Changing population characteristics, effect-measure modification, and cancer risk factor identification

1742-5573-4-10 1742-5573 Analytic Perspective Changing population characteristics, effect-measure modification, and cancer risk factor identification Slattery L Martha mslatter@hrc.utah.edu Murtaugh A Maureen mmurtaugh@hrc.utah.edu Quesenberry Charles Charles.Quesenberry@kp.org Caan J Bette Bette.Caan@kp.org Edwards Sandra sedwards@hrc.utah.edu Sweeney Carol csweeney@hrc.utah.edu

University of Utah, School of Medicine, Department of Medicine, Salt Lake City, Utah 84108 USA

Kaiser Permanente Medical Research Program, Department of Research, 3505 Broadway, Oakland CA 94611 USA

Epidemiologic Perspectives & Innovations 1742-5573 2007 4 1 10 http://www.epi-perspectives.com/content/4/1/10 17908309 10.1186/1742-5573-4-10 11 4 2005 01 10 2007 01 10 2007 2007 Slattery et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Epidemiologic studies have identified a number of lifestyle factors, e.g. diet, obesity, and use of certain medications, which affect risk of colon cancer. However, the magnitude and significance of risk factor-disease associations differ among studies. We propose that population trends of changing prevalence of risk factors explains some of the variability between studies when factors that change prevalence also modify the effect of other risk factors. We used data collected from population-based control who were selected as study participants for two time periods, 1991–1994 and 1997–2000, along with data from the literature, to examine changes in the population prevalence of aspirin and non-steroidal anti-inflammatory medication (NSAID) use, obesity, and hormone replacement therapy (HRT) over time. Data from a population-based colon cancer case-control study were used to estimate effect-measurement modification among these factors. Sizeable changes in aspirin use, HRT use, and the proportion of the population that is obese were observed between the 1980s and 2000. Use of NSAIDs interacted with BMI and HRT; HRT use interacted with body mass index (BMI). We estimate that as the prevalence of NSAIDs use changed from 10% to almost 50%, the colon cancer relative risk associated with BMI >30 would change from 1.3 to 1.9 because of the modifying effect of NSAIDs. Similarly, the relative risk estimated for BMI would increase as the prevalence of use of HRT among post-menopausal women increased. In conclusion, as population characteristics change over time, these changes may have an influence on relative risk estimates for colon cancer for other exposures because of effect-measure modification. The impact of population changes on comparability between epidemiologic studies can be kept to a minimum if investigators assess exposure-disease associations within strata of other exposures, and present results in a manner that allows comparisons across studies. Effect-measure modification is an important component of data analysis that should be evaluated to obtain a complete understanding of disease etiology.

Background

Epidemiologic investigations have detected associations between colon cancer and several diet and lifestyle risk factors, but the relative importance of risk factors has changed over time 12. Studies of diet and colon cancer conducted in the early 1980s showed that fat was statistically significantly associated with colon cancer, but more recent studies have not supported that association 3456. More recently use of aspirin and hormone replacement therapy (HRT) have emerged as being statistically significant risk factors for colon cancer as well as significantly interacting with other risk factors 789. For example, it has been shown that the association between obesity as indicated by a high body mass index (BMI) and increased relative risk of colon cancer is influenced by estrogen or hormone replacement therapy use 1011; reports suggest that aspirin may influence the colon cancer relative risk associated with fat intake 1213; other data support the role of effect modification on diet and lifestyle factors in colon cancer etiology 1214.

Inconsistencies in results for risk factor-disease associations for colon cancer have been attributed to a variety of non-biological factors. Since earlier results for colon cancer came primarily from case-control studies, while cohort studies contributed more to the recent literature, study design, i.e. the potential biased reporting in case-control studies, has been cited as a potential explanation for differences between results of older and more recent studies15. However, results of cohort studies may be affected by measurement error from imprecise exposure assessment when data are collected using a self-administered format. Thus case-control and cohort studies each have strengths and weaknesses, yet many risk factor associations are observed to be similar from both case-control and cohort studies. While random and systematic errors may explain differences in study results, even for similar, well-conducted studies, a very plausible explanation for a trend in study results is a change in prevalence of exposures that modify effects of other risk factors.

We propose that risk factors associated with colon cancer at the population level exist in a shifting context. We hypothesize that the ability of studies to estimate the influence of risk factors for colon cancer depends on the presence of effect-measure modifiers that are changing in the population. We hypothesize that change in the estimated relative risk from a given exposure can occur if prevalence of other exposures that modify its risk are changing. Thus, it is possible that differences in risk factors identified between studies can be attributed, at least in part, to differences in the prevalence of important population characteristics.

