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

Identifying vulnerabilities in essential health services: Analysing the effects of system shocks on childhood vaccination delivery in Lebanon

Shocks effects are under-theorised in the growing literature on health system resilience. Existing work has focused on the effects of single shocks on discrete elements within the health system, typically at national level. Using qualitative system dynamics, we explored how effects of multiple shocks interacted across system levels and combined with existing vulnerabilities to produce effects on essential health services delivery, through the prism of a case study on childhood vaccination in Lebanon. Lebanon has experienced a series of shocks in recent years, including large-scale refugee arrivals from neighbouring Syria, the COVID-19 pandemic and a political-economic crisis. We developed a causal loop diagram (CLD) to explore the effects of each shock individually, and in combination. The CLD was developed and validated using qualitative data from interviews with 38 stakeholders working in Lebanon's vaccination delivery system, in roles ranging from national level policy to facility-level service delivery, conducted between February 2020 and January 2022. We found that each of the shocks had different effects on service demand- and supply-side dynamics. These effects cascaded from national through to local levels. Both Syrian refugee movement and the COVID-19 pandemic primarily exposed vulnerabilities in service demand, mainly through slowly emerging knock-on effects on vaccination uptake behaviour among host communities, and fear of contracting infection in crowded health facilities respectively. The economic crisis exposed wider system vulnerabilities, including demand for vaccination as household income collapsed, and supply-side effects such as reduced clinic time for vaccination, declining workforce retention, and reduced availability of viable vaccine doses, among others. Finally, important pathways of interaction between shocks were identified, particularly affecting the balance between demand for vaccination through publicly supported facilities and private clinics. Future research should incorporate dynamic approaches to identifying within-system vulnerabilities and their potential impacts under different scenarios, as a precursor to improved resilience measurement, system preparedness, and intervention targeting.

Health workforce needs in Malawi: analysis of the Thanzi La Onse integrated epidemiological model of care

BACKGROUND

To make the best use of health resources, it is crucial to understand the healthcare needs of a population-including how needs will evolve and respond to changing epidemiological context and patient behaviour-and how this compares to the capabilities to deliver healthcare with the existing workforce. Existing approaches to planning either rely on using observed healthcare demand from a fixed historical period or using models to estimate healthcare needs within a narrow domain (e.g., a specific disease area or health programme). A new data-grounded modelling method is proposed by which healthcare needs and the capabilities of the healthcare workforce can be compared and analysed under a range of scenarios: in particular, when there is much greater propensity for healthcare seeking.

METHODS

A model representation of the healthcare workforce, one that formalises how the time of the different cadres is drawn into the provision of units of healthcare, was integrated with an individual-based epidemiological model-the Thanzi La Onse model-that represents mechanistically the development of disease and ill-health and patients' healthcare seeking behaviour. The model was applied in Malawi using routinely available data and the estimates of the volume of health service delivered were tested against officially recorded data. Model estimates of the "time needed" and "time available" for each cadre were compared under different assumptions for whether vacant (or established) posts are filled and healthcare seeking behaviour.

RESULTS

The model estimates of volume of each type of service delivered were in good agreement with the available data. The "time needed" for the healthcare workforce greatly exceeded the "time available" (overall by 1.82-fold), especially for pharmacists (6.37-fold) and clinicians (2.83-fold). This discrepancy would be largely mitigated if all vacant posts were filled, but the large discrepancy would remain for pharmacists (2.49-fold). However, if all of those becoming ill did seek care immediately, the "time needed" would increase dramatically and exceed "time supply" (2.11-fold for nurses and midwives, 5.60-fold for clinicians, 9.98-fold for pharmacists) even when there were no vacant positions.

CONCLUSIONS

The results suggest that services are being delivered in less time on average than they should be, or that healthcare workers are working more time than contracted, or a combination of the two. Moreover, the analysis shows that the healthcare system could become overwhelmed if patients were more likely to seek care. It is not yet known what the health consequences of such changes would be but this new model provides-for the first time-a means to examine such questions.

