with adjustment for neither tobacco smoking, nor exposure to asbestos.

* Meta-analysis with the inverse variance heterogeneity (IVhet) model (Doi et al. 2017), compared with the standard inverse variance meta-analysis with random effects.

* For meta-analyses with two or more individual effect estimates from the same study, conducted a meta-analysis with the individual effect estimates included individually, compared with a meta-analysis with the individual effect estimates first pooled in a fixed effect meta-analysis and then the pooled effect estimate included in the meta-analysis per study.

* For meta-analyses with one or more included effect estimates for which adjustment for one or both Tier 1 confounders was assumed but somewhat unclear (see Section 3.4.6), conducted a meta-analysis with these effect estimates included or compared with a meta-analysis with these effect estimates excluded.

* For case-control studies, meta-analyses with ORs converted to RR before conducting the quantitative meta-analysis, compared with meta-analyses with ORs pooled in the quantitative meta-analysis and then the pooled OR being converted to a RR.

* For cohort studies, those that reported RR as the effect estimate, compared with those that reported hazard ratios.

* Studies for which we calculated average effect estimates from two or more effect estimates of different exposure levels using the method developed by Borenstein et al. (2009), compared with studies for which we did not have to do such calculations.

* Studies for which we back-calculated the standard error from a p value, compared with studies for which we back-calculated the standard error from a 95 % CI.

* Following peer-review, we conducted meta-analyses with RR among non-smokers or never-smokers, compared with meta-analyses with RR among smokers.

We also conducted leave-one-out analyses to explore changes in heterogeneity and pooled effect estimates.

### 3.10. Assessed quality of evidence

We assessed quality of evidence using a modified version of the Navigation Guide quality of evidence assessment approach (Lam et al. 2016b). The approach is based on the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach (Schümemann et al. 2011) adapted specifically to systematic reviews in occupational and environmental health (Morgan et al. 2016).

A group of review authors comprising members of the working group of individual experts assessed quality of evidence for the entire body of evidence by outcome, through a consensus process. All review authors were subsequently given the opportunity to review and propose revisions to the assessments. The ratings and justifications presented here are the final consensus ratings and justifications of the working group of individual experts. We adapted the latest Navigation Guide instructions (Lam et al. 2016a) for assessing the quality of evidence and presented the adapted instructions in our protocol (Pega et al. 2020a). We graded the quality of the entire body of evidence by outcome, using the three Navigation Guide standard quality of evidence ratings: “high”, “moderate”, and “low” (Lam et al. 2016b). We downgraded the quality of evidence for the following five reasons: (i) risk of bias; (ii) inconsistency; (iii) indirectness; (iv) imprecision; and (v) publication bias (Balshem et al. 2011).

If our main meta-analysis included ten or more studies, we generated an Egger's funnel plot to judge our level of concern regarding publication bias (Egger et al. 1997). Additionally, we also produced a Doi plot and Luis Furuya-Kanamori (LFK) index. Briefly, the Doi plot is a variant of the normal quintile versus effect plot using a rank-based measure of precision (z-score), instead of the standard error, which is plotted against the effect size (Furuya-Kanamori et al. 2018). The most precise studies define the midpoint around which results scatter, whereas smaller, less precise studies produce an effect size that scatters increasingly widely, and the absolute z-score gradually increases for both smaller and larger effect sizes on either side of the precise studies. Doi plot asymmetry was quantified with the LFK index (Furuya-Kanamori et al. 2018; Furuya-Kanamori et al. 2020). The LFK index quantifies the difference between the two areas under the Doi plot, created by the perpendicular line to the X-axis from the effect size with the lowest absolute Z score on the Doi plot (Furuya-Kanamori et al. 2018). A symmetrical, mountain-like Doi plot and LFK index $<|1|$ indicate no asymmetry; an LFK index between $|1|$ and $|2|$ indicates minor
asymmetry; and an LFK index >|2| indicates major asymmetry (Furuya-Kanamori et al. 2018). In empirical simulation studies, these methods have demonstrated greater power to detect publication bias with as few as five estimates than p value driven methods (Furuya-Kanamori et al. 2020). If our main meta-analysis included four or fewer studies only, we judged the risk of publication bias qualitatively.

Within each of the relevant domains, we rated the concern for the quality of evidence, using the ratings “no or only minor concerns”, “serious concerns”, and “very serious concerns”. As per Navigation Guide, randomized studies start at “high” quality of evidence and observational studies at “moderate” quality of evidence”. Quality of evidence was downgraded for a rating of “serious concerns” by one level and for one of “very serious concerns” by two levels. We upgraded the quality of evidence for a large effect size, evidence of a dose-response relationship, and residual confounding and bias not plausibly explaining the effect. There had to be compelling reasons to upgrade or downgrade. If we had serious concerns for risk of bias in a body of evidence consisting of observational studies, but had no other concerns, and had no reasons for upgrading, then we downgraded the quality of evidence by one level from “moderate” to “low”.

Regarding large effect size, our protocol did not pre-specify criteria for judging what constitutes large and very large effect sizes in this systematic review (Pega et al. 2020a). Moreover, we judged the definitions of large and very large effect sizes provided in the GRADE handbook (2013) to not represent typical findings in environmental and occupational epidemiology even when materially important risks are observed (Huangfu and Atkinson 2020). Initially, we considered the cut-off of change in RR by ≥ 25 % (i.e., an RR of ≤ 0.75 or ≥ 1.25) as indicative of a large effect size, informed by a previous WHO/ILO systematic review on the effect of occupational exposure to noise on cardiovascular disease outcomes (Teixeira et al. 2021b), which had been informed by a WHO evidence review on the health effects of environmental exposure to noise (van Kempen et al. 2018). However, this criterion was adopted from a different risk factor-health outcome pair and may be seen as arbitrary. During the peer-review process it was suggested that we could adopt the strategy used in a recent WHO evidence review on the effect of long-term air pollution on mortality (Huangfu and Atkinson 2020), which calculated the so-called E-values and considered a reference confounder to assess the likelihood of residual confounding (Verbeek et al. 2021). Briefly, this approach attempts to judge the magnitude of an observed effect (pooled RR) of risk factor relative to a critical threshold (i.e., the E-value), which the effect of a plausible unmeasured confounder would have to exceed to attenuate the effect of said risk factor to 1.00. That is, if an unmeasured confounder (e.g., tobacco smoking or exposure to asbestos) has an effect on both the risk factor (i.e., occupational exposure to welding fumes welding) and the outcome (i.e., trachea, bronchus, and lung cancer prevalence, incidence or mortality) equal to or larger than the E-value, then it can be assumed that there is no evidence of a large effect, as the pooled RR of the risk factor could be reduced to null. Conversely, if this threshold is not exceeded by the effect of the confounder, one can assume a large effect of the risk factor. Since neither of the two approaches is beyond reproach, we applied them both judiciously and in tandem. We therefore relied on two approaches to determine whether the quality of evidence should be upgraded for a large effect size.

### 3.11. Assessed strength of evidence

Our systematic review included observational epidemiologic studies of human data only, and no other streams of evidence (e.g., no studies of non-human data). The standard Navigation Guide methodology (Lam et al. 2016b) allows for rating human and non-human animal studies separately, and then combining the strength of evidence for each stream for an overall strength of evidence rating. However, the Navigation Guide also allows for rating one stream of evidence based on the factors described above (i.e., risk of bias, indirectness, inconsistency,

imprecision, publication bias, large effect, dose-response, and residual confounding and bias) to arrive at an overall rating of the quality of evidence as “high”, “moderate” or “low” (see above and the protocol). The approach of evaluating only the human evidence stream is consistent with the GRADE methodology that has adopted the Bradford Hill considerations (Schünemann et al. 2011) So, using the method above based on the Navigation Guide incorporates the considerations of Bradford Hill (see Appendix 3 in the Supplementary data).

An additional step described in the protocol integrates the quality of the evidence (as described above) with other elements including direction of effect, confidence in the effect, and other compelling attributes of the data that may influence our certainty to allow for an overall rating that consists of “sufficient evidence of harmfulness”, “limited evidence of harmfulness”, “inadequate evidence of harmfulness”, and “evidence of lack of harmfulness” based on human evidence. This approach to evaluate only the human evidence has been applied in previous systematic reviews (Lam et al. 2016b; Lam et al. 2017) and verified by the US National Academy of Sciences (National Academies of Sciences Engineering and Medicine). It also provides two steps that integrate Bradford Hill criteria (evaluating the quality of the evidence and then evaluating the overall strength of evidence). Finally, the GRADE quality of evidence ratings (which are the same as for Navigation Guide and the IARC Monographs) are analogous to the final ratings from Bradford Hill for causality (Schünemann et al. 2011) (Table 3).

## 4. Results

### 4.1. Study selection

A flow diagram of the study selection is presented in Fig. 2. Forty-one records from 40 studies fulfilled the eligibility criteria and were included in the systematic review. For the 30 excluded studies that most closely resembled inclusion criteria the reasons for exclusion are listed in Appendix 4 in the Supplementary data. Of the 40 included studies, 35 were included in one or more quantitative meta-analyses.

### 4.2. Characteristics of included studies

The characteristics of the included studies (ordered alphabetically) are summarized in Table 4.

#### 4.2.1. Study type

Two-thirds of included studies (29) were case-control studies, and the other third were cohort studies (11). The type of effect estimate most commonly reported was ORs (29 studies), followed by risk or rate ratios (seven studies) and hazard ratios (four studies).

Thirty-eight studies adjusted for any of our pre-specified Tiers 1 and 2 confounders (including by restriction), but two studies did not adjust for any of these potential confounders. The confounders most commonly adjusted for were the two Tier 1 confounder of sex (37 studies, including 33 studies that restricted their study populations to males) and age (35 studies). Fewer studies adjusted for socioeconomic status (eight studies).

#### 4.2.2. Population studied

The effective (or analysis) sample sizes of the included studies captured >1,265,512 workers in total, with at least 22,761 females and 1,231,171 males studied.

Thirty-five and four studies examined male workers and workers of both sexes, respectively. No studies examined females only. For one study it was unclear whether females were included in the analyses.

For many studies, the age group being studied was unclear. Several studies did not indicate an upper or lower age range, instead reporting age ranges such as between < 40 years and > 70 years.

Over half of the included studies examined populations in the WHO European Region (23 studies from 17 countries, including two multi-national studies). Almost all of the remaining studies investigated
<table><caption>**Table 3**

Interpretation of the GRADE ratings of the overall quality of evidence, and the Navigation Guide and IARC Monographs ratings of strength of evidence evaluation.</caption><thead><tr><th>GRADE rating for quality of evidence</th><th>Interpretation of GRADE rating</th><th>Navigation Guide rating for strength of evidence for human evidence</th><th>Interpretation of Navigation Guide rating</th><th>IARC Monographs rating (descriptor) for strength of evidence descriptor for carcinogenicity in humans</th><th>Interpretation of IARC Monographs rating</th></tr></thead><tbody><tr><td>High</td><td>There is high confidence that the true effect lies close to that of the estimate of the effect.</td><td><strong>Sufficient evidence of harmfulness</strong><br><br><strong>Evidence of lack of harmfulness</strong></td><td>A positive relationship is observed between exposure and outcome where chance, bias, and confounding can be ruled out with reasonable confidence. The available evidence includes results from one or well-designed, well conducted studies, and the conclusion is unlikely to be strongly affected by the results of future studies.<br><br>The available evidence includes consistent results from well designed, well conducted studies, and the conclusion is unlikely to be strongly affected by the results of future studies; for human evidence, more than one study showed no effect on the outcome of interest at the full range of exposure levels that humans are known to encounter, and bias and confounding can be ruled out with reasonable confidence; the conclusion is limited to the age at exposure and/or other conditions and levels of exposure studied.</td><td><strong>Sufficient strength of evidence</strong></td><td>A causal association has been established: A positive association has been observed in the body of evidence on exposure to the agent and cancer in studies in which chance, bias, and confounding were ruled out with reasonable confidence.</td></tr><tr><td>Moderate</td><td>There is moderate confidence in the effect estimate: the true effect is likely to be close to the estimate of the effect, but there is a possibility that it is substantially different.</td><td><strong>Limited evidence of harmfulness</strong></td><td>A positive relationship is observed between exposure and outcome where chance, bias, and confounding cannot be ruled out with reasonable confidence. Confidence in the relationship is constrained by such factors as: the number, size or quality of individual studies or inconsistency of findings across individual studies. As more information becomes available, the observed effect could change, and this change may be large enough to alter the conclusion.</td><td><strong>Limited strength of evidence</strong></td><td>A causal interpretation of the positive association observed in the body of evidence on exposure to the agent and cancer is credible, but chance, bias, or confounding could not be ruled out with reasonable confidence.</td></tr><tr><td>Low</td><td>The panel's confidence in the effect estimate is limited: the true effect may be substantially different from the estimate of the effect.</td><td><strong>Inadequate evidence of harmfulness</strong></td><td>The available evidence is insufficient to assess effects of the exposure. Evidence is insufficient because of: the limited number or size of studies, low quality of individual studies or inconsistency of findings across individual studies. More information may allow an assessment of effects.</td><td><strong>Inadequate strength of evidence</strong></td><td>A causal interpretation of the positive association observed in the body of evidence on exposure to the agent and cancer is credible, but chance, bias, or confounding could not be ruled out with reasonable confidence.</td></tr><tr><td>Very low</td><td>There is little confidence in the effect estimate: the true effect is likely to be substantially different from the estimate of the effect.</td><td></td><td></td><td></td><td></td></tr></tbody></table>

Footnotes: Adapted from World Health Organization (2021), based on Schünemann et al. (2011), Lam et al. (2016a), and Samet et al. (2020).

populations in the Region of the Americas (16 studies from three countries, with Canada included in studies by itself and in one of the multinational studies). Only two studies from two countries were from the Western Pacific Region. The most commonly studied countries were the United States (10 studies), followed by Italy (six studies), Canada (five studies), and Germany (three studies).

Several studies included multiple or all industrial sectors, and/or several occupations.

### 4.2.3. Exposure studied

Out of the total of 40 studies, 22 measured occupational exposure to welding fumes using face-to-face surveys (although two used a combination of face-to-face and telephone surveys).

No studies measured exposure directly, and all studies measured exposure indirectly. Most studies measured exposure using self-reported data, e.g., occupation, job title, job tasks or self-reported exposures. For some studies, industrial hygienists assessed exposure based on these self-reports. Two studies relied on reports from people other than the
<figure><img src="image_4.png" /><figcaption>Fig. 2. PRISMA 2020 flow diagram of study selection. Footnote: PRISMA 2020 flow chart template sourced from Page et al. (2021a).</figcaption></figure>

workers themselves, namely their workplace supervisors, spouses or colleagues. Three studies used information on occupation as recorded in administrative records (i.e., death certificates or cancer registry records).

Some studies measured exposure to occupational exposure to welding fumes, whereas other studies assigned those who had the occupation or job title of “welder” as exposed. For some studies, exposure was required for a minimum period (e.g., six months or one year) for a participant to be assigned to be exposed.

### 4.2.4. Comparator studied

The comparator in studies was workers with no (or low) occupational exposure to welding fumes, which was often assigned by proxy of occupation as workers who did not have the occupation (or job title or equivalent) of “welder”.

### 4.2.5. Outcomes studied

The 40 studies in our systematic review and meta-analysis reported on two different health outcomes: trachea, bronchus, and lung cancer incidence (32 studies) and mortality (eight studies). No study reported on the prevalence of these cancer sites.

The outcome was most commonly assessed through medical records, often linked into population-based cancer registries. However, some studies used other sources for outcome assessment, such as death certificates.

## 4.3. Characteristics of studies awaiting classification

We identified two recently published studies (published since our original search was conducted, as identified in our updated search) that may potentially be eligible for inclusion in our systematic review but are still awaiting classification (Table 5).

## 4.4. Risk of bias within studies

The detailed justification for the risk of bias rating for each domain at the level of the included individual study by outcome (as shown in Figs. 3 and 4) is presented in Appendix 5 in the Supplementary data.

### 4.4.1. Acquired trachea, bronchus, and lung cancer (incidence)

The ratings for the 32 included studies for this outcome are presented in Fig. 3.

#### 4.4.1.1. Bias in selection of participants into the study. Are the study groups at risk of not representing their source populations in a manner that might introduce selection bias?

We judged the risk of bias in selection of participants into the study to be “low” for 17 studies, “probably low” for 13 studies, “probably high” for two studies, and “high” for no studies. The “probably high” ratings were due to insufficient descriptions of the participation rates between cases and controls, but indirect evidence suggested selection was inconsistent across groups.

#### 4.4.1.2. Bias due to a lack of blinding of study personnel. Was knowledge of the group assignments inadequately prevented (i.e., blinded or masked) during the study, potentially leading to subjective measurement of either exposure or outcome?

We rated the risk of bias due to a lack of blinding of study personnel as “low” for 21 studies, “probably low” for seven studies, “probably high” for four studies, and “high” for no studies. Our “probably high” ratings were given to studies for which we judged it unclear whether blinding occurred, and for which we deemed that a lack of blinding could have biased the exposure assessment.

