Health disparity: time delay in the treatment of breast cancer in Louisiana.
Previous studies have suggested that breast cancer patients with a
delay of three months or more in initiating radiotherapy after breast
conservative surgery, have increased recurrence and lower survival than
those without treatment delay.
In this study, the inequalities in receiving radiation on time after surgery were identified in five areas--patient's age at diagnosis, race, size of the facility where the patient received treatment, teaching status of the facilities and geographical location where the patient received treatment. All patients studied were female patients. Women younger than 50 years of age, of the black race, receiving treatment in a teaching hospital, and who were treated by surgeons in southeast and central Louisiana were more likely to experience delay in receiving radiotherapy after breast conservative surgery than their counterparts. Patient's insurance status, marital status and SES did not have any influence on treatment delay. Also, the surgeon's practice age at diagnosis, the surgeon's medical school, facility ownership status and Commission on Cancer approval status did not show significant effect.
(Care and treatment)
Health care disparities (Research)
Cancer (Care and treatment)
Kim, Min Su
Larson, James S.
|Publication:||Name: Journal of Health and Human Services Administration Publisher: Southern Public Administration Education Foundation, Inc. Audience: Academic Format: Magazine/Journal Subject: Government; Health Copyright: COPYRIGHT 2011 Southern Public Administration Education Foundation, Inc. ISSN: 1079-3739|
|Issue:||Date: Winter, 2011 Source Volume: 34 Source Issue: 3|
|Topic:||Event Code: 310 Science & research|
|Product:||Product Code: 8000432 Cancer Therapy NAICS Code: 621 Ambulatory Health Care Services|
|Geographic:||Geographic Scope: United States Geographic Name: Louisiana Geographic Code: 1USA United States; 1U7LA Louisiana|
Since the early 1990s, breast-conservative surgery (BCS) and radiotherapy have increased significantly. The NIH Consensus Statement on breast cancer treatment in 1990 recommended BCS as a preferable alternative to mastectomy, and as the primary treatment of localized breast cancer for most of the women with early stage breast cancer (Riley et al., 1999). This change in preferred treatment approach has increased the importance of radiotherapy as a component of standard treatment. An NIH fact sheet, entitled "Cancer Advances in Focus: Breast Cancer," says that breast conservative surgery followed by local radiotherapy has replaced mastectomy as the preferred surgical approach for treating women with localized breast cancer. Radiotherapy given after surgery significantly reduces the risk of local and regional recurrences, and is a positive influence on overall survival. (Poortmans, 2007).
In localized invasive breast cancer cases in general, radiotherapy is given after most cancer-directed surgery. The importance of radiotherapy after surgery to prevent loco-regional recurrence and thereby improve survival is demonstrated in many studies (Joslyn & West, 2000; Poortmans, 2007). Radiotherapy reduces the loco-regional recurrences by 70% (Abner et al., 1991). For every four loco-regional recurrences prevented at five years, one life at fifteen years will be saved (Abner et al., 1991). Delayed initiation of radiotherapy has been shown to increase the incidence of loco-regional recurrences (Huang et al., 2003). According to the Huang et al., the 5-year loco-regional recurrence rate is significantly higher in patients treated with radiotherapy more than 8 weeks after surgery, compared with those who started treatment within 8 weeks of surgery. Mikeljevic et al. (2004), in a retrospective study on 7,800 patients, found that delaying the initiation of radiotherapy for 20-26 weeks after the surgery is associated with a decreased survival after breast conservative surgery.
The interval between the surgery and the radiotherapy depend on various factors. These factors can be categorized into patient, surgeon and facility characteristics. Race is a significant factor in many studies. Black women are more likely to experience treatment delays, particularly very long delays, within any given stage (Lund et al., 2007). These delays are not explained by other demographic factors (Caplan et al., 2000; Dennis et al., 1975). Age also affects the interval between breast conservative surgery and radiotherapy. Women younger than 50 years are more likely to experience treatment delay than the women 50 years or older (Dennis et al., 1975, Gwyn et al., 2004). Other patient factors responsible for treatment delay include marital status, insurance status and other socioeconomic characteristics.
