Understanding the community interest of breast cancer in Indonesia: a digital epidemiology study using Google trends

Introduction. Breast cancer was count for 30.5% of all cancers diagnosed in Indonesia. Although preventable, breast cancer is mostly diagnosed in advanced stages and caused leading dead among females. Given the increasing growth of online information seeking behavior, adequate cancer health promotion through virtual setting is needed to tackle the massive increasing burden of breast cancer. Therefore this study aims to explore the community interest in breast cancer using Google Trends. Method. 5 years (from September 2013 to August 2018) information searches for breast cancer from Google were retrieved in Indonesian language. Data were downloaded at national and sub-region level to examine the pattern and distribution of queries. Results. Sporadic traces of information searches related to breast cancer from Google Trends presented the pattern and distribution of queries. Massive search happened in July 2015 and June 2017 following the died of Indonesian celebrity who suffered from breast cancer. However, the cancer awareness month every October does not impact the number of information searches. Considering the high influence of celebrities, many studies reveal the positive impact of celebrities involvement in health promotion. Celebrity can attract the public attention to health messages and increase the agreement of vaccination and screening for cancer. Thereby, celebrities involvement and availability of qualified breast cancer online information should be increased to win the breast cancer health promotion program in the digital era. Online information related to breast cancer could be disseminated through targetted risk population using Mobile JKN that has been downloaded by 1.5 million members. Conclusion. Google Trends could be potentially used as a novel tool to measure the dynamics of community interest in breast cancer in Indonesia. Therefore, adequate cancer health promotion through a virtual setting is the key to tackling the massive increasing burden of breast cancer in the digital era

Is health condition affect the online health information seeking behavior? a report from Indonesia

Background: Google Trends has increasingly received attention as the potential data source for diseases surveillance in the last two decades. In Indonesia, Google Trends was detected as a novel predictor for dengue outbreak in national and sub-national level. Although the accuracy depended on online information seeking behavior, no study was performed at the state level. This approach is necessary to be assessed in order to measure the representativeness of the online health information seeking pattern captured by Google Trends. Objective: This study aimed to examine the online health information seeking behavior according to the history of health condition among Indonesian aged 15-60 years old. Methods: Online health information seeking behavior’ survey was conducted in 2017, involved 385 respondents. Questions were asked in three different parts including the online health information seeking in general, the use of social media, and the use of search engines. Statistical analysis was conducted using Prevalence odds ratio (POR) in Stata version 13. Results: Prevalence odds ratio analysis shows that person who ever experiences ill in the last three months is 1.63 (CI 95% 1.06-2.50) more likely to have access to the online health information on the Internet. Online health information seeking behavior seem to be in-line both using social media and search engines. The person who ever experiences ill in last three months is more likely to have access to the online health information on social media (POR 1.60; CI 95% 0.95-2.74) and search engines (POR 2.89; CI 95% 1.63-5.28). Moreover, looking for disease information on social media (POR 1.61; CI 95% 1.04-2.49) and search engines (POR 2.23; CI 95% 1.43-3.51) also influenced by health condition. Conclusions: History of health condition affects online health information seeking behavior. Further research needs to assess the Indonesian online health information seeking behavior related to a certain disease

System Analysis of Dengue Virus Surveillance in BBTKL PP Surabaya Year 2012–2014

Changing the distribution of dengue virus serotypes has occurred in Indonesia. This condition should be monitored continuously through the Dengue Virus Surveillance. Implementation of Dengue Virus Surveillance also conducted by BBTKL PP Surabaya. The purpose of this study was to determine the workflow, identify problems, set priority problem, find the cause of the problem, and provide the alternative solution related to problems of Dengue Virus Surveillance in BBTKL PP Surabaya. This is a operational research and the informants are officers of Dengue Virus Surveillance in BBTKL PP Surabaya. Data in this study was analyzed descriptive and presented narrative. Results showed that the workflow of Dengue Virus Surveillance in BBTKL PP Surabaya are collecting of patient\u27s blood and vector specimen, vector survey and collecting the supporting data, Rapid Diagnostic Test (RDT) and Polymerase Chain Reaction (PCR), processing and data analysis, and dissemination the information. The main problems of Dengue Virus Surveillance in BBTKL PP Surabaya is the low quality of the information. Tree problem analysis showed that the cause of problem that can be intervene are incomplete supporting data and data storage. Alternative solution related to problems of Dengue Virus Surveillance in BBTKL PP Surabaya is use of Epi Info

Multimorbidity Patterns of Chronic Diseases among Indonesians: Insights from Indonesian National Health Insurance (INHI) Sample Data

Given the increasing burden of chronic diseases in Indonesia, characteristics of chronic multimorbidities have not been comprehensively explored. Therefore, this research evaluated chronic multimorbidity patterns among Indonesians using Indonesian National Health Insurance (INHI) sample data. We included 46 chronic diseases and analyzed their distributions using population-weighted variables provided in the datasets. Results showed that chronic disease patients accounted for 39.7% of total patients who attended secondary health care in 2015-2016. In addition, 43.1% of those were identified as having chronic multimorbidities. Findings also showed that multimorbidities were strongly correlated with an advanced age, with large numbers of patients and visits in all provinces, beyond those on Java island. Furthermore, hypertension was the leading disease, and the most common comorbidities were diabetes mellitus, cerebral ischemia/chronic stroke, and chronic ischemic heart disease. In addition, disease proportions for certain disease dyads differed according to age group and gender. Compared to survey methods, claims data are more economically efficient and are not influenced by recall bias. Claims data can be a promising data source in the next few years as increasing percentages of Indonesians utilize health insurance coverage. Nevertheless, some adjustments in the data structure are accordingly needed to utilize claims data for disease control and surveillance purposes

