Background
Between 2000 and 2007, there were 256,085 deaths in the United States (US) attributed to suicide. Approximately 32,000 people take their own lives every year in the US [
1]. In 2009, suicide was the tenth leading cause of death for all ages, the second leading cause of death among 25-34-year olds, and the third leading cause of death among 15-24-year olds [
1]. Firearms, suffocation, and poisoning are the most common methods of suicide; however, men and women differ in the methods used. In the same year, firearms were the most commonly used methods of suicide among males, while poisoning was the most commonly used mechanism in females. Males died by suicide at nearly four times the rate of females and represented 78.8% of all US suicides. During their lifetime though, women attempt suicide about two to three times as often as men [
1].
In Kentucky, suicide mortality rates have been steadily increasing since 1999. In 2006, suicides rose to 14.4 per 100,000 persons from the 1999 rate of 11.3 per 100,000 persons, a 27% increase. With an average of 13.4 suicides per 100,000 people annually (2000-2006), Kentucky ranks 16
th highest for suicide in the US [
1]. Additionally, medical costs and lost wages associated with suicide also take their toll on communities. In 2005, suicide cost society $26.7 billion in combined medical and work loss costs, while in Kentucky it was estimated to cost $481 million [
2].
A combination of demographic, individual, relational, community, and societal factors contribute to the risk of suicide. According to the World Health Organization's (WHO) report on violence and health, demographic factors such as age and sex, psychiatric, biological, social and environmental factors, as well as factors related to an individual's life history might play a role in making people more likely to attempt or commit suicide [
3].
Although much is understood about suicide at the individual level, including multiple factors associated with increased risk of suicide [
3], little has been done at the ecologic level to identify counties or neighborhoods with the greatest risk of suicide. Just as determining individual-level risk factors for suicide is vital for suicide prevention efforts, identifying high-risk areas and investigating spatial patterns for suicide provides a richer understanding of the determinants of suicide than the individual-level risk factors alone. Thus, identifying high-risk counties using spatial statistics may allow for a better targeting of resources and suicide intervention efforts so as to prevent future suicides.
Several studies have used spatial statistical techniques in assessing the presence of high-risk clusters including a brain cancer cluster study [
4], a study on networks of sexually transmitted infection [
5], and on breast cancer mortality disparities [
6]. Other studies incorporating similar methodologies have assessed high-risk clusters of La Crosse virus in West Virginia [
7], clusters of giardiasis in Canada [
8], and clustering of lung cancer in Italy [
9]. However, spatial studies of suicide clusters have been limited. Exeter and Boyle (2007) found a significant geographical cluster of suicide among young adults in east Glasgow across three time periods (1980 to 1982, 1990 to 1992, and 1999 to 2001), which were attributed to socioeconomic deprivation [
10]. Another study investigating suicide clusters in Queensland, Australia, found clusters in low socioeconomic areas [
11]. These studies provide some support that regions at high risk for suicide are those with greater socioeconomic deprivation.
Though suicide studies have used spatial statistical techniques in other countries, little has been published in the US. The present study has the potential to bridge the gap between suicide research and targeted prevention. The primary purpose of this study was to identify counties at the highest risk for suicide. Secondarily, this study also tests whether suicides are clustered temporally. Thus, the current study provides an analytical framework, using spatial statistics, to identify and target areas with the highest risk of suicide. Further, identification of counties at a greater risk for suicide is expected to guide resources and assist in policy decision making at the county level.
Discussion
We investigated the spatial epidemiology of suicides in Kentucky as reported in death certificate data files from the Kentucky Office of Vital Statistics between 1999 and 2008 using scan statistics and descriptive epidemiological methods. The results show evidence of hotspots of suicides across Kentucky counties, and describe the differences in suicide characteristics between genders, and for cases inside and outside high-risk spatial clusters. Further, when the purely spatial cluster analysis, SEB map, and cumulative incidence rates are jointly examined, they bolster the evidence of the existence of a high-risk suicide cluster in western Kentucky. To our knowledge this is the first study to investigate spatial and temporal patterns of suicide mortality at the county level in the US. This study allows for a better understanding of where to target resources and prevention efforts at the county level to reduce the burden of suicide in areas of greatest risk [
30,
31].
Moreover, suicide victim characteristics within the two spatial clusters allow for more targeted prevention efforts. For example, there is a moderately older age group of victims in the most likely cluster, and a greater proportion of 40-44 year old suicide victims inside the secondary cluster compared to outside the cluster. In addition, suicide cases inside the secondary cluster are more likely to self-poison than outside the cluster. Although our results reveal that females are more likely to self-poison than males (and this agrees with national data) [
1], there were not significantly more female suicides inside the secondary cluster than outside. These divergent characteristics of suicide cases inside versus outside clusters can inform interventions and guide future studies. Suicide risk was also found to be highest in 2006, providing evidence that suicide has been gradually increasing in Kentucky since 1999.
