1 Fred Hutchinson Cancer Research Center, Seattle, WA.
2 Department of Biostatistics, School of Public Health and Community Medicine, University of Washington, Seattle, WA.
3 Statistical Research and Applications Branch, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD.
4 Keck School of Medicine, University of Southern California/Norris Cancer Center, Los Angeles, CA.
5 Department of Epidemiology, School of Public Health and Community Medicine, University of Washington, Seattle, WA.
Received for publication February 26, 2004; accepted for publication June 24, 2004.
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ABSTRACT |
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computer simulation; confounding factors (epidemiology); mass screening; mortality; prostate-specific antigen; prostatic neoplasms
Abbreviations: Abbreviations: APC, annual percentage change; HT, hormone ablation therapy; PSA, prostate-specific antigen; SEER, Surveillance, Epidemiology, and End Results.
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INTRODUCTION |
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The rapid adoption of this test in the United States has occurred against a backdrop of controversy about its benefits and costs. Two large-scale clinical trials of PSA screening in Europe and the United States are ongoing, but results are not expected before 2008 (2, 3). In the absence of direct evidence about test efficacy, attention has focused on population trends in prostate cancer incidence and mortality. Declines in distant-stage incidence and prostate cancer mortality in the United States (46) have fueled speculation that the test may be saving lives (7, 8).
Ecologic studies of cancer screening examine trends in cancer mortality rates in relation to the use of population screening. International studies of PSA screening have generally yielded negative findings. One noted that mortality rates were declining in the United States but not in Australia, whereas the use of PSA screening had been high in both places (9). A second study observed that although PSA screening rates (as measured by prostate cancer incidence) in the United States and the United Kingdom were dramatically different, mortality rates in 19931995 were similar (10).
Ecologic analyses within countries have yielded conflicting results. An Austrian study found significantly lower prostate cancer mortality rates in the Tyrol region, where a mass screening program was introduced in 1993, than in the rest of the country (11). However, an Italian study found that while prostate cancer incidence differed dramatically between two regions in Tuscany, prostate cancer mortality rates did not (12). Similarly, a US study comparing Seattle-Puget Sound, Washington, and Connecticut found that although PSA testing, biopsy, and treatment rates in Seattle through 1993 were higher than those in Connecticut, mortality rates through 1997 in the two areas were virtually the same (13). A recent Canadian study also found no association between changes in prostate cancer incidence and mortality in 88 small health areas within the British Columbia Cancer Registry (14).
One common concern of ecologic studies is that results may be biased by the omission of important confounders. In screening studies, it is particularly important to consider changes in treatment patterns that may have occurred concurrently with dissemination of the screening test. For example, the increased use of PSA screening over time has been accompanied by a trend toward earlier and more frequent use of hormone ablation therapy (HT) after diagnosis (15). Thus, HT use could be a confounding variable in ecologic analyses of PSA screening and prostate cancer mortality.
In ecologic studies of screening, the principal outcome of interest is the change in disease-specific mortality following introduction of the screening test. However, there are different ways to express mortality changes over time. These include the percentage change in mortality from an earlier to a later calendar period or the annual percentage change (APC) in mortality over a specified interval. Different measures may yield different outcomes; moreover, results may be affected by the calendar period over which the study has been conducted. Studies conducted too soon after the test has been introduced may fail to show any association with mortality, even if screening is efficacious. In fact, updates of two studies (9, 10) comparing mortality rates in the United States with those in the United Kingdom and Australia have found growing mortality reductions in the United States and Australia with additional years of data (16, 17). The timing of mortality declines depends on the lead time, which is the time by which the test advances diagnosis. Therefore, knowledge of lead time is critical for study interpretation. With PSA screening, the mean lead time has been estimated to be 5 years (18) or more (19).
This article addresses these issues of bias and interpretation through an ecologic analysis within nine registries of the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute (Bethesda, Maryland). We directly measured the frequency of PSA screening use among men without a prostate cancer diagnosis in each registry by using data from a linked SEER-Medicare database (20). To assess sensitivity of our results to the choice of outcome, we considered three different measures of mortality change over time as well as different time horizons for estimating mortality declines. To illustrate how results might change when information on treatment trends is included, we conducted analyses with and without adjustment for the use of HT. To determine whether mortality differences across areas reflect those expected under different efficacy and lead-time assumptions, we used a computer model that simulates PSA testing and prostate cancer mortality in the US population.
