Upaya Keluarga Untuk Mencegah Penularan Dalam Perawatan Anggota Keluarga Dengan Tb Paru

Indonesia merupakan negara keempat dengan insiden kasus terbanyak untuk tuberkulosis (TB) paru didunia..Penelitian ini menggunakan desain kualitatif dengan pendekatan case study research, bertujuan untuk memberikan penjelasan tentang upaya keluarga untuk mencegah penularan dalam perawatan anggota keluarga dengan TB Paru. Dari hasil analisa data, didapatkan tiga tema dan tujuh subtema yaitu: (1) Modifikasi lingkungan dengan subtema modifikasi ventilasi yang memadai dan menjaga kebersihan. (2) Upaya memutus transmisi penyakit dengan subtema membuang dahak, pengunaan masker, dan menutup saat batuk. (3) Konsumsi obat dan kontrol rutin ke puskesmas dengan subtema pemantauan dari keluarga dalam minum obat (PMO), serta kontrol rutin ke Puskesmas.Berdasarkan hasil penelitian ini diharapkan Puskesmas dapat menambah dan memodifikasi program penanggulangan tuberkulosis (TB). Selain itu perlu dilakukan pengawasan secara berkala atau kunjungan rumah secara rutin untuk memantau pengobatan dan pencegahan penularan Tuberkulosis (TB) yang dilakukan keluarga di rumah

Peaked Interest: Public Interest in Hunger and the Economic Cycle

United Nations Sustainable Development Goal #2 is to end hunger. Private enterprises can aid in this effort if they have the right information. This paper creates an index to measure general interest in hunger in the United States (2004-2018). This ‘hunger interest index’ is based on keyword search frequency data from Google for a variety of hunger related keywords which appear in the mission statements of social businesses. We compare the ‘hunger interest index’ to broader economic trends and find that general interest in hunger increases as economic misery increases. Further, interest in hunger is shown to be positively related to interest in food banks and donations. The results provide valuable information to social enterprises for which combating hunger is a key value, and to social entrepreneurs looking to focus on hunger reduction in their new venture

Using Web-Based Search Data to Study the Public’s Reactions to Societal Events: The Case of the Sandy Hook Shooting

Background: Internet search is the most common activity on the World Wide Web and generates a vast amount of user-reported data regarding their information-seeking preferences and behavior. Although this data has been successfully used to examine outbreaks, health care utilization, and outcomes related to quality of care, its value in informing public health policy remains unclear. Objective: The aim of this study was to evaluate the role of Internet search query data in health policy development. To do so, we studied the public’s reaction to a major societal event in the context of the 2012 Sandy Hook School shooting incident. Methods: Query data from the Yahoo! search engine regarding firearm-related searches was analyzed to examine changes in user-selected search terms and subsequent websites visited for a period of 14 days before and after the shooting incident. Results: A total of 5,653,588 firearm-related search queries were analyzed. In the after period, queries increased for search terms related to “guns” (+50.06%), “shooting incident” (+333.71%), “ammunition” (+155.14%), and “gun-related laws” (+535.47%). The highest increase (+1054.37%) in Web traffic was seen by news websites following “shooting incident” queries whereas searches for “guns” (+61.02%) and “ammunition” (+173.15%) resulted in notable increases in visits to retail websites. Firearm-related queries generally returned to baseline levels after approximately 10 days. Conclusions: Search engine queries present a viable infodemiology metric on public reactions and subsequent behaviors to major societal events and could be used by policymakers to inform policy development. [JMIR Public Health Surveill 2017;3(1):e12

