Mapping the multicausality of Alzheimer's disease through group model building

Mapping complex public health problems with causal loop diagrams

This paper presents causal loop diagrams (CLDs) as tools for studying complex public health problems like health inequality. These problems often involve feedback loops-a characteristic of complex systems not fully integrated into mainstream epidemiology. CLDs are conceptual models that visualize connections between system variables. They are commonly developed through literature reviews or participatory methods with stakeholder groups. These diagrams often uncover feedback loops among variables across scales (e.g. biological, psychological and social), facilitating cross-disciplinary insights. We illustrate their use through a case example involving the feedback loop between sleep problems and depressive symptoms. We outline a typical step-by-step process for developing CLDs in epidemiology. These steps are defining a specific problem, identifying the key system variables involved, mapping these variables and analysing the CLD to find new insights and possible intervention targets. Throughout this process, we suggest triangulating between diverse sources of evidence, including domain knowledge, scientific literature and empirical data. CLDs can also be evaluated to guide policy changes and future research by revealing knowledge gaps. Finally, CLDs may be iteratively refined as new evidence emerges. We advocate for more widespread use of complex systems tools, like CLDs, in epidemiology to better understand and address complex public health problems.

Utilizing Group Model Building to Identify Barriers and Facilitators of Hypertension Management in Primary Health Care, China

Purpose

Group Model Building (GMB) is a qualitative method that refers to a participatory process. This project aims to identify barriers and facilitators of hypertension management in primary health care in China, through which, the leverage point for intervention may be found.

Methods

The GMB was used to identify the factors influencing hypertension management. Graphs over time and causal loop diagram (CLD) were main tools of GMB. To propose the influencing factors, key stakeholders were invited to participate in a workshop. During the workshop, stakeholders were encouraged to plot the graphs over time of the variables about research issues and give a descriptive explanation. And based on this, a CLD was initially developed to establish a model of the interaction of factors. After the workshop, the research group further improved the CLD through repeated mutual discussions, and gave feedback to the participants. The Vensim PLE 9.0 software package was used to build CLD.

Results

A total of 14 key stakeholders were invited to participate in the workshop. Finally, 26 influencing factors were identified, which were divided into three dimensions, including the institutional, the community health workers (CHWs), and the patient level. And 5 reinforcing loops and 4 balancing loops were formed in the CLD. Promoting the building of the Medical Community/Regional Medical Association, implementing the family doctor contract service (FDCS), and enhancing the motivation of CHWs may be potential leverage points for hypertension management in China.

Conclusion

By using GMB, we have identified key factors in the management of hypertension in primary health care and provided comprehensive suggestions to overcome the obstacles.

Quantifying reciprocal relationships between poverty and health: combining a causal loop diagram with longitudinal structural equation modelling

BACKGROUND

This study takes on the challenge of quantifying a complex causal loop diagram describing how poverty and health affect each other, and does so using longitudinal data from The Netherlands. Furthermore, this paper elaborates on its methodological approach in order to facilitate replication and methodological advancement.

METHODS

After adapting a causal loop diagram that was built by stakeholders, a longitudinal structural equation modelling approach was used. A cross-lagged panel model with nine endogenous variables, of which two latent variables, and three time-invariant exogenous variables was constructed. With this model, directional effects are estimated in a Granger-causal manner, using data from 2015 to 2019. Both the direct effects (with a one-year lag) and total effects over multiple (up to eight) years were calculated. Five sensitivity analyses were conducted. Two of these focus on lower-income and lower-wealth individuals. The other three each added one exogenous variable: work status, level of education, and home ownership.

RESULTS

The effects of income and financial wealth on health are present, but are relatively weak for the overall population. Sensitivity analyses show that these effects are stronger for those with lower incomes or wealth. Physical capability does seem to have strong positive effects on both income and financial wealth. There are a number of other results as well, as the estimated models are extensive. Many of the estimated effects only become substantial after several years.