#### 4.4.1.3. Bias due to exposure misclassification – Were exposure assessment methods lacking accuracy? We rated risk of bias in this domain as “low” for two studies, “probably low” for 15 studies, “probably high” for 13 studies, “probably low” for 3 studies, “probably high” for 10 studies, and “high” for 2 studies.
<table><caption>**Table 4**

Characteristics of included studies: the effect of occupational exposure to welding fumes on trachea, bronchus, and lung cancer.</caption><thead><tr><th>Study ID</th><th>Study population</th><th>Total number of study participants</th><th>Number of female study participants</th><th>Number of male study participants</th><th>Country of study population</th><th>Geographic location</th><th>Industrial sector</th><th>Occupation <sup>a</sup></th><th>Age</th><th>Formality of economy</th><th>Study type</th><th>Study design</th><th>Study period (month of first collection of any data and month of last collection of any data)</th><th>Follow-up period (period in months between exposure and outcome)</th></tr></thead><tbody><tr><td>(Becker 1999)</td><td>2901</td><td>Unclear</td><td>Unclear</td><td>Unclear</td><td>Germany</td><td>National</td><td>24 Manufacture of basic metals; 25 Manufacture of fabricated metal products, except machinery and equipment</td><td>7212</td><td>Unclear</td><td>Formal</td><td>Cohort study</td><td>1989-1995</td><td>Minimum 19 years (1970-89) -maximum 45 years (1950-95)</td></tr><tr><td>(Brenner 2010)</td><td>1393</td><td>798 (Cases 236, Controls 562)</td><td>595 (Cases 209, Controls 386)</td><td>Unclear</td><td>Canada</td><td>Region (Toronto)</td><td>Unclear</td><td>Unclear</td><td>&lt;35-&gt;75 years</td><td>Formal</td><td>Case-control study</td><td>1997-2002</td><td>Unclear</td></tr><tr><td>(Breslow 1954)</td><td>986</td><td>0</td><td>986</td><td>Unclear</td><td>United States</td><td>Region (California)</td><td>Unclear</td><td>7212, 7213</td><td>&lt;40-&gt;70 years</td><td>Both</td><td>Case-control study</td><td>1949-1952</td><td>At least 5 years up to entire working life</td></tr><tr><td>(Bruske-Hohlfeld 2000)</td><td>4517</td><td>0</td><td>4517</td><td>Unclear</td><td>Germany</td><td>Region (area surrounding Bremen and the Frankfurt area; West and East Germany, Nordrhein-Westfalen, Rheinland-Pfalz, Bayern, the Saarland, Thüringen, and Sachsen)</td><td>See footnote <sup>b</sup></td><td>87200, 87210, 87215, 87220, 87225, 87230, 87235 (ISCO 1968)</td><td>Unclear for sample in Kendzia et al. (2013). In original study record the average age of controls 60.4 (8.6) and of cases 60.4 (8.5)</td><td>Unclear</td><td>Case-control study</td><td>1988-1996</td><td>Unclear</td></tr><tr><td>(Buiatti 1985)</td><td>1157</td><td>0</td><td>1157</td><td>Unclear</td><td>Italy</td><td>Region (Toscana and Florence)</td><td>Unclear</td><td>7212</td><td>&lt;45-&gt;74 years</td><td>Unclear</td><td>Case-control study</td><td>1981-1983</td><td>Unclear</td></tr><tr><td>(Consonni 2010)</td><td>2840</td><td>0</td><td>2840</td><td>Unclear</td><td>Italy</td><td>Region (Lombardy)</td><td>See footnote <sup>b</sup></td><td>87200, 87210, 87215, 87220, 87225, 87230, 87235 (ISCO 1968)</td><td>Unclear for sample in Kendzia et al. (2013). In original study record, participants were 35-79 years of age at diagnosis (cases) or at sampling/enrolment (controls), and the average age of controls 66.8 (SD 7.9) and cases 65.8 (SD 8.1)</td><td>Both</td><td>Case-control study</td><td>2002-2005</td><td>Unclear</td></tr></tbody></table>

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<table><caption>Table 4 (continued)</caption><thead><tr><th rowspan="2">Study ID</th><th colspan="4">Study population</th><th rowspan="2">Country of study population</th><th rowspan="2">Geographic location</th><th rowspan="2">Industrial sector</th><th rowspan="2">Occupation <sup>a</sup></th><th rowspan="2">Age</th><th rowspan="2">Formality of economy</th><th colspan="3">Study type</th></tr><tr><th>Total number of study participants</th><th>Number of female study participants</th><th>Number of male study participants</th><th>Study design</th><th>Study period (month of first collection of any data and month of last collection of any data)</th><th>Follow-up period (period in months between exposure and outcome)</th></tr></thead><tbody><tr><td>(Corbin 2011)</td><td>536</td><td>0</td><td>536</td><td>New Zealand</td><td>National</td><td>See footnote <sup>b</sup></td><td>87200, 87210, 87215, 87220, 87225, 87230, 87235 (ISCO 1968)</td><td>Unclear for sample in Kendzia et al. (2013). In original study record the age breakdown at interview was: controls 10.2 % 20–50 years, 23.2 % 51–60 years, 53.5 % 61–70 years, 13.0 % ≥71 years; cases 9.4% 20–50 years, 25.8 % 51–60 years, 62.1 % 61–70 years, 2.6 % ≥71 years</td><td>Unclear</td><td>Case-control study</td><td>2007–2008</td><td>Unclear</td></tr><tr><td>(Danielsen 1993)</td><td>4,571</td><td>0</td><td>4,571</td><td>Norway</td><td>Local (shipyard workers)</td><td>30 Manufacture of other transport equipment</td><td>7212</td><td>Unclear for entire cohort. Range 16–74 years</td><td>Formal economy</td><td>Cohort study (retrospective)</td><td>Observations from 1953 to 1990</td><td>Workers employed between 1940 and 1979 followed until 1990</td></tr><tr><td>(Danielsen 2000)</td><td>Unclear</td><td>0</td><td>Unclear</td><td>Norway</td><td>Local (shipyard workers)</td><td>30 Manufacture of other transport equipment</td><td>7212</td><td>Unclear for entire cohort. Range 16–24 years</td><td>Formal economy</td><td>Cohort study (retrospective)</td><td>1945–1991</td><td>1945–1991</td></tr><tr><td>(Elci 2003)</td><td>2,873</td><td>0</td><td>2,873</td><td>Turkey</td><td>Region (Marmara)</td><td>Unclear</td><td>7212</td><td>Unclear</td><td>Formal economy</td><td>Case-control study</td><td>1979–1984</td><td>Unclear</td></tr><tr><td>(Finkelstein 1995)</td><td>3,788</td><td>0</td><td>3,788</td><td>Canada</td><td>Local (two cities in Ontario)</td><td>Unclear</td><td>8335 (SOC 1980)</td><td>45–75 years</td><td>Both</td><td>Case-control study</td><td>1979–1988</td><td>Working life</td></tr><tr><td>(Fortes 2003)</td><td>512</td><td>0</td><td>512</td><td>Italy</td><td>Region (Lazio)</td><td>See footnote <sup>b</sup></td><td>87200, 87210, 87215, 87220, 87225, 87230, 87235 (ISCO 1968)</td><td>35–90 years</td><td>Unclear</td><td>Case-control study</td><td>1993–1996</td><td>Unclear</td></tr><tr><td>(Gottlieb 1980)</td><td>5,606</td><td>0</td><td>5,606</td><td>United States</td><td>Region (Louisiana)</td><td>19 Manufacture of coke and refined petroleum products</td><td>7212</td><td>0–>35 years. For all industries combined the median age at death was 62.8 for cases and 61.4 for controls</td><td>Both</td><td>Case-control study</td><td>1960–1975</td><td>Unclear</td></tr></tbody></table>

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<table><caption>Table 4 (continued)</caption><thead><tr><th rowspan="2">Study ID</th><th colspan="3">Study population</th><th rowspan="2">Country of study population</th><th rowspan="2">Geographic location</th><th rowspan="2">Industrial sector</th><th rowspan="2">Occupation <sup>a</sup></th><th rowspan="2">Age</th><th rowspan="2">Formality of economy</th><th colspan="3">Study type</th></tr><tr><th>Total number of study participants</th><th>Number of female study participants</th><th>Number of male study participants</th><th>Study design</th><th>Study period (month of first collection of any data and month of last collection of any data)</th><th>Follow-up period (period in months between exposure and outcome)</th></tr></thead><tbody><tr><td>(Guida 2011; Matrat 2016)</td><td>5056</td><td>0</td><td>5056</td><td>France</td><td>National</td><td>Unclear</td><td>8-72 ISCO</td><td>&lt;50-≥70 years. Mean age for cases at recruitment 60 (SD 9.0), for controls 58 years (SD 9.9)</td><td>Both</td><td>Case-control study</td><td>2001-2007</td><td>Median 38 years between exposure and interview</td></tr><tr><td>(Gustavsson 2000)</td><td>3294</td><td>0</td><td>3294</td><td>Sweden</td><td>Region (Stockholm County)</td><td>See footnote <sup>b</sup></td><td>87200, 87210, 87215, 87220, 87225, 87230, 87235 (ISCO 1968)</td><td>40-75 years</td><td>Both</td><td>Case-control study</td><td>1985-1990</td><td>1950-1990</td></tr><tr><td>(Jöckel 1998)</td><td>1678</td><td>0</td><td>1678</td><td>Germany</td><td>Region (Bremen and Frankfurt)</td><td>See footnote <sup>b</sup></td><td>87200, 87210, 87215, 87220, 87225, 87230, 87235 (ISCO 1968)</td><td>Range 33-80 years. Mean age of cases 61.1 years (SD 8.9), controls 61.4 years (SD 9.0)</td><td>Both</td><td>Case-control study</td><td>1988-93</td><td>1926-1993</td></tr><tr><td>(Kazma 2012)</td><td>343</td><td>0</td><td>343</td><td>Canada, Czech Republic, Hungary, Poland, Romania, Russian Federation, Slovakia, and United Kingdom</td><td>Region</td><td>See footnote <sup>b</sup></td><td>87200, 87210, 87215, 87220, 87225, 87230, 87235 (ISCO 1968)</td><td>Mean age of cases 61.4 (SD 9.6) and 55.7 for controls (SD 14.1)</td><td>Unclear</td><td>Case-control study</td><td>Unclear</td><td>Working life</td></tr><tr><td>(Keller 1993)</td><td>13,449</td><td>0</td><td>13,449</td><td>United States</td><td>Region (Illinois)</td><td>060 (1980 United States Census Industrial Classification System)</td><td>783 (1980 United States Census Occupational Classification System)</td><td>Mean age of lung cancer cases 63.8 years among construction workers and 64.4 years among other subjects</td><td>Unclear</td><td>Case-control study</td><td>Unclear-cancer cases diagnosed 1986-1989</td><td>Unclear</td></tr><tr><td>(Kromhout 1992)</td><td>603</td><td>0</td><td>603</td><td>The Netherlands</td><td>Local</td><td>Unclear</td><td>Unclear</td><td>61-82 years at time of questionnaire on medical status</td><td>Both</td><td>Cohort</td><td>1960-1986</td><td>Exposure assessed 1977-1978 and health status last assessed in 1985</td></tr><tr><td>(Lauritsen 1996)</td><td>533</td><td>0</td><td>533</td><td>Denmark</td><td>National</td><td>Unclear</td><td>Unclear</td><td>Unclear</td><td>Formal</td><td>Case-control study (Nested case-control study)</td><td>1968-1985</td><td>1-21 + years</td></tr></tbody></table>

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<table><caption>Table 4 (continued)</caption><thead><tr><th rowspan="2">Study ID</th><th colspan="3">Study population</th><th rowspan="2">Country of study population</th><th rowspan="2">Geographic location</th><th rowspan="2">Industrial sector</th><th rowspan="2">Occupation <sup>a</sup></th><th rowspan="2">Age</th><th rowspan="2">Formality of economy</th><th colspan="3">Study type</th></tr><tr><th>Total number of study participants</th><th>Number of female study participants</th><th>Number of male study participants</th><th>Study design</th><th>Study period (month of first collection of any data and month of last collection of any data)</th><th>Follow-up period (period in months between exposure and outcome)</th></tr></thead><tbody><tr><td>(Lerchen 1987)</td><td>832</td><td>0</td><td>832</td><td>United States</td><td>Region (New Mexico)</td><td>Unclear</td><td>7212</td><td>25-84 years</td><td>Both</td><td>Case-control study</td><td>1980-1982</td><td>From 12 years of age until diagnosis</td></tr><tr><td>(Lopez-Cima 2007)</td><td>1343</td><td>0</td><td>1343</td><td>Spain</td><td>Region (Northern Spain)</td><td>See footnote <sup>b</sup></td><td>87200, 87210, 87215, 87220, 87225, 87230, 87235 (ISCO 1968)</td><td>30-85 years</td><td>Unclear</td><td>Case-control study</td><td>2000-2005</td><td>Unclear</td></tr><tr><td>(MacLeod 2017)</td><td>1,108,410</td><td>0</td><td>1,108,410</td><td>Canada</td><td>National</td><td>Unclear</td><td>J195 (SOC-91)</td><td>25-74 years</td><td>Unclear (maybe both)</td><td>Cohort (retrospective)</td><td>1991-2010</td><td>0-19 years</td></tr><tr><td>(Morabia 1992)</td><td>5021</td><td>0</td><td>5021</td><td>United States</td><td>Region</td><td>Unclear</td><td>7212</td><td>Unclear</td><td>Unclear (maybe both)</td><td>Case-control study</td><td>1980-1989</td><td>Working life</td></tr><tr><td>(Pezzotto 1999)</td><td>356</td><td>0</td><td>356</td><td>Argentina</td><td>Local (Rosario City)</td><td>See footnote <sup>b</sup></td><td>87200, 87210, 87215, 87220, 87225, 87230, 87235 (ISCO 1968)</td><td>Unclear</td><td>Both</td><td>Case-control study</td><td>1992-1998</td><td>Working life</td></tr><tr><td>(Richiardi 2004)</td><td>2003</td><td>0</td><td>2003</td><td>Italy</td><td>Region</td><td>See footnote <sup>b</sup></td><td>87200, 87210, 87215, 87220, 87225, 87230, 87235 (ISCO 1968)</td><td>Unclear from Kendzia et al. (2013), but in the original study cases had a mean age of 62.4 years (SD 7.4) and controls 63.3 years (SD 7.8)</td><td>Formal</td><td>Case-control study</td><td>1990-1992</td><td>Working life</td></tr><tr><td>(Ronco 1988)</td><td>510</td><td>0</td><td>510</td><td>Italy</td><td>Local</td><td>Unclear</td><td>7212</td><td>30-89 years for controls</td><td>Formal</td><td>Case-control study</td><td>Unclear</td><td>Working life</td></tr><tr><td>(Sankila 1990)</td><td>Unclear (6,878 cases)</td><td>0</td><td>Unclear (6,878 cases)</td><td>Finland</td><td>National</td><td>Unclear</td><td>655 (Nordic Classification of Occupations)</td><td>26-64 years</td><td>Unclear</td><td>Cohort study (retrospective)</td><td>1971-1980</td><td>Working life</td></tr><tr><td>(Schoenberg 1987)</td><td>1663</td><td>0</td><td>1663</td><td>United States</td><td>Region (New Jersey)</td><td>Unclear</td><td>Unclear</td><td>Unclear</td><td>Formal</td><td>Case-control study</td><td>1980-1981</td><td>Working life</td></tr><tr><td>(Siew 2008)</td><td>Unclear – original sample includes 1.2 million but the selected effect estimate relates only to the unexposed and high exposure groups</td><td>0</td><td>Unclear – original sample includes 1.2 million but the selected effect estimate relates only to the unexposed and high exposure groups</td><td>Finland</td><td>National</td><td>Unclear</td><td>Unclear</td><td>20-65 years</td><td>Both</td><td>Cohort study (retrospective)</td><td>1971-1995</td><td>25 years</td></tr></tbody></table>

