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The interrelationships between and contributions of background, cognitive, and environmental factors to colorectal cancer screening adherence

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Abstract

Objectives

We examined the interrelationships between and contributions of background, cognitive, and environmental factors to colorectal cancer (CRC) screening adherence.

Methods

In this study, 2,416 average risk patients aged 50–75 from 24 Veterans Affairs medical facilities responded to a mailed survey with phone follow-up (response rate 81%). Survey data (attitudes, behaviors, demographics) were linked to facility (organizational complexity) and medical records data (diagnoses, screening history). Patients with a fecal occult blood test within 15 months, sigmoidoscopy or barium enema within 5.5 years, or colonoscopy within 11 years of the survey were considered adherent. Logistic regressions estimated the association between adherence and background, cognitive, and environmental factors. Deviance ratios examined interrelationships between factors. Population attributable risks (PAR) were used to identify intervention targets.

Results

The association of background factors with adherence was partially explained by cognitive and environmental factors. The association of environmental factors with adherence was partially explained by cognitive factors. Cognitive and environmental factors contributed equally to adherence. Factors with the highest PARs for non-adherence were age 50–64, less than two comorbidities, and lack of physician recommendation.

Conclusions

Efforts to increase physician screening recommendations for younger, healthy patients at facilities with the lowest screening rates may improve CRC adherence in this setting.

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Acknowledgments

This project was supported by three grants from the United States’ Veterans Affairs Health Services Research & Development service: Investigator Initiated grant #IIR 04-042-2 (Partin); Career Development Award #RCD03-174 (Fisher); and Merit Review Entry Program award #MRP 04-412-1 (Burgess).

Author information

Authors and Affiliations

  1. Center for Chronic Disease Outcomes Research, Minneapolis Veterans Affairs Medical Center, 1 Veterans Drive (2E/152), Minneapolis, MN, 55417, USA

    Melissa R. Partin, Siamak Noorbaloochi, Joseph Grill, Diana J. Burgess, Joan M. Griffin, Adam A. Powell, Krysten Halek & Ann Bangerter

  2. Department of Medicine, University of Minnesota, Minneapolis, MN, USA

    Melissa R. Partin, Siamak Noorbaloochi, Diana J. Burgess, Joan M. Griffin & Adam A. Powell

  3. Department of Family Medicine and Community Health and Division of Epidemiology, University of Minnesota, Rm 225, 925 Delaware Street SE, Minneapolis, MN, 55414, USA

    Michelle van Ryn

  4. Durham Veterans Affairs Medical Center, Duke University Medical Center, 508 Fulton Street, Building 6, Durham, NC, 27705, USA

    Deborah A. Fisher

  5. Division of Health Promotion and Behavioral Sciences, University of Texas-Houston School of Public Health, 7000 Fannin Street, Suite 2560, Houston, TX, 77030, USA

    Sally W. Vernon

Authors
  1. Melissa R. Partin
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  2. Siamak Noorbaloochi
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  3. Joseph Grill
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  4. Diana J. Burgess
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  6. Deborah A. Fisher
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  8. Adam A. Powell
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  9. Krysten Halek
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  10. Ann Bangerter
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  11. Sally W. Vernon
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Corresponding author

Correspondence to Melissa R. Partin.

Appendix

Appendix

The ratio statistic for examining whether background factors are mediated by cognitive and environmental factors was calculated as follows:

  1. 1.

    A model including only the intercept was fit and its deviance, D(intercept), was computed.

  2. 2.

    A model including the intercept and X (all measures of background factors) was fit and its deviance, D(intercept, X), was computed.

  3. 3.

    We then computed the difference between these two deviances, DevDiff(X) = D(intercept) − D(intercept, X), which is a measure of the unadjusted association of X with Y.

  4. 4.

    A model including the intercept and M (all measures of cognitive and environmental factors) was fit and its deviance, D(intercept, M) was computed.

  5. 5.

    A model including the intercept, M, and X was fit, and its deviance, D(intercept, M, X), was computed.

  6. 6.

    We then computed the difference between these two deviances, DevDiff (X|M) = D(intercept, M) − D(intercept, M, X), which is a measure of the conditional (adjusted) association of X with Y after adjusting for M.

  7. 7.

    We then compute the ratio: \( {\frac{{{\text{DevDiff}}({\mathbf{X}}|{\mathbf{M}})}}{{{\text{DevDiff}}({\mathbf{X}})}}} \).

  8. 8.