Using simulation modelling to transform hospital planning and management to address health inequalities

Health inequalities are a perennial concern for policymakers and in service delivery to ensure fair and equitable access and outcomes. As health inequalities are socially influenced by employment, income, and education, this impacts healthcare services among socio-economically disadvantaged groups, making it a pertinent area for investigation in seeking to promote equitable access. Researchers widely acknowledge that health equity is a multi-faceted problem requiring approaches to understand the complexity and interconnections in hospital planning as a precursor to healthcare delivery. Operations research offers the potential to develop analytical models and frameworks to aid in complex decision-making that has both a strategic and operational function in problem-solving. This paper develops a simulation-based modelling framework (SimulEQUITY) to model the complexities in addressing health inequalities at a hospital level. The model encompasses an entire hospital operation (including inpatient, outpatient, and emergency department services) using the discrete-event simulation method to simulate the behaviour and performance of real-world systems, processes, or organisations. The paper makes a sustained contribution to knowledge by challenging the existing population-level planning approaches in healthcare that often overlook individual patient needs, especially within disadvantaged groups. By holistically modelling an entire hospital, socio-economic variations in patients' pathways are developed by incorporating individual patient attributes and variables. This innovative framework facilitates the exploration of diverse scenarios, from processes to resources and environmental factors, enabling key decision-makers to evaluate what intervention strategies to adopt as well as the likely scenarios for future patterns of healthcare inequality. The paper outlines the decision-support toolkit developed and the practical application of the SimulEQUITY model through to implementation within a hospital in the UK. This moves hospital management and strategic planning to a more dynamic position where a software-based approach, incorporating complexity, is implicit in the modelling rather than simplification and generalisation arising from the use of population-based models.

Cost-effectiveness of tuberculosis infection prevention and control interventions in South African clinics: a model-based economic evaluation informed by complexity science methods

INTRODUCTION

Nosocomial Mycobacterium tuberculosis (Mtb) transmission substantially impacts health workers, patients and communities. Guidelines for tuberculosis infection prevention and control (TB IPC) exist but implementation in many settings remains suboptimal. Evidence is needed on cost-effective investments to prevent Mtb transmission that are feasible in routine clinic environments.

METHODS

A set of TB IPC interventions was codesigned with local stakeholders using system dynamics modelling techniques that addressed both core activities and enabling actions to support implementation. An economic evaluation of these interventions was conducted at two clinics in KwaZulu-Natal, employing agent-based models of Mtb transmission within the clinics and in their catchment populations. Intervention costs included the costs of the enablers (eg, strengthened supervision, community sensitisation) identified by stakeholders to ensure uptake and adherence.

RESULTS

All intervention scenarios modelled, inclusive of the relevant enablers, cost less than US$200 per disability-adjusted life-year (DALY) averted and were very cost-effective in comparison to South Africa's opportunity cost-based threshold (US$3200 per DALY averted). Two interventions, building modifications to improve ventilation and maximising use of the existing Central Chronic Medicines Dispensing and Distribution system to reduce the number of clinic attendees, were found to be cost saving over the 10-year model time horizon. Incremental cost-effectiveness ratios were sensitive to assumptions on baseline clinic ventilation rates, the prevalence of infectious TB in clinic attendees and future HIV incidence but remained highly cost-effective under all uncertainty analysis scenarios.

CONCLUSION

TB IPC interventions in clinics, including the enabling actions to ensure their feasibility, afford very good value for money and should be prioritised for implementation within the South African health system.

Complexity and Evidence in Health Sector Decision Making: Lessons from Tuberculosis Infection Prevention in South Africa

