Summary of the Article
The article identified for the purpose of this assignment is, “Rates of suicide in China, 2004–2014: Comparing data from two sample-based mortality surveillance systems” which is a study conducted by Sha, Chang, Law, Hong, & Yip (2018). The authors concentrated on suicide which is a very pertinent global issue in public health today. This study generally focused on rates of suicide and how they had declined in a period of ten years in China.
According to the authors, the declining trend of China’s rates of suicide from 2004 to 2014 had contributed to the overall drop in the rates of suicide globally. However, the authors doubted the data accuracy of the rates of suicide in China that are provided by the Vital Registration System of the Ministry of Health of the Chinese government. The researchers conducted this study so as to compare this data with CDC’s system of Disease Surveillance Points and suggest an update to the rates of suicide statistics of China for the considered period of 2004 to 2014.
The data used for the study were obtained from the “Chinese Health Statistics Yearbooks and the National Cause-of-Death Surveillance Dataset”. Regression models of negative binomial were used to test how the sources of data were related to the rates of suicide. To test the statistical significance of the changes in crude rates of suicide from 2004 to 2014 in China.
The authors found out that both of the two systems considered in the study exhibited significant evidence of a decline in the rates of suicide in China from 2004 to 2014. However, before 2013, CDC’s system of DSP had been reporting significantly higher rates of suicide than the Ministry of Health’s system. It was also noted that some regions’ rates of suicide reversed in some specific years.
The study’s results suggested that the Ministry of Health’s system underestimated the rates of suicide in China. The CDC’s system of DSP was found to be better and provided more accurate suicide rate estimates of China. The authors recommended future studies to investigate the possibility of suicide rates reversing the trends in China that were observed during the study.
Available Data on Suicide
Suicide refers to any action that is intended to cause one’s own death. People commit suicide as a way of escaping pain or suffering. Every act of suicide has an effect on society because of the pain it causes those who are left behind. According to the World Health Organization (2018), approximately 800,000 deaths of people as a result of committing suicide annually were reported and more people attempted to end their own life. In 2016, suicide was said to be the second leading cause of mortality in young people aged between 15 and 19 years in the entire world (WHO, 2018). In the United States, suicide was 10th in the list of the leading causes of death as it claimed 47,000 people in 2017, according to the Centers for Disease Control and Prevention (2017). In as much as suicide is a serious public health challenge, it can be addressed with interventions that are evidence-based, timely and low-cost.
Suicide takes place in countries that are said to be developed as it does in developing and least developed countries. However, data from the WHO website reveals that in 2016, suicide accounted for 1.4% of all deaths globally and more than 79% of all the suicide cases took place in the developing and least developed countries (WHO 2018). Mental disorders such as depression and alcohol have been linked to suicide. However, in developed countries, many suicide cases have been as a result of the inability to cope or handle stresses of life like financial challenges, the break-up of relationships, illness or chronic pain. High rates of suicide cases have also been reported in vulnerable and minority groups that face discrimination like prisoners, gays, and lesbians, refugees and immigrants (WHO, 2018).
People can commit suicide through self-poisoning using pesticides. In approximately 20% of the suicides, the victims use self-poisoning method (WHO, 2018). Other methods that are commonly used to commit suicide are hanging and use of firearms. In the United States, firearms were reported to be the most commonly used suicide method that accounted for almost 50% of all the deaths as a result of suicide (National Institute of Mental Health, 2019). Males are the most frequent users of firearms as a method of suicide. Males’ deaths due to suicide were 3.54 times the deaths of females as a result of suicide in the United States (American Foundation for Suicide Prevention, 2017). Measures can be devised to prevent suicides and suicide attempts at various population levels. Some of those measures are restricting access to common means through which suicide can be committed like firearms and pesticides, identifying, treating and taking care of people with mental disorders, emotional distress, and chronic pain.
According to WHO (2018), suicide and attempted suicide data are not readily available and the available data are poor. Only a few countries have vital statistics that are of good quality and can be used to make estimations of rates of suicide. This challenge is due to underreporting due to underestimation. World Health Organization keeps and reports suicide data of countries all over the world and provides global estimates concerning rates of suicide and other related statistics. Country-specific suicide data can be obtained from a country’s vital statistic registry that usually records the cause of death. In the United States, the data on suicide can be obtained from the National Center for Health Statistics which is the custodian of the National Vital Statistics System.
According to Weir (2019), rates of suicide in the United States rose by 33% from 1999 to 2017. While rates of suicide in other countries like the United States are rising, the rates of suicide in China have been declining. In 2012, there was an estimated decline in rates of suicide in China by 59.6% (WHO, 2018). These statistics were obtained from China’s Ministry of Health Vital Registration System. However, Sha et al. (2018) argued that this system was less accurate that CDC’s system of DSP although both systems showed a decline in the rates of suicide in China.
