Health Systems Global (HSG)

Become a member
Alliance logo

The Health Systems Impact Modelling project

This community of practice emerges from a larger programme of work supported by the Alliance for Health Policy and Systems Research at WHO, with funding from The Gates Foundation. Read more

Resources and information

Browse a collection of helpful materials on health systems modelling. This section includes guides, reports, toolkits, and other resources to support learning, research, and practice in strengthening health systems. Do you have suggestions for other resources and information about health systems impact modelling that we could feature on this page? If so we’d love to hear from you.

Email your suggestion. Thank you!

  • Citation: Milstein B, Homer J, Hirsch G. Analyzing National Health Reform Strategies With a Dynamic Simulation Model. Am J Public Health.  2010;100:811-819. doi:10.2105/AJPH.2009.174490
  • Summary: This study uses a dynamic simulation model with a 25-year time horizon to analyze five health system reform strategies, finding that expanding insurance coverage and improving quality alone would improve health but increase costs and worsen inequity. The analysis demonstrates that a comprehensive strategy incorporating coverage expansion, quality improvements, primary care capacity building, and health protection interventions would be most effective at improving health outcomes, reducing inequities, and controlling costs over the long term.

  • Citation: Semwanga, A.R., Nakubulwa, S., & Adam, T. (2016). Applying a system dynamics modelling approach to explore policy options for improving neonatal health in Uganda. Health Research Policy and Systems, 14(1), 35. DOI: 10.1186/s12961-016-0101-8
  • Summary: This study developed NEOSIM, a system dynamics simulation model to explore policy interventions for improving neonatal health in Uganda by testing the effects of various health system interventions through four interconnected sectors: population, demand for services, health of mothers, and clinical care choices. The research found that the most impactful intervention package for reducing neonatal mortality rates was a combination of free delivery kits in settings with free delivery services and motorcycle coupons for emergency transportation to hospitals, demonstrating the importance of addressing socioeconomic barriers to healthcare access rather than focusing solely on clinical service improvements.

  • Citation: Leerapan, B., Teekasap, P., Urwannachotima, N., Jaichuen, W., Chiangchaisakulthai, K., Udomaksorn, K., Meeyai, A., Noree, T., & Sawaengdee, K. (2021). System dynamics modelling of health workforce planning to address future challenges of Thailand’s Universal Health Coverage. Human Resources for Health, 19(31). https://doi.org/10.1186/s12960-021-00572-5
  • Summary: This study used system dynamics modelling through group model building sessions with 110 stakeholders to analyze Thailand’s health workforce planning for 2018-2037 under Universal Health Coverage. The simulation modelling found that hospital utilization created a vicious cycle of increasing demand for hospital care and workforce shortages, confirming that shifting from hospital-centered care models to primary care, intermediate care, long-term care, palliative care, and end-of-life care would be a more effective high-leverage policy for health workforce planning, though difficult to implement in the short term.

  • Citation: Seid, M., Bridgeland, D., Bridgeland, A., & Hartley, D. M. (2021). A collaborative learning health system agent-based model: Computational and face validity. Learning Health Systems, 5(3), e10261. https://doi.org/10.1002/lrh2.10261
  • Summary: This paper presents the first theoretically-derived computational agent-based model of collaborative learning health systems (CLHSs), demonstrating initial computational and face validity by modelling patient and clinician agents who interact to optimize treatment selection through information sharing and knowledge accumulation. The model shows that increased patient influence leads to higher patient population activation, increased shared knowledge, and improved median patient outcomes, providing a foundational tool for understanding CLHS mechanisms and testing intervention strategies in health systems improvement.

  • Citation: Pitt, M., Monks, T., Crowe, S., & Vasilakis, C. (2016). Systems modelling and simulation in health service design, delivery and decision making. BMJ Quality & Safety, 25(1), 38-45. doi:10.1136/bmjqs-2015-004430
  • Summary: This review paper surveys the breadth of systems modelling and simulation approaches available for healthcare planning and decision-making, from qualitative problem structuring methods to quantitative techniques like discrete event simulation and system dynamics. Despite demonstrating potential through a detailed case study of stroke thrombolysis implementation that improved treatment rates from 5% to 15%, the authors highlight a persistent “implementation gap” where the majority of academic health systems modelling research fails to be applied effectively in practice due to barriers including lack of organizational capacity, insufficient stakeholder engagement, and limited evaluation of modelling effectiveness.

