Quantifying Industry Spending on Promotional Events Using Open Payments Data.

Importance: Sponsorship of promotional events for health professionals is a key facet of marketing campaigns for pharmaceuticals and medical devices; however, there appears to be limited transparency regarding the scope and scale of this spending. Objective: To develop a novel method for describing the scope and quantifying the spending by US pharmaceutical and medical companies on industry-sponsored promotional events for particular products. Design and Setting: This was a cross-sectional study using records from the Centers for Medicare & Medicaid's Open Payments database on payments made to prescribing clinicians from January 1 to December 21, 2022. Main Outcomes and Measures: An event-centric approach was used to define sponsored events as groupings of payment records with matching variables. Events were characterized by value (coffee, lunch, dinner, or banquet) and number of attendees (small vs large). To test the method, the number of and total spending for each type of event across professional groups were calculated and used to identify the top 10 products related to dinner events. To validate the method, we extracted all event details advertised on the websites of 4 state-level nurse practitioner associations that regularly hosted industry-sponsored dinner events during 2022 and compared these with events identified in the Open Payments database. Results: A total of 1 154 806 events sponsored by pharmaceutical and medical device companies were identified for 2022. Of these, 1 151 351 (99.7%) had fewer than 20 attendees, and 922 214 (80.0%) were considered to be a lunch ($10-$30 per person). Seven companies sponsored 16 031 dinners for the top 10 products. Of the 227 sponsored in-person dinner events hosted by the 4 state-level nurse practitioner associations, 168 (74.0%) matched events constructed from the Open Payments dataset. Conclusions and Relevance: These findings indicate that an event-centric analysis of Open Payments data is a valid method to understand the scope and quantify spending by pharmaceutical and medical device companies on industry-sponsored promotional events attended by prescribers. Expanding and enforcing the reporting requirements to cover all payments to all registered health professionals would improve the accuracy of estimates of the true extent of all sponsored events and their impact on clinical practice.

Characteristics of Advanced Practice Nurses Receiving Top Industry Payments and Their Practice Settings: a Cross-sectional Study.

BACKGROUND: The pharmaceutical industry promotes prescribing through the cultivation of key opinion leaders. Advanced practice nurses (APNs) are a growing and influential group of prescribers across generalist and specialty practice. Public reporting of industry payments to APNs allows for exploration of their influence within practice settings. OBJECTIVE: To understand the characteristics of APNs with top industry payments including their positions of influence and other payment recipients at the same address. DESIGN AND SETTING: Cross-sectional study of US national Open Payments reports of industry payments made between January 1, 2021, and December 31, 2021. PARTICIPANTS: APNs who received > $50,000 USD in industry payments for speaking, consulting, and honoraria ("personal fees"). MEASURES: Description of top APN recipients' practice setting type, clinical specialty, presence of other payment recipients, value of payments attributed to the same address, and top manufacturers and therapeutic categories associated with payments to top APN recipients. Structured content analysis of public-facing websites for evidence of APNs' clinical, research, and teaching leadership. RESULTS: A total of 99 APNs received > $50,000 USD in aggregate personal fees and a median $74,080 USD (IQR $57,303-101,702) in aggregate payments. They shared a practice setting with a median of 1 (IQR 0-5) physician and 0 (IQR 0-3) other APN payment recipients and were often the only (39%, 42/109) or the dominant (45%, 30/67) payment recipient in their practice setting. In total, 36% held clinical leadership positions, 25% led scientific research, and 18% had university appointments. Forty-two percent (37/88) owned a clinical practice, including cosmetic clinics (51%, 19/37) and mental/behavioral health clinics (24%, 9/37). CONCLUSIONS: Top APN payment recipients attracted high-value payments in practice settings and specialities associated with high-cost drug development; however, there may be little oversight of APNs' industry relationships. Policy development related to industry relationships must be inclusive of and responsive to the activities of interprofessional providers.

