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Diabetes is a chronic disease known as a "long life disease" which must be managed continuously. This study aims to build interventions to increase the independence of DM management with the DIEN model (Diabetic Self-Reliance, Intervention, Electronic basis, Network system). This mixed method exploratory sequential study went through four stages of research. The first stage identifies the behavioural determinants of DM management independence; the second stage identifies the interventions needed to build DM independence behaviour; the third stage of developing the DIEN model; fourth stage model trials. The DIEN model was based on the findings of stages one and two, has been tested and currently at the level seven technology development level.
ABSTRAK
Latar Belakang: Kanker payudara merupakan jenis kanker dengan insidensi tertinggi di Indonesia, dengan sebagian besar pasien terdiagnosis pada stadium lanjut salah satunya karena keterlambatan diagnosis di fasilitas kesehatan primer. Rendahnya kemampuan dokter umum dalam menegakkan diagnosis klinis kanker payudara serta alur rujukan yang panjang juga berkontribusi memperberat keterlambatan tersebut. Tujuan: Menghasilkan model prediksi diagnosis klinis kanker payudara di pelayanan kesehatan primer menggunakan skor malignansi “Probability of Breast Cancer (BOBAN)” Metode: Studi ini menggunakan mixed method, dengan pendekatan desain explanatory sequential, penelitian kuantitatif menggunakan desain potong lintang dilanjutkan penelitian kualitatif dengan desain studi kasus. Penelitian ini melibatkan 1.169 wanita usia ≥30 tahun yang melakukan deteksi dini di RS Kanker Dharmais (2020–2022). Variabel prediktor dianalisis dengan uji multivariat regresi logistik untuk penyusunan model skoring. Tahap kedua berupa uji akurasi (sensitivitas-spesifisitas) dan nilai probabilitas prediksi. Tahap ketiga berupa uji kualitatif melalui diskusi kelompok terfokus (FGD) dengan dokter umum di puskesmas. Hasil: Model prediksi terdiri dari tujuh variabel terpilih, yaitu usia, riwayat keluarga tingkat I, riwayat melahirkan, riwayat menyusui, benjolan payudara, kelenjar getah bening aksila, dan gejala lanjut kanker. Model ini memiliki nilai kalibrasi yang baik (p-value 0.826) dan nilai AUC pada ROC sebesar 0,920 (CI 95% 0,892 - 0,947; p-value 0,00) menunjukkan diskriminasi yang sangat baik. Total skor antara 0–177, dengan titik potong optimal pada skor 69 (sensitivitas 86,7%, spesifisitas 82,9%, dan nilai probabilitas 10,45%). Skor rendah (0-68) didiagnosis bukan kanker payudara dan skor tinggi (69-177) didiagnosis curiga kanker payudara. Evaluasi kualitatif menunjukkan bahwa skor malignansi BOBAN dapat diaplikasikan oleh dokter umum di fasilitas kesehatan pelayanan primer. Kesimpulan: Skor malignansi ini dapat memprediksi diagnosis klinis dan menghitung nilai probabilitas kanker payudara. Skor malignansi BOBAN direkomendasikan untuk digunakan sebagai instrumen deteksi dini kanker payudara di faskes primer dan dapat menjadi solusi bagi dokter umum untuk mempemudah skrining rujukan tatalaksana kanker payudara di Indonesia.
ABSTRACT
Background: Breast cancer is the most prevalent type of cancer in Indonesia, with the majority of patients diagnosed at an advanced stage, partly due to delayed diagnosis in primary healthcare settings. Limited diagnostic capabilities among general practitioners and lengthy referral processes contribute significantly to these delays. Objective: To develop a clinical prediction model for breast cancer diagnosis in primary healthcare using the "Probability of Breast Cancer (BOBAN)" malignancy score. Methods: This study employed a mixed-method approach, consisting of a quantitative cross-sectional study followed by a qualitative explanatory sequential design. A total of 1,169 women aged ≥30 years who underwent early detection at Dharmais Cancer Hospital (2020–2022) were included. Predictor variables were analyzed using multivariate logistic regression to construct the scoring model. The second phase involved evaluating the model’s diagnostic accuracy (sensitivity-specificity) and predictive probability values. The third phase included qualitative assessment through focus group discussions (FGDs) with general practitioners at community health centers (puskesmas). Results: The prediction model comprised seven selected variables: age, first-degree family history of breast cancer, childbirth history, breastfeeding history, presence of a breast lump, axillary lymph nodes, and advanced cancer symptoms. The model demonstrated good calibration (p-value = 0.826) and excellent discrimination with an AUC of 0.920 (95% CI: 0.892–0.947; p-value < 0.001). The total score ranged from 0–177, with an optimal cutoff score of 69 (sensitivity 86.7%, specificity 82.9%, predictive probability 10.45%). A low score (0–68) indicated a non-breast cancer diagnosis, while a high score (69–177) indicated suspected breast cancer. Qualitative evaluation indicated that the BOBAN malignancy score is feasible for implementation by general practitioners in primary care settings. Conclusion: The malignancy score is capable of predicting clinical diagnosis and estimating the probability of breast cancer. The BOBAN score is recommended as a screening tool for early detection in primary healthcare facilities and offers a practical solution for general practitioners to facilitate breast cancer management referrals in Indonesia.