Nowadays, most of the research on air pollution and its adverse effects on public health in China has focused on megacities and heavily-polluted regions. Fewer studies have focused on cities that are slightly polluted. Shenzhen used to have a favorable air environment, but its air quality has deteriorated gradually as a result of development in recent years. So far, no systematic investigations have been conducted on the adverse effects of air pollution on public health in Shenzhen. This research has applied a time series analysis model to study the possible association between different types of air pollution and respiratory hospital admission in Shenzhen in 2013. Respiratory hospital admission was divided into two categories for comparison analysis among various population groups: acute upper respiratory infection and acute lower respiratory infection. The results showed that short-term exposure to ambient air pollution was significantly associated with acute respiratory infection hospital admission in Shenzhen in 2013. Children under 14 years old were the main susceptible population of acute respiratory infection due to air pollution. PM₁₀, PM_2.5 and NO₂ were the primary air pollutants threatening respiratory health in Shenzhen. Though air pollution level is generally relatively low in Shenzhen, it will benefit public health to control the pollution of particulate matter as well as other gaseous pollutants.

Air pollutants have significant direct and indirect adverse effects on public health. To explore the relationship between air pollutants and meteorological conditions on the hospitalization for respiratory diseases, we collected a whole year of daily major air pollutants’ concentrations from Shenzhen city in 2013, including Particulate Matter (PM₁₀, PM_2.5), Nitrogen dioxide (NO₂), Ozone (O₃), Sulphur dioxide (SO₂), and Carbon monoxide (CO). Meanwhile, we also gained meteorological data. This study collected 109,927 patients cases with diseases of the respiratory system from 98 hospitals. We investigated the influence of meteorological factors on air pollution by Spearman correlation analysis. Then, we tested the short-term correlation between significant air pollutants and respiratory diseases’ hospitalization by Distributed Lag Non-linear Model (DLNM). There was a significant negative correlation between the north wind and NO₂ and a significant negative correlation between the south wind and six pollutants. Except for CO, other air pollutants were significantly correlated with the number of hospitalized patients during the lag period. Most of the pollutants reached maximum Relative Risk (RR) with a lag of five days. When the time lag was five days, the annual average of PM₁₀, PM_2.5, SO₂, NO₂, and O₃ increased by 10%, and the risk of hospitalization for the respiratory system increased by 0.29%, 0.23%, 0.22%, 0.25%, and 0.22%, respectively. All the pollutants except CO impact the respiratory system’s hospitalization in a short period, and PM10 has the most significant impact. The results are helpful for pollution control from a public health perspective.

How to maintain public transit safety and sustainability has become a major concern for the department of Road Traffic Administration. This study aims to analyze the risk factors that contribute to fatality in road traffic crashes using a 5-year police-reported dataset from the Wuhan Traffic Management Bureau. Four types of variables, including driving experience, environmental factor, roadway factor and crash characteristic, were examined in this research by a case-control study. To obtain a comprehensive understanding of crash fatality, this study explored a detailed set of injury-severity risk factors such as impact direction, light and weather conditions, crash characteristic, driving experience and high-risk driving behavior. Based on the results of statistical analyses, fatality risk of crash-involved individuals was significantly associated with driving experience, season, light condition, road type, crash type, impact direction, and high-risk driving behavior. This study succeeded in identifying the risk factors for fatality of crash-involved individuals using a police-reported dataset, which could provide reliable information for implementing remedial measures and improving sustainability in urban road network. A more detailed list of explanatory variables could enhance the accountability of the analysis.

In China, awareness about hypertension, the treatment rate and the control rate are low compared to developed countries, even though China’s aging population has grown, especially in those areas with a high degree of urbanization. However, limited epidemiological studies have attempted to describe the spatial variation of the geo-referenced data on hypertension disease over an urban area of China. In this study, we applied hierarchical Bayesian models to explore the spatial heterogeneity of the relative risk for hypertension admissions throughout Shenzhen in 2011. The final model specification includes an intercept and spatial components (structured and unstructured). Although the road density could be used as a covariate in modeling, it is an indirect factor on the relative risk. In addition, spatial scan statistics and spatial analysis were utilized to identify the spatial pattern and to map the clusters. The results showed that the relative risk for hospital admission for hypertension has high-value clusters in the south and southeastern Shenzhen. This study aimed to identify some specific regions with high relative risk, and this information is useful for the health administrators. Further research should address more-detailed data collection and an explanation of the spatial patterns.

Considering the high morbidity of hepatitis B in China, many epidemiological studies based on classic medical statistical analysis have been started but lack spatial information. However, spatial information such as the spatial distribution, autocorrelation and risk factors of the disease is of great help in studying patients with hepatitis B. This study examined 2851 cases of hepatitis B that were hospitalized in Shenzhen in 2010 and studied the spatial distribution, risk factors and spatial access to health services using spatial interpolation, Pearson correlation analysis and the improved two-step floating catchment area method. The results showed that the spatial distribution of hepatitis B, along with risk factors as well as spatial access to the regional medical resources, was uneven and mainly concentrated in the south and southwest of Shenzhen in 2010. In addition, the distribution characteristics of hepatitis B revealed a positive correlation between four types of service establishments and risk factors for the disease. The Pearson correlation coefficients are 0.566, 0.515, 0.626, 0.538 corresponding to bath centres, beauty salons, massage parlours and pedicure parlours (p < 0.05). Additionally, the allocation of medical resources for hepatitis B is adequate, as most patients could be treated at nearby hospitals.

Background: Hepatoma associated with hepatitis B infection is a major public health problem in Shenzhen (China) and worldwide. China has the largest number of people infected with the hepatitis B virus (HBV), and many studies have demonstrated that HBV infection is associated with hepatoma development. Shenzhen officials have been attempting to monitor and control these diseases for many years. The methodology and the results of this study may be useful in developing a system to monitor, prevent and control these diseases. Methods: The aim of the study was to analyze HBV infection and hepatoma distribution characteristics and patterns in Shenzhen by combining geographic information system (GIS) technology and spatial analysis. The study used data from patients at the district level from the 2010–2012 population censuses. Results: Only one-third of the patients were female, and 70.7% of all cases were 20–50 years of age. There was no global spatial correlation of the distribution of hepatitis B infections and hepatomas; however, there was a local spatial correlation, and certain sub-districts of the Nanshan district had significant agglomeration effects. Based on incidence density and rate maps, we can conclude that the Shenzhen special zone had a higher incidence density and rate of hepatitis B infections and hepatomas compared with the area outside of the Shenzhen special zone during 2010–2012. Conclusions: This study demonstrated substantial geographic variation in the incidence of hepatitis B infection and hepatoma in Shenzhen. The prediction and control of hepatitis B infections and hepatoma development and interventions for these diseases should focus on disadvantaged areas to reduce disparities. GIS and spatial analysis play an important role in public health risk-reduction programs and may become integral components in the epidemiologic description, analysis and risk assessment of hepatitis B and hepatoma.