SUPPLEMENTARY APPENDIX

Short-term exposure to carbon monoxide and myocardial infarction:

A systematic review and meta-analysis

Kuan Ken Lee MD*1, Nicholas Spath MD*1, Mark R Miller MD1, Nicholas L Mills MD1,2 and Anoop SV Shah MD1,2

1 BHF Centre for Cardiovascular Science, University of Edinburgh2 Usher Institute of Population Health Sciences and Informatics, University of Edinburgh

*Contributed equally

Correspondence and requests for reprints:Dr Anoop SV ShahBHF/University Centre for Cardiovascular Science,SU.305 Chancellor’s Building,University of Edinburgh,Edinburgh EH16 4SBUnited KingdomTel: +44 131 242 6432Fax: +44 131 242 6379Mobile: +44 7766 544156E-mail: Anoop.Shah@ed.ac.uk

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Appendix 1

Inclusion and exclusion criteria for each PECOS domain.

PECOS Inclusion criteria Exclusion criteria

Population General adult human population (age >18 years), developed and developing areas, both urban and rural. No geographical restrictions.

Exposure to the pollutant of interest via inhalation through ambient air predominantly.

Exposure to the pollutant of interest in occupational settings or as a result of indoor exposure exclusively

Exposure Short-term exposure (in the order of hours to 7 days) to ambient air CO from any source expressed in a concentration unit (mg/m3).

No exclusion criteria applied based on adjustment for co-pollutants.

Comparator

Increase in concentration unit of exposure to the air pollutant of interest in the same or in a similar population.

Outcome Health outcomes selected for short-term exposure include emergency department visits/hospital admissions and mortality due to myocardial infarction (ICD-9 code: 410 or ICD-10 codes: I21-I22)

Study Human epidemiological studies including:o time serieso case-crossovero systematic reviews of the above studies to

scan for references (or as a basis for an update)

Published (or accepted for publication i.e. in press) studies in peer reviewed indexed journals in any language (abstract in English language). If suitable articles are identified published in languages not known by the SR team, further assistance will be sought after (members of the GDG or external review team from different regions, colleagues, researcher networks, etc)

Qualitative studies Studies without individual level

data i.e. fully ecologicalcovariates

Reviews and methodological papers

Non-human studies (in vivo, in vitro, other)

Studies with complete geographical and temporal over-lap during meta-analysis (to avoid inclusion of patients across multiple studies)

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Appendix 2Search Strategy

Ovid EMBASE and MEDLINE (31st Oct 2011 to 30th Sept 2018)1. myocardial infarct*.mp.2. heart attack*.mp.3. exp Acute Coronary Syndrome/4. acute coronary syndrome*.mp.5. exp Myocardial Infarction/6. exp heart infarction/7. ("cardiac infarct*" or "cardial infarct*" or ("coronary arter*" adj3 occlusion) or (heart adj2

infarct*) or "myocardium infarct*" or "premonitory infarction sign" or "subendocardial infarct*" or "myocardial stunning" or "cardiogenic shock").mp. [mp=title, abstract, heading word, drug trade name, original title, device manufacturer, drug manufacturer, device trade name, keyword, floating subheading word]

8. or/1-79. exp air pollution/10. ((air or atmosphere or atmospheric) adj (pollution* or polluted or pollutant* or contamination or

contaminated)).mp. [mp=title, abstract, heading word, drug trade name, original title, device manufacturer, drug manufacturer, device trade name, keyword, floating subheading word]

11. exp Carbon Monoxide/12. exp Carbon Monoxide Poisoning/13. ("CO" adj3 (pollut* or contamin* or particulate or poisoning)).mp. [mp=title, abstract, heading

word, drug trade name, original title, device manufacturer, drug manufacturer, device trade name, keyword, floating subheading word]

14. ("carbon monoxide" or "carbonmonoxide").mp. [mp=title, abstract, heading word, drug trade name, original title, device manufacturer, drug manufacturer, device trade name, keyword, floating subheading word]

15. 9 or 10 or 11 or 12 or 13 or 1416. 8 and 1517. limit 16 to human18. limit 17 to dc=20111031-20180930

Cochrane Central Register of Controlled Trials (31st Oct 2011 to 30th Sept 2018)

“myocardial infarct” OR “heart attack” OR “acute coronary syndrome” OR “heart infarction” OR “cardiac infarct” OR “myocardial ischaemia”AND“air pollution” OR “carbon monoxide”ANDLimit to Oct 2011 and Sept 2018

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Appendix 3

Approach to assessing the certainty of evidence from

systematic reviews informing WHO global air

quality guidelines

By: the WHO Global Air Quality Guidelines Working Group on Certainty of Evidence

Assessment

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AcknowledgementsThis supplementary material consists of an approach to assessing the certainty of evidence from systematic reviews of epidemiologic studies of air quality and health, based on the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework.

