Application of a system dynamics model to inform investment in smoking cessation services in New Zealand

Implementation of Clinical Services for Adults with Obesity in Different Health Systems: A Scoping Review and Causal Loop Diagram

The medical needs of obesity have been underrecognized, though it has posed long-term and enormous challenges to global health. Correspondingly, clinical services for obesity are still uncommon and in their infancy across health systems. It is meaningful to sort out the implementation of such clinical services involving a multiplicity of factors to identify measures for service development, scaling-up and optimization. This study aims to generate a comprehensive understanding of key variables and factors in the utilization and delivery of clinical services for adult patients with obesity and their dynamic patterns and to explore viable options for improved implementation of such services in health systems. We conducted a scoping review of published articles in the database from the lens of system dynamics through causal loop diagramming. Based on the data obtained from the review, we employed the causal loop diagramming as a tool to capture the variables in the implementation of clinical obesity services and their causal relationships. Twenty-one studies were finally included in the review. Based on the evidence consolidated through the review, we developed a causal loop diagram containing 19 causal variables and 38 causal arrows in single directions centered around the service utilization and delivery in the clinical obesity service. The feedback loops revealed potential activation points to intervene to facilitate the service implementation, such as, promotion of obesity as a disease with medical needs and available clinical services, provision of obesity-specific medical education and training opportunities, and prioritization of obesity-specific procedures in clinical protocols. The possible intervention points identified through the causal loop analysis can facilitate the development, implementation, and optimization of clinical obesity services in health systems.

Optimal control strategies supported by system dynamics modelling: a study on hookworm disease in China

BACKGROUND

Hookworm disease remains a global health issue. In China, it persists with a 0.67% infection rate and uneven distribution in 2021. Optimized control strategies are needed. This study aims to optimize intervention strategies for hookworm disease in China.

METHODS

Structural analysis and parameter estimation were conducted using system dynamics theory. Key variables were identified via the Delphi method, leading to the creation of a causal loop diagram (CLD) and stock flow chart (SFC). Based on the SFC, parameter estimation and quantitative relationships were established and the model was validated. A cost-effectiveness model was then integrated into the intervention mechanism model. Various intervention measures were tested in the model to determine their cost-effectiveness ratio (CER) and effectiveness. Generalized linear models were constructed from simulation data, accounting for the impact of survey sites. The results were used to develop an optimized strategy for hookworm disease control.

RESULTS

In comparing drug treatment methods, whole population deworming (WPD) and key population deworming (KPD) showed lower CERs than examination and voluntarily deworming (EVD), saving 384.79-504.64 CNY and 354.35-506.21 CNY per infection reduced, respectively (P < 0.001). For WPD or KPD alone, CER decreased with increased drug coverage. For examination and deworming (ED) and EVD, CER was highest at 30% coverage for a 1-year intervention, but at 90% coverage for 2-5 years (P < 0.05). WPD, ED, and EVD had higher infection reduction rates than KPD, with ratios of 0.14-0.25, 0.10-0.19, and 0.08-0.17, respectively, over 1-5 years (P < 0.001). Continuous health education over 1-5 years showed that increasing coverage from a 10% baseline led to enhancing cost-effectiveness and intervention outcomes.

CONCLUSIONS

In high-endemic areas (infection rate ≥ 20%) in China, prioritize WPD for better cost-effectiveness and outcomes. In medium-endemic areas (5% ≤ infection rate < 20%) where WPD isn't feasible, use ED for cost-effectiveness and KPD for infection reduction, based on local needs. In low-endemic areas (infection rate < 5%), encourage voluntary examination and treatment due to limited cost-effectiveness of mass treatment. Combining drug treatment with extensive health education can enhance long-term control effect. This strategy can guide control efforts for hookworm diseases in China.

CLINICAL TRIAL NUMBER

Not applicable.

Using System Dynamics to Understand Transnational Corporate Power in Diet-Related Non-communicable Disease Prevention Policy-Making: A Case Study of South Africa

BACKGROUND

Complex interactions between political economy factors and corporate power are increasingly recognized to prevent transformative policy action on non-communicable disease (NCD) prevention. System science offers promising methods for analysing such causal complexity. This study uses qualitative system dynamics methods to map the political economy of diet-related NCD (DR-NCD) prevention policy-making aiming to better understand the policy inertia observed in this area globally.

METHODS

We interviewed 25 key policy actors. We analysed the interviews using purposive text analysis (PTA). We developed individual then combined casual loop diagrams to generate a shared model representing the DR-NCD prevention policy-making system. Key variables/linkages identified from the literature were also included in the model. We validated the model in several steps including through stakeholder validation interviews.

RESULTS

We identified several inter-linked feedback processes related to political economy factors that may entrench different forms of corporate power (instrumental, structural, and discursive) in DR-NCD prevention policy-making in South Africa over time. We also identified a number of feedback processes that have the potential to limit corporate power in this setting.

CONCLUSION

Using complex system methods can be useful for more deeply understanding DR-NCD policy inertia. It is also useful for identifying potential leverage points within the system which may shift the existing power dynamics to facilitate greater political commitment for healthy, equitable, and sustainable food system transformation.

Guidance on the use of complex systems models for economic evaluations of public health interventions

To help health economic modelers respond to demands for greater use of complex systems models in public health. To propose identifiable features of such models and support researchers to plan public health modeling projects using these models. A working group of experts in complex systems modeling and economic evaluation was brought together to develop and jointly write guidance for the use of complex systems models for health economic analysis. The content of workshops was informed by a scoping review. A public health complex systems model for economic evaluation is defined as a quantitative, dynamic, non-linear model that incorporates feedback and interactions among model elements, in order to capture emergent outcomes and estimate health, economic and potentially other consequences to inform public policies. The guidance covers: when complex systems modeling is needed; principles for designing a complex systems model; and how to choose an appropriate modeling technique. This paper provides a definition to identify and characterize complex systems models for economic evaluations and proposes guidance on key aspects of the process for health economics analysis. This document will support the development of complex systems models, with impact on public health systems policy and decision making.

