Regionalization of Percutaneous Transluminal Coronary Angioplasty and Implications for Patient Travel Distance FREE

The relationship between procedure volume and patient outcomes is well-established for percutaneous transluminal coronary angioplasty (PTCA).^1- 7 At operator and hospital levels, higher procedure volume is associated with lower rates of inpatient mortality, emergency coronary artery bypass graft surgery, and complications.^1- 3,5- 8 Minimum procedure volume thresholds have been proposed to improve outcomes among patients undergoing PTCA. Jollis et al¹ estimated significant improvements among patients whose operators performed at least 150 procedures annually and at hospitals with at least 400 procedures annually. The American College of Cardiology recommends that hospitals perform at least 400 procedures annually and that operators perform at least 75 procedures annually.⁹ The Leapfrog Group, a coalition of health care benefits organizations, encourages purchasers of health care to direct patients to hospitals that perform more than 400 procedures annually.¹⁰

Although regionalization of PTCA may reduce morbidity and mortality by diverting patients away from low-volume hospitals and operators, it may also affect access to care as low-volume centers in remote or rural areas stop providing services. Birkmeyer et al¹¹ examined this issue with respect to pancreatectomy and esophagectomy and found that low minimum volume standards would require approximately 15% of patients to change hospitals. Of those patients, 75% would have traveled no more than 30 additional minutes. Pancreatectomy and esophagectomy are relatively uncommon procedures. It is unclear how regionalization would affect patient travel burden for more common procedures that often are performed on an urgent or emergent basis.

We sought to determine the impact of 2 hypothetical minimum volume standards for PTCA on patient travel distances. We also conducted an exploratory analysis of patient characteristics associated with observed travel distances and, for patients who did not seek care from the closest hospital, characteristics associated with travel beyond the closest hospital. As a secondary outcome, we calculated the risk-adjusted mortality associated with hospital-operator pairs in different volume categories.

Data were from the State Inpatient Databases of the Healthcare Cost and Utilization Project, which comprised administrative databases containing inpatient discharge abstracts from nonfederal US hospitals in participating states. We used 2001 data from 3 states (New York, New Jersey, and Florida), which included surgeon identifiers, hospital identifiers, and 5-digit patient and hospital ZIP codes necessary for the analysis. We evaluated patients in New York and New Jersey as a single group because they were close enough to seek care at a hospital in either state. We did not evaluate patients who may have sought care in other neighboring states because data were not available for those states.

The study population included all patients with a documented PTCA as indicated by International Classification of Diseases, Ninth Revision (ICD-9) codes 36.01, 36.02, and 36.05. We calculated operator procedure volumes by summing procedures over unique physician identifiers, and hospital procedure volumes by summing procedures over unique American Hospital Association identification numbers. Hospitals and operators with less than 5 procedures per year were excluded because of possible errors in coding.² Each procedure was counted, even when PTCA was performed multiple times in the same patient during the same hospitalization, which occurred in 3% of all discharges.¹ For operators practicing at more than 1 hospital, volume reflected all procedures performed in that state.

In addition to calculating observed travel distances, we calculated expected travel distances for each patient under 2 hypothetical minimum volume standards based on existing proposals^1,3,9- 10: minimum annual operator volume of 175 at a hospital with a minimum annual volume of 400 (standard 1) and minimum annual operator volume of 75 at a hospital with a minimum annual volume of 400 (standard 2). To meet a minimum volume standard, the hospital-operator pair had to meet both the operator dimension and the hospital dimension of the standard. Hospital-operator pairs were classified according to the highest standard met. Thus, hospital-operator pairs that met standard 1 were considered to be class 1 organizations, pairs that met standard 2 but not standard 1 were considered to be class 2 organizations, and pairs that did not meet standard 2 were considered to be class 3 organizations.

