Study design

We conducted the modelling using Spectrum software, version 6.1 [12] incorporating two key modules: demographic module (DemPro) and family planning module (FamPlan) [13]. The DemPro module was employed to integrate demographic projections of births and deaths, providing the population structure and fertility assumptions necessary for accurate mortality estimates. The FamPlan module estimated the impact of family planning services on birth rates and fertility patterns, which influence the number of births and subsequent mortality risks [13]. The LiST has been previously used in Nepal to estimate the maternal, newborn and under-five childhood deaths, as well as stillbirths demonstrating its applicability in this context [14,15].

Data sources

According to recent 2021 census, Nepal’s estimated population was approximately 29.1 million, with nearly three million children under 5 years of age (10.31%) and about 0.41 million infants under one year (1.41%) [16] . Using this census data as the base year, LiST projected population changes over time, incorporating population growth rates and demographic shifts. Baseline values for intervention coverage were derived from the most recent DHS survey [17], multiple indicator cluster survey (MICS) [18], and other nationally representative data sources. For indicators lacking baseline values, proxy values were calculated based on the available regression models from previous published literatures [19,20]. The indicator definitions, along with their baseline and target values, and their sources are presented in S1 File.

Modelling strategy

Health intervention targets specific risk factors that ultimately influence morbidity or mortality, either directly or indirectly. The impact of an intervention is calculated as the product of its effectiveness and its coverage level, assuming all other interventions remained constant. When coverage increases, more newborn and children gain protection from preventable health risks. Conversely, the decline in coverage can lead to increased deaths, while maintaining constant yields no additional lives saved [13]. This relationship underscores the critical importance of expanding and sustaining high coverage of effective interventions within health systems.

The formula to calculate the impact is:

Effectiveness is defined as the as the proportional reduction in cause-specific mortality achievable at full coverage. It was sourced from global meta-analyses and systematic reviews to ensure standardized estimates [13]. For example, zinc treatment for diarrhea has the effectiveness of 23%, which means it can prevent 23% of diarrhoeal deaths among children aged 1–59 months when implemented at 100% coverage, assuming other factors remain constant. The model assumes a proportional relationship: 50% coverage level would therefore achieve 50% of the maximum potential impact (i.e., an 11.5% reduction in diarrheal deaths in this example) [10].

We selected 35 interventions specified in the NENAP (Table 1) for inclusion in our model as these are the core monitoring indicators prioritized by the national newborn health strategy to achieve its 2035 mortality reduction targets [8]. Baseline coverage for these interventions was set at 2025 estimates, or the nearest available data, with gradual scale-up projected to reach NENAP target coverage by 2035. Table 1 below shows the baseline and target values used for this modelling study.

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Table 1. Baseline and target coverage of selected intervention.

https://doi.org/10.1371/journal.pone.0337331.t001

Ethics issues and consent to participate

This study used publicly available secondary data and did not involve human subjects. Consequently, ethical approval and participant consent were not required, in accordance with standard research ethics guidelines for secondary data analysis.

Nepal’s initiative to improve neonatal health

Nepal has made significant progress in reducing child mortality, successfully achieving Millennium Development Goal (MDG) 4 by reducing U5MR from 141 to 36 per 1,000 live births between 1990–2015 [26,27]. This achievement was partly due to community-based interventions tailored to Nepal’s unique health system of Nepal. Neonatal health policies evolved from the broader child health focus of the Community-Based Integrated Management of Childhood Illness (CB-IMCI) to the more newborn-specific Community-Based Newborn Care Program (CB-NCP). These were later integrated into Community-Based Integrated Management of Neonatal and Childhood Illness (CB-IMNCI), which streamlined overlapping components to create a single, comprehensive program for managing illnesses in newborns and children under five. The substantial contribution of these community-based interventions to improving child health outcomes during the MDG period is well recognized [28]. A summary of the key policies and programs implemented to improve neonatal and child health after the year 2000 is provided in Table 3.

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Table 3. Policy development for strengthening newborn care in Nepal in last two decades with their implementation status.

https://doi.org/10.1371/journal.pone.0337331.t003

To meet the SDG targets of reducing NMR to 12 per 1,000 live births by 2030, Nepal must reduce its current rate by 42% [7,17]. Furthermore, the NENAP sets an even more ambitious targets of NMR of 11 per 1,000 live births, requiring a 47% reduction by 2035. Our results, which projects an NMR of 14 per 1,000 live births, provides evidence and underscore the urgent need for innovative strategies to improve neonatal survival. NENAP emphasizes high-impact intervention like KMC to manage preterm and low-birthweight (LBW) infants, which is a critical contributor to neonatal deaths in Nepal [8,38]. However, progress in implementing neonatal health infrastructure has been slow. For the past three fiscal years, the expansion of SNCUs and NICUs has stalled, highlighting a failure to turn policy into practice [23,35,39–41]. This underscores the pressing need for improved execution of neonatal health initiatives to accelerate reductions in neonatal mortality.