Substantial research has been done on the role of the facilities in providing treatment to cancer patients. The hospital factors associated with the BCS include university or teaching hospitals or both, hospitals located in urban areas, and large hospitals (Kotwall et al., 1996). However, little is known about the impact of facility characteristics on the interval between breast conservative surgery and radiotherapy. The number of beds in the facility, teaching status, the American College of Surgeons' Commission on Cancer (ACoS CoC) approval status and ownership classification--these characteristics are the most important facility characteristics affecting receipt of guideline concordant cancer treatment in a timely fashion (Kotwall et al., 1996).
The physician characteristics affecting cancer treatment also have been analyzed. Physician's age, sex, race, medical school, graduation year, license issue year and residency hospital are the characteristics found to be associated with breast cancer treatment (Cyran et al., 2001; Gilligan et al., 2007). Several surgeon characteristics are significantly associated with the variations in the breast cancer treatment received (Gilligan et al., 2007). But, in examining the effects of the physician characteristics on the interval between surgery and radiotherapy in localized invasive female breast cancer patients, the existing literature offers little information.
This research analyzes the factors affecting the interval (number of days) between the breast conservative surgery and radiotherapy. Although the maximum safe time interval between breast conservative surgery and radiotherapy has not yet been established (Mikeljevic et al., 2004), some previous studies have tried to explain the phenomenon based on recurrence rate and survival rate. For example, one study suggested that breast cancer patients with a delay of more than 90 days have lower survival rates than those without delay (Hershman et al., 2006). Another study found that long delays in beginning postoperative radiotherapy are linked to increased risk of local recurrence (Huang et al., 2003).
Huang et al. (2003) also suggest a negative impact of radiotherapy delay on local control probability. Their study of 46 breast cancer patients treated with breast conservative surgery analyzed the effect of delayed initiation or radiotherapy with respect to chemotherapy status. Poorer survival was associated with delays in initiating radiotherapy longer than seven weeks in the patients with no chemotherapy, while no significant effects on survival were found in patients who also received chemotherapy when their radiation treatment was initiated more than 24 weeks postoperatively (Ampil et al., 1999). Mikeljevic et al. (2004) suggest that adjuvant radiotherapy in breast conservative surgery patients should not be unnecessarily delayed and certainly should not commence later than 20 weeks following surgery.
To analyze the factors affecting the interval (number of days) between the breast conservative surgery and radiotherapy, this study used the data on patient characteristics derived from the database of the Louisiana Tumor Registry (LTR). Data on (census tract level) socioeconomic status came from the U.S. Bureau of Census 2000 (Census data). And, data on provider and facility characteristics were obtained from the Louisiana State Board of Medical Examiners Physician Database, as well as published American Hospital Association data.
This study population includes women who are both Louisiana residents, and who are newly diagnosed with localized invasive breast cancer. From the LTR database, a sample of 567 cases was selected. Missing data and other considerations relating to incomparable types of radiation treatment resulted in a total sample of 497 cases.
To determine which factors influence the treatment of localized invasive breast cancer in female patients, the interval between breast conservative surgery and radiotherapy is estimated as shown in the following equation:
Logit (P) = Log [P/ (1-P)] = [[beta].sub.0] + [[beta].sub.1][x.sub.1](Age at Diagnosis) + [[beta].sub.2][x.sub.2] (Race) + [[beta].sub.3][x.sub.3] (Insurance Status) + [[beta].sub.4][x.sub.4] (Marital Status)+ [[beta].sub.5][x.sub.5] (Education) + [[beta].sub.6][x.sub.6] (Urban-Rural status) + [[beta].sub.7][x.sub.7] (Practice Age in Louisiana) + [[beta].sub.8][x.sub.8] (Graduation School) + [[beta].sub.9][x.sub.9] (Practice Region) + [[beta].sub.10][x.sub.10] (Bed Size) + [[beta].sub.11][x.sub.11] (Ownership Status) + [[beta].sub.12][x.sub.12] (Teaching Status) + [[beta].sub.13][x.sub.13] (ACoS CoC Approval Status) + [e.sub.i]
The dependent variable in this study is the number of days between the breast conservative surgery and radiotherapy. Although the maximum safe time interval between breast conservative breast conservative surgery and radiotherapy has not been established (Mikeljevic et al., 2004), some studies have examined the relationship of treatment delay with recurrence and survival rates (Ampil et al., 1999; Hershman et al., 2006; Huang et al., 2003).
