The Effect of Patient and Visit Characteristics on Diagnosis of Depression in Primary Care

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Original Research

The Effect of Patient and Visit Characteristics on Diagnosis of Depression in Primary Care

J Fam Pract. 2001 December;50(12):1068

References

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Methods

Data

The study used data from the 1997 and 1998 National Ambulatory Medical Care Surveys (NAMCS). The NAMCS, which have been conducted every year since 1989 by the National Center for Health Statistics (NCHS), sample a nationally representative group of visits to physicians in office-based practices. The NCHS included weights in the NAMCS to enable the sample to represent all office visits in the United States. A detailed description of the NAMCS sample and sampling procedure, as well as a description of the survey instrument and survey administration procedures, is provided elsewhere.^18,19

There were 24,715 visits sampled in 1997 and 23,339 visits sampled in 1998. For each office visit, the survey provided information on physician specialty, up to 3 diagnoses, and up to 3 patient reasons for the visit. Because there were fewer than 200 visits with a diagnosis of depression sampled in each year, we combined the data from 1997 and 1998 to increase the power of the analysis. We limited our analysis to the 17,058 visits made during this interval by adults 18 years and older to primary care physicians. Primary care physicians included physicians with specialties of family practice, general practice, or internal medicine. Item nonresponse rates in the NAMCS data are low (<5%), and the NCHS provides imputed values for any missing information on demographic variables and duration of the visit in the NAMCS data.¹⁹

Diagnostic Groups

Patients were categorized on the basis of diagnoses assigned by providers during the index visit, using the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM). We classified depression visits as those with ICD-9 codes of 296.2 (major depressive disorder, single episode), 296.3 (major depressive disorder, recurrent), 300.4 (neurotic depression), 311 (depressive disorder, not elsewhere classified), and 298.0 (depressive type psychosis).

Patient and Visit Characteristics

Information on patient age, race, and ethnicity was recorded in the NAMCS survey, as was information on whether the visit was prepaid or fee-for-service and type of insurance coverage (eg, private, Medicaid, Medicare). The duration of the visit was also recorded. The survey reported physician specialty; we classified primary care physicians into 2 groups: family practice/general practice and internal medicine. The survey also indicated whether the physician had seen the patient previously. Information on up to 3 reasons for the visit, according to the patient, was collected in the survey at the time of the visit. Self-reported depressive symptoms were divided into 3 categories: (1) depressed mood, (2) physical symptoms of depression (eg, tiredness, general weakness or ill feeling, weight loss, restlessness, disturbance of sleep, abnormal appetite), and (3) other psychiatric symptoms associated with depression (eg, nervousness, fears and phobias, problems with self-esteem and identity, disturbance of memory, social adjustment problems, intentional self-mutilation, and suicidal ideation). The number of medications prescribed during the visit and the visit’s duration were recorded in the survey and used in the analysis.

Analysis

We sought to examine the role of patient and visit characteristics on the probability that a depression diagnosis was recorded during an office visit to a primary care physician. Specifically, we investigated the independent effect of factors such as age, race, sex, type of insurance, and duration of the visit on the probability of receiving a depression diagnosis, after controlling for patient-reported symptoms of depression, physician specialty, and other patient characteristics. Factors associated with having a depression diagnosis recorded were determined using weighted logistic regression models, and adjusted odds ratios and their 95% confidence intervals were calculated. Statistically significant differences in recognition rates were identified by reducing the sample weights by the proportion needed to downweight the sample to the size of a simple random sample with the same variance.²⁰ Although this method did not address problems caused by clustering within strata, it produced results that tend to overcompensate rather than undercompensate for artifacts produced from stratification.²¹ Significant differences were identified by testing the coefficients using a c2 test.

A sensitivity analysis was performed. We were concerned that patients with multiple medical conditions may be less likely to have a depression diagnosis recorded in the NAMCS because the survey only allows for 3 recorded diagnoses, and because these patients may not be randomly distributed by age, sex, race, type of physician, and so forth. A weighted logistic regression analysis was conducted on the subset of visits that recorded only 1 or 2 diagnoses (N=14,135). This should eliminate visits in which depression was recognized but a diagnosis was not recorded because 3 other conditions were perceived to be more important by the physician. The results of this analysis were then compared with results based on the full sample.