The low-cost and easy-to-use nature of rapidly developed PM_2.5 sensors provide an opportunity to bring breakthroughs in PM_2.5 research to resource-limited countries in Southeast Asia (SEA). This review provides an evaluation of the currently available literature and identifies research priorities in applying low-cost sensors (LCS) in PM_2.5 environmental and health research in SEA. The research priority is an outcome of a series of participatory workshops under the umbrella of the International Global Atmospheric Chemistry Project–Monsoon Asia and Oceania Networking Group (IGAC–MANGO). A literature review and research prioritization are conducted with a transdisciplinary perspective of providing useful scientific evidence in assisting authorities in formulating targeted strategies to reduce severe PM_2.5 pollution and health risks in this region. The PM_2.5 research gaps that could be filled by LCS application are identified in five categories: source evaluation, especially for the distinctive sources in the SEA countries; hot spot investigation; peak exposure assessment; exposure–health evaluation on acute health impacts; and short-term standards. The affordability of LCS, methodology transferability, international collaboration, and stakeholder engagement are keys to success in such transdisciplinary PM_2.5 research. Unique contributions to the international science community and challenges with LCS application in PM_2.5 research in SEA are also discussed.

Background: Air pollution is an important risk factor for the disease burden; however there is limited evidence in Indonesia on the effect of air pollution on health, due to lack of exposure and health outcome data. The objective of this study is to evaluate the potential use of the IFLS data for response part of urban-scale air pollution exposure–health response studies. Methods: Relevant variables were extracted based on IFLS5 documentation review. Analysis of the spatial distribution of respondent, data completeness, prevalence of relevant health outcomes, and consistency or agreement evaluation between similar variables were performed. Power for ideal sample size was estimated. Results: There were 58,304 respondents across 23 provinces, with the highest density in Jakarta (750/district). Among chronic conditions, hypertension had the highest prevalence (15–25%) with data completeness of 79–83%. Consistency among self-reported health outcome variables was 90–99%, while that with objective measurements was 42–70%. The estimated statistical power for studying air pollution effect on hypertension (prevalence = 17%) in Jakarta was approximately 0.6 (α = 0.1). Conclusions: IFLS5 data has potential use for epidemiological study of air pollution and health outcomes such as hypertension, to be coupled with high quality urban-scale air pollution exposure estimates, particularly in Jakarta.

(1) Background: The aim of this study was to understand the factors associated with vaccine hesitancy and refusal in Indonesia using the Social–Ecological Model (SEM). (2) Methods: Data on demographics, religiosity, family dynamics, and perceptions of public health efforts were collected through an online survey and compared to the rates of vaccine hesitancy and refusal. (3) Results: Income and sex were significantly associated with vaccine hesitancy. Based on a vaccine passport policy to enter public spaces, people who felt inhibited to enter public spaces or perceived privacy threats were twice as likely to exhibit vaccine hesitancy. Participants who believed that religious groups had a difficult time getting vaccinated were nearly twice as likely to exhibit vaccine hesitancy and three times more likely to exhibit vaccine refusal. However, participants who believed in a higher religious power were 58% less likely to exhibit vaccine hesitancy. Religious leaders significantly influenced participants to make the decision regarding vaccination. Individuals with vaccine refusal were more than twice as likely to share information with others without fact-checking. Notably, structural barriers such as distance and transportation were most strongly associated with vaccine hesitancy and refusal. (4) Conclusion: Cultural factors play a significant role in vaccine hesitancy and refusal. The SEM can be used to propose multi-level interventions with collaboration and communication among stakeholders to improve community health.

Although the field of medicine has made significant progress in recent decades, low and middle-income countries continue to face significant difficulties in addressing the unprecedented obstacles to improving health. Medical schools should play a critical role in driving health services reform and take on a new leadership role in strengthening the health system. This paper discusses a conceptual framework and outlines the Academic Health Systems (AHS) agenda in Indonesia. A comprehensive analysis was conducted on the existing literature and frameworks regarding AHS, applying the system thinking method, which engages stakeholders actively. The findings and interpretations from interviews, focus groups, and collaborative workshops were consolidated and a set of proposed frameworks, fundamental principles, and a route for enhancing the health system in Indonesia were put forward. Our recommendations include transformative learning, community engagement, and translational research as pillar principles of AHS program. These recommendations have the potential to be modified and implemented in other low- and middle-income countries.

Coronavirus Disease 2019 (COVID-19) spread quickly and reached epidemic levels worldwide. West Java is Indonesia’s most populous province and has a high susceptibility to the transmission of the disease, resulting in a significant number of COVID-19 cases. Therefore, this research aimed to determine the influencing factors as well as the spatial and temporal distribution of COVID-19 in West Java. Data on COVID-19 cases in West Java obtained from PIKOBAR were used. Spatial distribution was described using a choropleth, while the influencing factors were evaluated with regression analysis. To determine whether COVID-19s policies and events affected its temporal distribution, the cases detected were graphed daily or biweekly with information on those two variables. Furthermore, the cumulative incidence was described in the linear regression analysis model as being significantly influenced by vaccinations and greatly elevated by population density. The biweekly chart had a random pattern with sharp decreases or spikes in cumulative incidence changes. Spatial and temporal analysis helps greatly in understanding distribution patterns and their influencing factors, specifically at the beginning of the pandemic. Plans and strategies for control and assessment programs may be supported by this study material.

Although risk factors for hospitalization from a respiratory syncytial virus (RSV) are well known, RSV lower respiratory tract infections (LRIs) in the community are much less studied or understood, especially in developing countries. In a prospective, cohort study we studied factors predisposing Indonesian infants and children under 5 years of age to developing RSV LRIs. Subjects were enrolled in two cohorts: a birth cohort and a cross-sectional cohort of children <48 months of age. Subjects were visited weekly at home to identify any LRI, using the World Health Organization’s criteria. RSV etiology was determined through analysis of nasal washings by enzyme immunoassay and polymerase chain reaction. Risk factors for the development of the first documented RSV LRI were identified by multivariate analysis using logistic regression and Cox proportional hazard modeling. Of the 2014 children studied, 999 were enrolled within 30 days of birth. There were 149 first episodes of an RSV. Risk factors for an RSV LRI were poverty (p < 0.01), use of kerosene as a cooking fuel (p < 0.05), and household ownership of rabbits and chickens (p < 0.01). Our findings suggested that in a middle-income country such as Indonesia, with a substantial burden of RSV morbidity and mortality, lower socioeconomic status, environmental air quality, and animal exposure are predisposing factors for developing an RSV LRI.