Pesko’s central argument is that it does not matter that Friedman’s assessment of the effect of San Francisco’s ban on the sale of flavored tobacco products is not based on any data collected after the ban actually went into force. In particular, Friedman’s “after” data were collected in fall 2018, before the ordinance was enforced on January 1, 2019.[1] Pesko incredibly argues that Friedman’s “before-after” difference-in-difference analysis is valid despite the fact that she does not have any “after” data.
Pesko justifies this position on the grounds that the effective date of the San Francisco ordinance was July, 2018. While this is true, it is a matter of public record that the ordinance was not enforced until January 1, 2019 because of the need for time for merchant education and issuing implementing regulations.[2]
Friedman is aware of the fact that the enforcement of the ordinance started on January 1, 2019 and used that date in her analysis. In her response[3] to critiques[4] of her paper, she stated “retailer compliance jumped from 17% in December 2018 to 77% in January 2019 when the ban went into effect.” Friedman thought the YRBSS data was collected in Spring 2019; she only learned that the “2019” San Francisco YRBSS data she used were in fact collected in fall 2018 from our paper.[1]
Rather than simply accepting this as an honest error and suggesting Friedman withdraw her paper, Pesko is offering an after-the-fact justification for the cl...
Pesko’s central argument is that it does not matter that Friedman’s assessment of the effect of San Francisco’s ban on the sale of flavored tobacco products is not based on any data collected after the ban actually went into force. In particular, Friedman’s “after” data were collected in fall 2018, before the ordinance was enforced on January 1, 2019.[1] Pesko incredibly argues that Friedman’s “before-after” difference-in-difference analysis is valid despite the fact that she does not have any “after” data.
Pesko justifies this position on the grounds that the effective date of the San Francisco ordinance was July, 2018. While this is true, it is a matter of public record that the ordinance was not enforced until January 1, 2019 because of the need for time for merchant education and issuing implementing regulations.[2]
Friedman is aware of the fact that the enforcement of the ordinance started on January 1, 2019 and used that date in her analysis. In her response[3] to critiques[4] of her paper, she stated “retailer compliance jumped from 17% in December 2018 to 77% in January 2019 when the ban went into effect.” Friedman thought the YRBSS data was collected in Spring 2019; she only learned that the “2019” San Francisco YRBSS data she used were in fact collected in fall 2018 from our paper.[1]
Rather than simply accepting this as an honest error and suggesting Friedman withdraw her paper, Pesko is offering an after-the-fact justification for the claim that Friedman’s conclusion is still valid despite not being based on any data after the ordinance actually took effect.
In addition to this central issue, Pesko raised some other minor points that we address below.
Pesko criticised the CDC for providing unequal access to data. This is false. We simply used the request form on the CDC public website (https://www.cdc.gov/healthyyouth/data/yrbs/contact.htm) and were directed to reach the San Francisco School District that conducted the YRBSS to confirm these dates.
Pesko argued that our discussion of the tobacco industry promoting Friedman’s study is irrelevant. We disagree. The tobacco industry and its allies and front groups have widely used Friedman’s conclusion “that reducing access to flavored electronic nicotine delivery systems may motivate youths who would otherwise vape to substitute smoking”[5] to oppose local and state flavored tobacco sales restrictions.
References:
1 Liu J, Hartman L, Tan ASL, et al. Youth tobacco use before and after flavoured tobacco sales restrictions in Oakland, California and San Francisco, California. Tob Control 2022;:tobaccocontrol-2021-057135. doi:10.1136/tobaccocontrol-2021-057135
2 Vyas P, Ling P, Gordon B, et al. Compliance with San Francisco’s flavoured tobacco sales prohibition. Tob Control 2021;30:227–30. doi:10.1136/tobaccocontrol-2019-055549
3 Friedman AS. Further Considerations on the Association Between Flavored Tobacco Legislation and High School Student Smoking Rates-Reply. JAMA Pediatr 2021;175:1291–2. doi:10.1001/jamapediatrics.2021.3293
4 Maa J, Gardiner P. Further Considerations on the Association Between Flavored Tobacco Legislation and High School Student Smoking Rates. JAMA Pediatr 2021;175:1289–90. doi:10.1001/jamapediatrics.2021.3284
5 Friedman AS. A Difference-in-Differences Analysis of Youth Smoking and a Ban on Sales of Flavored Tobacco Products in San Francisco, California. JAMA Pediatr 2021;175:863–5. doi:10.1001/jamapediatrics.2021.0922
¶ I enjoyed reading this paper. I appreciate the author's use of difference-in-difference (DD) methodology. There were some things I found unclear that I would like to ask the authors to comment on.
¶ First, could the authors provide greater clarity on the model for column 1 of Table 1? Is the dependent variable here a yes/no for current cigarette use? The authors write, "Adolescents reported lifetime and prior month use of cigarettes, which we combined into a count variable of days smoked in the past month (0–30)." How does lifetime cigarette use help the authors to code the current number of cigarette days? The authors later state that they show that "increasing implementation of flavoured tobacco product restrictions was associated not with a reduction in the likelihood of cigarette use, but with a decrease in the level of cigarette use among users." Do the authors mean lifetime cigarette use here, or current cigarette use? The authors estimate this equation with an "inflation model," which I am not aware of. Could the authors provide more information on this modelling technique? This is not discussed in the "Analysis" section.
¶ Second, I felt like this statement is too strong. "Our findings suggest that[...] municipalities should enact stricter tobacco-control policies when not pre-empted by state law." Municipalities need to weigh many factors in making these decisions, including the effects of popu...
¶ I enjoyed reading this paper. I appreciate the author's use of difference-in-difference (DD) methodology. There were some things I found unclear that I would like to ask the authors to comment on.
¶ First, could the authors provide greater clarity on the model for column 1 of Table 1? Is the dependent variable here a yes/no for current cigarette use? The authors write, "Adolescents reported lifetime and prior month use of cigarettes, which we combined into a count variable of days smoked in the past month (0–30)." How does lifetime cigarette use help the authors to code the current number of cigarette days? The authors later state that they show that "increasing implementation of flavoured tobacco product restrictions was associated not with a reduction in the likelihood of cigarette use, but with a decrease in the level of cigarette use among users." Do the authors mean lifetime cigarette use here, or current cigarette use? The authors estimate this equation with an "inflation model," which I am not aware of. Could the authors provide more information on this modelling technique? This is not discussed in the "Analysis" section.
