Abstract
BACKGROUND:
With the expansion of non-small cell lung cancer (NSCLC) screening methods, the percentage of cases with early-stage NSCLC is anticipated to increase. Yet it remains unclear how the type and case volume of the health care facility at which treatment occurs may affect surgery selection and overall survival for cases with early-stage NSCLC.
METHODS:
A total of 332,175 cases with the American Joint Committee on Cancer (AJCC) TNM stage I and stage II NSCLC who were reported to the National Cancer Data Base (NCDB) by 1302 facilities were studied. Facility type was characterized in the NCDB as community cancer program (CCP), comprehensive community cancer program (CCCP), academic/research program (ARP), or integrated network cancer program (INCP). Each facility type was dichotomized further into high-volume or low-volume groups based on the case volume. Multivariate Cox proportional hazard models, the logistic regression model, and propensity score matching were used to evaluate differences in survival and surgery selection among facilities according to type and volume.
RESULTS:
Cases from ARPs were found to have the longest survival (median, 16.4 months) and highest surgery rate (74.8%), whereas those from CCPs had the shortest survival (median, 9.7 months) and the lowest surgery rate (60.8%). The difference persisted when adjusted by potential confounders. For cases treated at CCPs, CCCPs, and ARPs, high-volume facilities had better survival outcomes than low-volume facilities. In facilities with better survival outcomes, surgery was performed for a greater percentage of cases compared with facilities with worse outcomes.
CONCLUSIONS:
For cases with early-stage NSCLC, both facility type and case volume influence surgery selection and clinical outcome. Higher surgery rates are observed in facilities with better survival outcomes.
Keywords: facility type, facility volume, lung cancer, prognosis, surgery selection
INTRODUCTION
Lung cancer screening trials, such as the International Early Lung Cancer Action Program (I-ELCAP)1 and the National Lung Screening Trial (NLST),2 have shown great benefit in the detection of early-stage disease and improvement in the 10-year survival rate. The implementation of low-dose computed tomography screening is expected to increase the incidence of the diagnosis of early-stage non–small cell lung cancer (NSCLC).3 Early-stage NSCLC is a potentially curable condition and therefore treatment selection, especially receipt of potentially curative surgery, is a well-known determinant of survival, and has been included as the standard care for eligible cases with early-stage NSCLC in the European Society for Medical Oncology (ESMO) guidelines.4
The question of how teaching facility (TF) status affects care quality has been widely investigated, and a positive relationship between TF status and case outcomes has been demonstrated.5–8 In reported studies of facility type and surgical treatment of lung cancer,6,8–10 only a small percentage of studies have reported the overall survival outcome, whereas others have reported surgical resection outcomes. The limited population size in previous studies meant that stratification of cases by early and late stage of disease has not been performed. Moreover, the effect of non-TF type on survival outcome has not yet been reported.
The relationship between facility volume and care quality also has been investigated for >40 years, with mixed results.11 For cancer treatment, the majority of studies have found that higher volume was associated with a lower surgical morality rate and better survival outcomes for cases,6,12,13 whereas others have found no difference.14,15 One possible reason is that estimation of the performance of low-volume hospitals would be unstable because of the small sample size.16 In addition, to our knowledge, it remains unknown whether this association persists in all types of facilities, and if so, what the underlying reasons are.
To investigate these knowledge gaps, the current study was performed to evaluate the influence of facility type and volume on surgery selection and survival outcome in cases with early-stage NSCLC. Although outcomes based on facility volume and TF status have been widely investigated independently, to our knowledge the current study is the first study to stratify the volume effect by facility type. This also to our knowledge is the first study to investigate the associations among facility types, surgery rates, and survival outcomes in cases with early-stage lung cancer.
