Using the lives saved tool (LiST) to model mHealth impact on neonatal survival in resource-limited settings.
Ontology highlight
ABSTRACT:
SUBMITTER: Jo Y
PROVIDER: S-EPMC4094557 | biostudies-literature | 2014
REPOSITORIES: biostudies-literature
Publications
Using the lives saved tool (LiST) to model mHealth impact on neonatal survival in resource-limited settings.
Jo Youngji Y Labrique Alain B AB Lefevre Amnesty E AE Mehl Garrett G Pfaff Teresa T Walker Neff N Friberg Ingrid K IK
PloS one 20140711 7
While the importance of mHealth scale-up has been broadly emphasized in the mHealth community, it is necessary to guide scale up efforts and investment in ways to help achieve the mortality reduction targets set by global calls to action such as the Millennium Development Goals, not merely to expand programs. We used the Lives Saved Tool (LiST)--an evidence-based modeling software--to identify priority areas for maternal and neonatal health services, by formulating six individual and combined in ...[more]
Similar Datasets
Project description:BACKGROUND: Choosing 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 ASSUMPTIONS: LiST 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 LIST: LiST 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. CONCLUSIONS: The 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-other
Project description:BackgroundGuidelines recommend antiretroviral therapy (ART) initiation at CD4 <350 cells per microliter for HIV-infected individuals in resource-limited settings. However, funding for treatment expansion remains uncertain. We forecast the mortality impact of ART expansion alternatives in Haiti.MethodsWe used data from Haiti to develop a country-specific model of HIV disease. The model projects the mortality, total number of HIV-infected individuals, and number and coverage (percentage of those eligible) on ART by simulating cohorts of HIV-infected individuals over 10 years. Five ART expansion scenarios, ranging from fully expanded ART (best case) to No New ART (worst case), were assessed.ResultsBy 2010, the model predicts 103,500 individuals living with HIV in Haiti, of whom 27,300 were estimated to receive ART. Continuing ART initiation at current rates requires increasing the number on ART to 43,300 by 2020 (56% coverage), with 89,700 deaths estimated between 2010 and 2020. The number on ART could increase by 7400 (+17.1%, best case) or decrease by 25,600 (-59.1%, worst case), resulting in 19,500 deaths averted and 9900 fewer in care awaiting ART (best versus worst case). Results are sensitive to untreated disease progression and pre-ART loss from care. Increased HIV testing, linkage to care, and retention in care can avert additional deaths and achieve nearly 80% ART coverage with optimal policy improvements.ConclusionsIn resource-limited settings, continued improvements in HIV treatment access will save lives. Efforts to efficiently expand ART access should remain a global priority.
| S-EPMC3821389 | biostudies-literature
Project description:BackgroundInternational guidelines recommend HIV treatment expansion in resource-limited settings, but funding availability is uncertain. We evaluated the performance of a model that forecasts lives saved through continued HIV treatment expansion in Haiti.MethodsWe developed a computer-based, mathematical model of HIV disease and used incidence density analysis of patient-level Haitian data to derive model parameters for HIV disease progression. We assessed the internal validity of model predictions and internally calibrated model inputs when model predictions did not fit the patient-level data. We then derived uncertain model inputs related to diagnosis and linkage to care, pretreatment retention, and enrollment on HIV treatment through an external calibration process that selected input values by comparing model predictions to Haitian population-level data. Model performance was measured by fit to event-free survival (patient level) and number receiving HIV treatment over time (population level).ResultsFor a cohort of newly HIV-infected individuals with no access to HIV treatment, the model predicts median AIDS-free survival of 9.0 years precalibration and 6.6 years postcalibration v. 5.8 years (95% confidence interval, 5.1-7.0) from the patient-level data. After internal validation and calibration, 16 of 17 event-free survival measures (94%) had a mean percentage deviation between model predictions and the empiric data of <6%. After external calibration, the percentage deviation between model predictions and population-level data on the number on HIV treatment was <1% over time.ConclusionsValidation and calibration resulted in a good-fitting model appropriate for health policy decision making. Using local data in a policy model-building process is feasible in resource-limited settings.
