The Use of a Health Compliance Monitoring System During the COVID-19 Pandemic in Indonesia: Evaluation Study.

BACKGROUND: COVID-19 cases are soaring in Asia. Indonesia, Southeast Asia's most populous country, is now ranked second in the number of cases and deaths in Asia, after India. The compliance toward mask wearing, social distancing, and hand washing needs to be monitored to assess public behavioral changes that can reduce transmission. OBJECTIVE: This study aimed to evaluate this compliance in Indonesia between October 2020 and May 2021 and demonstrate the use of the Bersatu Lawan COVID-19 (BLC) mobile app in monitoring this compliance. METHODS: Data were collected in real time by the BLC app from reports submitted by personnel of military services, police officers, and behavioral change ambassadors. Subsequently, the data were analyzed automatically by the system managed by the Indonesia National Task Force for the Acceleration of COVID-19 Mitigation. RESULTS: Between October 1, 2020, and May 2, 2021, the BLC app generated more than 165 million reports, with 469 million people monitored and 124,315,568 locations under observation in 514 districts/cities in 34 provinces in Indonesia. This paper grouped them into 4 colored zones, based on the degree of compliance, and analyzed variations among regions and locations. CONCLUSIONS: Compliance rates vary among the 34 provinces and among the districts and cities of those provinces. However, compliance to mask wearing seems slightly higher than social distancing. This finding suggests that policy makers need to promote higher compliance in other measures, including social distancing and hand washing, whose efficacies have been proven to break the chain of transmission when combined with masks wearing.

Building on health security capacities in Indonesia: Lessons learned from the COVID-19 pandemic responses and challenges.

As an active member country of the WHO's International Health Regulation and Global Health Security Agenda, Indonesia, the world's fourth-most populous and largest archipelagic country has recorded the second-highest COVID-19 cases in Asia with over 1.8 million cases in early June 2021. This geographically and socially diverse country has a dynamic national and sub-national government coordination with decentralized authorities that can complicate a pandemic response which often requires nationally harmonized policies, adaptability to sub-national contexts and global interconnectedness. This paper analyses and reviews COVID-19 public data, regulations, guidance documents, statements and other related official documents to present a narrative that summarizes the government's COVID-19 response strategies. It further analyses the challenges and achievements of the country's zoonotic diseases preparedness and responses and lastly provides relevant recommendations. Findings are presented in four sections according to the Global Health Security Agenda capacities, namely epidemiological surveillance (detect capacity); laboratory diagnostic testing (respond capacity); data management and analysis (enable capacity); and the role of sub-national governments. The COVID-19 pandemic has been a catalyst for the rapid transformation of existing surveillance systems, inter-related stakeholder coordination and agile development from the pre-pandemic health security capacities. This paper offers several recommendations on surveillance, laboratory capacity and data management, which might be useful for Indonesia and other countries with similar characteristics beyond the COVID-19 response, such as achieving long-term health security, zoonoses and pandemic prevention, as well as a digital transformation of their governmental capacities.

A spatial-temporal description of the SARS-CoV-2 infections in Indonesia during the first six months of outbreak.

BACKGROUND: Since the first cases reported in Wuhan, China, in December 2019, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has spread worldwide. In Indonesia, the first case was reported in early March 2020, and the numbers of confirmed infections have been increasing until now. Efforts to contain the virus globally and in Indonesia are ongoing. This is the very first manuscript using a spatial-temporal model to describe the SARS-CoV-2 transmission in Indonesia, as well as providing a patient profile for all confirmed COVID-19 cases. METHOD: Data was collected from the official website of the Indonesia National Task Force for the Acceleration of COVID-19, from the period of 02 March 2020-02 August 2020. The data from RT-PCR confirmed, SARS-CoV-2 positive patients was categorized according to demographics, symptoms and comorbidities based on case categorization (confirmed, recovered, dead). The data collected provides granular and thorough information on time and geographical location for all 34 Provinces across Indonesia. RESULTS: A cumulative total of 111,450 confirmed cases of were reported in Indonesia during the study period. Of those confirmed cases 67.79% (75,551/111,450) were shown as recovered and 4.83% (5,382/111,450) of them as died. Patients were mostly male (50.52%; 56,300/111,450) and adults aged 31 to 45 years old (29.73%; 33,132/111,450). Overall patient presentation symptoms of cough and fever, as well as chronic disease comorbidities were in line with previously published data from elsewhere in South-East Asia. The data reported here, shows that from the detection of the first confirmed case and within a short time period of 40 days, all the provinces of Indonesia were affected by COVID-19. CONCLUSIONS: This study is the first to provide detailed characteristics of the confirmed SARS-CoV-2 patients in Indonesia, including their demographic profile and COVID-19 presentation history. It used a spatial-temporal analysis to present the epidemic spread from the very beginning of the outbreak throughout all provinces in the country. The increase of new confirmed cases has been consistent during this time period for all provinces, with some demonstrating a sharp increase, in part due to the surge in national diagnostic capacity. This information delivers a ready resource that can be used for prediction modelling, and is utilized continuously by the current Indonesian Task Force in order to advise on potential implementation or removal of public distancing measures, and on potential availability of healthcare capacity in their efforts to ultimately manage the outbreak.

