Berek and Hacker's Gynecologic Oncology, 5th Edition

6

Epidemiology and Biostatistics

Daniel W. Cramer

The disciplines of epidemiology and biostatistics apply to gynecologic oncology in defining cancer occurrence and survival, identifying risk factors, and implementing strategies for treatment or prevention, including the proper design of clinical trials. As such, epidemiology and biostatistics are essential to the practice of evidence-based medicine. In this chapter, some key principles of epidemiology and biostatistics are considered under the headings of descriptive statistics, etiologic studies, statistical inference and validity, and cancer risk and prevention. Readers should refer to standard statistical and epidemiologic texts for more detailed discussion and computational formulas (1,2).

Descriptive Statistics

Cancer is described in populations by statistics related to its occurrence and survival afterward. How cancer varies by age, ethnicity, and geography are of particular interest. Descriptive statistics about cancer in the United States can be obtained from the National Cancer Institute through its Web site: http://www.seer.cancer.gov/. Descriptive statistics about cancer in the world can be obtained from the International Agency for Research on Cancer through its Web site: http://www-dep.iarc.fr/.

Incidence

The incidence rate (IR) is defined as the number of new cases of disease in a population within a specified time period:

IR = New cases/Person-time

The fact that time is a component of the denominator should help clinicians avoid the misapplication of this term to prevalence, another measure of disease occurrence that includes both old and new cases.

Cancer Incidence and Mortality Cancer incidence or mortality is usually stated as cases (or deaths) per 100,000 people per year, or as cases per 100,000 person-years. Incidence or mortality is measured in a specific population over a specific period. For example, country or state cancer registries count the number of new cancer cases diagnosed or cases of dying among residents over a year and divide that figure by census estimates of the total population in the region.

Crude Incidence or Mortality Crude incidence or mortality is the total number of new cancers (or deaths) that occur over a specified time in the entire population.

Figure 6.1 Age-specific incidence curves for the gynecologic cancers in women in the United States, 1996 to 2000. Modified from Ries LAG MD, Krapcho M, Stinchcomb DG, Howlader N, Horner MJ, Mariotto A, et al., eds. SEER Cancer Statistics Review, 1975-2005. Bethesda, MD: National Cancer Institute. Available at: http://seer.cancer.gov/csr/1975_2005/. Based on November 2007 SEER data submission posted to the SEER Web site 2008.

Age-Specific Incidence or Mortality Age-specific incidence (or mortality) is the number of new cancers (or deaths) that occur over a specified time among individuals of a particular age group divided by the total population in that same age group. Age-specific incidence or mortality rates are the best way to describe the occurrence of cancer in a population and are commonly graphed in 5- or 10-year groups. Annual age-specific incidence and mortality curves for the common malignant gynecologic cancers in the United States based on all women in the Surveillance, Epidemiology, and End Results (SEER) survey area for 1996 to 2000 (3) are shown in Figures 6.1 and 6.2.

Invasive cervical cancer shows a gradual rise and plateau after 50 years of age at approximately 16 cases per 100,000 women-years. Cancer of the corpus (largely endometrium) rises during the perimenopause and peaks at approximately 90 cases per 100,000 women-years after 60 years of age. Cancer of the ovary also displays an increase during the perimenopause and peaks after age 70 years at approximately 60 cases per 100,000 women-years. Cancer mortality curves display similar age patterns, but ovarian cancer is revealed as the most lethal of the gynecologic cancers. In situ cervical cancers are no longer being tabulated by the SEER registries. The vast majority of these cases are seen between ages 20 and 50, with a peak occurrence of approximately 200 cases per 100,000 women per year at ages 25 to 29. In addition, SEER is no longer counting ovarian tumors of borderline malignancy, accounting for a decline of 21% in incidence and 6% in mortality between 2004 and 2006.

Figure 6.2 Age-specific mortality curves for the gynecologic cancers in women in the United States, 1996 to 2000. Modified from Ries LAG MD, Krapcho M, Stinchcomb DG, Howlader N, Horner MJ, Mariotto A, et al., eds. SEER Cancer Statistics Review, 1975-2005. Bethesda, MD: National Cancer Institute. Available at http://seer.cancer.gov/csr/1975_2005/. Based on November 2007 SEER data submission posted to the SEER Web site 2008.

Table 6.1 Lifetime Risk of Acquiring or Dying from Gynecologic Cancers in White and Black U.S. Women*

Risk of Acquiring (%) Risk of Dying (%) All White Black All White Black Cervix 0.7 0.7 1.0 0.2 0.2 0.4 Corpus 2.5 2.6 2.0 0.5 0.5 0.8 Ovary 1.4 1.5 1.0 1.0 1.1 0.8 *Data from 1975-2000. Ries LAG, Melbert D, Krapcho M, Stinchcomb DG, Howlader N, Horner MJ, et al., eds. SEER Cancer Statistics Review, 1975-2005.

Cumulative Incidence or Mortality Cumulative incidence (or mortality) may be thought of as the proportion of people who develop disease (or die from it) during some period of observation. Cumulative “incidence” is technically a misnomer because it does not contain time in the denominator but, rather, is expressed as a percentage. The cumulative IR (CIR) may be crudely approximated from age-specific IRs by the following formula:

where IR_i is the age-specific rate for the i age stratum and ΔT_i is the size of the age interval of the i stratum (usually 5 years). Cumulative incidence, summed over the age range 0 to 85 years, yields the “lifetime risk” for cancer occurrence or death. Lifetime risks that a woman in the United States will have or die from cancer of the cervix, corpus, or ovary are shown in Table 6.1 and confirm that a U.S. woman has a greater risk of acquiring cancer of the corpus than cervical or ovarian cancer but a higher risk of dying from ovarian cancer than cervical or endometrial cancer combined.