¶ Second, I felt like this statement is too strong. "Our findings suggest that[...] municipalities should enact stricter tobacco-control policies when not pre-empted by state law." Municipalities need to weigh many factors in making these decisions, including the effects of population health (not just to youth tobacco use). This study provides evidence from a single state that may not be generalizable to other states without preemption policies. Other studies have found unintended negative effects of flavor policies, and these studies should be referenced to balance the discussion section.
¶ I applaud the authors for providing an early data point on the effect of these policies, but certainly more work in this space is needed before policy recommendations can be made. The authors may also wish to consider for future difference-in-difference papers whether there is evidence in support of the parallel trends assumption , which is a crucial assumption underpinning the reliability of the model.
¶ References:
¶ Friedman, Abigail S. "A difference-in-differences analysis of youth smoking and a ban on sales of flavored tobacco products in San Francisco, California." JAMA pediatrics 175, no. 8 (2021): 863-865.
¶ Xu, Yingying, Lanxin Jiang, Shivaani Prakash, and Tengjiao Chen. "The Impact of Banning Electronic Nicotine Delivery Systems on Combustible Cigarette Sales: Evidence From US State-Level Policies." Value in Health (2022).
¶ The authors make some points in their article that are reasonable: 1) the generalizability of San Francisco's flavor ban compared to other places is an open question, and 2) the original study uses the San Francisco ban effective date rather than enforcement date. The original author (Friedman), who does not accept tobacco industry funding and is a well-respected scientist in the field, had pointed to both facts in her original article. So that information isn’t new.
¶ The current authors appear to construct a straw man argument claiming that Friedman argued that she was studying the effect of San Francisco enforcing its flavor ban policy. Friedman specifically wrote in her original article that she was studying, “a binary exposure variable [that] captured whether a complete ban on flavored tobacco product sales was in effect in the respondent’s district on January 1 of the survey year.” She specifically uses effect in the above sentence, so there is no ambiguity that she is studying effective date. San Francisco’s flavor ban effective date was July 2018 (Gammon et al. 2021).
¶ The authors found new information that the San Francisco YRBSS survey was collected between November to December of 2018. Gammon et al. 2021 (Appendix Figure 1) shows that flavored e-cigarette sales declined in San Francisco between the effective date and the end of August 2018 (compensating for a 30-day look-back period for the YRBSS question wording), even though the flavor ban...
¶ The authors make some points in their article that are reasonable: 1) the generalizability of San Francisco's flavor ban compared to other places is an open question, and 2) the original study uses the San Francisco ban effective date rather than enforcement date. The original author (Friedman), who does not accept tobacco industry funding and is a well-respected scientist in the field, had pointed to both facts in her original article. So that information isn’t new.
¶ The current authors appear to construct a straw man argument claiming that Friedman argued that she was studying the effect of San Francisco enforcing its flavor ban policy. Friedman specifically wrote in her original article that she was studying, “a binary exposure variable [that] captured whether a complete ban on flavored tobacco product sales was in effect in the respondent’s district on January 1 of the survey year.” She specifically uses effect in the above sentence, so there is no ambiguity that she is studying effective date. San Francisco’s flavor ban effective date was July 2018 (Gammon et al. 2021).
¶ The authors found new information that the San Francisco YRBSS survey was collected between November to December of 2018. Gammon et al. 2021 (Appendix Figure 1) shows that flavored e-cigarette sales declined in San Francisco between the effective date and the end of August 2018 (compensating for a 30-day look-back period for the YRBSS question wording), even though the flavor ban was not yet fully enforced. This could be due to early supply-side responses to the flavor ban (e.g., some businesses discontinuing selling flavored e-cigarettes immediately upon the law’s effective date), or demand for e-cigarettes falling due to publicity related to the flavor ban effective date. The fact that e-cigarette sales continued falling in the latter half of 2018 until full enforcement kicked in on 1/1/2019 does not by itself invalidate Friedman’s model specifically looking at effective date. Therefore, there is nothing flawed about the concept of studying the effect that the flavor ban effective date (which led to a documented decline in flavored e-cigarette sales in San Francisco between July 2018 through the end of August 2018) had on youth cigarette use measured in the San Francisco YRBSS in November to December of 2018 (compared to other locations not adopting flavor bans).
¶ The current TC paper makes many inaccurate statements that appear to undermine most of the paper.
¶ • "Thus, the San Francisco survey preceded the enforcement of its flavoured tobacco sales restriction (January 2019), making the 2019 YRBSS an inappropriate data source for evaluating the effects of the city’s flavoured tobacco sales restriction."
¶ This is not true. The decline in flavored e-cigarette sales between the July 2018 effective date to the end of August 2018 could have clearly resulted in spillover effects in the youth cigarette use marketplace. The authors provide no acknowledgement of this in their paper.
¶ • "If youth smoking rates increased similarly in Oakland following that city’s sales restriction, this would lend credence to the call for caution against flavoured tobacco sales restrictions. However, if the patterns differ, we should identify alternate explanations for the rise in San Francisco’s youth smoking prevalence."
¶ This is faulty logic. It's entirely possible that policies adopted in two separate cities could exhibit different effects (including one having an effect and the other having no effect) depending on the population's underlying preferences for tobacco products and different evasion opportunities. I don’t know if there is a reason that this could be the case or not, but that’s irrelevant. What is relevant is that the loose language as currently written is inaccurate and could lead people to conclude the wrong thing in other contexts. The authors also fail to provide statistical testing of their Oakland model as required by STROBE guidelines, nor do they acknowledge that unlike the original study their own pre-post analysis is limited by not having a counterfactual group of non-treated areas, and so there is no ability to control for trends over time.
¶ • "Since there was no ban on non-menthol cigarettes sales, we would have expected to see an increase in sales of cigarettes if youth had been switching products."
¶ • “The study actually found an overall trend of a reduction in both total tobacco sales and cigarette sales in San Francisco following the flavoured tobacco product sales restriction, further suggesting that flavoured products were not being substituted by other unflavoured tobacco products or cigarettes.”
¶ Assuming for a moment that we can observe cigarette sales sold to youth, it would be entirely possible that these cigarette sales could decline in San Francisco but decline by more in the control areas due to secular trends; therefore, suggesting the flavor ban would need to increase cigarette sales to youth is inaccurate. And of course the authors do not observe who buys these cigarettes (youth or adults), so sales data for the population as a whole does not necessarily refute youth use patterns.
¶ • “However, in order to imply causality, there cannot be ambiguous temporal precedence.”
¶ • “do not include the policy enactment and enforcement dates that are required to avoid erroneous conclusions like those in the recent analysis of the San Francisco flavoured sales restriction.”
