Increase in traffic injury crashes following the 2016 Kumamoto earthquake in Japan: A model comparison

OBJECTIVE

Following natural disasters, the number of motor vehicle crashes may increase as drivers are often forced to drive under stressful conditions. This study aims to analyze the changes in motor vehicle crashes that resulted in injury or death (injury crash) following the 2016 Kumamoto earthquake in Japan. An existing study reported that the increased crashes resulted in property damage following the earthquake; however, the effects on injury crashes remain unreported.

METHODS

Interrupted time series analysis is employed to investigate the changes in injury crashes following the earthquake. The results are compared based on several time series models, including negative binomial and autoregressive integrated moving average models. Monthly injury-crash data from 2011 to 2020 in Kumamoto and Fukuoka city is used.

RESULTS

The results reveal a 1,642-count or 20% increase (1.20-times increase, 95% confidence interval: 1.12, 1.27) in injury crashes due to the earthquake in Kumamoto city, where the earthquake damage was heavy. In contrast, statistically significant change is not detected in Fukuoka city, where the earthquake damage is negligible.

CONCLUSION

The results indicate that the earthquake has increased the motor-vehicle-crash risk and that traffic crash alerts are important following disasters.

Effects of sulfur dioxide and particulate matter pollution on hospital admissions for hypertensive cardiovascular disease: A time series analysis

Background and aims: Air pollution is a major environmental risk factor and the leading cause of disease burden with detrimental effects on cardiovascular systems. Cardiovascular diseases are predisposed by various risk factors, including hypertension, as the most important modifiable risk factor. However, there is a lack of sufficient data concerning the impact of air pollution on hypertension. We sought to study the associations of short-term exposure to Sulfur dioxide (SO₂) and particulate matter (PM₁₀) with the number of daily hospital admissions of hypertensive cardiovascular diseases (HCD). Methods: All hospitalized patients between March 2010 to March 2012 were recruited with the final diagnosis of HCD based on the International Classification of Diseases 10 (codes: I10-I15) from 15 hospitals in Isfahan, one of the most polluted cities in Iran. The 24-hour average concentrations of pollutants were obtained from 4 monitoring stations. In addition to single- and two-pollutant models, we used Negative Binomial and Poisson models with covariates of holidays, dew point, temperature, wind speed, and extracted latent factors of other pollutants controlling for multi-collinearity to examine the risk for hospital admissions for HCD affected by SO₂ and PM₁₀ exposures in the multi-pollutant model. Results: A total of 3132 hospitalized patients (63% female) with a mean (standard deviation) age of 64.96 (13.81) were incorporated in the study. The mean concentrations of SO2 and PM10 were 37.64 μg/m3 and 139.08 μg/m3, respectively. Our findings showed that a significantly increased risk of HCD-induced hospital admission was detected for a 10 μg/m3 increase in the 6-day and 3-day moving average of SO2 and PM₁₀ concentrations in the multi-pollutant model with a percent change of 2.11% (95% confidence interval: 0.61 to 3.63%) and 1.19% (0.33 to 2.05%), respectively. This finding was robust in all models and did not vary by gender (for SO₂ and PM₁₀) and season (for SO₂). However, people aged 35-64 and 18-34 years were vulnerable to SO2 and PM10 exposure-triggered HCD risk, respectively. Conclusions: This study supports the hypothesis of the association between short-term exposure to ambient SO₂ and PM₁₀ and the number of hospital admissions due to HCD.

