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COMMUNITY-BASED MASTER’S PROJECT

Substance abuse and different drug use as predictors for suicide ideation and risk among youth

Submitted by:

Geoffrey Kip MPH(c)

Email: gjk37@drexel.edu

Date: June 2015

Preceptor: Mr. Tita Atte, MPH, CPH

Adviser: Dr. M. Hovinga PhD, MPH

A Community Based Master’s Project presented to the faculty of Drexel University School of Public Health in partial fulfillment of the Requirement for the Degree of Masters of Public Health.

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ACKNOWLEDGMENTS

My sincere appreciation goes to my adviser Dr.Hovinga and my preceptor Mr.Atte for invaluable

suggestions and corrections, Dr.Diamond, my Heavenly Father above and my parents for

wisdom, strength and support and also my fellow colleague Maria Yilma with whom I worked

with.

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Abstract

The main objective of this study was to determine whether substance abuse was a significant

predictor for history of suicide in adolescents and young adults (14-24 years) and the specific

objectives were to:

• Assess whether substance abuse score is a significant predictor of suicide lifetime score.

• Determine how different drugs (alcohol, marijuana, smoking, or other illicit drugs)

predispose the youth to suicidal ideation and risk.

• Identify whether among different ethnic groups substance abuse predisposes them

differently to suicidal ideation and risk.

This study was cross-sectional with the data being derived from a dataset. The sample

included any person between the ages of 14-24 who gave their consent at any emergency

department, primary care office, school or location where the Behavioral Health Screen (BHS)

was administered. The sample was open to people of all race but they had to be able to speak

English or Spanish. Data Analysis was performed in SAS 9.3 and Excel was used to generate

graphs and tables. Logistic Regression was used to calculate odds ratios and confidence intervals

for the substance abuse exposure variables.

The results showed that substance abuse score, marijuana, alcohol, tobacco and other illicit

drugs were all significant predictors of history of suicide in participants. Race played a

significant role in predicting history of suicide in participants who were using any type of drug.

Future studies will need to be more generalizable (include people of all ages, more equal race

and gender distribution). In addition, a different study design such as a cohort or case control

study designs might make it easier to measure the risk of suicide.

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Background/Introduction

Globally 100,000 to 200,000 young adults (15-24y) commit suicide annually (Greydanus,

Patel, & Pratt, 2010). Suicide in adolescents and young adults continues to be a growing trend in

the world (Greydanus et al., 2010) as it is the third leading cause of death among the youth and is

responsible for greater number of deaths each year than the next 7 highest causes of death

combined (Anderson & Smith, 2003). Approximately 4000 deaths in 10 to 24 year olds are due

to suicide (Greydanus et al., 2010). Youth suicide is a huge problem that has drastic negative

consequences on young people and society (Wintersteen, 2010).

During the years of 2003-2005, there were 514 suicides in the youth in Pennsylvania

(Wintersteen, 2010b). In the 19 counties that had population densities large enough to calculate

suicides rates 15% of them had suicides rate twice or more compared to the national average

(Wintersteen, 2010b).

Self-harm behaviors which include suicidal thoughts and behaviors (STB) and non-

suicidal self-injury (NSSI), rise dramatically during adolescence and occur at high rates during

young adult years (Jenkins, Singer, Conner, Calhoun, & Diamond, 2014). Approximately 12.1%

of adolescents in the US contemplate suicide, 4.0% have a plan and 4.1% actually try to commit

suicide (Hoyert & Xu, 2012; Nock et al., 2013). NSSI serves as an early indicator of STB, so

studying the temporal relationship between them is of clinical importance (Whitlock et al.,

2013). Furthermore, findings in their study revealed that individuals who had a history of NSSI

had a higher risk for later or simultaneous STB and also showed mark differences from each

other in the severity of psychological and social markers (Whitlock et al., 2013).

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Several other variables and indicators when measured could help predict likelihood for

suicide in individuals. Jenkins et al. (2014) study indicated that youth who were at the biggest

risk for suicide were individuals who scored on average a 2.75 or above on the depression

subscale (scored on a 1-4 scale) and had a lifetime history of alcohol use. Due to the serious

nature of suicide, it is of interest to determine whether substance abuse is a major risk for suicide

among the youth.

Prior studies have shown a strong link between substance abuse and suicidal behavior

and that many of the factors that elevate risk for substance abuse, for example traumatic

experiences also increase the risk of STB (Dube et al., 2003). Alcohol use or abuse plays a large

role in unplanned suicides. Alcohol intoxication may have a significant role in unplanned

suicides because of a higher disinhibition and impulsive behavior, higher aggression and higher

cognitive restriction which affects the use of other coping strategies (Hufford, 2001; Sher, 2006).

Research has shown that drinking alcohol while depressed or sad leads to a three times increase

in the risk of self-reported suicide attempts among young individuals not reporting ideas of

suicide. This provides a different method to identify early intervention practices (Schilling,

Aseltine, Glanovsky, James, & Jacobs, 2009). In a large study results showed that heavy alcohol

consumption and binge drinking was linked to suicide attempts in adolescents aged 13 and older

even after adjusting for depressive symptoms (Hawton, Saunders, & O'Connor, 2012).

Between 2005-2007 there were 26,902 deaths in the National Vital Death Reporting

System (NVDRS) funded states with poisoning being the third highest manner to commit suicide

(Center for Disease Control (CDC), 2007). There were 3706 deaths due to poisoning and 75% of

these deaths was due to alcohol or drug overdose and 47% of those individuals were suffering

from an alcohol or substance abuse problem (CDC, 2007). Furthermore, 69% of suicide deaths

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related to substance overdose were due to the ingestion of one type of drug while 25% occurred

due to ingesting two or more drugs (CDC, 2007).

