Exploration of health dimensions to be included in multi-attribute health-utility assessment

Objective Measurement of health utility is important for quality improvement, but instruments vary in their content. Multi-attribute health utility measures typically assess a small number of health problems, e.g. the EuroQoL EQ-5D questionnaire explores five dimensions of health. We aimed to examine whether a small number of dimensions explains a sufficient amount of variance in self-perceived health, and what can be gained from adding additional dimensions. Design Cross-sectional mail survey that explored health utility and self-perceived health. Setting General resident population of French-speaking Switzerland. Participants Non-institutionalized adults. Main outcome measures EQ-5D (which measures mobility, self-care, usual activities, pain/discomfort, anxiety/depression and a visual analogue health scale between 0 and 100 (VAS)). A subsample rated five additional health dimensions (sleep, memory/concentration, energy/fatigue, sight/hearing, contacts with others). Results In total, 349 adults returned the extended 10-item questionnaire. All added items were strongly and significantly associated with the VAS for perceived health. The proportion of variance explained (R2) in the VAS was 0.47 for the original EQ-5D items (adjusted for attenuation: 0.65), 0.47 for the new items (adjusted for attenuation: 0.65) and 0.56 for the 10 items together (adjusted for attenuation: 0.78). Forty-four percent of the respondents who had a perfect health utility on the EQ-5D reported at least one problem in the new health dimensions. Conclusion Self-perceived health among the general public is influenced by more health dimensions than are typically measured in a multi-attribute health-utility instrumen

Does Prevalence Matter to Physicians in Estimating Post-test Probability of Disease? A Randomized Trial

ABSTRACT: BACKGROUND: The probability of a disease following a diagnostic test depends on the sensitivity and specificity of the test, but also on the prevalence of the disease in the population of interest (or pre-test probability). How physicians use this information is not well known. OBJECTIVE: To assess whether physicians correctly estimate post-test probability according to various levels of prevalence and explore this skill across respondent groups. DESIGN: Randomized trial. PARTICIPANTS: Population-based sample of 1,361 physicians of all clinical specialties. INTERVENTION: We described a scenario of a highly accurate screening test (sensitivity 99% and specificity 99%) in which we randomly manipulated the prevalence of the disease (1%, 2%, 10%, 25%, 95%, or no information). MAIN MEASURES: We asked physicians to estimate the probability of disease following a positive test (categorized as 99.9%). Each answer was correct for a different version of the scenario, and no answer was possible in the "no information” scenario. We estimated the proportion of physicians proficient in assessing post-test probability as the proportion of correct answers beyond the distribution of answers attributable to guessing. KEY RESULTS: Most respondents in each of the six groups (67%-82%) selected a post-test probability of 95-99.9%, regardless of the prevalence of disease and even when no information on prevalence was provided. This answer was correct only for a prevalence of 25%. We estimated that 9.1% (95% CI 6.0-14.0) of respondents knew how to assess correctly the post-test probability. This proportion did not vary with clinical experience or practice setting. CONCLUSIONS: Most physicians do not take into account the prevalence of disease when interpreting a positive test result. This may cause unnecessary testing and diagnostic error

Self-rated health: analysis of distances and transitions between response options

Purpose: We explored health differences between population groups who describe their health as excellent, very good, good, fair, or poor. Methods: We used data from a population-based survey which included self-rated health (SRH) and three global measures of health: the SF36 general health score (computed from the 4 items other than SRH), the EQ-5D health utility, and a visual analogue health thermometer. We compared health characteristics of respondents across the five health ratings. Results: Survey respondents (N=1.844, 49.2% response) rated their health as excellent (12.2%), very good (39.1%), good (41.9%), fair (6.0%), or poor (0.9%). The means of global health assessments were not equidistant across these five groups, for example, means of the health thermometer were 95.8 (SRH excellent), 88.8 (SRH very good), 76.6 (SRH good), 49.7 (SRH fair), and 33.5 (SRH poor, p<0.001). Recoding the SRH to reflect these mean values substantially improved the variance explained by the SRH, for example, the linear r 2 increased from 0.50 to 0.56 for the health thermometer if the SRH was coded as poor=1, fair=2, good=3.7, very good=4.5, and excellent=5. Furthermore, transitions between response options were not explained by the same health-related characteristics of the respondents. Conclusions: The adjectival SRH is not an evenly spaced interval scale. However, it can be turned into an interval variable if the ratings are recoded in proportion to the underlying construct of health. Possible improvements include the addition of a rating option between good and fair or the use of a numerical scale instead of the classic adjectival scal