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<table><caption>Table 4 (continued)</caption><thead><tr><th rowspan="2">Study ID</th><th colspan="3">Study population</th><th rowspan="2">Country of study population</th><th rowspan="2">Geographic location</th><th rowspan="2">Industrial sector</th><th rowspan="2">Occupation <sup>a</sup></th><th rowspan="2">Age</th><th rowspan="2">Formality of economy</th><th colspan="3">Study type</th></tr><tr><th>Total number of study participants</th><th>Number of female study participants</th><th>Number of male study participants</th><th>Study design</th><th>Study period (month of first collection of any data and month of last collection of any data)</th><th>Follow-up period (period in months between exposure and outcome)</th></tr></thead><tbody><tr><td>(Soskolne 2007)</td><td>358</td><td>0</td><td>358</td><td>Italy</td><td>Region (Campania)</td><td>Unclear</td><td>Unclear</td><td>35-74 years</td><td>Unclear</td><td>Case-control study</td><td>1988-1990</td><td>Working life</td></tr><tr><td>(Steenland 1986)</td><td>8679</td><td>Unclear</td><td>Unclear</td><td>United States</td><td>Local (Western Washington)</td><td>Unclear</td><td>7212</td><td>Unclear</td><td>Formal</td><td>Cohort study (retrospective)</td><td>1950-1976</td><td>1-27 years</td></tr><tr><td>(Steenland 2002)</td><td>8745</td><td>0</td><td>8745</td><td>United States</td><td>Region (mid-western United States)</td><td>Unclear</td><td>7212</td><td>Unclear</td><td>Formal</td><td>Cohort study (prospective)</td><td>1974-1998</td><td>Up to 37 years</td></tr><tr><td>(Stücker 2002)</td><td>522</td><td>0</td><td>522</td><td>France</td><td>Region (Paris and Besançon)</td><td>See footnote <sup>b</sup></td><td>87200, 87210, 87215, 87220, 87225, 87230, 87235 (ISCO 1968)</td><td>Unclear for sample in Kendzia et al. (2013). In original study record the average age of controls 59.3 (SD 9.6) and cases 59.6 (SD 9.9)</td><td>Unclear</td><td>Case-control study</td><td>1988-1992</td><td>1926-1992</td></tr><tr><td>('t Mannetje, 2012)</td><td>4492</td><td>0</td><td>4492</td><td>Czech Republic, Hungary, Poland, Romania, Russian Federation, Slovakia, and United Kingdom</td><td>National</td><td>Unclear</td><td>Unclear</td><td>&lt;45-≥65 years</td><td>Unclear</td><td>Case-control study</td><td>1998-2001</td><td>Working life</td></tr><tr><td>(Tse 2012)</td><td>2277</td><td>0</td><td>2277</td><td>People's Republic of China</td><td>Region (Hong Kong)</td><td>Unclear</td><td>Unclear</td><td>35-79 years</td><td>Both</td><td>Case-control study</td><td>2004-2006</td><td>Working life</td></tr><tr><td>(Vallieres 2012)</td><td>1416</td><td>0</td><td>1416</td><td>Canada</td><td>Local (Montreal)</td><td>See footnote <sup>b</sup></td><td>87200, 87210, 87215, 87220, 87225, 87230, 87235 (ISCO 1968)</td><td>35-75 years</td><td>Both</td><td>Case-control study</td><td>1976-1986 and 1996-2001</td><td>Working life</td></tr><tr><td>(van Loon 1997)</td><td>Unclear as unknown number of participants excluded due to lacking information on covariates (but participant on welding fume exposure held for 1828)</td><td>0</td><td>Unclear as unknown number of participant excluded due to lacking information on covariates (but information on welding fume exposure held for 1828)</td><td>The Netherlands</td><td>National</td><td>Unclear</td><td>Unclear</td><td>55-69 years. Mean age of 61.4 years among unexposed and 60.3 years among exposed</td><td>Both</td><td>Cohort study (prospective)</td><td>1986-1990</td><td>Working life</td></tr></tbody></table>

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<table><caption>Table 4 (continued)</caption><thead><tr><th rowspan="2">Study ID</th><th rowspan="2">Study population</th><th rowspan="2">Total number of study participants</th><th rowspan="2">Number of female study participants</th><th rowspan="2">Number of male study participants</th><th rowspan="2">Country of study population</th><th rowspan="2">Geographic location</th><th rowspan="2">Industrial sector</th><th rowspan="2">Occupation <sup>a</sup></th><th rowspan="2">Age</th><th rowspan="2">Formality of economy</th><th colspan="3">Study type</th></tr><tr><th>Study design</th><th>Study period (month of first collection of any data and month of last collection of any data)</th><th>Follow-up period (period in months between exposure and outcome)</th></tr></thead><tbody><tr><td>(Wong 2017)</td><td>50,983</td><td>21,678</td><td>29,305</td><td>United States</td><td>National</td><td>Unclear</td><td>7212</td><td>57-64 years</td><td>Both formal and informal economy</td><td>Cohort (prospective)</td><td>2002-2009</td><td>Working life</td></tr><tr><td>(Yiin 2007)</td><td>4388</td><td>285</td><td>4103</td><td>United States</td><td>Local (Portsmouth and New Hampshire)</td><td>30 Manufacture of other transport equipment</td><td>Unclear</td><td>Average age at vital status assessment for controls 83 and for cases 82 years</td><td>Formal</td><td>Case-control study (nested case-control study)</td><td>1952-1996</td><td>4-44 years</td></tr><tr><th rowspan="2">Study ID</th><th colspan="10">Exposure assessment</th></tr><tr><th>Exposure definition (i.e., how was the exposure defined?)</th><th>Unit for which exposure was assessed</th><th>Mode of exposure data collection</th><th>Exposure assessment methods</th><th>Type of exposure measurement or estimate</th><th>Dates covered by exposure assessment (years)</th><th>Shortest and longest exposure period</th><th>Levels/intensity of exposure (unit)</th><th>Number of study participants in exposed group</th><th>Number of study participants in unexposed group</th><th>Co-exposure with other occupational risk factors</th></tr><tr><td>(Becker 1999)</td><td>Arc welders exposed to fumes containing nickel and chromium</td><td>Individual level</td><td>Face-to-face survey</td><td>Indirectly reported by workplace supervisor</td><td>Unclear</td><td>1950-1970</td><td>≥6 months</td><td>Unclear</td><td>1213</td><td>1688</td><td>Unclear</td></tr><tr><td>(Brenner 2010)</td><td>Ever worked with or been exposed to welding fumes</td><td>Individual level</td><td>Face-to-face and telephone surveys</td><td>Self-reported</td><td>Lifetime prevalence</td><td>Participants' lifetime of exposure</td><td>Unclear</td><td>Unclear</td><td>76</td><td>1317</td><td>Asbestos<sup>c</sup>, solvents, paints or thinners, pesticides, grain elevator dust, wood dust, smoke-soot or exhaust<sup>c</sup>, and environmental tobacco smoke<sup>c</sup> (NB: factors considered separately, not in an adjusted model)</td></tr></tbody><tfoot><tr><td colspan="12">(continued on next page)</td></tr></tfoot></table>
<table><caption>Table 4 (continued)</caption><thead><tr><th>Study ID</th><th>Exposure assessment</th><th>Exposure definition (i. e., how was the exposure defined?)</th><th>Unit for which exposure was assessed</th><th>Mode of exposure data collection</th><th>Exposure assessment methods</th><th>Type of exposure measurement or estimate</th><th>Dates covered by exposure assessment (years)</th><th>Shortest and longest exposure period</th><th>Levels/intensity of exposure (unit)</th><th>Number of study participants in exposed group</th><th>Number of study participants in unexposed group</th><th>Co-exposure with other occupational risk factors</th></tr></thead><tbody><tr><td>(Breslow 1954)</td><td>Welder or sheet metal workers doing welding</td><td>Individual level</td><td>Face-to-face survey</td><td>Exposure assessment methods</td><td>Interviews coded by an industrial hygienist</td><td>Lifetime prevalence</td><td>Working life</td><td>Unclear</td><td>Welder or sheet metal workers doing welding for ≥5 years</td><td>16</td><td>970</td><td>Polycyclic aromatic hydrocarbons, mineral fibers and dusts<sup>c</sup>, and others according to various occupational codes (but not considered in adjusted models)</td></tr><tr><td>(Bruske-Hohlfeld 2000)</td><td>Ever worked as a welder (Kendzia et al. 2013)</td><td>Individual level</td><td>Face-to-face survey</td><td>Job title history</td><td>Lifetime prevalence</td><td>Working life</td><td>≥1 year (as in Kendzia et al. (2013); in original study record was ≥6 months)</td><td>Job title was "welder" for ≥1 year</td><td></td><td>150</td><td>4367</td><td>Adjustment for ever working in an occupation involving lung cancer risk, excluding welding-related occupations</td></tr><tr><td>(Buiatti 1985)</td><td>"Ever worked" in welding</td><td>Individual level</td><td>Face-to-face survey</td><td>Job title and self-reported chemical exposures</td><td>Lifetime prevalence</td><td>Working life</td><td>≥1 year</td><td>Worked in the occupation of welding for ≥1 year</td><td></td><td>12</td><td>1145</td><td>16 known carcinogens (although these exposures not detailed among welders)</td></tr><tr><td>(Consonni 2010)</td><td>Ever worked as a welder (Kendzia et al. 2013)</td><td>Individual level</td><td>Computer-administered face-to-face survey</td><td>Hygienists' assessment</td><td>Lifetime prevalence</td><td>Working life</td><td>≥1 year (as in Kendzia et al. (2013); in original study record was ≥6 months)</td><td>Job title was "welder" for ≥1 year</td><td></td><td>79</td><td>2761</td><td>Adjustment for ever working in an occupation involving lung cancer risk, excluding welding-related occupations</td></tr><tr><td>(Corbin 2011)</td><td>Ever worked as a welder (Kendzia et al. 2013)</td><td>Individual level</td><td>Face-to-face and telephone surveys</td><td>Job title and task description</td><td>Lifetime prevalence</td><td>Working life</td><td>≥1 year</td><td>Job title was "welder" for ≥1 year</td><td></td><td>30</td><td>506</td><td>Adjustment for ever working in an occupation involving lung cancer risk, excluding welding-related occupations</td></tr><tr><td>(Danielsen 1993)</td><td>Occupational inhalation exposure to fumes from</td><td>Individual level</td><td>Administrative records</td><td>The exposure was assessed according to worker's job title</td><td>Unclear</td><td>1940-1979</td><td>Unclear; ≤6 months to ≥10 years of employment</td><td>In 1973, The welding fume samples showed concentrations of</td><td></td><td>623</td><td>3948</td><td>Asbestos<sup>c</sup></td></tr></tbody></table>

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<table><caption>Table 4 (continued)</caption><thead><tr><th>Study ID</th><th>Exposure assessment</th><th>Unit for which exposure was assessed</th><th>Mode of exposure data collection</th><th>Exposure assessment methods</th><th>Type of exposure measurement or estimate</th><th>Dates covered by exposure assessment (years)</th><th>Shortest and longest exposure period</th><th>Levels/intensity of exposure (unit)</th><th>Number of study participants in exposed group</th><th>Number of study participants in unexposed group</th><th>Co-exposure with other occupational risk factors</th></tr></thead><tbody><tr><td></td><td>welding defined by work category of welder</td><td></td><td></td><td>and the environmental monitoring were also conducted</td><td></td><td></td><td>analysis split in to ≤5 years employment and >5 years employment</td><td>total dust from 0 to 8 to 9–5 mg/m³ (median 2–5 mg/m³). In 1985, Concentrations of total dust ranged from 0 to 6 to 22mg/m³ (median 2–6 mg/m³). All chromium concentrations were below 0–05 mg/m³. For nickel, the results in the welding shop ranged from the lowest detectable concentration</td><td></td><td></td><td>Potential co-exposure with other occupational risk factors (co-exposure and level of exposure)</td></tr><tr><td>(Danielsen 2000)</td><td>Occupational inhalation exposure to fumes from welding defined by work category of welder</td><td>Individual level</td><td>Administrative records</td><td>The exposure was assessed according to worker's job title and the environmental monitoring were also conducted</td><td>Unclear</td><td>1945–1991</td><td>Unclear</td><td>In 1973, the mean concentration of welding fumes in the work air was 14.5 mg/m³ ranging from 4.2 to 54.4 mg/m³. In 1977, The highest welding fume concentration was 2.1 mg/m³. In 1989, Among mild steel welders the mean concentration of welding fumes was 1.87 mg/m³, with concentrations inside air-stream helmets below 0.70 mg/m³. Stainless steel welders working in confined spaces were exposed to high levels of total fume, ranging from 7.0 to 38.0mg/m³.</td><td>Unclear</td><td>3619</td><td>Asbestos<sup>c</sup></td></tr></tbody></table>

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<table><caption>Table 4 (continued)</caption><thead><tr><th>Study ID</th><th>Exposure assessment</th><th>Unit for which exposure was assessed</th><th>Mode of exposure data collection</th><th>Exposure assessment methods</th><th>Type of exposure measurement or estimate</th><th>Dates covered by exposure assessment (years)</th><th>Shortest and longest exposure period</th><th>Levels/intensity of exposure (unit)</th><th>Number of study participants in exposed group</th><th>Number of study participants in unexposed group</th><th>Co-exposure with other occupational risk factors</th></tr></thead><tbody><tr><td>(Elci 2003)</td><td>Occupation of "welder"</td><td>Individual level</td><td>Face-to-face survey</td><td>Occupational history</td><td>Prevalence – unclear type</td><td>Occupational history was taken but unclear if this or current occupation was used</td><td>Unclear</td><td>with iron, chromium, manganese, and nickel as the principal elements. The stainless steel welders in the machine shop were exposed to total fume concentrations ranging from 0.28 to 1.03 mg/m³ inside air-stream helmets.</td><td>Unclear</td><td>Unclear</td><td>Unclear</td></tr><tr><td>(Finkelstein 1995)</td><td>Job and industry described in death certificate</td><td>Individual level</td><td>Administrative records</td><td>Death certificate</td><td>Lifetime prevalence</td><td>Working life</td><td>Unclear</td><td>Unclear</td><td>Unclear</td><td>Unclear</td><td>Unclear</td></tr><tr><td>(Fortes 2003)</td><td>Ever worked as a welder (Kendzia et al. 2013)</td><td>Individual level</td><td>Face-to-face survey</td><td>Self-reported occupational exposure to carcinogens</td><td>Lifetime prevalence (unclear in original record, but from information in Kendzia et al. 2013)</td><td>Working life (from Kendzia et al. 2013)</td><td>Unclear</td><td>Unclear</td><td>5</td><td>507</td><td>Unclear</td></tr><tr><td>(Gottlieb 1980)</td><td>Occupation of "welder"</td><td>Individual level</td><td>Administrative records</td><td>Death certificate</td><td>Prevalence – type unclear</td><td>Unclear</td><td>Unclear</td><td>Unclear</td><td>10</td><td>5596</td><td>Unclear</td></tr><tr><td>(Guida 2011; Matrat 2016)</td><td>At least one job period as welder in the strict sense of the word (i.e., coded 8–72 in ISCO code)</td><td>Individual level</td><td>Face-to-face survey</td><td>Occupational history and task description</td><td>Lifetime prevalence</td><td>Working life</td><td>Classified as ≤10 years or >10 years</td><td>Exposure classified in ≤35 and >35 years since first exposure and then into other two subclasses ≤10 and >10 ys of exposure. For regular welders also available frequency of welding, classified in ≤5% and >5%</td><td>167</td><td>3666</td><td>Asbestos<sup>c</sup></td></tr></tbody><tfoot><tr><td colspan="12" style="text-align: right;">(continued on next page)</td></tr></tfoot></table>
<table><caption>Table 4 (continued)</caption><thead><tr><th>Study ID</th><th>Exposure assessment</th><th>Exposure definition (i. e., how was the exposure defined?)</th><th>Unit for which exposure was assessed</th><th>Mode of exposure data collection</th><th>Exposure assessment methods</th><th>Type of exposure measurement or estimate</th><th>Dates covered by exposure assessment (years)</th><th>Shortest and longest exposure period</th><th>Levels/intensity of exposure (unit)</th><th>Number of study participants in exposed group</th><th>Number of study participants in unexposed group</th><th>Co-exposure with other occupational risk factors</th></tr></thead><tbody><tr><td>(Gustavsson 2000)</td><td>Ever worked as a welder (Kendzia et al. 2013)</td><td>Individual level</td><td>Face-to-face survey</td><td></td><td>Hygienist assessment (based on self-reports)</td><td>Lifetime prevalence</td><td>Working life</td><td>≥1 year (as in Kendzia et al. (2013))</td><td>Job title was "welder" for ≥1 year (as in Kendzia et al. (2013))</td><td>267</td><td>3081</td><td>Unclear</td></tr><tr><td>(Jöckel 1998)</td><td>Ever worked as a welder (Kendzia et al. 2013)</td><td>Individual level</td><td>Face-to-face survey</td><td></td><td>Self-reported</td><td>Lifetime prevalence</td><td>Working life</td><td>≥1 year (as in Kendzia et al. (2013))</td><td>Job title was "welder" for ≥1 year (as in Kendzia et al. (2013))</td><td>60</td><td>1334</td><td>Unclear</td></tr><tr><td>(Kazma 2012)</td><td>Ever worked as a welder (Kendzia et al. 2013)</td><td>Individual level</td><td>Face-to-face survey</td><td></td><td>Unclear</td><td>Lifetime prevalence</td><td>Working life</td><td>≥1 year</td><td>Job title was "welder" for ≥1 year</td><td>4</td><td>339</td><td>Adjustment for ever working in an occupation involving lung cancer risk, excluding welding-related occupations</td></tr><tr><td>(Keller 1993)</td><td>Current occupation code or longest lifetime occupation code equal to 783 in the 1980 US Census Occupation Classification System</td><td>Individual level</td><td>Administrative records</td><td></td><td>Occupation coded in cancer registry</td><td>Lifetime prevalence</td><td>Working life</td><td>Unclear</td><td>Current occupation code or longest lifetime occupation code</td><td>Unclear</td><td>Unclear</td><td>Unclear</td></tr><tr><td>(Kromhout 1992)</td><td>Self-reported occupational exposure to welding materials, welding fumes</td><td>Individual level</td><td>Questionnaire</td><td></td><td>Self-reported job title; job-exposure matrix</td><td>Lifetime prevalence</td><td>Working life prior to questionnaire</td><td>25 years</td><td>Exposure to welding materials, welding fumes during their job history</td><td>Unclear</td><td>Unclear</td><td>Twenty-six other chemicals or groups of chemical agents (including passive smoking<sup>c</sup>, dust (asbestos, cement, wood, chalk, quartz)<sup>c</sup>)</td></tr><tr><td>(Lauritsen 1996)</td><td>Welding ever, yes/no</td><td>Individual level</td><td>Questionnaire</td><td></td><td>Telephone or personal interview of spouses and/or colleagues</td><td>Lifetime prevalence</td><td>Lifetime exposure</td><td>Classified in groups from 1 to 5 years to ≥21 years</td><td>Welding ever, yes/no; time from first to last exposure</td><td>Unclear</td><td>Unclear</td><td>Welding, grinding, asbestos<sup>c</sup> and cutting oil exposures</td></tr><tr><td>(Lerchen 1987)</td><td>Occupation of welder for ≥6 months</td><td>Individual level</td><td>Questionnaire</td><td></td><td>Self-reported</td><td>Lifetime prevalence</td><td>Working life</td><td>≥6 months</td><td>Occupation as a welder for ≥6 months</td><td>29</td><td>803</td><td>Occupational exposure to 18 agents: asbestos<sup>c</sup>, radiation<sup>c</sup>, coal soot, tar or coke<sup>c</sup>, (continued on next page)</td></tr></tbody></table>
<table><caption>Table 4 (continued)</caption><thead><tr><th>Study ID</th><th>Exposure assessment</th><th>Exposure definition (i. e., how was the exposure defined?)</th><th>Unit for which exposure was assessed</th><th>Mode of exposure data collection</th><th>Exposure assessment methods</th><th>Type of exposure measurement or estimate</th><th>Dates covered by exposure assessment (years)</th><th>Shortest and longest exposure period</th><th>Levels/intensity of exposure (unit)</th><th>Number of study participants in exposed group</th><th>Number of study participants in unexposed group</th><th>Co-exposure with other occupational risk factors</th></tr></thead><tbody><tr><td>(Lopez-Cima 2007)</td><td>Ever worked as a welder (Kendzia et al. 2013)</td><td></td><td>Individual level</td><td>Computer-administered face-to-face survey</td><td>Hygienists' assessment</td><td>Lifetime prevalence (unclear in original record, but from information in Kendzia et al. 2013)</td><td>Working life (from Kendzia et al. 2013)</td><td>≥1 year</td><td>Job title was "welder" for ≥1 year</td><td>89</td><td>1254</td><td>nickel<sup>c</sup>, chromium<sup>c</sup>, arsenic<sup>c</sup>, chloromethyl ether, polyurethane, formaldehyde, insecticides and pesticides, vegetable and animal dust, wood dust, leather dust, petroleum and petroleum products, and solvents</td></tr><tr><td>(MacLeod 2017)</td><td>Standard Occupational Classification 1991 (SOC-91) code J195 for welders and soldering machine operators</td><td></td><td>Individual level</td><td>Administrative records</td><td>Self-reported</td><td>Point prevalence</td><td>1991</td><td>0-19 years</td><td>Occupation as a welder or solder machine operator</td><td>12,845</td><td>1,095,565</td><td>Adjustment for ever working in an occupation involving lung cancer risk, excluding welding-related occupations</td></tr><tr><td>(Morabia 1992)</td><td>Occupation as a welder or flame cutter</td><td></td><td>Individual level</td><td>Face-to-face survey</td><td>Occupational history (job title and self-reported chemical exposures)</td><td>Lifetime prevalence</td><td>Working life</td><td>Unclear</td><td>"Usual occupation" as a welder or flame cutter</td><td>Unclear</td><td>Unclear</td><td>Iron, manganese, aluminium, cadmium<sup>c</sup>, silica<sup>c</sup>, lead, ultraviolet radiation, and asbestos<sup>c</sup></td></tr><tr><td>(Pezzotto 1999)</td><td>Employed for more than one year as a welder</td><td></td><td>Individual level</td><td>Pen-and-paper survey</td><td>Questionnaire (job title, tasks, and self-reported chemical exposures)</td><td>Lifetime prevalence</td><td>Working life</td><td>≥1 year</td><td>Employed for more than one year as a welder</td><td>33</td><td>323</td><td>Interviewed about exposure to 44 different agents, but information not used in analysis on welding</td></tr></tbody></table>

