Changes in tobacco sales before, during, and after the COVID-19 pandemic in Japan: An interrupted time series analysis.

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Learn more: PMC Disclaimer | PMC Copyright Notice PLoS One . 2026 Jun 5;21(6):e0350820. doi: 10.1371/journal.pone.0350820 Changes in tobacco sales before, during, and after the COVID-19 pandemic in Japan: An interrupted time series analysis Kanae Kondo 1, 2, * , Ichizo Morita 2 , Shigemitsu Sakuma 3 , Isao Ohsawa 1 Editor: Fabrizio Ferretti 4 Author information Article notes Copyright and License information 1 Faculty of Health Science, Aichi Gakuin University, Nisshin, Aichi, Japan 2 Japanese Red Cross Toyota College of Nursing, Toyota, Aichi, Japan 3 Department of Fixed Prosthodontics and Oral Implantology, School of Dentistry, Aichi Gakuin University, Nagoya, Aichi, Japan 4 University of Modena and Reggio Emilia: Universita degli Studi di Modena e Reggio Emilia, ITALY ✉ * E-mail: k-kondo@rctoyota.ac.jp Competing Interests: The authors have declared that no competing interests exist. Roles Kanae Kondo : Conceptualization, Data curation, Formal analysis, Methodology, Supervision, Visualization, Writing – original draft Ichizo Morita : Conceptualization, Data curation, Formal analysis, Supervision, Visualization, Writing – original draft Shigemitsu Sakuma : Validation, Writing – review & editing Isao Ohsawa : Project administration, Writing – review & editing Fabrizio Ferretti : Editor Received 2026 Jan 27; Accepted 2026 May 19; Collection date 2026. © 2026 Kondo et al This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. PMC Copyright notice PMCID: PMC13240920  PMID: 42247397 Abstract Objectives This study aimed to longitudinally examine nationwide changes in smoking behavior among the Japanese population in response to the COVID-19 pandemic. Design and setting A secondary analysis was conducted using cigarette sales data from the Tobacco Institute of Japan, monthly tobacco expenditures from the Family Income and Expenditure Survey conducted by the Ministry of Internal Affairs and Communications, and smoking prevalences from the National Database of Open Data Japan. An interrupted time-series segmented regression model adjusted for seasonality and autocorrelation was used to examine changes in cigarette sales and monthly tobacco expenditures before and after the first declaration of a state of emergency. Participants Depending on the data source, data from 2014 to 2022, from 2015 to 2025, or from 2015 to February 2026 were used. Primary and secondary outcome measures Changes in level and slope were evaluated before and after the first state of emergency and after COVID-19 was downgraded to a common infectious disease. Results Cigarette sales declined at a significantly slower rate after the first state of emergency than before. Price-adjusted monthly tobacco expenditures increased significantly by 132 JPY compared with the previous month, although the expenditure findings were less robust in the quadratic sensitivity analysis. The smoking prevalence among men declined slightly each year, from 34.2% in 2014 to 31.9% in 2022. In contrast, the smoking prevalence among women remained relatively stable, ranging between 9.4% and 9.9%. Conclusions Although cigarette sales declined more slowly and household tobacco expenditures increased during the first state of emergency in Japan, smoking prevalences did not change substantially. These findings suggest changes in tobacco purchasing patterns, including stockpiling, and possible changes in smoking intensity among continuing smokers, rather than a meaningful population-level decline in smoking behavior. There was no robust evidence of a clear reversal after May 2023. Introduction Smoking is well known to cause many diseases and health problems [ 1 ]. Therefore, various organizations around the world have made efforts to reduce both the number of smokers and the amount they smoke [ 2 ]. These efforts range from encouraging individuals through education [ 3 ] and information provision to broader public health initiatives such as restricting smoking areas [ 4 ] and enacting anti-smoking legislation laws to curb smoking [ 5 ]. In Japan as well, smoking cessation has been promoted through the enactment of the Health Promotion Act [ 6 ]. However, while regulations are imposed on facility managers, the actual decision to refrain from smoking remains largely at the discretion of individuals. Smoking increases the risk of malignant tumors, including lung cancer—one of the most serious health consequences associated with smoking. These risks are cumulative, and smoking rarely causes cancer immediately, with onset typically occurring 10–20 years after exposure begins [ 7 ]. Therefore, people may not perceive the health risks of smoking as immediate or important. From the early stages of the COVID-19 pandemic, it was pointed out that a history of smoking increases the risk of severe illness [ 8 , 9 ]. This information was disseminated to the Japanese population via the Internet and mass media. The risk of developing life-threatening illness from COVID-19 due to smoking typically manifests within a few days to one or two weeks, in contrast to the decades-long latency period associated with smoking-related cancers. This shorter time frame for serious outcomes might have been expected to influence smokers’ behavior more strongly. However, many studies have reported an increase in smoking during the COVID-19 outbreak and lockdowns [ 10 – 12 ]. Moreover, an analysis of national data from the United States showed a decline in smoking cessation rates in 2020 compared to previous years [ 13 ]. This presents a paradox if individuals fully understood the risks associated with smoking. Some studies have also reported a decrease in smoking following the COVID-19 pandemic [ 14 , 15 ]. However, only a few have evaluated the impact of the pandemic on smoking behavior using a longitudinal design [ 16 ]. Japan’s first state of emergency did not involve a full lockdown, such as a citywide blockade or mandatory curfew, but instead included requests to limit outdoor activities and reduce interpersonal contact where feasible. Such pandemic-related changes in daily routines and coping behaviors may have influenced smoking-related behaviors during the restriction period. Understanding how the substantial societal changes associated with the COVID-19 pandemic influenced smoking behavior may provide valuable insight into effective strategies for reducing smoking. This study aimed to longitudinally clarify nationwide changes in smoking behavior among the Japanese population in response to the COVID-19 pandemic. Materials and methods Data sources The number of cigarette sales was obtained from tobacco statistics published on the website of the Tobacco Institute of Japan [ 17 ]. These data report the actual number of cigarettes sold and the total sales amount for each quarter across Japan. In this study, the quarterly number of cigarettes sold was used for the analysis of cigarette sales trends. The study utilized data from the first quarter of 2015 to the fourth quarter of 2025. National tobacco expenditures were obtained from the Family Income and Expenditure Survey conducted by the Ministry of Internal Affairs and Communications [ 18 ]. Monthly tobacco expenditures by households consisting of two or more members from January 2015 to February 2026 were included in the analysis. The retail price of tobacco is uniform across Japan, with price changes primarily driven by increases in the base price, tobacco tax, and consumption tax rate. Since 2015, tobacco prices have increased due to base price hikes in April 2016 and April 2017; tobacco tax increases in October 2018, October 2020, and October 2021; and a revision of the consumption tax rate in 2019. The analysis used tobacco expenditure amounts adjusted for price fluctuations due to consumption tax and tobacco tax based on cigarette prices from the Ministry of Internal Affairs and Communications’ Retail Price Statistics Survey [ 19 ]. Thus, the monthly analysis was based on price-adjusted tobacco expenditures