Systems Thinking and Complexity Science Methods and the Policy Process in Non-communicable Disease Prevention: A Systematic Scoping Review

BACKGROUND

Given the complex determinants of non-communicable diseases (NCDs), and the dynamic policy landscape, researchers and policymakers are exploring the use of systems thinking and complexity science (STCS) in developing effective policies. The aim of this review is to systematically identify and analyse existing applications of STCS-informed methods in NCD prevention policy.

METHODS

Systematic scoping review: We searched academic databases (Medline, Scopus, Web of Science, EMBASE) for all publications indexed by 13 October 2020, screening titles, abstracts and full texts and extracting data according to published guidelines. We summarised key data from each study, mapping applications of methods informed by STCS to policy process domains. We conducted a thematic analysis to identify advantages, limitations, barriers and facilitators to using STCS.

RESULTS

4681 papers were screened and 112 papers were included in this review. The most common policy areas were tobacco control, obesity prevention and physical activity promotion. Methods applied included system dynamics modelling, agent-based modelling and concept mapping. Advantages included supporting evidence-informed decision-making; modelling complex systems and addressing multi-sectoral problems. Limitations included the abstraction of reality by STCS methods, despite aims of encompassing greater complexity. Challenges included resource-intensiveness; lack of stakeholder trust in models; and results that were too complex to be comprehensible to stakeholders. Ensuring stakeholder ownership and presenting findings in a user-friendly way facilitated STCS use.

CONCLUSION

This review maps the proliferating applications of STCS methods in NCD prevention policy. STCS methods have the potential to generate tailored and dynamic evidence, adding robustness to evidence-informed policymaking, but must be accessible to policy stakeholders and have strong stakeholder ownership to build consensus and change stakeholder perspectives. Evaluations of whether, and under what circumstances, STCS methods lead to more effective policies compared to conventional methods are lacking, and would enable more targeted and constructive use of these methods.

Exploring personalized psychotherapy for depression: A system dynamics approach

Depressive disorders are the leading contributor to medical disability, yet only 22% of depressed patients receive adequate treatment in a given year. Response to treatment varies widely among individuals with depression, and poor response to one treatment does not signal poor response to others. In fact, half of patients who do not recover from a first-line psychotherapy will recover from a second option. Attempts to personalize psychotherapy to patient characteristics have produced better outcomes than usual care, but research on personalized psychotherapy is still in its infancy. The present study explores a new method for personalizing psychotherapy for depression through simulation modeling. In this study, we developed a system dynamics simulation model of depression based on one of the major mechanisms of depression in the literature and investigated the trend of depressive symptoms under different conditions and treatments. Our simulation outputs show the importance of individualized services with appropriate timing, and reveal a new method for personalizing psychotherapy to heterogeneous individuals. Future research is needed to expand the model to include additional mechanisms of depression.

Simulation of China’s Carbon Emission based on Influencing Factors

China is one of the world’s largest energy consumers and carbon emitters, and the situation of carbon emission reduction is serious. This paper forecasts the future trend of China’s carbon emissions by constructing a system dynamics model of China’s carbon emissions. The results show that China cannot fulfill its commitment to peak its carbon emissions in 2030 as scheduled. Secondly, the Logarithmic Mean Divisia Index model (LMDI) was used to analyze the influencing factors of China’s carbon emissions. The contribution rates of the five factors to China’s carbon emissions are as follows: economic development (226.30%), technological innovation (−105.92%), industrial structure (−26.55%), population scale (11.44%) and energy structure (−5.28%). Finally, this paper formulates five carbon emission reduction paths according to the size and direction of various factors that affect China’s carbon emissions. The paths of carbon emission reduction were simulated by using the system dynamics model of China’s carbon emissions. It is found that technological innovation is the key pathway for China to realize its commitment to carbon emission reduction. Slowing economic growth will delay the arrival time of peak carbon emissions and increase the intensity of carbon emissions. Optimizing the industrial structure, reducing the population scale and adjusting the energy structure can reduce the peak and carbon emissions in China, but the effect is small.

Insights from system dynamics applications in addiction research: A scoping review

BACKGROUND AND AIMS

Substance misuse and use disorders are dynamic and complex problems, situated within systems of interacting social, environmental, and neurobiological factors. System dynamics (SD) methods broaden, test, and improve understanding of complex systems and can help inform effective action. We sought to systematically review the use of SD tools in addiction-related research.

METHODS

Following PRISMA guidelines, we searched several databases from 1958 to 2019. We included studies focused on addiction-related screening and diagnosis, treatment, and return to use, as well as studies focused on earlier stages that may begin a path to addiction (e.g., experimentation, misuse onset).

RESULTS

We extracted information from 59 articles with a median publication year of 2014. In addition to using SD to understand the underlying complexity driving addiction-related trends, other commonly cited reasons for use of SD included assessing impacts of potential actions (n = 35), predicting future trends (n = 28), and supporting strategic planning processes (n = 22). Most studies included simulation models (n = 43); however, some presented insights from qualitative SD diagrams (n = 9) and concept models (n = 6). The majority of studies focused on stages leading to potential addiction: initiation/ experimentation (n = 42) and misuse onset (n = 38). One-third (n = 20) engaged persons with lived experience or other stakeholders during the modeling process.

CONCLUSIONS

Addiction-related SD research has increased over the last few decades with applications varying in several ways, from model purpose and types of data used to stakeholder involvement. Future applications should consider the benefits of stakeholder engagement throughout the modeling process and expanding models to include concomitant substance use.

