Cost Effectiveness and Resource Allocation最新文献

Background: Diabetes mellitus is highly prevalent in Sudan and requires long-term management, placing a considerable demand on healthcare resources. This study estimates the direct and indirect costs of managing diabetes mellitus in children in Khartoum State, Sudan, and identifies factors influencing these costs.

Methods: A cross-sectional study was conducted with 138 children with diabetes across three public diabetes clinics in Khartoum State. Data were collected through face-to-face interviews with caregivers via a standardized questionnaire adapted from the KNCV Tuberculosis Foundation. Direct costs included laboratory tests, medications, provider fees, food, transportation during diagnosis, follow-up visits, and hospitalizations. Indirect costs were estimated through productivity loss, whereas intangible costs were measured via the willingness-to-pay method. Regression analysis in SPSS was employed to identify factors affecting total costs.

Results: The median annual cost of managing diabetes was 314 USD, with direct costs accounting for 74% of this total. Major cost drivers included food, lab tests, medications, and hospitalizations, with families spending 41% of their household income on diabetes care. Regression analysis indicated that male sex, combined therapy, and recent hospitalizations were associated with higher costs, whereas longer disease duration was associated with reduced costs.

Conclusion: Diabetes imposes a substantial financial burden on families in Khartoum, consuming a significant portion of household income and straining resources. Targeted policy interventions are essential to improve healthcare access, expand insurance coverage, and provide financial support for families managing diabetes in resource-limited settings.

Integrating artificial intelligence (AI) into maternal and neonatal health (MNH) offers significant opportunities for enhancing patient care through advanced predictive modeling, early disease diagnosis, and ongoing monitoring of conditions such as preeclampsia or gestational diabetes. However, significant challenges in economic valuation persist, including data scarcity, complexity, and the nascent stage of AI implementation in clinical practice. There has been no consolidated empirical proof directly justifying widescale AI application in MNH so far, despite its potentially significant economic benefits and direct cost savings. This review demonstrates that AI systems can mitigate adverse drug reactions (ADRs) and enhance the operational efficiency of organizations. As the full economic potential has yet to be understood and quantified, this review examines several existing economic evaluation frameworks: Cost-Effectiveness Analysis (CEA), Cost-Utility Analysis (CUA), Cost-Benefit Analysis (CBA), and Budget Impact Analysis (BIA). A crucial gap exists between rapid technological advancements and robust economic evaluations, further compounded by a lack of standardized reporting frameworks that hinder the synthesis of available evidence. In addition, the review addresses key challenges, including how they affect the healthcare workforce and the economic impact of systemic errors and security breaches, and then discusses the clinical and liability risks posed by "black box" models. Furthermore, the frequent updates essential for the clinical efficacy and safety of AI tools in MNH are often tied to subscription-based models, creating significant financial strain, particularly in low and middle-income-countries (LMICs). To bridge this crucial research gap and the absence of uniform reporting, this paper proposes the AI-MNH economic evaluation lifecycle and a tailored CHEERS checklist. This multi-phase framework is designed to guide comprehensive, long-term economic evaluations and the adoption of a consolidated, standardized approach to support evidence-based policymaking and sustainable resource allocation.

Introduction: Clinical evidence indicates that atraumatic needles (ATNs) versus conventional needles (CNs) reduce diagnostic lumbar puncture (DLP) complications. Despite this, the use of CNs in DLP remains widespread. This analysis estimates the cost-effectiveness of ATNs versus CNs in DLP.

Methods: We constructed a model mapping DLP patient pathways and complications (limited to PDPH events and PDPH-related hospitalisations/epidural blood patches (EBP)). Model development was carried out in consultation with local clinical experts. Published data informed clinical data inputs (DLP characteristics and likelihood of PDPH) and resource estimates. Costs of PDPH management were estimated from UK NHS Reference Costs. Costs of LP were limited to needle costs. Model outputs included total PDPH, total costs, cost per PDPH avoided and numbers need to treat (NNT) to avoid one case of PDPH. Extensive one-way sensitivity analyses were conducted.

Results: Based on 100 patients undergoing DLP with CN (ATN), we estimated 31 (12) cases of PDPH with 7 (3) patients requiring EBP with total costs estimated at £9,469 (£4,257) i.e. 19 fewer cases of PDPH with ATN at a cost saving of £5,212. NNT to avoid one case of PDPH (hospitalised PDPH) was estimated at 5 (13). Clinical benefits and cost savings were robust to plausible input changes.

Discussion and conclusion: Our model findings support an economic case for use of ATN in preference to CN in DLP, with improved outcomes achieved at a cost saving. Local data collection is recommended but is not expected to change the model findings.