Hamburg Center for Health Economics, Universität Hamburg, 20354, Hamburg, Germany.
Eur J Health Econ. 2019 Jul;20(5):715-728. doi: 10.1007/s10198-019-01033-6. Epub 2019 Feb 9.
In this study, we investigated the relationship between changes in demand-side determinants and changes in hospital admissions. We used longitudinal market-wide data, including a novel detailed measure of population morbidity. To assess the effect of ageing, we interacted age with shifts in the population structure for both the surviving population and the population in their last year of life. We used fixed effects models and addressed the endogeneity of morbidity with instrumental variables. We found that changes in morbidity had the largest impact on changes in hospital admissions. Changes in the size of the surviving population had the second largest impact, which differed substantially across the age spectrum. There was a large response in admissions to changes in the size of the population aged 60-79 years. The end-of-life effect had the smallest impact and began to play a greater role only in the population aged 80 years and older. In many studies, end of life presumably approximates high morbidity. Our results demonstrated robustness in several tests. We performed estimations in separate major diagnostic categories and included changes in personal preferences. We argue that the determinants included in our estimations capture the vast majority of change on the demand side. Taken together, our findings provide evidence that these determinants explain one-fifth of changes in hospital admissions.
在这项研究中,我们调查了需求侧决定因素的变化与医院入院人数变化之间的关系。我们使用了纵向的全市场数据,包括一种新颖的详细人口发病率衡量标准。为了评估老龄化的影响,我们将年龄与幸存人口和生命最后一年人口结构的变化进行了交互。我们使用固定效应模型,并使用工具变量解决了发病率的内生性问题。我们发现,发病率的变化对医院入院人数的变化影响最大。幸存人口规模的变化次之,其在年龄谱上的差异很大。60-79 岁人群规模的变化对入院人数的反应很大。临终效应的影响最小,并且仅在 80 岁及以上人群中开始发挥更大的作用。在许多研究中,临终阶段大概相当于高发病率。我们的结果在几项测试中表现出了稳健性。我们在单独的主要诊断类别中进行了估计,并包括了个人偏好的变化。我们认为,我们的估计中包含的决定因素捕捉了需求侧变化的绝大多数。总的来说,我们的研究结果提供了证据,证明这些决定因素解释了医院入院人数变化的五分之一。