The approach was developed by external methodologist Jos Verbeek (Cochrane Work), with inputs from the WHO Global Air Quality Guidelines Working Group on Certainty of Evidence Assessment, convened by the WHO European Centre for Environment and Health (WHO Regional Office for Europe) in the context of the forthcoming WHO global air quality guidelines. The Working Group was composed of the Guideline Development Group members: Aaron Cohen (Health Effects Institute), Bert Brunekreef (Utrecht University), Francesco Forastiere (King’s College London), Nino Künzli (Swiss Tropical and Public Health Institute), and external methodologist: Rebecca Morgan (McMaster University); and, from the staff of the WHO Regional Office for Europe: Román Pérez-Velasco, Hanna Yang and Dorota Jarosińska. Additional comments were provided at different stages by external methodologist Eva Rehfuess (Cochrane Public Health Europe) and GDG members Michal Krzyzanowski (King’s College London), and Jonathan Samet (Colorado School of Public Health).

The WHO Regional Office for Europe acknowledges funding and in-kind contributions from the European Commission (Directorate-General for Environment); the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety; the German Federal Ministry of Health; the Government of the Republic of Korea; the Swiss Federal Office for the Environment; and the United States Environmental Protection Agency.

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Background

The Grading of Recommendations Assessment, Development and Evaluation (GRADE) has been developed to standardize the approach to judging the certainty of the effects of interventions (1). As such, the approach is currently the basis for evidence review in support of WHO Guidelines (2).

The main value of the system is that the comparability of the judgements increases when all assessors consider the same arguments underpinning their certainty in a similar manner. That is how the factors for downgrading and upgrading the certainty have been developed: to guide expert judgement. Behind each down- and upgrading factor in GRADE, there is a rationale for its importance and guidance for elaborating good reasons for downgrading or not downgrading. These ideas are well explained in the GRADE Handbook (1). Most of the reasoning in this framework can be equally well used for observational studies of exposure as for randomized studies of interventions (3). However, at some points there is a need for elaboration or clarification on how to use the GRADE criteria for observational studies of exposure.

Although different groups have adapted the approach for environmental exposures in recent years, no consensus has emerged among experts yet. Unlike some previous efforts, the aim of this work is not assessing the strength of evidence for causal inference by considering all the relevant streams of research (4), but to rate how certain one is that the ‘true’ estimate of the epidemiological association between an air pollutant and an adverse health effect lies within a particular range (5). Consistent with the standard GRADE framework, the certainty of the effect estimate is graded as high, moderate, low or very low. The ratings are subsequently used to select and underpin concentration−response functions in the process of deriving guideline exposure levels.

The current approach was designed specifically to assess the certainty of the evidence from the systematic reviews commissioned by WHO to inform the update of global air quality guidelines (AQGs). Its development benefitted from previous experiences in applying GRADE in the field of occupational and environmental health, as well as specific expertise in air pollution epidemiology. The approach was extensively discussed in two Guideline Development Group meetings, pilot tested by the members of the Systematic Review Team and improved iteratively according to the feedback received.

The Working Group accepted to start the rating of the certainty of the evidence for observational studies at moderate certainty evidence and not at high certainty, because of the risk of unmeasured confounding in observational studies. The certainty of the evidence from this level can then be downgraded or upgraded, based on the criteria per GRADE domain. The GRADE domains and the criteria considered when judging the certainty of the evidence are elaborated below.

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Reasons for downgrading

Limitations in studies: downgrade one or two levels

For risk of bias in studies, there should be serious concern about bias in the studies that have the most weight in the meta-analysis to rate down the certainty of the total body of evidence with one level. If there are very serious concerns, the certainty can be downgraded with two levels.

