Statistical procedures for analyzing mental health services data.
In mental health services research, analyzing service utilization data often poses serious problems, given the presence of substantially skewed data distributions. This article presents a non-technical introduction to statistical methods specifically designed to handle the complexly distributed datasets that represent mental health service use, including Poisson, negative binomial, zero-inflated, and zero-truncated regression models. A flowchart is provided to assist the investigator in selecting the most appropriate method. Finally, a dataset of mental health service use reported by medical patients is described, and a comparison of results across several different statistical methods is presented. Implications of matching data analytic techniques appropriately with the often complexly distributed datasets of mental health services utilization variables are discussed.
Elhai
Jon D
JD
Disaster Mental Health Institute, The University of South Dakota, 414 East Clark Street, Vermillion, SD 57069-2390, USA. jonelhai@fastmail.fm
Calhoun
Patrick S
PS
Ford
Julian D
JD
D003156:Community Mental Health Services / Q000706:statistics & numerical data; D017048:Health Care Costs / Q000706:statistics & numerical data; D006301:Health Services Needs and Demand / Q000191:economics / Q000706:statistics & numerical data; D006302:Health Services Research / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D001523:Mental Disorders / Q000453:epidemiology / Q000523:psychology; D008605:Mental Health Services / Q000706:statistics & numerical data* / Q000600:supply & distribution; D015233:Models, Statistical; D016012:Poisson Distribution; D012044:Regression Analysis; D014600:Utilization Review / Q000379:methods* / Q000706:statistics & numerical data