Optimizing an HIV testing program using a system dynamics model of the continuum of care

Identification of Key Components in Health System Using System Thinking Approach: A Scoping Review

Background

The dynamic and systemic planning and targeting in the health system require attention to all the system's components and investigation of their causal relationship in order to form a clear view and image of it. Therefore, the present study was designed with the aim of identifying the comprehensive dimensions of the system within a specific framework.

Methods

Key components in the health system were identified through the scoping review method. For this purpose, 61 studies with selected keywords were extracted from international databases, including Scopus, Web of Science, PubMed and Embase, and Persian language databases including Magiran and SID. Inclusion and exclusion criteria in this study were languages, time range, repeated studies, studies related to the health system, appropriateness of studies with the subject and purpose of the present study and the method used. The content of the selected studies and extracted themes were analyzed and categorized in the Balanced Scorecard (BSC) framework.

Results

In health system analysis, key components were divided into 18 main categories and 45 categories. Also, they were categorized according to the BSC framework into five dimensions of population health, service delivery, growth and development, financing, and governance & leadership.

Conclusion

For health system improvement, policymakers and planners should consider these factors in a dynamic system and a causal network.

The Application of Systems Thinking to the Prevention and Control of Sexually Transmissible Infections among Adolescents and Adults: A Scoping Review

Systems thinking is a mechanism for making sense of complex systems that challenge linear explanations of cause-and-effect. While the prevention and control of sexually transmissible infections (STIs) has been identified as an area that may benefit from systems-level analyses, no review on the subject currently exists. The aim of this study is to conduct a scoping review to identify literature in which systems thinking has been applied to the prevention and control of STIs among adolescent and adult populations. Joanna Briggs Institute guidelines for the conduct of scoping reviews were followed. Five databases were searched for English-language studies published after 2011. A total of n = 6102 studies were screened against inclusion criteria and n = 70 were included in the review. The majority of studies (n = 34) were conducted in African nations. Few studies focused on priority sub-populations, and 93% were focused on HIV (n = 65). The most commonly applied systems thinking method was system dynamics modelling (n = 28). The review highlights areas for future research, including the need for more STI systems thinking studies focused on: (1) migrant and Indigenous populations; (2) conditions such as syphilis; and (3) innovations such as pre-exposure prophylaxis and at-home testing for HIV. The need for conceptual clarity around 'systems thinking' is also highlighted.

Tools for Supporting the MCH Workforce in Addressing Complex Challenges: A Scoping Review of System Dynamics Modeling in Maternal and Child Health

OBJECTIVES

System Dynamics (SD) is a promising decision support modeling approach for growing shared understanding of complex maternal and child health (MCH) trends. We sought to inventory published applications of SD to MCH topics and introduce the MCH workforce to these approaches through examples to support further iteration and use.

METHODS

We conducted a systematic search (1958-2018) for applications of SD to MCH topics and characterized identified articles, following PRISMA guidelines. Pairs of experts abstracted information on SD approach and MCH relevance.

RESULTS

We identified 101 articles describing applications of SD to MCH topics.

APPROACH

27 articles present qualitative diagrams, 10 introduce concept models that begin to quantify dynamics, and 67 present more fully tested/analyzed models.

PURPOSE

The most common purposes described were to increase understanding (n = 55) and support strategic planning (n = 26). While the majority of studies (n = 53) did not involve stakeholders, 40 included what we considered to be a high level of stakeholder engagement - a strength of SD for MCH.

TOPICS

The two Healthy People 2020 topics addressed most frequently were early and middle childhood (n = 30) and access to health services (n = 26). The most commonly addressed SDG goals were "End disease epidemics" (n = 26) and "End preventable deaths" (n = 26).

CONCLUSIONS FOR PRACTICE

While several excellent examples of the application of SD in MCH were found, SD is still underutilized in MCH. Because SD is particularly well-suited to studying and addressing complex challenges with stakeholders, its expanded use by the MCH workforce could inform an understanding of contemporary MCH challenges.

