Jim Warren

Cohort Profile: The PREDICT Cardiovascular Disease Cohort in New Zealand Primary Care (PREDICT-CVD 19)

Cohort Profile: The PREDICT Cardiovascular Disease Cohort in New Zealand Primary Care (PREDICT-CVD 19) Sue Wells,* Tania Riddell, Andrew Kerr, Romana Pylypchuk, Carol Chelimo, Roger Marshall, Daniel J. Exeter, Suneela Mehta, Jeff Harrison, Cam Kyle, Corina Grey, Patricia Metcalf, Jim Warren, Tim Kenealy, Paul L. Drury, Matire Harwood, Dale Bramley, Geeta Gala and Rod Jackson School of Population Health, University of Auckland, Auckland, New Zealand, Middlemore Hospital, Cardiology Department, Auckland, New Zealand, School of Pharmacy, University of Auckland, Auckland, New Zealand, Endocrinology Services, Auckland District Health Board, Auckland, New Zealand, Computer Sciences, University of Auckland, School of Medicine, University of Auckland, Auckland, New Zealand, Waitemata District Health Board, Auckland, New Zealand and Northern Regional Alliance, Auckland, New Zealand

Cardiovascular disease risk prediction equations in 400 000 primary care patients in New Zealand: a derivation and validation study

BACKGROUNDnMost cardiovascular disease risk prediction equations in use today were derived from cohorts established last century and with participants at higher risk but less socioeconomically and ethnically diverse than patients they are now applied to. We recruited a nationally representative cohort in New Zealand to develop equations relevant to patients in contemporary primary care and compared the performance of these new equations to equations that are recommended in the USA.nnnMETHODSnThe PREDICT study automatically recruits participants in routine primary care when general practitioners in New Zealand use PREDICT software to assess their patients risk profiles for cardiovascular disease, which are prospectively linked to national ICD-coded hospitalisation and mortality databases. The study population included male and female patients in primary care who had no prior cardiovascular disease, renal disease, or congestive heart failure. New equations predicting total cardiovascular disease risk were developed using Cox regression models, which included clinical predictors plus an area-based deprivation index and self-identified ethnicity. Calibration and discrimination performance of the equations were assessed and compared with 2013 American College of Cardiology/American Heart Association Pooled Cohort Equations (PCEs). The additional predictors included in new PREDICT equations were also appended to the PCEs to determine whether they were independent predictors in the equations from the USA.nnnFINDINGSnOutcome events were derived for 401u2008752 people aged 30-74 years at the time of their first PREDICT risk assessment between Aug 27, 2002, and Oct 12, 2015, representing about 90% of the eligible population. The mean follow-up was 4·2 years, and a third of participants were followed for 5 years or more. 15u2008386 (4%) people had cardiovascular disease events (1507 [10%] were fatal, and 8549 [56%] met the PCEs definition of hard atherosclerotic cardiovascular disease) during 1u2008685u2008521 person-years follow-up. The median 5-year risk of total cardiovascular disease events predicted by the new equations was 2·3% in women and 3·2% in men. Multivariable adjusted risk increased by about 10% per quintile of socioeconomic deprivation. Māori, Pacific, and Indian patients were at 13-48% higher risk of cardiovascular disease than Europeans, and Chinese or other Asians were at 25-33% lower risk of cardiovascular disease than Europeans. The PCEs overestimated of hard atherosclerotic cardiovascular disease by about 40% in men and by 60% in women, and the additional predictors in the new equations were also independent predictors in the PCEs. The new equations were significantly better than PCEs on all performance metrics.nnnINTERPRETATIONnWe constructed a large prospective cohort study representing typical patients in primary care in New Zealand who were recommended for cardiovascular disease risk assessment. Most patients are now at low risk of cardiovascular disease, which explains why the PCEs based mainly on old cohorts substantially overestimate risk. Although the PCEs and many other equations will need to be recalibrated to mitigate overtreatment of the healthy majority, they also need new predictors that include measures of socioeconomic deprivation and multiple ethnicities to identify vulnerable high-risk subpopulations that might otherwise be undertreated.nnnFUNDINGnHealth Research Council of New Zealand, Heart Foundation of New Zealand, and Healthier Lives National Science Challenge.

Screening for risky behaviour and mental health in young people: the YouthCHAT programme

BackgroundThe prevalence of mental health concerns and risky health behaviours among young people is of global concern. A large proportion of young people in New Zealand (NZ) are affected by depression, suicidal ideation and other mental health concerns, but the majority do not access help. For NZ indigenous Māori, the burden of morbidity and mortality associated with mental health is considerably higher. Targeted screening for risky behaviours and mental health concerns among youth in primary care settings can lead to early detection and intervention for emerging or current mental health and psychosocial issues. Opportunistic screening for youth in primary care settings is not routinely undertaken due to competing time demands, lack of context-specific screening tools and insufficient knowledge about suitable interventions. Strategies are required to improve screening that are acceptable and appropriate for the primary care environment. This article outlines the development, utilisation and ongoing evaluation and implementation strategies for YouthCHAT.YouthCHATYouthCHAT is a rapid, electronic, self-report screening tool that assesses risky health-related behaviours and mental health concerns, with a ‘help question’ that enables youth to prioritise areas they want help with. The young person can complete YouthCHAT in the waiting room prior to consultation, and after completion, the clinician can immediately access a summary report which includes algorithms for stepped-care interventions using a strength-based approach. A project to scale up the implementation is about to commence, using a co-design participatory research approach to assess acceptability and feasibility with successive roll-out to clinics. In addition, a counter-balanced randomised trial of YouthCHAT versus clinician-administered assessment is underway at a NZ high school.ConclusionOpportunistic screening for mental health concerns and other risky health behaviours during adolescence can yield significant health gains and prevent unnecessary morbidity and mortality. The systematic approaches to screening and provision of algorithms for stepped-care intervention will assist in delivering time efficient, early, more comprehensive interventions for youth with mental health concerns and other health compromising behaviours. The early detection of concerns and facilitation to evidence-based interventions has the potential to lead to improved health outcomes, particularly for under-served indigenous populations.