The primary outcome measure was the effect of the hypothetical minimum volume standards on patient travel distance. We determined geographic centroids for patients and hospitals using a ZIP code centroid file (ZIPList5 Geocode, CD Light LLC, The Woodlands, Tex). Geographic centroids correspond with the geographic center of each ZIP code area. We assumed that all patients living in a ZIP code area lived at the geographic centroid and patient ZIP codes were based on the home address. We calculated straight-line travel distances using the latitude and longitude of each pair of patient and hospital-operator centroids with a formula (available upon request from the authors) that estimated distance within 0.01 miles (0.02 km). We excluded patients who lived more than 193 miles (309 km) from their hospital-operator (99th percentile) because of the strong likelihood that these patients were not close to their place of residence when they sought care. For each minimum volume standard, we calculated the expected travel distance between the patient ZIP code and the ZIP code of the nearest hospital-operator meeting the criteria of that standard. Patient travel burden was defined as the difference between the expected travel distance (under each minimum volume standard) and the observed travel distance. For transfer patients, travel distance was measured from the patient’s home address to the transfer facility. In a sensitivity analysis, patients from the study population who were transferred to the hospital from other facilities were removed because observed travel distances for these patients may not accurately reflect the patients’ choice of hospital-operator pair.

We compared patient characteristics across hospital-operator classes using χ² tests for categorical variables and 1-way analysis of variance for continuous variables. Statistical significance was defined as P<.05, and all tests were 2-sided. Multivariable log-linear regression was used to conduct an exploratory analysis on the independent effect of patient characteristics on observed travel distance and distance traveled beyond the closest hospital-operator pair. Patient characteristics incorporated in the model were identified on the basis of prior studies and clinical expertise and included age, sex, race/ethnicity, urgency of the procedure, state of residence, myocardial infarction (MI) status, comorbidity, insurance type, and admission source.^1- 2 For the race/ethnicity variable, the Healthcare Cost and Utilization Project (hospital discharge abstract) codes were used, which included black, white, Hispanic, Asian, and other. Race/ethnicity was assessed in this analysis because previous research has identified race/ethnicity as an important factor in the use of coronary revascularization procedures. For the comorbidity variable, the D’Hoore translation of the Charlson comorbidity index was used, which was developed for use with ischemic heart disease–related ICD-9 codes listed among secondary diagnoses in administrative databases.^12- 13 The admission source variable indicated whether the admission was routine (physician referral) from the emergency department, a transfer from another hospital, or a transfer from another health care facility. We converted distances of 0 to 0.00001 miles for the regression analysis.

To place our study population in the context of previous work demonstrating the relationship between procedure volume and mortality,^1- 7 we examined the risk-adjusted mortality associated with each hospital-operator class as a secondary outcome. A multivariable logistic regression model was constructed using generalized estimating equations to account for the nonindependence of patients within operators and hospitals. We excluded patients who were transferred to another hospital because outcome data at the transfer facility were not recorded in the database. Independent variables included patient age, sex, race/ethnicity, comorbidity, state of residence, MI status, and urgency.^1- 2 We then used the model to compute predicted mortality for each patient. We summed the predicted mortality of each patient to determine the expected number of deaths for each hospital-operator class. We then multiplied the observed-to-expected ratio for each class by the mortality rate for the overall population to determine the risk-adjusted mortality for each class.¹⁴ Statistical analyses were performed using SAS version 8.0 (SAS Institute, Cary, NC).

In 2001, 120 hospitals and 1418 operators performed PTCA on the 97 401 patients who were eligible for the study. A total of 23 148 patients (24%) received care from hospital-operator pairs considered to be class 3; these hospital-operator pairs failed to meet the hospital minimum volume standard of 400, the operator minimum procedure volume of 75, or both (Table 1).

Table 2 summarizes patient characteristics by hospital-operator class. Class 3 had the greatest percentage of patients admitted through the emergency department and, consistent with this finding, had the greatest percentage of procedures considered emergent. In addition, a greater percentage of patients receiving Medicare sought care from class 1 pairs compared with class 2 or class 3.

Median (interquartile range [IQR]) travel distance was 9.5 miles (4.7-18.2 miles [15.2 km; 7.5-29.2 km]). A total of 51 280 patients (53%) traveled less than 10 miles (<16 km) for the PTCA procedure, 41 157 (42%) traveled 10 to 50 miles (16 to 80 km), and 4964 (5%) traveled more than 50 miles (>80 km).

