Table 6.
Logistic regression results showing the relationship between annoyance and modelled SO2.
| Logistic regression analysis Dependent variable: annoyance (high annoyance) | ||||||
|---|---|---|---|---|---|---|
| Variables (reference) | Classification | Model I: OR (95% CI) | Model II: OR (95% CI) | Model III: OR (95% CI) | Model IV: OR (95% CI) | Model V: OR (95% CI) |
| Sulphur dioxide (1st quartile, low) | 2nd Quartile | 2.08 (1.03–4.21)* | 2.04 (1.00–4.15)* | 2.01 (0.98–4.11) | 1.87 (0.80–4.39) | 1.72 (0.73–4.05) |
| 3rd quartile | 4.15 (2.16–7.98)*** | 3.67 (1.88–7.17)*** | 3.69 (1.89–7.23)*** | 4.01 (1.82–8.81)** | 3.83 (1.73–8.47)** | |
| 4th quartile (high) | 3.60 (1.85–6.98)*** | 3.30 (1.67–6.51)** | 3.35 (1.70–6.63)*** | 4.18 (1.88–9.28)*** | 3.92 (1.76–8.74)** | |
| Age (18–24) | 25–44 | 1.28 (0.55–3.00) | 1.24 (0.53–2.91) | 1.13 (0.42–3.02) | 1.17 (0.43–3.18) | |
| 45–64 | 1.00 (0.43–2.31) | 1.01 (0.44–2.36) | 0.98 (0.37–2.61) | 1.04 (0.39–2.81) | ||
| 65+ | 0.59 (0.2–1.47) | 0.67 (0.27–1.67) | 0.71 (0.24–2.14) | 0.75 (0.24–2.28) | ||
| Gender (Male) | Female | 2.06 (1.34–3.17)** | 2.21 (1.43–3.43)*** | 1.80 (1.08–3.00)* | 1.71 (1.02–2.86)* | |
| Exposure to dust at work (not exposed) | Exposed | 1.57 (1.02–2.44)* | 1.31 (0.77–2.20) | 1.12 (0.64–1.98) | ||
| Employment (In the work force) | Not in work force | 1.24 (0.71–2.15) | 1.19 (0.68–2.08) | |||
| Health status (Very good/good/excellent) | Fair/poor | 1.65 (0.91–2.99) | 1.63 (0.89–2.98) | |||
| Cardinal symptoms (0–2 symptoms) | (≥ 3 symptoms) | 2.32 (1.39–3.88)** | 2.17 (1.25–3.75)** | |||
| Odours affect health (Neutral/disbelieve) | Believe | 5.33 (2.22–12.77)*** | 5.55 (2.31–13.35)*** | |||
| Odours in last 5years(Improved) | Did not improve | 1.80 (1.07–3.00)* | 1.76 (1.05–2.96)* | |||
| Coping with daily demands(able to cope | Not able to | 1.82 (0.92–3.62) | 1.75 (0.87–3.49) | |||
| Exposure to dust x cardinal symptoms | 4.79 (1.62–14.13)** | |||||
| Goodness of fit 1 | 0.04 | 0.06 | 0.07 | 0.20 | 0.21 | |
| Cox & Snell R Square | 0.03 | 0.05 | 0.06 | 0.16 | 0.17 | |
| Nagelkerke R Square | 0.06 | 0.09 | 0.10 | 0.27 | 0.29 | |
*
p-value< 0.05;
**
p-value< 0.01;
***
p-value< 0.001;
1
The goodness of fit is defined as one minus the ratio of the maximum log likelihood values of the fitted and constant only-term (null) models [43].