¶ The authors state that Friedman is ambiguous about the policy timing, but this is not the case as she clearly states she is studying effective date. That is not ambiguous. The authors also state that Friedman’s study has erroneous conclusions. I do not see anything erroneous about the limited scope of her research question studying effective date.
¶ The authors also refer in their references to conversation with the CDC-Office on Smoking and Health regarding the YRBSS data collection date. This reference is incomplete per STROBE guidelines, and should include a specific individual that the authors spoke with and a date of the conversation. Since this conversation was with a government employee it is especially important that there is not the perception of the government leaking information to certain groups of scientists but not others, so full disclosure is needed here. Other researchers have tried to get effective dates for the YRBSS survey from the CDC before but have been rebuffed, creating concerns regarding inequal access to data, as well as concerns regarding if this communication between the CDC and the researchers was authorized or not.
¶ Additionally, I found the author’s discussion of the tobacco industry promoting Friedman’s study as irrelevant. This discussion has the unfortunate effect of muddying the waters of what is supposed to be a focus on the science of Freidman’s article, and could easily lead people to conclude that Friedman herself has industry funding, which is not true. None of us are impervious to industry attempts to use our research for their own gain; in fact, if we start to attack researchers whose work is used by industry, this gives industry an easy way to discredit the researchers they are most threated by (by finding a way to cite their research in industry reports and publications, etc.). How research is used after the publication process is not relevant to this debate over the merits of the science of Friedman’s original article.
¶ Reference:
¶ Gammon, Doris G., Todd Rogers, Jennifer Gaber, James M. Nonnemaker, Ashley L. Feld, Lisa Henriksen, Trent O. Johnson, Terence Kelley, and Elizabeth Andersen-Rodgers . "Implementation of a comprehensive flavoured tobacco product sales restriction and retail tobacco sales." Tobacco Control (2021).
We would like to thank Mr. Wang for his feedback on our paper, Indicators of dependence and efforts to quit vaping and smoking among youth in Canada, England and the USA.
With regards to the ‘discrepancies’ in vaping and smoking prevalence between those reported in Table 1 and an earlier publication [1], we have previously published these same estimates [2], along with a description of the survey weighting procedures—which were modified since the first estimates were published (as outlined in a published erratum to the cited publication [3]). Briefly, since 2019, we have been able to incorporate the smoking trends from national ‘gold standard’ surveys in Canada and the US into the post-stratification sampling weights. A full description is provided in the study’s Technical Report [4], which is publicly available (see http://davidhammond.ca/projects/e-cigarettes/itc-youth-tobacco-ecig/).
Mr. Wang has also noted a change in the threshold used for a measure of frequent vaping/smoking: ≥20 days in past 30 days rather than ≥15 days, as previously reported [1]. We have adopted the convention of reporting using ≥20 days in past 30 days to align with the threshold commonly used by the US Centers for Disease Control for reporting data from the National Youth Tobacco Survey (NYTS), as well as the Population Assessment of Tobacco and Health (PATH) Study and the Mo...
We would like to thank Mr. Wang for his feedback on our paper, Indicators of dependence and efforts to quit vaping and smoking among youth in Canada, England and the USA.
With regards to the ‘discrepancies’ in vaping and smoking prevalence between those reported in Table 1 and an earlier publication [1], we have previously published these same estimates [2], along with a description of the survey weighting procedures—which were modified since the first estimates were published (as outlined in a published erratum to the cited publication [3]). Briefly, since 2019, we have been able to incorporate the smoking trends from national ‘gold standard’ surveys in Canada and the US into the post-stratification sampling weights. A full description is provided in the study’s Technical Report [4], which is publicly available (see http://davidhammond.ca/projects/e-cigarettes/itc-youth-tobacco-ecig/).
Mr. Wang has also noted a change in the threshold used for a measure of frequent vaping/smoking: ≥20 days in past 30 days rather than ≥15 days, as previously reported [1]. We have adopted the convention of reporting using ≥20 days in past 30 days to align with the threshold commonly used by the US Centers for Disease Control for reporting data from the National Youth Tobacco Survey (NYTS), as well as the Population Assessment of Tobacco and Health (PATH) Study and the Monitoring the Future (MTF) survey—three of the most widely cited sources of data for youth vaping.[5,6,7]
Mr. Wang questioned whether the process for ascertaining parental consent may bias the survey responses. Ascertaining parental consent among minors is a common and required practice in most jurisdictions. To the extent that young respondents may not have provided honest responses due to concerns about confidentiality, the likely impact would be to under-report smoking and vaping status. However, the recruitment process has not changed over the course of the study; thus, this is unlikely to account for the trends over time reported in our paper. In addition, the trends in the ITC Youth Tobacco and Vaping Surveys are very similar to the trends in vaping reported by national surveillance surveys in the US,[5] Canada, [8,9,10] and England.[11]
Regarding Mr. Wang’s assertion that “when asking participants quitting plans, it is better to clarify the quitting of traditional tobacco products or quitting nicotine products”, we can confirm that questions about intentions to quit and cessation were indeed asked separately for smoking and vaping. Thus, if a youth reported smoking cigarettes and vaping e-cigarettes, they would have been asked cessation-related questions in different sections of the survey for each of cigarettes and e-cigarettes/vaping.
Finally, Mr. Wang has noted seasonal variation in smoking and vaping rates. Despite some variations in the exact survey timing, the ITC Youth Tobacco and Vaping Surveys have been conducted over a similar time period in each year. For example, across the first three waves of the survey, 64%, 79% and 74% of surveys, respectively, were conducted in the month of August. As noted above, trends in vaping prevalence over time from the ITC surveys align very closely with other national surveys over the same period. With respect to specific findings reported in our Tobacco Control manuscript, we would not expect any material differences in levels of dependence, cessation-related outcomes or vaping brands due to the minor variation in data collection periods.
We hope this additional information will provide context for interpreting the study results and feedback on the manuscript.
References
1. Hammond D, Reid JL, Rynard VL, Fong GT, Cummings KM, McNeill A, Hitchman S, et al. Prevalence of vaping and smoking among adolescents in Canada, England, and the United States: repeat national cross sectional surveys. BMJ. 2019; 365: l2219. doi: 10.1136/bmj.l2219.
2. Hammond D, Rynard V, Reid JL. Changes in prevalence of vaping among youth in the United States, Canada, and England, 2017 to 2019. JAMA Pediatr. 2020;174(8):797-800. doi: 10.1001/jamapediatrics.2020.0901.