Mass media and the contagion of fear: The case of Ebola in America

Background: In the weeks following the first imported case of Ebola in the U. S. on September 29, 2014, coverage of the very limited outbreak dominated the news media, in a manner quite disproportionate to the actual threat to national public health; by the end of October, 2014, there were only four laboratory confirmed cases of Ebola in the entire nation. Public interest in these events was high, as reflected in the millions of Ebola-related Internet searches and tweets performed in the month following the first confirmed case. Use of trending Internet searches and tweets has been proposed in the past for real-time prediction of outbreaks (a field referred to as digital epidemiology ), but accounting for the biases of public panic has been problematic. In the case of the limited U. S. Ebola outbreak, we know that the Ebola-related searches and tweets originating the U. S. during the outbreak were due only to public interest or panic, providing an unprecedented means to determine how these dynamics affect such data, and how news media may be driving these trends. Methodology: We examine daily Ebola-related Internet search and Twitter data in the U. S. during the six week period ending Oct 31, 2014. TV news coverage data were obtained from the daily number of Ebola-related news videos appearing on two major news networks. We fit the parameters of a mathematical contagion model to the data to determine if the news coverage was a significant factor in the temporal patterns in Ebola-related Internet and Twitter data. Conclusions: We find significant evidence of contagion, with each Ebola-related news video inspiring tens of thousands of Ebola-related tweets and Internet searches. Between 65% to 76% of the variance in all samples is described by the news media contagion model. © 2015 Towers et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Mass Media and the Contagion of Fear: The Case of Ebola in America

abstract: Background In the weeks following the first imported case of Ebola in the U. S. on September 29, 2014, coverage of the very limited outbreak dominated the news media, in a manner quite disproportionate to the actual threat to national public health; by the end of October, 2014, there were only four laboratory confirmed cases of Ebola in the entire nation. Public interest in these events was high, as reflected in the millions of Ebola-related Internet searches and tweets performed in the month following the first confirmed case. Use of trending Internet searches and tweets has been proposed in the past for real-time prediction of outbreaks (a field referred to as “digital epidemiology”), but accounting for the biases of public panic has been problematic. In the case of the limited U. S. Ebola outbreak, we know that the Ebola-related searches and tweets originating the U. S. during the outbreak were due only to public interest or panic, providing an unprecedented means to determine how these dynamics affect such data, and how news media may be driving these trends. Methodology We examine daily Ebola-related Internet search and Twitter data in the U. S. during the six week period ending Oct 31, 2014. TV news coverage data were obtained from the daily number of Ebola-related news videos appearing on two major news networks. We fit the parameters of a mathematical contagion model to the data to determine if the news coverage was a significant factor in the temporal patterns in Ebola-related Internet and Twitter data. Conclusions We find significant evidence of contagion, with each Ebola-related news video inspiring tens of thousands of Ebola-related tweets and Internet searches. Between 65% to 76% of the variance in all samples is described by the news media contagion model.The article is published at http://journals.plos.org/plosone/article?id=10.1371/journal.pone.012917

Forecasting AIDS prevalence in the United States using online search traffic data

Over the past decade and with the increasing use of the Internet, the assessment of health issues using online search traffic data has become an integral part of Health Informatics. Internet data in general and from Google Trends in particular have been shown to be valid and valuable in predictions, forecastings, and nowcastings; and in detecting, tracking, and monitoring diseases’ outbreaks and epidemics. Empirical relationships have been shown to exist between Google Trends’ data and official data in several health topics, with the science of infodemiology using the vast amount of information available online for the assessment of public health and policy matters. The aim of this study is to provide a method of forecasting AIDS prevalence in the US using online search traffic data from Google Trends on AIDS related terms. The results at first show that significant correlations between Google Trends’ data and official health data on AIDS prevalence (2004–2015) exist in several States, while the estimated forecasting models for AIDS prevalence show that official health data and Google Trends data on AIDS follow a logarithmic relationship. Overall, the results of this study support previous work on the subject suggesting that Google data are valid and valuable for the analysis and forecasting of human behavior towards health topics, and could further assist with Health Assessment in the US and in other countries and regions with valid available official health data