How many days of indoor positioning system data are required to characterise typical movement behaviours of office workers?

Indoor Positioning Systems (IPS) appear to offer great potential to study the movement and interaction of people and their working environment, including office workplaces. But little is known about appropriate durations for data collection. In this study, location observations collected from 24 office workers on a 1220 m2 office floor over a 3-month period, were analysed to determine how many days are required to estimate their typical movement and spatial behaviours. The analysis showed that up to 8 days of data was sufficient to characterise participants' typical daily movement behaviours and 10 days were required to estimate their typical spatial mobility. However, the results also indicate that 5 weeks of data collection are required to gather the necessary 10 days of data from each participant. These findings will help researchers and workplace professionals to understand the capabilities and requirements of IPS when considering their use in indoor work environments.

Exposure, access and interaction: A global analysis of sponsorship of nursing professional associations.

AIM: To analyse the nature and extent of sponsorship of nursing professional associations and their major scientific conferences. DESIGN: Cross-sectional content analysis. METHODS: Data were extracted from the websites and conference documents of 156 national and international professional nursing associations in 2019 to identify sponsors. Sponsorship prospectuses were analysed to estimate the value and describe the nature of sponsorship arrangements. We analysed sponsorship patterns using social network analysis. RESULTS: Most associations (84/156, 54%) did not report any sponsors. Sponsorship was concentrated among specialty nursing associations in high-income countries. Half of identified sponsors promoted products used in clinical care (50%; 981/1969); the majority represented the medical device industry (69%; 681/981). Top sponsors generally favoured opportunities that promoted interaction with conference attendees. CONCLUSION: Globally, commercial sponsorship of nursing associations is a common, but not the dominant source of support for these activities. Half of sponsors were commercial entities that manufactured or distributed products used during clinical care, which presents a risk of commercial influence over education and ultimately, clinical practice. Sponsors favoured opportunities to interact directly with nurses, determine educational content, or foster continued interaction.

Activity space, office space: Measuring the spatial movement of office workers.

A key to the development of more effective interventions to promote movement and reduce physical inactivity in office workplaces may be to measure and locate individual's spatial movement. Using an activity space estimation method, high resolution location data collected from 15 office workers over 12 days were used to estimate and analyse the location and extent of their daily spatial movement whilst in an office work-based setting. The results indicated that the method, kernel density estimation, combined with location data offers significant opportunities to not only measure and compare spatial movement behaviours but also simultaneously identify the locations where the behaviours occur. Combined with other data streams, this method will allow researchers to further investigate the influence of different environmental characteristics on these behaviours, potentially leading the development of more effective, longer lasting interventions to promote movement and reduce stationary behaviour, ultimately improving the health of office workers.

Stop motion: using high resolution spatiotemporal data to estimate and locate stationary and movement behaviour in an office workplace.

Prolonged periods of stationary behaviour, a common occurrence in many office workplaces, are linked with a range of physical disorders. Investigating the physical context of this behaviour may be a key to developing effective interventions. This study aimed to estimate and locate the stationary and movement behaviours of office workers (n = 10) by segmenting spatiotemporal data collected over 5 days in an office work-based setting. The segmentation method achieved a balanced accuracy ≥85.5% for observation classification and ≥90% for bout classification when compared to reference data. The results show the workers spent the majority of their time stationary (Mean = 86.4%) and had on average, 28.4 stationary and 25.9 moving bouts per hour. While these findings accord with other studies, the segmented data was also visualised, revealing that the workers were stationary for periods ≥5 min at multiple locations and these locations changed across time. Practitioner Summary: This study applied a data segmentation method to classify stationary and moving behaviours from spatiotemporal data collected in an office workplace. The segmented data revealed not only what behaviours occurred but also their location, duration, and time. Segmenting spatiotemporal data may add valuable physical context to aid workplace research.

The Feasibility of Recruiting and Training Lay Leaders During Real-World Program Delivery and Scale-up: The Case of Walk With Ease.

