Assessing the dynamic impacts of non-pharmaceutical and pharmaceutical intervention measures on the containment results against COVID-19 in Ethiopia

The rapid spread of COVID-19 in Ethiopia was attributed to joint effects of multiple factors such as low adherence to face mask-wearing, failure to comply with social distancing measures, many people attending religious worship activities and holiday events, extensive protests, country election rallies during the pandemic, and the war between the federal government and Tigray Region. This study built a system dynamics model to capture COVID-19 characteristics, major social events, stringencies of containment measures, and vaccination dynamics. This system dynamics model served as a framework for understanding the issues and gaps in the containment measures against COVID-19 in the past period (16 scenarios) and the spread dynamics of the infectious disease over the next year under a combination of different interventions (264 scenarios). In the counterfactual analysis, we found that keeping high mask-wearing adherence since the outbreak of COVID-19 in Ethiopia could have significantly reduced the infection under the condition of low vaccination level or unavailability of the vaccine supply. Reducing or canceling major social events could achieve a better outcome than imposing constraints on people's routine life activities. The trend analysis found that increasing mask-wearing adherence and enforcing more stringent social distancing were two major measures that can significantly reduce possible infections. Higher mask-wearing adherence had more significant impacts than enforcing social distancing measures in our settings. As the vaccination rate increases, reduced efficacy could cause more infections than shortened immunological periods. Offsetting effects of multiple interventions (strengthening one or more interventions while loosening others) could be applied when the levels or stringencies of one or more interventions need to be adjusted for catering to particular needs (e.g., less stringent social distancing measures to reboot the economy or cushion insufficient resources in some areas).

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.

A simulation model to evaluate pharmaceutical supply chain costs in hospitals: the case of a Colombian hospital

BACKGROUND

Healthcare costs is one of the most studied issue in our days because of increasing demand and the aging of population. Final costs of medicines is one of the most important issue in patient treatment and determine its real value is an important task within hospitals. Simulation models and in this case system dynamics allows to build representations of reality considering the interaction of the whole variables that affect the system where first a causal loop diagram allows to represent and identify the interaction between variables for develop a stock flow diagram to determine the final results.

OBJECTIVE

Develop a simulation model that allows decision makers in Hospitals and Governments to identify the variables that affect the final cost of medicines and to determine the legal reimbursement allowed by national agencies.

METHODS

This paper presents a conceptual modeling framework using a causal loop diagram and a dynamic simulation model in the real case of a hospital in Colombia to explore how different internal charges for medicines affect the behavior of the final unit-dose cost of medicines, considering the complexity of the pharmaceutical system. We developed a simulation model to represent and characterize the pharmaceutical supply chain in a hospital and by using real data we validate the results of the model and conclude about the supply chain of medicines in Colombia using the legal regulations as a main factor of analysis.

RESULTS AND CONCLUSIONS

We found that in some cases the maximum reimbursement value is less than the final cost of medicines within the hospital, which means that hospitals lose money on the administration of medicines to patients. The benefit of this model is that with the result the hospital can determine the real final monetary value of medicines, including the different processes starting from the reception of the medicines, ending with the administration to patients.

Latent Tuberculosis Infection Testing Strategies for HIV-Positive Individuals in Hong Kong

IMPORTANCE

With immune recovery following early initiation of antiretroviral therapy (ART), the risk of tuberculosis (TB) reactivation among individuals with HIV could be reduced. The current strategy of annual latent TB infection (LTBI) testing should be revisited to increase cost-effectiveness and reduce the intensity of testing for individuals.

OBJECTIVE

To analyze the cost-effectiveness of LTBI testing strategies for individuals in Hong Kong with HIV who had negative LTBI test results at baseline.

DESIGN, SETTING, AND PARTICIPANTS

This decision analytical model study using a cost-effectiveness analysis included 3130 individuals with HIV in Hong Kong, China, which has an intermediate TB burden and a low incidence of HIV-TB coinfection. A system dynamics model of individuals with HIV attending a major HIV specialist clinic in Hong Kong was developed and parameterized by longitudinal clinical and LTBI testing records of patients during a 15-year period. The study population was stratified by age group, CD4 lymphocyte level, ART status, and right of abode. Alternative strategies for LTBI testing after a baseline test were compared with annual testing under different coverages of ART, LTBI testing, and LTBI treatment scenarios in the model. An annual discounting rate of 3.5% was used in cost-effectiveness analysis.

MAIN OUTCOMES AND MEASURES

Proportion of new TB cases averted above base case scenario, discounted quality-adjusted life-years gained (QALYG), incremental cost, and incremental cost-effectiveness ratios in 2017 to 2023.

RESULTS

A total of 3130 patients with HIV (2740 [87.5%] male and 2800 [89.5%] younger than 50 years at HIV diagnosis) with 16 630 person-years of follow-up data from 2002 to 2017 were analyzed. Of these, 94 patients (0.67 [95% CI, 0.51-0.91] per 100 person-years) developed TB. Model estimates of cumulative number of TB cases would reach 146 by 2023, with the annual number of new TB diagnoses ranging from 6 to 8. For patients who had negative LTBI test results at baseline, subsequent LTBI testing strategies were ranked by ascending effectiveness as follows: (1) no testing, (2) test by risk factors, (3) biennial testing for all, (4) up to 3 tests for all, and (5) annual testing for all. Applying a willingness-to-pay threshold of $50 000 per QALYG, none of the subsequent testing strategies were cost-effective. Test by risk factors and up to 3 tests for all were cost-effective only if the willingness-to-pay threshold was increased to $100 000 per QALYG and $200 000 per QALYG, respectively. More new TB cases would be averted by expanding LTBI testing and/or treatment coverage.