Age-Adjusted Incidence or Mortality Age-adjusted incidence (AAI) or mortality is obtained by summing weighted averages of the incidence or mortality rates for each age stratum.The weight is derived from the age distribution of a standard population:

where IR_i is the IR in the i age stratum, and W_i is the number of people in the i stratum in the standard population. Age-adjusted rates are better than crude rates for summarizing incidence or mortality when comparing cancer occurrence among populations that may differ in their age structure. An “old” population would have a higher crude incidence of ovarian cancer and a lower crude incidence of carcinoma in situ of the cervix than a “young” population, even though both populations might have identical age-specific incidences for each disease. Cancer rates adjusted to the “world population standard” are shown in Table 6.2.

Worldwide, cervical cancer is the most important of the gynecologic cancers and is second only to breast cancer in overall occurrence. Cervical cancer is most frequent in southern Africa and Central America and least frequent in North America and parts of Asia. Cancer of the corpus is least frequent in Africa and Asia and most frequent in North America. Ovarian cancer is least frequent in Africa and Asia and most frequent in northern Europe.

Prevalence

Prevalence (P) is the proportion of people who have a particular disease or condition at a specified time. Prevalence can be calculated by multiplying incidence times the average duration of disease:

Prevalence = Incidence × Average duration of disease

Table 6.2 Age-Adjusted Incidence Rate* for the Gynecologic Cancers in Comparison with Other Major Cancers in Women

Region Breast Colon Lung Stomach Cervix Corpus Ovary World 37.4 14.6 12.1 10.4 16.2 6.5 6.6 Northern Africa 23.2 4.0 2.2 2.5 12.1 2.4 2.6 Southern Africa 33.4 9.0 6.9 3.7 38.2 3.5 5.2 Eastern Africa 19.5 4.1 1.2 5.5 42.7 3.2 5.8 Western Africa 18.2 3.5 0.6 3.6 29.3 2.2 4.6 Northern America 99.4 32.9 35.6 3.4 7.7 22.0 10.7 Central America 25.9 7.4 6.5 10.8 30.6 4.5 7.2 South America 46.0 14.8 7.6 12.2 28.6 6.7 7.7 Eastern Asia 22.9 12.5 17.7 20.6 7.4 2.5 3.7 Southeast Asia 25.5 9.9 8.9 4.5 18.7 4.2 7.2 Western Asia 33.3 9.9 5.5 6.4 5.8 5.8 5.3 Northern Europe 82.5 26.4 21.3 6.0 9.0 12.2 13.3 Eastern Europe 42.6 20.1 8.7 12.8 14.5 11.8 10.2 Western Europe 84.6 29.8 12.0 6.6 10.0 12.5 11.3 Southern Europe 62.4 23.5 9.2 8.7 10.7 11.8 9.7 Australia-New Zealand 84.6 36.9 17.4 4.2 7.4 10.6 9.4 Micronesia 38.6 11.8 15.5 4.4 9.4 7.4 6.0 Polynesia 34.2 10.7 1.0 11.6 28.0 11.8 7.7 *Age adjusted to the world standard in cases per 100,000. Data from IARC Web site. Available at: http://www-dep.iarc.fr/globocan/globocan.html.

More commonly, prevalence is derived from cross-sectional studies in which the number of individuals alive with a particular condition is identified from a survey and stated as a percentage of the total number of people who responded to the survey. Other examples of studies that yield prevalence data are those based on autopsy findings and screening tests. The frequency of previously unidentified cancers found in a series of autopsies yields data on the prevalence of occult cancer. The first application of a screening test in a previously unscreened population yields the prevalence of preclinical disease.

Cancer Survival

When the proportion of patients surviving cancer is plotted against time, the pattern often fits an exponential function. To say that survival is exponential means that the rate of death is constant over time, which can be demonstrated by plotting the logarithm of the probability of survival against time and demonstrating a straight line. Summary measures for a survival curve commonly include median survival time or the point at which 50% of the patients have died and the probability of survival at 1, 2, and 5 years.

Relative Survival Relative survival is defined as the ratio of the observed survival rate for the patient group to the survival rate expected for a population with similar demographic characteristics. Relative survival rates for U.S. women diagnosed in 2000 are shown in Figure 6.3 for the major gynecologic cancers and reveal that survival is best after cancer of the corpus, worst after cancer of the ovary, and intermediate after cancer of the cervix. Five-year relative survival rates are shown in Table 6.3 by type and stage of gynecologic cancer for U.S. women.

Stage at presentation and 5-year survival are most favorable for cancer of the corpus and least favorable for cancer of the ovary. In general, African Americans tend to be diagnosed at more advanced stages and have poorer survival compared with whites, especially for cancer of the cervix and corpus.

Figure 6.3 Relative survival rates for invasive cancers of the cervix, corpus, and ovaries for women diagnosed in the United States in 1995. Modified from Ries LAG MD, Krapcho M, Stinchcomb DG, Howlader N, Horner MJ, Mariotto A, et al., eds. SEER Cancer Statistics Review, 1975-2005. Bethesda, MD: National Cancer Institute. Available at http://seer.cancer.gov/csr/1975_2005/. Based on November 2007 SEER data submission posted to the SEER Web site 2008.

