Impact of an e-cigarette tax on cigarette and e-cigarette use in a middle-income country: a study from Indonesia using a pre-post design

OBJECTIVES

Indonesia implemented its first e-cigarette regulation in 2018, a 57% tax on the retail price of e-cigarette liquid (e-liquid), exceeding the 40% average tax on cigarettes. Economic research suggests that this tax could unintentionally increase cigarette smoking among dual users, but this has not been examined in a low-income or middle-income country. We therefore investigated the effects of the e-liquid tax among adults in Indonesia.

DESIGN

Pre-post study.

SETTING

Indonesia.

PARTICIPANTS

Adults who currently used e-cigarettes and either currently or occasionally smoked cigarettes or recently quit were recruited using Facebook and Instagram ads. Our follow-up response rate was 79%. A final sample of 1039 adults participated.

PRIMARY OUTCOME MEASURES

E-cigarette and cigarette use.

RESULTS

Following the e-liquid tax, participants reported paying a 4.4% higher price for e-liquid (p=0.02). Participants also reported an average 0.5-day decrease in the number of days they used e-cigarettes in the past week (p<0.001), and the proportion of daily e-cigarette users decreased (75.9% to 63.6%; p<0.001). Overall, reported use of cigarettes also declined, on average, by nine cigarettes per week. Participants who reported decreasing their e-cigarette use had higher odds of reporting increasing their cigarette use rather than reporting no change (adjusted OR: 2.99; 95% CI: 1.95 to 4.59). Further, as participants reported using e-cigarettes less frequently, they reported using cigarettes more frequently (β=-2.41, p=0.007).

CONCLUSIONS

Following an e-liquid tax in Indonesia, prices of e-liquid increased slightly, e-cigarette and cigarette use declined, and people who reported decreasing their e-cigarette use reported increasing their cigarette use. To avoid encouraging cigarette use, a prudent approach would be to raise cigarette taxes concurrently with e-cigarette taxes.

Illicit Drug Use Pattern, Health-Risk Behaviors, and Social Contexts Among Indonesian Students

Health-risk behaviors (smoking, alcohol consumption, and premarital sex) among Indonesian students are important issues. Understanding the association of these health-risk behaviors and adolescents’ social contexts (family, peers, and neighborhood characteristics) to illicit drug use is required to develop strategies in preventing illicit drug use in Indonesia. The study used a multi-stage cluster random sampling method to collect data from the 2016 National Narcotics Survey of Indonesian students. The sample included 31,439 students. The result shows that the illicit drug use pattern was different from previous studies: the most prevalent was at the senior high school level. Multiple logistic regression analyses showed that smoking, alcohol consumption, premarital sex, peers, sex, family, and neighborhood were associated with illicit drug use. The study highlighted the importance of health-risk behaviors, social contexts of adolescents, and the illicit drug use pattern as considering factors in developing strategies to reduce illicit drug use prevalence.

Which type of tobacco product warning imagery is more effective and sustainable over time? A longitudinal assessment of smokers in Canada, Australia and Mexico

OBJECTIVE

This study examined smokers' responses to pictorial health warnings (PHWs) with different types of imagery under natural exposure conditions.

METHODS

Adult smokers from online panels in Canada (n=2357), Australia (n=1671) and Mexico (n=2537) were surveyed every 4 months from 2012 to 2013. Participants were shown PHWs on packs in their respective countries and asked about: (1) noticing PHWs; (2) negative affects towards PHWs; (3) believability of PHWs; (4) PHW-stimulated discussions; and (5) quit motivation due to PHWs. Country-specific generalised estimating equation models regressed these outcomes on time (ie, survey wave), PHW imagery type (ie, symbolic representations of risk, suffering from smoking and graphic depictions of bodily harm) and interactions between them.

RESULTS

In all countries, PHW responses did not significantly change over time, except for increased noticing PHWs in Canada and Mexico, increased negative affect in Australia and decreased negative affect in Mexico. For all outcomes, symbolic PHWs were rated lower than suffering and graphic PHWs in Canada (the only country with symbolic PHWs). Graphic PHWs were rated higher than suffering PHWs for negative affect (all countries), discussions (Canada) and quit motivation (Australia). Suffering PHWs were rated higher than graphic PHWs for noticing PHWs (Canada), believability (all countries), discussions (AustraliaandMexico) and quit motivation (Mexico). Changes in noticing, believability and discussions varied somewhat by imagery type across countries.

CONCLUSIONS

The different PHW imagery appears to have different pathways of influence on adult smokers. Reactions to specific PHWs are similar over 1-2 years, suggesting that wear-out of PHW effects is due to decreased attention rather than the diminishing effectiveness of content.

Detecting Smoking Events Using Accelerometer Data Collected Via Smartwatch Technology: Validation Study

BACKGROUND

Smoking is the leading cause of preventable death in the world today. Ecological research on smoking in context currently relies on self-reported smoking behavior. Emerging smartwatch technology may more objectively measure smoking behavior by automatically detecting smoking sessions using robust machine learning models.

