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 ads^1		Presence of discounts		Presence of flavored tobacco ads		Range of tobacco ad categories^2		Number of tobacco ads^3
	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 types^4		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.