Δημογραφικά και νοσολογικά χαρακτηριστικά των ασθενών του Αιγινητείου Νοσοκομείου κατά το πρώτο μισό του 20ου αιώνα
The demographic and nosological profile of patients in Aiginiteio Hospital during the first half of 20th century
KeywordsΑιγινήτειο ; Ασθενείς ; Ψυχικά νοσήματα ; Στατιστική ανάλυση ; Περιγραφική ανάλυση ; Λογιστική παλινδρόμηση ; Ανάλυση κατά συστάδες
This dissertation examines the patient records of the Aiginitio Hospital in Athens from October 1904 until January 1948, in order to study the main demographic and nosological characteristics as well as the relationship between them that may arise. The data of the patients enrolled in the statistical software IBM SPSS STATISTICS were: 1) Age 2) Year of importation 3) Sex 4) Marital status 5) Diagnosis 6) Illness outcome 7) Duration of hospitalization 8) Occupation 9) Geographic apartment of residence The first chapter of the work includes the history of the Aiginitio Hospital and its gradual evolution up to our days. The second chapter provides a brief review of the history of psychiatry over the years to the present in our country. All stages of the development of the science are presented in the Greek area, from the first mental hospital that was founded up to the system that prevails today. In the third chapter we begin to analyse the data of the hospital, on a descriptive basis. An overview of the distribution of patients is given on the basis of the data mentioned above, as well as the investigation of the existence of correlations between some of them. The fourth chapter examines the most populous diseases in the sample in relation to all variables, in order to investigate possible groups of patients who brought the specific diseases. The diseases examined are schizophrenia, melancholy and general paralysis. In the fifth chapter, the method of Binary Logistic Regression is applied, with the dependent variable being the outcome of the disease and independent all the others, in order to find how the odds ratio of non-negative outcomes of the disease is affected by the other variables in the model. The sixth chapter applies the grouping of the patients sample under the method of Cluster Analysis. The aim is to find groups of patients with specific common characteristics. Finally, the seventh chapter presents all the important findings that emerged from chapters 3, 4, 5 and 6, as a summary of the present dissertation.