Background: Potentially avoidable pediatric hospitalizations (PAPH) can now be identified using an analytical tool developed by the federal Agency for Healthcare Research and Quality (AHRQ). We apply this new tool to Tennessee inpatient discharge records for 2005 to determine the prevalence of PAPH and analyze the variation patterns of PAPH across racial, gender, and insurance status lines.
Methods: Retrospective analysis of administrative data based on the UB-92 claims forms submitted by all short-term acute-care hospitals in Tennessee for 2005.
Results: Tennessee had higher prevalence rates of PAPH than seen in the nation overall for four of the five Ambulatory-Care Sensitive Conditions (ACSC), identified by AHRQ as those hospitalizations which can potentially be avoided. Variations of the rates of PAPH across racial, gender and insurance subgroups were found to mirror those found for pediatric hospitalizations for all conditions. However, when PAPH were grouped according to whether they were chronic or acute in terms of their primary admitting condition, Black children were over-represented in PAPH for chronic conditions such as asthma and diabetes. In addition, Black children's average costs are significantly higher than those for White children irrespective of whether the admitting condition was chronic or acute.
Conclusions: The high rates of PAPH reported in this study imply a weakness in Tennessee's primary care for children. These high rates also point out opportunities for reducing expensive hospitalizations associated with poorly controlled diabetes, asthma exacerbations, and dehydration due to gastroenteritis.
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Spine Deform
January 2025
Department of Orthopedic Surgery, Mayo Clinic, 200 First Street S.W, Rochester, MN, 55906, USA.
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January 2025
Department of Pediatrics, Regency Hospital, Kanpur, Uttar Pradesh, India.
Eur J Pediatr
January 2025
Department of Digestive Endoscopy Center, School of Medicine, Shanghai Children's Hospital, Shanghai Jiao Tong University, Shanghai, 200062, China.
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Pediatric Endocrinology and Diabetes Unit, Department of Pediatrics, Mansoura Faculty of Medicine, Mansoura University Children's Hospital, Mansoura University, Gomhoria Street, Mansoura, 35516, Dakhlia, Egypt.
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January 2025
Children's Intensive Care Research Program, Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia.
Introduction: Variable selection is a common technique to identify the most predictive variables from a pool of candidate predictors. Low prevalence predictors (LPPs) are frequently found in clinical data, yet few studies have explored their impact on model performance during variable selection. This study compared the Random Forest (RF) algorithm and stepwise regression (SWR) for variable selection using data from a paediatric sepsis screening tool, where 18 out of 32 predictors had a prevalence < 10%.
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