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<table><caption>Table 4 (continued)</caption><thead><tr><th>Study ID</th><th>Exposure assessment</th><th>Exposure definition (i. e., how was the exposure defined?)</th><th>Unit for which exposure was assessed</th><th>Mode of exposure data collection</th><th>Exposure assessment methods</th><th>Type of exposure measurement or estimate</th><th>Dates covered by exposure assessment (years)</th><th>Shortest and longest exposure period</th><th>Levels/intensity of exposure (unit)</th><th>Number of study participants in exposed group</th><th>Number of study participants in unexposed group</th><th>Co-exposure with other occupational risk factors</th></tr></thead><tbody><tr><td>(Richiardi 2004)</td><td>Ever worked as a welder (Kendzia et al. 2013)</td><td>Individual level</td><td>Face-to-face survey</td><td>Occupational history (job title)</td><td>Lifetime prevalence</td><td>Working life</td><td>At least 1 year (as in Kendzia et al. (2013); in original study record was ≥6 months)</td><td>Job title was “welder” for ≥1 year</td><td>24</td><td>1358</td><td>Adjustment for ever working in an occupation involving lung cancer risk, excluding welding-related occupations</td></tr><tr><td>(Ronco 1988)</td><td>Occupation of welder for ≥6 months</td><td>Individual level</td><td>Face-to-face survey</td><td>Reported by next of kin</td><td>Lifetime prevalence</td><td>Working life</td><td>≥6 months</td><td>Employed for ≥6 months as a welder</td><td>13</td><td>497</td><td>Foundries and metal production</td></tr><tr><td>(Sankila 1990)</td><td>Welder (code 655)</td><td>Individual level</td><td>Administrative records</td><td>Unclear</td><td>Unclear</td><td>Unclear</td><td>Unclear</td><td>Working as a welder</td><td>57</td><td>Unclear</td><td>Unclear</td></tr><tr><td>(Schoenberg 1987)</td><td>“Combined welders” group as many of the same subjects were employed as welders or burners, sheetmetal workers, and boilermakers</td><td>Individual level</td><td>Face-to-face survey</td><td>Occupational history (job title, tasks, and self-reported chemical exposures)</td><td>Lifetime prevalence</td><td>Working life</td><td>≥3 months</td><td>Was in the “combined welders” group</td><td>28</td><td>Unclear</td><td>Unclear</td></tr><tr><td>(Siew 2008)</td><td>Occupational inhalation exposure to fumes from welding</td><td>Individual level</td><td>Industrial hygienists' assessment</td><td>Job-exposure matrix</td><td>Lifetime prevalence</td><td>Up to 1970</td><td>≤20 years</td><td>Cumulative exposure: No exposure; low: 0.1–99.9 mg/m³-years; medium: 100–199.9 mg/m³-years; and high: ≥200 mg/m³-years</td><td>Unclear</td><td>Unclear</td><td>Asbestos<sup>c</sup> and silica<sup>c</sup></td></tr><tr><td>(Soskolne 2007)</td><td>Exposed to welding fumes</td><td>Individual level</td><td>Face-to-face survey</td><td>Industrial hygienist assessment based on self-reported occupational history</td><td>Lifetime prevalence</td><td>Working life</td><td>Unclear</td><td>Exposed versus unexposed to welding fumes</td><td>Unclear (in the entire study 13 of 415 were exposed, but this was not the effective sample)</td><td>Unclear</td><td>No</td></tr><tr><td>(Steenland 1986)</td><td>Worked as a welder for ≥3 years (and a member of union 1950–1976)</td><td>Individual level</td><td>Administrative records</td><td>Unclear</td><td>Period prevalence</td><td>1950–76</td><td>≥3 years</td><td>Welder versus non-welder</td><td>3247</td><td>5432</td><td>Unclear</td></tr></tbody></table>

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<table><caption>Table 4 (continued)</caption><thead><tr><th>Study ID</th><th>Exposure assessment</th><th>Exposure definition (i.e., how was the exposure defined?)</th><th>Unit for which exposure was assessed</th><th>Mode of exposure data collection</th><th>Exposure assessment methods</th><th>Type of exposure measurement or estimate</th><th>Dates covered by exposure assessment (years)</th><th>Shortest and longest exposure period</th><th>Levels/intensity of exposure (unit)</th><th>Number of study participants in exposed group</th><th>Number of study participants in unexposed group</th><th>Co-exposure with other occupational risk factors</th></tr></thead><tbody><tr><td>(Steenland 2002)</td><td>Worked as a welder for ≥2 years</td><td>Individual level</td><td>Administrative records</td><td></td><td>Job title history</td><td>Period prevalence</td><td>1950–1987</td><td>Classified in groups of ≥2 years up to >20 years</td><td>Worked as a welder for ≥2 years versus non-welder</td><td>4459</td><td>4286</td><td>No</td></tr><tr><td>(Stücker 2002)</td><td>Ever worked as a welder (Kendzia et al. 2013)</td><td>Individual level</td><td>Face-to-face survey</td><td></td><td>Self-reported</td><td>Lifetime prevalence</td><td>Working life</td><td>≥1 year</td><td>Job title was “welder” for ≥1 year</td><td>14</td><td>308</td><td>Adjustment for ever working in an occupation involving lung cancer risk, excluding welding-related occupations</td></tr><tr><td>('t Mannetje 2012)</td><td>Ever exposed to welding fumes (each job on welding or gas cutting and if any welding or gas cutting was done near the subject)</td><td>Individual level</td><td>Face-to-face survey</td><td></td><td>Questionnaire including questions on each job in welding or gas cutting or if these tasks were carried out near the subject</td><td>Lifetime prevalence</td><td>Working life</td><td>1 year–>35 years</td><td>Exposed to welding fumes versus not exposed</td><td>1104</td><td>3388</td><td>Asbestos<sup>c</sup>, silica<sup>c</sup>, and metal in jobs not exposed to welding fumes</td></tr><tr><td>(Tse 2012)</td><td>Regular occupational exposure to welding fumes at least once a week for ≥6 months</td><td>Individual level</td><td>Face-to-face survey</td><td></td><td>Hygienist assessment (based on self-reports)</td><td>Lifetime prevalence</td><td>Working life</td><td>≥6 months</td><td>Regular occupational exposure to welding fumes at least once a week for ≥6 months versus not exposed</td><td>160</td><td>2117</td><td>No</td></tr><tr><td>(Vallieres 2012)</td><td>Ever worked as a welder (Kendzia et al. 2013)</td><td>Individual level</td><td>Face-to-face survey</td><td></td><td>Hygienist assessment (based on self-reports)</td><td>Lifetime prevalence</td><td>Working life (unclear if Kendzia et al. (2013) maintained categorisation of those participants exposed only in the 5-year period prior to recruitment as unexposed)</td><td>≥1 year</td><td>Job title was “welder” for ≥1 year</td><td>45</td><td>1371</td><td>Adjustment for ever working in an occupation involving lung cancer risk, excluding welding-related occupations</td></tr><tr><td>(van Loon 1997)</td><td>Exposure to welding fumes by job title</td><td>Individual level</td><td>Pen-and-paper survey</td><td></td><td>Hygienist assessment (based on self-reports)</td><td>Lifetime prevalence</td><td>Working life</td><td>Unclear</td><td>Ever versus never; no exposure to welding fumes, possible exposure (probability <30%), probable exposure (probability 30%-90%), and nearly certain exposure (probability >90%)</td><td>Unclear (but 202 exposed among those for whom welding fume exposure was held)</td><td>Unclear (but 1626 unexposed among those for whom welding fume exposure was held)</td><td>Other occupational exposures adjusted for (asbestos<sup>c</sup>, paint dust, polycyclic aromatic hydrocarbons)</td></tr></tbody></table>

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<table><caption>Table 4 (continued)</caption><thead><tr><th>Study ID</th><th>Exposure assessment</th><th>Exposure definition (i.e., how was the exposure defined?)</th><th>Unit for which exposure was assessed</th><th>Mode of exposure data collection</th><th>Exposure assessment methods</th><th>Type of exposure measurement or estimate</th><th>Dates covered by exposure assessment (years)</th><th>Shortest and longest exposure period</th><th>Levels/intensity of exposure (unit)</th><th>Number of study participants in exposed group</th><th>Number of study participants in unexposed group</th><th>Co-exposure with other occupational risk factors</th></tr></thead><tbody><tr><td>(Wong 2017)</td><td>Worked as a welder (but not a foundry worker) for ≥1 year</td><td>Individual level</td><td>Pen-and-paper survey</td><td></td><td>Self-reported</td><td>Lifetime prevalence</td><td>Working life</td><td>≥1 year-≥25 years</td><td>Worked as a welder for ≥1 year versus did not work as a welder</td><td>2311</td><td>48,672</td><td>Unclear</td></tr><tr><td>(Yiin 2007)</td><td>Exposure to welding fumes</td><td>Individual level</td><td>Administrative records</td><td></td><td>Shop and job-title combinations used with cumulative industrial hygiene monitoring records assessed by hygienist</td><td>Period prevalence</td><td>1952-1992</td><td>Unclear</td><td>Welding fume TLV level category &lt;0.5, 0.5-1, 1-2, 2-4, 4+</td><td>978</td><td>3410</td><td>Asbestos<sup>c</sup>, ionizing radiation<sup>c</sup></td></tr><tr><th>Study ID</th><th>Outcome assessment Definition of outcome</th><th>Which International Classification of Diseases (ICD) code was reported for the outcome (if any)?</th><th>Method of outcome assessment</th><th>Diagnostic assessment method</th><th>Specification of outcome</th><th>Number of cases with outcome of interest in exposed group</th><th>Number of non-cases (i.e., without outcome of interest) in exposed group</th><th>Number of cases with outcome of interest in unexposed group</th><th>Number of non-cases (i.e., without outcome of interest) in unexposed group</th><th>Comparator Definition of comparator (define comparator group, including specific level of exposure)</th></tr><tr><td>(Becker 1999)</td><td>Trachea, bronchus, and lung cancer</td><td>ICD-9 Code 162</td><td>Death certificate</td><td>Administrative record</td><td>Trachea, bronchus, and lung cancer mortality</td><td>28</td><td>1185</td><td>38</td><td>1650</td><td>Turners</td></tr><tr><td>(Brenner 2010)</td><td>Trachea, bronchus, and lung cancer</td><td>ICD O 3 (but specific code not reported)</td><td>Pathology</td><td>Medical records, cases were histologically confirmed</td><td>Trachea, bronchus, and lung cancer diagnosis</td><td>33</td><td>43</td><td>412</td><td>905</td><td>Never worked with or been exposed to welding fumes</td></tr><tr><td>(Breslow 1954)</td><td>Lung cancer</td><td>Unclear</td><td>Pathology</td><td>Physician diagnostic record</td><td>Lung cancer diagnosis</td><td>14</td><td>2</td><td>479</td><td>491</td><td>Had not worked as a welder or sheet metal worker doing welding</td></tr><tr><td>(Bruske-Hohlfeld 2000)</td><td>Lung cancer</td><td>Unclear</td><td>Pathology</td><td>Physician diagnostic record (histologically or cytologically confirmed)</td><td>Lung cancer diagnosis</td><td>101</td><td>49</td><td>2099</td><td>2268</td><td>Not worked in welding-related occupations for ≥1 year</td></tr><tr><td>(Buiatti 1985)</td><td>Trachea, bronchus, and lung cancer</td><td>ICD-8 code 162 (the study mentions lung cancer only and does not explicitly mention this code for the outcome, but controls excluded those with cancers with ICD-8 162)</td><td>Pathology</td><td>Physician diagnostic record</td><td>Trachea, bronchus, and lung cancer diagnosis</td><td>7</td><td>5</td><td>333</td><td>812</td><td>Not worked for ≥1 year in welding</td></tr></tbody></table>