MATERIALS AND METHODS
Data Collection
A total of 332,175 cases with early-stage (stage I or stage II under the American Joint Committee on Cancer (AJCC) TNM staging system) NSCLC were identified from the National Cancer Data Base (NCDB). The cases were independent and recorded by annual reports from all American College of Surgeons Commission on Cancer accreditation programs from 2004 to 2013. A total of 1299 facilities were involved and were categorized by the Commission on Cancer accreditation program into 4 program types as follows: 1) a community cancer program (CCP), which accessions 100 to 500 newly diagnosed cancer cases per year; 2) a comprehensive community cancer program (CCCP), which accessions ≥500 cases per year; 3) an academic/research program (ARP), which participates in cancer-related clinical research and mandates postgraduate education, including residency training; and 4) an integrated network cancer program (INCP), defined by the American College of Surgeons as “an organization that owns, operates, leases, or is part of a joint venture with multiple facilities providing integrated cancer care and comprehensive services” (https://www.facs.org/quality-programs/cancer/coc/info/incp. Accessed July 11, 2019) In the current study, facility volumes were determined by counting the number of cases with NSCLC reported annually according to the NCDB. The median volume for all facilities was determined and used to categorize each facility as high or low volume to study the joint effects of facility type and volume (Fig. 1). To separate the effect of case volume from facility type, the median facility volume for each facility type was determined and used to assign each facility as high or low volume within each facility type.
Figure 1.
Case survival outcomes from different facility types and volumes. (A) Kaplan-Meier plot of cases from different facility types and volumes, (B) Cox regression analysis comparing case survival outcomes from different facility types and volumes. High volume was defined as >17.9 cases of stage I/stage II disease annually, which was the median volume across all facilities. No data regarding integrated network cancer program (INCP) low-volume facilities are shown because volumes from all INCP facilities were >17.9 cases of stage I/stage II disease annually. ARP, academic/research program; CCCP, comprehensive community cancer program; CCP, community cancer program.
In addition to information regarding facility type, other demographic and clinical variables collected included age at diagnosis, sex, race, insurance status, comorbidity, treatment, and resident zip code—level characteristics (median household income collected from 2008 to 2012, percentage of residents without a high school diploma collected from 2008 to 2012, and urban/rural area collected in 2013). Comorbidity was represented by the Charlson/Deyo Score. Treatment information, including surgery, chemotherapy, and radiotherapy, was collected. Treatment information was stratified into 4 groups: 1) surgery performed; 2) no surgery but radiotherapy performed; 3) no surgery or radiotherapy but chemotherapy performed; and 4) no treatment received. The number of cases in each treatment approach group is listed in Supporting Table 1.
Statistical Analysis
To study the surgery selection, a multivariate logistic regression model adjusted for potential confounders was used to calculate the odds ratio (OR) of surgery selection among different facility types or volumes. Wald tests were used to determine whether the OR was 1. In survival analysis, overall survival was defined as the time from diagnosis to death from any reason or until last contact. Kaplan-Meier survival curves were used to visualize overall survival and Cox regression models and Wald tests were used to compare survival differences among different facility types or volumes in both univariate and multivariate analyses adjusted for potential confounders. To further rule out the effect of potential confounders, propensity score matching was used to weight and balance case groups with different clinical characteristics.17 The propensity score estimates the conditional probability of selecting a certain treatment condition given all the covariates that may affect this selection; thus, weighting cases under different conditions to balance propensity scores helps suggest the relationship between treatment condition and outcome independent of other covariates. All variables listed earlier were considered in propensity score matching. All P values were 2-sided, and results were considered statistically significant at P ≤ .05. All analyses were performed using R statistical software (version 3.4.2).18 R packages for “survival” (version 2.41-3) and “twang” (version 1.5) were used.
RESULTS
Case Characteristics Differed Across Facility Types
In total, 332,175 cases with early-stage NSCLC reported by 1299 facilities were included in the current analysis. Compared with other facility types, cases with NSCLC reported by ARPs were younger, more likely to be female, more likely to be nonwhite, had lower Charlson/Deyo comorbidity scores, and were more likely to have private insurance (Table 1). Cases reported by INCPs were more likely to come from areas with higher income and a higher educational level, and from metropolitan counties.
TABLE 1.