3. Published Erratum: Prevalence of vaping and smoking among adolescents in Canada, England, and the United States: repeat national cross sectional surveys. BMJ. 2020 Jul 10;370:m2579. doi: 10.1136/bmj.m2579.
4. Hammond D, Reid JL, Rynard VL, Burkhalter R. ITC Youth Tobacco and E-Cigarette Survey:
Technical Report – Wave 3 (2019). Waterloo, ON: University of Waterloo, 2020. http://davidhammond.ca/wp-content/uploads/2020/05/2019_P01P3_W3_Technica...
5. Park-Lee E, Ren C, Sawdey MD, et al. Notes from the Field: E-Cigarette Use Among Middle and High School Students — National Youth Tobacco Survey, United States, 2021. MMWR Morb Mortal Wkly Rep 2021;70:1387–1389. DOI: http://dx.doi.org/10.15585/mmwr.mm7039a4.
6. Hyland A, Kimmel HL, Borek N, on behalf of the PATH Study team. Youth and young adult acquisition and use of cigarettes and ENDS: The latest findings from the PATH Study (2013-2019). Society for Research on Nicotine & Tobacco Annual Conference, March 2020
7. Miech R, Johnston L, O’Malley PM, Bachman JG, Patrick ME. Trends in adolescent vaping, 2017-2019. N Engl J Med. 2019;381(15):1490-1491. doi:10.1056/NEJMc1910739.
8. Government of Canada. Detailed tables for the Canadian Student Tobacco, Alcohol and Drugs Survey 2016-17. Available from https://www.Canada.ca/en/health-Canada/services/canadian-student-tobacco...
9. Government of Canada. Canadian Tobacco, Alcohol and Drugs Survey (CTADS): Summary of Results for 2017. 2017. Available from https://www.Canada.ca/en/health-Canada/services/canadian-tobacco-alcohol....
10. Statistics Canada. Canadian Tobacco and Nicotine Survey, 2019. Available from https://www150.statcan.gc.ca/n1/daily-quotidien/200305/dq200305a-eng.htm.
11. Action on Smoking and Health UK. Use of e-cigarettes among young people in Great Britain, 2021. June 2021. Available from https://ash.org.uk/wp-content/uploads/2021/07/Use-of-e-cigarettes-among-...
There’s a published paper by Hammond and colleagues in 2019[1] using the same survey results, but there are some discrepancies.
1. The Table 2 of the 2019 paper, prevalence of vaping in 2018 for ever, past 30 days are 37.0% (1425), 14.6% (562) in Canada, 32.7% (1276), 8.9% (346) in England and 33.6% (1360), 16.2% (655) in the US, respectively. However, in this article’s Table 1, for vaping in the same year 2018 for ever, past 30 days are 33.2% (1275), 12.1% (463) in Canada, 33.1% (1283), 9.0% (351) in England and 33.1%(1336), 15.7% (635) in the US. More discrepancies can be found on cigarette smoking section as well. These numbers warrant further explanation particularly why numbers in Canada and the US decreased while numbers in England increased? Considering previous correction of numbers to the 2019 paper has raised serious concern among some readers[1], such timely clarification in this article will be very necessary.
2. The 2019 paper use the criteria of ≥15 days in past 30 days but the current paper adopts different criteria of ≥20 days in past 30 days, for both vaping and cigarette smoking. Further explanation is needed for such change.
Additionally, a few considerations on possible limitations of the paper’s findings:
1. Since the invitations were sent to nearly twice more parents than youth themselves according to the technical report[2], responds to survey questions might be biased because study has shown many...
There’s a published paper by Hammond and colleagues in 2019[1] using the same survey results, but there are some discrepancies.
1. The Table 2 of the 2019 paper, prevalence of vaping in 2018 for ever, past 30 days are 37.0% (1425), 14.6% (562) in Canada, 32.7% (1276), 8.9% (346) in England and 33.6% (1360), 16.2% (655) in the US, respectively. However, in this article’s Table 1, for vaping in the same year 2018 for ever, past 30 days are 33.2% (1275), 12.1% (463) in Canada, 33.1% (1283), 9.0% (351) in England and 33.1%(1336), 15.7% (635) in the US. More discrepancies can be found on cigarette smoking section as well. These numbers warrant further explanation particularly why numbers in Canada and the US decreased while numbers in England increased? Considering previous correction of numbers to the 2019 paper has raised serious concern among some readers[1], such timely clarification in this article will be very necessary.
2. The 2019 paper use the criteria of ≥15 days in past 30 days but the current paper adopts different criteria of ≥20 days in past 30 days, for both vaping and cigarette smoking. Further explanation is needed for such change.
Additionally, a few considerations on possible limitations of the paper’s findings:
1. Since the invitations were sent to nearly twice more parents than youth themselves according to the technical report[2], responds to survey questions might be biased because study has shown many parents are unaware of their children’s e-cigarette use[3]. Young participants who receive the questionnaire from their parents, in suspicion of their parents might know, may be unwilling to fill out honestly.
2. A clearer definition of stop smoking/quitting. In Table 3, when asking participants quitting plans, it is better to clarify the quitting of traditional tobacco products or quitting nicotine products. As some youth may treat vaping itself as quitting and some may not, the survey result might therefore, be ambiguous.
3. Seasonality. Previous studies have indicated seasonal variations in the initiation of smoking among adolescents[4], Unsupervised time out of school during the first months of summer vacation is a period of increased smoking behavior. However, significantly lower rates during September were observed which seem to be related to the beginning of school. Survey for this study were conducted July/August in 2017, August/September in 2018 and 2019. As cross-sectional study only offers a snapshot of the situation, to conduct surveys at different months with fluctuating trends would inevitably lower the liability of the result.
[1] Hammond, D., Reid, J. L., Rynard, V. L., Fong, G. T., Cummings, K. M., McNeill, A., Hitchman, S., et al. (2019). Prevalence of vaping and smoking among adolescents in Canada, England, and the United States: repeat national cross sectional surveys. BMJ (Clinical Research Ed.), 365, l2219.
[2] Hammond D, Reid JL, White CM. ITC Youth Tobacco and E-Cigarette Survey:
Technical Report – Wave 3 (2019. Waterloo, ON: University of Waterloo, 2020. http://
davidhammond.ca/wp-content/uploads/2020/05/2019_P01P3_W3_TechnicalReport_updated202005.pdf
[3] Wu T and Chaffee BW. Parental Awareness of Youth Tobacco Use and the Role of Household Tobacco Rules in Use Prevention. Pediatrics. 2020;146(5): e20194034
[4] Colwell, B., Ramirez, N., Koehly, L., Stevens, S., Smith, D. W., & Creekmur, S. (2006). Seasonal variations in the initiation of smoking among adolescents. Nicotine & Tobacco Research, 8(2), 239–243.
