The increasing of Independent National Health Insurance (NHI) membership is one of the indicators to achieve Universal Health Coverage. One of the challenges in extending such coverage is reaching it out to the informal sector. This study was aimed to analyze the determinants of Independent NHI ownership in Indonesia. This study used cross-sectional design, and the data were derived from Indonesian Family Live Survey (IFLS) 2014. 6,888 individuals aged ≥40 years were the sample of this study. To analyze the data, chi-square analysis and logistic regression were used. Based on the analysis, the proportion of respondent with Independent NHI is 16.6%. The ownership of independent NHI is influenced by the following factors: age 40-55 (PR=1.72 95%CI 1.41-2.09, p-value <0.001), Sumatra Island (PR=7.67 95%CI 5.55-10.59 p-value<0.001), very rich (PR = 2.26 95%CI 1.85-2.75 p-value <0.001), history of chronic disease (PR=1.33 95%CI 1.15-1.53 p-value<0.001), junior high school (PR = 2.21 95%CI 1.92-2.55 p-value<0.001), and urban (PR=1.79 95%CI 1.57-2.04 p-value <0.001). Region is the most dominant variable related to NHI ownership (p-value <0.001; Exp B= 7.03; 95% CI: 5.06-9.77). Independent NHI membership has not been maximal, yet. To increase this participation, the Social Security Administrator should approach each region with low NHI membership through promotion, socialization, and education about registration and the benefits of independent NHI.

Introduction: Delays in case-based surveillance are a significant obstacle to eliminating malaria. This study aims to optimise the current electronic malaria information system (eMIS), which uses an eMIS android-based mobile. Method: Qualitative research was conducted in the Lahat District, South Sumatra Province. We select informants directly involved in, or playing a role in, malaria control activities, such as program managers at community health centres (Puskemas) and health services. The system is built using the Framework for the Application of Systems Techniques (FAST), a structured method for analysing complex systems. It helps professionals break down problems, uncover hidden relationships, and make better decisions. FAST is widely used in engineering, project management, and business process optimisation to tackle complex challenges and improve problem-solving. Results: The paper revealed suggestions for designing and optimising the current eMIS. One recommended method for overcoming the obstacles is to optimise an eMIS Android-based mobile for malaria reporting. Eight key informants participated in the interviews. The main obstacles by key informants are the free time available for giving deep information and annoyance from outside during interviews due to their other activities. Hence, the researchers made appointments with informants and interviewed in a particular room, not the informant’s office. Conclusion: Our study demonstrates that eMIS android-based mobile is a better refinement of the current eMIS based on PC to support malaria surveillance for malaria elimination programs in an endemic area. Data entry with an eMIS android-based mobile is faster, more accurate, and allows real-time collaboration, outperforming sheet applications.

Many countries are trying to achieve Universal Health Coverage. Indonesia wanted to do this by implementing National Health Insurance in 2014. The purpose of this study is to explore the demand for health services based on visits to service providers, for both outpatient and inpatient care. This study used secondary data from wave five of the Indonesia Family Life Survey (IFLS). The sample used in this study comprised 34,177 individuals who were aged >15 years old. In this study, the demand for health services was measured based on whether respondents had visited a healthcare facility and their number of visits to healthcare facilities. Data was analysed using bivariate analysis with chi-square and multivariate analysis using the negative binomial regression model and probit model. The proportion of respondents visiting healthcare facilities for outpatient care was 16%, while for inpatient visits it was 5%. Both models produced almost the same effect in indicating the probability of individuals visiting a healthcare facility and their number of visits. Age, gender, marital status, education level, economic level, having health insurance, region, health status, chronic disease, and the number of diseases were statistically significant (P < 0.001) in influencing outpatient service demand. Age, gender, marital status, education level, economic level, having health insurance, regional status, health status, and the number of diseases were statistically significant (P < 0.001) in influencing inpatient service demand. Individual characteristics, demographics, and health status were independent factors associated with demand for healthcare services. The government should consider these factors in expanding health service demand in Indonesia.
Health Services