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Disparities in preventive procedures: comparisons of self-report and Medicare claims data
© Fiscella et al; licensee BioMed Central Ltd. 2006
Received: 25 May 2006
Accepted: 29 September 2006
Published: 29 September 2006
Racial/ethnic disparities are assessed using either self-report or claims data. We compared these two data sources and examined contributors to discrepancies in estimates of disparities.
We analyzed self-report and matching claims data from Medicare Beneficiaries 65 and older who participated in the Medicare Current Beneficiary Survey, 1999–2002. Six preventive procedures were included: PSA testing, influenza vaccination, Pap smear testing, cholesterol testing, mammography, and colorectal cancer testing. We examined predictors of self-reports in the absence of claims and claims in the absence of self-reports.
With the exception of PSA testing, racial/ethnic disparities in preventive procedures are generally larger when using Medicare claims than when using patients' self-report. Analyses adjusting for age, gender, income, educational level, health status, proxy response and supplemental insurance showed that minorities were more likely to self-report preventive procedures in the absence of claims. Adjusted odds ratios ranged from 1.07 (95% CI: 0.88 – 1.30) for PSA testing to 1.83 (95% CI: 1.46 – 2.30) for Pap smear testing. Rates of claims in the absence of self-report were low. Minorities were more likely to have PSA test claims in the absence of self-reports (1.55 95% CI: 1.17 – 2.06), but were less likely to have influenza vaccination claims in the absence of self-reports (0.69 95% CI: 0.51 – 0.93).
These findings are consistent with either racial/ethnic reporting biases in receipt of preventive procedures or less efficient Medicare billing among providers with large minority practices.
Racial and ethnic disparities in preventive procedures have been widely documented [1–9]. These findings are based primarily on either patient self-report or the use of claims data. Each has limitations. Self-report is associated with significant overestimation of rates of preventive procedures in most [10–17], but not all studies [18, 19]. In contrast, claims data may underestimate procedures . The size of disparities in mammography differs depending on survey question wording [21, 22], but more importantly, on whether self-report or claims data are used . Such discrepancies could represent either greater over-reporting by minorities of preventive procedures relative to majorities or less efficient Medicare billing procedures by providers servicing minority patients.
Determining whether different data sources yield disparate estimates of racial and ethnic disparities in preventive procedures is relevant to national monitoring of these disparities. A finding of similar disparities regardless of data source reinforces use of self-report data by the National Healthcare Disparities Report or for tracking progress towards Health People 2010 objectives. Discrepant findings suggest the need for further research to determine which is the more reliable data source for assessing disparities. Assessment of potential discrepancies is also critical to understanding disparities in health. Self-report data from the National Health Interview show small racial disparities in mammography , but analysis of Medicare claims show significantly larger racial disparities . Significantly, racial disparities in mammography based on Medicare claims have been linked to racial differences in stage at diagnosis .
Thus, the primary aim of this study is to determine whether estimates of racial and racial disparities in receipt of six different types of largely preventive procedures differ between self-report and Medicare claims data. To do so, we determine whether minority status, defined as African American or Hispanic (compared to non-Hispanic White) is associated with self-report of procedures in the absence of a claim and vice versa. We also examine whether any such associations are accounted for by other patient characteristics.
Throughout this paper we use the term preventive procedures rather than screening to indicate that while the majority of procedures are likely performed for screening purposes, some are performed for diagnostic or other reasons. Most published studies analyzing self-report data do not distinguish the reason for the procedure, and typically assume the procedure is for screening. Most national self-report surveys (including the Medicare Current Beneficiary Survey used here, the Medical Expenditure Panel Survey, and the Behavioral Risk Factor Surveillance System) do not have a question on the reason for testing. While the National Health Interview Survey currently does include a question on the reason for testing, the information is not used in the published procedure rates used to track the Healthy People 2010 goals , or in publications derived from the National Health Interview Survey [3, 26, 27]. Our analyses examine discrepancies between self-report and claims data, so we use an inclusive definition of preventive procedures to enable comparison to other self-report literature.
The study was approved by the University of Rochester Human Subjects Review Board. Data used were the Centers for Medicare & Medicaid Services' (CMS) Medicare Current Beneficiary Survey (MCBS). The MCBS includes an annual survey (for a maximum of four years per participant) to a rotating panel of Medicare beneficiaries. Participants are asked to recall tests and procedures they underwent during the proceeding year. After the first year, participants are given diaries to augment their memories; however, no information is available as to how widely these diaries are used by participants and self-reports are based on verbal response rather than examination of diaries. Selected subpopulations are over-sampled and appropriate longitudinal and cross-sectional weights are provided to allow for estimates for the entire Medicare population.
