Approximately 1 in 5 Americans lives in a rural area.1 Rural surgical patients are served by ≈20% of the nation’s general surgeons, who represent the second most common type of physician in rural America.2 Furthermore, ≈40% of hospitals are considered rural hospitals.3 Efforts to improve medical care for rural Americans are important, because this group is subject to higher rates of poverty and mortality relative to their urban counterparts.4
In addition, the travel required for comprehensive cancer care complicates the treatment of patients from rural communities.3 Previous research has demonstrated that patients living in rural areas are less likely to receive recommended cancer screenings5,6 and that these screening deficits have been shown to adversely affect colon cancer detection.7 Furthermore, colon cancer tends to be diagnosed at later stages in rural patients.8–10 We have previously explored the impact of the location of treatment on outcomes, specifically demonstrating that treatment at a rural hospital did not confer worse surgical mortality rates except in patients with complex cancers.11 However, an effective appraisal of quality cancer care should more broadly consider the structures, processes, and outcomes of cancer care.12 Although colon cancer represents the third most common cancer in the United States, investigation into the impact of rural residence across the entire continuum of colon cancer care has been sparse.
Our study, therefore, sought to examine the impact of patient rurality on quality colon cancer care. We hypothesized that patient rurality is associated with the following colon cancer care quality measures: 1) stage at diagnosis, 2) adequacy of lymphadenectomy at surgery, 3) receipt of chemotherapy, and 4) cancer-specific death. Our findings will help inform research-driven interventions to improve surgical cancer care for rural Americans.
The California Cancer Registry (CCR) is one of the largest and most diverse population-based cancer registries in the United States.13 All new cancer diagnoses are required by law to be reported to this registry; consequently, case reporting is estimated to be 98% complete.14 Data are collected from the 10 regional registries of California, encompassing its 58 counties, and are abstracted according to established statewide standards.15
Our cohort included patients with tumors of the colon as designated by International Classification of Diseases for Oncology, third edition, site code. We excluded patients with tumors located in the rectum, anal canal, or appendix. Patients age <18 years or >94 years were also excluded (N = 3081). Although case data were available beginning in 1988, complete reporting of patient rurality was not available until after 1995. Therefore, we limited our analysis to patients who were diagnosed between 1996 and 2008.
Patient Rurality and Other Demographic Measures
Rural residence was established based on the designation assigned to the patient’s medical service study area by the California Office of Statewide Health Planning and Development.16 Counties are subdivided in the state into multiple medical service study areas, each of which are classified by the California Office of Statewide Health Planning and Development as rural, urban, or frontier. Because of the small number of frontier-residing patients, frontier medical service study areas were also considered rural for the purposes of our study.
Age was categorized into the following groups: 18 to 35, 36 to 50, 51 to 65, 66 to 80, and 81 to 94 years. Payers were grouped by similar payment sources. Surgical treatment was grouped into the following 4 categories: none, local therapy (polypectomy, laser, or cautery), limited resection, and colectomy.
Colon Cancer Care Quality Measures
We chose 4 colon cancer care quality measures to assess quality at multiple places in the cancer care continuum. First, we examined stage at diagnosis. This is a crucial quality measure of cancer care, because it reflects the penetration of screening and outreach efforts. We assigned American Joint Committee on Cancer seventh edition TNM stage and overall stage to each patient based on tumor extension, nodal positivity, and metastasis, as reported by the CCR. Stage was then dichotomized into low stage (Tis, I, and II) and high stage (III and IV) for regression analysis.
For patients with stage I to III cancer who underwent surgical resection, we examined the adequacy of lymphadenectomy. An adequate lymphadenectomy was defined as recovery of >12 lymph nodes as specified by several specialty organizations.17 Patients with missing nodal evaluation or number of nodes were excluded from analysis.
For patients with stage III cancer <80 years of age who underwent surgery, we compared receipt of chemotherapy for rural and urban patients. CCR reports whether patients received, refused, or had a contraindication to chemotherapy. In our analysis, those who refused or had a contraindication to chemotherapy were considered to not have received chemotherapy. We further examined whether the proportion of patients who refused chemotherapy differed between rural and urban patients. This quality measure was selected because chemotherapy for stage III disease has been clearly shown to improve patient survival.18
Finally, cancer-specific death was assessed as our composite and final quality measure.