Table 3 summarizes expected travel distances under the hypothetical minimum volume standards. Under standard 1, 57 996 patients (60%) who received care from class 2 or class 3 pairs would have been required to move to a class 1 hospital-operator. Paradoxically, the median travel distance for these patients would have been shorter than the observed distance. This occurred because many patients were traveling to a low-volume hospital-operator pair when a higher-volume hospital-operator pair often was closer. Approximately 10% of patients would have traveled farther than their observed travel distance and 25% would have traveled a shorter distance. Most patients with longer travel distances under this standard would have traveled no more than 25 miles farther than their observed travel distance, and most patients with shorter travel distances would have saved no more than 10 miles. Of the remaining 65% who would have experienced no change in travel distance, 38% (25% of all patients) would have switched to a high-volume operator at the high-volume hospital at which they underwent the procedure.

Under standard 2, 23 148 patients (24%) who received care from a class 3 pair would have been required to switch to class 1 or class 2 pair. The median (IQR) travel distance for these patients was 6.8 (3.3-14.2) miles (10.9 [5.3-22.7] km). Class 1 was the closest alternative for approximately 79% of these patients and class 2 was the closest alternative for 21% of the patients. Only 2% of patients would have traveled farther under this standard and 11% would have traveled a shorter distance. Of the patients traveling farther, most would have traveled no more than 25 miles farther than their observed travel distance. For patients traveling a shorter distance, most would have saved no more than 10 miles. The majority of patients (87%) would have incurred no additional travel. Of these, 12% (11% of all patients) would have switched to a high-volume operator in the same high-volume hospital.

Under both minimum volume standards, less than 1% of patients would have traveled more than 50 miles farther than their observed travel distance. In addition, under both minimum volume standards, the changes in travel distance would have increased for more patients had all patients initially sought care from the closest hospital-operator pair (Table 4).

Younger patients, white patients, men, and patients not receiving Medicaid were more likely to travel farthest for PTCA. Patients who transferred from another hospital, patients without an MI, and patients admitted for an urgent PTCA also traveled farther. However, the regression model explained only 5% of the variance in observed travel distances. In the analysis of factors associated with travel beyond the closest hospital-operator, younger patients, white patients, men, patients not receiving Medicaid, patients without an MI, and patients transferred from another hospital were more likely to travel beyond the closest hospital-operator. Again, the regression model explained only 5% of the variance in travel distance beyond the closest hospital-operator. However, unlike the analysis of factors associated with observed travel distance, admission for an emergency procedure was also associated with greater travel distance beyond the closest hospital-operator, most likely because patients may not have been at home and were taken to the facility nearest their location.

Transfer patients had a median (IQR) travel distance of 12.2 (7.5-24.9) miles (19.6 [12.1-40.1] km). In sensitivity analysis, in which transfer patients were removed, median (IQR) observed travel distance was 8.6 (4.1-16.5) miles (13.8 [6.6-26.6] km). Approximately 57% of nontransfer patients traveled less than 10 miles, whereas only 39% of transfer patients traveled less than 10 miles. If nontransfer patients had initially traveled to the closest hospital-operator, 73% of these patients would have traveled less than 10 miles. Although nontransfer patients had shorter median travel distances than transfer patients, more than half of nontransfer patients were still not receiving care from the closest hospital-operator.

In the mortality calculation, we excluded 6710 patients who were discharged to another hospital and, therefore, could not be followed up. For the remaining 90 691 patients, unadjusted mortality increased with decreasing procedure volume. Patients receiving care from class 3 pairs had higher mortality (1.3%) than did patients receiving care from class 2 (0.9%) or class 1 (0.8%). Risk-adjusted mortality followed a similar trend. Patients receiving care from class 3 pairs had higher risk-adjusted mortality (1.2%) than did patients receiving care from class 2 (0.9%) and class 1 (0.8%) (P<.001 for both comparisons).

Our findings suggest that regionalization of PTCA would not increase travel distances for most patients in New York, New Jersey, and Florida. More than half of patients did not travel to the closest institution for PTCA; many bypassed hospitals with higher procedure volume and lower risk-adjusted mortality. Under our hypothetical minimum volume standards, therefore, median expected travel distances were shorter than observed travel distances. Observed travel distances would have been shorter than expected travel distances had all patients sought care from the closest hospital-operator.