NOT PEER REVIEWED
We thank Mr. Clive Bates (1) and Dr. Moira Gilchrist (2) for their reconsideration of our work (3) and previous response where we corrected some errors (4). We also reiterate that all data informing our Industry Watch are publicly available at Tobacco Watcher (https://tobaccowatcher.globaltobaccocontrol.org/) for anyone to analyze. As with any analyses of observational data, there are limitations and we do not disagree with some of the limitations that Gilchrist and Bates point out in our analyses (as we addressed nearly all of these in our previous response (4)). However, we remain unchanged in our conclusion that, as the title of our initial article stated, “Philip Morris International used the e-cigarette, or vaping, product use associated lung injury (EVALI) outbreak to market IQOS heated tobacco” (3).
While statistical analysis indicated a correlation between (a) PMI’s public statements regarding EVALI and their IQOS brand of heated tobacco posted to their corporate “media center” (5) and (b) trends in news coverage of EVALI and IQOS, our primary assertion is that PMI used EVALI to market IQOS. The necessary and sufficient analysis to substantiate this assertion is reporting what PMI publicly claimed, which we did by analyzing the statement made by PMI which promoted IQOS through mentioning, contrasting or describing it along with EVALI and/or vaping.
NOT PEER REVIEWED
We thank Mr. Clive Bates (1) and Dr. Moira Gilchrist (2) for their reconsideration of our work (3) and previous response where we corrected some errors (4). We also reiterate that all data informing our Industry Watch are publicly available at Tobacco Watcher (https://tobaccowatcher.globaltobaccocontrol.org/) for anyone to analyze. As with any analyses of observational data, there are limitations and we do not disagree with some of the limitations that Gilchrist and Bates point out in our analyses (as we addressed nearly all of these in our previous response (4)). However, we remain unchanged in our conclusion that, as the title of our initial article stated, “Philip Morris International used the e-cigarette, or vaping, product use associated lung injury (EVALI) outbreak to market IQOS heated tobacco” (3).
While statistical analysis indicated a correlation between (a) PMI’s public statements regarding EVALI and their IQOS brand of heated tobacco posted to their corporate “media center” (5) and (b) trends in news coverage of EVALI and IQOS, our primary assertion is that PMI used EVALI to market IQOS. The necessary and sufficient analysis to substantiate this assertion is reporting what PMI publicly claimed, which we did by analyzing the statement made by PMI which promoted IQOS through mentioning, contrasting or describing it along with EVALI and/or vaping.
The full text of the PMI’s public statement is available in the appendix of our original piece (the version available presently on the PMI website has been changed) and we analyze excerpts to make our case below.
PMI’s statement recounted the EVALI outbreak beginning: “Skepticism and fear around vaping has emerged following the cases of respiratory illness and deaths in the US associated with the use of e-cigarettes.” PMI then contrasted this against their IQOS heated tobacco product, writing “on April 30 2019, the FDA authorized IQOS for sale in the US, finding that marketing of the product would be ‘appropriate for the protection of public health’ [quotes used in the original release].” As we previously argued, some readers (especially novice consumers) can interpret this as implying the FDA endorses IQOS and such statements are not allowed. The Food, Drug and Cosmetic Act prohibits ‘any expressed or implied statement...that either conveys, or misleads or would mislead consumers into believing that [a tobacco] product is approved by the Food and Drug Administration’ (ACT 21 U.S.C. § 331(tt)). The same “appropriate for the protection of public health” statement also implies that IQOS is safer than vaping, a claim disallowed by the FDA.
While Bates and Gilchrist may still object to our assertions that PMI’s intent in publishing their statement was to promote IQOS using EVALI, this objection contrasts with public statements PMI executives have made that are perhaps even clearer in their intent. For example, a Wall Street Journal article began “tobacco giant Philip Morris International Inc. says vaping fears in the U.S. should give a boost to the recent launch of its IQOS smoking alternative” and cited PMI executives discussing how EVALI could be a boon for IQOS promotion just days after PMI published the materials we studied (6).
Bates and Gilchrist may similarly still object that other events (such as the failed merger talks between Altria and PMI (7)) may be responsible for news coverage of IQOS and EVALI. However, generating earned media about an issue is not a one-shot game. Any public relations expert could attest that a multi-modal strategy is required to get an issue in the news media agenda including a media statement, follow-up interviews, case studies, activations, integrations, etc.
Our assertion is that the EVALI outbreak was used by PMI as part of an overall strategy to promote IQOS - a fact we discovered in our Industry Watch piece. For example, the Irish Times reported that PM had called off merger talks with Altria, “…after it became apparent the US government crackdown on vaping could have a negative impact given Altria’s stake in Juul.” André Calantzopoulos, PMI’s chief executive, said that they had decided to focus on its launch of IQOS, which was not a vaping product (8) --thereby again leveraging EVALI to promote its new product.
We echo our previous call for the FDA to investigate the statement PMI published and subsequent news coverage for compliance and if a violation has occurred appropriate regulatory actions be taken. We call on the tobacco control community to invest in surveillance of industry speech and research on the potential impacts of industry speech that can inform additional regulations governing marketing channels such as websites, online news sources and other digital media.
Thank you for the corrections and for acknowledging the omission. The additional analysis performed by ITC is greatly appreciated and provides further insight into the impact of both interventions. Although unstated, Canada’s regional characterizing flavour bans contributed significantly to the development of a national menthol additive ban as chronicled by the U.S. Tobacco Control Legal Consortium[1] . I look forward to reading the full analysis when published.
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Reducing smoking rates in older smokers will achieve a far greater reduction in deaths & disease and do this much earlier than reducing already much lower smoking rates in teens & young populations. Tobacco harm reduction (THR) options, such as vaping, Heated Tobacco Products (HTP), snus & nicotine pouches, all avoid inhalation of smoke from tobacco combustion and are less risky than smoking cigarettes which are responsible for the death of more than 50% of long term smokers. Cigarette sales in Japan declined by over 40% in five years after HTPs entered the Japanese market in 2016. There are now many other examples of other THR options substituting for deadly cigarettes in other countries.