for households with two or more members. Information on smoking prevalence from 2014 to 2022 was obtained from the National Database (NDB) of Open Data [ 20 ]. In this survey, smokers are defined as individuals who have smoked more than 100 cigarettes in total or have smoked for more than six months and reported smoking within the past month. The information on smoking status included in the NDB Open Data originates from questionnaire surveys conducted as part of government-led health checkups, with 25–30 million individuals aged 40 years or older participating annually. Because these data were available as annual national percentages, smoking prevalence in men and women was summarized descriptively rather than analyzed using the interrupted time-series model. This study was a secondary analysis of these data and was approved by the Ethics Committee of the Japanese Red Cross Toyota College of Nursing (Project No. 2211). The data were accessed for research purposes on from 01 August 2023–05 May 2026. The authors had no access to information that could identify individual participants during or after data collection. Statistical methods An interrupted time-series (ITS) analysis [ 21 ] was conducted to examine changes in cigarette sales using a single segmented regression model with two intervention points: the first declaration of a state of emergency in mid-April 2020 (first intervention) and the reclassification of COVID-19 to a common infectious disease on May 8, 2023 (second intervention). For quarterly cigarette sales, the second intervention was aligned to the April–June 2023 quarter. The data period for analysis ranged from the first quarter (January – March) of 2015 to the fourth (October – December) quarter of 2025. Monthly tobacco expenditures were also analyzed using ITS analysis, with April 2020 and May 2023 defined as the first and second intervention points, respectively, within the same segmented regression framework. For interpretability and to facilitate comparisons between the tables and figures, the time variable was coded as a consecutive sequence and centered at 0 for the last pre-intervention time point (January–March 2020 for quarterly cigarette sales; March 2020 for monthly tobacco expenditures); therefore, the intercept represents the estimated outcome at these reference points. For quarterly cigarette sales, seasonality was modeled using quarter indicators (Q2–Q4), with Q1 as the reference category. For monthly tobacco expenditures, seasonality was modeled using month indicators (February–December), with January as the reference category. To account for autocorrelation, the models were fitted using linear mixed models with an AR(1) covariance structure. As a residual diagnostic, the Durbin–Watson statistic was examined in the corresponding ordinary least-squares segmented regression models. Following previous methodological guidance on segmented regression design specification [ 22 ], the level-change parameter for each intervention was defined as the immediate deviation at the first post-intervention time point from the value expected under continuation of the trend in the immediately preceding period, whereas the slope-change parameter was defined as the change in trend after the intervention relative to that immediately preceding period. In addition, as a sensitivity analysis, a quadratic time term (time²) was added to the fixed-effects part of the model to assess the robustness of the findings to possible nonlinearity in the underlying secular trend. Smoking prevalences (men and women) from NDB Open Data (2014–2022) were summarized descriptively by year and presented as percentages; no hypothesis testing was performed. All analyses were performed using IBM SPSS Statistics version 29.0 (IBM Corp., Armonk, NY, USA). Results Trends in cigarette sales and impact of the first state of emergency declaration and downgrade From the first quarter of 2015 to the first quarter of 2020, cigarette sales exhibited a downward trend with periodic fluctuations ( Fig 1 , Table 1 ), decreasing by approximately 1 billion cigarettes per quarter. In the quarter preceding the first state of emergency declaration (January–March 2020), cigarette sales amounted to 26.48 billion units. After adjustment for seasonality and autocorrelation, the level change at the first intervention was not statistically significant, whereas the slope became significantly less negative after the first declaration (p < 0.001), indicating a deceleration in the declining trend. In the first quarter of 2020, cigarette sales showed little change compared to the previous quarter; however, the post-intervention trend corresponded to a smaller quarterly decline (approximately 200 million cigarettes) compared with the pre-intervention period. After the second intervention (the reclassification of COVID-19 as a common infectious disease, reflected in the April–June 2023 quarter), there was no significant change in either the level or slope of cigarette sales. Fig 1. Trend in cigarette sales before and after the first state of emergency declaration. Open in a new tab Note: The solid line shows fitted values from the interrupted time-series segmented regression model adjusted for seasonality and autocorrelation. The dashed line shows the predicted number of cigarette sales had the COVID-19 pandemic and the first state of emergency not occurred, assuming continuation of the pre-intervention trend (counterfactual). The time variable was centered at 0 for January–March 2020; thus, the intercept represents the estimated cigarette sales in that quarter. Table 1. Parameter estimates from the interrupted time-series segmented regression model for quarterly cigarette sales adjusted for seasonality and autocorrelation. Estimate 95% confidence interval P-value Lower limit Upper limit Intercept 244.982 228.278 261.685 <0.001 Baseline trend −9.991 −11.214 −8.767 <0.001 After the first state of emergency declaration Level change −17.468 −41.080 6.143 0.133 Slope change 7.962 4.858 11.067 <0.001 After COVID-19 was downgraded to a common infectious disease Level change −1.481 −29.119 26.157 0.910 Slope change −0.924 −5.227 3.379 0.643 Q2 22.254 8.958 35.551 0.002 Q3 42.881 29.759 56.002 <0.001 Q4 19.110 5.973 32.248 0.006 Open in a new tab Note: Seasonality was modeled using quarter indicators (Q2–Q4), with Q1 as the reference category. Autocorrelation was accounted for using a linear mixed model with an AR(1) covariance structure. P-values were obtained from t-tests for fixed-effect parameters in the linear mixed model, using the denominator degrees-of-freedom method specified in SPSS. Values <0.05 were considered statistically significant. The quarter indicators were statistically significant, supporting the presence of seasonal variation in cigarette sales. The Durbin–Watson statistic in the corresponding ordinary least-squares segmented regression model was 2.100. As a sensitivity analysis, we additionally fitted a model including a quadratic time term (time²). For quarterly cigarette sales, the quadratic term was not statistically significant, and the direction and statistical significance of the intervention-related estimates were unchanged. Trends in monthly tobacco expenditures From January 2015 to March 2020, price-adjusted monthly tobacco expenditures exhibited a declining trend with periodic fluctuations, decreasing from approximately 1,000 JPY to 800 JPY ( Fig 2 , Table 2 ). After adjustment for seasonality and autocorrelation, in April 2020, when the government declared the first state of emergency, price-adjusted monthly tobacco expenditures increased significantly by 132 JPY compared to the previous month (p = 0.005). However, from that point through April 2023, the expenditures continued to decline; the change in slope after April 2020 was not statistically significant (p = 0.486), indicating no evidence that the rate of decline differed from the pre-pandemic trend. After May 2023, when COVID-19 was downgraded to a common infectious disease, the rate of decline in price-adjusted monthly tobacco expenditures became significantly less steep (p = 0.027), whereas the