Donatus U. Ekwueme, PhD

As the population ages, the financial amount spent on cancer care is expected to increase substantially. In this study, we projected cancer-related medical costs by state from 2010 through 2020.

We used pooled Medical Expenditure Panel Survey data for 2004 to 2008 and US Census Bureau population projections to produce state-level estimates of the number of people treated for cancer and the average cost of their treatment, from a health system perspective, by age group (18-44, 45-64, 

65 years) and sex. In the base model, we assumed that the percentage of people in each of the 6 age-by-sex categories who had been treated for cancer would remain constant and that the inflation-adjusted average cancer treatment cost per person would increase at the same rate as Congressional Budget Office projections of overall medical spending.

We projected that state-level cancer-related medical costs would increase by 34% to 115% (median = 72%) and that state-level costs in 2020 would range from $347 million to $28.3 billion in 2010 dollars (median = $3.7 billion).

The number of people treated for cancer and the costs of their cancer-related medical care are projected to increase substantially for each state. Effective prevention and early detection strategies are needed to limit the growing burden of cancer.

We project that the cost of medical care for cancer patients will increase substantially through the year 2020 in all US states.

		Effective prevention and early detection strategies are needed to limit the growing burden of cancer.

		These estimates provide a useful baseline against which to gauge the impact of current and future cancer policies.

		These estimates could be useful for budget allocations for investments in cancer prevention and early detection

Healthcare costs continue to rise nationally and impose greater burdens on state budgets.

Since cancer-related medical care costs constitute a substantial portion of overall US medical care costs,

accurate projections of future cancer-related care costs are critical. Over the past 20 years, the cost of treating cancer has nearly doubled nationally.

 As a result of an aging population and more expensive cancer treatments, the national costs of cancer care are expected to increase significantly in the near future.

 Although previous increases in spending on cancer have occurred despite the decreases in cancer incidence rates and increases in average survival times for patients with many types of cancers,

 researchers have noted many opportunities to further improve cancer detection and treatment while controlling costs.

To take advantage of these opportunities, state-administered insurance providers such as Medicaid and public healthcare providers such as the National Breast and Cervical Cancer Early Detection Program

 need state-level projections of future cancer care costs. Previous projections of cancer prevalence and cancer care costs have focused only on the national level.

This study produces state-level projections of cancer care costs through 2020. While our goal is not to explain differences across states, our projections do reflect projected changes in the distribution of state residents by age and sex during this period. They provide a useful baseline against which to gauge the impact of current and future cancer policies and could be useful for budget allocations for investments in cancer prevention and early detection.

First, we generated estimates of the number of adults who had been treated for cancer and the average cost of their treatment by age group (18-44, 45-64, 

65 years) and sex (male, female). The small number of children with cancer in our data prevented reliable estimates for children. Second, in our base projections, we assumed that the treatment rate for cancer in each of the 6 age-by-sex groups would remain constant and that the inflationadjusted initial average cancer treatment cost per person would increase at the same rate as Congressional Budget Office (CBO) projections of overall medical spending.

Third, we generated state-level projections of the total number of adults who will be treated for cancer and the costs of their treatment by multiplying treated cancer prevalence and average costs by the Census-projected population of each demographic cell. Therefore, the projections reflect expected changes in the distribution of state residents by sex and age group but assume that there will be no policy changes that could affect cancer treatment costs. For example, the projections do not account for possible changes in national healthcare policies mandated by the Affordable Care Act.

Projections of the Annual Number of US Adults Treated for Cancer

To estimate the number of adults in each state who will be treated for cancer, we used cancer prevalence data from the 2004 to 2008 Medical Expenditure Panel Survey (MEPS)

 and the US Census Bureau’s projections of state population counts for 2010 through 2020. The MEPS, a nationally representative survey of the civilian noninstitutionalized population administered by the Agency for Healthcare Research and Quality, provides data on participants’ use of medical services and on the costs of those services. MEPS provides a single, consistent data source to link disease prevalence and expenditures. Medical conditions are identified in the MEPS medical condition files; we restricted our condition indicators to those for which respondents received care within the interview year. Medical conditions were classified using the

International Classification of Diseases, Ninth Revision, Clinical Modification

) codes based on self-reported conditions that were transcribed by professional coders. Cancer was defined using clinical classification codes 11 through 43 and 45, which group 

We estimated logit models for the probability of cancer treatment that controlled for survey year and survey participants’ age, sex, and region of residence (northeast, south, midwest, and west). We used stepwise regressions to identify significant interactions among these variables to be included in the models. The significant interactions in the stepwise regressions represent age-by-sex-by-region categories with enough sample and power to detect differences in cancer treatment rates. We estimated cancer treatment rates (ie, the proportion of the population treated for cancer) for US adults in each age/sex/region group using coefficients from the logit regressions and adjusted these estimates to account for the nursing home care population using data from the 2004 National Nursing Home Survey.

We used the projected state population counts for 2010 through 2020 generated in 2008 by the US Census Bureau on the basis of data from the 2000 Census.

 For each state, we multiplied the predicted percentage of people treated for cancer in each of the 6 age-by-sex categories by the projected number of state residents in the corresponding category for each year from 2010 through 2020. We then aggregated these projections to project the total number of people who will be treated for cancer in each state in each year.

Projections of Direct Medical Care Costs of Cancer

MEPS measures total annual medical spending, including payments by insurers and out-of-pocket spending by patients (copayments, deductibles, and payments for noncovered services). The costs captured by MEPS represent payments (not charges) from the payer to the provider. MEPS spending data are obtained through a combination of self-reports by the respondents and validation of the self-reports from payers.

We projected direct cancer-related medical care costs of cancer in 6 steps. First, we estimated per-person medical costs as a function of cancer by using a 2-part regression model (a logit model to predict the probability of any expenditure and a generalized linear model with a gamma distribution and a log link to estimate total annual medical expenditures for people having any such expenditures). To choose among alternative nonlinear estimators, we used an algorithm recommended by Manning and Mullahy

 and found the generalized linear model was the most appropriate for the data. All regressions included the following variables: age; age

; sex; race/ethnicity; education; family income; other sources of health insurance; year indicators; and indicators for cancer, arthritis, asthma, back problems, congestive heart failure, chronic obstructive pulmonary disease, coronary heart disease, depression, diabetes, dyslipidemia, human immunodeficiency virus/acquired immunodeficiency syndrome, hypertension, injuries, other cardiovascular disease, other mental health/substance abuse, pneumonia, pregnancies, renal failure, skin disorders, and stroke.

Second, we calculated expenditures attributable to cancer by comparing predicted expenditures from the 2-part regression model. To ensure no double counting of expenditures for co-occurring diseases, we used the “complete classification” technique described in an earlier study.