Systems thinking for Aboriginal Health: Understanding the value and acceptability of group model building approaches

Systems thinking is increasingly applied to understand and address systemic drivers of complex health problems. In Australia, group model building, a participatory method from systems science, has been applied in various locations to engage communities in systems-based health promotion projects. To date there is limited evidence regarding GMB use with Australian Aboriginal communities. This study aimed to determine the value and acceptability of group model building (GMB) as a methodological approach in research with Aboriginal communities and identify any adaptations required to optimise its utility. Semi-structured interviews were undertaken with 18 Aboriginal health and university staff who had prior experience with a GMB research project. Interview transcripts were inductively analysed using thematic analysis and key themes were organised using an Indigenous research framework. Participants reported that GMB methods generally aligned well with Aboriginal ways of knowing, being, and doing. Participants valued the holistic, visual and collaborative nature of the method and its emphasis on sharing stories and collective decision-making. Group model building was viewed as a useful tool for identifying Aboriginal-led actions to address priority issues and advancing self-determination. Our findings suggest that by bringing together Aboriginal and non-Aboriginal knowledge, GMB is a promising tool, which Aboriginal communities could utilise to explore and address complex problems in a manner that is consistent with their worldviews. In adapting group model building methods, non-Aboriginal researchers should aspire to move beyond co-design processes and enable Aboriginal health research to be entirely led by Aboriginal people.

•

Group model building is a promising method for research with Aboriginal communities that is generally consistent with Aboriginal worldviews.

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Group Model Building may be a useful tool for identifying actions to address priority issues and advancing Aboriginal self-determination.

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Capacity building is required so that Group Model Building workshops, and ideally entire research projects, can be led by Aboriginal people.

Evaluation of public health interventions from a complex systems perspective: A research methods review

INTRODUCTION

Applying a complex systems perspective to public health evaluation may increase the relevance and strength of evidence to improve health and reduce health inequalities. In this review of methods, we aimed to: (i) classify and describe different complex systems methods in evaluation applied to public health; and (ii) examine the kinds of evaluative evidence generated by these different methods.

METHODS

We adapted critical review methods to identify evaluations of public health interventions that used systems methods. We conducted expert consultation, searched electronic databases (Scopus, MEDLINE, Web of Science), and followed citations of relevant systematic reviews. Evaluations were included if they self-identified as using systems- or complexity-informed methods and if they evaluated existing or hypothetical public health interventions. Case studies were selected to illustrate different types of complex systems evaluation.

FINDINGS

Seventy-four unique studies met our inclusion criteria. A framework was developed to map the included studies onto different stages of the evaluation process, which parallels the planning, delivery, assessment, and further delivery phases of the interventions they seek to inform; these stages include: 1) theorising; 2) prediction (simulation); 3) process evaluation; 4) impact evaluation; and 5) further prediction (simulation). Within this framework, we broadly categorised methodological approaches as mapping, modelling, network analysis and 'system framing' (the application of a complex systems perspective to a range of study designs). Studies frequently applied more than one type of systems method.

CONCLUSIONS

A range of complex systems methods can be utilised, adapted, or combined to produce different types of evaluative evidence. Further methodological innovation in systems evaluation may generate stronger evidence to improve health and reduce health inequalities in our complex world.

A dynamic modelling analysis of the impact of tobacco control programs on population-level nicotine dependence

According to the 'hardening hypothesis', average nicotine dependence will increase as less dependent smokers quit relatively easily in response to effective public health interventions, so that sustained progress in reducing smoking prevalence will depend on shifting the emphasis of tobacco control programs towards intensive treatment of heavily dependent smokers (who comprise an increasing fraction of continuing smokers). We used a system dynamics model of smoking behaviour to explore the potential for hardening in a population of smokers exposed to effective tobacco control measures over an extended period. Policy-induced increases in the per capita cessation rate are shown to lead inevitably to a decline in the proportion of smokers who are heavily dependent, contrary to the hardening hypothesis. Changes in smoking behaviour in Australia over the period 2001‒2016 resulted in substantial decreases in current smoking prevalence (from 23.1% in 2001 to 14.6% in 2016) and the proportion of heavily dependent smokers in the smoking population (from 52.1% to 36.9%). Public health interventions that have proved particularly effective in reducing smoking prevalence (tobacco tax increases, smoke-free environment legislation, antismoking mass media campaigns) are expected to also contribute to a decline in population-level nicotine dependence.

A Systems Thinking Approach to Inform Coherent Policy Action for NCD Prevention Comment on "How Neoliberalism Is Shaping the Supply of Unhealthy Commodities and What This Means for NCD Prevention"

Lencucha and Thow tackle the enormous public health challenge of developing non-communicable disease (NCD) policy coherence within a world structured and ruled by neoliberalism. Their work compliments scholarship on other causal mechanisms, including the commercial determinants of health, that have contributed to creating the risk commodity environment and barriers to NCD prevention policy coherence. However, there remain significant gaps in the understanding of how these causal mechanisms interact within a whole system. As such, public health researchers’ suggestions for how to effectively prevent NCDs through addressing the risk commodity environment tend to remain fragmented, incomplete and piecemeal. We suggest this is, in part, because conventional policy analysis methods tend to be reductionist, considering causal mechanisms in relative isolation and conceptualizing them as linear chains of cause and effect. This commentary discusses how a systems thinking approach offers methods that could help with better understanding the risk commodity environment problem, identifying a more comprehensive set of effective solutions across sectors and its utility more broadly for gaining insight into how to ensure recommended solutions are translated into policy, including though transformation at the paradigmatic level.

What is Systems Thinking? Expert Perspectives from the WPI Systems Thinking Colloquium of 2 October 2019

Systems thinking is an approach to reasoning and treatment of real-world problems based on the fundamental notion of ‘system.’ System here refers to a purposeful assembly of components. Thus, systems thinking is aimed at understanding relationships between components and their overall impact on system outcomes (i.e., intended and unintended) and how a system similarly fits in the broader context of its environment. There are currently several distinct flavors of systems thinking, both in practice and scholarship; most notably in the disciplines of systems science, systems engineering, and systems dynamics. Each of these, while similar in purpose, has a distinct history and a rich set of methods and tools for various application contexts. The WPI Systems Thinking Colloquium held on 2 October 2019 was aimed at exploring the diversity of perspectives on systems thinking from these disciplines. The colloquium brought together world-renowned experts from both industry and academia to share insights from their research and practice. This paper offers a compilation of summaries of the presentations given.

Youth smoking and anti-smoking policies in North Dakota: a system dynamics simulation study

BACKGROUND

The current study utilizes system dynamics to model the determinants of youth smoking and simulate effects of anti-smoking policies in the context of North Dakota, a state with one of the lowest cigarette tax rates in the USA.

