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1.
Pediatr Emerg Care ; 34(12): 862-865, 2018 Dec.
Article in English | MEDLINE | ID: mdl-27404462

ABSTRACT

OBJECTIVE: Emergency medical services providers may be called to a variety of sites to transport pediatric patients, whether it be a scene call for initial evaluation and care, a clinic for transportation of a patient who has been assessed by medical providers, or a hospital where assessment and stabilization have already begun. We hypothesize that there may be a direct relationship between adverse event rates and adverse event severity in transports from less medically stabilizing origins. METHODS: Emergency medical services records of all critical pediatric transports in an urban Oregon county in 2011 were reviewed and abstracted using a standardized tool. From this, UNSEMs (unintended injury, near miss, suboptimal action, error, management complication) were determined, and the potential severity of the issue was assessed. Then, UNSEMs were compared with the origin of transport using logistic regression. RESULTS: Four hundred ninety records were abstracted: 59 hospital transports, 48 clinic transports, and 384 scene transports. Furthermore, UNSEMs were noted in 24 hospital transports (40.7%), 33 clinic transports (68.8%), and 263 scene transports (68.5%). Severe UNSEMs were reported on 0 hospital transports (0.0%), 12 clinic transports (25.0%), and 65 scene transports (16.9%). The odds ratio of UNSEM occurrence from a hospital compared with nonmedical scenes was 0.35 (95% confidence interval, 0.20-0.60), and the odds ratio of a severe UNSEM from a hospital compared with nonmedical scenes was 0.09 (95% confidence interval, 0.01-0.63). CONCLUSIONS: In conclusion, UNSEMs involving the emergency medical services care of children are more likely to occur when transport originates from a clinic or scene compared with a hospital.


Subject(s)
Emergency Medical Services/statistics & numerical data , Medical Errors/statistics & numerical data , Transportation of Patients/statistics & numerical data , Adolescent , Child , Child, Preschool , Female , Humans , Infant , Infant, Newborn , Male , Oregon , Retrospective Studies
2.
Pediatrics ; 132(4): 624-30, 2013 Oct.
Article in English | MEDLINE | ID: mdl-24082000

ABSTRACT

BACKGROUND: In 2010, 9120 cases of pertussis were reported in California, more than any year since 1947. Although this resurgence has been widely attributed to waning immunity of the acellular vaccine, the role of vaccine refusal has not been explored in the published literature. Many factors likely contributed to the outbreak, including the cyclical nature of pertussis, improved diagnosis, and waning immunity; however, it is important to understand if clustering of unvaccinated individuals also played a role. METHODS: We analyzed nonmedical exemptions (NMEs) for children entering kindergarten from 2005 through 2010 and pertussis cases with onset in 2010 in California to determine if NMEs increased in that period, if children obtaining NMEs clustered spatially, if pertussis cases clustered spatially and temporally, and if there was statistically significant overlap between clusters of NMEs and cases. RESULTS: Kulldorff's scan statistics identified 39 statistically significant clusters of high NME rates and 2 statistically significant clusters of pertussis cases in this time period. Census tracts within an exemptions cluster were 2.5 times more likely to be in a pertussis cluster (odds ratio = 2.47, 95% confidence interval: 2.22-2.75). More cases occurred within as compared with outside exemptions clusters (incident rate ratios = 1.20, 95% confidence interval: 1.10-1.30). The association remained significant after adjustment for demographic factors. NMEs clustered spatially and were associated with clusters of pertussis cases. CONCLUSIONS: Our data suggest clustering of NMEs may have been 1 of several factors in the 2010 California pertussis resurgence.


Subject(s)
Pertussis Vaccine/therapeutic use , Vaccination , Whooping Cough/epidemiology , Whooping Cough/prevention & control , California/epidemiology , Child, Preschool , Cluster Analysis , Culture , Female , Humans , Male , Vaccination/ethics , Whooping Cough/diagnosis
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