Background: Hidradenitis suppurativa (HS) is a neglected chronic inflammatory disease with long delay in diagnosis. Besides pain, purulent discharge, and destruction of skin architecture, HS patients experience metabolic, musculoskeletal, and psychological disorders. Objectives: To determine the delay in HS diagnosis and its consequences for patients and the healthcare system. Methods: This was a prospective, multicenter, epidemiologic, non-interventional cross-sectional trial carried out in Germany and based on self-reported questionnaires and medical examinations performed by dermatologists. In total, data of 394 adult HS patients were evaluated. Results: The average duration from manifestation of first symptoms until HS diagnosis was 10.0 ± 9.6 (mean ± SD) years. During this time, HS patients consulted on average more than 3 different physicians-most frequently general practitioners, dermatologists, surgeons, gynecologists-and faced more than 3 misdiagnoses. Diagnosis delay was longer in younger and non-smoking patients. In most cases, HS was correctly diagnosed by dermatologists. The longer the delay of diagnosis, the greater the disease severity at diagnosis. Delayed HS diagnosis was also associated with an increased number of surgically treated sites, concomitant diseases, and days of work missed. Conclusion: This study demonstrates an enormous delay in the diagnosis of HS, which results in more severe disease. It also shows for the first time that a delay in diagnosis of a chronic inflammatory disease leads to a higher number of concomitant systemic disorders. In addition to the impaired health status, delayed diagnosis of HS was associated with impairment of the professional life of affected people.
BackgroundPreterm infants are at high risk of developing respiratory syncytial virus (RSV)-associated lower respiratory tract infection (LRTI). This observational epidemiologic study evaluated RSV disease burden and risk factors for RSV-associated LRTI hospitalization in preterm infants 33 weeks+0 days to 35 weeks+6 days gestational age not receiving RSV prophylaxis.MethodsPreterm infants ≤6 months of age during RSV season (1 October 2013–30 April 2014) were followed at 72 sites across 23 countries from September 2013–July 2014 (study period). RSV testing was performed according to local clinical practice. Factors related to RSV-associated hospitalization for LRTI were identified using multivariable logistic regression with backward selection.ResultsOf the 2390 evaluable infants, 204 and 127 were hospitalized for LRTI during the study period and RSV season, respectively. Among these subjects, 64/204 and 46/127, respectively, were hospitalized for confirmed RSV LRTI. Study period and RSV season normalized RSV hospitalization rates (per 100 infant years) were 4.1 and 6.1, respectively. Factors associated with an increased risk of RSV-related LRTI hospitalization in multivariable analyses were smoking of family members (P<0.0001), non-hemodynamically significant congenital heart disease diagnosis (P = 0.0077), maternal age of ≤25 years at delivery (P = 0.0009), low maternal educational level (P = 0.0426), household presence of children aged 4 to 5 years (P = 0.0038), age on 1 October ≤3 months (P = 0.0422), and presence of paternal atopy (P<0.0001).ConclusionsDuring the 2013–2014 RSV season across 23 countries, for preterm infants 33–35 weeks gestation ≤6 months old on 1 October not receiving RSV prophylaxis, confirmed RSV LRTI hospitalization incidence was 4.1 per 100 infant years during the study period and 6.1 per 100 infant years during the RSV season. This study enhances the findings of single-country studies of common risk factors for severe RSV infection in preterm infants and suggests that combinations of 4–6 risk factors may be used to accurately predict risk of RSV hospitalization. These findings may be useful in the identification of infants most at risk of severe RSV infection.
Functional magnetic resonance imaging (fMRI) is the most popular technique in human brain mapping, with statistical parametric mapping (SPM) as a classical benchmark tool for detecting brain activity. Smith and Fahrmeir (J Am Stat Assoc 102(478): 417-431, 2007) proposed a competing method based on a spatial Bayesian variable selection in voxelwise linear regressions, with an Ising prior for latent activation indicators. In this article, we alternatively link activation probabilities to two types of latent Gaussian Markov random fields (GMRFs) via a probit model. Statistical inference in resulting high-dimensional hierarchical models is based on Markov chain Monte Carlo approaches, providing posterior estimates of activation probabilities and enhancing formation of activation clusters. Three algorithms are proposed depending on GMRF type and update scheme. An application to an active acoustic oddball experiment and a simulation study show a substantial increase in sensitivity compared to existing fMRI activation detection methods like classical SPM and the Ising model
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