To examine factors associated with prostate-specific antigen (PSA) testing in the multiethnic California Men’s Health Study.

Cross-sectional analysis nested within a cohort of male health plan members (n = 55,278).

We extracted laboratory serum PSA values during the study period from 1998 to 2002. Using selected demographic and healthcare factors, we estimated the proportion of men who underwent PSA testing at least once during the 5-year period. Odds ratios and corresponding 95% confidence intervals were estimated to assess the association between these factors and PSA screening use.

African American men had substantially higher PSA screening prevalence than white men (82.6% vs 73.7%). Low PSA screening use was associated with Latino race/ethnicity, lower level of education, residency in the United States for 25 years or less, current smoking, and lack of PSA test discussion with healthcare providers. The strongest positive predictors of PSA testing were African American race/ethnicity (odds ratio, 1.66; 95% confidence interval, 1.50-1.83) and high concern about prostate cancer (odds ratio, 1.53; 95% confidence interval, 1.38-1.69). In contrast, when men did not discuss PSA testing with their physicians, they were 80% less likely to undergo screening.

In this insured population for whom financial barriers are minimized, PSA screening varied by race/ethnicity and by other patient and clinical factors, possibly reflecting inconsistencies in prostate cancer screening guidelines. Despite these differences, healthcare providers have a key role in patients’ likelihood of undergoing PSA screening.

Unlike previous studies of prostate cancer screening, this study adds to the literature by obtaining prostate-specific antigen (PSA) test utilization data from automated databases rather than by relying on self-reports.

Prostate-specific antigen screening varied by patient and clinical factors, perhaps reflecting the inconsistencies in prostate cancer screening guidelines.

African American men had significantly higher PSA test utilization than white men, while Latino men and Asian or Pacific Islander men had lower PSA test utilization.

Concern about prostate cancer was a strong predictor of PSA test use.

Men who discussed PSA screening with their clinician were more likely to get tested.

Approximately 218,890 men living in the United States were diagnosed as having prostate cancer in 2007, and 1 in 6 men nationally will receive the diagnosis in his lifetime.

Prostate cancer is the second leading cause of death among men in this country, and an estimated 27,350 US men died in 2006 of prostate cancer.

 Rates of prostate cancer are markedly higher among African American men versus men of other racial/ethnic groups (248.5 vs 156.7 cases per 100,000 men between 2001 and 2005).

 During the same period, the rates were 93.8 cases per 100,000 men among Asian or Pacific Islanders and 138.0 cases per 100,000 men among Latinos. Factors that predict whether a man undergoes prostate-specific antigen (PSA) testing among men of different  races/ethnicities are not well known.

While national consensus exists for breast and colorectal screening guidelines, controversy continues to surround guidelines for prostate screening.

 does not support routine testing among men of average risk, while the American Urologic Association (AUA)

 recommends annual PSA screening among men of average risk beginning at age 40 years. A 2008 study by the US Preventive Services Task Force (USPSTF)

 found that the current evidence was insufficient to assess the balance of benefits and harms of screening for prostate cancer among men younger than 75 years and recommended against screening among older men (≥75 years). Nevertheless, the USPSTF suggests that the group most likely to benefit from prostate cancer screening includes men aged 50 to 74 years and recommends testing every 1 to 4 years. Similar to most other major US medical associations, the USPSTF recommends that clinicians discuss the benefits and harms of prostate cancer screening with their patients and assist them with informed decision making regarding PSA testing.

Given the debate about prostate cancer screening guidelines, surprisingly little is known about the factors associated with screening behavior among men of diverse races/ethnicities. Data suggest that African American men are less likely to seek preventive healthcare compared with non-Hispanic white men.

 Because African American men are at greater risk of developing prostate cancer and dying of it, examination of variations in PSA testing rates by race/ethnicity is particularly warranted. Results of the few recent communitybased studies

 that compared PSA testing prevalence by  race/ethnicity suggest that African American men are substantially less likely to undergo screening than white men. The objective of this cross-sectional study was to examine factors associated with utilization of at least 1 screening PSA test during a 5-year period among men who were at average risk for prostate cancer and who were participants in the California Men’s Health Study (CMHS),

 a large multiethnic cohort established within the Kaiser Permanente Northern California and Kaiser Permanente Southern California health plans. Recommendations for PSA testing within these health plans generally follow those of the ACS; that is, asymptomatic men should be given information about the potential risks and benefits of the test, and the test should be provided if requested. For men with prostate gland symptoms, PSA screening or other screening modalities (eg, digital rectal examination) may be considered for men older than 50 years. Prostate-specific antigen screening is not recommended for men with life expectancy shorter than 10 years. Unlike several previous studies

 of prostate cancer screening, this population-based study adds to the research by obtaining PSA test utilization data from automated databases rather than by relying on self-reports, which tend to be an inaccurate measure of screening compliance. Furthermore, because out-of-pocket cost is an important known barrier to cancer screening, we were able to examine patient and healthcare factors in a population with equal access to care.

The Kaiser Permanente Medical Care Program is an integrated group practice prepaid health plan in 6 regions across the United States. The program cares for more than 6 million members in California. The analysis was nested within a prospective cohort study, the CMHS, described in detail previously.

 Briefly, the CMHS consists of male Kaiser Permanente Medical Care Program members aged 45 to 69 years at the time they completed self-administered questionnaires from January 2002 to December 2003. Participants were recruited in a 2-step process. In the first step, eligible health plan members were mailed a recruitment letter and a short questionnaire. In the second step, 134,060 men who completed the short questionnaire were then mailed a long questionnaire. The long questionnaire assessed information about demographics, personal and family history of cancer, healthcare utilization, health conditions, medication use, lifestyle, diet, and physical activity and was completed by 84,170 men. Participants, of whom approximately 40% self-reported being in a racial/ethnic minority group, closely reflected the sociodemographic diversity of the men residing in the state of California.

 After exclusions for length of membership, history of prostate cancer, and diagnostic PSA tests, the eligible study group included 55,278 men. The study was approved by the institutional review boards of Kaiser Permanente Northern California and Kaiser Permanente Southern California.

