Dataset Information

Projecting stunting and wasting under alternative scenarios in Odisha, India, 2015-2030: a Lives Saved Tool (LiST)-based approach.

ABSTRACT:

Objective

Although 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.

Design

This study primarily used data from National Family Health Survey 4, 2015-2016.

Measures

The 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.

Results

In 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.

Conclusion

Sustaining 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.

SUBMITTER: Mishra NR

PROVIDER: S-EPMC6549738 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Projecting stunting and wasting under alternative scenarios in Odisha, India, 2015-2030: a Lives Saved Tool (LiST)-based approach.

Mishra Nihar Ranjan NR Mohanty Sanjay K SK Mittra Devjit D Shah Mansi M Meitei Wahengbam Bigyananda WB

BMJ open 20190528 5

<h4>Objective</h4>Although 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.<h4>Design</h4>This study primarily used data from National Family Health Survey 4, 2 ...[more]

PMID: 31142537

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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.

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: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.

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

Projecting stunting and wasting under alternative scenarios in Odisha, India, 2015-2030: a Lives Saved Tool (LiST)-based approach.

Objective

Design

Measures

Results

Conclusion

Publications

Projecting stunting and wasting under alternative scenarios in Odisha, India, 2015-2030: a Lives Saved Tool (LiST)-based approach.

Similar Datasets

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