Table 1.
Univariate Regression on Predictors of Typhoid Fever Incidence
| Univariate Regression | |||
|---|---|---|---|
| Variables | Coefficient | 95% CI | AIC |
| Urban prevalencea | 2.83 | (2.47–3.18) | 4.24 |
| Improved toilet access (binary) | 2.72 | (0.15–5.29) | 34.35 |
| Education level (tertile) | 1.45 | (−0.76 to 3.55) | 36.77 |
| Access to vaccination (3rd dose, diphtheria, tetanus, and pertussis) | 6.54 | (1.98–11.11) | 31.79 |
| Wealth (quintile) | 0.78 | (0.19–1.37) | 32.61 |
| Household size | 0.02 | (−0.96 to 1.00) | 38.64 |
| Improved water access (binary) | 0.62 | (−4.99 to 6.24) | 38.59 |
| Stunting prevalence | −7.68 | (−11.96 to −3.39) | 29.31 |
| Underweight prevalence | −3.36 | (−8.15 to 1.41) | 36.51 |
Abbreviations: AIC, Akaike Information Criterion; CI, confidence interval.
The model in the univariate regression was a log linear regression using the 10 study sites (N = 10). In this regression test, the dependent variable was typhoid incidence at each study site (cases per 100 000 person-years). The coefficient represents a log transformation.
aUrban prevalence was computed as the average of a binary urban/rural household variable at the cluster level.