The analysis presented herein includes men without a personal history of prostate cancer (as identified by the tumor registry) who were continuously enrolled members of the health plans from 1998 to 2002. In addition, we excluded men who underwent PSA tests as ordered by a urologist and men who reported that they completed the PSA test because of symptoms or follow-up of previous abnormal test results. Such tests potentially would have been completed for diagnostic and not screening purposes.

Using unique medical record identifiers, we extracted data on PSA test utilization between 1998 and 2002 from the automated laboratory databases. For men who had multiple serum PSA tests during the 5-year window, only the first test was included in the analysis. We obtained information about overall healthcare utilization and about the department of each participant’s primary healthcare provider from other automated health plan databases. Self-reported information obtained from the questionnaire included the following: demographics (age, race/ethnicity, level of education, income, and residency in the United States), knowledge about PSA testing, family history of prostate cancer, occurrence of benign prostatic hyperplasia, prostatitis, lower urinary tract symptom severity using the AUA symptom index (AUASI) score, and smoking habits. The AUASI score provides an overall evaluation of lower urinary tract symptoms based on a questionnaire published by the AUA.

We estimated the proportion of men who underwent PSA testing by dividing the number of men with any serum PSA test in the 5-year period by the number of men at risk for being screened. The proportion was estimated for the cohort overall and was stratified by selected demographic and healthcare factors. Odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were estimated to assess the association between these factors and PSA screening use. We used unconditional logistic regression analysis to estimate the association of each factor, with adjustment for other factors. All demographic factors and healthcare factors were included in the multivariate model.

Of 84,067 men comprising the CMHS cohort, 66,400 had been enrolled in the health plans for 5 years before the baseline questionnaire. We excluded 11,122 men from the analysis who had prior prostate cancer (n = 3266) or a potentially diagnostic PSA test (as determined by self-report or if the test was ordered by a urologist) (n = 7856). Therefore, the eligible study population for this study included 55,278 men.

Overall, we identified 40,062 men (72.5%) who underwent any PSA screening as determined from automated laboratory data (

). The prevalence of any PSA screening was highest among African Americans (82.6%) and was lowest among Latinos (64.8%). As expected, PSA testing increased with age, with the sharpest rise occurring at age 50 years. In contrast, 36.1% of the men in the youngest group (45-49 years) had undergone PSA testing. In general, the highest prevalences of PSA testing were among the following: men with the highest level of education, men who were married or living with their partners, men who lived in the United States their whole lives or for longer than 25 years, and men who were past smokers. In multivariate analyses, African American men were more likely to have undergone a PSA test compared with white men (adjusted OR, 1.66; 95% CI, 1.50-1.83). In contrast, Latino men were less likely to have undergone a PSA test (adjusted OR, 0.89; 95% CI, 0.84-0.96). No significant difference was found between Asian or Pacific Islander men and white men. Compared with men 60 years or older, men aged 50 to 59 years were 40% less likely to have had PSA testing (adjusted OR, 0.59; 95% CI, 0.56-0.62). Prostate-specific antigen testing did not vary substantially by household income level. Yet, men who had lived in the United States for 25 years or less were dramatically less likely to have undergone any PSA testing compared with those who had lived in the United States longer (adjusted OR, 0.78; 95% CI, 0.70-0.86). Compared with nonsmokers, current smokers were 20% less likely to have undergone any PSA testing (adjusted OR, 0.80; 95% CI, 0.74-0.86).

Of all the factors examined, clinicians’ discussion of PSA testing was the strongest predictor of utilization (

). The prevalence of any PSA screening was 88.6% among men who reported having had a discussion with their clinician regarding this test. Clinicians more frequently discussed the test with African American men, men with higher AUASI scores, older men, and men with a college education (data not shown). A higher proportion of younger men among African Americans discussed the test with their clinician than among any other racial/ethnic group. In contrast to men who recalled a discussion, men who did not were 80% less likely to undergo screening (adjusted OR, 0.21; 95% CI, 0.20-0.22). Other factors strongly associated with PSA testing include having a high level of concern (“extremely concerned”) about prostate cancer (adjusted OR, 1.53; 95% CI, 1.38-1.69) and having a family history of prostate cancer (adjusted OR, 1.34; 95% CI, 1.24-1.44).

Men who reported having benign prostatic hyperplasia and men having an AUASI score in the severe range were more likely to undergo PSA testing. However, self-reported prostatitis was not associated with increased likelihood of having a PSA test (adjusted OR, 0.99; 95% CI, 0.88-1.11) (Table 2). Men who received their healthcare from providers in internal medicine and other primary care departments were substantially more likely to have a PSA test than participants who did not have an assigned primary care provider (73.8% vs 43.6%). Prostate-specific antigen testing prevalence increased with more outpatient visits (examined in quartiles). The associations in Table 1 and Table 2 did not change appreciably after adjusting for multiple covariates.

 This analysis identified the prevalence of PSA screening among insured men and the patient and healthcare factors associated with testing. Despite the controversy surrounding PSA testing, almost three-fourths of this multiethnic cohort had at least 1 PSA test in the prior 5 years. Consistent with recommendations of major US medical associations and the Kaiser Permanente Medical Care Program health plans, most men (88.6%) reported that their physician had discussed the PSA test with them (Table 2). Most important, African American men (who are at higher risk for prostate cancer) had significantly higher PSA test utilization than white men (82.6% vs 73.7%). This finding is in contrast to results from a 2001 population-based California survey of 8713 men, which found a lower prevalence of PSA testing among African American men (42%) than among white men (48%).

 The greater use of screening by African Americans in the managed care plans is noteworthy and may be the result of greater physician awareness about the higher risk among African Americans, as well as equivalent access to care, as reported among men in the Veterans Affairs system.

On the other hand, we found lower rates of PSA testing among Latino men compared with white men (possibly because Latino men have lower prostate cancer incidence in general). Similarly, men with less than a college education and those with 25 years or less of residence in the United States were significantly less likely to be tested. Smokers (who are at higher risk for many cancers) may also be at higher risk for prostate cancer—related morbidity and mortality as a result of their lower participation in PSA screening. These findings suggest that enhanced provider education and increased health plan efforts may be needed to help reduce a health disparities gap among certain groups of men. However, it may be that urgent problems or management of comorbidities was the focus of the outpatient visits utilized by these men.

