Table 1.

Estimated Prescribing Rates for Psychotropic Medications, Based on Mixed-Effects Logistic Regression Models With Patient- and Facility-Level Adjustments

Models	σb2 (b)	Predicted probability that the average patient will initiate treatment with a given psychotropic medication
		Average nursing home(c)	Range
			2.5 %ile	97.5 %ile
Antidepressants (index) vs. Atypical APMs (referent)		% Antidepressants
Unadjusted	1.17	60.7%	15. 7%	92.6%
Adjusted(a) for calendar year, patient characteristics and nursing home characteristics(d)	0.10	58.0%	42.7%	72.0%
Antidepressants (index) vs. Hypnotics (referent)		% Antidepressants
Unadjusted	0.18	48.2%	28.8%	68.2%
Adjusted for calendar year, patient characteristics and nursing home characteristics	0.12	47.9%	31.5%	64.8%
Atypical APMs (index) vs. Hypnotics (referent)		% Atypical APMs
Unadjusted	1.22	38.2%	6.7%	84.3%
Adjusted for calendar year, patient characteristics and nursing home characteristics	0.15	39.9%	23. 9%	58.4%

^(a)All adjustments made through estimation of a propensity score. The c-statistic for the propensity score regression models ranged between 0.52 and 0.82.

^(b)Estimate of the between-NH variation. The random intercept b_i is assumed to be normally distributed with mean 0 and variance σ_b². σ_b² represents the NH-specific deviation from β₀, the marginal (averaged across NHs) probability of initiating a given psychotropic medication class for a patient with the mean propensity score. With increasing levels of adjustment, there is less unexplained variation and σ_b² is expected to decrease.

^(c)Prescribing proportion for the ‘average’ patient, defined as a patient with a mean propensity score. The average differs slightly between models since different factors are being adjusted for in the various models.

^(d)Nursing home characteristics include quality of care indicators