G. Richard, MD, MSPH Naazneen Pal, MPH Eduardo C. Gonzalez, MD Jeanne M. Ferrante, MD Daniel J. Van Durme, MD John Z. Ayanian, MD, MPP Jeffrey P. Krischer, PhD Tampa, Florida, and Boston, Massachusetts Submitted, revised, July 21, 1999. From the Department of Family Medicine (R.G.R., N.P, E.C.G., J.M.F., D.J.V) and the H. Lee Moffitt Cancer Center and Research Institute (R.G.R., J.P.K), University of South Florida, Tampa, and the Division of General Medicine, Department of Medicine, Brigham & Women’s Hospital and the Department of Health Care Policy, Harvard Medical School (J.Z.A.). Reprint requests should be addressed to Richard Roetzheim, MD, MSPH, Department of Family Medicine, University of South Florida, 12901 Bruce B. Downs Blvd, MDC 13, Tampa, FL 33612.
References
To include information that is not routinely available from the FCDS (insurance payer, comorbidity, socioeconomic status), cases were linked with state discharge abstracts. The State of Florida Agency for Health Care Administration (AHCA) maintains discharge abstracts for admissions to all nonfederal acute care hospitals, ambulatory surgical centers, freestanding radiation therapy centers, and diagnostic imaging centers. The data abstracted include social security number, date of birth, sex, race-ethnicity, discharge diagnoses (up to 10), and insurance payer.
FCDS cases were linked with discharge abstracts through a matching process using social security number, sex, race-ethnicity, and date of birth. Cases that successfully matched on all variables were considered valid matches. Cases were also considered valid matches if the sole discrepancy was a social security number or date of birth that differed by only one digit (suggesting data entry errors). Using this method 82.8% of eligible cases were successfully matched, a rate similar to that achieved in a comparable study.37 Using 1990 US Census data, each individual was assigned the median income/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.38-41 Our study method was approved by the University of South Florida Institutional Review Board.
The main outcome, stage at diagnosis, was defined as the summary stage at the time of diagnosis using the Surveillance Epidemiology and End Results Site-Specific Summary Staging Guide.42 For these analyses, stage at diagnosis was classified as either early stage (in situ, local) or late stage (regional, distant). Stage at diagnosis was available for 8933 (93.5%) of the incident colorectal cancer cases. Unstaged patients were older (P = .001), had less education (P = .03) and income (P = .003), were more likely to be widowed (P = .001) and nonwhite (P = .02). There were no sex differences (P = .71) or insurance payer differences (P = .20) between staged and unstaged patients. The supply of total physicians (P = .48), primary care physicians (P = .25), and specialist physicians (P = .60) also did not differ between staged and unstaged patients.
We obtained data on physician supply from the 1994 American Medical Association (AMA) Physician Masterfile, which includes allopathic and osteopathic physicians regardless of AMA membership,43 and population estimates from the 1990 US Census. We created physician supply variables for total physicians, primary care physicians, non-primary-care physicians, and for the individual physician specialties. Primary specialty is self-designated by physicians as the area in which they spend the majority of their clinical time. Physicians were classified as primary care if their primary specialty was either family/general practice, obstetrics/gynecology, or general internal medicine, regardless of their secondary specialty designation.44,45 Primary care practice content has been verified for physicians meeting this definition.46 Physicians who indicated they were engaged in full-time direct patient care were counted as one full-time equivalent (FTE); those who indicated in the masterfile that they were either semi-retired, in residency training, or engaged in teaching or research were counted as 0.5 FTE.44 We excluded physicians who indicated they were no longer involved in direct patient care. Previous studies have validated the data contained in the 1994 AMA Physician Masterfile.43,46-48
Physician supply was measured from 2 perspectives: regionally by county and locally using ZIP codes. Because we thought individual ZIP codes were too small a unit to assess the availability of physicians, we created a composite measure of local physician supply. We geocoded cases and used their longitude and latitude to determine the 5 closest ZIP codes (by centroid) to the ZIP code of their residence. We then calculated the supply of physicians in patients’ ZIP code of residence and the surrounding 5 closest ZIP codes. Similar methods have been used in studies of health care access.49 To determine if results were sensitive to the number of ZIP codes chosen to define a cluster, we repeated the analyses using other cluster sizes (3, 7, and 10 ZIP codes).
Other variables used in multivariate analyses included insurance payer (Medicare, Medicare health maintenance organization [HMO], Medicaid, commercial indemnity, commercial preferred provider organization, commercial HMO, uninsured [includes self-pay, charity], or other [includes Civilian Health and Medical Program of Uniformed Services, Veterans Affairs, worker’s compensation, and other state and local government programs]); race-ethnicity, including white (non-Hispanic), black (non-Hispanic), Hispanic, or other; marital status (never married, married, divorced, separated, or widowed); and comorbidity. Comorbidity was determined using methods described by Deyo50 and Charlson.51 We used International Classification of Diseases, Ninth Revision, Clinical Modification mapping of comorbid conditions as described by Deyo.50 We excluded cancer-related conditions. We used the original weights described by Charlson51 in calculating a morbidity index (theoretical range = 0 - 23). We defined 3 categories of comorbidity (0, 1, 2+) on the basis of a patient’s index score. There were 1997 cases (21.3%) with one comorbid condition, and 739 cases (7.9%) with 2 or more comorbid conditions.
