Dataset Information

The impact of eliminating within-country inequality in health coverage on maternal and child mortality: a Lives Saved Tool analysis.

ABSTRACT: Inequality in healthcare across population groups in low-income countries is a growing topic of interest in global health. The Lives Saved Tool (LiST), which uses health intervention coverage to model maternal, neonatal, and child health outcomes such as mortality rates, can be used to analyze the impact of within-country inequality.Data from nationally representative household surveys (98 surveys conducted between 1998 and 2014), disaggregated by wealth quintile, were used to create a LiST analysis that models the impact of scaling up health intervention coverage for the entire country from the national average to the rate of the top wealth quintile (richest 20% of the population). Interventions for which household survey data are available were used as proxies for other interventions that are not measured in surveys, based on co-delivery of intervention packages.For the 98 countries included in the analysis, 24-32% of child deaths (including 34-47% of neonatal deaths and 16-19% of post-neonatal deaths) could be prevented by scaling up national coverage of key health interventions to the level of the top wealth quintile. On average, the interventions with most unequal coverage rates across wealth quintiles were those related to childbirth in health facilities and to water and sanitation infrastructure; the most equally distributed were those delivered through community-based mass campaigns, such as vaccines, vitamin A supplementation, and bednet distribution.LiST is a powerful tool for exploring the policy and programmatic implications of within-country inequality in low-income, high-mortality-burden countries. An "Equity Tool" app has been developed within the software to make this type of analysis easily accessible to users.

SUBMITTER: Clermont A

PROVIDER: S-EPMC5688502 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The impact of eliminating within-country inequality in health coverage on maternal and child mortality: a Lives Saved Tool analysis.

Clermont Adrienne A

BMC public health 20171107 Suppl 4

<h4>Background</h4>Inequality in healthcare across population groups in low-income countries is a growing topic of interest in global health. The Lives Saved Tool (LiST), which uses health intervention coverage to model maternal, neonatal, and child health outcomes such as mortality rates, can be used to analyze the impact of within-country inequality.<h4>Methods</h4>Data from nationally representative household surveys (98 surveys conducted between 1998 and 2014), disaggregated by wealth quinti ...[more]

PMID: 29143623

Similar Datasets

Project description:Background:As 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. Methods:A 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. Results:Our 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. Conclusion:Mozambique 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.

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.

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.

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.

Dataset Information

The impact of eliminating within-country inequality in health coverage on maternal and child mortality: a Lives Saved Tool analysis.

Publications

The impact of eliminating within-country inequality in health coverage on maternal and child mortality: a Lives Saved Tool analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets