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.

Causal loop diagrams (CLDs) are a systems thinking method that can be used to visualize and unpack complex health system behaviour. They can be employed prospectively or retrospectively to identify the mechanisms and consequences of policies or interventions designed to strengthen health systems and inform discussion with policymakers and stakeholders on actions that may alleviate sub-optimal outcomes. Whilst the use of CLDs in health systems research has generally increased, there is still limited use in low- and middle-income settings. In addition to their suitability for evaluating complex systems, CLDs can be developed where opportunities for primary data collection may be limited (such as in humanitarian or conflict settings) and instead be formulated using secondary data, published or grey literature, health surveys/reports and policy documents. The purpose of this paper is to provide a step-by-step guide for designing a health system research study that uses CLDs as their chosen research method, with particular attention to issues of relevance to research in low- and middle-income countries (LMICs). The guidance draws on examples from the LMIC literature and authors’ own experience of using CLDs in this research area. This paper guides researchers in addressing the following four questions in the study design process; (1) What is the scope of this research? (2) What data do I need to collect or source? (3) What is my chosen method for CLD development? (4) How will I validate the CLD? In providing supporting information to readers on avenues for addressing these key design questions, authors hope to promote CLDs for wider use by health system researchers working in LMICs.

Country-specific forecasts of the growing non-communicable disease (NCD) burden in ageing HIV-positive patients will be key to guide future HIV policies. We provided the first national forecasts for Italy and the Unites States of America (USA) and quantified direct cost of caring for these increasingly complex patients. We adapted an individual-based model of ageing HIV-positive patients to Italy and the USA, which followed patients on HIV-treatment as they aged and developed NCDs (chronic kidney disease, diabetes, dyslipidaemia, hypertension, non-AIDS malignancies, myocardial infarctions and strokes). The models were parameterised using data on 7,469 HIV-positive patients from the Italian Cohort Naïve to Antiretrovirals Foundation Study and 3,748 commercially-insured patients in the USA and extrapolated to national level using national surveillance data. The model predicted that mean age of HIV-positive patients will increase from 46 to 59 in Italy and from 49 to 58 in the USA in 2015–2035. The proportion of patients in Italy and the USA diagnosed with ≥1 NCD is estimated to increase from 64% and 71% in 2015 to 89% and 89% by 2035, respectively, driven by moderate cardiovascular disease (CVD) (hypertension and dyslipidaemia), diabetes and malignancies in both countries. NCD treatment costs as a proportion of total direct HIV costs will increase from 11% to 23% in Italy and from 40% to 56% in the USA in 2015–2035. HIV patient profile in Italy and the USA is shifting to older patients diagnosed with multiple co-morbidity. This will increase NCD treatment costs and require multi-disciplinary patient management.

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.

Background: The noncommunicable disease (NCD) burden in Kenya is not well characterized, despite estimates needed to identify future health priorities. We aimed to quantify current and future NCD burden in Kenya by human immunodeficiency virus (HIV) status.Methods: Original systematic reviews and meta-analyses of prevalence/incidence of cardiovascular disease (CVD), chronic kidney disease, depression, diabetes, high total cholesterol, hypertension, human papillomavirus infection, and related precancerous stages in Kenya were carried out. An individual-based model was developed, simulating births, deaths, HIV disease and treatment, aforementioned NCDs, and cancers. The model was parameterized using systematic reviews and epidemiological national and regional surveillance data. NCD burden was quantified for 2018–2035 by HIV status among adults.Results: Systematic reviews identified prevalence/incidence data for each NCD except ischemic heart disease. The model estimates that 51% of Kenyan adults currently suffer from ≥1 NCD, with a higher burden in people living with HIV (PLWH) compared to persons not living with HIV (62% vs 51%), driven by their higher age profile and partly by HIV-related risk for NCDs. Hypertension and high total cholesterol are the main NCD drivers (adult prevalence of 20.5% [5.3 million] and 9.0% [2.3 million]), with CVD and cancers the main causes of death. The burden is projected to increase by 2035 (56% in persons not living with HIV; 71% in PLWH), with population growth doubling the number of people needing services (15.4 million to 28.1 million) by 2035.Conclusions: NCD services will need to be expanded in Kenya. Guidelines in Kenya already support provision of these among both the general and populations living with HIV; however, coverage remains low.

Payment for performance (P4P) has been employed in low and middle-income (LMIC) countries to improve quality and coverage of maternal and child health (MCH) services. However, there is a lack of consensus on how P4P affects health systems. There is a need to evaluate P4P effects on health systems using methods suitable for evaluating complex systems. We developed a causal loop diagram (CLD) to further understand the pathways to impact of P4P on delivery and uptake of MCH services in Tanzania. The CLD was developed and validated using qualitative data from a process evaluation of a P4P scheme in Tanzania, with additional stakeholder dialogue sought to strengthen confidence in the diagram. The CLD maps the interacting mechanisms involved in provider achievement of targets, reporting of health information, and population care seeking, and identifies those mechanisms affected by P4P. For example, the availability of drugs and medical commodities impacts not only provider achievement of P4P targets but also demand of services and is impacted by P4P through the availability of additional facility resources and the incentivisation of district managers to reduce drug stock outs. The CLD also identifies mechanisms key to facility achievement of targets but are not within the scope of the programme; the activities of health facility governing committees and community health workers, for example, are key to demand stimulation and effective resource use at the facility level but both groups were omitted from the incentive system. P4P design considerations generated from this work include appropriately incentivising the availability of drugs and staffing in facilities and those responsible for demand creation in communities. Further research using CLDs to study heath systems in LMIC is urgently needed to further our understanding of how systems respond to interventions and how to strengthen systems to deliver better coverage and quality of care.