As would be expected, when men experienced urologic symptoms (eg, higher AUASI scores or a history of benign prostatic hyperplasia), they were more likely to undergo PSA testing. In our study, awareness of the potential threat of prostate cancer through having a family history of prostate cancer or expression of concern about prostate cancer was also associated with PSA testing.

Clearly, physicians were the most powerful influence on PSA testing in this cohort of middle-aged men. When men did not discuss a PSA test with their physician, they were 80% less likely to have a test. With the available data, we were unable to examine the content of the actual discussion. Future research that includes physician-patient interactions is needed to understand how the conversation affects decision making about PSA testing. Prostate-specific antigen testing increased significantly in proportion to the number of patient contacts with primary care providers. Yet, our examination of healthcare delivery identified missed opportunities for PSA screening. Only 3.4% of men without an assigned primary care physician had a PSA test compared with 4.0% of men with a regular source of care (data not shown).

This study has potential limitations. Despite the large size and racial/ethnic diversity of the CMHS cohort, our findings may not be generalizable to other populations. It is feasible that men with higher utilization of preventive health screening or with greater concern about prostate cancer were more likely to participate in the CMHS. However, the CMHS cohort members are similar to the male participants of a California population—based survey with respect to race/ethnicity, body mass index, level of education and income, marital status, and birthplace.

 Additional data are needed to examine PSA screening at earlier ages. Although digital rectal examination is also a modality for prostate cancer screening, information about this procedure was not comprehensively captured in the health plan electronic databases, and it is possible that prostate cancer screening use may be underestimated in the present study.

Because annual prostate cancer screening is mainly recommended for men considered to be at high risk, we did not specifically examine PSA screening frequency among our group of largely asymptomatic men. Most important, no clear guidelines exist about the frequency of PSA testing among men at average risk. Therefore, we examined the 5-year prevalence.

This study has several strengths. Out-of-pocket cost is an important known barrier to cancer screening. We were able to examine patient and healthcare factors in a population with equal access to care. This study occurred after the rapid rise of PSA testing in the late 1980s and early 1990s, enabling us to examine predictors associated with screening behavior after the initial diffusion of the test. Unlike previous studies, we based our analysis on PSA testing use extracted from laboratory databases, which eliminates recall bias. Previous studies

 may have inaccurately assessed screening prevalence because the rates were based on self-reports or may have included diagnostic tests. To differentiate screening from diagnostic PSA tests, we excluded men who had a history of prostate cancer and those whose tests were ordered by a urologist or in response to patient symptoms. The overall prevalence of PSA testing was 72.5% among the CMHS study population (1998-2002) (Table 1), and these results are comparable to those of other published studies. For example, 78% of individuals in a North Carolina study

 reported obtaining a PSA test in 2003 and 75% of individuals in the national 2001 Behavioral Risk Factor Surveillance System survey.

Although controversy exists about the balance of benefits and harms associated with PSA testing, rates of testing continue to climb nationally.

 In this study, overall PSA screening rates were high, and although some recommendations for prostate cancer screening call for PSA testing to begin at age 50 years, more than one-third of men 49 years or younger had a PSA test in the prior 5 years (Table 1). Given the age criterion for the initial survey, we were unable to examine PSA testing use among men younger than 45 years or men 70 years or older; however, many national guidelines (such as the ACS

) do not state the appropriate age at which to start and stop screening. Furthermore, the USPSTF

 does not recommend testing among men older than 75 years.

In summary, the present study identified factors positively and negatively associated with PSA screening utilization in a large diverse population. Prostate-specific antigen screening varied by several patient and clinical factors possibly because of inconsistencies in prostate cancer screening guidelines, which reflect existing controversies over the benefits and risks of detection of and treatment for prostate cancer. While PSA testing was higher among African American men compared with white men, we found substantially lower testing prevalence among Latino men. Although out-of-pocket expense is minimized in these health plans, greater attention should be paid to men who belong to lower socioeconomic groups and to men who smoke. Previous findings demonstrate that patient-reported barriers to the use of screening tests include absence of symptoms, worries about pain or injury, preference that any health problems remain unknown, busy lifestyle, perceived susceptibility, and lack of physician ecommendation.

 Our results support previous findings that patients are more likely to obtain screening when providers recommend PSA testing.

 In the large health plans providing comprehensive healthcare to the men in this study, most providers discussed PSA testing with their patients, and most men had the test.

We thank Virginia Cantrell, MS, for her contributions to the study, including project management, and Ailin Tsai, MS, for programming support.

: From Research & Evaluation (RH, SJJ, CCA, JS, VPQ), Kaiser Permanente Southern California, Pasadena; Division of Research (SKV, BC, BS), Kaiser Permanente Northern California, Oakland; and Research & Evaluation (RKL), Kaiser Permanente Southern California, Downey.

: The cohort was established with funds from grant 99-86883 from the California Cancer Research Program.

: Dr Jacobsen reports receiving research grants from Beckman Coulter. The other authors (RH, SKV, BC, CCA, JS, BS, RKL, and VPQ) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

: Concept and design (RH, SKV, SJJ, BC, CCA, BS, RKL, VPQ); acquisition of data (RH, SKV, SJJ, BC, CCA, BS, VPQ); analysis and interpretation of data (RH, SKV, SJJ, BC, CCA, JS, BS, RKL, VPQ); drafting of the manuscript (VPQ); critical revision of the manuscript for important intellectual content (RH, SKV, SJJ, BC, CCA, JS, BS, RKL, VPQ); statistical analysis (RH, SKV, SJJ, BC, CCA, JS, VPQ); provision of study materials or patients (RH, SKV, SJJ, BC, CCA, VPQ); obtaining funding (BS); and supervision (RH, SKV, SJJ, BC, CCA, RKL, VPQ).

: Reina Haque, PhD, MPH, Research & Evaluation, Kaiser Permanente Southern California, 100 S Los Robles Ave, 2nd Fl, Pasadena, CA 91101. E-mail: reina.haque@kp.org.

