| Literature DB >> 31528019 |
Amir Hamta1, Abedin Saghafipour2, Seyed Abbas Hosseinalipour3, Fatemeh Rezaei4.
Abstract
BACKGROUND AND AIM: Data mining in medical sciences provides countless opportunities for demonstrating hidden patterns of a data set. These patterns can help general physicians and health workers in preventing diseases. This study aimed to forecast delay times in post-exposure prophylaxis (PEP) to human animal bite injuries in central Iran using a decision tree analysis.Entities:
Keywords: Iran; decision tree analysis; human animal bite injuries; post-exposure prophylaxis
Year: 2019 PMID: 31528019 PMCID: PMC6702578 DOI: 10.14202/vetworld.2019.965-971
Source DB: PubMed Journal: Vet World ISSN: 0972-8988
Figure-1Position of Qom Province in Iran (left) and its geographical situation (right) [19].
Univariate analysis affecting factors on the delay of more than 48 h in the initiation of PEP.
| Factors for delay | Total | Delay | p-value | |
|---|---|---|---|---|
| No, n(%) | Yes, n(%) | |||
| Sex | ||||
| Male | 2009 | 1781 (88.7) | 228 (11.3) | <0.001 |
| Female | 405 | 328 (81.0) | 77 (19.0) | |
| Occupation | ||||
| Self-employed | 722 | 649 (89.9) | 73 (10.1) | <0.001 |
| Children | 150 | 128 (85.3) | 22 (14.7) | |
| Pupil | 376 | 320 (85.1) | 56 (14.9) | |
| housewives | 236 | 196 (83.1) | 40 (16.9) | |
| Rancher | 149 | 129 (86.6) | 20 (13.4) | |
| Student | 63 | 47 (74.6) | 16 (25.4) | |
| Driver | 64 | 62 (96.9) | 2 (3.1) | |
| Former | 99 | 88 (88.9) | 11 (11.1) | |
| Worker | 171 | 148 (86.5) | 23 (13.5) | |
| Employee | 120 | 110 (91.7) | 10 (8.3) | |
| Other | 259 | 231 (89.2) | 28 (10.8) | |
| Nationality | ||||
| Iranian | 2234 | 1958 (87.6) | 276 (12.4) | 0.09 |
| Other | 180 | 151 (83.9) | 29 (16.1) | |
| Residency place | ||||
| Rural | 474 | 398 (84.0) | 76 (16.0) | 0.006 |
| Urban | 1897 | 1678 (88.5) | 219 (11.5) | |
| Event description | ||||
| Exposure to suspected animal rabies | 19 | 11 (57.9) | 8 (42.1) | <0.01 |
| Animal bite | 2395 | 2098 (87.6) | 297 (12.4) | |
| Cause of animal bite occurrence | ||||
| Teasing animals | 502 | 440 (87.6) | 62 (12.4) | <0.01 |
| playing with the animal | 344 | 276 (80.2) | 68 (19.8) | |
| Human defense against animal attack | 52 | 46 (88.5) | 6 (11.5) | |
| Animal’s sudden attack on humans | 661 | 598 (90.5) | 63 (9.5) | |
| Because of feeding the animal and keeping it | 370 | 320 (86.5) | 50 (13.5) | |
| Because of hunting the animal | 98 | 90 (91.8) | 8 (8.2) | |
| Others | 387 | 339 (87.6) | 48 (12.4) | |
| Type of animal | ||||
| Cattle(Horse, Donkey, Cow, Sheep, Camel, Goat) | 75 | 50 (66.7) | 25 (33.3) | <0.001 |
| Carnivorous(Dog, Jackal, Pig, Fox) | 1187 | 1060 (89.3) | 127 (10.7) | |
| Cat | 1100 | 968 (88.0) | 132 (12.0) | |
| Other | 52 | 31 (59.6) | 21 (40.4) | |
| Being stray | ||||
| No | 1238 | 1077 (87.0) | 161 (13.0) | 0.32 |
| Yes | 1170 | 1026 (87.7) | 144 (12.3) | |
| Status animal | ||||
| Escaped | ||||
| Yes | 250 | 201 (80.4) | 49 (19.6) | <0.01 |
| No | 842 | 746 (88.6) | 96 (11.4) | |
| Killed | ||||
| No | 2376 | 2086 (87.8) | 290 (12.2) | <0.01 |
| Yes | 38 | 23 (60.5) | 15 (39.5) | |
| Place of injury in human body | ||||
| Lower limb of the human body | 693 | 625 (90.2) | 68 (9.8) | <0.01 |
| Upper limb of the human body | 1539 | 1333 (86.6) | 206 (13.4) | |
| Others | 160 | 137 (85.6) | 23 (14.4) | |
| Number of injury in human body | ||||
| 1 | 1311 | 1132 (86.3) | 179 (13.7) | <0.001 |
| 2 | 631 | 557 (88.3) | 74 (11.7) | |
| 3 | 255 | 226 (88.6) | 29 (11.4) | |
| More than 3 | 196 | 183 (93.4) | 13 (6.6) | |
| Entering the saliva of animal to human body | ||||
| Yes | 2405 | 2103 (87.4) | 302 (12.6) | 0.094 |
| No | 9 | 6 (66.7) | 3 (33.3) | |
| Puncture wounds | ||||
| No | 2131 | 1861 (87.3) | 270 (12.7) | 0.44 |
| Yes | 283 | 248 (87.6) | 35 (12.4) | |
| Scratches | ||||
| No | 202 | 162 (80.2) | 40 (19.8) | <0.01 |
| Yes | 2212 | 1947 (88.0) | 265 (12.0) | |
| Crush injuries | ||||
| No | 2375 | 2072 (87.2) | 303 (12.8) | 0.11 |
| Yes | 39 | 37 (94.9) | 2 (5.1) | |
| Time of event | ||||
| Before 7 am | 202 | 188 (93.1) | 14 (6.9) | <0001 |
| 7-13 | 661 | 542 (82.0) | 119 (18.0) | |
| 13-19 | 801 | 696 (86.9) | 105 (13.1) | |
| 19-24 | 693 | 643 (92.8) | 50 (7.2) | |
| Age | ||||
| <10 | 231 | 195 (84.4) | 36 (15.6) | 0.348 |
| 10-20 | 377 | 320 (84.9) | 57 (15.1) | |
| 20-30 | 612 | 537 (87.7) | 75 (12.3) | |
| 30-40 | 532 | 472 (88.7) | 60 (11.3) | |
| 40-50 | 286 | 254 (88.8) | 32 (11.2) | |
| >50 | 376 | 331 (88.0) | 45 (12.0) | |
Figure-2Final decision tree model for predicting delay of more than 48 h in the initiation of post-exposure prophylaxis using classification and regression trees algorithm.
Performance evaluation of the decision tree model for predicting delay of more than 48 h in the initiation of PEP.
| Statistic | Value(%) | 95% CI |
|---|---|---|
| Sensitivity | 63.61 | 57.9369.01 |
| Specificity | 63.02 | 60.91-65.08 |
| Positive predicted value | 19.92 | 18.35-21.59 |
| Negative predicted value | 92.29 | 91.14-93.31 |
| Accuracy | 63.09 | 61.13-65.02 |
PEP=Post-exposure prophylaxis