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 Table of Contents  
Year : 2018  |  Volume : 3  |  Issue : 2  |  Page : 49-55

Evaluating sociodemographic and psychiatric contributors to suicide in Sri Lanka: An ecological survey

1 Faculty of Medicine, University of Toronto, Toronto, Canada
2 Faculty of Medicine, University of Toronto; Centre for Addiction and Mental Health, Toronto, Canada
3 Department of Psychiatry, University of Kelaniya, Ragama, Sri Lanka
4 Centre for Addiction and Mental Health, Toronto, Canada

Date of Web Publication26-Jun-2018

Correspondence Address:
Arun V Ravindran
Centre for Addiction and Mental Health, 100 Stokes St., Toronto, M6J 1H4
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jncd.jncd_42_17

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Background: Suicide is a major public health concern worldwide. Sri Lanka has the fourth highest suicide rate in the world, with trends suggesting that underlying factors related to suicide risk remain poorly understood, and may differ from those observed in high-income countries (HICs).
Aim: The purpose of this study was twofold: First, to update country-wide suicide trends from 2009 to 2015 among geographic regions (districts), and second, to evaluate the association between suicide rates and demographic factors, social determinants, health-care access, and hospital presentations due to psychiatric illness.
Methods: National- and district-level suicide rates were calculated from 2009 to 2015 using data from the police statistics unit. These data were used in conjunction with sociodemographic and population health data from the 2012 Census to evaluate the interrelationships. Both correlational and regression statistical models were employed.
Results: Population density (PD), access to health resources, and social determinants including poverty, education, and employment were found to be associated with suicide rates in a correlational model. However, with regression analysis, low PD was the sole significant predictor of suicide risk. This finding supports the suggestion that suicide is a significant concern in rural areas, particularly in Sri Lanka and other LMICs. We found only a weak association between psychiatric morbidity and suicide rates in this study. While this is counterintuitive, it is in keeping with other reports from LMICs. It is suggested that LMICs may need unique suicide prevention strategies distinct from suicide prevention models adopted in HICs.
Conclusion: We found a positive association between rurality and suicide risk, and a weak association between psychiatric morbidity and suicide rate in Sri Lanka.

Keywords: Density, low- and middle-income countries, suicide

How to cite this article:
Whittall J, Sumner A, Ravindran AV, Rodrigo A, Walker ZJ. Evaluating sociodemographic and psychiatric contributors to suicide in Sri Lanka: An ecological survey. Int J Non-Commun Dis 2018;3:49-55

How to cite this URL:
Whittall J, Sumner A, Ravindran AV, Rodrigo A, Walker ZJ. Evaluating sociodemographic and psychiatric contributors to suicide in Sri Lanka: An ecological survey. Int J Non-Commun Dis [serial online] 2018 [cited 2022 May 20];3:49-55. Available from: https://www.ijncd.org/text.asp?2018/3/2/49/235212

FNx01Jonathan Whittall and Alison Sumner share first authorship, each contributed equally to the manuscript

  Introduction Top

Suicide is a major public health concern that results in over 800,000 deaths a year worldwide.[1] It has received significant public and scholarly attention in high-income countries (HICs); however, 73% of suicides occur in low- and middle-income countries (LMICs) where studies on suicide are scarce.[2] Causes and contributory factors of deliberate self-harm (DSH) and suicide differ in HICs and LMICs. Although the published literature is sparse, several contributory factors to suicide have been proposed across LMICs. These include interpersonal conflicts,[3] effects of adverse social determinants, including poverty, and by some accounts, severe mental illness.[4] In HICs, DSH is more likely to be associated with mental illness and previous self-harm attempts, when compared to LMICs.[5] Thus, it is of note that the rate of psychiatric morbidity among individuals who attempt and/or complete suicide has been reported to be much lower in LMICs [6],[7] compared to HICs.[8] While some have attributed this difference to systemic underreporting of mental health disorders,[9] others have suggested that cultural factors and the influence of adverse social determinants serve as contributing factors.[10]

