Classification of health needs: a cluster analysis of older adults in urban areas.

BACKGROUND: In this study, a cross-sectional survey was used to understand and analyze the health status and health needs of the elderly in the community. The cluster analysis method was used to explore the relationship between health needs items and investigate the commonness among health demand items, to provide a reference for the development of health management of the elderly with chronic diseases. METHODS: We used convenience sampling to recruit the participants (aged 60 and above) from four urban community centers in Jinzhou City, Liaoning Province, China, in this study. This study uses the Medical Outcomes Study(MOS)36-Item Short-Form Health Survey. The self-designed questionnaire includes sociodemographic characteristics, chronic diseases, physical examination conditions, illness in the past two weeks, and a health needs questionnaire. SPSS 18.0 was used for data entry and analysis. Data analysis methods included descriptive statistical analysis, t-test, one-way analysis, cluster analysis, and linear multiple regression analysis. RESULTS: The rate of health needs among the elderly in the community for various health services is 1.3-69.7%, of which the top three are: regular physical examination (69.7%), day Care Center (67.7%), the establishment of a Medical Alert Systems (66.1%). The health needs of the elderly in the community are divided into three categories: basic needs (24 items), health education (13 items), and first aid (2 items). The regression analysis found that the influencing factors of health status were age, revenue and expenditure, medical expenses, health education, basic needs, and first aid. CONCLUSIONS: The community should strengthen the management of chronic diseases of the elderly and the publicity and education of related knowledge, and provide complementary health care services according to the health needs of the elderly, improve the health of the elderly, and improve the quality of life of the elderly.

Influence of measurement depth on the stiffness assessment of healthy liver with real-time shear wave elastography.

The purpose of this study was to determine the measurement depth range within which liver stiffness can be reliably assessed using real-time shear wave elastography (SWE) technology. Measurements were performed on phantoms and healthy volunteers. In the first group of patients, measurements were performed at depths of 2-8 cm from the probe surface. In the second group of patients, measurements were conducted 0-7 cm below the liver capsule. Success rate of measurements (SRoM), success rate of patients (SRoS) and coefficients of variation (CVs) of repeated measurements were compared. The SRoMs at 3-7 cm and the CVs at 2-5 cm from the probe surface were significantly higher and lower than those at other depths (p < 0.001), respectively. SRoS was zero 0-1 cm below the liver capsule. Furthermore, the features of 2-D stiffness mapping images were also found to change with depth. According to our results, the depth range for the most reliable liver stiffness assessment using SWE should be 3-5 cm from the probe surface and simultaneously 1-2 cm below the liver capsule.

Computer-assisted assessment of ultrasound real-time elastography: initial experience in 145 breast lesions.

PURPOSE: To develop and evaluate a computer-assisted method of quantifying five-point elasticity scoring system based on ultrasound real-time elastography (RTE), for classifying benign and malignant breast lesions, with pathologic results as the reference standard. MATERIALS AND METHODS: Conventional ultrasonography (US) and RTE images of 145 breast lesions (67 malignant, 78 benign) were performed in this study. Each lesion was automatically contoured on the B-mode image by the level set method and mapped on the RTE image. The relative elasticity value of each pixel was reconstructed and classified into hard or soft by the fuzzy c-means clustering method. According to the hardness degree inside lesion and its surrounding tissue, the elasticity score of the RTE image was computed in an automatic way. Visual assessments of the radiologists were used for comparing the diagnostic performance. Histopathologic examination was used as the reference standard. The Student's t test and receiver operating characteristic (ROC) curve analysis were performed for statistical analysis. RESULTS: Considering score 4 or higher as test positive for malignancy, the diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 93.8% (136/145), 92.5% (62/67), 94.9% (74/78), 93.9% (62/66), and 93.7% (74/79) for the computer-assisted scheme, and 89.7% (130/145), 85.1% (57/67), 93.6% (73/78), 92.0% (57/62), and 88.0% (73/83) for manual assessment. Area under ROC curve (Az value) for the proposed method was higher than the Az value for visual assessment (0.96 vs. 0.93). CONCLUSION: Computer-assisted quantification of classical five-point scoring system can significantly eliminate the interobserver variability and thereby improve the diagnostic confidence of classifying the breast lesions to avoid unnecessary biopsy.