Estimates of screening mammography utilization rates are typically based on self-reported information, because surveys are an efficient method to obtain information on a large number of individuals. Medical records, such as those obtained in the BCSC, are generally considered to provide more accurate information on mammography usage than self reported data from state and national surveys; however, studies rarely collect both medical records and self-report to be able to actually compare the difference. This study is unusual because it examines the validity of survey response on a geographically defined population, using the Census as denominator, rather than focusing on a particular group such as members of an HMO. The approach presented provides an estimate of recent screening rates that is likely to be representative of the U.S. population.
The mammography model used is based largely on the BCSC data. Previous work has compared counties included in the BCSC to all counties in the U.S. to gauge representativeness of the BCSC to the U.S. (18
) A number of county level variables were similar between the U.S. and counties included in the BCSC, with BCSC counties appearing to have slightly higher income and education levels.
Our results are consistent with previously reported studies. The 17–28 percentage point difference between the observed rates of screening within the prior two years in Vermont and estimates based on self-report from the Vermont BRFSS is consistent with previous work. Values for sensitivity and specificity that would explain the differences between modeled screening rates and national survey estimates are in line with previously reported studies that sought to validate survey-based estimates for the percent of women that were screened in the previous two years. Estimates of screening mammography based on BCSC data are consistently lower then those reported by NHIS or BRFSS (19
Sensitivity of self-report of health behaviors are consistently high while specificity tends to be lower, resulting in an overestimation of the percent of the population actually adhering to recommended screening behavior. Therefore misreporting occurs mainly in women who have not had a mammogram in the previous two years and over reporting is greatest in the groups that have the lowest screening rates. We found the largest difference between modeled and self-reported screening rates among younger women and African Americans, both of whom had the lowest screening rates.
Lower screening rates among African American women results in more over reporting in African American women than in non-Hispanic white women even with similar values of sensitivities and specificities. Systematic underestimation of disparities between these two groups is a result of this pattern of misreporting. This is consistent with previous analyses that have shown a lower percent of self-reported mammogram utilization can be validated by medical reports for African American women compared to white women. For example, Holt et al. (11
) found self-reported mammography use similar between white and African American women, but lower rates of validated mammography among African American women. McPhee et al. (13
) also reported a lower validation rate for self-reported mammograms among African American women than white women.
Our results confirm other findings that Hispanic women may have different sensitivity and specificity values than white and African American women resulting in less over reporting. Hiatt et al. (6
) reported lower sensitivity and higher specificity for Hispanic as compared to non-Hispanic white women, and Lawrence et al.(20
) showed lower sensitivity in Mexican Americans compared to Euro-Americans. Other analyses (15
) have shown lower sensitivity and specificity in Puerto Rican women as compared to African American or non-Hispanic white women. In contrast with our results, previous studies have shown a lower validation rate in Hispanic women compared to non-Hispanic white women (6
). Hispanic women appear to have different patterns for misreporting mammography usage, and those patterns may vary within subgroups of the Hispanic population. A large percentage of Hispanic women in the BCSC come from New Mexico.
Over-reporting among women who have not received a mammogram also affects trends over time. During time-periods when screening rates have increased, the true amount of improvement will be masked since over-reporting will be decreasing at the same time that screening is increasing. For the first time since mammography rates have been ascertained, there was a reported decrease in the percent of the population reporting recent mammography use between 2000 and 2005 (21
). When screening rates decrease, we would expect that over reporting would increase, leading to an underestimation of the actual decrease in screening rates observed in 2005.
Women may over-report the use of mammography screening in survey situations for several reasons. The phenomenon of “telescoping” (i.e., remembering that an event occurred more recently then it actually did) can lead to systematic under-reporting of the time since last mammogram and over-reporting of the prevalence of women who adhere to a guideline-based screening interval, such as the past one or two years. The difference between the modeled and self-reported results may be related to the difference between being a “regular” screener and actually receiving the screening exam within the exact two year cut-off considered. Our model, as well as previous work (22
) show that even regular screeners often do not achieve the recommended interval. A woman who sees herself as a regular screener may report an interval of two years even if the interval was slightly longer. The phenomenon of telescoping leads women to underestimate the time since their last mammography, but it is possible that self reported rates better represent women who come in regularly for exams even if it is not within the exact two year time frame.
Since recommendations for annual or biennial breast cancer screening are well publicized, women may feel compelled to give a socially desirable response of having a recent screening mammogram even when untrue. Some women may lack the knowledge necessary to properly answer survey questions about prior mammography screening. Another possibility is that women who choose to answer the survey question have different screening behaviors than non-responders. NHIS 2000 had an overall response rate of 72% and the 2000 Vermont BRFSS had a response rate of 50%. Selection bias could also contribute to the differences observed.
Several data limitations were encountered when developing the mammography dissemination and usage model. The model consists of separate components for the time to first mammography examination and the time between mammography examinations. The time until a first mammography exam component is based on self-report data from surveys for whether or not a woman has ever received a mammogram, which is also subject to self-report bias. However, previous work suggests that self report is more accurate when measuring if a woman has ever had a mammography then measuring if she had a mammogram within some specified time period.(10
) Although both the NHIS and BRFSS surveys obtain information on the reason for the most recent mammogram, they do not contain similar information on all mammograms ever received. Therefore we can not directly determine if a woman has ever had a mammogram for the purposes of screening. This may result in underestimating the age at first screening mammography, ultimately leading to an overestimation of the amount of screening in the population.
Under certain circumstances the repeat mammography component of the model includes self-reported data. At each visit, women were asked when they had their last mammogram. If there was a discrepancy between the date of the last mammography recorded in the BCSC data and the date of self-reported last mammogram, the model included the minimum time estimate from the two sources when this discrepancy was greater than 6 months. This inclusion of self-report data was done to allow for the possibility that a woman received a mammogram at a facility that was not covered by the BCSC. The inclusion of self-reported data may overestimate frequency of mammography and result in an underestimation of the bias between self report and registry data on mammography use.
The modeling contains uncertainty on several levels. The data used to fit model parameters are subject to the limitations described above. Given the data, the parameter estimates have an associated variance. The parameter estimates are then use to simulate outcomes representing the US population. We do not include confidence intervals for the modeled screening rates because it would be difficult to quantify the true variance around these rates. Although bias in the modeled estimate may contribute to the difference reported, the results from the comparison of national estimates are very consistent with the Vermont comparison which is not subject to the potential modeling bias.
To obtain accurate information on screening behaviors, a consistent system of electronic medical records that links patients’ records from all sources of health care and then de-identifies them for purposes of research is needed. In the absence of such a system, recommendations described in Newell et al. (1
) may help maximize accuracy associated with self-report. Systematic errors in self-reported screening rates result in biased estimates of disparities. Sensitivity and specificity estimates can be used to adjust self-report data to better capture difference between groups and trends over time.