Booster Doses and Prioritizing Lives Saved.
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ABSTRACT:
SUBMITTER: Patel MK
PROVIDER: S-EPMC8693686 | biostudies-literature | 2021 Dec
REPOSITORIES: biostudies-literature
Publications
Booster Doses and Prioritizing Lives Saved.
The New England journal of medicine 20211208 26
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Project description: Not available
| S-EPMC11443335 | biostudies-literature
Project description:BackgroundAs one of several countries that pledged to achieve the Millennium Development Goals (MDGs), Mozambique sought to reduce child, neonatal, and maternal mortality by two thirds by 2015. This study examines the impact of Mozambique's efforts between 1997 and 2015, highlighting the increases in intervention coverage that contributed to saving the most lives.MethodsA retrospective analysis of available household survey data was conducted using the Lives Saved Tool (LiST). Baseline mortality rates, cause-of-death distributions, and coverage of child, neonatal, and maternal interventions were entered as inputs. Changes in mortality rates, causes of death, and additional lives saved were calculated as results. Due to limited coverage data for the year 2015, we reported most results for the period 1997-2011. For 2011-2015 we reported additional lives saved for a subset of interventions. All analyses were performed at national and provincial level.ResultsOur modelled estimates show that increases in intervention coverage from 1997 to 2011 saved an additional 422 282 child lives (0-59 months), 85 450 neonatal lives (0-1 month), and 6528 maternal lives beyond those already being saved at baseline coverage levels in 1997. Malaria remained the leading cause of child mortality from 1997 to 2011; prematurity, asphyxia, and sepsis remained the leading causes of neonatal mortality; and hemorrhage remained the leading cause of maternal mortality. Interventions to reduce acute malnutrition and promote artemisinin-based combination therapy (ACT) for malaria were responsible for the largest number of additional child lives saved in the 1997-2011 period. Increases in coverage of delivery management were responsible for most additional newborn and maternal lives saved in both periods in Mozambique.ConclusionMozambique has made impressive gains in reducing child mortality since 1997. Additional effort is needed to further reduce maternal and neonatal mortality in all provinces. More lives can be saved by continuing to increase coverage of existing health interventions and exploring new ways to reach underserved populations.
| S-EPMC6300161 | biostudies-literature
Project description:BackgroundChoosing an optimum set of child health interventions for maximum mortality impact is important within resource poor policy environments. The Lives Saved Tool (LiST) is a computer model that estimates the mortality and stillbirth impact of scaling up proven maternal and child health interventions. This paper will describe the methods used to estimate the impact of scaling up interventions on neonatal and child mortality.Model structure and assumptionsLiST estimates mortality impact via five age bands 0 months, 1-5 months, 6-11 months, 12-23 months and 24 to 59 months. For each of these age bands reductions in cause specific mortality are estimated. Nutrition interventions can impact either nutritional statuses or directly impact mortality. In the former case, LiST acts as a cohort model where current nutritional statuses such as stunting impact the probability of stunting as the cohort ages. LiST links with a demographic projections model (DemProj) to estimate the deaths and deaths averted due to the reductions in mortality rates.Using listLiST can be downloaded at http://www.jhsph.edu/dept/ih/IIP/list/ where simple instructions are available for installation. LiST includes default values for coverage and effectiveness for many less developed countries obtained from credible sources.ConclusionsThe development of LiST is a continuing process. Via technical inputs from the Child Health Epidemiological Group, effectiveness values are updated, interventions are adopted and new features added.
| S-EPMC3231906 | biostudies-literature
Project description:Malaria was the leading cause of post-neonatal deaths in Mozambique in 2017. The use of insecticide treated nets (ITNs) is recognized as one of the most effective ways to reduce malaria mortality in children. No previous analyses have estimated changes in mortality attributable to the scale-up of ITNs, accounting for provincial differences in mortality rates and coverage of health interventions. Based upon annual provincial ownership coverage of ITNs, the Lives Saved Tool (LiST), a multi-cause mathematical model, estimated under-5 lives saved attributable to increased household ITN coverage in 10 provinces of Mozambique between 2012 and 2018, and projected lives saved from 2019 to 2025 if 2018 coverage levels are sustained. An estimated 14,040 under-5 child deaths were averted between 2012 and 2018. If 2018 coverage levels are maintained until 2025, an additional 33,277 child deaths could be avoided. If coverage reaches at least 85% in all ten provinces by 2022, then a projected 36,063 child lives can be saved. From 2012 to 2018, the estimated number of lives saved was highest in Zambezia and Tete provinces. Increases in ITN coverage can save a substantial number of child lives in Mozambique. Without continued investment, thousands of avoidable child deaths will occur.
