Skip to main content

Table 2 Predictors of self-reported chronic conditions

From: No longer diseases of the wealthy: prevalence and health-seeking for self-reported chronic conditions among urban poor in Southern India

Predictor variables Overall chronic conditions Diabetes Hypertension
  Adjusted odds ratio* (95% CI) pvalue Adjusted odds ratio* (95% CI) pvalue Adjusted odds ratio* (95% CI) pvalue
Sex       
Male - - - - - -
Female 3.2 (2.6, 4.0) <0.001 2.5 (1.8, 3.5) <0.001 4.6 (3.6, 5.8) <0.001
Age groups (years)       
≤19 - - - - - -
20-39 6.7 (4.8, 9.5) <0.001 10.9 (4.9, 24.0) <0.001 12.2 (7.3, 20.3) <0.001
≥40 58.8 (36.3, 95.2) <0.001 106.8 (40.7, 280.2) <0.001 116.1 (59.5, 226.4) <0.001
Monthly per capita income
First quintile - - - - - -
Second quintile 0.8 (0.6, 1.0) 0.047 0.8 (0.5, 1.2) 0.226 0.8 (0.6, 1.1) 0.211
Third quintile 0.5 (0.3, 0.8) 0.002 0.5 (0.2, 1.1) 0.097 0.6 (0.4, 1.0) 0.056
Fourth quintile 0.4 (0.2, 0.7) 0.001 0.3 (0.1, 1.1) 0.072 0.4 (0.2, 0.9) 0.023
Fifth quintile 0.2 (0.1, 0.5) <0.001 0.2 (0.1, 1.1) 0.072 0.3 (0.1, 0.9) 0.026
Household poverty status       
Above the poverty line - - - - - -
Below the poverty line 3.0 (1.5, 5.8) 0.002 0.6 (0.5, 0.7) <0.001 1.9 (0.7, 4.9) 0.196
Religion       
Islam - - - - - -
Hinduism 0.9 (0.8, 1.1) 0.227 1.0 (0.8, 1.1) 0.527 0.9 (0.8, 1.1) 0.177
Christianity 1.2 (1.0, 1.5) 0.078 1.0 (0.8, 1.2) 0.665 1.2 (0.9, 1.5) 0.175
Interaction terms       
Sex*Religion 0.7 (0.6, 0.8) <0.001    0.7 (0.6, 0.8) <0.001
Sex* Monthly per capita income    0.8 (0.8, 0.9) <0.001   
Age group*Monthly per capita income 1.1 (1.1, 1.2) <0.001 1.2 (1.0, 1.4) 0.007 1.1 (1.0, 1.2) 0.019
Age group*Household poverty status 0.6 (0.5, 0.8) <0.001    0.7 (0.5,1.0) 0.039
  1. - 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 a likelihood-ratio test) until no further improvement was possible.