Non-Invasive Diagnosis of Liver Fibrosis in Chronic Hepatitis C using Mathematical Modeling and Simulation

Hepatitis C is a viral infection (HCV) that causes liver inflammation, and it was found that it affects over 170 million people around the world, with Egypt having the highest rate in the world. Unfortunately, serial liver biopsies, which can be invasive, expensive, risky, and inconvenient to patients, are typically used for the diagnosis of liver fibrosis progression. This study presents the development, validation, and evaluation of a prediction mathematical model for non-invasive diagnosis of liver fibrosis in chronic HCV. The proposed model in this article uses a set of nonlinear ordinary differential equations as its core and divides the population into six groups: Susceptible, Treatment, Responder, Non-Responder, Cured, and Fibrosis. The validation approach involved the implementation of two equivalent simulation models that examine the proposed process from different perspectives. A system dynamics model was developed to understand the nonlinear behavior of the diagnosis process over time. The system dynamics model was then transformed to an equivalent agent-based model to examine the system at the individual level. The numerical analysis and simulation results indicate that the earlier the HCV treatment is implemented, the larger the group of people who will become responders, and less people will develop complications such as fibrosis.

Psychosocial and behavioral correlates with HIV testing among men who have sex with men during the COVID‐19 pandemic in China

Objectives

Some of community mitigation efforts on COVID-19 created challenges to ongoing public health programs, including HIV care and prevention services among men who have sex with men (MSM). The goal of the current study was to explore sociodemographic factors and the impact of COVID-19 on HIV testing among Chinese MSM during state-enforced quarantine.

Methods

We conducted a community based survey between May 1st to June 30th, 2020 on COVID-19 related impacts on HIV testing among 436 China MSM during the COVID-19 state-enforced quarantine.

Results

One-third (33.7%) of MSM received HIV testing during the quarantine period. Few participants reported difficulty accessing facility-based testing (n = 13, 3.0%) or obtaining HIV self-test kit online (n = 22, 5.0%). However, 12.1% of participants reported being afraid of getting facility-based HIV test due to concerns about the risk of COVID-19. In the multivariate logistic regression model, participants who were married (aOR: 1.89, 95%CI: 1.19–3.01), reported increased quality of sleep (aOR: 2.07, 95%CI: 1.11–3.86), and increased difficulty in accessing health care (aOR: 2.34, 95%CI: 1.37–3.99) were more likely to get an HIV test during the state-enforced quarantine.

Conclusion

The mitigation measures of COVID-19 have created various barriers to access HIV related prevention services in China, including HIV testing. To mitigate these impacts on HIV prevention and care services, future programs need to address barriers to HIV-related services, such as providing high-quality HIV self-testing. Meanwhile, psychological services or other social services are needed to those experiencing mental distress.

Effective strategies to promote HIV self-testing for men who have sex with men: Evidence from a mathematical model

BACKGROUND

HIV testing is the gateway to HIV treatment and prevention. HIV self-testing (HIVST) has potential to increase testing; however, the potential population-level impact of HIVST on the HIV epidemic and the best strategies for promoting HIVST are unknown. Our aim is to inform public health approaches for promoting HIVST as part of a comprehensive strategy to reduce HIV incidence.

METHODS

Stochastic network-based HIV transmission models were used to estimate how different HIVST strategies would affect HIV incidence in Seattle and Atlanta over 10 years. We included four types of HIV testers and implemented nine replacement and eleven supplementation strategies for HIVST.

RESULTS

Replacement of clinic-based tests with HIVST increased HIV incidence in Seattle and Atlanta. The benefits of supplementary strategies depended on the tester type using HIVST. Targeting non-testers averted the highest number of cases per test. In Seattle 2.2 (95%SI=-77, 100.4) and 4.7 (95%SI=-35.7, 60.1) infections were averted per 1000 HIVST when non-testers used HIVST once or twice per year respectively. In Atlanta the comparable rates were 8.0 (95%SI=-60.3 to 77.7) and 6.7 (95%SI=-37.7, 41.0). Paradoxically, increasing testing among risk-based testers using HIVST increased incidence.

CONCLUSIONS

The population-level impact of HIVST depends on who is reached with HIVST, how kits are used, and by characteristics of the underlying epidemic and HIV care infrastructure. Targeted HIVST can be an effective component of a comprehensive HIV testing strategy. More work is needed to understand how to identify and target non-testers for self-testing implementation.