Changes in U.S. population during the last quarter of the 20th century in diet, physical activity, and weight, characteristics that affect colon cancer risk, have been described based on national surveys 16. In this paper we examine changes in the use of aspirin, HRT, and obesity in population-based samples from two western states between the early 1990s and the late 1990s. Data from these populations were rigorously collected using the same questionnaire and recruitment methods. We evaluate trends in use of HRT and aspirin over the last three decades of the 20th century as reported in the literature and in National Health and Nutrition Examination Survey (NHANES) data.

To test our hypothesis that effect-measure modifiers can influence population level risk of other factors, we consider the inter-relationships of aspirin and other NSAID use, BMI, and HRT in altering the risk of colon cancer. We focus particularly on these factors since their prevalence has changed dramatically over the past decades and they appear to play a major role in modulating colon cancer risk factors.

Methods

Study Population

Data from two case-control study populations are included in these analyses1718: a population-based case-control study of colon cancer of cases and controls selected between 1991 and 1994 and a population-based case-control study of rectal cancer where cases and controls were identified between 1997 and 2000. Both studies used identical methods to identify, recruit, and interview study participants. In both studies, participants were asked to recall exposures two years prior to the date of selection for the study. Methods for both studies have been described in the published literature and are summarized below1920. The colon cancer study data were used to estimate associations, including effect-measure modification or interaction between exposures. Controls from both cases-control studies were used to estimate changes in exposures over time. Participants in both studies were from the Kaiser Permanente Medical Care Program of Northern California (KPMCP) and the state of Utah. Response rates for controls from the two studies were comparable.

The colon cancer study included cases with a first primary of colon cancer (International Classification of Diseases of Oncology, 2nd edition codes 18.0, 18.2 to 18.9) diagnosed between October 1, 1991 and September 30, 1994. The rectal cancer study included cases with a first primary tumor in the rectosigmoid junction or rectum diagnosed between May 1997 and May 2001. For both studies, controls were frequency-matched on sex and 5-year age group to the cases. Case eligibility was determined by the Surveillance Epidemiology and End Results (SEER) Cancer Registries in Northern California and in Utah and identified using a rapid-reporting system. At the KPMCP, controls were randomly selected from membership lists. In Utah controls 65 years and older were randomly selected from Health Care Finance Administration (HCFA) lists and controls younger than 65 were randomly selected from driver's license lists. To be eligible for the study, participants had to be between 30 and 79 years of age at time of diagnosis, English speaking, mentally competent to complete the interview, and could not have had previous colorectal cancer. Cases with known (as indicated on the pathology report) familial adenomatous polyposis, ulcerative colitis, or Crohn's disease were excluded.

A total of 1346 colon cancer cases and 1544 matched controls were interviewed between January 1991 and December 1994 and 952 cases and 1205 controls were interviewed between October 1997 and January 2002 as part of the rectal cancer study and are included in these analyses. Of participants contacted between 1991 and 1994, 80.8% of cases and 71.8% of controls participated; between 1997 and 2001, 68.8% of controls participated.

Data Collection

Data were collected by trained and certified interviewers using laptop computers. For both the colon and rectal cancer studies, data were collected in an identical manner, using the same study questionnaire and the same quality control procedures 192021. The interview took approximately two hours. Quality control methods were used in both studies to assure that interview procedures were implemented consistently and have been described in detail 1920.

For both studies, dietary intake was ascertained using an adaptation of the CARDIA diet history 202223. Participants were asked to recall foods eaten, the frequency which they were eaten, serving size, and if fats were added in the preparation. Nutrient information was obtained by converting food intake data into nutrient data using the Minnesota Nutrition Coding Center (NCC) nutrient database. Height and weight were measured at the time of interview and weight also was reported for the two and five years prior to interview. The body mass index (BMI) of kg/m²was calculated for men and women. Other information of relevance to this analysis included reproductive history, use of aspirin and NSAIDs on a regular basis, and physical activity 24. The interview included questions for women on the use of exogenous hormones such as estrogen, progestin, or other female hormones for both contraceptive and non-contraceptive purposes. Dates of first and last use and duration of use of HRT were ascertained. Menopause history including reason for menopause, natural or surgery, and age at menopause, was obtained.

Statistical Methods

To evaluate differences in population characteristics for the two time periods (i.e. 1991–1994 and 1997–2001), we compare exposure prevalence among controls after adjusting for possible age and sex differences. Variables assessed included variables thought to be important effect-measure modifiers of other variables. These included, BMI, physical activity, NSAID use (includes use of aspirin), and HRT use among post-menopausal women.