Introduction. To maximize limited resources, many health promotion programs are designed to be delivered by volunteer lay leaders. But this model poses challenges to implementation in real-world settings and barriers to successfully scaling-up programs. This study examines the current lay leader training model for Walk With Ease, a Centers for Disease Control and Prevention-funded evidence-based arthritis program delivered at-scale. Method. Recruited volunteers (n = 106) opted into free online or in-person training and agreed to deliver one Walk With Ease program within the following year-only 49%, however, did. Using logistic regression models and qualitative interviews, we explored predictors of volunteer delivery. Results. Volunteers had higher odds of delivering programs if they trained online (odds ratio [OR] = 9.04, 95% confidence interval [CI: 2.30, 48.36]), previously taught health programs (OR = 15.52, 95% CI [3.51, 103.55]) or trained in the second year of implementation (OR = 27.08, 95% CI [2.63, 415.78]). Qualitative findings underscored that successful volunteers were readied by their previous health education experience. Conclusions. While online training modes appear effective to prepare experienced volunteers, lay leaders required additional support. This calls into question whether lay-led delivery models are suitable for scaling-up programs with limited resources. Given the many lay-led health interventions for chronic disease self-management, investing in common training and infrastructures for lay leader development could advance the quality and sustainability of real-world program delivery.

Core and peripheral organisations in prevention: Insights from social network analysis.

ISSUE: Formal (eg funded) community-level organisational collaborations are becoming more common in prevention. Rapid methods to assess organisational relationships could allow us to consider the significance of any pre-existing relationship patterns in communities that might impact on collaboration effectiveness. Insights may identify new options for practice. METHODS: We used social network analysis to study organisations engaged in prevention but not (yet) part of a formal purposive collaboration. Within a single community, we identified organisations providing programs in chronic disease prevention. We used whole network analysis methods to describe the extent to which organisations were aware, had contact, coordinated activity and/or collaborated more intensively. We also identified the contribution made to prevention locally. Results were compared with key informant interviews. RESULTS: There was an identifiable network structure, with more relationships across the network than one would expect by chance. The network had a core-periphery structure, meaning that, in terms of the relationships we measured, there were highly connected organisations who were strongly interlinked with each other (the core), alongside less connected organisations that were linked to the core but not to each other (the periphery). Core organisations were significantly more likely to have expertise in prevention and to have prevention staff. CONCLUSIONS: To our knowledge, it is new to identify inherent or "pre-existing" core-periphery structures in interorganisational health promotion. Yet, core-periphery structures are common in many social settings. They advantage entities in the core and are prone to further entrenchment. SO WHAT?: Our results map and quantify intuitive understandings about organisational "key players", thus enabling practitioners/organisations to critically reflect on what their role should be when it comes to activating communities ie to embed, or attempt to counterbalance, pre-existing power structures.

Movement at work: A comparison of real time location system, accelerometer and observational data from an office work environment.

Office workers can spend significant periods of time being stationary whilst at work, with potentially serious health consequences. The development of effective health interventions could be aided by a greater understanding of the location and environmental context in which this stationary behaviour occurs. Real time location systems (RTLS) potentially offer the opportunity to gather this much needed information, but they have not been extensively trialled in office workplaces, nor rigorously compared against more familiar devices such as accelerometers. The aim of this paper was to determine whether an RTLS can measure and spatially locate the non-stationary and stationary behaviours of adults working in an office work environment. Data collected from a series of comparison studies undertaken in a commercial office building suggests that RTLS can measure the velocity at which people are moving and locate them, when stationary, with an accuracy of 0.668 m (SD 0.389). This opens up significant opportunities to further understand how people move within buildings, the indoor physical environmental influences on that movement, and the development of effective interventions to help people to move more whilst at work.

Promotion or education: a content analysis of industry-authored oral health educational materials targeted at acute care nurses.

