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ABSTRAK Testosteron merupakan salah satu hormon androgen pada laki-laki, yang akan menurun seiring dengan bertambahnya usia Dua puluh persen dari pria berusia 60- 80 tahun, dan 35% dari pria yang berusia lebih dari 80 tahun, mempunyai konsentrasi testosteron di bawah batas normal. Beberapa faktor mempengaruhi terjadinya penurunan hormon testosteron, beberapa di antaranya dapat dimodifikasi, seperti indeks massa tubuh, asupan makan, gaya hidup, faktor penyakit, sehingga diharapkan dapat dilakukan upaya-upaya pencegahan. Penelitian ini dilakukan untuk mengetahui faktor-faktor yang berhubungan dengan hormon testosteron pada laki-laki, di antaranya adalah usia, indeks massa tubuh, asupan makan, gaya hidup seperti perilaku merokok, aktivitas fisik, dan faktor penyakit kronik yaitu Diabetes dan tekanan darah. Penelitian dilakukan dengan metode potong lintang. Data didapat dari data sekunder penelitian payung Andropause Trisakti-Puskesmas Cilandak tahun 2011. Sebanyak 249 responden laki-laki usia 40 tahun ke atas yang memenuhi kriteria masuk sebagai subyek penelitian. Terdapat hubungan yang signifikan antara indeks massa tubuh, Diabetes Melitus, serta merokok dengan testosteron total, dengan OR sebesar 2,1 (95% CI : 1,085 ? 4,058), 5,5 (95% CI : 2,442 ? 12,443), OR=0,485 (95% CI: 0,249 ? 0,944). Analisis multivariat dengan regresi logistik didapatkan faktor Diabetes Melitus merupakan faktor yang paling dominan terhadap hormon testosteron pada laki-laki usia 40 tahun ke atas (OR =5,49 , 95% CI : 2,427 ? 13,20).
Abstract Testosterone is one of the male?s androgen hormone, which it decrease according to age-ing. 20% male population from 60 to 80 years of age , and 35% of male population above 80 years of age, experincing lower than normal testosterone level. Several factors supposed to influence testosterone hormone decline, such as body mass index, food intake, lifestyle, and disease, and yet these factors are also modifiable to accomodate prevention efforts. This research had been conducted to further determine factors contribution to the influence,which were age, food intake, lifestyle such as smoking and physical activities, chronic disease (e.g diabetic mellitus, blood pressure) . The study design was cross sectional. The required data was retrieved as secondary data resulted from an umbrella androgen research in puskesmas Cilandak at 2011. The 249 males respondent, age above 40 years old, all eligible of the criterias, was included as test subjects. This study established a significant relation between body mass index (OR= 2,1; 95%CI:1.085 ? 4.058), diabetes mellitus (OR= 5,5; 95% CI:2,442-12.443) , and smoking (OR= 0.485; 95% CI: 0.249-0.944), towards total testosterone levels. Multivariate analysis rendered that diabetes mellitus is the most dominant factor to male above 40 years old testosterone level (OR=5,49, 95% CI: 2,427 ? 13,20)
Early warning and response system aims to detect early indications of outbreaks, pushing the program to respond to the alert that appears, knowing the tendency of potential disease outbreaks every week, evaluate the impact of the program interventions that appear alert and aware mapping any potential disease outbreak every week. The purpose of this study was to assess the completeness, precision, accuracy of Puskesmas in regency/city in selected provinces and the things that affect the quality of the data SKDR in Bangka Belitung, Bengkulu, Gorontalo, Central Kalimantan, Papua, West Sulawesi and North Sumatra. The method used is quantitative and qualitative. Completeness report SKDR at 1-20 weeks of 2015 in seven provinces by 47%. Timeliness of reporting by 29%. Accuracy of cases of disease in a population-based sample chi square test, found inaccurate data (value X ² count > X ² table), things that affect: not all health centers receive training SKDR, double job, means less than adequate, there are no special funds , servers are often impaired, any signal interference. Keywords: early
This study is seeking the impact of structure and process as the qualityaspect according to Donabedian 's theory that affecting to the Net Death Rate/NDR Stroke disease. Selected data source are Stroke patients, consisting ofIntracerebral Haemorrhage and Cerebral Infarction at Dr Kanujoso DjatiwibowoBalikpapan Hospital in the year 2014. This study is a qualitative research usingdescriptive analytic retrospective method. Structure and Process Factors that areinfluencing each other. It reveals that Structure Factors in hospitalization that areaffecting sequentially are the condition of the patient, facilities, policies andhuman resource. On the other hand it reveals that Process Factors includeobstacle on running the primary instruction and also Hospital AccociatedInfections/ HAIs occurs due to the nursing process. In Emergency Unit there isobstacle in Process Factor as the CT Scan service is not available sometime. It isrecommend to improve the quality of Stroke patient to overcome the Structure andProcess Factors and to develop the on stop service Stroke Unit.Keywords : Quality, Donabedian , NDR Stroke , Structure Factor, Process Factor.
Lymphedema is a chronic complication that commonly occurs after axillary lymph node dissection (ALND) in breast cancer patients. This study aimed to determine the incidence, risk factors, and prediction model for lymphedema after ALND in advanced-stage breast cancer patients. This was a retrospective cohort design on 174 patients at Dharmais Cancer Hospital. Cox regression was used to identify significant risk factors for lymphedema. The prediction accuracy of the model was assessed by calculating the area under the receiver operating characteristic curve (AUROC). The results showed that lymphedema was identified in 88/174 (50.6%) patients and most of them experienced lymphedema in the first 12 to 36 months after ALND. Risk factors associated with lymphedema include age, obesity, diabetes, neoadjuvant chemotherapy, and adjuvant chemotherapy. The prediction model showed good sensitivity (80.2%) in the study population with an AUC value of 0.706 (95% CI 0.629-0.783; p-value < 0.05). It can be concluded that the prediction model developed in this study is good enough to be implemented by clinicians in estimating the risk of lymphedema, especially for advanced-stage breast cancer patients in our hospital.
