Abstract
Background
Prostate cancer mortality trends and spatial clustering in Kazakhstan over the last two decades require detailed national and regional assessment using time-trend and spatial statistics.
Methods
A nationwide population-based observational study evaluated prostate cancer deaths (ICD-10 C61) among men in Kazakhstan from 2005 to 2024, calculating crude and age-standardized mortality rates, assessing temporal changes using joinpoint regression, and identifying spatial hot spots/cold spots using Getis-Ord Gi* across five predefined periods (2005-2012, 2013-2017, 2018-2019, 2020-2021, 2022-2024).
Results
A total of 7,411 deaths were recorded; the overall crude mortality rate (CMR) was 4.44 per 100,000 men and the age-standardized mortality rate (ASMR) was 6.54 per 100,000 men, with deaths concentrated in ages 65-79 (65-69: 19.2%; 70-74: 22.3%; 75-79: 18.9%) and age-specific mortality peaking at 103.04 per 100,000 in ages 80-84. National joinpoint analysis identified a statistically non-significant, essentially stable trend from 2005 to 2019 (annual percent change [APC] 0.7%, p = 0.397), followed by a statistically significant accelerated decline from 2019 to 2024 (APC =−8.4%, p < 0.001), with an overall average annual percent change (AAPC) of −2.8% (p=0.004) for 2005-2024. Regional ASMR varied substantially (3.1 per 100,000 in Kyzylorda to 10.5 per 100,000 in Almaty city), and mean regional ASMR decreased from 6.57 (2005-2012) to 4.30 (2022-2024) with shifting hot spot/cold spot patterns across periods.
Conclusions
Prostate cancer mortality in Kazakhstan declined significantly over 2005-2024, but substantial and evolving geographic disparities persisted, supporting the need for continued surveillance and targeted regional cancer-control actions.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-026-27549-0.
Keywords: Prostate cancer, Age-standardized mortality rate, Joinpoint regression, Spatial analysis, Hot spot, Cold spot, Kazakhstan
Introduction
Prostate cancer (PC) is the second most frequently diagnosed malignancy and ranks fifth among causes of cancer-related death in men globally [1]. According to Schafer et al., an estimated 1,460,000 new cases and 396,000 deaths were recorded in 2022, and projections indicate that the global burden could rise by 2040 to approximately 2.4 million cases and 712,000 deaths, with the largest projected increases in life-years lost occurring in Africa, Asia, and Latin America/Caribbean [2].
Across European countries, prostate cancer incidence varied widely over time, while mortality was lower and more consistent. Incidence ranged from 46 per 100,000 men (Ukraine) to 336 per 100,000 (France) around the mid-2000s, with up to a 20-fold cross-country difference overall, compared with roughly fivefold for mortality (23.7-35.6 per 100,000 in 1983-2006) [3]. A study showed that prostate cancer mortality declined more in the USA than in Australia from 1992 to 2011 which the USA fell from 30.0 per 100,000 (1992) to 16.5 (2010), while Australia dropped from 34.3 per 100,000 (1994) to 23.5 (2011), corresponding to an approximately 50% vs. 25% reduction [4]. In Italy incidence was estimated to rise sharply from 31 per 100,000 (1984) to 93 per 100,000 (2005) and then decline after 2005, while mortality started decreasing after around 1999 with an estimated reduction of about 2.3% per year and projections to about 12 per 100,000 by 2020, demonstrating a pattern of incidence “peaks” with later mortality improvement [5].
In China, Global Burden of Disease (GBD)-based analysis reported rising incidence (AAPC +2.75%) but declining mortality (AAPC −0.26%) from 1990 to 2017, again showing divergence between incidence and mortality trajectories [6].
Prostate-specific antigen (PSA) testing is repeatedly implicated as a key driver of incidence swings, while mortality responds more slowly and is influenced by both detection and treatment. In New South Wales (Australia), localized-stage incidence patterns “largely mirrored” PSA screening test rates, including a substantial spike in the mid-1990s and a second spike in 2008, followed by a significant decrease in localized-stage incidence from 2008 to 2015 (annual percent change [APC] −6.7; 95% CI [−8.2, −5.1]) alongside a decline in PSA screening test rates from 2008 to 2014 (APC −3.7; 95% CI [−5.1, −2.2]). Over a longer horizon, NSW prostate cancer mortality decreased from 1990 onward, with APC −1.7 from 1990 to 2006 and APC −3.8 from 2006 to 2017 [7].
In California (USA), a large population-based analysis identified 387,636 prostate cancer cases and 58,754 prostate cancer deaths from 2004 to 2021, and found that mortality declined by 2.6% per year from 2004 to 2012 but then plateaued from 2012 to 2021 (APC 0.1; 95%CI [− 0.6, 1.6]). At the same time, the incidence of distant-stage prostate cancer increased by 6.7% per year from 2011 to 2021 (95%CI [6.2, 7.3]) [8].
A recent Iranian study assessed long-term prostate cancer mortality trends using age–period–cohort modeling and joinpoint regression. Jafari and colleagues analyzed Global Burden of Disease (GBD) data on prostate cancer deaths in Iranian men from 1990 to 2021 (grouped in 5-year age bands) and reported that both crude and age-standardized mortality rates increased over time, with average annual percentage changes of 2.254% (95% CI: 2.099%-2.410%; p<0.001) and 0.257% (95%CI: 0.088%–0.428%; p<0.001), respectively. Their APC results also showed a strong age effect, with relative risk rising from 0.033 (95%CI: 0.023-0.046) at ages 20-24 to 16.183 (95%CI: 14.702-17.814) in men older than 95 years, alongside an increasing period effect from 1992 (RR 0.542; 95%CI: 0.516-0.570) to 2021 (RR 1.892; 95%CI: 1.809-1.979) [9].
The Kazakhstan nationwide incidence (2005-2024) study notes that 21,756 incident prostate cancer cases were registered, with a mean age at diagnosis of 69.8 years, and the age-standardized incidence rate increased from 11.9 per 100,000 in 2005 to 20.7 per 100,000 in 2024 (APC +2.6; p=0.002). These trends were non-linear, with four periods identified (2005-2008, 2008-2016, 2016-2020, 2020-2024), which the authors interpret in part through policy and system changes including the PSA screening program period and pandemic-era disruption [10].