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<table><caption>Table 4 (continued)</caption><thead><tr><th>Study ID</th><th>Outcome assessment<br>Definition of outcome</th><th>Which<br>International<br>Classification of<br>Diseases (ICD) code<br>was reported for the<br>outcome (if any)?</th><th>Method of<br>outcome<br>assessment</th><th>Diagnostic<br>assessment<br>method</th><th>Specification<br>of outcome</th><th>Number of<br>cases with<br>outcome of<br>interest in<br>exposed group</th><th>Number of<br>non-cases (i.e.,<br>without<br>outcome of<br>interest) in<br>exposed group</th><th>Number of<br>cases with<br>outcome of<br>interest in<br>unexposed<br>group</th><th>Number of<br>non-cases (i.e.,<br>without<br>outcome of<br>interest) in<br>unexposed<br>group</th><th>Comparator<br>Definition of<br>comparator<br>(define<br>comparator<br>group, including<br>specific level of<br>exposure)</th></tr></thead><tbody><tr><td>(Consonni<br>2010)</td><td>Kendzia et al. (2013) only<br>mentions lung cancer, but<br>original study specifies<br>trachea, bronchus, and lung<br>cancer. As effect estimate<br>taken from Kendzia et al.<br>(2013), included as lung in<br>subgroup analyses</td><td>ICD-O 3 (but specific<br>code not reported)</td><td>Pathology</td><td>Physician<br>diagnostic record</td><td>Lung cancer<br>diagnosis</td><td>40</td><td>39</td><td>1312</td><td>1449</td><td>Not worked in<br>welding-related<br>occupations for ≥1<br>year</td></tr><tr><td>(Corbin<br>2011)</td><td>Lung cancer</td><td>Unclear</td><td>Pathology</td><td>Administrative<br>record</td><td>Lung cancer<br>diagnosis</td><td>15</td><td>15</td><td>162</td><td>344</td><td>Not worked in<br>welding-related<br>occupations for ≥1<br>year</td></tr><tr><td>(Danielsen<br>1993)</td><td>Trachea, bronchus, and lung<br>cancer</td><td>ICD-7 162</td><td>Pathology</td><td>Administrative<br>record</td><td>Trachea,<br>bronchus, and<br>lung cancer<br>diagnosis</td><td>9</td><td>614</td><td>56</td><td>3892</td><td>Other shipyard<br>production<br>workers</td></tr><tr><td>(Danielsen<br>2000)</td><td>Trachea, bronchus, and lung<br>cancer</td><td>ICD-7 162</td><td>Pathology</td><td>Administrative<br>record</td><td>Trachea,<br>bronchus, and<br>lung cancer<br>diagnosis</td><td>4</td><td>Unclear</td><td>36</td><td>3583</td><td>Other shipyard<br>production<br>workers</td></tr><tr><td>(Elci 2003)</td><td>Trachea, bronchus, and lung<br>cancer</td><td>ICD-O codes 162.0,<br>162.2, 162.3, 162.4,<br>162.5, 162.9</td><td>Pathology</td><td>Administrative<br>record</td><td>Trachea,<br>bronchus, and<br>lung cancer<br>diagnosis</td><td>18</td><td>Unclear</td><td>1336</td><td>Unclear</td><td>Did not report<br>working as a<br>welder</td></tr><tr><td>(Finkelstein<br>1995)</td><td>Lung cancer</td><td>Unclear</td><td>Death certificate</td><td>Administrative<br>record</td><td>Lung cancer<br>mortality</td><td>18</td><td>45</td><td>949</td><td>2776</td><td>Occupation of<br>welder not<br>reported on death<br>certificate</td></tr><tr><td>(Fortes<br>2003)</td><td>Lung cancer</td><td>Unclear</td><td>Pathology</td><td>Physician<br>diagnostic record</td><td>Lung cancer<br>diagnosis</td><td>4</td><td>1</td><td>265</td><td>242</td><td>Not worked in<br>welding-related<br>occupations for ≥1<br>year</td></tr><tr><td>(Gottlieb<br>1980)</td><td>Lung cancer</td><td>Unclear</td><td>Death certificate</td><td>Administrative<br>record</td><td>Lung cancer<br>mortality</td><td>8</td><td>2</td><td>2795</td><td>2801</td><td>Occupation of<br>welder not<br>reported on death<br>certificate</td></tr><tr><td>(Guida 2011;<br>Matrat<br>2016)</td><td>Trachea, bronchus, and lung<br>cancer</td><td>ICD-O code C33 and<br>C34</td><td>Pathology</td><td>Administrative<br>record</td><td>Trachea,<br>bronchus, and<br>lung cancer<br>diagnosis</td><td>100</td><td>69</td><td>1629</td><td>2037</td><td>No welding</td></tr><tr><td>(Gustavsson<br>2000)</td><td>Original study states<br>bronchus and lung cancer</td><td>ICD-7 code 162.1</td><td>Pathology</td><td>Physician<br>diagnostic record</td><td>Bronchus and<br>lung cancer<br>diagnosis</td><td>99</td><td>168</td><td>923</td><td>2158</td><td>Not considered<br>exposed to<br>welding fumes</td></tr><tr><td>(Jöckel<br>1998)</td><td>Newly diagnosed cases with<br>histologically or<br>cytologically confirmed<br>lung cancer</td><td>Unclear</td><td>Pathology</td><td>Physician<br>diagnostic record</td><td>Lung cancer<br>diagnosis</td><td>42</td><td>18</td><td>637</td><td>697</td><td>Not worked in<br>welding-related<br>occupations for ≥1<br>year</td></tr><tr><td>(Kazma<br>2012)</td><td>Lung cancer</td><td>Unclear</td><td>Unclear</td><td>Administrative<br>record/Unclear</td><td>Lung cancer<br>diagnosis</td><td>2</td><td>2</td><td>138</td><td>201</td><td>Not worked in<br>welding-related<br>occupations for ≥1<br>year</td></tr></tbody></table>

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<table><caption>Table 4 (continued)</caption><thead><tr><th>Study ID</th><th>Outcome assessment Definition of outcome</th><th>Which International Classification of Diseases (ICD) code was reported for the outcome (if any)?</th><th>Method of outcome assessment</th><th>Diagnostic assessment method</th><th>Specification of outcome</th><th>Number of cases with outcome of interest in exposed group</th><th>Number of non-cases (i.e., without outcome of interest) in exposed group</th><th>Number of cases with outcome of interest in unexposed group</th><th>Number of non-cases (i.e., without outcome of interest) in unexposed group</th><th>Comparator Definition of comparator (define comparator group, including specific level of exposure)</th></tr></thead><tbody><tr><td>(Keller 1993)</td><td>Lung cancer</td><td>Unclear</td><td>Pathology</td><td>Administrative record</td><td>Lung cancer diagnosis</td><td>Unclear</td><td>Unclear</td><td>Unclear</td><td>Unclear</td><td>Current occupation code or longest lifetime occupation code not welder</td></tr><tr><td>(Kromhout 1992)</td><td>Trachea, bronchus, and lung cancer</td><td>ICD-8 code 162</td><td>Unclear – medical examination and self-reported morbidity (verified by contacting participant’s general practitioner)</td><td>Physician diagnostic record</td><td>Trachea, bronchus, and lung cancer diagnosis</td><td>Unclear</td><td>Unclear</td><td>Unclear</td><td>Unclear</td><td>No exposure to welding materials, welding fumes during their job history</td></tr><tr><td>(Lauritsen 1996)</td><td>Trachea, bronchus, and lung cancer</td><td>ICD-8 code 162.0–162.1</td><td>Pathology</td><td>Administrative record</td><td>Trachea, bronchus, and lung cancer mortality</td><td>46</td><td>Unclear</td><td>38</td><td>Unclear</td><td>No welding</td></tr><tr><td>(Lerchen 1987)</td><td>Primary lung cancer, other than bronchiolovalveolar carcinoma</td><td>Unclear</td><td>Pathology</td><td>Administrative record</td><td>Lung cancer diagnosis</td><td>19</td><td>10</td><td>314</td><td>489</td><td>Did not work as a welder for ≥6 months</td></tr><tr><td>(Lopez-Cima 2007)</td><td>Lung cancer</td><td>Unclear</td><td>Pathology</td><td>Hospital discharge record</td><td>Lung cancer diagnosis</td><td>51</td><td>38</td><td>651</td><td>603</td><td>Not worked in welding-related occupations for ≥1 year</td></tr><tr><td>(MacLeod 2017)</td><td>Lung cancer</td><td>Unclear</td><td>Histology</td><td>Administrative record</td><td>Lung cancer diagnosis</td><td>265</td><td>12,580</td><td>Unclear</td><td>Unclear</td><td>Non-welders</td></tr><tr><td>(Morabia 1992)</td><td>Trachea, bronchus, and lung cancer</td><td>ICD-9 codes 162.0–162.9</td><td>Pathology</td><td>Physician diagnostic record</td><td>Trachea, bronchus, and lung cancer diagnosis</td><td>18</td><td>22</td><td>1775</td><td>3206</td><td>Usual occupation not welder/flame cutter</td></tr><tr><td>(Pezzotto 1999)</td><td>Lung cancer</td><td>ICD-O (M) but codes used unclear</td><td>Pathology</td><td>Physician diagnostic record</td><td>Lung cancer diagnosis</td><td>11</td><td>22</td><td>98</td><td>225</td><td>Administrative staff</td></tr><tr><td>(Richiardi 2004)</td><td>Lung cancer</td><td>Unclear</td><td>Pathology</td><td>Hospital records</td><td>Lung cancer diagnosis</td><td>43</td><td>34</td><td>822</td><td>1104</td><td>Not worked in welding-related occupations for ≥1 year</td></tr><tr><td>(Ronco 1988)</td><td>Lung cancer</td><td>Unclear</td><td>Death certificate</td><td>Administrative record</td><td>Lung cancer mortality</td><td>6</td><td>7</td><td>Unclear</td><td>Unclear</td><td>Not employed as a welder for ≥6 months</td></tr><tr><td>(Sankila 1990)</td><td>Lung cancer</td><td>Unclear</td><td>Cancer registry</td><td>Administrative record</td><td>Lung cancer diagnosis</td><td>Unclear</td><td>Unclear</td><td>Unclear</td><td>Unclear</td><td>All other economically active Finnish men (i.e., not employed as a welder)</td></tr></tbody></table>

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<table><caption>Table 4 (continued)</caption><thead><tr><th>Study ID</th><th>Outcome assessment Definition of outcome</th><th>Which International Classification of Diseases (ICD) code was reported for the outcome (if any)?</th><th>Method of outcome assessment</th><th>Diagnostic assessment method</th><th>Specification of outcome</th><th>Number of cases with outcome of interest in exposed group</th><th>Number of non-cases (i.e., without outcome of interest) in exposed group</th><th>Number of cases with outcome of interest in unexposed group</th><th>Number of non-cases (i.e., without outcome of interest) in unexposed group</th><th>Comparator Definition of comparator (define comparator group, including specific level of exposure)</th></tr></thead><tbody><tr><td>(Schoenberg 1987)</td><td>Trachea, bronchus, and lung cancer</td><td>ICD code 162</td><td>Pathology and death certificates</td><td>Physician diagnostic record and administrative records</td><td>Trachea, bronchus, and lung cancer diagnosis</td><td>17</td><td>11</td><td>Unclear</td><td>Unclear</td><td>Did not work in the "combined welders" group</td></tr><tr><td>(Siew 2008)</td><td>Lung cancer</td><td>Unclear</td><td>Cancer registry</td><td>Administrative record</td><td>Lung cancer diagnosis</td><td>287 in medium exposure group, 67 in high exposure group</td><td>Unclear</td><td>27,192</td><td>Unclear</td><td>Not exposed to welding fumes (cumulative exposure &lt;0.1 mg/m³-years)</td></tr><tr><td>(Soskolne 2007)</td><td>Lung cancer</td><td>Unclear</td><td>Pathology</td><td>Physician diagnostic record</td><td>Lung cancer diagnosis</td><td>Unclear</td><td>Unclear</td><td>Unclear</td><td>Unclear</td><td>Not exposed to welding fumes</td></tr><tr><td>(Steenland 1986)</td><td>Lung cancer</td><td>Unclear</td><td>Death certificate</td><td>Administrative record</td><td>Lung cancer mortality</td><td>50</td><td>3197</td><td>87</td><td>5345</td><td>Non-welders (in the same union)</td></tr><tr><td>(Steenland 2002)</td><td>Trachea, bronchus, and lung cancer</td><td>ICD-9 code 162</td><td>Death certificate</td><td>Administrative record</td><td>Trachea, bronchus, and lung cancer mortality</td><td>108</td><td>4351</td><td>128</td><td>4158</td><td>Non-welders who had worked for ≥2 years</td></tr><tr><td>(Stücker 2002)</td><td>Lung cancer</td><td>Unclear</td><td>Pathology</td><td>Physician diagnostic record</td><td>Lung cancer diagnosis</td><td>7</td><td>7</td><td>258</td><td>250</td><td>Not worked in welding-related occupations for ≥1 year</td></tr><tr><td>('t Mannetje 2012)</td><td>Lung cancer</td><td>Unclear</td><td>Pathology</td><td>Physician diagnostic record</td><td>Lung cancer diagnosis</td><td>582</td><td>522</td><td>1615</td><td>1773</td><td>Never exposed to welding fumes</td></tr><tr><td>(Tse 2012)</td><td>Lung cancer</td><td>Unclear</td><td>Pathology</td><td>Hospital discharge record</td><td>Lung cancer diagnosis</td><td>112</td><td>48</td><td>1096</td><td>1021</td><td>Did not report regular occupational exposure to welding fumes</td></tr><tr><td>(Vallieres 2012)</td><td>Lung cancer</td><td>Unclear</td><td>Pathology</td><td>Hospital discharge record</td><td>Lung cancer diagnosis</td><td>29</td><td>16</td><td>595</td><td>776</td><td>Not worked in welding-related occupations for ≥1 year</td></tr><tr><td>(van Loon 1997)</td><td>Lung cancer</td><td>Unclear</td><td>Pathology</td><td>Hospital discharge record</td><td>Lung cancer diagnosis</td><td>Unclear (but 63 exposed cases among those for whom welding fume exposure information was held)</td><td>Unclear (but 139 exposed controls among those for whom welding fume exposure information was held)</td><td>Unclear (but 457 exposed controls among those for whom welding fume exposure information was held)</td><td>Unclear (but 1169 unexposed controls among those for whom welding fume exposure information was held)</td><td>No exposure to welding fumes</td></tr><tr><td>(Wong 2017)</td><td>Lung cancer</td><td>Unclear</td><td>Pathology</td><td>Physician diagnostic record</td><td>Lung cancer diagnosis</td><td>101</td><td>2210</td><td>1824</td><td>46,848</td><td>Never welder or foundry worker</td></tr><tr><td>(Yiin 2007)</td><td>Trachea, bronchus, and lung cancer</td><td>ICD-9 code 162; ICD-8 code 162; ICD-6 and ICD-7 codes 162 or 163</td><td>Death certificate</td><td>Administrative record</td><td>Trachea, bronchus, and lung cancer mortality</td><td>290</td><td>688</td><td>807</td><td>2603</td><td>Unexposed workers (TLV-1&lt;0.5)</td></tr></tbody></table>
<table><thead><tr><th rowspan="2">Study<br>Study ID</th><th rowspan="2">Prioritized model<br>Are two or more<br>alternative<br>models reported?</th><th rowspan="2">Which of the alternative<br>models was prioritized/<br>selected for use in the<br>review and/or meta-<br>analysis?</th><th rowspan="2">Reason for prioritization/<br>selection</th><th colspan="3">Adjustments of effect estimates in model prioritized by reviewers</th><th rowspan="2">Interactions<br>adjusted for<sup>g</sup></th><th rowspan="2">Adjustment<br>for clustering<br>(if any)</th></tr><tr><th>Adjusted for<br>confounding by<br>age</th><th>Adjusted for<br>confounding by sex</th><th>Other potential confounders<br>adjusted for (please specify)</th></tr></thead><tbody><tr><td>(Becker<br>1999)</td><td>No</td><td>N/A</td><td>N/A</td><td>Yes<sup>d</sup></td><td>Unclear - a previous<br>study record by the<br>authors suggests this<br>study could be<br>restricted to males</td><td>No (socioeconomic status not directly<br>adjusted for although, internal analyses<br>indirectly adjust for several potential<br>confounders including socioeconomic<br>status)</td><td>No</td><td>No</td></tr><tr><td>(Brenner<br>2010)</td><td>Yes - in original<br>study and in<br>Kendzia et al.<br>(2013)</td><td>Model from the original<br>study record including the<br>total population</td><td>The full population is included,<br>and the model is adjusted for<br>tobacco smoking, which is<br>preferable to restricting to non-<br>smokers, Kendzia et al. (2013)<br>does not include females</td><td>Yes<sup>d</sup></td><td>Yes<sup>d</sup></td><td>Pack-years of tobacco smoking among<br>ever and current smokers, education,<br>and ethnicity<sup>f</sup></td><td>No</td><td>No</td></tr><tr><td>(Breslow<br>1954)</td><td>No</td><td>N/A</td><td>N/A</td><td>No - controls were<br>matched within 5<br>years of age</td><td>Yes - restricted to<br>males<sup>e</sup></td><td>Patients were also matched by race</td><td>No</td><td>No</td></tr><tr><td>(Bruske-<br>Hohlfeld<br>2000)</td><td>Yes - in original<br>study and in<br>Kendzia et al.<br>(2013)</td><td>Ever versus never welder,<br>reported in Kendzia et al.<br>(2013)</td><td>The original study does not report<br>analyses on welding fumes/<br>welders</td><td>Yes<sup>d</sup></td><td>Yes - restricted to<br>males<sup>e</sup></td><td>Tobacco smoking as pack-years as a<br>continuous variable, time since<br>quitting tobacco smoking, ever<br>working in an occupation involving<br>lung cancer risk excluding welding-<br>related occupations, study location<sup>f</sup></td><td>No</td><td>No</td></tr><tr><td>(Buiatti<br>1985)</td><td>No</td><td>N/A</td><td>N/A</td><td>Yes<sup>d</sup></td><td>Yes - restricted to<br>males<sup>e</sup></td><td>Place of birth</td><td>No</td><td>No</td></tr><tr><td>(Consonni<br>2010)</td><td>Yes - in original<br>study and in<br>Kendzia et al.<br>(2013)</td><td>Ever versus never welder,<br>reported in Kendzia et al.<br>(2013)</td><td>Additional ever welders included<br>in Kendzia et al. (2013) analysis</td><td>Yes<sup>d</sup></td><td>Yes - restricted to<br>males<sup>e</sup></td><td>Tobacco smoking as pack-years as a<br>continuous variable, time since<br>quitting tobacco smoking, ever<br>working in an occupation involving<br>lung cancer risk excluding welding-<br>related occupations, study location<sup>f</sup></td><td>No</td><td>No</td></tr><tr><td>(Corbin<br>2011)</td><td>Yes - in original<br>study and in<br>Kendzia et al.<br>(2013)</td><td>Ever versus never welder,<br>reported in Kendzia et al.<br>(2013)</td><td>Additional ever welders included<br>in Kendzia et al. (2013) analysis</td><td>Yes<sup>d</sup></td><td>Yes - restricted to<br>males<sup>e</sup></td><td>Tobacco smoking as pack-years as a<br>continuous variable, time since<br>quitting tobacco smoking, ever<br>working in an occupation involving<br>lung cancer risk excluding welding-<br>related occupations, study location<sup>f</sup></td><td>No</td><td>No</td></tr><tr><td>(Danielsen<br>1993)</td><td>Yes</td><td>Model with lag time (10<br>years). Two effect estimates<br>were extracted:<br>employment ≤5 years and<br>employment > 5 years</td><td>It is likely that a lag time exists<br>between exposure to welding<br>fumes and lung cancer initiation</td><td>No</td><td>Yes - restricted to<br>males<sup>e</sup></td><td>No</td><td>No</td><td>No</td></tr><tr><td>(Danielsen<br>2000)</td><td>Yes</td><td>Worked ≥15 years as a<br>welder (Table 6) due to<br>latency period for trachea,<br>bronchus, and lung cancer</td><td>95% confidence intervals<br>provided (not provided for both<br>point estimates in stratified<br>analysis). Highest exposure level.</td><td>No</td><td>Yes - restricted to<br>males<sup>e</sup></td><td>No</td><td>No</td><td>No</td></tr><tr><td>(Elci 2003)</td><td>No</td><td>N/A</td><td>N/A</td><td>Yes<sup>d</sup></td><td>Yes - restricted to<br>males<sup>e</sup></td><td>Tobacco smoking (ever/never)<sup>f</sup></td><td>No</td><td>No</td></tr><tr><td>(Finkelstein<br>1995)</td><td>No</td><td>N/A</td><td>N/A</td><td>Yes<sup>d</sup></td><td>Yes - restricted to<br>males<sup>e</sup></td><td>Year of death, city of residence</td><td>No</td><td>No</td></tr><tr><td>(Fortes 2003)</td><td>No</td><td>N/A</td><td>N/A</td><td>Yes<sup>d</sup></td><td>Yes - restricted to<br>males<sup>e</sup></td><td>Tobacco smoking as pack-years as a<br>continuous variable, time since<br>quitting tobacco smoking, ever<br>working in an occupation involving</td><td>No</td><td>No</td></tr></tbody></table>

