How is the Lives Saved Tool (LiST) used in the global health community? Results of a mixed-methods LiST user study

Human factors methods in the design of digital decision support systems for population health: a scoping review

BACKGROUND

While Human Factors (HF) methods have been applied to the design of decision support systems (DSS) to aid clinical decision-making, the role of HF to improve decision-support for population health outcomes is less understood. We sought to comprehensively understand how HF methods have been used in designing digital population health DSS.

MATERIALS AND METHODS

We searched English documents published in health sciences and engineering databases (Medline, Embase, PsychINFO, Scopus, Comendex, Inspec, IEEE Xplore) between January 1990 and September 2023 describing the development, validation or application of HF principles to decision support tools in population health.

RESULTS

We identified 21,581 unique records and included 153 studies for data extraction and synthesis. We included research articles that had a target end-user in population health and that used HF. HF methods were applied throughout the design lifecycle. Users were engaged early in the design lifecycle in the needs assessment and requirements gathering phase and design and prototyping phase with qualitative methods such as interviews. In later stages in the lifecycle, during user testing and evaluation, and post deployment evaluation, quantitative methods were more frequently used. However, only three studies used an experimental framework or conducted A/B testing.

CONCLUSIONS

While HF have been applied in a variety of contexts in the design of data-driven DSSs for population health, few have used Human Factors to its full potential. We offer recommendations for how HF can be leveraged throughout the design lifecycle. Most crucially, system designers should engage with users early on and throughout the design process. Our findings can support stakeholders to further empower public health systems.

Using the Lives Saved Tool to inform global nutrition advocacy

Background

The 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.

Methods

The 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.

Results

Scaling 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.

Conclusions

This 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.

Return on investments in the Health Extension Program in Ethiopia

BACKGROUND

Since 2003, the government of Ethiopia has trained and deployed more than 42,000 Health Extension Workers across the country to provide primary healthcare services. However, no research has assessed the return on investments into human resources for health in this setting. This study aims to fill this gap by analyzing the return on investment within the context of the Ethiopian Health Extension Program.

METHODS

We collected data on associated costs and benefits attributed to the Health Extension Program from primary and secondary sources. Primary sources included patient exit interviews, surveys with Health Extension Workers and other health professionals, key informant interviews, and focus groups conducted in the following regions: Amhara, Oromia, Tigray, and the Southern Nations Nationalities and Peoples' Region. Secondary sources consisted of financial and administrative reports gathered from the Ministry of Health and its subsidiaries, as well as data accessed through the Lives Saved Tool. A long-run return on investment analysis was conducted considering program costs (personnel, recurrent, and capital investments) in comparison to benefits gained through improved productivity, equity, empowerment, and employment.

FINDINGS

Between 2008-2017, Health Extension Workers saved 50,700 maternal and child lives. Much of the benefits were accrued by low income, less educated, and rural women who had limited access to services at higher level health centers and hospitals. Regional return ranged from $1.27 to $6.64, with an overall return on investment in the range of $1.59 to $3.71.

CONCLUSION

While evidence of return on investments are limited, results from the Health Extension Program in Ethiopia show promise for similar large, sustainable system redesigns. However, this evidence needs to be contextualized and adapted in different settings to inform policy and practice. The Ethiopian Health Extension Program can serve as a model for other nations of a large-scale human resources for health program containing strong economic benefits and long-term sustainability through successful government integration.

Maternal and child nutrition in the Lives Saved Tool: Results of a recent update

Background

The Lives Saved Tool (LiST) is a mathematical modelling tool for estimating the survival, health, and nutritional impacts of scaling intervention coverage in low- and middle-income countries (LMICs). Various nutrition interventions are included in LiST and are regularly (and independently) reviewed and updated as new data emerge. This manuscript describes our latest in-depth review of nutrition evidence, focusing on intervention efficacy, appropriate population-affected fractions, and new interventions for potential inclusion in the LiST model.

