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Background: To understand the current measles mortality burden, and to mitigate the future burden, it is crucial to have robust estimates of measles case fatalities. Estimates of measles case-fatality ratios (CFRs) that are specific to age, location, and time are essential to capture variations in underlying population-level factors, such as vaccination coverage and measles incidence, which contribute to increases or decreases in CFRs. In this study, we updated estimates of measles CFRs by expanding upon previous systematic reviews and implementing a meta-regression model. Our objective was to use all information available to estimate measles CFRs in low-income and middle-income countries (LMICs) by country, age, and year. Method(s): For this systematic review and meta-regression modelling study, we searched PubMed on Dec 31, 2020 for all available primary data published from Jan 1, 1980 to Dec 31, 2020, on measles cases and fatalities occurring up to Dec 31, 2019 in LMICs. We included studies that previous systematic reviews had included or which contained primary data on measles cases and deaths from hospital-based, community-based, or surveillance-based reports, including outbreak investigations. We excluded studies that were not in humans, or reported only data that were only non-primary, or on restricted populations (eg, people living with HIV), or on long-term measles mortality (eg, death from subacute sclerosing panencephalitis), and studies that did not include country-level data or relevant information on measles cases and deaths, or were for a high-income country. We extracted summary data on measles cases and measles deaths from studies that fitted our inclusion and exclusion criteria. Using these data and a suite of covariates related to measles CFRs, we implemented a Bayesian meta-regression model to produce estimates of measles CFRs from 1990 to 2019 by location and age group. This study was not registered with PROSPERO or otherwise. Finding(s): We identified 2705 records, of which 208 sources contained information on both measles cases and measles deaths in LMICS and were included in the review. Between 1990 and 2019, CFRs substantially decreased in both community-based and hospital-based settings, with consistent patterns across age groups. For people aged 0-34 years, we estimated a mean CFR for 2019 of 1.32% (95% uncertainty interval [UI] 1.28-1.36) among community-based settings and 5.35% (5.08-5.64) among hospital-based settings. We estimated the 2019 CFR in community-based settings to be 3.03% (UI 2.89-3.16) for those younger than 1 year, 1.63% (1.58-1.68) for age 1-4 years, 0.84% (0.80-0.87) for age 5-9 years, and 0.67% (0.64-0.70) for age 10-14 years. Interpretation(s): Although CFRs have declined between 1990 and 2019, there are still large heterogeneities across locations and ages. One limitation of this systematic review is that we were unable to assess measles CFR among particular populations, such as refugees and internally displaced people. Our updated methodological framework and estimates could be used to evaluate the effect of measles control and vaccination programmes on reducing the preventable measles mortality burden. Funding(s): Bill & Melinda Gates Foundation;Gavi, the Vaccine Alliance;and the US National Institutes of Health.Copyright © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license
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OBJECTIVES: Disease transmission models are used in impact assessment and economic evaluations of infectious disease prevention and treatment strategies, prominently so in the COVID-19 response. These models rarely consider dimensions of equity relating to the differential health burden between individuals and groups. We describe concepts and approaches which are useful when considering equity in the priority setting process, and outline the technical choices concerning model structure, outputs, and data requirements needed to use transmission models in analyses of health equity. METHODS: We reviewed the literature on equity concepts and approaches to their application in economic evaluation and undertook a technical consultation on how equity can be incorporated in priority setting for infectious disease control. The technical consultation brought together health economists with an interest in equity-informative economic evaluation, ethicists specialising in public health, mathematical modellers from various disease backgrounds, and representatives of global health funding and technical assistance organisations, to formulate key areas of consensus and recommendations. RESULTS: We provide a series of recommendations for applying the Reference Case for Economic Evaluation in Global Health to infectious disease interventions, comprising guidance on 1) the specification of equity concepts; 2) choice of evaluation framework; 3) model structure; and 4) data needs. We present available conceptual and analytical choices, for example how correlation between different equity- and disease-relevant strata should be considered dependent on available data, and outline how assumptions and data limitations can be reported transparently by noting key factors for consideration. CONCLUSIONS: Current developments in economic evaluations in global health provide a wide range of methodologies to incorporate equity into economic evaluations. Those employing infectious disease models need to use these frameworks more in priority setting to accurately represent health inequities. We provide guidance on the technical approaches to support this goal and ultimately, to achieve more equitable health policies.
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Objective The health systems costs of COVID-19 are high in many countries, including Pakistan. Without increases in fiscal space, COVID-19 interventions are likely to displace other activities within the health system. We reflect on the inclusion of COVID-19 interventions in Pakistan's Essential Package of Health Services (EPHS) and, from a financial optimisation perspective, propose which interventions should be displaced to ensure the highest possible overall health utility within budgetary constraints. Methods We estimated the costs of all 88 interventions currently included in the EPHS and collected published data on their cost-effectiveness. We also estimated total costs and costeffectiveness of COVID-19 vaccination in Pakistan. We ranked all EPHS interventions and COVID-19 vaccination by costeffectiveness, determining which interventions are comparatively least cost-effective and, in the absence of additional funding, no longer affordable. Results The EPHS assumes a spending per capita of US $12.96, averting 40.36 million disability-adjusted life years (DALYs). From a financial optimisation perspective, and assuming no additional funds, the introduction of a COVID-19 vaccine (US$3 per dose) should displace 8 interventions out of the EPHS, making the EPHS more cost-effective by averting 40.62 million DALYs. A US$6 dose should displace a further intervention and avert 40.56 million DALYs. A US$10 dose would partially fall out of the package, displacing four additional interventions. If health spending per capita decreased to US$8, a US$3 dose would still be affordable, but not US$6 or US$10 doses. Discussion Cost-effectiveness is only one criterion considered when deciding which interventions are included in (or removed from) a health benefits package. While displacing certain interventions to create fiscal space for the COVID-19 vaccine may lead to a financially optimal scenario, doing so may be politically unfeasible or socially undesirable. We highlight the difficult trade-offs that health systems face in the era of COVID-19.
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COVID-19 , SARS-CoV-2 , COVID-19 Vaccines , Health Personnel , Humans , Population Density , VaccinationABSTRACT
BACKGROUND: The COVID-19 pandemic has disrupted routine measles immunisation and supplementary immunisation activities (SIAs) in most countries including Kenya. We assessed the risk of measles outbreaks during the pandemic in Kenya as a case study for the African Region. METHODS: Combining measles serological data, local contact patterns, and vaccination coverage into a cohort model, we predicted the age-adjusted population immunity in Kenya and estimated the probability of outbreaks when contact-reducing COVID-19 interventions are lifted. We considered various scenarios for reduced measles vaccination coverage from April 2020. RESULTS: In February 2020, when a scheduled SIA was postponed, population immunity was close to the herd immunity threshold and the probability of a large outbreak was 34% (8-54). As the COVID-19 contact restrictions are nearly fully eased, from December 2020, the probability of a large measles outbreak will increase to 38% (19-54), 46% (30-59), and 54% (43-64) assuming a 15%, 50%, and 100% reduction in measles vaccination coverage. By December 2021, this risk increases further to 43% (25-56), 54% (43-63), and 67% (59-72) for the same coverage scenarios respectively. However, the increased risk of a measles outbreak following the lifting of all restrictions can be overcome by conducting a SIA with ≥ 95% coverage in under-fives. CONCLUSION: While contact restrictions sufficient for SAR-CoV-2 control temporarily reduce measles transmissibility and the risk of an outbreak from a measles immunity gap, this risk rises rapidly once these restrictions are lifted. Implementing delayed SIAs will be critical for prevention of measles outbreaks given the roll-back of contact restrictions in Kenya.