level change was not statistically significant (p = 0.731). Fig 2. Trend in monthly tobacco expenditures per household (households with two or more members) before and after the first state of emergency declaration. Open in a new tab Note: The round markers indicate tobacco expenditures adjusted for price fluctuations based on cigarette prices from the Ministry of Internal Affairs and Communications’ Retail Price Statistics Survey. Price increases occurred because of base price adjustments in April 2016 and April 2017, tobacco tax increases in October 2018, October 2020, and October 2021, and a revision of the consumption tax rate in 2019. The solid line shows fitted values from the interrupted time-series segmented regression model adjusted for seasonality and autocorrelation. The dashed line shows the predicted monthly tobacco expenditures per household if the COVID-19 pandemic and the first state of emergency had not occurred, assuming continuation of the pre-intervention trend (counterfactual). The time variable was centered at 0 for March 2020; thus, the intercept represents the estimated monthly expenditure in March 2020. Table 2. Parameter estimates from the interrupted time-series segmented regression model for monthly tobacco expenditures adjusted for seasonality and autocorrelation. Estimate 95% confidence interval P-value Lower limit Upper limit Intercept 797.883 724.578 871.187 <0.001 Baseline trend −2.501 −4.037 −0.964 0.002 After the first state of emergency declaration Level change 132.004 42.281 221.727 0.005 Slope change −1.294 −5.039 2.451 0.486 After COVID-19 was downgraded to a common infectious disease Level change −17.578 −120.702 85.546 0.731 Slope change 5.892 0.731 11.053 0.027 February −51.703 −115.314 11.908 0.110 March 30.380 −41.112 101.873 0.402 April 3.547 −69.484 76.577 0.923 May 45.424 −28.406 119.255 0.225 June 46.667 −27.042 120.376 0.212 July 75.578 2.017 149.140 0.044 August 60.454 −12.967 133.875 0.106 September 133.170 59.912 206.429 <0.001 October −45.150 −118.046 27.747 0.222 November −42.058 −113.603 29.487 0.247 December 9.502 −55.873 74.877 0.774 Open in a new tab Note: Seasonality was modeled using month indicators (February–December), with January as the reference category. Autocorrelation was accounted for using a linear mixed model with an AR(1) covariance structure. P-values were obtained from t-tests for fixed-effect parameters in the linear mixed model, using the denominator degrees-of-freedom method specified in SPSS. Values <0.05 were considered statistically significant. Several month indicators were also statistically significant, supporting the presence of seasonal variation in tobacco expenditures. The Durbin–Watson statistic in the corresponding ordinary least-squares segmented regression model was 1.669. As a sensitivity analysis, we additionally fitted a model including a quadratic time term (time²). For monthly tobacco expenditures, the quadratic term was statistically significant ( Table 3 ). In this model, the immediate level change after the first intervention was no longer statistically significant, whereas the post-first-intervention slope change became statistically significant. Table 3. Parameter estimates from the interrupted time-series segmented regression model for monthly tobacco expenditures including a quadratic time term, adjusted for seasonality and autocorrelation. Estimate 95% confidence interval P-value Lower limit Upper limit Intercept 874.494 789.946 959.042 <.001 Baseline trend 5.140 −0.128 10.408 0.055 Quadratic time term (time²) 0.12 0.04 0.199 0.004 After the first state of emergency declaration Level change 76.389 −12.434 165.211 0.090 Slope change −13.265 −21.878 −4.652 0.004 After COVID-19 was downgraded to a common infectious disease Level change −27.931 −120.688 64.827 0.545 Slope change −2.513 −9.759 4.734 0.485 February −52.094 −115.462 11.274 0.106 March 30.795 −38.214 99.805 0.379 April 8.717 −61.118 78.552 0.805 May 51.888 −18.503 122.279 0.147 June 53.965 −16.313 124.243 0.131 July 83.462 13.291 153.634 0.020 August 68.681 −1.396 138.759 0.055 September 141.494 71.507 211.480 <.001 October −37.003 −106.837 32.831 0.296 November −34.492 −103.681 34.698 0.325 December 15.440 −49.707 80.587 0.639 Open in a new tab Note: Seasonality was modeled using month indicators (February–December), with January as the reference category. Autocorrelation was accounted for using a linear mixed model with an AR(1) covariance structure. P-values were obtained from t-tests for fixed-effect parameters in the linear mixed model, using the denominator degrees-of-freedom method specified in SPSS. Values <0.05 were considered statistically significant. The segmented regression model describes trends and changes in monthly tobacco expenditures across three phases: the period before the first state of emergency declaration, the period after the first declaration, and the period following the downgrading of COVID-19 to a common infectious disease. Baseline trend: January 2015 to March 2020. After the first state of emergency declaration: Level change — change from March 2020 to April 2020; Slope change — change in trend from before March 2020 to after April 2020. After COVID-19 was downgraded to a common infectious disease: Level change — change from April 2023 to May 2023; Slope change — change in trend from April 2020 to April 2023 to after May 2023. Changes in smoking prevalences The NDB Open Data included approximately 14–16 million men and 11–13 million women each year. The smoking prevalence among men exhibited a slight downward trend each year, declining from 34.2% in 2014 to 31.9% in 2022 ( Fig 3 ). In contrast, the smoking prevalence among women remained relatively stable, fluctuating between 9.4% and 9.9% during the same period. Fig 3. Trends in the prevalence of heavy smoking among individuals aged 40 years and older. Open in a new tab Note: Heavy smokers are defined as individuals who have smoked more than 100 cigarettes in total or for more than 6 months and who reported smoking within the past month. Discussion Cigarette sales volume Data from national tobacco product sales in the United States indicate that cigarette sales increased by approximately 12% during the pandemic lockdown period, possibly reflecting increased consumption or stockpiling of tobacco products [ 23 ]. In Japan, there was no clear increase or decrease in cigarette sales before or after the first state of emergency was declared. However, the observed slowdown in the declining trend may suggest a sustained change in purchasing and use patterns after the onset of the pandemic, including stockpiling or changes in smoking intensity among continuing smokers, similar to patterns reported in the United States. This pattern could be explained by reduced shopping frequency and heightened uncertainty early in the pandemic that encouraged advance purchases, increased time spent at home that shifted smoking opportunities, and pandemic-related stress and anxiety that may have increased cigarette use among some smokers. Consistent with a short-term shift in purchasing, price-adjusted monthly tobacco expenditures per household increased by 132 JPY in April 2020. Because this measure represents the average monthly expenditure across all households with two or more members, including households without current smokers, and reflects purchasing rather than direct consumption, the 132 JPY increase should be interpreted as modest in absolute terms, even though it corresponds to approximately a 17% increase relative to the pre-intervention level of about 800 JPY. This estimate should not be interpreted directly as the change per smoking household, because the denominator includes all households with two or more members, irrespective of smoking status. Using published Japanese household-based data indicating that 25.47% of households were classified as smoking households [ 24 ], the observed increase corresponds approximately to 518 JPY per smoking household, and the pre-intervention average corresponds approximately to 3,141 JPY per smoking household; however, these are only rough approximations and should be