METHODS

An explanatory model was built to replicate historical trends in the youth smoking rate. Three different policies were simulated: 1) an increase in cigarette excise taxes; 2) increased funding for CDC-recommended comprehensive tobacco control programs; and 3) enforcement of increased retailer compliance with age restrictions on cigarette sales.

RESULTS

The explanatory model successfully replicated historical trends in adolescent smoking behavior in North Dakota from 1992 to 2014. The policy model showed that increasing taxes to $2.20 per pack starting in 2015 was the most effective of the three policies, producing a 32.6% reduction in youth smoking rate by 2032. Other policies reduced smoking by a much lesser degree (7.0 and 3.2% for comprehensive tobacco control program funding and retailer compliance, respectively). The effects of each policy were additive.

CONCLUSIONS

System dynamics modeling suggests that increasing cigarette excise taxes are particularly effective at reducing adolescent smoking rates. More generally, system dynamics offers an important complement to conventional analysis of observational data.

The utility of a system dynamics approach for understanding cumulative health risk from exposure to environmental hazards

The potential of system dynamics modeling to advance our understanding of cumulative risk in the service of optimal health is discussed. The focus is on exploring system dynamics modeling as a systems science methodology that can provide a framework for examining the complexity of real-world social and environmental exposures among populations-particularly those exposed to multiple disparate sources of risk. The discussion also examines how system dynamics modeling can engage a diverse body of key stakeholders throughout the modeling process, promoting the collective assessment of assumptions and systematic gathering of critical data. Though not a panacea, system dynamics modeling provides a promising methodology to complement traditional research methods in understanding cumulative health effects from exposure to multiple environmental and social stressors.

Using SmartQuit®, an Acceptance and Commitment Therapy Smartphone application, to reduce smoking intake

Objective

SmartQuit® is a smartphone application (app) for smoking cessation based on Acceptance and Commitment Therapy, a behavioural therapy that encourages individuals to accept internal experiences, such as cravings to smoke, without acting on those experiences or urges. We used a single-subject (A-B-A) design with 10 participants to examine whether SmartQuit® use would reduce cigarette intake in a New Zealand sample.

Methods

10 smokers tallied their own cravings experienced and cigarettes smoked then sent those tallies to the first author every day until we observed stable patterns (Phase A1). We then gave the participants individual access to the SmartQuit® app (Phase B). When they advised that they had ceased using the app, they again recorded daily cravings and cigarettes smoked for a minimum of three days (Phase A2). We also collected follow-up smoking and craving data at 1, 2 and up to 13 months after completion of Phase A2.

Results

Using SmartQuit® reduced our participants' daily cigarette intake significantly in the short-term and three individuals remained smoke-free up to 13 months later. Cravings to smoke did not differ significantly across Phases A1, B and A2, but graphical analysis showed a trend for decreasing cravings.

Conclusion

Our results suggest that SmartQuit® provides another readily accessible intervention to help people stop smoking and is suited for use with a New Zealand population.

A Generic Simulation Model of the Relative Cost-Effectiveness of ICU Versus Step-Down (IMCU) Expansion

BACKGROUND

Many jurisdictions are facing increased demand for intensive care. There are two long-term investment options: intensive care unit (ICU) versus step-down or intermediate care unit (IMCU) capacity expansion. Relative cost-effectiveness of the two investment strategies with regard to patient lives saved has not been studied to date.

METHODS

We expand a generic system dynamics simulation model of emergency patient flow in a typical hospital, populated with empirical evidence found in the medical and hospital administration literature, to estimate the long-term effects of expanding ICU versus IMCU beds on patient lives saved under a common assumption of 2.1% annual increase in hospital arrivals. Two alternative policies of expanding ICU by two beds versus introducing a two-bed IMCU are compared over a ten-year simulation period. Russel equation is used to calculate total cost of patients' hospitalization. Using two possible values for the ratio of ICU to IMCU cost per inpatient day and four possible values for the percentage of patients transferred from ICU to IMCU found in the literature, nine scenarios are compared against the baseline scenario of no capacity expansion.

RESULTS

Expanding ICU capacity by two beds is demonstrated as the most cost-effective scenario with an incremental cost-effectiveness ratio of 3684 (US $) per life saved against the baseline scenario. Sensitivity analyses on the mortality rate of patients in IMCU, direct transfer of IMCU-destined patients to the ward upon completing required IMCU length of stay in the ICU, admission of IMCU patient to ICU, adding two ward beds, and changes in hospital size do not change the superiority of ICU expansion over other scenarios.

CONCLUSIONS

In terms of operational costs, ICU beds are more cost effective for saving patients than IMCU beds. However, capital costs of setting up ICU versus IMCU beds should be considered for a complete economic analysis.

Conceptualizing Productive Engagement in a System Dynamics Framework

Gerontologists have argued that the growing human capital of the aging population can be better marshaled as a resource for families, communities, and society at large. Additionally, this active, purposeful engagement can produce positive outcomes for older adults themselves. In this manuscript, we propose that existing conceptual frameworks articulating antecedents and outcomes of productive engagement, including working, volunteering, and caregiving can be improved using a system dynamics (SD) approach. Through a series of five unstructured group model-building sessions, experts from gerontology and systems science developed a qualitative SD model of the productive engagement of older adults. The model illustrates the reciprocal and dynamic nature of the stocks of human capital of older adults, social capital of older adults, and family resources; the engagement of older adults in productive activities; and the social and organizational variables that affect the flow and depletion of these stocks. Given this is the first attempt to develop a SD model for productive engagement in later life, the model is preliminary and heuristic. However, it offers a new approach to advancing theory and research on productive engagement in later life. Further, it can guide the development of mathematical models to estimate the effects of changes in any part of this system.

