Perbandingan Kesintasan Tiga Tahun pada Anak Leukemia Limfoblastik Akut antara Protokol Pengobatan 2006 dan 2013

Treatment of children with Acute lymphoblastic leukemia was developing, currently in Indonesia there are several commonly used protocols such as National protocol (Jakarta), WK-LLA 2000 protocol, LLA protocol 2006 and protocol LLA 2013. The purpose of this study to determine the probability of survival 3 years In children with acute lymphoblastic leukemia between protocols 2006 and 2013. This study used a mix method of retrospective cohorts and in-depth interviews. The population in this study were LLA children aged 1–15 years who received protocol 2006 and 2013 in RSKD Jakarta from 2008–2016 is 68 children with research time from April 2016 until June 2016. Data were analyzed using Cox Regression. The result of this study shows that the 3-year survival probability of LLA remission based on the 2006 treatment protocol is 30% and the treatment protocol of 2013 is 27%. A 3-year survival event remission occurred between 2006 and 2013 treatment protocols of HR 1.57 (90% CI 0.577–4,299), but the difference between the two protocols was not statistically significant with p-value 0.456.The results of in-depth interviews were also obtained in protocols 2006 and 2013 in the same principle but there remain some differences between the both of the treatment schedule and doses are cumulatively increased. The conclusions of these two protocols are in principle the same and there is not much difference in inputs and processes

Penemuan Kasus Tuberkulosis pada Pekerja Migran Indonesia

Indonesia menempati urutan ketiga dengan beban kasus TBC tertinggi di dunia dengan estimasi 824.000 kasus TBC pada tahun 2020. Pekerja migran adalah kelompok berisiko terkena TBC namun kasus TBC pada pekerja migran tidak diketahui dalam sistem surveilans TBC nasional.  Penelitian ini bertujuan untuk menjelaskan upaya penemuan kasus TBC pada pekerja migran pada tahapan sebelum bekerja dan masa tunggu Penelitian kualitatif dengan tipe Rapid Assessment Procedure. Pengumpulan data dilakukan dengan melakukan wawancara mendalam, diskusi kelompok, dan observasi ke sarana kesehatan pemeriksa calon PMI (CPMI), balai kesehatan kerja dan dinas kesehatan.Semua hasil wawancara direkam dan dibuatkan transkrip, kemudian dikelola, dikode, dan dianalisis. Data disajikan menggunakan analisis tematik  Skrining TBC di puskesmas belum sesuai Permenaker nomor 9 tahun 2019. Skrining TBC untuk menemukan kasus TBC sedini mungkin perlu diprioritaskan mengingat jumlah kasus TBC yang tinggi di Indonesia. Penemuan kasus TBC dapat dilakukan di sarana kesehatan pemeriksa CPMI (rumah sakit atau klinik), tempat penampungan dan Desmigratif. Penemuan kasus TBC pada PMI belum optimal pada tahapan sebelum bekerja dan masa tunggu. Hasil skrining TBC di puskesmas perlu diwajibkan sebelum pemeriksaan lebih lanjut di sarana kesehatan pemeriksa CPM

Decision Tree Clinical Algorithm for Screening of Mild Cognitive Impairment in the Elderly in Primary Health Care: Development, Test of Accuracy, and Time-Effectiveness Analysis

Mild cognitive impairment (MCI) is predicted to be a common cognitive impairment in primary health care. Early detection and appropriate management of MCI can slow the rate of deterioration in cognitive deficits. The current methods for early detection of MCI have not been satisfactory for some doctors in primary health care. Therefore, an easy, fast, accurate, and reliable method for screening MCI in primary health care is needed. This study intends to develop a decision tree clinical algorithm based on a combination of simple neurological physical examination and brief cognitive assessment for distinguishing elderly with MCI from normal elderly in primary health care. This is a diagnostic study, comparative analysis in elderly with normal cognition and those presenting with MCI. We enrolled 212 elderly people aged 60.04–79.92 years old. Multivariate statistical analysis showed that the existence of subjective memory complaints, history of lack of physical exercise, abnormal verbal semantic fluency, and poor one-leg balance were found to be predictors of MCI diagnosis ( p ≤ 0.001; p = 0.036; p ≤ 0.001; p = 0.013). The decision trees clinical algorithm, which is a combination of these variables, has fairly good accuracy in distinguishing elderly with MCI from normal elderly (accuracy = 89.62%; sensitivity = 71.05%; specificity = 100%; positive predictive value = 100%; negative predictive value = 86.08%; negative likelihood ratio = 0.29; and time effectiveness ratio = 3.03). These results suggest that the decision tree clinical algorithm can be used for screening of MCI in the elderly in primary health care