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A review of official data obtained from dog control records generated by the dog control service of county cork, Ireland during 2007.

ABSTRACT: BACKGROUND: There are no peer reviewed data on dog control records from an official agency in Ireland. In order to address this, a total of 2,669 official dog control service records generated during 2007 by Cork County Council dog control service were reviewed. RESULTS: Over 70 percent of records related to unwanted dogs and dogs not under their owners control. Stray dogs were collected by the service regularly throughout the year but with notable increase in voluntary surrenders by owners from January through to April. The majority of dogs collected or surrendered were male (2:1 ratio), of medium size, described as having a friendly temperament and were not wearing a neck collar. The Crossbreed and Greyhound breeds were more frequently collected as strays, while Greyhounds and German Shepherds were more frequently voluntarily surrendered by their owner. Restricted breeds such as Pit Bull terriers, German Shepherds and Rottweilers were more frequently reported by members of the public for aggressive behaviour while the only restricted breed reported for biting or snapping was the German Shepherd. CONCLUSIONS: Routine recording of dog control services in County Cork provide data on responsible dog ownership including the licensing of breeds, and surrender of owned dogs and the collection of stray dogs. Data capture and utilisation of dog control services by local authorities has potential to inform policy on responsible dog ownership and education programmes.

SUBMITTER: O'Sullivan EN

PROVIDER: S-EPMC3489852 | biostudies-other | 2012

REPOSITORIES: biostudies-other

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A review of official data obtained from dog control records generated by the dog control service of county cork, Ireland during 2007.

O'Sullivan Edmond N EN Hanlon Alison J AJ

Irish veterinary journal 20120608 1

<h4>Background</h4>There are no peer reviewed data on dog control records from an official agency in Ireland. In order to address this, a total of 2,669 official dog control service records generated during 2007 by Cork County Council dog control service were reviewed.<h4>Results</h4>Over 70 percent of records related to unwanted dogs and dogs not under their owners control. Stray dogs were collected by the service regularly throughout the year but with notable increase in voluntary surrenders b ...[more]

PMID: 22681751

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Project description:BACKGROUND:Falls in hospitals are the most common risk factor that affects the safety of inpatients and can result in severe harm. Therefore, preventing falls is one of the most important areas of risk management for health care organizations. However, existing methods for predicting falls are laborious and costly. OBJECTIVE:The objective of this study is to verify whether hospital inpatient falls can be predicted through the analysis of a single input-unstructured nursing records obtained from Japanese electronic medical records (EMRs)-using a natural language processing (NLP) algorithm and machine learning. METHODS:The nursing records of 335 fallers and 408 nonfallers for a 12-month period were extracted from the EMRs of an acute care hospital and randomly divided into a learning data set and test data set. The former data set was subjected to NLP and machine learning to extract morphemes that contributed to separating fallers from nonfallers to construct a model for predicting falls. Then, the latter data set was used to determine the predictive value of the model using receiver operating characteristic (ROC) analysis. RESULTS:The prediction of falls using the test data set showed high accuracy, with an area under the ROC curve, sensitivity, specificity, and odds ratio of mean 0.834 (SD 0.005), mean 0.769 (SD 0.013), mean 0.785 (SD 0.020), and mean 12.27 (SD 1.11) for five independent experiments, respectively. The morphemes incorporated into the final model included many words closely related to known risk factors for falls, such as the use of psychotropic drugs, state of consciousness, and mobility, thereby demonstrating that an NLP algorithm combined with machine learning can effectively extract risk factors for falls from nursing records. CONCLUSIONS:We successfully established that falls among hospital inpatients can be predicted by analyzing nursing records using an NLP algorithm and machine learning. Therefore, it may be possible to develop a fall risk monitoring system that analyzes nursing records daily and alerts health care professionals when the fall risk of an inpatient is increased.

Project description:ImportanceSuicide is the second leading cause of death among youths aged 10 to 19 years in the United States, with rates nearly doubling during the past decade. Youths in impoverished communities are at increased risk for negative health outcomes; however, the association between pediatric suicide and poverty is not well understood.ObjectiveTo assess the association between pediatric suicide rates and county-level poverty concentration.Design, setting, and participantsThis retrospective, cross-sectional study examined suicides among US youths aged 5 to 19 years from January 1, 2007, to December 31, 2016. Suicides were identified using International Statistical Classification of Diseases, Tenth Revision, Clinical Modification codes from the Centers for Disease Control and Prevention's Compressed Mortality File. Data analysis was performed from February 1, 2019, to September 10, 2019.ExposuresCounty poverty concentration and the percentage of the population living below the federal poverty level. Counties were divided into 5 poverty concentration categories: 0% to 4.9%, 5.0% to 9.9%, 10.0% to 14.9%, 15.0% to 19.9%, and 20.0% or more of the population living below the federal poverty level.Main outcomes and measuresThe study used a multivariable negative binomial regression model to analyze the association between pediatric suicide rates and county poverty concentration, reporting adjusted incidence rate ratios (aIRRs) with 95% CIs. The study controlled for year, demographic characteristics of the children who died (age, sex, and race/ethnicity), county urbanicity, and county demographic features (age, sex, and racial composition). Subgroup analyses were stratified by method.ResultsFrom 2007 to 2016, a total of 20 982 youths aged 5 to 19 years died by suicide (17 760 [84.6%] were aged 15-19 years, 15 982 [76.2%] male, and 14 387 [68.6%] white non-Hispanic). The annual suicide rate was 3.35 per 100 000 youths aged 5 to 19 years. In the multivariable model, compared with counties with the lowest poverty concentration (0%-4.9%), counties with poverty concentrations of 10% or greater had higher suicide rates in a stepwise manner (10.0%-14.9%: aIRR, 1.25 [95% CI, 1.06-1.47]; 15.0%-19.9%: aIRR, 1.30 [95% CI, 1.10-1.54]; and 20.0% or more: aIRR, 1.37 [95% CI, 1.15-1.64]). When stratified by method, firearm suicides had the strongest association with county poverty concentration (aIRR, 1.87; 95% CI, 1.41-2.49) in counties with 20% or higher poverty concentration compared with counties with 0% to 4.9% poverty concentration.Conclusions and relevanceThe findings suggest that higher county-level poverty concentration is associated with increased suicide rates among youths aged 5 to 19 years. These findings may guide research into upstream risk factors associated with pediatric suicide to inform suicide prevention efforts.

Dataset Information

A review of official data obtained from dog control records generated by the dog control service of county cork, Ireland during 2007.

Publications

A review of official data obtained from dog control records generated by the dog control service of county cork, Ireland during 2007.

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