Modeling Overdispersed Dengue Data via Poisson Inverse Gaussian Regression Model: A Case Study in the City of Campo Grande, MS, Brazil

Dengue fever is a tropical disease transmitted mainly by the female Aedes aegypti mosquito that affects millions of people every year. As there is still no safe and effective vaccine, currently the best way to prevent the disease is to control the proliferation of the transmitting mosquito. Since the proliferation and life cycle of the mosquito depend on environmental variables such as temperature and water availability, among others, statistical models are needed to understand the existing relationships between environmental variables and the recorded number of dengue cases and predict the number of cases for some future time interval. This prediction is of paramount importance for the establishment of control policies. In general, dengue-fever datasets contain the number of cases recorded periodically (in days, weeks, months or years). Since many dengue-fever datasets tend to be of the overdispersed, long-tail type, some common models like the Poisson regression model or negative binomial regression model are not adequate to model it. For this reason, in this paper we propose modeling a dengue-fever dataset by using a Poisson-inverse-Gaussian regression model. The main advantage of this model is that it adequately models overdispersed long-tailed data because it has a wider skewness range than the negative binomial distribution. We illustrate the application of this model in a real dataset and compare its performance to that of a negative binomial regression model.

Trends of Esophageal Cancer Incidence and Mortality and Its Influencing Factors in China

Purpose

To explore the esophageal cancer (EC) incidence and mortality trends and risk factors in China during 2005–2015.

Materials and Methods

The data were stratified by area (urban, rural), gender (male, female), and age groups (0 ~, 5 ~, …, 85 ~). The age-standardized incidence rate (ASIR) and mortality rate (ASMR), age-specific incidence and mortality were calculated to describe the trends, which were analyzed by Joinpoint software, negative binomial regression model, and age-period-cohort model.

Results

Trends in EC ASIR decreased markedly during 2010–2015 (APC=−6.14%, P<0.05), and the average annual percent change (AAPC) value was −8.07% (95% confidence interval (CI): −9.98~−6.12) for rural areas during 2005–2015. The ASMR was on a fast-downward trend after 2011 (APC=−6.67%, P<0.05), with AAPC values of −1.34% (95% CI: −2.56~−0.19) for males, −3.39% (95% CI: −5.65, −1.07) for females, and −9.67% (95% CI: −10.56~−8.77) for rural areas during 2005–2015. The age-specific incidence and mortality increased with age. The risk of EC for males was 3.1675 times higher than females (P<0.001), and for urban areas, it was 0.58 times larger than rural (P<0.001). The age and period effects presented an increasing trend, with a decreasing trend for the cohort effects in incidence and mortality risk. Later birth cohorts presented lower risks than previous birth cohorts.

Conclusion

ASIR and ASMR in China are higher in males than females, and higher in rural than urban areas, which have decreased during 2005–2015, especially in rural areas. The incidence increased with age up to the peak age group of 75. Area, gender, and age were independent risk factors for EC incidence.

Estimating the Threshold Effects of Climate on Dengue: A Case Study of Taiwan

Climate change is regarded as one of the major factors enhancing the transmission intensity of dengue fever. In this study, we estimated the threshold effects of temperature on Aedes mosquito larval index as an early warning tool for dengue prevention. We also investigated the relationship between dengue vector index and dengue epidemics in Taiwan using weekly panel data for 17 counties from January 2012 to May 2019. To achieve our goals, we first applied the panel threshold regression technique to test for threshold effects and determine critical temperature values. Data were then further decomposed into different sets corresponding to different temperature regimes. Finally, negative binomial regression models were applied to assess the non-linear relationship between meteorological factors and Breteau index (BI). At the national level, we found that a 1°C temperature increase caused the expected value of BI to increase by 0.09 units when the temperature is less than 27.21 °C, and by 0.26 units when the temperature is greater than 27.21 °C. At the regional level, the dengue vector index was more sensitive to temperature changes because double threshold effects were found in the southern Taiwan model. For southern Taiwan, as the temperature increased by 1°C, the expected value of BI increased by 0.29, 0.63, and 1.49 units when the average temperature was less than 27.27 °C, between 27.27 and 30.17 °C, and higher than 30.17 °C, respectively. In addition, the effects of precipitation and relative humidity on BI became stronger when the average temperature exceeded the thresholds. Regarding the impacts of climate change on BI, our results showed that the potential effects on BI range from 3.5 to 54.42% under alternative temperature scenarios. By combining threshold regression techniques with count data regression models, this study provides evidence of threshold effects between climate factors and the dengue vector index. The proposed threshold of temperature could be incorporated into the implementation of public health measures and risk prediction to prevent and control dengue fever in the future.

