Exposure to elevated levels of ambient particulate matter (PM 2.5 ) leads to premature mortality and considerable economic losses.
Background Eastern Europe and Central Asia faces an accelerating HIV epidemic among gay, bisexual, and other men who have sex with men (GBMSM), yet population-level effectiveness data for pre-exposure prophylaxis (PrEP) remain limited. In May 2021, Kazakhstan implemented a national PrEP programme for GBMSM. Methods We conducted interrupted time series (ITS) analysis of national HIV surveillance data from January 2020 through December 2024 (n = 60 months). The primary outcome was monthly HIV test positivity rate among GBMSM at substantial risk. Standard segmented regression was used to test for immediate and slope changes. We also performed counterfactual analysis to test the cumulative effects against projected pre-intervention trends. Sensitivity analyses controlled for testing volume and examined alternative outcomes. Results By December 2024, the programme achieved 20.1% coverage (1,936 of 9,630 GBMSM at substantial risk). Counterfactual analysis demonstrated significant cumulative reduction in monthly HIV test positivity (mean monthly divergence from projected trends: 0.537 percentage points, 95% CI: 0.393–0.681, p < 0.001). Descriptive comparison showed a 25.7% relative reduction from 3.38% to 2.51%. Effects accumulated gradually over the implementation period rather than as an immediate step-change. Findings remained robust after controlling for a 96% increase in testing volume and across alternative outcomes. Assuming 86% real-world effectiveness (from meta-analyses), the programme currently prevents an estimated 146 HIV infections annually, with a number needed to treat of 13. Conclusions Kazakhstan's PrEP programme demonstrates significant, robust population-level impact on HIV test positivity among GBMSM (25.7% reduction, p < 0.001). The programme currently prevents an estimated 146 infections annually (NNT = 13), with potential to prevent 510 infections annually at 70% coverage. This study provides quasi-experimental evidence for PrEP effectiveness in Kazakhstan, demonstrating that biomedical prevention can achieve measurable population-level impact in concentrated epidemics.
Introduction. Acid-base balance disorders are critical conditions in intensive care units requiring rapid and accurate management. The study explores the potential of large language models to serve as accessible clinical decision support systems to reduce iatrogenic errors. Aim. To compare the accuracy of diagnosing acid-base balance disorders and the rationality of therapeutic recommendations proposed by artificial intelligence based on ChatGPT and by intensive care physicians. Materials and Methods. Study design: retrospective, single-center, comparative study. The analysis included 302 clinical and laboratory cases of patients treated in an intensive care unit between 2024 and 2025. Using prompt engineering techniques, an adapted ChatGPT model named “ReanimatorKZ” was developed. A comparative expert evaluation was conducted to assess the conclusions of ChatGPT and intensive care physicians regarding acid–base disorders. Statistical analysis was performed using StatTech v.4.12.7 and SPSS Statistics 27.0.1. Results. In the group of doctors, diagnostic accuracy was 71.2% correct, 24.8% partially correct, and 4.0% incorrect conclusions. The AI demonstrated a lower rate of completely incorrect diagnostic conclusions (2.0%), while completely correct diagnoses accounted for 64.9%. The physicians’ treatment strategies were completely correct in 60.9% of cases, whereas the AI’s recommendations were completely correct in 89.7% of cases, with no completely incorrect therapeutic recommendations classified for the AI. Statistically significant differences were confirmed using paired tests (p < 0.05). Conclusion. An adapted version of ChatGPT demonstrated a high level of diagnostic accuracy in identifying acid–base disorders, comparable to that of intensive care physicians, and superior accuracy in formulating therapeutic recommendations for these conditions. Our study supports the potential for developing effective and readily scalable clinical decision support systems based on widely available artificial intelligence models. However, additional prospective validation is required before such systems can be implemented in routine clinical practice. Keywords: artificial intelligence, intensive care, clinical decision support systems, ChatGPT, diagnostic accuracy.
BackgroundArtificial intelligence (AI) is increasingly integrated into healthcare, yet the attitudes and knowledge of nurses, who are the key mediators of AI implementation, remain underexplored. This study aimed to evaluate the psychometric properties of a previously validated nine-item scale measuring nurses’ knowledge and attitudes toward AI and to describe preliminary findings from primary healthcare centre (PHC) nurses in Almaty, Kazakhstan.MethodsA cross-sectional survey was conducted among 400 nurses from eight randomly selected PHCs in Almaty. The English version of the questionnaire assessing sociodemographic characteristics, knowledge of AI, and attitudes toward AI among nurses was translated and adapted in Kazakh and Russian languages. Exploratory factor analysis (EFA) was performed on 60% of the sample (n = 240) to identify the factor structure, followed by confirmatory factor analysis (CFA) on the remaining 40% (n = 160). Internal consistency, composite reliability, and average variance extracted were calculated to evaluate reliability and convergent validity.ResultsMost participants were female (94%, n = 376), aged 20–39 years (51.5%,n = 206), and held post-secondary medical college education (52.5%,n = 210). About one third of the participants reported having no general awareness of AI, and nearly half (45.8%, n = 183) reported little to no understanding of the use of AI in nursing. EFA supported a two-factor structure “Operational and Workforce Impact” and “Clinical Benefits” explaining 72.7% of the variance. CFA confirmed the model with good model fit indices and high internal consistency (Cronbach's α overall=0.94; subscales 0.92 and 0.89). A substantial proportion of nurses recognized AI's potential to enhance patient care, decision-making, and workflow efficiency, though 38.8% were reluctant to adopt AI personally.ConclusionsThe validated scale demonstrated excellent psychometric properties in Kazakhstani context and has the potential to be used more broadly across the Central Asian region. While nurses exhibited positive perceptions of AI's clinical and operational benefits, gaps in specific knowledge suggest a need for targeted educational interventions.
Family planning (FP) is a major public health intervention that contributes to reducing maternal and infant mortality. In the Democratic Republic of the Congo, the prevalence of modern contraceptive use remains low despite ongoing national efforts. This study aimed to identify factors associated with the use of modern contraceptive methods (MCMs) among women aged 18–49 years in the N’djili Health Zone. A cross-sectional analytical study was conducted from May 1 to June 1, 2025, among 394 women selected using a three-stage probability sampling method. Data were collected through a structured questionnaire. Associations were assessed using chi-square tests and multivariable logistic regression, with statistical significance set at p < 0.05. The prevalence of modern family planning method use was 47%. The main reported reasons for use were avoiding pregnancy (56%) and spacing births (32%). In bivariate analysis, significant associations were observed with health area, occupation, number of children, wealth level, and knowledge of MCMs. After adjustment, only socioeconomic status and technical knowledge remained independent determinants. Women from poor (adjusted odds ratio, aOR = 0.34; p = 0.002) and middle-income households (aOR = 0.52; p = 0.037), as well as those with no knowledge of MCMs (aOR = 0.08; p = 0.001), were significantly less likely to use a modern method. The use of modern contraceptive methods in N’djili remains constrained by economic insecurity and limited knowledge. Strengthening contraceptive education and ensuring equitable access to family planning services are essential to improve coverage and reduce maternal health risks.