Hershman et al. (2006) suggest that breast cancer patients with a delay of more than 90 days have lower survival rates than those without delay. This study adopts the Hershman et al. (2006) findings and constructs the interval between breast conservative surgery and radiotherapy into a dichotomous variable, with a 90-day cut-off. Out of the 497 patients, 341 (68.6%) received radiation within 90 days of surgery and the remaining 156 (31.4%) had delays over 90 days. Table 1 provides a description of all variables.
The independent variables in the study include patient, surgeon and facility factors. The patient variables include age at diagnosis, race, marital status, insurance status, census tract level, socioeconomic factors (poverty level, education and occupation), and census tract level urban-rural status. The surgeon variables include the medical school from which the surgeon graduated, the number of years the surgeon practiced in Louisiana, and the geographical region in Louisiana where the surgeon practiced. The facility variables include the size of the facility, the ownership status, the teaching approval status, and the Commission on Cancer approval status.
The research shows that age affects the interval between breast conservative surgery and the radiotherapy. Women younger than 50 years are more likely than the older women to experience treatment delays (Dennis et al., 1975; Gwyn et al., 2004). Adaptation and quality of life after breast cancer diagnosis are more difficult for younger women compared to the older women (Wenzel et al., 1999). Because of child-rearing activities and employment outside the home, younger women experience more difficulties and disruptions from the disease and its treatment (Ganz et al., 1998). On the other hand, older women have public insurance coverage and tend not to have the same social responsibilities as young women. Although the some variation is lost, many similar studies have categorized the age variable into age groups where patients under age 50 were grouped together (Dennis, Gardner, & Lim, 1975; Ganz et al., 2002; Wenzel et al., 1999). In this study, we group age into two categories: patients 50 years and younger in one group (younger patients) coded as and patients 50 years and older in another group (older patients) coded as '0'.
Significant racial differences exist in the treatment for women with early-stage breast cancer (Joslyn, 2002). Black women with breast cancer are more likely than their White counterparts to experience treatment delays, particularly very long delays, within any given stage (Lund et al., 2007). Racial distribution in Louisiana is limited mainly to White and Black with a population distribution of 63.9% Whites and 32.5% Blacks (US Census Bureau). Other races contribute to the population in minor fractions. The Hispanic population is very small in Louisiana, as only 2.4% are of Hispanic origin. As this study population consists of Whites and Blacks only, a dichotomous variable was created where Whites were coded as '0' and Blacks were coded as
The marital status of the patient at the time of initial diagnosis and/or treatment also is used to estimate the interval between breast conservative surgery and radiotherapy. For the patients who are suffering a wide array of illnesses, marriage is often associated with longer life and better quality of life (Gore, Kwan, Saigal, & Litwin, 2005; Sorlie, Backlund, & Keller, 1995). Findings from recent studies on cancer patients are controversial in determining relationship between marital status and cancer survival. A study on bladder cancer found that marriage was associated with improved survival in patients with bladder carcinoma, independent of other factors known to influence mortality in this population (Gore et al., 2005). Another study on non-small cell lung cancer patients found no survival difference based on marital status, even after adjusting for variables of prognostic significance (Jatoi et al., 2007).
Insurance status is defined as the primary payer/insurance career at the time of initial diagnosis and/or treatment. The Insurance variable in this research is grouped into no insurance, public insurance and private insurance categories. In Louisiana, patients with no insurance usually receive treatment from the Charity Hospitals in different cities. Patients who do not have any insurance and are not eligible for Medicare or Medicaid, can apply for an insurance program called "Free Care." If approved, this covers all medical expenses. This group of no insurance is combined with public insurance to form a new group of public insurance and no insurance.
