## 3 Results
### Objective III  
The last objective of this study was to identify how various levels of individual predictors of an occupational injury can be used as an input to estimate the post-incident state, which elaborates the application of the LR model formula. Numerical values from Table 5 show that those specific variables that increased or decreased the odds of a disability post-incident state could be extracted. Considering all individual predictors and their levels, the ones that increased the odds of a disability post-incident state (i.e., these variables had positive coefficients) included:  
* Injured body part groups of lower extremities, multiple, neck, and trunk;
* Cause of injury groups of caught in, under, or between; fall, slip, or trip injury; and strain or injury by;
* Nature of injury groups of occupational disease and multiple injuries;  
Figure 4: Boxplot of disability post-incident state with respect to industry. (* shows the outlier data points). Figure 5: Boxplot of disability post-incident state with respect to occupation. (* shows the outlier data points; NOC: not otherwise classified). * Occupations including chauffeurs and helpers, farm machinery operations, fertilizer dry mixing, gas and oil dealers, hay grain or feed dealers, grain elevator operations, and poultry/egg production;
* Industries of grain milling plants, feed mills for livestock/pet foods, and food distributors. The formula for the LR model was written using the linear combination as in Equation (1) and the numerical values for coefficients \(\beta\) (Table 5) for all levels of individual factors. \[ \begin{array}{ll}\mathrm{z}=-2.12-0.40&*\mathrm{Body}-0.74*\mathrm{head}+0.4 9*\mathrm{lower}\mathrm{ extremities}+\cdots+0.18\\ &*\mathrm{grain\,\,milling\,\,plants}-0.060*\mathrm{p}\mathrm{oulty}-0.02* \mathrm{experience}+0.02\\ &*\mathrm{age}\end{array}  \tag{3}\]  
This z value can be turned into a logit function using Equation (2) to estimate the probability of a disability post-incident state. For instance, for a 50-year-old injured worker with 5 years of experience working in fertilizer blending and distribution under the job category of fertilizer dry mixing operations, an occupational injury in lower extremities caused by a fall or trip and classified as multiple injuries will have a z value of:  
\[\mathrm{z}=-2.12+0.49+0.62+0.21+0.08-0.07-0.02(5)+0.02(50)=0.11 \tag{4}\]  
The z-value of 0.11 and \(\mathrm{e}^{-\mathrm{z}}=0.89\) generated the probability of 1/(1 + 0.89) = 0.53. This injury was predicted to have a 53% chance of turning into a disability rather than a medical state. The same demographics with a head injury instead of an injury to lower extremities was predicted to have a 25% chance (z = -1.12, \(\mathrm{e}^{-\mathrm{z}}=3.06\), and 1/(1 + 3.06) = 0.25) of a disability post-incident state. The reason was that the coefficient of the head (\(\beta=-0.74\)) had a decreasing effect on the odds of the disability outcome. Similarly, the LR formula can be used to develop other incident conditions for the estimation and prediction of post-incident states. For example, an occupational incident in the grain elevator industry for a 65-year-old worker with 15 years of experience, whose injury was caused by the \"strain or injury by\" category and multiple injuries and occupational disease nature of injury groups, had an almost equal chance of turning into a medical (54%) or disability (46%) state, while specific injuries showed a lower probability of ending as a disability (32%). Considering the effect of body part group, the same worker showed a medical post-incident state (67-82%) if injured in upper extremities, trunk, neck, multiple body parts, whole body group, and head, respectively. However, the same injury had a 40% and 42% chance of turning into a disability if it occurred in the upper or lower extremities, respectively. When changing the age while keeping all other factors fixed, the model showed that the younger the injured worker, the lower the probabilities of a disability post-incident state.