 We treated each disease and combination of diseases as a separate entity and, for each unique combination of diseases, compared predicted expenditures with and without the disease(s) holding all else constant. For example, we treated cancer alone and cancer with hypertension as 2 different “diseases.” We then divided the total expenditures attributable to the combinations of diseases back to the constituent diseases using the parameters from the model to construct shares for each constituent disease within a combination (ie, a share of all cancer with hypertension disease costs that are attributable to cancer). The shares attribute a greater share of the joint expenditures to the disease with the larger coefficient in the main effect.

 We estimated per-person costs attributable to cancer for each age/sex/region category on the basis of coefficients from the national model.

Third, we used the 2004 National Nursing Home Survey and National Health Accounts to adjust our per-person cost estimates to account for nursing home spending. We assumed that average per-person, non-nursing home expenditures attributable to cancer were the same for the nursing home population as for the non-institutionalized population. Fourth, we used confidential MEPS data that identified the most populous 30 states and 9 Census divisions to generate state-specific per-person cost estimates. Sample sizes were not large enough for us to replicate the full analysis for each state. We regressed log (positive) medical expenditures on the variables in the model plus state/census division dummies. The coefficients on the dummies provided measures of the differences in average medical care costs across states that we used to scale the national estimates to make them state-specific.

Fifth, we estimated future costs by inflating dollar values in the MEPS data to the equivalent of 2010 values in accordance with recommendations from the Agency for Healthcare Research and Quality

 and then multiplied the projected perperson cost of cancer for people in each age/sex/state category by the number of people in the corresponding category that we projected will be treated for cancer in 2010 and in 2020. We then added the projections for each category to estimate total annual costs of cancer care.

Finally, we adjusted our cost projections on the basis of CBO assumptions that future healthcare costs not attributable to population growth and aging will increase by an average annual rate of 3.6% between 2010 and 2020.

We generated four 10-year projections of cancer care costs using the following assumptions about future US cancer prevalence rates: 1) no change in cancer incidence or survival rates (base projections), 2) continued trends in cancer incidence rates, 3) continued trends in cancer survival rates, and 4) continued trends in both incidence and survival rates. Incidence trends represent changes due to prevention and risk factor prevalence, and survival trends represent changes in early detection and treatment. We used trends in incidence and survival reported by Mariotto et al.

First, using Census projections for the year 2020, we converted their estimates of the number of cancer survivors for all sites under the 4 modeling assumptions

 to the implied cancer prevalence rates in each model. Second, we calculated the percentage difference in the predicted 2020 prevalence rates between the 3 alternative models and the base model. The differences between the alternative models and the base model hold the 2020 population constant and reflect differences due to the alternative assumptions. Third, we applied the percentage differences in 2020 prevalence rates between each of the 3 alternative assumptions and the base model at the national level to each of our age/sex/state categories in the year 2020. For years 2010 to 2020, we assumed linear growth from the 2010 value to the 2020 value by age, sex, and state.

We also generated cancer medical care cost projections using alternative assumptions of medical cost growth. Our baseline assumption was that per-person costs of cancer grew at the historical rate of growth of overall medical spending, 3.6% per year.

 In the sensitivity analysis, we applied the following growth rates to per-person cancer costs: 0%, 2%, and 5%.

In the base model, projected state-level changes in the number of residents treated for cancer between 2010 and 2020 ranged from −7% in the District of Columbia to 46% in Arizona (median = 17%; data not shown). The states with the largest projected increases in the number of people treated for cancer were Florida (353,000), California (351,000), and Texas (249,000). Projected percentage increases in cancer care costs between 2010 and 2020 ranged from 34% in the District of Columbia to 115% in Arizona (median = 72%) (

). Projected actual increases in costs ranged from $347 million in the District of Columbia to $28.3 billion in California (median, $3.7 billion) (

Our projections of cancer-related medical costs were not sensitive to alternative assumptions about future cancer incidence and survival rates (

). Accounting for trends in cancer incidence rates,

 projected medical costs were 3% lower than in the base model. Accounting for trends in cancer survival rates,

 projected medical costs were 10% higher than in the base model. However, accounting for both of these assumptions simultaneously, projected medical care costs were only 1% higher than in the base model.

Compared with our base model, projections based on the assumption of 0% increase in per capita medical care costs were 34% lower and those based on the assumption of 5% growth were 18% higher. Changes from 2010 to 2020 in projected state-level cancer care costs derived from the 0% cost-growth model, in which we assumed no change in treated cancer prevalence or inflation-adjusted per capita medical care costs, reflect solely the impact of projected changes in state populations and in the age and sex distribution of state residents (

). The combined impact of these 2 factors on projected changes in cancer-related medical costs between 2010 and 2020 ranged from a 12% decrease for the District of Columbia to a 41% increase for Arizona (median = 13% increase).

Articles

State-level estimates of the number of people treated for cancer and the average cost of their treatment by state from 2010 through 2020.

State-Level Projections of Cancer-Related Medical Care Costs: 2010 to 2020

 To establish current estimates and project potential reductions in the volume and cost of annual Pap tests administered at visits to physician office and hospital outpatient departments in light of cervical cancer screening changes and HPV vaccination.

 Assessment of baseline national administrative data and future projection.

We used data from the National Ambulatory Medical Care Survey (NAMCS) and the National Hospital Ambulatory Medical Care Survey (NHAMCS) to analyze physician office and hospital outpatient department visits made by female subjects 15 years and older from 2003 through 2005.

Pap tests were ordered annually at 30.2 million physician office and hospital outpatient department visits in the United States from 2003 through 2005. Among visits by young women aged 15 to 26 years, Pap tests were ordered at 5.8 million visits each year, representing 19.3% of all Pap tests ordered. Among visits made by women of childbearing age that included Pap tests, 76.0% occurred in obstetrics and gynecology offices or clinics. Using a simple projection model, we estimated an overall annual decrease of 1.2 million Pap tests for young women aged 15 to 26 years and a corresponding cost reduction of $77.6 million after routine HPV vaccination and HPV DNA testing. Among female subjects 15 years and older, the estimated potential decrease in Pap tests was 6.3 million, with an estimated $403.8 million in cost reduction.

The NAMCS and NHAMCS provide baseline data to estimate the effects of HPV vaccination and HPV DNA testing on cervical cancer screening policy. These future technologies may result in changes to cervical cancer screening policies and, when fully accepted and implemented, may reduce economic costs associated with cervical cancer in the United States.

	Assessment of the pre-human papillomavirus (HPV) vaccination burden of ambulatory care visits and procedures related to cervical cancer screening is provided, with projection of how HPV vaccination and testing may affect this burden.

