Assessing the efficiency of countries in making progress towards universal health coverage: a data envelopment analysis of 172 countries

Introduction: Maximising efficiency of resources is critical to progressing towards universal health coverage (UHC) and the sustainable development goal (SDG) for health. This study estimates the technical efficiency of national health spending in progressing towards UHC, and the environmental factors associated with efficient UHC service provision. Methods: A two-stage efficiency analysis using Simar and Wilson’s double bootstrap data envelopment analysis investigates how efficiently countries convert health spending into UHC outputs (measured by service coverage and financial risk protection) for 172 countries. We use World Bank and WHO data from 2015. Thereafter, the environmental factors associated with efficient progress towards UHC goals are identified. Results: The mean bias-corrected technical efficiency score across 172 countries is 85.7% (68.9% for low-income and 95.5% for high-income countries). High-achieving middleincome and low-income countries such as El Salvador, Colombia, Rwanda and Malawi demonstrate that peer-relative efficiency can be attained at all incomes. Governance capacity, income and education are significantly associated with efficiency. Sensitivity analysis suggests that results are robust to changes. Conclusion: We provide a 2015 baseline for cross-country UHC technical efficiency scores. If countries wish to improve their UHC outputs within existing budgets, they should identify their current efficiency and try to emulate more efficient peers. Policy-makers should focus on strengthening institutions and implementing known best practices to replicate efficient systems. Using resources more efficiently is likely to positively impact UHC coverage goals and health outcomes, and without addressing gaps in efficiency progress towards achieving the SDGs will be impeded

Incidence of Catastrophic Health Expenditure and Its Determinants in Cancer Patients: A Systematic Review and Meta-analysis

BACKGROUND: Cancer is the third leading cause of mortality in the world, and cancer patients are more exposed to financial hardship than other diseases. This paper aimed to review studies of catastrophic healthcare expenditure (CHE) in cancer patients, measure their level of exposure to CHE, and identify factors associated with incidence of CHE. METHODS: This study is a systematic review and meta-analysis. Several databases were searched until February 2020, including MEDLINE, Web of Science, Scopus, ProQuest, ScienceDirect and EMBASE. The results of selected studies were extracted and analyzed using a random effects model. In addition, determinants of CHE were identified. RESULTS: Among the 19 studies included, an average of 43.3% (95% CI 36.7–50.1) of cancer patients incurred CHE. CHE varied substantially depending on the Human Development Index (HDI) of the country in which a study was conducted. In countries with the highest HDI, 23.4% of cancer patients incurred CHE compared with 67.9% in countries with the lowest HDI. Key factors associated with incidence of CHE at the household level included household income, gender of the household head, and at the patient level included the type of health insurance, education level of the patient, type of cancer and treatment, quality of life, age and sex. CONCLUSION: The proportion of cancer patients that incur CHE is very high, especially in countries with lower HDI. The results from this review can help inform policy makers to develop fairer and more sustainable health financing mechanisms, addressing the factors associated with CHE in cancer patients

Measuring financial risk protection in health benefits packages: scoping review protocol to inform allocative efficiency studies

INTRODUCTION: To progress towards Universal Health Coverage (UHC), countries will need to define a health benefits package of services free at the point of use. Financial risk protection is a core component of UHC and should therefore be considered a key dimension of health benefits packages. Allocative efficiency modelling tools can support national analytical capacity to inform an evidence-based selection of services, but none are currently able to estimate financial risk protection. A review of existing methods used to measure financial risk protection can facilitate their inclusion in modelling tools so that the latter can become more relevant to national decision making in light of UHC. METHODS AND ANALYSIS: This protocol proposes to conduct a scoping review of existing methods used to measure financial risk protection and assess their potential to inform the selection of services in a health benefits package. The proposed review will follow the methodological framework developed by Arksey and O'Malley and the subsequent recommendations made by Levac et al. Several databases will be systematically searched including: (1) PubMed; (2) Scopus; (3) Web of Science and (4) Google Scholar. Grey literature will also be scanned, and the bibliography of all selected studies will be hand searched. Following the selection of studies according to defined inclusion and exclusion criteria, key characteristics will be collected from the studies using a data extraction tool. Key characteristics will include the type of method used, geographical region of focus and application to specific services or packages. The extracted data will then be charted, collated, reported and summarised using descriptive statistics, a thematic analysis and graphical presentations. ETHICS AND DISSEMINATION: The scoping review proposed in this protocol does not require ethical approval. The final results will be disseminated via publication in a peer-reviewed journal, conference presentations and shared with key stakeholders