In order to describe population changes in exposures to these factors over a longer time period, we used results from national surveys conducted in the U.S., and the published literature, to obtain estimates of exposure prevalence for obesity and NSAID use in the 1970s and 1980s. BMI for men and women was available from the NHANES surveys 25. We could not find suitable national survey data to estimate changes in the prevalence of aspirin use. Although NHANES surveys have asked about aspirin use, NHANES surveys over this time period have used questions with different definitions of aspirin use, and different groupings of aspirin, acetominophen, and other NSAIDs, so that the data are not comparable over time. We therefore estimated prevalence of NSAID use in the 1970s and 1980s as reported from other population-based studies that used questions similar to those in our own studies 2627.

For colon cancer relative risk associations, reported weight for the period two years prior to diagnosis was used to calculate BMI. Using aspirin or non-steroidal anti-inflammatory drugs on a regular basis (defined as at least three times per week for one month) within two years prior to diagnosis was considered positive use, while those not using aspirin or non-steroidal anti-inflammatory drugs during this time period were considered non-users. HRT use was defined as having used HRT within the past two years. Data were categorized into groups based on respondent response to using aspirin/NSAIDs, HRT, and BMI cut-points of 25 and 30. For physical activity levels and western diet, categories were based on distribution of these continuous variables as reported for the colon cancer study.

To provide an example of the potential for risk estimates to change due to change in the prevalence of effect-measure modifiers, we estimated odds ratios for the relative risk of colon cancer associated with obesity (BMI ≥ 30) compared to individuals whose BMI was ≤ 23 stratified by HRT users and non-users and by NSAID/aspirin users and nonusers. Unconditional logistic regression models were used to estimate odds ratios associated with effect-modification adjusting for confounding factors that included energy intake, physical activity level, dietary calcium and fiber, HRT (NSAID/aspirin model), and NSAIDs/aspirin (HRT use only model). Statistical testing for interaction was determined by the relative excess risk from interaction (RERI) 2829. The RERI was used to estimate the relative strength of the interactive effect and can be interpreted as the excess risk due to interaction relative to the risk without exposure. Using this model risk estimates are estimated for exposures with and without the presence of other exposures and it is estimated if the difference between the combined exposures are greater or less than would be expected when the risk from the two independent exposures are added. Statistical analyses were done using SAS statistical software (Cary, NC).

To illustrate the impact of changes in prevalence of use of aspirin and HRT over time on risk estimates for other risk factors, we estimated ORs for BMI (highest quartile vs. lowest) in hypothetical populations with different prevalence of NSAID/aspirin and HRT use. To develop these estimates we used the ORs for obesity or a BMI of 30 or more relative to those with a BMI of 23 or less that were observed in our study population. These risk estimates were calculated within strata of NSAID/aspirin users (risk estimate used was 2.5 from the stratified model) and non-users (risk estimate of 1.2 used from stratified model) and HRT users (risk estimate of 2.1 used for strata of users) and non-users (risk estimate of 0.8 used for strata of non-users). We assumed that risk estimates for BMI within these circumstances would be constant, although variation in exposure to NSAID/aspirin and HRT use in the population would influence the actual BMI detected risk. We calculated the population risk as the variable exposures of NSAIDS/aspirin and HRT using population distributions of 5% users, 95% non-users, then a population with 10% aspirin users, 90% non-users, etc. Additional File 1 shows the calculation for these estimates. These predicted population ORs are shown over a range of percentages of use of NSAIDs and HRT that are comparable to changes reported in these exposures over time.

Results

Figure 1 illustrates changes in obesity and aspirin use over the last two decades. Prevalence of obesity comes from NHANES data and data from our two control populations. In a 30 year time period, the prevalence of obesity has doubled. The prevalence of aspirin use dramatically increased between population-based studies conducted in the late 1970s and early 1980s, in which use was reported at 7% 26 and 15% 27 respectively, and one conducted in 1997–2000, in which use was over 40%. Our data demonstrate the dramatic change in reported use of HRT, increasing from 35.6% of post-menopausal women in 1991–1994 to 50.7% in 1997–2000 (Table 1). We further evaluated the joint distribution of risk factors over time, using data from the control subjects (Tables 2 and 3).

Figure 1

Changes in obesity and aspirin use between 1970 and 2000 in U. S. populations

Changes in obesity and aspirin use between 1970 and 2000 in U. S. populations. Obesity data prior to 1991 from NHANES [25], for 1991–94 from colon cancer case-control study [17], and for 1999–2000 from rectal cancer study [42]. Aspirin data from 1970s and 1980 from references [26, 27] and for 1991–94 from colon cancer case-control study [17], and for 1999–2000 from rectal cancer study [42].

