Assessing, communicating, and managing risk in general practice

Applying Communication Science to Substance Use Prevention Messaging

Despite a wealth of evidence-based messaging on youth alcohol and drug prevention, there remains a dearth of research on how to construct and deliver these messages effectively. Communication science is useful for increasing the efficacy of these messages in reducing substance use risk among youth. This study explores the perspectives of youth and youth-serving providers to identify theory-informed substance use prevention messages and strategies and how the content and delivery of prevention messages evolved during the COVID-19 pandemic. This is a secondary analysis of qualitative data derived from focus groups with 53 youth ages 13 to 18 years and 18 youth-serving providers conducted in the USA between 2021 and 2022. The results describe theory-informed strategies that are important to consider when constructing effective substance use prevention messaging for youth, including preferences around key communication framework constructs, including sources, content, channels, and context. An element that emerged across the communication constructs was the saliency of "connection" in youth substance use prevention messaging content. Findings point to the need to further explore connection related to having shared experiences and the extent to which these dimensions are critical ingredients to effective substance use prevention.

Utilizing shared frailty with the Cox proportional hazards regression: Post discharge survival analysis of CHF patients

Understanding patients' survival probability as well as the factors affecting it constitute a significant concern for researchers and practitioners, in particular for patients with severe chronic illnesses such as congestive heart failure (CHF). CHF is a clinical syndrome characterized by comorbidities and adverse medical events. Risk stratification to identify patients most likely to die shortly after hospital discharge can improve the quality of care by better allocating organizational resources and personalized interventions. Probability assessment improves clinical decision-making, contributes to personalized care, and saves costs. Although one of the most informative indices is the time to an adverse event for each patient, commonly analyzed using survival analysis methods, these are often challenging to implement due to the complexity of the medical data. Numerous studies have used the Cox proportional hazards (PH) regression method to generate the survival distribution pattern and factors affecting survival. This model, although advantageous for survival analysis, assumes the homogeneity of the hazard ratio across patients and independence of the observations in terms of survival time. These assumptions are often violated in real-world data, especially when the dataset is composed of readmission data for chronically ill patients, since these recurring observations are inherently dependent. This study ran the Cox PH regression on a feature set selected by machine learning algorithms from a rich hospital dataset. The event modeled here was patient mortality within 90 days post-hospital discharge. The sample was composed of medical records of patients hospitalized in the Israeli Sheba Medical Center more than once, with CHF as the primary diagnosis. We modeled the survival of CHF patients using the Cox PH regression with and without the shared frailty correction that addresses the shortcomings of the Cox Model. The results of the two models of the Cox PH regression - with and without the shared frailty correction were compared. The results demonstrate that the shared frailty correction, which was statistically significant in our analysis, improved the performance of the basic Cox PH model. While this is the main contribution, we also show that this model outperforms two commonly used measures (ADHERE and EFFECT) for predicting early mortality of CHF patients. Thus, the results illustrate how applying advanced analytics can outperform traditional methods. An additional contribution is the feature set selected using machine-learning methods that is different from those used in the extant literature.

Dementia prevention in memory clinics: recommendations from the European task force for brain health services

Observational population studies indicate that prevention of dementia and cognitive decline is being accomplished, possibly as an unintended result of better vascular prevention and healthier lifestyles. Population aging in the coming decades requires deliberate efforts to further decrease its prevalence and societal burden. Increasing evidence supports the efficacy of preventive interventions on persons with intact cognition and high dementia risk. We report recommendations for the deployment of second-generation memory clinics (Brain Health Services) whose mission is evidence-based and ethical dementia prevention in at-risk individuals. The cornerstone interventions consist of (i) assessment of genetic and potentially modifiable risk factors including brain pathology, and risk stratification, (ii) risk communication with ad-hoc protocols, (iii) risk reduction with multi-domain interventions, and (iv) cognitive enhancement with cognitive and physical training. A roadmap is proposed for concept validation and ensuing clinical deployment.

