Investigation of the impacts of climate change and rising temperature on food poisoning cases in Malaysia.

This study is an attempt to investigate climate-induced increases in morbidity rates of food poisoning cases. Monthly food poisoning cases, average monthly meteorological data, and population data from 2004 to 2014 were obtained from the Malaysian Ministry of Health, Malaysian Meteorological Department, and Department of Statistics Malaysia, respectively. Poisson generalised linear models were developed to assess the association between climatic parameters and the number of reported food poisoning cases. The findings revealed that the food poisoning incidence in Malaysia during the 11 years study period was 561 cases per 100 000 population for the whole country. Among the cases, females and the ethnic Malays most frequently experienced food poisoning with incidence rates of 313 cases per 100,000 and 438 cases per 100,000 population over the period of 11 years, respectively. Most of the cases occurred within the active age of 13 to 35 years old. Temperature gave a significant impact on the incidence of food poisoning cases in Selangor (95% CI: 1.033-1.479; p = 0.020), Melaka (95% CI: 1.046-2.080; p = 0.027), Kelantan (95% CI: 1.129-1.958; p = 0.005), and Sabah (95% CI: 1.127-2.690; p = 0.012) while rainfall was a protective factor in Terengganu (95% CI: 0.996-0.999; p = 0.034) at lag 0 month. For a 1.0°C increase in temperature, the excess risk of food poisoning in each state can increase up to 74.1%, whereas for every 50 mm increase in rainfall, the risk of getting food poisoning decreased by almost 10%. The study concludes that climate does affect the distribution of food poisoning cases in Selangor, Melaka, Kelantan, Sabah, and Terengganu. Food poisoning cases in other states are not directly associated with temperature but related to monthly trends and seasonality.

Short-Term Forecasting of Daily Confirmed COVID-19 Cases in Malaysia Using RF-SSA Model.

Novel coronavirus (COVID-19) was discovered in Wuhan, China in December 2019, and has affected millions of lives worldwide. On 29th April 2020, Malaysia reported more than 5,000 COVID-19 cases; the second highest in the Southeast Asian region after Singapore. Recently, a forecasting model was developed to measure and predict COVID-19 cases in Malaysia on daily basis for the next 10 days using previously-confirmed cases. A Recurrent Forecasting-Singular Spectrum Analysis (RF-SSA) is proposed by establishing L and ET parameters via several tests. The advantage of using this forecasting model is it would discriminate noise in a time series trend and produce significant forecasting results. The RF-SSA model assessment was based on the official COVID-19 data released by the World Health Organization (WHO) to predict daily confirmed cases between 30th April and 31st May, 2020. These results revealed that parameter L = 5 (T/20) for the RF-SSA model was indeed suitable for short-time series outbreak data, while the appropriate number of eigentriples was integral as it influenced the forecasting results. Evidently, the RF-SSA had over-forecasted the cases by 0.36%. This signifies the competence of RF-SSA in predicting the impending number of COVID-19 cases. Nonetheless, an enhanced RF-SSA algorithm should be developed for higher effectivity of capturing any extreme data changes.

Impact of Climate Change on Air Quality and Public Health in Urban Areas.

This review discusses how climate undergo changes and the effect of climate change on air quality as well as public health. It also covers the inter relationship between climate and air quality. The air quality discussed here are in relation to the 5 criteria pollutants; ozone (O3), carbon dioxide (CO2), nitrogen dioxide (NO2), sulfur dioxide (SO2), and particulate matter (PM). Urban air pollution is the main concern due to higher anthropogenic activities in urban areas. The implications on health are also discussed. Mitigating measures are presented with the final conclusion.

A study on exposure to cyanide among a community living near a gold mine in Malaysia.

Cyanidation in gold extraction has detrimental impacts on the ecosystem and the population in surrounding areas. The research described in this article aimed to assess the health effects of environmental exposure to cyanide in a community near a gold mining area in Malaysia. A total of 255 members of the exposed community and 117 members of the control community answered questionnaires on their exposure to a gold mine and health outcomes. Urine samples were collected for biomarker assessment (i.e., urinary thiocyanate). The authors found that the mean level of urinary thiocyanate was significantly higher among the exposed community (0.30 ± 0.26 mg/dL) than the control community (0.24 ± 0.23 mg/dL) (p = .0021). The exposed community had a significantly higher prevalence of headaches (prevalence odds ratio [pOR] = 2.6, p = .001), dizziness (pOR = 3.7, p < .001), skin irritation (pOR = 2.1, p = .040), and eye irritation (pOR = 5.1, p = .004). No association occurred between dietary exposure and urinary biomarkers. Smokers living near a gold mine had a higher urinary thiocyanate level (0.56 mg/dL) than smokers in the control community (0.49 mg/dL).