1. National Cancer Institute. Surveillance, Epidemiology, and End Results (SEER).

Cancer Stat Fact Sheets: Cancer: Prostate. 

Rockville, MD: National Institutes of Health; 2008. http://www.seer.cancer.gov/statfacts/html/prost.html. Accessed December 1, 2008.

United States Cancer Statistics (USCS): 1999-2005 Cancer Incidence and Mortality Data

. Atlanta, GA: Centers for Disease Control and Prevention; 2008. http://www.cdc.gov/uscs. Accessed December 1, 2008.

3. Smith RA, Cokkinides V, von Eschenbach AC, et al; American Cancer Society. 

American Cancer Society Guidelines for the early detection of cancer. 

Screening for prostate cancer: an update of the evidence for the U.S. Preventive Services Task Force. 

American Cancer Society guidelines for the early detection of cancer

. 2009. http://www.cancer.org/docroot/ped/contentped_2_3x_acs_cancer_detection_guidelines_36.asp. Accessed August 11, 2009.

Prostate-specific antigen best practice statement: 2009 update. 2009

. http://www.auanet.org/content/guidelines-and-quality-care/clinical-guidelines/main-reports/psa09.pdf. Accessed August 12, 2009.

 Screening for prostate cancer: U.S. Preventive Services Task Force recommendation statement. 

8. Woods VD, Montgomery SB, Herring RP, Gardner RW, Stokols D

. Social ecological predictors of prostate-specific antigen blood test and digital rectal examination in black American men. 

9. Etzioni R, Berry KM, Legler JM, Shaw P.

 Prostate-specific antigen testing in black and white men: an analysis of Medicare claims from 1991-1998. 

10. Swan J, Breen N, Coates RJ, Rimer BK, Lee NC

. Progress in cancer screening practices in the United States: results from the 2000 National Health Interview Survey. 

11. Spencer BA, Babey SH, Etzioni DA, et al.

 A population-based survey of prostate-specific antigen testing among California men at higher risk for prostate carcinoma. 

12. Enger SM, Van den Eeden SK, Sternfeld B, et al. 

California Men’s Health Study (CMHS): a multiethnic cohort in a managed care setting. 

13. Rauscher GH, Johnson TP, Cho YI, Walk JA

. Accuracy of self-reported cancer screening histories: a meta-analysis. 

14. Walter LC, Bertenthal D, Lindquist K, Konety BR. 

PSA screening among elderly men with limited life expectancies. 

Articles

ONCOLOGY FOCUS

Prostate-specific antigen screening was highest among African American men and those concerned about prostate cancer and lowest when physicians did not discuss the test.


Correlates of Prostate-Specific Antigen Testing in a Large Multiethnic Cohort

 To present nationally representative estimates of the effect of cancer care on out-of-pocket medical expenditures and lost productivity for the working-aged population.

Pooled data from the Medical Expenditure Panel Survey were used for the analysis. We constructed the following 4 respondent groups for comparison during the analysis period: (1) respondents with no cancer, and (among those who reported having cancer) (2) respondents with active cancer care, (3) respondents with follow-up

cancer care, and (4) respondents with no cancer care. Using regression analysis, we estimated the effect of being in each of the cancer care groups on out-of-pocket medical expenditures, the probability of being employed, and the annual number of workdays missed because of illness or injury.

Being actively treated for cancer increases the mean annual out-of-pocket medical expenditures by $1170 compared with not having cancer. Less intensive cancer care is associated with lower medical expenditures (but still higher than for those without cancer). Respondents undergoing active cancer care were less likely to be employed full-time. Among respondents who were employed, those undergoing active cancer care missed 22.3 more workdays per year than those without cancer.

Changes to the health system need to consider not only how to reduce inappropriate medical utilization but also how to ensure that those diagnosed as having cancer and other serious medical conditions will not be doubly burdened with poor health and high medical expenditures.

Increased out-of-pocket medical expenditures and the potential for reduced earnings suggest that a cancer diagnosis will lead to financial hardship at a time when an individual’s health and earning potential are most at risk.

Respondents undergoing active cancer care were less likely to be employed full-time compared with those not having cancer.

Among respondents who were employed, those undergoing active cancer care missed 22.3 more workdays per year than those without cancer.

Changes to the health system need to consider not only how to reduce inappropriate medical utilization but also how to ensure that those diagnosed as having cancer will not be doubly burdened with poor health and high medical expenditures.

A diagnosis of cancer is a life-changing event that leads to major alterations in the physical and psychological health of patients with cancer. Those diagnosed as having symptomatic tumors are often sick and unable to work. Asymptomatic patients diagnosed through screening may not be ill but are likely to experience treatmentrelated adverse effects that may interfere with the ability to work. In either case, the cancer diagnosis leads to increased medical expenditures and, for those in the labor market, may lead to a decreased ability to work and maintain one’s primary source of health insurance given that most workers today obtain insurance through their employer.

The focus of this study is to provide nationally representative estimates of the effect of cancer care on the following 3 important outcomes for the working-aged population: employment, out-of-pocket (OOP) medical expenditures, and workdays missed because of illness or injury. Previous studies of the costs of cancer have focused exclusively on medical costs

 or have been limited in geographic coverage.

 Although our estimates are based on cross-sectional prevalence rather than incidence, we use information on the types of cancer care received to quantify the effects for those actively being treated for cancer and for those in subsequent treatment phases. To our knowledge, this is the first study to report population-based nationally representative estimates of the effect of cancer on medical expenditures and labor market outcomes. These results allow for quantifying the cost implications of different treatment phases after a positive cancer diagnosis and can be used to assess the financial implications of how proposed changes to current healthcare financing and delivery will affect the roughly 10.5 million Americans living with cancer and the 1.4 million who are likely to be diagnosed in any given year.

Pooled data from the 2000-2005 Medical Expenditure Panel Survey (MEPS) were used for the analysis. The MEPS is a nationally representative survey of the civilian noninstitutionalized population administered by the Agency for Healthcare Research and Quality. The MEPS provides data on participants’ utilization of medical services and corresponding medical costs. It quantifies total annual medical spending, including insurance spending and annual OOP spending. The latter includes copayments, deductibles, and payments for noncovered services. The costs captured by the MEPS represent payments (not charges) from the payer to the provider. These are obtained through a combination of self-report and validation from payers (eg, private insurers). All US dollar amounts were inflated to 2005 using the medical care Consumer Price Index.