To better understand and plan health systems featuring multiple levels and complex causal elements, there have been increasing attempts to incorporate tools arising from complexity science to inform decisions. The utilization of new planning approaches can have important implications for the types of evidence that inform health policymaking and the mechanisms through which they do so. This paper presents an empirical analysis of the application of one such tool—system dynamics modelling (SDM)—within a tuberculosis control programme in South Africa in order to explore how SDM was utilized, and to reflect on the implications for evidence-informed health policymaking. We observed group model building workshops that served to develop the SDM process and undertook 19 qualitative interviews with policymakers and practitioners who partook in these workshops. We analysed the relationship between the SDM process and the use of evidence for policymaking through four conceptual perspectives: (1) a rationalist knowledge-translation view that considers how previously-generated research can be taken up into policy; (2) a programmatic approach that considers existing goals and tasks of decision-makers, and how evidence might address them; (3) a social constructivist lens exploring how the process of using an evidentiary planning tool like SDM can shape the understanding of problems and their solutions; and (4) a normative perspective that recognizes that stakeholders may have different priorities, and thus considers which groups are included and represented in the process. Each perspective can provide useful insights into the SDM process and the political nature of evidence use. In particular, SDM can provide technical information to solve problems, potentially leave out other concerns and influence how problems are conceptualized by formalizing the boundaries of the policy problem and delineating particular solution sets. Undertaking the process further involves choices on stakeholder inclusion affecting whose interests may be served as evidence to inform decisions.

Using systems thinking to increase understanding of the innovation system of healthcare organisations

PURPOSE

This paper applies systems thinking modelling to enhance the dynamic understanding of how to nurture an innovative culture in healthcare organisations to develop the innovation system in practice and speed up the innovative work. The model aims to provide a holistic view of a studied healthcare organisation's innovation processes, ranging from managerial values to its manifestation in improved results.

DESIGN/METHODOLOGY/APPROACH

The study is based on empirical material from a healthcare unit that, within a few years, changed from having no innovations to repeatedly generating innovations. The study uses the modelling language of causal loop diagrams (CLDs) in the system dynamics methodology to identify the key important aspects found in the empirical material.

FINDINGS

The proposed model, based on the stories of the interviewees, explores the dynamics of inertia when nurturing an innovative culture, identifying delays attributed to the internal change processes and system relationships. These findings underscored the need for perseverance when developing an innovative culture in the entrepreneurial phases.

PRACTICAL IMPLICATIONS

The approach of using systems thinking to make empirical healthcare research results more tangible through the visual notations of CLDs and mental simulations is believed to support exploring complex phenomena to induce and nurture both individual and organisational learning.

ORIGINALITY/VALUE

The results from this approach provide deepened analysis and provoke the systems view to explain how the nurturing of the culture can accelerate the innovation processes, which helps practitioners and researchers to further expand their understanding of their healthcare contexts.

Knowledge Management as a Domain, System Dynamics as a Methodology

For decades, system dynamics has been utilised as a framework for evaluating and interpreting various types of systems with varying degrees of complexity and knowledge demands. Knowledge management is strongly related to system dynamics on a thematic level. We did a thorough review to identify potential applications and analysed system dynamics and knowledge management domains. The systematic review followed the PRISMA method. We identified two major groups and one subgroup of the combination of system dynamics and knowledge management after examining and categorising 45 papers. Articles were searched for on Web of Science, Scopus, and LENS. We then concentrated on the categorisation of articles by theme. We discovered that system dynamics models were used as a component of a decision support tool or a knowledge management system in some instances, or the integration of knowledge management processes into specific systems. This study contributes to the growth of system dynamics as a methodology capable of generating novel ideas, highlighting limitations, and providing analogies for future research in a variety of academic areas.

Dynamic interaction nursing intervention on functional rehabilitation and self-care ability of patients after aneurysm surgery

BACKGROUND

Nursing practices based on the dynamic interaction model have been shown to be superior to generic nursing practices. However, whether this model is effective in patients recovering from intracranial aneurysm surgery is not well studied.

AIM

To investigate the effect of nursing based on a dynamic interaction model on functional rehabilitation of patients after aneurysm surgery.

METHODS

A total of 86 cases in our hospital with intracranial aneurysm from April 2019 to April 2021, were selected and divided into the study group and the control group, with 43 patients in each group. The control group received routine nursing, and the research group received nursing intervention based on a dynamic interaction model. The daily living ability (activities of daily living, ADL), cognitive function (Simple Intelligent Mental State Scale, MMSE), quality of life (Generic Quality of Life Inventory-74, GQOL-74), self-care ability (Exercise of Self-Care Agency scale), incidence of complications, and nursing satisfaction were recorded before and after intervention.