Taken together, these international findings suggest that prostate cancer mortality dynamics can reflect complex interactions among screening policies, diagnostic pathways, treatment access, and broader health-system trends [3, 4, 7, 8]. Despite the growing global literature on prostate cancer mortality trends, no study has comprehensively examined long-term temporal trends and spatial clustering of prostate cancer mortality across Kazakhstan’s regions over multiple policy-relevant periods. Understanding both the temporal dynamics and geographic distribution of prostate cancer mortality is essential for evidence-based cancer control planning in Kazakhstan. Accordingly, the present study examines prostate cancer mortality in Kazakhstan over two decades (2005-2024) using age-standardized mortality rates. By applying joinpoint regression to detect temporal changes and conducting geographical hot spot and cold spot analysis to identify spatial clustering, this work aims to provide an integrated national and regional picture of prostate cancer mortality dynamics in Kazakhstan.
Methods
Study design and setting
A nationwide, population-based observational time-trend study was conducted to evaluate prostate cancer mortality in Kazakhstan from 2005 to 2024. The unit of analysis was the male resident population at national and regional levels, with regions defined according to the country’s administrative structure to support longitudinal comparability [10].
Data source and case definition
Mortality data were obtained through formal requests to the Ministry of Healthcare of the Republic of Kazakhstan. Population denominators (mid-year estimates) were obtained from the Bureau of National Statistics, Agency for Strategic Planning and Reforms of the Republic of Kazakhstan (https://stat.gov.kz/en/). Mortality data were analyzed for deaths attributed to prostate cancer. Prostate cancer was identified using the International Classification of Diseases, 10th Revision (ICD-10) site code C61, consistent with population-based prostate cancer registry definitions used in comparable epidemiologic studies [7, 10]. The study assessed both crude mortality rates and age-standardized mortality rates to enable comparison over time independent of demographic aging.
Population denominators
Annual male population denominators (mid-year estimates), stratified by age group and region, were used to compute mortality rates. This approach aligns with standard population-based rate construction in large-scale prostate cancer surveillance studies [3, 10].
Outcomes: crude and age-standardized mortality
Crude prostate cancer mortality rates were calculated as annual deaths per 100,000 men. Age-standardized mortality rates were computed using direct standardization with 5-year age groups and a world standard population approach [11].
Joinpoint regression analysis
Temporal trends in age-standardized mortality were estimated using the Joinpoint Regression Program (version 5.4.0, National Cancer Institute, Bethesda, MD, USA). The primary model was chosen using the data-driven Weighted BIC among candidates with 0-3 joinpoints. Log-transformed rates were modeled [ln(y=xb], assuming uncorrelated errors. The minimum number of observations from either end of the data and between two joinpoints was set at 2. The number of joinpoints allowed ranged from 0 to 3. Model selection was based on the Weighted Bayesian Information Criterion (Weighted BIC). Annual percent change (APC), average annual percent change (AAPC), corresponding 95% confidence intervals, and p-values were estimated; confidence intervals for APC/AAPC were obtained using the Empirical Quantile method with 5001 resamples. AAPC was calculated for the entire study period (2005-2024). Statistical significance was defined as p<0.05 [12].
Geographical hot spot and cold spot analysis
Spatial analysis was employed to analysis hotspots and coldspots of regional age-standardized prostate cancer mortality rate across regions in five periods from 2005 to 2024. Getis-Ord Gi statistics have been used to discover areas with statistically significant hotspots and coldspots age-standardized prostate cancer mortality rates [13, 14]. The Getis-Ord Gi* statistic is a local spatial autocorrelation indicator that evaluates whether the clustering of high or low values around a given spatial unit is statistically significant compared to a random distribution [14]. A statistically significant positive Gi* z-score indicates a hot spot (clustering of high-mortality regions), while a significant negative z-score indicates a cold spot (clustering of low-mortality regions). A reference map showing the 16 administrative regions of Kazakhstan with their corresponding codes is provided in Supplementary Figure S1. The analysis was conducted using ArcGIS 10.8.2 (ESRI, Redlands, CA, USA) with a fixed-distance band conceptualization of spatial relationships. The distance threshold was determined using the incremental spatial autocorrelation tool to identify the distance at which spatial clustering was most pronounced. Results are reported at three confidence levels (90%, 95%, and 99%) to provide a graduated assessment of clustering intensity: 99% confidence identifies the most robust spatial clusters, 95% provides the conventional threshold, and 90% captures emerging or marginal clusters that may warrant attention for public health surveillance purposes. This graduated approach is standard in Getis-Ord Gi* analysis as implemented in ArcGIS and follows the methodology established by Getis and Ord (1992) and the ESRI spatial statistics framework [13, 14]. It should be noted that the Gi* statistic evaluates spatial clustering based on point estimates of regional ASMRs and does not incorporate the uncertainty (standard errors) of individual regional rate estimates. This is a recognized limitation of the method, and results from regions with small populations and wider confidence intervals should be interpreted with appropriate caution.
For conducting geographical hot spot and cold spot analysis, the study period was deliberately stratified into several consecutive intervals in order to account for major healthcare system changes and external factors that could substantially influence prostate cancer mortality trends.
The years 2005-2012 were defined as the pre-screening period, representing the baseline stage prior to the implementation of organized prostate cancer screening. This interval reflects natural mortality patterns in the absence of population-based early detection interventions.
The period 2013-2017 corresponds to the active screening phase, during which an organized, population-based pilot screening program for prostate cancer was officially implemented and operational. The program was launched in 2013 and implemented in 11 of 16 regions, targeting men aged 50-66 years with a 4-year screening interval. Participation was voluntary, with men being informed about the potential advantages and disadvantages of screening. The screening algorithm included total PSA testing, followed by Prostate Health Index (PHI) assessment for PSA values in the 4–10 ng/mL range and transrectal ultrasound (TRUS)-guided biopsy when indicated. Regions not covered by the pilot continued with traditional diagnosis or work-up on clinical presentation rather than organized screening. A standardized national participation-rate estimate for the full 2013-2017 pilot period is not available. This interval was selected to evaluate the potential impact of systematic early detection on prostate cancer mortality dynamics [15, 16].