Standardized Risk Analysis Approach Aimed to Evaluate the Last African Swine Fever Eradication Program Performance, in Sardinia

From more than 40 years African swine fever (ASF) is endemic in Sardinia. Historically, areas at higher risk are located throughout some inland parts of this island where domestic pigs are still illegally kept in semi-wild conditions, living in contact with the local wild boar population, thereby creating perfect conditions for disease endemicity. A new eradication plan (EP-ASF15-18) has been ongoing for the past 3 years, based on a comprehensive strategy adapted to the local situation and focused on strong actions on domestic pig farms, wild boars (WB), and the third Sardinian typical involved population [illegal free-ranging pigs (FRPs)]. A fundamental aspect of the plan is the classification of pig farms as "controlled" or "certified," based on clinical, structural, and biosecurity characteristics. The eradication plan also provides for strong action against illegal farms and pig meat marketing channels. In addition, this plan establishes specific control measures for WB hunting and ASF checks. Each control strategy is specifically based on municipality risk level, to focus actions and resources on areas at higher risk of endemic or re-emerging ASF. Thus, precise risk classification is fundamental to this goal. The aim of the present work was to establish an ASF risk index, to provide a summary measure of the risk level in the Sardinian municipalities. This synthetic measure can express the different aspects of a multidimensional phenomenon with a single numerical value, facilitating territorial and temporal comparisons. To this end, retrospective data (years 2011-2018) were used. The ASF risk index is the result of the algorithmic combination of numerical elementary indicators: disease prevalence in the suid populations, WB compliance with EP-ASF15-18, domestic pig compliance with EP-ASF15-18, and presence of FRPs. A negative binomial regression model has been applied and predictors calculated to obtain a risk index for each municipality. The result of the risk analysis was discussed and considered according to expert opinion and consensus. The results of this study, expressed as risk score and classified into five risk levels, can be used to help define actions to be carried out in each Sardinian municipality, according to the risk assessment for the territory.

Average Effects Based on Regressions with a Logarithmic Link Function: A New Approach with Stochastic Covariates

Researchers often use regressions with a logarithmic link function to evaluate the effects of a treatment on a count variable. In order to judge the average effectiveness of the treatment on the original count scale, they compute average treatment effects, which are defined as the average difference between the expected outcomes under treatment and under control. Current practice is to evaluate the expected differences at every observation and use the sample mean of these differences as a point estimate of the average effect. The standard error for this average effect estimate is based on the implicit assumption that covariate values are fixed, i.e., do not vary across different samples. In this paper, we present a new way of analytically computing average effects based on regressions with log link using stochastic covariates and develop new formulas to obtain standard errors for the average effect. In a simulation study, we evaluate the statistical performance of our new estimator and compare it with the traditional approach. Our findings suggest that the new approach gives unbiased effect estimates and standard errors and outperforms the traditional approach when strong interaction and/or a skewed covariate is present.

[Branch quantity distribution simulation for Pinus koraiensis plantation in Heilongjiang Pro-vince, China.]

Based on the measurement of 955 branch samples of 65 Korean pine (Pinus koraiensis) trees in 12 plots from Mengjiagang forest farm, Heilongjiang Province, and by using Poisson model and negative binomial model, the second-order branch count models for Korean pine were developed in this paper. AIC, Pseudo-R², RMSE and Vuong test were selected to compare the goodness-of-fit statistics of the models. The results indicated that the first-order branch count in a whorl was 3 to 5, with mean value of 4, and the first-order branch count in a whorl for Korean pine plantation associated with its own characteristics. The second-order branch count of the first-order standard branch had a large discrete degree. All subset regression techniques were used to develop the second-order branch count model. The negative binomial regression model E(Y)=exp(β₀+β₁lnRDINC+β₂RDINC²+β₃HT/DBH+β₄CL+β₅DBH) was selected as the optimal second-order branch count model (β represented the parameter, RDINC represented the relative depth into crown from tree apex, HT represented the total tree height, DBH represented the tree diameter at breast height, CL represented the crown length). Pseudo-R² of the optimal model was 0.79, the mean error was close to 0 and the mean absolute error was less than 7. For the developed model, the parameter values of lnRDINC, CL and DBH were negative, and the parameter values of RDINC² and HT/DBH were positive. With the increase of RDINC, the number of second-order branch had a peak value in the tree crown. On the whole, the precision of the second-order branch count model for Korean pine plantation was 96.4%, which would be suitable for predicting the second-order branch count for the study area and provide a theoretic basis for branch photosynthesis and biomass research.