During the 1990s, rates of suicide in China were among the top globally. The statistics showed China’s uniqueness in terms of gender with the highest rates of suicide which surprisingly were female. In terms of residence, unlike the other countries in the world, rural areas contributed to higher rates of suicide compared to urban areas. However, previous reports from WHO have continued to indicate that the rates of suicide in China have shown a significant rapid decline over the past decades compared to other countries in the world. However, data on rates of suicide is still significant to solve the problem completely and economic planning. Suicide in China still accounts for the high number of deaths (19% of all deaths annually) of the population of age between 15 and 34 years (Yang, Zhang, Sun, Sun, & Ye, 2015).
What Data does the Article use?
According to the authors of the article, the data used suicide counts of 10 years (from 2004 to 2014) that had been classified in terms of sex, age, location in the National Cause-of-Death Surveillance Dataset obtained from CDC’s system of DSP in China. The CDC-DSP system was established over 25 years ago to monitor national and regional mortality rates. Some data was also obtained from the Ministry of Health’s VR system published in the Chinese Health Statistics Yearbooks. This system was established over 70 years ago. According to the authors, the two systems were integrated after 2013.
Level of measurement, assumptions, and statistics.
The variables considered for this study were age, years, sex, residence, and source. Age and years were continuous variables measured on an interval scale. Sex, residence, and source were categorical variables that were measured on a nominal level of measurement. Descriptive statistics such as mean, mode, median, standard deviation could be calculated from the age and year variables. While frequency and percentage of counts could be calculated from the sex, residence and source variables. In addition to descriptive statistics, inferential statistics could be calculated from the data. Since the data contained a categorical variable, suitable categorical data analysis techniques such as chi-square could be calculated. Bivariate and multivariate analyses could also be carried out because of the presence of continuous variables age and years. It could be assumed that age was measured in years on a continuous measure. It was also assumed that the victims would be identified using only two categories of sex; male and female. Another possible assumption from the data would be that a person either resides in a rural or urban area and no other classification of residence. Since the data was not displayed in the article, it is not possible to evaluate the quality of the data. However, from the graphs that have been used to display the data visually, it can be concluded that the data quality was not far from good.
A study design involves a set of techniques that are applied in data collection and analysis with the aim of answering a research question or testing a hypothesis. For data collection, the authors used secondary data specifically obtained from government institutions records. Government records are usually considered reliable. Although not clearly mentioned in the article, it is clear that the authors adopted an empirical approach. An empirical approach involves the use of quantitative data as evidence to gain insights pertaining to a particular issue of interest.
The hypotheses of the study were clearly stated in the paper. The authors mentioned that their aim was to compare rates of suicide data from two sources and find out which system was more reliable and accurate. To achieve the mentioned objective, the authors hypothesized that the two systems provided significantly different rates of suicide data. Specifically, the authors hypothesized that, “nationally and within both rural and urban areas, the MOH-VR System reports lower rates of suicide than the CDC-DSP System; and, according to the CDC-DSP System, there are some reversing suicide trends in the more developed urban areas, such as the east and central urban areas in China” (Sha et al. 2018, pg 2). These hypotheses were tested using Poisson regression analysis because, “Poisson regression assumes a Poisson distribution that tends to fit suicide rate data better than the linear regression model” (Sha et al. 2018, pg 3). Negative binomial regression was also used. Rates were reported as percentages.
The authors wanted to compare rates of suicide as reported by two systems that are used by the Chinese government. When analyzed, the two systems showed significant variations in the rural rates compared to the urban rates for the period considered 2004 to 2014. Rates of residence, sex, and age were, however, found to be similar for the two systems in the last two years. This unique observation is due to the harmonization and merging of the two systems by the Chinese government from 2013 to present. The findings further indicated that the rural rates of suicide as consistently reported by the MOH-VR System were significantly lower than those reported by the CDC-DSP System. The MOH-VR System had also reported significant fluctuations between 2005 and 2010 in the urban rates of suicide and other fluctuations in the rural rates of suicide between 2007 and 2010. The fluctuations observed in the MOH-VR System, however, were not noticed in the CDC-DSP System. Further analysis using the negative binomial regression indicated that rates of suicide as reported by the CDC-DSP System were generally higher than the rates reported by the MOH-VR System controlling for sex, age and time. The source variable and the residence variable showed a significant interaction which was an indication that a significant correlation existed between the two variables.