Multimorbidity Patterns of Chronic Diseases among Indonesians: Insights from Indonesian National Health Insurance (INHI) Sample Data

Given the increasing burden of chronic diseases in Indonesia, characteristics of chronic multimorbidities have not been comprehensively explored. Therefore, this research evaluated chronic multimorbidity patterns among Indonesians using Indonesian National Health Insurance (INHI) sample data. We included 46 chronic diseases and analyzed their distributions using population-weighted variables provided in the datasets. Results showed that chronic disease patients accounted for 39.7% of total patients who attended secondary health care in 2015–2016. In addition, 43.1% of those were identified as having chronic multimorbidities. Findings also showed that multimorbidities were strongly correlated with an advanced age, with large numbers of patients and visits in all provinces, beyond those on Java island. Furthermore, hypertension was the leading disease, and the most common comorbidities were diabetes mellitus, cerebral ischemia/chronic stroke, and chronic ischemic heart disease. In addition, disease proportions for certain disease dyads differed according to age group and gender. Compared to survey methods, claims data are more economically efficient and are not influenced by recall bias. Claims data can be a promising data source in the next few years as increasing percentages of Indonesians utilize health insurance coverage. Nevertheless, some adjustments in the data structure are accordingly needed to utilize claims data for disease control and surveillance purposes

Correlation between Google Trends on dengue fever and national surveillance report in Indonesia

Background: Digital traces are rapidly used for health monitoring purposes in recent years. This approach is growing as the consequence of increased use of mobile phone, Internet, and machine learning. Many studies reported the use of Google Trends data as a potential data source to assist traditional surveillance systems. The rise of Internet penetration (54.7%) and the huge utilization of Google (98%) indicate the potential use of Google Trends in Indonesia. No study was performed to measure the correlation between country wide official dengue reports and Google Trends data in Indonesia. Objective: This study aims to measure the correlation between Google Trends data on dengue fever and the Indonesian national surveillance report. Methods: This research was a quantitative study using time series data (2012–2016). Two sets of data were analyzed using Moving Average analysis in Microsoft Excel. Pearson and Time lag correlations were also used to measure the correlation between those data. Results: Moving Average analysis showed that Google Trends data have a linear time series pattern with official dengue report. Pearson correlation indicated high correlation for three defined search terms with R-value range from 0.921 to 0.937 (p ≤ 0.05, overall period) which showed increasing trend in epidemic periods (2015–2016). Time lag correlation also indicated that Google Trends data can potentially be used for an early warning system and novel tool to monitor public reaction before the increase of dengue cases and during the outbreak. Conclusions: Google Trends data have a linear time series pattern and statistically correlated with annual official dengue reports. Identification of information-seeking behavior is needed to support the use of Google Trends for disease surveillance in Indonesia

Applications of google search trends for risk communication in infectious disease management: A case study of COVID-19 outbreak in Taiwan

OBJECTIVE: An emerging outbreak of COVID-19 has been detected in at least 26 countries worldwide. Given this pandemic situation, robust risk communication is urgently needed particularly in affected countries. Therefore, this study explored the potential use of Google Trends (GT) to monitor public restlessness toward COVID-19 epidemic infection in Taiwan. METHODS: We retrieved GT data for the specific locations of Taiwan nationwide and subregions using defined search terms related to coronavirus, handwashing, and face masks. RESULTS: Searches related to COVID-19 and face masks in Taiwan increased rapidly, following the announcements of Taiwan’ first imported case and reached its peak as local cases were reported. However, searches for handwashing were gradually increased in period of face masks shortage. Moreover, high to moderate correlations between Google relative search volume (RSV) and COVID-19 cases were found in Taipei (lag-3), New Taipei (lag-2), Taoyuan (lag-2), Tainan (lag-1), Taichung (lag0), and Kaohsiung (lag0). CONCLUSION: In response to the ongoing outbreak, our results demonstrated that GT could potentially define the proper timing and location for practicing appropriate risk communication strategies to the affected population

Effects of the Government Response and Community Mobility on the COVID-19 Pandemic in Southeast Asia

Preventive policies and mobility restrictions are believed to work for inhibiting the growth rate of COVID-19 cases; however, their effects have rarely been assessed and quantified in Southeast Asia. We aimed to examine the effects of the government responses and community mobility on the COVID-19 pandemic in Southeast Asian countries. The study extracted data from Coronavirus Government Response Tracker, COVID-19 Community Mobility Report, and Our World in Data between 1 March and 31 December 2020. The government responses were measured by containment, health, and economic support index. The community mobility took data on movement trends at six locations. Partial least square structural equation modeling was used for bi-monthly analyses in each country. Results show that the community mobility generally followed government responses, especially the containment index. The path coefficients of government responses to community mobility ranged from −0.785 to −0.976 in March to April and −0.670 to −0.932 in May to June. The path coefficients of community mobility to the COVID-19 cases ranged from −0.058 to −0.937 in March to April and from −0.059 to −0.640 in September to October. It suggests that the first few months since the mobility restriction implemented is the optimal time to control the pandemic