Socioeconomic status (SES) has been proposed as a key factor influencing the variability in suicide rates and has been studied extensively in HICs.[11],[12],[13] While the findings have been mixed in the past, more recent reports support an inverse relationship between the components of SES (education, poverty, and unemployment) and the risk of self-harm.[12] However, this relationship has not been well evaluated in LMICs, with few reports on the influence of socioeconomic markers on suicide. Of note, a recent review noted a significant association between poverty and increased vulnerability for suicide in LMICs, but the degree of such relationship varied when different indicators of poverty were considered separately.[14] Another systematic review, which evaluated the link between suicide and poverty more specifically in Southeast Asia, found low SES to be directly related to increased risk of suicide, but also noted the lack of high-quality studies from which to draw conclusions.[15] Furthermore, most of the investigations have focused on local population samples, with few investigations conducted at the regional and societal levels.[16] Further, the relationship between SES and suicide has not yet been studied while controlling for the effect of population density (PD).

Thus, there is limited information to develop effective suicide prevention strategies in LMICs, and those developed in HICs may be of limited applicability.

Suicide and deliberate self-harm in Sri Lanka

In spite of a trend for reduction in suicides over the past 20 years, Sri Lanka continues to have the fourth highest suicide rate in the world according to the most recent WHO data.[17] While rates of completed suicide have declined since the introduction of laws restricting access to highly toxic pesticides and insecticides, a 300% increase in admissions to state hospitals over the past 20 years for self-poisoning attempts suggest that the current prevention/intervention strategies are inadequate.[18] It has been proposed that the identification of vulnerability and risk factors, at both the individual and population levels, and effective interventions to address them are needed.[19] To date, there have been few country-wide studies investigating factors contributing to suicide and self-harm in Sri Lanka and in particular, the proposed relationship between increased risk of self-harm and adverse social determinants, social isolation, and mental illness needs further elucidation.[19]

The aim of this study was two-fold: first, we sought to update suicide trends by investigating and reporting country-level variations and district-specific statistics over the past 7 years (2009–2015). Second, we explored the associations between geographic administrative areas (referred to as districts), suicide rates, sociodemographics, and presentations to the hospital due to psychiatric morbidity. It was proposed that such comparisons will help identify vulnerability factors and yield an improved understanding of the pathophysiology of suicide, which may help to inform suicide prevention strategies for the Sri Lankan population.

  Methods Top

Study area and data collection

Sri Lanka has a population of approximately 20 million and the country is divided into 25 regions referred to as districts, each of which exhibits significant differences in demographics, socioeconomic parameters, and health resources. To update suicide trends over the study period (2009–2015), national- and district-level suicide rates were calculated using suicide data from the central police statistics unit (based in Colombo) and population data from the most recent Census, which was conducted in 2012.[20] Associations between predictor variables and rates of suicide were then determined by averaging district-level suicide rates over the study period and comparing them to 2012 sociodemographic and population health data. The average suicide rate for the districts of Mullaitivu and Kilinochchi were calculated using data from 2010 to 2015 only, due to missing suicide data for 2009 due to the civil war. To compare district-level suicide rates to population data, police districts were realigned to match administrative districts as close as possible. Candidate predictor variables related to suicide were identified using previously published literature, and three broad categories were identified: sociodemographic, psychiatric comorbidity, and health-care access. The specific socioeconomic and health-care variables evaluated in the current investigation included: PD, mean household income (MHI), poverty headcount index (PHI) (percentage of population living under the poverty line), educational attainment (percentage of population aged >25 having graduated ordinal level examinations; “% O/L,” which is equivalent to completion of high school), unemployment rate (UR), and availability of Health Care Workers (HCWs) (combined number of medical officers of health [public health physicians] and nurses per 100,000 persons). The key source of district-level sociodemographic data was the 2012 Census of population and housing.[20] Other sources of population data included the 2012 Annual Health Bulletin,[21] and the 2012 Labour Force Survey.[22] District-specific rates of presentation for psychiatric diagnoses were collected from the 2012 Annual Health Bulletin, as identified according to International Classification of Diseases-10 criteria. For the purposes of the present study, we subdivided the classifications of psychiatric diagnoses into four broad categories: Mood disorders (depression), anxiety disorders (neurotic, stress-related, and somatoform disorders), psychoses (schizophrenia, schizotypal, and delusional disorders), and substance-use disorders (alcohol and other psychoactive substance use). Given that there were no mental illness prevalence data collected systematically in Sri Lanka, we used as an alternative the number of “cases” of mental illness in each hospital during 2012, which included both the number of new diagnoses (i.e., the incidence for 2012) and the number of hospitalizations due to preexisting mental health diagnoses (mental health had to be the primary reason for hospitalization). The number of cases in each district was divided by the population in each district and was presented as the number of presentations to the hospital for mood/anxiety/psychosis/substance-use per 100,000 persons for the year 2012. Since mental health conditions are generally lifelong, with relapsing and remitting courses, incidence is correlated with the prevalence and commonly used to estimate the prevalence of mental disorders.[23]