Methods

An external advisory group (EAG) was assembled to review evidence from systematic reviews on intervention-outcome (I-O) pairs for women and children under five years of age. GRADE quality was assigned to each pair based on a LiST-specific checklist to facilitate consistent decisions during the consideration. For existing interventions with new information, the EAG was asked to recommend whether to update the default efficacy values and population-affected fractions. For the new interventions, the EAG decided whether there was sufficient evidence of benefit, and in affirmative cases, information on the efficacy and affected fraction values that could be used. Decisions were based on expert group consensus.

Results

Overall, the group reviewed 53 nutrition-related I-O pairs, including 25 existing and 28 new ones. Efficacy and population-affected fractions were updated for seven I-O pairs; three pairs were updated for efficacy estimates only, three were updated for population-affected fractions only; and nine new I-O pairs were added to the model, bringing the total of nutrition-related I-O pairs to 34. Included in the new I-O pairs were two new nutrition interventions added to LIST: zinc fortification and neonatal vitamin A supplementation.

Conclusions

For modelling tools like LiST to be useful, it is crucial to update interventions, efficacy and population-affected fractions as new evidence becomes available. The present updates will enable LiST users to better estimate the potential health, nutrition, and survival benefits of investing in nutrition.

Longitudinal trends in the health outcomes among children of the North Eastern States of India: a comparative analysis using national DHS data from 2006 to 2020

Background/Objectives

Northeastern Indian region has a high density of marginalised populations with a concerning quality of health services. We observed the trends in prevalence of infectious diseases and nutritional disorders among children under-five years from 2006 to 2020 in the Northeastern states. We also assessed the distribution of their burden by place of residence.

Methods

A secondary data analysis of select indicators on infectious diseases and nutritional disorders in seven Northeastern states across three rounds of the National Family Health Survey (2005–06, 2015–16, 2019–20) was undertaken. We calculated outcome indicator mean prevalence, relative change and average annual rates of reduction of the indicators.

Results

A significant relative reduction between 2006–2020 in the prevalence of diarrhoea (0.4 [95CI:0.7,0.1]) at p < 0.05; acute respiratory illness (ARI) (0.7 [95CI:0.1,0.4]), stunting (0.3 [95CI: 0.3,0.12]) and underweight (0.3 [95CI:0.5,0.2]) at p < 0.001 were noted. However, overweight prevalence increased (10.1[95CI:4.3,16.0, p < 0.001]) due to a low annual reduction rate. The highest annual reduction rates were observed in Sikkim and Tripura for diarrhoea and ARI respectively (>10.0%), and in Meghalaya for wasting and severely wasting (6.3%). Rural areas had a higher burden of stunting, wasting (including severe), underweight, anaemia and diarrhoea; overweight was seen in both rural and urban settings.

Conclusion

Significant reductions were observed in ARI, diarrhoea, stunting and underweight prevalence between 2006–2020, with sub-regional variations and a greater burden in rural areas. During this period, overweight prevalence worsened; and anaemia showed a large increase from 2016. To reduce the equity-gap, programmes should be adapted to meet the differential needs of the Northeastern states.

Nutrition modeling tools: a qualitative study of influence on policy decision making and determining factors

Nutrition modeling tools (NMTs) generate evidence to inform policy and program decision making; however, the literature is generally limited to modeling methods and results, rather than use cases and their impacts. We aimed to document the policy influences of 12 NMTs and identify factors influencing them. We conducted semistructured interviews with 109 informants from 30 low‐ and middle‐income country case studies and used thematic analysis to understand the data. NMTs were mostly applied by international organizations to inform national government decision making. NMT applications contributed to enabling environments for nutrition and influenced program design and policy in most cases; however, this influence could be strengthened. Influence was shaped by processes for applying the NMTs; ownership of the analysis and data inputs, and capacity building in NMT methods, encouraged uptake. Targeting evidence generation at specific policy cycle stages promoted uptake; however, where advocacy capacity allowed, modeling was embedded ad hoc into emerging policy discussions and had broader influence. Meanwhile, external factors, such as political change and resource constraints of local partner organizations, challenged NMT implementation. Importantly, policy uptake was never the result of NMTs exclusively, indicating they should be nested persistently and strategically within the wider evidence and advocacy continuum, rather than being stand‐alone activities.