Table 6.3 Stage at Diagnosis for the Gynecologic Cancersa and 5-Year Survival Rates for U.S. Women*

Stage Distribution at Diagnosis (%) 5-Year Survival Rate (%) All White Black All White Black Cervix All stages 71.2 72.5 61.8 Localized 51 52 44 91.7 92.6 85.9 Regional 35 34 38 55.9 56.7 47.5 Distant 10 9 8 16.6 17.5 8.9 Corpus All stages 82.9 84.7 61.1 Localized 69 71 54 95.5 96.2 83.7 Regional 17 17 22 67.5 69.7 45.0 Distant 9 8 17 23.6 25.3 13.9 Ovary All stages 45.5 45.3 38.4 Localized 19 18 17 92.7 92.7 92.5 Regional 7 7 7 71.1 71.8 58.5 Distant 67 68 67 30.6 30.9 22.7 *Data from 1996-2004. Information insufficient to stage 4% of cervical, 5% of corpus, and 7% of ovarian cases. Ries LAG, Melbert D, Krapcho M, Stinchcomb DG, Howlader N, Horner MJ, et al., eds. SEER Cancer Statistics Review, 1975-2005.

Etiologic Studies

In distinction to descriptive studies, etiologic studies examine the relationship between cancer occurrence and survival and personal factors such as diet and reproductive history. This relationship is often described by the epidemiologic parameters, relative risk, and attributable risk.

Figure 6.4 Case-control study design.

Relative risk (RR) is the risk of disease or death in a population exposed to some factor of interest divided by the risk in those not exposed. Absence of association is indicated by a RR of 1 (null value); a number greater than 1 may indicate that exposure increases the risk of disease and a number less than 1 that exposure decreases the risk of disease.

Attributable risk is the risk of disease or death in a population exposed to some factor of interest minus the risk in those not exposed. The null value is 0; a number greater than 0 may indicate that exposure increases the risk of disease and a number less than 0 that exposure decreases the risk.

Case-Control Study

In a case-control study, diseased and nondiseased populations are selected, and existing or past characteristics (exposures) are assessed to determine the possible relationship between exposure and disease. The investigator starts with diseased cases and nondiseased control subjects who are then studied to determine whether they had a particular exposure (before the illness). The odds that the cases were exposed (a/b) is compared with the odds that the control subjects were exposed (c/d) in a measure called the exposure odds ratio (Fig. 6.4).

Exposure Odds Ratio The odds of exposure among cases divided by the odds of exposure among the control subjects is the exposure odds ratio; it approximates the relative risk. If an entire population could be characterized by its exposure and disease status, then the exposure odds ratio would be mathematically identical to the relative risk obtained in a cohort study. Because it is feasible to study only subsets of cases and control subjects, the exposure odds ratio in the sampled population approximates the relative risk, as long as the cases and control subjects actually sampled have not been preferentially selected on the basis of their exposure status.

Attributable risk cannot be directly calculated in a case-control study.

Cohort Studies

In a cohort study, the groups to be studied (the cohorts) are defined by characteristics (or exposures) that occur before the disease of interest, and the study groups are followed to observe the risk of disease in the cohorts. The investigator starts with exposed and nonexposed individuals who are monitored over time to identify the number of diseased cases that develop. The initial sizes of the cohort and the number of years cohort members are studied determine the person-time contributed by the cohorts. The investigator then calculates the rates of disease in exposed and nonexposed subjects and determines the relative or attributable risk. For rare exposures, an investigator may use the general population as the unexposed group and calculate a parameter equivalent to the relative risk that is known as the standardized morbidity ratio (Fig. 6.5).

Figure 6.5 Cohort study design.

Standardized Morbidity or Mortality Ratio The standardized morbidity or mortality ratio (SMR) is the observed number of exposed cohort members in whom disease developed, divided by the number expected if general population disease rates had prevailed in the cohort.

Cohort studies are further distinguished by when the exposure and outcome occurred or will occur in relation to when the investigator begins the study.

Retrospective Cohort Study In a retrospective cohort study, the exposures and outcomes have already occurred when the study is begun. For example, studies of second cancers after therapeutic radiation are based on follow-up of women irradiated for cervical cancer 10 to 30 years previously. Medical records and death certificates are used to determine those who subsequently died of cancers other than cervical.

Prospective Cohort Study In a prospective cohort study, the relevant exposure may or may not have occurred when the study is begun, but the outcome has not yet occurred. After the cohort is selected, the investigator must wait for the disease or outcome to appear in the cohort members. The Nurses' Health Study (4) is a good example of a prospective cohort study.

Clinical Trial A clinical trial is a special type of prospective cohort study in which the investigator assigns a therapy or preventive agent in randomized fashion to minimize the possibility of bias accounting for different outcomes subsequently observed between treatment cohorts. Obviously, such studies cannot be used to assess a harmful effect of an exposure except as might occur as an unintended side effect of the therapy. Clinical trials are the only satisfactory way to assess the effect of different cancer therapies on disease recurrence or death because, in theory, they are able to overcome many of the biases that may affect casecontrol or cohort studies, as discussed in the next section.

Statistical Inference and Validity

Clinicians should understand issues affecting statistical significance and validity to evaluate studies claiming that some exposure causes cancer, a new therapy is superior to standard treatment, or a screening test can improve mortality.