| S-EPMC4297237 | 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:Achieving the Sustainable Development Goals will require careful allocation of resources in order to achieve the highest impact. The Lives Saved Tool (LiST) has been used widely to calculate the impact of maternal, neonatal and child health (MNCH) interventions for program planning and multi-country estimation in several Lancet Series commissions. As use of the LiST model increases, many have expressed a desire to cost interventions within the model, in order to support budgeting and prioritization of interventions by countries. A limited LiST costing module was introduced several years ago, but with gaps in cost types. Updates to inputs have now been added to make the module fully functional for a range of uses.This paper builds on previous work that developed an initial version of the LiST costing module to provide costs for MNCH interventions using an ingredients-based costing approach. Here, we update in 2016 the previous econometric estimates from 2013 with newly-available data and also include above-facility level costs such as program management. The updated econometric estimates inform percentages of intervention-level costs for some direct costs and indirect costs. These estimates add to existing values for direct cost requirements for items such as drugs and supplies and required provider time which were already available in LiST Costing.Results generated by the LiST costing module include costs for each intervention, as well as disaggregated costs by intervention including drug and supply costs, labor costs, other recurrent costs, capital costs, and above-service delivery costs. These results can be combined with mortality estimates to support prioritization of interventions by countries.The LiST costing module provides an option for countries to identify resource requirements for scaling up a maternal, neonatal, and child health program, and to examine the financial impact of different resource allocation strategies. It can be a useful tool for countries as they seek to identify the best investments for scarce resources. The purpose of the LiST model is to provide a tool to make resource allocation decisions in a strategic planning process through prioritizing interventions based on resulting impact on maternal and child mortality and morbidity.
| S-EPMC5688490 | biostudies-literature
Project description:BackgroundThe Missed Opportunity tool was developed as an application in the Lives Saved Tool (LiST) to allow users to quickly compare the relative impact of interventions. Global Financing Facility (GFF) investment cases have been identified as a potential application of the Missed Opportunity analyses in Democratic Republic of the Congo (DRC), Ethiopia, Kenya, and Tanzania, to use 'lives saved' as a normative factor to set priorities.MethodsThe Missed Opportunity analysis draws on data and methods in LiST to project maternal, stillbirth, and child deaths averted based on changes in interventions' coverage. Coverage of each individual intervention in LiST was automated to be scaled up from current coverage to 90% in the next year, to simulate a scenario where almost every mother and child receive proven interventions that they need. The main outcome of the Missed Opportunity analysis is deaths averted due to each intervention.ResultsWhen reducing unmet need for contraception is included in the analysis, it ranks as the top missed opportunity across the four countries. When it is not included in the analysis, top interventions with the most total deaths averted are hospital-based interventions such as labor and delivery management in the CEmOC and BEmOC level, and full treatment and supportive care for premature babies, and for sepsis/pneumonia.ConclusionsThe Missed Opportunity tool can be used to provide a quick, first look at missed opportunities in a country or geographic region, and help identify interventions for prioritization. While it is a useful advocate for evidence-based priority setting, decision makers need to consider other factors that influence decision making, and also discuss how to implement, deliver, and sustain programs to achieve high coverage.
| S-EPMC5688441 | biostudies-literature
Project description:BACKGROUND:The Lives Saved Tool (LiST) is a computer-based model that estimates the impact of scaling up key interventions to improve maternal, newborn and child health. Initially developed to inform the Lancet Child Survival Series of 2003, the functionality and scope of LiST have been expanded greatly over the past 10 years. This study sought to "take stock" of how LiST is now being used and for what purposes. METHODS:We conducted a quantitative survey of LiST users, qualitative interviews with a smaller sample of LiST users and members of the LiST team at Johns Hopkins University, and a literature review of studies involving LiST analyses. RESULTS:LiST is being used by donors, international organizations, governments, NGOs and academic institutions to assist program evaluation, inform strategic planning and evidenced-based decision-making, and advocate for high-impact interventions. Some organizations have integrated LiST into internal workflows and built in-house capacity for using LiST, while other organizations rely on the LiST team for support and to outsource analyses. In addition to being a popular stand-alone software, LiST is used as a calculation engine for other applications. CONCLUSIONS:The Lives Saved Tool has been reported to be a useful model in maternal, newborn, and child health. With continued commitment, LiST should remain as a part of the international health toolkit used to assess maternal, newborn and child health programs.