Dynamic modeling approaches to characterize the functioning of health systems: A systematic review of the literature

Universal Health Coverage (UHC) is one of the targets for the United Nations Sustainable Development Goal 3. The impetus for UHC has led to an increased demand for time-sensitive tools to enhance our knowledge of how health systems function and to evaluate impact of system interventions. We define the field of "health system modeling" (HSM) as an area of research where dynamic mathematical models can be designed in order to describe, predict, and quantitatively capture the functioning of health systems. HSM can be used to explore the dynamic relationships among different system components, including organizational design, financing and other resources (such as investments in resources and supply chain management systems) - what we call "inputs" - on access, coverage, and quality of care - what we call "outputs", toward improved health system "outcomes", namely increased levels and fairer distributions of population health and financial risk protection. We undertook a systematic review to identify the existing approaches used in HSM. We identified "systems thinking" - a conceptual and qualitative description of the critical interactions within a health system - as an important underlying precursor to HSM, and collated a critical collection of such articles. We then reviewed and categorized articles from two schools of thoughts: "system dynamics" (SD)" and "susceptible-infected-recovered-plus" (SIR+). SD emphasizes the notion of accumulations of stocks in the system, inflows and outflows, and causal feedback structure to predict intended and unintended consequences of policy interventions. The SIR + models link a typical disease transmission model with another that captures certain aspects of the system that impact the outcomes of the main model. These existing methods provide critical insights in informing the design of HSM, and provide a departure point to extend this research agenda. We highlight the opportunity to advance modeling methods to further understand the dynamics between health system inputs and outputs.

The effectiveness of naltrexone combined with current smoking cessation medication to attenuate post smoking cessation weight gain: a literature review

Background

Smoking is the number one cause of preventable morbidity and mortality globally and although many countries have invested heavily in smoking cessation programs, 21% of the global population still smoke. Post cessation weight gain has been identified as a barrier to attempting cessation and is implicated in the high rates of relapse. Naltrexone has been touted as a possible solution to address post smoking cessation weight gain.

Results

The results from seven original studies assessing the effectiveness of naltrexone in combination with existing smoking cessation medications to attenuate post smoking cessation weight gain were obtained and critically reviewed. Five returned positive results and two returned results that were statistically insignificant. The positive results were seen more often in those identified as more likely to exhibit hedonic eating behaviour for example women and participants who were categorised as overweight or obese.

Conclusion

The evidence suggests further investigation in to a combination of naltrexone and approved smoking cessation medications is warranted and could provide a solution to attenuate post smoking cessation weight gain especially in women and those classified as overweight or obese. This may provide the tool required to remove a perceived barrier to smoking cessation and improve global statistics.

A system dynamics modelling approach to assess the impact of launching a new nicotine product on population health outcomes

In 2012 the US FDA suggested the use of mathematical models to assess the impact of releasing new nicotine or tobacco products on population health outcomes. A model based on system dynamics methodology was developed to project the potential effects of a new nicotine product at a population level. A model representing traditional smoking populations (never, current and former smokers) and calibrated using historical data was extended to a two-product model by including electronic cigarettes use statuses. Smoking mechanisms, such as product initiation, switching, transition to dual use, and cessation, were represented as flows between smoking statuses (stocks) and the potential effect of smoking renormalisation through a feedback system. Mortality over a 50-year period (2000-2050) was the health outcome of interest, and was compared between two scenarios, with and without e-cigarettes being introduced. The results suggest that by 2050, smoking prevalence in adults was 12.4% in the core model and 9.7% (including dual users) in the counterfactual. Smoking-related mortality was 8.4% and 8.1%, respectively. The results suggested an overall beneficial effect from launching e-cigarettes and that system dynamics could be a useful approach to assess the potential population health effects of nicotine products when epidemiological data are not available.

The need for a comprehensive framework

To facilitate individual and population-level behavior change, we need policies based on science. We must develop coherent policies that explicitly consider the benefits and risks of different classes of nicotine delivery products, rather than continuing the current ad-hoc approach which fails to adequately address the product itself.

Translating research into prevention of high-risk behaviors in the presence of complex systems: definitions and systems frameworks

To impact population health, it is critical to collaborate across disciplinary and practice-based silos and integrate resources, experiences, and knowledge to exert positive change. Complex systems shape both the prevention outcomes researchers, practitioners, and policymakers seek to impact and how research is translated and can either impede or support movement from basic scientific discovery to impactful and scaled-up prevention practice. Systems science methods can be used to facilitate designing translation support that is grounded in a richer understanding of the many interacting forces affecting prevention outcomes across contexts. In this paper, we illustrate how one systems science method, system dynamics, could be used to advance research, practice, and policy initiatives in each stage of translation from discovery to translation of innovation into global communities (T0-T5), with tobacco prevention as an example. System dynamics can be applied to each translational stage to integrate disciplinary knowledge and document testable hypotheses to inform translation research and practice.

Modeling the Impact of School-Based Universal Depression Screening on Additional Service Capacity Needs: A System Dynamics Approach

Although it is widely known that the occurrence of depression increases over the course of adolescence, symptoms of mood disorders frequently go undetected. While schools are viable settings for conducting universal screening to systematically identify students in need of services for common health conditions, particularly those that adversely affect school performance, few school districts routinely screen their students for depression. Among the most commonly referenced barriers are concerns that the number of students identified may exceed schools' service delivery capacities, but few studies have evaluated this concern systematically. System dynamics (SD) modeling may prove a useful approach for answering questions of this sort. The goal of the current paper is therefore to demonstrate how SD modeling can be applied to inform implementation decisions in communities. In our demonstration, we used SD modeling to estimate the additional service demand generated by universal depression screening in a typical high school. We then simulated the effects of implementing "compensatory approaches" designed to address anticipated increases in service need through (1) the allocation of additional staff time and (2) improvements in the effectiveness of mental health interventions. Results support the ability of screening to facilitate more rapid entry into services and suggest that improving the effectiveness of mental health services for students with depression via the implementation of an evidence-based treatment protocol may have a limited impact on overall recovery rates and service availability. In our example, the SD approach proved useful in informing systems' decision-making about the adoption of a new school mental health service.

Mathematical Modeling in Tobacco Control Research: Initial Results From a Systematic Review

OBJECTIVES

The US Food and Drug Administration has expressed interest in using mathematical models to evaluate potential tobacco policies. The goal of this systematic review was to synthesize data from tobacco control studies that employ mathematical models.