Self-rated health: analysis of distances and transitions between response options

Purpose: We explored health differences between population groups who describe their health as excellent, very good, good, fair, or poor. Methods: We used data from a population-based survey which included self-rated health (SRH) and three global measures of health: the SF36 general health score (computed from the 4 items other than SRH), the EQ-5D health utility, and a visual analogue health thermometer. We compared health characteristics of respondents across the five health ratings. Results: Survey respondents (N=1.844, 49.2% response) rated their health as excellent (12.2%), very good (39.1%), good (41.9%), fair (6.0%), or poor (0.9%). The means of global health assessments were not equidistant across these five groups, for example, means of the health thermometer were 95.8 (SRH excellent), 88.8 (SRH very good), 76.6 (SRH good), 49.7 (SRH fair), and 33.5 (SRH poor, p<0.001). Recoding the SRH to reflect these mean values substantially improved the variance explained by the SRH, for example, the linear r 2 increased from 0.50 to 0.56 for the health thermometer if the SRH was coded as poor=1, fair=2, good=3.7, very good=4.5, and excellent=5. Furthermore, transitions between response options were not explained by the same health-related characteristics of the respondents. Conclusions: The adjectival SRH is not an evenly spaced interval scale. However, it can be turned into an interval variable if the ratings are recoded in proportion to the underlying construct of health. Possible improvements include the addition of a rating option between good and fair or the use of a numerical scale instead of the classic adjectival scal

Accuracy evaluation of CAD/CAM generated splints in orthognathic surgery: a cadaveric study

Introduction To evaluate the accuracy of CAD/CAM generated splints in orthognathic surgery by comparing planned versus actual post-operative 3D images. Methods Specific planning software (SimPlant® OMS Standalone 14.0) was used to perform a 3D virtual Le Fort I osteotomy in 10 fresh human cadaver heads. Stereolithographic splints were then generated and used during the surgical procedure to reposition the maxilla according to the planned position. Pre-operative planned and postoperative 3D CT scan images were fused and imported to dedicated software (MATLAB®) 7.11.) for calculating the translational and rotational (pitch, roll and yaw) differences between the two 3D images. Geometrical accuracy was estimated using the Root Mean Square Deviations (RMSD) and lower and upper limits of accuracy were computed using the Bland & Altman method, with 95 % confidence intervals around the limits. The accuracy cutoff was set at +/− 2 mm for translational and ≤ 4° for rotational measurements. Results Overall accuracy between the two 3D images was within the accuracy cutoff for all values except for the antero-posterior positioning of the maxilla (2.17 mm). The translational and rotational differences due to the splint were all within the accuracy cutoff. However, the width of the limits of agreement (range between lower and upper limits) showed that rotational differences could be particularly large. Conclusion This study demonstrated that maxillary repositioning can be accurately approximated and thus predicted by specific computational planning and CAD/CAM generated splints in orthognathic surgery. Further study should focus on the risk factors for inaccurate prediction

Experiences with a new biplanar low-dose X-ray device for imaging the facial skeleton: A feasibility study

Methods We evaluated 48 biplanar radiographs from 12 patients (posteroanterior/lateral), originally taken for a scoliosis examination with a biplanar low-dose X-ray device. For this study, the images were further evaluated for the perceptibility of 38 facial skeleton landmarks. To determine the reliability and reproducibility of perceptibility, two independent observers determined the landmarks twice, during a time interval of at least two weeks. Results Both interoperator and intraoperator reliability were excellent for all landmarks [intraclass correlation coefficient (ICC) > 0.92]. Conclusions The biplanar low-dose X-ray device demonstrated good feasibility for precisely assessing the anatomical landmarks of the facial skeleton. Given its excellent precision, the biplanar low-dose X-ray device data sets should be forwarded from the treating orthopedic surgeon or neurosurgeon to the orthodontist or dentist for further assessment in their field.For this study, no author has received any funding. During the time this retrospective study took place, the institute/laboratory in which Prof. P. Rouch works and Dr. A. Laville worked received funding from the EOS-Imaging company for other EOS studies. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Management of giant-cell arteritis in Switzerland: an online national survey.