CONCLUSIONS

Income and financial wealth appear to have limited effects on the health of the overall population of The Netherlands. However, there are indications that these effects may be stronger for individuals who are closer to the poverty threshold. Since the estimated effects of physical capability on income and financial wealth are more substantial, a broad recommendation would be that including physical capability in efforts that are aimed at improving income and financial wealth could be useful and effective. The methodological approach described in this paper could also be applied to other research settings or topics.

The need for systems thinking to advance Alzheimer's disease research

Despite extensive research efforts to mechanistically understand late-onset Alzheimer's disease (LOAD) and other complex mental health disorders, curative treatments remain elusive. We emphasize the multiscale multicausality inherent to LOAD, highlighting the interplay between interconnected pathophysiological processes and risk factors. Systems thinking methods, such as causal loop diagrams and systems dynamic models, offer powerful means to capture and study this complexity. Recent studies developed and validated a causal loop diagram and system dynamics model using multiple longitudinal data sets, enabling the simulation of personalized interventions on various modifiable risk factors in LOAD. The results indicate that targeting factors like sleep disturbance and depressive symptoms could be promising and yield synergistic benefits. Furthermore, personalized interventions showed significant potential, with top-ranked intervention strategies differing significantly across individuals. We argue that systems thinking approaches can open new prospects for multifactorial precision medicine. In future research, systems thinking may also guide structured, model-driven data collection on the multiple interactions in LOAD's complex multicausality, facilitating theory development and possibly resulting in effective prevention and treatment options.

A Scoping Review of Alzheimers Disease Hypotheses: An Array of Uni- and Multi-Factorial Theories

Background

There is a common agreement that Alzheimers disease (AD) is inherently complex; otherwise, a general disagreement remains on its etiological underpinning, with numerous alternative hypotheses having been proposed.

Objective

To perform a scoping review of original manuscripts describing hypotheses and theories of AD published in the past decades.

Results

We reviewed 131 original manuscripts that fulfilled our inclusion criteria out of more than 13,807 references extracted from open databases. Each entry was characterized as having a single or multifactorial focus and assigned to one of 15 theoretical groupings. Impact was tracked using open citation tools.

Results

Three stages can be discerned in terms of hypotheses generation, with three quarter of studies proposing a hypothesis characterized as being single-focus. The most important theoretical groupings were the Amyloid group, followed by Metabolism and Mitochondrial dysfunction, then Infections and Cerebrovascular. Lately, evidence towards Genetics and especially Gut/Brain interactions came to the fore.

Conclusions

When viewed together, these multi-faceted reports reinforce the notion that AD affects multiple sub-cellular, cellular, anatomical, and physiological systems at the same time but at varying degree between individuals. The challenge of providing a comprehensive view of all systems and their interactions remains, alongside ways to manage this inherent complexity.

Improving Cognitive Function with Nutritional Supplements in Aging: A Comprehensive Narrative Review of Clinical Studies Investigating the Effects of Vitamins, Minerals, Antioxidants, and Other Dietary Supplements

Cognitive impairment and dementia are burgeoning public health concerns, especially given the increasing longevity of the global population. These conditions not only affect the quality of life of individuals and their families, but also pose significant economic burdens on healthcare systems. In this context, our comprehensive narrative review critically examines the role of nutritional supplements in mitigating cognitive decline. Amidst growing interest in non-pharmacological interventions for cognitive enhancement, this review delves into the efficacy of vitamins, minerals, antioxidants, and other dietary supplements. Through a systematic evaluation of randomized controlled trials, observational studies, and meta-analysis, this review focuses on outcomes such as memory enhancement, attention improvement, executive function support, and neuroprotection. The findings suggest a complex interplay between nutritional supplementation and cognitive health, with some supplements showing promising results and others displaying limited or context-dependent effectiveness. The review highlights the importance of dosage, bioavailability, and individual differences in response to supplementation. Additionally, it addresses safety concerns and potential interactions with conventional treatments. By providing a clear overview of current scientific knowledge, this review aims to guide healthcare professionals and researchers in making informed decisions about the use of nutritional supplements for cognitive health.

