Table 3.
Predictor | Unadjusted POR (95% CI) |
Domain-specific model adjusted POR (95% CI)a |
Combined model adjusted POR (95% CI)b |
Final model adjusted POR (95% CI)c |
Beta coefficientd |
Scoree |
---|---|---|---|---|---|---|
Socio-demographic | ||||||
Age (18–29 years) | 2.2 (1.5–3.2) | 2.1 (1.4–3.0) | 2.1 (1.4–3.0) | 0.7 | 1 | |
Site | ||||||
Kilifi, Kenya | Ref | Ref | Ref | |||
Mombasa, Kenya | 0.8 (0.5–1.4) | 1.7 (0.3–8.9) | 1.7 (0.3–8.9) | |||
Lilongwe, Malawi | 5.8 (2.9–11.6) | 2.0 (0.8–4.7) | 2.0 (0.8–4.7) | |||
Durban, South Africa | 0.8 (0.4–1.7) | 2.9 (0.5–16.5) | 2.9 (0.5–16.5) | |||
Sex | 1.7 (1.1–2.6) | 1.1 (0.2–5.4) | 1.1 (0.2–5.4) | |||
Symptoms at evaluation visit | ||||||
Fever | 5.7 (4.0–8.1) | 1.9 (1.1–3.2) | 2.2(1.4–3.5) | 2.2(1.4–3.5) | 0.8 | 1 |
Diarrhoea | 4.7 (2.9–7.8) | 1.4 (0.8–2.4) | 1.8 (1.1–2.9)e | 1.8 (1.1–2.9) | 0.6 | 1 |
Fatigue | 7.5 (5.2–10.8) | 2.7 (1.6–4.8) | 2.6 (1.6–4.2) | 2.6 (1.6–4.2) | 0.9 | 1 |
Head ache | 3.9 (2.8–5.6) | 1.2 (0.7–2.1) | – | – | ||
Body pains | 6.8 (4.7–9.7) | 1.8 (1.1–2.9) | 2.3 (1.5–3.4) | 2.3 (1.5–3.4) | 0.8 | 1 |
Rash | 3.7 (1.8–7.6) | 1.5 (0.6–3.3) | – | – | ||
Sore throat | 5.0 (3.2–7.9) | 1.5 (0.9–2.6) | 1.7 (1.0–2.8) | 1.7 (1.0–2.8) | 0.5 | 1 |
Signs at evaluation visit | ||||||
Rash | 3.7 (1.8–7.6) | 0.7 (0.6–3.3) | – | – | ||
Any palpable lymph nodes | 6.8 (3.7–12.7) | 1.1 (0.6–2.1) | – | – | ||
Genital ulcer | 20.0 (12.9–31.1) | 19.8 (12.5–31.3) | 14.9 (8.6–26.0) | 14.9 (8.6–26.0) | 2.7 | 3 |
CI, confidence intervals; POR, prevalence odds ratio.
Factors associated with AHI at P ≤ 0.15 were included in two separate multivariable models for two domains: ‘symptoms’ and ‘signs’ findings. Each model is presented in this column, with a box indicating the results for each model.
Factors associated with AHI at P ≤ 0.15 in the initial domain-specific models were included in a combined model.
All variables in the final model, with the exception of sex or site, were associated with acute HIV infection at P ≤ 0.05.
Natural log of the adjusted prevalence odds ratio of the final model.
Predictor score is equal to its beta coefficient (natural log of the adjusted prevalence odds ratio) from the final generalized estimating equation model, rounded to the nearest integer.