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oai:figshare.com:article/34009314 2026-09-28T05:38:56Z category_427 portal_316 item_type_3 month_year_09_2026

Data Sheet 1_Nursing workforce trends in Kazakhstan, 2003–2024: an interrupted time series and joinpoint regression analysis in the context of educational reform.docx Dinara Ospanova (25119906) Raushan Issayeva (25119909) Lyazzat Alibekova (25119912) Makhigul Maxudova (25119915) Neilya Ussebayeva (25119918) Meruyert Suleimenova (25119921) Public Health and Health Services not elsewhere classified Central Asia educational reform health workforce interrupted time series analysis joinpoint regression Kazakhstan nursing workforce Introduction<p>Kazakhstan introduced the Applied Bachelor of Nursing programme in 2014 as part of a broader effort to upgrade nursing education to degree level. Over the same period, the population grew from 14.9 to 20.0 million and the hospital network contracted by 27%. Whether these simultaneous pressures on workforce supply and demand translated into measurable changes in nursing density has not been examined.</p>Methods<p>We conducted an ecological time series study using administrative data from the Bureau of National Statistics (2003 to 2024). The primary outcome was nursing and midwifery personnel per 10,000 population, a composite indicator of which nurses constitute approximately 77%. We combined interrupted time series analysis (ITSA) with a pre-specified 2014 intervention point and data-driven joinpoint regression to identify structural breakpoints. A generalized least squares (GLS) model with second-order autoregressive AR(2) errors served as the primary specification, supported by six sensitivity analyses, regional analysis, and autoregressive integrated moving average (ARIMA) forecasting to 2035.</p>Results<p>Nursing density rose from 77.4 per 10,000 in 2003 to a peak of 101.2 in 2012, then stabilized between 93.3 and 100.0 through 2024. Joinpoint regression placed the structural breakpoint at approximately 2012 (95% CI: 2010 to 2014), 2 years before the reform. Sensitivity analyses confirmed this was not a denominator artifact. The GLS model identified significant coefficients at 2014 (level change: minus 5.45, p = 0.001; slope change: minus 2.16 per year, p < 0.001), but these reflected the proximity of the cut point to the actual trend reversal rather than a causal effect. The nurse-to-physician ratio declined from 2.62 in 2012 to 2.30 in 2024, below the WHO-recommended 3:1. Regional density ranged from 72.0 to 134.8 per 10,000 in 2024. The forecast projected 111.0 per 10,000 by 2035 (95% prediction interval: 92.6 to 129.4).</p>Conclusions<p>The deceleration of nursing density growth preceded the 2014 reform by 2 years and coincided with the end of post-independence recovery and hospital contraction. Kazakhstan's density exceeds the WHO European Region average, but the nurse-to-physician ratio has been declining and educational output cannot keep pace with population growth. Expanding programme capacity and setting explicit ratio targets are needed.</p> 2026-09-28T05:38:56Z Dataset Dataset 10.3389/fpubh.2026.1913586.s001 https://figshare.com/articles/dataset/Data_Sheet_1_Nursing_workforce_trends_in_Kazakhstan_2003_2024_an_interrupted_time_series_and_joinpoint_regression_analysis_in_the_context_of_educational_reform_docx/34009314 CC BY 4.0