Donor coordination to support universal health coverage in Malawi.

Development assistance is a major source of financing for health in least developed countries. However, persistent aid fragmentation has led to inefficiencies and health inequities and constrained progress towards Universal Health Coverage (UHC). Malawi is a case study for this global challenge, with 55% of total health expenditure funded by donors and fragmentation across 166 financing sources and 265 implementing partners. This often leads to poor coordination and misalignment between government priorities and donor projects. To address these challenges, the Malawi Ministry of Health (MoH) has developed and implemented an architecture of aid coordination tools and processes. Using a case study approach, we documented the iterative development, implementation and institutionalization of these tools, which was led by the MoH with technical assistance from the Clinton Health Access Initiative. We reviewed the grey literature, including relevant policy documents, planning tools and databases of government/partner funding commitments, and drew upon the authors' experiences in designing, implementing and scaling up these tools. Overall, the iterative use and revision of these tools by the Government of Malawi across the national and subnational levels, including integration with the government's public financial management system, was critical to successful uptake. The tools are used to inform government and partner resource allocation decisions, assess financing and gaps for national and district plans and inform donor grant applications. As Malawi has launched the Health Sector Strategic Plan 2023-2030, these tools are being adapted for the 'One Plan, One Budget and One Report' approach. However, while the tools are an incremental mechanism to strengthen aid alignment, success has been constrained by the larger context of power imbalances and misaligned incentives between the donor community and the Government of Malawi. Reform of the aid architecture is therefore critical to ensure that these tools achieve maximum impact in Malawi's journey towards UHC.

Financial Implications of Tariffs for Medical Oxygen on Rwandan Public Hospitals' Finance Management During the Coronavirus Epidemic.

In Rwanda, provider reimbursements for oxygen are based on the duration of patient consumption at a fixed hourly tariff rate. This study sought to assess whether the current insurance tariff in Rwanda was adequate to cover the costs of oxygen used in oxygen therapy and to explore alternative tariff models.The assessment found that hospitals make a marginal surplus from low volume flow rate patients and incur losses from patients who require high volume flow rates. In high volume nonspecialized hospitals with a large pool of patients consuming medical oxygen, low flow rate usage patients (e.g., neonates) tend to subsidize high flow usage patients (surgery), if the number of patients consuming low flow oxygen is higher than the latter. The study found that the current tariff was sufficient before the exponential surge in demand for high flow usage during the peak of the COVID-19 pandemic. A variable tariff that factors both the duration (hours) and the volume (liters) used during the therapy may require more work but better reflects the cost of consumption in each ward. A case-based payment model provides a standard pricing framework based on the patient's diagnosis, intervention, and intensity of treatment.This study highlights the need for a transition from the time-based tariff structure to a case-based or volume-based tariff to incentivize sustainable production and provision (supply) of medical oxygen services at health facilities in Rwanda. Social health insurance reimbursement tariffs for medical oxygen need to reflect both duration and volume of consumption because oxygen therapy varies based on intervention, disease severity, patient age, length of stay, and responsiveness to treatment.

Health Sector Resource Mapping in Malawi: Sharing the Collection and Use of Budget Data for Evidence-Based Decision Making.

BACKGROUND: In 2011, the Ministry of Health in Malawi developed and institutionalized a resource-tracking process, known as resource mapping (RM), to collect information on planned funding flows across the health sector to support resource allocation and mobilization decisions. We analyze the RM process and tools and describe key uses of the data for health financing decision making to achieve universal health coverage (UHC). METHODS: We applied a case study approach, written as a collaboration between policy makers who have led the RM process in Malawi and the implementation team who have developed tools, collected data, and reported results over the period. It draws on our experiences in conducting RM in Malawi to document the RM process and data, key uses of data, implementation challenges, and lessons learned. We conducted a gray literature review to understand rounds of RM in which we did not participate. Finally, we conducted a search of published literature to situate our work in the international health resource-tracking literature. RESULTS: The RM exercise in Malawi is iteratively designed around the needs of the end users and policy priorities of the government, which in turn drives institutionalization of the exercise. We describe 4 ways in which RM data has been used, including national and district planning and budgeting; prioritization and coordination of existing funds by estimating resource availability; mobilization of new resources by conducting financial gap analysis against costed national strategic plans; and generation of evidence to support the national response to the coronavirus disease 2019 pandemic. DISCUSSION: To achieve UHC goals in Malawi, RM has equipped the government and development partners with critical data used for resource mobilization and coordination decisions. Lessons learned from RM in Malawi may be applicable to other countries starting or refining their own health resource-tracking exercise.

