Downloaded from http://heart.bmj.com/ on June 26, 2015 - Published by group.bmj.com
PostScript
CORRESPONDENCE
Ischaemic heart disease, influenza and influenza vaccination: a prospective case control study Dear Editor Influenza vaccination is critical to public health, and vaccine uptake should be widely encouraged. The effects of vaccination on non-influenza-specific outcomes —for example, on risk of acute myocardial infarction (AMI)—nonetheless remain controversial. Several studies reported no difference in either initial myocardial infarction1 or recurrent coronary events among individuals who received influenza vaccination relative to those who did not.2 However, a recent meta-analysis of randomised control trials suggests that influenza vaccination may offer additional benefit by preventing cardiac events among highrisk patients.3 The recent observational study of ischaemic heart disease and influenza by MacIntyre et al4 also suggests a substantial benefit of vaccination on AMI in a more general population. The critical result of this study is that influenza vaccination dramatically reduces risk of AMI, reported as an OR of 0.55 in Table 3.4 This table also reports that influenza infection itself is not associated with AMI. The contrast of these results, derived from a single multivariate regression model, raises several methodological concerns. The obvious mechanism by which influenza vaccine might prevent AMI is by preventing influenza infection; that is, infection is part of the hypothesised causal pathway between vaccination and AMI. However, because the authors control for infection in the regression model that includes vaccination, they report an effect of vaccination independent of influenza infection.5 Therefore, the estimated vaccine effect is not mediated by influenza. If the authors’ implicitly causal interpretation of the vaccine effect (eg, ‘influenza vaccination in the study year was significantly protective against AMI’) is correct, then the vaccine must operate through another pathway.5 What is the proposed, biologically plausible mechanism by which influenza vaccination substantially lowers AMI risk independently of influenza infection itself? We suggest that uncontrolled confounding is a more plausible interpretation of the strong negative association between vaccination and AMI. Specifically, Heart March 2014 Vol 100 No 6
confounding due to the healthy user bias (well documented in studies of influenza vaccines6 and other preventive medications7) may be responsible for the observed association, leading to exaggerated estimates of vaccine effectiveness. This is because people who choose to be vaccinated may on average be healthier and have healthier behaviours compared with people who do not get vaccinated, which makes them less likely to have an AMI regardless of receiving the vaccine. Many of these issues can be clarified using simple causal directed acyclic graphs (DAGs).8 In figure 1A, we show a proposed DAG relating influenza vaccine status to influenza infection and AMI. In the authors’ multivariate model, they controlled for infection and possible confounders of the infection–AMI relationship (Z1, indicated by boxes around those variables). In this analysis, the pathway from vaccination to AMI through infection is blocked. Thus, assuming figure 1A, the authors are attempting to assess only the effect of vaccination on AMI that is not mediated by influenza infection itself. This undefined causal pathway is indicated by the arrow with the question mark. While controlling for Z1 may have been sufficient to eliminate confounding of the infection–AMI relationship, there are likely additional confounders of the vaccination–AMI relationship that are distinct from Z1 and that are not considered by the authors. These additional confounders are represented by Z2 and likely include indicators of ‘healthy users’. Thus, the OR for vaccination reported by the authors, derived from a β coefficient from a model controlling for infection and Z1 in figure 1A, might be best interpreted as a potentially biased direct effect of vaccination on AMI not mediated by influenza infection.5 Incontrast, if we were interested in estimating a total effect of vaccination on AMI risk (ie, the effect by all pathways, including the direct pathway marked with the question mark and the pathway mediated by infection), we would not control for either infection or Z1, but rather only on Z2, as shown in figure 1B.5 We note that these interpretations are necessarily conditional on the hypothesised
DAG. If the true causal relationships diverge from what is represented by the DAG, then our interpretations might be in error. However, the underlying point will hold regardless. In general, each research question (in this case the separate effects of influenza infection and vaccination on AMI risk) requires a separate causal model and control of a separate (though likely overlapping) set of variables.5 Control selection from outpatient ophthalmology and orthopaedic clinics may have also contributed to the healthy user bias. Controls from outpatient clinics may not represent the exposure distribution of the population from which cases arose. In particular, people attending outpatient clinics may have healthier behaviours compared with the general population and, if so, might have had both a higher vaccination rate and a lower risk of AMI. Berkson described a similar case of problematic control selection in the comparison of hospitalised cases to controls with outpatient ophthalmologic refractive errors.9 The article by MacIntyre et al highlights several pervasive challenges in estimating the effectiveness of vaccines and other drugs in studies in which the intervention has not been randomly assigned. While we re-emphasise our full support of the authors’ conclusion that influenza vaccination is vital to public health, we do not find this evidence concerning the impact of vaccines on AMI risk to be convincing. Thank you. Elizabeth Rogawski, Leah McGrath, Nadja Vielot, Daniel Westreich Department of Epidemiology, UNC-Chapel Hill, Chapel Hill, North Carolina, USA Correspondence to Dr Daniel, Westreich, Epidemiology, UNC-Chapel Hill, Chapel Hill, NC, USA; [email protected] Contributors ER and DW conceived the letter. ER wrote the first draft with input from DW. LMcG and NV contributed ideas and editing to the content and specifics of the letter. All authors reviewed and approved the final product. Competing interests None. Provenance and peer review Not commissioned; internally peer reviewed. To cite Rogawski E, McGrath L, Vielot N, et al. Heart 2014;100:517–518.