This is a judgement and there are no clear pre-set cut-off points (6). A judgement is based on the number of studies and the impact they have in the meta-analysis, as well as the seriousness of the risk of bias in these studies. One small study with very serious risk of bias but hardly an influence on the meta-analysis should not be a reason to downgrade, but two big studies with a considerable weight in the meta-analysis should.

If the sensitivity analysis for risk of bias shows a considerable impact on the effect size, the conclusions could be based on the studies at low risk of bias only. In that case, there is no reason to downgrade because the body of evidence on which the conclusions are based is considered to be at low risk of bias only.

Indirectness: downgrade one or two levels

The assessors should consider the extent to which the Population, Exposure, Comparator, Outcome(s), Study Design (PECOS) of the studies in the meta-analysis reflects the original PECOS question formulated at the beginning of the systematic review process (7).

If there are considerable differences between the elements of the PECOS in the body of evidence compared to the original question, then the certainty of the body of evidence should be rated down with one level. This would, for example, be the case if the evidence consists of studies of occupational exposure instead of exposure in the general population.

Inconsistency: downgrade one or two levels

Inconsistency among studies means that there is a considerable difference in effect size between studies. For example, if there are studies in the body of evidence that show a harmful effect and also studies that show a preventive effect, this indicates serious inconsistency or heterogeneity.

Usually there is more heterogeneity in observational than in experimental studies, because more factors can influence the effect size. Therefore, it is important to try to explain the heterogeneity. The first step should be to consider the factors that are listed for subgroup analyses in the protocol, as those that are most likely to be moderators of effect sizes. Another source of heterogeneity can be variation in risk of bias. This may explain part of the heterogeneity, and evaluation of only studies at low risk of bias should then decrease the heterogeneity. The difference in effect sizes between the subgroups should be tested for statistical significance. A rule of thumb to be used is to check if the confidence intervals of the subgroup pooled effect sizes do not overlap.

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Ideally, a meta-regression should be conducted including all moderators of the effect size, to find out how much heterogeneity remains after allowing for previously established reasons for heterogeneity. In practice, it is unlikely that all studies in a systematic review will have the necessary information to do a complete meta-regression including all previously documented reasons for heterogeneity. This could then be done on subsets of studies having the relevant information.

Heterogeneity is often measured with the I2 statistic which varies between 0 and 100%, where 0% would indicate no heterogeneity and 100% large heterogeneity. Because the I2 statistic is a relative measure, it is difficult to make a judgement about the absolute amount of heterogeneity. As a result, the use of the prediction interval has been suggested (8-10).

The prediction interval provides an estimate of the distribution of the true effect sizes. To prevent overstating heterogeneity in observational studies, an 80% interval, and not the usual 95% interval, was chosen. For an 80% prediction interval, the true effect size for 80% of all populations would fall in this interval. This tells if the effect is consistent or if it varies substantially. It also tells if the effect is harmful in all populations, or if there is no effect in some populations or maybe even a preventive effect.

To make a judgement about the amount of heterogeneity that cannot be explained and that would be a reason for concern and a reason for downgrading, the following approach is proposed.If the 80% prediction interval for a specific meta-analysis of relative risks is of the same size as the confidence interval, this indicates that there is no more variation in effect sizes than the statistical uncertainty. Then there is no reason for concern about heterogeneity.

However, if the prediction interval is considerably wider than the confidence interval (e.g., double the size) and overlaps with 1, there is reason for concern about heterogeneity. The effect sizes of the studies vary so much that with different samples of studies the conclusions of the meta-analysis could be substantially different. For example, an alternative conclusion could be that there would be no risk. In this case, the certainty of the body of evidence would be downgraded with one level.

Assessors need to provide a rationale for downgrading or not downgrading by explicitly addressing all of the issues mentioned above. This includes an assessment of how much of the heterogeneity can be explained.

Imprecision: downgrade one or two levels

Precision of the pooled effect size is another domain to be judged for downgrading. If there are only a few participants and the confidence interval around the pooled effect size is wide, one is less inclined to believe that the results reflect the true effects. If there is considerable imprecision, there is a reason to downgrade.

