Optimizing Vaccine Delivery Costs: Modeling the Impact of Key Operational Levers in a Northern Nigerian State

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However, cost optimization remains a persistent challenge. This study assessed the cost drivers of vaccine delivery in Kano State, Nigeria, and modeled the effects of key operational levers to inform supply chain strengthening efforts. Methods: We conducted a retrospective cost analysis of vaccine deliveries to 390 health facilities in Kano State under both government-run and outsourced distribution models. Costs were categorized into labor, transportation, storage, building, and communication components. We modeled variations in delivery layers, delivery frequency, fleet types, and number of delivery points using Microsoft Excel and STATA 13 SE. Statistical tests included Kruskal-Wallis, Tukey post-hoc, and Mann-Whitney U analyses. Results: Five operational levers including automobile options, delivery frequency, number of delivery points, number of delivery layers, and responsibility for vaccine distribution, significantly influenced unit delivery costs. Streamlining delivery layers reduced costs by up to 38%, while transitioning from outsourced to government-run models lowered costs by up to 28% (p < 0.05). Increasing the number of delivery points and using motorcycles or tricycles instead of trucks further reduced unit costs by 15–34%. However, increased delivery frequency, while reducing unit costs per cycle, raised total annual operational costs. Conclusions: Optimizing supply chain design through strategic adjustments in delivery models, vehicle selection, and facility coverage can substantially lower vaccine delivery costs in resource-constrained settings. Policymakers should integrate cost-efficiency strategies into immunization system strengthening initiatives to enhance sustainability and resilience, particularly in the post-pandemic recovery era. Vaccine delivery Cost optimization Supply chain modelling Health logistics Figures Figure 1 Figure 2 Figure 3 Introduction An effective supply chain is fundamental to ensuring equitable access to vaccines, medicines, and other essential health supplies, ultimately driving improved health outcomes [ 1 ]. However, in low- and middle-income countries (LMICs), chronic underfunding and operational inefficiencies often compromise vaccine delivery, undermining efforts to reduce preventable mortality and morbidity [ 2 ]. The expanding portfolio of vaccines and increasingly complex immunization schedules continue to escalate the financial and logistical burden on already resource-constrained health systems such as those of Nigeria, Ethiopia, Malawi, and Kenya [ 3 , 4 ]. Recognizing the critical need for cost-effective immunization logistics, many countries have introduced innovations such as streamlining vaccine storage layers and outsourcing delivery operations to third-party logistics providers (3PLs) [ 5 – 7 ]. Nonetheless, there remains a paucity of empirical evidence quantifying the cost implications of these system adaptations, particularly in real-world programmatic contexts. Kano State, Nigeria, provides a valuable case study in vaccine delivery optimization. Beginning in 2012, a tripartite collaboration between the Kano State Government, the Gates Foundation, and the Dangote Foundation initiated a major redesign of the state's vaccine supply chain. By 2013, the system shifted from a multi-tiered distribution which basically entails passing through local government cold stores, to a streamlined model where vaccines were delivered directly from the state cold store to primary health centers equipped with solar refrigerators. Vaccines were subsequently distributed to peripheral facilities through designated ward technical officers. Throughout this period, Kano State alternated between government-managed and outsourced vaccine distribution models and adjusted delivery frequencies to optimize operational costs. These programmatic experiences offer critical insights into the cost dynamics of different vaccine delivery strategies, which are particularly relevant today as countries seek to strengthen immunization resilience post-COVID-19 [ 8 ]. This study examines the key operational levers influencing the cost of vaccine delivery in Kano State Nigeria, models the effects of varying these levers, and discusses implications for designing cost-efficient, context-appropriate supply chain systems in LMICs. Methods Study Design The study is operational research involving a retrospective cost analysis of the vaccine delivery systems implemented in Kano State, Nigeria. The study reviewed cost data for delivering vaccines to 390 health facilities under both government-run and outsourced models operating at a bi-weekly frequency. Identified cost levers were subsequently remodeled to estimate the impact of programmatic changes on the unit cost of vaccine delivery per facility. Study Setting This study was conducted in Kano State, located in northwestern Nigeria. With an estimated population exceeding 14 million, Kano is a critical hub for immunization programs due to its high disease burden, urban–rural mix, and logistical complexity. The state comprises 44 Local Government Areas (LGAs) and over 1,200 health facilities, including a wide range of primary health care centers, which serve as the primary points of vaccine delivery. Kano has long been at the center of Nigeria’s efforts to improve immunization performance, partly due to its history of polio transmission, low routine immunization coverage, and operational challenges in vaccine delivery. The state’s health system has undergone multiple reforms and partnerships, including a tripartite agreement signed in 2012 between the Kano State Government, the Gates Foundation, and the Dangote Foundation to strengthen routine immunization. This initiative introduced innovative delivery models such as direct vaccine distribution to health facilities and outsourcing logistics to third-party providers. Data Collection Cost data for outsourced vaccine deliveries were obtained from expenditure reports of the third-party logistics (3PL) personnel. For the government-run delivery model, data were sourced through market surveys and cost records of program officials. Capital costs were amortized over a one-year period [ 5 ]. Costs were categorized into five major components: labor, transportation, storage (cold chain equipment), building and maintenance, and communication. This categorization aligns with Portnoy et al.'s supply chain cost framework [ 9 , 10 ], with the addition of building and communication costs specific to the Kano context. Cost Components Labor costs Labor costs included salaries or stipends paid to data clerks, cold chain officers at state, zonal, and LGA levels, delivery coordinators, project managers (for outsourced deliveries), ward technical officers (WTOs), and drivers. Each state or satellite cold store employed one data clerk and one delivery coordinator, with a project manager stationed at the state level. Each Apex facility was assigned a WTO responsible for vaccine cascade deliveries to lower-level facilities. Transportation costs Transportation costs encompassed the amortized procurement cost of vehicles, motorcycles, or tricycles, along with related expenditures for driver licensing, GPS trackers, vehicle insurance, insurance for vaccines in transit, cold boxes, temperature loggers, fuelling, and maintenance. The number of vehicles required was calculated as: Number of automobiles = \(\:\frac{Total\:number\:of\:delivery\:sites}{Average\:number\:of\:delivery\:sites\:per\:day\:*\:no.