Characteristics of the 1299 Facilities Studied in the NCDB
| Characteristic | All | CCP | CCCP | ARP | INCP | Pa |
|---|---|---|---|---|---|---|
| No. of hospitals | 1299 | 433 | 598 | 232 | 36 | |
| No. of cases | 332,175 | 32,403 | 163,981 | 11,2673 | 23,118 | |
| Age, % | <.001 | |||||
| <65 y | 29.2 | 28.5 | 27.2 | 32.5 | 27.8 | |
| ≥65 y | 70.8 | 71.5 | 72.8 | 67.5 | 72.2 | |
| Sex, % | <.001 | |||||
| Male | 49.6 | 51.7 | 50.0 | 48.5 | 48.7 | |
| Female | 50.4 | 48.3 | 50.0 | 51.5 | 51.3 | |
| Race, % | <.001 | |||||
| White | 88.4 | 89.2 | 91.2 | 84.2 | 87.1 | |
| Black | 8.6 | 8.1 | 6.5 | 11.7 | 9.5 | |
| Other | 3.0 | 2.7 | 2.3 | 4.1 | 3.3 | |
| AJCC Stage of disease, % | <.001 | |||||
| I | 76.5 | 72.0 | 76.1 | 78.3 | 77.0 | |
| II | 23.5 | 28.0 | 23.9 | 21.7 | 23.0 | |
| Charlson/Deyo Score % | <.001 | |||||
| 0 | 53.7 | 52.5 | 51.8 | 57.4 | 50.2 | |
| 1 | 32.6 | 32.7 | 33.7 | 30.4 | 34.8 | |
| ≥2 | 13.7 | 14.8 | 14.4 | 12.2 | 15.0 | |
| Insurance status, % | <.001 | |||||
| Not insured | 1.6 | 1.7 | 1.4 | 1.9 | 1.7 | |
| Private insurance | 26.1 | 21.9 | 25.5 | 28.4 | 24.8 | |
| Medicaid | 3.9 | 4.9 | 3.3 | 4.6 | 3.8 | |
| Medicare | 65.3 | 68.6 | 67.5 | 60.6 | 68.4 | |
| Other government insurance | 1.2 | 1.0 | 1.0 | 1.5 | 0.7 | |
| Unknown status | 1.9 | 1.8 | 1.2 | 3.1 | 0.6 | |
| Surgery rate, % | <.001 | |||||
| Received | 69.9 | 60.8 | 68.4 | 74.8 | 69.8 | |
| Not received | 30.1 | 39.2 | 31.6 | 25.2 | 30.2 | |
| Resident Zip Code–Level Characteristics | ||||||
| Median household income, USD, % | <.001 | |||||
| <$38,000 | 19.4 | 20.8 | 19.6 | 19.4 | 15.9 | |
| $38,000-$47,999 | 25.4 | 31.7 | 26.6 | 22.3 | 23.5 | |
| $48,000-$62,999 | 26.8 | 26.7 | 27.8 | 24.9 | 30.0 | |
| ≥$63,000 | 28.3 | 20.9 | 26.0 | 33.4 | 30.6 | |
| No high school diploma, % | <.001 | |||||
| ≥21% | 17.0 | 19.4 | 16.7 | 17.3 | 14.6 | |
| 13%-20.9% | 27.9 | 31.9 | 28.6 | 26.2 | 25.1 | |
| 7%-12.9% | 33.6 | 35.3 | 33.9 | 32.1 | 36.2 | |
| <7% | 21.5 | 13.4 | 20.8 | 24.4 | 24.2 | |
| Urban/rural residence, % | <.001 | |||||
| Metropolitan counties | 81.7 | 70.1 | 79.9 | 85.8 | 90.6 | |
| Urban counties | 16.1 | 26.7 | 17.3 | 12.8 | 8.7 | |
| Rural counties | 2.2 | 3.2 | 2.8 | 1.4 | 0.7 |
Abbreviations: AJCC, American Joint Committee on Cancer; ARP, academic/research program; CCCP, comprehensive community cancer program; CCP, community cancer program; INCP, integrated network cancer program; NCDB, National Cancer Data Base; USD, US dollars.
The chi-square test was used to calculate the P value.
Facility Type Is Associated With Case Survival Outcome
Among different categories of facilities, ARPs were found to have the best survival outcome, with a median survival of 59.1 months, followed by INCPs (median survival of 49.9 months), CCCPs (median survival of 46.3 months), and CCPs (median survival of 36.0 months) (Fig. 2). To further investigate whether the facility type was an independent factor in survival outcome, a multivariate Cox regression analysis was performed to study the association between facility type and case survival adjusted by treatment selection, age, sex, race, stage, Charlson/Deyo Score, insurance status, income, educational level, and urban/rural area (Table 2). Propensity score matching through these confounders also demonstrated similar results (see Supporting Table 2). The significant differences in case survival persisted among all different facility types, indicating that facility type was an independent factor affecting the prognosis of cases with lung cancer.
