Faktor Risiko Terjadinya Kejadian Luar Biasa (KLB) Hepatitis A di Kabupaten Tangerang Tahun 2016

Hepatitis A adalah penyakit hati akibat virus hepatitis A yang dapat menyebabkan kesakitan ringan sampai berat. Dampak ekonomi dari wabah tersebut seperti epidemi Shanghai pada tahun 1988 yang menyerang sekitar 300.000 orang. Di negara-negara berkembang dengan kondisi sanitasi yang buruk dan praktek-praktek higienis, kebanyakan anak-anak (90%) telah terinfeksi hepatitis A virus sebelum usia 10 tahun. Di Indonesia Hepatitis A sering muncul dalam Kejadian Luar Biasa (KLB). Tahun 2014 tercatat 3 Provinsi dan 4 Kabupaten terjadi KLB dengan jumlah penderita 282. Penyelidikan epidemiologi ini bertujuan untuk mengetahui gambaran KLB dan mengidentifikasi faktor risiko KLB Hepatitis A di Kabupaten Tangerang tahun 2016. Desain studi yang digunakan dalam penelitian ini adalah desain kasus kontrol. Penyelidikan dilaksanakan pada bulan Maret 2016 di Kabupaten Tengerang. besar sampel yaitu kasus 44 dan control sebanyak 95. Data yang dikumpulkan dalam penyelidikan ini berupa data primer dan sekunder. Data primer meliputi identifikasi responden dan faktor risiko Hepatitis A. Penyelidikan dilakukan dengan metode wawancara menggunakan kuesioner terstruktur serta observasi lingkungan. Data sekunder diambil berdasarkan laporan puskesmas, catatan dinas kesehatan Kabupaten Tangerang dan data demografi. Data dianalisis dengan Stata menggunakan uji bivariate; Chi Square (X2) dan multivariate; regresi logistik. KLB terjadi pada bulan Februari-Maret 2016 dengan kasus sebanyak 44, kasus terbanyak terjadi pada minggu ke-10 pada bulan Maret 2016. KLB hepatitis A berdasarkan kelompok umur 6-10 tahun sebesar 3 orang (6.82%) lebih sedikit dibanding umur 11-16 tahun yaitu 41 orang (93.18%) dengan OR 1.78 (CI95% 0.43-10.48) . KLB hepatitis A berdasarkan jenis kelamin lebih banyak pada perempuan yaitu 24 orang (54.55%) dibanding laki – laki yaitu 20 orang (45.45%) dengan OR 0.71 (CI95% 0.32-1.56). Faktor risiko diantaranya tidak cuci tangan pakai sabun sehabis bab OR 7.90 (CI 95% 3.14 -19.88) dan jenis kantin yang digunakan (Warung 2) OR 2.92 (CI 95% 1.21 - 7.02). KLB hepatitis A terjadi karena berbagai faktor risiko diantaranya tidak cuci tangan pakai sabun sehabis bab dan jenis kantin yang digunakan (Warung 2). Selain itu PHBS penjamah makanan kurang baik dan sanitasi lingkungan juga buruk. Upaya pencegahan bisa dilakukan melalui perbaikan sanitasi sekolah dan penyuluhan tentang PHBS dan imunisasi hepatitis A. Faktor risiko diantaranya tidak cuci tangan pakai sabun sehabis bab OR 7.90 (CI 95% 3.14 -19.88) dan jenis kantin yang digunakan (Warung 2) OR 2.92 (CI 95% 1.21 - 7.02). KLB hepatitis A terjadi karena berbagai faktor risiko diantaranya tidak cuci tangan pakai sabun sehabis bab dan jenis kantin yang digunakan (Warung 2). Selain itu PHBS penjamah makanan kurang baik dan sanitasi lingkungan juga buruk. Upaya pencegahan bisa dilakukan melalui perbaikan sanitasi sekolah dan penyuluhan tentang PHBS dan imunisasi hepatitis A

Surveillance for Severe Acute Respiratory Infection as one approach to enhance Global Health Security in Indonesia