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<table><caption>Table 4 (continued)</caption><thead><tr><th>Study ID</th><th>Prioritized model Are two or more alternative models reported?</th><th>Which of the alternative models was prioritized/ selected for use in the review and/or meta-analysis?</th><th>Reason for prioritization/ selection</th><th>Adjustments of effect Adjusted for confounding by age</th><th>Adjusted for confounding by sex</th><th>Other potential confounders adjusted for (please specify)</th><th>Interactions adjusted for<sup>g</sup></th><th>Adjustment for clustering (if any)</th></tr></thead><tbody><tr><td>(Gottlieb 1980)</td><td>No</td><td>N/A</td><td>N/A</td><td>No – stratified by age at death</td><td>Yes – restricted to males<sup>e</sup></td><td>lung cancer risk excluding welding-related occupations, study location<sup>f</sup><br>Controls matched to the lung cancer cases by sex, race, year of death, parish of residence at death, and age of death<sup>f</sup></td><td>No</td><td>No</td></tr><tr><td>(Guida 2011; Matrat 2016)</td><td>Yes</td><td>Regular welders versus no welding</td><td>Compares regular welders</td><td>Yes<sup>d</sup></td><td>Yes – restricted to males<sup>e</sup></td><td>Department, number of jobs, cumulative tobacco smoking index, and asbestos<sup>f</sup></td><td>No</td><td>No</td></tr><tr><td>(Gustavsson 2000)</td><td>Yes – in original study and in Kendzia et al. (2013)</td><td>Ever versus never welder, reported in Kendzia et al. (2013)</td><td>Although the original study provides a larger number and analyses on exposure to welding fumes (rather than job title), Kendzia et al. (2013) provides a dichotomous analysis, without risk of overadjustment</td><td>Yes<sup>d</sup></td><td>Yes – restricted to males<sup>e</sup></td><td>Tobacco smoking as pack-years as a continuous variable, time since quitting tobacco smoking, ever working in an occupation involving lung cancer risk excluding welding-related occupations, study location<sup>f</sup></td><td>No</td><td>No</td></tr><tr><td>(Jöckel 1998)</td><td>Yes – in original study and in Kendzia et al. (2013)</td><td>Ever versus never welder, reported in Kendzia et al. (2013)</td><td>It is not clear that age was adjusted for in Jockel 1998 (only matched), however it adjusted for in Kendzia et al. (2013)</td><td>Yes<sup>d</sup></td><td>Yes – restricted to males<sup>e</sup></td><td>Tobacco smoking as pack-years as a continuous variable, time since quitting tobacco smoking, ever working in an occupation involving lung cancer risk excluding welding-related occupations, study location<sup>f</sup></td><td>No</td><td>No</td></tr><tr><td>(Kazma 2012)</td><td>Yes – in original study and in Kendzia et al. (2013)</td><td>Ever versus never welder, reported in Kendzia et al. (2013)</td><td>Kazma 2012 does not report analysis on welders</td><td>Yes<sup>d</sup></td><td>Yes – restricted to males<sup>e</sup></td><td>Tobacco smoking as pack-years as a continuous variable, time since quitting tobacco smoking, ever working in an occupation involving lung cancer risk excluding welding-related occupations, study location<sup>f</sup></td><td>No</td><td>No</td></tr><tr><td>(Keller 1993)</td><td>No</td><td>N/A</td><td>N/A</td><td>Yes<sup>d</sup></td><td>Yes – restricted to males<sup>e</sup></td><td>Known history of tobacco use<sup>f</sup></td><td>No</td><td>No</td></tr><tr><td>(Kromhout 1992)</td><td>Yes</td><td>Model with 25 years of follow-up, with occupational exposure classified in a strict way</td><td>Long lag period likely for the outcome</td><td>Yes<sup>d</sup></td><td>Yes – restricted to males<sup>e</sup></td><td>Tobacco smoking habits (pack-years up to 1960)<sup>f</sup></td><td>No</td><td>No</td></tr><tr><td>(Lauritsen 1996)</td><td>Yes</td><td>Adjusted ever/never welding exposure</td><td>Dichotomous exposed used in preference in main meta-analysis over years exposed</td><td>No – matched on 5 year age groups</td><td>Yes – restricted to males<sup>e</sup></td><td>Tobacco smoking (unclear, but at least never/rarely, daily smoker)<sup>f</sup></td><td>No</td><td>No</td></tr><tr><td>(Lerchen 1987)</td><td>Yes</td><td>Welders in all industries versus non-welders, logistic model adjusted for age, ethnicity and tobacco smoking</td><td>More adjusted and uncontaminated reference group</td><td>Yes<sup>d</sup></td><td>Yes – restricted to males<sup>e</sup></td><td>Tobacco smoking (never, ex-smoker, current), ethnicity<sup>f</sup></td><td>No</td><td>No</td></tr><tr><td>(Lopez-Cima 2007)</td><td>No (taken from Kendzia et al. (2013) as López-Cima 2007 does not report welding analysis)</td><td>N/A</td><td>N/A</td><td>Yes<sup>d</sup></td><td>Yes – restricted to males<sup>e</sup></td><td>Tobacco smoking as pack-years as a continuous variable, time since quitting tobacco smoking, ever working in an occupation involving lung cancer risk excluding welding-related occupations, study location<sup>f</sup></td><td>No</td><td>No</td></tr><tr><td></td><td>Yes</td><td></td><td></td><td>Yes<sup>d</sup></td><td></td><td>Region, education level<sup>f</sup></td><td>No</td><td>No</td></tr></tbody></table>

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<table><caption>Table 4 (continued)</caption><thead><tr><th>Study<br>Study ID</th><th>Prioritized model<br>Are two or more<br>alternative<br>models reported?</th><th>Which of the alternative<br>models was prioritized/<br>selected for use in the<br>review and/or meta-<br>analysis?</th><th>Reason for prioritization/<br>selection</th><th>Adjustments of effect<br>Adjusted for<br>confounding by<br>age</th><th>Adjusted for<br>confounding by<br>sex</th><th>Other potential confounders<br>adjusted for (please specify)</th><th>Interactions<br>adjusted for<sup>g</sup></th><th>Adjustment<br>for clustering<br>(if any)</th></tr></thead><tbody><tr><td>(MacLeod<br>2017)</td><td></td><td>Cox proportional hazards<br>analysis of welders,<br>adjusted for age, region,<br>and education level, with<br>non-welders as the<br>reference group</td><td>More adjusted and wider<br>reference group</td><td></td><td>Yes – restricted to<br>males<sup>e</sup></td><td></td><td></td><td></td></tr><tr><td>(Morabia<br>1992)</td><td>No</td><td>N/A</td><td>N/A</td><td>Yes<sup>d</sup></td><td>Yes – restricted to<br>males<sup>e</sup></td><td>Race, age, hospital, cigarette tobacco<br>smoking history (never smoker, ex-<br>smoker, current smoker of 1–19<br>cigarettes per day, current smoker of<br>20+ cigarettes per day)<sup>f</sup></td><td>No</td><td>No</td></tr><tr><td>(Pezzotto<br>1999)</td><td>No</td><td>N/A</td><td>N/A</td><td>Yes<sup>d</sup></td><td>Yes – restricted to<br>males<sup>e</sup></td><td>Tobacco smoking habit (non-smoker,<br>ex-smoker, smoker), and lifelong<br>cigarette consumption<sup>f</sup></td><td>No</td><td>No</td></tr><tr><td>(Richiardi<br>2004)</td><td>Yes – in original<br>study and in<br>Kendzia et al.<br>(2013)</td><td>Ever versus never welder,<br>reported in Kendzia et al.<br>(2013)</td><td>Additional ever welders included<br>in Kendzia et al. (2013) analysis</td><td>Yes<sup>d</sup></td><td>Yes – restricted to<br>males<sup>e</sup></td><td>Tobacco smoking as pack-years as a<br>continuous variable, time since<br>quitting tobacco smoking, ever<br>working in an occupation involving<br>lung cancer risk excluding welding-<br>related occupations, study location<sup>f</sup></td><td>No</td><td>No</td></tr><tr><td>(Ronco 1988)</td><td>Yes</td><td>Logistic regression</td><td>Adjusted for age and tobacco<br>smoking</td><td>Yes<sup>d</sup></td><td>Yes – restricted to<br>males<sup>e</sup></td><td>Tobacco smoking (cumulative life-long<br>cigarette consumption<br>was considered (non-smokers; up to<br>120,000 cigarettes<br>smoked, corresponding to a<br>consumption of about 15<br>cigarette a day for 20 years; 120,000–<br>240,000; 240,000–360,000;<br>>360,000)<sup>f</sup></td><td>No</td><td>No</td></tr><tr><td>(Sankila<br>1990)</td><td>No</td><td>N/A</td><td>N/A</td><td>Yes<sup>d</sup></td><td>Yes – restricted to<br>males<sup>e</sup></td><td>No</td><td>No</td><td>No</td></tr><tr><td>(Schoenberg<br>1987)</td><td>Yes</td><td>Model for “combined<br>welders”, restricting to<br>those not exposed to<br>asbestos</td><td>Presumably including more<br>workers due to wider exposure<br>definition. Controls for asbestos</td><td>Yes<sup>d</sup></td><td>Yes – restricted to<br>males<sup>e</sup></td><td>Tobacco smoking (never, smokers of<br>pipes/cigars/<10 cigarettes a day,<br>smokers of 10–29 cigarettes a day,<br>smokers of 30+ cigarettes a day)<sup>f</sup></td><td>No</td><td>Area</td></tr><tr><td>(Siew 2008)</td><td>Yes</td><td>Model for all lung cancers,<br>high cumulative exposure<br>versus no cumulative<br>exposure</td><td>Covers most diagnoses. It was not<br>possible to combine exposure<br>groups due to reporting of only<br>number of cases. High exposure<br>was chosen as provides the<br>greatest contrast in exposure.</td><td>Yes<sup>d</sup></td><td>Yes – restricted to<br>males<sup>e</sup></td><td>Tobacco smoking (proportion of those<br>in the occupation who smoked daily,<br>according to data from annual surveys<br>on the health behavior of the Finnish<br>adult population in 1978–1991),<br>period of follow-up, socioeconomic<br>status<sup>f</sup></td><td>No</td><td>No</td></tr><tr><td>(Soskolne<br>2007)</td><td>No</td><td>N/A</td><td>N/A</td><td>Yes<sup>d</sup></td><td>Yes – restricted to<br>males<sup>e</sup></td><td>Tobacco smoking (no/very low-level<br>smoking i.e., <5 cigarette pack-years,<br>low-level smoking i.e., 5–< to <30<br>cigarette pack-years; moderate-level<br>smoking i.e., 30–< to < 60 cigarette</td><td>No</td><td>No</td></tr></tbody></table>

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<table><caption>Table 4 (continued)</caption><thead><tr><th>Study ID</th><th>Prioritized model Are two or more alternative models reported?</th><th>Which of the alternative models was prioritized/ selected for use in the review and/or meta-analysis?</th><th>Reason for prioritization/ selection</th><th>Adjustments of effect Adjusted for confounding by age</th><th>Adjustments of effect Adjusted for confounding by sex</th><th>Other potential confounders adjusted for (please specify)</th><th>Interactions adjusted for<sup>g</sup></th><th>Adjustment for clustering (if any)</th></tr></thead><tbody><tr><td>(Steenland 1986)</td><td>Yes</td><td>Cox regression with age as the time variable age and dichotomous classification of welding</td><td>Able to calculate 95% confidence interval using method from Altman and Bland (see study record for details)</td><td>Yes<sup>d</sup></td><td>Yes<sup>d</sup></td><td>pack-years); high-level smoking (60+ cigarette pack-years)<sup>f</sup></td><td>Unclear – backwards stepwise procedure tested for interaction</td><td>No</td></tr><tr><td>(Steenland 2002)<br/>(Stücker 2002)</td><td>Yes<br/>Yes – in original study and in Kendzia et al. (2013)</td><td>Model for dichotomised exposure<br/>Ever versus never welder, reported in Kendzia et al. (2013)</td><td>Provides estimate for entire cohort<br/>The original study does not report analyses on welding fumes/ welders</td><td>Yes<sup>d</sup><br/>Yes<sup>d</sup></td><td>Yes – restricted to males<sup>e</sup><br/>Yes – restricted to males<sup>e</sup></td><td>Race, calendar time<sup>f</sup><br/>Tobacco smoking as pack-years as a continuous variable, time since quitting tobacco smoking, ever working in an occupation involving lung cancer risk excluding welding-related occupations, study location<sup>f</sup><br/>Tobacco consumption (continuous variable for cumulativelifetime tobacco use), asbestos, silica, and metals in the work environment, education<sup>f</sup></td><td>No<br/>No</td><td>No</td></tr><tr><td>('t Mannetje 2012)</td><td>Yes</td><td>Model from study reporting OR of ever exposure to welding fumes, adjusted for age, center, education, tobacco, and asbestos, silica and metals in the work environment</td><td>Considers exposure to welding fumes</td><td>Yes<sup>d</sup></td><td>Yes – restricted to males<sup>e</sup></td><td>Tobacco smoking status (never, ex-smoker, current smoker), tobacco smoking pack-years, alcohol drinking, residential radon exposure, history of lung diseases, and cancer in first-degree relatives, meat intake, education level, place of birth<sup>f</sup></td><td>No</td><td>Centre</td></tr><tr><td>(Tse 2012)</td><td>Yes</td><td>All lung cancer cases, with reference group of those never exposed to welding fumes</td><td>Considers exposure to welding fumes (as opposed to Kendzia et al. (2013), which considers occupation as welder). Does not restrict referents based on other exposures or sex.</td><td>Yes<sup>d</sup></td><td>Yes – restricted to males<sup>e</sup></td><td>Tobacco smoking as pack-years as a continuous variable, time since quitting tobacco smoking, ever working in an occupation involving lung cancer risk excluding welding-related occupations, study location<sup>f</sup><br/>Other occupational exposures, tobacco smoking (never/ex/current and pack-years), intake of vitamin C, β-carotene, and retinol<sup>f</sup></td><td>No</td><td>No</td></tr><tr><td>(Vallieres 2012)</td><td>Yes – in original study and in Kendzia et al. (2013)</td><td>Ever versus never welder, reported in Kendzia et al. (2013)</td><td>Estimates for arc and gas welders are reported separately in Vallieres et al. (2012), but the groups are not mutually exclusive so cannot be combined</td><td>Yes<sup>d</sup></td><td>Yes – restricted to males<sup>e</sup></td><td>Race/ethnicity, education, centred pack-years of tobacco smoking at randomisation, tobacco smoking status at randomisation (current or former), centred body mass index at baseline, first degree relative with lung cancer, history of diagnosed chronic bronchitis and emphysema, education level<sup>f</sup></td><td>No</td><td>No</td></tr><tr><td>(van Loon 1997)</td><td>Yes</td><td>Unexposed versus exposed</td><td>Dichotomous</td><td>Yes<sup>d</sup></td><td>Yes – restricted to males<sup>e</sup></td><td>Race/ethnicity, education, centred pack-years of tobacco smoking at randomisation, tobacco smoking status at randomisation (current or former), centred body mass index at baseline, first degree relative with lung cancer, history of diagnosed chronic bronchitis and emphysema, education level<sup>f</sup></td><td>No</td><td>No</td></tr><tr><td>(Wong 2017)</td><td>Yes</td><td>Ever welder, never foundry worker versus never welder, never foundry worker</td><td>Dichotomous</td><td>Yes<sup>d</sup></td><td>Yes<sup>d</sup></td><td>Race/ethnicity, education, centred pack-years of tobacco smoking at randomisation, tobacco smoking status at randomisation (current or former), centred body mass index at baseline, first degree relative with lung cancer, history of diagnosed chronic bronchitis and emphysema, education level<sup>f</sup></td><td>No</td><td>Trial arm</td></tr><tr><td>(Yiin 2007)</td><td>Yes – different effect estimates</td><td></td><td></td><td>Yes – conditional logistic regression</td><td>Yes – conditional logistic regression</td><td>Ionizing radiation monitoring status, tobacco smoking habit surrogates</td><td>No</td><td>No</td></tr></tbody></table>

