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Keywords:

  • mammography;
  • hormone therapy;
  • breast cancer;
  • National Health Interview Surveys;
  • women's health;
  • primary care

Abstract

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. FUNDING SOURCES
  7. CONFLICT OF INTEREST DISCLOSURES
  8. REFERENCES

BACKGROUND:

In 2005, mammography rates in the United States dropped nationally for the first time among age-eligible women. An increased risk of breast cancer related to hormone therapy (HT) use reported in 2002 led to a dramatic drop in its use by 2005. Because current users of HT also tend to have higher mammography rates, the authors examined whether concurrent drops in HT and mammography use were associated.

METHODS:

Multivariate logistic regression was used to test for an interaction between HT use and survey year, controlling for a range of measurable factors in data from the 2000 and 2005 National Health Interview Surveys (NHIS).

RESULTS:

Women ages 50 to 64 years were more likely to report a recent mammogram if they also reported more education, a usual source of care, private health insurance, any race except non-Hispanic Asian, talking with an obstetrician/gynecologist or other physician in the past 12 months, or were currently taking HT. Women aged ≥65 years were more likely to report a recent mammogram if they also reported younger age (ages 65-74 years), more education, a usual source of care, having Medicare Part B or other supplemental Medicare insurance, excellent health, any race except non-Hispanic Asian, talking with an obstetrician/gynecologist or other physician in the past 12 months, or were currently taking HT.

CONCLUSIONS:

The change in HT use was associated with the drop in mammography use for women ages 50 to 64 years but not for women aged ≥65 years. NHIS data explained 70% to 80% of the change in mammography use. Cancer 2011;. © 2011 American Cancer Society.

In 2005, US data indicated the first-ever drop in mammography rates.1, 2 This was surprising, because mammography rates had consistently risen since first monitored in 1987. In 2002, the Journal of the American Medical Association published a report from the Women's Health Initiative that HT use was associated with an increased risk of breast cancer.3 This widely publicized finding alerted physicians and women to potential problems with HT and led to a dramatic decline in the use of HT between 2000 and 2005.4, 5 Because current users of HT also tend to have higher mammography rates,6 we speculated that women who stopped taking HT also may have stopped screening with mammography. Our reasoning was that, if women typically need to consult a physician to renew their HT prescription, and if physicians typically take that opportunity to discuss mammography and order a mammogram, then stopping the HT prescription visits would result in a lost opportunity for physicians to remind women about mammograms.

In this study, we tested whether HT is associated with mammography use among the population of US women aged ≥50 years. Because the average age at menopause is slightly older than 50 years and because HT use is most common among postmenopausal women, we focused on women aged ≥50 years. We used a blended model (Fig. 1) that presents a patient-centered perspective and locates mammography appraisal within a multilevel, population-based public health approach.

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Figure 1. Population mammography screening is illustrated.

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The literature on mammography use suggests that the most important predictor of mammography use is the medical encounter.7, 8 Primary care constitutes the entry point to health care markets in the United States9 and provides the gateway to mammography. Although most Americans report having a usual source of health care, they need a specific reason to access the system. For example, if women want to obtain a mammogram or renew their HT prescription, then they need to contact a physician. In 2000 and 2003, approximately 70% of women aged ≥40 years reported a recent mammogram, and 30% did not. Nearly 50% of women ages 40 to 64 years and 75% of women aged ≥65 years who did not report a recent mammogram stated that they did not recall receiving a physician-recommendation for one in the previous year.10 Different rates of cancer screening use are associated with different practice settings,11 and greater health maintenance organization (HMO) market share is associated with greater use of mammography.12, 13 Because the use of HT is correlated directly with the use of mammography,6, 14 we examine whether women who used HT would continue to screen at the same higher levels when they stop taking HT.

