Supply Chain Determinants of Treatment Interruption and Mortality in Haemodialysis Centres: A Comprehensive Analysis of Sudan's Largest State

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This study examines the relationship between supply chain reliability, equipment maintenance, and clinical outcomes across all haemodialysis centres in this vast, remote region. Methods: We conducted a complete census of all 13 haemodialysis centres in Northern State, Sudan between October and December 2025. Using structured instruments, we collected comprehensive data on equipment functionality, supply availability, maintenance practices, and clinical outcomes. We computed two validated indices: Essential Availability Index (EAI) and Medication Availability Index (MAI). Statistical analyses included descriptive statistics and Pearson correlations. Results: The 13 centres served 919 patients (341 female, 578 male) across this vast territory. We identified critical supply chain fragmentation: essential supplies showed consistent availability (median EAI: 2.9, IQR: 2.8-3.0) while medications faced severe shortages (median MAI: 1.5, IQR: 1.1-1.9). MAI demonstrated a strong inverse correlation with mortality rates (r = -0.63, p=0.021), whereas EAI showed only weak association (r = -0.32, p=0.286). Machine downtime strongly predicted treatment interruptions (r = 0.72, p=0.006). Geographical remoteness disproportionately affected medication availability (r = -0.58, p=0.039). Only 46% of centres performed monthly preventive maintenance, with these centres demonstrating 59% lower machine downtime ratios than centres with no maintenance (0.09 vs 0.22). Conclusions: In Sudan's largest state, medication supply chain failures rather than essential supply shortages are the primary supply-side determinant of patient mortality. The vast geographical expanse exacerbates these challenges. Urgent interventions should prioritize reliable medication supply, standardized maintenance protocols, and geographically-equitable distribution to improve haemodialysis outcomes in this vulnerable population. Haemodialysis Supply Chain Resource-Limited Settings Sudan Essential Availability Index Medication Availability Index Renal Failure Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Chronic kidney disease represents a persistent and escalating challenge to healthcare systems worldwide, with recent epidemiological studies confirming its prevalence affects nearly one in ten adults globally [1]. As renal function deteriorates to end-stage renal disease, the imperative for renal replacement therapy becomes a matter of survival. Hemodialysis emerges as the predominant therapeutic intervention across numerous low- and middle-income countries, though its effectiveness is frequently compromised by structural limitations and resource allocation problems [2, 3]. The landscape of end-stage renal disease management throughout Sub-Saharan Africa reveals distressing patterns of care delivery. Current estimates indicate that a mere fraction of patients requiring dialysis—approximately 12–15%—actually obtain this life-sustaining treatment [4]. For the fortunate minority who commence hemodialysis, survival prospects remain grim, with mortality rates climbing precipitously during the initial treatment year [4]. This dire situation stems from multiple systemic deficiencies: inconsistent availability of dialysis consumables, repeated equipment malfunctions, erratic utility services, and critically understaffed facilities operating with minimal technical support [4–6]. Sudan's nephrology care infrastructure mirrors these regional difficulties. Ministry of Health figures document roughly 8,740 individuals receiving regular dialysis, though epidemiologists project the actual number needing treatment surpasses 13,000 [7]. Sudanese dialysis units commonly experience stock-outs of essential supplies ranging from vascular access needles to erythropoietin, with clinical staff reporting that these shortages directly influence dialysis adequacy and likely contribute to excess deaths [7, 8]. Northern State, spanning approximately 348,765 square kilometers and containing an estimated 936,000 residents [9], confronts particularly severe dialysis service challenges. Health authorities have documented that this region supports one of Sudan's highest patient-to-population ratios for hemodialysis dependence [10, 11]. The state's enormous territorial expanse, coupled with underdeveloped transportation networks and centralized supply distribution systems, creates formidable obstacles to consistent treatment delivery. Local medical directors have reported multiple instances where dialysis units suspended operations for days or weeks due to exhausted consumable inventories or non-functional equipment [11]. Existing scholarly work on Sudanese hemodialysis has predominantly examined patient demographics, etiology of kidney failure, and quality-of-life measures within Khartoum and other centrally-located states [12–14]. What remains inadequately explored is how operational variables—including supply chain continuity, dialysis machine uptime, and consistent resource availability—directly influence patient survival and treatment adherence metrics. Investigations from comparable Sub-Saharan African settings consistently demonstrate that interruptions in hemodialysis provision correlate strongly with missed treatments, increased emergency dialysis episodes, and higher mortality [4, 5, and 15]. Research from Cameroon and Ethiopia documents 27–36% mortality within the first year among incident hemodialysis patients, with service disruptions identified as significant contributing factors [15, 16]. These findings collectively suggest that operational reliability constitutes a fundamental determinant of clinical outcomes in hemodialysis populations. To address this knowledge gap, we undertook a comprehensive evaluation of all hemodialysis facilities operating within Northern State. Our investigation specifically sought to quantify how supply chain robustness and equipment maintenance practices correlate with patient mortality and treatment interruption frequencies. We postulated that centres facing more substantial supply disruptions and equipment failures would demonstrate elevated mortality and more frequent missed treatments, with these relationships intensified by the state's distinctive geographical constraints. Methods Study Design and Setting We conducted a cross-sectional census of all 13 haemodialysis centres operating in Northern State, Sudan between October and December 2025. Northern State encompasses approximately 348,765 km², making it the largest state in Sudan by area. The study represented a complete enumeration of all renal units serving the state's documented haemodialysis population of 919 patients (341 female, 578 male). Data Collection and Instrument Data collection employed a structured assessment instrument (see S1 File) adapted from the Sudanese Ministry of Health's standardized renal care monitoring system. The comprehensive instrument assessed multiple domains: centre characteristics and infrastructure, equipment inventory and functionality, supply availability and management, maintenance practices and schedules, logistical factors and supply chain operations, and clinical outcomes and service delivery metrics. Trained renal care coordinators conducted in-person visits to each centre to complete the assessments through direct observation, record review, and interviews with centre staff. Variable Definitions and Measurement Availability Indices: We measured supply availability using a standardized 4-point scale (0=None, 1=Rare, 2=Intermittent, 3=Always available). From these measurements, we computed two composite indices: Essential Availability Index (EAI): Mean score of dialyzer, bicarbonate, acid concentrate, and bloodline availability (Cronbach's α=0.85, demonstrating high internal consistency) Medication Availability Index (MAI): Mean score of heparin, erythropoietin, intravenous solutions, and iron dextran availability (Cronbach's α=0.79, indicating acceptable internal consistency) Operational Indicators: Machine downtime ratio: Proportion of non-operational machines to total machines Supply lead time: Average days between supply deliveries Preventive maintenance frequency: Categorized as monthly, quarterly, or none Transport disruption frequency: Number of transport disruptions per month Clinical Outcomes: Mortality rate: (Number of deaths / Total patients) × 100 Service disruption rate: (Missed sessions / Scheduled sessions) × 100 Emergency session rate: (Emergency sessions / Total sessions) × 100 Improvement rate: (Patients showing clinical improvement / Total patients) × 100 Data Analysis Given the complete population sampling (N=13 centres), we employed descriptive statistics including means, standard deviations, medians, and interquartile ranges to characterize centre operations and outcomes. We used Pearson correlation analysis to examine relationships between continuous variables. While traditional inferential statistics are primarily reserved for sample-based studies, we calculated p-values for correlation coefficients to provide additional context for the strength of observed relationships, while recognizing their interpretive limitations in a population census. Effect sizes were interpreted using conventional thresholds: small (|r|=0.1-0.3), medium (|r|=0.3-0.5), or large (|r|>0.5). All statistical analyses were conducted using SPSS version 28. Ethical Considerations The study protocol received approval from the Research Ethics Committee of the Research Ethics Committee of Ministry of Health, Northern State. All collected data were anonymized to protect centre confidentiality and patient privacy. The requirement