Unions, Health and Safety Committees, and Workplace Accidents in the Korean Manufacturing Sector

BACKGROUND

Despite the declining trend of workplace accidents in Republic of Korea, its level is still quite high compared with that in other developed countries. Factors that are responsible for high workplace accidents have not been well documented in Republic of Korea. The main purpose of this paper is to estimate the effects of unions and health and safety committees on workplace accidents in Korean manufacturing firms. We also allow for the interactions between unions and health and safety committees in the analysis. The results obtained in this paper will not only contribute to the literature in this field, but might also be useful for employers and worker representatives who are trying to find an effective way to reduce workplace accidents.

METHODS

This paper utilizes the 2012 Occupational Safety and Health Trend Survey data, which is a unique data set providing information on workplace injuries and illness as well as other characteristics of participatory firms, representative of the manufacturing industry in Republic of Korea.

RESULTS

In estimating the effects of unions and health and safety committees, we build a negative binomial regression model in which the interactions between unions and health and safety committees are permissible in reducing workplace accidents.

CONCLUSION

Health and safety committees were found to reduce the incidence of accidents whereas unionized establishments have higher incidence of accidents than nonunionized establishments. We also found that health and safety committees can more effectively reduce accidents in nonunionized establishments. By contrast, nonexclusive joint committees can more effectively reduce accidents in unionized establishments.

Children at risk: A comparison of child pedestrian traffic collisions in Santiago, Chile, and Seoul, South Korea

OBJECTIVE

We examine and compare pedestrian-vehicle collisions and injury outcomes involving school-age children between 5 and 18 years of age in the capital cities of Santiago, Chile, and Seoul, South Korea.

METHODS

We conduct descriptive analysis of the child pedestrian-vehicle collision (P-VC) data (904 collisions for Santiago and 3,505 for Seoul) reported by the police between 2010 and 2011. We also statistically analyze factors associated with child P-VCs, by both incident severity and age group, using 3 regression models: negative binomial, probit, and spatial lag models.

RESULTS

Descriptive statistics suggest that child pedestrians in Seoul have a higher risk of being involved in traffic crashes than their counterparts in Santiago. However, in Seoul a greater proportion of children are unharmed as a result of these incidents, whereas more child pedestrians are killed in Santiago. Younger children in Seoul suffer more injuries from P-VCs than in Santiago. The majority of P-VCs in both cities tend to occur in the afternoon and evening, at intersections in Santiago and at midblock locations in Seoul. Our model results suggest that the resident population of children is positively associated with P-VCs in both cities, and school concentrations apparently increase P-VC risk among older children in Santiago. Bus stops are associated with higher P-VCs in Seoul, and subway stations relate to higher P-VCs among older children in Santiago. Zone-level land use mix was negatively related to child P-VCs in Seoul but not in Santiago. Arterial roads are associated with fewer P-VCs, especially for younger children in both cities. A share of collector roads is associated with increased P-VCs in Seoul but fewer P-VCs in Santiago. Hilliness is related to fewer P-VCs in both cities. Differences in these model results for Santiago and Seoul warrant additional analysis, as do the differences in results across model type (negative binomial versus spatial lag models).

CONCLUSIONS

To reduce child P-VCs, this study suggests the need to assess subway station and bus stop area conditions in Santiago and Seoul, respectively; areas with high density of schools in Santiago; areas with greater concentrations of children in both cities; and collector roads in Seoul.