Table 1

BMI (kg/m²), HRT use, and current NSAID/aspirin use reported during 1991–1994 and 1997–2001 by two population-based control groups¹

1991–1994

1997–2000

p value²

N (%)³

N (%)

NSAIDs/aspirin:

938 (60.8)

692 (57.6)

0.02

Yes

605 (39.2)

510 (42.4)

HRT use:

364 (64.4)

188 (49.3)

<0.01

Yes

201 (35.6)

193 (50.7)

BMI¹

≤ 23

287 (18.8)

173 (14.5)

<0.01

24–25

272 (17.8)

174 (14.6)

26–29

507 (33.2)

427 (35.9)

≥ 30

461 (30.2)

417 (35.0)

¹Data from controls participating in population-based studies of colon and rectal cancer in Utah and Northern California [18]

²p values based on χ²statistic from contingency tables with Mantel-Haenszel adjustment.

³Numbers vary slightly because of missing data for the various variables; HRT use is limited to data from post-menopausal women.

Table 2

BMI and current NSAID/aspirin use stratified by HRT users and non-users¹in 1991–1994 and 1997–2001 [17, 18]

1991–1994

1997–2001

HRT Yes (36% of population) N (%)

HRT No (648% of population) N (%)

HRT Yes (51% of population) N (%)

HRT No (49% of population) N (%)

P value²

BMI (kg/m²)

≤ 23

49 (25.0)

80 (22.4)

41 (21.9)

32 (17.2)

<0.01

24–25

36 (18.4)

59 (16.5)

28 (15.0)

30 (16.1)

26–29

56 (28.6)

98 (27.5)

64 (34.2)

55 (29.6)

≥ 30

55 (28.1)

120 (33.6)

54 (28.9)

69 (37.1)

NSAIDs/aspirin

Yes

91 (45.3)

137 (37.7)

93 (48.2)

85 (45.7)

<0.01

110 (54.7)

226 (62.3)

100 (51.8)

101 (54.3)

¹Restricted to post-menopausal women

²p value compares differences in distribution over time using a χ²statistic.

Table 3

BMI among current NSAID/aspirin users and non-users in 1991–1994 and in 1997–2001 [17, 18]

1991–1994

1997–2001

NSAIDs/aspirin Yes (39% of population) N (%)

NSAIDs/aspirin No (61% of population) N (%)

NSAIDs/aspirin Yes (42% of population) N (%)

NSAIDs/aspirin No (58% of population) N (%)

P value¹

BMI (kg/m²)

≤ 23

113 (19.0)

174 (18.7)

63 (12.5)

109 (16.0)

0.65

24–25

104 (17.5)

168 (18.1)

73 (14.5)

99 (14.5)

26–29

200 (33.6)

306 (32.9)

181 (35.8)

246 (36.0)

≥ 30

178 (29.9)

283 (30.4)

188 (37.2)

229 (33.5)

¹p value compares differences in distribution over time using a χ²statistic.

We detected a significant interaction between NSAID use and HRT on colon cancer relative risk (RERI p value 0.06) (Table 4). The interaction is such that the only observed increased in colon cancer relative risk from (OR 2.0 95% CI 1.3–2.9) is among women who use neither NSAIDs nor HRT. Not using HRT did not result in an increased relative risk of colon cancer among women who used NSAIDs on a regular basis.

Table 4

Relative risk of colon cancer associated with NSAID/aspirin and HRT use, and their interaction, among post-menopausal women only, 1991–1994

All post-menopausal women

NSAID/aspirin Use Stratified

Cases/Controls (N)

Adjusted

Case/Control (N)

Yes

OR (95% CI)

Yes

OR¹(95% CI)

HRT use

Yes

138/198

1.0

52/89

86/109

1.0

1.3 (0.8–2.0)

344/361

1.4 (1.0–1.8)

90/136

254/225

1.1 (0.7–1.7)

2.0 (1.3–2.9)

RERI²(95% CI);p value

(-0.03, 1.21);0.06

¹Adjusted for age, energy intake, physical activity level, BMI, dietary calcium and fiber and NSAIDs/aspirin.

²Relative excess risk for interaction, a measure of the amount of effect-measure modification on the additive scale. A value of zero would represent no difference in ORs.

Odds ratios for associations between BMI and colon cancer relative risk differed significantly between subjects who did and did not use NSAIDS and HRT (Table 5). Among those who used NSAIDs on a regular basis, obesity significantly increased relative risk of colon cancer while among non-users there was little increase in risk associated with being obese. Similarly for HRT users, there was a significant increase relative risk associated with obesity while among post-menopausal women who did not use HRT, there was no increased relative risk associated with obesity.