A reinforcement learning model to inform optimal decision paths for HIV elimination

The 'Ending the HIV Epidemic (EHE)' national plan aims to reduce annual HIV incidence in the United States from 38,000 in 2015 to 9300 by 2025 and 3300 by 2030. Diagnosis and treatment are two most effective interventions, and thus, identifying corresponding optimal combinations of testing and retention-in-care rates would help inform implementation of relevant programs. Considering the dynamic and stochastic complexity of the disease and the time dynamics of decision-making, solving for optimal combinations using commonly used methods of parametric optimization or exhaustive evaluation of pre-selected options are infeasible. Reinforcement learning (RL), an artificial intelligence method, is ideal; however, training RL algorithms and ensuring convergence to optimality are computationally challenging for large-scale stochastic problems. We evaluate its feasibility in the context of the EHE goal. We trained an RL algorithm to identify a 'sequence' of combinations of HIV-testing and retention-in-care rates at 5-year intervals over 2015-2070 that optimally leads towards HIV elimination. We defined optimality as a sequence that maximizes quality-adjusted-life-years lived and minimizes HIV-testing and care-and-treatment costs. We show that solving for testing and retention-in-care rates through appropriate reformulation using proxy decision-metrics overcomes the computational challenges of RL. We used a stochastic agent-based simulation to train the RL algorithm. As there is variability in support-programs needed to address barriers to care-access, we evaluated the sensitivity of optimal decisions to three cost-functions. The model suggests to scale-up retention-in-care programs to achieve and maintain high annual retention-rates while initiating with a high testing-frequency but relaxing it over a 10-year period as incidence decreases. Results were mainly robust to the uncertainty in costs. However, testing and retention-in-care alone did not achieve the 2030 EHE targets, suggesting the need for additional interventions. The results from the model demonstrated convergence. RL is suitable for evaluating phased public health decisions for infectious disease control.

A multi-method approach to modeling COVID-19 disease dynamics in the United States

In this paper, we proposed a multi-method modeling approach to community-level spreading of COVID-19 disease. Our methodology was composed of interconnected age-stratified system dynamics models in an agent-based modeling framework that allowed for a granular examination of the scale and severity of disease spread, including metrics such as infection cases, deaths, hospitalizations, and ICU usage. Model parameters were calibrated using an optimization technique with an objective function to minimize error associated with the cumulative cases of COVID-19 during a training period between March 15 and October 31, 2020. We outlined several case studies to demonstrate the model's state- and local-level projection capabilities. We further demonstrated how model outcomes could be used to evaluate perceived levels of COVID-19 risk across different localities using a multi-criteria decision analysis framework. The model's two, three, and four week out-of-sample projection errors varied on a state-by-state basis, and generally increased as the out-of-sample projection period was extended. Additionally, the prediction error in the state-level projections was generally due to an underestimation of cases and an overestimation of deaths. The proposed modeling approach can be used as a virtual laboratory to investigate a wide range of what-if scenarios and easily adapted to future high-consequence public health threats.

A qualitative model of the HIV care continuum in Vancouver, Canada

A team of health care stakeholders and researchers collaboratively developed a qualitative model and graphic representation of the continuum of HIV care in Vancouver to inform delivery of antiretroviral therapy and other HIV health services. The model describes the patient journey through the HIV care continuum, including states of infection, health services, and care decisions. We used a Unified Modelling Language (UML) activity diagram to capture patient and provider activities and to guide the construction of a UML state machine diagram. The state machine diagram captures model agent states in a formalism that facilitates the development of system dynamics or agent-based models. These quantitative models can be applied to optimizing the allocation of resources, and to evaluate potential strategies for improved patient care and system performance. The novel approach of combining UML diagrams we present provides a general method for modelling capacity ---management strategies within complex health systems.

HIV supply chains in low- and middle-income countries: overview and research opportunities

Purpose

Despite HIV being reported as one of the major global health issues, availability and accessibility of HIV services and supplies remain limited, especially in low- and middle-income countries. The effective and efficient operation of HIV supply chains is critical to tackle this problem. The purpose of this paper is to give an introduction to HIV supply chains in low- and middle-income countries and identify research opportunities for the operations research/operations management (OR/OM) community.

Design/methodology/approach

First, the authors review a combination of the scientific and grey literature, including both qualitative and quantitative papers, to give an overview of HIV supply chain operations in low- and middle-income countries and the challenges that are faced by organizing such supply chains. The authors then classify and discuss the relevant OR/OM literature based on seven classification criteria: decision level, methodology, type of HIV service modeled, challenges, performance measures, real-life applicability and countries covered. Because research on HIV supply chains in low- and middle-income countries is limited in the OR/OM field, this part also includes papers focusing on HIV supply chain modeling in high-income countries.

