Trends, determinants and differences in antibiotic use in 68 residential aged care homes in Australia, 2014–2017: a longitudinal analysis of electronic health record data

Table 3 Marginal effects estimated by GEE regression of DOT/1000 resident days unadjusted and adjusted for resident characteristics and comorbidities

	Unadjusted model			Adjusted model
	Marginal effect	99% CI	p-value	Marginal effect	99% CI	p-value
Year	4.09	1.18, 6.99	< 0.001	3.12	−0.05, 6.29	0.011
Month
January	Reference			Reference
February	−0.19	−3.71, 3.33	0.889	0.98	−3.01, 4.96	0.527
March	−0.67	−4.71, 3.38	0.672	−0.03	−4.53, 4.46	0.985
April	−2.08	−6.12, 1.96	0.185	−1.23	−5.52, 3.06	0.459
May	3.52	−1.06, 8.11	0.048	5.14	0.2, 10.08	0.007
June	3.95	−0.78, 8.69	0.032	5.51	0.32, 10.7	0.006
July	8.27	3.32, 13.21	0.000	9.70	4.37, 15.03	< 0.001
August	10.51	5.38, 15.63	0.000	12.36	6.91, 17.81	< 0.001
September	5.48	0.79, 10.17	0.003	7.47	2.36, 12.59	< 0.001
October	0.01	−4.65, 4.66	0.998	1.36	−3.79, 6.51	0.496
November	0.06	−4.47, 4.6	0.971	1.83	−3.25, 6.92	0.352
December	3.38	−0.78, 7.55	0.036	5.40	0.59, 10.2	0.004
WHO Watch List Antibiotic	219.56	187.14, 251.97	< 0.001	247.04	206.61, 287.47	< 0.001
WHO Watch List antibiotic x year	−0.41	−5.96, 5.14	0.849	−1.64	−7.96, 4.67	0.503
Age				2.28	0.00, 3.91	0.000
Age at admission				−0.54	−2.05, 0.97	0.358
Men				−2.48	−12.99, 8.02	0.542
Comorbidities
Dementia				−9.93	−19.12, −0.74	0.005
Chronic respiratory disease				27.48	16.66, 38.31	< 0.001
Urinary incontinence				3.36	−4.57, 11.28	0.275
History of UTI				50.18	32.01, 68.36	< 0.001
History of wound				28.78	1.28, 56.28	0.007
History of SSTI				30.07	5.28, 54.86	0.002
Presence of resistant infectious organisms
MRSA				54.74	15.46, 94.03	< 0.001
Clostridioides difficile				−19.86	−94.4, 54.67	0.492
ESBL				106.52	− 101.52, 314.56	0.187
In-dwelling catheter				45.83	6.72, 84.94	0.003

GEE is generalised estimating equations. DOT is days of therapy. CI is confidence interval. MRSA is methicillin-resistant Staphylococcus aureus. ESBL is extended spectrum beta-lactamases. Model includes fixed effects for facilities to adjust for clustering within facilities. Marginal effects can be interpreted as the change in DOT/1000 resident days associated with a change in a given independent variable from the base/reference level, independent of all the other covariates in the model

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