What stops children with a chronic illness accessing health care: A mixed methods study in children with Chronic Fatigue Syndrome/Myalgic Encephalomyelitis (CFS/ME)

Background: Paediatric Chronic Fatigue Syndrome/Myalgic Encephalomyelitis (CFS/ME) is relatively common and disabling with a mean time out of school of more than one academic year. NICE guidelines recommend referral to specialist services immediately if severely affected, within 3 months if moderately affected and within 6 months if mildly affected. However, the median time-to-assessment by a specialist service in the UK is 18 months. This study used a mixed-methods approach to examine factors associated with time taken to access specialist services. Methods. Time-to-assessment was analysed as a continuous "survival-time" variable in Cox regression models using data from self-completed assessment forms for children attending a regional specialist CFS/ME service between January 2006 and December 2009. Semi-structured interviews about barriers experienced in accessing healthcare for their child were conducted with nine parents of children aged < 17 years (8 individual and one parent couple). Interviews were digitally recorded and analysed using "thematic analysis". Results: 405 children were assessed between 2006 and 2009 and information on school attendance was available on 388. Only 1/125 with severe CFS/ME and 49/263 (19%) with mild to moderate CFS/ME were seen within NICE recommended timeframe. Increased fatigue was associated with shorter time to assessment (HR = 1.15; 95% CI 1.03, 1.29 per unit increase in Chalder fatigue score; P = 0.01). Time-to-assessment was not associated with disability, mood, age or gender. Parents described difficulties accessing specialist services because of their own as well as their GP's and Paediatrician's lack of knowledge. They experienced negative attitudes and beliefs towards the child's condition when they consulted GPs, Paediatricians and Child Psychiatrists. Parents struggled to communicate an invisible illness that their child and not themselves were experiencing. Conclusions: GPs, Child Psychiatrists and Paediatricians need more knowledge about CFS/ME and the appropriate referral pathways to ensure timeliness in referral to specialist services. © 2011 Webb et al; licensee BioMed Central Ltd

The NANOGrav 15 yr Data Set: Search for Transverse Polarization Modes in the Gravitational-wave Background

\ua9 2024. The Author(s). Published by the American Astronomical Society.Recently we found compelling evidence for a gravitational-wave background with Hellings and Downs (HD) correlations in our 15 yr data set. These correlations describe gravitational waves as predicted by general relativity, which has two transverse polarization modes. However, more general metric theories of gravity can have additional polarization modes, which produce different interpulsar correlations. In this work, we search the NANOGrav 15 yr data set for evidence of a gravitational-wave background with quadrupolar HD and scalar-transverse (ST) correlations. We find that HD correlations are the best fit to the data and no significant evidence in favor of ST correlations. While Bayes factors show strong evidence for a correlated signal, the data does not strongly prefer either correlation signature, with Bayes factors ∼2 when comparing HD to ST correlations, and ∼1 for HD plus ST correlations to HD correlations alone. However, when modeled alongside HD correlations, the amplitude and spectral index posteriors for ST correlations are uninformative, with the HD process accounting for the vast majority of the total signal. Using the optimal statistic, a frequentist technique that focuses on the pulsar-pair cross-correlations, we find median signal-to-noise ratios of 5.0 for HD and 4.6 for ST correlations when fit for separately, and median signal-to-noise ratios of 3.5 for HD and 3.0 for ST correlations when fit for simultaneously. While the signal-to-noise ratios for each of the correlations are comparable, the estimated amplitude and spectral index for HD are a significantly better fit to the total signal, in agreement with our Bayesian analysis