We also found that insurance type, race/ethnicity, sex, age, and other patient characteristics were significantly associated with observed travel distances and with distance traveled beyond the closest hospital-operator. Our model explained only 5% of the variation in observed travel distance, suggesting that unmeasured factors account for most of the variation in observed travel distance. However, the association between insurance status and observed travel distances creates a concern that some health care plans may restrict choice of hospital-operator and that patients in these plans may travel farther despite having critical medical conditions. Time to the emergency department influences outcomes in situations requiring immediate intervention.^15- 16 Because PTCA is often an emergency procedure, increases in travel distance for some patients may not be worth improvements in mortality for other patients. In our study, if the 23 000 patients who visited a class 3 hospital-operator had instead presented to a class 1 hospital-operator, the associated 0.4% reduction in mortality would have saved an additional 92 lives. However, this finding is valid only if we assume that the greater mortality due to longer transit times for some patients balances out the lower mortality due to shorter transit times for other patients. Policies of regionalization must, therefore, take into account factors, such as payer requirements that may affect travel distance, as well as the effect of transit time on the outcome of the procedure itself.

To our knowledge, only 3 articles address the impact of regionalization on travel distances. Grumbach et al¹⁷ conducted a similar study of travel distances for patients undergoing coronary artery bypass graft surgery. After hospitals performing less than 100 coronary artery bypass graft procedures annually stopped providing services in a hypothetical scenario, travel distances increased for less than 5% of patients. Birkmeyer et al¹¹ studied pancreatectomy and esophagectomy and found that low minimum volume standards would have small effects on travel time. Chang and Klitzner¹⁸ studied the effect of regionalization on pediatric cardiac surgery and found that mean travel distance increased by 12.7 miles if all procedures were performed at high-volume hospitals. Most of these articles report small increases in travel distance or travel time; however, our study showed a decrease in travel distance for the majority of patients undergoing PTCA, a more common procedure.

Our analysis has some limitations. The distance calculations were made using straight-line distances rather than travel route distances or travel times. However, Grumbach et al¹⁷ report that correlation between straight-line distances and travel time is 0.987 in New York, except in New York City and Long Island. Also, under the minimum volume standards, we did not take into account that patients living along state borders may have been closest to hospitals in neighboring states, thereby further reducing travel distances. Furthermore, the travel distances may also be inaccurate because we measured distance from patients’ home addresses, although some patients requiring urgent or emergent PTCA may not have been at home at the time of the precipitating event. Using geographic centroids rather than population centroids to approximate patient and hospital locations may have introduced inaccuracies in the distance calculations. Also, our analysis assumes that all patients would make the most efficient choice and select the closest eligible hospital-operator if minimum volume standards were implemented.

The absence of clinical detail is an inherent limitation of administrative databases and makes risk adjustment imprecise. Although we excluded transfer patients from our mortality model, any patient who was discharged from one facility and readmitted to another would be included in the analysis, thus artificially depressing mortality estimates for the first facility. Similarly, the data set does not include postdischarge mortality, so the mortality estimates reflect in-hospital mortality only. Finally, the 3 states in our analysis have certificate-of-need laws. It is possible that states without certificate-of-need laws for angioplasty facilities may have more low-volume hospital-operator pairs, which would result in more patients traveling farther to reach a hospital-operator and meeting minimum volume requirements.

Proponents of regionalization often cite cost savings for payers and mortality differences between high-volume and low-volume hospital-operator pairs as the main advantages.¹⁷ If high-volume hospital-operator pairs have lower costs and mortality rates, it would be in payers’ interests to encourage patients to seek care from those hospital-operator pairs. Report cards documenting quality may help to identify high-volume hospital-operator pairs; however, payers must actively use quality data in contracting arrangements to affect quality of care.¹⁹

The potential consequences of regionalization are both positive and negative. Ceasing to provide one service may detract from other services at the same hospital.¹¹ For example, removing interventional cardiologists from a low-volume hospital may cause patients admitted for other noninterventional cardiac diagnoses to not receive high-quality care due to the hospital’s inability to attract and maintain an experienced cardiology staff. Alternatively, and contrary to the hypothesis of our study, low-volume hospital-operator pairs could precipitate a volume shift and avoid exiting the market by drawing procedures from nearby high-volume hospital-operator pairs that perform well above minimum volume standards. Available studies suggest that some degrees of regionalization can be achieved with minor consequences for patient travel. However, potential costs of regionalization not related to travel must be examined before such policies can be recommended.