for individual patient consent was waived as the study exclusively collected aggregate centre-level data without any patient-level identifiers or detailed clinical information. Results Centre Characteristics and Resource Availability The 13 haemodialysis centres distributed across Northern State's vast territory served a total of 919 patients (341 female, 578 male), with considerable variation in centre size and capacity. The median centre served 45 patients (IQR: 18-82), ranging from small units serving only 6 patients to large referral centres serving 312 patients. Equipment resources similarly varied substantially, with operational dialysis stations numbering from 3 to 33 per centre (Table 1). Table 1. Characteristics of Haemodialysis centres in Northern State, Sudan (N=13) Characteristic Mean ± SD Median (IQR) Range Operational machines 10.5 ± 7.9 8 (6-12) 3-33 Non-operational machines 1.5 ± 1.4 1 (0-2) 0-5 Total patients 70.7 ± 78.9 45 (31-82) 18-312 Monthly sessions 585.2 ± 782.4 360 (240-656) 144-2808 Machine downtime ratio 0.14 ± 0.13 0.11 (0.00-0.21) 0.00-0.42 Supply lead time (days) 25.4 ± 11.7 30 (20-30) 0-40 Distance from depot (km) 154.6 ± 126.1 150 (70-190) 5-480 Transport disruptions/month 1.07 ± 2.05 0 (0-0) 0-7 A critical and striking disparity emerged between the availability of essential dialysis supplies and vital medications. Centres consistently reported reliable availability of dialyzers, bicarbonate, and acid concentrates (median EAI: 2.9, IQR: 2.8-3.0), indicating "always available" status for these essential supplies. In stark contrast, the availability of heparin, erythropoietin, intravenous solutions, and iron dextran was substantially compromised (median MAI: 1.5, IQR: 1.1-1.9), falling between "rare" and "intermittent" availability. This disparity is visually summarized in Figure 1. Clinical Outcomes Mortality rates across the 13 centres averaged 6.9% (SD: 5.9%), with substantial variation between facilities (range: 0.0-17.8%). This represents approximately 63 deaths annually among the 919 patients. Service disruption affected a median of 0.9% of scheduled sessions (IQR: 0.0-4.8%), though several centres reported dramatically higher disruption rates (maximum: 52.6%). Emergency sessions constituted a median of 5.6% of all treatments delivered (IQR: 2.8-11.1%), indicating significant acute care needs within the patient population (Table 2). Table 2. Clinical Outcomes across Haemodialysis centres (N=13) Outcome Measure Mean ± SD Median (IQR) Range Mortality rate (%) 6.9 ± 5.9 5.3 (3.3-8.9) 0.0-17.8 Service disruption rate (%) 7.5 ± 15.7 0.9 (0.0-4.8) 0.0-52.6 Emergency session rate (%) 10.8 ± 17.2 5.6 (2.8-11.1) 0.6-66.7 Improvement rate (%) 8.5 ± 12.4 3.7 (0.0-10.0) 0.0-37.5 Associations between Supply Factors and Outcomes Correlation analysis revealed distinct and clinically important relationships between supply factors and patient outcomes (Table 3). The Medication Availability Index (MAI) demonstrated a strong, statistically significant inverse relationship with mortality rates (r = -0.63, p=0.021), indicating that centres with better medication access experienced substantially lower patient mortality. In contrast, the Essential Availability Index (EAI) showed only a weak, non-significant inverse relationship with mortality (r = -0.32, p=0.286). Machine downtime exhibited a strong positive relationship with service disruption rates (r = 0.72, p=0.006), clearly demonstrating that equipment failures directly and substantially impacted treatment delivery. The differential impact of these supply chain factors is illustrated in Figure 2. Table 3. Pearson Correlation Analysis of Supply Indices and Clinical Outcomes (N=13) Variable Mortality Rate Service Disruption Improvement Rate MAI EAI Machine Downtime Distance Mortality Rate 1 Service Disruption 0.41 1 Improvement Rate -0.48 -0.35 1 MAI -0.63* -0.38 0.52 1 EAI -0.32 -0.25 0.28 0.45 1 Machine Downtime 0.35 0.72* -0.41 -0.22 -0.15 1 Distance (km) 0.28 0.15 -0.31 -0.58* -0.49 0.11 1 *Note: * indicates large effect size (|r| > 0.5) with p < 0.05* The key correlations between supply chain factors and clinical outcomes are further visualized in Figure 3. Interpretation of Key Correlations: MAI and Mortality Rate (r = -0.63, p = 0.021): This large, statistically significant inverse correlation indicates that for every 1-point increase in medication availability on the 4-point scale, mortality rates decreased by approximately 4.2% (based on regression coefficient analysis). This powerful relationship suggests that medication shortages directly and substantially impact patient survival. EAI and Mortality Rate (r = -0.32, p = 0.286): This moderate, non-significant inverse correlation suggests that while essential supply availability may have some relationship with mortality, it is substantially weaker than the medication availability relationship. This indicates that the system successfully delivers basic dialysis supplies but critically fails to provide concomitant life-saving medications. Machine Downtime and Service Disruption (r = 0.72, p = 0.006): This large, statistically significant positive correlation demonstrates that equipment failures directly cause treatment interruptions. Each 10% increase in machine downtime ratio corresponded to approximately 15% more missed sessions, highlighting the operational impact of equipment reliability. Distance and MAI (r = -0.58, p = 0.039): This large, statistically significant inverse correlation shows that remote centres face substantially worse medication access. Centres located more than 150 km from the central depot demonstrated approximately 50% lower MAI scores than those within 50 km, indicating pronounced geographical inequities. Distance and EAI (r = -0.49, p = 0.092): This large, marginally significant inverse correlation indicates that geographical isolation also affects essential supplies, though less severely than medications. This pattern suggests that while both supply chains are distance-sensitive, the medication supply chain is particularly vulnerable to geographical challenges. MAI and Improvement Rate (r = 0.52, p = 0.072): This large, marginally significant positive correlation suggests that better medication availability may contribute to improved patient outcomes, though this relationship requires further investigation with larger samples. Differential Impact Analysis: The divergent correlation patterns between EAI and MAI reveal a critical system fragmentation. While EAI shows consistently weak correlations with all clinical outcomes (all |r| 0.28), MAI demonstrates strong, significant relationships with key outcome measures. This stark disparity highlights that the primary supply chain failure lies specifically in medication distribution rather than general supply logistics. Maintenance Practices and Equipment Reliability Preventive maintenance practices varied substantially across centres. Only 6 centres (46%) performed monthly preventive maintenance, while 3 centres (23%) conducted quarterly maintenance, and 4 centres (31%) reported no regular maintenance schedule. Centres implementing monthly maintenance demonstrated substantially lower machine downtime ratios (0.09) compared to those with no maintenance (0.22), representing a 59% reduction in downtime with regular maintenance. Centres without maintenance experienced 144% higher downtime rates (2.4 times more machine failures) than centres with monthly maintenance. The relationship between maintenance frequency and machine downtime is shown in Figure 4. Supply Sources and Adequacy Centres relied on multiple supply sources, with central government supply being most common (69% of centres), followed by donations (38%), and local purchase (31%). Self-rated supply adequacy assessments showed that only 5 centres (38%) rated their overall supply situation as "good" or "very good," while the majority (62%) reported "medium" or "poor" adequacy. Centres rating their supply as adequate had significantly higher MAI scores (2.10 vs 1.02, difference: 1.08 points), suggesting that staff perceptions of supply adequacy primarily reflected medication availability rather than essential supply status. Qualitative Findings Thematic analysis of open-ended responses from centre staff revealed three dominant, consistently reported challenges, as summarized in Figure 5. Medication and Consumable Shortages (12 centres, 92%): Centres consistently reported critical shortages of heparin, erythropoietin, iron dextran, and intravenous solutions. One centre manager stated: "The main challenge is providing all dialysis consumables including heparin, erythropoietin, iron, 50% dextrose, and saline solutions. Patients sometimes bring their own supplies or miss treatments." Financial and Management Constraints (5 centres, 38%): Multiple centres cited "poor financial management" and inadequate funding as systemic barriers. Staff reported that "limited and unpredictable funding prevents planned maintenance and bulk purchasing." Equipment and Maintenance Issues (4 centres, 31%): Centres reported prolonged repair times (up to 30 days) and lack of spare parts as major operational constraints. One technician noted: "We wait weeks for simple spare parts. Meanwhile, machines sit idle and patients miss treatments." Proposed solutions centred on three key areas: Supply chain reform (9 centres, 69%): Direct monthly delivery of consumables to centres Logistics improvement (4 centres, 31%): Central handling of transport costs and coordination Decentralization (3 centres, 23%): Local-level supply management and regional depots Discussion This comprehensive census of all haemodialysis centres in Northern State, Sudan's largest state by area, reveals a healthcare system characterized by selective supply chain failure - maintaining