scholarly journals Medical-attention injuries in community cricket: a systematic review

2020 ◽  
Vol 6 (1) ◽  
pp. e000670
Author(s):  
Geordie McLeod ◽  
Siobhán O’Connor ◽  
Damian Morgan ◽  
Alex Kountouris ◽  
Caroline F Finch ◽  
...  

ObjectivesThe aim was to identify and describe outcomes from original published studies that present the number, nature, mechanism and severity of medically treated injuries sustained in community-level cricket.DesignSystematic review.MethodsNine databases were systematically searched to December 2019 using terms “cricket*” and “injur*”. Original, peer-reviewed studies reporting injury for at least one injury descriptor (body region, nature of injury and/or mechanism of injury) in community-level cricketers of all ages were included. Qualitative synthesis, critical appraisal and descriptive summary results are reported within the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines.ResultsSix studies were included: five reported hospital-treated data and one reported insurance claims data. Two had a low risk of bias. In hospital-based studies, fractures were the most frequent injury type. Upper and lower limb injuries (age ≥15 years) and injuries to the head (age <15 years) were the most common body region injured. Being struck by the ball was the most common mechanism for injury presenting to hospitals. Children were also commonly struck by equipment. One study using insurance claims data reported soft tissue injuries as the main of injury type.ConclusionHospital treatment data were most prominent, which emphasised injuries of a more serious nature or requiring acute care. These injuries were primarily fractures, dislocation/sprain and strains, bruising and open wounds with the majority resulting from players being struck by the ball. Research into whether properly fitted protective equipment, at an approved standard, is worn and is effective, is recommended.

2021 ◽  
Vol Volume 13 ◽  
pp. 969-980
Author(s):  
Khulood Al Mazrouei ◽  
Asma Ibrahim Almannaei ◽  
Faiza Medeni Nur ◽  
Nagham Bachnak ◽  
Ashraf Alzaabi

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Michael Stucki ◽  
Janina Nemitz ◽  
Maria Trottmann ◽  
Simon Wieser

Abstract Background Decomposing health care spending by disease, type of care, age, and sex can lead to a better understanding of the drivers of health care spending. But the lack of diagnostic coding in outpatient care often precludes a decomposition by disease. Yet, health insurance claims data hold a variety of diagnostic clues that may be used to identify diseases. Methods In this study, we decompose total outpatient care spending in Switzerland by age, sex, service type, and 42 exhaustive and mutually exclusive diseases according to the Global Burden of Disease classification. Using data of a large health insurance provider, we identify diseases based on diagnostic clues. These clues include type of medication, inpatient treatment, physician specialization, and disease specific outpatient treatments and examinations. We determine disease-specific spending by direct (clues-based) and indirect (regression-based) spending assignment. Results Our results suggest a high precision of disease identification for many diseases. Overall, 81% of outpatient spending can be assigned to diseases, mostly based on indirect assignment using regression. Outpatient spending is highest for musculoskeletal disorders (19.2%), followed by mental and substance use disorders (12.0%), sense organ diseases (8.7%) and cardiovascular diseases (8.6%). Neoplasms account for 7.3% of outpatient spending. Conclusions Our study shows the potential of health insurance claims data in identifying diseases when no diagnostic coding is available. These disease-specific spending estimates may inform Swiss health policies in cost containment and priority setting.


2006 ◽  
Vol 48 (10) ◽  
pp. 1054-1061 ◽  
Author(s):  
Mark R. Cullen ◽  
Sally Vegso ◽  
Linda Cantley ◽  
Deron Galusha ◽  
Peter Rabinowitz ◽  
...  

2019 ◽  
Vol 51 (2) ◽  
pp. 327-334 ◽  
Author(s):  
Chirag M. Lakhani ◽  
Braden T. Tierney ◽  
Arjun K. Manrai ◽  
Jian Yang ◽  
Peter M. Visscher ◽  
...  

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