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The American Journal of Managed Care March 2013
Rates of Guideline Adherence Among US Community Oncologists Treating NSCLC
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Computed Tomography Scan Use Variation: Patient, Hospital, and Geographic Factors
Eric A. Vance, PhD; Xiaojin Xie, MS; Andrew Henry, BS; Christian Wernz, PhD; and Anthony D. Slonim, MD, DrPH
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Computed Tomography Scan Use Variation: Patient, Hospital, and Geographic Factors

Eric A. Vance, PhD; Xiaojin Xie, MS; Andrew Henry, BS; Christian Wernz, PhD; and Anthony D. Slonim, MD, DrPH
We observed small-area variation in computed tomography scan use for inpatients in New York State, even after controlling for relevant patient and hospital characteristics.
Objectives: To examine patient, hospital, and geographic characteristics influencing variation in computed tomography (CT) scan use in inpatients in New York State.

Study Design: Retrospective cohort study.

Methods: We used the 2007 Healthcare Cost and Utilization Project’s State Inpatient Database from the Agency for Healthcare Research and Quality and applied descriptive univariate statistics and logistic regression models to quantify the influence of each factor on CT scan use.

Results: The primary contributors to variation in CT scan use were the inpatients’ diagnosis, age, and hospital county, whereas inpatients’ sex and method of payment and hospitals’ teaching status and size had very little effect. Inpatients diagnosed with trauma had the highest CT scan use; CT scan use increased with age for inpatients over 30 years; and CT scan use varied widely between counties.

Conclusions: After controlling for patient and hospital characteristics, significant geographic variation remained at the level of the county, which indicates that additional research investigating the use of CT scans is necessary to understand the reasons behind small-area variation. Understanding the distribution and practice patterns of specific physician specialties may be helpful in curtailing underuse and overuse.

Am J Manag Care. 2013;19(3):e93-e99
An inpatient’s diagnosis and age were the strongest predictors of the likelihood of receiving a computed tomography (CT) scan. The third strongest predictor among 8 patient, hospital, and geographic characteristics was the county in which the scan was administered.

  •  Counties in New York State deviated between –11% and +9.4% from the state’s average CT scan rate.

  • Hospital teaching status and patient method of payment (insurance) played only a minor role in explaining variation in CT scan use.

  •  Additional research on county factors (eg, economic status, technology density, practice habits) is necessary to explain the observed small-area variation.
Since computed tomography (CT) was first introduced in the 1970s, its use has grown to an estimated 72 million CT scans performed annually in the United States.1 Computed tomography scans are currently a fundamental diagnostic tool in the evaluation of a multitude of conditions, including malignancies, cardiovascular disease, and infectious diseases, among others. The largest increase in CT scan use in recent years has been for procedures such as virtual colonoscopy, cardiac screening, and whole-body CT scans for asymptomatic patients as well as smokers and children.2 With the increased use of CT scans in low-risk populations, some have begun to question the public health implications of unnecessary radiation exposure of patients.3-5

The ubiquity of CT scanners provides access to accurate data to diagnose complex medical problems. However, the availability of CT scanners may have created a supply-induced demand, which may contribute to the variability in practice and CT scan use.6,7 Computed tomography scans improve diagnostic accuracy for certain conditions. However, in some instances, such as appendicitis, the benefit to patients’ health may not be justified.8,9

Almost 40 years ago, Wennberg and Gittelsohn10 observed that more care is not always better care. Numerous publications after this study observed variations in medical usage patterns (eg, a study on small-area variation in New York State for cesarean section11). Understanding smallarea variation in CT scan use can help identify underuse and overuse, both of which may be costly and negatively affect healthcare quality.4,12 We investigated how patient, hospital, and geographic characteristics influence CT scan use for inpatients with the goal that our research will lead to more effective CT scan use, lower costs, and higher quality.


Data Set

This research used the 2007 edition of the New York State Inpatient Database (SID) from the Healthcare Cost and Utilization Project. This data set resulted from a federal-state-industry partnership sponsored by the Agency for Healthcare Research and Quality. We analyzed 2,485,498 inpatients from 221 hospitals in 56 counties in New York State. In our analysis, we expunged fewer than 5% of the inpatient records in the SID because of missing values. No obvious patterns were evident in these expunged records compared with those that had complete data. As the data were de-identified, no institutional review board approval was requested or required.

Procedure Identification

The Agency for Healthcare Research and Quality created a utilization flag to indicate inpatient CT scan use based on International Statistical Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes, revenue codes, or both.13 In our study, an inpatient was considered to have received a CT scan if the inpatient had at least 1 report based on the utilization flag. We found that ICD-9-CM codes identified only 23.2% of inpatients receiving a CT scan, whereas revenue codes identified 98.8% of them.

Study Variables

We determined the association of CT scan use with the following categories of independent variables: (1) patient characteristics, (2) hospital characteristics, and (3) geographic characteristics. Patient demographics (including sex, race, and age), Major Diagnostic Category (MDC), payment method, and CT scan use were drawn directly from variables available in SID. For the age variable, we used small age group intervals to describe variation in CT scan use across age ranges. We followed conventional pediatric age groupings for ages <18 years (birth to 1 year, 1-4 years, 5-12 years, and 13- 17 years). The age groupings for adult inpatients were 18 to 29 years, 30 to 39 years, 40 to 64 years, 65 to 84 years, and >85 years. The MDCs divided patients into 25 mutually exclusive diagnostic areas based on the principal diagnosis from the ICD-9-CM codes.14

Hospital characteristics including hospital teaching status, location (urban or rural), and bed size were obtained from the New York State Department of Health.15 We used the Health Cost and Utilization Project’s classification to categorize hospitals as small, medium, or large based on their number of beds, location, and teaching status.16 The hospital’s county was the geographic characteristic we considered for this study.