New drug harm reduction interventions usually face fierce opposition for many years after their introduction. Needle syringe programs to reduce HIV spread among and from people who inject drugs were still strongly resisted long after the evidence for their effectiveness, safety and cost effectiveness was incontrovertible. It is not surprising to me therefore, as a veteran of many battles over new drug harm reduction interventions, to observe the acrimonious debate over THR.
If it is made easy for older smokers to switch to THR options, the benefits will not only be an acceleration in the decline of smoking related deaths and disease, but also a more rapid decline in cigarette sales.
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In his comment, Les Hagen brings up an important distinction between two types of restrictions on menthol: a menthol additive ban, and a menthol characterizing flavour ban. Canada's menthol ban across the provinces did indeed involve both types. Between May 2015 and July 2017, Nova Scotia, Alberta, Quebec, Ontario, Prince Edward Island, and Newfoundland & Labrador implemented characterizing flavour bans, whereas New Brunswick implemented a menthol additive ban [1]. When the Federal Government implemented a menthol additive ban in October 2017 [2] , it applied only to the remaining provinces—British Columbia, Saskatchewan, Manitoba—as well as Nunavut, Yukon, and the Northwest Territories. Thus, the "menthol cigarette ban" in Canada is a mixture of the two types.
Our article [3] evaluated the impact of menthol bans implemented between the 2016 and 2018 waves of the Canadian arm of the ITC Four Country Smoking and Vaping Surveys. Hagen incorrectly stated that "the analysis was performed exclusively on provincial characterizing flavour bans." In fact, the provinces evaluated in our study included both those that implemented characterizing flavour bans (Quebec, Ontario, Prince Edward Island, Newfoundland & Labrador) and those that implemented the Federal menthol additive ban (British Columbia, Saskatchewan, Manitoba).
In our original study, we did not test for differences between the two kinds of bans, beca...
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In his comment, Les Hagen brings up an important distinction between two types of restrictions on menthol: a menthol additive ban, and a menthol characterizing flavour ban. Canada's menthol ban across the provinces did indeed involve both types. Between May 2015 and July 2017, Nova Scotia, Alberta, Quebec, Ontario, Prince Edward Island, and Newfoundland & Labrador implemented characterizing flavour bans, whereas New Brunswick implemented a menthol additive ban [1]. When the Federal Government implemented a menthol additive ban in October 2017 [2] , it applied only to the remaining provinces—British Columbia, Saskatchewan, Manitoba—as well as Nunavut, Yukon, and the Northwest Territories. Thus, the "menthol cigarette ban" in Canada is a mixture of the two types.
Our article [3] evaluated the impact of menthol bans implemented between the 2016 and 2018 waves of the Canadian arm of the ITC Four Country Smoking and Vaping Surveys. Hagen incorrectly stated that "the analysis was performed exclusively on provincial characterizing flavour bans." In fact, the provinces evaluated in our study included both those that implemented characterizing flavour bans (Quebec, Ontario, Prince Edward Island, Newfoundland & Labrador) and those that implemented the Federal menthol additive ban (British Columbia, Saskatchewan, Manitoba).
In our original study, we did not test for differences between the two kinds of bans, because the number of menthol smokers across the seven provinces in our evaluation study was low (N=138). However, we did report that there were no statistically significant differences in smoking cessation outcomes between menthol and non-menthol smokers across the seven provinces, consistent with the possibility that there were no differences between a characterizing flavour ban and an additive ban. Hagen's comment did prompt us to do the explicit analysis, comparing the four provinces with characterizing flavour bans to the three provinces with menthol additive bans.
Consistent with our previously reported findings of no differences across the seven provinces, the explicit comparison found no significant differences in smoking cessation outcomes among daily and among all smokers between menthol smokers and non-menthol smokers in provinces with menthol additive bans vs provinces with menthol characterizing flavour bans. Thus, the findings do indeed, as Hagen aimed to highlight, point to the positive impact of the characterizing flavour ban, being not different from that of the national menthol additive ban ─ with the caution that the small sample sizes afforded low statistical power to test for differences.
Our follow-up analysis also showed that a significantly higher percentage of pre-ban menthol smokers reported that they still smoked menthols at follow-up in provinces with menthol characterizing flavour bans, compared with provinces with menthol additive bans (25.3% vs 8.4%, p=0.02). We will describe these results more fully in a forthcoming paper.
There are complexities in the distinctions between a characterizing flavour ban and an additive ban. Each would call upon different kinds of regulatory oversight. For example, the European Union's characterizing flavour ban under the 2016 Tobacco Products Directive [4] required the establishment of an Independent Advisory Panel to determine whether a particular tobacco product has a characterizing flavour, with input from a technical group of sensory and chemical assessors, whose methodology is "based on a comparison of the smelling properties of the test product with those of reference products." [5] In contrast, regulating an additive ban requires product testing to determine the presence of a banned additive.
As jurisdictions consider measures to eliminate the well-documented impact of menthol in increasing attractiveness and reducing harshness of combustible tobacco products [6], these differences in regulatory capacity need to be considered.
3. Chung-Hall J, Fong GT, Meng G, et al. Evaluating the impact of menthol cigarette bans on cessation and smoking behaviours in Canada: longitudinal findings from the Canadian arm of the 2016–2018 ITC Four Country Smoking and Vaping Surveys. Tobacco Control Published Online First: 05 April 2021. doi: 10.1136/tobaccocontrol-2020-056259
6. Tobacco Products Scientific Advisory Committee. Menthol cigarettes and public health: review of the scientific evidence and recommendations. Rockville, MD: Food and Drug Administration, 2011.
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We thank Pesko for his comments and the opportunity for us to respond and clarify.
First, we appreciate Pesko’s clarification that Cotti et al. (2020) clustered standard errors to account for clustering. In the present study, we used multilevel analysis not only to account for clustering of respondents (i.e., design effects) but also to incorporate different error terms for different levels of the data hierarchy which yields more accurate Type I error rates than nonhierarchical methods where all unmodeled contextual information ends up pooled into a single error term of the model.
Second, we understand that Cotti et al. (2020) evaluated the magnitude of e-cigarette tax values, which does not contradict to our statement because our study focused on the effects of e-cigarette excise tax policies on individual e-cigarette use and prevalence rather than aggregated sales at state or county levels. We also clearly described the reason why we examined the e-cigarette excise tax policy implementation indicator rather than its magnitude in our paper’s discussion section.
Third, our study used a nationally representative sample of young adults (rather than a nationally representative sample of general adult population). While we understand Pesko’s concern that a sample’s representativeness might be lost when subgroups are explored, we believe our use of sampling weights in analysis has reduced such a concern.