interpreted cautiously. These aggregate data cannot distinguish stockpiling from a true increase in cigarettes consumed per smoker. Moreover, because the Family Income and Expenditure Survey excludes single-person households, the findings may have limited generalizability to the overall household population. According to the 2020 Population Census, single-person households accounted for 38.1% of general households in Japan [ 25 ], indicating that a substantial segment of household types was not represented in the expenditure analysis. Because tobacco purchasing and smoking patterns may differ in single-person households, the direction and magnitude of any resulting bias cannot be determined a priori. The present study was limited to cigarette sales. In recent years, sales of heated tobacco products (HTPs) have increased, and their retail revenue now accounts for approximately 80% of that generated by conventional cigarettes [ 26 ]. Accordingly, substitution between cigarettes and HTPs may have affected the observed changes in cigarette sales and household tobacco expenditures and should be considered a potential confounder in the interpretation of the ITS results. One study conducted in Osaka, Japan, reported that during the state of emergency, only 2.2% of individuals switched from cigarettes to HTPs, while 0.6% switched in the opposite direction [ 10 ]. However, because this evidence was obtained in a single city during a limited period, it cannot be generalized to the national level over the longer study period. Possible changes in smoking behavior In this ecological analysis, smoking intensity was not measured directly at the individual level; therefore, any changes in intensity can only be inferred indirectly from population-level indicators. Since the onset of the COVID-19 pandemic, the medical community and mass media have consistently warned the public that smoking increases the risk of severe illness and death. Therefore, it was anticipated that smoking intensity would decrease due to heightened public concern about the associated health risks. However, only a few studies have reported reductions in smoking intensity during this period [ 14 ,15 27 ]. In contrast, substantial evidence indicates that a greater number of individuals increased their smoking intensity rather than reduced it during the pandemic [ 28 – 39 ]. Similar trends were observed in Japan. Even among high-risk populations—such as individuals with heart disease—an increase in smoking intensity was reported among smokers [ 12 , 40 ]. In our study, the slowdown in the declining trend of cigarette sales and the immediate increase in the level of price-adjusted tobacco expenditures in April 2020 may reflect changes in tobacco purchasing patterns, such as stockpiling, and possibly changes in smoking intensity among continuing smokers; however, these data cannot distinguish between these explanations. Sensitivity analyses including a quadratic time term did not materially change the cigarette sales results, but the monthly tobacco expenditure results were less stable. In particular, for monthly tobacco expenditures, the immediate level change after the first intervention was no longer statistically significant when the quadratic term was included. Accordingly, the expenditure findings should be interpreted with greater caution than the cigarette sales findings. Moreover, after May 2023, the change in the expenditure trend was significant in the model adjusted for seasonality and autocorrelation, but this result was not robust in the quadratic sensitivity analysis, further supporting a cautious interpretation of the monthly tobacco expenditure series. Mask-wearing may have played a role in shaping smoking opportunities and context, which could in turn affect smoking intensity among continuing smokers rather than smoking prevalence. In Japan, mask-wearing was strongly encouraged until March 2023, and the majority of individuals consistently wore masks beginning in April 2020, when the first state of emergency was declared [ 41 ]. Although there is limited evidence that mask use directly changed population-level cigarette sales, tobacco expenditures, or smoking prevalences, mask use may have reduced the convenience of smoking in public settings and contributed to shifts in where smoking occurred. Consistent with this possibility, reports of increased smoking at home—where mask use is not required—suggest that environmental constraints may have displaced smoking to private spaces, potentially influencing patterns of consumption without necessarily changing smoking prevalence [ 10 ]. Implications for cessation and prevention If individuals are unable to reduce or quit smoking despite the short-term risk of severe illness and mortality from COVID-19, then the long-term risk of developing malignancies 10 or 20 years after initiation may have limited influence on behavior modification. In this sense, the pandemic provides a natural experiment: even under a heightened and immediate health threat, cessation may remain difficult at the population level. Many countries and regions have attempted to deter smoking by placing health warnings on cigarette packaging. The broader literature suggests that warnings alone—particularly those emphasizing long-term health risks and mortality—may have limited effectiveness in promoting cessation for some smokers. These observations underscore the limitations of risk communication alone and suggest that effective strategies should also address stress and coping mechanisms. Furthermore, stress has been reported to influence smoking intensity [10 29 42 , 43 ]. Smoking control measures—such as displaying health warnings on cigarette packaging and restricting smoking areas—may inadvertently contribute to increased smoking intensity by inducing stress among smokers and the general population. Therefore, smoking cessation strategies that do not provoke stress may prove more effective. For instance, interventions that modify aspects of the choice architecture to influence behavior in a predictable manner—without restricting options or significantly altering economic incentives—such as “nudges [ 44 ],” are promising alternatives. Additionally, identifying the specific types of stress that trigger increased smoking may help develop more targeted prevention strategies. Although a deceleration in the declining trend of tobacco expenditures was observed after May 8, 2023, when COVID-19 was reclassified as a common infectious disease and legal restrictions on individual behaviors were lifted, this change should be interpreted cautiously because it was not robust in the quadratic sensitivity analysis. The absence of robust evidence for a clear shift in tobacco expenditures may reflect Japan’s prolonged economic stagnation [ 45 ] and a cultural tendency to avoid abrupt behavioral change. Smoking prevalence In Japan, the smoking prevalence among men had been gradually declining even before the first state of emergency was declared. There was no substantial change in smoking prevalence following the declaration. Because the data represent nationwide trends, it remains unclear whether the lack of change indicates that the COVID-19 pandemic had no effect on smoking behavior, or whether increases in new smokers were offset by an approximately equal number of individuals who quit smoking. A survey conducted in Osaka, Japan, reported that approximately 12% of individuals quit smoking during the first state of emergency in April and May 2020 [ 10 ]. However, this study only assessed behavioral changes over a two-month period during which activity was heavily restricted; longer-term behavior remains unknown. Similarly, reports from other countries have suggested that lockdowns and the pandemic contributed to small reductions in smoking prevalence [16 27 30 35 46 – 49 ]. These studies showed that the reductions were modest in all cases, with only one report indicating a decrease greater than 5% [ 49 ]. Conversely, one study from the United States found that serious smoking cessation activity among adults declined sharply and remained suppressed for over a year during the