Using electronic medication monitoring to guide differential management of tuberculosis patients at the community level in China

Table 2 Factors associated with TB patients who were switched to DOT due to non-adherence in the study city (n = 169)

Factors	Total		Switched to DOT		OR	(95% CI)	aOR^f	(95% CI)
Factors	n	(%)^a	n	(%)^b	OR	(95% CI)	aOR^f	(95% CI)
Sex
Male	123	72.8	9	7.3	ref		ref
Female	46	27.2	6	13.0	1.9	0.6–5.7	1.9	0.5–7.0
Age^c
< 44	44	26.0	7	15.9	2.6	0.7–9.5	1.9	0.4–9.7
45–64	66	39.1	4	6.1	0.9	0.2–3.7	0.9	0.2–4.3
> =65	59	34.9	4	6.8	ref		ref
Occupation
Farmer/Migrant worker	100	59.2	4	4.0	ref		ref
Other	69	40.8	11	15.9	4.6^e	1.4–15.0	4.2^e	1.1–15.4
Migrant^d
No	119	70.4	4	3.4	ref		ref
Yes	50	29.6	11	22.0	8.1^e	2.4–26.9	8.4^e	2.3–30.6
Category
New	151	89.3	12	7.9	ref		ref
Retreated	18	10.7	3	16.7	2.3	0.6–9.1	7.6^e	1.1–51.0
Classification
Bacteriologically confirmed	75	55.6	6	8.0	ref		ref
Clinically diagnosed	94	44.4	9	9.6	1.2	0.4–3.6	1.4	0.4–5.3

TB Tuberculosis, DOT Directly observed therapy, OR Odds ratio, aOR Adjusted odds ratio, CI Confidence interval
^aColumn percentages
^bRow percentages
^cOnly one patient was under 15 years old, so < 15 group was merged into < 44 group
^dMigrant defined as patient coming from another county
^eStatistically significant
^fEven though some variables didn’t show statistical significance in univariate analysis, considering the important influence of patient’s background and diagnosis in the treatment management, we included all the variables in the multivariable analysis

ISSN: 1471-2334