Hepatitis and tuberculosis testing are much less common than HIV testing among adults in Kisumu, Kenya: results from a cross-sectional assessment

Table 3 Spatial Location Hierarchical Modelling of Factors Associated with HIV Testing in the Year Prior to Enrollment

Characteristic	Adjusted Prevalence Ratio (95% Confidence Interval)	p
Fixed Effects
Education Level
Less than Secondary School	Reference
Secondary School or Higher	1.10 (1.02, 1.20)	0.02
Self-Assessed HIV Risk
No Risk	0.98 (0.86, 1.11)	0.72
Some Risk	Reference
High Risk	0.92 (0.84, 1.00)	0.06
Random Effects
Area
(1) West Seme	0.99 (0.91, 1.07)	0.78
(2) Central Seme	1.00 (0.96, 1.04)	0.92
(3) East Seme	1.00 (0.95, 1.04)	0.87
(4) Southwest, Central, and North Kisumu	1.00 (0.96, 1.04)	0.91
(5) West and Northwest Kisumu	1.00 (0.97, 1.04)	0.99
(6) Railways, Migosi, Shaurimoyo Kaloleni, Kondele	1.00 (0.97, 1.04)	0.91
(7) Market Milimani, Nayalenda B	1.01 (0.92, 1.10)	0.78
(8) Kajulu	0.99 (0.92, 1.07)	0.81
(9) Kolwa East	1.00 (0.96, 1.04)	0.95
(10) Manyatta B	1.00 (0.96, 1.03)	0.96
(11) Nyalenda A	1.01 (0.94, 1.07)	0.83
(12) Kolwa Central	1.00 (0.96, 1.04)	0.98
Overall Covariance Effect	1.00 (0.997, 1.004)	0.40

In order to assess whether prior testing was associated with aspects of differential access due to spatial location, we constructed a 2-tier hierarchical model with level 1 as the individual respondent and level 2 as the residential ward. Residential wards were aggregated into 12 groups based on expert knowledge from in-country staff as described in Fig. 1. Due to the number of geographic categories necessary to consider, the model was limited in the number of covariates that could be included as independent variables. Statistically significant (p < 0.05) prevalence ratios are shown in bold

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