Group decision making in health care: A case study of multidisciplinary meetings

Abstract Recent studies have demonstrated that Multi-Disciplinary Meetings (MDM) practiced in some medical contexts can contribute to positive health care outcomes. The group reasoning and decision-making in MDMs has been found to be most effective when deliberations revolve around the patient’s needs, comprehensive information is available during the meeting, core members attend and the MDM is effectively facilitated. This article presents a case study of the MDMs in cancer care in a region of Australia. The case study draws on a group reasoning model called the Reasoning Community model to analyse MDM deliberations to illustrate that many factors are important to support group reasoning, not solely the provision of pertinent information. The case study has implications for the use of data analytics in any group reasoning context.

Design of a technology-delivered motivational interviewing intervention for youth empowerment

Poor self-recognition of mental ill-health, as well as structural and attitudinal barriers often prevent youth from obtaining healthcare. In particular, minority groups cite stigma and loss of pride as reasons for not seeking treatment. The rising ubiquity of smartphones, even amongst lower socio-economic populations, can help ameliorate this problem by making healthcare more accessible and numerous groups have developed standalone e-therapy applications, although unsurprisingly, the effectiveness of such treatments depends on repeated use. Our work addresses this problem by creating youth-centered, mobile health applications that aim to empower help-avoidant adolescents to seek and continue treatment. Relationship-dependent forms of therapy such as motivational interviewing (MI) have previously proved challenging to effectively deliver electronically. However, through an iterative, co-design process with adolescents and clinicians, we have created a novel web-based application of the Values Card Sort which is a useful MI tool that can help resolve ambivalence and enhance motivation to change. As part of a larger suite of mobile therapy activities, the Values Card Sort serves to promote self-awareness and further encourage youth to pursue treatment.

Burden of atrial fibrillation in Māori and Pacific people in New Zealand: a cohort study

Atrial fibrillation (AF) is a major risk factor for ischaemic stroke and cardiovascular events. In New Zealand (NZ), Māori (indigenous New Zealanders) and Pacific people experience higher rates of AF compared with non‐Māori/non‐Pacific people.

Exploring reasoning mechanisms in ward rounds: a critical realist multiple case study

BackgroundWard rounds are an important and ubiquitous element of hospital care with a history extending well over a century. Although originally intended as a means of educating medical trainees and junior doctors, over time they have become focused on supporting clinical practice. Surprisingly, given their ubiquity and importance, they are under-researched and inadequately understood. This study aims to contribute knowledge in human reasoning within medical teams, meeting a pressing need for research concerning the reasoning occurring in rounds.MethodsThe research reported here aimed to improve the understanding of ward round reasoning by conducting a critical realist case study exploring the collaborative group reasoning mechanisms in the ward rounds of two hospitals in Victoria, Australia. The data collection involved observing rounds, interviewing medical practitioners and holding focus group meetings.ResultsNine group reasoning mechanisms concerning sharing, agreeing and recording information in the categories of information accumulation, sense-making and decision-making were identified, together forming a program theory of ward round reasoning. In addition, themes spanning across mechanisms were identified, further explaining ward round reasoning and suggesting avenues for future exploration. Themes included the use of various criteria, tensions involving mechanisms, time factors, medical roles and hierarchies.ConclusionsThis paper contributes to the literature by representing rounds in a manner that strengthens understanding of the form of the group reasoning occurring within, thus supporting theory-based evaluation strategies, redesigned practices and training enhancements.

Quantifying mood, content and dynamics of health forums

In this paper we examined content, mood and general dynamics of health forum discussions concerning vaccinations, genetically modified organisms (GMO) and a gluten-free diet and explored the ability to extract sentiment from social media. Using data from the social media website Reddit.com, we applied text mining techniques together with machine learning algorithms to derive insights. We used metadata from the source, text features, Latent Dirichlet Allocation (LDA) topic model outputs and manually annotated disposition labels that separate comments into affirmative or negative groups together with Gradient Boosted Models (GBM) to devise a set of disposition models inferring commentators sentiment towards each topic and expand our understanding of relevant arguments. Manual annotation resulted in moderate interrater agreement of an average 0.48 Fleiss-Kappa. Despite that, the disposition models for each topic were able to achieve a balanced successful prediction rates of between 68% and 74% providing a considerably better than chance assessment of a commentators disposition towards each topic. We observed changes in disposition over time and found areas of disagreement between the supporters and opponents of each topic. Despite the limitations associated with manual annotations, we obtained a wider view on the issues concerning the topics of interest than those offered by previous research.