Table 3.

Prevalence of Potentially Inappropriate Drug Classes, Unadjusted and Adjusted Sex Differences in Odds of prescribing at Least One Potentially Inappropriate Drug Class.

PIM category		Prevalence of PIM1, %				Unadjusted OR (women vs men (ref.)	Fully- Adjusted OR women vs men (ref.)2
	Overall	Women	Men	Women vs men, %	p-value
Any PIM	37.1	39.4	32.8	+6.6	<0.001	1.38 (1.26, 1.52)	1.30 (1.16, 1.46)
PIM category
Anticholinergics	10.2	11.5	7.9	+3.6	<0.001	1.55 (1.36, 1.78)	1.51 (1.28, 1.78)
Antidepressants	6.3	7.6	4.0	+3.6	<0.001	2.35 (1.86, 2.97)	2.17 (1.64, 2.87)
Antispasmodics	4.0	4.7	2.6	+2.1	<0.001	1.60 (1.26, 2.02)	1.53 (1.12, 2.08)
Antithrombotics	1.2	1.2	1.2	0	0.872	0.97 (0.62, 1.52)	0.71 (0.41, 1.22)
Benzodiazepines	10.6	10.9	9.9	+1.0	0.217	1.24 (1.08, 1.43)	1.33 (1.12, 1.59)
Cardiovascular	2.7	2.9	2.4	+0.5	0.226	1.35 (0.95, 1.90)	1.16 (0.79, 1.69)
Megestrol	1.8	1.7	1.9	−0.2	0.499	1.02 (0.80, 1.31)	0.88 (0.61, 1.26)
Pain drugs	2.8	2.5	3.3	−0.8	0.049	0.63 (0.52, 0.77)	0.64 (0.50, 0.82)
Skeletal muscle relaxants	6.4	7.4	4.7	+2.7	<0.001	1.58 (1.34, 1.85)	1.57 (1.29, 1.90)
Sulfonylureas	3.5	3.1	4.3	−1.2	0.021	0.69 (0.52, 0.92)	0.54 (0.38, 0.76)

Note: Drug types with >1% prevalence of use were considered for these analyses.

¹PIM-potentially inappropriate medication.

²The logistic regression models were conducted for each PIM class separately. Models were adjusted for: number of medications used >=90 days, number of all-cause ED visits, number of all-cause hospitalizations, number of physician visits and count of chronic disease, age group, years of education, race/ethnicity, marital status, region, BMI, current smoking status, and current alcohol use. Statistically significant at p = 0.05. Values in bold are statistically significant at p = 0.05.