Secular trends were investigated in the rates of suicide data as reported by the two systems. Secular trends are those that are neither cyclical nor seasonal but they are constant over a long period of time. This type of trend can be linear or non-linear. To estimate the secular trend of the rates of suicide data from the two systems, the authors considered a type of regression analysis known as Joinpoint Regression. Joinpoint regression analysis is a statistical technique that is utilized in identifying the best fitting points in the event that changes in a trend are statistically significant. The results of the analysis using joinpoint regression were displayed in the article with the estimated years when the trend in rates of suicide took place and the percentage change. The CDC-DSP system revealed a joinpoint in 2011 on the urban rates that were statistically significant while in rural rates of suicide, the joinpoint was observed in 2006. The MOH-VR System data did not show any joinpoints that were statistically significant. However, a declining trend which can be described as steady was observed.
Exploring further the crude rates of suicide from the subnational level for the age of 10 and above for the CDC-DSP System trends, indicated reversing trends on areas of urban areas to the east and central parts of the country. The authors then went ahead to decompose the reversing rates of suicide in order to improve the prediction in changes in rates of suicide. The authors displayed tables in the article that showed the relative contributions of various population proportion changes in the joinpoints that showed reversing trends. The authors found out that the crude suicide rate of ages above 10 for the years between 2011 and 2014 rose by 17.2% in the urban area to the east part of the country. Males of ages between 35 and 64 were observed to have an increase in the rates of suicide which was also a factor for the overall increase in rates of suicide. The authors also cited the increasing proportion of elderly people as a factor of increased rates of suicide. The years between 2008 and 2014 saw the crude rates of suicide of ages above 10 rises by 2.1% in the urban areas to the central part of the country. It is important to note that (as previously mentioned) middle-aged people had indicated a decline in rates of suicide. One would expect, therefore, that this decline would decrease the overall suicide rate by 6.7% but instead, the rates of suicide rose by 8.3% as a result of the rise in population proportion of the elderly people and the middle-aged people.
Interpretation of the findings
Generally, the CDC-DSP System reported higher rates of suicide across age, sex, and residence compared to the rates reported by the MOH-VR System between 2004 and 2012. From 2013 to present, the two systems were merged which implies that the data obtained afterward is the same for the two systems. To access the quality of the data, trends for the 10 years under consideration were examined. The authors think that the inconsistencies in the rates of suicide data as reported by the two systems may be due to poor representativeness and vague definition of urban and rural. This problem of the vague definition of the terms urban and rural and poor representativeness only appears in the MOH-VR System whose definitions are inconsistent. As a result, there was an underestimation of the total rates of suicide from 2004 to 2012. However, since 2004, the CDC-DSP System consistently adopted a sophisticated definition of rural and urban terms. The later system possessed a more representative sampling in addition to consistency in defining the two terms. Therefore, the CDC-DSP System may be regarded as the more reliable source of suicide data compared to the MOH-VR System.
The CDC-DSP System reported a notable rise in suicide trend since 2011. In terms of residence, the rates of suicide in the rural areas would previously be reported to be two or three times higher than the rates from the urban areas. This is contrary to the reports from the western countries which show suicide in both rural and urban areas having approximately the same rate. The decline in the rates of suicide in China is attributed to the social and economic developments that have taken place rapidly leading to urbanization and urban migration. The rising trend in the rates of suicide in the urban areas to the east from 2001 was as a result of the increase in the rates of suicide in the middle-aged and elderly people proportion increase. In the central region, the rising rate of suicide was caused by the decrease in the population of young people while there was a rise in rates of suicide of elderly people. Additionally, young people migrated massively from the central urban to the east urban hence leaving behind older people who recorded high rates of suicide since 2008. Because the rural-urban migration continues to increase in rates, it is predicted that national rates of suicide in urban areas will continue to increase.
In 50% of the countries that were analyzed by Fond, et al. (2016), rates of suicide of people aged between 35 and 54 years old were found to have significantly increased. This is consistent with the results in the article analyzed that found out the same results. Since 1990 to 2010, rates of suicide in the United States and the Western European countries mortality due to suicide has decreased according to Fond, et al. (2016). This trend was also witnessed in China regardless of the system used to obtain the data.
Park, Ahn, Lee, & Hong (2014) conducted a research to examine the changes in trends of suicide and its relationship with the suicide methods. They argued that, contrary to what the article in consideration opines that economic and social development are strong risk factors in suicide trend changes, suicide method is a strong factor. Park, Ahn, Lee, & Hong (2014) found that in the years 2001 to 2011, Korea’s male and female rates of suicide increased by more than 100% but the rates of suicide for Finland over the same period of time significantly dropped. It is expected that Korea, being on the same region of East Asia as China would register a decline in the rates of suicide in general. Additionally, with the economy of both Korea and China doing better off, then the factor of economic development does not seem to work with Korea to reduce rates of suicide.