In addition, studies have indicated that among the youth in a psychiatric hospital, the risk

for suicide attempts was four times greater in those who smoked compared to those who did not.

Adolescents who smoked also had a greater risk for NSSI (Mäkikyrö et al., 2004). Studies also

show that students who tend to consume alcohol and drugs have a greater likelihood of suicidal

tendencies than those who use no substances. For example, 8.5% of heavy drinkers (5 or more

drinks of alcohol in one sitting during the past 2 weeks) have contemplated suicide and 2.34%

have actually tried to commit suicide (Bussing-Burks, 2013).

Statement of the problem

In the context of Pennsylvania (PA), the three northeastern PA counties

(Lackawanna, Luzerne, and Schuykill) have suicide rates that are well above the national and

state average (Wintersteen, 2010b). Alcohol dependency is a significant risk factor for suicidal

behavior. The associations between suicide rates and measures of alcohol consumption have

been investigated in several studies (Sher, 2006). Substance abuse might cause social isolation,

low self-esteem, loss of work or school, estrangement from family and friends and all these

circumstances can contribute to stresses that may lead to suicidal tendencies. Substance abuse

also can increase impulsiveness and decrease inhibitions, making one more likely to act on

suicidal tendencies (Windham, 2014). According to Batra, Vulopas, Lozada and Siciliano

(2013) in Pennsylvania, in 2010, suicide was the third leading cause of death for ages 14-24;

third among males, second among females. Forty seven Pennsylvania counties reported at least

one suicide among ages 14-24 in 2010. Furthermore, there were 215 suicides among ages 14-24

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in the same year and 2,151 hospitalizations for self-inflicted injury. Among suicides, the likely

methods were suffocation (44%), firearms (43%), and poisoning (7%). Among self-injury

hospitalizations, the most methods were poisoning (75%) and cutting (17%). More males

accounted for the majority of suicides in this age group (83%), while females accounted for the

majority of self-injury hospitalizations in this age group (59%). For every female suicide among

ages 14-24, there were over 30 non-fatal self-injury inpatient hospitalizations; for every male

suicide among ages 14-24 there are five non-fatal self-injury inpatient hospitalizations (Batra,

Vulopas, Lozada & Siciliano, 2013). Therefore, the problem being addressed by this current

study is to investigate whether substance abuse is a significant predictor for suicide ideation and

risk among the youth (14-24 years) in Philadelphia.

Hypothesis

The hypothesis in this current study was as follows:

Among the youth population in Philadelphia, substance abuse is a significant predictor for

suicide ideation and risk.

Overall Objective/Aim:

The main objective of this study was to determine whether substance abuse is a

significant predictor for suicide ideation and risk among young individuals (14-24) in

Philadelphia.

Specific Objectives/Aims

The specific objectives set for the current study were to:

• Assess whether substance abuse score is a significant predictor of suicide lifetime score.

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• Determine how different drugs (alcohol, marijuana, smoking, or other illicit drugs)

predispose the youth to suicidal ideation and risk.

• Identify whether among different ethnic groups substance abuse predisposes them

differently to suicidal ideation and risk.

This study attempted to answer the following questions:

• Whether the numeric substance abuse score is significant as a predictor of the suicide

lifetime score?

• How do different drugs (alcohol, marijuana, cigarettes and other illicit drugs) predispose

the youth to suicidal ideation and risk?

• How does substance abuse among different ethnic groups predispose them differently to

suicidal ideation and risk?

Research Design and Methods

This section will outline the overview of the study and its design.

Overview of study and study design

The study was a non-intervention cross-sectional descriptive study with the data being

derived from a dataset. The study population consisted of males and females (all ethnicity) aged

between 14-24 years.

Description of Dataset

The data derived from the Behavioral Health Works (BH-Works) program which is a

web- based program that implements a screening tool known as the Behavioral Health Screen

(BHS) tool to test patients for a variety of risk behaviors and psychiatric symptoms. The BHS

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was created by the Center for Family Intervention Science at the Children’s Hospital of

Pennsylvania (now at Drexel University) and is targeted at an adolescent and young adult

population. It is made up of psychiatric symptom scales and risk behaviors that deal with all the

psychosocial areas.

The BHS tool consists of 55 crucial items with an extra 38 items that come up if specific items

are positively validated. The tool was created to be used in primary care, emergency department

(ED), universities/colleges, and mental health settings.

Approximately it takes about 10 minutes to finish the BHS varying on the number of

problems encountered. Several studies have analyzed the different parts of the BHS. During the

previous study where these data were collected, focus groups were performed at four hospitals in

Pennsylvania to discuss behavioral health issues and screening. The BHS works by 1) first

creating a file for the patient 2) the Patient logging in 3) Patient completing the screen 4)

Retrieving the Screen results 5) Reviewing and printing the screen results 6) Discussing with the

patient and lastly 7) storing the screen results in a medical record. Each participant is given a

unique Medical Record (MR #) so as to prevent duplicates in the dataset.

After a patient takes the web-based BHS, the facility administrators can export files from the

Data and Report section. The user may either export the file in raw data form or as coded values

and export the file in a text format or in raw data form. The data collected is saved in an excel

file and then analyzed either in SAS or SPSS. The data for this current study were collected from

this BHS database for analysis.