\:of\:days\:in\:a\:delivery\:cycle}\) + 1 back-up vehicle Two cold boxes were assigned per vehicle and one per motorcycle or tricycle. Fuel costs were based on vehicle fuel efficiency, total distance travelled, and fuel price per litre. Storage costs Storage costs included the amortized costs of Performance, Quality, and Safety (PQS)-certified walk-in cold rooms, refrigerators, freezers, and generators. One walk-in cold room was installed at each state or satellite store, complemented by backup refrigerators and generators. Storage costs at health facility level were excluded, as they were not directly attributable to delivery logistics. Building costs Building costs comprised office rent (proportional to vaccine storage space), furniture procurement and maintenance, janitorial services, and office supplies such as printers and stationery. Costs were incurred at each storage point but excluded health facilities. Communication costs Communication costs covered the procurement and maintenance of laptops and tablets, telephone airtime, internet bundles, and software support. Each delivery team and storage site manager were equipped with a communication device, with costs amortized using a straight-line depreciation model [ 11 ]. Cost Modeling The cost modeling study for vaccine delivery in Kano focused on identifying key supply chain cost drivers and evaluating different delivery architectures to determine their impact on delivery costs. Four models were analyzed; the 4-layer model (S-Z-L-F), which includes state, zone, LGA, and health facility; 3-layer models (S-Z-F and S-L-F), which reduce one distribution level; and the 2-layer model (S-F), which simplifies the supply chain by moving vaccines directly from the state to the facility. These various models are shown in Fig. 1. Kano currently uses the S-Z-L-F and S-Z-F models, but the S-L-F and S-F models were simulated for comparative analysis to explore how reducing distribution layers might lower costs and simplify the system. The study also varied key factors to assess their influence on cost outcomes, including the number of delivery points (from 25 to 400 facilities), delivery frequency (weekly, bi-weekly, monthly, quarterly), and vehicle types (trucks, motorcycles, or tricycles). The cost analysis aimed to assess how each factor impacted the unit cost of vaccine delivery. By examining the different delivery models and variations in delivery parameters, the study provided insights into how changes in scale, frequency, and transportation methods could optimize delivery efficiency and reduce costs. Although Kano state delivers to 390 apex facilities, we modelled the number of facilities to range from 25 to 400 health facilities (with increments of 25) Data Analysis Actual and modeled costs were analyzed using Microsoft Excel and STATA 13 SE. For each delivery model, the unit cost of delivering vaccines to a health facility was calculated as: $$\:C=\frac{t+i+s+b+c}{n\:X\:f}\:$$ where: C = Unit cost of delivery per facility t = Annual transportation cost i = Annual labor cost s = Annual storage cost b = Annual building cost c = Annual communication cost n = Number of health facilities served f = Delivery frequency (number of cycles per year) Mean unit costs were computed across different supply chain architectures, vehicle options, and delivery frequencies. Modeling also incorporated plausible variations not implemented in Kano (quarterly deliveries, full motorcycle substitution) based on prevailing market prices as of January 2019. Statistical comparisons were conducted to evaluate the influence of cost levers; We used Kruskal-Wallis tests with Tukey post-hoc analyses to assess differences in unit delivery costs across multiple delivery architectures and vehicle types. Mann-Whitney U tests were applied to compare government-run versus outsourced delivery models. Additionally, simple non-parametric linear regression was conducted to examine cost trends in relation to changes in delivery frequency and the number of delivery points. All tests were two-sided, with statistical significance set at p < 0.05. These analyses allowed us to quantify the impact of operational levers on delivery costs and identify the most cost-efficient strategies for vaccine distribution in the study setting. All statistical tests were two-sided, with a significance level set at p < 0.05. Where applicable, standard deviations were reported to describe variability across cost estimates. Ethical Considerations This study involved a retrospective review of programmatic cost data and did not include the collection of primary data from human participants. No personally identifiable information was accessed or analyzed. Therefore, ethical approval was not required according to prevailing research ethics guidelines. The data sources were administrative records and expenditure reports obtained with permission from the relevant program authorities in Kano State. All analyses were conducted in accordance with the principles of confidentiality and responsible data use. Results A review of cost data from the study location identified five operational levers significantly influencing the unit cost of vaccine deliveries: automobile options, delivery frequency, number of delivery points, number of delivery layers, and responsibility for vaccine distribution. Number of Delivery Layers The mean cost of delivering vaccines to 400 health facilities using a government-run model was $ 43.17 (standard deviation [SD] = 28.14) per facility under the 4-layer model (State-Zone-LGA-Facility; S-Z-L-F) (Table 1 ). For the 3-layer models, mean delivery costs were $ 38.50 (SD = 23.46) for the S-L-F model and $ 26.92 (SD = 12.92) for the S-Z-F model. The 2-layer model (S-F) had a higher mean delivery cost of $ 49.58 (SD = 9.54), which was significantly more expensive than the S-Z-F model (p < 0.05). Among the architectures assessed, the S-Z-F model was identified as the least costly. Table 1 Comparison of Unit Costs of Vaccine Deliveries Across Delivery Layers Delivery Layer Number of Layers Mean Cost per Facility (US $ ) Standard Deviation (US $ ) State → Zone → LGA → Facility (S-Z-L-F) 4 43.17 28.14 State → Zone → Facility (S-Z-F) 3 26.92 12.92 State → LGA → Facility (S-L-F) 3 38.50 23.46 State → Facility (S-F) 2 49.58 9.54 Note : Costs represent mean unit cost per health facility, based on government-run delivery models. Decision to Outsource Transportation Comparisons between the government-run and outsourced delivery models showed that outsourcing transportation increased vaccine delivery costs across all four delivery architectures (Table 2 ). Transitioning from an outsourced to a government-run model reduced delivery cost by up to 28% (p < 0.05). These comparisons assumed bi-weekly vaccine deliveries using trucks, as implemented in study location. Delivery costs associated with cascade facilities were excluded from the outsourcing analysis, as they did not influence transportation outsourcing decisions. Overall, government-run delivery systems demonstrated lower costs compared to outsourced models. Table 2 Comparison of Vaccine Delivery Costs: Government-Run vs. Outsourced Models Delivery Layer Government-Run Mean Cost (US $ ) Outsourced Mean Cost (US $ ) Cost Difference (US $ ) P-value State → Zone → LGA → Facility (S-Z-L-F) 74.63 85.13 10.50 0.002 State → LGA → Facility (S-L-F) 64.50 74.88 10.38 0.002 State → Zone → Facility (S-Z-F) 40.38 50.88 10.50 0.002 State → Facility (S-F) 26.50 37.06 10.56 0.003 Note : P-values derived from Mann-Whitney U tests. Lower costs associated with government-run models. Delivery Frequency The impact of delivery frequency on vaccine delivery cost is shown in Fig. 2. Regression analysis suggested that increasing delivery frequency reduced the unit cost of deliveries under the government-run approach. The greatest reduction in unit cost (by a factor of 7.24) occurred under the 4-layer S-Z-L-F model, while the smallest reduction (by a factor of 0.53) was observed under the 2-layer S-F model. Although higher delivery frequency lowered the cost per delivery cycle, total annual delivery costs would increase when accounting for the greater number of delivery cycles per year. Number of Delivery Points The effect of increasing the number of delivery points on unit costs is illustrated in Fig. 3 . Regression analysis indicated that expanding the