Assessing the Relationship between Retail Store Tobacco Advertising and Local Tobacco Control Policies: A Massachusetts Case Study
Table 4
Bivariate and multiple regression models showing the association between tobacco advertising variables and the strength of local tobacco control policies.
Variables
Presence of tobacco ads1
Presence of discounts
Presence of flavored tobacco ads
Range of tobacco ad categories2
Number of tobacco ads3
OR
AOR
OR
AOR
OR
AOR
OR
AOR
IRR
AIRR
Independent variable
Strength of other tobacco policies
0.84+
0.84+
0.99
1.01
0.71
0.78
0.89
0.88
0.91
0.91
Ban on discounts
0.44
0.63
Ban on flavored OTP
0.10
0.05
Control variables
Retail store level
Store type
Convenience stores (REF)#
Gas station
5.49
0.76
0.54
1.22
1.23
Chain retail/liquor stores
0.50
0.21
0.37
0.19
0.46
Nonchain retail/drug store/other store types4
0.45
0.11
1
0.22
0.49
Municipality level
Percent minority (% of minority population)
0.98
1.09
1.01
1.00
1.00
Median household income ($1,000)
1.00
1.00
1.00
1.00
1.00
Fit statistics
N
419
419
419
419
419
419
419
419
419
419
AIC
330.0
311.7
369.7
345.1
164.1
161.8
1629.1
1554.5
2485.0
2421.9
BIC
342.1
344.0
385.8
381.4
180.3
193.1
1665.4
1611.0
2501.2
2458.3
(one-tailed test). ,, and (two-tailed test). #Reference group. 1Mixed-effects logistic regression. 2Mixed-effects ordinal logistic regression. 3Mixed-effects negative binomial regression. 4Other store types include tobacco shops, big-box stores, fashion stores, bars, and private clubs. OR = odds ratio; AOR: adjusted odds ratio; IRR: incidence rate ratio; AIRR: adjusted incidence rate ratio.