Medicare claims data (diagnoses, and diagnostic and procedural medical services) are also available for each year respondents participate and can be linked to the survey data. This linkage at the patient level allows for direct comparison between participants' self-report of services received and those documented by the existence of Medicare claims. Further details about the survey are available at the CMS website
To yield sufficient power, survey, physician/supplier, and outpatient data were aggregated across four years (1998–2002) where possible. However, questions about cholesterol testing were asked only in 2001 and 2002, and questions about colorectal cancer testing only in 2000. Subjects' first year of participation in the survey was eliminated because a full year of corresponding claims in the preceding year is not available for first year participants in MCBS. Thus, comparison between self-report and existence of complete claims is available only for those completing at least 2 years of the survey. In analyses not reported, results consistent with those below were obtained using the nine months of claims data prior to the first survey year. The total number of observations available from 2 to 4 years of surveys was 88,509.
Respondents were excluded from our sample if they participated in facility interviews (i.e., resided in long-term care facilities, n = 6,462), were less than 65 years of age (i.e., were Medicare recipients due to having a qualifying disability, n = 12,852), reported race/ethnicity other than Hispanic, non-Hispanic African American, or non-Hispanic White, i.e. majority (n = 3,169 dropped due to small sample size of other race/ethnicities), were enrolled in a Medicare HMO (n = 15,262 dropped because claims were not available from medical encounters), or were not eligible for Medicare B (or Medicare A and B) coverage (n = 1,118) dropped due to incomplete claims. The resulting analytic sample contained 42,949 majority, 4,168 African American, and 2,528 Hispanic observations.
Minority status was defined as self-report of African American/Black race or Hispanic ethnicity: based on the responses to two questions: "(Are you/Is SP) of Hispanic or Latino origin?" [Yes or No] ; and "Looking at this card, what is (your/SP's) race?" [American Indian or Alaska Native; Asian; Black or African American; Native Hawaiian or other Pacific Islander; White; Another Race (Specify)]. Participants in our analyses were classified as a minority if they responded either "Yes" to the first question or "Black or African American" to the second question. This aggregate measure was used in the primary analyses to increase power; secondary analyses were conducted separately for African Americans and Hispanics.
Receipt of preventive procedures
Preventive procedure questions and CPT, HCPC, and BETOS codes
Have you a blood test for the detection of prostate cancer, known as PSA, in the past year?
84153, 84154, G0103
Did you have a flu shot for last winter?
90732, 90724, 90659, 90658, 90669, G0008, or a BETOS code of O1G
Pap smear testing
Have you had a PAP smear in the past year?
G0101, G0124, G0141, Q0091, P3001, G0123, G0143, G0144, G0145, G0147, G0148, 88142, 88143, 88147, 88148, 88150, 88151, 88152, 88153, 88154, 88156, 88157, 88164, 88165, 88166, 88167, 88174, 88175, P3000
When was the last time you had your blood cholesterol taken [Yes in the past year]?
82465, 83718, 83721, 83719, 80061.
Have you had a mammogram in the past year?
76090, 76091, 76092, or a BETOS code of I1C
Colorectal cancer testing
When was your most recent test done? [Yes in the past year to EITHER colonoscopy test OR blood stool test]
82270, 82272, 82274, 82270, G0328, G0107, or a BETOS code of P8C or P8D
To assess self-reported colorectal cancer testing, the combination of two measures (either submitting a kit for fecal occult blood testing or undergoing a sigmoidoscopy or colonoscopy in the past year) was used. Sigmoidoscopy was combined with colonoscopy because the self-report question did not distinguish them. Secondary analyses were also conducted separately for sigmoidoscopy/colonoscopy and fecal occult blood testing.
Analyses adjusted for the following MCBS respondent characteristics: age (categorized as: 65–69, 70–74, 75–79, 80–84, with 85 and older as the reference group), education (less than high school graduation vs. at least high school graduation), annual income (less than $25,000 vs. $25,000 or more), metropolitan residence (vs. not), whether the respondent lived alone (vs. not), availability of supplemental insurance (private insurance, Medicaid supplemental, vs. none), proxy response to the survey (vs. self-response), functional status – Activities of Daily Living scale (a 3-point impairment scale) , and respondents' estimate of their general health compared to others of their age (5-point scale). This type of comparative self-rating of health status has been shown to predict mortality [30, 31].