Education is a geographic-based proxy indicator. According to Census 2000 Brief on educational attainment (Bauman & Graf, 2003) 80.4% of the US population 25 years and older at least have a high school diploma, whereas, in Louisiana approximately 75% (74.8%) of the population 25 years and older at least have a high school diploma. Based on the percent of population with a high school diploma or higher level of education, all the census tracts in Louisiana were divided into high literacy and low literacy areas. The census tracts where 75% or more of the census tract population who are 25 years and older having at least high school equivalency were categorized as 'high literacy area' and coded as '0'. The census tracts with less than 75% of the 25 years or older population having high school diploma or more were categorized as 'low literacy area' and coded as '1'. Using the census tract of the patient at diagnosis, these data were assigned to each patient.
The urban-rural status of a patient is derived from the Census Bureau database, using the census tract of the patient at diagnosis. This variable is a geographic-based proxy indicator. While 20% of the US population lives in rural areas, only 9% of the physicians practice there (van Dis, 2002). Because rural patients have fewer healthcare providers per capita and increased transportation barriers, it is common for them to visit physicians less often than urban patients (Hughes & Rosenbaum, 1989). The percent of population living in urban areas was collected for each of the census tracts in Louisiana from the US Census Bureau 2000 Census database. The tracts where 100% of the population was dwelling in urban area were considered 'urban' and coded as '1', and the remaining tracts were categorized 'non-urban,' which includes the mixed tracts as well and were coded '0'.
The years in practice in Louisiana variable denotes the number of years the surgeon practiced in Louisiana. The main purpose of this variable is to examine if the young surgeons adhere to standard treatment. Those surgeons who practiced six years or more were termed experienced doctors and were coded '0', and those who practiced five years or less were termed as young doctors and were coded as '1'.
The Graduation School variable codes the medical school where the surgeon graduated. The patients treated by surgeons graduating from Louisiana State University Medical Schools were coded as '1', the patients treated by surgeons graduating from Tulane Medical School were coded as '2', and the patients treated by surgeons graduating from out-of-state or foreign medical schools were coded as '3'.
The Practice Region in Louisiana variable was derived from the business phone number of the surgeon performing the surgery. The telephone area code was used to identify regions of the state where they practiced, namely Southeast region (area codes 504 and 985), Central region (area codes 225 and 337), and North region (area code 318). The Bed Size variable is defined as the number of beds in the facility for the most definitive treatment. This variable was coded into a dichotomous variable where facilities with less than 300 beds were termed as small and medium-sized facilities and coded as '0' and the facilities with 300 or more beds were termed as large facilities and coded as '1'.
The Ownership variable is defined as the ownership status of the facility for the most definitive treatment. Patients treated in privately owned facilities were coded as '0', and the patients treated in government owned facilities were coded as '1'. The Teaching Status variable is defined as the teaching status of the facility for the most definitive treatment. Facilities which did not have approval for MD or DO programs during the year 2004 were coded as '0', and the facilities approved for MD or DO programs during the year 2004 were coded as '1'.
The American College of Surgeons Commission on Cancer (ACoS CoC) Approval Status variable is defined as the ACoS CoC approval status of the facility where the patient received the most definitive treatment. These facilities must meet certain criteria and perform certain duties set forth by the ACoS CoC to win and maintain this approval status. In general, this status should imply better cancer care, supervised by the ACoS CoC. Facilities which had ACoS CoC approval during the year 2004 were coded as '0,' and the facilities did not have ACoS CoC approval during the year 2004 were coded as '1'.
Table 2 provides frequencies for all variables. As shown in Table 2, out of the 497 patients, 341 (68.6%) received radiation within 90 days of surgery and the remaining 156 (31.4%) had delays over 90 days. The mean age at diagnosis for the study population was 61.04 years, range from 28 to 94, standard deviation of 12.6 years. Among the study population (N = 497) 102 (20.5%) patients were younger than 50 years at the time of diagnosis and 369 (74.2%) were White. A total of 71 or 14.6% of the population were never married, 277 (57.0%) were married (including common law), 7 (1.4%) were separated, 37 (7.6%) were divorced, and 94 (19.3%) were widowed.
