Correction: Service availability and readiness of malaria surveillance information systems implementation at primary health centers in Indonesia.

[This corrects the article DOI: 10.1371/journal.pone.0284162.].

Service availability and readiness of malaria surveillance information systems implementation at primary health centers in Indonesia.

One of the most important indicators in malaria eradication is the malaria surveillance information system (SISMAL) for recording and reporting medical cases. This paper aims to describe the availability and readiness of SISMALs at primary health centers (PHCs) in Indonesia. A cross-sectional survey was implemented in seven provinces for this study. The data was analyzed using bivariate, multivariate, and linear regression. The availability of the information system was measured by assessing the presence of the electronic malaria surveillance information system (E-SISMAL) at the studied PHCs. The readiness was measured by averaging each component of the assessment. From 400 PHC samples, only 58.5% had available SISMALs, and their level of readiness was only 50.2%. Three components had very low levels of readiness: (1) the availability of personnel (40.9%), (2) SISMAL integration and storage (50.2%), and (3) the availability of data sources and indicators (56.8%). Remote and border (DTPK) areas had a 4% better readiness score than non-DTPK areas. Endemic areas were 1.4% better than elimination areas, while regions with low financial capacity were 3.78% better than regions with high financial capacity, with moderate capacity (2.91%). The availability rate of the SISMAL at PHCs is only 58.5%. Many PHCs still do not have SISMALs. The readiness of the SISMAL at these PHCs is significantly related to DTPK/remote area, high endemicity status, and low financial capacity. This study found that the implementation of SISMAL is more accessible to malaria surveillance for the remote area and regions with low financial capacity. Therefore, this effort will well-fit to address barrier to malaria surveillance in developing countries.

Disparities of Health Program Information Systems in Indonesia: A Cross-Sectional Indonesian Health Facility Research 2019.

Although a recording and reporting format for health centers already exists for Indonesia's standard information system, numerous health applications still need to meet the needs of each program. Therefore, this study aimed to demonstrate the potential disparities in information systems in the application and data collection of health programs among Indonesian community health centers (CHCs) based on provinces and regions. This cross-sectional research used data from 9831 CHCs from the Health Facilities Research 2019 (RIFASKES). Significance was assessed using a chi-square test and analysis of variance (ANOVA). The number of applications was depicted on a map using the spmap command with STATA version 14. It showed that region 2, which represented Java and Bali, was the best, followed by regions 1, which comprised Sumatra Island and its surroundings, and 3, Nusa Tenggara. The highest mean, equaling that of Java, was discovered in three provinces of region 1, namely, Jambi, Lampung, and Bangka Belitung. Furthermore, Papua and West Papua had less than 60% for all types of data-storage programs. Hence, there is a disparity in the health information system in Indonesia by province and region. The results of this analysis recommend future improvement of the CHCs' information systems.

Determinants of COVID-19 Knowledge, Perception and Attitudes in Indonesia: A Cross-Sectional Survey.

Many countries, including Indonesia, were gravely affected by the COVID-19 pandemic. While younger people were rarely severely affected by an infection, they still served as important spreaders of the disease. Therefore, the knowledge, perception and attitudes regarding COVID-19 of a mostly younger population was assessed in this study using a quantitative survey and semi-structured questionnaire. Out of 15 questions on COVID-19, males answered fewer questions (-1.26) correctly. Persons reporting more diseases in the last year (+0.49 per disease) that lived in a central area of Indonesia, and that had a better socio-economic status defined through household condition scores, had better knowledge of the symptoms, causes of and measures against COVID-19. Better knowledge independently predicted more responsible attitudes and stated behavior. Knowledge and understanding should be enhanced through information campaigns targeted specifically towards men, persons with poor socio-economic backgrounds and those living in the periphery of the state.

Health information system concept in health services in the national health insurance (JKN) era in Indonesia: An environment and one health approach.

The health information system is a component of the healthcare system. The health information system in health services in Indonesia has experienced many problems in getting support for policy making, the implementation of the industrial revolution 4.0, and national health insurance (JKN). To answer the above problems, it is necessary to make a concept of health information systems in health services that based on environment and one health perspectives. This research was part of the thematic research of the 2019 JKN National Health Facilities Survey (Rifaskes) in Indonesia. The systems approach and cross-sectional research were carried out by collecting quantitative data. A structural equation model with Lisrel 88 software was used to model the health information system. The health information system produced a concept that included the following structured input components: governance, human resources, infrastructure, types of information system (IS) (program, JKN, management), and financing; process components: funding, technical guidance, and verification and validation; and output components: open access, standards and quality, utilization, bridging, and security. The concept for strengthening the health information system prioritizes improving the output components (standards, utilization, bridging, open access, and security) in the process components (funding, verification, technical guidance) while the input components (financing, human resources, governance, IS programs, infrastructure, IS JKN, IS management).