Our 4 quality measures, as well as patient- and tumor-related factors (age, sex, marital status, race, insurance status, tumor stage, and tumor grade), were compared by rural versus urban patient residence using χ2 analysis.
We first used multivariate regression to identify patient and tumor factors associated with rural residence. Multivariate logistic regression models were then constructed to examine the impact of rurality on the selected colon cancer care quality measures, including stage at diagnosis, adequate lymphadenectomy, and receipt of chemotherapy. We excluded from the models any cases lacking data needed to construct any response variables; 20,825 were excluded from the regression model for stage because of unknown stage at diagnosis, 3075 were excluded from the lymphadenectomy regression model because of the unknown number of lymph nodes harvested, and 3689 were excluded from the chemotherapy regression model because of unknown chemotherapy status.
Next, Cox proportional hazards models were created to examine the impact of rurality on cancer-specific death in the entire cohort, adjusting for stage, surgery, grade, age, lymphadenectomy, sex, race, marital status, insurance status, and year of diagnosis. A separate proportional hazards model was constructed to examine the impact of rurality on patients with stage III cancer and their cancer-specific survival to reflect chemotherapy guidelines. This model was adjusted for chemotherapy, surgery, grade, age, lymphadenectomy, sex, race, marital status, insurance status, and year of diagnosis. For both analyses, time to death was directly reported by the CCR, along with cause of death. Patients who had a noncancer-related death or an unknown cause of death were censored. Ties were handled using the Breslow method.
Interaction testing and sensitivity analyses were conducted to ensure that observed results were not attributed to our modeling decisions. The University of Minnesota Institutional Review Board reviewed this study (HSC# 1202E10466) and deemed it exempt from further review. All of the regressions were performed using SAS version 9.2 (SAS Institute Inc, Cary, NC).
Our cohort consisted of 123,129 patients. Of these, 18,735 (15.2%) resided in rural areas. Results from unadjusted analyses showed that rural patients differed by age, sex, marital status, race/ethnicity, adequate lymphadenectomy for stage I to III disease, and receipt of chemotherapy for stage III disease. Stage and receipt of surgery did not vary by patient rurality on unadjusted analysis (Table 1).
Multivariate Analysis of Demographic Factors Associated With Patient Rurality
Non-Hispanic blacks, Hispanics, and Asians/Pacific Islanders were less likely to live in rural areas compared with non-Hispanic whites (Table 2). However, American Indian patients were the most likely to live in rural areas (American Indian vs non-Hispanic whites[OR 3.406, 95% CI 2.744–4.227]). Furthermore, rural patients were less likely to have private insurance (OR 0.720, 95% CI 0.624–0.830).
Multivariate Analysis of Colon Cancer Quality Measures
After adjusting for covariates, patients living in rural areas were more likely to be diagnosed at later stages (III or IV) compared with their urban counterparts (OR 1.037, 95% CI 1.001–1.075, p = 0.043). The uninsured were also likely to be diagnosed at later stages as well (Table 3). Interaction testing revealed no significant interaction between rurality and age. However, an interaction was identified between rurality and race, reflecting varying effects of rurality on late stage at diagnosis by race. Therefore, we stratified our models by race to examine the effect of rurality on this measure within each race group. We then observed that late stage at diagnosis was statistically significantly associated with rurality for only non-Hispanic whites and American Indians (Table 4).
Rural patients with stage I to III disease were less likely to have ≥12 lymph nodes evaluated compared with their urban counterparts (OR 0.808, 95% CI 0.777–0.840, p < 0.001). Older patients, men, non-Hispanic blacks, and Asians/Pacific Islanders were also less likely to receive an adequate lymphadenectomy (Table 5). When this model was further adjusted by location of tumor (right vs left), there was no effect on the odds of adequate lymphadenectomy (OR 0.81, 95% CI 0.78–0.84, p < 0.001). Furthermore, interaction testing revealed that there was no meaningful interaction between year and the role of rurality for this outcome measure (interaction term, p = 0.44). Finally, we note that a greater proportion of rural patients compared with urban patients were missing nodal evaluation all together (3.6% vs 2.3%; p < 0.0001).
