OBJECTIVE: The aim of this study was to determine the association between wheezing symptoms among toddlers attending DCCs and indoor particulate matter, PM10, PM2.5, and microbial count level in urban DCCs in the District of Seremban, Malaysia.
METHODS: Data collection was carried out at 10 DCCs located in the urban area of Seremban. Modified validated questionnaires were distributed to parents to obtain their children's health symptoms. The parameters measured were indoor PM2.5, PM10, carbon monoxide, total bacteria count, total fungus count, temperature, air velocity, and relative humidity using the National Institute for Occupational Safety and Health analytical method.
RESULTS: All 10 DCCs investigated had at least one indoor air quality parameter exceeding the acceptable level of standard guidelines. The prevalence of toddlers having wheezing symptoms was 18.9%. There was a significant different in mean concentration of PM2.5 and total bacteria count between those with and those without wheezing symptoms (P = 0.02, P = 0.006).
CONCLUSIONS: Urban DCCs are exposed to many air pollutants that may enter their buildings from various adjacent sources. The particle concentrations and presence of microbes in DCCs might increase the risk of exposed children for respiratory diseases, particularly asthma, in their later life.
METHODS: The International Society of Global Health (ISoGH) used the Child Health and Nutrition Research Initiative (CHNRI) method to identify research priorities for future pandemic preparedness. Eighty experts in global health, translational and clinical research identified 163 research ideas, of which 42 experts then scored based on five pre-defined criteria. We calculated intermediate criterion-specific scores and overall research priority scores from the mean of individual scores for each research idea. We used a bootstrap (n = 1000) to compute the 95% confidence intervals.
RESULTS: Key priorities included strengthening health systems, rapid vaccine and treatment production, improving international cooperation, and enhancing surveillance efficiency. Other priorities included learning from the coronavirus disease 2019 (COVID-19) pandemic, managing supply chains, identifying planning gaps, and promoting equitable interventions. We compared this CHNRI-based outcome with the 14 research priorities generated and ranked by ChatGPT, encountering both striking similarities and clear differences.
CONCLUSIONS: Priority setting processes based on human crowdsourcing - such as the CHNRI method - and the output provided by ChatGPT are both valuable, as they complement and strengthen each other. The priorities identified by ChatGPT were more grounded in theory, while those identified by CHNRI were guided by recent practical experiences. Addressing these priorities, along with improvements in health planning, equitable community-based interventions, and the capacity of primary health care, is vital for better pandemic preparedness and response in many settings.