pandemic [ 13 ]. Overall, behavioral restrictions associated with COVID-19 do not appear to have significantly reduced smoking. The suggestion that the number of new smokers equaled the number of individuals who quit following the declaration of the state of emergency in Japan is not strongly supported by the available evidence, suggesting that the pandemic’s impact on national smoking prevalences was likely minimal. Overall, the available evidence suggests that the COVID-19 pandemic and associated behavioral restrictions had, at most, a minimal impact on national smoking prevalence in Japan. However, because the NDB Open Data cover only adults aged 40 years and older, these findings should be interpreted cautiously and should not be generalized directly to younger adults aged 20–39 years. If younger adults were more responsive to pandemic-related social and environmental changes, the present data may have underestimated changes in smoking prevalence in the overall adult population. Limitations This study has several limitations. First, we relied on aggregate indicators (cigarette sales, household tobacco expenditures, and national smoking prevalence), and thus the findings may be subject to ecological fallacy and should not be interpreted as individual-level changes in smoking behavior. Second, sales and expenditure data reflect purchasing rather than actual consumption; therefore, the observed increase in April 2020 may partly reflect stockpiling or shifts in purchasing timing. In addition, the Family Income and Expenditure Survey covers only households with two or more members, and the expenditure estimates represent averages across all such households, including those without current smokers; accordingly, they should not be interpreted as direct estimates of change per smoking household. Published household-based data suggest that smoking households account for only a minority of Japanese households, and thus the implied increase per smoking household would be substantially larger than the all-household average, although such calculations are approximate. This may limit generalizability to single-person households, which accounted for 38.1% of general households in Japan in the 2020 Population Census and may have different tobacco purchasing and smoking patterns. Because single-person households comprised a large proportion of households, the expenditure findings should be interpreted primarily as applying to households with two or more members rather than to the entire household population. Third, our sales series did not include heated tobacco products or other nicotine products, and substitution across products may have confounded inferences based on cigarette sales and household tobacco expenditures. Because the market share of HTPs increased substantially during the study period, this limitation should be considered when interpreting the ITS results. Fourth, smoking prevalence was derived from NDB Open Data health checkups among individuals aged ≥40 years and may be affected by reporting bias and pandemic-related changes in participation. Accordingly, the prevalence findings may not be generalizable to the entire adult population and may underestimate changes if smoking behavior was more responsive among younger adults during the pandemic. Finally, as with interrupted time-series analyses in general, our interpretation assumes that no other major contemporaneous factors influenced the outcomes around the intervention points; residual confounding and model specification choices may have affected the estimates. Sensitivity analyses using a quadratic time term suggested that the monthly tobacco expenditure results were less robust than the cigarette sales results. Conclusion During the first state of emergency in Japan, the declining trend in tobacco sales decelerated. Furthermore, household spending on tobacco increased by approximately 17% following the declaration, although this expenditure finding was less robust in the quadratic sensitivity analysis. In contrast, there was no significant change in smoking prevalence. From the early stages of the pandemic, smoking was believed to be closely associated with adverse outcomes of COVID-19. These health warnings could have prompted smokers to quit. However, they did not result in a substantial reduction in smoking prevalence and were more consistent with a shift in tobacco purchasing patterns, including stockpiling, and possible changes in smoking intensity among continuing smokers than with a meaningful population-level decline in smoking behavior. Moreover, there was no robust evidence of a clear reversal in these purchasing-related changes after May 2023 in the available data. Data Availability The dataset used to support the findings of this study is publicly available in Zenodo: https://doi.org/10.5281/zenodo.20324787 . This study used publicly available aggregate data from third-party sources, including quarterly cigarette sales data from the Tobacco Institute of Japan, monthly household tobacco expenditure data and cigarette price data from the Ministry of Internal Affairs and Communications, and annual smoking prevalence data from the NDB Open Data provided by the Ministry of Health, Labour and Welfare. No individual-level data were used in this study. Funding Statement The author(s) received no specific funding for this work. References 1. Larsson SC, Burgess S. Appraising the causal role of smoking in multiple diseases: A systematic review and meta-analysis of Mendelian randomization studies. EBioMedicine. 2022;82:104154. doi: 10.1016/j.ebiom.2022.104154 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 2. WHO. Tobacco. World Health Organization. https://www.who.int/news-room/fact-sheets/detail/tobacco [ Google Scholar ] 3. James J. 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Tob Induc Dis. 2022;20:98. doi: 10.18332/tid/155007 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] PLoS One. doi: 10.1371/journal.pone.0350820.r001 Decision Letter 0 Fabrizio Ferretti Author information Copyright and License information Roles Fabrizio Ferretti : Academic Editor © 2026 Fabrizio Ferretti This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. PMC Copyright notice 10 Apr 2026 -->PONE-D-26-02685-->-->Changes in tobacco sales before, during, and after the COVID-19 pandemic in Japan: an interrupted time series analysis-->-->PLOS One Dear Dr. Kondo, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. 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Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.--> Reviewer #1: Yes Reviewer #2: Yes ********** -->5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)--> Reviewer #1: The authors used several data sources to provide evidence on changes in cigarette sales and smoking in Japan before and after the COVID-19 pandemic. The authors found increases in cigarette sales and purchase after the pandemic compared to the pre-pandemic levels and trends. There are some major and minor concerns (not listed in order) that need to be considered. 1. Can you explain the data clearly, providing detailed information on what is available and how you used it in your analysis? 2. Model specification: I am not sure about the controls in the model, but the model should account for seasonality. For example, quarterly or monthly FE should be included in the models to account for seasonality. I suggest writing a formal model and defining terms. 3. Lines 104 to 106: Cite methodological studies that presented this approach as valid and how they are interpreted. Here is one of such studies: https://journals.sagepub.com/doi/abs/10.1177/00131640021970358 4. From Figure 1 and the results reported in Table 1, it is clear that the increase was not temporary. The effect of the pandemic on cigarette sales is a permanent change in the rate of decline, which lasted through 2024. I am not sure about how this was described as temporary. That change in slope never went away throughout the sample period. In Figure 2, we see a permanent shift in the level after the onset of the pandemic. That gap between the expected (counterfactual) and actual trends, although diminishing over time, persisted and even widened after May 2023. 