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MATERIALS AND METHODS |
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PSA screening use
Information on PSA testing among men aged 65 years or older was obtained for each SEER registry from linkage between the SEER database and Medicare claims (1, 21, 22). Medicare claims are the most complete record of PSA screening use over the study period because nationally representative surveys (23, 24) began including questions on PSA screening use in only the late 1990s. The SEER-Medicare files provide information on cancer cases as well as a 5 percent sample of controls without a history of a cancer diagnosis. We used data from both cases and controls, weighting controls by a factor of 20 to 1. The linkage data for this analysis contained claims from 1991 to 1998 and diagnosis information through 1996.
For each year, PSA screening use was defined as the percentage of men without a prostate cancer diagnosis who had at least one PSA test that year. Linkage with the SEER database allowed us to exclude all tests conducted after a prostate cancer diagnosis. For tests conducted prior to diagnosis, it was not possible to distinguish true screening tests from diagnostic tests because the reason for testing was not available. If a test was followed by another test within 3 months, only the first test was counted. We considered use up to age 84 years because men in this age group are generally considered eligible for prostate cancer screening.
To summarize the use of PSA screening over time within each registry, we computed the average proportion of men aged 6584 years tested each year from 1991 to 1996.
Mortality outcomes
Prostate cancer mortality rates through 1999 were obtained from the National Center for Health Statistics (Hyattsville, Maryland) by using SEER data. We considered three different outcomes to describe prostate cancer mortality trends.
The first outcome was the percentage change in prostate cancer mortality between two intervals I1 and I2. For I1, we used three different baseline intervals: 19851987, 19881990, and 19911993. The interval 19851987 represents the period before PSA screening was introduced, 19881990 was just prior to widespread adoption of PSA screening, and 19911993 spans the peak in prostate cancer mortality in the vast majority of registries. For I2, we used the interval 19971999. Three-year averages were calculated to reduce the effect of year-to-year variability in the observed rates within each registry.
Our second outcome was the APC in prostate cancer mortality between the years Y1 and Y2. A negative APC describes a decreasing trend. The APC is estimated by a least-squares regression line fit to the logarithm of the observed mortality rates between Y1 and Y2. For Y1, we used 1991; for Y2, we used 1999. In contrast to the percentage decline, which uses the mortality rates for just I1 and I2, the APC utilizes all data between Y1 and Y2 inclusive. However, the APC measure imposes a linear assumption on the temporal changes in the log of the prostate cancer mortality rate. Because most registries showed that mortality rates were level or increased slightly until about 1991 and then declined steadily afterward (figure 1), Y1 was chosen as 1991 to satisfy the linearity condition.
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Practice patterns
In analyzing practice patterns, we focused on trends in HT use within 2 years following diagnosis. Use of HT among men with early stage disease has increased dramatically over the past decade (15), and this change has been proposed as a possible explanation for observed declines in disease-specific mortality (6, 25). Using the SEER-Medicare linked database, we assessed the frequency of treatment with luteinizing hormone-releasing hormone agonists among localized/regional cases diagnosed from 1991 to 1996 who were treated initially with prostatectomy or radiation therapy. We used the Kaplan-Meier estimator to calculate the proportion of men who received any Medicare-covered HT up to 24 months after diagnosis (Health Care Common Procedure Coding System (HCPCS) codes J9202, J9217, J9218 for leuprolide/goserlin), with censoring due to death or loss to follow-up. Men were also censored if they received an orchiectomy within 24 months of diagnosis (International Classification of Diseases, Ninth Revision, procedure code 624x/HCPCS codes 54520, 54521, 54530). HT rates were averaged similarly to PSA screening rates, from 1991 to 1996.
Ecologic analysis
We conducted three statistical analyses to examine the association between PSA screening rates and prostate cancer mortality. First, we performed a weighted linear regression across registries to determine the association between percentage decline in mortality and PSA screening rates. Second, we conducted a weighted regression of the APC measure by registry and PSA screening rates. Third, we used Poisson regression to analyze the prostate cancer mortality rates at different points on the mortality curve for registries in which PSA screening rates were consistently high versus consistently low.