Understanding the complexity of individual developmental pathways: A primer on metaphors, models, and methods to study resilience in development

The modern study of resilience in development is conceptually based on a complex adaptive system ontology in which many (intersystem) factors are involved in the emergence of resilient developmental pathways. However, the methods and models developed to study complex dynamical systems have not been widely adopted, and it has recently been noted this may constitute a problem moving the field forward. In the present paper, I argue that an ontological commitment to complex adaptive systems is not only possible, but highly recommended for the study of resilience in development. Such a commitment, however, also comes with a commitment to a different causal ontology and different research methods. In the first part of the paper, I discuss the extent to which current research on resilience in development conceptually adheres to the complex systems perspective. In the second part, I introduce conceptual tools that may help researchers conceptualize causality in complex systems. The third part discusses idiographic methods that could be used in a research program that embraces the interaction dominant causal ontology and idiosyncratic nature of the dynamics of complex systems. The conclusion is that a strong ontological commitment is warranted, but will require a radical departure from nomothetic science.

Using network analysis to identify leverage points based on causal loop diagrams leads to false inference

Network analysis is gaining momentum as an accepted practice to identify which factors in causal loop diagrams (CLDs)-mental models that graphically represent causal relationships between a system's factors-are most likely to shift system-level behaviour, known as leverage points. This application of network analysis, employed to quantitatively identify leverage points without having to use computational modelling approaches that translate CLDs into sets of mathematical equations, has however not been duly reflected upon. We evaluate whether using commonly applied network analysis metrics to identify leverage points is justified, focusing on betweenness- and closeness centrality. First, we assess whether the metrics identify the same leverage points based on CLDs that represent the same system but differ in inferred causal structure-finding that they provide unreliable results. Second, we consider conflicts between assumptions underlying the metrics and CLDs. We recognise six conflicts suggesting that the metrics are not equipped to take key information captured in CLDs into account. In conclusion, using betweenness- and closeness centrality to identify leverage points based on CLDs is at best premature and at worst incorrect-possibly causing erroneous identification of leverage points. This is problematic as, in current practice, the results can inform policy recommendations. Other quantitative or qualitative approaches that better correspond with the system dynamics perspective must be explored.

Applications of Participatory System Dynamics Methods to Public Health: A Systematic Review

System dynamics, and specifically qualitative participatory applications of system dynamics, have potential to benefit public health research, scholarship, and practice. A systematic review was conducted to examine the existing applications of participatory system dynamics (PSD) to public health research. Three databases were searched using unique search terms related to PSD and methodological applications in public health research. A total of 57 unique articles met inclusion criteria and were included for review. The studies included for review were conducted globally and represent a wide breadth of public health issues. The review identified several advantages to adopting PSD methods in public health scholarship and practice. The PSD methods provide innovative frameworks for conceptualizing complex and nuanced public health problems. The participatory nature of PSD allows for increased community engagement and empowerment to address public health problems, as well as to mitigate existing power dynamics between research institutions and marginalized communities that are disproportionately impacted by social and health inequities. Finally, causal loop diagrams developed using PSD methods have unique potential to convey complex concepts to policy makers and interventionists. This systematic review reports evidence for PSD's potential to advance equity in public health research and practice.

