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Comorbid Illness and the Early Detection of Cancer.



ABSTRACT

Background Comorbidity may be associated with later detection of cancer.

Methods. Incident cases of colorectal, breast, and prostate cancer prostate cancer, cancer originating in the prostate gland. Prostate cancer is the leading malignancy in men in the United States and is second only to lung cancer as a cause of cancer death in men.  and melanoma melanoma: see skin cancer.
melanoma

Dark-coloured malignant tumour of skin cells that produce the protective skin-darkening pigment melanin.
 were determined from the 1994 Florida state tumor tumor: see neoplasm.  registry (N = 32,074). The relationship between comorbidity and late stage at diagnosis was examined using multiple logistic regression In statistics, logistic regression is a regression model for binomially distributed response/dependent variables. It is useful for modeling the probability of an event occurring as a function of other factors. .

Results. Patients with comorbid conditions had greater odds of late stage diagnosis for each of the four cancers (colorectal, melanoma, breast, and prostate). Higher mortality rates were observed among patients with comorbid illness, not as a result of later stage at diagnosis, but rather due to their underlying disease.

Conclusions. Comorbidity was associated with later stage diagnosis. Further research is needed to determine mechanisms by which comorbidity might influence stage at diagnosis.

COMORBIDHY is defined as the presence of concurrent chronic illnesses. [1] Comorbidity generally increases with advancing age and may be the reason behind age-related differences in cancer diagnosis, treatment, and outcome. [2-7] Because the incidence of most cancers increases with advancing age, it is not surprising that comorbidity has been frequently found among patients with cancer. [8] Comorbidity has often been associated with less aggressive treatment and poor cancer outcomes. [9-15] It is less clear, however, whether comorbidity influences the early detection of cancer.

Comorbidity could influence stage at diagnosis of cancer in a number of ways. Some have argued that comorbidity might mask early symptoms of cancer and lead to later stage diagnosis. [16,17] Comorbidity could also serve as a competing demand for primary care physicians, decreasing the likelihood of cancer screening recommendations. [18] Finally, patients and physicians may place less value on cancer screening in the face of competing causes of morbidity and mortality Morbidity and Mortality can refer to:
  • Morbidity & Mortality, a term used in medicine
  • Morbidity and Mortality Weekly Report, a medical publication
See also
  • Morbidity, a medical term
  • Mortality, a medical term
. [19] A number of clinic based studies have in fact found lower cancer screening rates among patients having comorbid conditions. [20-22]

On the other hand, some aspects of comorbidity could facilitate cancer screening. Patients having comorbid conditions generally have increased contact with the health care system and thus have more opportunities for preventive care Preventive care is a set of measures taken in advance of symptoms to prevent illness or injury. This type of care is best exemplified by routine physical examinations and immunizations. The emphasis is on preventing illnesses before they occur. See also
  • Public health
. Stange et al [23] found that family physicians delivered some preventive service the duty performed by the armed police in guarding the coast against smuggling.

See also: Preventive
 in 39% of visits for chronic disease. Some population-based studies have shown higher cancer screening rates among patients having comorbidity. [24-26]

Few studies have assessed the impact of comorbidity on cancer stage at diagnosis. Studies by Satariano and Ragland [14] and West et al [15] found trends toward earlier stage at diagnosis of breast cancer for patients having comorbid conditions. These studies were limited by not using multivariate analysis multivariate analysis,
n a statistical approach used to evaluate multiple variables.

multivariate analysis,
n a set of techniques used when variation in several variables has to be studied simultaneously.
 to examine the relationship between comorbidity and stage at diagnosis. A separate study by Satariano [27] with the use of multivariate analysis showed a statistically nonsignificant non·sig·nif·i·cant  
adj.
1. Not significant.

2. Having, producing, or being a value obtained from a statistical test that lies within the limits for being of random occurrence.
 trend toward earlier diagnosis for breast cancer patients having comorbidity.