| S-EPMC10022185 | biostudies-literature
Project description:BackgroundThe worldwide burden of stillbirths is large, with an estimated 2.6 million babies stillborn in 2015 including 1.3 million dying during labour. The Every Newborn Action Plan set a stillbirth target of ≤12 per 1000 in all countries by 2030. Planning tools will be essential as countries set policy and plan investment to scale up interventions to meet this target. This paper summarises the approach taken for modelling the impact of scaling-up health interventions on stillbirths in the Lives Saved tool (LiST), and potential future refinements.MethodsThe specific application to stillbirths of the general method for modelling the impact of interventions in LiST is described. The evidence for the effectiveness of potential interventions to reduce stillbirths are reviewed and the assumptions of the affected fraction of stillbirths who could potentially benefit from these interventions are presented. The current assumptions and their effects on stillbirth reduction are described and potential future improvements discussed.ResultsHigh quality evidence are not available for all parameters in the LiST stillbirth model. Cause-specific mortality data is not available for stillbirths, therefore stillbirths are modelled in LiST using an attributable fraction approach by timing of stillbirths (antepartum/ intrapartum). Of 35 potential interventions to reduce stillbirths identified, eight interventions are currently modelled in LiST. These include childbirth care, induction for prolonged pregnancy, multiple micronutrient and balanced energy supplementation, malaria prevention and detection and management of hypertensive disorders of pregnancy, diabetes and syphilis. For three of the interventions, childbirth care, detection and management of hypertensive disorders of pregnancy, and diabetes the estimate of effectiveness is based on expert opinion through a Delphi process. Only for malaria is coverage information available, with coverage estimated using expert opinion for all other interventions. Going forward, potential improvements identified include improving of effectiveness and coverage estimates for included interventions and addition of further interventions.ConclusionsKnown effective interventions have the potential to reduce stillbirths and can be modelled using the LiST tool. Data for stillbirths are improving. Going forward the LiST tool should seek, where possible, to incorporate these improving data, and to continually be refined to provide an increasingly reliable tool for policy and programming purposes.
| S-EPMC5688483 | biostudies-literature
Project description:BackgroundThe global nutrition community has been interested in investigating investment strategies that could be used to promote an increased focus and investment in nutrition programming in low- and middle-income countries.MethodsThe Lives Saved Tool (LiST) was used to evaluate lives saved and the costs of nutrition interventions in nine high-burden countries. In this case study, we detail the analyses that were conducted with LiST and how the results were packaged to develop Nourish the Future - a five-year proposal for the US government to scale up lifesaving malnutrition interventions.ResultsScaling up a proposed package of critical nutrition interventions including micronutrient supplementation for pregnant women, breastfeeding support, Vitamin A supplementation for children, and treatments for moderate and severe acute malnutrition is an effective and cost-effective way to avert millions of child deaths and stillbirths.ConclusionsThis is one of the few case studies that outlines how a nutrition modeling tool (in this case LiST) was used to engage in a prioritisation exercise to inform a US-based advocacy ask. We share reflections and provide practical insights into user motivation and preferences for existing and future modeling tool developers. This case study also emphasises how integral evidence translation and strategic advocacy are to ensure the use of the modeling results.
| S-EPMC11327894 | biostudies-literature
Project description:BackgroundIn 2010, the UK Government Department for International Development (DFID) committed through its 'Framework for results for reproductive, maternal and newborn health (RMNH)' to save 50,000 maternal lives and 250,000 newborn lives by 2015. They also committed to monitoring the performance of this portfolio of investments to demonstrate transparency and accountability. Methods currently available to directly measure lives saved are cost-, time-, and labour-intensive. The gold standard for calculating the total number of lives saved would require measuring mortality with large scale population based surveys or annual vital events surveillance. Neither is currently available in all low- and middle-income countries. Estimating the independent effect of DFID support relative to all other effects on health would also be challenging.MethodsThe Lives Saved Tool (LiST) is an evidence based software for modelling the effect of changes in health intervention coverage on reproductive, maternal, newborn and child mortality. A multi-country LiST-based analysis protocol was developed to retrospectively assess the total annual number of maternal and newborn lives saved from DFID aid programming in low- and middle-income countries.ResultsAnnual LiST analyses using the latest program data from DFID country offices were conducted between 2013 and 2016, estimating the annual number of maternal and neonatal lives saved across 2010-2015. For each country, independent project results were aggregated into health intervention coverage estimates, with and in the absence of DFID funding. More than 80% of reported projects were suitable for inclusion in the analysis, with 151 projects analysed in the 2016 analysis. Between 2010 and 2014, it is estimated that DFID contributed to saving the lives of 15,000 women in pregnancy and childbirth with health programming and 88,000 with family planning programming. It is estimated that DFID health programming contributed to saving 187,000 newborn lives.ConclusionsIt is feasible to estimate the overall contribution and impact of DFID's investment in RMNH from currently available information on interventions and coverage from individual country offices. This utilization of LiST, with estimated population coverage based on DFID program inputs, can be applied to similar types of datasets to quantify programme impact. The global data were used to estimate DFID's progress against the Framework for results targets to inform future programming. The identified limitations can also be considered to inform future monitoring and evaluation program design and implementation within DFID.