[Table 1] provides a summary of the variables used.
Table 1: Hypothesized predictor variables

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SPSS 24 (IBM Corp., Armonk, New York, USA) was used to investigate associations between the mean rate of suicide in each district and each of several key variables derived from a review of published literature. A negative binomial regression model was created to determine the association of each of the variables with district-level suicide rates while controlling for each of the other variables included in the model. A logarithm link was used, and the logarithm of the 2012 population was used as offset. A logarithm transformation was applied to the PD to determine the association between this predictor and the outcome linear. Household income, as a variable, was excluded from the regression model due to its high degree of collinearity with educational attainment, which was included. No significant differences were noted in terms of the impact on other predictor variables.

  Results Top

National-suicide trends (2009–2015)

The average national-suicide rate over the 7-year period was 17.47/100,000 individuals, a rate significantly above the global suicide rate of 10.7/100,000.[9] The national suicide rate declined each year over the 7-year period (2009–2015), at an average rate of 0.84 suicides/100,000 individuals [Figure 1]. Women had an average suicide rate of 7.62/100,000, while men had a rate of 27.95/100,000 (3.7 × higher) over the study period.
Figure 1: National suicide rates in men and women over the study period (2009–2015). Considerable variation was found in suicide rates across districts

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The highest average suicide rates (2009–2015) were reported in Mullaitivu (36.02/100,000) and Mannar (30.86/100,000), while the lowest suicide rates were found in Colombo (11.41/100,000) and Gampaha (11.65/100,000) [Figure 2].
Figure 2: Average district level suicide rates over the study period (2009–2015)

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Association between suicide and exposure variables

[Table 2] documents characteristics of each of the predictor variables considered. The Pearson correlation between district-specific average suicide rate and each of the predictor variables is also included. PD, MHI, and proportion of individuals having passed their ordinary level examination (% O/L) were found to have an inverse relationship with district-level suicide rate, while PHI was found to have a direct association (P< 0.01). The number of HCWs per 100,000 population (HCW) also had a significant inverse relationship with the rate of suicide (P< 0.05). However, there was no association noted between the mean suicide rate with UR, or with the rate of hospital presentations for psychiatric disorders in the individual districts. As anticipated, there was a high degree of collinearity between many of the predictor variables, including both sociodemographic variables and subclasses of psychiatric morbidity.
Table 2: Descriptive statistics and correlation of independent variables with suicide rate