MATERIALS AND METHODS

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. FUNDING SOURCES
  7. CONFLICT OF INTEREST DISCLOSURES
  8. REFERENCES

Data

We tested whether the drop in HT was correlated directly with the drop in mammography using data from the 2000 and 2005 National Health Interview Surveys (NHIS). Questions on HT and mammography that were asked in the 2000 and 2005 surveys allowed us to investigate whether the drop in HT use was associated with the drop in mammography use. Our study examined women aged ≥50 years who were interviewed in 2000 (N = 7125) and 2005 (N = 7387).

The NHIS is the principal source of health information on the civilian, noninstitutionalized, household population of the United States and is the largest population-based national sample on mammography use. The NHIS provides self-reported information. Questions on mammography use have been fielded periodically since 1987. Questions about mammography and HT were administered in 2000 and 2005. Periodic cancer-control supplements are administered to a randomly selected sample of adults in each household surveyed using computer-assisted personal interviewing. African American and Hispanic populations were over sampled in 2000 and 2005 to allow for more precise estimations of these groups. Data are collected using a complex sample design that involves stratification, clustering, and multistage sampling. NHIS public-use microdata files are released on an annual basis; and survey descriptions, including response rates, are available for each release at the NHIS Web site. Final response rates for the adult sample were 72% in 2000 and 69% in 2005.15, 16

Variable Definitions

Our dependent variable was receipt of recent mammography, which we defined as a mammogram reported within 2 years of the interview. Our covariates are described below. Categories are mutually exclusive. Educational attainment was categorized into less than high school, versus high school graduate only, versus some college or AA degree, versus college graduate (BA/BS) or higher.

Race-ethnicity was categorized into 5 groups using federal government definitions for Hispanic, non-Hispanic white, non-Hispanic black, non-Hispanic American Indian/Alaska Native, and non-Hispanic Asian.17Immigrant status categorized variables based on place of birth and time in the United States according to whether individuals were in the US <10 years, versus in the United States ≥10 years, versus born in the United States.

Income was not included in the model, because most mammograms are paid for by insurance.18Medical or health insurance was categorized slightly differently for women ages 50 to 64 years and for women aged ≥65 years because of near-universal coverage by Medicare for women aged ≥65 years. For women ages 50 to 64 years, insurance was grouped according to whether individuals had private HMO coverage, versus private non-HMO coverage, versus public coverage only, versus no insurance. For women aged ≥65 years, insurance was grouped according to whether patients had Medicare HMO coverage, versus fee-for-service coverage (private, medicaid/military/other government), versus Medicare fee-for-service, versus uninsured or Medicare Part A (hospital coverage) only.

Usual source of medical care was coded as yes versus no or hospital emergency room (ER). Saw or talked to a general physician in the past 12 months was coded as yes versus no. Saw or talked to an obstetrician-gynecologist in the past 12 months also was coded as yes versus no.

Whether a woman was currently taking HT was the key covariate of interest, and response items were coded yes versus no. HT requires a prescription, which requires contact with a physician. Self-reported health status was grouped as excellent/very good versus good/fair/poor.

Data Analysis

We applied the same computational methods that were used in 2000 to both years of data.19 We used statistical techniques to evaluate changes over time in the age-eligible population.

First, to examine the relation between population characteristics measured in the NHIS and the probability of receiving a recent mammogram, we fit a multivariate logistic regression model to pooled 2000 and 2005 data. The logistic model was stratified into 2 age groups, because lack of insurance and unmet health service needs are more likely for women ages 50 to 64 years20 than for women aged ≥65 years. Survey year was included as a covariate in the logistic model to represent changes in mammography that were not captured in the variables directly measured in the NHIS surveys. Then, we calculated 2 types of change in the population using the logistic regression model: 1) change in mammography rates associated with changes in population characteristics measured in the NHIS that have been associated in other studies with mammography, including HT use21; and 2) change in screening rates attributable to factors not captured in the items we selected from the NHIS survey. We started with the observed difference in the rate of recent mammography between 2000 and 2005 for each age group. Next, we calculated predictive margins.22 The difference between predicted marginals in 2000 and in 2005 represents change in recent mammography rates attributable to factors that were not captured in the items we selected from the NHIS survey. The remainder of the observed difference was associated with the variables included in the logistic regression. Partitioning the overall changes in screening into these 2 separate categories provided insight into the portion of the change in mammography use associated with factors that were measured by the NHIS and the portion related to factors that were not measured directly by the NHIS.