    To calculate a confidence interval for the ratio statistic, we used bootstrapping methods. Specifically, bootstrapping drawing n = 1,000 repeated samples of size = 2,416 with replacement were used to calculate the ratio for each sample. We then determined the mean and 2.5 and 97.5 percentiles of the resulting sampling distribution to obtain a point estimate and accompanying 95% CI for the ratio.

The ratio statistic for examining whether social environmental factors are mediated by cognitive factors was calculated as follows:

  1. 1.

    A model including only the intercept was fit and its deviance, D(intercept), was computed.

  2. 2.

    A model including the intercept and X (all measures of social environmental factors) was fit and its deviance, D(intercept, X), was computed.

  3. 3.

    We then computed the difference between these two deviances, DevDiff(X) = D(intercept) − D(intercept, X), which is a measure of the unadjusted association of X with Y.

  4. 4.

    A model including the intercept and M (all measures of cognitive factors) was fit and its deviance, D(intercept, M), was computed.

  5. 5.

    A model including the intercept, M, and X was fit, and its deviance, D(intercept, M, X), was computed.

  6. 6.

    We then computed the difference between these two deviances, DevDiff (X|M) = D(intercept, M) − D(intercept, M, X), which is a measure of the conditional (adjusted) association of X with Y after adjusting for M.

  7. 7.

    We then compute the ratio: \( {\frac{{{\text{DevDiff}}({\mathbf{X}}|{\mathbf{M}})}}{{{\text{DevDiff}}({\mathbf{X}})}}} \).

  8. 8.

    The same bootstrapping methods described earlier were used to calculate a confidence interval for this ratio statistic

The ratio statistic for examining whether medical environmental factors are mediated by cognitive factors was calculated as follows:

  1. 1.

    A model including only the intercept was fit and its deviance, D(intercept), was computed.

  2. 2.

    A model including the intercept and X (all measures of medical environmental factors) was fit and its deviance, D(intercept, X), was computed.

  3. 3.

    We then computed the difference between these two deviances, DevDiff(X) = D(intercept) − D(intercept, X), which is a measure of the unadjusted association of X with Y.

  4. 4.

    A model including the intercept and M (all measures of cognitive factors) was fit and its deviance, D(intercept, M) was computed.

  5. 5.

    A model including the intercept, M, and X was fit, and its deviance, D(intercept, M, X), was computed.

  6. 6.

    We then computed the difference between these two deviances, DevDiff (X|M) = D(intercept, M) − D(intercept, M, X), which is a measure of the conditional (adjusted) association of X with Y after adjusting for M.

  7. 7.

    We then compute the ratio: \( {\frac{{{\text{DevDiff}}({\mathbf{X}}|{\mathbf{M}})}}{{{\text{DevDiff}}({\mathbf{X}})}}} \).

  8. 8.

    The same bootstrapping methods described earlier were used to calculate a confidence interval for this ratio statistic

Steps involved in assessing whether environmental or cognitive factors had a greater association with adherence:

  1. 1.

    A model including the intercept and the background factors was fit and its deviance, D(intercept, background), was computed.

  2. 2.

    A model including the intercept, the background factors, and the cognitive factors was fit and its deviance, D(intercept, background, cognitive), was computed.

  3. 3.

    A model including the intercept, the background factors, and the environmental factors was fit and its deviance, D(intercept, background, environmental), was computed.

  4. 4.

    We then computed the following two deviance differences: DevDiff (cognitive|background) = D(intercept, background) − D(intercept, background, cognitive), a measure of the association of the cognitive factors on screening adherence adjusting for the background factors; and DevDiff (environmental|background) = D(intercept, background) − D(intercept, background, environmental), a measure of the association of the environmental factors with screening adherence adjusting for the background factors.

  5. 5.

    \( {\frac{{{\text{DevDiff}}({\text{cognitive}}|{\text{background}})}}{{{\text{DevDiff}}({\text{environmental}}|{\text{background}})}}} \)We then computed the ratio of these measures of association:

  6. 6.

    Bootstrapping methods as described earlier were then used to derive a point estimate and 95% confidence interval for the ratio.

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Partin, M.R., Noorbaloochi, S., Grill, J. et al. The interrelationships between and contributions of background, cognitive, and environmental factors to colorectal cancer screening adherence. Cancer Causes Control 21, 1357–1368 (2010). https://doi.org/10.1007/s10552-010-9563-0

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  • DOI: https://doi.org/10.1007/s10552-010-9563-0

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