Exploring the potential impact of multi-factor precision interventions in Alzheimer's disease with system dynamics

Numerous clinical trials based on a single-cause paradigm have not resulted in efficacious treatments for Alzheimer's disease (AD). Recently, prevention trials that simultaneously intervened on multiple risk factors have shown mixed results, suggesting that careful design is necessary. Moreover, intensive pilot precision medicine (PM) trial results have been promising but may not generalize to a broader population. These observations suggest that a model-based approach to multi-factor precision medicine (PM) is warranted. We systematically developed a system dynamics model (SDM) of AD for PM using data from two longitudinal studies (N=3660). This method involved a model selection procedure in identifying interaction terms between the SDM components and estimating individualized parameters. We used the SDM to explore simulated single- and double-factor interventions on 14 modifiable risk factors. We quantified the potential impact of double-factor interventions over single-factor interventions as 1.5 [95% CI: 1.5-2.6] and of SDM-based PM over a one-size-fits-all approach as 3.5 [3.1, 3.8] ADAS-cog-13 points in 12 years. Although the model remains to be validated, we tentatively conclude that multi-factor PM could come to play an important role in AD prevention.

European position paper on polypharmacy and fall-risk-increasing drugs recommendations in the World Guidelines for Falls Prevention and Management: implications and implementation

Falls prevention and management in older adults is a critical global challenge. One of the key risk factors for falls is the use of certain medications. Therefore, to prevent medication-related falls, the following is recommended in the recent World Guidelines for Falls Prevention and Management: (1) assess for fall history and the risk of falls before prescribing potential fall-risk-increasing drugs (FRIDs), (2) use a validated, structured screening and assessment tool to identify FRIDs when performing a medication review, (3) include medication review and appropriate deprescribing of FRIDs as a part of the multifactorial falls prevention intervention, and (4) in long-term care residents, if multifactorial intervention cannot be conducted due to limited resources, the falls prevention strategy should still always include deprescribing of FRIDs.In the present statement paper, the working group on medication-related falls of the World Guidelines for Falls Prevention and Management, in collaboration with the European Geriatric Medicine Society (EuGMS) Task and Finish group on FRIDs, outlines its position on how to implement and execute these recommendations in clinical practice.Preferably, the medication review should be conducted as part of a comprehensive geriatric assessment to produce a personalized and patient-centered assessment. Furthermore, the major pitfall of the published intervention studies so far is the suboptimal implementation of medication review and deprescribing. For the future, it is important to focus on gaining which elements determine successful implementation and apply the concepts of implementation science to decrease the gap between research and practice.

A causal loop diagram of older persons' emergency department visits and interactions of its contributing factors: a group model building approach

PURPOSE

Understanding the etiology of older persons' emergency department (ED) visits is highly needed. Many contributing factors have been identified, however, the role their interactions play remains unclear. Causal loop diagrams (CLDs), as conceptual models, can visualize these interactions and therefore may elucidate their role. This study aimed to better understand why people older than 65 years of age visit the ED in Amsterdam by capturing the interactions of contributing factors as perceived by an expert group in a CLD through group model building (GMB).

METHODS

Six qualitative online focus group like sessions, known as GMB, were conducted with a purposefully recruited interdisciplinary expert group of nine that resulted in a CLD that depicted their shared view.

RESULTS

The CLD included four direct contributing factors, 29 underlying factors, 66 relations between factors and 18 feedback loops. The direct factors included, 'acute event', 'frailty', 'functioning of the healthcare professional' and 'availability of alternatives for the ED'. All direct factors showed direct as well as indirect contribution to older persons' ED visits in the CLD through interaction.

CONCLUSION

Functioning of the healthcare professional and availability of alternatives for the ED were considered pivotal factors, together with frailty and acute event. These factors, as well as many underlying factors, showed extensive interaction in the CLD, thereby contributing directly and indirectly to older persons' ED visits. This study helps to better understand the etiology of older persons' ED visits and in specific the way contributing factors interact. Furthermore, its CLD can help to find solutions for the increasing numbers of older adults in the ED.