adequate availability of essential supplies while critically failing to provide necessary medications. The differential correlation patterns between EAI and MAI with clinical outcomes provide compelling evidence for targeted intervention priorities in this vast geographical region serving Sudan's second-largest haemodialysis population. The Medication-Mortality Relationship The strong, statistically significant inverse correlation between MAI and mortality (r = -0.63, p = 0.021) represents one of the most clinically important findings of this study. The strength of this relationship suggests that approximately 40% of the variance in mortality rates between centres can be explained by medication availability differences (R² = 0.397). This relationship likely operates through multiple physiological pathways: heparin shortages increasing clotting risks and blood loss during dialysis; erythropoietin deficiencies exacerbating anaemia-related cardiac strain and cardiovascular mortality; and intravenous fluid limitations compromising volume management and treatment tolerance. These findings align with established literature linking specific medication access to haemodialysis outcomes [9, 10], but demonstrate particular urgency in this resource-limited setting. The Essential Supply Paradox The non-significant correlation between EAI and mortality (r = -0.32, p = 0.286) reveals a system that successfully delivers basic dialysis materials while failing to provide concomitant medications. This paradox suggests that the existing infrastructure and logistics for bulk supply distribution function adequately, but the more complex and specialized medication supply chain remains critically compromised. The moderate correlation may indicate that essential supply disruptions do matter clinically, but their impact is substantially overshadowed by the more critical medication shortages. Geographical Inequities in a Vast Territory The stronger correlation between distance and MAI (r = -0.58) compared to EAI (r = -0.49) indicates that medications are disproportionately vulnerable to geographical challenges in this largest Sudanese state. This pattern likely reflects the additional logistical complexities of medication storage, cold chain requirements, and specialized procurement processes that make remote distribution particularly challenging across Northern State's 348,765 km². These geographical inequities have profound implications for health equity, as patients in remote areas face compounded disadvantages: greater disease burden, fewer healthcare resources, and now documented poorer access to life-saving medications. Maintenance Impact and Modifiable Factors The 59% lower downtime in centres with monthly maintenance, combined with the strong correlation between machine downtime and service disruption (r = 0.72), demonstrates that equipment reliability represents a readily modifiable factor directly affecting treatment delivery. In a region where geographical isolation already limits healthcare access, maintaining functional equipment becomes particularly crucial. The maintenance gap identified in this study represents a clear, addressable opportunity for systemic improvement through standardized protocols and adequate resource allocation. Integration with Qualitative Findings The convergence of strong quantitative correlations with consistent qualitative reports from frontline healthcare providers significantly strengthens the validity of our conclusions. The triad of medication shortages, financial constraints, and equipment issues identified through thematic analysis aligns precisely with the quantitative patterns, suggesting that these represent genuine, widely recognized system failures rather than isolated or perceived challenges. Systemic Implications and Intervention Priorities The combined quantitative and qualitative data suggest a clear, prioritized intervention approach for Northern State's haemodialysis system: first, emergency stabilization of the medication supply chain; second, standardization and funding of equipment maintenance protocols; and third, logistical reforms specifically designed to address the geographical disparities exacerbated by the state's vast territory. Limitations This study has several limitations that should be considered when interpreting the findings. The cross-sectional design precludes definitive causal inference, though the strong, consistent associations observed across multiple analysis methods suggest important relationships worthy of intervention. The relatively small number of centres, while representing the complete population of Northern State, limits the potential for more complex multivariate analysis. Some outcome measures, particularly mortality rates, may be influenced by case mix variations and patient characteristics not captured in our centre-level data. Additionally, the use of self-reported data may introduce potential reporting bias, though the high consistency between quantitative measurements and qualitative responses suggests good validity of the reported challenges. Conclusions and Recommendations Based on the compelling evidence of differential impact between essential and medication supply availability on clinical outcomes, we recommend a targeted, prioritized intervention strategy for Northern State's haemodialysis system, as outlined in Figure 6: Immediate Medication Supply Chain Emergency Response: Establish a dedicated medication task force with authority to implement emergency procurement mechanisms, establish strategic buffer stocks, and conduct weekly monitoring of MAI scores as key performance indicators for the renal care system. Maintenance Protocol Standardization and Funding: Implement and fund mandatory monthly preventive maintenance protocols across all centres, using the demonstrated 59% downtime reduction as compelling evidence for resource allocation to maintenance programs. Geographically-Targeted Logistics Reform: Develop a hub-and-spoke model specifically designed for medication distribution, with enhanced support, more frequent deliveries, and larger buffer stocks for centres located beyond 150 km from the central depot. Differentiated Monitoring and Evaluation: Establish separate tracking and evaluation systems for EAI and MAI, recognizing that these indices represent distinct supply chain challenges requiring different solutions, success metrics, and intervention approaches. These evidence-based interventions, specifically targeted at the identified system failures and adapted to Northern State's geographical challenges, offer the most promising path toward improving haemodialysis outcomes for the state's 919 vulnerable patients and similar resource-limited settings facing geographical healthcare delivery challenges. Abbreviations CKD: Chronic Kidney Disease ESRD: End-Stage Renal Disease HD: Haemodialysis LMICs: Low- and Middle-Income Countries EAI: Essential Availability Index MAI: Medication Availability Index IQR: Interquartile Range Declarations Ethics approval and consent to participate : The study was approved by the Research Ethics Committee of Ministry of Health, Northern State. The study was conducted in accordance with the principles of the Declaration of Helsinki. The requirement for individual informed consent was waived by the ethics committee as the study exclusively collected anonymized, aggregate centre-level data without any patient identifiers. All methods were carried out in accordance with relevant guidelines and regulations. Consent for publication : Not applicable. Availability of data and materials : The datasets used and analysed during the current study are available from the corresponding author on reasonable request. The structured data collection instrument used in this study is provided as S1 File. Competing interests : The authors declare that they have no competing interests. Funding : This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Authors' contributions Hamad Alneel Albagir Ahmed Ali: Supervision, Methodology, Formal Analysis, Writing – Original Draft. Umama Kamal Mohamed Ahmed: Investigation, Data Curation, Writing – Review & Editing. Muhammed Abdelwahed Muhammed Abdelrahman: Formal Analysis, Validation, Writing – Review & Editing. Muner Ali Yosif: Data Collection, Interviews. All authors read and approved the final manuscript. Acknowledgements : The authors gratefully acknowledge the haemodialysis centre staff across Northern State who participated in this study and shared their experiences and insights. References Luyckx VA, Tonelli M, Stanifer JW. The global burden of kidney disease and the sustainable development goals. Bull World Health Organ. 2018; 96:414–422. Naicker S, Ashuntantang G, Sarfo F, Osafo C, Arogundade F, et al. Outcomes of chronic kidney disease and end-stage kidney disease in sub-Saharan Africa: a systematic review. Kidney Int Rep. 2021; 6:1826–1839. Elamin S, Omer A, et al. Hemodialysis in Sudan: Challenges and Opportunities. Saudi J Kidney Dis Transpl. 2020; 31:1149–1156. Workie DL, et al. Treatment access, early mortality, and patient survival in dialysis in developing nations: evidence from Ethiopia. BMC Nephrol. 2022; 23:193. Olowu WA, et al. Bedside rationing and moral distress in nephrologists in sub-Saharan Africa. BMJ Glob Health. 2022; 7:e009134. Halle MP, Ashuntantang G, Kaze FF, et al. Fatal outcomes among patients on maintenance haemodialysis in sub-Saharan Africa: a 10-year audit from the Douala General Hospital in Cameroon. BMC Nephrol. 2016; 17:165. New Arab. Kidney failure on the rise in Sudan amid war, dire conditions. 2025. [Internet]. Sudan Events. Ministry of Health: Monthly cost of kidney medications is $2.5 million. 