The organized state-funded PSA pilot was not continued from 2018 onward. The available evidence indicates a combination of factors contributed to cessation: [1] the pilot revealed important implementation and diagnostic limitations, including false-positive and controversial results, pre-analytical problems, incomplete adherence to the reflex PHI/biopsy algorithm, and broader concerns about the specificity and cost-effectiveness of mass PSA screening; and [2] national policy priorities shifted toward other population screening programs, especially breast, cervical, and colorectal cancer, which became the main focus of subsequent cancer-control planning [15–17]. The early post-screening period 2018–2019 was therefore defined as a brief transitional phase to isolate residual screening effects after program cessation and to evaluate the robustness of screening-associated mortality changes before the onset of major external system-level shocks.
The interval 2020-2021 was defined as the COVID-19 pandemic period. This period was analyzed separately due to profound disruptions in healthcare delivery, including reduced access to diagnostic services, delays in cancer treatment, and increased competing mortality, all of which may have affected prostate cancer-specific mortality independently of screening policies.
Finally, the years 2022-2024 were classified as the post-pandemic, non-screening period. This interval represents a stabilized healthcare environment in which prostate cancer mortality trends are unlikely to be directly influenced by either organized screening programs or pandemic-related healthcare system constraints, allowing for the assessment of underlying mortality patterns under routine care conditions.
Ethics considerations
The study relied solely on publicly available administrative records, so no direct contact with participants was needed. Ethical approval was obtained from the Local Ethics Commission of the Central Asian Institute for Medical Research.
Results
Prostate cancer mortality in Kazakhstan 2005-2024
As shown in Table 1, a total of 7,411 prostate cancer deaths were recorded in Kazakhstan during 2005-2024. Mortality was overwhelmingly concentrated in older men, with approximately 60% of all deaths occurring in the 65-79 age range. Age-specific mortality rates increased sharply with advancing age, peaking at over 100 per 100,000 in the 80-84 age group (Table 1). Over the overall period, the crude mortality rate (CMR) was 4.44 (95%CI: 4.22-4.66) per 100,000 men and the age-standardized mortality rate (ASMR) was 6.54 (95%CI: 6.07-7.00) per 100,000 men.
Table 1.
Age-specific mortality rates for prostate cancer in Kazakhstan, 2005-2024
| Age group | Number (%) | Age-specific mortality rate (95% CI*) |
|---|---|---|
| 0-39 | 32 (0.4) | 0.01 (0.00-0.02) |
| 40-44 | 22 (0.3) | 0.20 (0.10-0.30) |
| 45-49 | 63 (0.9) | 0.61 (0.40-0.82) |
| 50-54 | 205 (2.8) | 2.32 (1.82-2.82) |
| 55-59 | 480 (6.5) | 6.60 (5.39-7.82) |
| 60-64 | 927 (12.5) | 18.33 (15.95-20.71) |
| 65-69 | 1423 (19.2) | 38.34 (35.06-41.62) |
| 70-74 | 1656 (22.3) | 64.47 (60.51-68.43) |
| 75-79 | 1397 (18.9) | 90.30 (83.04-97.57) |
| 80-84 | 846 (11.4) | 103.04 (92.41-113.66) |
| 85+ | 373 (5.0) | 99.13 (82.61-115.65) |
| Total | 7411 (100.0) | - |
| CMR** | 4.44 (4.22-4.66) | |
| ASMR*** | 6.54 (6.07-7.00) |
*CI Confidence interval, **CMR Crude mortality rate, ***ASMR Age-standardized mortality rate. Age-specific mortality rates are presented per 100,000 male population with 95% confidence intervals
Age standardized mortality rate trends of prostate cancer by region, 2005-2024
As shown in Table 2, notable regional variation in prostate cancer ASMR was observed during 2005-2024, ranging from 3.1 (95%CI: 2.6-3.6) per 100,000 men in Kyzylorda to 10.5 (95%CI: 9.3-11.8) per 100,000 men in Almaty city. Higher ASMR values were reported in East Kazakhstan (9.5), Astana city (9.5), and Pavlodar (9.3), whereas lower values were observed in Kyzylorda (3.1), Atyrau (3.2), and South Kazakhstan (3.7) per 100,000 men. Over the same period, the mortality-to-incidence (M/I) ratio ranged from 0.23 in Kostanay to 0.50 in South Kazakhstan.
Table 2.
Age-standardized mortality rate trends of prostate cancer by region, 2005-2024
| № | Region | Number of death | ASMR* / per 100,000 men (95% CI***) | ASIR** / per 100,000 men (95% CI***) | M/I**** |
|---|---|---|---|---|---|
| 1 | Kyzylorda | 118 | 3.1 (2.6-3.6) | 7.4 (5.1-9.7) | 0.42 |
| 2 | Atyrau | 90 | 3.2 (2.4-3.9) | 7.6 (6.1-9.0) | 0.42 |
| 3 | South-Kazakhstan | 478 | 3.7 (3.3-4.1) | 7.4 (6.5-8.3) | 0.50 |
| 4 | Aktobe | 183 | 4.0 (3.1-4.9) | 10.2 (8.4-12.0) | 0.39 |
| 5 | Almaty | 576 | 4.3 (4.0-4.6) | 10.3 (8.6-12.0) | 0.42 |
| 6 | Mangystau | 110 | 4.5 (3.5-5.4) | 9.2 (7.6-10.9) | 0.49 |
| 7 | Zhambyl | 302 | 4.7 (4.1-5.4) | 9.6 (8.1-11.1) | 0.49 |
| 8 | West-Kazakhstan | 256 | 5.8 (4.7-6.9) | 18.8 (16.3-21.3) | 0.31 |
| 9 | Kostanay | 482 | 5.9 (5.2-6.5) | 25.7 (22.2-29.3) | 0.23 |
| 10 | Akmola | 413 | 6.5 (5.7-7.4) | 15.4 (13.8-17.0) | 0.42 |
| 11 | North-Kazakhstan | 391 | 6.8 (6.0-7.7) | 21.7 (17.7-25.6) | 0.31 |
| 12 | Karaganda | 776 | 7.2 (6.4-8.1) | 22.4 (18.6-26.2) | 0.32 |
| 13 | Pavlodar | 549 | 9.3 (8.1-10.5) | 23.5 (19.4-27.7) | 0.40 |
| 14 | East-Kazakhstan | 1180 | 9.5 (8.6-10.5) | 25.9 (22.2-29.5) | 0.37 |
| 15 | Astana city | 343 | 9.5 (7.7-11.3) | 23.9 (20.7-27.0) | 0.40 |
| 16 | Almaty city | 1164 | 10.5 (9.3-11.8) | 28.0 (23.9-32.0) | 0.38 |
*ASMR Age standardized mortality rate, **ASIR Age standardized incidence rate, ***CI Confidence interval, ****M/I = Mortality to Incidence
Prostate cancer mortality trend
Trends in the age standardized mortality rate of prostate cancer in Kazakhstan for the period 2005-2024
Figure 1A shows that prostate cancer ASMR in Kazakhstan was statistically stable from 2005 to 2019 (APC −0.7%, 95%CI: −1.7 to 1.8; p=0.397), after which a significant decline was observed during 2019-2024 (APC −8.4%, 95%CI: −20.7 to − 3.9; p<0.001). Overall, the national trend for 2005-2024 was significantly downward (AAPC −2.8%, 95%CI: −4.2 to − 1.6; p=0.004).