		Human papillomavirus vaccination and testing may affect the volume of Pap tests and cervical cancer procedures in the future.

		National administrative data are used to assess baseline volumes of Pap tests and cervical cancer procedures and to project the effects of HPV vaccination and testing on numbers and cost.

	Human papillomavirus (HPV) testing, approved for routine cervical cancer screening in 2003, and the HPV vaccine, approved for routine vaccination of girls in 2006, are shifting the paradigm of cervical cancer screening and management. Recommendations for the use of HPV DNA testing for cervical cancer screening and management and HPV vaccination for cervical cancer prevention since 2003 and 2006, respectively, could dramatically affect the volume of Pap testing, a traditional and successful method of screening for cervical cancer. Management of cervical cancer—related procedures can be expensive: $2.3 billion to $6 billion are spent each year on direct medical costs from abnormal Pap test results and other low-grade lesions.

 Human papillomavirus vaccination and DNA testing are expected to decrease these costs by reducing the required numbers of annual Pap tests and cervical cancer—related procedures.

	In 2003, several organizations endorsed pairing HPV testing with Pap tests (HPV cotesting) as part of routine cervical cancer screening for women 30 years and older. Recent surveys of primary care providers estimate that about one-fifth of providers who offer Pap tests use the HPV test as a cotest.

 If most providers follow the recommended guideline of increasing the screening interval from 1 to 3 years for women who have both normal Pap tests and negative HPV test results, HPV cotesting is expected to decrease the number of Pap tests administered by 30%, from 65 million to 45.5 million, by 2010.

 By 2026, the HPV vaccine (approved for use in female subjects aged 11-26 years) is projected to decrease Pap test volume further by 13% as a result of shifting the age of Pap test initiation from 18 to 25 years among the fully vaccinated cohort of girls.

 Modeling studies also have projected a decrease in abnormal Pap test results and cervical cancer precursor lesions as a result of HPV vaccination. Sanders and Taira

 estimated a 21% reduction in the incidence of low-grade abnormal Pap test results over the lifetime of a vaccinated cohort of girls aged 12 years. Similarly, Kohli et al

 estimated a reduction in abnormal results of cytologies, colposcopies, and biopsies and in treatment of cervical intraepithelial neoplasia lesions over the lifetime of a 12-year-old cohort because of the HPV 16/18 vaccine. The numbers of colposcopies and other more serious procedures are projected to decrease by 12% and 42%, respectively, among women (negative to 14 HPV types at baseline) receiving the HPV vaccine based on evidence after 4 years of follow-up.

	Most projections of the number of Pap tests conducted annually are based on women’s self-reports.

 Self-reported data are subject to bias and could lead to an overestimation of adherence to screening.

 To date, no known study has used data abstracted from medical records to project the numbers of Pap tests and other cervical cancer—related procedures (ie, abnormal results of cytology, colposcopy, or biopsy and treatment of high-grade lesions) that are performed annually in the United States. Furthermore, few data are available on the number of abnormal Pap test results or colposcopies performed annually.

	The objectives of this study were to use data from the National Ambulatory Medical Care Survey (NAMCS) and the National Hospital Ambulatory Medical Care Survey (NHAMCS) from 2003 through 2005 to establish a baseline for the number of Pap tests ordered in physician offices and hospital outpatient departments (OPDs) and the number of follow-up procedures related to the management of abnormal Pap test results performed before the 2006 approval of the HPV vaccine. We use these estimates to project the degree to which HPV vaccination and HPV DNA testing could reduce the use of ambulatory cervical cancer—related tests and procedures and to estimate the potential reduction in screening and procedure costs from a reduction in cervical cancer–related tests and procedures because of HPV vaccination and HPV DNA testing.

	The NAMCS and NHAMCS are annual probability sample surveys conducted by the National Center for Health Statistics of the Centers for Disease Control and Prevention (CDC). The NAMCS uses a 3-stage probability sampling procedure that includes (1) 112 geographic primary sampling units, (2) a probability sample of physicians within primary geographic sampling units selected from the master files maintained by the American Medical Association and the American Osteopathic Association, and (3) a sample of about 30 visits during a randomly assigned reporting period of 1 week throughout the year. Sample design, sampling variance, and estimation procedures of the NAMCS have been described elsewhere.

 The NHAMCS uses a 4-stage probability sampling procedure that includes (1) the same 112 geographic primary sampling units used in the NAMCS, (2) a probability sample of nonfederal short-stay and general hospitals within the sampled primary sampling units selected from a publicly available database of all US hospitals, (3) emergency service areas within 24-hour emergency departments and clinics within OPDs, and (4) a sample of about 100 visits to emergency departments and 150 to 200 visits to OPDs during a randomly assigned reporting period of 4 weeks throughout the year; a published study14 describes the plan and operation of the NHAMCS. Only the OPD component of the NHAMCS was used in our analysis. The US Census Bureau is responsible for inducting NHAMCS hospitals and NAMCS physicians and for collecting sample encounter data. Additional information about the methods of the NAMCS and NHAMCS can be found on the Internet (http://www.cdc.gov/nchs/ahcd.htm).

	From 2003 through 2005, the survey response rates averaged 66% for NAMCS and 86% for NHAMCS OPDs. The annual numbers of participating NAMCS physicians and NHAMCS OPDs were 1300 and 220, respectively, and the annual numbers of patient record forms completed by physician offices and OPDs were 25,000 for the NAMCS and 32,000 for the NHAMCS. Estimates for visits by female subjects 15 years and older are based on 88,151 sample records that resulted in 5165 sample visits at which a Pap test was ordered during the 3-year study period.

	The same patient record form was used for NAMCS physicians and NHAMCS OPDs and contained demographic data about patients and information about the visit, including diagnosis, diagnostic and screening services, and ambulatory surgical procedures. As many as 3 diagnoses were coded according to the 

International Classification of Diseases, Ninth Revision, Clinical Modification 

 “Pap test” is a check box on the patient record form and is marked if the test is ordered or provided. “Procedures” that were ordered or provided were written in and coded using 

procedure codes. For this analysis, it was assumed that the Pap test or procedure was provided at this visit or at a later date. “Preventive care (eg, routine, prenatal, well-child, screening, insurance, and general examinations)” is a check box on the patient record form.

	We examined data from selected physician and OPD clinic specialties for which Pap test ordering is most likely to occur. Physician and clinic specialty is determined during the induction interviews for the NAMCS and NHAMCS, respectively. Physician specialty is not collected for the NHAMCS. For the NAMCS, obstetrics/gynecology (OB/GYN) was defined as a physician with that specialty, and general medicine (GM) was defined as a physician with the specialty of family medicine, internal medicine, or GM. For the NHAMCS, OB/GYN clinics included OPD clinics with that specialty. For the NHAMCS, GM clinics were defined more broadly than were GM physicians and may have included clinics that did not provide primary care.