Harnessing the Power of Networks of Care for Universal Health Coverage.

On the global health agenda, Universal Health Coverage has been displaced by the COVID-19 pandemic while disparities in COVID-19 outcomes have exposed stark gaps in quality, access, equity, and financial risk protection. These disparities highlight the importance of the core goals of Universal Health Coverage and the need for innovative approaches to working toward them. The newly codified concept of "Networks of Care" offers a promising option for implementation. The articles in this special issue present the Networks of Care lexicon and framework and demonstrate the development of leadership, responsibility, intra- and inter-facility cooperation, and dynamic cycles of quality improvement. These elements are associated with better access to services and better health outcomes, the ultimate goals of Universal Health Coverage. Increases in poverty, food insecurity, and deleterious impact on the status of women secondary to the COVID-19 pandemic add urgency to Universal Health Coverage, while the economic impact of pandemic mitigation may reduce availability of resources for years to come. The need for Universal Health Coverage and efficiency and flexibility in health spending, including the ability to contract directly, has become even more important. Countries where Universal Health Coverage efforts have yet to carry through to provision of good quality, accessible and equitable service delivery could potentially benefit from concurrent Networks of Care implementation. Documentation of Networks of Care in the context of Universal Health Coverage should be prioritized to understand how Networks of Care can be used to help realize the goals of Universal Health Coverage around the world.

Assessing The Value Of 40 Years Of Local Public Expenditures On Health.

The US public and private sectors now spend more than $3 trillion on health each year. While critical studies have examined the relationship between public spending on health and health outcomes, relatively little is known about the impact of broader public-sector spending on health. Using county-level public finance data for the period 1972-2012, we estimated the impact of local public hospital spending and nonhospital health spending on all-cause mortality in the county. Overall, a 10 percent increase in nonhospital health spending was associated with a 0.006 percent decrease in all-cause mortality one year after the initial spending. This effect was larger and significant in counties with greater proportions of racial/ethnic minorities. Our results indicate that county nonhospital health spending has health benefits that can help reduce costs and improve health outcomes in localities across the nation, though greater focus on population-oriented services may be warranted.

Testing survey-based methods for rapid monitoring of child mortality, with implications for summary birth history data.

INTRODUCTION: Under-five mortality estimates are increasingly used in low and middle income countries to target interventions and measure performance against global development goals. Two new methods to rapidly estimate under-5 mortality based on Summary Birth Histories (SBH) were described in a previous paper and tested with data available. This analysis tests the methods using data appropriate to each method from 5 countries that lack vital registration systems. SBH data are collected across many countries through censuses and surveys, and indirect methods often rely upon their quality to estimate mortality rates. METHODS AND FINDINGS: The Birth History Imputation method imputes data from a recent Full Birth History (FBH) onto the birth, death and age distribution of the SBH to produce estimates based on the resulting distribution of child mortality. DHS FBHs and MICS SBHs are used for all five countries. In the implementation, 43 of 70 estimates are within 20% of validation estimates (61%). Mean Absolute Relative Error is 17.7.%. 1 of 7 countries produces acceptable estimates. The Cohort Change method considers the differences in births and deaths between repeated Summary Birth Histories at 1 or 2-year intervals to estimate the mortality rate in that period. SBHs are taken from Brazil's PNAD Surveys 2004-2011 and validated against IGME estimates. 2 of 10 estimates are within 10% of validation estimates. Mean absolute relative error is greater than 100%. CONCLUSIONS: Appropriate testing of these new methods demonstrates that they do not produce sufficiently good estimates based on the data available. We conclude this is due to the poor quality of most SBH data included in the study. This has wider implications for the next round of censuses and future household surveys across many low- and middle- income countries.

Machine-Learning Algorithms to Code Public Health Spending Accounts.

