Abstract
Background
Obesity is associated with increased colon cancer mortality and lower rates of mammography and Pap testing.
Methods
We conducted a systematic review to determine if obesity is associated with lower rates of colon cancer screening. We searched the PubMed, CINAHL, and Cochrane Library databases. Two investigators reviewed citations, abstracts, and articles independently. Two investigators abstracted study information sequentially and evaluated quality independently using standardized forms. We included all studies in our qualitative syntheses. We used random effects meta-analyses to combine those studies providing screening results by the following BMI categories: Normal, 18.5–24.9 kg/m2 (reference); overweight, 25–29.9 kg/m2; class I obesity, 30–34.9 kg/m2; class II obesity, 35–39.9 kg/m2; and class III obesity, ≥ 40 kg/ m2.
Results
Of 5,543 citations, we included 23 articles. Almost all studies were cross-sectional and ascertained BMI and screening through self report. BMI was not associated with colon cancer screening overall. The subgroup of obese white women reported lower rates of colon cancer screening compared to those with a normal BMI with combined odds ratios (95% CI) of 0.87 (0.82 to 0.93), 0.80 (0.65 to 0.99), and 0.73 (0.54 to 0.94) for class I, II, and III obesity, respectively. Results were similar among white men with class II obesity.
Conclusions
Overall, BMI was not associated with colon cancer screening. Obese white men and women may be less likely to undergo colon cancer screening compared to those with a normal BMI.
Impact
Further investigation of this disparity may reduce the risk of obesity-related colon cancer death.
Keywords: Colon cancer screening, obesity, meta-analysis, screening, prevention, disparity
Introduction
Colorectal cancer is the third leading cause of cancer death in the United States (1). While screening for colon cancer with fecal occult blood testing (FOBT), flexible sigmoidoscopy, or colonoscopy decreases colon cancer risk (2) and mortality and is recommended for all adults between the ages of 50 and 75 years (3), rates of colon cancer screening are suboptimal. In 2005, only 20% of women and 24% of men over the age of 50 years reported endoscopic screening, and only 12% of women and men reported FOBT in the United States (4). Identification of barriers to screening can inform public health approaches to increase colon cancer screening and thus reduce colon cancer deaths.
In previous systematic reviews and meta-analyses, we have shown that obese women, especially obese white women, are less likely to undergo breast and cervical cancer screening compared to their normal weight counterparts (5, 6). Colon cancer mortality increases with increasing body mass index (BMI) (7), but whether obese persons are less likely to receive screening for colon cancer is unclear.
Therefore, we conducted a systematic review to: 1) Evaluate the association between obesity and colon cancer screening and 2) Determine if this association varies by race and sex. Based on our previous work, we hypothesized that class II and III obesity (BMI ≥35 kg/m2) would be associated with lower rates of colon cancer screening and that the obesity-related disparity would be most pronounced among white women.
Methods
Data Sources and Searches
We searched the PubMed, CINHAL, and Cochrane Library electronic databases from inception through November 1, 2006 using subject headings and key word terms for obesity and breast, cervical, and colon cancer screening (search terms available in Supplementary Tables 1–3). Results for mammography and Papanicolaou testing were published previously (5, 6). We completed an update of this search through February 9, 2011 with search terms that focused on obesity and colon cancer screening, and we report findings for articles on colon cancer screening identified from database inception through February 9, 2011 in this article. The manual search included review of the references of included articles and a review of the tables of contents of relevant journals. Two co-investigators reviewed titles, abstracts, and articles independently and resolved conflicts at the level of abstract and article review by consensus.
Study Selection
We included published, English language articles using original data to evaluate the relationship between obesity and colon cancer screening (fecal occult blood testing, sigmoidoscopy, and/or colonoscopy). We excluded studies not conducted in the United States because important determinants of cancer screening such as healthcare coverage (8) vary by country. We excluded studies conducted in special populations (e.g., subjects with a family history of colon cancer) for which screening recommendations and practices may differ (9, 10) and thus, obscure the association between obesity and cancer screening. We did not require a specific measure of adiposity or study design.
At the article review level, we identified several studies which analyzed the same data source, and we included only one article based on a given study population. Of the national studies, five analyzed data from the 2000 National Health Interview Survey (NHIS) (11–15), and two analyzed data from the 1999 Behavior and Risk Factor Surveillance System (BRFSS) (16, 17). The study population from the 2002 Maryland Cancer Survey was analyzed in three articles (18–20). For each of these cohorts, we selected the study meeting the most of the following criteria listed in descending order of importance: 1) The study provided adjusted results, 2) The study provided results using the following five BMI categories suggested by the National Heart, Lung, and Blood Institutes (21): Normal, 18.5–24.9 kg/m2; overweight, 25–29.9 kg/m2; class I obesity, 30–34.9 kg/m2; class II obesity, 35–39.9 kg/m2; and class III obesity, ≥ 40 kg/m2, and 3) The study included the largest study population. We provide information about the studies excluded based on duplicate data in the appendix (Supplementary Table 4).
Data Extraction and Quality Assessment
All members of the study team have advanced training in clinical investigation including two senior obesity researchers (FLB and JMC). Using standardized forms, two investigators abstracted study design characteristics and results sequentially. Quality evaluation forms were developed using the STROBE checklist for guidance (22). Two investigators reviewed the quality of each study independently, and conflicts were resolved through consensus.
We contacted authors for additional quantitative results with a focus on obtaining race-sex stratified (for black and white race) results based on a black-white difference in the association of higher BMI with lower rates of Papanicolaou testing and mammography found previously (5, 6).