2024. [Internet]. Central Bureau of Statistics (CBS). (2018). the Fifth Sudan Population and Housing Census — 2008: Priority Results. Republic of Sudan, Ministry of Cabinet Affairs. [Official government census report - primary source for population data]. Dabanga Radio. Technical problems lead to closure of Northern Sudan dialysis centre. Ed Debba case. [Internet]. Elamin S, Ahmed MM, et al. Quality of hemodialysis services in Northern Sudan: logistical challenges and patient outcomes. Ren Fail. 2021; 43:1180–1187. Salih RM, Ahmed MM, Mohammed MA, et al. Health-related quality of life among patients on maintenance hemodialysis in Khartoum State, Sudan. BMC Nephrol. 2020; 21:515. Causes of end-stage renal failure among haemodialysis patients in Khartoum State, Sudan. BMC Res Notes. 2015; 8:1509. Ishaq S, Mohamed N, Abdelrahim A, et al. Acute Kidney Injury in Sub-Sahara Africa: a single-center experience from Khartoum, Sudan. Ren Fail. 2018; 40:120–126. Adeniji OO, Uthman AO, Shittu MO. Global disparities in access and utilization of dialysis—Africa, the disadvantaged continent. Kidney Int Rep. 2023; 8:235–244. Workie DL, et al. Time to death and predictors among patients with CKD on hemodialysis in Addis Ababa, Ethiopia: a retrospective cohort study. BMC Nephrol. 2024; 25:xxx. Additional Declarations No competing interests reported. Supplementary Files S1FileStructuredAssessmentInstrument.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 15 Dec, 2025 Reviewers agreed at journal 15 Dec, 2025 Reviewers invited by journal 12 Dec, 2025 Editor assigned by journal 08 Dec, 2025 Editor invited by journal 14 Nov, 2025 Submission checks completed at journal 13 Nov, 2025 First submitted to journal 13 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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09:13:09","extension":"html","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":83592,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8031864/v1/4c01d8644853f537559f625e.html"},{"id":98778827,"identity":"86b7e57a-ab08-49b4-8ad8-04e17199d7a5","added_by":"auto","created_at":"2025-12-22 12:29:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":83528,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDisparity between Essential Availability Index (EAI) and Medication Availability Index (MAI) across 13 haemodialysis centres in Northern State, Sudan. EAI measures availability of dialyzers, bicarbonate, acid concentrate, and bloodlines; MAI measures availability of heparin, erythropoietin, intravenous solutions, and iron dextran.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8031864/v1/89429dcc37f9ef132b05eec5.png"},{"id":98778862,"identity":"b95bf4a0-21a1-4bdb-8bac-e5211b4fa7cd","added_by":"auto","created_at":"2025-12-22 12:29:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":107267,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDifferential Impact of Supply Chain Factors on Clinical Outcomes. This flow diagram illustrates how different supply chain components affect patient care outcomes. Medication availability (MAI) demonstrates a strong, statistically significant protective effect against mortality (r = -0.63, p = 0.021), indicating its crucial role in patient survival. In contrast, essential supply availability (EAI) shows only a weak, non-significant relationship with mortality (r = -0.32, p = 0.286). Machine downtime exhibits a strong detrimental effect on service continuity (r = 0.72, p = 0.006), directly increasing treatment disruptions. The color gradient (green for protective effects, red for limited impact, blue for operational effects) visually emphasizes the varying clinical significance of different supply chain elements, highlighting medication access as the most critical factor for patient outcomes in haemodialysis services.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8031864/v1/3433aeddc80ff865221913c7.png"},{"id":98751842,"identity":"4852b3d7-85b2-4151-a48d-e7848ea82454","added_by":"auto","created_at":"2025-12-22 09:13:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":57682,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eKey correlations between supply chain factors and clinical outcomes. MAI shows strong inverse correlation with mortality, while machine downtime strongly predicts service disruption. Distance from central depot negatively affects medication availability. p\u0026lt;0.01, p\u0026lt;0.05\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8031864/v1/669296b24bb97c5944c60345.png"},{"id":98751850,"identity":"5518d493-6451-48c6-badb-d3e00065ab94","added_by":"auto","created_at":"2025-12-22 09:13:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":71490,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePreventive Maintenance Practices and Machine Downtime in 13 Haemodialysis Centres. (A) Distribution of maintenance frequency showing that less than half of centres (46%) perform recommended monthly maintenance.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8031864/v1/27bbfb5e7ec7851de1acacbb.png"},{"id":98751848,"identity":"0bf3a537-e226-422b-9000-4bf82a94c6d4","added_by":"auto","created_at":"2025-12-22 09:13:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":84706,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFigure 4: Preventive Maintenance Practices and Machine Downtime in 13 Haemodialysis Centres. (B) Associated machine downtime ratios demonstrating a clear gradient where centres with monthly maintenance experience 59% lower downtime (0.09) compared to centres with no maintenance (0.22). Centres with quarterly maintenance show intermediate downtime levels (0.16). The colour gradient (green to red) visually emphasizes the relationship between maintenance frequency and equipment reliability, highlighting the operational consequences of inadequate maintenance protocols.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8031864/v1/f1acd3de00ef7300b8e2bbe7.png"},{"id":98751855,"identity":"0c1b6b3b-694d-4bd7-87e6-99f72ed71569","added_by":"auto","created_at":"2025-12-22 09:13:09","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":87521,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFigure 5: Primary challenges reported by haemodialysis centre staff in Northern State, Sudan\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8031864/v1/db57720bbd828a3661442ec3.png"},{"id":98777389,"identity":"8995650b-4782-463e-a6af-d2ac72ed87d1","added_by":"auto","created_at":"2025-12-22 12:26:46","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":96756,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFigure 6: Proposed intervention framework for improving haemodialysis outcomes in Northern State, Sudan\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8031864/v1/41b1d0aad9690a4b8f6dfe8d.png"},{"id":98783554,"identity":"5f85d454-46b3-4fca-829b-37686020c877","added_by":"auto","created_at":"2025-12-22 12:42:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1722873,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8031864/v1/adc2b50c-5a00-4fd7-ac25-abe298550f5b.pdf"},{"id":98751847,"identity":"76596237-323c-4675-ba98-f84550100959","added_by":"auto","created_at":"2025-12-22 09:13:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":401112,"visible":true,"origin":"","legend":"","description":"","filename":"S1FileStructuredAssessmentInstrument.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8031864/v1/e8294e7c348aedba8d844cb2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Supply Chain Determinants of Treatment Interruption and Mortality in Haemodialysis Centres: A Comprehensive Analysis of Sudan's Largest State","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic kidney disease represents a persistent and escalating challenge to healthcare systems worldwide, with recent epidemiological studies confirming its prevalence affects nearly one in ten adults globally [1]. As renal function deteriorates to end-stage renal disease, the imperative for renal replacement therapy becomes a matter of survival. Hemodialysis emerges as the predominant therapeutic intervention across numerous low- and middle-income countries, though its effectiveness is frequently compromised by structural limitations and resource allocation problems [2, 3].\u003c/p\u003e\n\u003cp\u003eThe landscape of end-stage renal disease management throughout Sub-Saharan Africa reveals distressing patterns of care delivery. Current estimates indicate that a mere fraction of patients requiring dialysis\u0026mdash;approximately 12\u0026ndash;15%\u0026mdash;actually obtain this life-sustaining treatment [4]. For the fortunate minority who commence hemodialysis, survival prospects remain grim, with mortality rates climbing precipitously during the initial treatment year [4]. This dire situation stems from multiple systemic deficiencies: inconsistent availability of dialysis consumables, repeated equipment malfunctions, erratic utility services, and critically understaffed facilities operating with minimal technical support [4\u0026ndash;6].\u003c/p\u003e\n\u003cp\u003eSudan\u0026apos;s nephrology care infrastructure mirrors these regional difficulties. Ministry of Health figures document roughly 8,740 individuals receiving regular dialysis, though epidemiologists project the actual number needing treatment surpasses 13,000 [7]. Sudanese dialysis units commonly experience stock-outs of essential supplies ranging from vascular access needles to erythropoietin, with clinical staff reporting that these shortages directly influence dialysis adequacy and likely contribute to excess deaths [7, 8].