CONCLUSIONS AND RELEVANCE

Changing the current testing strategy to less intense testing strategies is likely to be cost-effective in the presence of an increased coverage of baseline LTBI testing and/or treatment.

System dynamics modeling of public health services provided by China CDC to control infectious and endemic diseases in China

BACKGROUND

Infectious and endemic diseases are a serious public health concern worldwide, and their prevention and treatment are globally controversial. This study aimed to establish an system dynamics (SD) model to analyze the factors influencing public health services provided by the Chinese Centers for Disease Control and Prevention (China CDC) to implement infectious and endemic disease control in China, by establishing more effective interventions to provide public health services and thus achieving the goal of controlling infectious and endemic diseases.

MATERIALS AND METHODS

An SD model was constructed using the Vensim DSS program. Intervention experiments were performed using the SD model, which reflected the influences on disease control by adjusting the governmental investment and compensation level for public health products.

RESULTS

The experimental results showed that increasing the governmental investment in China CDC and compensation level for public health products will significantly increase the public health product rate provided by China CDC.

DISCUSSION

Problems with infectious and endemic disease prevention and treatment are the result of the system's incomplete functioning and limited health resources. To address the current problems and improve the system, the government should increase its investment in the public health service system and improve the compensation system to ensure smooth implementation of infectious and endemic disease prevention and treatment and, ultimately, improve public health in China.

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.

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.

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.

Evaluating the effectiveness of contact tracing on tuberculosis outcomes in Saskatchewan using individual-based modeling

Tuberculosis (TB) is a potentially fatal disease spread by an airborne pathogen infecting approximately one third of the globe. For decades, contact tracing (CT) has served a key role in the control of TB and many other notifiable communicable diseases. Unfortunately, CT is a labor-intensive and time-consuming process and is often conducted by a small and overworked nursing staff. To help improve the effectiveness of CT, we introduce a detailed, individual-based model of CT for the Canadian province of Saskatchewan. The model captures the detailed operation of TB CT, including loss to follow-up, and prophylactic and case treatment. This representation is used to assess the impact on active TB cases and TB infection prevalence of differential scoping, speed, prioritization of the CT process, and reduced loss to follow-up. Scenario results are broadly consistent with--but provide many additional insights beyond--our previously reported findings using an aggregate model. In the context of a stylized northern community, findings suggest that age- and ethnicity-prioritized schemes could improve CT effectiveness compared to unprioritized schemes by dramatically reducing TB infection and preventing on average roughly 11% (p < .0001) of active TB cases over a period of 20 years. Reducing loss to follow-up to 10% could yield 5.4% (p = .02) TB cases prevented on average with lower prevalence of TB infection, but improving the CT speed does not yield significant improvement in TB outcomes. Finally, although the work emphasized the value of social network analysis, we found that caution should be exercised in directly translating social network analysis-observed associations into prioritization recommendations.

Modeling US adult obesity trends: a system dynamics model for estimating energy imbalance gap

OBJECTIVES

We present a system dynamics model that quantifies the energy imbalance gap responsible for the US adult obesity epidemic among gender and racial subpopulations.

METHODS

We divided the adult population into gender-race/ethnicity subpopulations and body mass index (BMI) classes. We defined transition rates between classes as a function of metabolic dynamics of individuals within each class. We estimated energy intake in each BMI class within the past 4 decades as a multiplication of the equilibrium energy intake of individuals in that class. Through calibration, we estimated the energy gap multiplier for each gender-race-BMI group by matching simulated BMI distributions for each subpopulation against national data with maximum likelihood estimation.

RESULTS

No subpopulation showed a negative or zero energy gap, suggesting that the obesity epidemic continues to worsen, albeit at a slower rate. In the past decade the epidemic has slowed for non-Hispanic Whites, is starting to slow for non-Hispanic Blacks, but continues to accelerate among Mexican Americans.

CONCLUSIONS

The differential energy balance gap across subpopulations and over time suggests that interventions should be tailored to subpopulations' needs.

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.

Modelling the social determinants of health and simulating short-term and long-term intervention impacts for the city of Toronto, Canada

There is a substantial body of evidence highlighting the importance of the social determinants of health in shaping the health of urban populations in Canada. The low socio-economic status of marginalized, disadvantaged, and precarious populations in urban settings has been linked to adverse health outcomes including chronic and infectious disease, negative health behaviours, barriers to accessing health care services, and overall mortality. Given the dynamic complexities and inter-relationships surrounding the underlying drivers of population health outcomes and inequities, it is difficult to assess program and policy intervention tradeoffs, particularly when such interventions are studied with static models. To address this challenge, we have adopted a systems science approach and developed a simulation model for the City of Toronto, Canada, utilizing system dynamics modelling methodology. The model simulates changes in health, social determinants, and disparities from 2006 and projects forward to 2046 under different assumptions. Most of the variables in the model are stratified by ethnicity, immigration status, and gender, and capture the characteristics of adults aged 25-64. Intervention areas include health care access, behaviour, income, housing, and social cohesion. The model simulates alternative scenarios to help demonstrate the relative impact of different interventions on poor health outcomes such as chronic disease rates, disability rates, and mortality rate. It gives insight into how much, and how quickly, interventions can reduce mortality and morbidity. We believe this will serve as a useful learning tool to allow diverse stakeholders and policy makers to ask "what if" questions and map effective policy directions for complex population health problems, and will enable communities to think about their health futures.