The cut-offs for downgrading because of imprecision given by the standard GRADE approach are applicable to clinical decision-making. Since in environmental health there are no clinical decision thresholds involved, only the second criterion of optimal information size can be applied to air pollution and health studies.

Therefore, the proposed approach consists of calculating the number of participants needed for a single study that can measure the relative risk of interest with sufficient precision (11). If the number of participants in the meta-analysis is considerably lower than the number that would be needed for an adequately powered study, the certainty of the evidence is rated down. This is a relatively conservative approach, and implies that the information size of the meta-analysis would need to be larger than the single study because heterogeneity has to be taken into account.

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A method of calculating the sample size needed for a study with a specific relative risk and confidence interval was recently proposed by Rothman and Greenland (12). As guidance, the calculation of the sample size needed to be able to assess a relative risk for mortality of 1.05 per 10 µg/m3 increase of PM2.5 with a confidence interval with a width of 0.09 (1.01−1.10) is provided below.

The event rate of mortality would be 0.0116 per person-year as in Ostro et al., 2010 (13). This would lead to a number of about 940,000 person-years in the meta-analysis, containing sufficient information to assess the relative risk of interest with sufficient precision.

The event rate in the example above was observed over a five-year follow-up period in a cohort of female public school teachers aged around 54 years on average at baseline. As the confidence interval of the relative risk depends also and strongly on the event rate, the calculated number of about 940,000 person years should be viewed as indicative. It could be considerably smaller in older populations with higher event rates, and considerably larger in populations with lower event rates.

Separate calculations are needed for short-term studies which do not deal with person years but with numbers of daily events.

Publication bias: downgrade one level

Publication bias is assessed by a funnel plot and Egger’s test. If the funnel plot upon visual inspection shows that small studies with non-harmful effects are missing, this would be an indication of publication bias. This means that small (imprecise) studies that have a relative risk smaller than 1 are missing. If there is no indication for these missing studies in the funnel plot, there is no use for the Egger’s test, because significance will result from other factors causing heterogeneity (10). The Egger’s test would just be used to confirm suspected publication bias detected from the funnel plot.

It is important to note that the Egger’s test can easily produce statistical significance for other reasons than publication bias in case of heterogeneity. Members of the Working Group noted that the Egger’s test should not be used in case of heterogeneity, and that funnel plots should only include the studies included in the meta-analysis. Then, assessors should examine if small imprecise studies are missing in the funnel plots.

Other approaches to assessing reporting bias, such as a subgroup analysis of multi-centre studies compared to single city studies in case of evidence based on time series studies, an analysis of differences in effect estimates from earlier versus later studies, and a comparison to published results of attempts to quantify the magnitude of reporting bias, may help make a judgement.

Reasons for upgrading

The majority of the Working Group decided to recommend that upgrades for reasons of large effect size, all plausible confounding moving the relative risk estimate towards the null, and concentration−response gradient should be addressed independently from the results of applying the downgrading factors. Domains

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would be treated equally and independently, thus, leading to upgrading, downgrading or not changing the evidence level. A downgrade for any reason would not necessarily preclude upgrading for another reason.

Large magnitude of effect size: upgrade one level

The standard GRADE approach proposes upgrading the certainty of the evidence in observational studies if the pooled effect size is large or very large, so that ‘the study design that is more prone to bias is unlikely to explain all of the apparent benefit or harm’. The cut-off point for a large effect size for harm is a relative risk > 2, while for a very large effect size is a relative risk > 5 (13). These numbers are somehow arbitrary, and are not in the order of magnitude of the many relative risks reported in environmental health.

Instead of taking a certain value of the relative risk as the cut-off point, it is reasonable to judge whether confounding could have easily influenced the pooled effect size found in the meta-analysis. To this end, the application of the E-value approach is helpful (14-17). This statistic is based on an assessment of how easily unmeasured confounders could explain away the relationship found between the exposure and the health outcome. It is based on the mathematical calculation of how large the effect of a confounder should be to explain away the relative risk that has been found in a study. With ‘explain away’, it is meant that such a confounder would reduce the relative risk that resulted from the observations in the study to 1. This effect (or E-value) is a function of the relative risk that has been found in a study or in a meta-analysis and is calculated as follows: E-value = RR + sqrt {RR * (RR – 1)}. The idea behind it is very similar to the ‘large effect’ concept in the standard GRADE framework but does not use absolute cut-offs for large effect sizes.