Knowledge, Attitudes, and Behaviors on Utilizing Mobile Health Technology for TB in Indonesia: A Qualitative Pilot Study.

Tuberculosis (TB) infections remain a global health burden with a high incidence rate in South-East Asia, including Indonesia. TB control strategy is founded on early case detection and complete treatment to minimize transmission and prevent the emergence of drug resistance. However, many patients face challenges to comply with daily medication, causing many to adhere inconsistently or stop prematurely. Technological solutions could enhance adherence to treatment and support national screening and follow-up policies. These include telephone video communication, enabling health professionals to watch patients take their medication, address patients' concerns, and provide advice and support. This manuscript describes the outcome of a qualitative pilot study, based on a series of focus group discussions to assess the knowledge, attitudes, and behaviors, on the potential utilization of mobile technology for health purposes with a particular focus on TB treatment follow-up. The findings illustrate that general knowledge of mobile health technologies, of their legal framework of operations, and of their exact potential within the healthcare system is incomplete or poor. The novel findings are as follows: (a) the willingness of participants to learn about these technologies, (b) the open and welcoming attitude toward receiving such information even within frontline community settings, and (c) the willingness to back a government-supported, healthcare-driven set of such initiatives. Potential implementation barriers have also been highlighted. This study is an important first step toward understanding the attitudes and behaviors on utilizing mobile health technology for TB in Indonesia.

COVID-19 and informal settlements - implications for water, sanitation and health in India and Indonesia.

Informal settlements are home to over 1 billion people worldwide and are characterised by high population densities and poor environmental conditions. The authors identify the impact of COVID-19 on existing water and sanitation practices and potential pathways for the transmission of COVID-19 in informal settlements in India and Indonesia. In the short term, there is an urgent need for mobile and contactless hand washing, washing/bathing facilities and toilets. In the long term, COVID-19 provides an opportunity to invest in centralised water and sanitation networked solutions appropriate for high-density settings to integrate those settlements into cities and improve environmental conditions and health in these cities.

Surveillance and characterisation of influenza viruses among patients with influenza-like illness in Bali, Indonesia, July 2010-June 2014.

BACKGROUND: Although Indonesia has high fatality rate of human A/H5N1 cases, epidemiological and clinical data on influenza virus circulation among humans has been limited. Within Indonesia, Bali province is of interest due to high population densities of humans, pigs and poultry. This study aims to characterize and compare the epidemiological and clinical patterns of influenza viruses in humans through surveillance among patients with influenza-like illness (ILI) in Bali, Indonesia. METHODS: ILI patients were recruited at 21 sentinel health facilities across all nine regencies in Bali, from July 2010 to June 2014. PCR-based assays were used for detection and subtyping of influenza viruses. Demographic, behavioural and clinical data were tested for associations with influenza using chi-squared tests and logistic regression. RESULTS: Of 2077 ILI patients, 291 (14.0%) tested positive for influenza A, 152 (7.3%) for influenza B, and 16 (0.77%) for both influenza A and B. Of the influenza A isolates, the majority 61.2% were A/H3N2, followed by A/H1N1-pdm09 (80; 26.1%). Two A/H5N1 were identified. Influenza positive rates were significantly higher during wet season months (28.3%), compared with the dry season (13.8%; χ2 = 61.1; df = 1; p < 0.0001). Clinical predictors for infection varied by virus type, with measured fever (≥38 °C) more strongly associated with influenza B (AOR: 1.62; 95% CI: 1.10, 2.39). CONCLUSION: Influenza circulates year-round among humans in Bali with higher activity during the wet season. High contact rates with poultry and pigs, along with influenza virus detection that could not be subtyped through conventional assays, highlight the need for molecular studies to characterize epidemiological and evolutionary dynamics of influenza in this setting.

Human influenza A H5N1 in Indonesia: health care service-associated delays in treatment initiation.

BACKGROUND: Indonesia has had more recorded human cases of influenza A H5N1 than any other country, with one of the world's highest case fatality rates. Understanding barriers to treatment may help ensure life-saving influenza-specific treatment is provided early enough to meaningfully improve clinical outcomes. METHODS: Data for this observational study of humans infected with influenza A H5N1 were obtained primarily from Ministry of Health, Provincial and District Health Office clinical records. Data included time from symptom onset to presentation for medical care, source of medical care provided, influenza virology, time to initiation of influenza-specific treatment with antiviral drugs, and survival. RESULTS: Data on 124 human cases of virologically confirmed avian influenza were collected between September 2005 and December 2010, representing 73% of all reported Indonesia cases. The median time from health service presentation to antiviral drug initiation was 7.0 days. Time to viral testing was highly correlated with starting antiviral treatment (p < 0.0001). We found substantial variability in the time to viral testing (p = 0.04) by type of medical care provider. Antivirals were started promptly after diagnosis (median 0 days). CONCLUSIONS: Delays in the delivery of appropriate care to human cases of avian influenza H5N1 in Indonesia appear related to delays in diagnosis rather than presentation to health care settings. Either cases are not suspected of being H5N1 cases until nearly one week after presenting for medical care, or viral testing and/or antiviral treatment is not available where patients are presenting for care. Health system delays have increased since 2007.