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Description of the study population and eligibility criteria:

The study population comprised of young individuals aged between 14 to 24 years old

who visited the primary care offices, emergency departments and schools. Participants were of

diverse racial background and sexual orientation. The initial inclusion criterion during the

recruitment for the study was that individuals had to be able to speak English or Spanish. All

non-English and non-Spanish speakers were excluded from taking the BHS survey and psychotic

individuals were also excluded from the study.

No recruitment strategies were used to get participants to take the BHS screen, since

anyone who visited primary care offices and other locations with the screen and who met the

inclusion criteria were eligible to take the BHS. Consent forms were handed out to anyone who

took the BHS. During the validation phase of the BHS tool 1836 eligible young individuals

visited the locations with 1038 of them asked to participate (Diamond et al., 2010). Of these

individuals, 839 showed interests, 770 were contacted to schedule assessments, but 54 could not

be contacted and 100 lost interest. Furthermore, 190 individuals did not attend their scheduled

samples and so the final sample consisted of 415 young individuals who provided data that could

be used (Diamond et al., 2010). The final sample consisted of participants with a mean age 15.8

years with them being 66.5% female, 33.5% male, 77.5 % black, 10.7% white, 9.7% Hispanic

and 2.1% of other races (Diamond et al., 2010).

After the validation phase 1561 youth aged between 14 to 24 years who completed the BHS

survey in a primary physician’s office made the sample. This sample was used for analysis and

the mean age of participants was 17.68 years with 75.4% of them being female, 24.6% male and

78.3% were white, 2.0 % Black/African American, 1.5% Asian, 2.0% other, 10.3% claimed

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more than one racial, 5.9% were not sure and 14.3% considered their ethnicity as Hispanic

(Jenkins et al., 2014).

Definition and measurement of the outcome/dependent variables

Outcome Variables

The outcome variable for the study was the risk of suicide in the young individual. In the

dataset the variable that referred to the suicide risk was called the Suicide Current Score. The

suicide current score was connected to the suicide lifetime score. The Suicide lifetime score was

coded as if suicidelifetimescore > 0 then history=1 and if suicidelifetimescore <0 then history=0.

Furthermore, if history=0 and week=0 then there was “No History” but if history=1 and week=0

then there was a “History of suicide, but it was not current. If week=1 then the individual was

“Currently at risk for Suicide”. The Suicide current score was coded such as that if

suicidecurrentscore > 0 then week=1 meaning that that the individual was currently at risk for

suicide. If suicidecurrentscore =0 then week=0 and the individual was not currently at risk for

suicide. In addition, if history and week=0 then there was “No history” and if history=1 and

week=0, “there was a history of suicide but was not current”.

Covariates

Demographic variables that included in the study consisted of Gender, Age, Race, and if

the individuals consider themselves Hispanic or not. Gender was coded as female (1), Male (2),

Transgender-Female to Male (3), Transgender Male to Female (4) skip (-666), don’t know (-

888), and don’t want (-999). Age was coded as a continuous variable and accepts any numbers.

Race was coded as White (1), Black/African American (2), American Indian/Alaskan Native (3),

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Asian (4), Native Hawaiian/Other Pacific Islander (5), More than one Race (6), Not Sure (7),

skip (-666), don’t know (-888), and don’t want (-999). The variable indicating whether the

individuals consider themselves Hispanic or not was called HISPAN and it was coded as No (2),

Yes (1), Unsure (3), skip (-666), don’t know (-888), and don’t want (-999) .

The independent/exposure variables used was the substance abuse which included:

alcohol, cigarettes, chewing tobacco, snuff, marijuana and any drugs to relax or get high.

Substance abuse independent variables

The variable BHSSA01 defined the question, “Have you ever, in your whole life, even

once, used tobacco (i.e. cigarettes, chewing tobacco, snuff or others)? It is coded No (0),

Sometimes (2), Often (4), skip (-666), don’t know (-888), and don’t want (-999). The variable

BHSSA01a defined the question “In the past 30 days, how many days have you used tobacco?”

and was coded as a text-numeric variable and codes skip (-666), don’t know (-888), and don’t

want (-999). BHSSA01b defined the question “On average how many cigarettes do you smoke a

day and was coded as a text/numeric variable and coded skip (-666), don’t know (-888), and

don’t want (-999). The variable BHSSA02 defined the question “Have you ever, in your whole

life even once, used alcohol?” and was coded No (0), Yes (4), skip (-666), don’t know (-888),

and don’t want (-999). The variable BHSSA02a defined the question “In the past 30 days, how

many days have you used alcohol?” and was coded as a text-numeric variable and codes skip (-

666), don’t know (-888), and don’t want (-999). The variable BHSSA03 defined the question

“Have you ever, in your whole life, even once, used marijuana (i.e., weed, pot or blunts)?” and

was coded No (0), Yes (4), skip (-666), don’t know (-888), and don’t want (-999). BHSSA03a

defined the question “In the past 30 days, how many days have you used marijuana?” and was

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coded as a text-numeric variable and codes skip (-666), don’t know (-888), and don’t want (-

999). The variable BHSSA04 defined the question “Have you ever used any other type of

substance or medicine to get high or relax?” and was coded No (0), Yes (4), skip (-666), don’t

know (-888), and don’t want (-999). Finally, the variable BHSSA08 defined the question

“During the past year, have you kept using alcohol or drugs even though it has caused problems

in your relationships?” and was coded No (0), Yes (4), skip (-666), don’t know (-888), and don’t

want (-999).