The judgement is then to ascertain if an unmeasured confounder could easily have an association with the exposure and the outcome with relative risks as large as or larger than the E-value. It is important to note that this is always the covariate-adjusted association between the unmeasured confounder and the outcome, and also the covariate-adjusted association between the unmeasured confounder and exposure to air pollution. If such a confounder could realistically have such strong relationships with both exposure and outcome, then unmeasured confounding could explain away the observed pooled relative risk. If one judges that it would be very unlikely that an unmeasured confounder would attain a relative risk as high as the E-value, then one can conclude that unmeasured confounding is unlikely to explain away the relative risk that has been observed. In that case, the certainty of the evidence can be upgraded because of a large effect size.

It is important to note that a major part of the judgement is what a realistic value for the relative risk of the unmeasured confounder could possibly be. Preferably, this should be based on what is known about strong confounders for the association at hand. For the association air pollution– mortality, smoking would be an obvious choice about which much information is available concerning its relationship with all-cause and cause-specific mortality. However, the residual association between smoking and air pollution is highly variable across published studies, and calculations of E-values should report the covariate-adjusted associations with both air pollution and the outcome. The same logic applies to short-term studies where the covariate-adjusted associations between the confounder and the exposure (and the confounder–outcome) is relevant.

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All plausible confounding shifts the relative risk towards the null: upgrade one level

Another proposed reason for upgrading is if all plausible confounding would shift the relative risk towards the null and still there would be a significant relative risk. This requires considerable judgement of possible confounders.

In most air quality and health studies, there would be a long list of possible confounders that would shift the relative risk in both directions. However, if one can reasonably argue that all confounding would have reduced the relative risk towards 1, then this will be a reason to upgrade the certainty of the evidence with one level.

Concentration−response gradient: upgrade one level

The standard GRADE proposes upgrading the certainty of the evidence if there is a concentration−response relationship between exposure and adverse health outcomes.

This domain is readily applicable to air quality and health studies. If there is an increase in risk with increasing exposure, either linearly or non-linearly, the certainty of the evidence would be upgraded with one level.

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evidence and strength of recommendations. Updated October 2013. Hamilton, ON: The GRADE Working Group; 2013 (https://gdt.gradepro.org/app/handbook/handbook.html, accessed 21 February 2020).

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5. Hultcrantz M, Rind D, Akl EA, Treweek S, Mustafa RA, Iorio A, et al. The GRADE Working Group clarifies the construct of certainty of evidence. J Clin Epidemiol. 2017;87:4-13. doi: 10.1016/j.jclinepi.2017.05.006.

6. WHO Global Air Quality Guidelines Working Group on Risk of Bias Assessment. Risk of bias assessment instrument for systematic reviews informing WHO global air quality guidelines. Copenhagen: WHO Regional Office for Europe; 2020 (http://www.euro.who.int/__data/assets/pdf_file/0007/425518/Risk-of-bias-assessment-instrument-for-

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systematic-reviews-informing-who-global-air-quality-guidelines.pdf?ua=1, accessed 21 February 2020).

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10. Borenstein M. Common mistakes in meta-analysis and how to avoid them. Englewood, NJ: Biostat, Inc; 2019.

11. Rothman KJ, Greenland S. Planning study size based on precision rather than power. Epidemiology. 2018;29(5):599-603. doi: 10.1097/EDE.0000000000000876.

12. Ostro B, Lipsett M, Reynolds P, Goldberg D, Hertz A, Garcia C, et al. Long-term exposure to constituents of fine particulate air pollution and mortality: results from the California Teachers Study. Environ Health Perspect. 2010;118(3):363-9. doi: 10.1289/ehp.0901181.

13. Guyatt GH, Oxman AD, Sultan S, Glasziou P, Akl EA, Alonso-Coello P, et al. GRADE guidelines: 9. Rating up the quality of evidence. J Clin Epidemiol. 2011;64(12):1311-6. doi: 10.1016/j.jclinepi.2011.06.004.

14. VanderWeele TJ, Ding P. Sensitivity analysis in observational research: introducing the E-value. Ann Intern Med. 2017;167(4):268-274. doi: 10.7326/M16-2607.