Latar Belakang: Sistem surveilans nasional untuk infeksi saluran pernafasan akut berat (SARI) dapatmemberikan informasi penting tentang sirkulasi virus influenza, menyediakan sistem untuk mengendalikankejadian luar biasa yang mengancam keamanan dan keselamatan masyarakat serta menyediakandata untuk sistem surveilans influenza global (GISRS). Kemampuan Indonesia untuk mendeteksi dan mengendalikanpenyakit menular penting untuk keamanan kesehatan dunia. Penelitian ini bertujuan untukmenilai sistem surveilans ISPA berat Indonesia (SIBI) dan pemanfaatan untuk memantau patogen prioritaslainnya sebagai upaya meningkatkan keamanan kesehatan global. Metode: penilaian atribut surveilans melalui review laporan, analisis data dan interview staff yang terlibatdalam sistem surveilans. Semua kasus yang memenuhi kriteria SARI pada bulan Mei 2013 – April 2015 ikutserta dalam penelitian. Data epidemiologi dan virologi dianalisis. Kelengkapan dan kemudahan sistem untukmencapai tujuan surveilans influenza dan mendukung surveilans penyakit infeksi baru (emerging) dikaji. Hasil: Sebanyak 1,806 kasus SARI dan 1,697 (94%) spesimen dilakukan pemeriksaan virus influenza.Sebanyak 200 (12%) positif influenza, terdiri dari 46% influenza A(H3N2), 18% A(H1N1)pdm09 dan 37%influenza B. Hasil penilaian terhadap sistem surveilans didapatkan kesesuaian pelaksanaan untuk semuaatribut surveilans melebihi target >80%, kelengkapan laporan online 95%, kesesuaian kasus terhadapdefinisi kasus 100%, kasus yang diambil spesimen 94% dan hasil laboratorium diinput ke database secaraonline 100%. Sistem surveilans untuk dengue dan infeksi arbovirus lainnya sudah terlaksana di unitrawat jalan dan gawat darurat di sentinel SARI surveilans. Kesimpulan: SIBI dapat disesuaikan untuk menggabungkan surveilans penyakit lain yang menunjukkankegunaan dan fleksibilitas dalam mendukung keamanan kesehatan global. Kata kunci: keamanan kesehatan global, surveilans, influenza, Indonesia AbstractBackground: The existing national surveillance system for severe acute respiratory infection (SARI) providescritical information on influenza virus circulation, provides a system to control influenza outbreaks that threatenthe safety and security of the population and feeds data into the global influenza surveillance and responsesystem (GISRS). Indonesia’s ability to detect and control communicable diseases is critical for global healthsecurity. The aim of this study was to assess the SARI surveillance system and utility for monitoring other prioritypathogens as an effort to enhance global health security. Methods: Surveillance attributes were assessed by reviewing records, data analysis and through interviewedwith staffs involved in the surveillance system. All patients at six sentinel hospitals who meet the SARI casedefinition during May 2013 – April 2015 were enrolled. Epidemiological and virological data were analyzed.The surveillance system utility for its influenza surveillance objectives and flexibility to support surveillance ofemerging infectious diseases were assessed. Resuts: A total of 1,806 SARI cases were reported of which 1,697 (94%) had specimens tested for influenza viruses.Of those tested, 200 (12%) were positive, of which 46% were influenza A(H3N2), 18% A(H1N1)pdm09and 37% influenza B viruses. The system exceeded the targets of >80% adherence for most attributes: 95% forcompleteness of online reporting, 100% for cases adhering to the case definition, 94% for cases with specimenscollected and 100% of laboratory results uploaded to the online database. A surveillance system for dengue andother arbovirus infections was established in the outpatient/emergency units at the SARI surveillance sentinel.Conclusion: SIBI was adjusted to incorporate surveillance for other priority diseases indicating its utility andflexibility to support global health security Keywords: Global Health Security, surveillance, influenza, Indonesi