It is unclear, therefore, whether comorbidity affects stage at diagnosis for patients with breast cancer. Whether comorbidity affects the early diagnosis of other cancers amenable to screening also remains unknown. We used administrative data from the state of Florida to determine whether patients having comorbid conditions were more likely to have diagnosis at late stage and if so, whether resultant later stage at diagnosis had an impact on their survival. We hypothesized that cancer in patients having comorbidity would more likely be diagnosed at late stage and that these patients would have poorer survival than those without comorbid conditions.

METHODS

Data Sources

We studied 1994 Florida incident cases (the most recent year for which all relevant data were available) of four cancers for which screening is associated with detection of early stage disease: colorectal, breast (female only), prostate, and melanoma (N = 34,616). [28-36] Cervical cancer Cervical Cancer Definition

Cervical cancer is a disease in which the cells of the cervix become abnormal and start to grow uncontrollably, forming tumors.
 was not included because of different reporting requirements for this site (in situ In place. When something is "in situ," it is in its original location.  cervical cancers are not reportable). Incident cases were identified from the Florida Cancer Data System (FCDS FCDS Future Common Display System (US Navy Multi-Modal Workstation feature) ), Florida's population-based statewide cancer registry A cancer registry is a systematic collection of data about cancer and tumor diseases. The data is collected by Cancer Registrars. Cancer Registrars capture a complete summary of patient history, diagnosis, treatment, and status for every cancer patient in the United States, and . The FCDS has well-established methods to ensure complete case-finding, including cooperative arrangements with other state tumor registries, linkage with other databases, and ad hoc For this purpose. Meaning "to this" in Latin, it refers to dealing with special situations as they occur rather than functions that are repeated on a regular basis. See ad hoc query and ad hoc mode.  audits of reporting facilities. The FCDS is a member of the North American North American

named after North America.


North American blastomycosis
see North American blastomycosis.

North American cattle tick
see boophilusannulatus.
 Association of Central Cancer Registries, whose audits have estimated the completeness of case ascertainment for the period 1990 to 1994 to be 97%.

To include information that is not routinely available from the FCDS (insurance payer, comorbidity, socioeconomic status socioeconomic status,
n the position of an individual on a socio-economic scale that measures such factors as education, income, type of occupation, place of residence, and in some populations, ethnicity and religion.
, urban/ nonurban residence), cases were linked with state discharge abstracts and the 1990 US Census. The State of Florida Agency for Health Care Administration (AHCA AHCA Agency for Health Care Administration
AHCA American Health Care Association
AHCA American Hockey Coaches Association
AHCA American Highland Cattle Association
AHCA Australian Health Care Agreement
AHCA Austin Healey Club of America
) maintains discharge abstracts for admissions to all nonfederal acute care hospitals, and patient visits to ambulatory surgical centers, freestanding free·stand·ing  
adj.
Standing or operating independently of anything else: a freestanding bell tower; a freestanding maternity clinic.
 radiation therapy centers, and diagnostic imaging centers. Data abstracted include Social Security number, date of birth, sex, race-ethnicity, discharge diagnoses (up to 10), procedures done (up to 10), and insurance payer. The methods of linking FCDS and AHCA records have been previously described [37] and resulted in a match rate of 82.8%, a rate similar to that achieved in a comparable study. [28]

The 1990 United States Census The United States Census is a decennial census mandated by the United States Constitution.[1] The population is enumerated every 10 years and the results are used to allocate Congressional seats ("congressional apportionment"), electoral votes, and government program  was used to obtain aggregate measures of socioeconomic status by either Census tract A census tract, census area, or census district is a particular community defined for the purpose of taking a census. Usually these coincide with the limits of cities, towns or other administrative areas and several tracts commonly exist within a county.  or, if unavailable, by ZIP code zip code

System of postal-zone codes (zip stands for “zone improvement plan”) introduced in the U.S. in 1963 to improve mail delivery and exploit electronic reading and sorting capabilities.
. Each individual was assigned the median income and education level of either the Census tract (87% of cases) or ZIP code (13% of cases) of their residence. The use of Census-derived measures of socioeconomic status have been validated in previous studies. [39-12] Patients were defined as having an urban residence if they lived in a ZIP code that was classified as 100% urban by the US Census. Patients were defined as having nonurban residence if they lived in a ZIP code that contained outside urban or rural components.