As for receipt of adjuvant systemic chemotherapy for stage III colon cancer, rural patients were less likely to receive adjuvant chemotherapy (OR 0.863, 95% CI 0.799–0.932, p < 0.001). Men, non-Hispanic blacks, Hispanics, and the elderly were also less likely to receive adjuvant chemotherapy (Table 6). During interaction analysis, there was no significant interaction between rurality and age. However, an interaction was identified between rurality and race (p = 0.045). Therefore, we again stratified our models by race to independently look at the effect of rurality on chemotherapy use within each race group, finding that this effect was statistically significant for non-Hispanic whites only (Table 7). During sensitivity analysis, our results did not change when excluding patients for whom chemotherapy was contraindicated or those who died. Furthermore, specific secondary analysis of patients who refused chemotherapy revealed no difference in the proportion of refusers in rural versus urban patients (p = 0.914).
Proportional Hazards Regression of Cancer-Specific Mortality
We found that patients living in rural areas had a 4% higher risk of death from their cancer compared with patients living in urban areas (HR 1.038, 95% CI, 1.007–1.071; p = 0.016) even after adjustment for stage and other patient, tumor, and treatment factors. In addition, we demonstrate that non-Hispanic blacks had a higher risk of cancer-specific death than their non-Hispanic white counterparts despite multivariate adjustment (p < 0.001; Table 8). Asians/Pacific Islanders, however, had a lower risk of cancer-specific death compared with non-Hispanic whites (p < 0.001; Table 8). When this model was further adjusted for tumor location (right vs left), there was minimal change in the impact of rural residence (HR 1.04, 95% CI, 1.01–1.07; p = 0.014).
Similarly, in our analysis of patients with stage III cancer specifically, which included receipt of chemotherapy as a covariate, patient rurality predicted higher cancer-specific mortality (HR 1.24, 95% CI 1.19–1.30, p < 0.001). This effect was not seen in patients with stage 0, I, II, or IV cancer when the proportional hazards model was stratified for stage (Table 9). Finally, receiving no adjuvant chemotherapy predicted higher mortality as well (HR 1.24, 95% CI 1.19–1.30; p < 0.001). No race-rurality or age-rurality interactions were found in our proportional hazards models.
In this large, diverse, population-based study of 123,126 patients with colon cancer in the state of California, we found that rural patients were more likely to be diagnosed at late stage and less likely to receive adequate lymphadenectomies or chemotherapy for stage III disease. In addition, rural residence conferred worse cancer-specific mortality. To our knowledge, this study is the first to examine the impact of patient rurality across the entire continuum of colon cancer care at the population level.
The literature regarding the stage of diagnosis for rural patients with colon cancer is mixed. Consistent with our hypothesis, some researchers have found later-stage cancer diagnoses for rural compared with urban patients using the Nebraska Cancer Registry.5 However, other studies have found no difference in colon cancer stage at diagnosis7 or even an earlier stage at diagnosis among rural patients.19 It is important to note that these studies defined patient rurality by county of residence only, whereas our study used subdivisions of each county of residence.
Our results demonstrate that the largest rural-urban disparity occurs with adequate lymphadenectomy, where rural patients had significantly lower odds of having an adequate lymphadenectomy performed on multivariate analysis (OR 0.808, 95% CI 0.777–0.840). This illustrates the negative confounding present in the unadjusted results where rural patients actually did slightly better (73.6% vs 71.3%; p < 0.0001). This suggests that demographic and patient factors, particularly age, sex, race, and payer source, may mask the true impact of rural residence. Evaluation of ≥12 lymph nodes has been shown to improve overall survival after colectomy for cancer.17 We posit, as other authors have, that the adequacy of lymphadenectomy depends on the interplay between multiple structural elements of the patient care, including the surgeon specialty, the pathologist, case volume, and even the setting of care. As such, adequate lymphadenectomy can be seen as a surrogate marker of appropriate structures of care. Unfortunately, we must limit our analysis to the role of patient residence because of data available from the CCR. Thus, we cannot directly comment on the structural factors that may have contributed to the suboptimal number of lymph nodes examined in rural patients, but this finding certainly should be further investigated. Our group has in another study explored the impact of treatment location on outcomes,11 but we were conversely unable to assess the role of patient residence in that study.
