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It’s who you know: patient-sharing, quality, and costs of cancer survivorship care

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Abstract

Purpose

Cancer survivors frequently receive care from a large number of physicians, creating challenges for coordination. We sought to explore whether cancer survivors whose providers have more patients in common (e.g., shared patients) tend to have higher quality and lower cost care.

Methods

We performed a retrospective cohort study of 8,661 patients diagnosed with loco-regional breast, prostate, or colorectal cancer. We examined survivorship care from days 366 to 1,095 following their cancer diagnosis. Our primary independent variable was “care density,” a novel metric of the extent to which a patient’s providers share patients with one another. Our outcome measures were health care utilization, quality metrics, and costs.

Results

In adjusted analyses, we found that patients with high care density—indicating high levels of patient-sharing among their providers—had significantly lower rates of hospitalization (OR 0.87, 95 % CI 0.75–1.00) and higher odds of an eye examination for diabetes (OR 1.31, 95 % CI 1.03–1.66) compared to patients with low care density. High care density was not associated with emergency department visits, avoidable outcomes, lipid profile following an angina diagnosis, or odds of glycosylated hemoglobin testing for diabetes. Patients with high care density had significantly lower total costs of care over 24 months (beta coefficient −$2,116, 95 % CI −$3,107 to −$1,125) along with lower inpatient and outpatient costs.

Conclusion

Cancer survivors treated by physicians who share more patients with one another tend to have some higher aspects of quality and lower cost care.

Implications of cancer survivors

If validated, care density may be a useful indicator for monitoring care coordination among cancer survivors and potentially targeting interventions that seek to improve care delivery.

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Acknowledgments

We would like to thank Eric Roberts and Klaus Lemke. This was supported by the National Cancer Institute (R01CA149616 and K07 CA151910).

Conflict of interest

None.

Author information

Authors and Affiliations

  1. Johns Hopkins School of Medicine, Baltimore, MD, USA

    Craig Evan Pollack & Claire F. Snyder

  2. Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

    Craig Evan Pollack, Kevin D. Frick, Robert J. Herbert & Claire F. Snyder

  3. Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD, USA

    Craig Evan Pollack, Kevin D. Frick, Amanda L. Blackford, Antonio C. Wolff, Michael A. Carducci & Claire F. Snyder

  4. Consultant, Boston, MA, USA

    Bridget A. Neville

  5. Institute for Clinical Evaluative Sciences, Toronto, Canada

    Craig C. Earle

  6. 2024 E. Monument Street, Rm 2-521, Baltimore, MD, 21287, USA

    Craig Evan Pollack

Authors
  1. Craig Evan Pollack
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  2. Kevin D. Frick
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  3. Robert J. Herbert
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  4. Amanda L. Blackford
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  5. Bridget A. Neville
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  6. Antonio C. Wolff
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  7. Michael A. Carducci
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  8. Craig C. Earle
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  9. Claire F. Snyder
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Corresponding author

Correspondence to Craig Evan Pollack.

Appendix

Appendix

Table 3 Definition of potentially avoidable complications
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Table 4 Multivariable logistic regression analyses showing the association between care density and outcomes, stratified by cancer type and with care density defined using varying specifications of providers and timeframes
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Table 5 Multivariable regression analyses showing the association between care density and outcomes, stratified by cancer type and with care density defined using varying specifications of providers and timeframes
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Table 6 Multivariable regression analyses showing the association between care density and outcomes. Care density is constructed using all primary care, oncology specialists, and other providers during days 366 to 1,095 and log transformed. Patients for whom care density could not be calculated (<2 providers) are excluded
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Table 7 Multivariable regression analyses showing the association between care density and outcomes; models do not and do include the number of doctors visits as a covariate. Care density is constructed using all primary care, oncology specialists, and other providers during days 366 to 1,095
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Table 8 Multivariable regression analyses showing the association between care density and outcomes; models do not and do include the number of doctors visits as a covariate. Care density is constructed using all primary care, oncology specialists, and other providers during days 366 to 1,095
Full size table

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Pollack, C.E., Frick, K.D., Herbert, R.J. et al. It’s who you know: patient-sharing, quality, and costs of cancer survivorship care. J Cancer Surviv 8, 156–166 (2014). https://doi.org/10.1007/s11764-014-0349-3

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  • DOI: https://doi.org/10.1007/s11764-014-0349-3

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