Stage at diagnosis was defined using the SEER Site-Specific Summary Staging Guide. [43] Stage at diagnosis is based on a combination of pathologic pathologic /patho·log·ic/ (path?ah-loj´ik)
1. indicative of or caused by some morbid condition.

2. pertaining to pathology.
, operative, and clinical assessments available within 2 months of diagnosis. Stage categories included in situ, local (invasive disease confined con·fine  
v. con·fined, con·fin·ing, con·fines

v.tr.
1. To keep within bounds; restrict: Please confine your remarks to the issues at hand. See Synonyms at limit.
 to the organ of origin), regional (direct spread to adjacent structures or regional lymph nodes Lymph nodes
Small, bean-shaped masses of tissue scattered along the lymphatic system that act as filters and immune monitors, removing fluids, bacteria, or cancer cells that travel through the lymph system.
), and distant (distant metastases Metastasis (plural, metastases)
A tumor growth or deposit that has spread via lymph or blood to an area of the body remote from the primary tumor.

Mentioned in: Malignant Melanoma
). For these analyses, stage at diagnosis was reclassified as either early stage (insitu, local) or late stage (regional, distant). Stage at diagnosis was available for 32,074 FCDS cases (92.7% of all cases: colon 93.5%, melanoma 93.6%, breast 95.5%, prostate 88.9%). Vital status was assessed through December 31, 1997, using FCDS-derived mortality files. The length of time from diagnosis to death, or until the last follow-up contact, was measured in months.

Comorbidity was determined using methods described by Deyo et al [44] and Charison et al. [45] The Charlson comorbidity index was chosen because it has been validated specifically in studies of cancer patients. [15] The Charlson comorbidity index is not an exhaustive list of all possible comorbid conditions but is rather a weighted index of 19 selected categories of disease that were found to be associated with mortality and other important health outcomes. Charlson comorbid conditions (and their corresponding weightings) include myocardial infarction myocardial infarction: see under infarction.  (1), congestive heart failure congestive heart failure, inability of the heart to expel sufficient blood to keep pace with the metabolic demands of the body. In the healthy individual the heart can tolerate large increases of workload for a considerable length of time.  (1), peripheral vascular disease Peripheral Vascular Disease Definition

Peripheral vascular disease is a narrowing of blood vessels that restricts blood flow. It mostly occurs in the legs, but is sometimes seen in the arms.
 (1), cerebrovascular disease cerebrovascular disease Neurology Any vascular disease affecting cerebral arteries–eg ASHD, diabetic vasculopathy, HTN, which may cause a CVA or TIA with neurologic sequelae–speech, vision, movement of variable duration.  (1), dementia (1), chronic pulmonary disease (1), connective tissue disease connective tissue disease Autoimmune disease, collagen-vascular disease Any of the diseases affecting connective tissues, with an autoimmune component, and immunologic/inflammatory defects Clinical Arthritis, connective tissue defects, endocarditis, myositis,  (1), peptic ulcer disease Peptic ulcer disease (PUD)
A stomach disorder marked by corrosion of the stomach lining due to the acid in the digestive juices.

Mentioned in: Indigestion

peptic ulcer disease See Duodenal ulcer, Gastric ulcer, GERD.
 (1), mild liver disease Liver Disease Definition

Liver disease is a general term for any damage that reduces the functioning of the liver.
Description

The liver is a large, solid organ located in the upper right-hand side of the abdomen.
 (1), moderate/severe liver disease (3), diabetes without complications (1), diabetes with complications (2), hemiplegia hemiplegia /hemi·ple·gia/ (-ple´jah) paralysis of one side of the body.hemiple´gic

alternate hemiplegia  paralysis of one side of the face and the opposite side of the body.
 (2), renal disease Renal disease
Kidney disease.