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We thank Pesko for his comments and the opportunity for us to respond and clarify.
First, we appreciate Pesko’s clarification that Cotti et al. (2020) clustered standard errors to account for clustering. In the present study, we used multilevel analysis not only to account for clustering of respondents (i.e., design effects) but also to incorporate different error terms for different levels of the data hierarchy which yields more accurate Type I error rates than nonhierarchical methods where all unmodeled contextual information ends up pooled into a single error term of the model.
Second, we understand that Cotti et al. (2020) evaluated the magnitude of e-cigarette tax values, which does not contradict to our statement because our study focused on the effects of e-cigarette excise tax policies on individual e-cigarette use and prevalence rather than aggregated sales at state or county levels. We also clearly described the reason why we examined the e-cigarette excise tax policy implementation indicator rather than its magnitude in our paper’s discussion section.
Third, our study used a nationally representative sample of young adults (rather than a nationally representative sample of general adult population). While we understand Pesko’s concern that a sample’s representativeness might be lost when subgroups are explored, we believe our use of sampling weights in analysis has reduced such a concern.
Fourth, in Table 3, please note that vaping product excise tax policy indicator is a time-variant variable in Model 1. However, to present results of a standard difference-in-differences model with a binary indicator, the policy implementation status was operationalized as a time-invariant variable in Model 2, which is not unusual.
Disclosure: We did not receive any funding from the tobacco industry.
References
1. Cotti, C. D., Courtemanche, C. J., Maclean, J. C., Nesson, E. T., Pesko, M. F., & Tefft, N. (2020). The effects of e-cigarette taxes on e-cigarette prices and tobacco product sales: evidence from retail panel data. National Bureau of Economic Research. NBER Working Paper No. w26724.
Pesko’s central argument is that it does not matter that Friedman’s assessment of the effect of San Francisco’s ban on the sale of flavored tobacco products is not based on any data collected after the ban actually went into force. In particular, Friedman’s “after” data were collected in fall 2018, before the ordinance was enforced on January 1, 2019.[1] Pesko incredibly argues that Friedman’s “before-after” difference-in-difference analysis is valid despite the fact that she does not have any “after” data.
Pesko justifies this position on the grounds that the effective date of the San Francisco ordinance was July, 2018. While this is true, it is a matter of public record that the ordinance was not enforced until January 1, 2019 because of the need for time for merchant education and issuing implementing regulations.[2]
Friedman is aware of the fact that the enforcement of the ordinance started on January 1, 2019 and used that date in her analysis. In her response[3] to critiques[4] of her paper, she stated “retailer compliance jumped from 17% in December 2018 to 77% in January 2019 when the ban went into effect.” Friedman thought the YRBSS data was collected in Spring 2019; she only learned that the “2019” San Francisco YRBSS data she used were in fact collected in fall 2018 from our paper.[1]
Rather than simply accepting this as an honest error and suggesting Friedman withdraw her paper, Pesko is offering an after-the-fact justification for the cl...
Show More¶ I enjoyed reading this paper. I appreciate the author's use of difference-in-difference (DD) methodology. There were some things I found unclear that I would like to ask the authors to comment on.
¶ First, could the authors provide greater clarity on the model for column 1 of Table 1? Is the dependent variable here a yes/no for current cigarette use? The authors write, "Adolescents reported lifetime and prior month use of cigarettes, which we combined into a count variable of days smoked in the past month (0–30)." How does lifetime cigarette use help the authors to code the current number of cigarette days? The authors later state that they show that "increasing implementation of flavoured tobacco product restrictions was associated not with a reduction in the likelihood of cigarette use, but with a decrease in the level of cigarette use among users." Do the authors mean lifetime cigarette use here, or current cigarette use? The authors estimate this equation with an "inflation model," which I am not aware of. Could the authors provide more information on this modelling technique? This is not discussed in the "Analysis" section.
¶ Second, I felt like this statement is too strong. "Our findings suggest that[...] municipalities should enact stricter tobacco-control policies when not pre-empted by state law." Municipalities need to weigh many factors in making these decisions, including the effects of popu...
Show More¶ The authors make some points in their article that are reasonable: 1) the generalizability of San Francisco's flavor ban compared to other places is an open question, and 2) the original study uses the San Francisco ban effective date rather than enforcement date. The original author (Friedman), who does not accept tobacco industry funding and is a well-respected scientist in the field, had pointed to both facts in her original article. So that information isn’t new.
Show More¶ The current authors appear to construct a straw man argument claiming that Friedman argued that she was studying the effect of San Francisco enforcing its flavor ban policy. Friedman specifically wrote in her original article that she was studying, “a binary exposure variable [that] captured whether a complete ban on flavored tobacco product sales was in effect in the respondent’s district on January 1 of the survey year.” She specifically uses effect in the above sentence, so there is no ambiguity that she is studying effective date. San Francisco’s flavor ban effective date was July 2018 (Gammon et al. 2021).
¶ The authors found new information that the San Francisco YRBSS survey was collected between November to December of 2018. Gammon et al. 2021 (Appendix Figure 1) shows that flavored e-cigarette sales declined in San Francisco between the effective date and the end of August 2018 (compensating for a 30-day look-back period for the YRBSS question wording), even though the flavor ban...
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We would like to thank Mr. Wang for his feedback on our paper, Indicators of dependence and efforts to quit vaping and smoking among youth in Canada, England and the USA.
With regards to the ‘discrepancies’ in vaping and smoking prevalence between those reported in Table 1 and an earlier publication [1], we have previously published these same estimates [2], along with a description of the survey weighting procedures—which were modified since the first estimates were published (as outlined in a published erratum to the cited publication [3]). Briefly, since 2019, we have been able to incorporate the smoking trends from national ‘gold standard’ surveys in Canada and the US into the post-stratification sampling weights. A full description is provided in the study’s Technical Report [4], which is publicly available (see http://davidhammond.ca/projects/e-cigarettes/itc-youth-tobacco-ecig/).
Mr. Wang has also noted a change in the threshold used for a measure of frequent vaping/smoking: ≥20 days in past 30 days rather than ≥15 days, as previously reported [1]. We have adopted the convention of reporting using ≥20 days in past 30 days to align with the threshold commonly used by the US Centers for Disease Control for reporting data from the National Youth Tobacco Survey (NYTS), as well as the Population Assessment of Tobacco and Health (PATH) Study and the Mo...
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There’s a published paper by Hammond and colleagues in 2019[1] using the same survey results, but there are some discrepancies.
