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Geographic distribution of amyotrophic lateral sclerosis through motor neuron disease mortality data
Author(s) -
Raffaella Uccelli,
Alessandra Binazzi,
Pierluigi Altavista,
Stefano Belli,
Pietro Comba,
Marina Mastrantonio,
Nicola Vanacore
Publication year - 2007
Publication title -
european journal of epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.825
H-Index - 111
eISSN - 1573-7284
pISSN - 0393-2990
DOI - 10.1007/s10654-007-9173-7
Subject(s) - amyotrophic lateral sclerosis , medicine , motor neuron , epidemiology , disease , physical medicine and rehabilitation , public health , distribution (mathematics) , pathology , mathematical analysis , mathematics
Amyotrophic lateral sclerosis (ALS) is a rare and devastating neurological disorder of the adult age with a prognosis of about 2-3 years from the onset of the disease. No clear cause has been identified but it seems to be a multifactorial disease with genetic and environmental components involved. Increments of mortality rates were observed since 1980 both in Italy and in many other countries. The objective of the present study is to describe the distribution of ALS mortality in Italy in the period 1980-2001 detecting single municipalities or clusters with high mortality levels for motor neuron disease (MND). ALS represents the main part (85%) of the MND group which is globally identified by the IX ICD (International Classification of Diseases and Causes of Death) 335.2 code. Death numbers and standardized mortality ratios (SMR) for MND were calculated for all Italian municipalities through the ENEA mortality database system (data source: National Institute of Statistics-ISTAT), using national mortality rates as reference. Subsequently, in order to detect municipal clusters, spatial analysis was performed. Out of the 8,099 Italian municipalities, 132 where characterized by SMR values higher than expected. Moreover 16 clusters with significant high relative risk values (RR) were identified, 12 out of them including only a single municipality. Only 22 of the municipalities with high SMR were included in the clusters. In conclusion, the two different epidemiological methodologies demonstrated to be widely complementary in detecting the geographical distribution of the disease in terms of risk for populations. A first selection of the priority areas where analytical studies should be carried on, in order to identify risk factors associated to ALS, is tentatively suggested.

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