To accommodate the complex survey design of the MCBS, including the multiple years of enrollment in the survey, SAS SURVEY procedures were used (SAS Institute, Cary, NC, Version 9.1, 2002–2003,). Survey weights were used to adjust for over-sampling and non-response to yield population parameter estimates. Data were analyzed with logistic regression to assess the adjusted relationship between minority status and self-report in the absence of a claim, as well as for a claim in the absence of a self-report.
Percentages of sample, by socio-demographic and survey characteristics
Pap Smear Testing
Colorectal Cancer Testing*
N = 7,009
N = 18,315
N = 4,784
N = 10,421
N = 7,404
N = 1,474
Hispanic, African American
MCBS Survey Years
Activities of Daily Living1
Self-assessed health status2
Overall agreement between self-report and claims was generally lower for minorities. The kappa statistic measures the level of agreement two values with values ranging from "0" for no agreement to "1.0" for perfect agreement. A kappa between self-report and claims data for minorities ranged from 0.19 (for colorectal cancer testing) to 0.58 (for mammography). For majorities, these statistics ranged from 0.37 (for colorectal cancer testing) to 0.70 (for mammography). The different ranges of kappa between majorities and minorities are largely due to higher rates of self-reported preventive procedures in the absence of a claim for minorities.
Effect of Minority Status on reported receipt of preventive health care procedures, in the absence of a corresponding claim, and a claim in the absence of a self-report.
Odds Ratios for Minorities Compared to Majoritiesa
Pap Smear Testing
Colorectal Cancer Testing
Self-Report in the Absence of Claim
Crude Odds Ratio
Adjusted Odds Ratio
Claim in the Absence of Self-report
Crude Odds Ratio
Adjusted Odds Ratio
Rates of a claim in the presence of self-report of non-receipt were appreciably lower than self-report in the absence of a claim. Again, the crude and adjusted odds ratios were similar (Table 3). Minorities were more likely than Majorities to have a PSA test claim in the absence of a self-report (1.55 95% CI: 1.17 – 2.06), but were less likely to have an influenza vaccination claim in the absence of a self-report (0.69 95% CI: 0.51 – 0.93). The remaining odds ratios did not reach statistical significance.
To address the differences in report of true preventive services versus diagnostic services, analyses for PSA testing, mammography, and colorectal cancer testing were recomputed using only true preventive codes . Those models produced results very similar to models described above: similar minority effects were apparent for mammography and colorectal cancer testing; no minority effects were observed for PSA testing.
Previous research has shown larger disparities in mammography between African Americans and majorities when receipt of mammography is based on claims data rather than on self-report [23, 32]. The current study extends these findings to other preventive procedures. With the exception of PSA testing, greater discrepancies were observed between self-reported receipt of preventive care and documentation by Medicare claims among minorities. These effects persisted after controlling for a range of patient characteristics and were observed across different types of preventive procedures. These discrepancies resulted in differing estimates of racial/ethnic disparities in preventive care with generally larger disparities observed using claims than self-report data.
Unfortunately, there is no clear gold standard in this study; we cannot determine whether self-report or a Medicare claim is a more accurate reflection of procedures received. It is not clear whether higher rates of self-report in the absence of a claim represent a greater tendency among minorities to "over-report" across many procedures or whether these findings represent suboptimal Medicare billing by providers who serve minorities.
Each of these explanations has some plausibility. Based on medical record documentation, self-report tends to overestimate actual receipt of preventive procedures partly through underestimation of the time interval since the previous test [12, 14, 15, 17, 33, 34]. Some studies suggest this reporting bias may be greater among minorities than among majorities [13, 14, 35, 36]. Moreover, reports of preventive care by minorities seem to be more sensitive than non-minority reports to survey question wording [21, 22]. Potential explanations for these examples of over-reporting include cultural differences in perception of time elapsed, effects of cross racial/ethnic interviewer-respondent effects [37–39], and social desirability [40–43].
