Frontiers in public health · 2026
Nursing workforce trends in Kazakhstan, 2003-2024: an interrupted time series and joinpoint regression analysis in the context of educational reform.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Ospanova Dinara, Issayeva Raushan, Alibekova Lyazzat, Maxudova Makhigul, Ussebayeva Neilya, Suleimenova Meruyert
Аннотация
INTRODUCTION: 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.
METHODS: 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.
RESULTS: 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).
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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).
CONCLUSIONS: 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.