Elsevier

Psychiatry Research

Volume 144, Issues 2–3, 15 November 2006, Pages 167-175
Psychiatry Research

Diagnostic variability for schizophrenia and major depression in a large public mental health care system dataset

https://doi.org/10.1016/j.psychres.2005.12.002Get rights and content

Abstract

Administrative datasets can provide information about mental health treatment in real world settings; however, an important limitation in using these datasets is the uncertainty regarding psychiatric diagnosis. To better understand the psychiatric diagnoses, we investigated the diagnostic variability of schizophrenia and major depression in a large public mental health system. Using schizophrenia and major depression as the two comparison diagnoses, we compared the variability of diagnoses assigned to patients with one recorded diagnosis of schizophrenia or major depression. In addition, for both of these diagnoses, the diagnostic variability was compared across seven types of treatment settings. Statistical analyses were conducted using t tests for continuous data and chi-square tests for categorical data. We found that schizophrenia had greater diagnostic variability than major depression (31% vs. 43%). For both schizophrenia and major depression, variability was significantly higher in jail and the emergency psychiatric unit than in inpatient or outpatient settings. These findings demonstrate that the variability of psychiatric diagnoses recorded in the administrative dataset of a large public mental health system varies by diagnosis and by treatment setting. Further research is needed to clarify the relationship between psychiatric diagnosis, diagnostic variability and treatment setting.

Introduction

Understanding the delivery of mental health care in the real world is critical to improving mental health care (NAMHC Clinical Treatment and Services Research Workgroup, 1999). One tool for increasing the knowledge about the delivery of mental health care is the use of administrative datasets from large public mental health systems, which contain a wealth of information about treatments and outcomes for a large number of patients. In addition, these datasets offer the advantage of ease of use and low cost of data collection (Wang et al., 2000). The Saskatchewan Health databases, in particular, have been used to investigate schizophrenia (Rawson et al., 1997), depression and non-mental health conditions including hysterectomy (Edouard and Rawson, 1996) and ischemic heart disease (Rawson and Malcolm, 1995). In the U.S., the databases of state Medicaid programs (Busch et al., 2004), health maintenance organizations (Goodwin et al., 2003) and the Veteran Affairs system (Rosenheck and Seibyl, 1998) have been used to investigate mental health treatment. However, uncertainty regarding each patient's psychiatric diagnosis complicates the use of these datasets for mental health services research (Busch et al., 2004, Rawson and D'Arcy, 1998, West et al., 2000).

In a few prior studies, the investigators have compared the diagnoses in the administrative dataset with the patients' records. Lurie et al. (1992) compared the psychiatric diagnoses recorded in a Medicaid dataset with the clinical information that supported the diagnosis recorded in patients' charts (based on DSM-III-R criteria); 87% of the patients with a diagnosis of schizophrenia in the Medicaid dataset had symptoms in their charts to support a definite or probable diagnosis of schizophrenia. Another investigation compared the psychiatric diagnoses in a hospital's computerized dataset with the diagnosis in patients' charts, finding that for patients with schizophrenia the agreement was 94%, whereas for depression the agreement was only 58% (Rawson et al., 1997).

However, many investigations that have used administrative data did not have access to either the patients or their medical records, and instead determined psychiatric diagnoses based entirely on the administrative data. Furthermore, these studies varied in how psychiatric diagnoses were determined. A study of mental health services in California that used the state's administrative dataset defined a person's psychiatric diagnosis as “the psychiatric diagnosis recorded at admission to the mental health system” (Zhang et al., 2000). A report comparing the effects of lithium and valproic acid on suicidal behavior in patients with bipolar disorder used administrative datasets from two large HMOs, and defined bipolar as any patient with “at least one diagnosis of bipolar disorder, excluding those with a recorded diagnosis of schizophrenia” (Goodwin et al., 2003). Two investigations of patients with schizophrenia required that a patient receive one inpatient or two outpatient diagnoses of schizophrenia to be included in the study (Lurie et al., 1992, Dixon et al., 2001). In prior reports utilizing administrative data, we have employed a hierarchical classification scheme, with schizophrenia being at the top of the hierarchy, followed in descending order of priority by bipolar disorder, other psychotic disorders, major depression, and finally other psychiatric disorders (Gilmer et al., 2004, Gilmer et al., 2003, Folsom et al., 2005, Lindamer et al., 2003, Barrio et al., 2003). To our knowledge, no prior investigations have studied the issue of variability of major psychiatric diagnoses in large administrative datasets.

The present report focuses on comparing the diagnostic variability of schizophrenia with that of major depression among patients treated in the San Diego County's Adult and Older Adult Mental Health System (AOAMHS). We chose these diagnoses because they accounted for the two largest groups of patients in the dataset and have been compared in prior investigations (Rawson et al., 1997). We hypothesized that the diagnosis of schizophrenia would be less variable than major depression. In addition, we compared the diagnostic variability of these two diagnoses across different treatment settings, testing the hypothesis that inpatient and outpatient mental health settings would have less diagnostic variability than the psychiatric emergency room or jail.

Section snippets

Methods

San Diego County is the sixth largest county in the United States and has a diverse ethnic composition. The AMHS is the public mental health system in San Diego. Clients who receive treatment in this system are medically indigent (no insurance) or have Medi-Cal (Medicaid in California). At the time of this investigation, approximately one-third of outpatient care was provided by AMHS employees at five county-operated mental health centers. The AMHS also operated a 19-bed inpatient psychiatric

Results

The patients with at least one diagnosis of schizophrenia were more likely to be male, to have Medi-Cal insurance, and to have had more treatment episodes than those with major depression (Table 1). Patients with schizophrenia were more likely to be African American and less likely to be Caucasian than patients with major depression. Rates of marriage and current employment were low for both groups, but the patients with schizophrenia had lower rates than those with depression. Patients with a

Discussion

The main finding of this study is that the diagnosis of schizophrenia was less variable than that of major depression in the administrative dataset of a large public mental health system. In addition, we found that for both patient groups, the diagnostic variability differed by treatment setting. For patients with schizophrenia, the lowest variability was in case management and the greatest in the EPU and jail. For patients with major depression, the lowest variability was in crisis residential

Acknowledgments

This work was supported, in part, by the National Institute of Mental Health grants MH067895, MH49671, MH43693, MH59101, and MH19934, and by the Department of Veterans Affairs VISN 22 MIRECC. The authors gratefully acknowledge the County of San Diego Health and Human Services Agency Adult and Older Adult Mental Health Services for access to the management information systems and assistance in the preparation in this article and Anne Bailey for help with initial work on this project.

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