METHODS

We searched five electronic databases on July 1, 2013 to identify published studies that used a mathematical model to project a tobacco-related outcome and developed a data extraction form based on the ISPOR-SMDM Modeling Good Research Practices. We developed an organizational framework to categorize these studies and identify models employed across multiple papers. We synthesized results qualitatively, providing a descriptive synthesis of included studies.

RESULTS

The 263 studies in this review were heterogeneous with regard to their methodologies and aims. We used the organizational framework to categorize each study according to its objective and map the objective to a model outcome. We identified two types of study objectives (trend and policy/intervention) and three types of model outcomes (change in tobacco use behavior, change in tobacco-related morbidity or mortality, and economic impact). Eighteen models were used across 118 studies.

CONCLUSIONS

This paper extends conventional systematic review methods to characterize a body of literature on mathematical modeling in tobacco control. The findings of this synthesis can inform the development of new models and the improvement of existing models, strengthening the ability of researchers to accurately project future tobacco-related trends and evaluate potential tobacco control policies and interventions. These findings can also help decision-makers to identify and become oriented with models relevant to their work.

A System Dynamics Model of Serum Prostate-Specific Antigen Screening for Prostate Cancer

Since 2012, US guidelines have recommended against prostate-specific antigen (PSA) screening for prostate cancer. However, evidence of screening benefit from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening trial and the European Randomized Study of Screening for Prostate Cancer has been inconsistent, due partly to differences in noncompliance and contamination. Using system dynamics modeling, we replicated the PLCO trial and extrapolated follow-up to 20 years. We then simulated 3 scenarios correcting for contamination in the PLCO control arm using Surveillance, Epidemiology, and End Results (SEER) incidence and survival data collected prior to the PSA screening era (scenario 1), SEER data collected during the PLCO trial period (1993-2001) (scenario 2), and data from the European trial's control arm (1991-2005) (scenario 3). In all scenarios, noncompliance was corrected using incidence and survival rates for men with screen-detected cancer in the PLCO screening arm. Scenarios 1 and 3 showed a benefit of PSA screening, with relative risks of 0.62 (95% confidence interval: 0.53, 0.72) and 0.70 (95% confidence interval: 0.59, 0.83) for cancer-specific mortality after 20 years, respectively. In scenario 2, however, there was no benefit of screening. This simulation showed that after correcting for noncompliance and contamination, there is potential benefit of PSA screening in reducing prostate cancer mortality. It also demonstrates the utility of system dynamics modeling for synthesizing epidemiologic evidence to inform public policy.

Systems science and systems thinking for public health: a systematic review of the field

OBJECTIVES

This paper reports on findings from a systematic review designed to investigate the state of systems science research in public health. The objectives were to: (1) explore how systems methodologies are being applied within public health and (2) identify fruitful areas of activity.

DESIGN

A systematic review was conducted from existing literature that draws on or uses systems science (in its various forms) and relates to key public health areas of action and concern, including tobacco, alcohol, obesity and the social determinants of health.

DATA ANALYSIS

117 articles were included in the review. An inductive qualitative content analysis was used for data extraction. The following were systematically extracted from the articles: approach, methodology, transparency, strengths and weaknesses. These were then organised according to theme (ie, commonalities between studies within each category), in order to provide an overview of the state of the field as a whole. The assessment of data quality was intrinsic to the goals of the review itself, and therefore, was carried out as part of the analysis.

RESULTS

4 categories of research were identified from the review, ranging from editorial and commentary pieces to complex system dynamic modelling. Our analysis of each of these categories of research highlighted areas of potential for systems science to strengthen public health efforts, while also revealing a number of limitations in the dynamic systems modelling being carried out in public health.

CONCLUSIONS

There is a great deal of interest in how the application of systems concepts and approach might aid public health. Our analysis suggests that soft systems modelling techniques are likely to be the most useful addition to public health, and align well with current debate around knowledge transfer and policy. However, the full range of systems methodologies is yet to be engaged with by public health researchers.

Mathematical modelling in tobacco control research: protocol for a systematic review

INTRODUCTION

Tobacco control researchers have recently become more interested in systems science methods and mathematical modelling techniques as a means to understand how complex inter-relationships among various factors translate into population-level summaries of tobacco use prevalence and its associated medical and social costs. However, there is currently no resource that provides an overview of how mathematical modelling has been used in tobacco control research. This review will provide a summary of studies that employ modelling techniques to predict tobacco-related outcomes. It will also propose a conceptual framework for grouping existing modelling studies by their objectives.

METHODS AND ANALYSIS

We will conduct a systematic review that is informed by Cochrane procedures, as well as guidelines developed for reviews that are specifically intended to inform policy and programme decision-making. We will search 5 electronic databases to identify studies that use a mathematical model to project a tobacco-related outcome. An online data extraction form will be developed based on the ISPOR-SMDM Modeling Good Research Practices. We will perform a qualitative synthesis of included studies.

ETHICS AND DISSEMINATION

Ethical approval is not required for this study. An initial paper, published in a peer-reviewed journal, will provide an overview of our findings. Subsequent papers will provide greater detail on results within each study objective category and an assessment of the risk of bias of these grouped studies.

What will it take to get to under 5% smoking prevalence by 2025? Modelling in a country with a smokefree goal

BACKGROUND

New Zealand has a goal of becoming a smokefree nation by the year 2025. Smoking prevalence in 2012 was 17%, but is over 40% for Māori (indigenous New Zealanders). We forecast the prevalence in 2025 under a business-as-usual (BAU) scenario, and determined what the initiation and cessation rates would have to be to achieve a <5% prevalence.

METHODS

A dynamic model was developed using Census and Health Survey data from 1981 to 2012 to calculate changes in initiation by age 20 years, and net annual cessation rates, by sex, age, ethnic group and time period. Similar parameters were also calculated from a panel study for sensitivity analyses. 'Forecasts' used these parameters, and other scenarios, applied to the 2011-2012 prevalence.