RESULTS

Before intervention, ADL (52.09 ± 6.44), MMSE (18.03 ± 4.11), and GQOL-74 (53.68 ± 4.34) scores in the study group were not significantly different from those in the control group (ADL: 50.97 ± 7.32, MMSE: 17.59 ± 3.82, GQOL-74: 55.06 ± 3.98) (P > 0.05). After intervention, ADL (86.12 ± 5.07), MMSE (26.64 ± 2.66), and GQOL-74 (83.13 ± 5.67) scores in the study group were higher than those in the control group (ADL: 79.81 ± 6.35, MMSE: 24.51 ± 3.00, and GQOL-74: 77.96 ± 6.27) (P < 0.05). Before intervention, self-concept (17.46 ± 4.44), self-care skills (25.22 ± 4.20), self-care knowledge (22.35 ± 4.74), and self-care responsibility (15.06 ± 3.29) scores in the study group was similar to those in the control group (self-concept: 16.89 ± 5.53, self-care skills: 24.59 ± 4.46, self-care knowledge: 21.80 ± 3.61, and self-care responsibility: 14.83 ± 3.11) (P > 0.05). After the intervention, self-concept (26.01 ± 3.18), self-care skills (37.68 ± 6.05), self-care knowledge (45.56 ± 5.83), and self-care responsibility (22.01 ± 3.77) scores in the study group were higher than those in the control group (self-concept: 22.97 ± 3.46, self-care skills: 33.02 ± 5.65, self-care skills knowledge: 36.81 ± 5.54, and self-care responsibility: 17.97 ± 3.56 points) (P < 0.05). The incidence of complications in the study group (4.65%) was lower than that in the control group (18.60%) (P < 0.05). Nursing satisfaction in the study group (95.35%) was higher than that in the control group (81.40%) (P < 0.05).

CONCLUSION

Nursing intervention based on a dynamic interaction model can improve postoperative cognitive function, daily living ability, self-care ability, quality of life, and patient satisfaction, while reducing the risk of complications.

Collaboration-competition dilemma in flattening the COVID-19 curve

Testing for COVID-19 is a key intervention that supports tracking and isolation to prevent further infections. However, diagnostic tests are a scarce and finite resource, so abundance in one country can quickly lead to shortages in others, creating a competitive landscape. Countries experience peaks in infections at different times, meaning that the need for diagnostic tests also peaks at different moments. This phase lag implies opportunities for a more collaborative approach, although countries might also worry about the risks of future shortages if they help others by reallocating their excess inventory of diagnostic tests. This article features a simulation model that connects three subsystems: COVID-19 transmission, the diagnostic test supply chain, and public policy interventions aimed at flattening the infection curve. This integrated system approach clarifies that, for public policies, there is a time to be risk-averse and a time for risk-taking, reflecting the different phases of the pandemic (contagion vs. recovery) and the dominant dynamic behavior that occurs in these phases (reinforcing vs. balancing). In the contagion phase, policymakers cannot afford to reject extra diagnostic tests and should take what they can get, in line with a competitive mindset. In the recovery phase, policymakers can afford to give away excess inventory to other countries in need (one-sided collaboration). When a country switches between taking and giving, in a form of two-sided collaboration, it can flatten the curve, not only for itself but also for others.

Fasting blood glucose in a Ghanaian adult is causally affected by malaria parasite load: a mechanistic case study using convergent cross mapping

BACKGROUND

Adults with diabetes mellitus (DM) in malaria-endemic areas might be more susceptible to Plasmodium infection than healthy individuals. Herein, the study was aimed at verifying the hypothesis that increased fasting blood glucose (FBG) promotes parasite growth as reflected by increased parasite density.

METHODS

Seven adults without DM were recruited in rural Ghana to determine the relationships between FBG and malaria parasite load. Socio-economic data were recorded in questionnaire-based interviews. Over a period of 6 weeks, FBG and Plasmodium sp. Infection were measured in peripheral blood samples photometrically and by polymerase chain reaction (PCR)-assays, respectively. Daily physical activity and weather data were documented via smartphone recording. For the complex natural systems of homeostatic glucose control and Plasmodium sp. life cycle, empirical dynamic modelling was applied.