5. Lines 205 – 207: Should be in the intro before setting up the hypothesis. 6. Line 217: Again, I don't know why the authors claim there was a temporary increase. You moved up to a different intercept and continued with the same trend. Levels never shifted to the original, so how is that temporary? It was a rather permanent increase in the level. 7. The figure title and captions in the text are more confusing and make reading difficult to distinguish. Add “Note:” to figure captions. Reviewer #2: This paper uses segmented interrupted time series (ITS) models to analyze three national-level data sources (cigarette sales, household tobacco expenditures, and smoking prevalence) to examine how the COVID-19 pandemic affected smoking behavior in Japan. The authors report that the declining trend in cigarette sales decelerated after the first state of emergency (April 2020) and household tobacco expenditures spiked by approximately 19%, but smoking prevalence did not meaningfully change. The topic is policy-relevant, and the multi-data-source design is a strength. However, there are several concerns that should be addressed. 1. Autocorrelation and seasonality. Monthly and quarterly time-series data are expected to exhibit autocorrelation, and tobacco purchasing has well-documented seasonal patterns. Standard OLS-based segmented regression will produce incorrect standard errors and p-values if autocorrelation is not addressed. The manuscript mentions that only in the limitations section as a caveat, rather than having it tested or corrected. Seasonality was likewise not modeled. I recommend the authors account for autocorrelation and seasonality adjustments, and provide standard diagnostic tests (e.g., Durbin-Watson statistic, Ljung-Box test) for the residuals. 2. Absence of sensitivity analyses. There is no exploration of alternative model specifications, for example, different polynomial functions (now is all assumed linear). ITS results can be sensitive to polynomial choices. 3. Overstatement of conclusions relative to the data. The abstract and conclusion state that "smoking behavior among smokers appeared to intensify" and that these effects "persisted beyond May 2023." Neither claim is adequately supported by the data results. First, the sales/expenditure variables measure purchasing, not consumption. The authors themselves acknowledge that "these data cannot distinguish stockpiling from a true increase in cigarettes consumed per smoker," yet the abstract and conclusion reassert the intensification interpretation. Second, the "persistence" claim rests on a non-significant result at the second intervention point. This is a type II error concern: the non-significant estimate reflects absence of evidence, not evidence of persistence. I recommend the authors revise the abstract and conclusions to focus on changes in sales/purchasing patterns (even just to be more consistent with your title), with the “intensifying” and “persistence” interpretations discussed just as possibilities rather than conclusive findings. 4. The 153 JPY increase is translated to a ~19% rise, but the pre-treatment average of 800 JPY is calculated across all households with two or more members, even including non-smoking households. To aid interpretation, the authors should report (or estimate from published data) what fraction of Japanese households include a current smoker. This would allow readers to gauge the approximate per-smoker-household effect. Additionally, since it also excludes single-person households, the authors should note what proportion of Japanese households are single-person (e.g., approximately 38% as of the 2020 Population Census of Japan) and discuss how this exclusion may affect generalizability. Particularly, single-person households may have very different smoking patterns. 5. The authors note that heated tobacco products (HTPs) are excluded from the analysis, though its revenue now represents approximately 80% of conventional cigarette revenue in Japan, a substantial market shift occurring over the study period. If smokers transitioned between cigarettes and HTPs during the pandemic, the observed changes in the cigarette sales/expenditure could be partially attributable to cross-product substitution. I know that the authors cite one Osaka survey showing low switching rates (2.2%), but this is limited evidence from a single city during a short window and cannot be generalized to the national level over a longer time period. This limitation should be discussed more prominently and explicitly acknowledged as a potential confounder in the ITS results, rather than treated as a secondary limitation. 6. The NDB Open Data include only adults aged 40+, excluding younger adults (i.e., 20–39), a demographic likely with very different tobacco use patterns and pandemic-related behavioral changes compared to older adults. In Japan, smoking prevalence among younger men has historically been higher than among older age groups, and younger adults may be more responsive to social and environmental changes. This limitation is mentioned only briefly; it should be more explicitly flagged when interpreting the prevalence trends, and the authors should discuss the direction in which this exclusion might bias their findings. ********** -->6. PLOS authors have the option to publish the peer review history of their article ( what does this mean? ). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy .--> Reviewer #1: No Reviewer #2: No ********** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] To ensure your figures meet our technical requirements, please review our figure guidelines: https://journals.plos.org/plosone/s/figures You may also use PLOS’s free figure tool, NAAS, to help you prepare publication quality figures: https://journals.plos.org/plosone/s/figures#loc-tools-for-figure-preparation . NAAS will assess whether your figures meet our technical requirements by comparing each figure against our figure specifications. PLoS One. 2026 Jun 5;21(6):e0350820. doi: 10.1371/journal.pone.0350820.r002 Author response to Decision Letter 1 Article notes Copyright and License information Collection date 2026. PMC Copyright notice 18 May 2026 Reviewer #1: The authors used several data sources to provide evidence on changes in cigarette sales and smoking in Japan before and after the COVID-19 pandemic. The authors found increases in cigarette sales and purchase after the pandemic compared to the pre-pandemic levels and trends. There are some major and minor concerns (not listed in order) that need to be considered. Dear Reviewer 1, Thank you very much for your careful review and thoughtful comments on our manuscript. We sincerely appreciate the time and effort you devoted to evaluating our work. Your comments were extremely helpful and led us to improve the manuscript substantially. In the process of revision, additional analyses became necessary in response to the reviewers’ comments. During this period, newer data also became available. Accordingly, we updated the data sets and re-conducted the analyses using the most recent data available. Specifically, for the number of cigarette sales, the study period was updated from the first quarter of 2015 to the first quarter of 2025 to the first quarter of 2015 to the fourth quarter of 2025. For National tobacco expenditures, the study period was updated from January 2015 to May 2025 to January 2015 to February 2026. These updates are reflected in the revised manuscript and in our point-by-point response. We are very grateful for your valuable comments, which have helped us strengthen the manuscript. 