Data Synthesis and Analysis
We constructed tables to qualitatively describe the study design and report the results of each included study. Analyses were conducted using Stata version 11 (College Station, TX). We performed meta-analyses using a random effects model to combine effect estimates across studies (23). To perform a meta-analysis, we required that at least three studies address the research question overall or for a race-sex subgroup, and we only included studies in meta-analyses which reported body mass index in categories of normal (reference), overweight, class I obesity, class II obesity, and class III obesity. We defined moderate statistical heterogeneity as an I-squared statistic > 50% indicating that more than 50% of the variability across studies is due to heterogeneity (24). We investigated substantial heterogeneity using meta-regression for the following factors: Study type (nationally representative, national, regional, state, or local), sex of study participants (male, female, or both included), and adjustment of statistical models (unadjusted or adjusted) (25). We conducted sensitivity analyses evaluating the effect of removing any one study from each meta-analysis. To evaluate for bias resulting from the absence of small studies (often termed, “publication bias”), we examined funnel plots visually for asymmetry and also used the method of Egger et al to formally test for funnel plot asymmetry (26). We also used meta-regression to evaluate sex and race-sex category as sources of heterogeneity in the subset of studies which provided sex-specific and race-sex-specific results, respectively.
Results
Description of Included Studies
Of 5,543 citations reviewed, twenty-three articles met our inclusion criteria (Supplementary Figure 1) (13, 15, 17, 18, 27–45). We describe the design of the included studies in Table 1. The included studies represented ten national studies and three cancer-based cohorts. All other studies were regional, state-based, or local. Three studies included white subjects predominantly (29, 30, 41), two studies included only black subjects (33, 39), a single study included only Latino subjects (43), and another study included only Native American men (38). All other studies (N=16) were multi-ethnic. All studies were cross-sectional with the exception of one retrospective cohort study (44). Screening and BMI data were self reported in all studies except for four, in which at least some data came from medical records (28, 31, 36, 44) and another in which height was measured (40). All studies reported BMI as the measure of adiposity. Two studies used fecal occult blood testing as the sole component of their screening definition (33, 38); the remainder included sigmoidoscopy or colonoscopy or both.
Table 1.
Design of Included Studies
| Author, Year | N | Study setting | % Womena | Race/ethnicitya | Data Collection |
|---|---|---|---|---|---|
| National | |||||
| Banerjea, 2008 (27) | 4,256 | MEPS 2003 | 100% | NR | Self-report via in-person interview |
| Chang, 2010 (28) | 37,864 | VA and MCBS | NR | NR | Self-report via in-person interview (MCBS); medical record review (VA) |
| Chao, 2004 (30) | 129,246 | Cancer Prevention Study II Nutrition Cohort | 55% | White: 98%b Black: 1% |
Self-report via mailed survey |
| Heo, 2004 (32) | 84,284 | BRFSS 2001 | 62% | White: 82% | Self-report via telephone interview |
| Ioannou, 2003 (17) | 58,915 | BRFSS 1999 | 61% | White: 85% Black: 6% Hispanic: 5.9% Asian: 1% |
Self-report via telephone interview |
| Leone, 2010 (34) | 7,469 | NHIS 2005 | 100% | White: 86% Black: 14% |
Self-report via in-person interview |
| Liang, 2006 (13) | 11,779 | NHIS 2003c | NR | NR | Self-report via in-person interview |
| McQueen, 2006 (37) | 2,686 | HINTS | 63% | White: 74% | Self-report via telephone interview |
| Wee, 2005 (15) | 11,427 | NHIS 2000 | NR | NR | Self-report via in-person interview |
| Yancy, 2010 (44) | 1,699,219d | VA 2000–2005 | NR | NR | Medical record review |
| Regional | |||||
| Ferrante, 2006 (31) | 1,297 | Mid-Atlantic primary care practices | 49% | White: 80%e Black: 9% Asian/Pacific Islander: 3% Other: 8% Hispanic: 4% |
Medical record review |
| Matthews, 2007 (36) | 104 | Midwestern medical clinics | 68% | White: 62% Black: 47.8% |
Self-report via in-person interview and verification by medical record review |
| Muus, 2009 (38) | 2,447 | National Resource Center on Native American Aging | 0% | Native American: 100% | Self-report via in-person interview |
| Slattery, 2004 (40) | 2,479 | Controls from HMO (Northern California) and community-based (Utah) case-control study | 45% | White: 82–87% | In-person self-report of weight, measured height |
| Tessaro, 2006 (41) | 802 | 16 Appalachian churches | 65% | White: 98% | Self-administered survey completed in church |
| State | |||||
| James, 2008 (33) | 378 | Churches in rural North Carolina | 72% | Black: 100% | Self-report via telephone interview |
| Lian, 2008 (35) | 2,987 | BRFSS 2006 (Missouri) | 62% | White: 87% Black: 7% Hispanic: 5% |
Self-report via telephone interview |
| Menis, 2006 (18) | 3,017 | Maryland Cancer Survey 2002 | 62% | White: 79% Black: 17% Other: 4% |
Self-report via telephone interview |
| Satia, 2007 (39) | 405 | North Carolina Department of Motor Vehicles rosters | 56% | Black: 100% | Self-report via mailed survey, telephone, or Internet |
| Yang, 2009 (45) | 2,478 | BRFSS 2004–06 (Nevada) | NR | White: 85%b Hispanic: 15% |
Self-report via telephone interview |
| Local | |||||
| Chao, 1987 (29) | 11,888 | Retirement community-based cohort study in Southern California | 64% | Predominantly White | Self-report via mailed survey |
| Vlahov, 2005 (42) | 5,362 | New York Cancer Project (New York City) | 67% | White: 55.9% Black: 14.7% Asian: 10.3% Hispanic: 15.2% |
Self-report via in-person interview |
| Winkleby, 2003 (43) | 98 | Monterrey County (California) in residential community and agricultural camp | 55%b | Latino: 100% | Self-report via telephone interview (men and women) and in-person interview (men in agricultural camp) |
Abbreviations: NR, not reported; VA, Veterans’ Administration; MCBS, Medicare Beneficiary Survey; BRFSS, Behavior Risk Factor and Surveillance Survey; NHIS, National Health Interview Survey; HINTS, Health Information Trends Survey
Proportion based on obesity-colon cancer screening analysis.
Proportions calculated using results kindly provided by authors.