Findings

The authors conclude this study by identifying several tendencies and gaps and by proposing future research directions for OR/OM research.

Originality/value

To the best of the authors’ knowledge, this paper is the first literature review addressing this specific topic from an OR/OM perspective.

Bringing a health systems modelling approach to complex evaluations: multicountry applications in HIV, TB and malaria

INTRODUCTION

Understanding how to deliver interventions more effectively is a growing emphasis in Global Health. Simultaneously, health system strengthening is a key component to improving delivery. As a result, it is challenging to evaluate programme implementation while reflecting real-world complexity. We present our experience in using a health systems modelling approach as part of a mixed-methods evaluation and describe applications of these models.

METHODS

We developed a framework for how health systems translate financial inputs into health outcomes, with in-country and international experts. We collated available data to measure framework indicators and developed models for malaria in Democratic Republic of the Congo (DRC), and tuberculosis in Guatemala and Senegal using Bayesian structural equation modelling. We conducted several postmodelling analyses: measuring efficiency, assessing bottlenecks, understanding mediation, analysing the cascade of care and measuring subnational effectiveness.

RESULTS

The DRC model indicated a strong relationship between shipment of commodities and utilisation thereof. In Guatemala, the strongest model coefficients were more evenly distributed. Results in Senegal varied most, but pathways related to community care had the strongest relationships. In DRC, we used model results to estimate the end-to-end cost of delivering commodities. In Guatemala, we used model results to identify potential bottlenecks and understand mediation. In Senegal, we used model results to identify potential weak links in the cascade of care, and explore subnationally.

CONCLUSION

This study demonstrates a complementary modelling approach to traditional evaluation methods. Although these models have limitations, they can be applied in a variety of ways to gain greater insight into implementation and functioning of health service delivery.

Optimization Models for HIV/AIDS Resource Allocation: A Systematic Review

OBJECTIVE

This study reviews optimization models for human immunodeficiency virus (HIV) and acquired immunodeficiency syndrome (AIDS) resource allocation.

METHODS

We searched 2 databases for peer-reviewed articles published from January 1985 through August 2019 that describe optimization models for resource allocation in HIV/AIDS. We included models that consider 2 or more competing HIV/AIDS interventions. We extracted data on selected characteristics and identified similarities and differences across models. We also assessed the quality of mathematical disease transmission models based on the best practices identified by a 2010 task force.

RESULTS

The final qualitative synthesis included 23 articles that used 14 unique optimization models. The articles shared several characteristics, including the use of dynamic transmission modeling to estimate health benefits and the inclusion of specific high-risk groups in the study population. The models explored similar HIV/AIDS interventions that span primary and secondary prevention and antiretroviral treatment. Most articles were focused on sub-Saharan African countries (57%) and the United States (39%). There was notable variation in the types of optimization objectives across the articles; the most common was minimizing HIV incidence or maximizing infections averted (87%). Articles that utilized mathematical modeling of HIV disease and transmission displayed variable quality.

CONCLUSIONS

This systematic review of the literature identified examples of optimization models that have been applied in different settings, many of which displayed similar features. There were similarities in objective functions across optimization models, but they did not align with global HIV/AIDS goals or targets. Future work should be applied in countries facing the largest declines in HIV/AIDS funding.

A System Dynamics Simulation Applied to Healthcare: A Systematic Review

In recent years, there has been significant interest in developing system dynamics simulation models to analyze complex healthcare problems. However, there is a lack of studies seeking to summarize the available papers in healthcare and present evidence on the effectiveness of system dynamics simulation in this area. The present paper draws on a systematic selection of published literature from 2000 to 2019, in order to form a comprehensive view of current applications of system dynamics methodology that address complex healthcare issues. The results indicate that the application of system dynamics has attracted significant attention from healthcare researchers since 2013. To date, articles on system dynamics have focused on a variety of healthcare topics. The most popular research areas among the reviewed papers included the topics of patient flow, obesity, workforce demand, and HIV/AIDS. Finally, the quality of the included papers was assessed based on a proposed ranking system, and ways to improve the system dynamics models' quality were discussed.