Table 5

Colon cancer relative risk associated with BMI (kg/m²), by NSAID/aspirin and HRT use,1991–1994

NSAIDS/Aspirin

Everyone

Adjusted

Yes

Case/Control N

OR (95% CI)¹

Case/Control N

OR (95% CI)

BMI

≤ 23

202/307

1.0

41/115

161/192

1.0

2.4 (1.6,3.6)

24–25

233/296

1.2 (0.9–1.5)

71/120

162/176

1.6 (1.02,2.6)

2.5 (1.6,3.8)

26–29

459/528

1.3 (1.0–1.6)

136/189

323/339

1.9 (1.3,3.0)

2.6 (1.7,3.8)

≥ 30

443/404

1.6 (1.2–2.0)

179/177

264/227

2.7 (1.8,4.1)

3.0 (2.0,4.5)

RERI²(95% CI); p value

-1.09 (-2.30, 0.11); 0.07

HRT³

Everyone

Adjusted

Yes

Case/Control N

OR (95% CI)⁴

Case/Control N

OR (95% CI)

BMI

≤ 23

109/145

1.0

27/64

82/81

1.0

2.6 (1.5,4.6)

24–25

97/109

1.2 (0.8–1.7)

39/44

58/66

2.1 (1.1,4.0)

2.3 (1.3,4.1)

26–29

139/151

1.2 (0.9–1.7)

35/49

105/102

1.7 (0.9,3.3)

2.6 (1.5,4.5)

≥ 30

137/154

1.1 (0.8–1.5)

37/41

100/113

2.1 (1.1,4.0)

2.1 (1.2,3.5)

RERI⁴(95% CI); p value

-1.68 (-3.53,0.17); 0.07

¹Adjusted for age, sex, energy intake, physical activity level, dietary calcium and fiber, NSAIDs/aspirin

²Relative excess risk from interaction, a measure of the amount of effect-measure modification on the additive scale. A value of zero would represent no difference in ORs.

³Analysis restricted to post-menopausal women

⁴Adjusted for age, sex, energy intake, physical activity level, dietary calcium and fiber, NSAIDs/aspirin, and HRT; restricted to post-menopausal women

Figure 2 illustrates the relative risk associated with obesity by various levels of exposure to NSAIDs and HRT. We estimate that for a population in which 5% use aspirin or NSAIDs, the overall OR for obesity (BMI of 30 or more) that would be detected would be less than 1.3, while the OR that would be detected at the population level would increase to over 1.8 in a population with 50% NSAIDs users. Similarly, the OR for obesity among women in a population with few HRT users that would be detected would be close to null, but would increase to 1.45 among women if the prevalence of HRT use was 50%.

Figure 2

Predicted ORs for colon cancer associated with obesity (BMI ≥ 30 kg/m²versus lowest quartile, BMI ≤ 23 kg/m²) where obesity is held constant but with varying prevalence of NSAID or HRT use

Predicted ORs for colon cancer associated with obesity (BMI ≥ 30 kg/m²versus lowest quartile, BMI ≤ 23 kg/m²) where obesity is held constant but with varying prevalence of NSAID or HRT use. ORs calculated from multiple logistic regression models for stratum-specific ORs for obesity in users and non-users of aspirin, and users and non-users of HRT among post-menopausal women. Adjusted for age, sex (NSAIDS/aspirin use only), energy intake, physical activity level, dietary calcium and fiber, NSAIDs/aspirin (HRT model only).

Conclusion

We have demonstrated using data from our studies and the literature that BMI, HRT use, and aspirin use have changed over time. In addition to documenting these changes, we have shown, through assessment of interaction, the potential impact of those changes on ORs for colon cancer relative risk estimates in the population. The presence of exposures such as aspirin and HRT use appear to modify relative risk associated with other the important colon cancer relative risk factors such as obesity. Therefore, changes in prevalence of aspirin and HRT use over time will affect colon cancer relative risk estimates associated with these other factors. The result of multiple risk factors changing overtime further illustrate the complexity of multi-factorial diseases, such as colon cancer, both in terms of risk factor identification and in understanding the disease process itself.

As documented here and by others, important diet and lifestyle factors that are associated with colorectal cancer have changed over time 1630313233. Obesity has been documented as increasing worldwide 34. In the study by Cronin and colleagues16, changes in five colon cancer risk factors, vegetable intake, red meat intake, physical activity, weight, and alcohol, were evaluated over a 20-year time period. Although they noted that changes had occurred in these factors over time, they summarized that the overall effect on colon cancer incidence associated with these changes was minimal since changes in risk and protective factors offset each other. They did not evaluate the impact of effect-measure modification when factors such as NSAIDs that change over time modify the observed relative risk associated with other exposures. Looking at a similar time period as we did in this study, Li and colleagues evaluated changes in diet, physical activity, and weight between 1990 and 1996 35. They observed that increases in fruit and vegetable intake were restricted to those who were active and had a normal weight, suggesting that differential shifts in the population are occurring.