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

Background: Countries are increasingly defining health benefits packages (HBPs) as a way of progressing towards Universal Health Coverage (UHC). Resources for health are commonly constrained, so it is imperative to allocate funds as efficiently as possible. We conducted allocative efficiency analyses using the Health Interventions Prioritization tool (HIPtool) to estimate the cost and impact of potential HBPs in three countries. These analyses explore the usefulness of allocative efficiency analysis and HIPtool in particular, in contributing to priority setting discussions. / Methods and findings: HIPtool is an open-access and open-source allocative efficiency modelling tool. It is preloaded with publicly available data, including data on the 218 cost-effective interventions comprising the Essential UHC package identified in the 3rd Edition of Disease Control Priorities, and global burden of disease data from the Institute for Health Metrics and Evaluation. For these analyses, the data were adapted to the health systems of Armenia, Côte d’Ivoire and Zimbabwe. Local data replaced global data where possible. Optimized resource allocations were then estimated using the optimization algorithm. In Armenia, optimized spending on UHC interventions could avert 26% more disability-adjusted life years (DALYs), but even highly cost-effective interventions are not funded without an increase in the current health budget. In Côte d’Ivoire, surgical interventions, maternal and child health and health promotion interventions are scaled up under optimized spending with an estimated 22% increase in DALYs averted–mostly at the primary care level. In Zimbabwe, the estimated gain was even higher at 49% of additional DALYs averted through optimized spending. / Conclusions: HIPtool applications can assist discussions around spending prioritization, HBP design and primary health care transformation. The analyses provided actionable policy recommendations regarding spending allocations across specific delivery platforms, disease programs and interventions. Resource constraints exacerbated by the COVID-19 pandemic increase the need for formal planning of resource allocation to maximize health benefits

Optima TB: A tool to help optimally allocate tuberculosis spending.

Approximately 85% of tuberculosis (TB) related deaths occur in low- and middle-income countries where health resources are scarce. Effective priority setting is required to maximise the impact of limited budgets. The Optima TB tool has been developed to support analytical capacity and inform evidence-based priority setting processes for TB health benefits package design. This paper outlines the Optima TB framework and how it was applied in Belarus, an upper-middle income country in Eastern Europe with a relatively high burden of TB. Optima TB is a population-based disease transmission model, with programmatic cost functions and an optimisation algorithm. Modelled populations include age-differentiated general populations and higher-risk populations such as people living with HIV. Populations and prospective interventions are defined in consultation with local stakeholders. In partnership with the latter, demographic, epidemiological, programmatic, as well as cost and spending data for these populations and interventions are then collated. An optimisation analysis of TB spending was conducted in Belarus, using program objectives and constraints defined in collaboration with local stakeholders, which included experts, decision makers, funders and organisations involved in service delivery, support and technical assistance. These analyses show that it is possible to improve health impact by redistributing current TB spending in Belarus. Specifically, shifting funding from inpatient- to outpatient-focused care models, and from mass screening to active case finding strategies, could reduce TB prevalence and mortality by up to 45% and 50%, respectively, by 2035. In addition, an optimised allocation of TB spending could lead to a reduction in drug-resistant TB infections by 40% over this period. This would support progress towards national TB targets without additional financial resources. The case study in Belarus demonstrates how reallocations of spending across existing and new interventions could have a substantial impact on TB outcomes. This highlights the potential for Optima TB and similar modelling tools to support evidence-based priority setting

Optima TB: A tool to help optimally allocate tuberculosis spending

Approximately 85% of tuberculosis (TB) related deaths occur in low- and middle-income countries where health resources are scarce. Effective priority setting is required to maximise the impact of limited budgets. The Optima TB tool has been developed to support analytical capacity and inform evidence-based priority setting processes for TB health benefits package design. This paper outlines the Optima TB framework and how it was applied in Belarus, an upper-middle income country in Eastern Europe with a relatively high burden of TB. Optima TB is a population-based disease transmission model, with programmatic cost functions and an optimisation algorithm. Modelled populations include age-differentiated general populations and higher-risk populations such as people living with HIV. Populations and prospective interventions are defined in consultation with local stakeholders. In partnership with the latter, demographic, epidemiological, programmatic, as well as cost and spending data for these populations and interventions are then collated. An optimisation analysis of TB spending was conducted in Belarus, using program objectives and constraints defined in collaboration with local stakeholders, which included experts, decision makers, funders and organisations involved in service delivery, support and technical assistance. These analyses show that it is possible to improve health impact by redistributing current TB spending in Belarus. Specifically, shifting funding from inpatient- to outpatient-focused care models, and from mass screening to active case finding strategies, could reduce TB prevalence and mortality by up to 45% and 50%, respectively, by 2035. In addition, an optimised allocation of TB spending could lead to a reduction in drug-resistant TB infections by 40% over this period. This would support progress towards national TB targets without additional financial resources. The case study in Belarus demonstrates how reallocations of spending across existing and new interventions could have a substantial impact on TB outcomes. This highlights the potential for Optima TB and similar modelling tools to support evidence-based priority setting