Routine HIV testing in acute care hospitals: Changing practice to curb a local HIV epidemic in Vancouver, BC

Early treatment of HIV infection increases life expectancy and reduces infectivity; however, delayed HIV diagnosis remains common. Implementation and sustainability of hospital-based routine HIV testing in Vancouver, British Columbia, was evaluated to address a local HIV epidemic by facilitating earlier diagnosis and treatment. Public health issued a recommendation in 2011 to offer HIV testing to all patients presenting to three Vancouver hospitals as part of routine care, including all patients admitted to medical/surgical units with expansion to emergency departments (ED). We evaluated acceptability, feasibility, and effectiveness from 2011 to 2014 and continued monitoring through 2016 for sustainability. Between October 2011-December 2016, 114,803 HIV tests were administered at the three hospitals; an 11-fold increase following implementation of routine testing. The rate of testing was sustained and remained high through 2018. Of those tested, 151 patients were diagnosed with HIV for a testing yield of 0.13%. Review of 12,996 charts demonstrated 4935/5876 (96·9%) of admitted patients agreed to have an HIV test when offered. People diagnosed in hospital were significantly more likely to be diagnosed with acute stage (aOR 1·96, 95% CI 1·19, 3·23) infection, particularly those diagnosed in the ED. This study provides practice-based evidence of the feasibility, acceptability, and effectiveness of implementing a recommendation for routine HIV testing among inpatient and emergency department admissions, as well as the ability to normalize and sustain this change. Routine hospital-based HIV testing can increase diagnoses of acute HIV infection and facilitate earlier initiation of antiretroviral treatment.

Optimizing Interventions Across the HIV Care Continuum: A Case Study Using Process Improvement Analysis

UNAIDS' 90-90-90 goal for 2020 is for 90% of HIV-infected people to know their status, 90% of infected individuals to receive antiretroviral therapy (ART), and 90% of those on ART to achieve viral suppression. To achieve these ambitious goals, effective care delivery programs are needed. In this paper we present a case study showing how HIV care can be improved by viewing the patient care process as a production process and applying methods of process improvement analysis. We examine the continuum of HIV care at a hospital-based HIV clinic in Kingston, Jamaica. We perform qualitative analysis to identify key programmatic, personnel, and clinical areas for process improvement. We then perform quantitative analysis. We develop a stochastic model of the care process which we use to evaluate the effects of potential process improvements on the number of patients who receive ART and the number who achieve viral suppression. We also develop a model for optimal investment of a fixed budget among interventions aimed at improving the care cascade and we use the model to determine the optimal investment among three interventions that the clinic could invest in. By viewing the patient care process as a production process and applying qualitative and quantitative process improvement analysis, our case study illustrates how clinics can identify the best ways to maximize clinical outcomes. Our methods are generalizable to other HIV care clinics as well as to clinics that provide care for other chronic conditions (e.g., diabetes, hepatitis B, or opioid use disorder).

The impact of high-dose opioid prescription on mortality rates among people living with HIV: A retrospective cohort study

OBJECTIVES

High-dose opioid use is associated with increased morbidity, mortality, and healthcare utilization. People living with HIV (PLHIV) are frequently prescribed these medications to manage their pain. However, little is known about the relationship between being prescribed high doses of opioids (> 90 MME/d) and mortality risk among this population. The objective of this study was to examine the trends in mortality and the relationship between high-dose opioid analgesic prescribing and mortality among PLHIV.

METHODS

Utilizing the STOP HIV/AIDS cohort--a population-level linked database of treatment of PLHIV in British Columbia--we conducted bivariable and multivariable generalized estimating equation (GEE) models with a Poisson distribution to examine the relationship between high-dose opioid prescription and all-cause mortality rates in the study sample.

RESULTS

Between 1996 and 2015, 9272 PLHIV were included in the study. Age- and sex-adjusted mortality rate (using the 2011 Canadian population as the reference) was 30.99 per 1000 person-years (95% confidence interval [CI]: 28.11-33.88). In a multivariable GEE model with adjustment for various demographic and clinical confounders, there was a positive and independent association between being prescribed high-dose opioids and all-cause mortality rates (adjusted rate ratio [ARR] = 3.01; 95%CI: 2.47-3.66).

DISCUSSION

We found that mortality rates were significantly higher among PLHIV who were prescribed high-dose opioids compared to those who were prescribed lower doses. Our results highlight the risk associated with the prescribing of high-dose opioids to manage HIV-related pain and emphasize the need to explore non-opioid approaches to pain management.