Fig. 1.
Trends in the age standardized mortality rate of prostate cancer in Kazakhstan and its regions for the period 2005-2024. A Trends in the age standardized mortality rate of prostate cancer in Kazakhstan for the period 2005-2024. B Trends in the age standardized mortality rate of prostate cancer in the regions for the period 2005-2024
Trends in the age standardized mortality rate of prostate cancer in the regions for the period 2005-2024
Figure 1B shows marked regional heterogeneity in prostate cancer age-standardized mortality trends across Kazakhstan over 2005-2024. In most regions, ASMR demonstrated either a declining or statistically stable long-term pattern, whereas three regions (Aktobe, Almaty city, and Astana) showed significant temporal inflections with one joinpoint each.
Among the regions with 0 joinpoints, statistically significant declines were observed in East Kazakhstan (APC −2.5%, 95%CI: −3.9 to −1.2; p<0.001), Karaganda (APC −3.8%, 95%CI: −5.5 to −1.7; p<0.001), Kostanay (APC −2.9%, 95%CI: −4.8 to −1.0; p=0.004), and Pavlodar (APC −2.9%, 95%CI: −4.6 to −0.1; p=0.036). By contrast, the remaining regions showed no statistically significant long-term change, including West Kazakhstan (APC −2.3%, 95%CI: −6.7 to 0.2; p=0.052), Akmola (APC −0.7%, 95%CI: −3.9 to 2.5; p=0.623), Almaty region (APC −0.8%, 95%CI: −2.1 to 0.5; p=0.241), Atyrau (approximately stable APC −0.03%, 95%CI: −4.8 to 5.9; p=0.884), Zhambyl (APC +1.6%, 95%CI: −1.5 to 3.9; p = 0.392), Kyzylorda (APC −1.7%, 95%CI: −3.4 to 3.5; p=0.967), Mangystau (APC + 1.1%, 95%CI: −6.2 to 5.3; p=0.814), North Kazakhstan (APC −1.0%, 95%CI: −4.3 to 2.5; p=0.545), and South Kazakhstan (APC 0.9%, 95%CI: −3.0 to 1.4; p=0.455).
A different pattern was observed in the three regions with joinpoints. Aktobe Region had 1 joinpoint in 2010: ASMR increased sharply during 2005-2010 (APC +27.7%) but then declined during 2010-2024 (APC −5.2%), although neither segment reached statistical significance individually. The two metropolitan areas each had 1 joinpoint. In Almaty city, the joinpoint was identified in 2019: ASMR was essentially stable during 2005-2019 (APC −0.02%, p>0.05), followed by a sharp and significant decline during 2019-2024 (APC − 15.1%, p0.001). Astana city showed the opposite early pattern, with a significant increase in ASMR during 2005-2017 (APC + 8.4%, p<0.001), followed by a pronounced decline after 2017 (APC −19.3%, 95%CI: −26.5 to −13.9; p<0.001). Despite this initial rise, ASMR in Astana city showed a significant overall decline over 2005-2024 (AAPC −3.3%, 95%CI: −5.6 to −1.0; p=0.008).
Hot spot and cold spot of regional age-standardized prostate cancer mortality in the five different periods
In Table 3, the regional age-standardized prostate cancer mortality rate is summarized across Kazakhstan’s regions for the five predefined periods (2005-2012, 2013-2017, 2018-2019, 2020-2021, and 2022-2024). The regional mean (standard deviation [SD]) was 6.57 (2.75) in 2005-2012 and 6.92 (3.22) in 2013-2017, followed by 5.95 (2.85) in 2018-2019, 5.73 (1.94) in 2020-2021, and 4.30 (1.40) in 2022-2024. The maximum regional value was observed in Almaty city in 2005-2012 (11.2) and in Astana in 2013-2017 (13.5) and 2018-2019 (11.5), whereas East Kazakhstan Region recorded the highest value in 2020-2021 (9.0) and 2022-2024 (7.1). The minimum regional value was observed in Kyzylorda Region in 2005-2012 (3.2), 2013-2017 (3.1), and 2022-2024 (1.9), and in Atyrau Region in 2018-2019 (1.7) and 2020-2021 (2.7).
Table 3.