Plasmodium vivax Malaria Endemicity in Indonesia in 2010

BACKGROUND: Plasmodium vivax imposes substantial morbidity and mortality burdens in endemic zones. Detailed understanding of the contemporary spatial distribution of this parasite is needed to combat it. We used model based geostatistics (MBG) techniques to generate a contemporary map of risk of Plasmodium vivax malaria in Indonesia in 2010. METHODS: Plasmodium vivax Annual Parasite Incidence data (2006-2008) and temperature masks were used to map P. vivax transmission limits. A total of 4,658 community surveys of P. vivax parasite rate (PvPR) were identified (1985-2010) for mapping quantitative estimates of contemporary endemicity within those limits. After error-checking a total of 4,457 points were included into a national database of age-standardized 1-99 year old PvPR data. A Bayesian MBG procedure created a predicted PvPR(1-99) endemicity surface with uncertainty estimates. Population at risk estimates were derived with reference to a 2010 human population surface. RESULTS: We estimated 129.6 million people in Indonesia lived at risk of P. vivax transmission in 2010. Among these, 79.3% inhabited unstable transmission areas and 20.7% resided in stable transmission areas. In western Indonesia, the predicted P. vivax prevalence was uniformly low. Over 70% of the population at risk in this region lived on Java and Bali islands, where little malaria transmission occurs. High predicted prevalence areas were observed in the Lesser Sundas, Maluku and Papua. In general, prediction uncertainty was relatively low in the west and high in the east. CONCLUSION: Most Indonesians living with endemic P. vivax experience relatively low risk of infection. However, blood surveys for this parasite are likely relatively insensitive and certainly do not detect the dormant liver stage reservoir of infection. The prospects for P. vivax elimination would be improved with deeper understanding of glucose-6-phosphate dehydrogenase deficiency (G6PDd) distribution, anti-relapse therapy practices and manageability of P. vivax importation risk, especially in Java and Bali

Karakteristik Epidemiologi Kasus-kasus Flu Burung di Indonesia Juli 2005-oktober 2006

Influenza A (H5N1) human cases started to he reported since 1997. In Indonesia, the first human cases were reported in July 2005, as a cluster consisted of a father and two daughters; two of them were fatal confirmed cases. As of 31 October 2006, 72 cases have heen identified (25 were classified into 10 cluster) with a case fatality rate (CFR) of 76.4%. The patients were from 9 (27%) of the 33 provinces in Indonesia. The ratio between male and female patients was 4 to 3. with an extremely high CFR of the women (87%). Most patients were of young adult ages, 39% of them were less than 15 years old. Indirect and direct contact with sick or dead poultry was reported from 81% of these confirmed cases

Avian Influenza H5N1 Transmission in Households, Indonesia

BACKGROUND: Disease transmission patterns are needed to inform public health interventions, but remain largely unknown for avian influenza H5N1 virus infections. A recent study on the 139 outbreaks detected in Indonesia between 2005 and 2009 found that the type of exposure to sources of H5N1 virus for both the index case and their household members impacted the risk of additional cases in the household. This study describes the disease transmission patterns in those outbreak households. METHODOLOGY/PRINCIPAL FINDINGS: We compared cases (n = 177) and contacts (n = 496) in the 113 sporadic and 26 cluster outbreaks detected between July 2005 and July 2009 to estimate attack rates and disease intervals. We used final size household models to fit transmission parameters to data on household size, cases and blood-related household contacts to assess the relative contribution of zoonotic and human-to-human transmission of the virus, as well as the reproduction number for human virus transmission. The overall household attack rate was 18.3% and secondary attack rate was 5.5%. Secondary attack rate remained stable as household size increased. The mean interval between onset of subsequent cases in outbreaks was 5.6 days. The transmission model found that human transmission was very rare, with a reproduction number between 0.1 and 0.25, and the upper confidence bounds below 0.4. Transmission model fit was best when the denominator population was restricted to blood-related household contacts of index cases. CONCLUSIONS/SIGNIFICANCE: The study only found strong support for human transmission of the virus when a single large cluster was included in the transmission model. The reproduction number was well below the threshold for sustained transmission. This study provides baseline information on the transmission dynamics for the current zoonotic virus and can be used to detect and define signatures of a virus with increasing capacity for human-to-human transmission

Quantifying primaquine effectiveness and improving adherence: a round table discussion of the APMEN Vivax Working Group.