While self-reports overestimate receipt of preventive procedures, administrative billing data may underestimate actual use . Minorities are more likely to been seen by safety net providers such as hospital clinics and community health centers . These providers often receive substantial revenue through Medicaid or Medicare prospective payment system [45, 46]. As a result, there may be less financial incentive among these providers to optimize patient Medicare encounter billing since much of their patient revenue is not as strongly dependent on billing coding for specific visits . Furthermore, greater use of paper submission of claims or slower adoption of Medicare prevention codes  could result in higher claim rejection rates by Medicare. This, in turn, could affect data sources that rely on the presence of claims to document receipt of procedures.
There are several ways these different explanations could be distinguished in future research. Rates of self-reported receipt of preventive procedures and Medicare claims data by race or ethnicity could be compared based on chart validation or through other sources of validation. For example, Medicare claims for mammography were recently compared to a mammography registry showing claims data to be moderately sensitive . In addition, studies are needed to compare Medicare billing patterns including payment denial according to the racial composition of the physician's practice. For example, a finding of higher preventive procedure claim denial rates for those physicians' practices having large numbers of minorities would suggest that the use of claims data may be biased for those practitioners.
Distinguishing among these competing explanations has important policy implications. If, on one hand, minorities are more likely to "over-report" receipt of preventive procedures, estimates of racial/ethnic disparities in those procedures based on survey self-report will underestimate the actual magnitude of disparities. This could result in the premature conclusion that disparities in preventive care have been eliminated. For example, self-report survey data have shown that African American women have caught up with majority women in most areas of preventive procedures and actually exceed them in others [8, 49]. Differences in survey wording appear to reduce disparities by reducing reporting bias associated with minority status . Increasing use of electronic health records may eventually minimize reliance on billing and survey data to assess disparities in preventive care.
If, on the other hand, if these discrepancies are primarily driven by inconsistencies in billing, this suggests caution in the use of claims data, not only as a proxy for procedures performed, but for comparisons among providers who serve a greater proportion of minority patients. That is, differential billing efficiency may bias comparisons among different types of providers . Such bias could penalize minority care providers, as many current pay-for-performance systems rely on claims data to assess care and reward performance. Further, failure to successfully bill for procedures could result in decreased payments, and thus compromise resources available to provide quality care.
The limitations of our analyses should be noted. First, the sample was confined to community dwelling, Medicare beneficiaries aged 65 and older, and those not enrolled in managed care. The extent to which these findings generalize to other groups cannot be assessed. Second, colorectal cancer testing survey questions were only asked in one year. As a result, the small sample size limited the power of these analyses. Third, although the findings summarized in Figure 1 suggest a general pattern of larger disparities based on claims compared to self report data, the discrepancies are relatively modest and beyond the power of the study to demonstrate statistically significant difference in differences. Fourth, we used a broad definition of preventive procedures because these data do not allow us to clearly distinguish between screening or diagnostic tests. However, comparable effects to those presented above were found when analyses were repeated using preventive codes only. Fifth, and finally, our selection of procedures might be differentially affected by the systematic lack of claims. For some procedures and selected years, Medicare did not reimburse for screening, e.g., Pap smear testing is reimbursed biennially and reimbursement for PSA screening and colonoscopy did not begin in 2001. As a result, physicians may have billed for screening services using diagnostic codes (which is why we included both codes). However, limitation of analyses to 2001 and/or exclusion of colonoscopy did not appreciably alter the findings.
Embedded in the problem of distinguishing screening and diagnostic testing is a critical issue for monitoring disparities in screening. Some claims-based evidence suggests that disparities for diagnostic testing are narrower than those for screening . Such evidence is consistent with the observation that minority patients present later in the course of cancer  (and thus obtain "diagnostic" rather than "screening" tests). Thus, self-report studies that, in general, do not distinguish screening and diagnostic testing may underestimate disparities in screening.
This study shows that estimates of racial/ethnic disparities, across a variety of preventive care procedures, vary depending on whether self-report or claims are used to assess them. Whether these differences reflect biases in participant report or in billing claims is unclear. These competing explanations have profoundly different policy implications, and thus warrant careful study. Future monitoring of disparities in screening will require more careful distinction of screening from diagnostic uses of preventive procedures.