New variables called marijuanafreq, alcoholfreq and tobacfreq were created to measure

the frequency of marijuana, alcohol and tobacco use were created. These variables converted the

open ended questions that asked about how many days in the past 30 days the respondents used

the specific drug. These frequency variables were split into 1-10 days, 11-20 days, 21-29 days

and daily to measure how often current users of these drugs were using them.

In addition, another variable called druguse was created to subset only the population

who had used drugs. Druguse was coded 1 or positive if the participants had either used alcohol,

or tobacco, or marijuana or any other medical substance to get high. Druguse was coded as 0 or

negative if the participants had used none of the drugs mentioned above even once. Druguse was

used to split the population by race and ethnicity for those who only used drugs to see if race or

ethnicity had a significant impact on the lifetime risk of suicide or not.

Plan of analysis:

The plan of analysis involved first carrying out a univariate analysis of the data to get a

good description, the range and distribution of the reference variables (substance abuse

variables). All the values of each variable had already been coded by the dataset and have

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defined ranges already set so there should not be any problems with the quality of the data being

used. Calculation of interquartile ranges, means and medians of continuous variables was

performed to get a good sense of the distribution.

This was a quantitative study and the data were analyzed using the SAS Statistical

Package version 9.3 and figures, tables and graphs were generated in Microsoft excel.

Descriptive statistical analysis was utilized. The data were analyzed according to the research

objectives.

Additionally, data were displayed in figures, percentages and proportions in the form of pie and

bar charts as well as histograms and line graphs. All these were applied to describe data and to

give a view of the distribution of the study results. Frequency distribution tables for categorical

variables were used. Data were checked for any implausible values or outliers that do not make

sense and these values were excluded from the analysis. If there was any presence of incorrect

responses and inconsistencies in the data, then the data were cleaned in SAS using if-then

statements to correct these discrepancies in any answers.

Therefore, if there was missing data for any specific variables then the statistical program

SAS was coded to ignore any missing fields in the data. All missing data were excluded from the

analysis. The skip (-666), don’t know (-888) and don’t want (-999) categories were grouped into

one category which was coded as the numerical value=3 for each of the variables.

SAS was used to recode values for the variables. For example if Yes=1 and No=2 then

the values were recoded to No=0 and Yes=1 to make the data more easy to understand.

Furthermore, the nature and distribution of the variables was explored using SAS

and appropriate statistical models depending on the distribution of the variables and the types of

variables were chosen.

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A univariate analysis of the demographic data (gender, age, language, race, school status

and job status) was performed and a table and some graphs of race, age and gender were

generated to give a graphical representation.

Once a univariate analysis had been performed, then a bivariate analysis was

carried out. The bivariate analysis was conducted between the independent variables and the

dependent variable and between several independent variables in an attempt to look for

confounders. SAS was used to create a new variable called “liferisk” from the variable

lifetimesuicidescore. The suicide lifetime score assessed the risk of suicide using a scale of 0-4

with 4 identifying the participants who were at the highest risk of suicide and those who were at

0 were at no life risk for suicide. Any individual with any score greater than 0 means he/she has a

history of suicide. Liferisk was coded such that any individual whose suicidelifetimescore was

greater than 0 had a history of suicide and those whose suicidelifetimescore was equal to 0 then

had no history of suicide.

A bivariate analysis was performed between all the independent variables for alcohol use

e.g BHSSA02 (alcohol use) and Liferisk to assess whether if the usage of alcohol was a

significant predictor for the outcome variable suicidelifetimescore. In addition, a bivariate

analysis of all the substance abuse variables eg BHSSA1 (tobacco use) and Liferisk was used to

examine or determine if these two independent variables were significant predictors of each

other. For example, if one individual uses tobacco, is this a significant predictor if they have a

history of suicide or not.

Finally, a multivariate analysis was performed that predicts suicidelifetimescore from all

the other predictors such as the substance abuse variables. The best model that fits the data was

used in the multivariate analysis. A logistic model was used to model the data since the outcome

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variable liferisk was binary. For the full model liferisk was the outcome variable and the

predictors were the BHSSA01 BHSSA02 BHSSA03 BHSSA04 BHSSA08 substance abuse

variables and it was adjusted for Race, Gender Age and Depression Score. Model 1 was the

unadjusted model and only included the exposure variable. Model 2 was adjusted for age and

gender, model 3 further adjusted for race and model 4 further adjusted for depression.

In summary, cross-tabulations were used to review and analyze these data. After

frequency distributions and different types of cross-tabulations, further statistical analyses were

performed to determine whether the differences and associations found were significant or were

just a consequence of chance. Tests of significance estimated the likelihood that an observed

study result (for example a difference between two variables) was due to chance.

Additionally, chi-squares (χ²) and p-values were calculated to test the significance of the

relationships between categories or variables. A table of χ² values was used to accept or reject the

null hypothesis.

Human Subjects Section

During the BHS study (Jenkins et al., 2014) it was mentioned that the second page of the

screening tool presented the patients with an IRB-approved consent form which asked whether

their de-identified data could be used for research purposes. Only those who consented were

included in that study. The report further indicated that parental consent was not considered

necessary as Pennsylvania law stipulates that adolescents aged 14 and above can consent to

mental health services without parental consent. This right has been extended to low-risk

research that provides access to care (Diamond, Wintersteen, Fein, Tien, & Briner, 2014). All

procedures were approved by the Children’s Hospital of Philadelphia and Drexel University

Institutional Review Board (IRB).