Table 3 provides the logistic regression results for the surgery and radiotherapy interval model shown in equation (1). Out of 497 cases in the study population, 456 cases were included in the final analyses. For the remaining 41 cases, one or more fields were missing or unknown. The overall percentage of patient, surgeon and facility variables correctly predicted 75.2% of the variation in the dependent variable- the interval between surgery and radiotherapy. Five (age, race, bed size, residency approval status and Louisiana regions) out of the thirteen independent variables were found to be statistically significant.
Examination for multicollinearity among the independent variables was necessary to perform the analyses. Tolerances and Variance Inflation Factors for each variable are suggestive of no statistically significant multicollinearity. In the age at diagnosis variable, the patients with age 20 to 49 years at diagnosis were coded as '1' and the patients with age 50 years or over were coded as '0'. It was hypothesized that younger patients were more likely to have delays in treatment. For this variable, the logistic coefficient was 1.391, the Wald statistic was 27.524 and the age at diagnosis was significant at p <.05. The odds ratio tells us that as age changes from 50 years or over (0) to less than 50 years (1) the changes in the odds of getting treatment after 90 days compared to getting treatment within 90 days is 4.02. The odds of a young patient having delays in treatment compared to no delay are 4.02 times more than for an old patient. In short, younger patients are more likely to have delay in treatment.
The race of the patient significantly predicted whether they had a time delay in treatment of breast cancer (coefficient = 0.654, Wald statistic = 5.335, P < .05). The estimated odds ratio was 1.924, which means that for the black women with localized breast cancer, the odds of experiencing delay in treatment is almost twice as great as that of their white counterparts. For race variable, this research fails to reject the null hypothesis that there is no difference of treatment delay between Black patients and White patients and draws inference that Black patients are more likely to have delays 90 days or more between breast conservative surgery and radiotherapy.
The education level of the patient, however, did not significantly predict whether they had a time delay in treatment, and neither did the urban-rural variable. Note that although these predictors are not significant, the coefficients were the same sign. Contrary to expectation, both marital status and insurance status variables were shown to decrease delay in treatment. Although these variables do exhibit the unexpected sign, the p-values of these effects suggest the variables were not significant determinants.
Turning to the analysis of surgeon characteristics effect, Table 3 shows that the surgeon's practice region in Louisiana significantly predicted whether patients had a time delay in treatment of breast cancer. For the Southeast variable, the estimated odds ratio of 2.024 suggests that the odds to have delay in treatment for the patients treated in southeast region are almost twice as great as that of the patients treated in north regions. For the Central variable, the estimated odds ratio of 2.426 means that the odds to have delay in treatment for the patients treated in central region are almost twice as great as that of the patients treated in north regions.
The number of years the surgeon practiced in Louisiana did not significantly predict whether patients had time delays in treatment of breast cancer. Among the facility characteristics variables, it was hypothesized that delays in treatment would be different between these two bed- size groups of hospitals. For this variable, the logistic coefficient was negative and it was -1.548, the Wald statistic was 15.352, and it was significant at p < 0.05. The estimated odds ratio of 0.206 suggests that the odds of having delay in treatment of patients treated in hospitals with less than 300 beds are 1/0.206, almost five times as great as that of the patients treated in hospitals with 300 or more beds. In other words, the patients treated in smaller hospitals have greater tendencies to experience delays in treatment.
For the teaching status variable, the estimated odds ratio was 2.727, which means that patients treated in teaching hospitals were almost 2.7 times as likely to have delays than patients treated in non-teaching hospitals. For teaching status variable, this research fails to reject the null hypothesis that there is no difference between the teaching and non-teaching hospitals and draws inference that patients treated at teaching hospitals are more likely to have delays 90 days or more between breast conservative surgery and radiotherapy.
Finally, the ownership status of facility, however, did not significantly predict whether patients had time delay in treatment of breast cancer. The approval of the ACosCoc was not significant in this crucial aspect of cancer treatment.