	Abnormal Pap test result and cervical dysplasia were defined as 

diagnosis codes 795.0 (without 622.1) and 622.1 (without 795.0), respectively. The 

 procedure codes were used to define cervical procedures. Colposcopy was coded as 70.21 (vaginoscopy). Other cervical procedures were coded as 67.1 (diagnostic procedures on cervix), 67.2 (conization of cervix), and 67.3 (other excision or destruction of lesion or tissue of cervix, excluding 67.31).

	We presented our data by various age categories (15-26, 27-44, and >45 years). The first age category is a group that could be eligible for receipt of the HPV vaccine under the current guidelines.

 The second and third age categories would not be eligible for HPV vaccine. 

	The NAMCS and NHAMCS data were weighted to produce national estimates, and 3 years of data were combined to increase the reliability of the estimates. The NAMCS weight includes the following 4 components: selection probability, nonresponse adjustment, physician-population weighting ratio adjustment, and weight smoothing. The NHAMCS weight includes the following 3 components: selection probability, nonresponse adjustment, and ratio adjustment to fixed totals. SUDAAN software 9.0.1 (Research Triangle Institute, Research Triangle Park, NC) was used for all statistical analyses.

	The determination of statistical significance was based on χ2 test and 2-tailed t test (with a .05 level of significance). Bonferroni inequality was used to establish the critical value for statistically significant differences on the basis of the number of possible comparisons within a particular variable, or combination of variables, of interest. The complex sample designs of the NAMCS and NHAMCS were figured into the standard errors used to calculate the 95% confidence intervals around the estimates. Estimates based on fewer than 30 cases in the sample data did not meet CDC standards of reliability or precision.

	The NAMCS and NHAMCS protocols were approved by the Research Ethics Review Board of the CDC’s National Center for Health Statistics. Requirements to obtain informed consent from patients and patient authorization for healthcare providers to release medical record data were waived.

	We anticipate that the initial cervical cancer screening  age among the vaccinated cohort would change because of decreased HPV prevalence of HPV 16 and HPV 18, the lower positive predictive value of cytology, the rarity of cervical cancer in younger women, and anticipated guidelines change.

 We used the estimated baseline data and published assumptions to assume that HPV vaccination delayed the onset of Pap testing until age 26 years. This differs slightly from the age (25 years) presented in an article by Eltoum and Roberson

 because we wanted to make our categories mutually exclusive. We also assumed that the use of HPV DNA tests in screening decreased Pap testing volume among women 30 years and older by at least 30% (range, 29%-32%)

). Furthermore, we assumed that HPV vaccination and HPV DNA testing would reduce the annual incidence of colposcopy and other cervical cancer—related procedures by a mean of 24.5% (range, 12.0%-37.3%)

 (R. Haupt, MD, Gardasil Update, CDC Cancer Conference, August 2007). We used these data to project the potential annual reduction in the number of Pap tests because of HPV vaccination and HPV DNA testing. The potential annual reduction was obtained by using the estimated number of annual Pap tests ordered, the percentage reduction in Pap testing because of HPV vaccination, and the vaccination coverage rate. We also estimated the potential annual reduction in costs of Pap testing and other cervical procedures performed as a result of the potential annual reduction in the number of Pap tests ordered because of HPV vaccination and HPV DNA testing. We used a simple static model to calculate the potential annual reduction in screening and procedure costs. Formally, this model is expressed as follows: Potential Reduction in Screening Costs and Other Procedures per Year = (BPT × VCR × % RPT) × AC, where BPT is number of baseline Pap tests and other procedures per year; VCR is vaccination coverage rate; RPT is the annual percentage reduction in Pap tests and other procedures; and AC is the annual mean cost of Pap tests and other procedures.

	The input variables used to calculate the potential reduction in net cost for the numbers of screening and other procedures performed are given in Table 1. The medical care component of the consumer price index from the US Bureau of Labor Statistics was used to adjust all costs to 2005 US dollars.

 The mean cost of a Pap test was $63.67 (range, $36.47-$93.78).

 The mean cost for colposcopy with biopsy and office visit was $286.61 (range, $108.37-$433.47).

 The baseline HPV vaccination coverage rate of 70% was obtained from a study by Chesson and colleagues,

 and we varied this rate from 65% to 100% to calculate the minimum and maximum values.

	From 2003 through 2005, there were more than 519 million annual visits to physician offices and hospital OPD clinics made by female subjects 15 years and older, of which 18.1% were for preventive care. Where a subject went for preventive care varied by age. Among young women aged 15 to 18 years, almost half of visits for preventive care were to OB/GYN physician offices or hospital OPD clinics; the other visits were split equally between GM providers and other providers, mostly pediatricians. About 73% of preventive care visits made by women aged 19 to 44 years were to OB/GYN offices and clinics. Fewer preventive care visits were to OB/GYN offices and clinics beginning at age 45 years versus at ages 19 to 44 years (P <.05) (

). For visits at which a Pap test was ordered, a similar pattern was observed, with the proportion of visits to OB/GYN offices and clinics decreasing with age (P <.05) (

). A higher proportion of visits at which a Pap test was ordered occurred in OB/GYN offices and clinics among female subjects aged 15 to 44 years (76.0%) versus 45 years and older (63.2%) (P <.05).

	Pap tests were ordered at 30.2 million visits made by female subjects 15 years and older (25.7 visits per 100 female subjects), representing 5.8% of all visits. For visits by female subjects aged 15 to 26 years, Pap tests were ordered at 5.8 million visits, representing 8.8% of all visits among this age group and 19.3% of Pap test visits among female subjects 15 years and older. Among female subjects 15 years and older, a diagnosis of abnormal Pap test result (without a cervical dysplasia diagnosis) accounted for 1,371,000 visits, and a diagnosis of cervical dysplasia (without a diagnosis of abnormal Pap test result) accounted for 1,400,000 visits each year (

). Rates of abnormal Pap test result and cervical dysplasia varied by age, with young women aged 15 to 26 years having higher rates of abnormal Pap result and cervical dysplasia diagnoses than women in the other 2 categories combined (P <.05) (