Mentioned in: Glycogen Storage Diseases

hypertension High blood pressure Cardiovascular disease An abnormal ↑ systemic arterial pressure, corresponding to a systolic BP of > 160 mm Hg
 (2), and acquired immunodeficiency syndrome acquired immunodeficiency syndrome, see AIDS.  (6). Increasing scores on the Charlson comorbidity index reflect an increasing burden of comorbid conditions. [15,45,46]

We identified Charlson comorbid conditions using all inpatient and ambulatory discharge abstracts for the calendar year 1994. We used methods described by Deyo et al [44] that were specifically developed to measure Charlson comorbidity from administrative databases. Patients having no Charlson comorbid condition identified in discharge abstracts were assigned a comorbidity score of zero, as were patients who had no record of inpatient or outpatient admissions during the calendar year. We created two variables related to comorbidity: a dichotomous di·chot·o·mous  
adj.
1. Divided or dividing into two parts or classifications.

2. Characterized by dichotomy.



di·chot
 variable indicating the presence or absence of comorbid conditions and a second categorical That which is unqualified or unconditional.

A categorical imperative is a rule, command, or moral obligation that is absolutely and universally binding.

Categorical is also used to describe programs limited to or designed for certain classes of people.
 variable defined by three levels of comorbidity (0, 1, 2+) based on the patient's index score.

Data Analysis

All analyses were conducted separately by site. The proportion of cases diagnosed at a late stage (regional or distant) was first compared for patients having any comorbid condition using the chi-square test chi-square test: see statistics. . We then used the Mantel-Haenszel [[chi].sup.2] test for trend to examine whether the likelihood of late stage diagnosis increased with increasing severity of overall comorbidity.

The multivariate The use of multiple variables in a forecasting model.  relationship between late stage at diagnosis and comorbidity was then examined using multiple logistic regression. Indicator variables were created for the presence or absence of comorbid conditions, and for the three comorbid index categories (0, 1, 2+). Based on previous research, the following variables were included in all logistic models logistic models,
n.pl statistical models that describe the relationship between a qualitative dependent variable (that is, one that can take only certain discrete values, such as the presence or absence of a disease) and an independent variable.
 to control for potential confounding confounding

when the effects of two, or more, processes on results cannot be separated, the results are said to be confounded, a cause of bias in disease studies.


confounding factor
: age, sex (if appropriate), race-ethnicity, marital status marital status,
n the legal standing of a person in regard to his or her marriage state.
, educational level, income level, urban residence, and insurance payer. [47-49] The statistical significance of individual indicator variables was assessed using the chi-square likelihood ratio test. [50]

To determine whether findings might differ, we also repeated logistic models with cases restricted to invasive cancers only. For colorectal, breast, and prostate cancers, we also repeated analyses with cases restricted to ages for which screening is most often recommended and for which physician agreement is high (ages 50 to 75 years) We also repeated analyses after excluding those FCDS cases that did not match with inpatient and ambulatory discharge abstracts.

Survival was examined for patients having Charlson comorbid conditions compared with those who did not. Survival curves were constructed using the Kaplan-Meier product-limit method. [54] The duration of potential follow-up varied from 36 months to a maximum of 48 months; depending on the patient's date of diagnosis. Survival curves were compared using the Mantel-Cox log-rank test. [54] We examined the adjusted risk of death from all-cause mortality for patients with and without comorbidity using Cox proportional-hazards regression analysis In statistics, a mathematical method of modeling the relationships among three or more variables. It is used to predict the value of one variable given the values of the others. For example, a model might estimate sales based on age and gender. . Hazard rates were adjusted for other factors that might be associated with mortality, including age, sex, marital status, smoking status, cancer stage at diagnosis, and community measures of socioeconomic status. To determine the degree to which greater mortality among patients with comorbidity was the result of later stage at diagnosis, we repeated models both with and without variables for stage at diagnosis. We did not have data on the cause of death to allow analysis o f cancer specific mortality.

All analysis was conducted using SAS (1) (SAS Institute Inc., Cary, NC, www.sas.com) A software company that specializes in data warehousing and decision support software based on the SAS System. Founded in 1976, SAS is one of the world's largest privately held software companies. See SAS System.  statistical software (LOGISTIC, LIFETEST, PHREG procedures). [55] We present 95% confidence intervals confidence interval,
n a statistical device used to determine the range within which an acceptable datum would fall. Confidence intervals are usually expressed in percentages, typically 95% or 99%.
 for adjusted odds and risk ratios and unless specified, all P values are two-tailed. Statistical significance was determined using an [alpha] level of .05.