number of target facilities consistently reduced the unit delivery cost. When increasing the number of facilities by a factor of 25, the S-Z-L-F model achieved the highest cost reduction (factor of 5.69), while the S-F model achieved the lowest reduction (factor of 0.76). Choice of Fleet Cost comparisons for different vehicle types are presented in Table 3 . Substituting trucks with motorcycles or tricycles for deliveries resulted in estimated unit cost reductions of 15–34% (p < 0.05). When comparing motorcycles and tricycles, deliveries with motorcycles were 4–12% less expensive (p < 0.05). The cost differences related to vehicle type were statistically significant for the S-F model but not for all delivery architectures. Table 3 Comparison of unit cost of vaccine deliveries with different automobile options Delivery Layers S-Z-L-F S-Z-F S-L-F S-F Automobile Option Trucks only -A ( $ ) 74.63 40.38 64.50 26.50 Truck &Tricycle -B ( $ ) 63.75 27.19 53.50 20.75 Truck & Motorcycle -C ( $ ) 61.00 26.56 50.81 18.25 Mean difference B & A ( $ ) 10.88 13.19 11.00 5.75 T-test 9.11 14.58 8.88 2.68 p-value 0.000 0.000 0.000 0.009 Mean difference C & A ( $ ) 13.63 13.81 13.69 8.25 T-test 11.19 15.59 11.31 3.52 p-value 0.000 0.000 0.000 0.002 Mean difference C & B ( $ ) 2.75 0.63 2.69 2.50 T-test 10.33 4.04 9.44 9.68 p-value 0.000 0.001 0.000 0.000 Note : Costs represent mean unit delivery costs per facility. Cost reductions were statistically significant (p < 0.05) for substitutions of motorcycles or tricycles compared to trucks. Discussion This study identified five key operational levers influencing the cost of vaccine deliveries in Kano State: automobile options, delivery frequency, number of delivery points, number of delivery layers, and responsibility for vaccine distribution. Each of these factors demonstrated significant effects on unit delivery costs, as detailed in our findings. Understanding how these operational choices affect delivery efficiency is critical, particularly in LMICs where immunization programs face persistent financial and logistical constraints. Recent global evidence increasingly highlights the importance of optimizing supply chain architecture and delivery models to enhance the cost-effectiveness and resilience of vaccine programs [ 12 , 13 ]. Our results show that streamlining vaccine delivery layers substantially reduces unit delivery costs, consistent with findings from Niger, where the removal of regional storage levels decreased logistics costs by 17% [ 13 ]. Similarly, recent modeling studies in Ethiopia and Bangladesh confirm that consolidating intermediate cold chain points reduces both operational complexity and financial burden [ 14 , 15 ]. In our study location, the 3-layer S-Z-F model was notably more cost-efficient compared to the 4-layer and 2-layer models, emphasizing that supply chain redesigns must balance distance logistics with administrative feasibility. The decision to retain government-run vaccine distribution over outsourcing was supported by cost savings of up to 28%, which aligns with emerging evidence. A 2022 multi-country review found that while outsourcing logistics can improve timeliness and transparency, it often introduces overhead and coordination costs that are underestimated in initial planning [ 16 ]. Recent review regarding outsourcing of supply logistics for health commodities across Africa and experience from India post-COVID-19 highlighted the need for strong public-private partnership frameworks when outsourcing, to avoid unintended cost escalations and service fragmentation [ 17 , 18 ]. Regarding delivery frequency, although increasing delivery frequency lowered unit costs, it raised total annual delivery costs which is an important consideration for sustainability. Post-pandemic evaluations indicate that many countries that scaled up delivery cycles for COVID-19 vaccination campaigns are now recalibrating frequency to manage operating costs without compromising stock availability [ 19 ]. Thus, program managers must weigh unit cost efficiencies against broader programmatic affordability. Expanding the number of delivery points was shown to reduce unit costs, driven by economies of scale in Kano's delivery system. This finding is supported by recent supply chain analyses showing that higher facility coverage per logistics cycle can dilute fixed capital costs, provided that delivery vehicles and human resources are adequately optimized [ 20 ]. Substituting trucks with motorcycles or tricycles also lowered delivery costs, particularly in dense urban and peri-urban areas. However, this is context-specific. Difficult terrain, poor road networks, and security risks as experienced in many northern Nigerian states and parts of East Africa can limit the feasibility of lighter vehicle use [ 21 ]. Recent pilot studies in Kenya and Uganda suggest that motorcycles are effective for deliveries within 30–50 km ranges but require robust maintenance and safety planning [22]. Conclusion This study demonstrated that key operational levers; delivery layers, frequency, number of delivery points, vehicle choice, and mode of delivery management, significantly influence the unit cost of vaccine deliveries in a the study location. Through retrospective cost modeling, we found that strategic adjustments to these levers can produce measurable cost savings without compromising coverage. Notably, streamlining the number of delivery layers and increasing the number of delivery points reduced per-facility delivery costs, while transitioning from outsourced to government-run delivery models consistently yielded lower expenditures. Also, shifting from truck-based deliveries to motorcycles or tricycles offered further cost reductions, especially for shorter delivery routes and lower-volume facilities. These findings align with global efforts to strengthen immunization supply chains, particularly in resource-constrained settings where fiscal efficiency is essential to achieving universal coverage. As countries continue to recover from the COVID-19 pandemic and integrate new vaccines into routine immunization schedules, the need for adaptive, cost-effective delivery strategies is more urgent than ever. This study adds evidence to support context-specific supply chain redesign and provides a replicable approach for similar evaluations in other low- and middle-income countries. Future research should incorporate dynamic costing methods that adjust for inflation, currency fluctuations, and changing fuel and labor markets. Integrating real-time data into delivery planning and developing robust costing frameworks across subnational levels will be vital for sustaining efficient vaccine delivery systems. Policymakers and immunization program managers must consider these operational levers as part of broader health system strengthening and financial sustainability strategies. Limitations This study is limited by its reliance on Kano's administrative and logistical structure, which may differ from other states or countries with fewer cold chain facilities or more dispersed populations. Furthermore, cost data were collected for the period up to early 2020, and while major structural shifts are unlikely, inflation, fuel price volatility, and new health security priorities (outbreak responses for instance) may affect cost assumptions. Future studies could incorporate dynamic modeling to adjust for post-COVID-19 market fluctuations. Declarations Ethics approval and consent to participate This study involved secondary analysis of programmatic cost and delivery data and did not involve human participants or access to identifiable personal data. Therefore, ethical approval and informed consent were not required, in line with national and institutional guidelines. All cost data were obtained with appropriate permission from relevant program authorities in Kano State, Nigeria. Consent for publication Not applicable. Availability of data and materials The dataset supporting the conclusions of this article is publicly available on the Open Science Framework (OSF) at the following DOI: https://doi.org/10.17605/OSF.IO/H3N6T. Competing interests The authors declare that they have no competing interests. Funding This study received no external funding. Authorship Contribution MA and UI conceptualised the study and developed the methodology. LJ and CO collected the data and participated in its analysis and interpretation. MA, LJ, OP and AB developed the manuscripts while all authors provided critical review and approved the final version. All authors agreed on the content of the manuscripts. Acknowledgements We would like to acknowledge Kano State Government, through the Northern Nigerian RI Strengthening Program for providing a ground for this work to be conducted. We also acknowledge the Kano State Primary Health Care Management Board and the private sector logistics provider for giving us access to their program data. References Yadav P, Yadav P. Health product supply chains in developing countries: diagnosis of the root causes of underperformance and an agenda for reform. 