Soil transmitted helminths (STH) infection is a major health problem in tropical countries such as Indonesia. Albendazole is an effective and widely used anthelmintic agent to treat STH; however, it is not effective towards T. trichiura and its effectiveness varies between populations. Hence, we conducted a study to determine the effectiveness of triple dose albendazole in children of Perobatang village, Southwest Sumba, Indonesia. A pre-post study was carried out in Perobatang village on July 2016. Children aged 1-15 years old were enrolled in the study and asked to collect stool samples which were then examined using Kato-Katz method. The children infected with STH were given albendazole 400 mg for three consecutive days. From 246 subjects examined, 192 (78%) were positive for any STH consisting of T. trichiura (64%), A. lumbricoides (60%), and hookworms (10%). After treatment, the prevalence of STH decreased significantly (McNemar test, p<0.001) to 27%, T. trichiura 25%, A. lumbricoides 2%, and hookworm 0%. Cure rate for T. trichiura, A. lumbricoides, and hookworms was 61%, 97%, and 100%, respectively. Significant decrease of eggs per gram of feces was found in all STH (Wilcoxon test, p value <0.001 for A. lumbricoides and T. trichiura, p value = 0.027 for hookworms); egg reduction rate for T. trichiura was 91%, A. lumbricoides was 100%, and hookworms was 100%. In conclusion, triple dose albendazole is effective in controlling STH in children of Perobatang village, Southwest Sumba, Indonesia.

Background: Analyses of correlates of SARS-CoV-2 infection or mortality have usually assessed individual predictors. This study aimed to determine if patterns of combined predictors may better identify risk of infection and mortality. Methods: For the period of March 2nd to 10th 2020, the first 9 days of the COVID-19 pandemic in Indonesia, we selected all 18 confirmed cases, of which 6 died, and all 60 suspected cases, of which 1 died; and 28 putatively negative patients with pneumonia and no travel history. We recorded data for travel, contact history, symptoms, haematology, comorbidities, and chest x-ray. Hierarchical cluster analyses (HCA) and principal component analyses (PCA) identified cluster and covariance patterns for symptoms or haematology which were analysed with other predictors of infection or mortality using logistic regression. Results: For univariate analyses, no significant association with infection was seen for fever, cough, dyspnoea, headache, runny nose, sore throat, gastrointestinal complaints (GIC), or haematology. A PCA symptom component for fever, cough, and GIC tended to increase risk of infection (OR 3.41; 95% CI 1.06 - 14; p=0.06), and a haematology component with elevated monocytes decreased risk (OR 0.26; 0.07 - 0.79; 0.027). Multivariate analysis revealed that an HCA cluster of 3-5 symptoms, typically fever, cough, headache, runny nose, sore throat but little dyspnoea and no GIC tended to reduce risk (aOR 0.048; <0.001 - 0.52; 0.056). In univariate analyses for death, an HCA cluster of cough, fever and dyspnoea had increased risk (OR 5.75; 1.06 - 31.3, 0.043), but no other individual predictor, cluster or component was associated. Other significant predictors of infection were age >= 45, international travel, contact with COVID-19 patient, and pneumonia. Diabetes and history of contact were associated with higher mortality. Conclusions: Cluster groups and co-variance patterns may be stronger correlates of SARS-CoV-2 infection than individual predictors. Comorbidities may warrant careful attention as would COVID-19 exposure levels.

Vaccine hesitancy can be a challenge for those with autoimmune diseases. This study investigated the acceptance of COVID-19 vaccination by patients with autoimmune diseases in Indonesia using the integrated behavioral model (IBM). This cross-sectional study was conducted from December 2021 to February 2022. A total of 404 patients with autoimmune diseases completed the survey. The majority of respondents (57.9%) said they intended to get vaccinated against COVID-19. The IBM model with added demographic variables explained 54.1% of the variance of vaccination intention (R2 = 0.541). Self-efficacy, perceived norms, experiential attitude, and instrumental attitude are significantly correlated with vaccination intention in components of health behavior theories. Self-efficacy is the most critical factor influencing vaccination intention in patients with autoimmune diseases (F(2, 401) = 96.9, p < 0.001, R2 = 0.326). In the multivariate analysis, vaccine intention was found to be positively associated with patients’ occupation as health-care workers (β = 0.105). Meanwhile, having a personal history of contracting COVID-19 and having co-morbidities other than autoimmune diseases were negatively correlated to the willingness to be vaccinated against COVID-19. This study confirms the viability of the IBM model for predicting the COVID-19 vaccination intention of patients with autoimmune diseases. It is essential to provide patients with autoimmune diseases with information that is clear and supported by evidence-based medicine.

Latar Belakang: Pembatasan jumlah kunjungan pasien selama pandemi COVID-19 mengakibatkan perilaku pembelian konsumen beralih dengan pesat ke media digital.  Frost & Sullivan menyatakan pendapatan digital health di Indonesia diperkirakan akan meningkat dari 85 juta USD pada tahun 2017 menjadi 973 juta USD pada tahun 2022 dengan tingkat CAGR lebih dari 60%.  Rumah sakit perlu beradaptasi untuk menggunakan aplikasi e-health secara tepat dan cepat sesuai dengan permintaan pasar. Tujuan: Studi ini bertujuan untuk mengetahui strategi beberapa rumah sakit dalam menilai peluang pasar e-health. Metode: Metode yang digunakan dalam penelitian ini adalah metode deskriptif dengan menggunakan studi literatur terhadap 4 rumah sakit swasta yang terdiri dari 2 rumah sakit di posisi growth and build dan 2 rumah sakit di posisi hold and maintain. Hasil: Penilaian peluang pasar e-health rumah sakit perlu dilakukan dengan cara menganalisis situasi baik faktor internal maupun eksternal rumah sakit, menentukan posisi rumah sakit berdasarkan matriks EFE, IFE, dan IE yang nantinya akan berpengaruh pada strategi yang akan diambil dalam menghadapi pasar e-health. Studi ini mendapatkan bahwa posisi rumah sakit yang berbeda-beda tetap dapat menangkap peluang pasar e-health yang disesuaikan dengan kondisi internal masing-masing. Kesimpulan: Dalam menilai peluang pasar e-health rumah sakit perlu melakukan analisis situasi baik faktor internal maupun eksternal rumah sakit.