Publications

Development of microneedle array patches (MAPs) for potential use in immunization is ongoing, but the cost of manufacturing is expected to be higher than that of existing needle-and-syringe vial systems. The potential benefits of MAPs in reaching previously unvaccinated populations have been touted, but affordability, especially in low- and middle-income countries, remains an open question. In this study, we quantify the expected impact on operational costs of switching to MAPs for immunization for measles-rubella, human papilloma virus, and typhoid in both routine and campaign-based delivery modes. We endeavor to make a comprehensive estimate, including the costs of labor, syringes, waste management (i.e., sharps and trash), wastage (unused vaccine), freight and in-country cold chain transportation. We examined five potential use cases and our results show that in total, operational cost savings from a switch to MAPs are expected to range from a low of $0.24 per dose delivered (HPV, 1-dose vial, campaign) up to $0.61 per dose delivered (MR, 10-dose vial, routine). Excluding the allocated cost of labor, the estimated range of cost savings are $0.18 and $0.43, respectively. Confidence intervals are wide, due to the uncertainty in the assumptions, but in all five use cases tested, there was at least an 87 % probability of savings. These results show that operational savings from a switch to MAPs may offset at least part of the expected incremental manufacturing costs, which will make the transition more viable in settings with limited budget space. With this in mind, development agencies should continue to invest in MAPs technology and, if the product does come to market, use this evidence as part of total value of vaccines assessments and to inform investment strategies for implementation of vaccine MAPs, including alignment with policy makers.

Background Primary care networks (PCNs) are increasingly being adopted in low- and middle-income countries (LMICs) to improve the delivery of primary health care (PHC). Kenya has identified PCNs as a key reform to strengthen PHC delivery and has passed a law to guide its implementation. PCNs were piloted in two counties in Kenya in 2020 and implemented nationally in October 2023. This protocol outlines methods for a study that examines the impact, implementation experience and political economy of the PCN reform in Kenya. Methods We will adopt the parallel databases variant of convergent mixed methods study design to concurrently but separately collect quantitative and qualitative data. The two strands will be mixed during data collection to refine questions, with findings triangulated during analysis and interpretation to provide a comprehensive understanding of PCN implementation. The quantitative study will use a controlled before and after study design and collect data using health facility and client exit surveys. The primary outcome measure will be the service delivery readiness of PHC facilities. We will use a random sample of 228 health facilities and 2560 clients in four currently implementing PCNs, four planning to implement and four control counties at baseline and post-implementation. We shall undertake a preliminary cross-sectional analysis of the data at baseline from October to December 2023, followed by a difference-in-difference analysis at the endline from October to December 2024 to compare the outcome differences between the intervention and control counties over a 12-month period. The qualitative study will include a cross-sectional process evaluation and political economy analysis (PEA) using document reviews and approximately 80 in-depth interviews with national and sub-national stakeholders. The process evaluation will assess the emergence of PCN reforms, the implementation experience, the mechanism of impact and how the context affects implementation and outcomes. The PEA will examine the interaction of structural factors, institutions and actors/stakeholders’ interests and power relations in implementing PCNs. We will also examine the gendered effects of the PCNs, including power relations and norms, and their implications on PHC from the supply and demand sides. We shall undertake a thematic analysis of the qualitative data. Discussion This evaluation will contribute robust evidence on the impact, implementation experience, political economy and gendered implications of PCNs in a LMIC setting, as well as guide the refining of PCN implementation in Kenya and other LMICs implementing or planning to implement PCNs to enhance their effectiveness.

To improve service delivery of Nigeria’s primary health care (PHC) system, the government tested two approaches for facility-level financing: performance-based financing (PBF) and decentralized facility financing (DFF). Facilities also had increased autonomy, supervision, and community oversight. We examine how the approach, funding level, and state context affected breadth of services and structural quality. We use health facility surveys previously collected in 2014 and 2017, covering three years of implementation, in which districts were randomly assigned PBF or DFF and compared to matched districts in control states. We use log-linear regressions and non-parametric statistics to estimate the effect size of the financing approach and level of funding per capita. Service availability was highest in PBF facilities, while DFF also outperformed control on most measures. Results showed that structural readiness and service offerings both increased with more funding, especially under DFF. DFF and PBF facilities were better equipped to provide services that they claimed to offer, which was not the case for controls. Overall, PBF outperformed DFF, partially explained by funding levels. The rate of offering complimentary services followed a pattern of easiest-to-hardest to deliver. PBF and DFF both improved the breadth and structural quality of services, although DFF performance was more sensitive to funding levels. Improvements were observed at relatively low levels of funding, but larger investments were associated with better performance. Most DFF facilities exceeded the performance of higher-funded controls, implying that funding was more valuable in the context of autonomy, increased supervision, and community oversight.