Figure 2.
Case survival outcomes from different facility types. (A) Kaplan-Meier plot of cases from different facility types, (B) Cox regression analysis comparing case survival outcomes from different facility types. Academic/research programs (ARPs) demonstrate the lowest hazard ratio and the longest median survival. CCCP indicates comprehensive community cancer program; CCP, community cancer program; INCP, integrated network cancer program.
TABLE 2.
HR of Cases With Lung Cancer Treated in Different Types of Facilities by Multivariate Cox Regressiona
| HR (95% CI) | P | |
|---|---|---|
| Individual-Level Characteristics | ||
| Facility type | ||
| CCP | Reference | |
| CCCP | 0.94 (0.92-0.95) | <.001 |
| ARP | 0.86 (0.84–0.87) | <.001 |
| ICNP | 0.91 (0.88–0.93) | <.001 |
| Age at diagnosis, y | ||
| <65 | Reference | |
| ≥65 | 1.4 (1.37-1.42) | <.001 |
| Sex | ||
| Male | Reference | |
| Female | 0.74 (0.73–0.75) | <.001 |
| Race | ||
| Black | Reference | |
| Other | 0.86 (0.83-0.89) | <.001 |
| White | 1.05 (1.03-1.07) | <.001 |
| Insurance status | ||
| Not insured | Reference | |
| Private insurance | 0.81 (0.78-0.85) | <.001 |
| Medicaid | 1.08 (1.03-1.13) | <.001 |
| Medicare | 0.99 (0.95-1.04) | .73 |
| Other government insurance | 0.94 (0.88-1.00) | .05 |
| Unknown status | 0.92 (0.87-0.97) | <.001 |
| Charlson/Deyo Score | ||
| 0 | Reference | |
| 1 | 1.17 (1.15-1.18) | <.001 |
| ≥2 | 1.44 (1.42-1.46) | <.001 |
| AJCC Stage of disease | ||
| I | Reference | |
| II | 1.6 (1.58-1.62) | <.001 |
| Treatment approach | ||
| Surgery received | Reference | |
| No surgery; RT received | 2.48 (2.45-2.51) | <.001 |
| No surgery or RT; CT received | 3.67 (3.57-3.77) | <.001 |
| No treatment received | 4.43 (4.36-4.5) | <.001 |
| Resident zip code-level characteristics | ||
| Median household income, USD | ||
| <$38,000 | Reference | |
| $38,000-$47,999 | 0.97 (0.87-0.97) | <.001 |
| $48,000-$62,999 | 0.94 (0.92-0.95) | <.001 |
| ≥$63,000 | 0.87 (0.85-0.89) | <.001 |
| Percentage without high school diploma | ||
| ≥21% | Reference | |
| 13%-20.9% | 1 (0.99-1.02) | .76 |
| 7%-12.9% | 1 (0.99-1.02) | .79 |
| <7% | 0.97 (0.95-0.99) | <.001 |
| Urban/rural residence | ||
| Metropolitan counties | Reference | |
| Urban counties | 1.01 (1.00-1.02) | .15 |
| Rural counties | 1.04 (1.00-1.07) | .03 |
Abbreviations: AJCC, American Joint Committee on Cancer; ARP, academic/research program; CCCP, comprehensive community cancer program; CCP, community cancer program; CT, chemotherapy; HR, hazard ratio; INCP, integrated network cancer program; RT, radiotherapy; USD, US dollars.
Number of cases is shown in Supporting Table 1.
Facility Type Is Associated With Surgery Selection
To determine whether different facility types had different tendencies to perform surgery, multivariate logistic regression was used to calculate the OR of surgery selection, adjusted by all other available demographic and clinical variables, including stage of disease (Table 3). The likelihood of performing surgery was found to be significantly different between different facility types. Specifically, compared with CCPs, the ARP facilities were the most likely to treat cases with surgery (OR, 1.81), followed by INCPs (OR, 1.44) and CCCPs (OR, 1.36). It is interesting to note that this was the same order of facility types, from best to worst, when analyzed for case survival. Propensity score matching across the 4 facility types demonstrated similar results and support this finding (see Supporting Table 3).
