| 1. BackgroundSuicide, as a social concern, has attracted more and more attention from researchers with the social-economic development. According to data from WHO, the suicide rates around the world have experienced a significant decrease from 14.5/100.000 in 2000 to 11.4/100,000 in 2012. Recent studies have reported that the suicide rates in China also show a downtrend with a decrease from 15.68/100.000 in 2002 to 8.14/100.000 in 2011. And suicide pattern in China has varied in recent years. Suicide risk is multifactorial, with risk factors involving psychosocial, cultural, and biological factors. Most researches explored the risk factors of suicide on the base of individual information, while a minority of ecological studies based on population. Researches conducted in Western countries accounted for the majority of the existing ecological studies on suicide, and found a relationship between suicide rates and temperature, sunlight duration, poverty rate, unemployment, and tobacco consumption, etc. However, because of the differences in culture and demographic characteristics, findings in the West may not be generalized to Chinese populations. It’s still unclear which factors are related to suicide rates in the level of population. Thus, this study employed an ecological study design to comprehensively explore the possible relationship between suicide and factors of meteorology, population structure, social economy and living conditions. It’s purposed to provided basis and reference for macroscopic strategy of suicide prevention.Time series analysis is a mature method to fit and forecast the variation trend of variables. It was widely used in analyzing and forecasting the process of various diseases. However, few researches adopted time series analysis to analyze the varying pattern of suicide rates in China. In this study, suicide rates in each month were regarded as time series, and an autoregressive integrated moving average (ARIMA) model was built to forecast the trend of suicide rate.2. Objectives2.1 To understand the behavior characteristics of suicide and the trend of suicide rate, describe the distribution of suicide rate across gender and age groups.2.2 To explore the association between suicide rate and meteorological, socioeconomic factors, and find related factors.2.3 To build ARIMA model for suicide rates across gender and forecast the trend of suicide rate.3. MethodsAn ecological study design was adopted to find related factors of suicide.3.1 Data sourcesEleven rural counties (Gaomi, Gaotang, Jiaonan, Jvnan, Yiyuan, Ningyang, Penglai, Rushan, Shouguang, Wucheng, Zoucheng,) were selected from the disease surveillance points. Suicide deaths in each county during 2006 to 2013 were provided by Shandong Center for Disease Prevention and Control (CDC). For each suicide victim, information of age, gender, marital status, education, address, death time and cause of death were collected. The population data of each county in these years were also provided by Shandong CDC.Data on meteorological conditions, population structure, socioeconomic development and living conditions in each county were obtained from the statistic yearbooks of Shandong Province and each city. Detailed information of each county were collected during 2006-2012, including annual average temperature, annual precipitation, sunshine hours, population density, gender ratio, proportion of people over the age of 65, birth rate, natural growth rate, GDP per capita, proportion of primary industry, proportion of tertiary industry, proportion of rural population, pesticide application amount per capita, net income per capita, Engel’s coefficient, expenditure proportion of transportation and communication, expenditure proportion of medical care, amount of beds in health institutions per 10,000 persons, amount of medical and technical personnel per 100,000 person, and enrolment rate.3.2 Data StatisticsThe 2010 Chinese standard population was used to work out age-adjusted suicide rates. Microsoft Excel (Version 2013) was used to graph suicide rate to describe the trend and distribution of suicide rate. Bivariate correlation was employed to analyze the correlationship between suicide rate and each variate. Suicide rates in every month during 2006-2012 were used to build an ARIMA model, and fitting effect and predictive effect of the model were assessed. Another ARIMA model were built for suicide rates during 2006-2013, and used to forecast suicide rate in the year of 2014.4. Results4.1 Characteristics of suicide deathsSuicide claimed 10038 deaths in the 11 counties during 2006-2013, accounting for 2.25% of all-cause mortality. The average age of suicide victims was 59.56±18.13, and 43.8% of the victims aged 65 or more. There were more man committed suicide than women, with a gender ratio of 1.24. More suicides took place in spring and summer rather than autumn or winter. Pesticide (48.90%), hanging (38.85%) and medicine overdose (5.14%) were the three most commonly used method to suicide. Violent suicide method was used more frequent in men than women (47.6% VS. 41.1%, X2=42.11,P<0.01).4.2 Suicide rate and its distributionThe age-adjusted suicide rate was 14.23/100,000 in Shandong during 2006-2013. There was a downtrend in suicide rate, with a decrease from 17.20/100,00 in 2006 to 11.38/100,000 in 2013, and an annual falling speed of 6.08%. Men had higher suicide rates than woman in each year, and the gender ratio was 1.32. Suicide rate increased with age, and it had an exponential increase among people aged 65 or above. In terms of temporal distribution, suicide rate peaked in March to June, and bottomed in December.4.3 Related factors of suicide rateDuring 2006-2012, factors positively correlated to suicide were annual precipitation, gender ratio, proportion of tertiary industry and amount of beds in health institutions per 10,000 persons. Engel’s coefficient, as well as amount of medical and technical personnel per 100,000 persons, had the trend to positively correlate to suicide rate. Meanwhile, birth rate and proportion of rural population had the trend to negatively correlate to suicide rate. Among the above factors, factors of population structure and meteorological conditions were significant in earlier years, while factors reflecting social-economy and living conditions in more recent years.4.4 Time series analysis of suicide rateFor suicide rates during 2006-2012, ARIMA (0, (1,12), (1,5) model was built. This model had a good fitting and predictive effect, with no actual value beyond the 95% CI.ARIMA (0, (1,12),1) x(0, (1,12), 1)12 was built as the optimal model for suicide rates during 2006-2013. With all actual values fall into the 95% CI, this model had a good fitting. Suicide rate in 2014 was forecasted as 11.35/100,000 with the model, slightly below the rate in 2013.5. Conclusions5.1 Suicide accounted for 2.25% of all deaths. The average age of suicide victims was 59.56±18.13, and the elderly accounted for 43.8% of all suicide deaths. Pesticide and hanging were the most commonly used method to suicide.5.2 Suicide rate was 14.23/100,000 in Shandong during 2006-2013, and it had a significant downtrend. Suicide rate was higher in men than women, with the gender ratio of 1.32. An increase with age was found in suicide rate. Men and the elderly should be the focus groups of suicide prevention.5.3 Annual precipitation, gender ratio, proportion of tertiary industry and amount of beds in health institutions per 10,000 persons were positively correlated to suicide rate. Factors having a trend to correlate to suicide rate included Engel’s coefficient, amount of medical and technical personnel per 100,000 persons, birth rate and proportion of rural population.5.4 ARIMA model is suitable for fitting and forecast of suicide rate. Suicide rate in 2014 may have a slight decrease. |
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