RESULTS

Study Population

The study population consisted of the 32,074 Florida residents who had colorectal, breast, or prostate cancer or melanoma diagnosed in 1994 and for whom information on stage was available (Table 1). Reflecting the demographics The attributes of people in a particular geographic area. Used for marketing purposes, population, ethnic origins, religion, spoken language, income and age range are examples of demographic data.  of the state, most patients were over age 65, and Medicare was the most common type of insurance. The majority of the breast and prostate cancers and melanomas were diagnosed at an early stage (either in situ or local). The majority of colorectal cancers colorectal cancer

Malignant tumour of the large intestine (colon) or rectum. Risk factors include age (after age 50), family history of colorectal cancer, chronic inflammatory bowel diseases, benign polyps, physical inactivity, and a diet high in fat.
, however, were diagnosed at a late stage (either regional or distant).

The percentage of patients having Charlson comorbid conditions varied from 7% for melanoma to 30% for colorectal cancer (Table 1). Patients having any comorbid condition were more likely to have diagnosis at late stage for each of the four cancer sites examined (Table 2). For breast and prostate cancer, the likelihood of late stage diagnosis increased in a dose-response fashion with increasing levels of comorbidity. This was not the case for patients with colorectal cancer or melanoma. In multivariate analysis, the presence of any comorbid condition was a significant predictor of late stage diagnosis for all four cancer sites that were examined (Table 3). The magnitude of the effect ranged from 17% greater odds of late stage diagnosis for colorectal cancer to a 62% increased odds of late stage diagnosis for patients with melanoma. Again, only for breast and prostate cancers did the effects of comorbidity demonstrate a dose-response, patients with comorbidity scores of 2 or greater having greater likelihood o f late stage diagnosis than patients with a score of 1.

Results were similar to those described when logistic models were repeated with cases restricted to invasive cancers only. In addition, results did not vary when cases were restricted to ages for which screening is most often recommended (ages 50 to 75 years) or when unmatched FCDS cases were excluded from the analysis (data not presented). We also did not find evidence of statistical interaction between the effects of comorbidity and other patient characteristics (age, sex, race-ethnicity).

Survival for patients with and without Charlson comorbidity is presented in Figures 1 through 4. For each cancer type examined, the proportion of patients surviving through the 4-year follow-up period was lower for patients having Charlson comorbidity than for those who did not (colorectal 58.8% versus 79.3%; breast 71.8% versus 85.4%; prostate 75.8% versus 82.8%; melanoma 58.8% versus 79.3%). Table 3 presents the results of proportional hazards regression analysis. Hazard rates were controlled for other potential predictors of mortality (age, sex, race-ethnicity, marital status, stage at diagnosis, smoking status, socioeconomic status, urban versus rural residence). The presence of comorbidity was associated with higher mortality rates for each cancer type examined. Adjusting for stage at diagnosis, however, did not alter this finding, suggesting that the higher mortality observed among patients with comorbid illness is primarily the result of their underlying illness, rather than later stage at diagnosis.

CONCLUSIONS

We found that the presence of comorbidity among patients with cancer was associated with later stage at diagnosis and greater overall mortality. The magnitude of the effects of comorbidity varied considerably by cancer type, however. Among patients with melanoma, those having comorbidity had 62% greater odds of late stage diagnosis and more than double the mortality rate of patients lacking comorbidity. The effects of comorbidity were least among patients with colorectal cancer, for which comorbidity was associated with 17% greater odds of late stage diagnosis and a 27% higher mortality rate.