| S-EPMC5688469 | biostudies-literature
Project description:BackgroundDiarrhea is a leading cause of morbidity and mortality among children under five years of age. The Lives Saved Tool (LiST) is a model used to calculate deaths averted or lives saved by past interventions and for the purposes of program planning when costly and time consuming impact studies are not possible.DiscussionLiST models the relationship between coverage of interventions and outputs, such as stunting, diarrhea incidence and diarrhea mortality. Each intervention directly prevents a proportion of diarrhea deaths such that the effect size of the intervention is multiplied by coverage to calculate lives saved. That is, the maximum effect size could be achieved at 100% coverage, but at 50% coverage only 50% of possible deaths are prevented. Diarrhea mortality is one of the most complex causes of death to be modeled. The complexity is driven by the combination of direct prevention and treatment interventions as well as interventions that operate indirectly via the reduction in risk factors, such as stunting and wasting. Published evidence is used to quantify the effect sizes for each direct and indirect relationship. Several studies have compared measured changes in mortality to LiST estimates of mortality change looking at different sets of interventions in different countries. While comparison work has generally found good agreement between the LiST estimates and measured mortality reduction, where data availability is weak, the model is less likely to produce accurate results. LiST can be used as a component of program evaluation, but should be coupled with more complete information on inputs, processes and outputs, not just outcomes and impact.SummaryLiST is an effective tool for modeling diarrhea mortality and can be a useful alternative to large and expensive mortality impact studies. Predicting the impact of interventions or comparing the impact of more than one intervention without having to wait for the results of large and expensive mortality studies is critical to keep programs focused and results oriented for continued reductions in diarrhea and all-cause mortality among children under five years of age.
| S-EPMC4234397 | biostudies-literature
Project description:BackgroundDiarrhea remains a leading cause of mortality among young children in low- and middle-income countries. Although the evidence for individual diarrhea prevention and treatment interventions is solid, the effect a comprehensive scale-up effort would have on diarrhea mortality has not been estimated.Methods and findingsWe use the Lives Saved Tool (LiST) to estimate the potential lives saved if two scale-up scenarios for key diarrhea interventions (oral rehydration salts [ORS], zinc, antibiotics for dysentery, rotavirus vaccine, vitamin A supplementation, basic water, sanitation, hygiene, and breastfeeding) were implemented in the 68 high child mortality countries. We also conduct a simple costing exercise to estimate cost per capita and total costs for each scale-up scenario. Under the ambitious (feasible improvement in coverage of all interventions) and universal (assumes near 100% coverage of all interventions) scale-up scenarios, we demonstrate that diarrhea mortality can be reduced by 78% and 92%, respectively. With universal coverage nearly 5 million diarrheal deaths could be averted during the 5-year scale-up period for an additional cost of US$12.5 billion invested across 68 priority countries for individual-level prevention and treatment interventions, and an additional US$84.8 billion would be required for the addition of all water and sanitation interventions.ConclusionUsing currently available interventions, we demonstrate that with improved coverage, diarrheal deaths can be drastically reduced. If delivery strategy bottlenecks can be overcome and the international community can collectively deliver on the key strategies outlined in these scenarios, we will be one step closer to achieving success for the United Nations' Millennium Development Goal 4 (MDG4) by 2015.
| S-EPMC3062532 | biostudies-literature
Project description:BackgroundWith multiple coronavirus disease 2019 (COVID-19) vaccines available, understanding the epidemiologic, clinical, and economic value of increasing coverage levels and expediting vaccination is important.MethodsWe developed a computational model (transmission and age-stratified clinical and economics outcome model) representing the United States population, COVID-19 coronavirus spread (February 2020-December 2022), and vaccination to determine the impact of increasing coverage and expediting time to achieve coverage.ResultsWhen achieving a given vaccination coverage in 270 days (70% vaccine efficacy), every 1% increase in coverage can avert an average of 876 800 (217 000-2 398 000) cases, varying with the number of people already vaccinated. For example, each 1% increase between 40% and 50% coverage can prevent 1.5 million cases, 56 240 hospitalizations, and 6660 deaths; gain 77 590 quality-adjusted life-years (QALYs); and save $602.8 million in direct medical costs and $1.3 billion in productivity losses. Expediting to 180 days could save an additional 5.8 million cases, 215 790 hospitalizations, 26 370 deaths, 206 520 QALYs, $3.5 billion in direct medical costs, and $4.3 billion in productivity losses.ConclusionsOur study quantifies the potential value of decreasing vaccine hesitancy and increasing vaccination coverage and how this value may decrease with the time it takes to achieve coverage, emphasizing the need to reach high coverage levels as soon as possible, especially before the fall/winter.
| S-EPMC8136017 | biostudies-literature