Household-level consumption data can be redistributed for individual-level Optifood diet modeling: analysis from four countries

A barrier to using Optifood linear programming (LP), which identifies nutrient gaps and supports population-specific food-based recommendation (FBR) development, is the requirement for dietary intake data. We investigated whether Household Consumption and Expenditure Surveys (HCESs) could be used instead of individual-level 24-h recalls (24HRs). The 24HR data from 12- to 23-month-old breastfeeding children in rural Kenya, Uganda, Guatemala, and Bangladesh were paired with HCES food consumption data from similar areas (n = 8) and time periods. HCES food intakes (g/week) were estimated using adult male equivalents, adjusted for breastfeeding. Paired HCES- and 24HR-defined LP inputs and outputs were compared using percentage agreement. Mean overall percentage agreements were 42%, 63%, and 80%, for food, food subgroup, and food-group model parameters, respectively. HCES food lists were on average 1.3 times longer than 24HR. Similar nutrient gaps (77-100% agreement), food sources of nutrients (71-100% agreement), and FBRs (80-100% agreement) were identified. The results suggest that HCES data can be used in Optifood analyses for 12- to 23-month-old children, despite recognized challenges of using it to estimate dietary intakes of young children compared with older age groups. Further analyses, however, are required for different age groups and locations to confirm expectations that it would perform equally well.

Impact of mass distribution of insecticide-treated nets in Mozambique, 2012 to 2025: Estimates of child lives saved using the Lives Saved Tool

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.

Oxygen systems strengthening as an intervention to prevent childhood deaths due to pneumonia in low-resource settings: systematic review, meta-analysis and cost-effectiveness

OBJECTIVES

Increasing access to oxygen services may improve outcomes among children with pneumonia living in low-resource settings. We conducted a systematic review to estimate the impact and cost-effectiveness of strengthening oxygen services in low-income and middle-income countries with the objective of including oxygen as an intervention in the Lives Saved Tool.

DESIGN

We searched EMBASE and PubMed on 31 March 2021 using keywords and MeSH terms related to 'oxygen', 'pneumonia' and 'child' without restrictions on language or date. The risk of bias was assessed for all included studies using the quality assessment tool for quantitative studies, and we assessed the overall certainty of the evidence using Grading of Recommendations, Assessment, Development and Evaluations. Meta-analysis methods using random effects with inverse-variance weights was used to calculate a pooled OR and 95% CIs. Programme cost data were extracted from full study reports and correspondence with study authors, and we estimated cost-effectiveness in US dollar per disability-adjusted life-year (DALY) averted.

RESULTS

Our search identified 665 studies. Four studies were included in the review involving 75 hospitals and 34 485 study participants. We calculated a pooled OR of 0.52 (95% CI 0.39 to 0.70) in favour of oxygen systems reducing childhood pneumonia mortality. The median cost-effectiveness of oxygen systems strengthening was $US62 per DALY averted (range: US$44-US$225). We graded the risk of bias as moderate and the overall certainty of the evidence as low due to the non-randomised design of the studies.

CONCLUSION

Our findings suggest that strengthening oxygen systems is likely to reduce hospital-based pneumonia mortality and may be cost-effective in low-resource settings. Additional implementation trials using more rigorous designs are needed to strengthen the certainty in the effect estimate.

Indirect effects of the SARS CoV-2 pandemic on the prevalence of breastfeeding: Modeling its impact

INTRODUCTION

Breastfeeding has a protective effect against acute respiratory and diarrheal infections. There are psychological and social effects due to physical isolation in the population in the mother-child group.

OBJECTIVE

To assess the impact on infant mortality due to a decrease in the prevalence of breastfeeding during 2020 due to the physical isolation against the SARS CoV-2 (COVID-19) pandemic in Colombia.

MATERIALS AND METHODS

We used the population attributable risk approach taking into account the prevalence of breastfeeding and its potential decrease associated with the measures of physical isolation and the relative risk (RR) of the association between exclusive breastfeeding and the occurrence of acute infection consequences in the growth (weight for height) of children under the age of five through a mathematical modeling program.

RESULTS

We found an increase of 11.39% in the number of cases of growth arrest in the age group of 6 to 11 months with a 50% decrease in breastfeeding prevalence, as well as an increase in the number of diarrhea cases in children between 1 and 5 months of age from 5% (5.67%) on, and an increased number of deaths in children under 5 years (9.04%) with a 50% decrease in the prevalence of exclusive breastfeeding.