The goal to eliminate malaria from the Asia-Pacific by 2030 will require the safe and widespread delivery of effective radical cure of malaria. In October 2017, the Asia Pacific Malaria Elimination Network Vivax Working Group met to discuss the impediments to primaquine (PQ) radical cure, how these can be overcome and the methodological difficulties in assessing clinical effectiveness of radical cure. The salient discussions of this meeting which involved 110 representatives from 18 partner countries and 21 institutional partner organizations are reported. Context specific strategies to improve adherence are needed to increase understanding and awareness of PQ within affected communities; these must include education and health promotion programs. Lessons learned from other disease programs highlight that a package of approaches has the greatest potential to change patient and prescriber habits, however optimizing the components of this approach and quantifying their effectiveness is challenging. In a trial setting, the reactivity of participants results in patients altering their behaviour and creates inherent bias. Although bias can be reduced by integrating data collection into the routine health care and surveillance systems, this comes at a cost of decreasing the detection of clinical outcomes. Measuring adherence and the factors that relate to it, also requires an in-depth understanding of the context and the underlying sociocultural logic that supports it. Reaching the elimination goal will require innovative approaches to improve radical cure for vivax malaria, as well as the methods to evaluate its effectiveness

\u3ci\u3ePlasmodium vivax\u3c/i\u3e Malaria Endemicity in Indonesia in 2010

Background: Plasmodium vivax imposes substantial morbidity and mortality burdens in endemic zones. Detailed understanding of the contemporary spatial distribution of this parasite is needed to combat it. We used model based geostatistics (MBG) techniques to generate a contemporary map of risk of Plasmodium vivax malaria in Indonesia in 2010. Methods: Plasmodium vivax Annual Parasite Incidence data (2006–2008) and temperature masks were used to map P. vivax transmission limits. A total of 4,658 community surveys of P. vivax parasite rate (PvPR) were identified (1985–2010) for mapping quantitative estimates of contemporary endemicity within those limits. After error-checking a total of 4,457 points were included into a national database of age-standardized 1–99 year old PvPR data. A Bayesian MBG procedure created a predicted PvPR1–99 endemicity surface with uncertainty estimates. Population at risk estimates were derived with reference to a 2010 human population surface

KARAKTERISTIK EPIDEMIOLOGI KASUS-KASUS FLU BURUNG DI INDONESIA JULI 2005-OKTOBER 2006

Influenza A (H5N1) human cases started to he reported since 1997. In Indonesia, the first human cases were reported in July 2005, as a cluster consisted of a father and two daughters; two of them were fatal confirmed cases. As of 31 October 2006, 72 cases have heen identified (25 were classified into 10 cluster) with a case fatality rate (CFR) of 76.4%. The patients were from 9 (27%) of the 33 provinces in Indonesia. The ratio between male and female patients was 4 to 3. with an extremely high CFR of the women (87%). Most patients were of young adult ages, 39% of them were less than 15 years old. Indirect and direct contact with sick or dead poultry was reported from 81% of these confirmed cases.  

The <i>Plasmodium vivax</i> malaria <i>Pv</i>PR_1–99 endemicity map.

<p>Model-based geostatistical point estimates of the annual mean <i>Pv</i>PR_1–99 for 2010 within the stable spatial limits of <i>P. vivax</i> malaria transmission, displayed as a continuum of light green to red from 0% to 7% (see map legend). Areas within the stable limits in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037325#pone-0037325-g001" target="_blank">Figure 1</a> that were predicted with high certainty (>0.9) to have <i>Pv</i>PR_1–99 less than 1% were classified as unstable areas (shaded medium grey areas). The rest of the land area was defined as unstable risk (medium grey areas, where <i>Pv</i>API<0.1 per 1,000 pa) or no risk (light grey, where <i>Pv</i>API = 0 per 1,000 pa).</p