Bacterial meningitis (BM) is a public health burden in developing countries, including Central Asia. This disease is characterized by a high mortality rate and serious neurological complications. Delay with the start of adequate therapy is associated with an increase in mortality for patients with acute bacterial meningitis. Cerebrospinal fluid culture, as a gold standard in bacterial meningitis diagnosis, is time‐consuming with modest sensitivity, and this is unsuitable for timely decision‐making. It has been shown that bacterial meningitis differentiation from viral meningitis could be done through different parameters such as clinical signs and symptoms, laboratory values, such as PCR, including blood and cerebrospinal fluid (CSF) analysis. In this study, we proposed the method for distinguishing the bacterial form of meningitis from enteroviral one. The method is based on the machine learning process deriving making decision rules. The proposed fast‐and‐frugal trees (FFTree) decision tree approach showed an ability to determine procalcitonin and C‐reactive protein (CRP) with cut‐off values for distinguishing between bacterial and enteroviral meningitis (EVM) in children. Such a method demonstrated 100% sensitivity, 96% specificity, and 98% accuracy in the differentiation of all cases of bacterial meningitis in this study. These findings and proposed method may be useful for clinicians to facilitate the decision‐making process and optimize the diagnostics of meningitis.

The purpose of this research was to study the particularities of the immune status of workers in the field of chrysotile asbestos production, depending on their work experience and professional risk of being affected by chrysotile dust. The research covered 125 men, who were workers at the only enterprise dealing with the extraction and beneficiation of chrysotile ores in Kazakhstan. Indicants of cell immunity were detected by flow cytometry; IgA, IgM, and IgG were detected by a multiplex immunological assay. It was found that, among workers impacted by chrysotile asbestos for more than 15 years, compared with individuals who were not impacted by asbestos dust, the level of CD3+ T-cells was decreased (t = −8.76, p < 0.001), as well as the number of CD4+ T-cells (U = 1246.0, p < 0.001). Moreover, CD8+ T-cells increased (t = 5.308, p = 0.001), and neutrophil phagocytic activity also increased, by 1.2 times (U = 305.5, p < 0.001). It was found that working under the condition of professional contact with chrysotile asbestos dust modifies the indicants of humoral immunity, IgA, IgM, and IgG, to a lesser extent than those of cellular immunity.

Age-specific mortality forecasting in Kazakhstan plays a crucial role in public health planning and healthcare management. By predicting mortality rates across different age groups, policymakers, healthcare providers, and researchers can make informed decisions that improve health outcomes and allocate resources more effectively. We analyzed Kazakhstan’s annual mortality data from 1991 to 2023. The Lee–Carter model and its extensions were used to predict mortality. But they did not give satisfactory results for predicting mortality. Including external socio-economic factors in the model did not improve the forecasting accuracy. The accuracy of the forecast increased with a separate analysis of the subpopulations of children and adults. This was because, since 1991 in the children subpopulation there has been a pronounced linear downward trend, while in the adult subpopulation the global trend in mortality dynamics is nonlinear. As a result, it is possible to make forecasts for 7 years with a high degree of accuracy (error < 10%) and forecast for the 8th, 9th, and 10th years with a “good” degree of accuracy (error 10–20%). In 2024–2033, a further mortality decline is expected in most age groups. Only in groups over 80 years old is a slight increase in mortality predicted in the coming year, but then a downward trend will be observed again.

Background. It is necessary to monitor the health disorders of healthcare practitioners exposed to a complex of occupational hazards to optimize their activities and substantiation of preventive health measures. The purpose of the study was the hygienic assessment of occupational hazards, analysis of data on self-assessment of the state of health (SASH questionnaire) among healthcare practitioners (MRI diagnostics specialists, ultrasound diagnostics specialists, and ophthalmologists, as a control group) exposed to nonionizing radiation, ultrasound, and noise. Materials and Methods. An analysis of hygienic parameters of the working environment (workplace) and questionnaire data of healthcare practitioners were carried out. The number of illnesses and absences from work, the level of quality of life, the level of depression, and the self-assessment of the state of health were evaluated according to the questionnaire. Results. An analysis of workloads made it possible to rank the severity and intensity of labor. Health disorders (according to the self-assessment of the state of health), the number of absences from work due to illness, an increase in complaints and level of depression, and a decrease in the level of quality of life were corresponded to the severity of labor. It was revealed that healthcare practitioners were exposed to a number of hazards during their work (above the threshold limit value (TLV)), which caused an increase in health disorders. Conclusions. During the organization of workplace ergonomics and implementation of preventive measures directed to maintaining health and early diagnosis of diseases, one should take into account the hygienic indicators of the working environment that exceed TLV, the results of self-assessment of the state of health (complaints) and morbidity of healthcare practitioners, the level of quality of life and the level of depression.

Navigating the labyrinth of Autism diagnosis in Kazakhstan can be an arduous journey for families. The scarcity of specialized professionals, coupled with limited awareness and resources, poses significant hurdles for early identification and intervention. This study aimed to offer a comprehensive examination of caregivers' experiences in navigating the process of diagnosing autism in their children within the context of Kazakhstan.Objective: The overarching aim of this study is to illuminate caregivers' experiences in navigating the diagnostic journey for Autism Spectrum Disorder in Kazakhstan, shedding light on factors influencing parental decision-making and elucidating barriers faced by families seeking diagnostic support for their children.Methods. This study involved a mixed interview of caregivers of children diagnosed with childhood autism, atypical autism, about difficulties in the process from initial concern about the child's development to formal diagnosis.Results. On average, autism concerns emerged at 1.91 years, with specialist contact at 2.19 years. Median diagnosis age was 4.51 years, taking 2.45 years from concern. Comorbidities included Attention deficit hyperactivity disorder (16.98%), psycho-speech delay (15.09%), and mental retardation (9.43%). Satisfaction with diagnosis was low (9.43%), yet 41.5% were content. Caregivers struggled with timely diagnosis, facing awareness and support deficits from specialists and society, along with logistical and psychological challenges.Conclusion.The study sheds light on the challenges faced by caregivers in Kazakhstan during the diagnosis of autism in their children. It highlights the delayed age of diagnosis, the prevalence of comorbidities, and the dissatisfaction with diagnostic assistance, underscoring the urgent need for improved access to timely and effective diagnostic services, as well as enhanced support for caregivers navigating the complexities of autism diagnosis.Keywords: autism spectrum disorder, parents’ experience, Kazakhstan.

The distribution of the health workforce affects the availability of health service delivery to the public. In practice, the demographic and geographic maldistribution of the health workforce is a long-standing national crisis. In this study, we present an approach based on Functional Principal Component Analysis (FPCA) of data to identify patterns in the availability of health workers across different regions of Kazakhstan in order to forecast their needs up to 2033. FPCA was applied to the data to reduce dimensionality and capture common patterns across regions. To evaluate the forecasting performance of the model, we employed rolling origin cross-validation with an expanding window. The resulting scores were forecasted one year ahead using Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) methods. LSTM showed higher accuracy compared to ARIMA. The use of the FPCA method allowed us to identify national and regional trends in the dynamics of the number of doctors. We identified regions with different growth rates, highlighting where the most and least intensive growth is taking place. Based on the FPSA, we have predicted the need for doctors in each region in the period up to 2033. Our results show that the FPCA can serve as a significant tool for analyzing the situation relating to human resources in healthcare and be used for an approximate assessment of future needs for medical personnel.