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Background: The US adult heart allocation policy was changed on October 18, 2018. This study aims to evaluate its impact on orthotopic heart transplantation (OHT) for adults with congenital heart disease (ACHD).
Methods: The United Network for Organ Sharing database was used to perform 2 comparisons: waitlist outcomes among listed ACHD candidates, and post-transplant outcomes in those transplanted. Waitlisted candidates were stratified by date of waitlisting: Period 1: 2010 to 2013; Period 2: 2014 to October 17, 2018 and Period 3: October 18, 2018 to March 20, 2020. Transplanted ACHD patients were similarly stratified but by date of transplantation. Competing risk regression for waitlist outcomes was performed. Post-transplant survival was analyzed using the Kaplan-Meier method and multivariable Cox regression.
Results: Nine hundred and seventy-six patients with ACHD were waitlisted for OHT in our study: 343(35.1%), 466(47.8%), and 167(17.1%) in periods 1, 2, and 3. Post-policy change, 1-year cumulative incidence of waitlist mortality or deterioration decreased (p = 0.02). Six hundred and forty-eight patients were transplanted: 221(34.1%), 329(50.8%) and 98(15.1%) respectively. In those transplanted, post-policy median waitlist time (174, 161 and 38 days, p < 0.001) decreased and the use of intra-aortic balloon pumps increased (1.4%, 4.9% and 19.4%, p < 0.001). Compared to periods 1 and 2, risk-adjusted post-transplant 1-year mortality was similar to period 3 (HR 1.10, 95% CI 0.52-2.32; p = 0.81) (HR 1.19, 95% CI 0.58-2.46, p = 0.63).
Conclusions: The recent US allocation policy change may have resulted in reduced waitlist times and 1-year waitlist mortality for OHTs in ACHD patients. Early post-transplant outcomes appear comparable post-policy change.
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http://dx.doi.org/10.1016/j.healun.2021.11.006 | DOI Listing |
Int J Health Care Qual Assur
September 2025
Department of Electrical and Computer Engineering, Graduate University of Advanced Technology, Kerman, Iran.
Purpose: Neonatal mortality is a significant global health issue, particularly in low- and middle-income countries. This study aims to identify and understand the factors contributing to high neonatal mortality rates in the cities of Kerman and Bam, Iran, to develop effective strategies for improvement.
Design/methodology/approach: We employed systems dynamics to develop Causal Loop Diagrams that capture qualitative interactions among determinants of neonatal mortality.
Afr J Prim Health Care Fam Med
September 2025
Department of Physiotherapy, School of Therapeutic Sciences, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg.
Background: The Framework and Strategy for Disability and Rehabilitation (FSDR) in South Africa aims to improve rehabilitation services for individuals with disabilities. However, research related to its implementation process is limited.
Aim: To explore the experiences of the implementation process of FSDR among stakeholders in Gauteng, South Africa.
Afr J Prim Health Care Fam Med
August 2025
Department of Family and Emergency Medicine, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town.
Background: Mental health disorders are increasing globally. In South Africa, primary healthcare (PHC) services are tasked with mental healthcare, with limited resources. A task-sharing approach between PHC role-players has also been met with barriers, including negative attitudes towards mental health care, organisational constraints and insufficiently trained staff.
View Article and Find Full Text PDFHealth Econ Policy Law
September 2025
Department of Health Policy, London School of Economics and Political Science, London, UK.
While a substantial amount of evidence exists on factors associated with positive health technology assessment (HTA) outcomes, the evidence on the same regarding rejections is scarce. Using a proprietary dataset of HTA outcomes in seven Organisation for Economic Co-operation and Development (OECD) countries, we empirically examine the factors associated with HTA rejections and study the magnitude of inter-agency differences in technology appraisals. Data were extracted from HTA reports between 2009 and 2020.
View Article and Find Full Text PDFHealth Econ
September 2025
The CHOICE Institure, School of Pharmacy, University of Washington, Seattle, Washington, USA.
This paper demonstrates how optimal policy learning can inform the targeted allocation of Indonesia's two subsidized health insurance programmes. Using national survey data, we develop policy rules aimed at minimizing "catastrophic health expenditure" among enrollees of APBD or APBN, the two government-funded schemes. Employing a super learner ensemble approach, we use regression and machine learning methods of varying complexity to estimate conditional average treatment effects and construct policy rules to optimize program benefits, both with and without budget constraints.
View Article and Find Full Text PDF