Statistical Inference

Statistical inference is a process of drawing conclusions from data by hypothesis testing, during which a decision is made either to reject or not reject a null hypothesis. Hypothesis testing involves the following steps:

• Observations are made and summarized by some statistical parameter such as a mean, a proportion, a relative risk, and so forth.
• A research question is stated in terms of a null hypothesis claiming no difference between the observed parameter and some theoretical value.
• A statistical test is chosen based on the study design and nature of the parameters being studied.
• The test statistic is calculated, and its associated p value is read from the appropriate statistical table or generated from a statistical program.
• A p value less than the traditional 5% leads to the decision to reject the null hypothesis, whereas a value greater than 5% leads to the decision not to reject the null hypothesis. Errors are possible with either decision.
• A confidence interval on the parameter may be constructed from the test results and defines the range in which the true value of the parameter is expected to fall.Precision refers to a characteristic of a parameter falling into a narrow confidence interval, a desirable feature of large studies.

Type I Error

The degree of conflict between the parameter observed and that assumed by the null hypothesis is summarized by the p value, alpha, or type I error and indicates the probability of incorrectly rejecting the null hypothesis. In practice, an alpha level is chosen a priori, usually p = 0.05, and if the association tested has a p value less than the predetermined alpha level, then results are considered statistically significant. It is important to note, however, that when many tests are performed, some results will be observed by chance. For instance, if 100 tests are performed and the alpha level is set at 0.05, then an estimated five significant results will be observed by chance. One way to address this multiple testing issue, called Bonferroni correction, is to divide the alpha level by the number of tests being performed. If this method is used and 100 tests are performed, then the alpha level will be 0.05/100 or 0.0005.

Type II Error

A type II, or beta error, indicates the probability of failing to reject the null hypothesis when, in reality, it is false. To calculate a beta error, an alternate hypothesis must be stated.

Power

Power is 1 minus the beta error and reflects the ability of a study to detect an actual effect. More precisely, power is the ability of a test statistic to detect differences of a specified size in test parameters. In planning a clinical trial, an investigator often calculates the power that a study will have to detect an association, given a certain study size and certain assumptions about the nature of the association. Small clinical trials that find no significant difference among therapies may be cited as evidence of “no effect of therapy” when the statistical power may have been well below the accepted target of 80% for a meaningful difference in response rates.

Statistical Distributions and Tests

There are no simple rules for determining which statistical test is appropriate in every situation. The choice depends on whether the variable is qualitative (nominal) or quantitative (numerical), what assumptions are made about the distribution of the parameter being measured, what is the nature of the study question, and the number of groups or variables being studied. For example, a chi-square test is used to test the null hypothesis that proportions are equal or that nominal variables are independent. The unpaired t-test is used to compare two means from independent samples, whereas the paired t-test compares the difference or change in a numerical variable for matched or paired groups or samples.

Validity

Validity has two components: internal validity and external validity. Internal validity means freedom from bias. Bias refers to a systematic error in the design, conduct, or analysis of a study that results in a mistaken conclusion and is commonly divided into observation bias, selection bias, and confounding. The external validity of a study refers to the ability to generalize the results observed in one study population to another. Although there is controversy about what characteristics of a study make for generalizability, it is clear that external validity is only an issue for those studies that possess internal validity, which is the main focus of this discussion.

Observation Bias

Observation bias or misclassification occurs when subjects are classified incorrectly with respect to exposure or disease. If misclassification was equally likely to occur whether the subject was a case or control or an exposed or nonexposed cohort member, then the observation bias would be nondifferential and would cause the relative risk to be biased toward the null value, 1. Alternatively, if misclassification was more likely to occur for case than control subjects or for exposed than nonexposed cohort members, then a falsely elevated (or decreased) relative risk might occur (e.g., if cases preferentially recalled or admitted to a particular exposure compared with control subjects). Criteria for exposure or disease should be clearly defined to minimize observation bias and, whenever possible, exposure or disease confirmed from medical records. Ideally, researchers recording disease status in a cohort study or exposure status in a case-control study should be unaware of the subject's study group or blinded to key hypotheses. In a clinical trial, observation bias may be minimized by double blindness, when neither the subject nor investigator knows which specific treatment the subject is receiving.

Selection Bias

Selection bias is an error that results from systematic differences in the characteristics of subjects who are and are not selected for study. For example, a selection bias might occur in a case-control study if exposed cases did much better or worse than nonexposed cases. If the case group consisted of long-term survivors, then they might have a different frequency of the exposure than newly diagnosed individuals. Selection bias may also occur in the process of selecting control subjects; for example, control subjects might be selected from hospitalized patients in a disease category that may, itself, relate to the exposure. Selection bias is less likely to occur in cohort studies or in population-based case-control studies, where most cases in a particular area are studied and control subjects are selected from the general population.

Confounding

Confounding occurs when some factor not considered in the design or analysis accounts for an association because that factor is correlated with both exposure and disease.Potential confounders for any cancer study are age, ethnicity, and socioeconomic status. Confounding may be controlled during the design of a study by matching cases to control subjects on key confounding variables or during the analytic phase of the study by stratification or multivariate analysis. Stratifying means examining the association of interest within groups that are similar with respect to a potential confounder, whereas multivariate analysis is a statistical technique that controls for a number of confounders simultaneously.