 

  • Citation: Chang, A.Y., Ogbuoji, O., Atun, R., & Verguet, S. (2017). Dynamic modelling approaches to characterize the functioning of health systems: A systematic review of the literature. Social Science & Medicine, 194, 160-167. https://doi.org/10.1016/j.socscimed.2017.09.005
  • Summary: This systematic review identifies and categorizes dynamic modelling approaches used in health systems modelling (HSM), examining 43 relevant articles and defining HSM as the use of dynamic mathematical and computational models to understand how health system inputs translate into outputs and outcomes. The authors identify two main modelling approaches – system dynamics (SD) and “susceptible-infected-recovered-plus” (SIR+) models – and highlight opportunities to advance quantitative methods for better understanding the relationships between health system investments and their impacts on coverage, quality, and health outcomes.

  • Citation: Verguet S, Feldhaus I, Jiang Kwete X, Aqil A, Atun R, Bishai D, Cecchini M, Guerra Junior AA, Habtemariam MK, Jbaily A, Karanfil O, Kruk ME, Haneuse S, Norheim OF, Smith PC, Tolla MT, Zewdu S, Bump J. Health system modelling research: towards a whole-health-system perspective for identifying good value for money investments in health system strengthening. BMJ Global Health 2019;4:e001311. doi:10.1136/bmjgh-2018-001311
  • Summary: This paper argues that new approaches in economic evaluation are needed to identify good value for money investments in health system strengthening (HSS), requiring development of analytic models that capture dynamic interactions between health system components rather than focusing on single disease interventions. The authors describe priority health system modelling research areas that can guide economic evaluations of HSS interventions by incorporating system-wide delivery platforms, recognizing constraints within and outside the health sector, and quantifying the interconnected relationships between financing, delivery, individual behavior, and community decision-making.

  • Citation: Cassidy, R., Singh, N.S., Schiratti, P.R., Semwanga, A., Binyaruka, P., Sachingongu, N., Chama-Chiliba, C.M., Chalabi, Z., Borghi, J., & Blanchet, K. (2019). Mathematical modelling for health systems research: a systematic review of system dynamics and agent-based models. BMC Health Services Research, 19, 845. https://doi.org/10.1186/s12913-019-4627-7
  • Summary: This systematic review identified 28 papers using system dynamics models (SDM) and 11 papers using agent-based models (ABM) to simulate health system behavior, finding that both methods are increasingly used to model emergency/acute care and elderly/long-term care services primarily in high-income countries. The authors recommend expanding the use of these mathematical modelling approaches to low- and middle-income countries to better understand health system functioning and enable stakeholders to assess policy impacts before implementation.

  • Citation: Witter, S., Palmer, N., Balabanova, D., Mounier-Jack, S., Martineau, T., Klicpera, A., Jensen, C., Pugliese-Garcia, M., & Gilson, L. (2019). Health system strengtheningReflections on its meaning, assessment, and our state of knowledge. International Journal of Health Planning and Management, 34(4), e1980-e1989. DOI: 10.1002/hpm.2882
  • Summary: This paper argues for clearer definitions and evaluation frameworks for health system strengthening (HSS) interventions, proposing that HSS should encompass scope (cross-cutting effects), scale (national reach), sustainability (addressing systemic blockages), and effects (impacting health outcomes, equity, and financial protection). The authors highlight major gaps in HSS evaluation methods and advocate for moving beyond narrow outcome assessments toward comprehensive frameworks that capture spillover effects and contribution to overarching health system process goals

  • Citation: Bertone, M. P., Palmer, N., Kruja, K., & Witter, S. (2023). How do we design and evaluate health system strengthening? Collaborative development of a set of health system process goals. International Journal of Health Planning and Management, 38(2), 279-288. DOI: 10.1002/hpm.3607
  • Summary: The authors developed a framework of 22 health system process goals organized under four domains (ownership/participation/accountability, learning/resilience, use of resources, and service delivery) to guide health systems strengthening (HSS) design, monitoring, and evaluation beyond traditional building blocks approaches. The framework aims to capture systemic effects of HSS interventions by focusing on desirable attributes of strong health systems rather than just inputs and outputs, providing an earlier assessment tool that can support participatory dialogue and identify both intended and unintended consequences of health system investments.

  • Citation: Health Systems Strengthening Evidence Collaborative (HSSEC). (2022, July). Modelling the Effects of Health Systems Strengthening Investments. Technical Brief. Itad. [No DOI provided]
  • Summary: This technical brief proposes a four-level causal modelling framework for estimating the impact of health systems strengthening (HSS) investments on health outcomes, linking HSS activities to health system attributes, then to service delivery cascades, and finally to intervention coverage and health outcomes. The approach aims to address limitations of existing models by providing an adaptable, comprehensible framework that goes from upstream HSS investments to downstream health outcomes while remaining mathematically tractable and avoiding the complexity of systems dynamics models.