Results presented for NHIS 2003 since other studies provided results from NHIS 2000
Number of participants in entire study population and not limited to obesity-colon cancer screening analysis
Racial composition of included practices, not specifically for the study population
Quality of Included Studies
Yang et al evaluated ethnicity as a predictor of colon cancer screening and adjusted this analysis for BMI, but the BMI-colon cancer screening methods and results were not reported specifically (45); thus, the following description of quality of included studies focuses on the remaining 22 studies. One study did not specify objectives or hypotheses regarding the BMI-colon cancer screening analyses (29). One study included subjects aged 41–49 years in analyses of colon cancer screening (39). Two studies did not specify the BMI categories clearly (35, 38), and two studies did not specify the colon cancer screening definition clearly (27, 33). Fifteen studies did not report on missing data for the BMI-screening analyses (13, 15, 27, 29, 31, 32, 34–40, 42, 44), and five reported <10% missing data (17, 28, 33, 41, 43). Two studies reported ≥10% missing data and did not address this issue in the analyses (18, 30). Twenty studies described all covariates completely, and two described most covariates (29, 38). The description of statistical methods was adequate (n=20) or fair (n=2) (38, 40) for all studies. Eleven studies accounted for confounding variables adequately (15, 17, 27, 28, 30, 32, 34, 35, 40, 42, 44); five fairly (18, 31, 33, 39, 43); and six inadequately (13, 29, 36–38, 41). Details on how the studies addressed confounding are provided in Supplementary Table 5.
Of four studies based in part on medical record review, none reported fully on procedures for data abstraction (28, 31, 36, 44). Additional quality information is provided in Supplementary Table 6.
Meta-analyses
Obesity was not significantly associated with colon cancer screening in the unstratified meta-analyses (Figure 1). There was evidence of substantial statistical heterogeneity (range of I2 between 82 and 98%) for combined odds ratios across BMI categories (Supplementary Figure 2). Meta-regression did not reveal study type, sex, or adjustment of statistical models as a source of heterogeneity. Sensitivity analyses including only nationally representative studies (NHIS and BRFSS) provided a lower range of heterogeneity (I2 between 0 and 64%) and confirmed the absence of a significant inverse association between BMI and colon cancer screening. The results of studies not included in the meta-analyses showed little evidence of a significant relationship between obesity and colon cancer screening and generally corroborated our meta-analysis results (Table 2).
Figure 1. Combined odds ratios for colon cancer screening by BMI category.
BMI Categories: normal, 18.5–24.9 kg/m2 (reference); overweight, 25–29.9 kg/m2; class I obesity, 30–34.9 kg/m2; class II obesity, 35–39.9 kg/m2; and class III obesity, ≥ 40 kg/m2
Number of studies included in meta-analysis: Overweight, 15; Class I obesity, 12; Class II/III obesity, 11
Table 2.
Relationship between Obesity and Colon Cancer Screening
| Author, Year | Reference BMI | Screening definitiona | Results (Adjusted OR, 95%CI) |
|---|---|---|---|
| National | |||
| Chang, 2010 (28) | Normal BMI | Any screening | Medicare Current Beneficiary Survey |
| Overweight: 0.98 (0.88 to 1.10) | |||
| Obese: 0.98 (0.86 to 1.12) | |||
| Veterans Administration | |||
| Overweight: 1.12 (1.04 to 1.20) | |||
| Obese: 1.02 (0.95 to 1.09) | |||
| Chao, 2004 (30)b | Normal BMI | Endoscopy in past 5 years | Overweight: 0.93 (0.90 to 0.976) |
| Class I: 0.88 (0.84 to 0.93) | |||
| Class II: 0.81 (0.73 to 0.90) | |||
| Class III: 0.71 (0.60 to 0.85) | |||
| Heo, 2004 (32) | Normal BMI | Any screening: | Overweight: 1.15 (1.02 to 1.31) |
| FOBT in past year | Class I: 1.21 (1.09 to 1.35) | ||
| Flexible sigmoidoscopy in past 5 yearsc | Class II: 1.17 (1.04 to 1.44) | ||
| Class III: 1.27 (1.05 to 1.58) | |||
| Ioannou, 2003 (17) | <25 kg/m2 | FOBT in past year | Class I+II: 1.0 |
| Endoscopy in past 5 years | Class III: 0.8d | ||
| Liang, 2006 (13)a,e | Normal BMI | Any screening | Overweight: 1.03 (0.91 to 1.18) |
| Class I: 1.06 (0.90 to 1.25) | |||
| Class II: 0.85 (0.66 to 1.10) | |||
| Class III: 0.90 (0.66 to 1.21) | |||
| McQueen, 2006 (37)a | < 25 kg/m2 | Endoscopy in last 10 yearse,f | <25: 49% |
| FOBT in past year | Overweight: 47% | ||
| Obese: 50% | |||
| Wee, 2005 (15) | Normal BMI | Any screening | Overweight: 1.1 (1.0 to 1.2) |
| Class I: 1.0 (0.9 to 1.2) | |||
| Class II: 1.1 (0.9 to 1.4) | |||
| Class III: 1.1 (0.8 to 1.5) | |||
| Regional | |||
| Ferrante, 2006 (31) | < 30 kg/m2 | Any screening: | Obese: 0.75 (0.61 to 0.91) |
| FOBT in past year | |||
| Flexible sigmoidoscopy in past 5 years | |||
| Colonoscopy in past 10 years | |||
| Barium enema in past 5 years | |||
| Matthews, 2007 (36) | < 25 kg/m2 | Any screening | Overweight/obese: 1.98 (0.65 to 6.02)e |
| Slattery, 2004 (40) | < 25 kg/m2 | Flexible sigmoidoscopy in past 10 yrs | Overweight: 1.5 (1.2 to 2) |
| Obese: 1.2 (0.9 to 1.6) | |||
| Tessaro, 2006 (41)a,e | Normal BMI | Any screening: | Overweight: 0.92 (0.87 to 0.97) |
| FOBT in past year | Class I: 1.11 (1.02 to 1.21) | ||
| Flexible sigmoidoscopy in past 5 years | Class II: 1.08 (0.85 to 1.39) | ||
| Colonoscopy in past 10 years | Class III: 0.21 (0.10 to 0.87) | ||
| Barium enema in past 5 years | |||
| State | |||
| James, 2008 (33)a,e | Normal BMI | FOBT in past year | Overweight: 0.75 (0.41 to 1.40) |
| Class I: 0.52 (0.26 to 1.03) | |||
| Class II: 0.70 (0.26 to 1.84) | |||
| Class III: 0.80 (0.26 to 2.45) | |||
| Lian, 2008 (35) | < 25 kg/m2 | Any screening | Overweight: 1.40 (1.04 to 1.88) |
| Obese: 1.57 (1.13 to 2.18) | |||
| Satia, 2007 (39) | Normal BMI | Endoscopy in past 10 years | Obese: 1.25 |
| Menis, 2006 (18)a | < 25 kg/m2 | Any screening: | Overweight: 1.03 (0.81 to 1.32) |
| Class I: 0.91 (0.68 to 1.23) | |||
| Class II: 0.66 (0.42 to 1.04) | |||
| Class III: 0.66 (0.37 to 1.18) | |||
| Yang, 2009 (45)a | Normal BMI | Ever had endoscopy at/after age 50 | Overweight: 1.14 (0.87 to 1.50) |
| Class I: 1.46 (1.09 to 1.96) | |||
| Class II: 1.29 (0.74 to 2.27) | |||
| Class III: 0.86 (0.49 to 1.52) | |||
| Local | |||
| Vlahov, 2005 (42)a,e | Normal BMI | Endoscopy in past 5 years | Overweight: 0.92 (0.8 to 1.07) |
| Class I: 1 (0.83 to 1.2) | |||
| Class II: 0.93 (0.7 to 1.23) | |||
| Class III: 0.64 (0.46 to 0.9) |
Abbreviations: BMI, body mass index; OR, odds ratio; FOBT, fecal occult blood testing.