In a clinical trial, confounding is avoided by randomization; that is, subjects are allocated to treatment groups by a chance mechanism such that prejudices of the investigator or preferences by the subject do not influence allocation of treatment. In practice, participant randomization assignments can be determined using computer-generated random numbers or random number tables found in most statistic textbooks (5,6). The initial table in the report of a clinical trial usually shows how the treatment groups compared with respect to age, ethnicity, or other important variables to demonstrate whether randomization indeed balanced key variables. Similar tables are helpful in case-control and cohort studies.

Other Criteria for Judging an Epidemiologic Study

In addition to validity, other criteria applied to judging an epidemiologic study include consistency, whether a dose response is present, and whether the association has biologic credibility.

Consistency

Measurements that are in close agreement when repeated are said to be consistent. In the context of an epidemiologic association, relative risks that are consistent among studies, especially those in which different study methods have been used, provide evidence for a causal association. However, the possibility that a systematic bias affected all the studies should also be considered. Consistency can be assessed in a formal manner by performing a study called a metaanalysis. In a metaanalysis, results from independent studies examining the same exposure (or treatment) and outcome are combined so that a more powerful test of the null hypothesis may be conducted. As part of the metaanalysis, a test for heterogeneity is performed to indicate whether there are statistical differences among the results of different studies. The metaanalysis has become an important component of evidenced-based medical reviews. For example, oral contraceptive use was less common in ovarian cancer cases compared to controls in 45 studies; when the studies results were combined in a metaanalysis, women who had ever used oral contraceptives had an estimated 17% reduction in ovarian cancer risk compared to women who had never used oral contraceptives (7).

Dose Response

Dose response refers to a relationship between exposure and disease such that a change in the duration, amount, or intensity of an exposure is associated with an increase or decrease in disease risk.

Biologic Credibility

An association has biologic credibility if it is supported by a framework of diverse observations from the natural history or demographics of the disease and from relevant experimental models.

Cancer Risk and Prevention

In this section, risk factors for the gynecologic cancers are discussed, along with the application of this information to cancer prevention. Table 6.4 summarizes major epidemiologic risk factors for cervical, endometrial, and ovarian cancer.

Table 6.4 Risk Factors for Gynecologic Cancers

Factor Cervix Endometrium Ovary Sexual Increased risk associated with coitus at an early age, multiple partners, or “high-risk men” Increased risk in women who have never married Increased risk in women who have never married Contraception Barrier methods protective; oral contraceptives may increase risk Oral contraceptives protective Oral contraceptives and tubal ligation protective Childbirth Increasing risk with increasing parity Decreasing risk with increasing parity Decreasing risk with increasing parity Age at menopause No clear association Late menopause increases risk No clear association Menopausal hormones No clear association Increased risk from “unopposed estrogen” Weak increased risk with “unopposed estrogen” Family history Weak evidence of familial tendency Mutations of DNA mismatch repair genes increase risk Mutations of BRCA1, BRCA2, and DNA mismatch repair increase risk Body habitus, diet Carotene, vitamin C, and folic acid potentially protective Obesity a strong risk factor No clear association Smoking Increased risk Decreased risk Varies by epithelial type; increased risk of mucinous type Other exposures Douching may increase risk Association with estrogen-producing tumors of the ovary, liver disease, tamoxifen use Foreign bodies (talc) per vagina may increase risk; acetaminophen may decrease risk

Cervical Cancer

Invasive squamous cell carcinoma of the cervix is the end stage of a process beginning with atypical transformation of cervical epithelium at the squamocolumnar junction, leading to cervical intraepithelial neoplasia (CIN) of advancing grades and eventual invasive disease. Thus, risk factors for cervical cancer are those associated with atypical transformation and those that influence persistence and progression of disease.

Factors associated with atypical transformation largely relate to sexual practices that increase the opportunity for human papilloma virus (HPV) infection. Early age at first intercourse may also be important because adolescence is a period of heightened squamous metaplasia, and intercourse at this time may increase the likelihood of atypical transformation (8). The woman who has had intercourse with multiple partners or with a “high-risk” male who has himself had contact with multiple partners increases the likelihood of her exposure to HPV. An estimated 27% of U.S. women ages 14 to 59 have a prevalent HPV infection. Prevalence is highest among women ages 20 to 24 at 45% (9). The link with HPV infection means that a woman can decrease her risk of cervical cancer by safe sexual practices and use of barrier methods of contraception (10). Male circumcision would appear to decrease the risk of male HPV infection and cervical cancer in their partners (11).

The recent development of HPV vaccines offers an exciting approach to true primary prevention of this important cancer worldwide (12). It is important to note that currently available HPV vaccines only target HPV types 16 and 18, which cause an estimated 70% of cervical cancers (13,14). Consequently, screening with Papanicolaou (Pap) smears continues to be an important cervical cancer prevention strategy (14,15). Unfortunately, vaccination does not accelerate clearance of prevalent HPV infections, which means that the vaccine is not effective for women who have already been infected (16). Thus, females should receive the vaccine before sexual debut. Vaccine trials in males are ongoing because HPV vaccines may prevent not only anogenital warts and anogenital cancer but also a subset of anal, penile, oral, head, and neck cancers as well as juvenile respiratory papillomatosis in their children (17). Mathematical models suggest that male vaccination will have little impact on preventing cervical cancer in females (18). Although implementation of HPV vaccination has gone fairly smoothly in developed countries, the cost of the vaccine ($335-$360) has made distribution difficult in developing countries, where 80% of cervical cancers occur (19).