Few studies have reported on changes in aspirin or HRT use over time and different definitions of regular use make comparisons across studies difficult. One of the first studies to detect an association between aspirin use and colon cancer was conducted by Rosenberg and colleagues who reported 7% of the population using aspirin in the late 1970s 26. In 1976 the National Health Survey observed that roughly 20% of the population used any aspirin 36. A survey conducted in Minnesota in 1981–82 indicated that among those between 55 and 74 years 14.6% of women and 13.6% of men took aspirin regularly (at least 7 of the prior 14 days), and that this increased to 19.8 % and 19.4% by 1985–86 37. That study noted that an increase in the proportion of study participants who reported use of aspirin for cardiovascular disease protection accounted for much of the change. A study of nurses reported that 11.6% of those younger than 52 years reported using NSAIDs over 3 times a week in 1997 and 18.8% and 27.9% of nurses over 52 years reported NSAID and aspirin use over 3 times per week 2938. In the late 1990s, NSAID use 3 or more times per week for at least one month was reported by 42% of our study population. Although difficulties in monitoring trends in aspirin use exist, taken together these reports are consistent with an upward trend in use over this time period.

While our study data indicate that HRT use increased during the 1990s, recent research findings regarding potential adverse effects from HRT use 39 are resulting in a dramatic reduction in the proportion of post-menopausal women using HRT40. Estrogen and HRT also have been identified as important modifiers of other risk factors, such as obesity, and may explain many of the differences in colon cancer obesity associations observed for men and women 1011.

There are several strengths to the study, including a large sample to look at interactions to test our hypothesis, and the ability to look at data collected in the same manner from two population-based control groups, to identify exposure changes overtime. Response rates were slightly lower for the rectal cancer study than for the colon cancer study, although good response rates overall were obtained in both studies. However, because the study in the early 1990s was one of colon cancer and the one in the late 1990s was of rectal, we are unable to directly calculate relative risk estimates for the two time periods at the population level.

These findings also have important study design and analysis implications because they suggest that disease associations are complicated when factors that modify risk change. Aspirin, NSAID, and HRT use are examples of these types of effect-measure modifiers. Our results suggest that the risks associated with obesity and HRT is influenced by NSAIDs. In this scenario, case-control studies provide a snapshot of what risk factors are in the population at a given point in time. Studies with sufficiently large sample size can define subsets of the population that have different characteristics. Cohort studies accumulate cases over time; in order to take into account changing population characteristics, changes in risk factors for should be taken into account by incorporating repeated exposure measurements. The importance of identification of effect-measure modification should be stressed since results can have implications for understanding the data and the disease process. Often evaluation of effect-measure modification is secondary and stratified estimates are not presented unless significant effect-measure modification is detected. However, studies are often underpowered to detect statistically significant effect-measure modification 41. When effect-measure modification between two risk factors has been reported, stratified odds ratios should be reported so that results can be compared between studies. Failure to evaluate effect-measure modification when knowledge of biological mechanisms, or prior data, suggest that it exists may lead to inability to recognize and understand associations that are important.

In summary, populations are dynamic and risk factors exist in a shifting context. Of particular importance are factors that modify the risk of other factors. NSAID use, HRT, and obesity are three factors that appear to be changing over time as well as having an impact as effect-measure modifiers of other diet and lifestyle factors. The impact of population changes on the epidemiologic literature can be kept to a minimum if investigators assess exposure-disease associations within strata of other exposures, and present results in a manner that allows comparisons across studies.

Additional file 1

Numbers used to estimate population risk shown in Figure 2. The data provide a guide to calculations made for Figure 2

Click here for file

Acknowledgements

This study was funded by NCI grants CA48998 to Dr. Slattery. This research also was supported by the Utah Cancer Registry, which is funded by Contract #N01-PC-67000 from the National Cancer Institute, with additional support from the State of Utah Department of Health, the Northern California Cancer Registry, and the Sacramento Tumor Registry. The contents of this manuscript are solely the responsibility of the authors and do not necessarily represent the official view of the National Cancer Institute. We would like to acknowledge the contributions of Karen Curtin, Roger Edwards, Leslie Palmer, Donna Schaffer, and Judy Morse for data collection and management.

