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Objective: This studi aims to find the risk factors and develop risk scorefor undiagnosed diabetes and prediabetes. Method: Risk score madebased on available data from Basic Health Research 2013 in Indonesia,with criteria 18-55 years old, newly diagnosed diabetes, and not affectedby chronic /infectious diseases before.β coeff value from multinomiallogistic regression analysis results of predictive models are used todevelop risk score. The accuracy of risk score assessed with ROC(Receiver Operating Characteristic). Result: 2 prediction models are useto develop risk score. The accuracy form 7 predictors for undiagnoseddiabetes in model 1 are AUC 73.5%, sen 62.2%, spes 70.8%, PPV 12.8%,NPV 96.5%, cut off ≥22. The accuracy form 5 predictors for undiagnoseddiabetes in model 2 are AUC 72.4%, sen 68.3%, spes 64.7%, PPV 11.8%,NPV 96.7%, cut off ≥20 . Score predikction for diabetes not developed,because of poor accuray, but the result of analysis can showed prediabetesdominant risk factors. Conclusion: Indonesia may have a risk scorecalculation for predicting undiagnosed diabetes based on data from HealthResearch provided. The risk score can be used by health workers toindentified individuals with high-risk and the general public are able touse these scores.Keyword : prediabetes, undiagnosed diabetes, risk factor, score
Cardiovascular disease is the first leading cause of death globally. Riskesdas data of 2013 shows that the highest prevalence of cardiovascular disease in Indonesia is coronary heart disease, which is 1.5%. This study aims to analyze the risk level of coronary heart disease (CHD) in workers at gold mining company PT Cibaliung Sumberdaya, Pandeglang 2017 based on risk factors of blood pressure, body mass index, smoking habit, diabetes mellitus, cholesterol, physical activity, and eating habits. The study design used in this research is descriptive cross sectional study with quantitative and qualitative approach. The sample of this study were amounted to 88 workers with quota sampling method. The risk level of CHD was calculated using the scoring method of Jakarta Cardiovascular Score. The results showed that 39 workers (44.3%) had low risk of CHD, 31 workers (35.2%) had medium risk of CHD and 18 workers (20.5%) had a high risk of CHD. Therefore, it is necessary to conduct treatment promptly on workers who have a high risk level and take anticipatory action on workers who have medium risk level as a form of promotive and preventive measures to prevent workers from coronary heart disease.
Indonesia telah menjadi pelopor dalam pengelolaan program jaminan kesehatan sosial (JKN) terbesar di dunia. Program ini diinisiasi sejak tahun 2011 berdasarkan Undang-Undang No 40 tahun 2004 tentang Sistem Jaminan Sosial Nasional dengan pencapaian kepesertaan JKN sebesar 96% terhadap jumlah penduduk pada Desember 2023. Cakupan kesehatan semesta sebagai salah satu upaya dalam program JKN tidak hanya berkaitan dengan kepesertaan, tetapi juga mencakup manfaat yang diterima serta mekanisme pembiayaannya. Ekuitas sebagai salah satu asas dalam memenuhi persyaratan Universal Health Coverage (UHC) masih menjadi masalah dalam pelaksanaan program JKN ini, hal ini terlihat dari data grafik yang dianalisa oleh Ascobat Gani pada tahun 2019 bahwa masih terjadi disparitas yang sangat signifikan antara wilayah propinsi di Indonesia. Tujuan dari penelitian ini untuk melakukan analisis terhadap faktor dari sisi penyedia atau supply side yang mempengaruhi terjadinya disparitas pemanfaatan layanan kesehatan pada fasilitas kesehatan tingkat pertama program JKN. Penelitian ini menggunakan data sekunder BPJS Kesehatan, dan data publikasi dari Kementrian Keuangan dan Kemntrian Dalam Negeri serta Badan Pusat Statistik. Data dianalisis secara univariat, bivariat, dan multivariat dengan menggunakan metode regresi linier berganda. Secara statistik, pemanfaatan layanan kesehatan ditingkat pertama dipengaruhi sebesar 30% oleh kondisi geografis melalui alat ukur status keterpencilan Desa melalui variabel Skor Indeks Desa Membangun (IDM), dan kondisi sosioekonomi melalui alat ukur kapasitas fiskal dan persentase Pendapatan Asli Daerah (PAD) terhadap APBD kabupaten dan kota. Skor IDM dan Persentase PAD terhadap APBD secara signifikan berpengaruh positif sedangkan rasio kapasitas fiskal daerah secara signifikan berpengaruh negatif dengan nilai signifikansi P < 0,05 terhadap kontak rate kunjungan rawat jalan tingkat pertama
Indonesia has become a pioneer in managing the largest social health insurance (JKN) program in the world. This program was initiated in 2011 based on Law No. 40 of 2004 concerning the National Social Security System with the achievement of JKN membership of 96% of the total population in December 2023. Universal health coverage as one of the efforts in the JKN program is not only related to membership, but also includes the benefits received and the financing mechanism. Equity as one of the principles in fulfilling Universal Health Coverage (UHC) requirements is still a problem in the implementation of the JKN program. This can be seen from graphic data analyzed by Ascobat Gani in 2019 that there are still very significant disparities between provincial regions in Indonesia. The aim of this research is to conduct an analysis of factors from the provider side or supply side that influence disparities in health service utilization in first level health facilities of the JKN program. This research uses secondary data from BPJS Kesehatan, and published data from the Ministry of Finance and Ministry of Home Affairs as well as the Central Statistics Agency. Data were analyzed univariately, bivariately and multivariately using multiple linear regression methods. Statistically, the utilization of health services at the first level is influenced by 30% by geographical conditions through measuring village remoteness status through the Village Development Index Score (IDM) variable, and socio-economic conditions through measuring fiscal capacity and the percentage of Regional Original Income (PAD) to the district APBD and city. The IDM score and the percentage of PAD to APBD have a significant positive effect, while the regional fiscal capacity ratio has a significant negative effect with a significance value of P < 0.05 on the contact rate of first level outpatient visits.
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.