\u003c/p\u003e\n\u003cp\u003eNorthern State, spanning approximately 348,765 square kilometers and containing an estimated 936,000 residents [9], confronts particularly severe dialysis service challenges. Health authorities have documented that this region supports one of Sudan\u0026apos;s highest patient-to-population ratios for hemodialysis dependence [10, 11]. The state\u0026apos;s enormous territorial expanse, coupled with underdeveloped transportation networks and centralized supply distribution systems, creates formidable obstacles to consistent treatment delivery. Local medical directors have reported multiple instances where dialysis units suspended operations for days or weeks due to exhausted consumable inventories or non-functional equipment [11].\u003c/p\u003e\n\u003cp\u003eExisting scholarly work on Sudanese hemodialysis has predominantly examined patient demographics, etiology of kidney failure, and quality-of-life measures within Khartoum and other centrally-located states [12\u0026ndash;14]. What remains inadequately explored is how operational variables\u0026mdash;including supply chain continuity, dialysis machine uptime, and consistent resource availability\u0026mdash;directly influence patient survival and treatment adherence metrics.\u003c/p\u003e\n\u003cp\u003eInvestigations from comparable Sub-Saharan African settings consistently demonstrate that interruptions in hemodialysis provision correlate strongly with missed treatments, increased emergency dialysis episodes, and higher mortality [4, 5, and 15]. Research from Cameroon and Ethiopia documents 27\u0026ndash;36% mortality within the first year among incident hemodialysis patients, with service disruptions identified as significant contributing factors [15, 16]. These findings collectively suggest that operational reliability constitutes a fundamental determinant of clinical outcomes in hemodialysis populations.\u003c/p\u003e\n\u003cp\u003eTo address this knowledge gap, we undertook a comprehensive evaluation of all hemodialysis facilities operating within Northern State. Our investigation specifically sought to quantify how supply chain robustness and equipment maintenance practices correlate with patient mortality and treatment interruption frequencies. We postulated that centres facing more substantial supply disruptions and equipment failures would demonstrate elevated mortality and more frequent missed treatments, with these relationships intensified by the state\u0026apos;s distinctive geographical constraints.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design and Setting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a cross-sectional census of all 13 haemodialysis centres operating in Northern State, Sudan between October and December 2025. Northern State encompasses approximately 348,765 km\u0026sup2;, making it the largest state in Sudan by area. The study represented a complete enumeration of all renal units serving the state\u0026apos;s documented haemodialysis population of 919 patients (341 female, 578 male).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection and Instrument\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData collection employed a structured assessment instrument (see S1 File) adapted from the Sudanese Ministry of Health\u0026apos;s standardized renal care monitoring system. The comprehensive instrument assessed multiple domains: centre characteristics and infrastructure, equipment inventory and functionality, supply availability and management, maintenance practices and schedules, logistical factors and supply chain operations, and clinical outcomes and service delivery metrics. Trained renal care coordinators conducted in-person visits to each centre to complete the assessments through direct observation, record review, and interviews with centre staff.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVariable Definitions and Measurement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability Indices:\u003c/strong\u003e We measured supply availability using a standardized 4-point scale (0=None, 1=Rare, 2=Intermittent, 3=Always available). From these measurements, we computed two composite indices:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eEssential Availability Index (EAI): Mean score of dialyzer, bicarbonate, acid concentrate, and bloodline availability (Cronbach\u0026apos;s \u0026alpha;=0.85, demonstrating high internal consistency)\u003c/li\u003e\n \u003cli\u003eMedication Availability Index (MAI): Mean score of heparin, erythropoietin, intravenous solutions, and iron dextran availability (Cronbach\u0026apos;s \u0026alpha;=0.79, indicating acceptable internal consistency)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eOperational Indicators:\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eMachine downtime ratio: Proportion of non-operational machines to total machines\u003c/li\u003e\n \u003cli\u003eSupply lead time: Average days between supply deliveries\u003c/li\u003e\n \u003cli\u003ePreventive maintenance frequency: Categorized as monthly, quarterly, or none\u003c/li\u003e\n \u003cli\u003eTransport disruption frequency: Number of transport disruptions per month\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Outcomes:\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eMortality rate: (Number of deaths / Total patients) \u0026times; 100\u003c/li\u003e\n \u003cli\u003eService disruption rate: (Missed sessions / Scheduled sessions) \u0026times; 100\u003c/li\u003e\n \u003cli\u003eEmergency session rate: (Emergency sessions / Total sessions) \u0026times; 100\u003c/li\u003e\n \u003cli\u003eImprovement rate: (Patients showing clinical improvement / Total patients) \u0026times; 100\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven the complete population sampling (N=13 centres), we employed descriptive statistics including means, standard deviations, medians, and interquartile ranges to characterize centre operations and outcomes. We used Pearson correlation analysis to examine relationships between continuous variables. While traditional inferential statistics are primarily reserved for sample-based studies, we calculated p-values for correlation coefficients to provide additional context for the strength of observed relationships, while recognizing their interpretive limitations in a population census. Effect sizes were interpreted using conventional thresholds: small (|r|=0.1-0.3), medium (|r|=0.3-0.5), or large (|r|\u0026gt;0.5). All statistical analyses were conducted using SPSS version 28.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol received approval from the Research Ethics Committee of the Research Ethics Committee of Ministry of Health, Northern State. All collected data were anonymized to protect centre confidentiality and patient privacy. The requirement for individual patient consent was waived as the study exclusively collected aggregate centre-level data without any patient-level identifiers or detailed clinical information.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eCentre Characteristics and Resource Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 13 haemodialysis centres distributed across Northern State\u0026apos;s vast territory served a total of 919 patients (341 female, 578 male), with considerable variation in centre size and capacity. The median centre served 45 patients (IQR: 18-82), ranging from small units serving only 6 patients to large referral centres serving 312 patients. Equipment resources similarly varied substantially, with operational dialysis stations numbering from 3 to 33 per centre (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Characteristics of Haemodialysis centres in Northern State, Sudan (N=13)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRange\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOperational machines\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.5 \u0026plusmn; 7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8 (6-12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3-33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-operational machines\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.5 \u0026plusmn; 1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (0-2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0-5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal patients\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e70.7 \u0026plusmn; 78.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45 (31-82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18-312\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonthly sessions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e585.2 \u0026plusmn; 782.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e360 (240-656)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e144-2808\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMachine downtime ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.14 \u0026plusmn; 0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.11 (0.00-0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00-0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSupply lead time (days)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25.4 \u0026plusmn; 11.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30 (20-30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0-40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistance from depot (km)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e154.6 \u0026plusmn; 126.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e150 (70-190)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5-480\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTransport disruptions/month\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.07 \u0026plusmn; 2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0-0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0-7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eA critical and striking disparity emerged between the availability of essential dialysis supplies and vital medications. Centres consistently reported reliable availability of dialyzers, bicarbonate, and acid concentrates (median EAI: 2.9, IQR: 2.8-3.0), indicating \u0026quot;always available\u0026quot; status for these essential supplies. In stark contrast, the availability of heparin, erythropoietin, intravenous solutions, and iron dextran was substantially compromised (median MAI: 1.5, IQR: 1.1-1.9), falling between \u0026quot;rare\u0026quot; and \u0026quot;intermittent\u0026quot; availability.