The data also include information on each respondent’s health insurance status, source of payment, employment status and wages, and demographic characteristics (eg, race/ethnicity, sex, and education). Because we focused on medical expenditures and labor market outcomes, the study sample was restricted to working-aged participants (age range, 25-64 years) (N = 89,520).

Medical conditions are identified in the MEPS medical condition files based on self-reports of conditions affecting the respondent within the interview year. Medical conditions are classified using 

International Classification of Diseases, Ninth Revision, Clinical Modification

) codes based on self-reported conditions that were transcribed by professional coders. In addition to

 codes, the MEPS medical conditions files report clinical classification codes that aggregate 

 codes into clinically meaningful categories.10 We defined cancer using clinical classification codes 11 through 43 and code 45. Because of differences in care and prognosis, nonmelanoma skin cancer was included as a regressor but was not included in our cancer estimates.

We constructed the following 4 respondent groups for comparison during the analysis period: (1) respondents who did not report having cancer (n = 87,580); and (among those who reported having cancer) (2) respondents with a claim for active cancer care (ie, inpatient stay, chemotherapy, radiation therapy, surgery, or prescription for antineoplastic agents) during the survey year (n = 679); (3) respondents with a claim for follow-up cancer care, defined as any claim for a service not listed as active care with a cancer diagnosis (n = 968); and (4) respondents without a claim for cancer care during the survey year (n = 293). 

Using regression analysis, we estimated the effect of being in each of the cancer care groups on (1) OOP medical expenditures, (2) the probability of being employed, and (3) the annual number of workdays missed because of illness or injury. A 2-part regression model was used for OOP medical expenditures. The first part of the 2-part model used logistic regression analysis to predict the probability of any OOP expenditures, and the second part analyzed OOP expenditures among those with any OOP expenditures. To select the appropriate cost estimation model for the second part of the 2-part model, we used the algorithm for choosing among alternative nonlinear estimators recommended by Manning and Mullahy.

 Results of those tests led us to use a generalized linear model with log link and gamma variance function. The analysis sample for OOP expenditures totaled 69,107. A logistic model was used to estimate the effect of cancer care on the probability of being employed at some point in the survey year. The annual number of workdays missed because of illness or injury was defined as the number of times the respondent lost a half day or more from work because of illness, injury, or mental or emotional problems and was modeled using a negative binomial model. The analysis sample for the number of workdays missed was restricted to the employed (n = 65,692).

In addition to the cancer indicators, the models controlled for potentially costly or prevalent medical conditions and demographic variables. Regression analyses included the following comorbidities as independent variables: nonmelanoma skin cancer, heart disease, congestive heart failure, hypertension, stroke, high cholesterol, human immunodeficiency virus/AIDS, diabetes mellitus, injuries, pneumonia, chronic obstructive pulmonary disease, asthma, depression, other mental health or substance abuse disorders, arthritis, pregnancy, back problems, and renal failure. 

Consistent with prior studies, regression analyses also included the following demographics as independent variables: age, age squared, sex, study year indicators, census region (Northeast [referent], Midwest, South, or West), race/ethnicity (white [referent], black, Hispanic, Asian, or other), education (education missing, less than high school, high school diploma, or beyond high school [referent]), and primary source of insurance, defined as coverage during any part of the year (uninsured [referent], Medicaid, private insurance, or other).

As a sensitivity check, we also examined our outcomes using propensity score matching, which selects comparison groups for each cancer category from the set of individuals without cancer based on the estimated propensity of being in each cancer care category.

 Unlike regression models, propensity score matching restricts comparisons between respondents in our different groups to those with significant overlap in the independent variables. We estimated separate propensity scores for each cancer care group (ie, the probability of being in a cancer care group) using a probit model with the same set of explanatory variables as in the regression models. Nearest neighbor matches based on predicted propensity scores were used with 5 control matches for every respondent in the cancer care group. Contracting the number of matches to 1 and expanding the number of matches to 10 did not alter the results.

Without adjusting for confounders, the mean OOP expenditures are higher, employment is lower, and missed workdays are more frequent for those with cancer compared with those without cancer, and these are increased with more intensive cancer care (

). The mean annual OOP medical expenditures for a respondent with cancer in the active care group were $870 higher than those for a respondent in the no cancer care group (ie, those who once had cancer but are not currently receiving treatment) and $1190 higher than those for a respondent without evidence of cancer. Respondents receiving active cancer care missed a mean of 16.7 more workdays per year than respondents without evidence of cancer (20.8 compared with 4.1 workdays). However, respondents with cancer differed from the noncancer group in other dimensions. They were more likely to be older, female, white, and insured and to have higher education and other comorbidities.

After controlling for observable differences between the cancer and noncancer samples, estimates from the 2-part regression model reveal that being actively treated for cancer increases the mean annual OOP medical expenditures by $1170 compared with not having cancer (

). Approximately 10% of actively treated patients with cancer are estimated to have OOP medical expenditures exceeding $2000 (data not shown). Less intensive cancer care is associated with lower OOP medical expenditures, but they are still higher than expenditures for those without cancer ($460 higher per year for patients receiving follow-up cancer care and $290 higher per year for patients receiving no cancer care) (data not shown). 

Regression analysis results also reveal that, compared with those without cancer, patients undergoing active cancer care have a 3.3% absolute (4.5% relative) decrease in the probability of employment. There are no statistically significant effects of cancer on employment for the other 2 cancer care groups. Among respondents who were employed, those undergoing active cancer care missed 22.3 more workdays per year than those without cancer (Table 2). Follow-up cancer care is associated with about 1 more workday missed per year compared with those without cancer.

The propensity matching model produces estimates that are slightly smaller than the regression model estimates for OOP expenditures and workdays missed and slightly larger in absolute value for employment, with point estimates not statistically significantly different from the regression estimates (

). The primary patterns remain for individuals receiving active care for cancer: their OOP medical expenditures are higher than those for individuals without cancer (and these expenditures increase with the intensity of care received), and individuals who are employed miss significantly more days from work.