2015;8604:April. Kodali PB. Achieving universal health coverage in low- and middle-income countries: challenges for policy post-pandemic and beyond. Risk Manag Healthc Policy. 2023;16:607–21. Ashok A, Brison M, Letallec Y. Improving cold chain systems: challenges and solutions. Vaccine. 2017;35(17):2217–23. Uzochukwu B, Chukwuogo O, Onwujekwe O. Financing immunization for results in Nigeria: who funds, who disburses, who utilizes, who accounts? 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12:53:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6949544/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6949544/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87396707,"identity":"a354f7f0-0888-4d12-93a3-091c949ec0f0","added_by":"auto","created_at":"2025-07-23 10:54:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":70869,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6949544/v1/983b86449c677a18f463bad2.png"},{"id":87396709,"identity":"e96072f7-53fc-4896-80b9-14c645002f11","added_by":"auto","created_at":"2025-07-23 10:54:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":196760,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6949544/v1/7b90381ac35d43c6401b4ae5.png"},{"id":87396708,"identity":"03083910-19e4-45b0-9f17-d64d5bf5f186","added_by":"auto","created_at":"2025-07-23 10:54:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":54117,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6949544/v1/e6e888cc0d71c59e88e6b3d9.png"},{"id":87399097,"identity":"84018eac-4815-4399-a5ed-6ce36cbfbd7e","added_by":"auto","created_at":"2025-07-23 11:26:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1011509,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6949544/v1/54cc4d54-87fa-4ec6-a415-3c310f7e6b8e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimizing Vaccine Delivery Costs: Modeling the Impact of Key Operational Levers in a Northern Nigerian State","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAn effective supply chain is fundamental to ensuring equitable access to vaccines, medicines, and other essential health supplies, ultimately driving improved health outcomes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, in low- and middle-income countries (LMICs), chronic underfunding and operational inefficiencies often compromise vaccine delivery, undermining efforts to reduce preventable mortality and morbidity [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The expanding portfolio of vaccines and increasingly complex immunization schedules continue to escalate the financial and logistical burden on already resource-constrained health systems such as those of Nigeria, Ethiopia, Malawi, and Kenya [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRecognizing the critical need for cost-effective immunization logistics, many countries have introduced innovations such as streamlining vaccine storage layers and outsourcing delivery operations to third-party logistics providers (3PLs) [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Nonetheless, there remains a paucity of empirical evidence quantifying the cost implications of these system adaptations, particularly in real-world programmatic contexts.\u003c/p\u003e\u003cp\u003eKano State, Nigeria, provides a valuable case study in vaccine delivery optimization. Beginning in 2012, a tripartite collaboration between the Kano State Government, the Gates Foundation, and the Dangote Foundation initiated a major redesign of the state's vaccine supply chain. By 2013, the system shifted from a multi-tiered distribution which basically entails passing through local government cold stores, to a streamlined model where vaccines were delivered directly from the state cold store to primary health centers equipped with solar refrigerators. Vaccines were subsequently distributed to peripheral facilities through designated ward technical officers.\u003c/p\u003e\u003cp\u003eThroughout this period, Kano State alternated between government-managed and outsourced vaccine distribution models and adjusted delivery frequencies to optimize operational costs. These programmatic experiences offer critical insights into the cost dynamics of different vaccine delivery strategies, which are particularly relevant today as countries seek to strengthen immunization resilience post-COVID-19 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study examines the key operational levers influencing the cost of vaccine delivery in Kano State Nigeria, models the effects of varying these levers, and discusses implications for designing cost-efficient, context-appropriate supply chain systems in LMICs.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy Design\u003c/h2\u003e\n \u003cp\u003eThe study is operational research involving a retrospective cost analysis of the vaccine delivery systems implemented in Kano State, Nigeria. The study reviewed cost data for delivering vaccines to 390 health facilities under both government-run and outsourced models operating at a bi-weekly frequency. Identified cost levers were subsequently remodeled to estimate the impact of programmatic changes on the unit cost of vaccine delivery per facility.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eStudy Setting\u003c/h3\u003e\n\u003cp\u003eThis study was conducted in Kano State, located in northwestern Nigeria. With an estimated population exceeding 14\u0026nbsp;million, Kano is a critical hub for immunization programs due to its high disease burden, urban\u0026ndash;rural mix, and logistical complexity. The state comprises 44 Local Government Areas (LGAs) and over 1,200 health facilities, including a wide range of primary health care centers, which serve as the primary points of vaccine delivery.\u003c/p\u003e\n\u003cp\u003eKano has long been at the center of Nigeria\u0026rsquo;s efforts to improve immunization performance, partly due to its history of polio transmission, low routine immunization coverage, and operational challenges in vaccine delivery. The state\u0026rsquo;s health system has undergone multiple reforms and partnerships, including a tripartite agreement signed in 2012 between the Kano State Government, the Gates Foundation, and the Dangote Foundation to strengthen routine immunization. This initiative introduced innovative delivery models such as direct vaccine distribution to health facilities and outsourcing logistics to third-party providers.\u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eCost data for outsourced vaccine deliveries were obtained from expenditure reports of the third-party logistics (3PL) personnel. For the government-run delivery model, data were sourced through market surveys and cost records of program officials. Capital costs were amortized over a one-year period [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eCosts were categorized into five major components: labor, transportation, storage (cold chain equipment), building and maintenance, and communication. This categorization aligns with Portnoy et al.