CONCLUSIONS

A lower prevalence of breastfeeding has an impact on infant morbidity and mortality in the short and medium-term. As a public health policy, current maternal and childcare strategies must be kept in order to reduce risks in the pediatric population.

Building resource constraints and feasibility considerations in mathematical models for infectious disease: A systematic literature review

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Mathematical model capabilities to explore complex systems now enable priority-setting to consider local resource constraints.

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Common objectives of model-based analyses incorporating constraints are to assess real-world feasibility or allocate resources efficiently.

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Constraints may be incorporated via (i) model-based estimation; (ii) linkage of mathematical and health system models; or (iii) optimisation.

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Models can then project constrained intervention effects and costs and resource requirement s for delivering interventions at full scale.

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'Health system constraints' should be systematically defined for routine operationalisation in model-based priority-setting.

Priority setting for infectious disease control is increasingly concerned with physical input constraints and other real-world restrictions on implementation and on the decision process. These health system constraints determine the ‘feasibility’ of interventions and hence impact. However, considering them within mathematical models places additional demands on model structure and relies on data availability. This review aims to provide an overview of published methods for considering constraints in mathematical models of infectious disease.

We systematically searched the literature to identify studies employing dynamic transmission models to assess interventions in any infectious disease and geographical area that included non-financial constraints to implementation. Information was extracted on the types of constraints considered and how these were identified and characterised, as well as on the model structures and techniques for incorporating the constraints.

A total of 36 studies were retained for analysis. While most dynamic transmission models identified were deterministic compartmental models, stochastic models and agent-based simulations were also successfully used for assessing the effects of non-financial constraints on priority setting. Studies aimed to assess reductions in intervention coverage (and programme costs) as a result of constraints preventing successful roll-out and scale-up, and/or to calculate costs and resources needed to relax these constraints and achieve desired coverage levels. We identified three approaches for incorporating constraints within the analyses: (i) estimation within the disease transmission model; (ii) linking disease transmission and health system models; (iii) optimising under constraints (other than the budget).

The review highlighted the viability of expanding model-based priority setting to consider health system constraints. We show strengths and limitations in current approaches to identify and quantify locally-relevant constraints, ranging from simple assumptions to structured elicitation and operational models. Overall, there is a clear need for transparency in the way feasibility is defined as a decision criteria for its systematic operationalisation within models.

Early estimates of the indirect effects of the COVID-19 pandemic on maternal and child mortality in low-income and middle-income countries: a modelling study

BACKGROUND

While the COVID-19 pandemic will increase mortality due to the virus, it is also likely to increase mortality indirectly. In this study, we estimate the additional maternal and under-5 child deaths resulting from the potential disruption of health systems and decreased access to food.

METHODS

We modelled three scenarios in which the coverage of essential maternal and child health interventions is reduced by 9·8-51·9% and the prevalence of wasting is increased by 10-50%. Although our scenarios are hypothetical, we sought to reflect real-world possibilities, given emerging reports of the supply-side and demand-side effects of the pandemic. We used the Lives Saved Tool to estimate the additional maternal and under-5 child deaths under each scenario, in 118 low-income and middle-income countries. We estimated additional deaths for a single month and extrapolated for 3 months, 6 months, and 12 months.

FINDINGS

Our least severe scenario (coverage reductions of 9·8-18·5% and wasting increase of 10%) over 6 months would result in 253 500 additional child deaths and 12 200 additional maternal deaths. Our most severe scenario (coverage reductions of 39·3-51·9% and wasting increase of 50%) over 6 months would result in 1 157 000 additional child deaths and 56 700 additional maternal deaths. These additional deaths would represent an increase of 9·8-44·7% in under-5 child deaths per month, and an 8·3-38·6% increase in maternal deaths per month, across the 118 countries. Across our three scenarios, the reduced coverage of four childbirth interventions (parenteral administration of uterotonics, antibiotics, and anticonvulsants, and clean birth environments) would account for approximately 60% of additional maternal deaths. The increase in wasting prevalence would account for 18-23% of additional child deaths and reduced coverage of antibiotics for pneumonia and neonatal sepsis and of oral rehydration solution for diarrhoea would together account for around 41% of additional child deaths.