BMI categorization: Normal BMI (18.5 – 24.9 kg/m2); overweight (25– 29.9 kg/m2); class I obesity (30 – 34.9 kg/m2); class II obesity (35 – 39.9 kg/m2); class III obesity (≥ 40 kg/m2)
Any screening: FOBT in past year, flexible sigmoidoscopy in past 5 years, or colonoscopy in past 10 years
Authors kindly provided additional results upon request.
Authors report results for flexible sigmoidoscopy in past 5 years and report that BMI not associated with FOBT in past year.
P > 0.05 for all analyses.
Unadjusted results
Endoscopy results reported
We present results from studies reporting BMI-colon cancer screening analyses by sex only in Supplementary Table 7. Among studies providing quantitative results restricted on or stratified by sex, meta-regression revealed sex as a source of statistical heterogeneity for the class I obesity category (P = 0.039) while sex was not significantly predictive of screening for the other BMI categories (P value range, 0.159 to 0.334).
Among studies providing quantitative results restricted on or stratified by race and sex, meta-regression did not reveal race-sex dyad as a source of statistical heterogeneity (P = 0.93, 0.33, 0.37, and 0.53 for the overweight and class I, II, and III obesity categories, respectively). Within the subgroup of white women, obese white women reported significantly lower rates of colon cancer screening compared to those with a normal BMI, and this inverse association strengthened with increasing BMI category: Combined OR (95% CI) were 0.98 (0.89 to 1.08), 0.87 (0.82 to 0.93), 0.80 (0.65 to 0.99), and 0.73 (0.58 to 0.94) for the overweight and class I, II, and III obesity categories compared to normal BMI, respectively, Figure 2). We found moderate heterogeneity for the meta-analyses comparing white women with class II (I2 = 61%) and III (I2 = 53%) obesity to those with a normal BMI (Supplementary Figure 3). With the exception of two studies (15, 45), the effect measures from all studies were consistent with the combined estimate of the odds ratio for the class II obesity category; omission of either of these two studies did not change the inference for this meta-analysis. For the class III obesity category, only the odds ratio estimate from Yang et al (45) was not consistent with the combined odds ratio, and omission of this study did not change the results. Meta-regression suggested study type as a possible source of heterogeneity (P < 0.001) for this BMI category.
Figure 2. Combined odds ratios for colon cancer screening by BMI category by race and sex.
Panel A . White men, Panel B . White women, Panel C . Black men, Panel D . Black women
BMI Categories: normal, 18.5–24.9 kg/m2 (reference); overweight, 25–29.9 kg/m2; class I obesity, 30–34.9 kg/m2; class II obesity, 35–39.9 kg/m2; and class III obesity, ≥ 40 kg/m2
Number of studies included in meta-analysis: Panel A, 6; Panel B, 7; Panel C, 5 (class III obesity, 4); Panel D, 6
White men with class II obesity reported significantly lower odds of colon cancer screening compared to those with a normal BMI (combined OR (95% CI), 0.83 ( 0.72 to 0.96)), and the combined OR white men with class III obesity did not reach statistical significance. We did not find a consistent inverse association between obesity and colon cancer screening among black men and women (Figure 2). Meta-analyses among class I obese white men demonstrated moderate heterogeneity (I2 = 62%), and meta-regression did not reveal study type or statistical adjustment as a source of heterogeneity. Forest plots for these meta-analyses are provided in Supplementary Figures 3–6, and information about studies not included in the meta-analyses is provided in Supplementary Table 8.
No single study significantly influenced the meta-analysis results.
Bias Due Lack of Small Studies (Publication Bias)
Unstratified analyses comparing class II obese to normal weight individuals suggested a lack of small studies in which class II obese persons were more likely to undergo screening; inclusion of such studies would not likely change the inference of the meta-analysis result of no association. We also observed a paucity of small studies showing an association between class III obesity and increased screening among black men; this evaluation of bias was limited by the small number of studies (n=4). Publication bias was not apparent for all other analyses.
Discussion
BMI was not associated with lower rates of colon cancer screening overall. In the subgroup of white women, class I, II, and III obesity were associated with 13, 20, and 27% lower rates of colon cancer screening, respectively, relative to a normal BMI, and results suggested this inverse association may exist among white men with class II obesity as well. We did not find this association consistently in the subgroups of black men and women. Of the observational studies yielding these results, approximately 1/3 did not handle confounding adequately through statistical adjustment, stratification, or restriction. Our findings are consistent with previous systematic reviews of breast and cervical cancer screening suggesting an inverse association between BMI and mammography and Pap testing among white, but not black, women (5, 6). A prior systematic review of the association between obesity and colon cancer screening among women was inconclusive regarding the association (46); our study includes 15 additional studies and evaluates the impact of male sex and race.