Disease evolution and risk-based disease trajectories in congestive heart failure patients

Congestive Heart Failure (CHF) is among the most prevalent chronic diseases worldwide, and is commonly associated with comorbidities and complex health conditions. Consequently, CHF patients are typically hospitalized frequently, and are at a high risk of premature death. Early detection of an envisaged patient disease trajectory is crucial for precision medicine. However, despite the abundance of patient-level data, cardiologists currently struggle to identify disease trajectories and track the evolution patterns of the disease over time, especially in small groups of patients with specific disease subtypes. The present study proposed a five-step method that allows clustering CHF patients, detecting cluster similarity, and identifying disease trajectories, and promises to overcome the existing difficulties. This work is based on a rich dataset of patients' records spanning ten years of hospital visits. The dataset contains all the health information documented in the hospital during each visit, including diagnoses, lab results, clinical data, and demographics. It utilizes an innovative Cluster Evolution Analysis (CEA) method to analyze the complex CHF population where each subject is potentially associated with numerous variables. We have defined sub-groups for mortality risk levels, which we used to characterize patients' disease evolution by refined data clustering in three points in time over ten years, and generating patients' migration patterns across periods. The results elicited 18, 23, and 25 clusters respective to the first, second, and third visits, uncovering clinically interesting small sub-groups of patients. In the following post-processing stage, we identified meaningful patterns. The analysis yielded fine-grained patient clusters divided into several finite risk levels, including several small-sized groups of high-risk patients. Significantly, the analysis also yielded longitudinal patterns where patients' risk levels changed over time. Four types of disease trajectories were identified: decline, preserved state, improvement, and mixed-progress. This stage is a unique contribution of the work. The resulting fine partitioning and longitudinal insights promise to significantly assist cardiologists in tailoring personalized interventions to improve care quality. Cardiologists could utilize these results to glean previously undetected relationships between symptoms and disease evolution that would allow a more informed clinical decision-making and effective interventions.

Communication of health risk in substance-dependent populations: A systematic review of randomised controlled trials

ISSUES

Individuals with substance use problems are at greater risk of chronic diseases due to their unhealthy lifestyle behaviours (e.g. alcohol use, smoking, physical inactivity, poor nutrition). There is increasing evidence that health risk communication is crucial in improving risk perception and knowledge of chronic diseases, and both factors are associated with health behaviour change. The aim of this systematic review is to provide a comprehensive overview of the current state of evidence on health risk communication on people with substance use problems.

APPROACH

A systematic search identified peer reviewed studies from the databases MEDLINE, PsycINFO, CINAHL and Scopus. Data were extracted from the included studies and a narrative synthesis of the results was conducted.

KEY FINDINGS

Eight articles, representing five unique studies, were included in the review. The overall risk of bias of the included studies was considered to be low. The studies evaluated the use of message framing and personalised/customised recommendations across smoking cessation and patient engagement with methadone maintenance treatment. Results revealed that message framing, specifically gain-framed messages, had a positive impact on smoking cessation. Risk perception, sex and level of nicotine dependence were also found to be associated with smoking cessation.

IMPLICATIONS AND CONCLUSIONS

The limited number of studies provides preliminary evidence that health risk communication promotes smoking cessation. However, studies included in the review were characterised by heterogeneous methods and measures. Further investigation of health risk communication using adequately powered randomised controlled trial is warranted.

Statins for primary prevention of cardiovascular disease: modelling guidelines and patient preferences based on an Irish cohort

BACKGROUND

Changes in clinical guidelines for primary prevention of cardiovascular disease (CVD) have widened eligibility for statin therapy.

AIM

To illustrate the potential impacts of changes in clinical guidelines.

DESIGN AND SETTING

Modelling the impacts of seven consecutive European guidelines based on a cohort of people aged ≥50 years from the Irish Longitudinal Study on Ageing.

METHOD

The eligibility for statin therapy of a sample of people without a history of CVD was established, according to changing guideline recommendations and modelled associated potential costs. The authors calculated the numbers needed to treat (NNT) to prevent one major vascular event in patients at the lowest baseline risk for which each of the seven guidelines recommended treatment, and for those at low, medium, high, and very-high risk according to 2016 guidelines. These were compared with the NNT that patients reported as required to justify taking a daily medicine.