OBJECTIVES: To assess the nature, quality and independence of scientific evidence provided in support of claims in industry-authored educational materials in oral health. DESIGN: A content analysis of educational materials authored by the four major multinational oral health product manufacturers. SETTING: Acute care settings. PARTICIPANTS: 68 documents focused on oral health or oral care, targeted at acute care clinicians and identified as 'educational' on companies' international websites. MAIN OUTCOME MEASURES: Data were extracted in duplicate for three areas of focus: (a) products referenced in the documents, (b) product-related claims and (c) citations substantiating claims. We assessed claim-citation pairs to determine if information in the citation supported the claim. We analysed the inter-relationships among cited authors and companies using social network analysis. RESULTS: Documents ranged from training videos to posters to brochures to continuing education courses. The majority of educational materials explicitly mentioned a product (59/68, 87%), a branded product (35/68, 51%), and made a product-related claim (55/68, 81%). Among claims accompanied by a citation, citations did not support the majority (91/147, 62%) of claims, largely because citations were unrelated. References used to support claims most often represented lower levels of evidence: only 9% were systematic reviews (7/76) and 13% were randomised controlled trials (10/76). We found a network of 20 authors to account for 37% (n=77/206) of all references in claim-citation pairs; 60% (12/20) of the top 20 cited authors received financial support from one of the four sampled manufacturers. CONCLUSIONS: Resources to support clinicians' ongoing education are scarce. However, caution should be exercised when relying on industry-authored materials to support continuing education for oral health. Evidence of sponsorship bias and reliance on key opinion leaders suggests that industry-authored educational materials have promotional intent and should be regulated as such.

Learning relationships in community-based service-learning: a social network analysis.

BACKGROUND: Little is known about the social learning of students within community-based clinical placements and ways in which it can be supported. In an allied health service-learning program, we analysed students' learning relationships to quantify what, and from whom students learnt. METHODS: We conducted a social learning network survey in four domains of learning (clinical knowledge, procedural skills, professional development, and complex determinants of health) to explore learning relationships (ties) with other people (alters) that students (egos) formed during their placement. We quantified how different roles (supervisors, health professionals, administrators, peers, schoolteachers, and clients) contributed to the students' learning in each of the four domains. We used exponential random graph models (ERGMs) to test which relational processes contributed to the structure of the observed learning networks. RESULTS: Data was available from a complete cohort of 10 students on placement in a network of 69 members, thus providing information on 680 potential learning relations. Students engaged in similar ways in the domains of clinical knowledge, procedural skills, and professional development. Learning relations with academic supervisors were significantly more likely. Also students reported reciprocal learning relations with peers - i.e. they formed learning pairs. This effect was absent in learning networks about complex determinants of health (including socio-economic and cultural factors). Instead, local administrative staff were significantly more often the source of learning about the local contextual factors. CONCLUSIONS: Understanding the structure of student learning networks through social network analysis helps identify targeted strategies to enhance learning in community-based service-learning programs. Our findings suggest students recognised important learning from each other and from administrative personnel that is unrelated to the content of their placement. Based on this insight clinical educators could prepare students to become agentic learners, learning with each other and from sources outside their program.

Data sharing practices of medicines related apps and the mobile ecosystem: traffic, content, and network analysis.