DISCUSSION AND CONCLUSION
This research attempts to examine health disparity using the quantitative findings from the logistic regression analyses of the interval between breast conservative surgery and radiotherapy in localized female breast cancer patients diagnosed in 2004 in Louisiana. Through the logistic regression analyses, we identify the specific variables contributing inequality. As noted earlier, findings have shown that the inequalities in receiving radiation on time after surgery were identified in five areas--patient's age at diagnosis, race, size of the facility where the patient received treatment, teaching status of the facilities and geographical location where the patient received treatment. According to results, young women who were diagnosed with localized breast cancer and underwent breast conservative surgery and radiotherapy were clearly susceptible to experiencing delays between breast conservative surgery and radiotherapy. Younger women appear to experience more difficulties and disruptions from the disease and its treatments because of child-rearing activities and employment outside the home (P. A. Ganz et al., 1998).
The study also supports other studies finding statistically significant racial differences in the treatment of women with early-stage breast carcinoma (Joslyn, 2002). Black women were more likely to experience treatment delays, particularly very long delays, within any given stage (Lund et al., 2007) and these delays were not explained by other demographic factors (Caplan et al., 2000; Dennis et al., 1975). Although numerous efforts have been made to ensure racial equity and social justice, since the Civil Rights movement, the racial disparity identified in this research conforms again to the findings of other studies establishing disparity grounded in racial difference in different areas of health and welfare.
Patients treated in teaching hospitals are more likely than those treated in non-teaching hospitals to experience delays, which supports the findings from literature. Although teaching hospitals are historically well known for state of the art treatment of their patients, many of the teaching hospitals are financially stressed compared to the non-teaching hospitals (Ayanian & Weissman, 2002). With everything in place, patients treated at large hospitals are experiencing shorter delays than small and medium hospitals. Such finding may be explained by the fact that the level of care is not optimal in small and medium hospitals for patients with breast cancer. After surgery is performed in a small or medium sized hospital, the patients are finding themselves in a comparatively difficult situation in terms of receiving radiotherapy. The characteristics of the patients who are getting treatment in large hospitals may be different from those who are getting treatment from smaller hospitals. In addition, the practice region was statistically significant in influencing interval between breast conservative surgery and radiotherapy. Local socioeconomic factors have an influence.
This research provides a cross sectional view of the inequalities present in the area of breast cancer treatment. Women who are younger than 50 years are extremely vulnerable to treatment delays. The safety net we created for the poor in society does not seem to work for this group, and our policies need to be revisited. To ensure fairness to all, it is necessary to eliminate the barriers to proper care for young women. Although we have numerous policies in place with a goal to eliminate racial disparity, race is still playing a significant role in healthcare delivery. At the providers end, the disparity between large and small facilities and teaching and non-teaching facilities needs to be explored further to identify why this is occurring. In institutional settings, these problems can be solved efficiently with proper policy recommendation and implementation.
In conclusion, a formal policy evaluation in each area where significant inequality was identified would enable policymakers and policy scientists to propose reforms to minimize or eliminate inequalities in those areas. Geographic variation in treatment interval also needs to be explored in future research. Although geographic variation in treatment and treatment outcomes has been identified by many studies, little policy research has been done on specific areas of variation to identify why those anomalies are occurring. Further research is necessary to answer why there is geographic variation in the intervals between breast conservative surgery, radiotherapy and other areas of breast cancer patterns of care.
