Ari Voutilainen1, Sirpa Hartikainen2, Paula R Sherwood3, Heidi Taipale2, Anna-Maija Tolppanen4, Katri Vehviläinen-Julkunen5. 1. Department of Nursing Science, University of Eastern Finland, Finland ari.voutilainen@uef.fi. 2. Kuopio Research Centre of Geriatric Care, School of Pharmacy, University of Eastern Finland, Finland. 3. Department of Acute and Tertiary Care, School of Nursing, University of Pittsburgh, USA. 4. Research Centre for Comparative Effectiveness and Patient Safety, School of Pharmacy, University of Eastern Finland, Finland. 5. Department of Nursing Science, University of Eastern Finland, Finland.
Abstract
AIMS: The epidemiological aim was to draw a general picture of spatial patterns of diseases, socio-demographics, and land use in Finland to detect possible under-recognized associations between the patterns. The methodological purpose was to compare and combine two statistical techniques to approach the data from different viewpoints. METHODS: Two different statistical methods, the self-organizing map and principal coordinates of neighbor matrices with variation partitioning, were used to search for spatial patterns of 15 non-infectious diseases and 17 direct or indirect risk factors. The dataset was gathered from five Finnish registries and pooled over the years 1991-2010. The statistical unit in the analyses was a municipality (n=303). RESULTS: Variables referring to urban living were related to low incidences of all other diseases but cancer, whereas variables referring to rural living were related to low incidences of cancer and high incidences of other diseases, especially coronary heart disease (CHD), hypertension, diabetes, asthma/chronic obstructive pulmonary disease, and serious mental illnesses at the municipal level. The relationships between diseases other than cancer and risk factors related to socio-demographics and land use variables were stronger than those between cancer and risk factors. CONCLUSIONS: The structuration of spatial patterns was dominated by CHD together with land use features and unemployment rate. The relationship between unemployment and spatial health inequalities was emphasized. On the basis of the present study, it is suggested that large heterogeneous datasets are clustered and analyzed simultaneously with more than one statistical method to recognize the most significant and generalizable results.
AIMS: The epidemiological aim was to draw a general picture of spatial patterns of diseases, socio-demographics, and land use in Finland to detect possible under-recognized associations between the patterns. The methodological purpose was to compare and combine two statistical techniques to approach the data from different viewpoints. METHODS: Two different statistical methods, the self-organizing map and principal coordinates of neighbor matrices with variation partitioning, were used to search for spatial patterns of 15 non-infectious diseases and 17 direct or indirect risk factors. The dataset was gathered from five Finnish registries and pooled over the years 1991-2010. The statistical unit in the analyses was a municipality (n=303). RESULTS: Variables referring to urban living were related to low incidences of all other diseases but cancer, whereas variables referring to rural living were related to low incidences of cancer and high incidences of other diseases, especially coronary heart disease (CHD), hypertension, diabetes, asthma/chronic obstructive pulmonary disease, and serious mental illnesses at the municipal level. The relationships between diseases other than cancer and risk factors related to socio-demographics and land use variables were stronger than those between cancer and risk factors. CONCLUSIONS: The structuration of spatial patterns was dominated by CHD together with land use features and unemployment rate. The relationship between unemployment and spatial health inequalities was emphasized. On the basis of the present study, it is suggested that large heterogeneous datasets are clustered and analyzed simultaneously with more than one statistical method to recognize the most significant and generalizable results.