These results reveal the financial implications that are likely to result from a cancer diagnosis. Consider the case of a typical household earning roughly $49,240 per year and paying $2630 (5% of their annual income) toward health insurance premiums, copayments, and deductibles.13 During years that include active cancer care, expected mean annual OOP medical expenditures increase by an additional $1170 if 1 of the adults in the household is diagnosed as having cancer. Moreover, if the person is an hourly wage earner, selfemployed, or otherwise experiences the financial loss resulting from the roughly 22 additional workdays missed (ie, does not receive paid sick leave), then annual income could be reduced to $45,396. In this instance, the person’s increased annual healthcare bill now represents 8% of annual earnings, a significant share for almost every family. Although paid sick leave reduces the financial burden to the employee, the value of the lost work time remains unchanged, but the burden of payment shifts to the employer.

Our results are consistent with findings from a similar study

 that used data from the Health and Retirement Survey to quantify the financial and labor market outcomes resulting from the onset of serious medical conditions. That study reported that the onset of a serious illness was associated with an increase in OOP medical costs, a decrease in the likelihood of working, and a reduction in weekly hours worked. Our employment results are also consistent with findings reported by Bradley and coauthors15 that focused on labor market outcomes among Detroit, Michigan, area residents diagnosed as having breast or prostate cancer. They found that there was a dip in work and earnings 6 months after diagnosis but that employment was back up to prediagnosis levels by 1 year. Similarly, we found significant decreases in employment and increases in absenteeism only during years with active cancer care.

As already illustrated, a diagnosis of cancer can result in financial hardship at a time when an individual’s health and earning potential (through a decreased probability of employment) are reduced. Although families may be able to finance the costs via borrowing or savings, access to capital may prove difficult if the person diagnosed is the primary wage earner in the household. Moreover, our results suggest that the increased medical expenditures are likely to continue beyond the initial treatment year, possibly because of the costs of ongoing medical care and surveillance or potential increases in insurance premiums resulting from a cancer diagnosis. A cancer diagnosis may also limit insurance options in the future because insurance companies may deny coverage or charge exceedingly high rates for individuals who have had a recent cancer diagnosis.

Looking ahead, the OOP expenditures associated with cancer care are likely to grow because of increases in the cost of procedures and drugs to treat cancer

 and the trend toward consumer-driven healthcare options such as health savings accounts and high-deductible health plans, which require greater levels of consumer cost sharing. In 2008, 6.1 million Americans were covered under these types of plans, almost 4 times as many as in 2005.

 Higher cost sharing could result in a larger financial burden for patients at a time when their ability to earn a living may be substantially reduced. 

One possible solution to this problem that is consistent with consumer-driven healthcare is the broader dissemination of value-based insurance designs. Value-based insurance designs establish pricing schemes that encourage the use of “high-value” (highly cost-effective) prevention and disease management goods and services and discourage the use of low-value services.

 In value-based insurance designs, consumer cost sharing for high-value services is reduced (often to zero) to encourage their use, but cost sharing for low-value services is increased to discourage their use. An example of a high-value service is colorectal cancer screening,20 while an example of a low-value service is radical prostatectomy for early-stage prostate cancer.

 This approach could reduce wasteful medical spending, while ensuring that those diagnosed as having serious medical conditions such as cancer will receive cost-effective care at a low OOP cost.

These analyses have strengths and limitations. The strengths include the single source of data for quantifying medical expenditures, employment, and work loss attributable to cancer. However, because the MEPS public use data files do not include information on copayments and deductibles, we could not quantify the effect of higher cost sharing on these outcomes. The findings do not reflect cancer survivors who were employed but had reduced hours because of their health, which may underestimate the cost burden of cancer for these patients.

 Analysis of more specific cancer sites was not possible because of the small sample sizes within care categories. In addition, medical conditions in the MEPS are self-reported and can be used to quantify treated (or affected) prevalence only. There are likely to be individuals who had cancer at some point but, perhaps because of its long-term remission or the lack of required treatment, did not self-report it. The types of claims made within the study year were used as proxies for cancer treatment phase. Finally, the true burden of cancer includes costs for travel and child care, expenses incurred by caretakers, and nontangible amounts associated with psychological pain and stress.

 Future studies should include these additional measures of burden.

: From the Division of Health Services Research (EAF), Duke—National University of Singapore Graduate Medical School, Singapore; Public Health Economics Program (JGT), RTI International, Research Triangle Park, NC; and the Division of Cancer Prevention and Control (FKT, SAS, LCR), Centers for Disease Control and Prevention, Atlanta, GA.

: This work was supported by the Centers for Disease Control and Prevention under contract 200-2002-00575 to RTI International. The findings and conclusions are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

: The authors (EAF, FKT, JGT, SAS, LCR) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

: Concept and design (EAF, FKT, JGT); acquisition of data (EAF, JGT); analysis and interpretation of data (EAF, FKT, JGT, SAS, LCR); drafting of the manuscript (EAF, JGT); critical revision of the manuscript for important intellectual content (EAF, FKT, JGT, SAS, LCR); statistical analysis (EAF, JGT); obtaining funding (EAF, FKT); administrative, technical, or logistic support (EAF); and supervision (FKT, SAS). 

: Eric A. Finkelstein, PhD, Division of Health Services Research, Duke—National University of Singapore Graduate Medical School, Singapore. E-mail: eric.finkelstein@duke-nus.edu.sg.

1. Brown ML, Riley GF, Schussler N, Etzioni R.

 Estimating health care costs related to cancer treatment from SEER-Medicare data. 

Health insurance and spending among cancer patients. 

 2003;suppl Web exclusives:W3-189-W3-198.

 National estimates of medical costs incurred by nonelderly cancer patients. 

4. Yabroff KR, Lawrence WF, Clauser S, Davis WW, Brown ML.

 Burden of illness in cancer survivors: findings from a population-based national sample. 

5. Bradley CJ, Neumark D, Bednarek HL, Schenk M. 

Short-term effects of breast cancer on labor market attachment: results from a longitudinal study. 

