Cluster Analysis Demonstrates the Need to Individualize Care for Cancer Survivors.

Abstract

BACKGROUND

In efforts to inform clinical screening and development of survivorship care services, we sought to characterize patterns of health care needs among cancer survivors by (a) identifying and characterizing subgroups based on self-reported health care needs and (b) assessing sociodemographic, clinical, and psychosocial factors associated with these subgroups.

RESULTS

Among 292 respondents, the highest unmet needs were related to the domains of side effects (53%), self-care (51%), and emotional coping (43%). Our analysis identified four clusters of survivors: (a) low needs (n = 123, 42%), (b) mainly physical needs (n = 46, 16%), (c) mainly psychological needs (n = 57, 20%), and (d) both physical and psychological needs (n = 66, 23%). Compared with cluster 1, those in clusters 2, 3, and 4 were younger (p < .03), those in clusters 3 and 4 had higher levels of psychological distress (p < .05), and those in clusters 2 and 4 reported higher levels of fatigue (p < .05).

IMPLICATIONS FOR PRACTICE

The characterization of patients as having few needs, predominantly physical needs, predominantly psychological needs, or substantial needs that are both physical and psychological provides a productive framework for clinical care of cancer survivors and to guide further research in this field. Further research is needed to define the tailored information and services appropriate for each group of patients and to define optimal screening tools to efficiently identify the needs of individuals in oncology practice.

CONCLUSION

Unmet needs among cancer survivors are prevalent; however, a substantial group of survivors report low or no health care needs. The wide variation in health care needs among cancer survivors suggests a need to screen all patients, followed by tailored interventions in clinical care delivery and research.

METHODS

We conducted a cross-sectional self-administered survey among patients presenting for routine follow-up care for early-stage cancer at our academic medical center. Latent class cluster analysis was used to identify clusters of survivors based on survivorship care needs within seven domains. Multiple logistic regression analyses were used to assess factors associated with these clusters.