\u0026apos;s supply chain cost framework [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e], with the addition of building and communication costs specific to the Kano context.\u003c/p\u003e\n\u003ch3\u003eCost Components\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eLabor costs\u003c/h2\u003e\n \u003cp\u003eLabor costs included salaries or stipends paid to data clerks, cold chain officers at state, zonal, and LGA levels, delivery coordinators, project managers (for outsourced deliveries), ward technical officers (WTOs), and drivers. Each state or satellite cold store employed one data clerk and one delivery coordinator, with a project manager stationed at the state level. Each Apex facility was assigned a WTO responsible for vaccine cascade deliveries to lower-level facilities.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eTransportation costs\u003c/h2\u003e\n \u003cp\u003eTransportation costs encompassed the amortized procurement cost of vehicles, motorcycles, or tricycles, along with related expenditures for driver licensing, GPS trackers, vehicle insurance, insurance for vaccines in transit, cold boxes, temperature loggers, fuelling, and maintenance. The number of vehicles required was calculated as:\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eNumber of automobiles =\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{Total\\:number\\:of\\:delivery\\:sites}{Average\\:number\\:of\\:delivery\\:sites\\:per\\:day\\:*\\:no.\\:of\\:days\\:in\\:a\\:delivery\\:cycle}\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003e+ 1 back-up vehicle\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eTwo cold boxes were assigned per vehicle and one per motorcycle or tricycle. Fuel costs were based on vehicle fuel efficiency, total distance travelled, and fuel price per litre.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eStorage costs\u003c/h3\u003e\n\u003cp\u003eStorage costs included the amortized costs of Performance, Quality, and Safety (PQS)-certified walk-in cold rooms, refrigerators, freezers, and generators. One walk-in cold room was installed at each state or satellite store, complemented by backup refrigerators and generators. Storage costs at health facility level were excluded, as they were not directly attributable to delivery logistics.\u003c/p\u003e\n\u003ch3\u003eBuilding costs\u003c/h3\u003e\n\u003cp\u003eBuilding costs comprised office rent (proportional to vaccine storage space), furniture procurement and maintenance, janitorial services, and office supplies such as printers and stationery. Costs were incurred at each storage point but excluded health facilities.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eCommunication costs\u003c/h2\u003e\n \u003cp\u003eCommunication costs covered the procurement and maintenance of laptops and tablets, telephone airtime, internet bundles, and software support. Each delivery team and storage site manager were equipped with a communication device, with costs amortized using a straight-line depreciation model [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eCost Modeling\u003c/h2\u003e\n \u003cp\u003eThe cost modeling study for vaccine delivery in Kano focused on identifying key supply chain cost drivers and evaluating different delivery architectures to determine their impact on delivery costs. Four models were analyzed; the 4-layer model (S-Z-L-F), which includes state, zone, LGA, and health facility; 3-layer models (S-Z-F and S-L-F), which reduce one distribution level; and the 2-layer model (S-F), which simplifies the supply chain by moving vaccines directly from the state to the facility. These various models are shown in Fig.\u0026nbsp;1.\u003c/p\u003e\n \u003cp\u003eKano currently uses the S-Z-L-F and S-Z-F models, but the S-L-F and S-F models were simulated for comparative analysis to explore how reducing distribution layers might lower costs and simplify the system. The study also varied key factors to assess their influence on cost outcomes, including the number of delivery points (from 25 to 400 facilities), delivery frequency (weekly, bi-weekly, monthly, quarterly), and vehicle types (trucks, motorcycles, or tricycles).\u003c/p\u003e\n \u003cp\u003eThe cost analysis aimed to assess how each factor impacted the unit cost of vaccine delivery. By examining the different delivery models and variations in delivery parameters, the study provided insights into how changes in scale, frequency, and transportation methods could optimize delivery efficiency and reduce costs.\u003c/p\u003e\n \u003cp\u003eAlthough Kano state delivers to 390 apex facilities, we modelled the number of facilities to range from 25 to 400 health facilities (with increments of 25)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eData Analysis\u003c/h2\u003e\n \u003cp\u003eActual and modeled costs were analyzed using Microsoft Excel and STATA 13 SE. For each delivery model, the unit cost of delivering vaccines to a health facility was calculated as:\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:C=\\frac{t+i+s+b+c}{n\\:X\\:f}\\:$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eC\u0026thinsp;=\u0026thinsp;Unit cost of delivery per facility\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003et\u0026thinsp;=\u0026thinsp;Annual transportation cost\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003ei\u0026thinsp;=\u0026thinsp;Annual labor cost\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003es\u0026thinsp;=\u0026thinsp;Annual storage cost\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eb\u0026thinsp;=\u0026thinsp;Annual building cost\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003ec\u0026thinsp;=\u0026thinsp;Annual communication cost\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;Number of health facilities served\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003ef\u0026thinsp;=\u0026thinsp;Delivery frequency (number of cycles per year)\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eMean unit costs were computed across different supply chain architectures, vehicle options, and delivery frequencies. Modeling also incorporated plausible variations not implemented in Kano (quarterly deliveries, full motorcycle substitution) based on prevailing market prices as of January 2019.\u003c/p\u003e\n \u003cp\u003eStatistical comparisons were conducted to evaluate the influence of cost levers; We used Kruskal-Wallis tests with Tukey post-hoc analyses to assess differences in unit delivery costs across multiple delivery architectures and vehicle types. Mann-Whitney U tests were applied to compare government-run versus outsourced delivery models. Additionally, simple non-parametric linear regression was conducted to examine cost trends in relation to changes in delivery frequency and the number of delivery points. All tests were two-sided, with statistical significance set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. These analyses allowed us to quantify the impact of operational levers on delivery costs and identify the most cost-efficient strategies for vaccine distribution in the study setting. All statistical tests were two-sided, with a significance level set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Where applicable, standard deviations were reported to describe variability across cost estimates.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eEthical Considerations\u003c/h2\u003e\n \u003cp\u003eThis study involved a retrospective review of programmatic cost data and did not include the collection of primary data from human participants. No personally identifiable information was accessed or analyzed. Therefore, ethical approval was not required according to prevailing research ethics guidelines.\u003c/p\u003e\n \u003cp\u003eThe data sources were administrative records and expenditure reports obtained with permission from the relevant program authorities in Kano State. All analyses were conducted in accordance with the principles of confidentiality and responsible data use.