Analytic Methods

To investigate the association of the independent variables with CT scan use, we calculated univariate statistics and reported mean values with their odds ratios (ORs)

and confidence intervals (CIs). We used a logistic regression model to predict the odds of an inpatient receiving a CT scan based on the independent variables discussed above. The urban or rural location of an inpatient’s hospital was not included in the logistic regression model because this variable was too highly collinear with the other variables in the model. We calculated the Schwarz criterion to determine the relative influence of each independent variable on explaining variation in CT scan use. The Schwarz criterion was calculated for the full model and separate submodels to evaluate the influence of each independent variable. For each submodel, we eliminated 1 independent variable and determined the difference in Schwarz criterion values between this submodel and the full model. The greater the difference between the Schwarz criterion values, the greater was the explanatory power of the independent variable.


CT Scan Use by Inpatients

Figure 1 shows how the percentage of inpatients who received at least 1 CT scan deviated from the state average for each county. Across the state, 28.0% of inpatients received at least 1 CT scan. Cortland County had the highest rate with 37.4%, closely followed by Rockland County with 37.2%. As shown in Figure 1, these 2 counties had CT scan use of +9.4% and +9.2% over the state average, respectively. In contrast, Otsego County and Schuyler County had the lowest rates of CT scan use and the largest deviation magnitude. Their rates were 17.0% and 17.3%, respectively, which translates into deviations from the state average of -11.0% and -10.7%.

Overall, we found considerable geographic variation. The median absolute deviation from the state average was 3.6%. No correlation existed between these CT scan use percentages and county population, even when adjusted for numbers of inpatients (r = -0.01, n =56 counties, 95% CI, -0.27 to 0.26). Also, no correlation existed between CT scan use percentages and number of hospitals per county (r = -0.008, 95% CI, -0.27 to 0.26).

Patient Characteristics Associated With Inpatient CT Scan Use

Patient Demographics and CT Scan Use. Appendix A shows inpatient CT scan use according to various patient characteristics. In this univariate analysis, there was statistically significant variation in inpatient CT scan based on sex, race, payer, age, and diagnosis. Males were more likely to have CT scans performed than females, with an OR of 1.172 (95% CI, 1.165-1.178). White inpatients were more likely to have CT scans than other racial groups. The ORs for racial groups compared with whites ranged from 0.687 (95% CI, 0.675- 0.697) for Asians to 0.881 (95% CI, 0.875-0.888) for blacks. Medicare benefi ciaries had the highest likelihood of receiving a CT scan, with an OR of 2.237 (95% CI, 2.222-2.252) compared with inpatients with private insurance.

Inpatient Age and CT Use Rate. We found significant variation in CT scan use for different ages. We calculated the percentage of inpatients of a given age who received a CT scan (Figure 2). Inpatients aged 88 years had the highest rate of CT scan use, at 47.2% of 20,783 inpatients aged 88 years. Only 1.7% of inpatients younger than 1 year received a CT scan (n = 260,503). Use of CT scans increased by age from birth to 8 years (23.3%, n = 4082), stayed relatively fl at until age 15 years (23.7%, n = 8077), decreased until age 30 years (14.7%, n = 27,604), and then steadily increased until age 88 years. The percentages of CT scan use for the oldest inpatients in the data set were slightly lower than the percentage for the peak age of 88.

Inpatient Medical Condition and CT Use Rate. Appendix B shows the association of MDCs with CT scan use. As expected, there was considerable variability in CT scan use across diagnostic categories. Trauma was the diagnostic category with the highest CT scan use (89.8%, n = 3377), while Newborn and Other Neonates (Perinatal Period) was the diagnostic category with the lowest use (1.0%, n = 234,290).

Hospital Characteristics Associated With Inpatient CT Scan Use

Appendix C lists the CT scan use rates for differences in hospitals’ teaching status, location, and bed size. The inpatient CT scan use was 30.7% for nonteaching and 27.1% for teaching hospitals. The inpatient CT scan use was 28.1% for urban and 26.3% for rural hospitals. Only a weak positive correlation could be observed between CT scan use and hospital bed size (r = 0.15, n = 221 hospitals, 95% CI, 0.02-0.27).

Logistic Regression Results

To control for patient and hospital characteristics and determine residual geographic variation in CT scan use at the county level, we used a logistic regression model. Figure 3 shows how the percentage of inpatients who received at least 1 CT scan deviated from the model’s prediction for each county. Based on its patient and hospital characteristics, Schoharie County had the highest predicted CT scan use, 39.7%. Its actual use was 30.1%, showing a deviation of -9.6%. Jefferson County had the lowest predicted CT scan use, 21.5%. Its actual use was 20.7%, showing a deviation of -0.8%. Delaware County had the largest magnitude deviation. The model predicted that 33.9% of its inpatients would receive at least 1 CT scan, but only 20.7% of its inpatients did, for a deviation of -13.2%. Rockland County had the largest positive deviantion, +10.1%, from its model prediction. Its prediction was 27.2%, but 37.2% of its inpatients received at least 1 CT scan. Appendix D lists for each county the observed percentage of inpatients receiving a CT scan, the expected percentage, the deviation, and the standardized ratio of observed percentage to expected percentage.

After controlling for patient and hospital characteristics, we still found considerable variation between counties. The median absolute deviation from the state average was 3.1%. Before using the logistic regression model (see Figure 1), the median absolute deviation was 3.6%.

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