Applying systems thinking to unravel the mechanisms underlying orthostatic hypotension related fall risk

Orthostatic hypotension (OH) is an established and common cardiovascular risk factor for falls. An in-depth understanding of the various interacting pathophysiological pathways contributing to OH-related falls is essential to guide improvements in diagnostic and treatment opportunities. We applied systems thinking to multidisciplinary map out causal mechanisms and risk factors. For this, we used group model building (GMB) to develop a causal loop diagram (CLD). The GMB was based on the input of experts from multiple domains related to OH and falls and all proposed mechanisms were supported by scientific literature. Our CLD is a conceptual representation of factors involved in OH-related falls, and their interrelatedness. Network analysis and feedback loops were applied to analyze and interpret the CLD, and quantitatively summarize the function and relative importance of the variables. Our CLD contains 50 variables distributed over three intrinsic domains (cerebral, cardiovascular, and musculoskeletal), and an extrinsic domain (e.g., medications). Between the variables, 181 connections and 65 feedback loops were identified. Decreased cerebral blood flow, low blood pressure, impaired baroreflex activity, and physical inactivity were identified as key factors involved in OH-related falls, based on their high centralities. Our CLD reflects the multifactorial pathophysiology of OH-related falls. It enables us to identify key elements, suggesting their potential for new diagnostic and treatment approaches in fall prevention. The interactive online CLD renders it suitable for both research and educational purposes and this CLD is the first step in the development of a computational model for simulating the effects of risk factors on falls.

Implementing online group model building to unravel complex geriatric problems, a methodological description

BACKGROUND

Group model building (GMB), is a qualitative focus group like study design from the field of system dynamics, that leads a group of topic experts (often key stakeholders of a problem), through a set of scripted activities to create a conceptual model of their shared view on this problems' key contributing factors and their interactions. By offering a specific step wise approach to the complexity of a problem, GMB has provided better understanding and overview of complex problems across different scientific domains, in addition to traditional research methods. As the development of geriatric syndromes and organization of geriatric care are often complex issues that are difficult to research, understand and resolve, GMB might be a useful methodology to better address these issues. This study aimed to describe the methodology of online GMB using a geriatric case study.

METHODS

Four online GMB sessions were designed by two clinician researchers. A GMB methodology expert was consulted for optimal design. Scriptapedia scripts formed the core of the sessions. These scripts were adapted to the online format. Experts were recruited purposefully and included seven local health care professionals, one patient representative and one healthcare insurance data analyst. The outcome was a conceptual model of older adults' emergency department visits, which was discussed in a separate article.

RESULTS

During implementation of these four sessions, the sessions were adjusted and two extra (non-scripted) sessions were added because defining unambiguous contributing factors to the geriatric case was challenging for the experts. Paraphrasing, categorizing, iterative plenary reflection, and reserving extra time were used to help experts overcome this challenge. All sessions were held in April and May 2021.

CONCLUSION

This study shows that GMB can help unravel complex problems in geriatrics, both pathophysiological as organizational, by creating step wise overview of their key contributing factors and interactions. Furthermore, it shows that GMB can be used by clinicians, researchers and health policy makers to better understand complex geriatric problems. Moreover, this paper can help to overcome specific implementational challenges in the geriatric field.

The use of participatory systems mapping as a research method in the context of non-communicable diseases and risk factors: a scoping review

CONTEXT

Participatory systems mapping is increasingly used to gain insight into the complex systems surrounding non-communicable diseases (NCDs) and their risk factors.

OBJECTIVES

To identify and synthesize studies that used participatory systems mapping in the context of non-communicable diseases.

DESIGN

Scoping review.

ELIGIBILITY CRITERIA

Peer-reviewed studies published between 2000 and 2022.

STUDY SELECTION

Studies that focused on NCDs and/or related risk factors, and included participants at any stage of their system's mapping process, were included.

CATEGORIES FOR ANALYSIS

The main categories for analysis were: (1) problem definition and goal-setting, (2) participant involvement, (3) structure of the mapping process, (4) validation of the systems map, and (5) evaluation of the mapping process.

RESULTS

We identified 57 studies that used participatory systems mapping for a variety of purposes, including to inform or evaluate policies or interventions and to identify potential leverage points within a system. The number of participants ranged from 6 to 590. While policymakers and professionals were the stakeholder groups most often included, some studies described significant added value from including marginalized communities. There was a general lack of formal evaluation in most studies. However, reported benefits related mostly to individual and group learning, whereas limitations described included a lack of concrete actions following from systems mapping exercises.

