Patterns and determinants of premature mortality from non-communicable diseases in Aktobe region, Kazakhstan: a...

Non-communicable diseases (NCDs) cause approximately 17 million premature deaths annually and are largely associated with modifiable risk factors.

This sequential explanatory mixed-methods study combined descriptive trend analysis of premature NCD mortality with qualitative inquiry. Quantitative data from the National Scientific Center for Health Development (Aktobe region, 2019-2023) were analyzed using Joinpoint Regression and Statistical Package for the Social Sciences (SPSS) statistical software packages. Premature mortality was defined as death before age 75, and crude (non-age-standardized) rates per 100,000 population were calculated. Trends were summarized using average annual percentage change (AAPC) with 95% confidence intervals (CIs). Semi-structured interviews with 10 healthcare managers explored awareness of premature mortality indicators, perceived risk factors, and the use of data in decision-making.

Premature mortality fluctuated over the study period, peaking in 2021 and declining by 2023 (AAPC -4.9%; 95% CI: -18.8 to 11.3), indicating no statistically significant overall trend. Cardiovascular diseases (CVDs) and cancer were the leading causes, with consistently higher rates among men and urban populations. Statistically significant declines were observed for cancer and diabetes in some subgroups, while other trends, including respiratory mortality, were not statistically significant and showed substantial variability. Qualitative findings highlighted gaps in the use of epidemiological data for strategic decision-making and identified system-level barriers to effective data utilization.

Premature mortality from NCDs remains substantial in the Aktobe region, with persistent gender and urban-rural disparities. While these findings provide context-specific insights for regional health planning, they should be interpreted with caution given the descriptive design, the use of crude rates, and the short observation period. Strengthening data-driven decision-making and preventive strategies may help address observed disparities, but further analytical and longitudinal research is needed to better understand the underlying drivers.