Regional age-standardized prostate cancer mortality rate across Kazakhstan’s regions for the five periods
| Region | Regional age-standardized prostate cancer mortality rate | |||||
|---|---|---|---|---|---|---|
| 2005-2012 (95% CI) | 2013-2017 (95% CI) | 2018-2019 (95% CI) | 2020-2021 (95% CI) | 2022-2024 (95% CI) | ||
| Pavlodar Region | 10.0 (5.9-14.2) | 11.0 (5.8-16.1) | 8.9 (8.8-9.0) | 7.3 (5.6-8.9) | 5.9 (0.9-11.0) | |
| Jambyl Region | 4.1 (1.1-7.0) | 6.1 (3.5-8.8) | 4.0 (1.0-7.0) | 4.9 (3.5-6.3) | 4.6 (2.2-7.0) | |
| Kostanay Region | 6.6 (3.5-9.8) | 6.0 (3.6-8.4) | 5.6 (4.3-6.9) | 5.1 (4.5-5.7) | 4.2 (1.1-7.3) | |
| Mangystau Region | 4.2 (0.8-9.1) | 6.1 (3.1-9.1) | 3.9 (0.8-8.7) | 3.8 (1.1-6.6) | 3.4 (1.4-5.4) | |
| Karaganda Region | 8.9 (6.7-11.1) | 7.1 (4.5-9.8) | 5.8 (5.0-6.7) | 6.6 (4.1-9.2) | 4.4 (1.5-7.2) | |
| Kyzylorda Region | 3.2 (1.2-5.2) | 3.1 (1.9-4.2) | 3.0 (0.8-5.2) | 4.6 (2.6-6.5) | 1.9 (0.5-4.2) | |
| East Kazakhstan Region | 10.9 (7.0-14.8) | 9.3 (5.3-13.2) | 9.0 (9.0-9.0) | 9.0 (8.4-9.6) | 7.1 (6.4-7.7) | |
| Aktobe Region | 4.4 (0.8-9.6) | 4.5 (1.7-7.3) | 4.8 (4.0-5.5) | 3.1 (0.9-5.3) | 2.4 (1.1-3.8) | |
| Atyrau Region | 3.2 (0.0-6.4) | 4.0 (0.0-7.9) | 1.7 (0.4-3.8) | 2.7 (0.9-4.4) | 3.0 (0.5-5.5) | |
| South Kazakhstan Region | 3.9 (2.3-5.5) | 3.9 (2.3-5.5) | 3.2 (1.2-5.2) | 4.4 (3.2-5.6) | 3.0 (0.5-5.6) | |
| Akmola Region | 6.5 (3.8-9.2) | 6.7 (0.9-12.5) | 8.4 (7.7-9.1) | 7.0 (4.9-9.1) | 4.7 (3.0-6.3) | |
| Almaty Region | 4.7 (2.9-6.4) | 3.9 (2.9-4.9) | 4.5 (4.5-4.5) | 4.7 (4.1-5.2) | 3.8 (3.1-4.5) | |
| North Kazakhstan Region | 7.1 (3.3-10.8) | 7.9 (3.4-12.5) | 5.2 (4.6-5.8) | 7.2 (5.4-8.9) | 5.3 (3.7-6.9) | |
| Almaty city | 11.2 (9.1-13.3) | 12.8 (6.7-19.0) | 10.4 (10.3-10.4) | 8.9 (5.7-12.0) | 6.3 (3.3-9.3) | |
| West Kazakhstan Region | 7.3 (0.9-13.7) | 4.8 (2.7-6.9) | 5.3 (4.5-6.2) | 4.9 (3.7-6.1) | 4.4 (1.7-7.1) | |
| Astana city | 8.9 (2.7-15.2) | 13.5 (5.8-21.2) | 11.5 (9.6-13.3) | 7.4 (6.2-8.7) | 4.4 (2.9-5.8) | |
Hot spot and cold spot of regional age-standardized prostate cancer mortality rate, 2005-2012
As shown in Fig. 2A, one 90% hot spot was identified in Astana (code 15).
Fig. 2.
Hot spot and cold spot of regional age-standardized prostate cancer mortality rate in the five periods. A Hot Spot and Cold Spot of Regional Age-Standardized Prostate cancer Mortality in 2005-2012. B Hot Spot and Cold Spot of Regional Age-Standardized Prostate cancer Mortality in 2013-2017. C Hot Spot and Cold Spot of Regional Age-Standardized Prostate cancer Mortality in 2018-2019. D Hot Spot and Cold Spot of Regional Age-Standardized Prostate cancer Mortality in 2020-2021. E Hot Spot and Cold Spot of Regional Age-Standardized Prostate cancer Mortality in 2022-2024. 0- Pavlodar Region, 1- Jambyl Region, 2- Kostanay Region, 3- Mangystau Region, 4- Karaganda Region, 5- Kyzylorda Region, 6- East Kazakhstan Region, 7- Aktobe Region, 8- Atyrau Region, 9- South Kazakhstan Region, 10- Akmola Region, 11- Almaty Region, 12- North Kazakhstan Region, 13- Almaty, 14- West Kazakhstan Region, and 15- Astana
Figure 2A also shows cold spots in South Kazakhstan Region (code 9) at 95% confidence and Kyzylorda Region (code 5) at 90% confidence, while all remaining regions were not statistically significant (n = 13).
Hot spot and cold spot of regional age-standardized prostate cancer mortality rate, 2013-2017
As shown in Fig. 2B, hot spots were detected in Akmola Region (code 10) and Astana (code 15) at 90% confidence and in Pavlodar Region (code 0) at 95% confidence. In Fig. 6B, no statistically significant cold spots were observed (0 at 90%, 95%, or 99% confidence), and the remaining regions were not statistically significant (n=13).
Hot spot and cold spot of regional age-standardized prostate cancer mortality rate, 2018-2019
As shown in Fig. 2C, hot spots were identified in Akmola Region (code 10) and Astana (code 15) at 90% confidence and in Pavlodar Region (code 0) at 95% confidence. Figure 6C further indicates cold spots at 90% confidence in Mangystau Region (code 3) and South Kazakhstan Region (code 9), whereas the remaining regions were not statistically significant (n=11).
Hot spot and cold spot of regional age-standardized prostate cancer mortality rate, 2020-2021
As shown in Fig. 2D, hot spots at 90% confidence were observed in Akmola Region (code 10) and Astana (code 15). In Fig. 2D, cold spots were detected in Aktobe Region (code 7) and Atyrau Region (code 8) at 95% confidence and in Mangystau Region (code 3) at 90% confidence; all other regions were not statistically significant (n=11).
Hot spot and cold spot of regional age-standardized prostate cancer mortality rate, 2022-2024
As shown in Fig. 2E, a 90% hot spot was detected in Almaty Region (code 11), while Almaty city (code 13) was not statistically significant. Figure 2E also shows cold spots in Kyzylorda Region (code 5) at 95% confidence and in Aktobe Region (code 7) and Atyrau Region (code 8) at 90% confidence, with the remaining regions not statistically significant (n=12).