Previous studies have shown that facility autonomy, especially control over budget allocation, can have a modest positive effect on performance, but the findings depend on the context. Similarly, management practices are often cited as important contributors to facility performance, but the evidence is limited and usually qualitative. Data from the large-scale randomized evaluation of the Nigeria States Health Investment Project (NSHIP) offers an opportunity to quantitatively examine these relationships in the context of a lower middle-income country. We utilize non-parametric statistics to test for difference in means and apply regression analysis to test the hypothesis that autonomy and management affected facility performance. Our results show that facilities with greater autonomy, more budget control, and better management practices generally outperform their peers on a range of facility readiness and service delivery measures. For example, regression results found that facilities with high autonomy held on average 2.1 more outreach sessions per month than those without, and facilities with an annual business plan offered 1.8 additional outreach services. Supervision practices, such as more frequent visits and use of a quantitative checklist, are associated with 26% higher productivity and up to a 28.6% increase in equipment availability (percentage points), respectively. We conduct sensitivity analyses on our variable selection and use a random forest approach to validate that results are robust to changes in the model structure. We conclude that facility-level autonomy and especially budget control can improve primary healthcare facility readiness and service availability, even in resource-constrained contexts, Further, this can be achieved through good management practices that are reinforced through supportive supervision and routine performance monitoring to maximize the gains that result from incremental financing. This shows that these policies and practices can be critical contributors to efficiently achieving the goals of universal healthcare policies in the context of limited resources.

Previous work has shown that primary healthcare facilities can benefit from both in-kind support (e.g., medication shipments) as well as increased cash-on-hand to spend to address service readiness gaps. However, there is limited evidence on how facility managers choose to spend available cash or how their decisions to manage their facility budgets are affected by in-kind support. Economic theory suggests that the optimal allocation of cash resources would depend on the context and constraints to how it can be spent, and expenditures would in turn affect the availability of supplies and medications. We test this theory using regression analysis on data from the Nigeria Service Delivery Indicators for Health (SDI), a health facility survey from twelve states in 2013 that included both hospitals and primary healthcare centers (PHCs). We find that facilities with financial resources available to them have higher availability of essential medicines, especially if the facility had earmarked some cash for medication expenditures. However, earmarking for other expenditure categories did not have the same effect on medication availability, which indicates that budgeting processes are an important factor in ensuring medication availability. We find that cash support had large effect (p < 0.001) on availability and that in-kind donations had a negative effect on the probability of expenditure of medications. Additionally, we find the difference between hospitals and PHCs is due to their financial situation (variables become insignificant once support variables were in regressions). Regression analyses also showed that facilities that received in-kind medications had higher availability, but this only had a significant effect in facilities that did not have cash available to spend on medications, implying that facilities are able to address their own supply needs when they have resources available to them. Thus, in-kind supplies should be targeted to facilities that cannot otherwise procure them. Overall, facilities appear to be making effective trade-offs in the context of limited resources and they should receive both cash and support for appropriate budgeting and procurement practices.

Importance
The MORDOR (Macrolides Oraux pour Réduire les Décès avec un Oeil sur la Résistance) trial demonstrated that mass azithromycin administration reduced mortality by 18% among children aged 1 to 59 months in Niger. The identification of high-risk subgroups to target with this intervention could reduce the risk of antimicrobial resistance.

Objective
To evaluate whether distance to the nearest primary health center modifies the effect of azithromycin administration to children aged 1 to 59 months on child mortality.

Design, Setting, and Participants
The MORDOR cluster randomized trial was conducted from December 1, 2014, to July 31, 2017; this post hoc secondary analysis was conducted in 2023 among 594 clusters (communities or grappes) in the Boboye and Loga departments in Niger. All children aged 1 to 59 months in eligible communities were evaluated.