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Results

The findings of the study are presented in this section. The first part outlines the brief

personal profile of the study participants as summarized in Table 1. This includes participants'

gender, age, educational status, race/ethnicity, employment status and language.

Table 1: Socio-Demographics

Demographics Frequency PercentGenderFemale 3628 66.45Male 1822 33.37Transgender (Female to Male) 8 0.15Transgender (Male to Female) 2 0.04RaceWhite 3586 75.05Black/African American 347 7.26American Indian/Alaskan Native 38 0.80Asian 138 2.89Native Hawaiian/Other Pacific Islander 73 1.53More than One Race 596 12.47LanguageEnglish-Speaking 4051 99.41Spanish-Speaking 2447 0.59Age (at Screening)14-17 3935 71.9918-20 962 17.6021-24 569 10.41Currently Attending School or Planning to return in the fall?Yes 4912 90.00No 546 10.00Do you currently have a JobYes 1534 28.10No 3925 71.90

Table 1 shows the socio- demographics for the sample population. Of the 5466 who took

the BHS , the majority of the sample population’s gender was female ( n=3628, 66.45%) as

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compared to male (n=1822, 33%) and with a very few percentage of the population being

transgender (n= 10, <1%). The response category transgender was only added recently and this

might explain why it had such low numbers. The majority (n=3935, 72%) were between 14-17

years old, 962 (17.6%) of the participants were between 18-20 years and 569 (10.41%) were 21-

24 years old. Different ethnic groups were also presented in this study and it was observed that

the majority of the sample population was white (n=3586; 75%), Black/African Americans were

347 (7.26%) and 596 (12.47%) claimed more than one race. Furthermore, the findings revealed

that the majority ( n=4912; 90%) were currently attending school or planning to return in the fall,

while 10% (n=546) were not attending or not planning to return to school in the fall. With regard

to employment status at the time of the survey, it was observed that 1534 (28.1%) were

employed while 3925 (71.9%) did not have a job.

Table 2: No lifetime suicide ideation, lifetime suicide ideation and lifetime suicide attempts only Mean and standard deviations

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No Lifetime Suicide Ideation (N=4597)

Lifetime Suicide Ideation Only (N=582)

Lifetime Suicide Attempts Only (N=282)

Age 16.68 (2.54)

16.83 (2.51)

17.26 (2.77)

% Female 64.60 72.60 83.20% Male 35.31 26.72 15.71%White 67.10 65.30 55.00%Black 6.48 5.23 7.90% More than one Race 9.95 15.51 18.93% Native Hawaiin/ Other Pacific Islander

1.36 1.05 1.79

% American India/ Alaskann Native

0.81 0.17 0.00

% Asian 2.5 3.31 1.79Substance Abuse Score 0.06

(0.37) 0.20 (0.58)

0.40 (0.88)

Note: Mean and standard deviation (or percentages) are presented ; Substance Abuse diagnostic subscale scores are means and range from 0-4.

In addition, as shown in Table 2, the mean age was 16.68 years for those with no lifetime

suicide ideation, 16.83 years for those with lifetime suicide ideation only and 17.26 years for

individuals with suicide attempts only. It was further noted that, the percentage of females who

had suicide attempts only was much greater than the males who had suicide attempts only (83.2

% vs 15.71%). Furthermore, the percentage of the white population was 67.1% for no lifetime

suicide ideation, 65.3% for suicide ideation only and 55% for suicide attempts only.

Additionally, the mean substance abuse score for individuals who had no lifetime suicide

ideation was 0.06, was 0.20 for suicide ideation only and 0.40 for suicide attempt only.

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Table 3: Numeric Substance Abuse Score of Participant vs lifetime risk of suicide

No Lifetime Risk of Suicide Lifetime Risk of SuicideSubstance Abuse Score

Frequency Percent Frequency Percent p-value

0-1 4517 82.65 812 14.86 <0.0011.0-2.0 45 0.82 32 0.592.0-3.0 25 0.46 18 0.333.0-4.0 9 0.16 7 0.13Total 4596 84.10 869 15.90

Table 3 shows that the numeric substance abuse score was a significant predictor for the

lifetime risk of suicide (p-value= <0.001). Those who had a substance abuse score between 0-1

had a 14.86% had a history of suicide. Thus, a total of 869 (15.9%) had a history of suicide.

Table 4: Tobacco Use, Alcohol use, marijuana use, and other medical substance use vs lifetime risk of suicide

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No Lifetime Risk of Suicide

Lifetime Risk of Suicide

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Used Tobacco once in their life? Frequency

% Frequency % p-value

No 3660 66.96 481 8.80 <0.0001Yes 928 16.98 387 7.08Don’t know, don’t want, skip 9 0.16 1 0.02Total 4597 84.1 869 15.9

Tobacco Frequency: In the past 30 days how many days have you used tobacco?1-10 153 21.79 70 9.97 0.441511-20 53 7.55 29 4.1321-29 22 3.13 16 2.28Daily 250 35.61 109 15.53Total 478 68.09 224 31.91

Average Number of Cigarettes Smoked per day1-4 111 20.11 64 11.59 0.20535-14 170 30.80 73 13.2215-24 67 12.14 44 7.9725+ 13 2.36 10 1.81Total 361 65.40 191 34.60

Used Alcohol Once in their life?No 3209 58.71 398 7.28 <0.0001Yes 1371 25.08 468 8.56Don’t know, don’t want, skip 17 0.31 3 0.05Total 4597 84.1 869 15.9