AIMS OF THE STUDY To assess current practices in diagnosing, treating, and following-up giant-cell arteritis by specialists in Switzerland and to identify the main barriers to using diagnostic tools. METHODS We performed a national survey of specialists potentially caring for patients with giant-cell arteritis. The survey was sent by email to all members of the Swiss Societies of Rheumatology and for Allergy and Immunology. A reminder was sent to nonresponders after 4 and 12 weeks. Its questions covered the following dimensions: respondents' main characteristics, diagnosis, treatment, and imaging's role during follow-up. The main study results were summarized using descriptive statistics. RESULTS Ninety-one specialists, primarily aged 46-65 years (n = 53/89; 59%), working in academic or nonacademic hospitals or private practice, and treating a median of 7.5 (interquartile range [IQR]: 3-12) patients with giant-cell arteritis per year participated in this survey. Ultrasound of temporal arteries/large vessels (n = 75/90; 83%) and positron-emission-tomography-computed tomography (n = 52/91; 57%) or magnetic resonance imaging (n = 46/90; 51%) of the aorta/extracranial arteries were the most common techniques used to diagnose giant-cell arteritis with cranial or large vessel involvement, respectively. Most participants reported a short time to obtain imaging tests or arterial biopsy. The glucocorticoid tapering scheme, glucocorticoid-sparing agent, and glucocorticoid-sparing treatment duration varied among the participants. Most physicians did not follow a predefined repeat imaging scheme for follow-up and mainly relied on structural changes (vascular thickening, stenosis, or dilatation) to drive treatment choice. CONCLUSIONS This survey indicates that imaging and temporal biopsy are rapidly accessible for diagnosing giant-cell arteritis in Switzerland but highlights heterogeneous practice in many disease management areas

What differences are detected by superiority trials or ruled out by noninferiority trials? A cross-sectional study on a random sample of two-hundred two-arms parallel group randomized clinical trials

BACKGROUND: The smallest difference to be detected in superiority trials or the largest difference to be ruled out in noninferiority trials is a key determinant of sample size, but little guidance exists to help researchers in their choice. The objectives were to examine the distribution of differences that researchers aim to detect in clinical trials and to verify that those differences are smaller in noninferiority compared to superiority trials. METHODS: Cross-sectional study based on a random sample of two hundred two-arm, parallel group superiority (100) and noninferiority (100) randomized clinical trials published between 2004 and 2009 in 27 leading medical journals. The main outcome measure was the smallest difference in favor of the new treatment to be detected (superiority trials) or largest unfavorable difference to be ruled out (noninferiority trials) used for sample size computation, expressed as standardized difference in proportions, or standardized difference in means. Student t test and analysis of variance were used. RESULTS: The differences to be detected or ruled out varied considerably from one study to the next; e.g., for superiority trials, the standardized difference in means ranged from 0.007 to 0.87, and the standardized difference in proportions from 0.04 to 1.56. On average, superiority trials were designed to detect larger differences than noninferiority trials (standardized difference in proportions: mean 0.37 versus 0.27, P = 0.001; standardized difference in means: 0.56 versus 0.40, P = 0.006). Standardized differences were lower for mortality than for other outcomes, and lower in cardiovascular trials than in other research areas. CONCLUSIONS: Superiority trials are designed to detect larger differences than noninferiority trials are designed to rule out. The variability between studies is considerable and is partly explained by the type of outcome and the medical context. A more explicit and rational approach to choosing the difference to be detected or to be ruled out in clinical trials may be desirable