Table 2.

Factors associated with CD4 < 350 among ART clients in Uganda

Characteristic	(1) Unadjusted OR (95% CI)	(2) Adjusted OR (95% CI)	(3) Adjusted OR (95% CI) with adherence and VL suppression
Any food insecurity	1.22 (1.05–1.40)**	1.47 (1.24–1.74)***	1.47 (1.21–1.77) ***
Age (per 10 years)	1.03 (0.96–1.12)
Male (vs female)	1.74 (1.49–2.02)***	1.66 (1.39–1.98)***	1.91 (1.57–2.32)***
Less than high school education	1.30 (1.12–1.51)***	1.46 (1.22–1.76)***	1.39 (1.13–1.71)**
Employed	1.07 (0.92–1.23)
Asset index ╪(continuous)	0.97 (0.94–1.00)	1.03 (0.99–1.08)	1.04 (0.99–1.10)
Married	1.07 (0.94–1.22)
Problem drinking	1.75 (1.20–1.79)**	1.60 (1.02–2.49)*	1.45 (0.84–2.49)
Tobacco use, past 30 days	0.92 (0.67–1.26)
ART naïve at baseline	1.53 (1.34–1.75)***	1.73 (1.48–2.02) ***	1.63 (1.38–1.94) ***
Pre ART cd4 per 100	0.41 (0.38–0.45)***	0.39 (0.36–0.43) ***	0.38 (0.34–0.42)***
VL<400	3.62 (2.77–4.73)***		1.69 (1.15–2.49)**
Adherence < 90%	1.76 (1.31–2.37)***		1.58 (1.11–2.23)*

^***p<0.001

^**p<0.01

^*p<0.05

Note: Column (1) shows results from an unadjusted model of the bivariate association between CD4<350 and each characteristic. Column (2) shows results from a regression model of food insecurity on CD4<350 adjusting for all factors significant at α=0.2 from column (1). Column (3) adds viral suppression and adherence to the model from column (2) to explore their role as potential mediators of the relationship between food insecurity and CD4<350.

^╪Asset index created using 25 variables related to assets following the methodology proposed by Filmer and Pritchett [28]. Higher values indicating greater household wealth