Barg PAC3

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Published on January 10, 2008

Author: Regina1

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Unmet psychosocial needs of Pennsylvanians with cancer The Pennsylvania Cancer Resource Evaluation Study (PA CaRES):  Unmet psychosocial needs of Pennsylvanians with cancer The Pennsylvania Cancer Resource Evaluation Study (PA CaRES) Best practices Quality of life/survivorship PA CaRES Project team:  PA CaRES Project team Frances K. Barg, PhD Peter Cronholm, MD, MSCE Shimrit Keddem, Project coordinator Katie Knott, MS, RN Steven Palmer, PhD Joseph Straton, MD, MSCE Special thanks to:  Special thanks to Annie Cooper Seth Degginger Shira Gidding Toni Mae King Nicola White Goal:  Goal To determine whether existing programs and services are meeting the psychosocial needs of people with cancer in Pennsylvania Objectives:  Objectives Identify met and unmet psychosocial needs of people with cancer and their family members Describe the distribution of met and unmet needs among subpopulations in PA Compare psychosocial needs in 2005 with those identified in 1986 Background:  Background Houts, P., Yasko, J. M., Kahn, S. B., Schelzel, G. W., & Marconi, K. (1986). Unmet psychological, social and economic needs of persons with cancer in Pennsylvania. Cancer, 58(10), 2355-2361. Houts, P. S., Yasko, J. M., Harvey, H. A., Kahn, S. B., Hartz, A. J., Hermann, J. F., Schelzel, G.W., Bartholomew, MJ. (1988). Unmet needs of persons with cancer in Pennsylvania during the period of terminal care. Cancer, 62(3), 627-634. Methods:  Methods Phase one: Open ended semi-structured interviews with patients and/or family members to identify needs and develop surveys Contacted 297 pwc 37% urban, 63% rural Interviewed 32 patients and family members Recently diagnosed (n=17):  Recently diagnosed (n=17) Family members (n=15):  Family members (n=15) Qualitative data analysis:  Qualitative data analysis QSR N6 Constant comparative method Identification of themes Team discussion and verification Intercoder reliability Results:  Results Prominent themes Information Family What I want from my doctor Instrumental and structural issues Survey redesign– newly diagnosed :  Survey redesign– newly diagnosed EXISTING DOMAINS Demographics Healthcare Utilization Medical History Cancer Treatment Needs ADLs \ IADLs Transportation Financial / Insurance / Employment Information Seeking Treatment Support Feeding Physical / Spiritual / Emotional / Social Complementary Medicine Household / Family Coping REDESIGN OF SURVEY Finer exploration of the sufficiency of support Treatment-Specific Needs Barriers to Support Quality of Life Measure Survey redesign—End of life :  Survey redesign—End of life Existing domains Symptom distress Emotional distress Access to physician ADLs/IADLs Employment Transportation Financial needs Home care/hospice Additional domains Expanded symptom assessment Physician-patient communication Decision-making Access to information Caregiver support Phase II: survey implementation :  Phase II: survey implementation Sampling: Recently diagnosed sample n=4000 (from a possible 44,990) people diagnosed within the last three years Stratified by age, ethnicity and urban vs. rural setting and oversampling for African American ethnicity and urban setting Phase II: survey implementation:  Phase II: survey implementation Sampling frame: Recently diagnosed 15.5% African American 53.9% female 31% under the age of 50 50% under the age of 61 at the time of diagnosis Phase II: survey implementation:  Phase II: survey implementation Sampling: End-of-life sample n=2000 (from a possible 24,694) next-of-kin of people diagnosed with cancer in 2000-1 who died between 2001-2003 Stratified by age, ethnicity and urban vs. rural setting and oversampling for African American ethnicity and urban setting Phase II: survey implementation:  Phase II: survey implementation Sampling frame: End-of-life sample 20.5% African American 48.8% female Phase II: survey implementation:  Phase II: survey implementation Physician assent Phase II: survey implementation:  Phase II: survey implementation Survey and City of Hope Quality of Life instrument Data analysis:  Data analysis Determine representativeness of the sample Frequencies and distributions Bivariate associations among demographic characteristics and unmet needs Multivariate logistic regression to determine relative importance of characteristics to need status Comparison with Houts’ data Challenges:  Challenges Use of the registry Physician assent Response rate

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