The comparison between the periods
As shown in Fig. 2A, significant clustering was limited and included a 90% hot spot in Astana (code 15) and cold spots in South Kazakhstan Region (code 9; 95%) and Kyzylorda Region (code 5; 90%). In comparison, Fig. 2B shows a north-central/northeastern hot spot configuration (Akmola Region [10] and Astana [15] at 90%, and Pavlodar Region (0) at 95%), with no statistically significant cold spots (and 13 not significant regions). From 2013 to 2017 to 2018–2019, the mean decreased from 6.92 to 5.95. The maximum value decreased from 13.5 (Astana) to 11.5 (Astana), and the minimum shifted from Kyzylorda Region (3.1) to Atyrau Region (1.7). Comparing the maps, Fig. 2C retains the same hot spot set as Fig. 2B (Akmola [10] and Astana [15] at 90%, Pavlodar (0) at 95%) and additionally shows cold spots at 90% confidence in Mangystau Region [3] and South Kazakhstan Region [9]. Between 2018 and 2019 and 2020–2021, the mean decreased from 5.95 to 5.73. The maximum shifted from Astana (11.5) in 2018-2019 to East Kazakhstan Region (9.0) in 2020–2021, while the minimum remained in Atyrau Region (1.7 to 2.7). In the spatial analysis, Fig. 2D shows hot spots at 90% confidence in Akmola [10] and Astana [15], while the cold spots shift to western/southwestern regions: Aktobe [7] and Atyrau [8] at 95% confidence and Mangystau [3] at 90% confidence (11 not significant regions).
From 2020 to 2021 to 2022-2024, the mean declined from 5.73 to 4.30.
The maximum remained in East Kazakhstan Region but decreased from 9.0 to 7.1, and the minimum returned to Kyzylorda Region (1.9). As shown in Fig. 2E, a 90% hot spot was detected in Almaty Region [11], and cold spots were identified in Kyzylorda [5] at 95% confidence and in Aktobe [7] and Atyrau [8] at 90% confidence, with 12 regions not statistically significant. The summary of geographical variation of hot spot and cold spot in the five periods has been provided in the Table 4.
Table 4.
The summary of geographical variation of hot spot and cold spot in the five periods
| Period (Figure) | Mean across regions | Min (region) | Max (region) | Hot spots (confidence; regions/codes) | Cold spots (confidence; regions/codes) | Not significant (n) |
|---|---|---|---|---|---|---|
| 2005-2012 (Fig. 2A) | 6.57 | 3.2 (Kyzylorda Region) | 11.2 (Almaty) | 90%: Astana (15) | 95%: South Kazakhstan (9); 90%: Kyzylorda (5) | 13 |
| 2013-2017 (Fig. 2B) | 6.92 | 3.1 (Kyzylorda Region) | 13.5 (Astana) | 95%: Pavlodar (0); 90%: Akmola (10), Astana (15) | None | 13 |
| 2018-2019 (Fig.2C) | 5.95 | 1.7 (Atyrau) | 11.5 (Astana) | 95%: Pavlodar (0); 90%: Akmola (10), Astana (15) | 90%: Mangystau (3), South Kazakhstan (9) | 11 |
| 2020-2021 (Fig. 2D) | 5.73 | 2.7 (Atyrau) | 9.0 (East Kazakhstan) | 90%: Akmola (10), Astana (15) | 95%: Aktobe (7), Atyrau (8); 90%: Mangystau (3) | 11 |
| 2022-2024 (Fig. 2E) | 4.30 | 1.9 (Kyzylorda) | 7.1 (East Kazakhstan) | 90%: Almaty Region (11) | 95%: Kyzylorda (5); 90%: Aktobe (7), Atyrau (8) | 12 |
Discussion
Summary of key findings
During 2005-2024, a total of 7,411 prostate cancer deaths were registered in Kazakhstan, with mortality overwhelmingly concentrated in older men. The largest proportions of deaths occurred in the 70-74 (22.3%), 65-69 (19.2%), and 75-79 (18.9%) age groups, together accounting for approximately 60% of all deaths. Age-specific mortality rates increased sharply with advancing age, peaking at over 100 per 100,000 at ages 80-84. Over the full period, the CMR was 4.44 per 100,000 men and the ASMR was 6.54 per 100,000, indicating a moderate national burden after adjusting for population age structure.
Interpretation of temporal trends
Joinpoint analysis identified a two-phase temporal pattern: a statistically non-significant, essentially stable trend from 2005 to 2019 (APC=0.7%, 95% CI: −1.7 to 1.8; p=0.397), followed by a statistically significant accelerated decline from 2019 to 2024 (APC=−8.4%, 95%CI: −20.7 to −3.9; p<0.001), yielding an overall AAPC of −2.8% (95%CI: −4.2 to −1.6; p=0.004). The magnitude of decline in the later segment is notably larger than the post - 2001 mortality decline reported in Canada (−2.9% per year) [18], suggesting that the recent improvement in Kazakhstan is comparatively rapid. However, the accelerated decline resembles patterns seen in Lithuania, where prostate cancer mortality declined from 2007 onward by −1.7% annually [19]. In contrast, some countries have experienced increasing mortality, such as Serbia (AAPC +2.2%, 1991-2010) [20], highlighting that declining mortality is not universal.
International comparisons
In global GBD-based estimates, the 2021 worldwide ASMR was reported as 5.26 per 100,000, which places Kazakhstan’s 2005-2024 average ASMR (6.54) slightly above the 2021 global level reported in that analysis [21]. At the same time, Kazakhstan’s ASMR is far lower than the very high standardized mortality rates reported for Brazilian men aged ≥40 years (about 75 per 100,000 in both 1990 and 2019), underscoring the wide cross-country variation that arises from different denominators and age structures used in reporting [22].
While the temporal coincidence of the 2019 joinpoint with the post-screening period is noteworthy, this ecological study cannot establish a causal relationship between screening cessation and the observed mortality changes. Multiple concurrent factors may have contributed to the accelerated decline after 2019, including: (a) delayed effects of the 2013-2017 PSA screening pilot, as cancers detected during screening may have been treated at earlier stages, with mortality benefits manifesting with a lag of several years; (b) improvements in treatment access and oncological care capacity during this period; (c) evolving clinical practice aligned with international guideline changes; and (d) for 2020-2021, pandemic-related disruptions that may have affected both cancer diagnosis and death certification. The joinpoint identifies a change in trend direction but does not attribute causation. Continued monitoring beyond 2024 is needed to confirm whether this accelerated decline represents a sustained trend or is partly an artifact of the short post - 2019 observation period (~ 5 years). Caution is warranted: this terminal segment is relatively short for joinpoint regression, and potential contributing factors beyond genuine mortality reduction include statistical instability and pandemic-related healthcare disruptions during 2020-2021.