\u0026nbsp;This disparity is visually summarized in Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMortality rates across the 13 centres averaged 6.9% (SD: 5.9%), with substantial variation between facilities (range: 0.0-17.8%). This represents approximately 63 deaths annually among the 919 patients. Service disruption affected a median of 0.9% of scheduled sessions (IQR: 0.0-4.8%), though several centres reported dramatically higher disruption rates (maximum: 52.6%). Emergency sessions constituted a median of 5.6% of all treatments delivered (IQR: 2.8-11.1%), indicating significant acute care needs within the patient population (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Clinical Outcomes across Haemodialysis centres (N=13)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome Measure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRange\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMortality rate (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.9 \u0026plusmn; 5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.3 (3.3-8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0-17.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eService disruption rate (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.5 \u0026plusmn; 15.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.9 (0.0-4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0-52.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmergency session rate (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.8 \u0026plusmn; 17.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.6 (2.8-11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.6-66.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eImprovement rate (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.5 \u0026plusmn; 12.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.7 (0.0-10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0-37.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAssociations between Supply Factors and Outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrelation analysis revealed distinct and clinically important relationships between supply factors and patient outcomes (Table 3). The Medication Availability Index (MAI) demonstrated a strong, statistically significant inverse relationship with mortality rates (r = -0.63, p=0.021), indicating that centres with better medication access experienced substantially lower patient mortality. In contrast, the Essential Availability Index (EAI) showed only a weak, non-significant inverse relationship with mortality (r = -0.32, p=0.286). Machine downtime exhibited a strong positive relationship with service disruption rates (r = 0.72, p=0.006), clearly demonstrating that equipment failures directly and substantially impacted treatment delivery. The differential impact of these supply chain factors is illustrated in Figure 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Pearson Correlation Analysis of Supply Indices and Clinical Outcomes (N=13)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"651\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eMortality Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eService Disruption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003eImprovement Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eMAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eEAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eMachine Downtime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003eDistance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eMortality Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eService Disruption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eImprovement Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eMAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-0.63*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eEAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eMachine Downtime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.72*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e-0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e-0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eDistance (km)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e-0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e-0.58*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e-0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Note: * indicates large effect size (|r| \u0026gt; 0.5) with p \u0026lt; 0.05*\u003c/p\u003e\n\u003cp\u003eThe key correlations between supply chain factors and clinical outcomes are further visualized in Figure 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterpretation of Key Correlations:\u003c/strong\u003e\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003eMAI and Mortality Rate\u003c/strong\u003e (r = -0.63, p = 0.021): This large, statistically significant inverse correlation indicates that for every 1-point increase in medication availability on the 4-point scale, mortality rates decreased by approximately 4.2% (based on regression coefficient analysis). This powerful relationship suggests that medication shortages directly and substantially impact patient survival.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEAI and Mortality Rate\u003c/strong\u003e (r = -0.32, p = 0.286): This moderate, non-significant inverse correlation suggests that while essential supply availability may have some relationship with mortality, it is substantially weaker than the medication availability relationship. This indicates that the system successfully delivers basic dialysis supplies but critically fails to provide concomitant life-saving medications.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMachine Downtime and Service Disruption\u003c/strong\u003e (r = 0.72, p = 0.006): This large, statistically significant positive correlation demonstrates that equipment failures directly cause treatment interruptions. Each 10% increase in machine downtime ratio corresponded to approximately 15% more missed sessions, highlighting the operational impact of equipment reliability.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDistance and MAI\u003c/strong\u003e (r = -0.58, p = 0.039): This large, statistically significant inverse correlation shows that remote centres face substantially worse medication access. Centres located more than 150 km from the central depot demonstrated approximately 50% lower MAI scores than those within 50 km, indicating pronounced geographical inequities.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDistance and EAI\u003c/strong\u003e (r = -0.49, p = 0.092): This large, marginally significant inverse correlation indicates that geographical isolation also affects essential supplies, though less severely than medications. This pattern suggests that while both supply chains are distance-sensitive, the medication supply chain is particularly vulnerable to geographical challenges.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMAI and Improvement Rate\u003c/strong\u003e (r = 0.52, p = 0.072): This large, marginally significant positive correlation suggests that better medication availability may contribute to improved patient outcomes, though this relationship requires further investigation with larger samples.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eDifferential Impact Analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe divergent correlation patterns between EAI and MAI reveal a critical system fragmentation. While EAI shows consistently weak correlations with all clinical outcomes (all |r| \u0026lt; 0.35, all p \u0026gt; 0.28), MAI demonstrates strong, significant relationships with key outcome measures. This stark disparity highlights that the primary supply chain failure lies specifically in medication distribution rather than general supply logistics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaintenance Practices and Equipment Reliability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePreventive maintenance practices varied substantially across centres. Only 6 centres (46%) performed monthly preventive maintenance, while 3 centres (23%) conducted quarterly maintenance, and 4 centres (31%) reported no regular maintenance schedule. Centres implementing monthly maintenance demonstrated substantially lower machine downtime ratios (0.09) compared to those with no maintenance (0.22), representing a 59% reduction in downtime with regular maintenance. Centres without maintenance experienced 144% higher downtime rates (2.4 times more machine failures) than centres with monthly maintenance.\u003c/p\u003e\n\u003cp\u003eThe relationship between maintenance frequency and machine downtime is shown in Figure 4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupply Sources and Adequacy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCentres relied on multiple supply sources, with central government supply being most common (69% of centres), followed by donations (38%), and local purchase (31%). Self-rated supply adequacy assessments showed that only 5 centres (38%) rated their overall supply situation as \u0026quot;good\u0026quot; or \u0026quot;very good,\u0026quot; while the majority (62%) reported \u0026quot;medium\u0026quot; or \u0026quot;poor\u0026quot; adequacy. Centres rating their supply as adequate had significantly higher MAI scores (2.10 vs 1.02, difference: 1.08 points), suggesting that staff perceptions of supply adequacy primarily reflected medication availability rather than essential supply status.