Second, we tested for an association between changes in HT use and changes in mammography use in the population of women aged ≥50 years. Our hypothesis has 2 explicit scenarios, which are illustrated in Figure 2. Because current users of HT also tend to have higher rates of current screening than women who do not use HT,6, 14 a question that follows is whether women who stop taking HT would continue to screen at higher levels. Our objective was to test whether women who stopped using HT between 2000 and 2005 also reduced their mammography use. Because NHIS is a cross-sectional survey, it does not capture longitudinal data on HT use. Therefore, we tested for an interaction between HT and survey year (2000 and 2005) as a predictor of mammography use in the population of age-eligible women. With this interaction test, we were able to evaluate whether the influence of HT on mammography differed in 2005 compared with 2000. It is unclear whether HT is an independent predictor of mammography screening or whether mammography use and HT are correlated jointly with other factors, such as education, insurance, income, use of preventive services, and physician visits. Therefore, we considered 2 extreme possibilities to illustrate how changes in current HT use would affect mammography rates. The numbers presented in Figure 2 were chosen for ease of computation.

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Figure 2. This graphic illustrates what would happen to mammography use in the 24% of the population who stopped taking hormone therapy (HT).

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Hypothetical Scenario 1

In Hypothetical Scenario 1 (Fig. 1), HT and mammography use are related only through factors other than HT. Thus, women who stop taking HT would maintain the same higher screening level that they had when they were current users of HT, and mammography use would remain the same for the overall population and for the women who are current users of HT. However, for the group of women who are not current users of HT, screening rates would increase, because this group would now include the women who stopped HT with their higher level of mammography use. This scenario would be represented in the logistic model by a significant interaction term between HT use and survey year, indicating a change the relation between HT and mammography between 2000 and 2005.

Hypothetical Scenario 2

In Hypothetical Scenario 2 (Fig. 1), HT is an independent predictor of mammography use. Thus, women who stop taking HT would change their mammography use to resemble the level in nonusers of HT. If a portion of the population stops using HT, the rates of mammography use for the overall population would decline, because more of the population is now in the HT nonuser group. This scenario would be represented in the logistic model by no significant interaction between HT use and survey year, because mammography rates in the current HT user groups remain the same, and mammography rates among nonusers of HT remain the same in 2000 and in 2005.

To determine whether women who stopped HT use changed their mammography use, we tested an interaction term for current HT use and survey year in a pooled logistic regression for each of the two age groups. The logistic regression included all covariates described above (see Materials and Methods). All analyses were weighted by the NHIS sample weights to account for the unequal probabilities of selection and the complex survey design. SUDAAN release 9 (RTI International, Research Triangle Park, NC) was used to calculate frequencies and to compute logistic regression results. The sample weights for analyses using pooled data were modified by adding the NHIS sample weights for each survey and dividing them by 2.

RESULTS

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. FUNDING SOURCES
  7. CONFLICT OF INTEREST DISCLOSURES
  8. REFERENCES

Table 1 lists the characteristics of the population in 2000 and 2005 for women ages 50 to 64 years and aged ≥65 years. Population distributions for each group changed significantly between 2000 and 2005. It is difficult to predict the net effect on screening rates, because some characteristics are associated positively with screening (eg, education, recent physician visit), whereas others are associated negatively (eg, drop in HMO coverage and in current HT use).