	Cervical procedures were ordered or performed at 1.7 million visits; 1.2 million of these visits included a colposcopy, and another 712,000 visits included other cervical procedures. These 1.7 million visits represented 0.3% of all visits among female subjects 15 years and older. Visits that included a colposcopy or other cervical procedure accounted for 31.2% of visits with a diagnosis of abnormal Pap test result (without a cervical dysplasia diagnosis) or cervical dysplasia and represented 32.4% of visits with a diagnosis of cervical dysplasia (without an abnormal Pap test result diagnosis) (data not shown). There was no significant difference in the race/ethnicity distribution of visits with a diagnosis of abnormal Pap test result versus cervical dysplasia. Almost half (49.1%) of the visits at which colposcopies and other cervical procedures were ordered or performed were made by female subjects aged 15 to 26 years. There was no significant difference in the proportion of visits for colposcopy in the 2 older age categories (28.1% and 22.8%) (

	We estimated an overall annual decrease of 1.2 million Pap tests for young women aged 15 to 26 years, with a corresponding reduction in screening costs of $77.6 million (range, $39.9 million-$174.1 million) after routine HPV vaccination and HPV DNA testing (

). Among female subjects 15 years and older, the estimated potential decrease in Pap tests was 6.3 million (range, 5.7 million-9.7 million), with an estimated $403.8 million (range, $207.6 million-$906.3 million) reduction in screening costs. The estimated potential decrease in the number of cervical procedures and the estimated annual reduction in their costs are given in Table 3.

	We used NAMCS and NHAMCS data to derive baseline data on the annual number of ambulatory care visits in the United States at which Pap tests (30.2 million) and colposcopies and other cervical cancer—related procedures (1.7 million) were provided. We used these baseline data to estimate the potential annual reduction in the number of Pap tests administered and other cervical cancer–related procedures performed because of the widespread implementation of HPV vaccination and HPV DNA testing as part of cervical cancer screening. On the basis of these baseline data and other assumptions, we estimated an annual 6.3 million reduction in the number of Pap tests, with a corresponding cost reduction of $403.8 million and a 291,550 reduction in the number of colposcopies and other cervical cancer–related procedures, with a corresponding cost reduction of $83.6 million.

	It is difficult to project whether the use of the HPV vaccine will result in any changes in cervical cancer screening immediately or in the future, especially given the reluctance of providers to screen patients at older ages and issues of medicolegal liabilities.

 Furthermore, the use of HPV DNA testing may not result in any extension of screening intervals but rather may promote annual HPV and Pap testing, as predicted by experts.

 If this scenario occurs, then a baseline number of Pap tests performed among various age categories serves as a useful marker to measure the adoption of screening and vaccine recommendations. The estimated reduction in the screening costs and costs of other cervical cancer—related procedures illustrates how screening and other procedural costs can be contained if HPV vaccination and HPV DNA testing lead to a policy of less frequent screening intervals.

	Our study has some limitations. Because midlevel healthcare providers were not sampled, the numbers of visits for Pap tests and other outcomes may have been underestimated. We reported 30.2 million annual Pap tests from 2003 through 2005 among female subjects 15 years and older. The 2007 article by Solomon et al

 reports 65.6 million annual Pap tests among women 18 years and older in 2003. The self-report likely overestimates the number, as has been seen in the literature.12 We suspect that the NAMCS and NHAMCS data may underestimate because of several reasons. However, midlevel providers may have been included in the hospital OPDs because clinics, not providers, were sampled. Other surveys show that midlevel providers perform many Pap tests and follow-up procedures.

 Pap tests and other procedures also may be performed in settings not covered by the NAMCS or NHAMCS such as college health units, military clinics, or ambulatory surgical centers. In addition, health departments, community health centers, family planning clinics, large group practices, health maintenance organizations, and faculty practice plans have a low probability of coverage by the surveys. The NAMCS and NHAMCS collect diagnoses that are coded to 

 codes. We were unable to examine abnormal Pap test or cervical dysplasia results in more detail because the 

codes that existed during the study period did not allow us to differentiate the abnormal Pap test result category from the cervical dysplasia category, as is now possible. In addition, the 

codes for abnormal Pap test results and cervical dysplasia have the potential for misclassification because many providers may not discriminate between cytology and histology. The 2005 edition of the 

 contains several changes for coding cervical cytology to differentiate between diagnoses that are based on Pap test results (code 795) and diagnoses that are based on biopsy results (code 622). New entries under code 795.0 (abnormal Papanicoloau smear of cervix and cervical HPV) were expanded to reflect more accurately the terminology used in the revised Bethesda system for atypical cells of undetermined significance, atypical glandular cells of undetermined significance, cervical dysplasia, unsatisfactory cell sampling, and nonspecific abnormalities.

	Another potential limitation is that our simple model did not take into account the dynamic changes occurring in cervical cancer screening. Such changes may include increases among the populations of women in various age ranges, changes in the prevalence of hysterectomy, and an increase in the use of liquid-based cytology and computerized screening. These omissions may have resulted in underestimating or overestimating the true potential reduction in annual Pap tests, procedures, and associated costs. Another potential limitation is the use of data on different age groups from the literature to quantify potential reduction in Pap testing and other cervical procedures. These data are based on assumptions that may not be realized. For example, in estimating the potential decrease in Pap tests for young women aged 15 to 26 years, we used data from women 30 years and older. Newer guidelines have already been released that recommend screening women at age 21 years.

 Therefore, the estimated results are as good as the input data and assumptions. We did not include the cost of HPV vaccination and HPV DNA testing because our study was not designed to include such cost in our estimations. Because these new screening technologies are more expensive compared with conventional methods, the estimated reduction in the costs of cervical screening may be overstated. Based on these limitations, our simple model offers only a rough approximation of the potential reduction in screening and procedure costs from a reduction in cervical cancer—related tests and procedures because of HPV vaccination and HPV DNA testing. Data from the NAMCS and NHAMCS have the advantage of being obtained from medical records rather than from self-reports. Similar to other abstraction, there may not be 100% compliance of the tests or procedures in the medical records, so we may have overestimated some of the prevalence.

	These limitations notwithstanding, our study reported 30.2 million annual Pap tests from 2003 through 2005 among female subjects 15 years and older. This is clearly lower than 2-fold higher estimates using a self-reported survey of 65.6 million among women 18 years and older in 2003.

 confirm that women are significantly more likely to overreport Pap tests compared with documentation of Pap tests in the medical record (range, 1.2-1.8 higher self-reports compared with the medical records), consistent with our findings. The NAMCS and NHAMCS are the only national surveys in the United States that collect data on procedures performed in physician offices and OPDs. Furthermore, they allow for trend analysis of Pap testing and colposcopy visits over time. Long-standing surveys such as the NAMCS and NHAMCS have provided a baseline and an opportunity to analyze the future effects of the HPV vaccine and newer screening recommendations as vaccine coverage and HPV testing increase in the future. If newer screening recommendations that include HPV DNA testing are used appropriately, they could decrease the number of Pap tests performed annually. After taking into account the health effects and costs of HPV tests and HPV vaccines, these technologies might offer an opportunity to further reduce the health burden and economic costs of cervical cancer in the United States during the coming decades.