Alcohol Frequency: In the past 30 days how many days have you used alcohol?1-10 659 72.66 205 22.60 0.012111-20 26 2.87 11 1.2121-29 2 0.22 0 0Daily 2 0.22 2 0.22Total 689 75.96 218 24.04

Used Marijuana once in their life?No 3987 72.94 545 9.97 <0.0001Yes 589 10.78 321 5.87Don’t know, don’t want, skip 21 0.38 3 0.05Total 4597 84.1 869 15.9

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Marijuana Frequency: In the past 30 days how many days have you used marijuana?1-10 178 54.6 85 26.07 0.038311-20 17 5.21 19 5.8321-29 3 0.92 5 1.53Daily 12 3.68 7 2.15Total 210 64.42 116 35.58

Used any medical substance to get high or relax?No 4456 81.52 732 13.39 <0.0001Yes 129 2.36 135 2.47Don’t know, don’t want, skip 12 0.22 2 0.04Total 4597 84.1 869 15.9

Have you kept using alcohol or drugs even though it has caused problems in relationships?No 1435 26.25 436 7.98 <0.0001Yes 75 1.37 79 1.45Don’t know, don’t want, skip 3087 56.48 354 6.48Total 4597 84.1 869 15.9

Table 4 shows that 66.96% of those who had not used tobacco once in their life had no

history of suicide while 7.08% who had used tobacco at least once in their life had a history of

suicide. Tobacco use was a significant predictor for lifetime suicide score. (p-value <0.0001).

For the current users of tobacco who used it daily 15.53% had a history of suicide. In addition,

9.97% of users who used tobacco 1-10 days in the past 30 days had a history of suicide.

However, the number of days tobacco was used in the past 30 day was not a significant predictor

of lifetime risk of suicide (p-value=0.4415).

It was further noted that the average number of cigarettes smoked a day was also

insignificant at predicting the outcome lifetime suicide risk (p-value=0.2053). A total of 361

(65.40%) participants who smoked cigarettes had no history of suicide while 191 (34.60%)

24

participants who smoked had a history of suicide. Furthermore, out of the participants who

smoked 1-4 cigarettes a day, 11.59% had a history of suicide and those who smoked 15-24

cigarettes a day, 13.22% had a history of suicide.

It was also observed that more than fifty eighty percent (58.71%) who had not even used

alcohol once in their life had no history of suicide. Of those individuals who had used alcohol at

least once in their life, 8.56% had a history of suicide. A response to “Whether or not they had

used alcohol once in their life”, was a significant predictor for history of suicide (p-value

<0.0001). Furthermore, 22.6% of participants who had used alcohol 1-10 days in the past 30

days had a history of suicide. The frequency of alcohol use was a significant predictor for

lifetime risk of suicide (p-value=0.0121).

Further results show that 72.94% of those who had not used marijuana once in their life

had no history of suicide while 9.97% had a history of suicide (see table 4). Out of the

participants who had used marijuana at least once, 5.87% had a history of suicide. Using

Marijuana at least once was found to be a significant predictor for lifetime risk of suicide (p-

value <0.0001).

Among the current users of marijuana, those who used marijuana 1-10 days during the last 30

days, 26.07% had a history of suicide and marijuana frequency was found to be a significant

predictor of lifetime risk of suicide (p-value=0.0383) (Table 4).

In addition it was also noted that 2.47% who had used any kind of medical substance to

get high or relax, had also history of suicide. Furthermore, 81.52% who had not used any other

medical substances to get high or relax had no history of suicide. Using a medical substance to

get high or relax was a significant predictor for lifetime risk of suicide (p-value <0.0001).

Furthermore, 26.25% of participants who did not keep using alcohol or other drugs even though

25

it caused problems in relationships had no history of suicide. Out of those who kept using drugs

despite the problems it caused in relationships, 1.45% had a history of suicide. This variable was

found to be a significant predictor for lifetime risk of suicide (p-value <0.0001) as shown in

Table 4.

Table 5: The impact of race and ethnicity for those using drugs vs lifetime risk of suicide

No Lifetime Risk of Suicide

Lifetime Risk of Suicide

Race Frequency Percent Frequency Percent p-valueWhite 1221 59.91 375 18.4 0.0004Black/African American 110 5.40 36 1.77American India/Alaskan Native 12 0.59 1 0.05Asian 18 0.88 15 0.74Native Hawaiian/Other Pacific Islander

19 0.93 5 0.25

More than one race 148 7.26 78 3.83Total 1528 74.98 510 25.02

EthnicityNot Hispanic 1334 61.50 434 20.01 0.1063Hispanic 287 13.23 114 5.26Total 1621 74.73 548 25.27

Table 5 shows the lifetime risk of suicide by race and ethnicity for only the participants

who had used either alcohol, tobacco, marijuana or any other medical substance to get high once

in their life. It was observed that, 18.4% of white population who had used one of those drugs at

least once in their life, had a history of suicide. Of the black population who had used one of

those drugs had a 5.4% had no history of suicide while 1.77% had a history of suicide.

Furthermore, 3.83% of those individuals with more than one race had a history of suicide.

Among those who had used at least one of the drugs, race was found to be a significant predictor

for lifetime risk of suicide (p-value=0.00004). In addition, among those who were not Hispanic

26

20.01 % had a history of suicide while 5.26% of those who were Hispanic had a history of

suicide. Ethnicity was not a significant predictor for lifetime risk of suicide for those that

population who had used drugs once in their life (p-value=0.1063) as shown in Table 5.