Regional disparities and spatial patterns
Regional inequality within Kazakhstan was pronounced, with ASMR ranging from 3.1 per 100,000 in Kyzylorda to 10.5 per 100,000 in Almaty city (more than a threefold difference). The M/I ratio varied from 0.23 (Kostanay) to 0.51 (South Kazakhstan), which may indicate differences in stage at diagnosis, data completeness, and/or treatment effectiveness across regions. Comparable within-country disparities are documented in Ecuador [23] and in Canada, where socioeconomic gradients in prostate cancer mortality persisted even as national mortality declined [24]. Regions with higher mortality (e.g., Almaty city, East Kazakhstan, Astana, Pavlodar) tend to be more urbanized with older population structures and potentially greater diagnostic capacity, which may lead to better case ascertainment rather than truly elevated underlying risk. Conversely, lower mortality in southern and western regions may partly reflect underdiagnosis or underreporting in areas with less developed healthcare infrastructure. Furthermore, several regional APC estimates are very large in magnitude (e.g., Aktobe 2005-2010: APC+27.7%; Astana 2017-2024: APC=-−19.3%). These extreme values likely reflect small numbers of annual deaths, highly unstable year-to-year rates, and potential administrative factors (e.g., boundary changes, population estimate revisions, and improvements in diagnostic infrastructure) rather than true epidemiological shifts of this magnitude. These regional estimates should be interpreted with caution, particularly for regions with small populations and few annual prostate cancer deaths.
Interpretation of spatial patterns and contextual factors
The Getis-Ord Gi* analysis revealed that spatial clustering of prostate cancer mortality in Kazakhstan was dynamic across the five policy-relevant periods, with both hot spots and cold spots shifting over time (Table 4; Fig. 2A and E). Mean regional ASMR initially increased from 6.57 (SD 2.75) in 2005-2012 to 6.92 (SD 3.22) in 2013-2017, then declined progressively to 5.95 (SD 2.85) in 2018-2019, 5.73 (SD 1.94) in 2020-2021, and 4.30 (SD 1.40) in 2022-2024. The concurrent decrease in inter-regional standard deviation suggests convergence toward lower mortality rates across all regions in the most recent period. In the earliest period (2005-2012), hot spot clustering was limited to a single 90% hot spot in Astana, with cold spots identified in southern regions (South Kazakhstan at 95%, Kyzylorda at 90%). During 2013-2017 and 2018-2019, the hot spot configuration expanded to a north-central/northeastern cluster encompassing Pavlodar (95%), Akmola (90%), and Astana (90%), while cold spots appeared in Mangystau and South Kazakhstan at 90% confidence in 2018-2019. By 2020-2021, hot spots persisted in Akmola and Astana (90%), and cold spots shifted to the western regions (Aktobe and Atyrau at 95%, Mangystau at 90%). In the most recent period (2022-2024), a notable transition occurred: the hot spot shifted from the north-central cluster to Almaty Region (90%), while cold spots consolidated in the western belt (Kyzylorda at 95%, Aktobe and Atyrau at 90%). This dynamic pattern implies that high-burden clustering is not fixed but evolves over time, potentially reflecting differential impacts of screening cessation, changing healthcare capacity, and demographic shifts across regions. Persistent hot spot regions (Astana, Pavlodar, Akmola) share common characteristics: higher urbanization, relatively older population age structures, greater diagnostic and oncological capacity, and potentially better case ascertainment - all of which may inflate recorded mortality relative to regions with weaker healthcare infrastructure. Conversely, recurrent cold spots in Kyzylorda and western/southwestern regions may reflect genuinely lower underlying risk, but could also result from underdiagnosis or underreporting in areas with less developed diagnostic services. Given the limited number of spatial units (n=16 regions), the Gi* results should be interpreted as exploratory indicators of potential spatial clustering rather than definitive evidence of statistically robust hot spots or cold spots. The statistical power of local indicators of spatial association is inherently limited with small numbers of spatial units, and results may differ with alternative spatial weight definitions or period boundaries. The COVID-19 pandemic period (2020-2021) may have affected prostate cancer mortality through multiple pathways: delayed diagnosis due to reduced healthcare access, deferred treatment, competing causes of death, and disrupted vital registration systems, making it difficult to isolate pandemic-specific effects on mortality trends [25–27]. The shift in cold spots toward western regions during this period could partly reflect pandemic-related disruptions in death certification or diagnostic activity. It should also be acknowledged that the five analysis periods are of unequal length (8, 5, 2, 2, and 3 years, respectively), which may influence the stability and comparability of period-specific ASMR estimates; shorter periods (2018-2019 and 2020-2021) are inherently more susceptible to year-to-year variability, and their hot spot/cold spot designations should be interpreted with particular caution. Regarding targeted screening, the evidence on PSA screening remains mixed: while the European Randomized Study of Screening for Prostate Cancer (ERSPC) demonstrated a mortality reduction of approximately 20% with screening over 13 years, this benefit must be weighed against overdiagnosis and overtreatment risks [28]. If PSA screening were reconsidered in Kazakhstan, a targeted approach focused on persistent high-burden regions (e.g., north-central Kazakhstan) and specific age groups (e.g., men aged 55-69) with shared decision-making frameworks may offer a more favorable benefit-harm balance than universal screening. Such a program should incorporate lessons from the 2013-2017 pilot, including improved algorithms to reduce false-positive rates and strategies to enhance regional participation.