OBJECTIVE

This study aimed to examine the feasibility of detecting smoking behavior using smartwatches. The second aim of this study was to compare the success of observing smoking behavior with smartwatches to that of conventional self-reporting.

METHODS

A convenience sample of smokers was recruited for this study. Participants (N=10) recorded 12 hours of accelerometer data using a mobile phone and smartwatch. During these 12 hours, they engaged in various daily activities, including smoking, for which they logged the beginning and end of each smoking session. Raw data were classified as either smoking or nonsmoking using a machine learning model for pattern recognition. The accuracy of the model was evaluated by comparing the output with a detailed description of a modeled smoking session.

RESULTS

In total, 120 hours of data were collected from participants and analyzed. The accuracy of self-reported smoking was approximately 78% (96/123). Our model was successful in detecting 100 of 123 (81%) smoking sessions recorded by participants. After eliminating sessions from the participants that did not adhere to study protocols, the true positive detection rate of the smartwatch based-detection increased to more than 90%. During the 120 hours of combined observation time, only 22 false positive smoking sessions were detected resulting in a 2.8% false positive rate.

CONCLUSIONS

Smartwatch technology can provide an accurate, nonintrusive means of monitoring smoking behavior in natural contexts. The use of machine learning algorithms for passively detecting smoking sessions may enrich ecological momentary assessment protocols and cessation intervention studies that often rely on self-reported behaviors and may not allow for targeted data collection and communications around smoking events.

Evaluation of strategies to communicate harmful and potentially harmful constituent (HPHC) information through cigarette package inserts: a discrete choice experiment

BACKGROUND

The US Food and Drug Administration (FDA) has regulatory authority to use inserts to communicate with consumers about harmful and potentially harmful constituents (HPHCs) in tobacco products; however, little is known about the most effective manner for presenting HPHC information.

METHODS

In a discrete choice experiment, participants evaluated eight choice sets, each of which showed two cigarette packages from four different brands and tar levels (high vs low), accompanied by an insert that included between-subject manipulations (ie, listing of HPHCs vs grouping by disease outcome and numeric values ascribed to HPHCs vs no numbers) and within-subject manipulations (ie, 1 of 4 warning topics; statement linking an HPHC with disease vs statement with no HPHC link). For each choice set, participants were asked: (1) which package is more harmful and (2) which motivates them to not smoke; each with a 'no difference' option. Alternative-specific logit models regressed choice on attribute levels.

RESULTS

1212 participants were recruited from an online consumer panel (725 18-29-year-old smokers and susceptible non-smokers and 487 30-64-year-old smokers). Participants were more likely to endorse high-tar products as more harmful than low-tar products, with a greater effect when numeric HPHC information was present. Compared with a simple warning statement, the statement linking HPHCs with disease encouraged quit motivation.

CONCLUSIONS

Numeric HPHC information on inserts appears to produce misunderstandings that some cigarettes are less harmful than others. Furthermore, brief narratives that link HPHCs to smoking-related disease may promote cessation versus communications that do not explicitly link HPHCs to disease.

Does Reactance against Cigarette Warning Labels Matter? Warning Label Responses and Downstream Smoking Cessation amongst Adult Smokers in Australia, Canada, Mexico and the United States

Objective

Some researchers have raised concerns that pictorial health warning labels (HWLs) on cigarette packages may lead to message rejection and reduced effectiveness of HWL messages. This study aimed to determine how state reactance (i.e., negative affect due to perceived manipulation) in response to both pictorial and text-only HWLs is associated with other types of HWL responses and with subsequent cessation attempts.

Methods

Survey data were collected every 4 months between September 2013 and 2014 from online panels of adult smokers in Australia, Canada, Mexico, and the US were analyzed. Participants with at least one wave of follow-up were included in the analysis (n = 4,072 smokers; 7,459 observations). Surveys assessed psychological and behavioral responses to HWLs (i.e., attention to HWLs, cognitive elaboration of risks due to HWLs, avoiding HWLs, and forgoing cigarettes because of HWLs) and cessation attempts. Participants then viewed specific HWLs from their countries and were queried about affective state reactance. Logistic and linear Generalized Estimating Equation (GEE) models regressed each of the psychological and behavioral HWL responses on reactance, while controlling for socio-demographic and smoking-related variables. Logistic GEE models also regressed having attempted to quit by the subsequent survey on reactance, each of the psychological and behavioral HWL responses (analyzed separately), adjustment variables. Data from all countries were initially pooled, with interactions between country and reactance assessed; when interactions were statistically significant, country-stratified models were estimated.

Results

Interactions between country and reactance were found in all models that regressed psychological and behavioral HWL responses on study variables. In the US, stronger reactance was associated with more frequent reading of HWLs and thinking about health risks. Smokers from all four countries with stronger reactance reported greater likelihood of avoiding warnings and forgoing cigarettes due to warnings, although the association appeared stronger in the US. Both stronger HWLs responses and reactance were positively associated with subsequent cessation attempts, with no significant interaction between country and reactance.

Conclusions

Reactance towards HWLs does not appear to interfere with quitting, which is consistent with its being an indicator of concern, not a systematic effort to avoid HWL message engagement.