INTERPRETATION

Our estimates are based on tentative assumptions and represent a wide range of outcomes. Nonetheless, they show that, if routine health care is disrupted and access to food is decreased (as a result of unavoidable shocks, health system collapse, or intentional choices made in responding to the pandemic), the increase in child and maternal deaths will be devastating. We hope these numbers add context as policy makers establish guidelines and allocate resources in the days and months to come.

FUNDING

Bill & Melinda Gates Foundation, Global Affairs Canada.

Early estimates of the indirect effects of the COVID-19 pandemic on maternal and child mortality in low-income and middle-income countries: a modelling study

BACKGROUND

While the COVID-19 pandemic will increase mortality due to the virus, it is also likely to increase mortality indirectly. In this study, we estimate the additional maternal and under-5 child deaths resulting from the potential disruption of health systems and decreased access to food.

METHODS

We modelled three scenarios in which the coverage of essential maternal and child health interventions is reduced by 9·8-51·9% and the prevalence of wasting is increased by 10-50%. Although our scenarios are hypothetical, we sought to reflect real-world possibilities, given emerging reports of the supply-side and demand-side effects of the pandemic. We used the Lives Saved Tool to estimate the additional maternal and under-5 child deaths under each scenario, in 118 low-income and middle-income countries. We estimated additional deaths for a single month and extrapolated for 3 months, 6 months, and 12 months.

FINDINGS

Our least severe scenario (coverage reductions of 9·8-18·5% and wasting increase of 10%) over 6 months would result in 253 500 additional child deaths and 12 200 additional maternal deaths. Our most severe scenario (coverage reductions of 39·3-51·9% and wasting increase of 50%) over 6 months would result in 1 157 000 additional child deaths and 56 700 additional maternal deaths. These additional deaths would represent an increase of 9·8-44·7% in under-5 child deaths per month, and an 8·3-38·6% increase in maternal deaths per month, across the 118 countries. Across our three scenarios, the reduced coverage of four childbirth interventions (parenteral administration of uterotonics, antibiotics, and anticonvulsants, and clean birth environments) would account for approximately 60% of additional maternal deaths. The increase in wasting prevalence would account for 18-23% of additional child deaths and reduced coverage of antibiotics for pneumonia and neonatal sepsis and of oral rehydration solution for diarrhoea would together account for around 41% of additional child deaths.

INTERPRETATION

Our estimates are based on tentative assumptions and represent a wide range of outcomes. Nonetheless, they show that, if routine health care is disrupted and access to food is decreased (as a result of unavoidable shocks, health system collapse, or intentional choices made in responding to the pandemic), the increase in child and maternal deaths will be devastating. We hope these numbers add context as policy makers establish guidelines and allocate resources in the days and months to come.

FUNDING

Bill & Melinda Gates Foundation, Global Affairs Canada.

Prioritizing the Scale-Up of Evidence-Based Nutrition and Health Interventions to Accelerate Stunting Reduction in Ethiopia

Despite some progress, stunting prevalence in many African countries including Ethiopia remains unacceptably high. This study aimed to identify key interventions that, if implemented at scale through the health sector in Ethiopia, can avert the highest number of stunting cases. Using the Lives Saved Tool (LiST), the number of stunting cases that would have been averted, if proven interventions were scaled-up to the highest wealth quintile or to an aspirational 90% coverage was considered. Stunting prevalence was highest among rural residents and households in the poorest wealth quintile. Coverage of breastfeeding promotion and vitamin A supplementation were relatively high (>50%), whereas interventions targeting women were limited in number and had particularly low coverage. Universal coverage (90%) of optimal complementary feeding, preventive zinc supplementation, and water connection in homes could have each averted 380,000-500,000 cases of stunting. Increasing coverage of water connection to homes to the level of the wealthiest quintile could have averted an estimated 168,000 cases of stunting. Increasing coverage of optimal complementary feeding, preventive zinc supplementation, and Water, Sanitation and Hygiene (WASH) services is critical. Innovations in program delivery and health systems governance are required to effectively reach women, remote areas, rural communities, and the poorest proportion of the population to accelerate stunting reduction.