15. Haneuse S, VanderWeele TJ, Arterburn D. Using the E-value to assess the potential effect of unmeasured confounding in observational studies. JAMA. 2019;321(6):602-603. doi: 10.1001/jama.2018.21554.

16. Ioannidis JPA, Tan YJ, Blum MR. Limitations and misinterpretations of E-values for sensitivity analyses of observational studies. Ann Intern Med. 2019;170(2):108-111. doi: 10.7326/M18-2159.

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Appendix 4Assessment of risk of bias.

Author Publication year

Domain 1(Confounding)

Domain 2(Selection bias)

Domain 3(Exposure

assessment)

Domain 4(Outcome

measurement)

Domain 5(Missing data)

Domain 6(Selective reporting)

Wang et al 2015 High Low Low Low Low LowMilojevic at al 2014 Moderate Low Low Low Low Low

Tsai et al 2012 Moderate Low Low Low Low LowPoloniecki et al 1997 Moderate Low Low Low Low Low

Cendon et al 2006 High Low Low Low Low LowBarnett et al 2006 Moderate Low Low Low Low LowCheng et al 2009 Moderate Low Low Low Low LowHseih et al 2010 Moderate Low Low Low Low Low

Caussin et al 2015 High Low Low Low Low LowMann et al 2002 High Moderate Low Low Low LowLiu et al 2017 Moderate Low Low Low Low Low

Bard et al 2014 Low Low Moderate Low Moderate LowZanobetti et al 2006 Moderate Low High Low Low Low

Wang et al 2015 High Low Low Low Low LowEilstein et al 2001 Low Low Low Low Low Low

Sharovsky et al 2004 High Low Low Low Low LowLanki et al 2006 High Moderate Low Low Moderate LowLinn et al 2000 Moderate Low Low Low Low Low

Peters et al 2005 Moderate Low Low Low Low LowSullivan et al 2005 High Low Low Low Low Low

Lin et al 2013 Moderate Low Low Low Low LowBerglind et al 2010 Moderate High Moderate Low Moderate Low

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D'Ippoliti et al 2003 Moderate Low Low Low Low LowNuvolone et al 2011 Moderate High Low Low Low Low

Peters et al 2001 High High High Low Low LowRoye et al 2018 Moderate Low Low Low Low Low

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Appendix 5 List of included and excluded studies

Included studies Author ReferenceCaussin et al International Journal of Cardiology / 2015;183:17-23Wang et al BMJ Open / 2015;5(11):e009169Wang et al PLoS ONE / 2015;10(7):e0132769Milojevic et al Heart / 2014;100(14):1093-1098Mann et al Environmental Health Perspective / 2002;110:1247-1252Eilstein et al Rev Epidemiol Sante Publique. 2001;49(1):13-25.Sharovsky et al Brazilian Journal of Medical and Biological Research / 2004;37:1651-1657Lanki et al Occup Environ Med / 2006;63:844-851Poloniecki et al Occup Environ Med / 1997; 54:535-540Linn et al Environmental Health Perspective / 2000; 108:427-434Peters et al Res Rep Health Eff Inst. 2005;(124):1-66.Sullivan et al Epidemiology. 2005;16(1): 41-48.Tsai et al Journal of Toxicology and Environmental Health - Part A: Current Issues /

2012;75(4):242-251Liu et al American Journal of Cardiology/2017 Sep 1;120(5):753-758Lin et al Atmospheric Environment / 2013;76:68-73Bard et al PLoS ONE / 2014;9(6):e100307Cendon et al Rev Saúde Pública 2006;40(3):414-9Berglind et al Int J Environ Res Public Health. 2010 Apr;7(4):1486-99.Barnett et al Environ Health Perspect. 2006 Jul; 114(7): 1018–1023.Cheng et al J Toxicol Environ Health A. 2009;72(19):1135-40. D'Ippoliti et al Epidemiology 2003;14: 528–535 Hseih et al Journal of Toxicology and Environmental Health, Part A, 73:757–765,

2010 Nuvolone et al Am J Epidemiol. 2011;174(1):63–71 Peters et al Circulation. 2001;103:2810-2815Zanobetti et al J Epidemiol Community Health 2006;60:890–895.Roye et al Rev Esp Cardiol 2018.