Our study differs from Exeter and Boyle's (2007) Scotland suicide cluster analysis in several ways [
10]. Exeter and Boyle's study, which was limited to young adult suicides (15 to 44 years old), found high risk clusters in Glasgow, Scotland, across three time periods with relative risks ranging from 1.53 to 2.41, while our study revealed clusters with relative risks ranging from 1.24 to 1.38. Unlike our study, where 67% of suicides were firearm related (65% for adults 15 to 44 years old), Exeter and Boyle found that 63% of suicides among young adults were from poisonings, hangings, strangulation and suffocation. In addition, Scotland's suicide rate among adults aged 15 to 44 years in 1999 to 2001 was 24.3 per 100,000 persons, which was markedly higher than Kentucky's suicide rate of 13.5 per 100,000 persons among adults aged 15 to 44 years during the same period [
1]. This difference in suicide method and rates suggests that suicide may be better explained by other factors-such as low socioeconomic status [
3,
10], less access to mental health care [
32], sudden unemployment [
33], depression [
34], and alcoholism [
35]-than by firearm access alone [
36], given that Scotland's suicide rate is higher than Kentucky's despite there being lower access to firearms in Scotland than Kentucky [
37]. Thus, this comparison suggests that firearm availability may not be the sole driving force behind increased regional suicide rates. Both studies reveal that suicide does not occur randomly in space, and that the characteristics of suicide cases inside clusters tend to be different than outside; specifically in Scotland where suicide clusters have been explained by the concentration of socioeconomic deprivation [
10].
Strengths, limitations, and future research
In addition to the previously mentioned advantages of using scan statistics over other epidemiology methods, scan statistics do not assume that observations are spatially independent, but rather test for spatial randomness, or in other words, spatial independence of the observations. An additional strength of this study is that it uses novel spatial techniques to provide ecological information on suicide risk, thus having the potential to guide interventions in those high-risk counties.
This study used data from death certificates which can be affected by errors. Pierce and Denison (2006) assessed place-of-residence errors on death certificates in only two Texas counties and found a 14% error rate in recording county of residence for deaths [
38]. Within our dataset, cases where the underlying cause of death and the manner of death did not match were excluded. Further, excluded cases due to this discrepancy came mainly from 2000 and 2001. This differential distribution in excluded cases may have biased the temporal analysis results away from the null hypothesis. Although 2006 has the highest suicide incidence rate, this introduced bias may have inflated that excess risk because of the seemingly lower rates in 2000 and 2001. Additionally, suicide mortality may not reflect current prevention needs. This study also used county-level data, which does not discriminate among suicide mortality risk in different parts of the county.
We are inclined to recommend future studies be undertaken in the high-risk counties to identify reasons for the high rates observed. Although Exeter and Boyle's (2007) study found high-risk suicide clusters to be explained by greater social deprivation [
10], implying that greater suicide risk is dependent on a relatively stable regional risk factor, several interventions such as physician education in depression recognition and treatment, along with greater restriction of access to lethal methods have been shown to be effective in reducing suicide rates [
39]. It is important to note that since suicide is affected by sociocultural factors, effective interventions in a certain population may not work elsewhere [
40]. Regardless, our study does offer evidence to support increasing availability and access to mental health care facilities, and targeted prevention efforts across the identified high-risk clusters in western Kentucky. Given that suicide is highly associated with poor mental health and depression [
3,
41], it is appropriate to make mental health facilities more available to provide services to those populations that most need it. We believe that future studies assessing suicide risk may provide more insight into regional-specific interventions that are most appropriate for western Kentucky.
In guiding future suicide spatial research, we recommend that individual-level circumstance data from the National Violent Death Reporting System (NVDRS) in Kentucky be linked to socioeconomic and demographic data, and vital statistics data to offer a richer understanding of those persons who define the cluster. It is also recommended that spatial analyses be performed using NVDRS suicide data at the census-tract level to allow comparisons between the county-level analysis and to offer a lower level (i.e., finer) visualization of suicide risk. This study also guides future small area analysis research: i.e., given that two high-risk suicide clusters have been identified, we recommend a spatial analysis limited to each cluster, with those high-risk counties divided into smaller census-tracts to identify those tracts within high-risk counties that are at greatest risk for suicide. Moreover, a further investigation determining the factors associated with high-risk clusters of suicide is recommended using regression analysis by linking various sociodemographic and environmental county (or census-tract) characteristics to the vital statistics data. This approach would offer an ecological understanding of the county-level (or census-tract) characteristics that explain suicide risk. We hypothesize that similar results may be found in Kentucky as in Glasgow, UK, and Queensland, Australia, where socioeconomic deprivation has been associated with high-risk suicide clusters [
10,
11]. Without more rigorous regression analyses, however, we would only be speculating as to what explains high-risk county clusters of suicide in Kentucky.
Beyond regional social deprivation, several other factors may be contributing to the higher rates of suicide in these relatively rural regions (i.e., all seven counties in the most likely cluster are rural, and three out of eight counties in the secondary cluster are rural) [
42]. Specifically, rurality has been found to be a likely regional risk factor for suicide in the United States and Australia [
43,
44]. Rural populations have less access to mental health care facilities, putting those with mental health disorders at a greater risk for suicide than their urban counterparts [
44,
45]. Furthermore, data collected from the Kentucky Violent Death Reporting System (KVDRS) from 1999-2008 suggest that county-level unemployment may be statistically associated with higher rates of suicide (Sabrina Walsh, unpublished data).
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
DMS conceived the research idea, study design, performed all analyses, managed data, interpreted results, and wrote the manuscript. AO was involved in study design, results interpretation, and editing of the manuscript. AB drafted the Introduction and was involved in data acquisition. SW was involved in data acquisition, results interpretation, as well as review and editing of the manuscript. All authors certify that they have participated sufficiently in the research to believe in its overall validity and have read and approved the final manuscript.