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Our Lady Of The Lake College
MIN SU KIM
JAMES S. LARSON
Table 1: Variables and Data Sources Variable Description and data source Dependent variable Interval between Dichotomous variable coded 1if the patients surgery and who received radiotherapy after 90 days of radiotherapy breast conservative surgery, and 0 otherwise; Source: Louisiana Tumor Registry Patient characteristics Age Dichotomous variable coded 1if the patient is 50 years old or younger at diagnosis, 0 otherwise; Source: Louisiana Tumor Registry Race Dichotomous variable coded 1if the patient is black, 0 otherwise; Source: Louisiana Tumor Registry Insurance Dichotomous variable coded 1if the patient has public or no insurance, 0 otherwise; Source: Louisiana Tumor Registry Marital status Dichotomous variable coded 1if the patient is not married, 0 otherwise; Source: Louisiana Tumor Registry Education Dichotomous variable coded 1if the patient census track is in the area less than 75% of the 25 years or older population having high school diploma or more, 0 otherwise; Source: U.S. Census Bureau Urban-rural status Dichotomous variable coded 1if the patient census track is in the area where 100% of the population was dwelling in urban area, 0 otherwise; Source: U.S. Census Bureau Surgeon characteristics Years of practice Dichotomous variable coded 1if the surgeon practiced six years or more in Louisiana, 0 otherwise; Source: Louisiana State Board of Medical Examiners The Medical School where the surgeon graduated; Source: Louisiana Graduation school State Board of Medical Examiners Practice region The regions of the state where the surgeon practiced; Source: Louisiana State Board of Medical Examiners Facility characteristics Bed size Dichotomous variable coded 1if the facilities with 300 or more beds, 0 otherwise; Source: American Hospital Association Ownership Dichotomous variable coded 1if the patients classification treated in government-owned facilities, 0 Teaching status otherwise; Source: American Hospital Association Dichotomous variable coded 1 if the facilities did not have approval for MD or DO programs, 0 otherwise; Source: American Hospital Association AcoS CoC Dichotomous variable coded 1 if the facilities approval status did not have American College of Surgeons Commission on Cancer (ACoS CoC) Approval, 0 otherwise; Source: American Hospital Association Table 2 Frequency Distribution Frequency distribution Variable Co Dependent variable Within 90 days Interval between surgery 341 (68.6%) After 90 days and radiotherapy 156 (31.4%) Patient characteristics Age 50 yrs or Less than50 more yrs 395 (79.5%) 102 (20.5%) Race White Black 369 (74.2%) 128 (20.5%) Insurance Private Public or no 356 (17.6%) 133 (26.8%) Maritial status Married Not married 284 (57.1%) 202 (40.6%) Education High Low education education 298 (60.0%) 199 (40.0%) Urban-rural status Rural Urban 221 (44.5%) 276 (55.5%) Surgeon characteristics Years of practice 6 yrs or less than 6 more yrs 417 (83.9%) 55 (11.1%) Graduation school Other LSU Tulane 190 (38.2%) 223 (44.9%) 59 (11.9%) Practice region North Southeast Central 127 (25.6%) 178 (35.8%) 167 (33.6%) Facility characteristics Bed size Small or Medium Large 269 (54.1%) 228 (45.9%) Ownership classification Private Public 352 (70.8%) 145 (29.2%) Teaching status No teaching Teaching 312 (62.8%) 185 (37.2%) AcoS CoC approval No approval Approval status 138 (27.8%) 359 (72.2%) Table 3 Regression estimates for the surgery and radiotherapy interval R Squared= .752 Coefficie Odd Variable nt ratio S.E Wald P-Val Patient Characteristics Age at Diagnosis 1.391 4.020 0.265 27.524 0.000 *** Race 0.654 1.924 0.283 5.335 0.021 * Marital Status -0.052 0.950 0.253 0.041 0.839 Insurance Status -0.374 0.688 0.302 1.533 0.216 Education 0.017 1.017 0.254 0.004 0.948 Urban-Rural Status -0.014 0.987 0.258 0.003 0.958 Surgeon characteristics Graduation School LSU 0.148 1.160 0.274 0.292 0.589 Tulane 0.463 1.589 0.393 1.392 0.238 Practice Regions Southeast 0.705 2.024 0.339 4.339 0.037 * Central 0.886 2.426 0.348 6.501 0.011 * Years of Practice 0.409 1.505 0.367 1.243 0.265 Facility characteristics Bed Size 0.000 ** -1.578 0.206 0.403 15.352 * Teaching Status 1.003 2.727 0.380 6.980 0.008 ** Ownership Status 0.514 1.672 0.291 3.118 0.077 ACoS CoC Approval 0.166 1.181 0.295 0.317 0.574 Constant 0.000 ** -1.921 0.146 0.410 22.011 * Note: *** denotes significance at the 0.1% level, ** at the 1% * at the 5%. * =
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