This project was supported by funding from the Agency for Healthcare Research and Quality R01 HS 013173-01A1
- O'Malley AS, Forrest CB, Feng S, Mandelblatt J: Disparities despite coverage: gaps in colorectal cancer screening among Medicare beneficiaries. Archives of Internal Medicine. 2005, 165: 2129-2135. 10.1001/archinte.165.18.2129.View ArticlePubMedGoogle Scholar
- Felix-Aaron K, Moy E, Kang M, Patel M, Chesley FD, Clancy C: Variation in quality of men's health care by race/ethnicity and social class. Med Care. 2005, 43: I72-I81. 10.1097/00005650-200503001-00011.View ArticlePubMedGoogle Scholar
- 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. Cancer. 2003, 97: 1528-1540. 10.1002/cncr.11208.View ArticlePubMedGoogle Scholar
- Hebert PL, Frick KD, Kane RL, McBean AM: The Causes of Racial and Ethnic Differences in Influenza Vaccination Rates among Elderly Medicare Beneficiaries. Health Services Research. 2005, 40: 517-538. 10.1111/j.1475-6773.2005.0e371.x.View ArticlePubMedPubMed CentralGoogle Scholar
- Cooper GS, Koroukian SM: Racial disparities in the use of and indications for colorectal procedures in Medicare beneficiaries. Cancer. 2004, 100: 418-424. 10.1002/cncr.20014.View ArticlePubMedGoogle Scholar
- Stewart SH, Silverstein MD: Racial and ethnic disparity in blood pressure and cholesterol measurement. Journal of General Internal Medicine. 2002, 17: 405-411. 10.1046/j.1525-1497.2002.10524.x.View ArticlePubMedPubMed CentralGoogle Scholar
- Centers for Disease Control and Prevention (CDC): Access to health-care and preventive services among Hispanics and non-Hispanics--United States, 2001-2002. MMWR - Morb Mortal Wkly Rep. 2004, 53: 937-941.Google Scholar
- Sambamoorthi U, McAlpine DD: Racial, ethnic, socioeconomic, and access disparities in the use of preventive services among women. Preventive Medicine. 2003, 37: 475-484. 10.1016/S0091-7435(03)00172-5.View ArticlePubMedGoogle Scholar
- Gilligan T, Wang PS, Levin R, Kantoff PW, Avorn J: Racial differences in screening for prostate cancer in the elderly. Archives of Internal Medicine. 2004, 164: 1858-1864. 10.1001/archinte.164.17.1858.View ArticlePubMedGoogle Scholar
- Paskett ED, Tatum CM, Mack DW, Hoen H, Case LD, Velez R: Validation of self-reported breast and cervical cancer screening tests among low-income minority women. Cancer Epidemiol Biomarkers Prev. 1996, 5: 721-726.PubMedGoogle Scholar
- R. MD, Baken L, Nelson A, Nichol KL: Validation of self-report of influenza and pneumococcal vaccination status in elderly outpatients. Am J Prev Med. 1999, 16: 173-177. 10.1016/S0749-3797(98)00159-7.View ArticleGoogle Scholar
- May DS, Trontell AE: Mammography use by elderly women: a methodological comparison of two national data sources. Ann Epidemiol. 1998, 8: 439-444. 10.1016/S1047-2797(98)00010-6.View ArticlePubMedGoogle Scholar
- McPhee SJ, Nguyen TT, Shema SJ, Nguyen B, Somkin C, Vo P, Pasick R: Validation of recall of breast and cervical cancer screening by women in an ethnically diverse population. Preventive Medicine. 2002, 35: 463-473. 10.1006/pmed.2002.1096.View ArticlePubMedGoogle Scholar
- McGovern PG, Lurie N, Margolis KL, Slater JS: Accuracy of self-report of mammography and Pap smear in a low-income urban population. Am J Prev Med. 1998, 14: 201-208. 10.1016/S0749-3797(97)00076-7.View ArticlePubMedGoogle Scholar
- Warnecke RB, Sudman S, Johnson TP, O'Rourke D, Davis AM, Jobe JB: Cognitive aspects of recalling and reporting health-related events: Papanicolaou smears, clinical breast examinations, and mammograms. Am J Epidemiology. 1997, 146: 982-992.View ArticleGoogle Scholar
- Armstrong K, Long JA, Shea JA: Measuring adherence to mammography screening recommendations among low-income women. Preventive Medicine. 2004, 38: 754-760. 10.1016/j.ypmed.2003.12.023.View ArticlePubMedGoogle Scholar