1. The Table 2 of the 2019 paper, prevalence of vaping in 2018 for ever, past 30 days are 37.0% (1425), 14.6% (562) in Canada, 32.7% (1276), 8.9% (346) in England and 33.6% (1360), 16.2% (655) in the US, respectively. However, in this article’s Table 1, for vaping in the same year 2018 for ever, past 30 days are 33.2% (1275), 12.1% (463) in Canada, 33.1% (1283), 9.0% (351) in England and 33.1%(1336), 15.7% (635) in the US. More discrepancies can be found on cigarette smoking section as well. These numbers warrant further explanation particularly why numbers in Canada and the US decreased while numbers in England increased? Considering previous correction of numbers to the 2019 paper has raised serious concern among some readers[1], such timely clarification in this article will be very necessary.
2. The 2019 paper use the criteria of ≥15 days in past 30 days but the current paper adopts different criteria of ≥20 days in past 30 days, for both vaping and cigarette smoking. Further explanation is needed for such change.
Additionally, a few considerations on possible limitations of the paper’s findings:
1. Since the invitations were sent to nearly twice more parents than youth themselves according to the technical report[2], responds to survey questions might be biased because study has shown many...
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We thank Mr. Clive Bates (1) and Dr. Moira Gilchrist (2) for their reconsideration of our work (3) and previous response where we corrected some errors (4). We also reiterate that all data informing our Industry Watch are publicly available at Tobacco Watcher (https://tobaccowatcher.globaltobaccocontrol.org/) for anyone to analyze. As with any analyses of observational data, there are limitations and we do not disagree with some of the limitations that Gilchrist and Bates point out in our analyses (as we addressed nearly all of these in our previous response (4)). However, we remain unchanged in our conclusion that, as the title of our initial article stated, “Philip Morris International used the e-cigarette, or vaping, product use associated lung injury (EVALI) outbreak to market IQOS heated tobacco” (3).
While statistical analysis indicated a correlation between (a) PMI’s public statements regarding EVALI and their IQOS brand of heated tobacco posted to their corporate “media center” (5) and (b) trends in news coverage of EVALI and IQOS, our primary assertion is that PMI used EVALI to market IQOS. The necessary and sufficient analysis to substantiate this assertion is reporting what PMI publicly claimed, which we did by analyzing the statement made by PMI which promoted IQOS through mentioning, contrasting or describing it along with EVALI and/or vaping.
The full text...
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Thank you for the corrections and for acknowledging the omission. The additional analysis performed by ITC is greatly appreciated and provides further insight into the impact of both interventions. Although unstated, Canada’s regional characterizing flavour bans contributed significantly to the development of a national menthol additive ban as chronicled by the U.S. Tobacco Control Legal Consortium[1] . I look forward to reading the full analysis when published.
1. Kerry Cork, Tobacco Control Legal Consortium, Leading from Up North: How Canada Is Solving the Menthol Tobacco Problem (2017). https://www.publichealthlawcenter.org/sites/default/files/resources/tclc...
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Reducing smoking rates in older smokers will achieve a far greater reduction in deaths & disease and do this much earlier than reducing already much lower smoking rates in teens & young populations. Tobacco harm reduction (THR) options, such as vaping, Heated Tobacco Products (HTP), snus & nicotine pouches, all avoid inhalation of smoke from tobacco combustion and are less risky than smoking cigarettes which are responsible for the death of more than 50% of long term smokers. Cigarette sales in Japan declined by over 40% in five years after HTPs entered the Japanese market in 2016. There are now many other examples of other THR options substituting for deadly cigarettes in other countries.
New drug harm reduction interventions usually face fierce opposition for many years after their introduction. Needle syringe programs to reduce HIV spread among and from people who inject drugs were still strongly resisted long after the evidence for their effectiveness, safety and cost effectiveness was incontrovertible. It is not surprising to me therefore, as a veteran of many battles over new drug harm reduction interventions, to observe the acrimonious debate over THR.
If it is made easy for older smokers to switch to THR options, the benefits will not only be an acceleration in the decline of smoking related deaths and disease, but also a more rapid decline in cigarette sales.
NOT PEER REVIEWED
In his comment, Les Hagen brings up an important distinction between two types of restrictions on menthol: a menthol additive ban, and a menthol characterizing flavour ban. Canada's menthol ban across the provinces did indeed involve both types. Between May 2015 and July 2017, Nova Scotia, Alberta, Quebec, Ontario, Prince Edward Island, and Newfoundland & Labrador implemented characterizing flavour bans, whereas New Brunswick implemented a menthol additive ban [1]. When the Federal Government implemented a menthol additive ban in October 2017 [2] , it applied only to the remaining provinces—British Columbia, Saskatchewan, Manitoba—as well as Nunavut, Yukon, and the Northwest Territories. Thus, the "menthol cigarette ban" in Canada is a mixture of the two types.
Our article [3] evaluated the impact of menthol bans implemented between the 2016 and 2018 waves of the Canadian arm of the ITC Four Country Smoking and Vaping Surveys. Hagen incorrectly stated that "the analysis was performed exclusively on provincial characterizing flavour bans." In fact, the provinces evaluated in our study included both those that implemented characterizing flavour bans (Quebec, Ontario, Prince Edward Island, Newfoundland & Labrador) and those that implemented the Federal menthol additive ban (British Columbia, Saskatchewan, Manitoba).
In our original study, we did not test for differences between the two kinds of bans, beca...
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We thank Pesko for his comments and the opportunity for us to respond and clarify.
First, we appreciate Pesko’s clarification that Cotti et al. (2020) clustered standard errors to account for clustering. In the present study, we used multilevel analysis not only to account for clustering of respondents (i.e., design effects) but also to incorporate different error terms for different levels of the data hierarchy which yields more accurate Type I error rates than nonhierarchical methods where all unmodeled contextual information ends up pooled into a single error term of the model.
Second, we understand that Cotti et al. (2020) evaluated the magnitude of e-cigarette tax values, which does not contradict to our statement because our study focused on the effects of e-cigarette excise tax policies on individual e-cigarette use and prevalence rather than aggregated sales at state or county levels. We also clearly described the reason why we examined the e-cigarette excise tax policy implementation indicator rather than its magnitude in our paper’s discussion section.
Third, our study used a nationally representative sample of young adults (rather than a nationally representative sample of general adult population). While we understand Pesko’s concern that a sample’s representativeness might be lost when subgroups are explored, we believe our use of sampling weights in analysis has reduced such a concern.
Fourth, in Table 3,...
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