Responses to this symptom measure were used to create clusters or groups of patients with similar severity profiles.23 Four distinct groups were identified: low severity, moderate anxiety/minor mood, moderate anxiety/severe mood, and high severity. The groups were distinguished by their relative levels of severity across both mood and anxiety symptoms rather than by clustering mood and anxiety symptoms into individual groups. As previously reported, these groups varied on indicators of physical comorbidity, income and occupational status, and measures of HRQOL.
Health Care Costs. We obtained billing data from UTMB Hospital Information Services for the 15-month period composed of the 3 months before and the 12 months after each patient’s index visit. These data included physician charges and charges for other technical services. Details regarding those services were not obtained. Outpatient pharmacy data was not included. The data reflected all activity within the UTMB Hospitals and Clinics, and therefore included inpatient, outpatient, and mental health services. Because we recognize that any mental health symptomatology captured at the index visit reflected morbidity that had been present for an unknown period, we included charge data from 3 months preceding the index visit. We are confident we captured the majority of health care use in our study population because of the dominant presence of the UTMB Hospitals and Clinics in the local health care market.
All charges were divided into 3-month intervals, then summed. Because missing charge data could (1) indicate the patient had left the area, or (2) indicate the patient received no charges during the period in question, we developed the following procedure. Where charge data for a patient was missing within a 3-month period, we examined the subsequent 3-month periods, including 3 months beyond the period of study. If charge data existed for any subsequent period, we assumed that the patient was still active in the UTMB health system but no charges had been recorded during the intervening period(s). In this situation, a zero was recorded as the amount billed. If, in contrast, all subsequent periods were void of charge data, we assumed the patient had left the UTMB health system, and the data was treated as “system missing” and not used in any data analysis calculations. This conservative approach would reduce the average charges for each group, though more for the low-severity group, which had the lowest medical comorbidity.
Other Variables. We also examined age, sex, ethnicity, income level, medical comorbidities, and the presence or absence of a mood or anxiety disorder as independent variables.
Adjustment for medical comorbidities was based on a count of diagnoses of chronic health problems from patients’ problem lists that were found predictive of Short Form-36 Physical Component Summary scale (SF-36 PCS) scores.25 Chronic health problems, grouped by International Classification of Diseases diagnosis codes, indicative of PCS scale scores were identified through a linear regression model that used the SF-36 PCS scale scores as the dependent variable. These chronic disease states, representing chronic health problems seen commonly in primary care patients, were included in the comorbidity index. The index was confirmed by testing a validation subset of a randomly selected group of cases. The comorbidity index was also shown to have predictive validity for future health care costs. This approach was modeled after the adaptation by Deyo26 of the Charlson Index,27 a widely used and validated clinical comorbidity adjustment index developed in a hospital-based patient population.
The presence of a mood or anxiety disorder was determined in the original study through use of the mood and anxiety modules of the PRIME-MD instrument.6 We included anxiety disorders in this study because we used those symptoms in the original study that produced the severity groups. Disorders included major depression, partial remission or recurrence of a major depressive disorder, dysthymia, bipolar disorder, generalized anxiety disorder, and panic disorder. Prevalence estimates for these disorders in our sample were consistent with those obtained by the PRIME-MD 1000 study, with the exception of major depressive disorder, which was identified in 18.2% of our sample compared with 12% in the PRIME-MD 1000 study.
Data Analysis
After aggregating the charge data for each 3-month period, we normalized the data using a logarithmic transformation. We calculated unadjusted utilization costs for each 3-month period surrounding the study index visit. Associated 95% confidence limits were also estimated. Analyses of variance were used to test for differences between symptom severity groups. T tests were performed to examine charge differences between patients with and without a diagnosed mood or anxiety disorder as determined by the PRIME-MD.
We next evaluated whether the differences seen between the symptom severity groups would remain after adjusting for significant covariates. To ensure that our analyses did not overestimate the contributions of a mood or anxiety disorder or symptom-severity group membership, analyses of covariance were used to test for interactions between these variables.