- Mandelson MT, LaCroix AZ, Anderson LA, Nadel MR, Lee NC: Comparison of self-reported fecal occult blood testing with automated laboratory records among older women in a health maintenance organization. Am J Epidemiol. 1999, 150: 617-621.View ArticlePubMedGoogle Scholar
- Baier M, Calonge N, Cutter G, McClatchey M, Schoentgen S, Hines S, Marcus A, Ahnen D: Validity of self-reported colorectal cancer screening behavior. Cancer Epidemiology, Biomarkers & Prevention. 2000, 9: 229-232.Google Scholar
- Jordan TR, Price JH, King KA, Masyk T, Bedell AW: The validity of male patients' self-reports regarding prostate cancer screening. Preventive Medicine. 1999, 28: 297-303. 10.1006/pmed.1998.0430.View ArticlePubMedGoogle Scholar
- Freeman JL, Klabunde CN, Schussler N, Warren JL, Virnig BA, Cooper GS: Measuring breast, colorectal, and prostate cancer screening with medicare claims data. Med Care. 2002, 40: 36-42. 10.1097/00005650-200208001-00005.View ArticleGoogle Scholar
- Fiscella K, Franks P, Meldrum S: Estimating racial/ethnic disparity in mammography rates: it all depends on how you ask the question. Preventive Medicine. 2004, 39: 399-403. 10.1016/j.ypmed.2004.02.002.View ArticlePubMedGoogle Scholar
- Siegel PZ, Qualters JR, Mowery PD, Campostrini S, Leutzinger C, McQueen DV: Subgroup-specific effects of questionnaire wording on population-based estimates of mammography prevalence. Am J Public Health. 2001, 91: 817-820.View ArticlePubMedPubMed CentralGoogle Scholar
- Kagay CR, Quale C, Smith-Bindman R: Screening mammography in the american elderly. American Journal of Preventive Medicine. 2006, 31: 142-149. 10.1016/j.amepre.2006.03.029.View ArticlePubMedGoogle Scholar
- Statistics NCH: Health, United States 2005. 2006, Hyattsville, M.D., U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health StatisticsGoogle Scholar
- Smith-Bindman R, Miglioretti DL, Lurie N, Abraham L, Barbash RB, Strzelczyk J, Dignan M, Barlow WE, Beasley CM, Kerlikowske K: Does utilization of screening mammography explain racial and ethnic differences in breast cancer?. Ann Intern Med. 2006, 144: 541-553.View ArticlePubMedGoogle Scholar
- Finney Rutten LJ, Nelson DE, Meissner HI: Examination of population-wide trends in barriers to cancer screening from a diffusion of innovation perspective (1987-2000). Preventive Medicine. 2004, 38: 258-268. 10.1016/j.ypmed.2003.10.011.View ArticlePubMedGoogle Scholar
- Lees KA, Wortley PM, Coughlin SS: Comparison of racial/ethnic disparities in adult immunization and cancer screening. American Journal of Preventive Medicine. 2005, 29: 404-411. 10.1016/j.amepre.2005.08.009.View ArticlePubMedGoogle Scholar
- CMS: Medicare Current Beneficiary Survey. 2006, [http://www.cms.hhs.gov/mcbs]Google Scholar
- Katz S, Branch LG, Branson MH, Papsidero JA, Beck JC, Greer DS: Active life expectancy. N Engl J Med. 1983, 309: 1218-1224.View ArticlePubMedGoogle Scholar
- Grand A, Grosclaude P, Bocquet H, Pous J, Albarede JL: Disability, psychosocial factors and mortality among the elderly in a rural French population. J Clin Epidemiol. 1990, 43: 773-782. 10.1016/0895-4356(90)90237-J.View ArticlePubMedGoogle Scholar
- Manderbacka K, Kareholt I, Martikainen P, Lundberg O: The effect of point of reference on the association between self-rated health and mortality. Social Science & Medicine. 2003, 56: 1447-1452. 10.1016/S0277-9536(02)00141-7.View ArticleGoogle Scholar
- Holt K, Franks P, Meldrum S, Fiscella K: Mammography self-report and mammography claims: racial, ethnic, and socioeconomic discrepancies among elderly women. Med Care. 2006, (In press): x.Google Scholar
- Vacek PM, Mickey RM, Worden JK: Reliability of self-reported breast screening information in a survey of lower income women. Prev Med. 1997, 26: 287-291. 10.1006/pmed.1997.0138.View ArticlePubMedGoogle Scholar
