The Comparison of Urinary Cadmium (UCd) and Urinary Lead (UPb) between 2007 and 2015 in a Population Living in a Zinc Contaminated Area

This paper compares urinary cadmium (UCd) and lead (UPb) between 2007 and 2015 in a population living in an area of zinc contamination and classified in terms of year, subdistrict, gender and gender broken down by age. A total of 441 participants from zinc contaminated areas gave urine samples in 2007 and again in 2015 for analysis of cadmium and lead concentrations. Urine was divided into 2 parts for: 1) cadmium and lead analysis by ICP-MS and 2) urinary creatinine (Cr) measurement by the modified Jaffe’s reaction method. The statistical analysis includes mean, frequency and percentage, paired t-test and ANOVA. The results show a statistically significant decrease in the urinary concentrations of cadmium and lead in 2015 compared to 2007 for: 1) all subdistricts, 2) year, 3) age group, 4) gender and 5) gender by age. The reduction was greater in gender by age of females than in that of males, but this was not statistically significant. The conclusion illustrates that UCd and UPb in terms of years, sub districts (Prathadpadeang, Mae Tao and Mae Ku), gender, and gender by age (a cross tabulation of gender and age) show a statistically significant decrease from 2007 to 2015

A systematic review of the physical health impacts from non-occupational exposure to wildfire smoke

Background: Climate change is likely to increase the threat of wildfires, and little is known about how wildfires affect health in exposed communities. A better understanding of the impacts of the resulting air pollution has important public health implications for the present day and the future. Method: We performed a systematic search to identify peer-reviewed scientific studies published since 1986 regarding impacts of wildfire smoke on health in exposed communities. We reviewed and synthesized the state of science of this issue including methods to estimate exposure, and identified limitations in current research. Results: We identified 61 epidemiological studies linking wildfire and human health in communities. The U.S. and Australia were the most frequently studied countries (18 studies on the U.S., 15 on Australia). Geographic scales ranged from a single small city (population about 55,000) to the entire globe. Most studies focused on areas close to fire events. Exposure was most commonly assessed with stationary air pollutant monitors (35 of 61 studies). Other methods included using satellite remote sensing and measurements from air samples collected during fires. Most studies compared risk of health outcomes between 1) periods with no fire events and periods during or after fire events, or 2) regions affected by wildfire smoke and unaffected regions. Daily pollution levels during or after wildfire in most studies exceeded U.S. EPA regulations. Levels of PM10, the most frequently studied pollutant, were 1.2 to 10 times higher due to wildfire smoke compared to non-fire periods and/or locations. Respiratory disease was the most frequently studied health condition, and had the most consistent results. Over 90% of these 45 studies reported that wildfire smoke was significantly associated with risk of respiratory morbidity.Conclusion: Exposure measurement is a key challenge in current literature on wildfire and human health. A limitation is the difficulty of estimating pollution specific to wildfires. New methods are needed to separate air pollution levels of wildfires from those from ambient sources, such as transportation. The majority of studies found that wildfire smoke was associated with increased risk of respiratory and cardiovascular diseases. Children, the elderly and those with underlying chronic diseases appear to be susceptible. More studies on mortality and cardiovascular morbidity are needed. Further exploration with new methods could help ascertain the public health impacts of wildfires under climate change and guide mitigation policies

SARIMA Model Forecasting Performance of the COVID-19 Daily Statistics in Thailand during the Omicron Variant Epidemic

This study aims to identify and evaluate a robust and replicable public health predictive model that can be applied to the COVID-19 time-series dataset, and to compare the model performance after performing the 7-day, 14-day, and 28-day forecast interval. The seasonal autoregressive integrated moving average (SARIMA) model was developed and validated using a Thailand COVID-19 open dataset from 1 December 2021 to 30 April 2022, during the Omicron variant outbreak. The SARIMA model with a non-statistically significant p-value of the Ljung–Box test, the lowest AIC, and the lowest RMSE was selected from the top five candidates for model validation. The selected models were validated using the 7-day, 14-day, and 28-day forward-chaining cross validation method. The model performance matrix for each forecast interval was evaluated and compared. The case fatality rate and mortality rate of the COVID-19 Omicron variant were estimated from the best performance model. The study points out the importance of different time interval forecasting that affects the model performance