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQualitative Findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThematic analysis of open-ended responses from centre staff revealed three dominant, consistently reported challenges, as summarized in Figure 5.\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003eMedication and Consumable Shortages\u003c/strong\u003e (12 centres, 92%): Centres consistently reported critical shortages of heparin, erythropoietin, iron dextran, and intravenous solutions. One centre manager stated: \u0026quot;The main challenge is providing all dialysis consumables including heparin, erythropoietin, iron, 50% dextrose, and saline solutions. Patients sometimes bring their own supplies or miss treatments.\u0026quot;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eFinancial and Management Constraints\u003c/strong\u003e (5 centres, 38%): Multiple centres cited \u0026quot;poor financial management\u0026quot; and inadequate funding as systemic barriers. Staff reported that \u0026quot;limited and unpredictable funding prevents planned maintenance and bulk purchasing.\u0026quot;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEquipment and Maintenance Issues\u003c/strong\u003e (4 centres, 31%): Centres reported prolonged repair times (up to 30 days) and lack of spare parts as major operational constraints. One technician noted: \u0026quot;We wait weeks for simple spare parts. Meanwhile, machines sit idle and patients miss treatments.\u0026quot;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eProposed solutions centred on three key areas:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eSupply chain reform\u003c/strong\u003e (9 centres, 69%): Direct monthly delivery of consumables to centres\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eLogistics improvement\u003c/strong\u003e (4 centres, 31%): Central handling of transport costs and coordination\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDecentralization\u003c/strong\u003e (3 centres, 23%): Local-level supply management and regional depots\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis comprehensive census of all haemodialysis centres in Northern State, Sudan\u0026apos;s largest state by area, reveals a healthcare system characterized by selective supply chain failure - maintaining adequate availability of essential supplies while critically failing to provide necessary medications. The differential correlation patterns between EAI and MAI with clinical outcomes provide compelling evidence for targeted intervention priorities in this vast geographical region serving Sudan\u0026apos;s second-largest haemodialysis population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Medication-Mortality Relationship\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe strong, statistically significant inverse correlation between MAI and mortality (r = -0.63, p = 0.021) represents one of the most clinically important findings of this study. The strength of this relationship suggests that approximately 40% of the variance in mortality rates between centres can be explained by medication availability differences (R\u0026sup2; = 0.397). This relationship likely operates through multiple physiological pathways: heparin shortages increasing clotting risks and blood loss during dialysis; erythropoietin deficiencies exacerbating anaemia-related cardiac strain and cardiovascular mortality; and intravenous fluid limitations compromising volume management and treatment tolerance. These findings align with established literature linking specific medication access to haemodialysis outcomes [9, 10], but demonstrate particular urgency in this resource-limited setting.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Essential Supply Paradox\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe non-significant correlation between EAI and mortality (r = -0.32, p = 0.286) reveals a system that successfully delivers basic dialysis materials while failing to provide concomitant medications. This paradox suggests that the existing infrastructure and logistics for bulk supply distribution function adequately, but the more complex and specialized medication supply chain remains critically compromised. The moderate correlation may indicate that essential supply disruptions do matter clinically, but their impact is substantially overshadowed by the more critical medication shortages.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGeographical Inequities in a Vast Territory\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe stronger correlation between distance and MAI (r = -0.58) compared to EAI (r = -0.49) indicates that medications are disproportionately vulnerable to geographical challenges in this largest Sudanese state. This pattern likely reflects the additional logistical complexities of medication storage, cold chain requirements, and specialized procurement processes that make remote distribution particularly challenging across Northern State\u0026apos;s 348,765 km\u0026sup2;. These geographical inequities have profound implications for health equity, as patients in remote areas face compounded disadvantages: greater disease burden, fewer healthcare resources, and now documented poorer access to life-saving medications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaintenance Impact and Modifiable Factors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 59% lower downtime in centres with monthly maintenance, combined with the strong correlation between machine downtime and service disruption (r = 0.72), demonstrates that equipment reliability represents a readily modifiable factor directly affecting treatment delivery. In a region where geographical isolation already limits healthcare access, maintaining functional equipment becomes particularly crucial. The maintenance gap identified in this study represents a clear, addressable opportunity for systemic improvement through standardized protocols and adequate resource allocation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntegration with Qualitative Findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe convergence of strong quantitative correlations with consistent qualitative reports from frontline healthcare providers significantly strengthens the validity of our conclusions. The triad of medication shortages, financial constraints, and equipment issues identified through thematic analysis aligns precisely with the quantitative patterns, suggesting that these represent genuine, widely recognized system failures rather than isolated or perceived challenges.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSystemic Implications and Intervention Priorities\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe combined quantitative and qualitative data suggest a clear, prioritized intervention approach for Northern State\u0026apos;s haemodialysis system: first, emergency stabilization of the medication supply chain; second, standardization and funding of equipment maintenance protocols; and third, logistical reforms specifically designed to address the geographical disparities exacerbated by the state\u0026apos;s vast territory.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has several limitations that should be considered when interpreting the findings. The cross-sectional design precludes definitive causal inference, though the strong, consistent associations observed across multiple analysis methods suggest important relationships worthy of intervention. The relatively small number of centres, while representing the complete population of Northern State, limits the potential for more complex multivariate analysis. Some outcome measures, particularly mortality rates, may be influenced by case mix variations and patient characteristics not captured in our centre-level data. Additionally, the use of self-reported data may introduce potential reporting bias, though the high consistency between quantitative measurements and qualitative responses suggests good validity of the reported challenges.\u003c/p\u003e"},{"header":"Conclusions and Recommendations","content":"\u003cp\u003eBased on the compelling evidence of differential impact between essential and medication supply availability on clinical outcomes, we recommend a targeted, prioritized intervention strategy for Northern State\u0026apos;s haemodialysis system, as outlined in Figure 6:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003eImmediate Medication Supply Chain Emergency Response:\u003c/strong\u003e Establish a dedicated medication task force with authority to implement emergency procurement mechanisms, establish strategic buffer stocks, and conduct weekly monitoring of MAI scores as key performance indicators for the renal care system.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMaintenance Protocol Standardization and Funding:\u003c/strong\u003e Implement and fund mandatory monthly preventive maintenance protocols across all centres, using the demonstrated 59% downtime reduction as compelling evidence for resource allocation to maintenance programs.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eGeographically-Targeted Logistics Reform:\u003c/strong\u003e Develop a hub-and-spoke model specifically designed for medication distribution, with enhanced support, more frequent deliveries, and larger buffer stocks for centres located beyond 150 km from the central depot.