Smoking also has been associated with increased risk for cervical cancer, even after adjustment for a number of confounding factors (20). This association has biologic credibility because potentially mutagenic substances are secreted in the cervical mucus of smokers (21). In third world countries, chronic exposure to wood smoke may increase the risk for cervical cancer in HPV-infected women (22).

Besides factors that affect the risk for cervical cancer by initiating atypical transformation, others may modulate risk for cervical cancer by affecting the likelihood that a preinvasive lesion will persist or progress. A factor indisputably related to the progression of CIN is the frequency of cervical cytologic screening. Population studies have demonstrated a correlation between cytologic screening and declining mortality from cervical cancer (23). Casecontrol studies demonstrate that women who have had Pap smears at least every 3 years have one-tenth the risk of developing invasive disease compared with women who have never had a Pap test (24). The recent introduction of HPV testing has improved cervical cancer screening even further (described in more detail under “Screening Strategies”).

Other factors that relate to disease progression may include oral contraceptive use and diet. Long-term oral contraceptive use has been reported to increase the risk of high-grade intraepithelial lesions and invasive cervical cancer (25), and a link to adenocarcinomas of the cervix has also been postulated (26). Butterworth et al. attributed the potential harmful effects of oral contraceptives on the cervix to folate deficiency and recommended supplementation (27). More recent studies found that high homocysteine levels may correlate with risk for invasive cervical cancer, again suggesting the importance of folates and vitamins B₁₂ or B₆ (28). Finally, progression of CIN is likely to be greater in immunosuppressed women, such as those with human immunodeficiency virus infection (29), or after kidney transplantation (30).

Endometrial Cancer

Risk for adenocarcinoma of the endometrium is largely attributed to estrogen (31). States that lead to an excess of estrogen over progesterone or increase lifetime exposure to estrogen also increase endometrial cancer risk. For instance, early age at menarche and late age at menopause increase endometrial cancer risk (32,33,34,35,36,37,38). Furthermore, obesity, which leads to increased estrogen production through the peripheral conversion of androstenedione, accounts for an estimated 57% of all endometrial cancers in the United States (39). Alternatively, protective factors are those associated with decreased estrogen production. Surgical castration at an early age with retention of the uterus is a strong protective factor (40). Leanness and regular exercise lower estrogen levels and protect against endometrial cancer (41). Smoking also lowers estrogen and protects against endometrial cancer but obviously cannot be encouraged as a preventive measure (42). Endometrial cancer as a consequence of decreased degradation of estrogen is illustrated by case reports of the disease in women with cirrhosis of the liver (43).

Endometrial cancer as a consequence of exogenous estrogen is demonstrated by the impressive evidence that unopposed estrogen administered for the menopause increases the risk of endometrial cancer in a dose-response fashion (44). Tamoxifen, with its estrogen antagonist effects in the breast and agonist effects in the uterus, has also been shown to increase the risk for endometrial cancer in clinical trial data (45). Alternatively, menopausal estrogen taken with a progestin has not been shown to increase risk (46), and past use of combination birth control pills has been reported to decrease the risk of endometrial cancer (47). Clinical trials have suggested very low rates of hyperplasia occurring with a continuous regimen of 0.625 mg of conjugated estrogen and 2.5 mg of medroxyprogesterone acetate (48).

Fitting with key roles for estrogen and progesterone in this disease, risk for endometrial cancer may be modified by genetic polymorphisms of the progesterone receptor (49), estrogen receptor alpha (50), estrogen metabolism genes (51), and aromatase, which catalyzes the conversion of androgens to estrogens (52). It is less clear how the DNA mismatch repair genes that are associated with increased risk for colorectal and endometrial cancers would operate through the “estrogen excess” model (53). Although the majority of risk factors for endometrial cancer are nicely explained by estrogen excess, the consistent observation that even inert intrauterine devices (IUDs) decrease risk suggests that immune factors related to the low-grade inflammation that occurs with IUDs may also play a role (54).

Ovarian Cancer

Ovarian cancer has been associated with a number of diverse findings with a variety of theories offered to explain them. Consistently observed risk factors for ovarian cancer include a protective effect of pregnancy, breast-feeding, and oral contraceptive use. A popular theory to account for these findings is that these events lead to a break in monthly ovulations and, therefore, repeated disruption and healing of the surface of the ovary (incessant ovulation), which is the cause of ovarian cancer (55). Not readily explained by this model, however, are the facts that the peak occurrence of ovarian cancer is well beyond the cessation of ovulation. In addition, very low rates of the disease are observed in Japan, where there are both low birthrates and little use of oral contraceptives.

An alternative theory to incessant ovulation is that ovarian cancer may arise from excessive gonadotropin stimulation of the ovary (56). Classic animal models for ovarian cancer involved disruption of ovarian-pituitary feedback either by prematurely destroying oocytes using radiation or chemical toxins (57,58) or by transplanting the animal's ovary to its spleen, leading to enhanced metabolism of ovarian hormones before they could exert feedback inhibition (59). A role for gonadotropins was indicated by observations that ovarian tumors did not develop in rodents who were hypophysectomized before the experimental treatment or who were given estrogen, which inhibited gonadotropin release (60,61). More recently, it has been shown that gonadal stromal tumors invariably developed in mice with a targeted deletion of the gene for the gonadotropin down regulator, α-inhibin (62), unless the mice were also incapable of secreting gonadotropins (63). Most of these experimental tumors were stromal in origin, and their relevance to the epithelial types observed in women has been debated. However, monthly ovulators, in contrast to rodents, have inclusion cysts and an abundant stromal-epithelial admixture, which might lead to epithelial proliferation as the principal manifestation of ovarian stromal stimulation in humans.

