Dataset Information

Estimating the changing nature of Scotland's health inequalities by using a multivariate spatiotemporal model.

ABSTRACT: Health inequalities are the unfair and avoidable differences in people's health between different social groups. These inequalities have a huge influence on people's lives, particularly those who live at the poorer end of the socio-economic spectrum, as they result in prolonged ill health and shorter lives. Most studies estimate health inequalities for a single disease, but this will give an incomplete picture of the overall inequality in population health. Here we propose a novel multivariate spatiotemporal model for quantifying health inequalities in Scotland across multiple diseases, which will enable us to understand better how these inequalities vary across disease and have changed over time. In developing this model we are interested in estimating health inequalities between Scotland's 14 regional health boards, who are responsible for the protection and improvement of their population's health. The methodology is applied to hospital admissions data for cerebrovascular disease, coronary heart disease and respiratory disease, which are three of the leading causes of death, from 2003 to 2012 across Scotland.

SUBMITTER: Jack E

PROVIDER: S-EPMC6563432 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Estimating the changing nature of Scotland's health inequalities by using a multivariate spatiotemporal model.

Jack Eilidh E Lee Duncan D Dean Nema N

Journal of the Royal Statistical Society. Series A, (Statistics in Society) 20190409 3

Health inequalities are the unfair and avoidable differences in people's health between different social groups. These inequalities have a huge influence on people's lives, particularly those who live at the poorer end of the socio-economic spectrum, as they result in prolonged ill health and shorter lives. Most studies estimate health inequalities for a single disease, but this will give an incomplete picture of the overall inequality in population health. Here we propose a novel multivariate s ...[more]

PMID: 31217673

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Project description:SEE STEPHAN ET AL DOI101093/AWW120 FOR A SCIENTIFIC COMMENTARY ON THIS WORK: Real world information is often abstract, dynamic and imprecise. Deciding if changes represent random fluctuations, or alterations in underlying contexts involve challenging probability estimations. Dysfunction may contribute to erroneous beliefs, such as delusions. Here we examined brain function during inferences about context change from noisy information. We examined cortical-subcortical circuitry engaging anterior and dorsolateral prefrontal cortex, and midbrain. We hypothesized that schizophrenia-related deficits in prefrontal function might overestimate context change probabilities, and that this more chaotic worldview may subsequently gain familiarity and be over-reinforced, with implications for delusions. We then examined these opposing information processing biases against less expected versus familiar information patterns in relation to genetic risk for schizophrenia in unaffected siblings. In one experiment, 17 patients with schizophrenia and 24 normal control subjects were presented in 3 T magnetic resonance imaging with numerical information varying noisily about a context integer, which occasionally shifted up or down. Subjects were to indicate when the inferred numerical context had changed. We fitted Bayesian models to estimate probabilities associated with change inferences. Dynamic causal models examined cortical-subcortical circuitry interactions at context change inference, and at subsequent reduced uncertainty. In a second experiment, genetic risk for schizophrenia associated with similar cortical-subcortical findings were explored in an independent sample of 36 normal control subjects and 35 unaffected siblings during processing of intuitive number sequences along the number line, or during the inverse, less familiar, sequence. In the first experiment, reduced Bayesian models fitting subject behaviour suggest that patients with schizophrenia overestimated context change probabilities. Here, patients engaged anterior prefrontal cortex relatively less than healthy controls, in part driven by reduced effective connectivity from dorsolateral prefrontal cortex to anterior prefrontal cortex. In processing subsequent information indicating reduced uncertainty of their predictions, patients engaged relatively increased mid-brain activation, driven in part by increased dorsolateral prefrontal cortex to midbrain connectivity. These dissociable reduced and exaggerated prefrontal and subcortical circuit functions were accentuated in patients with delusions. In the second experiment, analogous dissociable reduced anterior prefrontal cortex and exaggerated midbrain engagement occurred in unaffected siblings when processing less expected versus more familiar number sequences. In conclusion, patients overestimated ambiguous context change probabilities with relatively reduced anterior frontal engagement. Subsequent reduced uncertainty about contextual state appeared over-reinforced, potentially contributing to confirmation bias and a cascade of aberrant belief processing about a more chaotic world relevant to delusions. These opposing cortical-subcortical effects relate in part to genetic risk for schizophrenia, with analogous imbalances in neural processing of less expected versus familiar information patterns.

Project description:PurposeGeneration Scotland (GS) is a large family-based cohort study established as a longitudinal resource for research into the genetic, lifestyle and environmental determinants of physical and mental health. It comprises extensive genetic, sociodemographic and clinical data from volunteers in Scotland.ParticipantsA total of 24 084 adult participants, including 5501 families, were recruited between 2006 and 2011. Within the cohort, 59% (approximately 14 209) are women, with an average age at recruitment of 49 years. Participants completed a health questionnaire and attended an in-person clinic visit, where detailed baseline data were collected on lifestyle information, cognitive function, personality traits and mental and physical health. Genotype array data are available for 20 026 (83%) participants, and blood-based DNA methylation (DNAm) data for 18 869 (78%) participants. Linkage to routine National Health Service datasets has been possible for 93% (n=22 402) of the cohort, creating a longitudinal resource that includes primary care, hospital attendance, prescription and mortality records. Multimodal brain imaging is available in 1069 individuals.Findings to dateGS has been widely used by researchers across the world to study the genetic and environmental basis of common complex diseases. Over 350 peer-reviewed papers have been published using GS data, contributing to research areas such as ageing, cancer, cardiovascular disease and mental health. Recontact studies have built on the GS cohort to collect additional prospective data to study chronic pain, major depressive disorder and COVID-19.Future plansTo create a larger, richer, longitudinal resource, 'Next Generation Scotland' launched in May 2022 to expand the existing cohort by a target of 20 000 additional volunteers, now including anyone aged 12+ years. New participants complete online consent and questionnaires and provide postal saliva samples, from which genotype and salivary DNAm array data will be generated. The latest cohort information and how to access data can be found on the GS website (www.generationscotland.org).

Dataset Information

Estimating the changing nature of Scotland's health inequalities by using a multivariate spatiotemporal model.

Publications

Estimating the changing nature of Scotland's health inequalities by using a multivariate spatiotemporal model.

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