Accessible Modelling of Complexity in Health (AMoCH) and associated data flows: asthma as an exemplar

Liyanage, Harshana; Luzi, Daniela; Lusignan, Simon de; Pecoraro, Fabrizio; McNulty, Richard; Tamburis, Oscar; Krause, Paul; Rigby, Michael; Blair, Mitch

doi:10.14236/jhi.v23i1.863

Cited by 12 publications

(11 citation statements)

References 14 publications

(10 reference statements)

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“…The study outcome measure was a new diagnosis of asthma during the follow‐up period. A patient was classified as an asthma “case” if they had an asthma diagnostic code recorded (eg, atopic asthma) or if they had a process of care code recorded (eg, “asthma medication review”) . A full list of the Read codes used to detect cases is included in the Supplementary Materials.…”

Section: Methodsmentioning

confidence: 99%

“…A patient was classified as an asthma "case" if they had an asthma diagnostic code recorded (eg, atopic asthma) or if they had a process of care code recorded (eg, "asthma medication review"). 16 A full list of the Read codes used to detect cases is included in the Supplementary Materials. Additional data collected included: age, gender, BMI, socioeconomic status as measured using IMD score (the official national measure of social deprivation), baseline smoking status and comorbidities (angina, previous stroke or transient ischaemic attack, previous myocardial infarction and hypertension).…”

Section: Study Population and Definition Of Variablesmentioning

confidence: 99%

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The impact of therapy on the risk of asthma in type 2 diabetes

Rayner

McGovern

Sherlock

et al. 2019

Clinical Respiratory J

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Background and Objectives There are limited data about the risk of asthma in people with diabetes. We examined the incidence of asthma in subjects with type 2 diabetes (T2DM) compared to controls, and the association with metformin, sulphonylureas and insulin therapy. Materials and Methods We conducted a retrospective cohort study using a representative UK primary care database (N = 894 646 adults). We used 1:1 propensity score matching (age, gender, socio‐economic deprivation, body mass index and smoking status) to match 29 217 pairs of T2DM cases and controls. We used Cox proportional hazard regression to compare the incidence of asthma in both groups over 8 years of follow‐up. In those with T2DM, we used Cox proportional hazard regression to assess for any impact of antidiabetic medications on asthma incidence. Results Individuals with T2DM were less likely to develop asthma than matched controls (hazard ratio [HR] 0.85, 95% CI 0.77‐0.93). Insulin increased the risk of incident asthma (HR 1.25, 95% CI 1.01‐1.56), whilst metformin and sulphonylureas were associated with reduced risk (HR 0.80, 95% CI 0.69‐0.93 and HR 0.76, 95% CI 0.60‐0.97, respectively). There was no association with diabetes duration, complications or glycaemic control. Conclusions T2DM may have a protective effect against asthma development. Insulin use was associated with an increased risk of asthma, while metformin and sulphonylureas reduced the risk in those with T2DM.

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Section: Methodsmentioning

confidence: 99%

Section: Study Population and Definition Of Variablesmentioning

confidence: 99%

The impact of therapy on the risk of asthma in type 2 diabetes

Rayner

McGovern

Sherlock

et al. 2019

Clinical Respiratory J

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“…As one approach, business modelling has been applied successfully in the health and health research area. It has been used, for example, to perform a requirements analysis of the barriers to conducting research linking of primary care, genetic and cancer data 7 , to model the complexity of health and associated data flow in asthma 8 and to provide a generic architecture for a type 2 diabetes mellitus care system 9 . We decided not to apply the full spectrum of business process modelling (BPMN), but to use only basic elements to give a notational and terminological basis for further work.…”

Section: Discussionmentioning

confidence: 99%

Classification of processes involved in sharing individual participant data from clinical trials

Ohmann¹,

Canham²,

Banzi

et al. 2018

F1000Res

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In recent years, a cultural change in the handling of research Background: data has resulted in the promotion of a culture of openness and an increased sharing of data. In the area of clinical trials, sharing of individual participant data involves a complex set of processes and the interaction of many actors and actions. Individual services and tools to support data sharing are becoming available, but what is missing is a detailed, structured and comprehensive list of processes and subprocesses involved and the tools and services needed.: Principles and recommendations from a published consensus Methods document on data sharing were analysed in detail by a small expert group. Processes and subprocesses involved in data sharing were identified and linked to actors and possible supporting services and tools. Definitions adapted from the business process model and notation (BPMN) were applied in the analysis.A detailed and comprehensive tabulation of individual processes Results: and subprocesses involved in data sharing, structured according to 9 main processes, is provided. Possible tools and services to support these processes are identified and grouped according to the major type of support.The identification of the individual processes and Conclusions: subprocesses and supporting tools and services, is a first step towards development of a generic framework or architecture for the sharing of data from clinical trials. Such a framework is needed to provide an overview of how the various actors, research processes and services could interact to form a sustainable system for data sharing.

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Section: Discussionmentioning

confidence: 99%

Classification of processes involved in sharing individual participant data from clinical trials

et al. 2018

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Background: In recent years, a cultural change in the handling of data from research has resulted in the strong promotion of a culture of openness and increased sharing of data. In the area of clinical trials, sharing of individual participant data involves a complex set of processes and the interaction of many actors and actions. Individual services/tools to support data sharing are available, but what is missing is a detailed, structured and comprehensive list of processes/subprocesses involved and tools/services needed. Methods: Principles and recommendations from a published data sharing consensus document are analysed in detail by a small expert group. Processes/subprocesses involved in data sharing are identified and linked to actors and possible services/tools. Definitions are adapted from the business process model and notation (BPMN) and applied in the analysis. Results: A detailed and comprehensive list of individual processes/subprocesses involved in data sharing, structured according to 9 main processes, is provided. Possible tools/services to support these processes/subprocesses are identified and grouped according to major type of support. Conclusions: The list of individual processes/subprocesses and tools/services identified is a first step towards development of a generic framework or architecture for sharing of data from clinical trials. Such a framework is strongly needed to give an overview of how various actors, research processes and services could form an interoperable system for data sharing.

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Accessible Modelling of Complexity in Health (AMoCH) and associated data flows: asthma as an exemplar

Cited by 12 publications

References 14 publications

The impact of therapy on the risk of asthma in type 2 diabetes

The impact of therapy on the risk of asthma in type 2 diabetes

Classification of processes involved in sharing individual participant data from clinical trials

Classification of processes involved in sharing individual participant data from clinical trials

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