6. Yabroff KR, Bradley CJ, Mariotto AB, Brown ML, Feuer EJ.

 Estimates and projections of value of life lost from cancer deaths in the United States. 

7. Bradley CJ, Yabroff KR, Dahman B, Feuer EJ, Mariotto A, Brown ML.

 Productivity costs of cancer mortality in the United States: 2000-2020. 

8. Ries LAG, Melbert D, Krapcho M, et al, eds.

. Bethesda, MD: National Cancer Institute; based on November 2007 SEER data submission, posted to the SEER Web site 2008. http://seer.cancer.gov/csr/1975_2005/. Accessed September 25, 2008.

. http://www.bls.gov/cpi/. Accessed August 12, 2009.

10. Healthcare Cost and Utilization Project

. Clinical Classifications Software (CCS) for 

. http://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp. Accessed July 29, 2009.

 Estimating log models: to transform or not to transform? 

. The central role of the propensity score in observational studies for causal effects. 

 Changes in financial burdens for health care: national estimates for the population younger than 65 years, 1996 to 2003. 

Healthy bodies and thick wallets: the dual relation between health and economic status. 

Being actively treated for cancer increases the mean annual out-of-pocket medical expenditures by $1170 compared with not having cancer.

The Personal Financial Burden of Cancer for the Working-Aged Population

To test the efficacy of an electronic asthma monitoring system (AMS) to reduce pediatric emergency department (ED) visits and hospitalizations for asthma.

Randomized clinical trial. Methods: Families of pediatric patients with asthma aged 8 to 17 years were recruited at 6 medical centers. Children were randomly assigned to the American Medical Alert Corporation pediatric AMS or a paper diary. The numbers of and costs associated with ED visits and hospitalizations for the 2 groups in the year following randomization were compared using 

Of 59 children recruited to the trial, 29 were randomized to the AMS and 30 to the diary. The 2 groups were similar in demographic and clinical characteristics. During their study year, 24 AMS group members logged on a mean (SD) of 211.0 (117.3) days; 13 diary group members provided data on a mean (SD) of 136.6 (128.0) days. During the 32 months that the study was in progress, the case managers logged on a mean (SD) of 171.0 (97.2) days. Overall, 35 children had at least 1 ED visit, but only 7 children were hospitalized. The 2 groups had no statistically significant differences in the numbers of or charges associated with ED visits or hospitalizations.

Electronic devices are being developed to make chronic disease management easier for patients and their families, but they should not be adopted without careful study, including randomized trials, to ascertain their use, costs, and benefits.

In a randomized trial, children with asthma who used an electronic asthma monitoring system had as many emergency department visits and hospitalizations as children who used a paper diary.

Clinical trial methods can be a valuable aid to decision making about reimbursement for costly new technologies.

Case managers are typically busy dealing with urgent conditions and needy patients. Expecting them to log on to a Web site on a daily basis without a prompt may be unrealistic.

Whether adding a prompt would yield better results for the electronic system can be determined but would require further research.

Asthma is the most common chronic disease in children. Despite intensive asthma control efforts during the past decade, the disease is the number one cause of emergency department (ED) visits and hospitalizations. Asthma impairs the quality of life of patients and families in the short term, and frequent absences from school may limit the aspirations of children with poorly controlled asthma, as well as their potential contributions to society in the long term. For patients with more than mild asthma with infrequent exacerbations, treatment involves daily use of a controller medication, usually an inhalable corticosteroid, plus rescue medication for use during exacerbations.

 Good outcomes for patients with asthma rely on effective patient education, regular and correct use of medications, and early recognition of symptoms requiring medication or medical attention. Poor outcomes in the treatment of asthma may be attributed in part to suboptimal patient adherence to the asthma action plan (treatment plan), ineffective communication between patient and physician,

 and inadequate or nonexistent asthma action plans.

 To assess adherence to the treatment plan, physicians often ask their patients to keep an asthma diary, but compliance with diaries is generally poor. Sharek et al

 studied children who averaged 0.41 hospitalizations and 3.5 ED asthma “sick” visits in the 2 months before enrollment in the study. They found that, among parents of 119 disadvantaged inner-city children with moderate-to-severe asthma, compliance with an asthma diary was 64%. However, new technologies offer the prospect of improving adherence,

In New York City, approximately 300,000 children (17%) have been diagnosed as having asthma at some time in their lives.

 The prevalence of asthma is highest among the low-income neighborhoods of the South Bronx, East Harlem, and Central Harlem. Children who live in those neighborhoods are almost 3 times as likely to be hospitalized for asthma as those in higher-income neighborhoods in part because of inadequate control of their condition.

The hospitals of the New York City Health and Hospitals Corporation (HHC) are primary providers of healthcare for low-income children in New York City. In 2002, more than 20,000 pediatric visits to HHC EDs and urgent clinics carried a primary diagnosis of asthma (6% of all pediatric ED visits and 35% of urgent outpatient visits). Since 1997, pediatric ED visits and hospitalizations have declined somewhat,

 but better asthma management seems achievable and may reduce the economic and human costs of this chronic condition.

In 2002, in an attempt to reduce the ED and hospitalization rates among his most difficult pediatric patients with asthma,

 the director of pediatrics at HHC’s Coney Island Hospital provided the Health Buddy, a computerized device developed by the American Medical Alert Corporation (AMAC [Oceanside, NY]) and programmed with their pediatric asthma monitoring system (AMS), to 69 children aged 8 to 16 years who had been diagnosed as having moderate-to-severe asthma. The patients had had a mean of 2.4 ED visits per month and 1 hospitalization every 7 weeks during the previous winter. Based on the prior winter’s data from Coney Island Hospital, the children were expected to have 2 to 3 ED visits per month and 3 to 4 hospitalizations during the 6-month study period from October 2002 to March 2003. In fact, only 1 child had an ED visit and was hospitalized (W. Seigel, MD, and M. Rutstein, RN, oral communication, July 2003). These results stimulated considerable interest in the new technology. We initiated a randomized controlled trial to test the hypothesis that among children with moderate-to-severe asthma those assigned to use the AMS would have fewer ED visits and hospitalizations than those assigned to use a paper diary.