RESULTS

At baseline, four men and three women (median age, 33 years; interquartile range, 30-48) showed a median FBG of 5.5 (5.1-6.0 mmol/L); one participant had an asymptomatic Plasmodium sp. infection (parasite density: 240/µL). In this participant, convergent cross mapping (CCM) for 34 consecutive days, showed that FBG was causally affected by parasite density (p < 0.02), while the reciprocal relationship was not discernible (p > 0.05). Additionally, daily ambient temperature affected parasite density (p < 0.01).

CONCLUSION

In this study population living in a malaria-endemic area, time series analyses were successfully piloted for the relationships between FBG and Plasmodium sp. density. Longer observation periods and larger samples are required to confirm these findings and determine the direction of causality.

Economic evaluations of health system strengthening activities in low-income and middle-income country settings: a methodological systematic review

Objective

Health system strengthening (HSS) activities should accompany disease-targeting interventions in low/middle-income countries (LMICs). Economic evaluations provide information on how these types of investment might best be balanced but can be challenging. We conducted a systematic review to evaluate how researchers address these economic evaluation challenges.

Methods

We identified studies about economic evaluation of HSS activities in LMICs using a two-stage approach. First, we conducted a broad search to identify areas where economic evaluations of HSS activities were being conducted. Next, we selected specific interventions for more targeted literature review. We extracted study characteristics using the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist. Finally, we summarised authors’ modelling decisions using a framework that examines how models are developed to emphasise generalisability, precision, or realism.

Findings

Our searches produced 1978 studies, out of which we included 36. Most studies used data from prospective trials and calculated cost-effectiveness directly from these trial inputs, rather than using simulation methods. As a group, these studies primarily emphasised precision and realism over generalisability, meaning that their results were best suited to specific settings.

Conclusions

The number of included studies was small. Our findings suggest that most economic evaluations of HSS do not leverage methods like sensitivity analyses or inputs from literature review that would produce more generalisable (but potentially less precise) results. More research into how decision-makers would use economic evaluations to define the expansion path to strengthening health systems would allow for conceptualising impactful work on the economic value of HSS.

Using system dynamics modelling to estimate the costs of relaxing health system constraints: a case study of tuberculosis prevention and control interventions in South Africa

Health system constraints are increasingly recognized as an important addition to model-based analyses of disease control interventions, as they affect achievable impact and scale. Enabling activities implemented alongside interventions to relax constraints and reach the intended coverage may incur additional costs, which should be considered in priority setting decisions. We explore the use of group model building, a participatory system dynamics modelling technique, for eliciting information from key stakeholders on the constraints that apply to tuberculosis infection prevention and control processes within primary healthcare clinics in South Africa. This information was used to design feasible interventions, including the necessary enablers to relax existing constraints. Intervention and enabler costs were then calculated at two clinics in KwaZulu-Natal using input prices and quantities from the published literature and local suppliers. Among the proposed interventions, the most inexpensive was retrofitting buildings to improve ventilation (US$1644 per year), followed by maximizing the use of community sites for medication collection among stable patients on antiretroviral therapy (ART; US$3753) and introducing appointments systems to reduce crowding (US$9302). Enablers identified included enhanced staff training, supervision and patient engagement activities to support behaviour change and local ownership. Several of the enablers identified by the stakeholders, such as obtaining building permissions or improving information flow between levels of the health systems, were not amenable to costing. Despite this limitation, an approach to costing rooted in system dynamics modelling can be successfully applied in economic evaluations to more accurately estimate the 'real world' opportunity cost of intervention options. Further empirical research applying this approach to different intervention types (e.g. new preventive technologies or diagnostics) may identify interventions that are not cost-effective in specific contexts based on the size of the required investment in enablers.

Modelling the effect of infection prevention and control measures on rate of Mycobacterium tuberculosis transmission to clinic attendees in primary health clinics in South Africa

BACKGROUND

Elevated rates of tuberculosis in healthcare workers demonstrate the high rate of Mycobacterium tuberculosis (Mtb) transmission in health facilities in high-burden settings. In the context of a project taking a whole systems approach to tuberculosis infection prevention and control (IPC), we aimed to evaluate the potential impact of conventional and novel IPC measures on Mtb transmission to patients and other clinic attendees.