 From the Division of Cancer Prevention and Control (MS, LFM, DUE), Centers for Disease Control and Prevention, Atlanta, GA. Funding Source: No funding was provided for this study.

The authors (MS, LFM, DUE) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article. The findings and conclusions in this study are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

Concept and design (MS); acquisition of data (MS, LFM); analysis and interpretation of data (MS, LFM, DUE); drafting of the manuscript (MS, LFM, DUE); critical revision of the manuscript for important intellectual content (MS, LFM, DUE); statistical analysis (LFM); and supervision (MS).

The pre-HPV vaccination burden of ambulatory care visits and procedures related to cervical cancer screening is assessed, with projected effects of HPV vaccination and testing.

Ambulatory Care Visits for Pap Tests, Abnormal Pap Test Results, and Cervical Cancer Procedures in the United States

: To assess factors that may influence men’s preference for surgery versus nonsurgical options among newly diagnosed patients considering treatments for local-stage prostate cancer.

: Prostate cancer patients were approached at urology clinics after diagnosis but prior to starting treatment in California, South Carolina, and Texas. Using a survey about the treatment decision-making process, patients were asked about their likes and dislikes of 5 common treatment options: surgery (prostatectomy), brachytherapy, external beam radiation therapy, hormone therapy, and watchful waiting.

: Logistic regression identified associations between treatment characteristics and choice of prostatectomy compared with nonsurgical options, controlling for demographic, clinical, and psychological covariates.

: Of the 198 eligible men who returned the baseline survey, 59% indicated they only considered surgery and 41% considered at least 1 nonsurgical option. In multivariate analysis, patients who thought treatment efficacy was a primary concern were significantly more likely to prefer surgery only (odds ratio [OR] = 6.20, 95% confidence interval [95% CI] = 1.74, 22.10); those indicating concern about personal burden were significantly more likely to prefer nonsurgical options (OR = 0.07, 95% CI = 0.02, 0.22). Advice of friends and relatives and concerns over side effects were not significantly associated with preference for surgery versus other treatments.

: Men’s perceptions about treatment efficacy and the personal burden of treatment dominated preferences for surgery versus nonsurgical options. Interventions to aid treatment decision making should account for these elements to minimize the impact of physician biases and patient misperceptions on men’s decisions as how best to manage their prostate cancer.

We conducted a multisite survey of men with local-stage prostate cancer to understand their preferences for available treatments. The most influential factors were perceptions of efficacy and personal burden of prostatectomy relative to nonsurgical options.

Men misperceived the efficacy (cure) of prostatectomy relative to other options.

Personal burdens associated with treatment (eg, recovery time) were more influential than long-term side effects.

Policymakers and clinicians should consider wider use of decision aids, which have been shown to improve men’s understanding of management options, and may counter what appear to be biases influencing the decision processes.

Most men with prostate cancer are diagnosed at early stage and have several treatment options available, including prostatectomy, external beam radiation therapy, brachytherapy, and watchful waiting. Overall survival following any management choice approaches 99% at 5 years.

 A recent review found no compelling evidence favoring one approach versus another.

 Despite the lack of consensus about treatment, prostatectomy is commonly selected by men with early-stage prostate cancer, particularly those with low-risk disease.

 The reasons for this preference are unclear. To understand why men favor prostatectomy versus other options, we conducted a prospective survey of a diverse sample of men. Participants included men newly diagnosed with local-stage prostate cancer who were considering their treatment options.

We based our model on the taxonomy described by Holmboe and Concato, who categorized treatment attributes (likes and dislikes) into 4 axes: (1) external information, (2) intrinsic characteristics of treatment, (3) personal impressions, and (4) economic concerns.

 Using this taxonomy, we conducted focus groups with prostate cancer patients to develop relevant attributes for each management approach (including watchful waiting) that patients considered important. Based on our focus groups and literature review, we grouped characteristics into 5 domains: personal beliefs, treatment efficacy, others’ experience, personal burden, and avoiding side effects (see the 

We hypothesized that patients who indicated 

 as a main concern would be more likely to prefer prostatectomy only. Second, we hypothesized that men who indicated that a 

 influenced their decision (for reasons other than perceived efficacy) would favor surgery because perceptions about curing other common cancers involve removing the tumor (eg, breast cancer). Third, persons who listed 

 as highly important to their decision would be less likely to prefer prostatectomy, as surgery has been associated with higher rates of incontinence and impotence compared with other options.

experience of a family member or friend with prostate cancer 

who had surgery would be more likely to prefer prostatectomy versus nonsurgical options. Patients who have undergone surgery have been reported to be strong advocates for surgery,

 and many men choose surgical options, which increases the “exposure” to this management option. Finally, we hypothesized that patients concerned with the 

 of a treatment would prefer nonsurgical options because of the family and time burdens associated with recovery and managing a catheter. We also examined associations between treatment choice and general physical and emotional quality of life, prostate-specific symptoms, and the beliefs/perceptions patients had about prostate cancer and treatment.

Survey Development: Focus Groups and Cognitive Interviews

To develop questions that reflected the conceptual model, we conducted focus groups and cognitive interviews. Participants were identified through the Puget Sound Cancer Surveillance System, the Surveillance, Epidemiology and End Results (SEER) cancer registry for Western Washington State. Sampling randomly from SEER records for 2 urban counties, we contacted men diagnosed with local-stage prostate cancer within 6 months prior to the sampling date. Following physician notification to 69 patients, 14 men agreed to participate. Prior to the focus groups, participants received a set of likes and dislikes about treatment options based on the conceptual model.

 During the discussions, focus group participants ranked likes about the treatment they received and dislikes about treatments they did not select. Based on focus group feedback, we reduced the items in the final survey to 10 reasons (likes) for considering each treatment and 14 reasons (dislikes) for not considering a treatment. Survey items listing reasons for considering a treatment included doctor recommendation and opinions that the treatment would be more effective, have the fewest side effects, or have the least impact on the patient’s life. Survey items listing reasons for not considering a treatment included the converse of the previous 3 items and insurance not covering the treatment or out-of-pocket costs being too high. See the Appendix for the complete list.

We also included items from previous surveys developed for prostate cancer patients as well as surveys assessing general health,

 (see the Appendix). Using identical patient recruitment methods, these questions and the revised items were presented to 9 men in one-on-one cognitive interviews. The objectives were to confirm that patients understood the survey items and to assess survey fatigue. During the cognitive interviews, the participants completed semifinal versions of the survey while discussing the responses with an interviewer.