  • Citation: Cheryl Cashin, Nguyen Khanh Phuong, Ryan Shain, Tran Thi Mai Oanh & Nguyen Thi Thuy (2015) A simple simulation model as a tool to assess alternative health care provider payment reform options in Vietnam, Global Public Health, 10:sup1, S104-S119, DOI: 10.1080/17441692.2014.986156
  • Summary: This study uses a simple spreadsheet-based, micro-simulation model to analyze the potential impacts of different provider payment reform scenarios on resource allocation and expenditure distribution in Vietnam’s health insurance system across three provinces. The model demonstrates that eliminating the fund-holding arrangement for district hospitals could reduce their high financial risk without dramatically shifting total spending or distribution patterns, providing empirical evidence to support health policy reform decisions in Vietnam’s capitation system.

  • Citation: Morton, A., Thomas, R., & Smith, P. C. (2016). Decision rules for allocation of finances to health systems strengthening. Journal of Health Economics, 49, 97-108. DOI: 10.1016/j.jhealeco.2016.06.001
  • Summary: This paper develops mathematical programming models to guide optimal allocation of funding between disease-specific “vertical projects” and “horizontal programmes” that strengthen health systems as a whole. The authors demonstrate that their models are computationally tractable and provide insights into the complex task of prioritizing health systems strengthening investments, illustrating with examples of infectious disease funding allocation in sub-Saharan Africa and showing that optimal solutions depend heavily on context-specific factors and the returns to health systems investment.

  • Citation: Chou VB, Walker N, Kanyangarara M (2019) Estimating the global impact of poor quality of care on maternal and neonatal outcomes in 81 low- and middle-income countries: A modelling study. PLoS Med 16(12): e1002990. https://doi.org/10.1371/journal.pmed.1002990
  • Summary: This modelling study used the Lives Saved Tool (LiST) to quantify the potential impact of improving quality of care for maternal and neonatal health interventions across 81 low- and middle-income countries by linking health facility assessments with population-based surveys. The analysis found that improving quality of care to match current utilization levels could prevent an estimated 28% of maternal deaths, 28% of neonatal deaths, and 22% of stillbirths, potentially saving 86,000 maternal lives and 670,000 neonatal lives while preventing 520,000 stillbirths by 2020.

  • Citation: Jbaily, A., Feldhaus, I., Bigelow, B., Kamareddine, L., Tolla, M.T., Bouvier, M., Kiros, M., & Verguet, S. (2020). Toward health system strengthening in low- and middle-income countries: insights from mathematical modelling of drug supply chains. BMC Health Services Research, 20, 776. https://doi.org/10.1186/s12913-020-05549-z
  • Summary: This study develops a mathematical model using dynamical system methods to analyze drug supply chains in low-income countries, examining how health system interventions (such as road improvements, workforce expansion, and digital tracking systems) affect drug stock levels and prevent stockouts at health facilities. The model demonstrates that implementing a digital drug tracking system with feedback loops can effectively predict and prevent drug stockouts and expiration, while various health system strengthening interventions show different impacts on supply chain performance and drug availability.

  • Citation: Love-Koh J, Mirelman A, Suhrcke M. Equity and economic evaluation of system-level health interventions: A case study of Brazil’s Family Health Program. Health Policy and Planning. 2021;36(3):229-238. doi: 10.1093/heapol/czaa181
  • Summary: This study applies distributional cost-effectiveness analysis to evaluate Brazil’s Family Health Program (PSF), a community-based primary care intervention, finding it cost-effective at $2640 per disability-adjusted life year averted with greater health benefits for lower-income states. The research demonstrates how to incorporate equity concerns into health economic evaluations of population and system-level interventions, though it reveals significant methodological challenges including the need for treatment effect heterogeneity estimates and complex health opportunity cost calculations across multiple government budget levels.

  • Citation: Fraser-Hurt, N., Hou, X., Wilkinson, T., Duran, D., Abou Jaoude, G. J., Skordis, J., Chukwuma, A., Lao Pena, C., Tshivuila Matala, O. O., Gorgens, M., & Wilson, D. P. (2021). Using allocative efficiency analysis to inform health benefits package design for progressing towards Universal Health Coverage: Proof-of-concept studies in countries seeking decision support. PLoS ONE, 16(11), e0260247. https://doi.org/10.1371/journal.pone.0260247
  • Summary: This study demonstrates the application of the Health Interventions Prioritization tool (HIPtool) for allocative efficiency analysis in Armenia, Cote d’Ivoire, and Zimbabwe to optimize health benefits package design and resource allocation toward Universal Health Coverage. The analysis showed potential DALY gains of 26%, 22%, and 49% respectively through optimized spending, with results emphasizing the importance of community and primary care interventions for maximizing health impact within constrained budgets.