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Excluded studies

Author Reference Reason for exclusionRius et al European Heart Journal / 2015;36:418 Conference abstractCaussin et al European Heart Journal / 2012;33:296-

297Conference abstract

Chiu et al Journal of Toxicology and Environmental Health - Part A: Current Issues / 2017;80(5):251-257

Did not analyse the main air pollutant of interest

Collart et al Revue d'Epidemiologie et de Sante Publique / 2015;63(2):97-103

Did not analyse the main air pollutant of interest

Collart et al International Journal of Environmental Health Research / 2015;25(6):601-613

Did not analyse the main air pollutant of interest

Collart et al International Journal of Environmental Health Research / 2017;27(1):68-81

Did not analyse the main air pollutant of interest

Goggins et al International Journal of Cardiology / 2013;168(1):243-249

Did not analyse the main air pollutant of interest

Julius et al Biosciences Biotechnology Research Asia / 2011;8(2):853-856

Did not analyse the main air pollutant of interest

Lee et al European Heart Journal / 2014;35:560 Conference abstractLee et al Clinical and Experimental Pharmacology

and Physiology / 2017;44(6):631-638Did not evaluate outcome of interest

Milojevic et al Epidemiology / 2012;23(5 SUPPL. 1):S740

Duplicate study

Polikutina et al European Heart Journal / 2015;36:553-554

Did not analyse the main air pollutant of interest

Rha et al Journal of the American College of Cardiology / 2016;67(13 SUPPL. 1):38

Did not evaluate outcome of interest

Sen et al American Journal of Emergency Medicine / 2016;34(3):449-454

Did not analyse the main air pollutant of interest

Winchmann et al PLoS ONE / 2013;8(1):e53684 Did not evaluate outcome of interest

Wang et al Journal of the American College of Cardiology / 2015;66(16 SUPPL. 1):C146

Duplicate study

Yamaji et al American Journal of Cardiology / 2017;119(6):872-880

Did not analyse the main air pollutant of interest

You et al European Heart Journal / 2015;36:553 Conference abstractArfaeinia et al International Journal of Pharmacy and

Technology / 2015;7(3):9811-9824Did not evaluate outcome of interest

Bhaskaran et al BMJ. 2011; 343: d5531. Complete geographical and temporal overlap with subsequent study

Baneras et al International journal of cardiology / 2018;250(gqw, 8200291):35-42

Did not analyse the main air pollutant of interest

Akbarzadeh et al Environmental Research / 2018;161:299-303

Long term exposure analysis

Sen et al Am J Emerg Med. 2016 Mar;34(3):449- Did not analyse the main air

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54. pollutant of interestZuin et al International Journal of Cardiology /

2017;236: 49-53Did not compare outcome per unit increment of exposure

Stieb et al Environmental Health / 2009;8(25) Did not report risk estimates for outcome of interest

Hoek et al J Air Waste Manag Assoc. 2000; 50(8):1380-1389

Did not report risk estimates for outcome of interest

Wang et al Journal of Geriatric Cardiology / 2016;13(2):132-137

Exposure levels reported in article are implausible.

Liu et al Lancet Planet Health 2018; 2: e12-18. Did not report risk estimates for outcome of interest

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Appendix 6

Sensitivity analysis excluding 3 studies that reported outlying results

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Appendix 7

Summary of the certainty of evidence assessment.

Domain Judgement Down/Up Grade

Limitations in studies 10 studies were assessed to be high risk of bias due to inadequate adjustment for confounding. Subgroup analysis did not demonstrate a statistically significant difference in risk estimates between studies at low/moderate risk of bias versus those at high risk of bias.

No downgrading

Indirectness All included studies were consistent with our prespecified PECOS.

No downgrading

Inconsistency The 80% prediction interval was 0.871-1.271. However, most of this is driven by 3 studies that reported outlying results. Sensitivity analysis excluding these studies had a 80% prediction interval of 1.002-1.030.

No downgrading

Imprecision Although the number of participants included in the review (1.5 million) was significantly lower than the estimated sample size required (12.1 million), risk estimates reported by the studies are sufficiently precise.

No downgrading

Publication Bias Visual inspection of the funnel plot does not indicate significant asymmetry.

No downgrading

Large Effect Size Overall relative risk was 1.052. Based on this, an E-value of 1.29 was calculated. However there is insufficient information to determine strength of unmeasured confounders.

