Keyword: Resource-Limited Settings

ABSTRACT: Introduction: Ovarian cancer is a major cause of cancer death in women, mostly due to late detection. Quality control (QC) in molecular genetics laboratories is essential for accurate testing of BRCA1/2 and other mutations. This study evaluated QC practices in molecular genetics laboratories in Kazakhstan conducting ovarian cancer diagnostics in a resource-limited setting and compared them with international standards. Aim: To assess the quality control practices of molecular genetics laboratories involved in ovarian cancer diagnostics in Kazakhstan.
Methods: A descriptive cross-sectional study was conducted among 25 laboratory employees from three molecular genetics laboratories in Almaty, Kazakhstan. The questionnaire assessed internal quality control (IQC), external quality assessment (EQA) participation, SOP compliance, and operational challenges. Data were analysed using SPSS v.28. A systematic literature review based on PRISMA 2020 guidelines was also performed.
Results: Daily use of positive and negative controls was reported by 68% of respondents, while 52% performed daily DNA quality checks and 60% reported full SOP compliance. Equipment calibration was conducted weekly or monthly (44% each) rather than daily. Major challenges included sample contamination (56%), unreliable reagents (48%), and inadequate funding (68%). EQA participation was 76%. Respondents recommended improved training (52%), automation (36%), and better sample handling (32%). The review indicated that daily controls, high-depth NGS, and automation achieved 98–99% accuracy in BRCA1/2 testing.
Discussion: QC practices in Kazakh laboratories are reasonable but reveal gaps in calibration frequency, sample integrity, and resources. Daily calibration, affordable automation, local EQA programs, and staff training could improve diagnostic accuracy in resource-limited settings.

ABSTRACT: The COVID-19 pandemic accelerated the adoption of telemedicine and artificial intelligence (AI), transforming healthcare delivery worldwide. These technologies hold promise for improving access, efficiency, and diagnostic accuracy, but their benefits remain unevenly distributed. In many low- and middle-income countries (LMICs), persistent gaps in infrastructure, affordability, literacy, and governance risk turning digital innovation into a driver of health inequities. This paper examines the digital divide as a multidimensional health determinant encompassing infrastructure, affordability, human capacity, sociocultural inclusion, and governance. Using illustrative case studies from Africa, South Asia, Latin America, and high-income countries, this study highlights how telehealth and AI can enhance accessibility and enable task-shifting, while also demonstrating how exclusionary design and weak systems may perpetuate disparities. Building on these insights, the paper proposes a multi-sector framework for inclusive digital health, integrating investments in infrastructure, affordable and scalable models, digital literacy, culturally sensitive design, governance reform, sustainable financing, and public–private partnerships. To operationalize this framework, we recommend measurable indicators (e.g., affordability thresholds, literacy benchmarks, governance readiness indices) and propose implementation tools, including a logic model and barrier-to-action checklist. We argue that digital equity must be treated not as a peripheral issue but as a moral imperative for global health justice. Achieving this requires embedding equity into design, financing, and governance from the outset so that telehealth and AI reduce, rather than exacerbate, disparities in healthcare.