1. Can you explain the data clearly, providing detailed information on what is available and how you used it in your analysis? Thank you for this helpful comment. The Data sources section was revised to clarify what information was available from each dataset and how it was used in the present study. Specifically, the revised text now states that the cigarette sales analysis used the quarterly number of cigarettes sold, that the household expenditure analysis used price-adjusted monthly tobacco expenditures for households with two or more members, and that smoking prevalence data were annual percentages and therefore were summarized descriptively rather than analyzed in the interrupted time-series model. The following sentences were added to the Data sources section of the revised manuscript: New (P3, L95) In this study, the quarterly number of cigarettes sold was used for the analysis of cigarette sales trends. New (P4L, 106) Thus, the monthly analysis was based on price-adjusted tobacco expenditures for households with two or more members. New (P4L, 113) Because these data were available as annual national percentages, smoking prevalence in men and women was summarized descriptively rather than analyzed using the interrupted time-series model. 2. Model specification: I am not sure about the controls in the model, but the model should account for seasonality. For example, quarterly or monthly FE should be included in the models to account for seasonality. I suggest writing a formal model and defining terms. Thank you for this helpful comment. We agree that autocorrelation and seasonality should be handled explicitly in the present time-series analyses. In response, we revised the analytic approach to account for seasonality using quarter indicators for the quarterly sales data and month indicators for the monthly expenditure data, and to account for autocorrelation using linear mixed models with an AR(1) covariance structure. The following sentences were added. New (P4, L133): For quarterly cigarette sales, seasonality was modeled using quarter indicators (Q2–Q4), with Q1 as the reference category. For monthly tobacco expenditures, seasonality was modeled using month indicators (February–December), with January as the reference category. To account for autocorrelation, the models were fitted using linear mixed models with an AR(1) covariance structure. The results tables have been revised as follows. Table 1. Parameter estimates from the interrupted time-series segmented regression model for quarterly cigarette sales adjusted for seasonality and autocorrelation. 95% confidence interval Estimate Lower limit Upper limit P-value Intercept 244.982 228.278 261.685 <0.001 Baseline trend -9.991 -11.214 -8.767 <0.001 After the first state of emergency declaration Level change -17.468 -41.080 6.143 0.133 Slope change 7.962 4.858 11.067 <0.001 After COVID-19 was downgraded to a common infectious disease Level change -1.481 -29.119 26.157 0.910 Slope change -0.924 -5.227 3.379 0.643 Q2 22.254 8.958 35.551 0.002 Q3 42.881 29.759 56.002 <0.001 Q4 19.110 5.973 32.248 0.006 Note: Seasonality was modeled using quarter indicators (Q2–Q4), with Q1 as the reference category. Autocorrelation was accounted for using a linear mixed model with an AR(1) covariance structure. P-values were obtained from t-tests for fixed-effect parameters in the linear mixed model, using the denominator degrees-of-freedom method specified in SPSS. Values <0.05 were considered statistically significant. Table 2. Parameter estimates from the interrupted time-series segmented regression model for monthly tobacco expenditures adjusted for seasonality and autocorrelation. 95% confidence interval Estimate Lower limit Upper limit P-value Intercept 797.883 724.578 871.187 <0.001 Baseline trend -2.501 -4.037 -0.964 0.002 After the first state of emergency declaration Level change 132.004 42.281 221.727 0.005 Slope change -1.294 -5.039 2.451 0.486 After COVID-19 was downgraded to a common infectious disease Level change -17.578 -120.702 85.546 0.731 Slope change 5.892 0.731 11.053 0.027 February -51.703 -115.314 11.908 0.110 March 30.380 -41.112 101.873 0.402 April 3.547 -69.484 76.577 0.923 May 45.424 -28.406 119.255 0.225 June 46.667 -27.042 120.376 0.212 July 75.578 2.017 149.140 0.044 August 60.454 -12.967 133.875 0.106 September 133.170 59.912 206.429 <0.001 October -45.150 -118.046 27.747 0.222 November -42.058 -113.603 29.487 0.247 December 9.502 -55.873 74.877 0.774 Note: Seasonality was modeled using month indicators (February–December), with January as the reference category. Autocorrelation was accounted for using a linear mixed model with an AR(1) covariance structure. P-values were obtained from t-tests for fixed-effect parameters in the linear mixed model, using the denominator degrees-of-freedom method specified in SPSS. Values <0.05 were considered statistically significant. The figures presenting the results have been revised as follows. New: Fig 1. Trend in cigarette sales before and after the first state of emergency declaration. Note: The solid line shows fitted values from the interrupted time-series segmented regression model adjusted for seasonality and autocorrelation. The dashed line shows the predicted number of cigarette sales had the COVID-19 pandemic and the first state of emergency not occurred, assuming continuation of the pre-intervention trend (counterfactual). The time variable was centered at 0 for January–March 2020; thus, the intercept represents the estimated cigarette sales in that quarter. New: Fig 2. Trend in monthly tobacco expenditures per household (households with two or more members) before and after the first state of emergency declaration. Note: The round markers indicate tobacco expenditures adjusted for price fluctuations based on cigarette prices from the Ministry of Internal Affairs and Communications’ Retail Price Statistics Survey. Price increases occurred because of base price adjustments in April 2016 and April 2017, tobacco tax increases in October 2018, October 2020, and October 2021, and a revision of the consumption tax rate in 2019. The solid line shows fitted values from the interrupted time-series segmented regression model adjusted for seasonality and autocorrelation. The dashed line shows the predicted monthly tobacco expenditures per household if the COVID-19 pandemic and the first state of emergency had not occurred, assuming continuation of the pre-intervention trend (counterfactual). The time variable was centered at 0 for March 2020; thus, the intercept represents the estimated monthly expenditure in March 2020. The relevant text in the Results section has been revised as follows. Old (P5, L118): Using a segmented regression ITS model with two intervention points (April–June 2020 and April–June 2023), the level change at the first intervention was not statistically significant, whereas the slope became significantly less negative after the first declaration (p = 0.002), indicating a deceleration in the declining trend. In the first quarter of 2020, cigarette sales showed little change compared to the previous quarter; however, the post-intervention trend corresponded to a smaller quarterly decline (approximately 300 million cigarettes) compared with the pre-intervention period. Nwe (P5, L166): After adjustment for seasonality and autocorrelation, the level change at the first intervention was not statistically significant, whereas the slope became significantly less negative after the first declaration (p < 0.001), indicating a deceleration in the declining trend. In the first quarter of 2020, cigarette sales showed little change compared to the previous quarter; however, the post-intervention trend corresponded to a smaller quarterly decline (approximately 200 million cigarettes) compared with the pre-intervention period. Nwe (P5, L174): The quarter indicators were statistically significant, supporting the presence of seasonal variation in cigarette sales. Old (P6, L142): From January 2015 to March 2020, price-adjusted monthly tobacco expenditures exhibited a declining trend with periodic fluctuations, decreasing from approximately 1,000 JPY to 800 JPY (Fig 2, Table 2). In April 2020, when the government declared the first state of emergency, price-adjusted monthly tobacco expenditures rose sharply by 153 JPY compared to the previous month (p < 0.001). However, from that point through April 2023, the expenditures continued to decline; the change in slope after April 2020 was not statistically significant (p = 0.251), indicating no evidence that the rate of decline differed