Table 6: Odds Ratios and Confidence Intervals for Tobacco, Alcohol, Marijuana and Other Illicit Drugs

Further analyses as shown in Table 6 (the multivariate logistic model analyses) show the

odd ratios for the exposure variables (substance abuse variables). Model 1 was the unadjusted

model, Model 2 was adjusted for Age and Gender, Model 3 further adjusted for Race and Model

4 further adjusted for depression. The findings in this table show that the youth who used

27

tobacco once in their life were 3.17( 2.73-3.69 CI) times more likely to have a history of suicide

(lifetime suicide risk) than those who did not use tobacco. Furthermore, when adjusted for race,

gender, age and depression, youth who used tobacco were 2.6 (2.13-3.17 CI) times more likely to

have a history of suicide as compared to those who did not. Youth who used alcohol were 2.75

(2.37-3.19 CI) times more likely to have a history of suicide as compared to those who did not

use alcohol when unadjusted for any other covariates (Table 6). Adjusting for age and gender

increased the odds ratio to 3.07 and further adjusting for race further increased the odds ratio to

3.2 times.

When adjusted for covariates such as race, gender, age and depression, youth who used

alcohol were 2.26 times (1.87-2.73 CI) more likely to have a history of suicide as compared to

those do did not use alcohol.

Youth who used marijuana were 3.99 (3.39-4.69 CI) times more likely to have a history

of suicide as compared to those who did not when unadjusted for covariates. Adjusting for Age

and gender increased the odds ratio to 4.08 and adjusting for race further increased it to 4.15. In

addition, when adjusting for depression the odds ratio decreased. Model 4 shows that when

adjusting for other covariates, youth who used marijuana were 2.7 (2.22-3.29 CI) times more

likely to have a history of suicide compared to those who did not.

In addition, youth who used any medical substance to get high or relax were 6.37 (4.95-

8.21 CI) times more likely to have a history of suicide as compared to those who did not when

not adjusted for other covariates. Furthermore, when adjusted for other covariates such as

gender, race, age and depression, the odds decreased to 3.96 (2.90-5.41 CI).

Discussion

28

This current study attempted to answer the question whether substance abuse is a

significant predictor for lifetime suicide risk in adolescents and the young adult population.

Suicide is a serious public health problem that causes immeasurable pain, suffering, and loss to

individuals, families, and communities in the United States (U.S. Department of Health and

Human Services, 2012). In 2009, 1,852 young people between the ages of 13 to 19 years died by

suicide in the United States. Suicide was the third leading cause of death for ages 14-24; third

among males, second among females (Batra, Vulopas, Lozada & Siciliano, 2013; U.S.

Department of Health and Human Services, 2012). The findings of this current study showed that

the majority who participated in the BHS were females (66.5%) as compared to males (33%).

Using the BHS, it was observed that the whites; those who were attending school or planning to

return in the fall and the unemployed were in the majority who participated. In another study, the

rates of suicide deaths among 13 – 24 year olds were as follows: American Indian/Alaska

Native: 22.11 per 100,000, White: 9.47 per 100,000, Asian/Pacific Islander: 6.32 per 100,000,

Hispanic: 6.46 per 100,000 and Black: 5.74 per 100,000 (U.S. Department of Health and Human

Services, 2012).

Substance abuse is a major risk factor for suicidal behavior among young people (U.S.

Department of Health and Human Services, 2012). The current study results show that the mean

age for lifetime suicide attempts (17.26) is greater than the mean ages for lifetime suicide

ideation only (16.83) and for no lifetime suicide ideation (16.68). Literature shows that suicide is

generally rare in childhood and early adolescence and becomes more common as the age

increases. The latest worldwide annual rates of suicide per 100,000 show were 0.5 for females

and 0.9 for males for 5-14 years old and 12.0 for females and 14.2 for males among 15-24 year

olds, respectively (Pelkonen & Marttunen, 2003). Particularly, in older adolescent males,

29

substance abuse (drug use and alcohol use) when occurring with mood disorders or disruptive

disorders have greatly increase the risk of suicide (Cash & Bridge, 2009).

Interestingly and contrary to the prior studies, the results of this study also noted that the

percentage of females with lifetime suicide attempts (83.20%) and lifetime suicide ideation only

(72.60 %) is higher compared to the males in the sample. Most literature found that men are

more likely to commit suicide when compared to women. In the United States, the suicide rate

for men is four times that of women (Stack & Wasserman, 2009).In addition, in 2009,

approximately 78 percent of the suicides were males and 22 percent were females. During the

same year, an additional 2,702 young people between the ages of 20 and 24 years died by suicide

(US department of health and human services, 2012). Further to this, about 84 percent of these

fatalities were young men and 16.0 percent were young women (US department of health and

human services, 2012). Reasons for this include that men have higher rates of alcoholism,

substance abuse while women have higher social integration and also more religious which

protects them from being at risk for suicide attempts (Stack & Wasserman, 2009). Furthermore,

men when using a gun to commit suicide are more likely than women to shoot themselves in the

head as compared to the body which results in more successful suicide attempts for men when

compared to women (Stack & Wasserman, 2009). Therefore, in this study sample, some further

investigation must be done to figure out as to why the females have a higher percentage of

suicide attempts as compared to males.

In addition, the substance abuse score increases from no lifetime suicide ideation (0.06)

to lifetime suicide ideation only (0.20) and lifetime suicide attempts only (0.40). This is

generally expected as participants who have greater substance abuse problems and alcohol abuse

30

is more likely to have suicidal attempts when compared to those who do not have substance

abuse problems. According to Wines et al. 28% of inpatients in drug abuse treatment centers had

a past of suicidal ideation and 21% had attempted suicide.