Risk factors for colon neoplasia--epidemiology and biology Potter JD Eur J Cancer 1995 31A 7-8 1033 1038 10.1016/0959-8049(95)00125-3 7576987 Modifiable risk factors for colon cancer Giovannucci E Gastroenterol Clin North Am 2002 31 4 925 943 10.1016/S0889-8553(02)00057-2 12489270 Colon cancer and dietary fat, phosphorus, and calcium in Hawaiian-Japanese men Heilbrun LK Hankin JH Nomura AMY Stemmermann GN Am J Clin Nutr 1986 43 306 309 3946295 Dietary fats and colon cancer: assessment of risk associated with specific fatty acids Slattery ML Potter JD Duncan DM Berry TD Int J Cancer 1997 73 5 670 677 10.1002/(SICI)1097-0215(19971127)73:5<670::AID-IJC10>3.0.CO;2-A 9398044 Dietary intake and colon cancer: sex- and anatomic site-specific associations West DW Slattery ML Robison LM Schuman KL Ford MH Mahoney AW Lyon JL Sorensen AW Am J Epidemiol 1989 130 5 883 894 2554725 Relation of meat, fat, and fiber intake to the risk of colon cancer in a prospective study among women Willett WC Meir MD Stampfer J Colditz GA Rosner BA Speizer FE New England Journal of Medicine 1990 323 24 1664 1672 2172820 Aspirin and other nonsteroidal anti-inflammatory agents in the prevention of colorectal cancer Sandler RS Important Adv Oncol 1996 123 137 8791132 Aspirin, non-steroidal anti-inflammatory drugs and protection from colorectal cancer: a review of the epidemiological evidence Weiss HA Forman D Scand J Gastroenterol Suppl 1996 220 137 141 8898452 Hormone replacement therapy and the risk of colorectal cancer: a meta-analysis Nanda K Bastian LA Hasselblad V Simel DL Obstet Gynecol 1999 93 5 Pt 2 880 888 10.1016/S0029-7844(98)00424-4 10912438 Body mass index and colon cancer: an evaluation of the modifying effects of estrogen (United States) Slattery ML Ballard-Barbash R Edwards S Caan BJ Potter JD Cancer Causes Control 2003 14 1 75 84 10.1023/A:1022545017867 12708728 Obesity and colorectal cancer risk in women Terry PD Miller AB Rohan TE Gut 2002 51 2 191 194 1773328 12117878 10.1136/gut.51.2.191 Classification tree analysis: a statistical tool to investigate risk factor interactions with an example for colon cancer (United States) Camp NJ Slattery ML Cancer Causes Control 2002 13 9 813 823 10.1023/A:1020611416907 12462546 Trans-fatty acids and colon cancer Slattery ML Benson J Ma KN Schaffer D Potter JD Nutr Cancer 2001 39 2 170 175 10.1207/S15327914nc392_2 11759276 Physical activity and colon cancer: confounding or interaction? Slattery ML Potter JD Med Sci Sports Exerc 2002 34 6 913 919 10.1097/00005768-200206000-00002 12048315 Weight, weight gain, activity, and major illnesses: the Nurses' Health Study Colditz GA Coakley E Int J Sports Med 1997 18 Suppl 3 S162 70 10.1055/s-2007-972709 9272843 Evaluating the impact of population changes in diet, physical activity, and weight status on population risk for colon cancer (United States) Cronin KA Krebs-Smith SM Feuer EJ Troiano RP Ballard-Barbash R Cancer Causes Control 2001 12 4 305 316 10.1023/A:1011244700531 11456226 Energy balance and colon cancer--beyond physical activity Slattery ML Potter J Caan B Edwards S Coates A Ma KN Berry TD Cancer Res 1997 57 1 75 80 8988044 Physical activity and colorectal cancer Slattery ML Edwards S Curtin K Ma K Edwards R Holubkov R Schaffer D Am J Epidemiol 2003 158 3 214 224 10.1093/aje/kwg134 12882943 Objective system for interviewer performance evaluation for use in epidemiologic studies Edwards S Slattery ML Mori M Berry TD Caan BJ Palmer P Potter JD Am J Epidemiol 1994 140 11 1020 1028 7985650 A computerized diet history questionnaire for epidemiologic studies Slattery ML Caan BJ Duncan D Berry TD Coates A Kerber R J Am Diet Assoc 1994 94 7 761 766 10.1016/0002-8223(94)91944-5 8021418 Response rates among control subjects in case-control studies Slattery ML Edwards SL Caan BJ Kerber RA Potter JD Ann Epidemiol 1995 5 3 245 249 10.1016/1047-2797(94)00113-8 7606315 The CARDIA