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA review of cost data from the study location identified five operational levers significantly influencing the unit cost of vaccine deliveries: automobile options, delivery frequency, number of delivery points, number of delivery layers, and responsibility for vaccine distribution.\u003c/p\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eNumber of Delivery Layers\u003c/h2\u003e\n \u003cp\u003eThe mean cost of delivering vaccines to 400 health facilities using a government-run model was \u003cspan\u003e$\u003c/span\u003e43.17 (standard deviation [SD]\u0026thinsp;=\u0026thinsp;28.14) per facility under the 4-layer model (State-Zone-LGA-Facility; S-Z-L-F) (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eFor the 3-layer models, mean delivery costs were \u003cspan\u003e$\u003c/span\u003e38.50 (SD\u0026thinsp;=\u0026thinsp;23.46) for the S-L-F model and \u003cspan\u003e$\u003c/span\u003e26.92 (SD\u0026thinsp;=\u0026thinsp;12.92) for the S-Z-F model.\u003c/p\u003e\n \u003cp\u003eThe 2-layer model (S-F) had a higher mean delivery cost of \u003cspan\u003e$\u003c/span\u003e49.58 (SD\u0026thinsp;=\u0026thinsp;9.54), which was significantly more expensive than the S-Z-F model (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cp\u003eAmong the architectures assessed, the S-Z-F model was identified as the least costly.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of Unit Costs of Vaccine Deliveries Across Delivery Layers\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDelivery Layer\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of Layers\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean Cost per Facility (US\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStandard Deviation (US\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eState \u0026rarr; Zone \u0026rarr; LGA \u0026rarr; Facility (S-Z-L-F)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eState \u0026rarr; Zone \u0026rarr; Facility (S-Z-F)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eState \u0026rarr; LGA \u0026rarr; Facility (S-L-F)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eState \u0026rarr; Facility (S-F)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003cstrong\u003eNote\u003c/strong\u003e: Costs represent mean unit cost per health facility, based on government-run delivery models.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eDecision to Outsource Transportation\u003c/h2\u003e\n \u003cp\u003eComparisons between the government-run and outsourced delivery models showed that outsourcing transportation increased vaccine delivery costs across all four delivery architectures (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eTransitioning from an outsourced to a government-run model reduced delivery cost by up to 28% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cp\u003eThese comparisons assumed bi-weekly vaccine deliveries using trucks, as implemented in study location. Delivery costs associated with cascade facilities were excluded from the outsourcing analysis, as they did not influence transportation outsourcing decisions.\u003c/p\u003e\n \u003cp\u003eOverall, government-run delivery systems demonstrated lower costs compared to outsourced models.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of Vaccine Delivery Costs: Government-Run vs. Outsourced Models\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDelivery Layer\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGovernment-Run Mean Cost (US\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOutsourced Mean Cost (US\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCost Difference (US\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eState \u0026rarr; Zone \u0026rarr; LGA \u0026rarr; Facility (S-Z-L-F)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eState \u0026rarr; LGA \u0026rarr; Facility (S-L-F)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eState \u0026rarr; Zone \u0026rarr; Facility (S-Z-F)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eState \u0026rarr; Facility (S-F)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cstrong\u003eNote\u003c/strong\u003e: P-values derived from Mann-Whitney U tests. Lower costs associated with government-run models.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eDelivery Frequency\u003c/h2\u003e\n \u003cp\u003eThe impact of delivery frequency on vaccine delivery cost is shown in Fig.\u0026nbsp;2.\u003c/p\u003e\n \u003cp\u003eRegression analysis suggested that increasing delivery frequency reduced the unit cost of deliveries under the government-run approach.\u003c/p\u003e\n \u003cp\u003eThe greatest reduction in unit cost (by a factor of 7.24) occurred under the 4-layer S-Z-L-F model, while the smallest reduction (by a factor of 0.53) was observed under the 2-layer S-F model.\u003c/p\u003e\n \u003cp\u003eAlthough higher delivery frequency lowered the cost per delivery cycle, total annual delivery costs would increase when accounting for the greater number of delivery cycles per year.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eNumber of Delivery Points\u003c/h2\u003e\n \u003cp\u003eThe effect of increasing the number of delivery points on unit costs is illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eRegression analysis indicated that expanding the number of target facilities consistently reduced the unit delivery cost.\u003c/p\u003e\n \u003cp\u003eWhen increasing the number of facilities by a factor of 25, the S-Z-L-F model achieved the highest cost reduction (factor of 5.69), while the S-F model achieved the lowest reduction (factor of 0.76).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003eChoice of Fleet\u003c/h2\u003e\n \u003cp\u003eCost comparisons for different vehicle types are presented in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eSubstituting trucks with motorcycles or tricycles for deliveries resulted in estimated unit cost reductions of 15\u0026ndash;34% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cp\u003eWhen comparing motorcycles and tricycles, deliveries with motorcycles were 4\u0026ndash;12% less expensive (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cp\u003eThe cost differences related to vehicle type were statistically significant for the S-F model but not for all delivery architectures.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of unit cost of vaccine deliveries with different automobile options\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDelivery Layers\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS-Z-L-F\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS-Z-F\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS-L-F\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS-F\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAutomobile Option\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrucks only -A (\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTruck \u0026amp;Tricycle -B (\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTruck \u0026amp; Motorcycle -C (\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean difference B \u0026amp; A (\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean difference C \u0026amp; A (\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean difference C \u0026amp; B (\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cstrong\u003eNote\u003c/strong\u003e: Costs represent mean unit delivery costs per facility. Cost reductions were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for substitutions of motorcycles or tricycles compared to trucks.