1
40
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Text
A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text.
URL Address
<a href="http://doi.org/10.1111/jep.13042" target="_blank" rel="noreferrer noopener">http://doi.org/10.1111/jep.13042</a>
Pages
1293–1309
Issue
6
Volume
24
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
A name given to the resource
Exploring comorbid depression and physical health trajectories: A case-based computational modelling approach.
Publisher
An entity responsible for making the resource available
Journal of evaluation in clinical practice
Date
A point or period of time associated with an event in the lifecycle of the resource
2018
2018-12
Subject
The topic of the resource
artificial intelligence; case-based modelling; Child Abuse; cluster analysis; comorbid depression and physical health; Comorbidity; complexity theory; Computer Simulation; Depression – Therapy; differential equations; Health Status; Human; Intimate Partner Violence; longitudinal analysis; Models; nonlinear dynamics; primary care; Primary Health Care; Prospective Studies; Questionnaires; Research Personnel; Scales; Theoretical
Creator
An entity primarily responsible for making the resource
Castellani Brian; Griffiths Frances; Rajaram Rajeev; Gunn Jane
Description
An account of the resource
While comorbid depression/physical health is a major clinical concern, the conventional methods of medicine make it difficult to model the complexities of this relationship. Such challenges include cataloguing multiple trends, developing multiple complex aetiological explanations, and modelling the collective large-scale dynamics of these trends. Using a case-based complexity approach, this study engaged in a richly described case study to demonstrate the utility of computational modelling for primary care research. N = 259 people were subsampled from the Diamond database, one of the largest primary care depression cohort studies worldwide. A global measure of depressive symptoms (PHQ-9) and physical health (PCS-12) were assessed at 3, 6, 9, and 12 months and then annually for a total of 7 years. Eleven trajectories and 2 large-scale collective dynamics were identified, revealing that while depression is comorbid with poor physical health, chronic illness is often low dynamic and not always linked to depression. Also, some of the cases in the unhealthy and oscillator trends remain ill without much chance of improvement. Finally, childhood abuse, partner violence, and negative life events are greater amongst unhealthy trends. Computational modelling offers a major advance for health researchers to account for the diversity of primary care patients and for developing better prognostic models for team-based interdisciplinary care.
Identifier
An unambiguous reference to the resource within a given context
<a href="http://doi.org/10.1111/jep.13042" target="_blank" rel="noreferrer noopener">10.1111/jep.13042</a>
Rights
Information about rights held in and over the resource
Article information provided for research and reference use only. All rights are retained by the journal listed under publisher and/or the creator(s).
2018
Artificial Intelligence
case-based modelling
Castellani Brian
Child Abuse
Cluster Analysis
comorbid depression and physical health
Comorbidity
complexity theory
Computer Simulation
Depression – Therapy
differential equations
Griffiths Frances
Gunn Jane
Health Status
Human
Intimate Partner Violence
Journal of evaluation in clinical practice
longitudinal analysis
Models
Nonlinear Dynamics
primary care
Primary Health Care
Prospective Studies
Questionnaires
Rajaram Rajeev
Research Personnel
Scales
Theoretical
-
Text
A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text.
URL Address
<a href="http://doi.org/10.1111/jep.13042" target="_blank" rel="noreferrer noopener">http://doi.org/10.1111/jep.13042</a>
Rights
Article information provided for research and reference use only. All rights are retained by the journal listed under publisher and/or the creator(s).
Pages
1293-1309
Issue
6
Volume
24
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
A name given to the resource
Exploring comorbid depression and physical health trajectories: A case-based computational modelling approach.
Publisher
An entity responsible for making the resource available
Journal of evaluation in clinical practice
Date
A point or period of time associated with an event in the lifecycle of the resource
2018
2018-12
Subject
The topic of the resource
Adult; Female; Humans; Male; Middle Aged; Socioeconomic Factors; Aged; Chronic Disease; *Computer Simulation; Longitudinal Studies; Comorbidity; *Health Status; Artificial Intelligence; cluster analysis; Life Change Events; primary care; Artificial Intelligence; case-based modelling; comorbid depression and physical health; complexity theory; differential equations; longitudinal analysis; nonlinear dynamics; Systems Analysis; Adult Survivors of Child Abuse/statistics & numerical data; Depression/*epidemiology/*physiopathology; Health Services Research/*methods; Intimate Partner Violence/statistics & numerical data; Primary Health Care/organization & administration
Creator
An entity primarily responsible for making the resource
Castellani Brian; Griffiths Frances; Rajaram Rajeev; Gunn Jane
Description
An account of the resource
While comorbid depression/physical health is a major clinical concern, the conventional methods of medicine make it difficult to model the complexities of this relationship. Such challenges include cataloguing multiple trends, developing multiple complex aetiological explanations, and modelling the collective large-scale dynamics of these trends. Using a case-based complexity approach, this study engaged in a richly described case study to demonstrate the utility of computational modelling for primary care research. N = 259 people were subsampled from the Diamond database, one of the largest primary care depression cohort studies worldwide. A global measure of depressive symptoms (PHQ-9) and physical health (PCS-12) were assessed at 3, 6, 9, and 12 months and then annually for a total of 7 years. Eleven trajectories and 2 large-scale collective dynamics were identified, revealing that while depression is comorbid with poor physical health, chronic illness is often low dynamic and not always linked to depression. Also, some of the cases in the unhealthy and oscillator trends remain ill without much chance of improvement. Finally, childhood abuse, partner violence, and negative life events are greater amongst unhealthy trends. Computational modelling offers a major advance for health researchers to account for the diversity of primary care patients and for developing better prognostic models for team-based interdisciplinary care.
Identifier
An unambiguous reference to the resource within a given context
<a href="http://doi.org/10.1111/jep.13042" target="_blank" rel="noreferrer noopener">10.1111/jep.13042</a>
*Computer Simulation
*Health Status
2018
Adult
Adult Survivors of Child Abuse/statistics & numerical data
Aged
Artificial Intelligence
case-based modelling
Castellani Brian
Chronic Disease
Cluster Analysis
comorbid depression and physical health
Comorbidity
complexity theory
Depression/*epidemiology/*physiopathology
differential equations
Female
Griffiths Frances
Gunn Jane
Health Services Research/*methods
Humans
Intimate Partner Violence/statistics & numerical data
Journal of evaluation in clinical practice
Life Change Events
longitudinal analysis
Longitudinal Studies
Male
Middle Aged
Nonlinear Dynamics
primary care
Primary Health Care/organization & administration
Rajaram Rajeev
Socioeconomic Factors
Systems Analysis