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDifferentiated Monitoring and Evaluation:\u003c/strong\u003e Establish separate tracking and evaluation systems for EAI and MAI, recognizing that these indices represent distinct supply chain challenges requiring different solutions, success metrics, and intervention approaches.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThese evidence-based interventions, specifically targeted at the identified system failures and adapted to Northern State\u0026apos;s geographical challenges, offer the most promising path toward improving haemodialysis outcomes for the state\u0026apos;s 919 vulnerable patients and similar resource-limited settings facing geographical healthcare delivery challenges.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCKD: Chronic Kidney Disease\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;ESRD: End-Stage Renal Disease\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;HD: Haemodialysis\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;LMICs: Low- and Middle-Income Countries\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;EAI: Essential Availability Index\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;MAI: Medication Availability Index\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;IQR: Interquartile Range\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e:\u0026nbsp;The study was approved by the Research Ethics Committee of Ministry of Health, Northern State. The study was conducted in accordance with the principles of the Declaration of Helsinki. The requirement for individual informed consent was waived by the ethics committee as the study exclusively collected anonymized, aggregate centre-level data without any patient identifiers. All methods were carried out in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e:\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e:\u0026nbsp;The datasets used and analysed during the current study are available from the corresponding author on reasonable request. The structured data collection instrument used in this study is provided as S1 File.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e:\u0026nbsp;The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e:\u0026nbsp;This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e Hamad Alneel Albagir Ahmed Ali: Supervision, Methodology, Formal Analysis, Writing \u0026ndash; Original Draft.\u003cbr\u003e\u0026nbsp;Umama Kamal Mohamed Ahmed: Investigation, Data Curation, Writing \u0026ndash; Review \u0026amp; Editing.\u003cbr\u003e\u0026nbsp;Muhammed Abdelwahed Muhammed Abdelrahman: Formal Analysis, Validation, Writing \u0026ndash; Review \u0026amp; Editing.\u003cbr\u003e\u0026nbsp;Muner Ali Yosif: Data Collection, Interviews.\u003cbr\u003e\u0026nbsp;All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e: The authors gratefully acknowledge the haemodialysis centre staff across Northern State who participated in this study and shared their experiences and insights.\u003c/p\u003e"},{"header":"References","content":"\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eLuyckx VA, Tonelli M, Stanifer JW. The global burden of kidney disease and the sustainable development goals. Bull World Health Organ. 2018; 96:414\u0026ndash;422.\u003c/li\u003e\n \u003cli\u003eNaicker S, Ashuntantang G, Sarfo F, Osafo C, Arogundade F, et al. Outcomes of chronic kidney disease and end-stage kidney disease in sub-Saharan Africa: a systematic review. Kidney Int Rep. 2021; 6:1826\u0026ndash;1839.\u003c/li\u003e\n \u003cli\u003eElamin S, Omer A, et al. Hemodialysis in Sudan: Challenges and Opportunities. Saudi J Kidney Dis Transpl. 2020; 31:1149\u0026ndash;1156.\u003c/li\u003e\n \u003cli\u003eWorkie DL, et al. Treatment access, early mortality, and patient survival in dialysis in developing nations: evidence from Ethiopia. BMC Nephrol. 2022; 23:193.\u003c/li\u003e\n \u003cli\u003eOlowu WA, et al. Bedside rationing and moral distress in nephrologists in sub-Saharan Africa. BMJ Glob Health. 2022; 7:e009134.\u003c/li\u003e\n \u003cli\u003eHalle MP, Ashuntantang G, Kaze FF, et al. Fatal outcomes among patients on maintenance haemodialysis in sub-Saharan Africa: a 10-year audit from the Douala General Hospital in Cameroon. BMC Nephrol. 2016; 17:165.\u003c/li\u003e\n \u003cli\u003eNew Arab. Kidney failure on the rise in Sudan amid war, dire conditions. 2025. [Internet].\u003c/li\u003e\n \u003cli\u003eSudan Events. Ministry of Health: Monthly cost of kidney medications is $2.5 million. 2024. [Internet].\u003c/li\u003e\n \u003cli\u003eCentral Bureau of Statistics (CBS). (2018). the Fifth Sudan Population and Housing Census \u0026mdash; 2008: Priority Results. Republic of Sudan, Ministry of Cabinet Affairs. [Official government census report - primary source for population data].\u003c/li\u003e\n \u003cli\u003eDabanga Radio. Technical problems lead to closure of Northern Sudan dialysis centre. Ed Debba case. [Internet].\u003c/li\u003e\n \u003cli\u003eElamin S, Ahmed MM, et al. Quality of hemodialysis services in Northern Sudan: logistical challenges and patient outcomes. Ren Fail. 2021; 43:1180\u0026ndash;1187.\u003c/li\u003e\n \u003cli\u003eSalih RM, Ahmed MM, Mohammed MA, et al. Health-related quality of life among patients on maintenance hemodialysis in Khartoum State, Sudan. BMC Nephrol. 2020; 21:515.\u003c/li\u003e\n \u003cli\u003eCauses of end-stage renal failure among haemodialysis patients in Khartoum State, Sudan. BMC Res Notes. 2015; 8:1509.\u003c/li\u003e\n \u003cli\u003eIshaq S, Mohamed N, Abdelrahim A, et al. Acute Kidney Injury in Sub-Sahara Africa: a single-center experience from Khartoum, Sudan. Ren Fail. 2018; 40:120\u0026ndash;126.\u003c/li\u003e\n \u003cli\u003eAdeniji OO, Uthman AO, Shittu MO. Global disparities in access and utilization of dialysis\u0026mdash;Africa, the disadvantaged continent. Kidney Int Rep. 2023; 8:235\u0026ndash;244.\u003c/li\u003e\n \u003cli\u003eWorkie DL, et al. Time to death and predictors among patients with CKD on hemodialysis in Addis Ababa, Ethiopia: a retrospective cohort study. BMC Nephrol. 2024; 25:xxx.\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":"Haemodialysis, Supply Chain, Resource-Limited Settings, Sudan, Essential Availability Index, Medication Availability Index, Renal Failure","lastPublishedDoi":"10.21203/rs.3.rs-8031864/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8031864/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Northern State, the largest state in Sudan by area (348,765 km²), hosts the country's second-largest haemodialysis population (919 patients), creating unique logistical challenges for renal care delivery. This study examines the relationship between supply chain reliability, equipment maintenance, and clinical outcomes across all haemodialysis centres in this vast, remote region.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We conducted a complete census of all 13 haemodialysis centres in Northern State, Sudan between October and December 2025. Using structured instruments, we collected comprehensive data on equipment functionality, supply availability, maintenance practices, and clinical outcomes. We computed two validated indices: Essential Availability Index (EAI) and Medication Availability Index (MAI). Statistical analyses included descriptive statistics and Pearson correlations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The 13 centres served 919 patients (341 female, 578 male) across this vast territory. We identified critical supply chain fragmentation: essential supplies showed consistent availability (median EAI: 2.9, IQR: 2.8-3.0) while medications faced severe shortages (median MAI: 1.5, IQR: 1.1-1.9). MAI demonstrated a strong inverse correlation with mortality rates (r = -0.63, p=0.021), whereas EAI showed only weak association (r = -0.32, p=0.286). Machine downtime strongly predicted treatment interruptions (r = 0.72, p=0.006). Geographical remoteness disproportionately affected medication availability (r = -0.58, p=0.039). Only 46% of centres performed monthly preventive maintenance, with these centres demonstrating 59% lower machine downtime ratios than centres with no maintenance (0.09 vs 0.22).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e In Sudan's largest state, medication supply chain failures rather than essential supply shortages are the primary supply-side determinant of patient mortality. The vast geographical expanse exacerbates these challenges. Urgent interventions should prioritize reliable medication supply, standardized maintenance protocols, and geographically-equitable distribution to improve haemodialysis outcomes in this vulnerable population.\u003c/p\u003e","manuscriptTitle":"Supply Chain Determinants of Treatment Interruption and Mortality in Haemodialysis Centres: A Comprehensive Analysis of Sudan's Largest State","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 09:13:04","doi":"10.21203/rs.3.rs-8031864/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-12-15T09:35:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"45832113179176924189107242696632111876","date":"2025-12-15T07:57:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-12T11:29:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-08T18:54:51+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-14T11:31:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-13T08:38:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2025-11-13T08:35:28+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":"6b2d9046-0f8d-48e6-957f-293dc1bb416b","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-22T09:13:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 09:13:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8031864","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8031864","identity":"rs-8031864","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}