OBJECTIVES: To investigate whether and how user data are shared by top rated medicines related mobile applications (apps) and to characterise privacy risks to app users, both clinicians and consumers. DESIGN: Traffic, content, and network analysis. SETTING: Top rated medicines related apps for the Android mobile platform available in the Medical store category of Google Play in the United Kingdom, United States, Canada, and Australia. PARTICIPANTS: 24 of 821 apps identified by an app store crawling program. Included apps pertained to medicines information, dispensing, administration, prescribing, or use, and were interactive. INTERVENTIONS: Laboratory based traffic analysis of each app downloaded onto a smartphone, simulating real world use with four dummy scripts. The app's baseline traffic related to 28 different types of user data was observed. To identify privacy leaks, one source of user data was modified and deviations in the resulting traffic observed. MAIN OUTCOME MEASURES: Identities and characterisation of entities directly receiving user data from sampled apps. Secondary content analysis of company websites and privacy policies identified data recipients' main activities; network analysis characterised their data sharing relations. RESULTS: 19/24 (79%) of sampled apps shared user data. 55 unique entities, owned by 46 parent companies, received or processed app user data, including developers and parent companies (first parties) and service providers (third parties). 18 (33%) provided infrastructure related services such as cloud services. 37 (67%) provided services related to the collection and analysis of user data, including analytics or advertising, suggesting heightened privacy risks. Network analysis revealed that first and third parties received a median of 3 (interquartile range 1-6, range 1-24) unique transmissions of user data. Third parties advertised the ability to share user data with 216 "fourth parties"; within this network (n=237), entities had access to a median of 3 (interquartile range 1-11, range 1-140) unique transmissions of user data. Several companies occupied central positions within the network with the ability to aggregate and re-identify user data. CONCLUSIONS: Sharing of user data is routine, yet far from transparent. Clinicians should be conscious of privacy risks in their own use of apps and, when recommending apps, explain the potential for loss of privacy as part of informed consent. Privacy regulation should emphasise the accountabilities of those who control and process user data. Developers should disclose all data sharing practices and allow users to choose precisely what data are shared and with whom.

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

BACKGROUND: The association rules method is a novel methodology to ascertain patterns of medication use and combinations associated with adverse drug events. OBJECTIVES: The aim of this case-crossover study was to apply the association rules method to ascertain medication combinations contributing to the risk of fractures in older adults. METHODS: A nationwide representative sample of New Zealanders aged ≥65 years was sourced from the pharmaceutical collection. The first-time coded diagnosis of fracture was extracted from the National Minimum Dataset. Association rule method is a data mining technique that can be used to quickly traverse big datasets to identify a combination of items that co-occur. The association rules method were applied to identify frequent 11 medication combinations in the case and the control periods (1-14 days as hazard period, with 35-day washout period), and the association of fractures with each frequent medication combination were tested by computing a matched odd ratio (OR) and its 95% CI. RESULTS: We identified a total of 72 184 individuals (mean age 81.5 years) from 2005 to 2014 with incident fracture and exposed to at least 1 medication of interest. The association rules method revealed codeine phosphate (aOR = 11.50, 95% CI, 7.09-15.20, concomitantly with ibuprofen), zopiclone (aOR = 2.34, 95% CI, 1.49-3.67, concomitantly with morphine) and quetiapine (OR = 1.95, 95% CI, 1.28-2.98, concomitantly with zopiclone) were associated with fractures. CONCLUSION: The association rules method identified medication exposure combinations containing psychotropic medications and codeine are frequently associated with fractures. This novel methodology applied to big data can be an important tool to ascertain medication combinations associated with adverse drug events.

Growing health partnerships in rural and remote communities: what drives the joint efforts of primary schools and universities in maintaining service learning partnerships?

AimThis study explored the partnership between universities and local primary schools to deliver a classroom-based paediatric communication impairment service provided by undergraduate speech pathology students. It aimed to understand how partnerships work to facilitate programme replication. BACKGROUND: The partners included universities sending students on rural clinical placement, local host academic units and primary schools who worked together to provide paediatric speech and language services in primary schools in three sites in Australia. Rural and remote communities experience poorer health outcomes because of chronic workforce shortages, social disadvantage and high Aboriginality, poor access to services and underfunding. METHODS: The study was in twofold: qualitative analysis of data from interviews/focus group with the partners in the university and education sectors, and quantitative social network analysis of data from an electronic survey of the partners.FindingsFactors supporting partnerships were long-term, work and social relationships, commitment to community, trust and an appetite for risk-taking. We postulate that these characteristics are more likely to exist in rural communities.