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

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.

Antenatal care data sources and their policy and planning implications: a Palestinian example using the Lives Saved Tool

Background

Policy making in healthcare requires reliable and local data. Different sources of coverage data for health interventions can be utilized to populate the Lives Saved Tool (LiST), a commonly used policy-planning tool for women and children’s health. We have evaluated four existing sources of antenatal care data in Palestine to discuss the implications of their use in LiST.

Methods

We identified all intervention coverage and health status indicators around the antenatal period that could be used to populate LiST. These indicators were calculated from 1) routine reported data, 2) a Multiple Indicator Cluster Survey (MICS), 3) paper-based antenatal records and 4) the eRegistry (an electronic health information system) for public clinics in the West Bank, Palestine for the most recent year available. We scaled coverage of each indicator to 90%, in public clinics only, and compared this to a no-change scenario for a seven-year period.

Results

Eight intervention coverage and health status indicators needed to populate the antenatal section of LiST could be calculated from both paper-based antenatal records and the eRegistry. Only two could be calculated from routine reports and three from a national survey. Maternal lives saved over seven years ranged from 5 to 39, with percent reduction in the maternal mortality ratio (MMR) ranging from 1 to 6%. Pre-eclampsia management accounted for 25 to 100% of these lives saved.

Conclusions

The choice of data source for antenatal indicators will affect policy-based decisions when used to populate LiST. Although all data sources have their purpose, clinical data collected directly in an electronic registry during antenatal contacts may provide the most reliable and complete data to populate currently unavailable but needed indicators around specific antenatal care interventions.

Electronic supplementary material

The online version of this article (10.1186/s12889-019-6427-8) contains supplementary material, which is available to authorized users.

Intervention heroes of Mozambique from 1997 to 2015: estimates of maternal and child lives saved using the Lives Saved Tool

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.

Neglected Tropical Disease Control - The Case for Adaptive, Location-specific Solutions

The world is experiencing environmental and social change at an unprecedented rate, with the effects being felt at local, regional, and international scales. This phenomenon may disrupt interventions against neglected tropical diseases (NTDs) that operate on the basis of linear scaling and 'one-size-fits-all'. Here we argue that investment in field-based data collection and building modelling capacity is required; that it is important to consider unintended consequences of interventions; that inferences can be drawn from wildlife ecology; and that interventions should become more location-specific. Collectively, these ideas underpin the development of adaptive decision-support tools that are sufficiently flexible to address emerging issues within the Anthropocene.

All things to all people: trade-offs in pursuit of an ideal modeling tool for maternal and child health

BACKGROUND

Modeling tools have potential to aid decision making for program planning and evaluation at all levels, but are still largely the domain of technical experts, consultants, and global-level staff. One model that can improve decision making for maternal and child health is the Lives Saved Tool (LiST). We examined respondents' perceptions of LiST's strengths and weaknesses, to identify ways in which LiST - and similar modeling tools - can adapt to be more accessible and helpful to policy makers.

METHODS

We interviewed 21 purposefully sampled LiST users. First, we identified the characteristics that respondents explicitly stated, or implicitly implied, were important in a modeling tool, and then used these results to create a framework for reviewing a modeling tool. Second, we used this framework to categorize the strengths and weaknesses of LiST that respondents articulated.

RESULTS

Two overarching qualities were important to respondents: usability and accuracy. For some users, LiST already meets these criteria: it allows for customized input parameters to increase specificity; the interface is intuitive; the assumptions and calculations are scientifically sound; and the standard metric of "additional lives saved" is understood and comparable across settings. Other respondents had different views, although their complaints were typically not that the tool is unusable or inaccurate, but that aspects of the tool could be better explained or easier to understand.

CONCLUSION

Government and agency staff at all levels should be empowered to use the data available to them, including the use of models to make full use of these data. For this, we need tools that meet a threshold of both accuracy, so results clarify rather than mislead, and usability, so tools can be used readily and widely, not just by select experts. With these ideals in mind, there are ways in which LiST might continue to be improved or adapted to further advance its uptake and impact.