One possible mechanism by which comorbidity could affect stage at diagnosis is by influencing cancer screening. Comorbidity would seemingly have two opposing influences on cancer screening. Comorbidity should increase the number of patient encounters with the health care system, increasing opportunities for cancer screening recommendations. [23] On the other hand, comorbidity may decrease the likelihood of cancer screening discussions during encounters by serving as a competing demand for primary care physicians, and by decreasing the perceived importance of cancer screening. [18,19] If comorbidity does have separate and opposing influences on cancer screening, our results suggest than in balance it reduces the likelihood of early detection. Further clinical studies examining screening behavior among patients with comorbid illness would be helpful in understanding these issues.

Many would argue that because of competing causes of mortality, cancer screening has less value in patients of advanced age and comorbidity. [14,56] If so, one might expect that comorbidity would have greater effects on stage at diagnosis among patients of advanced age. We found no evidence, however, that the effects of comorbidity on stage at diagnosis increased with advancing age.

Comorbidity showed a dose-response effect only for breast and prostate cancers. Charlson comorbidity index scores are based on the number of comorbid conditions and their assigned weights. Weights were chosen, however, to predict outcomes related to inpatient care inpatient care Managed care Services delivered to a Pt who needs physician care for > 24 hrs in a hospital , such as in-hospital mortality, length of stay, and health care resource use. It is possible that a different assignment of weights would be required to predict early cancer detection activities that largely occur outside of the hospital.

This study has a number of limitations that should be considered. First, we relied on administrative data only, the accuracy of which could not be independently verified. The inherent limitations in assessing comorbidity using administrative data sources have been well described. [57,58] We also did not have information on the severity of individual comorbidities, which may have affected our results. [59-61] We used the Charlson method to assess comorbidity, and it is possible that another method of assessment would have yielded different results. Most current measures of comorbidity have proven to be highly correlated and have shown similar effects, however. [62,63] We also did not have information on the cause of death to allow differentiation of cancer mortality from all-cause mortality. Information about whether patients had cancer screening tests was also not available and would have helped in understanding our findings. Finally, our study was restricted to incident cases in Florida, and our findings mi ght not be generalizable gen·er·al·ize  
v. gen·er·al·ized, gen·er·al·iz·ing, gen·er·al·iz·es

v.tr.
1.
a. To reduce to a general form, class, or law.

b. To render indefinite or unspecific.

2.
 to other parts of the country.

In conclusion, comorbidity was associated with later stage at diagnosis and higher overall mortality rates among patients with colorectal cancer, female breast cancer, prostate cancer, and melanoma. Further research is needed to confirm these findings and if confirmed, to determine the mechanisms by which comorbidity might influence stage at diagnosis. A better understanding of the degree to which the benefits of cancer screening are attenuated Attenuated
Alive but weakened; an attenuated microorganism can no longer produce disease.

Mentioned in: Tuberculin Skin Test


attenuated

having undergone a process of attenuation.
 with comorbidity and how comorbidity influences physicians' and patients' decisions to pursue cancer screening is also needed.

From the Department of Family Medicine, University of South Florida


    [
, and the H. Lee Moffitt Cancer Center and Research Institute, Tampa, Fla.

Supported by a Robert Wood Johnson Foundation Robert Wood Johnson Foundation, charitable organization devoted exclusively to health care issues. It was established in 1936 by Robert Wood Johnson (1893–1968), board chairman of the Johnson & Johnson medical products company.  Generalist gen·er·al·ist
n.
A physician whose practice is not oriented in a specific medical specialty but instead covers a variety of medical problems.


generalist 
 Physician Faculty Scholars Award (Dr. Roetzheim).

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(Hebrew: “priest”) Jewish priest descended from Zadok (a descendant of Aaron), priest at the First Temple of Jerusalem. The biblical priesthood was hereditary and male.
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[Graph omitted]

[Graph omitted]

[Graph omitted]

[Graph omitted]
TABLE 1

Characteristics of Men and Women With Selected Cancers Diagnosed in
Florida, 1994 (N = 32,074)


                                        Colorectal
                                        (n = 8,933)

Characteristics [*]                    Median (SD)

Age in years                            71.5 (11.6)
Household income                     $28,929 (10,593)
                                         No. (%)