Simulating system dynamics of the HIV care continuum to achieve treatment as prevention

The continuing HIV pandemic calls for broad, multi-sectoral responses that foster community control of local prevention and care services, with the goal of leveraging high quality treatment as a means of reducing HIV incidence. Service system improvements require stakeholder input from across the care continuum to identify gaps and to inform strategic plans that improve HIV service integration and delivery. System dynamics modeling offers a participatory research approach through which stakeholders learn about system complexity and about ways to achieve sustainable system-level improvements. Via an intensive group model building process with a task force of community stakeholders with diverse roles and responsibilities for HIV service implementation, delivery and surveillance, we designed and validated a multi-module system dynamics model of the HIV care continuum, in relation to local prevention and care service capacities. Multiple sources of data were used to calibrate the model for a three-county catchment area of central Connecticut. We feature a core module of the model for the purpose of illustrating its utility in understanding the dynamics of treatment as prevention at the community level. We also describe the methods used to validate the model and support its underlying assumptions to improve confidence in its use by stakeholders for systems understanding and decision making. The model’s generalizability and implications of using it for future community-driven strategic planning and implementation efforts are discussed.

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.

Making Smarter Decisions Faster: Systems Engineering to Improve the Global Public Health Response to HIV

PURPOSE OF REVIEW

This review offers an operational definition of systems engineering (SE) as applied to public health, reviews applications of SE in the field of HIV, and identifies opportunities and challenges of broader application of SE in global health.

RECENT FINDINGS

SE involves the deliberate sequencing of three steps: diagnosing a problem, evaluating options using modeling or optimization, and providing actionable recommendations. SE includes diverse tools (from process improvement to mathematical modeling) applied to decisions at various levels (from local staffing decisions to planning national-level roll-out of new interventions). Contextual factors are crucial to effective decision-making, but there are gaps in understanding global decision-making processes. Integrating SE into pre-service training and translating SE tools to be more accessible could increase utilization of SE approaches in global health. SE is a promising, but under-recognized approach to improve public health response to HIV globally.

System Dynamics Approaches and Collective Action for Community Health: An Integrative Review

System dynamics (SD) methods, from qualitative causal loop diagramming to quantitative simulation modeling, have the potential to be powerful tools for engaging community stakeholders interested in improving health. However, the extent to which SD drives collective action to improve community health is unclear. The objective of this review was to understand how often, why, and how SD has been used by cross-sector community collectives. Of 409 identified manuscripts describing application of SD to community health, only 31 (7.6%) documented cross-sector collective use of these tools. All 31 had as a purpose using SD to better understand community health problems, but only seven (22.6%) documented a collective action taken as the result. In nine of the 31 articles (29.0%), no learning, decisions, or action was documented. The most common form of collective participation in SD work among the seven collectives reporting resulting action was building the SD model, with implementing a new program or practice the most frequently mentioned collective action resulting. Cost and access were the most common system outcomes studied, and chronic diseases and prevention were commonly mentioned as the focal health outcomes. Overall, SD methods seem underutilized for engaging cross-sector collectives in addressing complex community health problems.

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.

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.

Modelling self-management pathways for people with diabetes in primary care

BACKGROUND

Self-management support to facilitate people with type 2 diabetes to effectively manage their condition is complex to implement. Organisational and system elements operating in relation to providing optimal self-management support in primary care are poorly understood. We have applied operational research techniques to model pathways in primary care to explore and illuminate the processes and points where people struggle to find self-management support.

METHODS

Primary care clinicians and support staff in 21 NHS general practices created maps to represent their experience of patients' progress through the system following diagnosis. These were collated into a combined pathway. Following consideration of how patients reduce dependency on the system to become enhanced self-managers, a model was created to show the influences on patients' pathways to self-management.

RESULTS

Following establishment of diagnosis and treatment, appointment frequency decreases and patient self-management is expected to increase. However, capacity to consistently assess self-management capabilities; provide self-management support; or enhance patient-led self-care activities is missing from the pathways. Appointment frequencies are orientated to bio-medical monitoring rather than increasing the ability to mobilise resources or undertake self-management activities.

CONCLUSIONS

The model provides a clear visual picture of the complexities implicated in achieving optimal self-management support. Self-management is quickly hidden from view in a system orientated to treatment delivery rather than to enhancing patient self-management. The model created highlights the limited self-management support currently provided and illuminates points where service change might impact on providing support for self-management. Ensuring professionals are aware of locally available support and people's existing network support has potential to provide appropriate and timely direction to community facilities and the mobilisation of resources.