To evaluate the net effect, we examined the data using multivariate logistic regression. Table 2 provides the adjusted results for each age group. Women ages 50 to 64 years were more likely to report a recent mammogram if they also reported more education, a usual source of care, private health insurance, any race except non-Hispanic Asian, talking with an obstetrician/gynecologist or other physician in the past 12 months, or were currently taking HT. Women aged ≥65 years were more likely to report a recent mammogram if they also reported younger age (ages 65-74 years), more education, a usual source of care, having Medicare Part B or other supplemental Medicare insurance, excellent health, any race except non-Hispanic Asian, talking with an obstetrician/gynecologist or other physician in the past 12 months, or were currently taking HT. Findings for interactions for each age group are discussed below.

Table 1. Reported Characteristics and Recent Mammography Among Women Aged ≥50 Years: 2000 and 2005 National Health Interview Survey (Computed Using the 2000 Method)a,b
 Ages 50-64 YearsAged ≥65 Years
 20002005 20002005 
CharacteristicNo.% (95% CI)No.% (95% CI)Chi-Square PNo.% (95% CI)No.% (95% CI)Chi-Square P
  • Abbreviations: AIAN, American Indian/Alaska Native; CI, confidence interval; ER, emergency room; FFS, fee for service; HMO, health maintenance organization; HT, hormone therapy; Ob/Gyn, obstetrician/gynecologist.

  • a

    Excluding women who reported a personal history of breast cancer.

  • b

    Source: National Health Interview Survey.