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.

Tracing the Potential Flow of Consumer Data: A Network Analysis of Prominent Health and Fitness Apps.

BACKGROUND: A great deal of consumer data, collected actively through consumer reporting or passively through sensors, is shared among apps. Developers increasingly allow their programs to communicate with other apps, sensors, and Web-based services, which are promoted as features to potential users. However, health apps also routinely pose risks related to information leaks, information manipulation, and loss of information. There has been less investigation into the kinds of user data that developers are likely to collect, and who might have access to it. OBJECTIVE: We sought to describe how consumer data generated from mobile health apps might be distributed and reused. We also aimed to outline risks to individual privacy and security presented by this potential for aggregating and combining user data across apps. METHODS: We purposively sampled prominent health and fitness apps available in the United States, Canada, and Australia Google Play and iTunes app stores in November 2015. Two independent coders extracted data from app promotional materials on app and developer characteristics, and the developer-reported collection and sharing of user data. We conducted a descriptive analysis of app, developer, and user data collection characteristics. Using structural equivalence analysis, we conducted a network analysis of sampled apps' self-reported sharing of user-generated data. RESULTS: We included 297 unique apps published by 231 individual developers, which requested 58 different permissions (mean 7.95, SD 6.57). We grouped apps into 222 app families on the basis of shared ownership. Analysis of self-reported data sharing revealed a network of 359 app family nodes, with one connected central component of 210 app families (58.5%). Most (143/222, 64.4%) of the sampled app families did not report sharing any data and were therefore isolated from each other and from the core network. Fifteen app families assumed more central network positions as gatekeepers on the shortest paths that data would have to travel between other app families. CONCLUSIONS: This cross-sectional analysis highlights the possibilities for user data collection and potential paths that data is able to travel among a sample of prominent health and fitness apps. While individual apps may not collect personally identifiable information, app families and the partners with which they share data may be able to aggregate consumer data, thus achieving a much more comprehensive picture of the individual consumer. The organizations behind the centrally connected app families represent diverse industries, including apparel manufacturers and social media platforms that are not traditionally involved in health or fitness. This analysis highlights the potential for anticipated and voluntary but also possibly unanticipated and involuntary sharing of user data, validating privacy and security concerns in mobile health.

Social learning in a longitudinal integrated clinical placement.

Recent research has demonstrated that longitudinal integrated placements (LICs) are an alternative mode of clinical education to traditional placements. Extended student engagement in community settings provide the advantages of educational continuity as well as increased service provision in underserved areas. Developing and maintaining LICs require a differing approach to student learning than that for traditional placements. There has been little theoretically informed empirical research that has offered explanations of which are the important factors that promote student learning in LICs and the relationships between those factors. We explored the relationship between student learning, student perceptions of preparedness for practice and student engagement, in the context of a rural LIC. We used a sequential qualitative design employing thematic, comparative and relational analysis of data from student interviews (n = 18) to understand possible processes and mechanisms of student learning in the LIC. Through the theoretical lens of social learning systems, we identified two major themes; connectivity and preparedness for practice. Connectivity described engagement and relationship building by students, across formal and informal learning experiences, interprofessional interactions, social interactions with colleagues, interaction with patients outside of the clinical setting, and the extent of integration in the wider community. Preparedness for practice, reflected students' perceptions of having sufficient depth in clinical skills, personal and professional development, cultural awareness and understanding of the health system, to work in that system. A comparative analysis compared the nature and variation of learning across students. In a relational analysis, there was a positive association between connectivity and preparedness for practice. Connectivity is a powerful enabler of students' agentic engagement, collaboration, and learning within an LIC. It is related to student perceptions of preparedness for practice. These findings provide insight for institutions wishing to develop similar programmes, by encouraging health professional educators to consider all of the potential elements of the placements, which most promote connectivity.

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 one's 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.

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 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.