Interventions
Biannual (twice-yearly) administration of a single dose of oral azithromycin or matching placebo over 2 years.

Main Outcomes and Measures
A population-based census was used to monitor mortality and person-time at risk (trial primary outcome). Community distance to a primary health center was calculated as kilometers between the center of each community and the nearest health center. Negative binomial regression was used to evaluate the interaction between distance and the effect of azithromycin on the incidence of all-cause mortality among children aged 1 to 59 months.

Results
Between December 1, 2014, and July 31, 2017, a total of 594 communities were enrolled, with 76 092 children (mean [SD] age, 31 [2] months; 39 022 [51.3%] male) included at baseline, for a mean (SD) of 128 (91) children per community. Median (IQR) distance to the nearest primary health center was 5.0 (3.2-7.1) km. Over 2 years, 145 693 person-years at risk were monitored and 3615 deaths were recorded. Overall, mortality rates were 27.5 deaths (95% CI, 26.2-28.7 deaths) per 1000 person-years at risk in the placebo arm and 22.5 deaths (95% CI, 21.4-23.5 deaths) per 1000 person-years at risk in the azithromycin arm. For each kilometer increase in distance in the placebo arm, mortality increased by 5% (adjusted incidence rate ratio, 1.05; 95% CI, 1.03-1.07; P < .001). The effect of azithromycin on mortality varied significantly by distance (interaction P = .02). Mortality reduction with azithromycin compared with placebo was 0% at 0 km from the health center (95% CI, −19% to 17%), 4% at 1 km (95% CI, −12% to 17%), 16% at 5 km (95% CI, 7% to 23%), and 28% at 10 km (95% CI, 17% to 38%).

Conclusions and Relevance
In this secondary analysis of a cluster randomized trial of mass azithromycin administration for child mortality, children younger than 5 years who lived farthest from health facilities appeared to benefit the most from azithromycin administration. These findings may help guide the allocation of resources to ensure that those with the least access to existing health resources are prioritized in program implementation.

Background
Effective healthcare systems need adequate numbers of well-trained human resources for health (HRH). To support evidence-based strategic planning, modeling is sometimes used to estimate the number of required health workers and to allocate them appropriately. However, despite the demonstrated utility of models, there are several limitations to existing tools, including the inability to reflect the stochastic nature of workload and parameter uncertainty, or to incorporate seasonal variations. Additionally, some tools are proprietary or no longer supported, which makes them difficult for decision makers to adopt.

Methods
To address these issues, we have created an open-source, freely available modeling tool called the Population-Aware Capacity Estimator for Human Resources for Health (PACE-HRH). The modeling platform has two components: an Excel-based workbook for data input and scenario management, and a stochastic Monte Carlo simulation package and analysis pipeline written in R. PACE-HRH has a demographics model that projects future populations, a task time model that estimates workload from both variable responsibilities and overhead, an optional seasonality model, and an optional cadre allocation model.

Results
To establish the utility of PACE-HRH, we run a demonstrative model based on a subset of eight clinical service categories, populated with Ethiopian data. The projections show an increase in weekly workload for a baseline population from 37.8 (36.0, 39.7) hours in 2021 to 44.0 (37.9, 49.8) hours in 2035. The ability to calculate a confidence interval is unique to PACE-HRH, as is the option to calculate the monthly variation in workload, which in this case amounts to seasonal amplitude of 6.8%. These results are demonstrative only and more curated input assumptions would be needed in order for the results to support decision making.

Conclusions
Modeling HRH requirements is valuable to planning processes. The PACE-HRH modeling package takes a novel approach to generating these estimates and is designed to be an easy-to-use platform that reduces barriers to use. There is a shortage of observational data on task times, which are key model assumptions, and time and motion studies are needed. However, even without improved data, PACE-HRH is an advancement in the field of HRH modeling and can be used to support evidence-based planning processes.

Background: Primary healthcare systems require adequate staffing to meet the needs of their local population. Guidelines typically use population ratio targets for healthcare workers, such as Ethiopia’s goal of two health extension workers for every five thousand people. However, fixed ratios do not reflect local demographics, fertility rates, disease burden (e.g., malaria endemicity), or trends in these values. Recognizing this, we set out to estimate the clinical workload to meet the primary healthcare needs in Ethiopia by region.