from the pre-pandemic trend. After May 2023, when COVID-19 was downgraded to a common infectious disease, the rate of decline in price-adjusted monthly tobacco expenditures slowed, although the change was not statistically significant. Nwe (P6, L208): From January 2015 to March 2020, price-adjusted monthly tobacco expenditures exhibited a declining trend with periodic fluctuations, decreasing from approximately 1,000 JPY to 800 JPY (Fig 2, Table 2). After adjustment for seasonality and autocorrelation, in April 2020, when the government declared the first state of emergency, price-adjusted monthly tobacco expenditures increased significantly by 132 JPY compared to the previous month (p = 0.005). However, from that point through April 2023, the expenditures continued to decline; the change in slope after April 2020 was not statistically significant (p = 0.486), indicating no evidence that the rate of decline differed from the pre-pandemic trend. After May 2023, when COVID-19 was downgraded to a common infectious disease, the rate of decline in price-adjusted monthly tobacco expenditures became significantly less steep (p = 0.027), whereas the level change was not statistically significant (p = 0.731). Several month indicators were also statistically significant, supporting the presence of seasonal variation in tobacco expenditures. 3. Lines 104 to 106: Cite methodological studies that presented this approach as valid and how they are interpreted. Here is one of such studies: https://journals.sagepub.com/doi/abs/10.1177/00131640021970358 Thank you for this helpful comment. We revised the Statistical methods section to clarify the interpretation of the segmented regression coefficients. Specifically, we now state explicitly that the level-change parameter represents the immediate change at the first post-intervention time point relative to the value expected from the trend in the immediately preceding period, whereas the slope-change parameter represents the change in trend after the intervention relative to that preceding period. We also added the methodological reference by Huitema and McKean to support this clarification. The following sentences and reference were changed and added at the Statistical methods. New (P5, L149) Following previous methodological guidance on segmented regression design specification [22], the level-change parameter for each intervention was defined as the immediate deviation at the first post-intervention time point from the value expected under continuation of the trend in the immediately preceding period, whereas the slope-change parameter was defined as the change in trend after the intervention relative to that immediately preceding period. 22. Huitema BE, McKean JW. Design specification issues in time-series intervention models. Educ Psychol Meas. 2000;60(1):38-58. https://doi.org/10.1177/0013164002197035 4. From Figure 1 and the results reported in Table 1, it is clear that the increase was not temporary. The effect of the pandemic on cigarette sales is a permanent change in the rate of decline, which lasted through 2024. I am not sure about how this was described as temporary. That change in slope never went away throughout the sample period. In Figure 2, we see a permanent shift in the level after the onset of the pandemic. That gap between the expected (counterfactual) and actual trends, although diminishing over time, persisted and even widened after May 2023. Thank you for this important comment. The term “temporary” was imprecise and has been revised. Specifically, the description of cigarette sales was modified to avoid implying a temporary increase and to better reflect the sustained deceleration in the declining trend after the onset of the pandemic. In addition, the description of household tobacco expenditures was revised from “temporary increase” to “immediate increase in the level” in April 2020, which is more consistent with Figure 2 and Table 2. The following sentences were changed to the Discussion section. Old (P7, L192): However, the observed slowdown in the declining trend may suggest changes in purchasing and use patterns, including stockpiling or a temporary increase in smoking intensity among continuing smokers, similar to patterns reported in the United States. New (P9, L306): However, the observed slowdown in the declining trend may suggest a sustained change in purchasing and use patterns after the onset of the pandemic, including stockpiling or changes in smoking intensity among continuing smokers, similar to patterns reported in the United States. Old (P8, L227): In our study, the slowdown in the declining trend of cigarette sales and the temporary increase in price-adjusted tobacco expenditures immediately after April 2020 may reflect changes in purchasing and use patterns, such as stockpiling and/or a short-term increase in smoking intensity among continuing smokers; however, these data cannot distinguish between these explanations. New (P10, L364): In our study, the slowdown in the declining trend of cigarette sales and the immediate increase in the level of price-adjusted tobacco expenditures in April 2020 may reflect changes in tobacco purchasing patterns, such as stockpiling, and possibly changes in smoking intensity among continuing smokers; however, these data cannot distinguish between these explanations. Old (P10, L309): However, they did not result in a substantial reduction in smoking prevalence and were more consistent with a shift in purchasing and smoking patterns among continuing smokers—such as temporary stockpiling and/or a modest increase in smoking intensity—than with a meaningful population-level decline in smoking behavior. New (P13, L537): However, they did not result in a substantial reduction in smoking prevalence and were more consistent with a shift in tobacco purchasing patterns, including stockpiling, and possible changes in smoking intensity among continuing smokers than with a meaningful population-level decline in smoking behavior. 5. Lines 205 – 207: Should be in the intro before setting up the hypothesis. Th Attachment Submitted filename: Response to Reviewers_C19Smoking-07.docx pone.0350820.s002.docx (93.5KB, docx) PLoS One. doi: 10.1371/journal.pone.0350820.r003 Decision Letter 1 Fabrizio Ferretti Author information Copyright and License information Roles Fabrizio Ferretti : Academic Editor © 2026 Fabrizio Ferretti This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. PMC Copyright notice 19 May 2026 <p>Changes in tobacco sales before, during, and after the COVID-19 pandemic in Japan: an interrupted time series analysis PONE-D-26-02685R1 Dear Dr. Kondo, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. 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Kind regards, Fabrizio Ferretti, Ph.D. Academic Editor PLOS One Additional Editor Comments (optional): Reviewers' comments: PLoS One. doi: 10.1371/journal.pone.0350820.r004 Acceptance letter Fabrizio Ferretti Author information Copyright and License information Roles Fabrizio Ferretti : Academic Editor © 2026 Fabrizio Ferretti This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. PMC Copyright notice PONE-D-26-02685R1 PLOS One Dear Dr. Kondo, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS One. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. 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Supplementary Materials Attachment Submitted filename: Response to Reviewers_C19Smoking-07.docx pone.0350820.s002.docx (93.5KB, docx) Data Availability Statement The dataset used to support the findings of this study is publicly available in Zenodo: https://doi.org/10.5281/zenodo.20324787 . This study used publicly available aggregate data from third-party sources, including quarterly cigarette sales data from the Tobacco Institute of Japan, monthly household tobacco expenditure data and cigarette price data from the Ministry of Internal Affairs and Communications, and annual smoking prevalence data from the NDB Open Data provided by the Ministry of Health, Labour and Welfare. No individual-level data were used in this study. 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