IntroductionMaximising 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.MethodsA 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.ResultsThe 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 middle-income 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.ConclusionWe 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.

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 considered were identified.Results: Among the 19 studies included, an average of 43.3% (95% CI 36.7-50.1%) cancer patients incurred CHE. CHE among cancer patients 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 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.

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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.