  • Citation: Schroeder, L.F., Dei-Adomakoh, Y., DeStigter, K., Idigbe, E.O., Flanigan, J., Ekpale, P.M.A., Adjei, E., Roa, L., Wilson, M.L., & Horton, S. (2022). Rational design of an essential diagnostics network to support Universal Health Coverage: a modelling analysis. BMC Health Services Research, 22, 1224. https://doi.org/10.1186/s12913-022-08558-2
  • Summary: This paper presents a relational modelling approach to systematically allocate diagnostic tests (laboratory and imaging) across different health system tiers based on disease burden, treatment pathways, and infrastructure constraints, using Ghana as an exemplar. The model recommended 111 unique diagnostic investigations distributed across primary (17), secondary (45), and tertiary (49) care levels with estimated capital costs of $8,330 at primary tier and $571,000-$777,000 at secondary tier, providing a transparent framework for countries to customize diagnostic planning to support Universal Health Coverage.

  • Citation: Wagner AD, Gimbel S, sbjrnsdttir KH, Cherutich P, Coutinho J, Crocker J, Cruz E, Cuembelo F, Cumbe V, Eastment M, Einberg J, Floriano F, Gaitho D, Guthrie BL, John-Stewart G, Kral AH, Lambdin BH, Liu S, Maina M, Manaca N, Matsuzaki M, Mattox L, Mburu N, McClelland RS, Micek MA, Mocumbi AO, Muanido A, Nduati R, Njuguna IN, Oluoch G, Oyiengo LB, Ronen K, Soi C, Wagenaar BH, Wanje G, Wenger LD, Sherr K. Cascade Analysis: An Adaptable Implementation Strategy Across HIV and Non-HIV Delivery Platforms. Journal of Acquired Immune Deficiency Syndromes. 2019;82(Supplement 3):S322-S331. DOI: 10.1097/QAI.0000000000002210.
  • Summary: This paper describes the development and adaptation of the Cascade Analysis Tool (CAT), an Excel-based simulation model that identifies the step within a health system cascade that could most improve overall system performance by calculating “cascade gain” for each sequential step. The authors demonstrate CAT’s adaptability across seven different health delivery platforms including HIV care, hypertension management, mental health services, and opioid overdose reversal, showing how this implementation strategy enables frontline health workers to use routine data for prioritizing system improvements and resource allocation.

  • Citation: Uneke, C.J., Sombie, I., Uro-Chukwu, H.C., Johnson, E. (2018). Using equitable impact sensitive tool (EQUIST) to promote implementation of evidence informed policymaking to improve maternal and child health outcomes: a focus on six West African Countries. Globalization and Health, 14, 104. https://doi.org/10.1186/s12992-018-0422-1
  • Summary: This study used UNICEF’s EQUitable Impact Sensitive Tool (EQUIST) to analyze maternal, newborn and child health outcomes across six West African countries (Benin, Burkina Faso, Ghana, Mali, Nigeria, and Senegal), identifying the poorest populations with highest mortality rates and determining cost-effective intervention strategies to address health disparities. The analysis revealed that delivery by skilled professionals could avert the highest number of maternal and under-five deaths, with significant variations in intervention costs between countries (ranging from $0.39 per capita in Ghana to $4.0 in Benin), demonstrating EQUIST’s value as a health systems modelling tool for evidence-informed policymaking focused on equity and cost-effectiveness.

  • Citation: Mangal TD, Mohan S, Colbourn T, Collins JH, Graham M, Jahn A, Janouakov E, Li Lin I, Manning Smith R, Mnjowe E, Molaro M, Mwenyenkulu TE, Nkhoma D, She B, Tamuri A, Revill P, Phillips AN, Mfutso-Bengo J, Hallett TB. Assessing the effect of health system resources on HIV and tuberculosis programmes in Malawi: a modelling study. Lancet Glob Health 2024; 12: e1638-48. DOI: https://doi.org/10.1016/S2214-109X(24)00275-X
  • Summary: This study uses the Thanzi la Onse individual-based simulation model to demonstrate that health system resource constraints, particularly medicine stockouts, significantly compromise the effectiveness of HIV and tuberculosis program scale-up in Malawi, potentially causing 58,700 additional deaths and requiring 14.3 million extra health worker hours compared to unconstrained scenarios. The research highlights that ignoring health system frailties in disease elimination projections risks overestimating health impacts and underestimating resource requirements, emphasizing the critical need for simultaneous health system strengthening alongside program expansion.

Health Systems Impact Modelling

Organizations and initiatives icon

Organizations and initiatives

Webinar recordings icon

Webinar series

An illustrated globe image with multicoloured lines running through it

Health Systems Impact Modelling