- Champion VL, Menon U, McQuillen DH, Scott C: Validity of self-reported mammography in low-income African-American women. Am J Prev Med. 1998, 14: 111-117. 10.1016/S0749-3797(97)00021-4.View ArticlePubMedGoogle Scholar
- Caplan LS, Mandelson MT, Anderson LA, Organization HM: Validity of self-reported mammography: examining recall and covariates among older women in a Health Maintenance Organization. Am J Epidemiol. 2003, 157: 267-272. 10.1093/aje/kwf202.View ArticlePubMedGoogle Scholar
- Zapka JG, Bigelow C, Hurley T, Ford LD, Egelhofer J, Cloud WM, Sachsse E: Mammography use among sociodemographically diverse women: the accuracy of self-report. Am J Public Health. 1996, 86: 1016-1021.View ArticlePubMedPubMed CentralGoogle Scholar
- Warnecke RB, Johnson TP, Chavez N, Sudman S, O'Rourke DP, Lacey L, Horm J: Improving question wording in surveys of culturally diverse populations. Ann Epidemiol. 1997, 7: 334-342. 10.1016/S1047-2797(97)00030-6.View ArticlePubMedGoogle Scholar
- Cotter PR, Cohen J, Coulter PB: Race-of-interviewer effects in telephone interviews. Public Opinion Quarterly. 1982, 46: 278-284. 10.1086/268719.View ArticleGoogle Scholar
- Weeks MF, Moore RP: Ethnicity-of-interviewer effects on ethnic respondents. Public Opinion Q. 1981, 52: 245-249. 10.1086/268655.View ArticleGoogle Scholar
- Anderson BA, Silver BD, Abramson PR: The effects of race of the interviewer on measures of electoral participation by Blacks in SRC National Election Studies. Public Opinion Q. 1988, 52: 78-83.Google Scholar
- Calsyn RJ, Winter JP: Understanding and controlling response bias in needs assessment studies. Evaluation Rev. 1999, 23: 399-417.View ArticleGoogle Scholar
- Johnson TP, O'Rourke DP, Chavez N, al : Social cognition and response to surveys questions among culturally diverse populations. Survey Measurement and Process Quality. 1997, New York, John Wilery & Sons, 87-113.Google Scholar
- Klassen D, Hornstra RK, Anderson PB: Influence of social desirability on symptom and mood reporting in a community survey. J Consult Clin Psychology. 1975, 43: 448-452. 10.1037/h0076863.View ArticleGoogle Scholar
- Forrest CB, Whelan EM: Primary care safety-net delivery sites in the United States: A comparison of community health centers, hospital outpatient departments, and physicians' offices. JAMA. 2000, 284: 2077-2083. 10.1001/jama.284.16.2077.View ArticlePubMedGoogle Scholar
- Taylor J: The Fundamental of Community Health Centers: NHPF Background Paper. 2004, Washington, D.C., National Health Policy ForumGoogle Scholar
- Centers for Medicare & Medicaid Services (CMS): Medicare program; changes to the hospital outpatient prospective payment system for calendar year 2002. Final rule. Federal Register. 2001, 66: 59855-60125.Google Scholar
- Steinwachs DM, Stuart ME, Scholle S, Starfield B, Fox MH, Weiner JP: A comparison of ambulatory Medicaid claims to medical records: a reliability assessment. American Journal of Medical Quality. 1998, 13: 63-69.View ArticlePubMedGoogle Scholar
- Smith-Bindman R, Quale C, Chu PW, Rosenberg R, Kerlikowske K: Can Medicare Billing Claims Data Be Used to Assess Mammography Utilization Among Women Ages 65 and Older?. Med Care. 2006, 44: 463-470. 10.1097/01.mlr.0000207436.07513.79.View ArticlePubMedGoogle Scholar
- Blanchard J, Lurie N: Preventive care in the United States: are blacks finally catching up?. Ethnic Dis. 2005, 15: 498-504.Google Scholar
- Pham HH, Schrag D, Hargraves JL, Bach PB: Delivery of preventive services to older adults by primary care physicians. JAMA. 2005, 294: 473-481. 10.1001/jama.294.4.473.View ArticlePubMedGoogle Scholar
- Polednak AP: Trends in late-stage breast, cervical and colorectal cancers in blacks and whites. Ethnic Dis. 2000, 10: 60-68.Google Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1472-6963/6/122/prepub
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