Weighted linear regression requires an assessment of variability in the outcome. For percentage decline, we used a Monte Carlo approach. First, we assumed that the observed number of prostate cancer deaths within a given 5-year age group followed a Poisson distribution. For the two time periods of interest, we independently simulated numbers of prostate cancer deaths within each age group by using observed counts as mean parameters. Corresponding age-adjusted mortality rates were then calculated to yield a percentage decline from the earlier to the later calendar period. This procedure was repeated 10,000 times to produce an empirical estimate for the variance of the percentage-decline measure within each registry. The inverse of this variance was used as the weight in the regression analysis. For APC, we used the standard variance estimate from least-squares regression.
To compare the age-adjusted mortality curves for high- versus low-use areas, we used Poisson regression. The Poisson models compared mortality rates over specified 3-year intervals (19851987, 19881990, 19911993, 19941996, and 19971999). In a supporting analysis, we calculated the percentage decline and APC summary measures for the high- and low-use groups and compared them by using a t test.
Interpretation of results
To determine whether divergence of mortality declines across areas would be expected within the time horizon of the present study, we used a computer model developed previously by Etzioni et al. (26). This model translates assumptions about mean lead time in the population and screening efficacy into declines in age-adjusted prostate cancer mortality relative to pre-PSA-era levels. Screening efficacy is expressed as the reduction in annual risk of prostate cancer death (relative risk) following the projected date of diagnosis in the absence of screening. Thus, for a screen-detected case, the model assumes that the hazard of prostate cancer death is zero until the original date of diagnosis, after which it is equal to the relative risk times the hazard of death in the absence of screening.
Input data for the model include registry-specific population sizes (table 1) and PSA screening use trends (figure 2). We used the model to project expected mortality declines within each registry, beginning in 1988 and assuming a mean lead time of 5 years and a 50 percent relative risk. The 50 percent relative risk assumption corresponds to the efficacy assumed in the design of the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (3, 27). We ran the model 1,000 times for each site to obtain an empirical distribution for the site-specific annual declines in prostate cancer mortality. We then compared the model results with the observed percentage decline estimates for I1 = 19851987, which represents the most recent calendar interval prior to the era of PSA testing. For an analysis of sensitivity, we repeated this procedure by assuming a mean lead time of 10 years (19) and a 50 percent relative risk, as well as a mean lead time of 5 years and a 70 percent relative risk.
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RESULTS |
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Figure 2 shows annual frequencies of PSA screening use. Seven registries exhibited either a consistently high-use or a consistently low-use trend. Atlanta and Detroit had generally high levels of PSA screening, whereas the frequencies in Connecticut, Hawaii, Iowa, New Mexico, and Utah were consistently about 1015 percent lower. Use in San Francisco and Seattle could not be categorized as consistently high or low.
HT use
The proportion of local-regional cases receiving HT ranged from 7 percent to 15 percent across registries. From 1991 to 1996, overall use of HT within 24 months increased from 8 percent to 22 percent. Intensity of HT was not strongly correlated with the use of PSA screening (figure 3). For example, Detroit had both a high PSA screening rate and one of the highest intensities of HT, but Atlanta, which also had a high rate of PSA screening, had one of the lowest frequencies of HT use. Similarly, both Utah and Connecticut had low-PSA-use profiles but very different intensities of HT.
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Figure 5 presents the annual prostate cancer mortality rates for sites associated with high versus low PSA screening rates. Mortality in the early 1990s was greater in the high-use group (p = 0.08 for Poisson regression applied to 19911993 rates), but this difference had disappeared by the end of the study period (p > 0.5). Table 4 presents the percentage decline in prostate cancer mortality in each group for the different baseline periods, together with the APC estimates for 19911999. For a baseline interval of 19911993, the high-use registries showed a decline of 22.2 percent (95 percent confidence interval: 15.2, 28.9) and the low-use registries a decline of 16.3 percent (95 percent confidence interval: 11.5, 20.9; p = 0.17). Differences between high- and low-use registries were considerably smaller when earlier baseline intervals were used. Greater declines were estimated based on the APC: 24 percent for low-use areas and 33 percent for high-use areas from 1991 to 1999.