No upgrading

Plausible confounding towards null

Direction of effect of other confounding is unknown.

No upgrading

Dose-response relation

None of the studies reported the dose-response relationship

No upgrading

Conclusion Moderate certainty of evidence

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Appendix 8: PRISMA Checklist

Section/topic # Checklist item Reported on page #

TITLE Title 1 Identify the report as a systematic review, meta-analysis, or both. 1ABSTRACT Structured summary 2 Provide a structured summary including, as applicable: background; objectives; data sources; study eligibility

criteria, participants, and interventions; study appraisal and synthesis methods; results; limitations; conclusions and implications of key findings; systematic review registration number.

2

INTRODUCTION Rationale 3 Describe the rationale for the review in the context of what is already known. 3Objectives 4 Provide an explicit statement of questions being addressed with reference to participants, interventions,

comparisons, outcomes, and study design (PICOS). 3

METHODS Protocol and registration 5 Indicate if a review protocol exists, if and where it can be accessed (e.g., Web address), and, if available,

provide registration information including registration number. 4

Eligibility criteria 6 Specify study characteristics (e.g., PICOS, length of follow-up) and report characteristics (e.g., years considered, language, publication status) used as criteria for eligibility, giving rationale.

4

Information sources 7 Describe all information sources (e.g., databases with dates of coverage, contact with study authors to identify additional studies) in the search and date last searched.

4

Search 8 Present full electronic search strategy for at least one database, including any limits used, such that it could be repeated.

Appendix 1

Study selection 9 State the process for selecting studies (i.e., screening, eligibility, included in systematic review, and, if applicable, included in the meta-analysis).

4

Data collection process 10 Describe method of data extraction from reports (e.g., piloted forms, independently, in duplicate) and any processes for obtaining and confirming data from investigators.

5

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Data items 11 List and define all variables for which data were sought (e.g., PICOS, funding sources) and any assumptions and simplifications made.

5

Risk of bias in individual studies

12 Describe methods used for assessing risk of bias of individual studies (including specification of whether this was done at the study or outcome level), and how this information is to be used in any data synthesis.

5

Summary measures 13 State the principal summary measures (e.g., risk ratio, difference in means). 6Synthesis of results 14 Describe the methods of handling data and combining results of studies, if done, including measures of

consistency (e.g., I2) for each meta-analysis. 6-7

Risk of bias across studies 15 Specify any assessment of risk of bias that may affect the cumulative evidence (e.g., publication bias, selective reporting within studies).

6

Additional analyses 16 Describe methods of additional analyses (e.g., sensitivity or subgroup analyses, meta-regression), if done, indicating which were pre-specified.

7

RESULTS Study selection 17 Give numbers of studies screened, assessed for eligibility, and included in the review, with reasons for

exclusions at each stage, ideally with a flow diagram. 10

Study characteristics 18 For each study, present characteristics for which data were extracted (e.g., study size, PICOS, follow-up period) and provide the citations. 10

Risk of bias within studies 19 Present data on risk of bias of each study and, if available, any outcome level assessment (see item 12). 10-11Results of individual studies 20 For all outcomes considered (benefits or harms), present, for each study: (a) simple summary data for each

intervention group (b) effect estimates and confidence intervals, ideally with a forest plot. Table 1

Synthesis of results 21 Present results of each meta-analysis done, including confidence intervals and measures of consistency. Figure 1Risk of bias across studies 22 Present results of any assessment of risk of bias across studies (see Item 15). 10-11Additional analysis 23 Give results of additional analyses, if done (e.g., sensitivity or subgroup analyses, meta-regression [see Item

16]). 12-13

DISCUSSION Summary of evidence 24 Summarize the main findings including the strength of evidence for each main outcome; consider their

relevance to key groups (e.g., healthcare providers, users, and policy makers). 14

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Limitations 25 Discuss limitations at study and outcome level (e.g., risk of bias), and at review-level (e.g., incomplete retrieval of identified research, reporting bias). 15

Conclusions 26 Provide a general interpretation of the results in the context of other evidence, and implications for future research. 16

FUNDING Funding 27 Describe sources of funding for the systematic review and other support (e.g., supply of data); role of funders

for the systematic review. 17

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