While several factors may affect receipt of screening by obese patients (e.g., the presence of co-morbid conditions (47) precluding discussions of screening, provider discrimination (obesity bias) (48), and difficulties with endoscopy regarding bowel preparation and airway difficulties (49)), how race and sex might influence this, in particular why colon cancer screening rates may be lower for obese white persons, especially women, is unknown. Obese patients may avoid screening, when recommended, because of embarrassment related to disrobing in the setting of pervasive obesity stigmatization (48, 50, 51). This weight stigma seems to foster a negative body image in women more than men and, particularly in white women (48, 52). In a study in which white and black women rated magazine images of “thin, average weight, and large” black and white women, white women rated large white women lower in interpersonal and career domains while black women did not stigmatize large black women in this way (52).
A strength of this systematic review is the inclusion of results from a large number of studies evaluating predictors of colon cancer screening in both community-based and nationally representative study populations. To our knowledge, this is the first review to provide systematic and comprehensive evidence for the relationship between obesity and colon cancer screening in both men and women. Based on our prior work (5, 6), we designed this study to focus on this relationship in race-sex subgroups; in addition to conducting a thorough literature search, we contacted authors for the additional results that we report for race-sex subgroups.
A limitation of our evidence synthesis is heterogeneity of the definitions of adiposity and colon cancer screening across studies. While we included all studies in the qualitative synthesis regardless of adiposity measure used, we only included studies with BMI in specific categories in our meta-analyses. The impact of this requirement on our results is uncertain, but we felt that homogeneity in BMI categorization was important for the quantitative synthesis. Studies varied in their screening definition by modality (FOBT or endoscopy) and the screening interval (e.g., ever or only within the past year). Generally, definitions including both modalities and more permissive intervals should be more sensitive, but we do not know how the relationship between BMI and colon cancer screening might be different with different screening definitions. Therefore, we are unable to predict the effect of this heterogeneity, if any, on our results. Since screening definitions were more similar across studies for the race-sex meta-analyses, we anticipate that any possible effects of this heterogeneity were minimal for this aspect of our study.
While a key objective of our study was to evaluate the association between BMI and colon cancer screening across race-sex subgroups, we identified a relatively small number of studies for the race-sex analyses. Thus, the use of meta-regression to evaluate race-sex subgroup as a source of heterogeneity is likely underpowered. Point estimates from the meta-analyses conducted in race-sex subgroups, however, do lend support to our a priori hypothesis.
Limitations related to the design of the included studies also deserve mention. The included studies were observational and thus susceptible to both residual and unmeasured confounding which may bias our meta-analysis results. To address this, we included studies with adjusted results when possible, but adjustment variables and restrictions did vary across studies. Also, both BMI and receipt of colon cancer screening were self reported in most studies. Self reported BMI is highly correlated with measured BMI but is generally underestimated (53). Women tend to underestimate BMI more than men, but this gender difference narrows after the age of 40 (53), the age of our study populations. Furthermore, the National Health and Nutrition Epidemiologic Survey 2001–2006 did not find a significant difference in reporting of BMI by blacks compared to whites (53). In total, the underestimation of BMI should not affect the overall qualitative inference that increasing BMI is associated with lower rates of colon cancer screening in white women. A prior validation study found a sensitivity and specificity of self-reported screening endoscopy to be 79% and 90%, respectively, which did not differ by gender (54). Evidence comparing performance of self report between black and white participants was lacking (54). Thus, in our systematic review, colon cancer screening was likely underreported; whether this self-report of screening was differential by BMI is unknown but unlikely.
Identification of obesity as a possible barrier to colon cancer screening in white men and women underscores an important public health issue given the prevalence of obesity (55), suboptimal screening rates (4), and the substantial benefit of colon cancer screening for decreasing colon cancer risk and death (2, 56). One third of white adults in the United States are obese (55), and less than 40% of men and women over the age of 50 report colon cancer screening (4). It is therefore plausible that obesity-related under-screening contributes to the observed rates of obesity-related colon cancer incidence (35% and 13% increase in risk of colon cancer per 5 kg/m2 increase in BMI in white men and women, respectively) (57) and obesity-related colon cancer death (relative risks in obese compared to normal weight ranging from 1.47 to 1.84 in white men and 1.33 to 1.46 in white women) (7). Our study suggests that obese white persons, an at-risk segment of the United States population for colon cancer morbidity and mortality, are not receiving an effective preventive service. Future research should identify cultural mediators of this relationship to address this disparity.
In summary, our systematic review demonstrates that obesity is not associated with lower rates of colon cancer screening in general but that there may be a graded relationship between increasing BMI and lower rates of colon cancer screening in obese white women and to a lesser extent in obese white men. While interventions to increase colon cancer screening rates across the population are necessary, the further investigation of the possible obesity-related disparity in white men and women may decrease colon cancer risk and death in the United States.
Supplementary Material
Acknowledgments
Additional Contributions: We thank the researchers who provided additional results from their studies upon our request: Carmela Groves, RN, MS, Center for Cancer Surveillance and Control, Maryland Department of Health and Mental Hygiene; Su-Ying Liang, PhD, Center for Translational Research and Policy Research on Personalized Medicine, University of California San Francisco; Lucia Leone, PhD, Department of Nutrition, University of North Carolina Gillings School of Global Public Health; Vijay Nandi, MPH, Center for Urban Epidemiologic Studies, The New York Academy of Medicine; Eileen Steinberger, MD, MS, Department of Epidemiology and Public Health, University of Maryland; Irene Tessaro, DrPH, MS, MSN, School of Nursing, West Virginia University; Christina Wee, MD, MPH, Division of General Internal Medicine and Primary Care, Beth Israel Deaconess Medical Center, Harvard Medical School; Marilyn Winkleby, PhD, MPH, Stanford University School of Medicine; Wei Yang, MD, PhD, University of Nevada, Reno.
