CURRENT ISSUE

ABSTRACT: Introduction: Thyroid cancer is one of the most common endocrine malignancies. According to global studies, its prevalence has been increasing worldwide and continues to grow. Although there have been global epidemiological studies on thyroid cancer, there is limited data on its epidemiology in Central Asian countries, including Kazakhstan.
Materials and Methods: A retrospective study utilized data from the Unified National Electronic Health System on thyroid cancer patients in Kazakhstan from 2014 to 2021. A descriptive analysis of patients was performed based on key demographic factors. Survival analysis was conducted using the Kaplan-Meier estimator and Cox proportional hazards regression.
Results: In total, 4,877 cases of thyroid cancer have been included during the period from 2014 to 2021 in Kazakhstan. Most of the diagnosed patients throughout the given period were females. The highest incidence and prevalence rates were found in the age group of 51-70 years old, while the highest mortality rate was among patients older than 70 years. Increasing age and male sex were the major predictors of mortality among thyroid cancer patients.
Conclusion: The obtained data coaligned with global data on thyroid cancer. Increased age, male sex, and living area were associated with poor prognosis. The study was limited by missing information on comorbidities and treatment types that the patients could have received. Therefore, further research is needed to assess the impact of such significant factors on survival.

ABSTRACT: Infectious disease surveillance has long been vital in public health, but traditional methods often fall short in detecting emerging threats and understanding pathogen evolution. Recent advances in Next-Generation Sequencing (NGS) have revolutionized genomic surveillance, enabling near real-time monitoring of pathogens at the genetic level. This study explores the integration of real-time genomic surveillance with epidemiological models to enhance disease intervention planning. We examine how combining genomic data with models like Susceptible-Infectious-Recovered (SIR) and Susceptible-Exposed-Infectious-Recovered (SEIR) improves outbreak forecasting, facilitates early detection of new variants, and provides actionable insights for targeted interventions. The integration of NGS data allows for more precise transmission network mapping, better-informed resource allocation, and dynamic policy adjustments. However, challenges persist, including technical limitations, data privacy concerns, and equity in global surveillance capacities. The findings suggest that genomic integration enhances epidemic prediction and response but requires robust policy frameworks, equitable data-sharing practices, and continuous capacity-building efforts in low- and middle-income regions. The future of infectious disease control hinges on advancing technologies like artificial intelligence (AI), cloud computing, and machine learning to improve predictive accuracy and support real-time decision-making. This review underscores the potential of genomic surveillance to transform public health strategies and outlines key steps for effective global collaboration.

ABSTRACT: Antimicrobial resistance (AMR) poses an escalating threat to global public health, undermining the effectiveness of infectious disease prevention and treatment and placing sustained pressure on health systems worldwide. Advances in genomic technologies, including whole-genome sequencing and metagenomic analyses, have substantially enhanced the resolution and scope of AMR surveillance. However, despite growing investments in genomic surveillance, the routine translation of genomic data into public health policy and action remains limited. This review examines the persistent data-to-decision (D2D) gap that constrains the public health impact of genomic AMR surveillance. Using a narrative review approach, the literature on genomic AMR surveillance, public health surveillance systems, and decision-making frameworks was synthesized to assess how genomic data are generated, interpreted, and operationalized within public health systems. The review integrates evidence from international and national surveillance initiatives, policy analyses, and implementation studies, with particular attention to organizational, analytical, and governance factors influencing data use. Findings indicate that while genomic surveillance offers high potential for early detection of resistance, transmission tracking, and proactive intervention, its public health utility is frequently limited by insufficient integration with decision-making structures, lack of standardized reporting and interpretation frameworks, and unclear action thresholds. The review highlights emerging best practices, including standardized translational reporting, decision-support tools, predefined genomic action triggers, and multidisciplinary collaboration, as critical mechanisms for closing the D2D gap. Persistent inequities in access to genomic surveillance capacity, particularly in low- and middle-income countries, further underscore the need for governance models that prioritize sustainability, local ownership, and equitable capacity building. Overall, this review argues that realizing the full public health value of genomic AMR surveillance requires moving beyond technological advancement toward intentional systems-level integration that aligns genomic intelligence with timely, evidence-informed public health decision-making.