Epidemiologic data support the relevance of these models to human ovarian cancer. Ovarian cancer incidence rises sharply between ages 45 and 54 and remains elevated for the remainder of a woman's life, paralleling gonadotropin levels over this period. The strong protective association between oral contraceptives and ovarian cancer (64) duplicates the effects of exogenous estrogen in the animal models. Also relevant to the animal models are cohort studies that demonstrate that ovarian cancer occurs after radiation for cervical cancer after a 10- to 15-year lag period (65,66).

Parmley and Woodruff proposed that epithelial ovarian cancers might be ovarian mesotheliomas that arise from transformation of the surface lining of the ovary exposed to pelvic contaminants (67). One such contaminant might be talc used in genital hygiene, which has fairly consistently been identified as a risk factor (68). Besides talc, another pelvic “contaminant” might be the menstrual products that are believed to flow out of the fallopian tubes during menstruation to explain endometriosis (69). Indeed, prior endometriosis is a risk factor for ovarian cancer (70), especially the endometrioid and clear cell types (71). The pelvic contamination theory might also explain why tubal ligation decreases the risk of ovarian cancer (72). Other theories have suggested roles for androgens, progesterone, and inflammation and offer alternate but not necessarily competing explanations for ovarian cancer risk factors (73,74).

Uninterrupted ovulations could have immune consequences related to the surface glycoprotein and tumor marker, human mucin 1 (MUC1) (75). MUC1 is a high molecular weight protein expressed in a highly glycosylated form at low levels by many types of normal epithelial cells and in an underglycosylated form at high levels by most epithelial adenocarcinomas, including endometrial, breast, and ovarian cancer (76). In cancer patients, anti-MUC1 antibodies may correlate with a more favorable prognosis (77,78). Interestingly, anti-MUC1 antibodies are also found in healthy individuals, especially in women during pregnancy and lactation, leading to the hypothesis that a natural immunity against tumor MUC1 might develop and account for the long-term protective effect of pregnancy or breast-feeding on the risk of breast cancer (79). This led us to propose the broader theory that a variety of seemingly disparate events associated with ovarian cancer risk—including tubal ligation, mastitis, bone fracture, and possibly IUD use—may be operating by their ability to raise anti-MUC1 antibodies and enhance immunity or lower anti-MUC1 antibodies to increase immune tolerance to cancers expressing MUC1 (80).

Finally, there are a number of genetic risk factors emerging for ovarian cancer. Having a mother or sister with the disease increases a woman's risk for ovarian cancer approximately two- to threefold (81). Specific genetic factors include mutations of the BRCA1 and BRCA2 as well as the DNA mismatch genes (82). Although these genetic factors are more likely to be found in families in which a number of relatives have been affected with breast or ovarian cancer, they may be found in a surprising number of women with “sporadic” ovarian cancer: 10% in one series (83) and as much as 40% among women with a Jewish ethnic background (84).

Other Gynecologic Neoplasms

Other than clear cell adenocarcinomas of the vagina associated with maternal use of diethylstilbestrol (85), vaginal carcinoma is primarily a disease of women older than 50 years of age. Vulvar cancer has an age-incidence distribution similar to vaginal cancer. HPV infection appears to play a role in both vaginal and vulvar cancers (86,87,88). In a population based casecontrol study, HPV was detected in more than 80% of the tumor blocks from patients with in situ vaginal cancer and in 60% of those from invasive vaginal cancers (87). For vulvar cancer, two types of vulvar cancer have been defined (86,88). The first, which affects younger women, is associated with HPV and has a preinvasive stage. The second type occurs in older women and arises in areas with non-neoplastic epithelial disorders such as lichen sclerosis. It is not surprising that risk factors known to exist for cervical neoplasms also pertain to vulvar and vaginal neoplasms, including sexual history and smoking (87,89,90,91). Further study of dietary factors, especially folates and the carotenoids, would be worthwhile.

Trophoblastic neoplasms include complete and partial hydatidiform moles, invasive moles, and choriocarcinoma. The epidemiology of hydatidiform mole is probably better understood than that of other trophoblastic diseases, and it is likely to be relevant because of the association between molar pregnancy and subsequent invasive mole or choriocarcinoma. The prevalence of molar pregnancy varies from 1 per 100 deliveries in Asia, Indonesia, and other third world countries to 1 per 1,000 to 1,500 in the United States (92). Clearly, the risk of having a molar pregnancy increases with maternal age (93,94), but it is less certain whether adolescents are also at increased risk (95). A previous hydatidiform mole is also a strong risk factor; subsequent pregnancies have a 1% risk of also being a hydatidiform mole (96). The peculiar cytogenetic patterns of complete and partial hydatidiform moles are discussed in Chapter 15 and may indicate the importance of aberrant germ cells in the origin of these disorders.