Sex
    Male                               4,555 (51.0)
    Female                             4,375 (49.0)
Race
    White, non-Hispanic                7,626 (85.4)
    Black, non-Hispanic                  534 (6.0)
    Hispanic                             701 (7.8)
    Other                                 72 (0.6)
Education
    High school graduate or less       4,162 (46.9)
    More than high school education    4,715 (53.1)
Marital status
    Currently married                  5,399 (60.4)
    Not married                        3,534 (39.6)
Insurance payer
    Medicare                           5,736 (70.9)
    Medicaid                             119 (1.4)
    Commercial insurance                 709 (8.8)
    Commercial HMO                       662 (8.2)
    Commerical PPO                       484 (6.0)
    Uninsured                            234 (2.9)
    Other                                146 (1.8)
Stage
    In situ                              612 (6.9)
    Local                              2,858 (32.0)
    Regional                           3,977 (44.5)
    Distant                            1,486 (16.6)
Comorbidity Index
    0                                  6,298 (70.5)
    1                                  1,929 (21.6)
    2+                                   706 (7.9)




                                         Melanoma
                                        (n = 1,884)

Characteristics [*]                    Median (SD)

Age in years                            62.1 (16.7)
Household income                     $31,550 (12,193)
                                         No. (%)

Sex
    Male                               1,117 (59.3)
    Female                               767 (40.7)
Race
    White, non-Hispanic                1,763 (93.6)
    Black, non-Hispanic                   15 (0.8)
    Hispanic                              62 (3.3)
    Other                                 44 (2.4)
Education
    High school graduate or less         650 (34.7)
    More than high school education    1,225 (65.3)
Marital status
    Currently married                  1,268 (67.3)
    Not married                          616 (32.7)
Insurance payer
    Medicare                             713 (47.9)
    Medicaid                              28 (1.9)
    Commercial insurance                 274 (17.9)
    Commercial HMO                       180 (11.8)
    Commerical PPO                       186 (12.1)
    Uninsured                             72 (4.7)
    Other                                 58 (3.8)
Stage
    In situ                              295 (15.7)
    Local                              1,346 (71.4)
    Regional                             129 (6.8)
    Distant                              114 (6.1)
Comorbidity Index
    0                                  1,754 (93.1)
    1                                    106 (5.6)
    2+                                    24 (1.3)




                                          Breast
                                        (n = 10,976)

Characteristics [*]                    Median (SD)

Age in years                            64.0 (13.8)
Household income                     $29,794 (10,913)
                                         No. (%)

Sex
    Male                                    -
    Female                            10,976 (100)
Race
    White, non-Hispanic                9,217 (84.0)
    Black, non-Hispanic                  768 (7.0)
    Hispanic                             830 (7.6)
    Other                                161 (1.5)
Education
    High school graduate or less       4,275 (43.5)
    More than high school education    5,557 (56.5)
Marital status
    Currently married                  6,188 (56.4)
    Not married                        4,788 (43.6)
Insurance payer
    Medicare                           4,912 (49.7)
    Medicaid                             249 (2.5)
    Commercial insurance               1,653 (16.7)
    Commercial HMO                     1,081 (10.9)
    Commerical PPO                     1,161 (11.7)
    Uninsured                            472 (4.8)
    Other                                360 (3.7)
Stage
    In situ                            1,391 (12.7)
    Local                              6,372 (58.1)
    Regional                           2,632 (24.0)
    Distant                              581 (5.3)
Comorbidity Index
    0                                  9,601 (87.5)
    1                                  1,120 (10.2)
    2+                                   255 (2.3)




                                         Prostate
                                        (n = 10,281)

Characteristics [*]                    Median (SD)

Age in years                            69.8 (8.1)
Household income                     $29,563 (11,201)
                                         No. (%)

