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An efficient allocation of limited resources in low-income settings offers the opportunity to improve population-health outcomes given the available health system capacity. Efforts to achieve this are often framed through the lens of "health benefits packages" (HBPs), which seek to establish which services the public healthcare system should include in its provision. Analytic approaches widely used to weigh evidence in support of different interventions and inform the broader HBP deliberative process however have limitations. In this work, we propose the individual-based Thanzi La Onse (TLO) model as a uniquely-tailored tool to assist in the evaluation of Malawi-specific HBPs while addressing these limitations. By mechanistically modelling—and calibrating to extensive, country-specific data—the incidence of disease, health-seeking behaviour, and the capacity of the healthcare system to meet the demand for care under realistic constraints on human resources for health available, we were able to simulate the health gains achievable under a number of plausible HBP strategies for the country. We found that the HBP emerging from a linear constrained optimisation analysis (LCOA) achieved the largest health gain—∼8% reduction in disability adjusted life years (DALYs) between 2023 and 2042 compared to the benchmark scenario—by concentrating resources on high-impact treatments. This HBP however incurred a relative excess in DALYs in the first few years of its implementation. Other feasible approaches to prioritisation were assessed, including service prioritisation based on patient characteristics, rather than service type. Unlike the LCOA-based HBP, this approach achieved consistent health gains relative to the benchmark scenario on a year- to-year basis, and a 5% reduction in DALYs over the whole period, which suggests an approach based upon patient characteristics might prove beneficial in the future. Author summary: All publicly funded healthcare systems face difficult decisions about how limited resources should be allocated to achieve the greatest possible return in health. These decisions are particularly pressing in lower-income countries (LICs) like Malawi, where resources are extremely limited and their inefficient allocation results in larger morbidity and mortality. In this work, we introduce a new analytical tool to inform such decisions based on an "all diseases, whole healthcare system" simulation specifically tailored to Malawi, the Thanzi La Onse (TLO) model. The TLO model is able to forecast the health burden that should be expected from different resource-allocation strategies in Malawi specifically, allowing policy-makers to explore a wide range of policy options in a safe and theoretical fashion. In this analysis, we compare the forecasted health burden from a set of common resource-prioritisation strategies, and draw some general conclusions as to what makes certain strategies more or less effective in reducing the health burden incurred. [ABSTRACT FROM AUTHOR]

Background: To make the best use of health resources, it is crucial to understand the healthcare needs of a population—including how needs will evolve and respond to changing epidemiological context and patient behaviour—and how this compares to the capabilities to deliver healthcare with the existing workforce. Existing approaches to planning either rely on using observed healthcare demand from a fixed historical period or using models to estimate healthcare needs within a narrow domain (e.g., a specific disease area or health programme). A new data-grounded modelling method is proposed by which healthcare needs and the capabilities of the healthcare workforce can be compared and analysed under a range of scenarios: in particular, when there is much greater propensity for healthcare seeking. Methods: A model representation of the healthcare workforce, one that formalises how the time of the different cadres is drawn into the provision of units of healthcare, was integrated with an individual-based epidemiological model—the Thanzi La Onse model—that represents mechanistically the development of disease and ill-health and patients' healthcare seeking behaviour. The model was applied in Malawi using routinely available data and the estimates of the volume of health service delivered were tested against officially recorded data. Model estimates of the "time needed" and "time available" for each cadre were compared under different assumptions for whether vacant (or established) posts are filled and healthcare seeking behaviour. Results: The model estimates of volume of each type of service delivered were in good agreement with the available data. The "time needed" for the healthcare workforce greatly exceeded the "time available" (overall by 1.82-fold), especially for pharmacists (6.37-fold) and clinicians (2.83-fold). This discrepancy would be largely mitigated if all vacant posts were filled, but the large discrepancy would remain for pharmacists (2.49-fold). However, if all of those becoming ill did seek care immediately, the "time needed" would increase dramatically and exceed "time supply" (2.11-fold for nurses and midwives, 5.60-fold for clinicians, 9.98-fold for pharmacists) even when there were no vacant positions. Conclusions: The results suggest that services are being delivered in less time on average than they should be, or that healthcare workers are working more time than contracted, or a combination of the two. Moreover, the analysis shows that the healthcare system could become overwhelmed if patients were more likely to seek care. It is not yet known what the health consequences of such changes would be but this new model provides—for the first time—a means to examine such questions. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "The economic costs of orthopaedic services: a health system cost analysis of tertiary hospitals in a low-income country." The correction addresses an error in the affiliation of the second author, David Watkins, which was mistakenly stated as "University of Washington, Washington, DC, USA" instead of the correct affiliation, "University of Washington, Seattle, WA, USA." The correction has been made to the online version of the article. The publisher, Springer Nature, remains neutral regarding jurisdictional claims and institutional affiliations. The article was authored by Pakwanja Twea, David Watkins, Ole Frithjof Norheim, Boston Munthali, Sven Young, Levison Chiwaula, Gerald Manthalu, Dominic Nkhoma, and Peter Hangoma. [Extracted from the article]

