Spatial and temporal variation in the Heinz Nixdorf Recall data and their effects on the risk of depression at the district level

Background/Aim:

Temporal autocorrelations of health outcomes occur because the data relate to largely the same population in consecutive time periods. In addition, spatial patterns occur either because of neighborhood effect or the omission of spatially patterned risk factors for the response variable. Aim here is to (a) estimate the risk of depression at the district level using data of the population- based Heinz Nixdorf Recall Study (HNRS), (b) analyze temporal trends and spatial variations in the model and (c) further test the impact of greenness on the risk of depression.

Methods:

Data of 4,814 participants (51% women) are aggregated in the 108 non-overlapping districts (spatial unit) of Bochum, Essen and Mülheim, with known adjacency structure. Repeated measurements of depressive symptoms (CES-D, n = 9 within 12 years) were used. Greenness is defined as mean normalized difference vegetation index at the district level. We computed spatio-temporal Poisson models to estimate the risk of depression in each district accounting for covariate effects (greenness, age, socio-economic risk factors). The spatio-temporal autocorrelation is modelled by the latent random effect, using Conditional Autoregressive (CAR) type prior distributions and spatio-temporal extensions thereof.

Results:

The results show low spatial correlation in the data after adjusting for covariate effects, while the temporal correlation is very strong. The risk of depression for 1 unit increase in green is 0.90 [0.85,0.96] for the basic model, with no change when spatial residual variation is considered. The effect of green is 0.98 for the fully adjusted model, both for the model that considers and neglects spatial effects 95% CI [0.91, 1.06].

Conclusion:

The low spatial correlation at the district level is indicative for neglecting it. Some covariate effects may explain spatial variation in the model.