TABLE 3.
Adjusted ORs of Performing Surgery for Each Facility Type
| Facility Types | OR (95% CI)a | P |
|---|---|---|
| CCP | 1 (Reference) | |
| CCCP | 1.36 (1.33-1.40) | <.001 |
| ARP | 1.81 (1.76-1.87) | <.001 |
| INCP | 1.44 (1.39-1.50) | <.001 |
Abbreviations: ARP, academic/research program; CCCP, comprehensive community cancer program; CCP, community cancer program; INCP, integrated network cancer program; OR, odds ratio.
Adjusted by age, sex, race, Charlson/Deyo Score, AJCC stage I/II disease, insurance status, income, educational level, and urban/rural status.
Joint Effects of Facility Type and Case Volume on Case Survival Outcome
The distribution of facility volume is shown in Supporting Figure 1. When stratified further by the median annual volume of all facilities (17.9 cases per year), case survival outcomes and facility types demonstrated a similar trend, but within a facility type, high-volume facilities had better survival than low-volume facilities (Fig. 1). Thus, we further investigated the effects of facility type as a primary factor and facility volume as a secondary factor on surgery selection and case outcome in the following analysis.
Effects of Case Volume Within the Same Facility Type
As a secondary factor, the effect of facility volume on overall survival then was investigated individually for each facility type. Within each facility type, the facilities were dichotomized into high-volume and low-volume groups using the median facility volume as a cutoff value. Consistent with previous reports, for CCPs, CCCPs, and ARPs, the high-volume group showed better survival outcomes than the low-volume group on univariate analysis, whereas no significant survival difference was detected for INCPs (see Supporting Fig. 2). However, multivariate analysis demonstrated that only in ARPs and CCCPs was higher volume found to be an independent factor associated with a better survival outcome. In INCPs, no difference was found between high-volume and low-volume facilities. Surprisingly, in CCPs, high-volume facilities had modestly worse survival outcomes compared with low-volume facilities (hazard ratio, 1.05; 95% CI, 1.02-1.10) (see Supporting Table 4). Propensity score matching also demonstrated similar hazard ratio results, which led to the same conclusion (see Supporting Table 5). To investigate the differences in surgery selection between high-volume and low-volume facility groups, multivariate logistic regression was used to calculate the OR of surgery selection (Table 4). It is interesting to note that for each facility type, high-volume facilities were more likely to select surgery than low-volume facilities. Propensity score matching results closely followed these trends (see Supporting Table 6).
TABLE 4.
Adjusted ORs of Performing Surgery Between High-Volume and Low-Volume Facilitiesa by Different Facility Types
| Facility Type | OR (95% CI)b High Volume Vs Low Volume | P |
|---|---|---|
| CCP | 1.46 (1.38-1.54) | <.001 |
| CCCP | 1.12 (1.09-1.15) | <.001 |
| ARP | 1.31 (1.27-1.36) | <.001 |
| INCP | 1.09 (1.02-1.16) | .01 |
Abbreviations: ARP, academic/research program; CCCP, comprehensive community cancer program; CCP, community cancer program; INCP, integrated network cancer program; OR, odds ratio.
High-volume status and low-volume status were defined using the median volume within each facility type.
Adjusted by age, sex, race, Charlson/Deyo Score, insurance status, stage of disease, income, educational level, and urban/rural status.
DISCUSSION
The current study examined a large cohort of 332,175 cases with early-stage (stage I and stage II) NSCLC from the NCDB to study the relationship between facility type, surgery selection, and case outcome. The large sample size and multifacility data collection greatly improved the statistical power and generalizability of the current study. Although outcomes based on facility volume and TF status have been widely investigated independently, to our knowledge the current study is the first to stratify the volume effect by facility type, including TF status.
Cases treated at ARPs demonstrated the best overall survival, a finding that is consistent with previous reports.5,6,9 To our knowledge, the current study is the first to identify a gradient in adjusted long-term survival, with the best outcomes noted among cases treated in ARP sites followed by those treated in INCPs, CCCPs, and CCPs (with the worst outcomes). This ranking persisted after multivariate adjustment by age, sex, race, treatment, and other socioeconomic status data, indicating that facility type is an independent predictor of survival outcome for cases with NSCLC.
