We performed a time series analysis to investigate the potential association between exposure to ambient air pollution and type 2 diabetes (T2D) incidence in the Chinese population. Monthly time series data between 2008 and 2015 on ambient air pollutants and incident T2D (N = 25,130) were obtained from the Environment Monitoring Center of Ningbo and the Chronic Disease Surveillance System of Ningbo. Relative risks (RRs) and 95% confidence intervals (95% CIs) of incident T2D per 10 μg/m³ increases in ambient air pollutants were estimated from Poisson generalized additive models. Exposure to particulate matter < 10 μm (PM₁₀) and sulfur dioxide (SO₂) was associated with increased T2D incidence. The relative risks (RRs) of each increment in 10 μg/m³ of PM₁₀ and SO₂ were 1.62 (95% CI, 1.16–2.28) and 1.63 (95% CI, 1.12–2.38) for overall participants, whereas for ozone (O₃) exposure, the RRs were 0.78 (95% CI, 0.68–0.90) for overall participants, 0.78 (95% CI, 0.69–0.90) for males, and 0.78 (95% CI, 0.67–0.91) for females, respectively. Exposure to PM₁₀ and SO₂ is positively associated with T2D incidence, whereas O₃ is negatively associated with T2D incidence.

Most studies of short-term exposure to ambient air pollution and cerebrovascular diseases focused on specific stroke-related outcomes, and results were inconsistent due to data unavailability and limited sample size. It is unclear yet how ambient air pollution contributes to the total cardiovascular mortality in central China. Daily deaths from cerebrovascular diseases were obtained from the Disease Surveillance Point System (DSPs) of Wuhan Center for Disease Control and Prevention during the period from 2013 to 2019. Air pollution data were obtained from Wuhan Ecology and Environment Institute from 10 national air quality monitoring stations, including average daily PM₂.₅, PM₁₀, SO₂, NO₂, and O₃. Average daily temperature and relative humidity were obtained from Wuhan Meteorological Bureau. We performed a Poisson regression in generalized additive models (GAM) to examine the association between ambient air pollution and cerebrovascular disease mortality. We observed a total of 84,811 deaths from cerebrovascular diseases from 1 January 2013 to 31 December 2019 in Wuhan. Short-term exposure to PM₂.₅, PM₁₀, SO₂, and NO₂ was positively associated with daily deaths from cerebrovascular diseases, and no significant association was found for O₃. The largest effect on cerebrovascular disease mortality was found at lag0 for PM₂.₅ (ERR: 0.927, 95% CI: 0.749–1.105 per 10 μg/m3) and lag1 for PM₁₀ (ERR: 0.627, 95% CI: 0.493–0.761 per 10 μg/m³), SO₂ (ERR: 2.518, 95% CI: 1.914, 3.122 per 10 μg/m³), and NO₂ (ERR: 1.090, 95% CI: 0.822–1.358 per 10 μg/m³). The trends across lags were statistically significant. The stratified analysis demonstrated that females were more susceptible to SO₂ and NO₂, while elder individuals aged above 65 years old, compared with younger people, suffered more from air pollution, especially from SO₂. Short-term exposure to PM₂.₅, PM₁₀, SO₂, and NO₂ were significantly associated with a higher risk of cerebrovascular disease mortality, and elder females seemed to suffer more from air pollution. Further research is required to reveal the underlying mechanisms.

The scarlet fever incidence has increased drastically in recent years in China. However, the long-term relationship between climate variation and scarlet fever remains contradictory, and an early detection system is lacking. In this study, we aim to explore the potential long-term effects of variations in monthly climatic parameters on scarlet fever and to develop an early scarlet-fever detection tool. Data comprising monthly scarlet fever cases and monthly average climatic variables from 2004 to 2017 were retrieved from the Notifiable Infectious Disease Surveillance System and National Meteorological Science Center, respectively. We used a negative binomial multivariable regression to assess the long-term impacts of weather parameters on scarlet fever and then built a novel forecasting technique by integrating an autoregressive distributed lag (ARDL) method with a nonlinear autoregressive neural network (NARNN) based on the significant meteorological drivers. Scarlet fever was a seasonal disease that predominantly peaked in spring and winter. The regression results indicated that a 1 °C increment in the monthly average temperature and a 1-h increment in the monthly aggregate sunshine hours were associated with 17.578% (95% CI 7.674 to 28.393%) and 0.529% (95% CI 0.035 to 1.025%) increases in scarlet fever cases, respectively; a 1-hPa increase in the average atmospheric pressure at a 1-month lag was associated with 12.996% (95% CI 9.972 to 15.919%) decrements in scarlet fever cases. Based on the model evaluation criteria, the best-performing basic and combined approaches were ARDL(1,0,0,1) and ARDL(1,0,0,1)-NARNN(5, 22), respectively, and this hybrid approach comprised smaller performance measures in both the training and testing stages than those of the basic model. Climate variability has a significant long-term influence on scarlet fever. The ARDL–NARNN technique with the incorporation of meteorological drivers can be used to forecast the future epidemic trends of scarlet fever. These findings may be of great help for the prevention and control of scarlet fever.

Cholera is a global public health problem with high endemicity in many developing countries in Africa. In 2014, Ghana experienced its largest epidemic with more than 20,000 cases and 200 deaths; most of it occurred in the Accra Metropolitan Area (AMA). Ghana’s disease surveillance system is mainly clinically based and focused on case detection and management. Environmental exploration for the etiological agents is missing from the surveillance strategy. This study therefore assessed the occurrence of toxigenic Vibrio cholerae in water storage systems in selected high risk areas in the AMA area prior to the 2014 outbreak. Three hundred twenty water samples from 80 households’ water storage systems were analyzed for toxigenic Vibrio cholerae using the bacterial culture method. Presumptive V. cholerae was isolated from 83.8% of households’ water storage systems. The viable cells ranged from 1 to 1400 CFU/100 ml. Vibrio cholerae O1 serotype was isolated from five households in Old Fadama, one household in Shiabu, and one household in Bukom in the month of May and a similar trend was observed for the months of June and July. The presence of Vibro cholerae in the water storage vessels used for drinking confirms the need to consider environmental surveillance for toxigenic Vibro cholerae particularly in high-risk areas to strengthen the existing surveillance system.

An outbreak of food-poisoning caused by a urea-hydrolyzing strain of Vibrio parahaemolytictis is presented. In the outbreak, urea-hydrolyzing V. parahaemolyticus was the only suspected etiological agent isolated from patients. In other respects, the strains conformed to the general characteristics of V. parahaemolyticus. The isolates were positive to the Kanagawa phenomenon and thermal direct hemolysin test. Urea-hydrolyzing strains of V, parahaemolyticus have rarely been reported and have not been described previously as a cause of food-poisoning in Japan. Eleven urea-hydrolyzing strains were found among the strains of V. parahaemolyticus isolated in our laboratory previously from various materials. Six of them were isolated from travellers to South-East Asia. A total of 16 strains of urea-hydrolyzing V.pamkaemolyticus was typed into 7 serovars (e. g. O4: K12, O6: K18, O1 : KUT. O3: K15 etc.).