Figure 1 Directed acyclic graphs showing hypothesized causal relations among key study variables in MacIntyre et al. 517
Downloaded from http://heart.bmj.com/ on June 26, 2015 - Published by group.bmj.com
PostScript Published Online First 16 January 2014
▸ http://dx.doi.org/10.1136/heartjnl-2013-305435
Heart 2014;100:517–518. doi:10.1136/heartjnl-2013-305406
REFERENCES 1
2
3
4
5
6
7
8
9
Heffelfiner JD, Heckbert SR, Psaty BM, et al. Influenza Vaccination and Risk of Incident Myocardial Infarction. Human Vaccines 2006;2:161–6. Jackson LA, Yu O, Heckbert SR, et al. Influenza Vaccination Is Not Associated with a Reduction in the Risk of Recurrent Coronary Events. Am J Epidemiol 2002;156:634–40. Udell JA, Zawi R, Bhatt DL, et al. Association between influenza vaccination and cardiovascular outcomes in high-risk patients: A meta-analysis. JAMA 2013;310:1711–20. MacIntyre CR, Heywood AE, Kovoor P, et al. Ischaemic heart disease, influenza and influenza vaccination: a prospective case control study. Heart 2013;99:1843–8. Westreich D, Greenland S. The Table 2 Fallacy: Presenting and Interpreting Confounder and Modifier Coefficients. Am J Epidemiol 2013;177:292–8. Jackson LA, Jackson ML, Nelson JC, et al. Evidence of bias in estimates of influenza vaccine effectiveness in seniors. Int J Epidemiol 2006;35:337–44. Brookhart MA, Patrick AR, Dormuth C, et al. Adherence to lipid-lowering therapy and the use of preventive health services: an investigation of the healthy user effect. Am J Epidemiol 2007;166: 348–54. Greenland S, Pearl J, Robins J. Causal Diagrams for Epidemiologic Research. Epidemiol 1999;10: 37–48. Berkson J. Limitations of the Application of Fourfold Table Analysis to Hospital Data. Biometrics Bulletin 1946;2:47–53.
The Bradford-Hill criteria and evidence of association between influenza vaccination and ischaemic heart disease The Authors’ reply: In their letter, Rogawski et al1 have misinterpreted our data.2 They state “However, because the authors control for infection in the regression model that includes vaccination, they report an effect of vaccination independent of influenza infection. Therefore, the estimated vaccine effect is not mediated by influenza.” We would like to clarify that the independent effect of vaccination as described in our paper refers to an epidemiological, not a biological effect. That is, after adjustment, vaccination remains independently predictive and is not confounded by the known confounders which were adjusted. In the paper we make the point that the study had far greater statistical power to examine vaccination as a 518
predictor of acute myocardial infarction (AMI), with 276 vaccinated subjects, than it did for influenza infection, which was a rarer event (15 cases in total with a fourfold rise in antibody or positive nucleic acid testing (NAT)). As stated in the paper, this is the most likely explanation for why vaccination but not infection significantly predicted AMI. We believe infection was not significant because the study was underpowered for infection, not because the effect on AMI is unrelated to prevention of influenza. Clearly, vaccination, if it protects against AMI, works through preventing influenza infection. Rogawski et al suggest that including influenza vaccination and infection in our single multivariate model is flawed. We recognise that they are correlated variables. However, influenza vaccine can prevent infection, but may also prevent severe disease in people who get infection despite vaccination. This was the logic of including both predictors in the model. When separate models are run to include only one of these two variables (influenza infection or influenza vaccination), there are no changes to the resulting model outputs for these variables as shown in tables 1 and 2 below. All other included variables remain significant. It should be further noted that the OR for influenza infection is in the direction of being a risk factor for AMI, while not reaching statistical significance.