Methods: We utilize the open-source modeling package PACE-HRH for our analysis. This is a stochastic Monte Carlo simulation model, which samples annually from distributions for fertility, mortality, disease burden, and the trends in these rates. Inputs were drawn from literature, DHS, and UN population estimates. We model seven regions and two charted cities of Ethiopia, based on data availability and the anticipated reliability of historical trends into the future.

Results: All regions and charted cities are expected to experience increased workload between 2021 and 2035 for a starting catchment of five thousand people. The expected (mean) clinical workload varied from 2,930 hours (Addis) to 3,752 (Gambela) and increased by 19-28% over fifteen years. This results from a decline in per capita workload (due to declines in fertility and infectious diseases), overpowered by total population growth. Pregnancy, non-communicable diseases, sick child care, and nutrition remain the largest service categories, but their priority shifts substantially in some regions by 2035. Sensitivity analysis shows that fertility assumptions have major implications for workload. We incorporate seasonality and estimate monthly variation of up to 8.9% (Somali), though most services with high variability are declining.

Conclusions: Regional variation in demographics, fertility, seasonality, and disease trends all affect the workload estimates. This results in differences in expected clinical workload, the level of uncertainty in those estimates, and relative priorities between service categories. By showing these differences, we demonstrate the inadequacy of a fixed population ratio for staffing allocation. Policy-makers and regulators need to consider these factors in designing their healthcare systems, or they risk sub-optimally allocating workforce and creating inequitable access to care.

The national deployment of polyvalent community health workers (CHWs) is a constitutive part of the strategy initiated by the Ministry of Health to accelerate efforts towards universal health coverage in Haiti. Its implementation requires the planning of future recruitment and deployment activities for which mathematical modelling tools can provide useful support by exploring optimised placement scenarios based on access to care and population distribution. We combined existing gridded estimates of population and travel times with optimisation methods to derive theoretical CHW geographical placement scenarios including constraints on walking time and the number of people served per CHW. Four national-scale scenarios that align with total numbers of existing CHWs and that ensure that the walking time for each CHW does not exceed a predefined threshold are compared. The first scenario accounts for population distribution in rural and urban areas only, while the other three also incorporate in different ways the proximity of existing health centres. Comparing these scenarios to the current distribution, insufficient number of CHWs is systematically identified in several departments and gaps in access to health care are identified within all departments. These results highlight current suboptimal distribution of CHWs and emphasize the need to consider an optimal (re-)allocation.

Innovative game-based training methods that leverage the ubiquity of cellphones and familiarity with phone-based interfaces have the potential to transform the training of public health practitioners in low-income countries such as Liberia. This article describes the design, development, and testing of a prototype of the Figure It Out mobile game. The prototype game uses a disease outbreak scenario to promote evidence-based decision-making in determining the causative agent and prescribing intervention measures to minimize epidemiological and logistical burdens in resource-limited settings. An initial prototype of the game developed by the US team was playtested and evaluated by focus groups with 20 University of Liberia Masters of Public Health (UL MPH) students. Results demonstrate that the learning objectives—improving search skills for identifying scientific evidence and considering evidence before decision-making during a public health emergency—were considered relevant and important in a setting that has repeatedly and recently experienced severe threats to public health. However, some of the game mechanics that were thought to enhance engagement such as trial-and-error and choose-your-own-path gameplay, were perceived by the target audience as distracting or too time-consuming, particularly in the context of a realistic emergency scenario. Gameplay metrics that mimicked real-world situations around lives lost, money spent, and time constraints during public health outbreaks were identified as relatable and necessary considerations. Our findings reflect cultural differences between the game development team and end users that have emphasized the need for end users to have an integral part of the design team; this formative evaluation has critically informed next steps in the iterative development process. Our multidisciplinary, cross-cultural and cross-national design team will be guided by Liberia-based public health students and faculty, as well as community members who represent our end user population in terms of experience and needs. These stakeholders will make key decisions regarding game objectives and mechanics, to be vetted and implemented by game design experts, epidemiologists, and software developers.