Berkowitz et al. (97) suggested that deficiency of the vitamin A precursor, carotene, or of animal fats necessary for its absorption might be a factor in the cause of this disease. Vitamin A deficiency causes fetal wastage and aberrancy of epithelial development in female animals and degeneration of seminiferous epithelium with poor gamete development in male animals (98,99,100). In addition, regions where molar pregnancy is common have a high incidence of night blindness (101). Interestingly, paternal blood group combinations may influence risk of a molar pregnancy. Mothers with group A blood and fathers with group A or 0 had an increased risk compared with all other blood group combinations (102,103,104). Oral contraceptives are associated with an increased risk of hydatidiform mole that increases with duration of use. Ten or more years of use is associated with a more than twofold increase in risk (105). Smoking also doubles the risk of hydatidiform mole and quadruples risk with ten or more years of smoking (102,106,107). The role of alcohol and infections (HPV, adenoassociated virus, and tuberculosis) have also been considered (108).

Cancer Prevention

Cancer prevention may occur at the level of primary prevention (the identification and modification of risk factors for disease), secondary prevention (the detection of the disease at earlier, more treatable stages), or tertiary prevention (effective treatment of clinical disease). This section addresses primary and secondary measures of prevention.

Methods of primary prevention are by no means certain, but suggestions include the following.

• For cervical cancer, use of barrier methods of contraception, avoidance of tobacco, and maintaining a diet high in folates, B vitamins, and β-carotene may be beneficial. HPV vaccines have proven benefits and should be used by women before sexual debut.
• For endometrial cancer, maintenance of ideal body weight, avoidance of a high-fat diet, and avoidance of unopposed estrogen therapy during menopause may be beneficial.
• For ovarian cancer, use of oral contraceptives, if not medically contraindicated, and avoidance of talc in genital hygiene may be beneficial. Women known to carry a predisposing mutation should undergo prophylactic salpingo-oophorectomy after they have completed childbearing.

Secondary Prevention

Cancer deaths may also be prevented by detecting disease at a stage when it is more curable. The secondary prevention of cervical cancer has been successful, and screening programs for the other gynecologic cancers may eventually be devised. To be successful, a screening program must be directed at a “suitable” disease with a “suitable” screening test (109). A suitable disease must be one that has serious consequences, as most cancers do. Treatment must be available so that when such therapy is applied to screen-detected (preclinical) disease, it will be more effective than when applied after symptoms of the disease have appeared. Also, the preclinical phase of the disease must be long enough that the chances are good that a person will be screened. There must also be a suitable screening test as defined by simplicity, acceptability to patients, low cost, and high validity (defined by the measures in Table 6.5).

Sensitivity

The sensitivity of a test is defined as the proportion of people with a true positive screening result of all those who have the disease.

Specificity

The specificity of a test is defined as the proportion of people with a true negative screening result of all those who do not have the disease.

Predictive Value

The predictive value of a positive test is defined as the proportion of true positives out of all those who screened positive. The alternate formula shown in Table 6.5 reveals that predictive value is a function of sensitivity, specificity, and disease prevalence. This function implies that a positive screening test is more likely to indicate disease in a high-risk population than in a low-risk population (Table 6.5).

Screening Strategies

Cervical cytology represents one of the most effective screening tests for cancer ever developed; controversies relate to how to make it more efficient. Recent guidelines suggested by the American Cancer Society (110) are that the interval between screenings may be safely lengthened to 3 years in women who have had at least three consecutive negative screens and are at otherwise low risk (e.g., no history of immunosuppression). Similar guidelines were proposed by the American College of Obstetricians and Gynecologists in 2000, although the less frequent intervals were to be at the discretion of the physician (111). A recent analysis of the potential effects of extending screening intervals concluded that an average excess risk of three cases of cervical cancer per 100,000 women screened would result (112). However, to avert one additional case of cancer by screening women annually for 3 years would necessitate approximately 280,000 additional Pap tests and 15,000 colposcopic examinations. HPV testing, which involves detection of HPV DNA in a cervical swab, can greatly improve the sensitivity of the traditional Pap smear. In a study of 10,154 women ages 30 to 69, the HPV test had a much higher sensitivity (95%) than the Pap smear (55%) and similar specificity (95% for HPV testing and 97% for the Pap smear). Using HPV testing together with the Pap smear increases the sensitivity to 100% (113). A cost-benefit analysis revealed that the maximum number of lives were saved when combined HPV and Pap smear screening was performed every 2 years until death (114).

Table 6.5 Measures of Validity for a Screening Procedure

Status Determined by Screening True Disease Status Positive Negative Total Positive a (true positives) b (false positives) a + b (all screened positives) Negative c (false negatives) d (true negatives) c + d (all screened negatives) Total a + c (all diseased) b + d (all nondiseased) N (all subjects) Measure Definition Formulaa Sensitivity True positives; all diseased Specificity True negatives; all nondiseased Predictive value of a positive screen True positives; all screened positives or a SN, sensitivity; SP, specificity; P, prevalence of disease

Screening for endometrial cancer in asymptomatic women in the general population is not justified, but endometrial biopsies or assessment of the endometrial stripe by transvaginal ultrasound may be appropriate for perimenopausal or postmenopausal women at risk for endometrial cancer, including those who are obese, are exposed to unopposed estrogen, use tamoxifen, or who come from families with both colon and endometrial cancer. Based on expert opinion only, women at high risk for ovarian cancer by virtue of a BRCA1 orBRCA2 mutation are recommended to have annual or semiannual screening with transvaginal ultrasound and CA125 measurements (115). Alarge trial is under way in the United Kingdom to determine whether use of annual CA125 measurements with secondary ultrasonic screening would be effective at reducing ovarian cancer mortality in postmenopausal women at normal risk (116).

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