We do not agree with Rogawski et al that our estimates are exaggerated by confounding due to the healthy user effect. The prevalence of chronic conditions in cases and controls was high, with approximately 70% self-reporting two or more chronic conditions. Over 50% of cases and controls reported hypertension and high cholesterol, a quarter of participants had diabetes and 15% had chronic obstructive pulmonary disease (COPD). Furthermore, annual uptake of influenza vaccination in the funded 65+ years age group in Australia exceeds 70%. While health conscious adults may be likely to receive the annual influenza vaccine, vaccination of those at risk of complications from influenza infection is an established and funded public health programme in Australia, with high rates of vaccine uptake. In fact, the highest rates of influenza vaccination in Australia are in those with risk factors.3 Rogawski et al cite a study conducted in 1946 regarding hospital-based case control studies to suggest that our control selection contributed to a healthy user bias. We selected cases and controls from the same hospital, the same geographical area and a range of outpatient clinics. Our outpatient population is not comparable with that cited by Rogawski et al. Our controls (who were more likely to be vaccinated) were patients in the ophthalmology and orthopaedic outpatient clinics,
Heart March 2014 Vol 100 No 6
Downloaded from http://heart.bmj.com/ on June 26, 2015 - Published by group.bmj.com
Ischaemic heart disease, influenza and influenza vaccination: a prospective case control study Elizabeth Rogawski, Leah McGrath, Nadja Vielot and Daniel Westreich Heart 2014 100: 517-518 originally published online January 16, 2014
doi: 10.1136/heartjnl-2013-305406 Updated information and services can be found at: http://heart.bmj.com/content/100/6/517
These include:
References Email alerting service
This article cites 9 articles, 5 of which you can access for free at: http://heart.bmj.com/content/100/6/517#BIBL Receive free email alerts when new articles cite this article. Sign up in the box at the top right corner of the online article.
Notes
To request permissions go to: http://group.bmj.com/group/rights-licensing/permissions To order reprints go to: http://journals.bmj.com/cgi/reprintform To subscribe to BMJ go to: http://group.bmj.com/subscribe/
PostScript
CORRESPONDENCE
Ischaemic heart disease, influenza and influenza vaccination: a prospective case control study Dear Editor Influenza vaccination is critical to public health, and vaccine uptake should be widely encouraged. The effects of vaccination on non-influenza-specific outcomes —for example, on risk of acute myocardial infarction (AMI)—nonetheless remain controversial. Several studies reported no difference in either initial myocardial infarction1 or recurrent coronary events among individuals who received influenza vaccination relative to those who did not.2 However, a recent meta-analysis of randomised control trials suggests that influenza vaccination may offer additional benefit by preventing cardiac events among highrisk patients.3 The recent observational study of ischaemic heart disease and influenza by MacIntyre et al4 also suggests a substantial benefit of vaccination on AMI in a more general population. The critical result of this study is that influenza vaccination dramatically reduces risk of AMI, reported as an OR of 0.55 in Table 3.4 This table also reports that influenza infection itself is not associated with AMI. The contrast of these results, derived from a single multivariate regression model, raises several methodological concerns. The obvious mechanism by which influenza vaccine might prevent AMI is by preventing influenza infection; that is, infection is part of the hypothesised causal pathway between vaccination and AMI. However, because the authors control for infection in the regression model that includes vaccination, they report an effect of vaccination independent of influenza infection.5 Therefore, the estimated vaccine effect is not mediated by influenza. If the authors’ implicitly causal interpretation of the vaccine effect (eg, ‘influenza vaccination in the study year was significantly protective against AMI’) is correct, then the vaccine must operate through another pathway.5 What is the proposed, biologically plausible mechanism by which influenza vaccination substantially lowers AMI risk independently of influenza infection itself? We suggest that uncontrolled confounding is a more plausible interpretation of the strong negative association between vaccination and AMI. Specifically, Heart March 2014 Vol 100 No 6
