Digital detection for tobacco control: online reactions to the 2009 U.S. cigarette excise tax increase

Abstract

Introduction: The Internet is revolutionizing tobacco control, but few have harnessed the Web for surveillance. We demonstrate for the first time an approach for analyzing aggregate Internet search queries that captures precise changes in population considerations about tobacco.

Methods: We compared tobacco-related Google queries originating in the United States during the week of the State Children's Health Insurance Program (SCHIP) 2009 cigarette excise tax increase with a historic baseline. Specific queries were then ranked according to their relative increases while also considering approximations of changes in absolute search volume.

Results: Individual queries with the largest relative increases the week of the SCHIP tax were "cigarettes Indian reservations" 640% (95% CI, 472-918), "free cigarettes online" 557% (95% CI, 432-756), and "Indian reservations cigarettes" 542% (95% CI, 414-733), amounting to about 7,500 excess searches. By themes, the largest relative increases were tribal cigarettes 246% (95% CI, 228-265), "free" cigarettes 215% (95% CI, 191-242), and cigarette stores 176% (95% CI, 160-193), accounting for 21,000, 27,000, and 90,000 excess queries. All avoidance queries, including those aforementioned themes, relatively increased 150% (95% CI, 144-155) or 550,000 from their baseline. All cessation queries increased 46% (95% CI, 44-48), or 175,000, around SCHIP; including themes for "cold turkey" 19% (95% CI, 11-27) or 2,600, cessation products 47% (95% CI, 44-50) or 78,000, and dubious cessation approaches (e.g., hypnosis) 40% (95% CI, 33-47) or 2,300.

Conclusions: The SCHIP tax motivated specific changes in population considerations. Our strategy can support evaluations that temporally link tobacco control measures with instantaneous population reactions, as well as serve as a springboard for traditional studies, for example, including survey questionnaire design.

Figure 1.

Time trends for all tobacco-related queries. Figure shows time trends before and after the SCHIP tax for all tobacco-related queries analyzed (N = 181). Each line indicates estimated search volume (relative search volume) for a specific query. The red line indicates the mean across all queries, and the shaded region highlights the 7-day period around, with the dashed line indicating the day of, SCHIP.

Figure 2.

Ranking of relative and absolute changes for specific tobacco-related queries around the SCHIP tax. Each node represents the mean percent increase (with node size corresponding to estimates of excess absolute volume from Google Adwords, with triangle nodes indicating no volume estimate was returned by Adwords), and each line represents the 95% CI for that increase, as estimated from an interrupted time series, comparing the ratio of search volume (relative search volume, RSV) for the 7 days (including three before and three after) around SCHIP by search volume before SCHIP. Queries are ranked by the mean percent increase according to RSV, with only statistically significant associations shown (N = 162).

Figure 3.

Time trends for tax avoidance and smoking cessation queries around the SCHIP tax. Each panel shows time trends before and after SCHIP for (a) tax avoidance–related queries and (b) smoking cessation–related queries. Each dot indicates an estimated search volume (relative search volume) for a specific query, including 73 and 74 related queries for (a) and (b). Dark red solid lines indicates the mean trend from specific queries, and the shaded region highlights the 7-day period around, with the dashed line indicating the day of, the SCHIP tax.

Figure 4.

Ranking of relative changes for post-hoc identified categories of tobacco-related queries around the SCHIP tax. Each bar represents the mean percent increase in search volume, and each line represents the 95% CI for that increase, as estimated from an interrupted time series, comparing the ratio of search volume (relative search volume, RSV) for the 7 days (including three before and three after) around SCHIP by search volume before SCHIP. Queries are ranked by the mean percent increase according to RSV. Each bar is indicated by a group of similar queries, with the specific queries included in each category described in the text.