| S-EPMC5688436 | biostudies-literature
Project description:Calls have been made for improved measurement of coverage for maternal, newborn and child health interventions. Recently, methods linking household and health facility surveys have been used to improve estimation of intervention coverage. However, linking methods rely the availability of household and health facility surveys which are temporally matched. Because nationally representative health facility assessments are not yet routinely conducted in many low and middle income countries, estimates of intervention coverage based on linking methods can be produced for only a subset of countries. Estimates of intervention coverage are a critical input for modelling the health impact of intervention scale-up in the Lives Saved Tool (LiST). The purpose of this study was to develop a data-driven approach to estimate coverage for a subset of antenatal care interventions modeled in LiST.Using a five-step process, estimates of population level coverage for syphilis detection and treatment, case management of diabetes, malaria infection, hypertensive disorders, and pre-eclampsia, were computed by linking household and health facility surveys. Based on data characterizing antenatal care and estimates of coverage derived from the linking approach, predictive models for intervention coverage were developed. Updated estimates of coverage based on the predictive models were compared, first with current default proxies, then with estimates based on the linking approach. Model fit and accuracy were assessed using three measures: the coefficient of determination, Pearson's correlation coefficient, and the root mean square error (RMSE).The ability to predict intervention coverage was fairly accurate across all interventions considered. Predictive models accounted for 20-63% of the variance in intervention coverages, and correlation coefficients ranged from 0.5 to 0.83. The predictive model used to estimate coverage of management of pre-eclampsia performed relatively better (RMSE = 0.11) than the model estimating coverage of diabetes case management (RMSE = 0.19).The new approach to estimate coverage represents an improvement over current default proxies in LiST. As the availability of reliable coverage data improves, impact estimates generated by LiST will improve. This study underscores the need for continued efforts to improve coverage measurement, while bringing to the fore the importance of health facility assessments as complementary data sources.
| S-EPMC5688485 | biostudies-other
Project description:ObjectiveAlthough nutrition-specific interventions are designed based on maternal, household and community-level correlates, no attempt has been made to project stunting and wasting and identify intervention priorities in India. The objective of this paper is to model the stunting and wasting in the state of Odisha, India by scaling up maternal and child health interventions under alternative scenarios.DesignThis study primarily used data from National Family Health Survey 4, 2015-2016.MeasuresThe LiST (Lives Saved Tool) software is used to model the nutritional outcomes and prioritise interventions. The projections were carried out under four alternative scenarios: scenario 1-if the coverage indicators continued based on past trends; scenario 2-scaled up to the level of the richest quintile; scenario 3-scaled up to that of Tamil Nadu; and scenario 4-scaled up to an aspirational coverage level.ResultsIn 2015, out of 3.52 million under-5 children in Odisha, around 1.20 million were stunted. By 2030, the numbers of stunted children will be 1.11 million under scenario 1, 1.07 million under scenario 2, 1.09 million under scenario 3 and 0.89 million under scenario 4. The projected stunting level will be 25% under scenario 4 and around 31% under all other scenarios. By 2030, the level of wasting will remain unchanged at 20% under the first three scenarios and 4.3% under scenario 4. Appropriate complementary feeding would avert about half of the total stunting cases under all four scenarios, followed by zinc supplementation. Water connection at home, washing hands with soap and improved sanitation are other effective interventions.ConclusionSustaining the maternal and child health interventions, promoting evidence-based stunting and wasting reduction interventions, and a multisectoral approach can achieve the World Health Assembly targets and Sustainable Development Goals of undernutrition in Odisha.
| S-EPMC6549738 | biostudies-literature
Project description:The 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.The 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.High 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.Known 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