METHODS

An individual-based model of patient movements through clinics, ventilation in waiting areas, and Mtb transmission was developed, and parameterised using empirical data from eight clinics in two provinces in South Africa. Seven interventions-codeveloped with health professionals and policy-makers-were simulated: (1) queue management systems with outdoor waiting areas, (2) ultraviolet germicidal irradiation (UVGI) systems, (3) appointment systems, (4) opening windows and doors, (5) surgical mask wearing by clinic attendees, (6) simple clinic retrofits and (7) increased coverage of long antiretroviral therapy prescriptions and community medicine collection points through the Central Chronic Medicine Dispensing and Distribution (CCMDD) service.

RESULTS

In the model, (1) outdoor waiting areas reduced the transmission to clinic attendees by 83% (IQR 76%-88%), (2) UVGI by 77% (IQR 64%-85%), (3) appointment systems by 62% (IQR 45%-75%), (4) opening windows and doors by 55% (IQR 25%-72%), (5) masks by 47% (IQR 42%-50%), (6) clinic retrofits by 45% (IQR 16%-64%) and (7) increasing the coverage of CCMDD by 22% (IQR 12%-32%).

CONCLUSIONS

The majority of the interventions achieved median reductions in the rate of transmission to clinic attendees of at least 45%, meaning that a range of highly effective intervention options are available, that can be tailored to the local context. Measures that are not traditionally considered to be IPC interventions, such as appointment systems, may be as effective as more traditional IPC measures, such as mask wearing.

Role of systems science in preventing and controlling emerging infectious diseases: protocol for a scoping review

INTRODUCTION

In recent history, many new infectious diseases have affected humans for the first time or have appeared in previously unaffected areas of the world; these diseases are known as emerging infectious diseases (EIDs). Examples of EIDs include COVID-19, Middle East respiratory syndrome and Ebola virus disease. EIDs are known for their complexity. Multiple factors play a role in their spread, including increases in human population, conflicts, urbanisation, air travel, global trade and inequalities in wealth distribution and access to healthcare. In order to gain a better understanding of such complexity, we aim to explore the role of systems science, which allows us to view EIDs in the context of complex adaptive systems rather than simple causes and effects. The objectives of this scoping review are to explore and map the theoretical concepts and key characteristics of studies that use systems methods in controlling EIDs, to identify the gaps in knowledge and disseminate the results.

METHODS

We will follow the Joanna Briggs Institute guidance for this scoping review, comprising the following stages: formulating the research question and subquestions, scanning the literature for available data, selecting relevant publications, charting the data by two independent reviewers, aggregating the findings, reporting, summarising and disseminating the results. We will review peer-reviewed articles, preprints and grey literature available in all languages.

DISCUSSION

We intend that this scoping review will contribute to a better understanding of the use of systems methods to inform policymakers about how to prevent and control EIDs.

ETHICS AND DISSEMINATION

Research ethics approval is not required for a scoping review because it is based on reviewing and collecting data from publicly available sources. To disseminate the findings, results will be shared through academic publications, seminars and conferences.

Telehealth and the COVID-19 Pandemic: International Perspectives and a Health Systems Framework for Telehealth Implementation to Support Critical Response

OBJECTIVES

Telehealth implementation is a complex systems-based endeavour. This paper compares telehealth responses to (COrona VIrus Disease 2019) COVID-19 across ten countries to identify lessons learned about the complexity of telehealth during critical response such as in response to a global pandemic. Our overall objective is to develop a health systems-based framework for telehealth implementation to support critical response.

METHODS

We sought responses from the members of the International Medical Informatics Association (IMIA) Telehealth Working Group (WG) on their practices and perception of telehealth practices during the times of COVID-19 pandemic in their respective countries. We then analysed their responses to identify six emerging themes that we mapped to the World Health Organization (WHO) model of health systems.

RESULTS

Our analysis identified six emergent themes. (1) Government, legal or regulatory aspects of telehealth; (2) Increase in telehealth capacity and delivery; (3) Regulated and unregulated telehealth; (4) Changes in the uptake and perception of telemedicine; (5) Public engagement in telehealth responses to COVID-19; and (6) Implications for training and education. We discuss these themes and then use them to develop a systems framework for telehealth support in critical response.

