Table 2. Association of Pharmaceutical Company Opioid Marketing With Prescription Opioid Overdose Deaths Across All US Counties^a.

Characteristic	aRR (95% CI)b
	Model A	Model B	Model C
Marketing value, $ per 1000 population per year	1.09 (1.05-1.12)	NA	NA
No. of payments, per 1000 population per year	NA	1.18 (1.14-1.21)	NA
No. of physicians receiving payments, per 1000 population per year	NA	NA	1.12 (1.08-1.16)
Age group, %
18-34 y	1.05 (1.03-1.07)	1.04 (1.02-1.06)	1.05 (1.03-1.06)
35-64 y	1.10 (1.07-1.12)	1.09 (1.07-1.12)	1.09 (1.07-1.12)
≥65 y	1.01 (0.99-1.02)	1.01 (0.99-1.02)	1.01 (1.00-1.03)
Male, %	0.93 (0.91-0.95)	0.94 (0.92-0.96)	0.94 (0.92-0.96)
White, %	1.01 (1.01-1.02)	1.01 (1.01-1.02)	1.01 (1.01-1.02)
High school or lower education, %	1.00 (1.00-1.01)	1.00 (1.00-1.01)	1.00 (1.00-1.01)
Unemployment, %	1.03 (1.01-1.04)	1.03 (1.02-1.05)	1.03 (1.01-1.04)
Poverty, %	1.03 (1.01-1.04)	1.03 (1.01-1.04)	1.03 (1.01-1.04)
Median household income ($1000)	1.00 (1.00-1.01)	1.00 (1.00-1.01)	1.00 (1.00-1.01)
Gini indexc	1.01 (1.00-1.02)	1.00 (1.00-1.02)	1.01 (1.00-1.02)
Metropolitan area	1.21 (1.11-1.31)	1.13 (1.04-1.22)	1.20 (1.10-1.30)

Abbreviations: aRR, adjusted relative risk; NA, not applicable.

^aN = 9398 county-years for each analysis.

^bModel A includes marketing value (in dollars) as the independent variable, model B includes number of payments as the independent variable, and model C includes number of physicians receiving payments as the independent variable. Each model also includes all other covariates listed in the table.

^cGini index of income inequality ranges from 0, representing perfect income equality (ie, all incomes within a county are the same), to 1, representing perfect inequality (ie, 1 individual within a county holds all the county’s income, and all others in the same county have no income).^20,21,22,23