Sex
    Male                              10,281 (100)
    Female                                  -
Race
    White, non-Hispanic                8,187 (79.6)
    Black, non-Hispanic                  938 (9.1)
    Hispanic                           1,066 (10.4)
    Other                                 90 (0.9)
Education
    High school graduate or less       4,697 (43.1)
    More than high school education    6,217 (57.0)
Marital status
    Currently married                  8,068 (78.5)
    Not married                        2,213 (21.5)
Insurance payer
    Medicare                           5,737 (65.7)
    Medicaid                              96 (1.1)
    Commercial insurance                 957 (11)
    Commercial HMO                       864 (9.9)
    Commerical PPO                       596 (6.8)
    Uninsured                            245 (2.8)
    Other                                233 (2.7)
Stage
    In situ                               60 (0.6)
    Local                              8,254 (80.3)
    Regional                           1,373 (13.4)
    Distant                              594 (5.8)
Comorbidity Index
    0                                  8,876 (86.3)
    1                                  1,099 (10.7)
    2+                                   306 (3.0)



(*)Numbers for individual categories may not sum to total sample size
because of missing data.
TABLE 2

Bivariate Relationship of Comorbidity With Stage at Diagnosis


                   No. (%) of Cancers
                Diagnosed at Late Stage

                       Colorectal                Melanoma
                      (n = 8,933)              (n = 1,884)

Comorbid Index
     0          3,748/6,298 (59.5%) [**]  214/1,754 (12.2%) [+]
     1          1,264/1,929 (65.5%)        24/106 (22.6%)
     2+           451/706 (63.9%)           5/24 (20.8 %)
Comorbidity
     None       3,748/6,298 (59.5%) [**]  214/1,754 (12.2%) [**]
     Any        1,715/2,635 (65.1%)        29/130 (22.3%)







                         Breast                   Prostate
                      (n = 10,976)              (n = 10,281)

Comorbid Index
     0          2,762/9,601 (28.8%) [**]  1,626/8,876 (18.3%) [**]
     1            356/1,120 (31.8%)         248/1,099 (22.6%)
     2+            95/255 (37.3%)            93/306 (30.4%)
Comorbidity
     None       2,762/9,601 (28.8%) [+]   1,626/8,876 (18.3%) [**]
     Any          451/1,375 (32.8%)         341/1,405 (24.3%)



(*)p [less than].05 for chi-square.
(+)P [less than].01 for chi-square.
(**)P [less than].001 for chi-square.
TABLE 3

Effects of Comorbidity on the Odds of Late Stage Diagnosis [++]


                     Colorectal            Melanoma
                     (n = 8,035)           (n = 1,524)

Comorbid conditions
  No                 1.00                  1.00
  Yes                1.17 (1.06-1.29) [+]  1.62 (1.01-2.60) [*]
Comorbidity Index
  0                  1.00                  1.00
  1                  1.19 (1.06-1.32) [+]  1.73 (1.04-2.88) [*]
  2+                 1.12 (0.95-1.33)      1.20 (0.40-3.62)




                     Breast                 Prostate
                     (n = 9,832)            (n = 8,659)

Comorbid conditions
  No                 1.00                   1.00
  Yes                1.24 (1.09-1.41) [**]  1.30 (1.14-1.50) [**]
Comorbidity Index
  0                  1.00                   1.00
  1                  1.18 (1.02-1.35) [*]   1.19 (1.02-1.40) [*]
  2+                 1.56 (1.20-2.03) [**]  1.75 (1.35-2.26) [**]



(*)P [less than] .05

(+)P [less than] .01

(**)P [less than] .001

(++)Odds ratios for late stage diagnosis (regional/distant stage)
are adjusted for age, sex (if appropriate), race-ethnicity, marital
status, educational level, income level, urban residence, and
insurance payer.


KEY POINTS

* Comorbidity was associated with later stage diagnosis for breast cancer, colorectal cancer, prostate cancer, and melanoma.

* Comorbidity was associated with higher mortality for breast cancer, colorectal cancer, prostate cancer, and melanoma.

* Higher mortality appeared to be the result of underlying illness rather than later stage diagnosis.
COPYRIGHT 2001 Southern Medical Association
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Copyright 2001, Gale Group. All rights reserved. Gale Group is a Thomson Corporation Company.

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Author:ROETZHEIM, RICHARD G.
Publication:Southern Medical Journal
Geographic Code:1USA
Date:Sep 1, 2001
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