Prevalence of Health Problems and Primary Care Physicians’ Specialty Referral Decisions

,

Original Research

Prevalence of Health Problems and Primary Care Physicians’ Specialty Referral Decisions

J Fam Pract. 2001 May;50(5):427-432

References

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Clinical Factors

To examine the referral characteristics of different types of conditions, International Classification of Diseases-ninth revision-Clinical Modification (ICD-9-CM) codes were grouped into clinically similar categories that we called expanded diagnosis clusters (EDCs). The EDCs were based on the diagnosis clusters originally developed by Schneeweiss and coworkers.¹⁴ EDCs were assigned to the vast majority of primary care visits in the data set, matching 93.6% of the unique diagnosis codes. EDCs were grouped into clinical domains based on the nature of the problems and the specialty most responsible for the care. We also classified each EDC according to whether the primary treatment approach was medical or surgical.

A practice-prevalence ratio was calculated for each condition. The numerator was the number of visits for the EDC under consideration, and the denominator was the total number of visits in the data set. These ratios were further divided into tertiles (ie, high-, medium-, and low-frequency EDCs.)

To account for the number and severity of comorbid conditions with which patients presented, we assigned a comorbidity index to each visit. The index was based on the aggregated diagnostic groups (ADGs), the building blocks of The Johns Hopkins Adjusted Clinical Groups Case-Mix system.¹⁵ The 32 ADGs represent morbidity groups that contain ICD-9-CM diagnosis codes that are similar with respect to likelihood of persistence, expected need for health care resources, and other clinical criteria. In the NAMCS, up to 3 ICD-9-CM codes were assigned per visit; thus, each visit was assigned 1 to 3 ADGs. A comorbidity index score was obtained by summing ADG-specific resource intensity weights.* Larger weights suggest more complex health problems and greater expected resource intensity. In previous research,¹³ the comorbidity index increased with patient age and distinguished type of primary care delivery site by morbidity burden. The comorbidity index was divided into tertiles representing high, medium, and low levels of comorbidity.

Data Analysis

The unadjusted association of each clinical factor with the probability of referral was evaluated in bivariate analyses using the chi-square statistic. We further analyzed the relationship between a condition’s practice prevalence and its referral rate using scatter plots and Pearson correlation coefficients. Because both the practice prevalence and referral rate variables were right skewed, we used a logarithmic transformation to normalize both. For the scatter plots we excluded conditions with unstable referral rate estimates because of small sample sizes. Specifically, a condition was included if the ratio of the difference between the 95% confidence interval upper and lower limits of the referral rates divided by the condition-specific referral rate was less than 1. For example, multiple sclerosis was excluded because it presented at a rate of just 6 per 10,000 visits, had a referral rate of 7.1%, and the difference between the 95% confidence interval limits divided by the referral rate was 2.7.

We conducted a multiple logistic regression analysis with the patient visit as the unit of analysis for examining the independent effect of practice prevalence of the presenting problem on the chances of referral. Controlling variables in this analysis included age, sex, comorbidity index tertile, and the medical versus surgical nature of the principal diagnosis. To determine if the clinical complexity of a visit as assessed by the comorbidity index modified the effect of practice prevalence, we also added interaction terms for these variables to the final model. The goodness of fit of the final model was tested using the Hosmer-Lemeshow statistic, which suggested adequate model fit.¹⁶ The regression analyses used the generalized estimating equation¹⁷ to control for the intraphysician correlation of clustered visits.

Results

The distribution of clinical characteristics is shown in Table 1. The population was somewhat more represented by women (57.5%) than men, and by children/adolescents (33.3%) and seniors (39.4%) than middle-aged persons. The average comorbidity index value was 0.63, but there was considerable variability and the distribution was right skewed (median=0.31; interquartile range=0.05-0.83). The mean EDC practice-prevalence estimate for patient visits was 51 per 1000 primary care visits, but this distribution was also right skewed (median=25; interquartile range=10-106).

In the bivariate analyses, the probability of referral was significantly related to age, sex, practice prevalence of the condition, patient comorbidity, and whether the condition was a surgical problem Table 2. For the 65 condition categories with stable referral rate estimates Figure 1, the correlation between the logarithms of the practice-prevalence ratios and the referral rates was -0.87 (P <.001). Also, this relationship remained after stratifying for the 35 medical (r=-0.82; P <.001) and 24 surgical conditions (r=-0.88; P=.001).

Table 3 shows the multiple logistic regression results modeling the probability of referral during an office visit as a function of the clinical factors. There were no significant differences in the chances of referring patients aged 11 to 64 years once measures of morbidity, such as patient comorbidity and practice prevalence of the principal diagnosis, were controlled. Men were 19% more likely to be referred than women, and medical conditions were 39% less likely to be referred than surgical ones.