OBJECTIVES: Government public health expenditure data sets require time- and labor-intensive manipulation to summarize results that public health policy makers can use. Our objective was to compare the performances of machine-learning algorithms with manual classification of public health expenditures to determine if machines could provide a faster, cheaper alternative to manual classification. METHODS: We used machine-learning algorithms to replicate the process of manually classifying state public health expenditures, using the standardized public health spending categories from the Foundational Public Health Services model and a large data set from the US Census Bureau. We obtained a data set of 1.9 million individual expenditure items from 2000 to 2013. We collapsed these data into 147 280 summary expenditure records, and we followed a standardized method of manually classifying each expenditure record as public health, maybe public health, or not public health. We then trained 9 machine-learning algorithms to replicate the manual process. We calculated recall, precision, and coverage rates to measure the performance of individual and ensembled algorithms. RESULTS: Compared with manual classification, the machine-learning random forests algorithm produced 84% recall and 91% precision. With algorithm ensembling, we achieved our target criterion of 90% recall by using a consensus ensemble of ≥6 algorithms while still retaining 93% coverage, leaving only 7% of the summary expenditure records unclassified. CONCLUSIONS: Machine learning can be a time- and cost-saving tool for estimating public health spending in the United States. It can be used with standardized public health spending categories based on the Foundational Public Health Services model to help parse public health expenditure information from other types of health-related spending, provide data that are more comparable across public health organizations, and evaluate the impact of evidence-based public health resource allocation.

Maternal and newborn care practices in Sierra Leone: a mixed methods study of four underserved districts.

Despite recent progress, Sierra Leone's lifetime risk of maternal death remains high (1 in 21), as does neonatal mortality (35 per 1000 live births). We present findings on maternal and neonatal care practices from a mixed methods study conducted in four districts during July­August 2012. We conducted a household cluster survey with data on maternal and newborn care practices collected from women ages 15­49 years who had ever given birth. We also conducted focus group discussions and in-depth interviews in two communities in each of the four districts. Participants included pregnant women, mothers of young children, older caregivers, fathers, community health volunteers, traditional birth attendants (TBAs) and health workers. We explored personal experiences and understandings of pregnancy, childbirth, the newborn period and social norms. Data analysis was conducted using STATA (quantitative) and thematic analysis using Dedoose software (qualitative). Antenatal care was high (84.2%, 95% CI: 82.0­86.3%), but not timely due to distance, transport, and social norms to delay care-seeking until a pregnancy is visible, particularly in the poorer districts of Kambia and Pujehun. Skilled delivery rates were lower (68.9%, 95% CI: 64.8­72.9%), particularly in Kambia and Tonkolili where TBAs are considered effective. Clean cord care, delaying first baths and immediate breastfeeding were inadequate across all districts. Timely postnatal checks were common among women with facility deliveries (94.1%, 95% CI: 91.9­96.6%) and their newborns (94.5%, 95% CI: 92.5­96.5%). Fewer women with home births received postnatal checks (53.6%, 95% CI: 46.2­61.3%) as did their newborns (75.8%, 95% CI: 68.9­82.8%). TBAs and practitioners are well-respected providers, and traditional beliefs impact many behaviours. Challenges remain with respect to maternal and neonatal health in Sierra Leone; these were likely exacerbated by service interruptions during the 2014­2016 Ebola Virus Disease epidemic. The reasons behind existing practices must be examined to identify appropriate strategies to improve maternal and newborn survival.

Monitoring Change in Child Mortality through Household Surveys.

BACKGROUND: Most low- and middle-income countries lack fully functional civil registration systems. Measures of under-five mortality are typically derived from periodic household surveys collecting detailed information from women on births and child deaths. However, such surveys are expensive and are not appropriate for monitoring short-term changes in child mortality. We explored and tested the validity of two new analysis methods for less-expensive summary histories of births and child deaths for such monitoring in five African countries. METHODS AND FINDINGS: The first method we explored uses individual-level survey data on births and child deaths to impute full birth histories from an earlier survey onto summary histories from a more recent survey. The second method uses cohort changes between two surveys in the average number of children born and the number of children dead by single year of age to estimate under-five mortality for the inter-survey period. The first method produces acceptable annual estimates of under-five mortality for two out of six applications to available data sets; the second method produced an acceptable estimate in only one of five applications, though none of the applications used ideal data sets. CONCLUSIONS: The methods we tested were not able to produce consistently good quality estimates of annual under-five mortality from summary birth history data. The key problem we identified was not with the methods themselves, but with the underlying quality of the summary birth histories. If summary birth histories are to be included in general household surveys, considerable emphasis must be placed on quality control.