School of Health Sciences, Massey University, Wellington, New Zealand.
School of Health, Victoria University of Wellington, Easterfield Building on Kelburn Parade, Wellington, 6012, New Zealand.
Qual Life Res. 2021 Aug;30(8):2161-2170. doi: 10.1007/s11136-021-02827-z. Epub 2021 Apr 11.
Maintaining or improving quality of life (QoL) in later life has become a major policy objective. Yet we currently know little about how QoL develops at older ages. The few studies that have modelled QoL change across time for older adults have used 'averaged' trajectories. However, this ignores the variations in the way QoL develops between groups of older adults.
We took a theoretically informed 'capabilities approach' to measuring QoL. We used four waves of data, covering 6 years, from the New Zealand Health, Work and Retirement Study (NZHWR) (N = 3223) to explore whether distinct QoL trajectories existed. NZHWR is a nationally representative longitudinal study of community-dwelling adults aged 50 + in New Zealand. Growth mixture modelling was applied to identify trajectories over time and multinomial regressions were calculated to test baseline differences in demographic variables (including age, gender, ethnicity, education and economic living standards).
We found five QoL trajectories: (1) high and stable (51.94%); (2) average and declining (22.74%); (3) low and increasing (9.62%); (4) low and declining (10.61%); (5) low and stable (5.09%). Several differences across profiles in baseline demographic factors were identified, with economic living standards differentiating between all profiles.
The trajectory profiles demonstrate that both maintaining and even improving QoL in later life is possible. This has implications for our capacity to develop nuanced policies for diverse groups of older adults.
提高老年人的生活质量(QoL)已成为一项主要的政策目标。然而,我们目前对老年人的 QoL 如何发展知之甚少。少数研究通过“平均”轨迹来模拟老年人的 QoL 随时间的变化。然而,这忽略了 QoL 在不同老年人群体之间的发展方式的差异。
我们采用了一种理论上的“能力方法”来衡量 QoL。我们使用了来自新西兰健康、工作和退休研究(NZHWR)的四个波次数据(覆盖 6 年)(N = 3223),以探索是否存在不同的 QoL 轨迹。NZHWR 是一项对新西兰 50 岁以上社区居民进行的全国代表性纵向研究。增长混合物模型被用于识别随时间的轨迹,而多项回归被用于测试人口统计学变量(包括年龄、性别、种族、教育和经济生活水平)的基线差异。
我们发现了五种 QoL 轨迹:(1)高且稳定(51.94%);(2)平均且下降(22.74%);(3)低且上升(9.62%);(4)低且下降(10.61%);(5)低且稳定(5.09%)。在基线人口统计学因素方面,在各轨迹之间发现了几个差异,经济生活水平可以区分所有轨迹。
轨迹分布表明,在晚年保持甚至提高 QoL 是可能的。这对我们为不同老年人群体制定细致入微的政策的能力产生了影响。