Purpose
Middle-aged women often experience weight gain, particularly as visceral fat, due to hormonal changes associated with menopause. Visceral fat, which accumulates in the abdomen, poses significant risks to cardiometabolic health. This cross-sectional study aimed to compare the cardiometabolic risks associated with Visceral Fat Obesity (VFO) and Subcutaneous Fat Obesity (SFO) in middle-aged Korean women and to identify factors that influence VFO.
Methods
Women aged 40 to 64 with overweight or obesity were recruited from March to April 2019. The study involved anthropometric measurements, fasting blood tests, and low-volume abdominal computed tomography. Additionally, participants provided self-reported sociodemographic, health-related, and lifestyle information, including Physical Activity (PA) and dietary intake.
Results
Of all participants, 70.8% were post-menopausal, and 55.1% had VFO. Those with VFO exhibited significantly higher mean values for waist circumference, total cholesterol, low-density lipoprotein cholesterol, triglycerides, fasting glucose, high sensitivity C-reactive protein, and the Framingham risk score compared to those with SFO. The factors influencing VFO were age (odds ratio (OR)=1.14; 95% confidence interval (CI), 1.032~1.247), body mass index (OR=1.47; 95% CI, 1.151 ~1.875), days of vigorous PA per week (OR=0.42; 95% CI, 0.244~0.735), and intake of animal calcium (OR=0.99; 95% CI, 0.988~0.997).
Conclusion
The findings indicate that middle-aged women with VFO face increased cardiometabolic risks. Since menopause is inevitable in women, targeting modifiable behaviors to reduce weight, particularly visceral fat, is crucial for lowering cardiometabolic risk.

Purpose
This study introduces information value chain analysis by identifying essential information for use in gout care management. Part I reviews the essential concepts of information value chain analysis first introduced by Porter. Part II applies the analysis to determine the information values of patient health information and explores ways in which health information technologies can be best utilized to provide that information to patients with gout. Methods We combined value chain analysis with natural language processing and machine learning techniques to develop algorithms that can identify patients with gout flares using clinical notes. As one of the first signs that the disease was not being controlled, variables found to be associated with gout flares were considered valuable information for patients with gout. Results The best performing model, in terms of both gout flare prediction and association identification, was the comprehensive model that not only included concepts from all stages of the value chain but also designated natural language processing concepts from every care stage as surrogate variables. Additionally, all administrative codes traditionally associated with gout and its treatment were included as surrogate outcome variables. Conclusion This study introduced information value chain analysis and applied it to develop a computer-based method with theoretical underpinnings to identify the concepts associated with gout flares. The findings can be used as a starting point for filtering the vast amounts of information patients must go through and identifying the most valuable information for patient with gout to adequately manage their symptoms.

Purpose
Information value is created by providing care for specific medical conditions. To assess the appropriate content and time of delivery, a research framework to examine information values at different stages of the care continuum is needed. This study identified essential information to recommend for different stages of Systemic Lupus Erythematosus (SLE) management.
Methods
Using Porter's value chain analysis, we conducted a content analysis of the research literature, clinical practice guidelines, and patient education materials in an education-enabled environment regarding patient with SLE. We also used a natural language processing technique to automatically map the essential information identified into authorized concepts in the National Library of Medicine’s Unified Medical Language Systems.
Results
The essential contents in the diagnosis stage pertained to a general understanding of disease manifestation such as SLE definition, pathophysiology, etiology, prognosis, and progress. The intervening stage highlights information about prominent spheres of therapeutic regimens and administration as well as diverse care providers with relevance to their specific roles. While screening information, such as self-awareness of SLE signs, is valued prior to a clinical visit, the monitoring information follows clinical visits to avoid flaring events. The key concepts identified were "butterfly rash" (C0277942), "anti-inflammatory drugs" (C0003211), "SLE" (C0024141), and "antinuclear antibodies" (C0151480).
Conclusion
Communication of essential information identified at appropriate care stages can increase patient knowledge and reduce anxiety levels to improve self-care.

Citations

• Application of Information Value Chain in Gout Management
  Maranda Russell, Sujin Kim
  Korean Journal of Adult Nursing.2022; 34(4): 351.     CrossRef