Our results are in agreement with others who have found differing rates of chemotherapy among rural patients. Previous work has shown that rural patients have more difficulty than their urban counterparts in accessing chemotherapy because of geographic and infrastructure barriers.20 Although 2 studies21,22 have recently found no impact of rurality on chemotherapy use in patients with colon cancer, both studies used cases linked to Medicare claims and, therefore, limited their entire study cohort to elderly patients.
Finally, our results demonstrate the modest impact of patient rurality on cancer-specific death. On stratified analysis, we demonstrate that this effect is primarily driven by patients with stage III cancer, even when receipt of chemotherapy is adjusted for. We speculate that this may be because of the failure to complete full-course of chemotherapy even if the initial infusions were administered. In terms of other predictors of cancer-specific death, we observe similar racial trends in our multivariable analysis as seen in the literature, demonstrating a higher risk of cancer-specific death for non-Hispanic blacks and a lower risk of cancer-specific death for Asians/Pacific Islanders when compared with non-Hispanic whites.23,24 Furthermore, we demonstrate that younger patients were less likely to have cancer-related mortality compared with their older counterparts. This is in agreement with other population-based studies, which show a similar pattern.25
The current analysis has several positive attributes in addition to the fundamental strength of its population-based design. First, because cancer reporting is mandatory in California, we were able to study nearly all of the colon cancers diagnosed in that state during the study period. Second, because of California’s diverse population, we think that our study results may be more applicable to broader populations compared with studies based on more ethnically homogeneous registries. Third, we were able to assess key quality-care metrics of structures, processes, and outcomes. Finally, with the unique use of designations by medical service study area, we were able to classify patient rurality at a level more detailed than just by county of residence.
Our study has several important limitations related to the CCR. Some authors have shown greater impact of socioeconomic status on certain races when considering colorectal cancer-specific survival.26 Unfortunately, we could not account for socioeconomic status from the registry, although we were able to adjust for insurance status as a surrogate measure. We did not have access to information regarding treatment location or how far rural patients traveled for their care. We also did not have access to the specific medical study service area in which each patient lived. Therefore, we were unable to conduct a cluster analysis to determine whether residence in certain rural areas accounted for a greater proportion of the observed effects. Furthermore, we were not able to adjust for hospital case volume, surgeon specialty, or surgeon case volume. Finally, we did not have information on patient comorbidities, emergent versus elective surgical case status, or postoperative complications, which potentially could affect the receipt of chemotherapy.
Our results highlight the importance of identifying the barriers to health care for the rural population. Caring for rural patients will likely become increasingly challenging, because the increasing centralization of care may increase financial pressures on both rural hospitals and rural providers. Rural patients may not be able to find quality care close to home and may travel increasing distances as these trends continue to evolve.
A significant portion of patients treated for colon cancer live in rural areas. In this study, using the largest population-based state registry in the United States, rural residence was associated with later stage at diagnosis, inadequate lymphadenectomy, lower likelihood of receiving chemotherapy, and worse cancer-specific mortality. Although the magnitude of each of these differences was relatively modest, further research should investigate how rural patient outcomes can be affected by treatment location, provider volume, provider specialty, hospital volume, and other structures of care. In addition, research should be directed toward linking the rurality of the patient and the rurality of the treating facility. Because more and more surgical graduates are choosing subspecialization and urban practice,2 resources should be devoted to bolstering the rural surgical workforce. Methods should be investigated that could improve care for rural patients, and key stakeholders should reward treating this critical population in an efficient yet effective manner.
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Cancer-specific survival; Chemotherapy; Colon cancer; Lymphadenectomy; Outcomes; Rurality
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