Participants were recruited through informed consent procedures approved by an institutional review board and were reimbursed $25 for their involvement.

Patients were recruited for the Family And Cancer Therapy Selection (FACTS) study. This study was initiated to understand the full range of domains involved in decision making by patients and partners, and how they make prostate cancer treatment decisions together. Newly diagnosed patients were approached in urology clinics at 3 sites: the Medical University of South Carolina (the University Urology Department and Ralph H. Johnson Veterans Administration Medical Center, Charleston), the University of Southern California (3 university-affiliated medical centers in Los Angeles), and the University of Texas Health Science Center at San Antonio (University Physicians Group and Audie Murphy Veterans Administration Hospital). Eligible patients included those diagnosed with local-stage disease (prostate-specific antigen [PSA] <50; tumor, node, metastasis [TNM] stages T1-T2, N0, M0) who had not initiated treatment other than hormone therapy at the time of approach. Clinic study coordinators reviewed lists of newly diagnosed local-stage prostate cancer patients scheduled for clinic visits. Interested patients signed consent forms and received a take-home survey to return by mail. Patients who indicated they had started treatment prior to the survey return date were excluded from this analysis (n = 42).

Study materials were approved by the institutional review board at each accrual site and the coordinating center. Participants received $25 after completing the baseline survey.

We conducted bivariate analyses of the relationships with each covariate and the primary outcome “preferring surgery only versus preferring at least one nonsurgical option.” We collapsed the outcome variable rather than keep individual nonprostatectomy categories because of the small number of observations in the nonprostatectomy categories. Chi-square tests were used for all categorical variables, and 

 tests were used to assess bivariate relationships for continuous variables. We used logistic regression to assess the association between our hypothesized likes and dislikes, and preferring only surgery versus nonsurgical options, adjusting for the demographic, clinical, and patient psychological factors noted in the Results section. We used a fixed-effects model, including recruitment site as the fixed effect in the model. This allowed for a withinsite assessment of the other variables. One limitation of this approach is the potential for other, unmeasured variables to confound the primary association between the outcome and our hypothesized likes and dislikes. Adjusting for the demographic, clinical, and patient psychological factors in the Results section addressed confounding to some extent. However, we recognize that there may be variables we were not aware of that could affect our results. The Hosmer-Lemeshow goodness-of-fit test was used to assess the overall adequacy of the model. All variables were retained in the model to assess their joint association on the outcome. For the continuous variables including the cancer worry scale,

 the Expanded Prostate Index Composite scores,

 nonparametric smoothing was used to assess the unadjusted functional form between each of these variables and choice of surgery. For all these variables, the functional form was approximately linear; thus, each of these variables was entered linearly into the logistic regression model. No adjustments were made for multiple tests. All tests for statistical significance used a 

A total of 423 patients were approached for participation. Of these, 240 met eligibility criteria and 198 (83%) returned the survey prior to initiating treatment; the analysis included the 198 patients (

). The median time from diagnosis to participant survey return was 70 days. The average age of the patient at the time of diagnosis was 63.0 years (SD = 8.1 years) and 36% were under the age of 60; 71% were white, 59% were employed, and 48% were college graduates (

). Eighty-six percent had a PSA level of <10, 62% had a Gleason score of <6 at diagnosis, and 55% were categorized as low risk based on the combination of these factors.

 Thirty-six percent listed 2 or more noncancer comorbidities at diagnosis (Table 1).

Overall, 116 (59%) patients indicated they were considering only surgery. More than three-quarters (76%) of all patients were only considering a single treatment option, with 12% considering 2 treatments, 8% considering 3 treatments, and 2% considering 4 or 5 treatment options. We examined all options considered by patients, including those considering multiple options: 71% reported they were considering prostatectomy, 19% brachytherapy, 15% external beam radiation therapy, 15% watchful waiting, and 8% hormone therapy (data not shown).

On bivariate analysis, men preferring prostatectomy only were more likely to be less than 70 years old, have private insurance, be married, and be employed compared with those considering at least 1 nonsurgical option (Table 1). Men preferring prostatectomy scored higher on the SF-12 physical component, but not the mental component, than patients preferring other options. Sexual function was significantly higher for men preferring prostatectomy only (Table 1).

The survey domains were commonly ranked as important to the treatment decision. Nearly half (45%) of the patients considering nonsurgical options reported personal burden as important to their decision compared with 10% of men only considering surgery (

). The experience of others was more likely to influence men considering surgery only. Both groups listed avoidance of side effects and personal beliefs nearly evenly in their likes and dislikes about treatment options; treatment efficacy was more commonly listed by surgeryonly men as important to their decision (see the Appendix).

Men preferring prostatectomy only were no different from those preferring other options regarding their trust in the healthcare system, levels of prostate cancer anxiety and worry, and feelings of cancer control and self-efficacy (Table 2). For those preferring prostatectomy only, there was no significant difference in the likelihood of receiving multiple different treatment recommendations compared with men considering other options.

Men concerned about the personal burden of treatment were significantly more likely to prefer nonsurgical options, and men who gave weight to the experiences of others were more likely to prefer prostatectomy only. Those concerned with treatment efficacy were significantly more likely to consider only prostatectomy. There were no significant differences in preferences for prostatectomy or nonsurgical options between those who indicated that personal beliefs or avoiding side effects was a key factor in their treatment decision.

Of the hypothesized likes and dislikes explored in the multivariate logistic regression analysis, treatment efficacy and personal burden remained strongly associated with treatment preference after adjusting for demographic, clinical, and psychological factors (

). The experience of others was not significantly associated with treatment preference. Those who indicated that treatment efficacy was a main reason for their treatment choice were significantly more likely to prefer surgery only (odds ratio [OR] = 6.20, 95% confidence interval [95% CI] = 1.74, 22.10). Those who considered personal burden a top reason for their treatment preference were much less likely to prefer surgery (OR = 0.07, 95% CI = 0.02, 0.22). General and prostate-specific measures of quality of life were not significantly associated with preferring surgery only. In the multivariate model, Hispanics and Asians were more likely to only consider surgery compared with whites (OR = 10.77, 95% CI = 1.77, 65.33).

lists the top reasons for considering and not considering each treatment choice. Patients considering brachytherapy most often believed it had the fewest side effects. The most common reason stated by those considering external radiation was research they had done on their own. The most common reason men stated for not considering prostatectomy, brachytherapy, external radiation, and/or hormone therapy was that the doctor recommended other treatment(s) (Table 4). The most common reason for not considering watchful waiting was fear about the cancer spreading.