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DISCUSSION |
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Do these findings indicate lack of test efficacy? We urge caution in making such an inference. First, disease-specific mortality in the late 1980s and early 1990s was greater in the high-use areas. If this early difference in mortality rates is real, then later convergence of the mortality curves is not inconsistent with some degree of test efficacy. However, it is possible that misattributing other causes of death to prostate cancer could be more pronounced in high-use areas, which would explain the early inflation in mortality (26). Continuing misattribution, even at a constant level, would then act against mortality declines attributable to PSA screening because of increased disease prevalence in these areas. Understanding trends in cause-of-death misattribution is key to reconciling the different analyses and interpreting study results. Studies among prostate cancer patients have yielded conflicting estimates of the extent of misattribution (2830).
Knowledge of mortality declines that would be expected under screening efficacy is also critical to understanding study results. When a clinically significant level of screening efficacy (relative risk = 0.5) and a mean lead time of 5 years were assumed, the model-projected mortality declines matched those observed through 1999. These results are consistent with an overall impact of PSA screening on population mortality. However, the model predicted greater mortality differences across areas than we observed in our ecologic analysis. This discrepancy could mean that additional sources of variation are making it difficult to detect mortality differences across areas using an ecologic approach. Note that the model projects mortality changes in the absence of other factors such as HT, socioeconomic status, and access to care. These factors could induce sufficient variability to obscure any ecologic associationexcept, perhaps, by assuming extreme differences in screening use between areas. In the present study, differences in PSA screening rates between the high- and low-use areas were far smaller than those observed in the positive Tyrol study of Bartsch et al. (11). The modest differences in PSA screening rates, together with additional sources of variation, could have produced a negative ecologic result.
Other models have been used to project the impact of PSA screening based on declines in the incidence of distant-stage disease that began in 1990. Feuer et al. (31) demonstrated that overall declines in distant-stage disease would be expected to induce a decline in mortality of approximately 18 percent by 1999. Figure 6 shows that there was a modest difference in declines of the incidence of distant-stage disease between high- and low-use registries (approximately 2025 percent of the overall decline by 1999). On the basis of the Feuer et al. model, we estimate that this would translate into a difference of approximately 4 percent between the declines in mortality in the two groups. This difference is lower than projected by our baseline model, which assumed that survival benefits apply to all screen-detected cancers rather than being restricted to cancers shifted out of the distant stage by screening.
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One of our incidental findings was that an ecologic association appears to exist between intensity of HT within the first 2 years after diagnosis and declines in mortality. If early HT is associated with improved survival, as has been suggested by some studies (6, 3234), such use could explain, for example, why prostate cancer mortality declined less in Atlanta and more in San Francisco than was predicted on the basis of PSA screening use alone.
It is possible that changes in other treatment modalities may also be affecting mortality. However, PSA screening and HT use were novel changes in clinical care that began during the 1990s. Changes in the use of other treatments, such as radical prostatectomy and radiation, occurred over longer periods of time. In general, use of these therapies increased according to all registries during the 1980s and stabilized during the 1990s, but differences between the registries were not always consistent throughout this time period. Quantifying the impact of these changes would require in-depth modeling, which is beyond the scope of this analysis.
Since we conducted the analyses presented in this article, the SEER data have been updated to provide cancer mortality rates through 2001. Between 1999 and 2001, prostate cancer mortality declined from 152 to 138.3 per 100,000 among White men aged 6584 years. We reran the regression analyses on the updated data, which showed greater mortality declines in the low-use areas but similar declines in the high-use areas relative to the earlier data. The updated regression results showed a weaker association between PSA screening rates and mortality declines than in the earlier data. Regression slopes for all analyses decreased, and the corresponding p values increased.
In conclusion, ecologic analyses of PSA screening and prostate cancer mortality should take into account the range of screening use across areas, the simultaneous dissemination of other cancer control methods, and the expected timing of any mortality declines that could plausibly arise as a result of the screening intervention. It is important to recognize that results of these analyses do not provide quantitative information about any individual survival benefit that may be associated with PSA screening. For better evidence regarding test efficacy, we therefore await the results of the ongoing randomized PSA screening trials.
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ACKNOWLEDGMENTS |
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The authors thank Kristin Berry for supplying the PSA screening rates and biopsy frequencies from the SEER-Medicare data and Seth Falcon for assisting with output generation for the simulation model runs.
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NOTES |
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REFERENCES |
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