Institute of General Practice and Family Medicine, University Hospital Bonn, University of Bonn, Bonn, Germany.
Institute of General Practice and Family Medicine, University Hospital Bonn, University of Bonn, Bonn, Germany.
Public Health. 2024 Nov;236:338-346. doi: 10.1016/j.puhe.2024.09.010. Epub 2024 Sep 19.
Limited healthcare availability impacts population health. Regional disparities in GP density across Germany raise questions about their association with regional socioeconomic characteristics.
This longitudinal nationwide ecological German study used regional data at the county level (n = 401) from 2015 to 2019 provided by the Federal Institute for Research on Building, Urban Affairs and Spatial Development (BBSR). The outcome was general practitioners (GPs) density, defined as the number of GPs per 10,000 inhabitants.
Univariate Moran's I, cluster analysis (LISA), and spatial lag of X (SLX) models were employed to analyse the spatial distribution of GP density and its correlation with various regional socioeconomic characteristics from a cross-sectional and longitudinal perspective.
In contrast to the univariate analysis, rural counties showed the highest GP density the multivariate model. Several counties were identified as embedded in low- or high-GP-density clusters. In 2015 and 2019, larger household size (2015: std. β = -2.31, p = 0.021; 2019: std. β = -4.14, p < 0.001) and higher unemployment rate (2015: std. β = -2.84, p = 0.005; 2019: std. β = -5.47, p < 0.001) were associated with lower GP density. In the longitudinal model, a greater increase in the unemployment rate was related to a greater decrease in GP density (std. β = -2.17, p = 0.030).
A higher regional unemployment rate is linked to lower GP availability in Germany, and a greater increase in the unemployment rate was related to a greater decrease in GP availability over time. This necessitates policy intervention to avoid socioeconomic disparities in GP care.
医疗保健的可及性有限会影响人口健康。德国全科医生密度在地区间存在差异，这引发了人们对其与地区社会经济特征之间关系的质疑。
这是一项在全国范围内使用纵向生态方法的德国研究，使用了联邦建筑、城市事务和空间发展研究所（BBSR）提供的 2015 年至 2019 年县级（n=401）的区域数据。结果是全科医生密度，定义为每 10000 名居民中的全科医生人数。
采用单变量 Moran's I、聚类分析（LISA）和空间滞后 X（SLX）模型，从横断面和纵向角度分析全科医生密度的空间分布及其与各种区域社会经济特征的相关性。
与单变量分析相比，多元模型显示农村县的全科医生密度最高。确定了几个县处于低或高全科医生密度聚类中。2015 年和 2019 年，家庭规模较大（2015 年：标准 β=-2.31，p=0.021；2019 年：标准 β=-4.14，p<0.001）和失业率较高（2015 年：标准 β=-2.84，p=0.005；2019 年：标准 β=-5.47，p<0.001）与较低的全科医生密度相关。在纵向模型中，失业率的更大增加与全科医生密度的更大下降相关（标准 β=-2.17，p=0.030）。
德国地区失业率较高与全科医生的可及性较低有关，失业率的更大增加与全科医生的可及性随时间的推移而更大下降有关。这需要政策干预，以避免全科医生护理方面的社会经济差异。
