A Bayesian Model of Sample Selection with a Discrete Outcome Variable: Detecting Depression in Older Adults

Depression as a major mental illness among older adults has attracted a lot of research attention. However, the problem of sample selection, inevitable in most health surveys, has been largely ignored. To fill in this gap, this paper formally models selection into the sample jointly with a discrete outcome variable for depression. A Bayesian model of sample selection is developed from a multivariate probit by (i) allowing missing depression status for nonselected respondents, and (ii) using Cholesky factorization of the inverse variance matrix to avoid a Metropolis-Hastings step in the Gibbs sampler. Non-selected respondents are less likely to suffer from depression.Multivariate probit model; Sample selection; Bayesian methods; Gibbs sampler

Association between a longer duration of illness, age and lower frontal lobe grey matter volume in schizophrenia

The frontal lobe has an extended maturation period and may be vulnerable to the long-term effects of schizophrenia. We tested this hypothesis by studying the relationship between duration of illness (DoI), grey matter (GM) and cerebro-spinal fluid (CSF) volume across the whole brain. Sixty-four patients with schizophrenia and 25 healthy controls underwent structural MRI scanning and neuropsychological assessment. We performed regression analyses in patients to examine the relationship between DoI and GM and CSF volumes across the whole brain, and correlations in controls between age and GM or CSF volume of the regions where GM or CSF volumes were associated with DoI in patients. Correlations were also performed between GM volume in the regions associated with DoI and neuropsychological performance. A longer DoI was associated with lower GM volume in the left dorsomedial prefrontal cortex (PFC), right middle frontal cortex, left fusiform gyrus (FG) and left cerebellum (lobule III). Additionally, age was inversely associated with GM volume in the left dorsomedial PFC in patients, and in the left FG and CSF excess near the left cerebellum in healthy controls. Greater GM volume in the left dorsomedial PFC was associated with better working memory, attention and psychomotor speed in patients. Our findings suggest that the right middle frontal cortex is particularly vulnerable to the long-term effect of schizophrenia illness whereas the dorsomedial PFC, FG and cerebellum are affected by both a long DoI and aging. The effect of illness chronicity on GM volume in the left dorsomedial PFC may be extended to brain structure–neuropsychological function relationships

Applicants\u27 Experience of Social Support in the Process of Seeking Psychotherapy

Examined informal social support during the process of seeking psychotherapy, conceptualized as four steps: realizing there is a problem; deciding therapy might help; deciding to seek therapy; and contacting the clinic. 315 psychotherapy applicants (aged 18–62 yrs) completed a questionnaire asking them whether they had talked to anyone about the problem prior to seeking therapy, whether they had help at any of the steps, and from whom they had obtained such help. Results indicated that social support was important across the process. Almost three-fourths of all Ss had help with at least 2 of the 4 steps, and almost one-third had help at all 4. Younger applicants had more help. Males more frequently had help from a spouse or romantic partner than females. Medical professionals were used primarily for referral to the clinic, rather than as sources of help at earlier steps

Economic inequalities in burden of illness, diagnosis and treatment of five long-term conditions in England: panel study

We compared the distribution by wealth of self-reported illness burden (estimated from validated scales, biomarker and reported symptoms) for angina, cataract, depression, diabetes and osteoarthritis, with the distribution of self-reported medical diagnosis and treatment. We aimed to determine if the greater illness burden borne by poorer participants was matched by appropriately higher levels of diagnosis and treatment

A Sensitivity Matrix Methodology for Inverse Problem Formulation

We propose an algorithm to select parameter subset combinations that can be estimated using an ordinary least-squares (OLS) inverse problem formulation with a given data set. First, the algorithm selects the parameter combinations that correspond to sensitivity matrices with full rank. Second, the algorithm involves uncertainty quantification by using the inverse of the Fisher Information Matrix. Nominal values of parameters are used to construct synthetic data sets, and explore the effects of removing certain parameters from those to be estimated using OLS procedures. We quantify these effects in a score for a vector parameter defined using the norm of the vector of standard errors for components of estimates divided by the estimates. In some cases the method leads to reduction of the standard error for a parameter to less than 1% of the estimate