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. 2013 Aug 13;13:306. doi: 10.1186/1472-6963-13-306

Table 4.

Predictors of seeking care from government health services (opposed to private health services)

Predictor variables
Overall chronic conditions (n = 3844)
Diabetes (n = 1760)
Hypertension (n = 2756)
  Adjusted odds ratio* (95% CI) p value Adjusted odds ratio* (95% CI) p value Adjusted odds ratio* (95% CI) p value
Age groups (years)
 
 
 
 
 
 
  ≤40
-
-
-
-
-
-
  40-50
1.1 (0.7, 1.8)
0.584
5.3 (1.6, 17.3)
0.006
1.2 (0.7, 2.0)
0.599
  50-60
1.7 (0.9, 3.1)
0.106
13.5 (2.7, 67.5)
0.002
1.6 (0.8, 3.4)
0.175
  ≥60
3.7 (1.6, 8.3)
0.002
40.2 (5.0,325.7)
0.001
3.4 (1.3, 8.8)
0.010
Monthly per capita income
  First quintile
-
-
-
-
-
-
  Second quintile
0.7 (0.5, 1.0)
0.028
0.7 (0.4, 1.2)
0.235
0.4 (0.2, 0.6)
<0.001
  Third quintile
0.5 (0.3, 0.7)
0.001
0.6 (0.3, 1.1)
0.106
0.2 (0.1, 0.4)
<0.001
  Fourth quintile
0.4 (0.2, 0.7)
0.001
1.1 (0.7, 1.9)
0.617
0.1 (0.1, 0.4)
<0.001
  Fifth quintile
0.3 (0.1, 0.5)
<0.001
0.6 (0.3, 1.0)
0.066
0.1 (0.0, 0.3)
<0.001
Household poverty status
 
 
 
 
 
 
  Above the poverty line
-
-
-
-
-
-
  Below the poverty line
1.4 (1.0, 2.0)
0.069
1.3 (0.7, 2.4)
0.392
5.2 (1.6, 17.1)
0.007
Religion
 
 
 
 
 
 
  Islam
-
-
 
 
-
-
  Hinduism
0.8 (0.5, 0.1)
0.185
 
 
0.9 (0.5, 1.5)
0.676
  Christianity
0.4 (0.2, 0.8)
0.011
 
 
0.3 (0.1, 0.8)
0.019
Tiers of health services
 
 
 
 
 
 
  Clinics/health centres
-
-
-
-
-
-
  Referral hospitals
2.4 (1.5, 3.8)
<0.001
5.3 (1.9, 14.7)
0.001
1.6 (0.9, 3.0)
0.115
  Super-specialty hospitals
30.3 (14.4, 63.8)
<0.001
99.9 (16.2, 614.1)
<0.001
9.2 (3.0, 28.2)
<0.001
Interaction terms
 
 
 
 
 
 
  Age group*Tiers of health services
0.8 (0.7, 0.9)
<0.001
0.6 (0.5, 0.9)
0.002
0.8 (0.7, 0.9)
0.008
  Monthly per capita income *Religion
1.2 (1.0, 1.3)
0.010
 
 
1.2 (1.0, 1.4)
0.019
  Monthly per capita income *Tiers of health service
 
 
 
 
1.1 (1.0, 1.3)
0.017
  Household poverty status*Religion         0.4 (0.2, 0.9) 0.021

- Referent category. *Adjusted odds ratio as obtained from multivariable logistic regression models. All the predictor variables were included in the initial model, including two-way interaction terms that were significant at p < 0.05 during binominal logistic regression. Similar to a backward elimination technique, the predictors that were not significant at p < 0.05 were then dropped individually, and the resultant models were compared for goodness of fit (using likelihood-ratio test) until no further improvement was possible.