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study identified five key operational levers influencing the cost of vaccine deliveries in Kano State: automobile options, delivery frequency, number of delivery points, number of delivery layers, and responsibility for vaccine distribution. Each of these factors demonstrated significant effects on unit delivery costs, as detailed in our findings. Understanding how these operational choices affect delivery efficiency is critical, particularly in LMICs where immunization programs face persistent financial and logistical constraints.\u003c/p\u003e\u003cp\u003eRecent global evidence increasingly highlights the importance of optimizing supply chain architecture and delivery models to enhance the cost-effectiveness and resilience of vaccine programs [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Our results show that streamlining vaccine delivery layers substantially reduces unit delivery costs, consistent with findings from Niger, where the removal of regional storage levels decreased logistics costs by 17% [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Similarly, recent modeling studies in Ethiopia and Bangladesh confirm that consolidating intermediate cold chain points reduces both operational complexity and financial burden [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In our study location, the 3-layer S-Z-F model was notably more cost-efficient compared to the 4-layer and 2-layer models, emphasizing that supply chain redesigns must balance distance logistics with administrative feasibility.\u003c/p\u003e\u003cp\u003eThe decision to retain government-run vaccine distribution over outsourcing was supported by cost savings of up to 28%, which aligns with emerging evidence. A 2022 multi-country review found that while outsourcing logistics can improve timeliness and transparency, it often introduces overhead and coordination costs that are underestimated in initial planning [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Recent review regarding outsourcing of supply logistics for health commodities across Africa and experience from India post-COVID-19 highlighted the need for strong public-private partnership frameworks when outsourcing, to avoid unintended cost escalations and service fragmentation [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRegarding delivery frequency, although increasing delivery frequency lowered unit costs, it raised total annual delivery costs which is an important consideration for sustainability. Post-pandemic evaluations indicate that many countries that scaled up delivery cycles for COVID-19 vaccination campaigns are now recalibrating frequency to manage operating costs without compromising stock availability [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Thus, program managers must weigh unit cost efficiencies against broader programmatic affordability.\u003c/p\u003e\u003cp\u003eExpanding the number of delivery points was shown to reduce unit costs, driven by economies of scale in Kano's delivery system. This finding is supported by recent supply chain analyses showing that higher facility coverage per logistics cycle can dilute fixed capital costs, provided that delivery vehicles and human resources are adequately optimized [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSubstituting trucks with motorcycles or tricycles also lowered delivery costs, particularly in dense urban and peri-urban areas. However, this is context-specific. Difficult terrain, poor road networks, and security risks as experienced in many northern Nigerian states and parts of East Africa can limit the feasibility of lighter vehicle use [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Recent pilot studies in Kenya and Uganda suggest that motorcycles are effective for deliveries within 30\u0026ndash;50 km ranges but require robust maintenance and safety planning [22].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrated that key operational levers; delivery layers, frequency, number of delivery points, vehicle choice, and mode of delivery management, significantly influence the unit cost of vaccine deliveries in a the study location. Through retrospective cost modeling, we found that strategic adjustments to these levers can produce measurable cost savings without compromising coverage. Notably, streamlining the number of delivery layers and increasing the number of delivery points reduced per-facility delivery costs, while transitioning from outsourced to government-run delivery models consistently yielded lower expenditures. Also, shifting from truck-based deliveries to motorcycles or tricycles offered further cost reductions, especially for shorter delivery routes and lower-volume facilities.\u003c/p\u003e\u003cp\u003eThese findings align with global efforts to strengthen immunization supply chains, particularly in resource-constrained settings where fiscal efficiency is essential to achieving universal coverage. As countries continue to recover from the COVID-19 pandemic and integrate new vaccines into routine immunization schedules, the need for adaptive, cost-effective delivery strategies is more urgent than ever. This study adds evidence to support context-specific supply chain redesign and provides a replicable approach for similar evaluations in other low- and middle-income countries.\u003c/p\u003e\u003cp\u003eFuture research should incorporate dynamic costing methods that adjust for inflation, currency fluctuations, and changing fuel and labor markets. Integrating real-time data into delivery planning and developing robust costing frameworks across subnational levels will be vital for sustaining efficient vaccine delivery systems. Policymakers and immunization program managers must consider these operational levers as part of broader health system strengthening and financial sustainability strategies.\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eThis study is limited by its reliance on Kano's administrative and logistical structure, which may differ from other states or countries with fewer cold chain facilities or more dispersed populations. Furthermore, cost data were collected for the period up to early 2020, and while major structural shifts are unlikely, inflation, fuel price volatility, and new health security priorities (outbreak responses for instance) may affect cost assumptions. Future studies could incorporate dynamic modeling to adjust for post-COVID-19 market fluctuations.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study involved secondary analysis of programmatic cost and delivery data and did not involve human participants or access to identifiable personal data. Therefore, ethical approval and informed consent were not required, in line with national and institutional guidelines. All cost data were obtained with appropriate permission from relevant program authorities in Kano State, Nigeria.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset supporting the conclusions of this article is publicly available on the Open Science Framework (OSF) at the following DOI: https://doi.org/10.17605/OSF.IO/H3N6T.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMA and UI conceptualised the study and developed the methodology. LJ and CO collected the data and participated in its analysis and interpretation. MA, LJ, OP and AB developed the manuscripts while all authors provided critical review and approved the final version. All authors agreed on the content of the manuscripts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge Kano State Government, through the Northern Nigerian RI Strengthening Program for providing a ground for this work to be conducted. We also acknowledge the Kano State Primary Health Care Management Board and the private sector logistics provider for giving us access to their program data. \u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eYadav P, Yadav P. Health product supply chains in developing countries: diagnosis of the root causes of underperformance and an agenda for reform. 2015;8604:April.\u003c/li\u003e\n \u003cli\u003eKodali PB. Achieving universal health coverage in low- and middle-income countries: challenges for policy post-pandemic and beyond. Risk Manag Healthc Policy. 2023;16:607\u0026ndash;21.