confounding due to the healthy user bias (well documented in studies of influenza vaccines6 and other preventive medications7) may be responsible for the observed association, leading to exaggerated estimates of vaccine effectiveness. This is because people who choose to be vaccinated may on average be healthier and have healthier behaviours compared with people who do not get vaccinated, which makes them less likely to have an AMI regardless of receiving the vaccine. Many of these issues can be clarified using simple causal directed acyclic graphs (DAGs).8 In figure 1A, we show a proposed DAG relating influenza vaccine status to influenza infection and AMI. In the authors’ multivariate model, they controlled for infection and possible confounders of the infection–AMI relationship (Z1, indicated by boxes around those variables). In this analysis, the pathway from vaccination to AMI through infection is blocked. Thus, assuming figure 1A, the authors are attempting to assess only the effect of vaccination on AMI that is not mediated by influenza infection itself. This undefined causal pathway is indicated by the arrow with the question mark. While controlling for Z1 may have been sufficient to eliminate confounding of the infection–AMI relationship, there are likely additional confounders of the vaccination–AMI relationship that are distinct from Z1 and that are not considered by the authors. These additional confounders are represented by Z2 and likely include indicators of ‘healthy users’. Thus, the OR for vaccination reported by the authors, derived from a β coefficient from a model controlling for infection and Z1 in figure 1A, might be best interpreted as a potentially biased direct effect of vaccination on AMI not mediated by influenza infection.5 Incontrast, if we were interested in estimating a total effect of vaccination on AMI risk (ie, the effect by all pathways, including the direct pathway marked with the question mark and the pathway mediated by infection), we would not control for either infection or Z1, but rather only on Z2, as shown in figure 1B.5 We note that these interpretations are necessarily conditional on the hypothesised
DAG. If the true causal relationships diverge from what is represented by the DAG, then our interpretations might be in error. However, the underlying point will hold regardless. In general, each research question (in this case the separate effects of influenza infection and vaccination on AMI risk) requires a separate causal model and control of a separate (though likely overlapping) set of variables.5 Control selection from outpatient ophthalmology and orthopaedic clinics may have also contributed to the healthy user bias. Controls from outpatient clinics may not represent the exposure distribution of the population from which cases arose. In particular, people attending outpatient clinics may have healthier behaviours compared with the general population and, if so, might have had both a higher vaccination rate and a lower risk of AMI. Berkson described a similar case of problematic control selection in the comparison of hospitalised cases to controls with outpatient ophthalmologic refractive errors.9 The article by MacIntyre et al highlights several pervasive challenges in estimating the effectiveness of vaccines and other drugs in studies in which the intervention has not been randomly assigned. While we re-emphasise our full support of the authors’ conclusion that influenza vaccination is vital to public health, we do not find this evidence concerning the impact of vaccines on AMI risk to be convincing. Thank you. Elizabeth Rogawski, Leah McGrath, Nadja Vielot, Daniel Westreich Department of Epidemiology, UNC-Chapel Hill, Chapel Hill, North Carolina, USA Correspondence to Dr Daniel, Westreich, Epidemiology, UNC-Chapel Hill, Chapel Hill, NC, USA; [email protected] Contributors ER and DW conceived the letter. ER wrote the first draft with input from DW. LMcG and NV contributed ideas and editing to the content and specifics of the letter. All authors reviewed and approved the final product. Competing interests None. Provenance and peer review Not commissioned; internally peer reviewed. To cite Rogawski E, McGrath L, Vielot N, et al. Heart 2014;100:517–518.
Figure 1 Directed acyclic graphs showing hypothesized causal relations among key study variables in MacIntyre et al. 517
Downloaded from http://heart.bmj.com/ on June 26, 2015 - Published by group.bmj.com
PostScript Published Online First 16 January 2014
▸ http://dx.doi.org/10.1136/heartjnl-2013-305435
Heart 2014;100:517–518. doi:10.1136/heartjnl-2013-305406
REFERENCES 1
2
3
4
5
6
7
8
9
Heffelfiner JD, Heckbert SR, Psaty BM, et al. Influenza Vaccination and Risk of Incident Myocardial Infarction. Human Vaccines 2006;2:161–6. Jackson LA, Yu O, Heckbert SR, et al. Influenza Vaccination Is Not Associated with a Reduction in the Risk of Recurrent Coronary Events. Am J Epidemiol 2002;156:634–40. Udell JA, Zawi R, Bhatt DL, et al. Association between influenza vaccination and cardiovascular outcomes in high-risk patients: A meta-analysis. JAMA 2013;310:1711–20. MacIntyre CR, Heywood AE, Kovoor P, et al. Ischaemic heart disease, influenza and influenza vaccination: a prospective case control study. Heart 2013;99:1843–8. Westreich D, Greenland S. The Table 2 Fallacy: Presenting and Interpreting Confounder and Modifier Coefficients. Am J Epidemiol 2013;177:292–8. Jackson LA, Jackson ML, Nelson JC, et al. Evidence of bias in estimates of influenza vaccine effectiveness in seniors. Int J Epidemiol 2006;35:337–44. Brookhart MA, Patrick AR, Dormuth C, et al. Adherence to lipid-lowering therapy and the use of preventive health services: an investigation of the healthy user effect. Am J Epidemiol 2007;166: 348–54. Greenland S, Pearl J, Robins J. Causal Diagrams for Epidemiologic Research. Epidemiol 1999;10: 37–48. Berkson J. Limitations of the Application of Fourfold Table Analysis to Hospital Data. Biometrics Bulletin 1946;2:47–53.