Abstract Background The district resource allocation formula in Malawi was recently reviewed to include stunting as a proxy measure of socioeconomic status. In many countries where the concept of need has been incorporated in resource allocation, composite indicators of socioeconomic status have been used. In the Malawi case, it is important to ascertain whether there are differences between using single variable or composite indicators of socioeconomic status in allocations made to districts, holding all other factors in the resource allocation formula constant. Methods Principal components analysis was used to calculate asset indices for all districts from variables that capture living standards using data from the Malawi Multiple Indicator Cluster Survey 2006. These were normalized and used to weight district populations. District proportions of national population weighted by both the simple and composite indicators were then calculated for all districts and compared. District allocations were also calculated using the two approaches and compared. Results The two types of indicators are highly correlated, with a spearman rank correlation coefficient of 0.97 at the 1% level of significance. For 21 out of the 26 districts included in the study, proportions of national population weighted by the simple indicator are higher by an average of 0.6 percentage points. For the remaining 5 districts, district proportions of national population weighted by the composite indicator are higher by an average of 2 percentage points. Though the average percentage point differences are low and the actual allocations using both approaches highly correlated (ρ of 0.96), differences in actual allocations exceed 10% for 8 districts and have an average of 4.2% for the remaining 17. For 21 districts allocations based on the single variable indicator are higher. Conclusions Variations in district allocations made using either the simple or composite indicators of socioeconomic status are not statistically different to recommend one over the other. However, the single variable indicator is favourable for its ease of computation.

Background: In all health-care systems, decisions need to be made regarding allocation of available resources. Evidence is needed for these decisions, especially in low-income countries. We aimed to estimate how health-care resources provided by the public sector were used in Malawi during 2015-19 and to estimate the effects of strengthening health-care services., Methods: For this modelling study, we used the Thanzi La Onse model, an individual-based simulation model. The scope of the model was health care provided by the public sector in Malawi during 2015-19. Health-care services were delivered during health-care system interaction (HSI) events, which we characterised as occurring at a particular facility level and requiring a particular number of appointments. We developed mechanistic models for the causes of death and disability that were estimated to account for approximately 81% of deaths and approximately 72% of disability-adjusted life-years (DALYs) in Malawi during 2015-19, according to the Global Burden of Disease (GBD) estimates; we computed DALYs incurred in the population as the sum of years of life lost and years lived with disability. The disease models could interact with one another and with the underlying properties of each person. Each person in the Thanzi La Onse model had specific properties (eg, sex, district of residence, wealth percentile, smoking status, and BMI, among others), for which we measured distribution and evolution over time using demographic and health survey data. We also estimated the effect of different types of health-care system improvement., Findings: We estimated that the public-sector health-care system in Malawi averted 41·2 million DALYs (95% UI 38·6-43·8) during 2015-19, approximately half of the 84·3 million DALYs (81·5-86·9) that the population would otherwise have incurred. DALYs averted were heavily skewed to children aged 0-4 years due to services averting DALYs that would be caused by acute lower respiratory tract infection, HIV or AIDS, malaria, or neonatal disorders. DALYs averted among adults were mostly attributed to HIV or AIDS and tuberculosis. Under a scenario whereby each appointment took the time expected and health-care workers did not work for longer than contracted, the health-care system in Malawi during 2015-19 would have averted only 19·1 million DALYs (95% UI 17·1-22·4), suggesting that approximately 21·3 million DALYS (20·0-23·6) of total effect were derived through overwork of health-care workers. If people becoming ill immediately accessed care, all referrals were successfully completed, diagnostic accuracy of health-care workers was as good as possible, and consumables (ie, medicines) were always available, 28·2% (95% UI 25·7-30·9) more DALYS (ie, 12·2 million DALYs [95% UI 10·9-13·8]) could be averted., Interpretation: The health-care system in Malawi provides substantial health gains with scarce resources. Strengthening interventions could potentially increase these gains, so should be a priority for investigation and investment. An individual-based simulation model of health-care service delivery is valuable for health-care system planning and strengthening., Funding: The Wellcome Trust, UK Research and Innovation, the UK Medical Research Council, and Community Jameel., Competing Interests: Declaration of interests TBH receives research funding from the UK Medical Research Council, UK Research and Innovation, and The Wellcome Trust, paid to their institution. NA receives research funding from the UK Medical Research Council and the Bill & Melinda Gates Foundation, paid to their institution. VC receives research funding from the UK Medical Research Council and UK Research and Innovation, paid to their institution, and consulting fees from Source Market Access. MC receives research funding from The Wellcome Trust, paid to their institution. JHC receives research funding from UK Research and Innovation and The Wellcome Trust, paid to their institution. JC receives research funding from UK Research and Innovation and The Wellcome Trust, paid to their institution. EJ receives research funding from UK Research and Innovation and The Wellcome Trust, paid to their institution. BLJ receives research funding from the UK Medical Research Council and the UK Department for International