Total34951003949100 36301003438100 
Mammogram in the past 2 y          
 Yes248678.2 (76.3-79.9)262173.4 (71.7-75).0002219167 (65.1-68.9)190864.7 (62.5-66.9).1118
 No76021.8 (20.1-23.7)98826.6 (25-28.3) 115233 (31.1-34.9)111835.3 (33.1-37.5) 
Education          
 <High school69116.4 (14.9-18.1)65214.2 (12.9-15.6).0005126232.4 (30.4-34.4)100027.3 (25.3-29.4).0001
 High school graduate116535.7 (33.8-37.7)117332.1 (30.2-34) 126538 (36.2-40)120637.5 (35.4-39.6) 
 Some college or AA degree88025.6 (24-27.3)109627.5 (26-29.1) 68419.3 (17.9-20.7)70421.4 (19.8-23.1) 
 College graduate (BA/BS)72322.2 (20.7-23.9)98926.1 (24.5-27.9) 36410.3 (9.2-11.5)46113.8 (12.3-15.3) 
Usual source of care          
 None or hospital ER2667.1 (6.2-8.1)3167.4 (6.4-8.6).64171514.4 (3.6-5.3)1173.3 (2.6-4.2).0619
 Yes319892.9 (91.9-93.8)360292.6 (91.4-93.6) 346095.6 (94.7-96.4)329396.7 (95.8-97.4) 
Health insurance, age <65 y          
 Private HMO102929.9 (28-31.8)86222.4 (20.8-24).0000 
 Private non-HMO152748.8 (46.7-50.9)194153.0 (51.1-54.9)  
 Public only4319.1 (8.1-10.2)56311.8 (10.7-13)  
 Uninsured49212.2 (11-13.5)56512.8 (11.6-14.1)  
Health insurance, age ≥65 y          
 Medicare HMO 58415.6 (14.3-17.1)39710.8 (9.7-12).0000
 Private 201459.2 (57.3-61)181155 (53.1-57) 
 Medicaid, military, other government 3958.9 (7.9-10)45011.5 (10.3-12.7) 
 Medicare FFS 56614.9 (13.6-16.3)73221.2 (19.6-22.7) 
 Uninsured or Medicare Part A only 571.4 (1-1.9)451.5 (1.1 to-−2.1) 
Reported health status          
 Excellent/very good183055.2 (53.2-57.1)201653.1 (51.2-55).1440139338.4 (36.6-40.2)133239.1 (37.2-41).5996
 Good/fair/poor166244.8 (42.9-46.8)193346.9 (45-48.8) 223461.6 (59.8-63.4)210560.9 (59-62.8) 
Race/ethnicity          
 Hispanic4367.6 (6.8-8.5)5128.7 (7.8-9.7).23793306 (5.1-7)3086.5 (5.6-7.6).0069
 Non-Hispanic white242278.4 (76.8-79.9)273476.4 (74.9-77.9) 281583.6 (81.9-85.1)263480.9 (79.1-82.7) 
 Non-Hispanic black53610.9 (9.7-12.2)56511 (10-12.1) 4328.8 (7.7-10)4069.2 (8-10.6) 
 Non-Hispanic AIAN200.6 (0.3-1.1)290.8 (0.5-1.3) 90.3 (0.1-0.6)90.2 (0.1-0.5) 
 Non-Hispanic Asian752.5 (2-3.2)1053 (2.4-3.8) 421.4 (0.9-2.2)793.1 (2.4-4.1) 
Immigration          
 In US <10 y591.5 (1.1-2)491.2 (0.9-1.7).0029240.9 (0.5-1.4)160.6 (0.3-1).0634
 In US ≥10 y3447.8 (6.9-8.9)46410.2 (9.1 to −11.3) 3458.4 (7.4-9.6)38110.1 (9-11.3) 
 Born in US305290.6 (89.6-91.6)342288.6 (87.4-89.7) 322090.7 (89.4-91.9)303189.3 (88.2-90.4) 
Saw/talked to general physician past 12 mo          
 Yes261876.6 (74.8-78.2)297376.9 (75.4-78.3).7721302584.3 (82.8-85.7)294087 (85.5-88.3).0054
 No83523.4 (21.8-25.2)91923.1 (21.7-24.6) 56715.7 (14.3-17.2)44713 (11.7-14.5) 
Saw/talked to Ob/Gyn past 12 mo          
 Yes137140.7 (38.7-42.8)142937.5 (35.8-39.2).017071020.6 (19.1-22.2)60718.5 (17-20.1).0543
 No207359.3 (57.2-61.3)246562.5 (60.8-64.2) 287579.4 (77.8-80.9)277181.5 (79.9-83) 
Currently taking HT          
 Yes124940.5 (38.7-42.3)55415.8 (14.5-17.2).000060619.4 (17.9-21)2629.8 (8.5-11.3).0000
 No200959.5 (57.7-61.3)308884.2 (82.8-85.5) 278380.6 (79-82.1)284090.2 (88.7-91.5) 
Table 2. Results of Logistic Regression Among Women Aged ≥50 Years: Pooled 2000 and 2005 National Health Interview Surveys—Computed Using the 2000 Method (Response Variable: Had Mammogram Within the Past 2 Years: Yes vs No)ab
 Ages 50-64 Years (N=6709)Aged ≥65 Years (N=6212)
VariablePredictive MarginsSEWald F PPredictive MarginsSEWald F P
  • Abbreviations: AIAN, American Indian-Alaska Native; ER, emergency room; FFS, fee for service; HMO, health maintenance organization; HT, hormone therapy; Ob/Gyn, obstetrician/gynecologist; SE, standard error.

  • a

    Excluding women who reported a personal history of breast cancer.

  • b

    Source: National Health Interview Survey.