CONCLUSIONS

Based on the findings of this review, we argue that research using participatory systems mapping would benefit from considering three different but intertwined actions: explicitly considering how different participants and the power imbalances between them may influence the participatory process, considering how the results from a systems mapping exercise may effectively inform policy or translate into action, and including and reporting on evaluation and outcomes of the process, wherever possible.

Complexity in Epidemiology and Public Health. Addressing Complex Health Problems Through a Mix of Epidemiologic Methods and Data

Public health and the underlying disease processes are complex, often involving the interaction of biologic, social, psychologic, economic, and other processes that may be nonlinear and adaptive and have other features of complex systems. There is therefore a need to push the boundaries of public health beyond single-factor data analysis and expand the capacity of research methodology to tackle real-world complexities. This article sets out a way to operationalize complex systems thinking in public health, with a particular focus on how epidemiologic methods and data can contribute towards this end. Our proposed framework comprises three core dimensions-patterns, mechanisms, and dynamics-along which complex systems may be conceptualized. These dimensions cover seven key features of complex systems-emergence, interactions, nonlinearity, interference, feedback loops, adaptation, and evolution. We relate this framework to examples of methods and data traditionally used in epidemiology. We conclude that systematic production of knowledge on complex health issues may benefit from: formulation of research questions and programs in terms of the core dimensions we identify, as a comprehensive way to capture crucial features of complex systems; integration of traditional epidemiologic methods with systems methodology such as computational simulation modeling; interdisciplinary work; and continued investment in a wide range of data types. We believe that the proposed framework can support the systematic production of knowledge on complex health problems, with the use of epidemiology and other disciplines. This will help us understand emergent health phenomena, identify vulnerable population groups, and detect leverage points for promoting public health.

Role of endocrine PACAP in age-related diseases

Pituitary adenylate cyclase activating polypeptide (PACAP) is a conserved neuropeptide, which confers diverse anti-aging endocrine and paracrine/autocrine effects, including anti-apoptotic, anti-inflammatory and antioxidant action. The results of the in vivo and in vitro experiments show that increasing emphasis is being placed on the diagnostic/prognostic biomarker potential of this neuropeptide in a wide array of age-related diseases. After the initial findings regarding the presence and alteration of PACAP in different body fluids in physiological processes, an increasing number of studies have focused on the changes of its levels in various pathological conditions associated with advanced aging. Until 2016 - when the results of previous human studies were reviewed - a vast majority of the studies had dealt with age-related neurological diseases, like cerebrovascular and neurodegenerative diseases, multiple sclerosis, as well as some other common diseases in elderly such as migraine, traumatic brain injury and post-traumatic stress disorder, chronic hepatitis and nephrotic syndrome. The aim of this review is to summarize the old and the new results and highlight those 'classical' and emerging clinical fields in which PACAP may become subject to further investigation as a diagnostic and/or prognostic biomarker in age-related diseases.

A Bibliometric and Visual Analysis of Exercise Intervention Publications for Alzheimer’s Disease (1998–2021)

