外科肿瘤学
医学
癌症
肿瘤科
家庭医学
内科学
重症监护医学
作者
W. Mehari,Yinghui Sun,Yunhong Lei,Sisay Shine,Elias Seyoum,Mikiyas Amare Getu
出处
期刊:BMC Cancer
[BioMed Central]
日期:2025-08-02
卷期号:25 (1): 1260-1260
标识
DOI:10.1186/s12885-025-14607-w
摘要
Abstract Introduction Identifying and managing unmet supportive care needs is a vital aspect of providing comprehensive healthcare to cancer patients. Planning and redesigning services can be informed by assessment of unmet supportive care need. Objectives This study aimed to assess unmet supportive care needs and associated factors among cancer patients in Ethiopia. Methods A cross-sectional study design was conducted among 260 cancer patients. The sample size was obtained using a simple random sampling method. The collected data were entered and cleaned using EpiData 4.6 and exported to SPSS version 25 for analysis. Binary and multiple logistic regression analyses were performed to identify factors associated with the outcome variable. In the multivariate analysis, adjusted odds ratio (AOR) with 95% confidence interval (CI) were used as measures of association, and a p -value of less than 0.05 was considered statistically significant for unmet supportive care needs. Results A total of 260 individuals were initially approached to participate, yielding a response rate of 93.1%. One hundred eleven participants (45.9%) reported at least one unmet supportive care needs. The most prevalent unmet supportive care needs were physical needs (61.6%) and health system needs (62.4%). Logistic regression analysis revealed age, sex, educational level, and stage of cancer diagnosis were significantly associated with unmet supportive care needs. Conclusion The study highlights a high prevalence of unmet supportive care needs among cancer patients, primarily related to physical needs. The findings suggest potential opportunities for intervention in addressing physical needs. Age, sex, educational level, and stage of cancer at diagnosis were significantly associated with unmet supportive care need. Addressing these factors through tailored interventions can improve patient outcomes and guide policy decisions aimed at reducing unmet cancer support services.
科研通智能强力驱动
Strongly Powered by AbleSci AI