Cancer survivors' preference for follow-up care providers: a cross-sectional study from the population-based PROFILES-registry.

Abstract

RESULTS

Of all survivors, 29% reported no preference, 40% reported a preference for the medical specialist, 20% reported a preference for both the medical specialist and the general practitioner and 11% reported a preference for both the medical specialist and the oncology nurse. Survivors who were older, lower/intermediate educated and women were more likely to have a preference for the medical specialist. Lower educated survivors were less likely to have a preference for both the medical specialist and the general practitioner. Overall, survivors prefer to discuss diet, physical fitness and fatigue with the general practitioner, and hereditary and recurrence with the medical specialist. Only a small minority favored to discuss cancer-related problems with the oncology nurse.

BACKGROUND

The best practice for the organization of follow-up care in oncology is under debate, due to growing numbers of cancer survivors. Understanding survivors' preferences for follow-up care is elementary for designing patient-centred care. Based on data from prostate cancer and melanoma survivors, this study aims to identify: 1) preferences for follow-up care providers, for instance the medical specialist, the oncology nurse or the general practitioner; 2) characteristics associated with these preferences and 3) the preferred care provider to discuss cancer-related problems.

CONCLUSION

Survivors reported different preferences for follow-up care providers based on age, education level, gender and satisfaction with the general practitioner, showing a need for tailored follow-up care in oncology. The results indicate an urgency to educate patients about transitions in follow-up care.

MATERIAL AND METHODS

Survivors diagnosed with prostate cancer (N = 535) and melanoma (N = 232) between 2007 and 2013 as registered in The Netherlands Cancer Registry returned a questionnaire (response rate was 71% and 69%, respectively). A latent class cluster model analysis was used to define preferences and a multinomial logistic regression analysis was used to identify survivor-related characteristics associated with these preferences.