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[Table 3] shows the results of the negative binomial regression model. Since the model uses a logarithm link, the exponentiated coefficients were utilized rather than the raw coefficient from [Table 2]. We found that higher population densities were associated with lower suicide rates, with the latter decreasing by approximately 26% for each increase of 1 in the logarithm of density. The inference, as an example, is that a district with a PD of 100 would be expected to have 26% less suicides than a district with a PD of 10. Several other variables were also significantly associated with suicide rate in the univariate analysis [Table 2], however, in the final model they were no longer significant [Table 3]. Once we accounted for PD in the negative binomial regression model, other independent variables (including MHI, % O/L, and HCW) were no longer found to be significant. On the other hand, rate of presentations to the hospital due to psychosis and substance use-related disorders were both found to be significantly associated with increased suicide rates in the final model but did not show a significant correlation in the univariate analysis. We found that districts with higher PD tend to have lower suicide rates but higher rates of hospital presentation for psychosis and substance use-related psychiatric morbidity. When we controlled for PD, a significant direct correlation was found between suicide rates and presentations to the hospital due to psychosis and substance use disorders.
Table 3: Output from negative binomial regression model

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  Discussion Top

We found PD and available health resources, as well as social determinants, including income, poverty, and education, to have a relationship to suicide rates when evaluated directly. However, when analyzed in a regression model, PD was found to be the only significant predictor of suicide risk. It is likely that the high collinearity between PD and these factors contributed to this finding.

The observation that low PD predicts higher rates of suicide supports a growing body of evidence that suicide is a greater concern in rural areas in the low-income regions of the world. Specifically in South and Southeast Asia, studies have shown considerably higher rates of suicide in rural regions, when compared to urban centers.[24],[25] Proposed factors contributing to this association include reduced access to services [26] and social isolation.[27] Rural regions also tend to show the persistence of traditional cultural norms,[28] as well as increased availability of toxic agents due to the dominant agricultural economy.[29]

The majority (87%) of the suicides in Sri Lanka occur by poisoning, with pesticides being the most commonly ingested substance.[30] However, the use of pharmaceuticals, such as paracetamol for self-harm purposes, has dramatically increased in urban areas over the past decade.[30] Thus, it is well accepted that accessibility plays a key role in the selection of the ingested agent. Individuals living in less densely populated agricultural areas may have easier access to pesticides, such as organophosphates, which typically have a higher case fatality ratio than commonly abused pharmaceutical agents.[31] Case fatalities associated with poisoning may thus be higher in rural agricultural areas. Similar trends in choice and lethality of poisoning agents and fatality rates have been reported in other countries in South Asia.[26]

Social isolation has been identified as another factor that may explain the inverse association between suicide rates and PD. Sri Lankan studies have found that working in agriculture predicted higher risk of both attempted and completed suicide,[32] and have speculated that social isolation associated with living in farming areas may further increase the vulnerability to such behavior. Most acts of self-harm occur impulsively and are often preceded by interpersonal conflict, and maladaptive coping strategies have also been found to be an important predictor of suicidal behavior in Sri Lanka.[28],[33] Urban centers often offer social and recreational opportunities that may help individuals distance themselves from trigger situations or cope with stress.[34] In the absence of such resources, there may be a greater risk of developing maladaptive coping strategies.

It has been proposed that certain cultural norms in rural areas may also increase risk of self-harm behavior.[35] Jayaweera [36] argued that gender roles tend to be stronger and more rigid in rural settings of Sri Lanka compared to the cities. For example, the outward expression of emotion by men is often discouraged, as it is considered a sign of weakness. This may, in turn, result in external (behavioral) expressions, such as suicide.