dietary history: development, implementation, and evaluation McDonald A Van Horn L Slattery M Hilner J Bragg C Caan B Jacobs D Jr. Liu K Hubert H Gernhofer N Betz E Havlik D J Am Diet Assoc 1991 91 9 1104 1112 1918764 A study of the reliability and comparative validity of the cardia dietary history Liu K Slattery M Jacobs D Jr. Cutter G McDonald A Van Horn L Hilner JE Caan B Bragg C Dyer A Ethn Dis 1994 4 1 15 27 7742729 Physical activity and colon cancer: a public health perspective Slattery ML Edwards SL Ma KN Friedman GD Potter JD Ann Epidemiol 1997 7 2 137 145 10.1016/S1047-2797(96)00129-9 9099401 National Health and Nutrition Examination Survey Data CDC 2004 A hypothesis: nonsteroidal anti-inflammatory drugs reduce the incidence of large-bowel cancer Rosenberg L Palmer JR Zauber AG Warshauer ME Stolley PD Shapiro S Journal of the National Cancer Institute 1991 83 355 358 10.1093/jnci/83.5.355 1759994 Aspirin and the risk of colorectal cancer in women Giovannucci E Egan KM Hunter DJ Stampfer MJ Colditz GA Willett WC Speizer FE N Engl J Med 1995 333 10 609 614 10.1056/NEJM199509073331001 7637720 Confidence interval estimation of interaction Hosmer DW Lemeshow S Epidemiology 1992 3 5 452 456 10.1097/00001648-199209000-00012 1391139 Interaction as departure from additivity in case-control studies: a cautionary note Skrondal A Am J Epidemiol 2003 158 3 251 258 10.1093/aje/kwg113 12882947 Secular trends in variables associated with the metabolic syndrome of North American children and adolescents: a review and synthesis Eisenmann JC Am J Hum Biol 2003 15 6 786 794 10.1002/ajhb.10214 14595870 Rapid increase in colorectal cancer rates in urban Shanghai, 1972-97, in relation to dietary changes You WC Jin F Devesa S Gridley G Schatzkin A Yang G Rosenberg P Xiang YB Hu YR Li Q J Cancer Epidemiol Prev 2002 7 3 143 146 12665213 Trends in colorectal cancer rates in urban shanghai, 1972-1996, in relation to dietary changes You W Jin F Gridley G Schatzkin A Yang G Rosenberg P Xiang Y Hu Y Li Q Ann Epidemiol 2000 10 7 469 10.1016/S1047-2797(00)00098-3 11018398 A case-control study on risk of changing food consumption for colorectal cancer Zhang B Li X Nakama H Zhang X Wei N Zhang L Cancer Invest 2002 20 4 458 463 10.1081/CNV-120002145 12094540 Weight Control and Physical activity IARC Handbooks of Cancer Prevention Lyon, France , IARC Press 2002 6 Trends in fruit and vegetable consumption among adults in 16 US states: Behavioral Risk Factor Surveillance System, 1990-1996 Li R Serdula M Bland S Mokdad A Bowman B Nelson D Am J Public Health 2000 90 5 777 781 1446230 10800429 Use habits among adults of cigarettes, coffee, aspirin, and sleeping pills, United States, 1976 Hyattsville, Md , U.S. Department of Health, Education, and Welfare National Health Survey 1979 Use of aspirin for prevention of cardiovascular disease--1981-82 to 1985-86: the Minnesota Heart Survey Folsom AR Iso H Sprafka JM Edlavitch SA Luepker RV Am Heart J 1988 116 3 827 830 10.1016/0002-8703(88)90344-4 3261930 Frequency of use of acetaminophen, nonsteroidal anti-inflammatory drugs, and aspirin in US women Curhan GC Bullock AJ Hankinson SE Willett WC Speizer FE Stampfer MJ Pharmacoepidemiol Drug Saf 2002 11 8 687 693 10.1002/pds.732 12512245 Postmenopausal hormone replacement therapy: scientific review Nelson HD Humphrey LL Nygren P Teutsch SM Allan JD Jama 2002 288 7 872 881 10.1001/jama.288.7.872 12186605 Changes in the use of postmenopausal hormone therapy after the publication of clinical trial results Haas JS Kaplan CP Gerstenberger EP Kerlikowske K Ann Intern Med 2004 140 3 184 188 14757616 Interaction, subgroup analysis and sample size Cuzick J IARC Sci Publ 1999 109 121 10493253 Energy balance and rectal cancer: an evaluation of energy intake, energy expenditure, and body mass index Slattery ML Caan BJ Benson J Murtaugh M Nutr Cancer 2003 46 2 166 171 10.1207/S15327914NC4602_09 14690792