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1.
Epidemics ; 38: 100552, 2022 03.
Article in English | MEDLINE | ID: covidwho-1757327

ABSTRACT

COVID-19 disease models have aided policymakers in low-and middle-income countries (LMICs) with many critical decisions. Many challenges remain surrounding their use, from inappropriate model selection and adoption, inadequate and untimely reporting of evidence, to the lack of iterative stakeholder engagement in policy formulation and deliberation. These issues can contribute to the misuse of models and hinder effective policy implementation. Without guidance on how to address such challenges, the true potential of such models may not be realised. The COVID-19 Multi-Model Comparison Collaboration (CMCC) was formed to address this gap. CMCC is a global collaboration between decision-makers from LMICs, modellers and researchers, and development partners. To understand the limitations of existing COVID-19 disease models (primarily from high income countries) and how they could be adequately support decision-making in LMICs, a desk review of modelling experience during the COVID-19 and past disease outbreaks, two online surveys, and regular online consultations were held among the collaborators. Three key recommendations from CMCC include: A 'fitness-for-purpose' flowchart, a tool that concurrently walks policymakers (or their advisors) and modellers through a model selection and development process. The flowchart is organised around the following: policy aims, modelling feasibility, model implementation, model reporting commitment. Holmdahl and Buckee (2020) A 'reporting standards trajectory', which includes three gradually increasing standard of reports, 'minimum', 'acceptable', and 'ideal', and seeks collaboration from funders, modellers, and decision-makers to enhance the quality of reports over time and accountability of researchers. Malla et al. (2018) A framework for "collaborative modelling for effective policy implementation and evaluation" which extends the definition of stakeholders to funders, ground-level implementers, public, and other researchers, and outlines how each can contribute to modelling. We advocate for standardisation of modelling processes and adoption of country-owned model through iterative stakeholder participation and discuss how they can enhance trust, accountability, and public ownership to decisions.


Subject(s)
COVID-19 , Health Policy , COVID-19/epidemiology , Humans , Pandemics , Policy Making
2.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-308591

ABSTRACT

Background: Mathematical models have been used throughout the COVID-19 pandemic to inform policymaking decisions. The COVID-19 Multi-Model Comparison Collaboration (CMCC) was established to provide country governments, particularly low- and middle-income countries (LMICs), and other model users with an overview of the aims, capabilities and limits of the main multi-country COVID-19 models to optimise their usefulness in the COVID-19 response. Methods: Seven models were identified that satisfied the inclusion criteria for the model comparison and had creators that were willing to participate in this analysis. A questionnaire, extraction tables and interview structure were developed to be used for each model, these tools had the aim of capturing the model characteristics deemed of greatest importance based on discussions with the Policy Group. The questionnaires were first completed by the CMCC Technical group using publicly available information, before further clarification and verification was obtained during interviews with the model developers. The fitness-for-purpose flow chart for assessing the appropriateness for use of different COVID-19 models was developed jointly by the CMCC Technical Group and Policy Group. Results: : A flow chart of key questions to assess the fitness-for-purpose of commonly used COVID-19 epidemiological models was developed, with focus placed on their use in LMICs. Furthermore, each model was summarised with a description of the main characteristics, as well as the level of engagement and expertise required to use or adapt these models to LMIC settings. Conclusions: This work formalises a process for engagement with models, which is often done on an ad-hoc basis, with recommendations for both policymakers and model developers and should improve modelling use in policy decision making.

3.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-295420

ABSTRACT

Background: This modeling study aims to measure the impact COVID-19-related tuberculosis (TB) service disruptions had on key TB outcomes in Indonesia, Kyrgyzstan, Malawi, Mozambique, and Peru, and the mitigation of that impact through catch-up strategies in each country.<br><br>Methods: Quarterly epidemiological estimates and programmatic TB data capturing disruption levels to each TB service were collected by National TB Programmes (NTPs) in 2019, for a pre-COVID-19 baseline, and throughout 2020. These data, together with the NTP’s COVID-19 response plans, were used within Optima TB models to project TB incidence and deaths over five years.<br><br>Findings: Countries reported disruptions of up to 64% to passive TB case finding. TB treatment experienced lower levels of disruption of up to 21%. We predicted that under the worse-case scenario new latent TB infections, new active TB infections, and TB-related deaths could increase by up to 23%, 11%, and 20%, respectively, by 2024. However, three of the five countries were on track to mitigate these increases to 3% or less by maintaining TB services in 2021 and 2022 and by implementing proposed catch-up strategies thereafter. Indonesia was already experiencing the worse-case scenario, which could lead to 270,000 additional active TB infections and 36,000 additional TB-related deaths by the end of 2024.<br><br>Interpretation: The COVID-19 pandemic is projected to negatively affect progress towards 2035 End TB targets, especially in countries already off-track. Findings highlight the need to proactively maintain TB service availability under a range of scenarios, including potential new waves of COVID-19 caused by more transmissible variants.<br><br>Funding Information: UNAIDS<br><br>Declaration of Interests: None to declare.

4.
PLoS One ; 16(11): e0260247, 2021.
Article in English | MEDLINE | ID: covidwho-1542186

ABSTRACT

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.


Subject(s)
Clinical Decision-Making , Proof of Concept Study , Resource Allocation , Universal Health Insurance , Armenia , Humans , Public Policy , Zimbabwe
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