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<table><caption>Table 4 (continued)</caption><thead><tr><th>Study ID</th><th>Prioritized model Are two or more alternative models reported?</th><th>Which of the alternative models was prioritized/ selected for use in the review and/or meta-analysis?</th><th>Reason for prioritization/ selection</th><th>Adjustments of effect estimates in model prioritized by reviewers Adjusted for confounding by age</th><th>Adjusted for confounding by sex</th><th>Other potential confounders adjusted for (please specify)</th><th>Interactions adjusted for<sup>g</sup></th><th>Adjustment for clustering (if any)</th></tr><tr><th>Study ID</th><th>Estimate of effect of exposure on outcome Model prioritized by reviewers (if more than one potentially eligible model reported)</th><th>Effect estimate measure type</th><th>Point estimate<sup>h</sup></th><th>Standard deviation</th><th>Lower confidence limit<sup>h</sup></th><th>Upper confidence limit<sup>h</sup></th><th>Included in a meta-analysis?</th><th>Exposure-response (or dose-response) analysis conducted</th></tr></thead><tbody><tr><td>(Becker 1999)</td><td>N/A – the model presents risk ratios of the welders in relative to the internal comparison group of turners</td><td>Risk ratio</td><td>1.3</td><td>Not reported</td><td>0.8</td><td>2.12</td><td>Yes – supporting evidence (cohort studies), mortality</td><td>Table 5 presents standardized mortality rates for duration of exposure</td></tr><tr><td>(Brenner 2010)</td><td>Total population adjusted for pack-years of tobacco smoking, age, sex, education, and ethnicity</td><td>Odds ratio</td><td>1.7</td><td>Not reported</td><td>1.0</td><td>3.0</td><td>Yes – main meta-analysis, incidence</td><td>No</td></tr><tr><td>(Breslow 1954)</td><td>Not reported, but possible to calculate odds ratios from Table 10</td><td>Odds ratio</td><td>1.77</td><td>Not reported</td><td>1.46</td><td>2.16</td><td>No – not included as insufficient adjustment for age</td><td>No</td></tr><tr><td>(Bruske-Hohlfeld 2000)</td><td>Ever versus never welder, reported in Kendzia et al. (2013)</td><td>Odds ratio</td><td>1.75</td><td>Not reported</td><td>1.14</td><td>2.61</td><td>Yes – main meta-analysis, incidence</td><td>No</td></tr><tr><td>(Buiatti 1985)</td><td>N/A</td><td>Odds ratio</td><td>2.8</td><td>Not reported</td><td>0.9</td><td>8.5</td><td>Yes – main meta-analysis, incidence</td><td>No</td></tr><tr><td>(Consonni 2010)</td><td>Ever versus never welder, reported in Kendzia et al. (2013)</td><td>Odds ratio</td><td>0.94</td><td>Not reported</td><td>0.56</td><td>1.59</td><td>Yes – main meta-analysis, incidence</td><td>No</td></tr><tr><td>(Corbin 2011)</td><td>Ever versus never welder, reported in Kendzia et al. (2013)</td><td>Odds ratio</td><td>1.43</td><td>Not reported</td><td>0.57</td><td>3.58</td><td>Yes – main meta-analysis, incidence</td><td>No</td></tr><tr><td>(Danielson 1993)</td><td>Model with lag time (10 years). Two effect estimates were extracted: employment ≤5 years and employment >5 years</td><td>Risk ratio</td><td>Employment ≤5 years 1.8; employment >5 years 3.2</td><td>Not reported</td><td>Employment ≤5 years 0.5; employment >5 years 1.3</td><td>Employment ≤5 years 5.7; employment >5 years 8.1</td><td>No – not included as insufficient adjustment for age</td><td>Employment of ≤5 years or >5 years- but different analyses</td></tr><tr><td>(Danielson 2000)</td><td>Worked ≥15 years as a welder (Table 6) due to latency period for trachea, bronchus, and lung cancer</td><td>Risk ratio</td><td>1.9</td><td>Not reported</td><td>0.67</td><td>5.38</td><td>No – not included as insufficient adjustment age</td><td>Yes – duration of employment as welder at the yard</td></tr><tr><td>(Elci 2003)</td><td>N/A</td><td>Odds ratio</td><td>0.9</td><td>Not reported</td><td>0.5</td><td>1.7</td><td>Yes – main meta-analysis, incidence</td><td>No</td></tr><tr><td>(Finkelstein 1995)</td><td>N/A</td><td>Odds ratio</td><td>1.07</td><td>Not reported</td><td>0.57</td><td>1.91</td><td>Yes – main meta-analysis, mortality</td><td>No</td></tr><tr><td>(Fortes 2003)</td><td>N/A</td><td>Odds ratio</td><td>7.65</td><td>Not reported</td><td>0.59</td><td>99.8</td><td>Yes – main meta-analysis, incidence</td><td>No</td></tr><tr><td>(Gottlieb 1980)</td><td>N/A</td><td>Odds ratio</td><td>Age at death <60: 1.89; age at death ≥60 years: 0.93</td><td>Not reported</td><td>Age at death <60: 0.48; age at death ≥60 years: 0.25</td><td>Age at death <60: 7.37; age at death ≥60 years: 3.46</td><td>No – not included as no adjustment for age</td><td>No</td></tr><tr><td>(Guida 2011; Matrat 2016)</td><td>Risk of lung cancer associated with welding among regular welders</td><td>Odds ratio</td><td>1.66</td><td>Not reported</td><td>1.11</td><td>2.49</td><td>Yes – main meta-analysis, incidence</td><td>Yes</td></tr><tr><td>(Gustavsson 2000)</td><td>Ever versus never welder, reported in Kendzia et al. (2013)</td><td>Odds ratio</td><td>1.52</td><td>Not reported</td><td>0.86</td><td>2.67</td><td>Yes – main meta-analysis, incidence</td><td>Yes – in original study record</td></tr><tr><td>(Jöckel 1998)</td><td>Exposure to welding fumes analysis presented in Jöckel 1998</td><td>Odds ratio</td><td>1.87</td><td>Not reported</td><td>1.03</td><td>3.42</td><td>Yes – main meta-analysis, incidence</td><td>Yes – in original study record</td></tr><tr><td>(Kazma 2012)</td><td>Ever versus never welder, reported in Kendzia et al. (2013)</td><td>Odds ratio</td><td>0.37</td><td>Not reported</td><td>0.02</td><td>9.03</td><td>Yes – main meta-analysis, incidence</td><td>No</td></tr><tr><td>(Keller 1993)</td><td>N/A</td><td>Odds ratio</td><td>1.68</td><td>Not reported</td><td>1.03</td><td>2.76</td><td>Yes – main meta-analysis, incidence</td><td>No</td></tr></tbody></table>

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<table><caption>Table 4 (continued)</caption><thead><tr><th>Study ID</th><th>Estimate of effect of exposure on outcome Model prioritized by reviewers (if more than one potentially eligible model reported)</th><th>Effect estimate measure type</th><th>Point estimate<sup>h</sup></th><th>Standard deviation</th><th>Lower confidence limit<sup>h</sup></th><th>Upper confidence limit<sup>h</sup></th><th>Included in a meta-analysis?</th><th>Exposure-response (or dose-response) analysis conducted</th></tr></thead><tbody><tr><td>(Kromhout 1992)</td><td>Model with 25 years of follow-up, with occupational exposure classified in a strict way</td><td>Hazard ratio</td><td>1.54</td><td>Not reported</td><td>0.37</td><td>6.30</td><td>Yes – supporting evidence (cohort studies), incidence</td><td>No</td></tr><tr><td>(Lauritsen 1996)</td><td>Adjusted ever/never welding exposure</td><td>Odds ratio</td><td>1.5</td><td>Not reported</td><td>1.0</td><td>2.4</td><td>No – not included as no adjustment for age</td><td>Yes</td></tr><tr><td>(Lerchen 1987)</td><td>Welders in all industries versus non-welders, logistic model adjusted for age, ethnicity, and tobacco smoking</td><td>Odds ratio</td><td>3.2</td><td>Not reported</td><td>1.4</td><td>7.4</td><td>Yes – main meta-analysis, incidence</td><td>No</td></tr><tr><td>(Lopez-Cima 2007)</td><td>Ever versus never welder, reported in Kendzia et al. (2013)</td><td>Odds ratio</td><td>1.09</td><td>Not reported</td><td>0.66</td><td>1.80</td><td>Yes – main meta-analysis, incidence</td><td>No</td></tr><tr><td>(MacLeod 2017)</td><td>Cox proportional hazards analysis of welders, adjusted for age, region, and education level, with non-welders as the reference group</td><td>Hazard ratio</td><td>1.16</td><td>Not reported</td><td>1.03</td><td>1.31</td><td>Yes – supporting evidence (cohort studies), incidence</td><td>No</td></tr><tr><td>(Morabia 1992)</td><td>N/A</td><td>Odds ratio</td><td>1.5</td><td>Not reported</td><td>0.8</td><td>2.7</td><td>Yes – main meta-analysis, incidence</td><td>No</td></tr><tr><td>(Pezzotto 1999)</td><td>N/A</td><td>Odds ratio</td><td>1.1</td><td>Not reported</td><td>0.4</td><td>3.1</td><td>Yes – main meta-analysis, incidence</td><td>No</td></tr><tr><td>(Richiardi 2004)</td><td>Ever versus never welder, reported in Kendzia et al. (2013)</td><td>Odds ratio</td><td>1.77</td><td>Not reported</td><td>1.05</td><td>2.98</td><td>Yes – main meta-analysis, incidence</td><td>No</td></tr><tr><td>(Ronco 1988)</td><td>Logistic regression</td><td>Odds ratio</td><td>2.93</td><td>Not reported</td><td>0.87</td><td>9.82</td><td>Yes – main meta-analysis, mortality</td><td>No</td></tr><tr><td>(Sankila 1990)</td><td>N/A</td><td>Risk ratio</td><td>1.51</td><td>Not reported</td><td>1.16</td><td>1.95</td><td>Yes – supporting evidence (cohort studies), incidence</td><td>No</td></tr><tr><td>(Schoenberg 1987)</td><td>Model for “combined welders”, restricting to those not exposed to asbestos</td><td>Odds ratio</td><td>2.5</td><td>Not reported</td><td>1.1</td><td>5.5</td><td>Yes – main meta-analysis, incidence</td><td>No</td></tr><tr><td>(Siew 2008)</td><td>Model for all lung cancers, high cumulative exposure versus no cumulative exposure</td><td>Risk ratio</td><td>1.15</td><td>Not reported</td><td>0.90</td><td>1.46</td><td>Yes – supporting evidence (cohort studies), incidence</td><td>Yes</td></tr><tr><td>(Soskolne 2007)</td><td>N/A</td><td>Odds ratio</td><td>3.91</td><td>Not reported</td><td>1.03</td><td>14.95</td><td>Yes – main meta-analysis, incidence</td><td>No</td></tr><tr><td>(Steenland 1986)</td><td>Cox regression with age as the time variable and dichotomous classification of welding (with 95% confidence interval calculated from p value)</td><td>Hazard ratio</td><td>1.29</td><td>Not reported</td><td>0.89</td><td>1.87</td><td>Yes – supporting evidence (cohort studies), mortality</td><td>No</td></tr><tr><td>(Steenland 2002)</td><td>Model for dichotomised exposure</td><td>Risk ratio</td><td>1.22</td><td>Not reported</td><td>0.93</td><td>1.59</td><td>Yes – supporting evidence (cohort studies), mortality</td><td>Yes</td></tr><tr><td>(Stücker 2002)</td><td>Ever versus never welder, reported in Kendzia et al. (2013)</td><td>Odds ratio</td><td>0.56</td><td>Not reported</td><td>0.18</td><td>1.70</td><td>Yes – main meta-analysis, incidence</td><td>No</td></tr><tr><td>(‘t Mannetje 2012)</td><td>Model from study reporting OR of ever exposure to welding fumes, adjusted for age, center, education, tobacco, and asbestos, silica, and metals in the work environment</td><td>Odds ratio</td><td>1.18</td><td>Not reported</td><td>1.01</td><td>1.38</td><td>Yes – main meta-analysis, incidence</td><td>Yes</td></tr><tr><td>(Tse 2012)</td><td>All lung cancer cases, with reference group of those never exposed to welding fumes</td><td>Odds ratio</td><td>1.69</td><td>Not reported</td><td>1.11</td><td>2.58</td><td>Yes – main meta-analysis, incidence</td><td>No</td></tr><tr><td>(Vallieres 2012)</td><td>Ever versus never welder, reported in Kendzia et al. (2013)</td><td>Odds ratio</td><td>2.21</td><td>Not reported</td><td>1.10</td><td>4.41</td><td>Yes – main meta-analysis, incidence</td><td>No</td></tr><tr><td>(van Loon 1997)</td><td>Unexposed versus exposed</td><td>Risk ratio</td><td>0.86</td><td>Not reported</td><td>0.46</td><td>1.58</td><td>Yes – main meta-analysis, incidence</td><td>Yes</td></tr></tbody></table>

(continued on next page)
<table><caption>Table 4 (continued)</caption><thead><tr><th>Study<br>Study ID</th><th>Estimate of effect of exposure on outcome<br>Model prioritized by reviewers (if more than<br>one potentially eligible model reported)</th><th>Effect<br>estimate<br>measure type</th><th>Point estimate<sup>h</sup></th><th>Standard<br>deviation</th><th>Lower confidence<br>limit<sup>h</sup></th><th>Upper confidence<br>limit<sup>h</sup></th><th>Included in a meta-<br>analysis?</th><th>Exposure-response (or<br>dose-response) analysis<br>conducted</th></tr></thead><tbody><tr><td>(Wong 2017)</td><td>Ever welder, never foundry worker versus never<br>welder, never foundry worker</td><td>Hazard ratio</td><td>1.12</td><td>Not<br>reported</td><td>0.91</td><td>1.37</td><td>Yes – supporting<br>evidence (cohort<br>studies), incidence</td><td>Yes</td></tr><tr><td>(Yiin 2007)</td><td>Levels combined using Borenstein et al. (2009)<br>method</td><td>Odds ratio</td><td>1.28</td><td>Not<br>reported</td><td>1.03</td><td>1.59</td><td>Yes – supporting<br>evidence (cohort<br>studies), incidence</td><td>Yes</td></tr></tbody></table>

Footnotes:

<sup>a</sup> ISCO-08 code specified unless the study used a different classification system (in which case this is stated); for subgroup analyses, relevant crosswalks were used where identified (Statistics Canada https://www.statcan.gc.ca/en/subjects/standard/concordances/soc1980-soc1991#n1, U.S. Bureau of Labor Statistics https://www.bls.gov/soc/soccrosswalks.htm, Kendzia et al. (2013), Hardy et al. (2018) to ascertain whether codes were equivalent to ISCO-08 7212. Where a crosswalk was not identified, the job title was used and studies specifying occupation as welder were assumed to be ISCO-08 code 7212.