Funding/support: Dr. Bolen’s time was supported by Grant number 1KL2 RR024990, National Center for Research Resources (NCRR) at the National Institutes of Health (NIH) and NIH Roadmap for Medical Research. Dr. Maruthur’s time was supported by a training grant (5T32 HL007180, National Heart, Lung, and Blood Institute of the NIH) and the Johns Hopkins Clinical Research Scholars Program (Grant number 1KL2 RR025006, NCRR at the NIH and NIH Roadmap for Medical Research). The contents of this manuscript are solely the responsibility of the authors and do not necessarily represent the official view of NCRR (58) or NIH.
Dr. Brancati’s time was supported by Grant number K24 DK62222 from the National Institute for Diabetes and Digestive and Kidney Disease (NIDDK) of the NIH and Grant number P60 DK079637 (Diabetes Research and Training Center) from the NIDDK of the NIH. Dr. Gudzune’s time was supported by a training grant from the Health Resources and Service Administration (Grant number T32 HP10025-16-00).
Footnotes
Disclosures: The authors have no conflicts of interest to disclose.
Prior presentation: Preliminary results presented at the 2009 Society of General Internal Medicine National Meeting (Miami).
References
- 1.Jemal A, Siegel R, Ward E, Hao Y, Xu J, Murray T, et al. Cancer statistics, 2008. CA Cancer J Clin. 2008;58(2):71–96. doi: 10.3322/CA.2007.0010. Epub 2008/02/22. [DOI] [PubMed] [Google Scholar]
- 2.Brenner H, Chang-Claude J, Seiler CM, Rickert A, Hoffmeister M. Protection from colorectal cancer after colonoscopy. Annals of Internal Medicine. 2011;154(1):22–30. doi: 10.7326/0003-4819-154-1-201101040-00004. [DOI] [PubMed] [Google Scholar]
- 3.United States Preventive Services Task Force. Screening for colorectal cancer: U.S. Preventive Services Task Force recommendation statement. Annals of Internal Medicine. 2008;149(9):627–637. doi: 10.7326/0003-4819-149-9-200811040-00243. [DOI] [PubMed] [Google Scholar]
- 4.Swan J, Breen N, Graubard BI, McNeel TS, Blackman D, Tangka FK, et al. Data and trends in cancer screening in the United States. Cancer. 2010;116(20):4872–4881. doi: 10.1002/cncr.25215. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Maruthur NM, Bolen SD, Brancati FL, Clark JM. The association of obesity and cervical cancer screening: A systematic review and meta-analysis. Obesity (Silver Spring, Md) 2009;17(2):375–381. doi: 10.1038/oby.2008.480. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Maruthur NM, Bolen S, Brancati FL, Clark JM. Obesity and mammography: A systematic review and meta-analysis. Journal of General Internal Medicine. 2009;24(5):665–677. doi: 10.1007/s11606-009-0939-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Calle EE, Rodriguez C, Walker-Thurmond K, Thun MJ. Overweight, obesity, and mortality from cancer in a prospectively studied cohort of U.S. Adults. N Engl J Med. 2003;348(17):1625–1638. doi: 10.1056/NEJMoa021423. Epub 2003/04/25. [DOI] [PubMed] [Google Scholar]
- 8.Ward E, Halpern M, Schrag N, Cokkinides V, DeSantis C, Bandi P, et al. Association of insurance with cancer care utilization and outcomes. CA Cancer J Clin. 2008;58(1):9–31. doi: 10.3322/CA.2007.0011. Epub 2007/12/22. [DOI] [PubMed] [Google Scholar]
- 9.Felsen CB, Piasecki A, Ferrante JM, Ohman-Strickland PA, Crabtree BF. Colorectal cancer screening among primary care patients: Does risk affect screening behavior? J Community Health. 2011 doi: 10.1007/s10900-010-9348-0. Epub 2011/01/05. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Ochoa EM, Gomez-Acebo I, Rodriguez-Cundin P, Navarro-Cordoba M, Llorca J, Dierssen-Sotos T. Relationship between family history of breast cancer and health-related behavior. Behavioral Medicine. 2010;36(4):123–129. doi: 10.1080/08964289.2010.516783. [DOI] [PubMed] [Google Scholar]
- 11.Ata A, Elzey JD, Insaf TZ, Grau AM, Stain SC, Ahmed NU. Colorectal cancer prevention: Adherence patterns and correlates of tests done for screening purposes within United States populations. Cancer Detect Prev. 2006;30(2):134–143. doi: 10.1016/j.cdp.2006.02.003. Epub 2006/04/28. [DOI] [PubMed] [Google Scholar]
- 12.Coups EJ, Manne SL, Meropol NJ, Weinberg DS. Multiple behavioral risk factors for colorectal cancer and colorectal cancer screening status. Cancer Epidemiol Biomarkers Prev. 2007;16(3):510–516. doi: 10.1158/1055-9965.EPI-06-0143. Epub 2007/03/21. [DOI] [PubMed] [Google Scholar]
- 13.Liang SY, Phillips KA, Nagamine M, Ladabaum U, Haas JS. Rates and predictors of colorectal cancer screening. Prev Chronic Dis. 2006;3(4):A117. Epub 2006/09/19. [PMC free article] [PubMed] [Google Scholar]
- 14.Seeff LC, Nadel MR, Klabunde CN, Thompson T, Shapiro JA, Vernon SW, et al. Patterns and predictors of colorectal cancer test use in the adult U.S. Population. Cancer. 2004;100(10):2093–2103. doi: 10.1002/cncr.20276. Epub 2004/05/13. [DOI] [PubMed] [Google Scholar]
- 15.Wee CC, McCarthy EP, Phillips RS. Factors associated with colon cancer screening: The role of patient factors and physician counseling. Prev Med. 2005;41(1):23–29. doi: 10.1016/j.ypmed.2004.11.004. Epub 2005/05/27. [DOI] [PubMed] [Google Scholar]
