Geographical and ecological analyses of multiple myeloma in Denmark:Identification of potential hotspot areas and impact of urbanisation

BACKGROUND: The aetiology of multiple myeloma (MM) is unknown but various environmental exposures are suspected as risk factors. We present the first paper analysing the geographical distribution of MM in Denmark at the municipal level to investigate variations that could be explained by environmental exposures.METHODS: Patients diagnosed with MM in Denmark during 2005-2020 were identified from nationwide registries and grouped into the 98 Danish municipalities based on residence. The age- and sex-standardised incidence rate (SIR) of each municipality was compared to the national incidence in a funnel plot with 95% control limits. Differences in SIRs of rural, suburban, and urban areas were evaluated with incidence rate ratios.RESULTS: In total, 5243 MM patients were included. Overall, we found a heterogeneous geographical distribution of MM and a potential hotspot in southern Denmark. This hotspot contains three municipalities with SIRs above the 95% control limit assuming considerably higher rate of MM compared to the national incidence rate. A significant higher SIR was found in rural areas compared to urban areas.CONCLUSION: The geographical distribution of MM in Denmark indicates that the risk of developing MM depends on place of residence probably due to environmental factors.</p

Modelling spine locations on dendrite trees using inhomogeneous Cox point processes

Dendritic spines, which are small protrusions on the dendrites of a neuron, are of interest in neuroscience as they are related to cognitive processes such as learning and memory. We analyse the distribution of spine locations on six different dendrite trees from mouse neurons using point process theory for linear networks. Besides some possible small-scale repulsion, we find that one of the spine point pattern data sets may be described by an inhomogeneous Poisson process model, while the other point pattern data sets exhibit clustering between spines at a larger scale. To model this we propose an inhomogeneous Cox process model constructed by thinning a Poisson process on a linear network with retention probabilities determined by a spatially correlated random field. For model checking we consider network analogues of the empirical $F$-, $G$-, and $J$-functions originally introduced for inhomogeneous point processes on a Euclidean space. The fitted Cox process models seem to catch the clustering of spine locations between spines, but also posses a large variance in the number of points for some of the data sets causing large confidence regions for the empirical $F$- and $G$-functions