CONCLUSION

COVID-19 has introduced new challenges for telehealth support in times of critical response. Our themes and systems framework extend the WHO systems model and highlight that telemedicine usage in response to the COVID-19 pandemic is complex and multidimensional. Our systems-based framework provides guidance for telehealth implementation as part of health systems response to a global pandemic such as COVID-19.

System dynamics modelling of health workforce planning to address future challenges of Thailand's Universal Health Coverage

BACKGROUND

System dynamics (SD) modelling can inform policy decisions under Thailand's Universal Health Coverage. We report on this thinking approach to Thailand's strategic health workforce planning for the next 20 years (2018-2037).

METHODS

A series of group model building (GMB) sessions involving 110 participants from multi-sectors of Thailand's health systems was conducted in 2017 and 2018. We facilitated policymakers, administrators, practitioners and other stakeholders to co-create a causal loop diagram (CLD) representing a shared understanding of why the health workforce's demands and supplies in Thailand were mismatched. A stock and flow diagram (SFD) was also co-created for testing the consequences of policy options by simulation modelling.

RESULTS

The simulation modelling found hospital utilisation created a vicious cycle of constantly increasing demands for hospital care and a constant shortage of healthcare providers. Moreover, hospital care was not designed for effectively dealing with the future demands of ageing populations and prevalent chronic illness. Hence, shifting emphasis to professions that can provide primary care, intermediate care, long-term care, palliative care, and end-of-life care can be more effective.

CONCLUSIONS

Our SD modelling confirmed that shifting the care models to address the changing health demands can be a high-leverage policy of health workforce planning, although very difficult to implement in the short term. of health workforce planning, although very difficult to implement in the short term.

Resources management impact on neonatal services performance in the United Kingdom: A system dynamics modelling approach

Demand for neonatal care in the United Kingdom (UK) has increased in recent years. This care is provided by neonatal services, which are chronically saturated due to years of budget austerity in the UK. The aim of this paper is to investigate the possible impact of increasing resources to these services to improve their operational performance and alleviate the pressure they are facing. To achieve this aim, a system dynamics (SD) simulation model was built and validated in a UK neonatal unit. The SD model was used initially to evaluate the impact of increasing resources on the unit performance and the results showed that this policy will have a limited effect on performance. The model was then extended to predict the effect of reducing the length of stay (LoS) in conjunction with increasing resources. These joint interventions will have a positive impact on the unit performance if LoS is reduced for all care categories and resources are slightly increased. Results' implications and SD's modelling usefulness to guide decision making in complex health settings are discussed.

A realist review to assess for whom, under what conditions and how pay for performance programmes work in low- and middle-income countries

Pay for performance (P4P) programmes are popular health system-focused interventions aiming to improve health outcomes in low-and middle-income countries (LMICs). This realist review aims to understand how, why and under what circumstance P4P works in LMICs.We systematically searched peer-reviewed and grey literature databases, and examined the mechanisms underpinning P4P effects on: utilisation of services, patient satisfaction, provider productivity and broader health system, and contextual factors moderating these. This evidence was then used to construct a causal loop diagram.We included 112 records (19 grey literature; 93 peer-reviewed articles) assessing P4P schemes in 36 countries. Although we found mixed evidence of P4P's effects on identified outcomes, common pathways to improved outcomes include: community outreach; adherence to clinical guidelines, patient-provider interactions, patient trust, facility improvements, access to drugs and equipment, facility autonomy, and lower user fees. Contextual factors shaping the system response to P4P include: degree of facility autonomy, efficiency of banking, role of user charges in financing public services; staffing levels; staff training and motivation, quality of facility infrastructure and community social norms. Programme design features supporting or impeding health system effects of P4P included: scope of incentivised indicators, fairness and reach of incentives, timely payments and a supportive, robust verification system that does not overburden staff. Facility bonuses are a key element of P4P, but rely on provider autonomy for maximum effect. If health system inputs are vastly underperforming pre-P4P, they are unlikely to improve only due to P4P. This is the first realist review describing how and why P4P initiatives work (or fail) in different LMIC contexts by exploring the underlying mechanisms and contextual and programme design moderators. Future studies should systematically examine health system pathways to outcomes for P4P and other health system strengthening initiatives, and offer more understanding of how programme design shapes mechanisms and effects.