Jeon, Reither, & Masters (2016), like Sha et al. (2018) did in China, also noted the significant differences in rates of suicide across age in Japan and South Korea. Jeon, Reither, & Masters (2016) further noted that the high rates of suicide observed in Korea were as a result of increased suicide cases among elderly people. However, despite the geographical and cultural similarities, China and Korea have sharply contrasting trends in the rates of suicide for the period of 2001 to 2011.
The authors warned that although they concluded that the CDC-DSP System is more accurate than the MOH-VR System, it also has its limitations. The authors noted that “Same as many other mortality surveillance systems, its information regarding suicides also suffers from the issues of under-reporting and misclassifications” (Sha et al., 2018, pg 8). Therefore, the authors recommended that when using the CDC-DSP System, it should not be assumed that the data reported is precisely classified and properly reported.
This study reported a significant variation in the rates of suicide data reported by the two systems in the previous years before the systems were merged. This means that previous studies that used raw data obtained from the MOH-VR System may have underreported the suicide statistics in China making it look too low especially in the rural areas. The WHO statement in 2012 that placed the two systems’ quality of the data is not satisfactory. The previous research that may have used MOH-VR System raw data may, therefore, be misleading and the author recommends that the readers be cautious of that.
As observed previously, the suicide trends in China using either of the systems is has been constantly declining. However, trends observed at the subnational level indicated that the areas that are considered as developed in China have stopped registering the rates of suicide decline and are now rising since 2010. This rising trend at the subnational level may be an indication of a reverse national trend in the near future. The authors, therefore, recommended that there is a need for constant monitoring by future studies to note any peculiar trend.
Changes I would make
The authors have used data from the two systems from 2004 to 2014. However, it is clear that the data for 2013-2014 was the same due to the fact that the two systems were merged. This means that the authors used only 9 years for the analysis instead of 10. It is not clear if the authors made that distinction during the analysis because it could affect the trend and even the estimation function. Had I been the author, I would make sure that it was clear to my authors why I collected the 2013-2014 data even when it was the same for both systems.
The authors have utilized various data visualization techniques to display the results of their data analysis. Tables are clear and simple to understand. However, the authors failed to display the data they used to come up with those tables and graphs or at least provide descriptive statistics of the two data sets from the systems compared. Had I been the author, I would make available at the appendix the data that I used for the analysis. If providing the data would be difficult, I would be sure to provide all the descriptive statistics so that the readers know how the data looked like.
The authors failed to provide the limitations of the methods they used and substantive recommendations at the end of their article. One line on recommendation, in my view, was not enough where more was expected. I would recommend the researchers using previous studies that utilized any of the two systems data to be cautious of their analyses because of the weaknesses identified in this study. Since the two systems had already been merged after 2013, I would recommend further studies to evaluate the new system’s level of accuracy.
The authors mentioned that they utilized a Poisson regression modeling but they did not display the analysis results or the resulting model. Although this is not a statistical error, it is an omission that may make the readers doubt the reason the authors decided to use negative binomial regression instead. There was no other statistical error that could be observed.
Correct data used
The authors used the right data to test the hypotheses that were mentioned in the article. For the first hypothesis, the authors wanted to compare the two systems on the national level and the urban and rural areas at the subnational level. Evidently, from the tables and graphs of the results provided, the authors compared the two systems on a national level and discovered disparities. Also, the two systems were also compared on and changes were observed on rural and urban areas. According to the authors, the data that was used was obtained from a national surveillance system which is a government source. Therefore, the credibility of the source is assured. For the second hypothesis, trends in the rates of suicide data from the CDC-DSP System were analyzed for developed urban areas. Only the data from the urban areas to the east and central China were considered. There is no other evidence to suggest that wrong data was used to test a hypothesis. So I am confident that the correct data was used.
Availability of the correct data
The data used for the analysis in this article is secondary data. Secondary sources are those that have been created by a different party for different purposes other than the one the researcher is interested in. One advantage of using secondary data is that it is relatively easy to obtain, saves time and at a low cost (Cheng & Phillips, 2014). One limitation of secondary data, however, is that it is not collected with a particular hypothesis in mind. This might imply that the researchers who are using the secondary data may not be necessarily the ones who designed data collection methods. The authors of the article did not mention any difficulty in obtaining the data used. Therefore, it is assumed that the secondary data was easily available and all the required variables were obtained.