Although Nepal’s neonatal mortality reduction program align with WHO standards for quality newborn care [42], significant gaps in implementation limit their effectiveness. For example, despite the proven benefits of KMC, its utilization remains underutilized due to inadequate integration at community-level and limited hospital capacity. The services face inadequate infrastructure, equipment, trained service providers and financial resources [43,44]. Additionally, while the guidelines exist to improve the quality of SSNB care in hospitals, overall quality of these services remains suboptimal. Moreover, the focus on inpatient care through NICU and SNCU addresses only a fraction of neonates in Nepal, leaving significant proportion of newborn underserved. This limited reach reduces the potential impact of these programs on the wider newborn population.

Alignment of current policy interventions with LiST results

Our LiST model analysis builds on existing research by providing quantitative evidence to guide prioritization of interventions. It shows which interventions will save the most lives, offering a clear strategy for where to allocate resources. Nepal has been implementing several interventions that align closely with the findings of the LiST model. The community-focused strategy like FCHV program supports the expansion of KMC through strengthening community outreach for management of low birth weight. Capacity-building initiatives like FB-IMNCI and Point of Care Quality Improvement (PoCQI) training aims to enhance the skills of healthcare workers in managing sick newborn and reducing mortality [8,23,30]. Furthermore, the government has invested in physical infrastructure, including the establishment of NICU and SNCU, along with strengthening supply-chain system to ensure that sick newborn can access hospital care. However, the expansion of health facilities has been stagnant since fiscal year 2020/21 as shown in Fig 3.

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Fig 3. Expansion of SNCU and NICU facilities over time (Fiscal year 2018/19 to 2022/23).

https://doi.org/10.1371/journal.pone.0337331.g003

The NENAP emphasizes improving healthcare quality by updating the health management information system (HMIS) to monitor newborn health outcomes more effectively [23,39]. The KMC guideline prioritizes capacity building, infrastructure upgrades, community programs, hypothermia monitoring using BEMPU bracelets, and public awareness campaigns to increase demand for KMC services [37]. Since 2023, Family Welfare Division has allocated budgets for training across all 61 SNCU hospitals, with 378 nurses and 79 medical officers trained by 2024. Collectively, these efforts strengthen healthcare workers’ capacity, strengthen health system functionality, and engage communities—key components necessary for scaling up effective newborn care in Nepal.

Despite sustained efforts to improve newborn care, several critical misalignments hinder in effective implementation of neonatal health interventions in Nepal. First, the absence of dedicated CB-IMNCI officers at provincial and municipal levels, along with insufficient specialized staff in SNCUs, NICUs and KMC units creates coordination gaps across the three tiers of governments during implementation [39]. This challenge is exacerbated by weakness in the referral system, causing delays in timely and effective treatment for newborn [8]. Second, Nepal’s financing model for the FNC program, which includes Packages A (USD 7.41), B (USD 14.81), and C (USD 37.04), to cover newborn care costs at birthing centres, SNCUS and NICUs [45]. This amount does not cover the full operational costs [45,46] or reduce out-of-pocket expenditure for families [47], thereby limiting both the access and quality of newborn care. Third, frequent supply chain interruption; including periodic stockouts of essential supplies, and inadequate engagement of private sector disrupts the uninterrupted supply and delivery of newborn care services [35,39]. For instance, outcome of neonatal resuscitation depends on uninterrupted supply of equipment and quality care, which Nepal often struggles to maintain. Fourth, Nepal lacks robust data system to track SNCU training activities conducted by federal and provincial governments; ongoing training programs have rarely been systematically evaluated for their effectiveness in provider skill retention, equipment sufficiency or readiness of health facilities. Lastly, while community-based interventions are contextually appropriate for Nepal, they lacks capacity to manage complex cases like LBW and small for gestational age (SGA) cases, which requires inpatient care [35]. These challenges — workforce shortages, financing constraints, supply chain interruptions, inadequate training evaluation, and limitations of community care pose major barriers to scaling up and sustaining effective newborn health interventions in Nepal.

Strength and limitation

There are some strengths of this paper. This study estimates the combined impact of selected interventions when delivered at the coverage levels estimated by NENAP. Thus, this model incorporates maximum number of interventions possible in this analysis. Additionally, for those indicators whose baseline values are lacking in DHS or MICS survey, this study calculated the proxy estimates as suggested by Spectrum manual [11] and regression equations suggested by previous studies [19,20].

Despite these strengths, this paper has some limitations that should be considered when interpreting the findings. First, we used the most recent coverage data available for Nepal, with DHS 2022 [5] and MICS 2018 data [18] (for WASH indicators), both of which predate the 2025 baseline period. Second, the effect size of individual interventions in the LiST model are drawn from global or regional evidence; as a result, national projections may not fully reflect Nepal’s unique context. Variation in healthcare access, service utilization, disease burden, cultural practices and health system characteristics could lead to overor underestimation of intervention impacts in our model. Third, the modelling primarily relies on DHS data, which focuses on coverage, but not account for service quality which is critical factor for health system outcome. Fourth, this study does not include sensitivity analyses or alternative coverage scenarios (example different coverage level), limiting assessment of how changes in coverage might affect outcomes.