Predicting mental health improvement and deterioration in a large community sample of 11- to 13-year-olds

Multivariate analysis of psychological dat

Predicting mental health improvement and deterioration in a large community sample of 11- to 13-year-olds.

Of children with mental health problems who access specialist help, 50% show reliable improvement on self-report measures at case closure and 10% reliable deterioration. To contextualise these figures it is necessary to consider rates of improvement for those in the general population. This study examined rates of reliable improvement/deterioration for children in a school sample over time. N = 9074 children (mean age 12; 52% female; 79% white) from 118 secondary schools across England provided self-report mental health (SDQ), quality of life and demographic data (age, ethnicity and free school meals (FSM) at baseline and 1 year and self-report data on access to mental health support at 1 year). Multinomial logistic regressions and classification trees were used to analyse the data. Of 2270 (25%) scoring above threshold for mental health problems at outset, 27% reliably improved and 9% reliably deteriorated at 1-year follow up. Of 6804 (75%) scoring below threshold, 4% reliably improved and 12% reliably deteriorated. Greater emotional difficulties at outset were associated with greater rates of reliable improvement for both groups (above threshold group: OR = 1.89, p < 0.001, 95% CI [1.64, 2.17], below threshold group: OR = 2.23, p < 0.001, 95% CI [1.93, 2.57]). For those above threshold, higher baseline quality of life was associated with greater likelihood of reliable improvement (OR = 1.28, p < 0.001, 95% CI [1.13, 1.46]), whilst being in receipt of FSM was associated with reduced likelihood of reliable improvement (OR = 0.68, p < 0.01, 95% CI [0.53, 0.88]). For the group below threshold, being female was associated with increased likelihood of reliable deterioration (OR = 1.20, p < 0.025, 95% CI [1.00, 1.42]), whereas being from a non-white ethnic background was associated with decreased likelihood of reliable deterioration (OR = 0.66, p < 0.001, 95% CI [0.54, 0.80]). For those above threshold, almost one in three children showed reliable improvement at 1 year. The extent of emotional difficulties at outset showed the highest associations with rates of reliable improvement

3-D Local Radon Power Spectra for Seismic Attribute Extraction

Conference paperIn this paper we discuss a method for volume attribute extraction that is based on a new type of local Radon power spectrum. The new algorithm results in robust and geologically meaningful volume attributes, such as volume dip and azimuth. Seismic volume attribute analysis greatly facilitates the interpretation of large 3-D seismic data volumes. However, horizon attribute maps are generally more easy to interpret than volume attribute images, which are usually time slices or cross-sections. We show that, for dip estimation, the volume attribute image is very similar to the horizon dip map

3-D Local Radon power Spectra for seismic Attribute Extraction

In this paper we discuss a method for volume attribute extraction that is based on a new type of local Radon power spectrum. The new algorithm results in robust and geologically meaningful volume attributes, such as volume dip and azimuth. Seismic volume attribute analysis greatly facilitates the interpretation of large 3-D seismic data volumes. However, horizon attribute maps are generally more easy to interpret than volume attribute images, which are usually time slices or crosssections. We show that, for dip estimation, the volume attribute image is very similar to the horizon dip map

Decomposition of seismic signals via time-frequency representations

Conference PaperIn this paper we discuss the use of a time-frequency representation, the Wigner distribution, for the decomposition and characterization of seismic signals. The advantage of the Wigner distribution over other representations, such as the wavelet and sliding window Fourier transform, is its sharp localization properties in the time-frequency plane. However, the Wigner distribution is a not a linear transformation. This non-linearity complicates the use of the Wigner distribution for time-frequency filtering and decomposition. We present an optimization method for the reconstruction of a time signal from its Wigner distribution. The reconstruction technique enables a decomposition of a signal into its time-frequency components, where the reconstructed components are stripped off from the signal one by one. The method is illustrated a real data example. We also demonstrate how the decomposition can be used for suppression and enhancement of events in the time-frequency plane