- 16.Rosen AB, Schneider EC. Colorectal cancer screening disparities related to obesity and gender. J Gen Intern Med. 2004;19(4):332–338. doi: 10.1111/j.1525-1497.2004.30339.x. Epub 2004/04/06. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Ioannou GN, Chapko MK, Dominitz JA. Predictors of colorectal cancer screening participation in the United States. Am J Gastroenterol. 2003;98(9):2082–2091. doi: 10.1111/j.1572-0241.2003.07574.x. Epub 2003/09/23. [DOI] [PubMed] [Google Scholar]
- 18.Menis M, Kozlovsky B, Langenberg P, Zhan M, Dwyer DM, Israel E, et al. Body mass index and up-to-date colorectal cancer screening among Marylanders aged 50 years and older. Prev Chronic Dis. 2006;3(3):A88. Epub 2006/06/17. [PMC free article] [PubMed] [Google Scholar]
- 19.Griffith KA. Biological, psychological and behavioral, and social variables influencing colorectal cancer screening in African Americans. Nurs Res. 2009;58(5):312–320. doi: 10.1097/NNR.0b013e3181ac143d. Epub 2009/09/16. [DOI] [PubMed] [Google Scholar]
- 20.Griffith KA, McGuire DB, Royak-Schaler R, Plowden KO, Steinberger EK. Influence of family history and preventive health behaviors on colorectal cancer screening in African Americans. Cancer. 2008;113(2):276–285. doi: 10.1002/cncr.23550. Epub 2008/06/11. [DOI] [PubMed] [Google Scholar]
- 21.North American Association for the Study of Obesity and the National Heart, Lung, and Blood Institute. Practical guide to the identification, evaluation, and treatmetn of overweight and obesity in adults. NIH Publication. 2000 (Number 00-4084) [Google Scholar]
- 22.von Elm E, Altman DG, Egger M, Pocock SJ, Gotzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: Guidelines for reporting observational studies. Ann Intern Med. 2007;147(8):573–577. doi: 10.7326/0003-4819-147-8-200710160-00010. Epub 2007/10/17. [DOI] [PubMed] [Google Scholar]
- 23.DerSimonian R, Laird N. Meta-analysis in clinical trials. Controlled clinical trials. 1986;7(3):177–188. doi: 10.1016/0197-2456(86)90046-2. [DOI] [PubMed] [Google Scholar]
- 24.Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Statistics in medicine. 2002;21(11):1539–1558. doi: 10.1002/sim.1186. [DOI] [PubMed] [Google Scholar]
- 25.Newton HJ, editor. [cited 2011 March 28];Meta-analysis regression. Stata Technical Bulletin [Internet] 1998 42(March). Available from: http://www.stata.com/products/stb/journals/stb42.pdf.
- 26.Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ (Clinical research ed) 1997;315(7109):629–634. doi: 10.1136/bmj.315.7109.629. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Banerjea R, Findley PA, Sambamoorthi U. Disparities in preventive care by body mass index categories among women. Women Health. 2008;47(4):1–17. doi: 10.1080/03630240802099261. Epub 2008/10/11. [DOI] [PubMed] [Google Scholar]
- 28.Chang VW, Asch DA, Werner RM. Quality of care among obese patients. JAMA. 2010;303(13):1274–1281. doi: 10.1001/jama.2010.339. Epub 2010/04/08. [DOI] [PubMed] [Google Scholar]
- 29.Chao A, Paganini-Hill A, Ross RK, Henderson BE. Use of preventive care by the elderly. Prev Med. 1987;16(5):710–722. doi: 10.1016/0091-7435(87)90053-3. Epub 1987/09/01. [DOI] [PubMed] [Google Scholar]
- 30.Chao A, Connell CJ, Cokkinides V, Jacobs EJ, Calle EE, Thun MJ. Underuse of screening sigmoidoscopy and colonoscopy in a large cohort of us adults. Am J Public Health. 2004;94(10):1775–1781. doi: 10.2105/ajph.94.10.1775. Epub 2004/09/29. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Ferrante JM, Ohman-Strickland P, Hudson SV, Hahn KA, Scott JG, Crabtree BF. Colorectal cancer screening among obese versus non-obese patients in primary care practices. Cancer Detect Prev. 2006;30(5):459–465. doi: 10.1016/j.cdp.2006.09.003. Epub 2006/10/28. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Heo M, Allison DB, Fontaine KR. Overweight, obesity, and colorectal cancer screening: Disparity between men and women. BMC Public Health. 2004;4:53. doi: 10.1186/1471-2458-4-53. Epub 2004/11/10. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.James AS, Leone L, Katz ML, McNeill LH, Campbell MK. Multiple health behaviors among overweight, class I obese, and class II obese persons. Ethn Dis. 2008;18(2):157–162. Epub 2008/05/30. [PubMed] [Google Scholar]
- 34.Leone LA, Campbell MK, Satia JA, Bowling JM, Pignone MP. Race moderates the relationship between obesity and colorectal cancer screening in women. Cancer Causes Control. 2010;21(3):373–385. doi: 10.1007/s10552-009-9469-x. Epub 2009/11/27. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Lian M, Schootman M, Yun S. Geographic variation and effect of area-level poverty rate on colorectal cancer screening. BMC Public Health. 2008;8:358. doi: 10.1186/1471-2458-8-358. Epub 2008/10/18. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Matthews BA, Nattinger AB, Venkatesan T, Shaker R, Anderson RC. Objective risk, subjective risk, and colorectal cancer screening among a clinic sample. Psychol Health Med. 2007;12(2):135–147. doi: 10.1080/13548500500429312. Epub 2007/03/17. [DOI] [PubMed] [Google Scholar]
- 37.McQueen A, Vernon SW, Meissner HI, Klabunde CN, Rakowski W. Are there gender differences in colorectal cancer test use prevalence and correlates? Cancer Epidemiol Biomarkers Prev. 2006;15(4):782–791. doi: 10.1158/1055-9965.EPI-05-0629. Epub 2006/04/15. [DOI] [PubMed] [Google Scholar]