Age, y      
 65-7471.61.0.0000
 ≥75 60.71.0 
Education      
 <High school72.31.6.003859.51.3.0000
 High school graduate75.31.0 67.91.1 
 Some college or AA degree76.21.0 69.11.4 
 College graduate (BA/BS)79.01.1 73.01.9 
Usual source of care      
 None or hospital ER63.92.2.000049.93.8.0000
 Yes76.90.6 66.70.8 
Health insurance, age <65 y      
 Private HMO78.81.1.0000
 Private non-HMO79.50.8  
 Public only69.91.8  
 Uninsured62.71.9  
Health insurance, age ≥65 y      
 Medicare HMO70.22.0.0000
 Private 67.90.9 
 Medicaid, military, other government 60.22.0 
 Medicare FFS 62.01.7 
 Uninsured or Medicare Part A only 47.25.4 
Reported health status      
 Excellent/very good76.90.9.068369.71.1.0000
 Good/fair/poor74.70.9 63.90.9 
Race/ethnicity      
 Hispanic74.92.1.008969.32.6.0036
 Non-Hispanic white75.80.7 65.80.8 
 Non-Hispanic black78.81.3 69.11.9 
 Non-Hispanic AIAN79.15.8 71.58.8 
 Non-Hispanic Asian63.74.5 51.34.8 
Immigration      
 In US <10 y77.44.5.416856.211.0.4076
 In US ≥10 y78.21.8 68.42.4 
 Born in US75.50.7 66.00.8 
Saw/talked to general physician past 12 mo      
 Yes79.70.6.000068.70.8.0000
 No63.71.3 50.01.9 
Saw/talked to Ob/Gyn past 12 mo      
 Yes88.90.8.000088.61.0.0000
 No68.70.8 61.30.8 
Currently taking HT      
 Yes85.21.0.000082.11.7.0000
 No73.00.7 63.70.8 
Year of survey      
 200076.30.9.485766.40.9.9046
 200575.50.8 65.51.1 
Currently taking HT year of survey      
 Yes, 200086.31.8.0000
 Yes, 2005 77.92.9 
 No, 2000 63.61.0 
 No, 2005 63.81.1 

Figure 3 compares the change in recent mammography between 2000 and 2005 for the two age groups associated with changes in the population characteristics modeled in Table 2. These associations predict most of the observed change in mammography use between 2000 and 2005 for both age groups. Recent screening declined 4.7 percentage points for women ages 50 to 64 years and 2.5 percentage points for women aged ≥65 years. By using the predicted marginal values for survey year, we observed that 0.8 percentage points (standard error, 1.1 percentage points) of the estimated decline for women ages 50 to 64 and 0.8 percentage points (standard error, 1.3 percentage points) of the estimated decline for women aged ≥65 years were not associated with the variables included in the regression analysis. These estimates suggest that 3.9 of the 4.7 percentage point decline observed in recent screening (80% of the total decline) for women ages 50 to 64 years and 1.7 of the 2.5 percentage point decline observed (70% of the total decline) for women aged ≥65 years were associated with the variables included in the model. Although we were able to estimate the portion of the decline associated with factors in the model, the large standard errors demonstrate the difficulty in obtaining precise estimates for these small effects. Nevertheless, we believe they are worth reporting, because this was our best estimate.

thumbnail image

Figure 3. The decline in recent screening between 2000 and 2005 is illustrated.

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We also tested for an association between changes in HT use and changes in mammography use for the population of women aged ≥50 years. Table 2 provides results from the multivariate logistic regression separately for women ages 50 to 64 years and women aged ≥65 years. The interaction between current HT use and survey year was significant only for women aged ≥65 years, so the results for women ages 50 to 64 years are provided without this interaction.

Relation Between Change in HT Use and Mammography Use

For women ages 50 to 64 years, we observed no significant interaction between current use of HT and survey year. This scenario, which is illustrated in Figure 2 (Hypothetical 2), suggests that HT use and mammography use are related. Women who stopped HT had lower screening rates after they stopped than when they were taking HT, thus reducing their mammography rates to the level previously observed in nonusers of HT.

For women aged ≥65 years, a significant interaction was observed. This scenario, which also is illustrated in Figure 2 (Hypothetical 1), suggests that HT use and mammography use are not related. Women who reported that they were current users of HT had lower rates of mammography in 2005 than in 2000, whereas women who reported nonuse of HT had similar rates of mammography use in 2005 and 2000. Our model suggests that the change in HT use had less effect on mammography use in older women compared with younger women.

DISCUSSION

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. FUNDING SOURCES
  7. CONFLICT OF INTEREST DISCLOSURES
  8. REFERENCES

To our knowledge, this study is the first to demonstrate that a drop in HT use was associated with a drop in mammography use in the general population. Variables from the NHIS were associated with 70% to 80% of the change in mammography for the women we studied. In addition to HT, age was strongly associated with mammography use. Thus, our study is the first to demonstrate a difference by age group: A change in HT use was associated with the drop in mammography use for women ages 50 to 64 years but not for women aged ≥65 years.