The authors performed a Poisson regression analysis to find out if there was any relationship between the source variable and the controlling variables. Suicide can be described as a rare event. According to Moksony & Hegedűs (2014), such rare events whose dependent variable is distributed with high skewness can be modeled with Poisson regression. Additionally, the authors also recognize the fact that this kind of regression modeling fits suicide better more than linear regression modeling. One characteristic of the Poisson distribution, however, is that the mean and the variance must be equal. If this condition fails, using Poisson regression would be a mistake. For this reason, the authors decided to utilize a negative binomial regression in place of a Poisson regression model because the rates of suicide seemed to be over-dispersed.
Negative binomial regression requires that the dependent variable is a count that is observed with a negative binomial distribution. It is a version of Poisson regression analysis except that it does not require the variance and the mean to be equal as required by the Poisson model. To this far, the authors are rightly justified to used negative binomial distribution to compare the source variable and the controlling variables.
Data to collect
I would want to collect the data for 2003 for both systems so that I could replace the 2013-2014 data with it. This is because the authors have already told the readers that the 2013-2014 data is not different for both systems because the systems had been merged. This means that comparison could not take place and this could affect trend analysis. To make sure that the trend is analyzed with consistent data, one more pair of data that is different for both system is necessary. Perhaps this would give more insights as to why the systems reported varied suicide data rates.
Statistical findings and the authors’ conclusions
The authors concluded that previous studies that may have used the MOH-VR Systems data are likely to have underestimated rates of suicide in China especially the rates from the country’s rural areas. This conclusion is supported by the findings which pointed out that the MOH-VR Systems data was less reliable compared to CDC-DSP data in terms of accuracy.
The article analyzed in this assignment is, “Rates of suicide in China, 2004–2014: Comparing data from two sample-based mortality surveillance systems” which is a study conducted by Sha, Chang, Law, Hong, & Yip (2018). The article is about rates of suicide in China and how two different systems were compared in terms of their accuracy of suicide data provided. Suicide is a pertinent issue in public health and it required urgent attention. However, it is common that quality data on suicide is not adequate for most countries making it difficult to address this issue. Even the available data provided by governments are not adequate or are not accurate enough to be used to address the issue.
The data used for this article was obtained from two government systems which collected suicide data. The data from the two systems were compared with each other so as to find out if significant disparities existed between the rates of suicide reported by each. It was discovered that the disparities were significant and that one of the two systems provided more accurate data.
American Foundation for Suicide Prevention. (2017). Suicide Statistics. Retrieved from American Foundation for Suicide Prevention: https://afsp.org/about-suicide/suicide-statistics/
Cheng, H. G., & Phillips, M. R. (2014). Secondary analysis of existing data: Opportunities and implementation. Shanghai Archives of Psychiatry, 371-375.
Fond, G., Llorca, P.-M., Boucekine, M., Zendjidjian, X., Brunel, L., Lancon, C., . . . Boyer, L. (2016). Disparities in suicide mortality trends between United States of America and 25 European countries: a retrospective analysis of WHO mortality database. Nature Scientific Reports, 1-9.
Jeon, S. Y., Reither, E. N., & Masters, R. K. (2016). A population-based analysis of increasing rates of suicide mortality in Japan and South Korea, 1985–2010. BMC Public Health, 1-9.
Moksony, F., & Hegedűs, R. (2014). The use of Poisson regression in the sociological study of suicide. Corvinus Journal of Sociology and Social Policy, 97–114.
National Institute of Mental Health. (2019, April 1). Suicide. Retrieved from National Institute of Mental Health: https://www.nimh.nih.gov/health/statistics/suicide.shtml
Park, S., Ahn, M. H., Lee, A., & Hong, J. P. (2014). Associations between changes in the pattern of suicide methods and rates in Korea, the US, and Finland. International Journal of Mental Health Systems, 1-7.
Sha, F., Chang, Q., Law, Y. W., Hong, Q., & Yip, P. S. (2018). Rates of suicide in China, 2004–2014: Comparing data from two sample-based mortality surveillance systems. BMC Public Health, 1-9. Retrieved from https://doi.org/10.1186/s12889-018-5161-y
Weir, K. (2019, March 01). Worrying trends in U.S. rates of suicide. Retrieved from American Psychological Association: https://www.apa.org/monitor/2019/03/trends-suicide
World Health Organization. (2018). Suicide data. Retrieved from World Health Organization: https://www.who.int/mental_health/prevention/suicide/suicideprevent/en/
Yang, L.-S., Zhang, Z.-H., Sun, L., Sun, Y.-H., & Ye, D.-Q. (2015). Prevalence of suicide attempts among college students in China: A meta-analysis. Plos One, 1-13.
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