Development, paid to their institution. SM receives research funding from UK Research and Innovation and The Wellcome Trust, paid to their institution, and consulting fees from The Global Fund to Fight AIDS, Tuberculosis and Malaria. MM receives research funding from UK Research and Innovation and The Wellcome Trust, paid to their institution. TC receives research funding from UK Research and Innovation and The Wellcome Trust, paid to their institution; receives consulting fees from the UN Economic Commission for Africa; and is on a data safety monitoring board for a trial of adolescent mental health in Nepal. ANP receives research funding from The Wellcome Trust, paid to their institution; receives research funding from the Bill & Melinda Gates Foundation, The Wellcome Trust, the US National Institutes of Health, the UK National Institute for Health and Care Research, and the EU, paid to their institution; and receives consulting fees from WHO. All other authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Background: Medical consumable stock-outs negatively affect health outcomes not only by impeding or delaying the effective delivery of services but also by discouraging patients from seeking care. Consequently, supply chain strengthening is being adopted as a key component of national health strategies. However, evidence on the factors associated with increased consumable availability is limited., Methods: In this study, we used the 2018-19 Harmonised Health Facility Assessment data from Malawi to identify the factors associated with the availability of consumables in level 1 facilities, ie, rural hospitals or health centres with a small number of beds and a sparsely equipped operating room for minor procedures. We estimate a multilevel logistic regression model with a binary outcome variable representing consumable availability (of 130 consumables across 940 facilities) and explanatory variables chosen based on current evidence. Further subgroup analyses are carried out to assess the presence of effect modification by level of care, facility ownership, and a categorisation of consumables by public health or disease programme, Malawi's Essential Medicine List classification, whether the consumable is a drug or not, and level of average national availability., Findings: Our results suggest that the following characteristics had a positive association with consumable availability-level 1b facilities or community hospitals had 64% (odds ratio [OR] 1·64, 95% CI 1·37-1·97) higher odds of consumable availability than level 1a facilities or health centres, Christian Health Association of Malawi and private-for-profit ownership had 63% (1·63, 1·40-1·89) and 49% (1·49, 1·24-1·80) higher odds respectively than government-owned facilities, the availability of a computer had 46% (1·46, 1·32-1·62) higher odds than in its absence, pharmacists managing drug orders had 85% (1·85, 1·40-2·44) higher odds than a drug store clerk, proximity to the corresponding regional administrative office (facilities greater than 75 km away had 21% lower odds [0·79, 0·63-0·98] than facilities within 10 km of the district health office), and having three drug order fulfilments in the 3 months before the survey had 14% (1·14, 1·02-1·27) higher odds than one fulfilment in 3 months. Further, consumables categorised as vital in Malawi's Essential Medicine List performed considerably better with 235% (OR 3·35, 95% CI 1·60-7·05) higher odds than other essential or non-essential consumables and drugs performed worse with 79% (0·21, 0·08-0·51) lower odds than other medical consumables in terms of availability across facilities., Interpretation: Our results provide evidence on the areas of intervention with potential to improve consumable availability. Further exploration of the health and resource consequences of the strategies discussed will be useful in guiding investments into supply chain strengthening., Funding: UK Research and Innovation as part of the Global Challenges Research Fund (Thanzi La Onse; reference MR/P028004/1), the Wellcome Trust (Thanzi La Mawa; reference 223120/Z/21/Z), the UK Medical Research Council, the UK Department for International Development, and the EU (reference MR/R015600/1)., Competing Interests: Declaration of interests Besides funding from the Wellcome Trust and UK Research and Innovation going towards authors’ institutions, some authors took on private projects, outside the submitted work. SM declares receiving consulting fees from The Global Fund. TC declares consulting fees from the UN Economic Commission for Africa, and non-paid work chairing a Trial Steering Committee for a trial of adolescent mental health interventions in Nepal. ANP declares receiving consulting fees from the Bill & Melinda Gates Foundation. All other authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.)

Malawi, like many low-income and middle-income countries, has used health benefits packages (HBPs) to allocate scarce resources to key healthcare interventions. With no widely accepted method for their development, HBPs often promise more than can be delivered, given available resources. An analytical framework is developed to guide the design of HBPs that can identify the potential value of including and implementing different interventions. It provides a basis for informing meaningful discussions between governments, donors and other stakeholders around the trade-offs implicit in package design. Metrics of value, founded on an understanding of the health opportunity costs of the choices faced, are used to quantify the scale of the potential net health impact (net disability adjusted life years averted) or the amount of additional healthcare resources that would be required to deliver similar net health impacts with existing interventions (the financial value to the healthcare system). The framework can be applied to answer key questions around, for example: the appropriate scale of the HBP; which interventions represent 'best buys' and should be prioritised; where investments in scaling up interventions and health system strengthening should be made; whether the package should be expanded; costs of the conditionalities of donor funding and how objectives beyond improving population health can be considered. This is illustrated using data from Malawi. The framework was successfully applied to inform the HBP in Malawi, as a core component of the country's Health Sector Strategic Plan II 2017-2022., Competing Interests: Competing interests: None declared.