To better understand the decision-making process for men who have been diagnosed with local-stage prostate cancer, we conducted a prospective, multicenter study of patients prior to initial therapy. We asked participants to report the management options they were considering and their reasons for liking and disliking each option. Our primary aim was to identify differences in likes and dislikes that influence treatment choice, focusing on differences between men preferring prostatectomy only and men preferring at least 1 nonsurgical option. Approximately 59% of men preferred only prostatectomy. We identified 2 strong influences on patient preferences. First, men who cited a strong concern for treatment efficacy were significantly more likely to prefer surgery. Second, men concerned with personal burdens of treatment were significantly more likely to prefer nonsurgical options. Notably, factors including disease risk among this population with local-stage cancer, side effects, and quality-of-life considerations did not emerge as strong predictors of preference for or against only considering surgery.

Prior studies of prostate cancer decision making surveyed men weeks to months after they had started or completed therapy.

 The FACTS study is unique because men provided insight into their treatment preferences as they were making their decision. A second feature of our study is our attempt to include a racially diverse cohort. Interestingly, we found that Hispanics and Asians, both of whom have a lower likelihood of prostate cancer than whites and blacks, are much more likely to prefer surgery only. This finding is consistent with a study of Hispanic men by Denberg and colleagues, who found that Latinos received prostatectomy more often than whites.

 We are not aware of studies of decision making among Asian men.

Many men in our sample appeared to believe that prostatectomy has superior efficacy compared with other options. This finding has been observed in multiple studies among men who were asked to recall reasons for their treatment choice,

 but is not consistent with current evidence.

 In contrast to our hypothesis about side effects and decisions, and despite evidence showing differential risk for side effects among management options,

 we found that concern about side effects appeared not to influence men’s preferences for surgery and consideration of nonsurgical options. This finding, combined with the apparent positive relationship between men’s concern about efficacy and preference for prostatectomy, suggests that many men have either limited access to or a poor understanding of the evidence regarding management options for local-stage prostate cancer. To address this issue, medical professionals who support prostate cancer patients during their decision process may need to take additional measures to ensure patients have a clear and balanced picture of the risks and benefits of all management options. Decision aids (eg, online or in the form of DVDs) have been shown to help patients make more informed decisions about treatment.

Many men in this study expressed concern with the impact of treatment on daily life (ie, personal burden). Perceived treatment burdens had a substantial negative impact on patients’ willingness to undergo prostatectomy. Holmboe and Concato also found burden of treatment to be a powerful factor in treatment decision making.

 Gwede and colleagues noted that persons who chose options other than prostatectomy cited burden issues as reasons to avoid surgery.

 These findings may explain the recent popularity of robotic prostatectomy, which has been touted to reduce recovery time compared with open prostatectomy.

The results of the multivariate analysis did not support our hypotheses regarding the influence of affected friends or family members on patients’ decisions. In contrast to our study, Diefenbach and colleagues found that the influence of fellow patients was second only to physicians in the decision process.

 It is possible that our sample included men who were different in some respects from men in prior studies and who were less likely to consider the opinions of friends and family. This possible difference may be due to the academic environment of our study sites, where second opinions are common.

We note some limitations of the FACTS study. We conducted it in academic urology clinics in 3 states. Although there was considerable diversity in this sample, our findings may not be generalizable to other settings. Additionally, many of the men in the sample (56%) were seeking second opinions. Our sample, therefore, may include a cohort of self-selected men with a preference for surgery. Although this factor was not an important predictor of surgery preference in the multivariate model, we acknowledge that our findings may be unique to urology settings with many patients seeking a second opinion. Second, although all patients were approached prior to treatment and the majority of men in our study returned their surveys prior to starting therapy, a portion returned surveys after initiating treatment. Although we excluded from the analysis men who had started treatment, it is possible that men who received treatment soon after diagnosis may have different perceptions than men who completed the survey prior to treatment. Third, a substantial proportion of our participants favored prostatectomy as their sole treatment consideration, thus limiting our statistical power to detect differences among the various nonsurgical treatments. We did not differentiate between robotic or open surgical procedures. Future studies of patient decision making could oversample patients considering nonsurgical options to better delineate differences between those management strategies. Although we observed differences in beliefs about treatment efficacy and personal burdens, we did not specifically measure patient knowledge about prostate cancer treatment. Future studies should examine whether inaccurate perceptions about treatment persist among men who receive accurate information, as measured by consistency in knowledge about treatment outcomes.

Patients’ personal beliefs about the value of one treatment versus another, as well as their assessments of the efficacy, burden, and side effects of various treatments, are influenced by several factors. These factors include their doctor’s advice and information available through a variety of other sources such as pamphlets, books, and the Internet. Further research is needed to characterize what leads to men’s likes and dislikes of various treatment choices. In particular, the content of the physician—patient interaction and its influence on patient considerations need further evaluation.

The authors gratefully acknowledge the contributions made by Catherine Fedorenko, MMSc, and Megan Fairweather, BS, in preparing this manuscript.

From the Health Services Research & Development Center of Excellence (SBZ), VA Puget Sound Health Care System, Seattle, WA; Public Health Sciences Division (SBZ, CMM, SDR), Fred Hutchinson Cancer Research Center, Seattle, WA; School of Pharmacy (DKB), University of Washington, Seattle, WA; Department of Urology (DFP), University of Southern California, Los Angeles, CA; Division of Cancer Prevention and Control (IJH, JLS, DUE), Centers for Disease Control and Prevention, Atlanta, GA; Department of Urology (IMT), University of Texas Health Science Center, San Antonio, TX; and Division of Urology Services (TEK), Medical University of South Carolina, Charleston, SC. Dr Penson is now with the Vanderbilt Institute for Medicine and Public Health, Vanderbilt University Medical Center, Nashville, TN.

This publication was supported by Cooperative Agreement 1-U48-DP-000050 from the Centers for Disease Control and Prevention, Prevention Research Centers Program, through the University of Washington Health Promotion Research Center. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

Case ascertainment for the focus group research was supported by the Cancer Surveillance System of the Fred Hutchinson Cancer Research Center, which is funded by Contract N01-PC-35142 from the Surveillance, Epidemiology and End Results (SEER) Program of the National Cancer Institute with additional support from the Fred Hutchinson Cancer Research Center and the State of Washington.

The authors (SBZ, CMM, DKB, DFP, IJH, JLS, DUE, IMT, TEK, SDR) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

In a survey of local-stage prostate cancer patients, preference for prostatectomy was influenced by perceptions of its efficacy and personal burden versus nonsurgical options.


Donatus U. Ekwueme, PhD

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