Strengths and limitations
A key strength of this study is its nationwide, population-based design spanning two decades (2005-2024), which provides a comprehensive assessment of prostate cancer mortality patterns at both national and regional levels. The use of ICD-10 code C61 for case definition and the presentation of both crude and age-standardized mortality rates improve comparability over time by accounting for demographic ageing. Methodologically, applying joinpoint regression enabled detection of statistically meaningful changes in temporal trends (including identification of an inflection point), rather than assuming a single constant trend across the full period. In addition, the integration of spatial hot spot/cold spot analysis (Getis-Ord Gi*) across predefined policy-relevant periods (pre-screening, screening, post-screening transition, pandemic, and post-pandemic) adds value by highlighting evolving geographic clustering and supporting more targeted public health actions. Finally, reliance on publicly available administrative mortality records allowed national coverage without participant recruitment, which reduces selection bias related to non-response and supports reproducibility.
Several limitations should be considered when interpreting these findings. First, the study is ecological and observational, so it cannot establish causality between changes in mortality and specific drivers such as PSA screening, diagnostic intensity, or treatment improvements, because individual-level exposures and clinical pathways were not available. Second, cause-of-death coding in administrative records may be subject to misclassification or temporal changes in certification practices. Over the 20-year study period, Kazakhstan’s vital registration system has undergone improvements, and earlier years may have higher rates of ill-defined or misattributed causes of death; this could influence both the absolute level and temporal trends of prostate cancer mortality, particularly in regions with less developed healthcare infrastructure. Third, the analysis did not include stage at diagnosis, tumor aggressiveness, treatment uptake, or survival outcomes, limiting the ability to explain the mechanisms underlying regional differences in ASMR, M/I variation, or the post-joinpoint decline. Fourth, the Getis-Ord Gi* spatial analysis was conducted on only 16 administrative regions, which limits the statistical power of local indicators of spatial association; results should be interpreted as exploratory indicators of potential clustering rather than definitive evidence. The Gi* statistic operates on point estimates and does not incorporate the uncertainty of individual regional rate estimates, and results may differ with alternative spatial weight definitions. Fifth, joinpoint regression was applied independently to each of the 16 regions without adjustment for multiple comparisons; the risk of false-positive joinpoints increases with the number of models tested, and some identified joinpoints or extreme APC values may represent chance findings. Sixth, spatial analyses are sensitive to regional aggregation and administrative boundary definitions, and within-region heterogeneity may be masked (modifiable areal unit problem), while period stratification reduces the number of years per interval and increases uncertainty in period-specific averages. The unequal length of time intervals was driven by policy-relevant considerations but may introduce variability in rate stability, particularly in shorter periods. Finally, because age-standardized and crude rates depend on population denominators, any inaccuracy in age- and region-specific population estimates could affect calculated mortality rates.
Conclusion
Between 2005 and 2024, Kazakhstan experienced an overall significant decline in prostate cancer mortality (AAPC−2.8%, 95%CI: −4.2 to −1.6; p=0.004), characterized by a joinpoint in 2019 and a subsequent accelerated reduction (APC=−8.4%, 95%CI: −20.7 to −3.9; p<0.001). Mortality was concentrated in older men, with the highest age-specific rates in the 75-84 year age range, indicating that prevention, timely diagnosis, and optimized treatment in older age groups remain central to mortality reduction. Large and shifting regional disparities (ASMR ranging from 3.1 to 10.5 per 100,000) and evolving hot spot patterns indicate that geographically targeted interventions are essential to sustain national progress and reduce inequities. These findings have several implications for Kazakhstan’s cancer control strategy: strengthening cancer registry completeness and mortality coding across all regions; ensuring equitable access to diagnostic and treatment services, particularly in regions with persistently high mortality; careful evaluation of targeted PSA screening in identified hotspot regions, balancing mortality reduction benefits against overdiagnosis risks with shared decision-making approaches; and continued long-term regional surveillance to confirm whether the post - 2019 decline is sustained. These findings may also be relevant for other Central Asian countries with similar health system contexts and evolving cancer screening policies. Future research should include individual-level studies to examine the effect of PSA screening exposure on mortality, survival analyses incorporating stage at diagnosis and treatment data, investigation of healthcare access disparities contributing to regional mortality differences, and continued spatial surveillance using updated data to monitor geographic clustering patterns.
Supplementary information
Acknowledgements
The authors express their sincere gratitude to the Ministry of Healthcare of the Republic of Kazakhstan for providing the data essential for this research. Selected findings from this study form the basis of the Candidate of Medical Sciences dissertation of Nariman Yermek, being conducted within the postgraduate program of the I.K. Akhunbaev Kyrgyz State Medical Academy.
Authors’ contributions
GI: Data curation, Methodology, Writing – original draft, Validation. NI: Funding acquisition, Writing – review & editing, Project administration, Supervision. NY: Investigation, Conceptualization, Writing – review &editing. IK: Writing – review & editing, Project administration, Supervision. AH: Formal analysis, Writing – review & editing, Writing – original draft, Conceptualization. ZT: Investigation, Data curation, Writing – review & editing. DT: Validation, Methodology, Writing – original draft. SD: Writing – review & editing, Formal analysis, Methodology. DB: Formal analysis, Writing – review & editing. ZB: Funding acquisition, Writing – review & editing, Project administration, Supervision.
Funding
The author(s) declare financial support was received for the research and/or publication of this article. This work was supported by the Ministry of Science and Higher Education of the Republic of Kazakhstan (project No. AP23484167: “The burden of prostate cancer in Kazakhstan: assessment of the current situation, the role of determinants and innovative ways to solve it”).
Data availability
The datasets analyzed in this study were obtained through formal requests to the relevant authorized institutions of the Republic of Kazakhstan. Population data were extracted from publicly accessible official statistics published by the Bureau of National statistics Agency for Strategic planning and reforms of the Republic of Kazakhstan ( https://stat.gov.kz/en/ ). Data may be obtained from the corresponding authorities upon reasonable request.
Declarations
Ethics approval and consent to participate
This study relied solely on publicly available administrative records and involved no direct contact with participants; ethical approval was obtained from the Local Ethics Commission of the Central Asian Institute for Medical Research.
Competing interests
The authors declare no competing interests.
Consent for publication
Not applicable.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets analyzed in this study were obtained through formal requests to the relevant authorized institutions of the Republic of Kazakhstan. Population data were extracted from publicly accessible official statistics published by the Bureau of National statistics Agency for Strategic planning and reforms of the Republic of Kazakhstan ( https://stat.gov.kz/en/ ). Data may be obtained from the corresponding authorities upon reasonable request.