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.

Mathematical modelling for health systems research: a systematic review of system dynamics and agent-based models

BACKGROUND

Mathematical modelling has been a vital research tool for exploring complex systems, most recently to aid understanding of health system functioning and optimisation. System dynamics models (SDM) and agent-based models (ABM) are two popular complementary methods, used to simulate macro- and micro-level health system behaviour. This systematic review aims to collate, compare and summarise the application of both methods in this field and to identify common healthcare settings and problems that have been modelled using SDM and ABM.

METHODS

We searched MEDLINE, EMBASE, Cochrane Library, MathSciNet, ACM Digital Library, HMIC, Econlit and Global Health databases to identify literature for this review. We described papers meeting the inclusion criteria using descriptive statistics and narrative synthesis, and made comparisons between the identified SDM and ABM literature.

RESULTS

We identified 28 papers using SDM methods and 11 papers using ABM methods, one of which used hybrid SDM-ABM to simulate health system behaviour. The majority of SDM, ABM and hybrid modelling papers simulated health systems based in high income countries. Emergency and acute care, and elderly care and long-term care services were the most frequently simulated health system settings, modelling the impact of health policies and interventions such as those targeting stretched and under resourced healthcare services, patient length of stay in healthcare facilities and undesirable patient outcomes.

CONCLUSIONS

Future work should now turn to modelling health systems in low- and middle-income countries to aid our understanding of health system functioning in these settings and allow stakeholders and researchers to assess the impact of policies or interventions before implementation. Hybrid modelling of health systems is still relatively novel but with increasing software developments and a growing demand to account for both complex system feedback and heterogeneous behaviour exhibited by those who access or deliver healthcare, we expect a boost in their use to model health systems.

Health system modelling research: towards a whole-health-system perspective for identifying good value for money investments in health system strengthening

Global health research has typically focused on single diseases, and most economic evaluation research to date has analysed technical health interventions to identify ‘best buys’. New approaches in the conduct of economic evaluations are needed to help policymakers in choosing what may be good value (ie, greater health, distribution of health, or financial risk protection) for money (ie, per budget expenditure) investments for health system strengthening (HSS) that tend to be programmatic. We posit that these economic evaluations of HSS interventions will require developing new analytic models of health systems which recognise the dynamic connections between the different components of the health system, characterise the type and interlinks of the system’s delivery platforms; and acknowledge the multiple constraints both within and outside the health sector which limit the system’s capacity to efficiently attain its objectives. We describe priority health system modelling research areas to conduct economic evaluation of HSS interventions and ultimately identify good value for money investments in HSS.

Dynamics of intervention adoption, implementation, and maintenance inside organizations: The case of an obesity prevention initiative

Overall impact of public health prevention interventions relies not only on the average efficacy of an intervention, but also on the successful adoption, implementation, and maintenance (AIM) of that intervention. In this study, we aim to understand the dynamics that regulate AIM of organizational level intervention programs. We focus on two well-documented obesity prevention interventions, implemented in food carry-outs and stores in low-income urban areas of Baltimore, Maryland, which aimed to improve dietary behaviour for adults by providing access to healthier foods and point-of-purchase promotions. Building on data from field observations, in-depth interviews, and data discussed in previous publications, as well as the strategy and organizational behaviour literature, we developed a system dynamics model of the key processes of AIM. With simulation analysis, we show several reinforcing mechanisms that span stakeholder motivation, communications, and implementation quality and costs can turn small changes in the process of AIM into big difference in the overall impact of the intervention. Specifically, small changes in the allocation of resources to communication with stakeholders of intervention could have a nonlinear long-term impact if those additional resources can turn stakeholders into allies of the intervention, reducing the erosion rates and enhancing sustainability. We present how the dynamics surrounding communication, motivation, and erosion can create significant heterogeneity in the overall impact of otherwise similar interventions. Therefore, careful monitoring of how those dynamics unfold, and timely adjustments to keep the intervention on track are critical for successful implementation and maintenance.