Determinants of adherence to treatment in patients with ophthalmic conditions

Figures & data

Figure 1. Distribution of factors for patients who were adherent and non-adherent to ophthalmic drugs.

Table 1. Baseline characteristics.

Characteristic
Sex, n (%)
Male	346 (31.83)
Female	741 (68.17)
Age (years), mean ± SD	54.56 ± 17.19
Study time (days), mean ± SD	59.33 ± 31.66
Principal disease, n (%)
Conjunctivitis (viral or bacterial)	37 (3.40)
Dry eye disease	565 (51.98)
Glaucoma or ocular hypertension	175 (16.10)
Post cataract-surgery	214 (19.69)
Pterygium	96 (8.83)
Severity of medical condition, n (%)
Acute medical condition (AMC)	251 (23.09)
Chronic medical condition (CMC)	661 (60.81)
Chronic degenerative disease (CDD)	175 (16.10)
Drop treatments, n (%)
Antibiotic	37 (3.40)
Antibiotic + corticosteroid	36 (3.31)
Corticosteroid	178 (16.38)
Lubricants	614 (56.49)
Hypotensive	175 (16.10)
NSAID	47 (4.32)
Dosage, n (%)
Every day (QD)	96 (8.83)
Twice a day (BID)	108 (9.94)
Three times a day (TID)	112 (10.30)
Four times a day (QID)	668 (63.29)
Five times per day	37 (3.40)
Six times per day	46 (4.23)
Co-morbidities, n (%)	557 (51.24)
Hypertension	138 (24.77)
Diabetes Mellitus	70 (12.56)
Hypothyroidism	17 (3.10)
Tear Film Insufficiency	12 (2.2)
Gastritis	10 (1.8)
Other	310 (55.7)
Concomitant drug uses, n (%)	673 (61.91)
Incidence of adverse events, n (%)	401 (36.89)

Abbreviations: NSAID, non-steroidal anti-inflammatory drugs; SD, standard deviation.

Figure 2. Polychoric correlation matrix for all study factors. NA= not available.

Figure 3. Factors significantly associated with adherence to ophthalmic drugs. *p < 0.05, **p < 0.01, ***p < 0.001; horizontal lines: 95% CI; vertical line: OR = 1 (no effect).

Table 2. Metrics for evaluate the LR models performance considering class imbalanced.

Model	n (valid)	Link test^1	H-L (Significance)	AIC	BIC	Precision (%)	Recall (%)	F1 score (%)	PR-AUC (%)	ROC-AUC (95% CI)
M1	1069	p(_hat) = 0.000; p(_hastsq) = 0.707	X^2(8) = 4.38 (0.822)	450.465	554.929	97.1	80	87.724	97.81	0.901 [0.867–0.933]
M2	1069	p(_hat) = 0.000; p(_hastsq) = 0.705	X^2(8) = 4.38 (0.822)	450.465	554.929	97.1	80	87.724	97.81	0.901 [0.868–0.933]
M3	1069	p(_hat) = 0.000; p(_hastsq) = 0.708	X^2(8) = 4.38 (0.822)	452.462	561.901	97.1	80	87.724	97.81	0.901 [0.868–0.934]

¹Significance predict (_hat) and square predict (_hastsq). Youden’s Index (probability cutoff) = 0.906.

Abbreviations: AIC, Akaike’s information criterion; BIC, Bayesian information criterion; CI, confidence interval; H-L, Hosmer-Lemeshow goodness of fit; PR-AUC, precision-recall curve; ROC-AUC, area under ROC curve.

Figure 4. The observed and expected adherence/non-adherence for model 1 (M1).

Data availability statement

The dataset analyzed for this study can be found in the Open Science Framework repository (https://osf.io) as DOI 10.17605/OSF.IO/2WJKV.