Weekly Forecasting Model for Dengue Hemorrhagic Fever Outbreak in Thailand

A dengue virus causes diseases, including dengue hemorrhagic fever (DHF) which induces several sicknesses and deaths in Thailand. DHF is categorized as one of the most dangerous communicable diseases by the Ministry of Public Health Thailand (MoPH); moreover, the MoPH also sets strict protocols and encourages forecasting techniques for monitoring and dealing with the outbreaks. This research aims to utilize the data that were gathered from external sources, e.g. Google Trends data and meteorology data, to forecast the number of cases that will occur within the 7 day-interval in the next 1–4 weeks. Six provinces—including Chiang Rai, Mukdahan, Pattani, Phichit, Ayutthaya, and Ratchaburi—were selected as they represent the unique patterns of dengue outbreaks in Thailand. The machine learning models—including Random Forest, AdaBoost, Extra-Trees, and Regularized Regressions—were used to forecast the number of the cases. The performances of these models were compared to the performances of the traditional time series model including Naïve model and Moving Average. The proposed machine learning models for Chiang Rai, Mukdahan, and Pattani yield better results than those of the traditional models

Predicting referendum results in the Big Data Era

In addressing the challenge of Big Data Analytics, what has been of notable significance is the analysis of online search traffic data in order to analyze and predict human behavior. Over the last decade, since the establishment of the most popular such tool, Google Trends, the use of online data has been proven valuable in various research fields, including -but not limited to- medicine, economics, politics, the environment, and behavior. In the field of politics, given the inability of poll agencies to always well approximate voting intentions and results over the past years, what is imperative is to find new methods of predicting elections and referendum outcomes. This paper aims at presenting a methodology of predicting referendum results using Google Trends; a method applied and verified in six separate occasions: the 2014 Scottish Referendum, the 2015 Greek Referendum, the 2016 UK Referendum, the 2016 Hungarian Referendum, the 2016 Italian Referendum, and the 2017 Turkish Referendum. Said referendums were of importance for the respective country and the EU as well, and received wide international attention. Google Trends has been empirically verified to be a tool that can accurately measure behavioral changes as it takes into account the users’ revealed and not the stated preferences. Thus we argue that, in the time of intelligence excess, Google Trends can well address the analysis of social changes that the internet brings

Infoveillance of infectious diseases in USA: STDs, tuberculosis, and hepatitis

Big Data Analytics have become an integral part of Health Informatics over the past years, with the analysis of Internet data being all the more popular in health assessment in various topics. In this study, we first examine the geographical distribution of the online behavioral variations towards Chlamydia, Gonorrhea, Syphilis, Tuberculosis, and Hepatitis in the United States by year from 2004 to 2017. Next, we examine the correlations between Google Trends data and official health data from the ‘Centers for Disease Control and Prevention’ (CDC) on said diseases, followed by estimating linear regressions for the respective relationships. The results show that Infoveillance can assist with exploring public awareness and accurately measure the behavioral changes towards said diseases. The correlations between Google Trends data and CDC data on Chlamydia cases are statistically significant at a national level and in most of the states, while the forecasting exhibits good performing results in many states. For Hepatitis, significant correlations are observed for several US States, while forecasting also exhibits promising results. On the contrary, several factors can affect the applicability of this forecasting method, as in the cases of Gonorrhea, Syphilis, and Tuberculosis, where the correlations are statistically significant in fewer states. Thus this study highlights that the analysis of Google Trends data should be done with caution in order for the results to be robust. In addition, we suggest that the applicability of this method is not that trivial or universal, and that several factors need to be taken into account when using online data in this line of research. However, this study also supports previous findings suggesting that the analysis of real-time online data is important in health assessment, as it tackles the long procedure of data collection and analysis in traditional survey methods, and provides us with information that could not be accessible otherwise

The urban transformation with new legal regulations

All around the world, cities need projects and applications for renewal, transformation, resettlement and improvement due to reasons such as economic reasons, inadequacy in social development, excessive population accumulation, wrong place selection and natural disasters. Many project application examples are available in the world and in our country. They vary in their purpose, form of implementation, organizational patterns and outcomes. In the process of reclaiming troubled areas of cities; a spatial transformation is also being studied at the same time to ensure the social and cultural development. In this context, the process of urban transformation in our country has been evaluated from the perspective of new legal regulations. In the study, firstly, the urban transformation was very briefly defined and it was focused on the historical development of urban transformation in the Turkey and world. Secondly, Past legal regulations and current legal regulations regard to urban transformation were examined. Lastly, the new legislation was critically discussed. Also the current trends related to urban transformation are investigated