Literature DB >> 28103182

Trends in Nonfatal Agricultural Injury in Maine and New Hampshire: Results From a Low-Cost Passive Surveillance System.

Erika Scott1, Erin Bell2, Liane Hirabayashi1,3, Nicole Krupa3, Paul Jenkins3.   

Abstract

OBJECTIVES: Agriculture is a dangerous industry, and although data on fatal injuries exist, less is known about nonfatal injuries. The purpose of this study is to describe trends in agricultural morbidity in Maine and New Hampshire from 2008 to 2010 using a newly established passive surveillance system. This passive system is supplied by injury cases gathered from prehospital care reports and hospital data.
METHODS: Demographics and specifics of the event were recorded for each incident case.
RESULTS: The average age of injured people in Maine and New Hampshire was 41.7. Women constituted 43.8% of all agricultural injuries. Machinery- (n = 303) and animal- (n = 523) related injuries accounted for most agricultural incidents. Of all injured women, over 60% sustained injuries due to animal-related causes. Agricultural injuries were spread across the two states, with clustering in southern New Hampshire and south central Maine, with additional injuries in the Aroostook County area, which is located in the northeast part of the state. Seasonal variation in agricultural injuries was evident with peaks in the summer months. There was some overlap between the agricultural and logging industry for tree-related work.
CONCLUSIONS: Our methods are able to capture traumatic injury in agriculture in sufficient detail to prioritize interventions and to evaluate outcomes. The system is low-cost and has the potential to be sustained over a long period. Differences in rates of animal- and machinery-related injuries suggest the need for state-specific safety prioritization.

Entities:  

Keywords:  Agriculture; Maine; New Hampshire; injury; time trends

Mesh:

Year:  2017        PMID: 28103182     DOI: 10.1080/1059924X.2017.1282908

Source DB:  PubMed          Journal:  J Agromedicine        ISSN: 1059-924X            Impact factor:   1.675


  2 in total

1.  The development of a machine learning algorithm to identify occupational injuries in agriculture using pre-hospital care reports.

Authors:  Erika Scott; Liane Hirabayashi; Alex Levenstein; Nicole Krupa; Paul Jenkins
Journal:  Health Inf Sci Syst       Date:  2021-07-29

2.  Using hospitalization data for injury surveillance in agriculture, forestry and fishing: a crosswalk between ICD10CM external cause of injury coding and The Occupational Injury and Illness Classification System.

Authors:  Erika Scott; Liane Hirabayashi; Judy Graham; Nicole Krupa; Paul Jenkins
Journal:  Inj Epidemiol       Date:  2021-02-15
  2 in total

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