The Bradford-Hill criteria and evidence of association between influenza vaccination and ischaemic heart disease The Authors’ reply: In their letter, Rogawski et al1 have misinterpreted our data.2 They state “However, because the authors control for infection in the regression model that includes vaccination, they report an effect of vaccination independent of influenza infection. Therefore, the estimated vaccine effect is not mediated by influenza.” We would like to clarify that the independent effect of vaccination as described in our paper refers to an epidemiological, not a biological effect. That is, after adjustment, vaccination remains independently predictive and is not confounded by the known confounders which were adjusted. In the paper we make the point that the study had far greater statistical power to examine vaccination as a 518
predictor of acute myocardial infarction (AMI), with 276 vaccinated subjects, than it did for influenza infection, which was a rarer event (15 cases in total with a fourfold rise in antibody or positive nucleic acid testing (NAT)). As stated in the paper, this is the most likely explanation for why vaccination but not infection significantly predicted AMI. We believe infection was not significant because the study was underpowered for infection, not because the effect on AMI is unrelated to prevention of influenza. Clearly, vaccination, if it protects against AMI, works through preventing influenza infection. Rogawski et al suggest that including influenza vaccination and infection in our single multivariate model is flawed. We recognise that they are correlated variables. However, influenza vaccine can prevent infection, but may also prevent severe disease in people who get infection despite vaccination. This was the logic of including both predictors in the model. When separate models are run to include only one of these two variables (influenza infection or influenza vaccination), there are no changes to the resulting model outputs for these variables as shown in tables 1 and 2 below. All other included variables remain significant. It should be further noted that the OR for influenza infection is in the direction of being a risk factor for AMI, while not reaching statistical significance.
We do not agree with Rogawski et al that our estimates are exaggerated by confounding due to the healthy user effect. The prevalence of chronic conditions in cases and controls was high, with approximately 70% self-reporting two or more chronic conditions. Over 50% of cases and controls reported hypertension and high cholesterol, a quarter of participants had diabetes and 15% had chronic obstructive pulmonary disease (COPD). Furthermore, annual uptake of influenza vaccination in the funded 65+ years age group in Australia exceeds 70%. While health conscious adults may be likely to receive the annual influenza vaccine, vaccination of those at risk of complications from influenza infection is an established and funded public health programme in Australia, with high rates of vaccine uptake. In fact, the highest rates of influenza vaccination in Australia are in those with risk factors.3 Rogawski et al cite a study conducted in 1946 regarding hospital-based case control studies to suggest that our control selection contributed to a healthy user bias. We selected cases and controls from the same hospital, the same geographical area and a range of outpatient clinics. Our outpatient population is not comparable with that cited by Rogawski et al. Our controls (who were more likely to be vaccinated) were patients in the ophthalmology and orthopaedic outpatient clinics,
Heart March 2014 Vol 100 No 6
Downloaded from http://heart.bmj.com/ on June 26, 2015 - Published by group.bmj.com
Ischaemic heart disease, influenza and influenza vaccination: a prospective case control study Elizabeth Rogawski, Leah McGrath, Nadja Vielot and Daniel Westreich Heart 2014 100: 517-518 originally published online January 16, 2014
doi: 10.1136/heartjnl-2013-305406 Updated information and services can be found at: http://heart.bmj.com/content/100/6/517
These include:
References Email alerting service
This article cites 9 articles, 5 of which you can access for free at: http://heart.bmj.com/content/100/6/517#BIBL Receive free email alerts when new articles cite this article. Sign up in the box at the top right corner of the online article.
Notes
To request permissions go to: http://group.bmj.com/group/rights-licensing/permissions To order reprints go to: http://journals.bmj.com/cgi/reprintform To subscribe to BMJ go to: http://group.bmj.com/subscribe/