Alzheimer’s disease (AD) is the most common cause of dementia worldwide, posing a considerable economic burden to patients and society as a whole. Exercise has been confirmed as a non-drug intervention method in the related literature on AD. However, at present, there are still few bibliometric studies on AD exercise research. In order to fill the gap, this paper aims to intuitively analyze the growth in AD exercise literature published from 1998 to 2021 using bibliometrics, providing historical insights for scientific research circles. The main source of literature retrieval is the Web of Science database. Using the Boolean operator tools “OR” and “AND” combined with keywords related to “exercise” and “Alzheimer’s disease”, we conducted a title search and obtained 247 documents. Using Microsoft Excel, Datawrapper, and Biblioshiny, this study carried out a bibliometric analysis of countries, institutions, categories, journals, documents, authors, and keyword plus terms. The study found that the number of papers published from 2016 to 2021 had the greatest increase, which may have been influenced by the Global Dementia Report 2015 and COVID-19. Interdisciplinary cooperation and the research results published in high-scoring journals actively promoted research and development in the AD exercise field. The United States and the University of Minnesota system play a central role in this field. In future, it will be necessary to explore the effectiveness and feasibility of multi-mode interventions on an active lifestyle, including exercise, in different groups and environments worldwide. This study may provide a direction and path for future research by showing the global overview, theme evolution, and future trends of research results in the AD exercise field.

A scoping review of systems approaches for increasing physical activity in populations

Introduction

The past decade has increasingly seen systems approaches as a featured theme in public health studies and policy documents. This trend is evident in the area of physical activity, which is a significant global health risk factor that is addressed in WHO’s Global Action Plan on Physical Activity. We undertook a comprehensive scoping review to characterize the application of systems approaches to physical activity, to develop a typology of the objectives, themes and methods of research papers that purported to apply systems thinking to this issue.

Methods

We searched electronic databases (PubMed, Web of Science, Scopus and PsycINFO) for studies published during the period 2010–2021 that explicitly applied systems approaches or methods to investigate and/or address population physical activity. A framework using systems-based methodological approaches was adapted to classify physical activity studies according to their predominant approach, covering basic descriptive, complex analytical and advanced forms of practice. We selected case studies from retained studies to depict the current “state of the art”.

Results

We included 155 articles in our narrative account. Literature reporting the application of systems approaches to physical activity is skewed towards basic methods and frameworks, with most attention devoted to conceptual framing and predictive modelling. There are few well-described examples of physical activity interventions which have been planned, implemented and evaluated using a systems perspective. There is some evidence of “retrofitted” complex system framing to describe programmes and interventions which were not designed as such.

Discussion

We propose a classification of systems-based approaches to physical activity promotion together with an explanation of the strategies encompassed. The classification is designed to stimulate debate amongst policy-makers, practitioners and researchers to inform the further implementation and evaluation of systems approaches to physical activity.

Conclusion

The use of systems approaches within the field of physical activity is at an early stage of development, with a preponderance of descriptive approaches and a dearth of more complex analyses. We need to see movement towards a more sophisticated research agenda spanning the development, implementation and evaluation of systems-level interventions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12961-022-00906-2.

Population Attributable Fractions for Modifiable Risk Factors of Incident Dementia in Cognitively Normal and Mild Cognitively Impaired Older Adults: Data from Two Cohort Studies

BACKGROUND

Recent global meta-analyses show that 40% of dementia cases can be attributed to twelve modifiable risk factors.

OBJECTIVE

To investigate how health promotion strategies may differ in specific populations, this study estimated population attributable fractions (PAFs) of these risk factors for dementia in cognitively normal (CN) individuals and individuals with mild cognitive impairment (MCI) in United States and Greek cohorts.

METHODS

We re-analyzed data from the National Alzheimer's Coordinating Centre (NACC, n = 16,147, mean age 75.2±6.9 years, 59.0% female) and the Hellenic Longitudinal Investigation of Aging and Diet (HELIAD, n = 1,141, mean age 72.9±5.0 years, 58.0% female). PAFs for the total samples and CN and MCI subgroups were calculated based on hazard ratios for the risk of dementia and risk factor prevalence in NACC (9 risk factors) and HELIAD (10 risk factors).

RESULTS

In NACC, 2,630 participants developed MCI (25.1%) and 3,333 developed dementia (20.7%) during a mean follow-up of 4.9±3.5 years. Weighted overall PAFs were 19.4% in the total sample, 15.9% in the CN subgroup, and 3.3% in the MCI subgroup. In HELIAD, 131 participants developed MCI (11.2%) and 68 developed dementia (5.9%) during an average follow-up of 3.1±0.86 years. Weighted overall PAFs were 65.5% in the total sample, 65.8% in the CN subgroup and 64.6% in the MCI subgroup.

