Psychische Erkrankungen im ländlichen Raum - Gemeindestrukturen und Aufnahmeraten im ländlichen Einzugsgebiet der psychiatrischen Klinik des Bezirkskrankenhauses Günzburg

Abstract

Background: Researching environmental risk factors for mental illnesses in urban areas has a history since the 1930s, but research in rural areas on this topic is lacking. Methods: For the years 2006 to 2009, hospital admission rates for depression and schizophrenia were identified for 174 rural municipalities in the catchment area of the state psychiatric hospital in Günzburg. Relationships of overall and diagnosis-specific admission rates with municipality characteristics were analysed by means of negative binomial regression models. Results: Admission rates for both diagnoses combined decrease with population growth, population density, average income and green areas, while they are positively correlated with commuter balance, income inequality, unemployment rates and traffic areas. Admission rates for schizophrenia alone are negatively correlated with population growth, average income and agricultural areas, but positively correlated with mobility index, income inequality and unemployment rate. Admission rates for affective disorders are negatively correlated with population growth, population density, average income and green areas, while higher admission rates are correlated with commuter balance, high income inequality, unemployment rate and traffic-related areas. Discussion: As expected from former urban studies, the study shows that economic factors are related to admission rates on the municipality level in rural areas. Population density shows a different effect: low population density was associated with higher admission rates. While stress could be the key to the findings in urban areas, the lack of social networks and fixed social norms could be behind the findings here. Land use types are rarely researched in rural areas, which is why the findings are really astonishing and need further investigation, especially since evidence for underlying mechanisms is missing.