FINDINGS

Since 2002-2003, prevalence at age 20 years has decreased annually by 3.1% (95% uncertainty interval 0.8% to 5.7%) and 1.1% (-1.2% to 3.2%) for non-Māori males and females, and by 4.7% (2.2% to 7.1%) and 0.0% (-2.2% to 1.8%) for Māori, respectively. Annual net cessation rates from the dynamic model ranged from -3.0% to 6.1% across demographic groups, and from 3.0% to 6.0% in the panel study. Under BAU, smoking prevalence is forecast to be 11% and 9% for non-Māori males and females by 2025, and 30% and 37% for Māori, respectively. Achieving <5% by 2025 requires net cessation rates to increase to 10% for non-Māori and 20% for Māori, accompanied by halving or quartering of initiation rates.

CONCLUSIONS

The smokefree goal of <5% prevalence is only feasible with large increases in cessation rates.

Applications of systems science in biomedical research regarding obesity and noncommunicable chronic diseases: opportunities, promise, and challenges

Interest in the application of systems science (SS) in biomedical research, particularly regarding obesity and noncommunicable chronic disease (NCD) research, has been growing rapidly over the past decade. SS is a broad term referring to a family of research approaches that include modeling. As an emerging approach being adopted in public health, SS focuses on the complex dynamic interaction between agents (e.g., people) and subsystems defined at different levels. SS provides a conceptual framework for interdisciplinary and transdisciplinary approaches that address complex problems. SS has unique advantages for studying obesity and NCD problems in comparison to the traditional analytic approaches. The application of SS in biomedical research dates back to the 1960s with the development of computing capacity and simulation software. In recent decades, SS has been applied to addressing the growing global obesity epidemic. There is growing appreciation and support for using SS in the public health field, with many promising opportunities. There are also many challenges and uncertainties, including methodologic, funding, and institutional barriers. Integrated efforts by stakeholders that address these challenges are critical for the successful application of SS in the future.

Progression to multi-scale models and the application to food system intervention strategies

The aim of this article is to discuss how the systems science approach can be used to optimize intervention strategies in food animal systems. It advocates the idea that the challenges of maintaining a safe food supply are best addressed by integrating modeling and mathematics with biological studies critical to formulation of public policy to address these challenges. Much information on the biology and epidemiology of food animal systems has been characterized through single-discipline methods, but until now this information has not been thoroughly utilized in a fully integrated manner. The examples are drawn from our current research. The first, explained in depth, uses clinical mastitis to introduce the concept of dynamic programming to optimize management decisions in dairy cows (also introducing the curse of dimensionality problem). In the second example, a compartmental epidemic model for Johne's disease with different intervention strategies is optimized. The goal of the optimization strategy depends on whether there is a relationship between Johne's and Crohn's disease. If so, optimization is based on eradication of infection; if not, it is based on the cow's performance only (i.e., economic optimization, similar to the mastitis example). The third example focuses on food safety to introduce risk assessment using Listeria monocytogenes and Salmonella Typhimurium. The last example, practical interventions to effectively manage antibiotic resistance in beef and dairy cattle systems, introduces meta-population modeling that accounts for bacterial growth not only in the host (cow), but also in the cow's feed, drinking water and the housing environment. Each example stresses the need to progress toward multi-scale modeling. The article ends with examples of multi-scale systems, from food supply systems to Johne's disease. Reducing the consequences of foodborne illnesses (i.e., minimizing disease occurrence and associated costs) can only occur through an understanding of the system as a whole, including all its complexities. Thus the goal of future research should be to merge disciplines such as molecular biology, applied mathematics and social sciences to gain a better understanding of complex systems such as the food supply chain.

Tobacco use and quit behaviour assessment in the Global Adult Tobacco Survey (GATS): invalid responses and implications

BACKGROUND

Tobacco use and quit attempts are two key indicators of the Global Adult Tobacco Survey (GATS) that assess quit attempts among current as well as former tobacco users. The relevant data have inherent policy implications for tobacco cessation programme evaluation. This study aimed to review the concepts of quit attempt assessment and quantifying invalid responses considering GATS-India data.

MATERIALS AND METHODS

GATS assessment of tobacco use and quit attempts were examined in the current literature. Two categories of invalid responses were identified by stratified analysis of the duration of last quit attempt among current users and duration of abstinence among former users. Category A included absolute invalid responses when time- frame of assessment of current tobacco use and less than former tobacco use were violated. Category B included responses that violated the unit of measurement of time.

RESULTS

Current daily use, current less than daily use and former use in GATS were imprecisely defined with overlapping of time-frame of assessment. Overall responses of 3,102 current smokers, 4,036 current smokeless users, 1,904 former smokers and 1,343 former smokeless users were analyzed to quantify invalid responses. Analysis indicated overall 21.2% (category A: 7.32%; category B: 17.7%) and 22.7% (category A: 8.05%; category B: 18.1%) invalid responses among current smokers and smokeless users respectively regarding their duration of last quit attempt. Similarly overall 6.62% (category A: 4.7%; category B: 2.3%) and 10.6% (category A: 8.6%; category B: 3.5%) invalid responses were identified among former smokers and smokeless users respectively regarding their duration of abstinence.

CONCLUSIONS

High invalid responses for a single assessment are due to the imprecise definition of current use, former use and quit attempt; and failure to utilize opportunity of direct data entry interface use during the survey to validate responses instantly. Redefining tobacco use and quit attempts considering an appropriate timeframe would reduce invalid responses.

Social ecology of asthma: engaging stakeholders in integrating health behavior theories and practice-based evidence through systems mapping

This article describes a process for integrating health behavior and social science theories with practice-based insights using participatory systems thinking and diagramming methods largely inspired by system dynamics methods. This integration can help close the gap between research and practice in health education and health behavior by offering a systematic approach to bring together stakeholders across multiple domains. In this process we create a diagram using constructs from multiple health behavior theories at all levels of the social ecological framework as variables in causal loop diagrams. The goal of this process is to elucidate the reciprocal relationships between explanatory factors at various levels of the social ecological framework that render so many public health problems intractable. To illustrate, we detail a theory-based, replicable process for creating a qualitative diagram to enrich understanding of caregiver and provider behavior around adherence to pediatric asthma action plans. We describe how such diagramming can serve as the foundation for translating evidence into practice to address real-world challenges. Key insights gained include recognition of the complex, multilevel factors affecting whether, and how effectively, parents/caregivers and medical providers co-create an asthma action plan, and important "feedback" dynamics at play that can support or derail ongoing collaboration. Although this article applies this method to asthma action plan adherence in children, the method and resulting diagrams are applicable and adaptable to other health behaviors requiring continuous, daily action.

