MOESM1 of Factors associated with prescribing costs: analysis of a nationwide administrative database
Creators: name: O. Hirsch
name: M. Schulz
name: M. Erhart
name: N. Donner-Banzhoff
Publisher: name: figshare
Year: 2018
Subjects: subject: Biotechnology
subject: Immunology
subject: FOS: Clinical medicine
subject: FOS: Clinical medicine
subject: 69999 Biological Sciences not elsewhere classified
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: 80699 Information Systems not elsewhere classified
subject: FOS: Computer and information sciences
subject: FOS: Computer and information sciences
subject: Science Policy
subject: 111714 Mental Health
subject: FOS: Health sciences
subject: FOS: Health sciences

description: Additional file 1: Table S1. Descriptive statististics (mean, standard deviation, and median) regarding the potential predictors median age of patients, proportion of pensioners, morbidity index, proportion of prescriptions issued (PPI), proportion of DDD issued (PDDD), proportion of polypharmacy patients, and proportion of polypharmacy patients in ≥ 2 quarters in GPs and family physicians (n=30,325). Table S2. Descriptive statististics (mean, standard deviation, and median) regarding the potential predictors median age of patients, proportion of pensioners, morbidity index, proportion of prescriptions issued (PPI), proportion of DDD issued (PDDD), proportion of polypharmacy patients, and proportion of polypharmacy patients in ≥ 2 quarters in cardiologists (n=559). Table S3. Descriptive statististics (mean, standard deviation, and median) regarding the potential predictors median age of patients, proportion of pensioners, morbidity index, proportion of prescriptions issued (PPI), proportion of DDD issued (PDDD), proportion of polypharmacy patients, and proportion of polypharmacy patients in ≥ 2 quarters in gastroenterologists (n=485). Table S4. Descriptive statististics (mean, standard deviation, and median) regarding the potential predictors median age of patients, proportion of pensioners, morbidity index, proportion of prescriptions issued (PPI), proportion of DDD issued (PDDD), proportion of polypharmacy patients, and proportion of polypharmacy patients in ≥ 2 quarters in neurologists and psychiatrists (n=3080). Table S5. Descriptive statististics (mean, standard deviation, and median) regarding the potential predictors median age of patients, proportion of pensioners, morbidity index, proportion of prescriptions issued (PPI), proportion of DDD issued (PDDD), proportion of polypharmacy patients, and proportion of polypharmacy patients in ≥ 2 quarters in pulmologists (n=642). Table S6. Descriptive statististics (mean, standard deviation, and median) regarding the potential predictors median age of patients, proportion of pensioners, morbidity index, proportion of prescriptions issued (PPI), proportion of DDD issued (PDDD), proportion of polypharmacy patients, and proportion of polypharmacy patients in ≥ 2 quarters in oncologists and haematologists (n=303). Table S7. Results of the multivariate robust regression model (R2 =.54) with dependent variable „costs per patient“ in the complete sample of GPs/family physicians (n=30,325). Table S8. Results of the multivariate model (R2 =.41) with dependent variable „costs per patient“ in cardiologists (sample 1, n=280). Table S9. Results of the multivariate model (R2 =.61) with dependent variable „costs per patient“ in cardiologists (sample 2, n=279). Table S10. Results of the multivariate model (R2 =.12) with dependent variable „costs per patient“ in gastroenterologists (n=485). Table S11. Results of the multivariate model (R2 =.20) with dependent variable „costs per patient“ in neurologists and psychiatrists (n=3080). Table S12. Results of the multivariate model (R2 =.60) with dependent variable „costs per patient“ in pulmologists (n=642).