Population changes in breast cancer incidence related to HT use may reflect the combination of a reduction in breast cancer risk and slightly lower screening rates.23 We observed an association between HT and mammography in a representative sample for women aged ≥50 years. In an earlier article,1 we indicated that the extent to which the decline in incidence rates for breast cancer was associated with the drop in mammography needed to be investigated; however, because there is no measure of breast cancer incidence in NHIS, our study could not examine associations between mammography, HT, and incidence. Caan et al used HMO data to examine whether women who were regular users of HT had different patterns of mammography compared with women who continued using or never used hormones and observed that, among women who stopped using HT after publication of the Women's Health Initiative study, both mammography use and breast cancer incidence dropped.24 Robbins and Clarke compared secular trends in HT use and breast cancer incidence in California and observed that each 1% decrease in the prevalence of HT use was associated with a decrease of 3.1 cases per 100,000 women in breast cancer incidence.25 More recently, a study of trends in invasive breast cancer incidence among women undergoing regular screening mammography revealed a significant decrease in invasive breast cancer incidence between 2002 to 2006 among women ages 50 to 69 years and ages 70 to 79 years.26

To understand the potential impact of the drop in mammography use compared with the drop in HT use on breast cancer incidence, it is important to note that HT use dropped from 41% to 16% among women ages 50 to 64 years and from 19% to 10% among women aged ≥65 years; and biennial mammography use dropped from 78% to 73% among women ages 50 to 64 years and from 67% to 65% among women aged ≥65 years. The drop in HT use represented approximately 6.4 million women ages 50 to 64 years and approximately 2.0 million aged ≥65 years, whereas mammography represented far less (approximately 1.2 million women ages 50-64 years and approximately 0.5 million women aged ≥65 years). Although we conclude that the reduction of HT use is associated with reduced mammography, our analysis cannot assess the separate contributions of each to the reduction in incidence. Our results are consistent with the preponderance of evidence from other studies indicating that the decline in incidence is accounted for largely by a reduction in risk caused by HT cessation, and our findings continue to raise the possibility that reduced mammography use also may have played a role in the relation between HT and incidence in the population.

Our findings are significant and important in understanding the relation between HT and mammography use and the impact of mammography on mortality. Observed disparities in cancer mortality, survival, and incidence have motivated the study of societal-level influences on the etiology of cancer.27 The literature indicates that the practice setting is key to whether a woman gets a mammogram, and the NHIS captures this aspect of screening for individual women with a national sample. A limitation is that self-report may overestimate use.28-30 Although, ideally, this analysis would be done with longitudinal data to directly examine the screening rates among women who stopped using HT,24 our data would not allow that. Other available sources of data also were problematic: HMO data are not representative of the general population, and Medicare data are limited to women aged ≥65 years, whereas we expect the biggest impact to be among women ages 50 to 70 years.

Finally, it is worth noting the considerable controversy over details of the mortality benefit of mammography in the scientific literature during the period that mammography use dropped.31-35 An opportunistic system of screening leaves it to individual women to assess information in making a decision about the importance of having a mammogram relative to her resources. Therefore, different communication channels, because they reach women with different educational levels, may lead to different behaviors. In an earlier analysis, we observed that women who had higher education and more resources were significantly less likely to screen in 2005 than in 2000.1 An important exception was women enrolled in organized systems. Organized systems develop and deploy protocols based on scientific evidence, and women enrolled in organized systems tend to have high rates of mammography use regardless of personal characteristics.36 Organized systems often add to their advantage by using electronic health records to maintain patient records, remind patients and physicians when tests are due, and provide feedback to clinicians to help them improve their practice patterns.37, 38

REFERENCES

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. FUNDING SOURCES
  7. CONFLICT OF INTEREST DISCLOSURES
  8. REFERENCES