Practice variation, bias, and experiential learning in cesarean delivery: a data-based system dynamics approach

OBJECTIVES

To simulate physician-driven dynamics of delivery mode decisions (scheduled cesarean delivery [CD] vs. vaginal delivery [VD] vs. unplanned CD after labor), and to evaluate a behavioral theory of how experiential learning leads to emerging bias toward more CD and practice variation across obstetricians.

DATA SOURCES/STUDY SETTING

Hospital discharge data on deliveries performed by 300 randomly selected obstetricians in Florida who finished obstetrics residency and started practice after 1991.

STUDY DESIGN

We develop a system dynamics simulation model of obstetricians' delivery mode decision based on the literature of experiential learning. We calibrate the model and investigate the extent to which the model replicates the data.

PRINCIPAL FINDINGS

Our learning-based simulation model replicates the empirical data, showing that physicians are more likely to schedule CD as they practice longer. Variation in CD rates is related to the way that physicians learn from outcomes of past decisions and accumulate experience.

CONCLUSIONS

The repetitive nature of medical decision making, learning from past practice, and accumulating experience can account for increases in CD decisions and practice variation across physicians. Policies aimed at improving medical decision making should account for providers' feedback-based learning mechanisms.

Predictors of quit attempts and abstinence among smokers not currently interested in quitting

INTRODUCTION

Rates of quitting smoking remain stagnant, and thus it is becoming increasingly important to identify determinants of successful quitting behavior. The primary purpose of the current study was to examine predictors of quit attempts and 7-day point prevalence abstinence in a large nationally based sample. The study population consisted exclusively of smokers with minimal interest in quitting in the immediate future, for whom the need to identify facilitating factors of cessation is highly significant.

METHODS

Participants consisted of 849 smokers participating in a nationwide population-based randomized controlled trial (RCT) to promote quit attempts and cessation; all participants were not currently interested in cessation.

RESULTS

After adjusting for treatment group, and using a multivariate logistic approach, a combination of motivational and self-efficacy variables consistently predicted quit attempts, regardless of how quit attempts were defined (i.e., any self-defined vs. 24 hr). Additionally, a greater number of previous quit attempts significantly predicted making future quit attempts. In terms of achieving short-term abstinence, regardless of whether analyses were restricted to individuals who made prior quit attempts or not, self-efficacy emerged as the only significant consistent predictor.

CONCLUSIONS

Unlike previous studies, we did not find strong evidence suggesting unique predictors for making a quit attempt compared with achieving abstinence. Our findings demonstrate that even among smokers not currently interested in quitting, self-efficacy and motivation are key factors in the cessation process. Overall, the findings have important implications, as they highlight factors to target for future treatment.

Exposure to school and community based prevention programs and reductions in cigarette smoking among adolescents in the United States, 2000-08

Smoking remains prevalent among US youth despite decades of antismoking efforts. Effects from exposure to prevention programs at national level may provide informative and compelling data supporting better planning and strategy for tobacco control. A national representative sample of youth 12-17 years of age from the National Survey on Drug Use and Health was analyzed. A 3-stage model was devised to estimate smoking behavior transitions using cross-sectional data and the Probabilistic Discrete Event System method. Cigarette smoking measures (prevalence rates and odds ratios) were compared between exposed and non-exposed youth. More than 95% of the sample was exposed to prevention programs. Exposure was negatively associated with lifetime smoking and past 30-day smoking with a dose-response relation. Reduction in smoking was related to increased quitting in 2000-02, to increased quitting and declined initiation in 2003-05, and to initiation, quitting and relapse in 2005-08. Findings of this analysis suggest that intervention programs in the United States can reduce cigarette smoking among youth. Quitting smoking was most responsive to program exposure and relapse was most sensitive to funding cuts since 2003. Health policy and decision makers should consider these factors in planning and revising tobacco control strategies.

Systems science methods in public health: dynamics, networks, and agents

Complex systems abound in public health. Complex systems are made up of heterogeneous elements that interact with one another, have emergent properties that are not explained by understanding the individual elements of the system, persist over time, and adapt to changing circumstances. Public health is starting to use results from systems science studies to shape practice and policy, for example in preparing for global pandemics. However, systems science study designs and analytic methods remain underutilized and are not widely featured in public health curricula or training. In this review we present an argument for the utility of systems science methods in public health, introduce three important systems science methods (system dynamics, network analysis, and agent-based modeling), and provide three case studies in which these methods have been used to answer important public health science questions in the areas of infectious disease, tobacco control, and obesity.

A 'whole of system' approach to compare options for CVD interventions in Counties Manukau

OBJECTIVE

To assess the usefulness of a national and a local system dynamics model of cardiovascular disease to planning and funding decision makers.

METHODS

In an iterative process, an existing national model was populated with local data and presented to stakeholders in Counties Manukau, New Zealand. They explored the model's plausibility, usefulness and implications. Data were collected from 30 people using questionnaires, and from field notes and interviews; both were thematically analysed.

RESULTS

Potential users readily understood the model and actively engaged in discussing it. None disputed the overall model structure, but most wanted extensions to elaborate areas of specific interest to them. Local data made little qualitative difference to data interpretation but were nevertheless considered a necessary step to support confident local decisions.

CONCLUSION

Some limitations to the model and its use were recognised, but users could allow for these and still derive use from the model to qualitatively compare decision options.

IMPLICATIONS

The system dynamics modelling process is useful in complex systems and is likely to become established as part of the routinely used suite of tools used to support complex decisions in Counties Manukau District Health Board.