<sup>b</sup> Classification of welders in industries in Kendzia et al. (2013) was as follows (ISIC revision 2): Construction, plumbers, and other building services: 5000, 9100, 4101, 9310, 4102, 4103, 6320, 8324, 8310; Manufacture of machines, equipment, appliances: 3819, 3813, 3829, 3811, 3824, 3800, 3821, 3822, 3833, 3831, 3812, 3823, 3851, 3830, 3832, 3839, 3810, 3820, 3825; Manufacture of motor vehicles, motor bikes, bikes: 3843, 3844; Shipbuilding and repairing: 3841; Repair of transport equipment: 3842, 3845, 6100, 7111, 9513, 7112, 7110, 3849, 7100, 7131; Others: 3710, 3699, 1110, 3511, 3530, 2100, 3320, 3513, 3529, 3909, 2901, 3720, 3833, 3831, 3812, 3823, 3851, 3830, 3832, 3839, 3810, 3820, 3825, 3843, 3844, 3841, 3842, 3845, 6100, 7111, 9513, 7112, 7110, 3849, 7100, 7131, 3710, 3699, 1110, 3511, 3530, 2100, 3320, 3513, 3529, 3909, 2901.

<sup>c</sup> Group 1 carcinogen for the IARC category of Lung cancer, as classified in the IARC Monographs 1–132 (International Agency for Research on Cancer 2022).

<sup>d</sup> Adjusted for Tier 1 confounder (see Fig. 1).

<sup>e</sup> Restricted on Tier 1 confounder (see Fig. 1).

<sup>f</sup> Adjusted for one or more Tier 2 confounders (see Fig. 2).

<sup>g</sup> Interaction occurs when two or more exposures are greater than they would be if purely additive.

<sup>h</sup> Presented to the decimal place reported in the study record or capped at two decimal places.
studies, and “high” for two studies. We assigned “high” and “probably high” ratings where we considered assignment of exposure by proxy of occupation or job title as “welder” could lead to misclassification of occupational exposure to welding fumes through other tasks or participants were asked to recall past exposure to particular carcinogens, with risk of recall bias.

4.4.1.4. *Bias due to outcome misclassification – Were outcome assessment methods lacking accuracy?*. Risk of this bias was rated as “low” for three-quarters of the included studies (29 studies), “probably low” for two studies, “probably high” for one study, and “high” for no studies. The “probably high” rating was assigned when we judged the described methods were not robust, as described by our pre-specified criteria.

4.4.1.5. *Confounding – Was potential confounding inadequately incorporated?*. We judged risk of confounding was “low” in 11 studies, “probably low” in 16 studies, “probably high” in four studies, and “high” for one study. Risk of confounding was judged to be “high” or “probably high” when there was a lack of adjustment for Tier 1 or Tier 2 confounders.

4.4.1.6. *Bias due to incomplete outcome data – Were incomplete outcome data inadequately addressed?*. Risk of this bias was rated as “low” for three-quarters of the included (29 studies)) and “probably low” for three studies. We judged no study to have a “high” risk of bias in this domain.

4.4.1.7. *Bias due to selective reporting – Does the study report appear to have selective outcome reporting?*. Three-quarters of the included studies were judged as at “low” risk of bias due to selective reporting. Four studies were judged as “probably low”. We judged no studies to be at “probably high” or “high” risk of this bias. Most studies were case-control studies, which we generally considered at low risk of bias due to selective reporting. We note, however, that we did not find any study protocols.

4.4.1.8. *Bias due to conflict of interest*. Did the study receive any support from a company, study author, or other entity having a financial interest in any of the exposures studied?

The risk of this bias was rated as “low” for one quarter of included studies (nine studies), “probably low” for half of the studies (21 studies), “probably high” for two study, and “high” for no studies. For most studies, either the authors declared no conflicts of interest or there we had no reasons to believe that conflicts of interest would have affected the study results (i.e., author affiliations were from government or academic/non-industry settings).

4.4.1.9. *Other bias – Did the study appear to have other problems that could put it at a risk of bias?*. We judged almost all of the included studies to be at “low” risk of other bias, with the exception of one study, which was judged to be “probably high” because cancer patients served as control subjects, which hinders generalization of findings to the general working population (Morabia et al. 1992).

4.4.2. *Died from trachea, bronchus, and lung cancer (mortality)*

The ratings for the eight included studies for this outcome are presented in Fig. 4.

4.4.2.1. *Bias in selection of participants into the study*. Are the study groups at risk of not representing their source populations in a manner that might introduce selection bias?

We rated risk of bias in selection of participants into the study as “low” for over half (five) of the included studies, “probably low” for one study, “probably high” for two studies, and “high” for no studies. For one study rated as “probably high” risk in this domain, the study sample was built from previous cohort studies. For the other, the effective sample

was only a proportion of the total cohort. In neither case were the methods reported in sufficiently detail.

4.4.2.2. *Bias due to a lack of blinding of study personnel*. Was knowledge of the group assignments inadequately prevented (i.e., blinded or masked) during the study, potentially leading to subjective measurement of either exposure or outcome?

Risk in this bias domain was rated as “low” in over half of the studies (five studies), “probably low” in two studies, “probably high” for one study, and “high” for no studies. For most studies, we judged blinding was effective or that lack of blinding was unlikely to influence the exposure and/or outcome assessments.

4.4.2.3. *Bias due to exposure misclassification – Were exposure assessment methods lacking accuracy?*. Risk of bias due to exposure misclassification was rated as “low” for no studies. Over half of the included studies had a “probably low” risk of this bias. Two studies and one study had a “probably high” and “high” risk in this bias domain. We rated studies as “high” and “probably high” risk if they assigned exposure via proxy of occupation at time of death (from the death certificate) or job title as recalled by next of kin. Exposure misclassification could have occurred because occupation at time of death could not reflecting usual job history and because of recall bias, respectively.

4.4.2.4. *Bias due to outcome misclassification – Were outcome assessment methods lacking accuracy?*. We judged six studies to be at “low” risk of bias due to outcome misclassification. The other two studies were rated as “probably low” risk of this bias. No studies were at “probably high” or “high” risk. Deaths were ascertained either with a death certificate or extracted from administrative registers.

4.4.2.5. *Confounding – Was potential confounding inadequately incorporated?*. Risk of confounding was judged to be “low” for one study, “probably low” for three studies, “probably high” for three studies and “high” for one study.

4.4.2.6. *Bias due to incomplete outcome data – Were incomplete outcome data inadequately addressed?*. Almost all (seven) studies were rated as at “low” risk of bias due to incomplete outcome data. The eighth study was judged as at “probably low” risk of this bias. No studies were at “probably high” or “high” risk. The studies were either case-control studies, in which outcome was known at the start of the study or we judged follow-up was long enough to assess mortality well.

4.4.2.7. *Bias due to selective reporting – Does the study report appear to have selective outcome reporting?*. Seven studies were judged at “low” risk of bias due to selective reporting. The eighth study was “probably low” in reporting bias risk. No studies had “probably high” or “high” risk. We did not find any study protocols to compare reporting against. We nevertheless judged all studies to be free of selective reporting.

4.4.2.8. *Bias due to conflict of interest*. Did the study receive any support from a company, study author, or other entity having a financial interest in any of the exposures studied?

We judged none of the included studies to be at “low” risk of bias due to conflict of interest. Almost all studies were rated as “probably low” risk of this bias. We judged one study had a “probably high” risk of bias due to conflict of interest, but none as at “high” risk for this bias.

4.4.2.9. *Other bias – Did the study appear to have other problems that could put it at a risk of bias?*. Risk of other bias was rated as “low” for all studies and “probably low”, “probably high” and “high” for no studies. We did not identify other sources of substantive bias in included studies.
<table><caption>**Table 5**

Characteristics of studies awaiting classification.</caption><thead><tr><th rowspan="2">Study ID</th><th colspan="3">Study population</th><th rowspan="2">Study type</th><th colspan="2">Exposure assessment</th><th rowspan="2">Comparator</th><th colspan="2">Outcome assessment</th></tr><tr><th>Total number of study participants</th><th>Country of study population</th><th>Industrial sector/ Occupation</th><th>Exposure definition</th><th>Exposure measurement</th><th>Outcome definition</th><th>Outcome measurement</th></tr></thead><tbody><tr><td>DeBono 2021 (DeBono et al. 2021)</td><td>2,188,300</td><td>Canada</td><td>Multiple</td><td>Cohort study</td><td>Welding and flame cutting occupations</td><td>Occupation coded by Workplace Safety and Insurance Board as part of the claim review process</td><td>All other occupations</td><td>Malignant neoplasms of the pleura (ICD-10 code C38.4) or mesothelioma (ICD-10 code: C45) including peritoneal disease (ICD-10 code: C45.1)</td><td>Compensation claims with Canadian province of Ontario Cancer Registry</td></tr><tr><td>Chung 2021 (Chung et al. 2021)</td><td>6326</td><td>Republic of Korea</td><td>Shipyard workers</td><td>Cohort study</td><td>Workers exposed to nickel, chromium, and welding fumes (CO<sup>2</sup> welding, stainless steel welding)</td><td>Occupational environment assessment of the shipyard showed the geometric mean exposure to welding fumes was 0.63 mg/m<sup>3</sup> (maximum value: 41.53 mg/m<sup>3</sup>), to nickel was 0.88 µg/m<sup>3</sup> (maximum value: 73.00 µg/m<sup>3</sup>), and to chromium was 1.38 µg/m<sup>3</sup> (maximum value: 63.60 µg/m<sup>3</sup>)</td><td>Unexposed workers (based on assessment of their work environment)</td><td>Lung cancer (Lung-RADS category ≥ 3)</td><td>Lung imaging reporting and data system (Lung-RADS) (imaging classification system using a larger minimum nodule size than the NLST criteria)</td></tr></tbody></table>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Summary of risk of bias in included studies on trachea, bronchus, and lung cancer incidence. Footnote: <sup>a</sup> Study IDs marked in grey were included in the main meta-analysis.</figcaption></figure>

## 4.5. Evidence synthesis

Our search did not identify any evidence on prevalence of trachea, bronchus, and lung cancer but we did find evidence for incidence and mortality of trachea, bronchus, and lung cancer.

### 4.5.1. Acquired trachea, bronchus, and lung cancer (incidence)

A total of 32 studies (24 case-control studies and eight cohort studies) with over 1,227,096 participants reported estimates on the effect of any (or high) occupational exposure to welding fumes on the outcome of trachea, bronchus, and lung cancer incidence, compared with no (or low) occupational exposure to welding fumes. The total
<table><thead><tr><th rowspan="2">Navigation Guide risk of bias domain</th><th colspan="7">Study ID <sup>a</sup></th></tr><tr><th>Becker 1999</th><th>Finkelstein 1995</th><th>Gottlieb 1980</th><th>Lauritsen 1996</th><th>Ronco 1988</th><th>Steenland 1986</th><th>Steenland 2002</th><th>Yiin 2007</th></tr></thead><tbody><tr><td>1. Are the study groups at risk of not representing their source populations in a manner that might introduce selection bias?</td><td>L</td><td>L</td><td>PL</td><td>L</td><td>L</td><td>PH</td><td>L</td><td>PH</td></tr><tr><td>2. Was knowledge of the group assignments inadequately prevented (i.e., blinded or masked) during the study, potentially leading to subjective measurement of either exposure or outcome?</td><td>L</td><td>L</td><td>PL</td><td>PL</td><td>L</td><td>PH</td><td>L</td><td>L</td></tr><tr><td>3. Were exposure assessment methods lacking accuracy?</td><td>PL</td><td>H</td><td>PH</td><td>PL</td><td>PH</td><td>PL</td><td>PL</td><td>PL</td></tr><tr><td>4. Were outcome assessment methods lacking accuracy?</td><td>L</td><td>PL</td><td>PL</td><td>L</td><td>L</td><td>L</td><td>L</td><td>L</td></tr><tr><td>5. Was potential confounding inadequately incorporated?</td><td>PH</td><td>PH</td><td>H</td><td>PL</td><td>PL</td><td>L</td><td>PL</td><td>PH</td></tr><tr><td>6. Were incomplete outcome data inadequately addressed?</td><td>L</td><td>L</td><td>L</td><td>L</td><td>L</td><td>PL</td><td>L</td><td>L</td></tr><tr><td>7. Does the study report appear to have selective outcome reporting?</td><td>L</td><td>L</td><td>L</td><td>L</td><td>L</td><td>PL</td><td>L</td><td>L</td></tr><tr><td>8. Did the study receive any support from a company, study author, or other entity having a financial interest in any of the exposures studied?</td><td>PH</td><td>PL</td><td>PL</td><td>PL</td><td>PL</td><td>PL</td><td>PL</td><td>PL</td></tr><tr><td>9. Did the study appear to have other problems that could put it at a risk of bias?</td><td>L</td><td>L</td><td>L</td><td>L</td><td>L</td><td>L</td><td>L</td><td>L</td></tr></tbody></table>

<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Summary of risk of bias in included studies on trachea, bronchus, and lung cancer mortality. Footnote: <sup>a</sup> Study IDs marked in grey were included in the main meta-analysis.</figcaption></figure>

sample size comprised 22,476 (1.8 %) females and 1,204,620 (98.2 %) males, counting effective sample sizes (Table 4). Occupational exposure to welding fumes was generally assessed via survey, and most studies included in their exposure group workers who had been employed or reported their occupation as job title as a welder (i.e., exposure assignment via proxy; Table 4). We meta-analysed evidence from different study designs separately (as per our pre-published protocol). In our risk of bias assessment (Section 4.4.1), we judged that case-control studies generally have a lower risk of confounding from tobacco smoking. Additionally, more of the case-control studies consider exposure to asbestos for this outcome. Moreover, the pooled effect estimate from the meta-analysis of the case-control studies for the outcome is based on a much larger cumulative sample size and has better temporal coverage, as we have many more case-control studies in our dataset, including recently published ones. Our main meta-analysis for this outcome is consequently that of the relevant case-control studies as the prioritized evidence. We consider the evidence from the cohort study as supporting evidence.

4.5.1.1. Case-control studies (prioritised evidence). Twenty-four studies comprising 57,931 participants (798 [1.4 %] females and 58,119 [98.6 %] males) from three WHO regions (Region of the Americas, European Region, and Western Pacific Region) reported estimates of the effect of occupational exposure to welding fumes on acquiring (incident) trachea, bronchus, and lung cancer, compared with no (or low) occupational exposure to welding fumes. Of these, all but one could be included in a quantitative meta-analysis. We converted ORs to RR using the median baseline risk in the unexposed population (i.e., 24.9 cases per 100,000 persons) reported for this outcome in the cohort studies included in this systematic review (Danielsen et al. 1993; van Loon et al. 1997; Danielsen et al. 2000; Siew et al. 2008; Wong et al. 2017), and present the

conversions in Table 6. For ‘t Mannetje, 2012, we added the effect estimate from the original study record, as opposed to the individual effect estimates reported for different countries in the Kendzia et al. (2013) individual participant data analysis. Additionally, instead of the effect estimate in Kendzia et al. (2013), we included the effect estimate from the original study records for Brenner et al. (2010) due to the inclusion of females, and for Tse et al. (2012) due to the consideration of exposure to welding fumes (rather than via proxy of ever/never welder). These studies that we pooled in our meta-analysis were somewhat heterogeneous in their exposure definitions (in that some studies assigned exposure based on occupation or job title of “welder”, whereas others measured occupational exposure to welding fumes more directly), the comparator (in that some studies included participants who had been exposed for less than a specified period of time as unexposed), and the outcome (in that some studies considered only lung cancer, but others considered two or more of trachea, bronchus, and lung cancer). However, we considered the studies similar enough to warrant inclusion in the meta-analysis. Compared with no (or low) occupational exposure to welding fumes, any (or high) occupational exposure to welding fumes increased the risk of acquiring trachea, bronchus, and lung cancer (RR 1.48, 95 % confidence interval [CI] 1.29–1.70, 23 studies, 57,931 participants, I² 24 %, Fig. 5). We also used an alternative method to run the meta-analysis, in which we included ORs in the meta-analysis and then converted the pooled OR to a RR (Appendix 6 in the Supplementary data). This resulted in a pooled OR of 1.50 (95 % CI 1.29–1.73, I² 29 %). Converting this gave a summary RR of 1.48 (95 % CI 1.28–1.72). We judged this to be comparable to the pooled RR from the main meta-analysis.

One case-control study that was included in the systematic review was excluded from the meta-analysis (Breslow et al. 1954). The reason for this exclusion from the meta-analysis was that this study did not