MetroPlus Health Plan, a wholly owned subsidiary of HHC and its primary managed care plan, covers more than 105,000 children, mainly through Medicaid. We conducted the study among MetroPlus Health Plan member children receiving care for asthma at the following 6 HHC medical centers: Bellevue Medical Center, Elmhurst Hospital Center, Harlem Hospital Center, Jacobi Medical Center, North Central Bronx Hospital, and Woodhull Medical and Mental Health Center. Together these hospitals receive approximately 3000 emergent visits per year from children aged 8 to 17 years with a primary diagnosis of asthma.

The AMAC electronic monitoring systems are used to assist patients with self-management of various chronic conditions, including congestive heart failure and diabetes mellitus. The pediatric AMS is installed in patients’ homes to help them track their status with respect to their condition, to remind them to take their medication as prescribed, to instruct them to consult their provider when necessary, and to keep their case manager apprised of their status. The AMS uses patient dialogues written for a fourthgrade reading level but is considered interpretable by most children at least 8 years old. The hardware is a hand-sized electronic device with 4 keys and cords that plug into an electrical outlet and a telephone jack. It operates toll free.

Daily at a predetermined time, the device beeps and invites the child to answer a short list of questions (varied from encounter to encounter) about his or her asthma symptoms and use of medications since the last check. If the responses to these questions indicate that the child is having an asthma exacerbation, the system responds with appropriate instructions. The data are uploaded to a central site, where a clinician or case manager is responsible for reviewing them; he or she may then telephone the home to assess the child’s status, adjust the child’s medications if necessary, or have the child come in to see the physician.

Questions and answers are designed to identify symptoms early. Additional trivia questions are designed to keep children engaged while they learn about managing their condition.

) were designed to replicate, to the extent that paper could do so, the questions and information that the AMS used. However, the primary purpose of the AMS was not data collection but facilitation of asthma self-management. The diaries provided the control group with a tool that, although commonly used for the same purpose, lacked the electronic reminder features of the AMS. We viewed it as a kind of placebo. For each month, a cover sheet contained instructions for self-monitoring of peak expiratory flow, definitions of asthma zones, and the names of the child’s controller and rescue medications. On subsequent pages were questions about whether the child had gone to school, how the day had gone, the morning and evening peak expiratory flows, medication use, any symptoms, and what the child had done about the symptoms. Columns for each day of the week contained “Ys” for yes, “Ns” for no, or emoticons to be circled as appropriate. Each day’s column was a different color. Each child received a large white 3-ring binder with 12 tabs (labeled for each month of the study year) and a cover sheet and five 1-week diary pages per tab. In a pocket of the binder were 12 addressed and postage-paid envelopes in which each month’s diaries could be mailed to the case managers. The binders were attractively personalized with the child’s name.

Patients aged 8 to 17 years who had 2 or more ED visits or 1 hospitalization with a primary diagnosis of asthma at 1 of 6 participating HHC medical centers in the year before recruitment were invited to participate in the trial. Continuous eligibility in MetroPlus Health Plan was not a requirement for participation in the study. The study was approved by the institutional review boards of the participating hospitals and Columbia University.

After giving their informed consent (via parent or guardian) and assent, participating children were randomly assigned to receive the AMS or to be control subjects. All study materials were made available in English and Spanish. In a few cases, interpreters translated the consent and assent forms into other languages for the parents.

Each hospital assigned a clinical case manager to be responsible for daily review of and, if appropriate, response to the information that the AMS automatically sent to a passwordprotected Web site every night. Upon enrolling a child, the case manager sent the child’s contact information to MetroPlus Health Plan, which held the randomization codes and forwarded the information to AMAC staff or the diary health educators, depending on the randomization assignment.

The AMAC staff or the diary health educators then arranged to visit the child, to bring the AMS or diary, and to train the child in its use. All children were taught the definitions of the green, yellow, and red zones commonly used in asthma action plans.

All training included instruction in the correct use of the peak expiratory flow monitor, determination of the child’s personal best and yellow and red zone ranges, and directions to monitor peak expiratory flow and to use the AMS or diary daily. All participating facilities used uniform clinical protocols for managing pediatric patients with asthma. All study participants received appropriate standard care during their trial participation. AMAC trained the case managers and set up access to the AMS Web site for the patients on their computers. Case managers were instructed to contact patients and to arrange for them to see their provider when they entered the red zone.

Upon enrollment, all study participants completed a demographic and behavioral questionnaire. The primary outcomes of interest were ED visits for asthma, hospitalizations for asthma, and their costs. These data were obtained from the MetroPlus Health Plan member utilization database.

Children in the AMS and diary groups were compared with respect to demographic characteristics and asthma status at baseline. 

 tests were used to assess the statistical significance of differences between the 2 groups. The mean numbers of and charges for ED visits and hospitalizations in the year following enrollment were compared between groups using t tests and 

From August 2004 through April 2006, a total of 59 subjects were recruited (

), of whom 29 were randomized to the AMS and 30 to the diary. The groups did not differ with respect to sex (30 girls and 29 boys), age at baseline (age range, 8-15 years for both), race/ethnicity (23 non-Hispanic black, 26 Hispanic, and 10 other or unknown), or recent history of asthma attacks. Overall, 50 subjects had had at least 1 ED visit in the prior year, and 24 subjects had had at least 1 hospitalization.

Children who used an electronic monitoring system had as many emergency department visits and hospitalizations for asthma as children who used a paper diary.


November 2009

Correlates of Prostate-Specific Antigen Testing in a Large Multiethnic Cohort

The Personal Financial Burden of Cancer for the Working-Aged Population

Randomized Trial of an Electronic Asthma Monitoring System Among New York City Children

A Payer's Perspective on the Cost Effectiveness of Gastric Bypass Surgery

Cost-Sharing and Adherence to Antihypertensives for Low and High Adherers

Effectiveness of Care Coordination and Health Counseling in Advancing Illness

Cancer Care Disparities: Research Regarding Timeliness and Potential Coordination

Delayed Radiotherapy for Breast Cancer Patients in Integrated Delivery Systems

Efficiency and Its Measurement: What Practitioners Need to Know

Prescriber Compliance With Black Box Warnings in Older Adult Patients