CONCLUSION

Translation of global meta-analysis data on modifiable risk factors should be carefully carried out per population. The PAFs of risk factors differ substantially across populations, directing health policy making to tailored risk factor modification plans.

Causal machine learning for healthcare and precision medicine

Causal machine learning (CML) has experienced increasing popularity in healthcare. Beyond the inherent capabilities of adding domain knowledge into learning systems, CML provides a complete toolset for investigating how a system would react to an intervention (e.g. outcome given a treatment). Quantifying effects of interventions allows actionable decisions to be made while maintaining robustness in the presence of confounders. Here, we explore how causal inference can be incorporated into different aspects of clinical decision support systems by using recent advances in machine learning. Throughout this paper, we use Alzheimer's disease to create examples for illustrating how CML can be advantageous in clinical scenarios. Furthermore, we discuss important challenges present in healthcare applications such as processing high-dimensional and unstructured data, generalization to out-of-distribution samples and temporal relationships, that despite the great effort from the research community remain to be solved. Finally, we review lines of research within causal representation learning, causal discovery and causal reasoning which offer the potential towards addressing the aforementioned challenges.

Individual Workplace Well-Being Captured into a Literature- and Stakeholders-Based Causal Loop Diagram

This study demonstrates an innovative approach to capture the complexity of individual workplace well-being, improving our understanding of multicausal relationships and feedback loops involved. The literature shows that a high number of interacting factors are related to individual workplace well-being. However, many studies focus on subsets of factors, and causal loops are seldomly studied. The aim of the current study was, therefore, to capture individual workplace well-being in a comprehensive conceptual causal loop diagram (CLD). We followed an iterative, qualitative, and transdisciplinary systems-thinking approach including literature search, group model building sessions, retrospective in-depth interviews with employees, and group sessions with human resource professionals, managers, job coaches, and management consultants. The results were discussed with HR and well-being officers of twelve organizations for their critical reflection on the recognizability and potential of the developed CLD. The final result, a conceptual individual workplace well-being CLD, provides a comprehensive overview of multiple, measurable key factors relating to individual workplace well-being and of the way these factors may causally interact over time, either improving or deteriorating workplace well-being. In future studies, the CLD can be translated to a quantitative system dynamics model for simulating workplace well-being scenarios. Ultimately, these simulations could be used to design effective workplace well-being interventions.

Mapping the complexity of dementia: factors influencing cognitive function at the onset of dementia

Background

Dementia is a multi-factorial condition rather than a natural and inevitable consequence of ageing. Some factors related to dementia have been studied much more extensively than others. To gain an overview of known or suspected influential factors is a prerequisite to design studies that aim to identify causal relationships and interactions between factors. This article aims to develop a visual model that a) identifies factors related to cognitive decline that signal the onset of dementia, b) structures them by different domains and c) reflects on and visualizes the possible causal links and interactions between these factors based on expert input using a causal loop diagram.

Method

We used a mixed-method, step-wise approach: 1. A systematic literature review on factors related to cognitive decline; 2. A group model building (GMB) workshop with experts from different disciplines; 3. Structured discussions within the group of researchers. The results were continuously synthesized and graphically transformed into a causal loop diagram.

Results

The causal loop diagram comprises 73 factors that were structured into six domains: physical (medical) factors (23), social health factors (21), psychological factors (14), environmental factors (5), demographic factors (5) and lifestyle factors (3). 57 factors were identified in the systematic literature review, additionally 16 factors, mostly of the social health cluster, were identified during the GMB session and the feedback rounds.

Conclusion

The causal loop diagram offers a comprehensive visualisation of factors related to cognitive decline and their interactions. It supports the generation of hypotheses on causal relationships and interactions of factors within and between domains.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-022-02955-2.