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. 2019 Jul;109(7):e1–e8. doi: 10.2105/AJPH.2019.305106

TABLE 2—

Exact Logistic Models Fit With Industry Funding or Affiliation Source as Predictors for Pro–Tobacco Harm Reduction (Pro-THR) Stance and Research Article Type: January 1, 1992, to July 26, 2016

OR (95% CI) Industry-Funded Pro-THR Articles, No.
Is presence of industry funding or affiliation associated with pro-THR stance? (n = 749)a
E-cigarette industry 20.9 (5.3, 180.7) 53
Tobacco industry 59.4 (10.1, +Infinity) 54
Pharmaceutical industry 2.18 (1.3, 3.7) 88
Is presence of industry funding or affiliation associated with type of research article (empirical)? (n = 826)
E-cigarette industry 0.7 (0.4, 1.3) 20
Tobacco industry 2.3 (1.3, 4.2) 35
Pharmaceutical industry 1.3 (0.9, 2.2) 56
Among nonempirical research articles, is industry funding associated with pro-THR stance? (n = 459)b
E-cigarette industry 20.1 (3.2, 860.2) 34
Tobacco industry 19.1 (3.0, 799.5) 22
Pharmaceutical industry 2.4 (1.1, 5.3) 51
Among empirical research articles, is industry funding associated with pro-THR stance? (n = 258)
E-cigarette industry 16.4 (2.46, 701.76) 19
Tobacco industryc . . . 32
Pharmaceutical industry 2.1 (1.03, 4.45) 37

Note. CI = confidence interval; OR = odds ratio; THR = tobacco harm reduction.

a

The articles with THR stance coded as neutral or mixed (n = 77) were excluded from this analysis. Models take sparse data into account.

b

Here, 77 articles taking a neutral THR stance were excluded.

c

In addition to excluding the 77 neutral THR articles, all 32 articles funded by or affiliated with the tobacco industry were dropped from the analysis as they were all pro-THR (perfect prediction).