To understand the factors accounting for the survival difference among different facilities, it is worth noting that facilities with improved overall survival were more likely to perform surgery. Because surgery is the preferred treatment modality for cases with early-stage lung cancer,4,19 the correlation between surgery rate and survival outcomes rank suggests that survival outcomes among facility types partly result from the selected treatment modality. This result should alert hospital facilities to ensure that the most appropriate treatment modality is chosen when evaluating cases, and surgery should be selected when clinically appropriate. The correlation found between surgery rate and survival outcome also demonstrates an urgent need for researchers to determine the specific reasons why surgery is performed more frequently at ARP facilities and whether there are any other factors that explain outcome differences between facility types, which will require future analysis.
It is interesting to note that the differences in both overall survival and surgery rates among different facilities still were significant, although decreased, after adjusting for multiple confounders. This observation suggests that there may be several uncaptured factors leading to the differences. First, although the Charlson/Deyo Score was provided by the NCDB to describe comorbidities, it was not enough to indicate the presence of contraindications to surgery, which may be a major potential confounder for the recommendation of surgery. Second, high-volume and ARP facilities may have a greater availability of cardiothoracic surgeons who are experienced in lung cancer surgery, and therefore these institutions are more likely to offer surgical treatments to cases with early-stage NSCLC. In addition, high-volume and ARP facilities may provide more accurate staging through radiology and pathology, leading to the selection of an appropriate treatment modality. All of these could be confounding factors for treatment selection. In any case, the survival analysis combined with the surgery selection tendency indicated the existence of care quality differences between facility types. Third, the presence of a multidisciplinary team operating within a regular disease-specific tumor conference has been reported as affecting case assessment and treatment selection,20 and thus is another possible confounder.
In the current multivariate analysis, the effect of volume was modest and smaller than the effect of facility type. It is interesting to note that for CCP facilities, high-volume facilities performed even worse than low-volume facilities after adjustment for other confounders, which is inconsistent with previous studies.6,9 Adjustment by clinical and demographic confounders and the use of a nationwide database largely reduced potential bias due to the cases’ own characteristics, which might explain this inconsistency with other reports. However, it still was possible that other quality measures, such as surgical mortality ratio, were improved in high-volume CCP facilities, a finding that requires further study.
The results of the current study support the idea that for cases with cancer, selecting hospitals of an ARP facility type that perform a high number of lung surgeries would be reasonable when no other quality measures are available.21 However, such a recommendation should be approached carefully within the context of a few limitations. First, cost was not considered in our research because cost information was not available from the NCDB. Because it has been reported that the average cost was 60% higher in teaching hospitals but lower in high-volume facilities,22,23 it is an important consideration in addition to facility type and volume when considering health care outcomes. Second, the survival outcome in multivariate analysis did not favor high-volume facilities in CCPs, which means that if a CCP facility was being considered, a high-volume facility would not necessarily be a better choice. Third, facility volume rather than procedure volume was considered in our research. It might be reasonable to choose a hospital according to surgeon volume or type rather than facility volume because some previous studies have found improved outcomes when cardiothoracic surgeons with high individual case volumes perform lung cancer surgery.24,25 This information was not available from the NCDB and therefore was not included in the current study.
Supplementary Material
Acknowledgments
FUNDING SUPPORT
Supported by the National Institutes of Health (grants P50CA70907, 5P30CA1425431, R01GM115473, and 1R01CA172211), a National Cancer Institute Midcareer Investigator Award in Patient-Oriented Research (K24 CA201543-01 to David E. Gerber), the Cancer Prevention and Research Institute of Texas (RP180805), and the Agency for Healthcare Research and Quality (R24 HS 22418 to Ethan A. Halm).
We thank the National Cancer Data Base (NCDB) project for collecting this invaluable information and making it publicly available. The NCDB is a joint project of the Commission on Cancer of the American College of Surgeons and the American Cancer Society. The data used in the study were derived from a deidentified NCDB file. The American College of Surgeons and the Commission on Cancer have not verified and are not responsible for the analytic or statistical methodology used, or the conclusions drawn from these data by the investigators.
Footnotes
CONFLICT OF INTEREST DISCLOSURES
The authors made no disclosures.
Additional supporting information may be found in the online version of this article.
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