RESULTS

The proportion of patients eligible for statins increased from approximately 8% in 1987 to 61% in 2016; associated costs rose from €13.9 million to €107.1 million per annum. The NNT for those at the lowest risk for which each guideline recommended treatment rose from 40 to 400. By 2016, the NNT for low-risk patients was 400 compared to ≤25 very-high risk patients. The proportion of patients eligible for statins achieving NNT levels that patients regarded as justified to taking a daily medicine fell as guidelines changed over time.

CONCLUSION

Increased eligibility for statin therapy impacts large proportions of the present population and healthcare budgets. Decisions to take and reimburse statins should be considered on the basis of expected cost-effectiveness and acceptability to patients.

Evaluation of clinical quality improvement interventions: feasibility of an integrated approach

BACKGROUND

Cardiovascular diseases (CVD) are the largest cause of death and disability in Australia. Australian national guidelines for the primary prevention of CVD recommend that all adults without CVD and aged 45 years or more are screened for their absolute risk of CVD every 2 years. Despite the compelling evidence to address CVD risk, treatment gaps remain and evidence suggests that much of the shortcomings are attributed to the performance of primary care practices. To address this issue, a quality improvement initiative is being implemented in a large urban multidisciplinary primary care practice in the South West region of Victoria, Australia. The key outcome of this intervention will be to increase the use and acceptability of CVD risk assessment guidelines. To ensure the intervention is tracking toward its objectives, a robust monitoring and evaluation framework was established.

METHOD/DESIGN

A novel framework that assimilates key traditional and theory-driven evaluation practices was developed to assess the impact of the intervention. The framework approach is termed the integrated model of evaluation (IMoE). Researchers and stakeholders convened several times to discuss and develop the evaluation protocol and align it with the quality intervention. The main objective here is to explore the feasibility of an integrated approach to evaluating clinical quality improvement interventions. The sub-objectives are to test the alignment of the IMoE to clinical quality improvement projects and its ability to derive findings to the satisfaction of stakeholders. The design and establishment of the evaluation approach is discussed in further detail in this article.

DISCUSSION

The novel feature of the IMoE is its emphasis on tracking 'change' in practices that lead to quality improvement. This emphasis suits the quality improvement theme of this initiative as identification of change elements and explanation behind change is necessary to sustain and promote quality improvement. The other principle behind development of this model, which emphasises practicality in implementation, is to ensure stakeholders gain greatest value from the commissioning of program evaluation. By incorporating practical components and leaving out esoteric concepts, this approach ensures evaluation can be undertaken in realistic timeframes.

ETHICS APPROVAL

The quality improvement intervention and evaluation framework received approval from the Deakin University Human Research Ethics Committee (Approval Number: 2017-313).

Using quantitative risk information in decisions about statins: a qualitative study in a community setting

Background

A large literature informs guidance for GPs about communicating quantitative risk information so as to facilitate shared decision making. However, relatively little has been written about how patients utilise such information in practice.

Aim

To understand the role of quantitative risk information in patients’ accounts of decisions about taking statins.

Design and setting

This was a qualitative study, with participants recruited and interviewed in community settings.

Method

Semi-structured interviews were conducted with 34 participants aged >50 years, all of whom had been offered statins. Data were analysed thematically, using elements of the constant comparative method.

Results

Interviewees drew frequently on numerical test results to explain their decisions about preventive medication. In contrast, they seldom mentioned quantitative risk information, and never offered it as a rationale for action. Test results were spoken of as objects of concern despite an often-explicit absence of understanding, so lack of understanding seems unlikely to explain the non-use of risk estimates. Preventive medication was seen as ‘necessary’ either to treat test results, or because of personalised, unequivocal advice from a doctor.

Conclusion

This study’s findings call into question the assumption that people will heed and use numerical risk information once they understand it; these data highlight the need to consider the ways in which different kinds of knowledge are used in practice in everyday contexts. There was little evidence from this study that understanding probabilistic risk information was a necessary or valued condition for making decisions about statin use.