乳癌病患的照護品質與生活品質之相關性研究：應用核心測量指標與多層次分析

Abstract

背景：各國對於乳癌病患的照護品質與生活品質逐漸受到重視，亦針對乳癌不同期別病患的皆發展治療指引。國民健康局委由台大公衛學院之研究團隊發展完成六大癌症之核心測量指標，目的讓各癌症照護機構藉此工具達到內部改善及外部監測。乳癌為目前國內診療資料蒐集較齊全的癌症，然而國內卻鮮少文獻探討乳癌病患照護品質與生活品質之間的關係，本研究透過以個案為基礎的測量方式，以核心測量指標遵從度測量乳癌病患照護品質，並探討其與生活品質的相關性，另從醫院層次的角度，探討乳癌病患個人層次變項對乳癌病患生活品質是否會受到醫院層次變項影響有跨層次的交互作用。 方法：本研究採橫斷式研究設計，研究對象為2011年1月1日至7月31日，立意取樣至全國北、中、南，共19家醫院就診的乳癌病患。研究分成兩個部分進行，第一部分以自擬結構式問卷收集乳癌病患主觀的病人特性、疾病相關資訊及生活品質；第二部分將問卷收集到的初級資料串聯癌症診療資料庫與健保資料庫客觀的病人疾病特徵、就醫醫院特性與治療情形。照護品質採用乳癌核心測量指標個人層級計算遵從度，主觀的核心測量指標遵從度以問卷取得資訊，客觀的核心測量指標遵從度以資料庫取得資訊；生活品質採用「EORTC QLQ-C30台灣中文版」與「EORTC QLQ-BR23台灣中文版」測量。本研究將兩部分收集到的資料以描述性統計、獨立樣本t檢定、單因子變異數分析、複迴歸進行分析，並將第一部分資料以階層線性模型進行多層次分析。 結果：本研究第一部份有效樣本共有670人，於19家醫院中；第二部分有效樣本共有204人。本研究經複迴歸分析結果發現，控制病人特性、疾病特徵及醫院特性後，主觀核心測量指標遵從度與QLQ-C30 及QLQ-BR23的10個構面上有顯著相關，並可解釋QLQ-C30與QLQ-BR23生活品質構面15.3%-44.2%的變異量；客觀核心測量指標遵從度與QLQ-C30 及QLQ-BR23的4個構面上有顯著相關，並可解釋QLQ-C30與QLQ-BR23生活品質構面12.1%-21.4%的變異量。階層線性模型分析結果發現，乳癌病患QLQ-C30與QLQ-BR23的10個構面中，分別有3.53%-9.1%的差異來自於醫院層次；在完整模型中，共有4個生活品質構面的醫院層次變項調節個人層次變項有達統計顯著的影響效果。 結論：乳癌病患個人層次的核心測量指標遵從度與其生活品質(QLQ-C30 & QLQ-BR23)之整體生活品質、角色功能、情緒功能、身體意象、性功能、性生活、未來展望、疼痛、財務困難、系統性治療影響、乳房症狀等構面相關。且乳癌病患就醫醫院層次變項，如醫院地區別與層級別會調節個人層次變項，如乳癌病患病理期別、家庭總收入、教育程度及核心測量指標遵從度對生活品質(QLQ-C30 & QLQ-BR23)之身體功能、財務困難、性功能、未來展望的影響效果。

Background: Quality of care and quality of life in patients with breast cancer are becoming important in every contries. And there are some treatment guidelines for patients with breast cancer. In order to improve care quality and benchmark among cancer care hospitals, the Bureau of Health Promotion supported a project for quality improvement mechanism that included establishing six cancer core measure sets by research team of College of Public Health, National Taiwan University. There are more breast cancer researches than other kinds of cancers. However, there are limited searches to discuss the relationship between quality of care and quality of life. This study attempt to using breast cancer core measure set to measure the quality of care in patients with breast cancer, and investigate the relationship among quality of care and quality of life in ptients. Furthermore, it also verifies the influence of hospital-level on patient-level. Method: This is a cross-sectional study. The patients were recruited at 19 hospitals in Taiwan. All samples came from convenient sampling. There were 670 valid samples with collection period from January first to July 31, year 2011. The study divided into two parts. The first part, a survey was conducted by using structured questionnaires to collect patients’ demographic, clinical characteristics and quality of life. The second part, we using TCDB and NHI database to collect patients’ demographic and clinical characteristics, hospital characteristics and volume, as well as the treatment. To measure the quality of care, this study use case-based approach to find out the care services each patient received in the whole care process from pretreatment to treatment. Quality of life was measured by the EORTC QLQ-C30 and QLQ-BR23. The subjective core measure compliance was obtained from questionnaires, and the objective core measure compliance was obtained from database. The data is analyzed by methods of descriptive statistics, independent-samples t-test, one-way ANOVA, and Hierarchical Linear Modeling(HLM). Results: In the first part, a total of 670 samples were valid at 19 hospitals. And in the second part, a total of 204 samples were valid. From multiple regression analysis, after controlled for patients’ demographic, clinical characteristics and hospital characteristics, patient’s subjective core measure compliance is related to 10 dimensions in QLQ-C30 and QLQ-BR23. The predictive power of whole model toward quality of life crossed from 15.3%-44.2%. However, patient’s objective core measure compliance is related to 4 domains in QLQ-C30 and QLQ-BR23. The predictive power of whole model toward quality of life crossed from 15.3%-44.2%. From HLM, the cross-level effect for “hospital-level variables” is confirmed between “patient-level variables” and “quality of life’’ in this research. Conclusion: In regression models, higher core measure compliance was associated with better overall QOL, role functioning, emotional functioning, body image, sexual functioning, sexual enjoyment, future perspective, pain, financial difficulties, systemic therapy side effects and breast symptoms, controlling for covariates. And the hospital-level variables (hospital accreditation level and location), does not only explain the different quality of life (physical functioning, financial difficulties, sexual functioning, future perspective) of patients among across the hospital, but also moderate the association between the patients’ demographic (education attainment, and household income), clinical characteristics (stage) and core measure compliance.