Fabian Held

Association Rules Analysis of Comorbidity and Multimorbidity: The Concord Health and Aging in Men Project

BACKGROUND Comorbidity and multimorbidity are common in older people. Here we used a novel analytic approach called Association Rules together with network analysis to evaluate multimorbidity (two or more disorders) and comorbidity in old age. METHODS A population-based cross-sectional study was undertaken where 17 morbidities were analyzed using network analysis, cluster analysis, and Association Rules methodology. A comorbidity interestingness score was developed to quantify the richness and variability of comorbidities associated with an index condition. The participants were community-dwelling men aged 70 years or older from the Concord Health and Ageing in Men Project, Sydney, Australia, with complete data (n = 1,464). RESULTS The vast majority (75%) of participants had multimorbidity. Several morbidity clusters were apparent (vascular cluster, metabolic cluster, neurodegenerative cluster, mental health and other cluster, and a musculoskeletal and other cluster). Association Rules revealed unexpected comorbidities with high lift and confidence linked to index diseases. Anxiety and heart failure had the highest comorbidity interestingness scores while obesity, hearing impairment, and arthritis had the lowest (zero) scores. We also performed Association Rules analysis for the geriatric syndromes of frailty and falls to determine their association with multimorbidity. Frailty had a very complex and rich set of frequent and interesting comorbidities, while there were no frequent and interesting sets associated with falls. CONCLUSIONS Old age is characterized by a complex pattern of multimorbidity and comorbidity. Single disease definitions do not account for the prevalence and complexity of multimorbidity in older people and a new lexicon may be needed to underpin research and health care interventions for older people.

Medicine information exchange networks among healthcare professionals and prescribing in geriatric medicine wards

AIMS Effective transfer of information is vital for rational drug therapy. This is particularly important for older patients, who have a high prevalence of polypharmacy and are managed by multidisciplinary teams. We aimed to assess medicine information exchange (MIE) networks in geriatric medicine wards and whether they are associated with prescribing patterns. METHODS We conducted network analysis in acute geriatric medicine wards from four hospitals to characterize MIE networks among multidisciplinary team members. Corresponding patient data were collected to analyze high-risk prescribing in conjunction with network characteristics. RESULTS We found that junior doctors, senior nurses and pharmacists were central to MIE across all four hospitals. Doctors were more likely than other professions to receive medicines information in three hospitals. Reciprocity and the tendency to communicate within ones own profession also influenced network formation. No difference was observed in prescribing practice between hospitals. CONCLUSIONS Understanding MIE networks can identify gaps in multidisciplinary communication that can be addressed. Networks may identify targets for dissemination of interventions to improve prescribing.

Polypharmacy in older adults: Association Rule and Frequent-Set Analysis to evaluate concomitant medication use

ABSTRACT The aim of this study was to apply Association Rule and Frequent‐Set analysis, and novel means of data visualisation to ascertain patterns of medication use and medication combinations contributing to medication group clusters according to geriatric syndrome status in older adults. Participants were community‐dwelling men (aged ≥70 years, n = 1686), Sydney, Australia. Medication exposure was categorised at medication class level and data were analysed according to geriatric syndrome status (presence of at least one syndrome including frailty, falls, cognitive impairment and urinary incontinence). Association Rule and Frequent‐Set analysis were performed to identify “interesting” patterns of medication combinations that occur together. This analysis involves advanced computer algorithms that investigated all possible combinations of medications in the dataset in order to identify those which are observed more or much less frequently than expected. Frequent‐Set Analysis demonstrated one unexpected medication combination, antiulcer and antidiabetic medications (3.5% of participants) in the overall population (n = 1687). Frequency of medication combinations was similar in participants with (n = 666) and without (n = 1020) geriatric syndromes. Among participants with geriatric syndromes, the most frequent combinations included antigout with lipid‐lowering agents (5.7%) followed by angiotensin II and diuretics combination (22%). This novel methodology can be used to detect common medication combinations overall by data visualisation, and against specific adverse drug reactions such as geriatric syndromes. This methodology may be a valuable pharmacovigilance approach to monitor large databases for the safety of medications.

Association rules method and big data: Evaluating frequent medication combinations associated with fractures in older adults

The association rules method is a novel methodology to ascertain patterns of medication use and combinations associated with adverse drug events.

Computer Simulation and Agent-Based Models as a Research Method

Computer simulations are a different way of doing science from induction and deduction, they are about creating artificial worlds to study real worlds by analogy. Here we focus on a particular type of simulation model, Agent Based Models, that are capable of representing the essential features of complex social and business systems. We explain the role and value of these types of models and how they are built, tested and interpreted. We show how they can be used to better understand the behaviour of complex systems and also guide practitioners and policymakers.

A Social Network Analysis of the Financial Links Backing Health and Fitness Apps

Objectives To identify the major stakeholders in mobile health app development and to describe their financial relationships using social network analysis. Methods We conducted a structured content analysis of a purposive sample of prominent health and fitness apps available in November 2015 in the United States, Canada, and Australia. We conducted a social network analysis of apps’ developers, investors, other funding sources, and content advisors to describe the financial relationships underpinning health app development. Results Prominent health and fitness apps are largely developed by private companies based in North America, with an average of 4.7 (SD = 5.5) financial relations, including founders, external investors, acquiring companies, and commercial partnerships. Network analysis revealed a core of 41 sampled apps connected to 415 other entities by 466 financial relations. This core largely comprised apps published by major technology, pharmaceutical, and fashion corporations. About one third of apps named advisors, many of whom had commercial affiliations. Conclusions Public health needs to extend its scrutiny and advocacy beyond the health messages contained within apps to understanding commercial influences on health and, when necessary, challenging them.