We did not find significant associations between presentations to the hospital due to mental illness and suicide rates. However, two subsets – psychoses and substance use disorders– were found to be significant in the negative binomial regression model, but with a relatively low effect magnitude. Our findings add to a growing body of evidence suggesting that psychiatric comorbidity may be a less important risk factor for suicide in LMICs, such as Sri Lanka, compared to HICs.[7],[8] Nonetheless, the finding is somewhat surprising but explainable as a possible measurement error of underestimation. According to the 2012 Health Bulletin, the rate of presentation to hospitals for psychiatric diagnoses in Sri Lanka was 203.8/100,000 people, which is significantly lower than that reported for HICs.[21] For example, in England, the annual admission rate for mental illness was reported to be 320/100,000 people, and admission rates involving depression and anxiety in emergency rooms in the US was 3945/100,000 people.[37],[38] While it is true that diagnoses reported in the Health Bulletin only reflect those in which psychiatric diagnosis was the primary diagnosis and further, only account for diagnoses made in government hospitals, the reported rate of admission to hospitals for psychiatric conditions in Sri Lanka appears to be far below that of other countries.

In addition to the stigma which may deter individuals from seeking help, a shortage of mental health services may also help explain the lower rates of presentation for mental health conditions reported in Sri Lanka. According to the policy study report conducted in 2009, <1.6% of publicly funded health care spending is dedicated to mental health in Sri Lanka, compared to 13% in Britain.[39] As of 2014, there was only one publicly funded psychiatrist for every 286,000 people in Sri Lanka, and most of these specialists are concentrated in urban areas.[21] Of note, there exists a particular dearth of mental health services in the Northern regions of the country, specifically those most affected by the 1983–2009 civil war.[40] Although the WHO Sri Lanka Country Cooperation Strategy (2006–2011) was launched to decentralize mental health services, the continued lack of mental health professionals in rural areas suggests that a renewed commitment to the provision of community-based mental health services is needed.


A major limitation of this study is that it relies on aggregate data (combined rates for each district) rather than individual case data. Thus, the findings reflect population trends only, and cannot be used to make conclusions about individual-level risk. In addition, due to the aggregate nature of the data, we were unable to control for factors such as sex and age, which may confound the relationships between suicide and the chosen exposure variables.

Another limitation is the high likelihood of underreporting of suicides in Sri Lanka, with reluctance from families and the public to disclose the true cause of death to police after a completed suicide. Research suggests that the stigma surrounding mental illness and suicidal behavior is more pronounced in rural areas compared to urban centers.[3] If this is the case, individuals living in rural areas may be more likely to conceal suicide deaths of family and friends. Discrepancies in the rate of suicide reporting between rural and urban areas may, therefore, confound the relationship between PD and suicide rates.

A third limitation is the use of the frequency of presentations to the hospital with a diagnosis of mental illness as a measure of psychiatric comorbidity, which may be an underestimate of the true burden. A more reliable measure would have been that of prevalence rates for mental disorders using systematic countrywide standards or community surveys. Such data, however, are lacking in Sri Lanka, underscoring the need for epidemiological studies in this population.

Finally, it should be noted that considering districts based on their PD ignores the impact of their geographical distribution. While no association has been noted between country-wide suicide rates and the onset and end of the 1983–2009 civil war, district-level suicide statistics suggest otherwise. Mannar and Mullaitivu, the two districts most affected by the civil war, reported the highest rates of suicide between 2009 and 2015 (30.86/100,000 and 30.88/100,000, respectively). Lasting effects of the civil war may have had a confounding effect on the relationship between PD and district-level suicide rate. More studies are needed to better understand the residual impacts of the war on self-harm behavior.

  Conclusion Top

The findings of this investigation add to the growing body of evidence for an association between rurality and suicide risk, a finding previously reported in several countries across South and Southeast Asia.[24],[25] The study also found only a weak association between psychiatric morbidity and suicide rate in this region, which is in keeping with existing literature from other LMICs in this region.[5] It is proposed that modes of suicide prevention developed in HICs need to be modified and culturally adapted to effectively to address this major public health problem in Sri Lanka and other LMICs in the region.


We would like to thank the University of Toronto Medical Alumni Association and CREMS-sponsored Dr. Elva Mary Rowe Fund for their generous support of this project. We also thank Marcos Sanches for his help with the statistics and data analysis.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.

  References Top

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  [Figure 1], [Figure 2]

  [Table 1], [Table 2], [Table 3]


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