- 38.Muus KJ, Baker-Demaray T, McDonald LR, Ludtke RL, Allery AJ, Bogart TA, et al. Body mass index and cancer screening in older American Indian and Alaska Native men. J Rural Health. 2009;25(1):104–108. doi: 10.1111/j.1748-0361.2009.00206.x. Epub 2009/01/27. [DOI] [PubMed] [Google Scholar]
- 39.Satia JA, Galanko JA. Demographic, behavioral, psychosocial, and dietary correlates of cancer screening in African Americans. J Health Care Poor Underserved. 2007;18(4 Suppl):146–164. doi: 10.1353/hpu.2007.0114. Epub 2007/12/11. [DOI] [PubMed] [Google Scholar]
- 40.Slattery ML, Kinney AY, Levin TR. Factors associated with colorectal cancer screening in a population-based study: The impact of gender, health care source, and time. Prev Med. 2004;38(3):276–283. doi: 10.1016/j.ypmed.2003.11.009. Epub 2004/02/10. [DOI] [PubMed] [Google Scholar]
- 41.Tessaro I, Mangone C, Parkar I, Pawar V. Knowledge, barriers, and predictors of colorectal cancer screening in an Appalachian church population. Prev Chronic Dis. 2006;3(4):A123. Epub 2006/09/19. [PMC free article] [PubMed] [Google Scholar]
- 42.Vlahov D, Ahern J, Vazquez T, Johnson S, Philips LA, Nash D, et al. Racial/ethnic differences in screening for colon cancer: Report from the New York Cancer Project. Ethn Dis. 2005;15(1):76–83. Epub 2005/02/22. [PubMed] [Google Scholar]
- 43.Winkleby MA, Snider J, Davis B, Jennings MG, Ahn DK. Cancer-related health behaviors and screening practices among Latinos: Findings from a community and agricultural labor camp survey. Ethn Dis. 2003;13(3):376–386. Epub 2003/08/05. [PubMed] [Google Scholar]
- 44.Yancy WS, Jr, McDuffie JR, Stechuchak KM, Olsen MK, Oddone EZ, Kinsinger LS, et al. Obesity and receipt of clinical preventive services in veterans. Obesity (Silver Spring) 2010;18(9):1827–1835. doi: 10.1038/oby.2010.40. Epub 2010/03/06. [DOI] [PubMed] [Google Scholar]
- 45.Yang W, Queadan F, Smith-Gagen J. The hispanic epidemiological paradox in the fastest-growing state in the United States. Hispanic Health Care International. 2009;7(3):130–140. [Google Scholar]
- 46.Cohen SS, Palmieri RT, Nyante SJ, Koralek DO, Kim S, Bradshaw P, et al. Obesity and screening for breast, cervical, and colorectal cancer in women: A review. Cancer. 2008;112(9):1892–1904. doi: 10.1002/cncr.23408. Epub 2008/03/26. [DOI] [PubMed] [Google Scholar]
- 47.Lenz M, Richter T, Muhlhauser I. The morbidity and mortality associated with overweight and obesity in adulthood: A systematic review. Dtsch Arztebl Int. 2009;106(40):641–648. doi: 10.3238/arztebl.2009.0641. Epub 2009/11/06. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Puhl RM, Heuer CA. The stigma of obesity: A review and update. Obesity. 2009;17(5):941–964. doi: 10.1038/oby.2008.636. [DOI] [PubMed] [Google Scholar]
- 49.Schreiner MA, Fennerty MB. Endoscopy in the obese patient. Gastroenterol Clin North Am. 2010;39(1):87–97. doi: 10.1016/j.gtc.2009.12.009. Epub 2010/03/06. [DOI] [PubMed] [Google Scholar]
- 50.Clark MA, Rogers ML, Armstrong GF, Rakowski W, Bowen DJ, Hughes T, et al. Comprehensive cancer screening among unmarried women aged 40–75 years: Results from the Cancer Screening Project for Women. J Womens Health (Larchmt) 2009;18(4):451–459. doi: 10.1089/jwh.2008.1046. Epub 2009/04/14. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Klabunde CN, Vernon SW, Nadel MR, Breen N, Seeff LC, Brown ML. Barriers to colorectal cancer screening: A comparison of reports from primary care physicians and average-risk adults. Med Care. 2005;43(9):939–944. doi: 10.1097/01.mlr.0000173599.67470.ba. Epub 2005/08/24. [DOI] [PubMed] [Google Scholar]
- 52.Hebl MR, Heatherton TF. The stigma of obesity in women: The difference is black and white. Personality and Social Psychology Bulletin. 1998;24(4):417–426. [Google Scholar]
- 53.Stommel M, Schoenborn CA. Accuracy and usefulness of bmi measures based on self-reported weight and height: Findings from the NHANES & NHIS 2001–2006. BMC Public Health. 2009;9:421. doi: 10.1186/1471-2458-9-421. Epub 2009/11/20. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 54.Rauscher GH, Johnson TP, Cho YI, Walk JA. Accuracy of self-reported cancer-screening histories: A meta-analysis. Cancer Epidemiology Biomarkers & Prevention. 2008;17(4):748–757. doi: 10.1158/1055-9965.EPI-07-2629. [DOI] [PubMed] [Google Scholar]
- 55.Flegal KM, Carroll MD, Ogden CL, Curtin LR. Prevalence and trends in obesity among us adults, 1999–2008. JAMA: The Journal of the American Medical Association. 2010;303(3):235–241. doi: 10.1001/jama.2009.2014. [DOI] [PubMed] [Google Scholar]
- 56.Whitlock EP, Lin JS, Liles E, Beil TL, Fu R. Screening for colorectal cancer: A targeted, updated systematic review for the U.S. Preventive services task force. Ann Intern Med. 2008;149(9):638–658. doi: 10.7326/0003-4819-149-9-200811040-00245. Epub 2008/10/08. [DOI] [PubMed] [Google Scholar]
- 57.Renehan AG, Tyson M, Egger M, Heller RF, Zwahlen M. Body-mass index and incidence of cancer: A systematic review and meta-analysis of prospective observational studies. Lancet. 2008;371(9612):569–578. doi: 10.1016/S0140-6736(08)60269-X. Epub 2008/02/19. [DOI] [PubMed] [Google Scholar]
- 58. [Cited 2011 November 23]; Available from: http://www.ncrr.nih.gov.
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