\u003c/li\u003e\n \u003cli\u003eAshok A, Brison M, Letallec Y. Improving cold chain systems: challenges and solutions. Vaccine. 2017;35(17):2217\u0026ndash;23.\u003c/li\u003e\n \u003cli\u003eUzochukwu B, Chukwuogo O, Onwujekwe O. Financing immunization for results in Nigeria: who funds, who disburses, who utilizes, who accounts? Financing bottlenecks and accountability challenges. 2014.\u003c/li\u003e\n \u003cli\u003eAina M, Igbokwe U, Jegede L, Fagge R, Thompson A, Mahmoud N. Preliminary results from direct-to-facility vaccine deliveries in Kano, Nigeria. Vaccine. 2017;35(17):2175\u0026ndash;82.\u003c/li\u003e\n \u003cli\u003eBotes J, Bam W, De Kock I. Public-private supply chain integration as a possible means to improve public health supply chains. In: SAIIE29 Proceedings. Spier, Stellenbosch, South Africa; 2018 Oct 24\u0026ndash;26.\u003c/li\u003e\n \u003cli\u003eZaffran M, Vandelaer J, Kristensen D, Melgaard B, Yadav P, Antwi-Agyei KO, et al. The imperative for stronger vaccine supply and logistics systems. Vaccine. 2013;31 Suppl 2:B73\u0026ndash;80.\u003c/li\u003e\n \u003cli\u003ePortnoy A, Ozawa S, Grewal S, Norman BA, Rajgopal J, Gorham KM, et al. Costs of vaccine programs across 94 low- and middle-income countries. Vaccine. 2015;33 Suppl 1:A99\u0026ndash;108.\u003c/li\u003e\n \u003cli\u003eBrown ST, Schreiber B, Cakouros BE, Wateska AR, Dicko HM, Connor DL, et al. The benefits of redesigning Benin\u0026apos;s vaccine supply chain. Vaccine. 2014;32(32):4097\u0026ndash;103.\u003c/li\u003e\n \u003cli\u003eAdebayo AG. Accounting for depreciation: empirical analyses of the application of depreciation methods in small and medium enterprises in Nigeria. J Account Financ Manag. 2016;2(6):17.\u003c/li\u003e\n \u003cli\u003eWorld Health Organization, United Nations Children\u0026apos;s Fund. The big catch-up: an essential immunization recovery plan for 2023 and beyond. Geneva: World Health Organization; 2023.\u003c/li\u003e\n \u003cli\u003eAssi TM, Brown ST, Kone S, Norman BA, Djibo A, Connor DL, et al. Removing the regional level from the Niger vaccine supply chain. Vaccine. 2013;31(26):2828\u0026ndash;34.\u003c/li\u003e\n \u003cli\u003ePATH, World Health Organization. Ethiopia National Immunization Logistics Strategy. 2023.\u003c/li\u003e\n \u003cli\u003eUNICEF Bangladesh. Supply chain optimization for immunization. 2022.\u003c/li\u003e\n \u003cli\u003eProsser W, Sagar K, Seidel M, Alva S. Ensuring vaccine potency and availability: how evidence shaped Gavi\u0026rsquo;s immunization supply chain strategy. BMC Health Serv Res. 2022;22(1):1237.\u003c/li\u003e\n \u003cli\u003eTetteh EK. Outsourcing supply logistics for health commodities in Africa. J Health Care Poor Underserved. 2024;35(3):995\u0026ndash;1010.\u003c/li\u003e\n \u003cli\u003eWHO-India. Evaluation of COVID-19 vaccination logistics system. 2023.\u003c/li\u003e\n \u003cli\u003eKana BD, Arbuthnot P, Botwe BK, Choonara YE, Hassan F, Louzir H, et al. Opportunities and challenges of leveraging COVID-19 vaccine innovation and technologies for developing sustainable vaccine manufacturing capabilities in Africa. Lancet Infect Dis. 2023;23(8):e288\u0026ndash;300.\u003c/li\u003e\n \u003cli\u003eUSAID Deliver Project. Scaling logistics coverage for immunization. 2022.\u003c/li\u003e\n \u003cli\u003eSinnei DK, Karimi PN, Maru SM, Karengera S, Bizimana T. Evaluation of vaccine storage and distribution practices in rural healthcare facilities in Kenya. J Pharm Policy Pract. 2023;16(1):25.\u003c/li\u003e\n \u003cli\u003eHerawatie D, Siswanto N, Widodo E. Motorcycle taxi in shared mobility and informal transportation: a bibliometric analysis. J Inf Syst Eng Bus Intell. 2024;10(2):250\u0026ndash;69.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Vaccine delivery, Cost optimization, Supply chain modelling, Health logistics","lastPublishedDoi":"10.21203/rs.3.rs-6949544/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6949544/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Efficient vaccine delivery systems are critical for sustaining high immunization coverage in low- and middle-income countries (LMICs). However, cost optimization remains a persistent challenge. This study assessed the cost drivers of vaccine delivery in Kano State, Nigeria, and modeled the effects of key operational levers to inform supply chain strengthening efforts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We conducted a retrospective cost analysis of vaccine deliveries to 390 health facilities in Kano State under both government-run and outsourced distribution models. Costs were categorized into labor, transportation, storage, building, and communication components. We modeled variations in delivery layers, delivery frequency, fleet types, and number of delivery points using Microsoft Excel and STATA 13 SE. Statistical tests included Kruskal-Wallis, Tukey post-hoc, and Mann-Whitney U analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Five operational levers including automobile options, delivery frequency, number of delivery points, number of delivery layers, and responsibility for vaccine distribution, significantly influenced unit delivery costs. Streamlining delivery layers reduced costs by up to 38%, while transitioning from outsourced to government-run models lowered costs by up to 28% (p \u0026lt; 0.05). Increasing the number of delivery points and using motorcycles or tricycles instead of trucks further reduced unit costs by 15–34%. However, increased delivery frequency, while reducing unit costs per cycle, raised total annual operational costs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Optimizing supply chain design through strategic adjustments in delivery models, vehicle selection, and facility coverage can substantially lower vaccine delivery costs in resource-constrained settings. Policymakers should integrate cost-efficiency strategies into immunization system strengthening initiatives to enhance sustainability and resilience, particularly in the post-pandemic recovery era.\u003c/p\u003e","manuscriptTitle":"Optimizing Vaccine Delivery Costs: Modeling the Impact of Key Operational Levers in a Northern Nigerian State","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-23 10:54:05","doi":"10.21203/rs.3.rs-6949544/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-08-09T09:03:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-08T19:23:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-08T05:44:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-05T21:06:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"99224973964934360074772525595798150798","date":"2025-08-04T11:19:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"141056979971268813939388285653597491411","date":"2025-07-29T19:01:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"261129911838990598093086982869871213704","date":"2025-07-29T18:54:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"278258574541785859092243651595210686839","date":"2025-07-28T16:54:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"117658743843691575523828324081630199919","date":"2025-07-28T10:36:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"335292162848046356863615315861570494798","date":"2025-07-23T03:18:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-21T03:15:15+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-25T12:27:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-25T02:24:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-25T02:24:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2025-06-22T12:38:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"54451d52-855b-4930-acd9-aa2e4ad314a8","owner":[],"postedDate":"July 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-07-23T10:54:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-23 10:54:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6949544","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6949544","identity":"rs-6949544","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}