Table 5.
Logistic regression results showing the relationship between annoyance and modelled NO2.
| 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) |
| Nitrogen dioxide (1st quartile, low) | 2nd Quartile | 0.96 (0.51–1.81) | 0.87 (0.45–1.68) | 0.86 (0.45–1.67) | 1.46 (0.64–3.28) | 1.43 (0.63–3.24) |
| 3rd quartile | 1.89 (1.07– 3.34)* | 1.84 (1.02–3.32)* | 1.85 (1.02–3.36)* | 3.06 (1.46–6.41)** | 3.05 (1.45–6.40)** | |
| 4th quartile (high) | 2.30 (1.32– 4.02)** | 2.29 (1.28–4.11)** | 2.31 (1.28–4.16)** | 3.52 (1.68–7.39)** | 3.32 (1.57–7.01)** | |
| Age (18–24) | 25–44 | 1.02 (0.45–2.30) | 1.00 (0.44–2.29) | 1.00 (0.38–2.61) | 0.99 (0.38–2.63) | |
| 45 – 64 | 0.77 (0.34–1.73) | 0.80 (0.35– 1.81) | 0.97 (0.37–2.56) | 1.03 (0.38–2.74) | ||
| 65+ | 0.48 (0.20–1.16) | 0.56 (0.23– 1.37) | 0.77 (0.25–2.31) | 0.81 (0.26–2.46) | ||
| Gender (Male) | Female | 2.17 (1.41–3.33)*** | 2.35 (1.51–3.63)*** | 1.96 (1.17–3.27)* | 1.84 (1.09–3.09)* | |
| Exposure to dust at work (not exposed) | Exposed | 1.57 (1.01–2.42)* | 1.39 (0.82–2.34) | 1.22 (0.70–2.13) | ||
| Employment (In the work force) | Not in work force | 1.22 (0.70–2.13) | 1.16 (0.66–2.04) | |||
| Health status (Very good/good/excellent) | Fair/poor | 1.49 (0.81–2.72) | 1.45 (0.78–2.69) | |||
| Cardinal symptoms (0–2 symptoms) | (≥3 symptoms) | 1.84 (1.11–3.06)* | 1.69 (0.98–2.92) | |||
| Odours affect health(Neutral/disbelieve) | Believe | 4.96 (2.18–11.30)*** | 5.08 (2.22–11.62)*** | |||
| Odours in last 5years (Improved) | Did not improve | 1.75 (1.05–2.93)* | 1.73 (1.03–2.90)* | |||
| Community satisfaction(satisfied) | dissatisfied | 1.58 (0.96–2.61) | 1.65 (0.99–2.75) | |||
| Coping with daily demands (able to cope) | Not able to cope | 2.11 (1.07–4.16)* | 2.06 (1.03–4.10)* | |||
| Exposure to dust x cardinal symptoms | 4.87 (1.68–14.09)** | |||||
| Goodness of fit 1 | 0.02 | 0.05 | 0.06 | 0.19 | 0.21 | |
| Cox & Snell R Square | 0.02 | 0.04 | 0.05 | 0.15 | 0.16 | |
| Nagelkerke R Square | 0.03 | 0.08 | 0.08 | 0.26 | 0.28 | |
*
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].