The results further showed that substance abuse score is a significant at predicting the

lifetime risk of suicide in the sample population. Majority of the youth had a substance abuse

score between 0-1 which was not significant and fewer participants had a substance abuse score

greater than 1.0001 which puts them at risk for a substance abuse problem. Initially, it was

expected that youth which had a substance abuse score in the range of 3-4 would have a greater

percentage for lifetime risk of suicide. However, because of the low number of participants in the

3-4 range, compared to the 0-1 range the percentages are lower. Conversely, the chi-square test

showed that the substance abuse score is a significant predictor for the lifetime risk of suicide.

Possible reasons as to why there were fewer participants with a score greater than 1 could be due

to social desirability and the negative stigma associated with using drugs. Since the survey was

self-reported, the youth might have possibly underreported their drug use habits underestimating

the true numbers.

It was further observed that tobacco, alcohol, marijuana and any other medical substance

to get high or relax even just once was significant at predicting the outcome lifetime risk of

suicide. Ironically, tobacco frequency was insignificant as a predictor for lifetime risk of suicide.

According to (Bohnert et al., 2014) study, individuals that had substance abuse disorder and who

were followed for a period of time had a 1.88 times greater risk of suicide death as compared to

those without current substance abuse disorder. In this study, the youth who had used tobacco at

least once in their life were 2.6 times more likely to have a history of suicide as compared to

those who did not use tobacco after adjusting for other covariates (age, gender, race and

31

depression) (Table 6). Those who used tobacco on a daily basis were at greatest risk for having a

suicidal past with 15.5% of all the participants having a history of suicide. However, another

unexpected result was that the average number of cigarettes was found to be insignificant as a

predictor for lifetime suicide score. In this study, cigarette smoking in the sample was not a

direct cause of lifetime risk of suicide. One explanation for this finding might be that cigarette

smoking may not directly cause suicide but instead may cause depression or illness which are

linked with and contribute to suicide (Smith, Phillips, & Neaton, 1992). The main mechanisms as

to why smoking might lead to suicide are that 1) smokers may have pre-existing conditions that

heighten raise their risk for suicide, 2) smoking leads to conditions that are painful and

inhibiting and 3) smoking decreases serotonin and monoamine oxidase levels (Hughes, 2008). It

is possible that since the sample population in this study is a younger population that they may

not have existing conditions or may not develop conditions from smoking yet and so this may

explain why smoking was not a significant predictor. Further research will need to be done to

determine why smoking is not a significant predictor for lifetime risk.

Alcohol use even once and the frequency of alcohol use were significant predictors for

lifetime suicide risk. Most of the youth used alcohol for between 1-10 days a month. These

results are consistent with Jenkins et al. (2014) study which found a history of ever drinking

alcohol is the best predictor of attempts among youth engaging in NSSI. Alcohol is thought to

lead to increased impulsivity and aggression which may to someone to purposely harm

themselves (Sher, 2006). (Schilling et al., 2009) study found that the association between heavy

drinking and suicide attempt is significant and 8.8% of students who reported drinking heavily in

the past year, also reported a suicide attempt compared to 3.3% of students who did not engage

in any heavy drinking. This current study revealed that youth who used alcohol once in their life

32

were 2.26 times more likely to have a history of suicide compared to those who did not use

alcohol after adjusting for other covariates (Table 6).

In addition, both marijuana use even once and the frequency of marijuana use were

significant predictors of lifetime risk of suicide. Some studies have shown that any use of

cannabis or marijuana in the early adolescent years has been shown to be a strong independent

predictor for attempted suicide in young adulthood (Clarke et al., 2014). The earlier the use of

cannabis the greater the toxic effects on the brain are (Clarke et al., 2014). This study found that

those who used marijuana were 2.7 times more likely to have a history of suicide compared to

those who did not after adjusting for all other covariates (Table 6). Frequent marijuana use

possibly leads to grey matter volume reduction in a number of brain areas including the medial

temporal cortex, the parahippocampal gyrus, the insula and orbitofrontal regions (Batistella, n.d).

According to the National Household Survey of Drug Abuse found that young people

ages 12–17 who used alcohol or illegal drugs were more likely to be at risk for suicide than

young people who did not use alcohol or drugs. For example, 19.6 percent of young people who

reported using alcohol were found to be at risk of suicide. Furthermore, those who were using

illicit drugs were 3.96 times more likely to have a history of suicide compared to those who did

not use illicit drugs after adjusting for all other covariates. While only 8.6 percent of young

people who did not report using alcohol were at risk, 25.4 percent of young people who reported

using illicit drugs were found to be at risk of suicide and only 9.2 percent of young people who

did not report using drugs were at risk as well as 29.4 percent of young people who reported

using an illicit drug other than marijuana were found to be at risk of suicide while only 10.1

percent of those who did not report using a drug other than marijuana were at risk (U.S.

Department of Health and Human Services, 2012).

33

As presented in Table 5, the white young population had higher history of suicide

(18.4%) compared with other ethnic groups such as those with more than one race (3.83%) as

well as the black population (1.77%). Literature has shown that white people are more likely to

have suicidal thoughts and behavior compared to any other race (Morrison & Downey, 2000)).

However, in the context of suicide, people who are part of underrepresented groups, are more

likely than European Americans to be “hidden ideators” who reveal their suicidal thoughts more

reluctantly (Morrison & Downey, 2000). Furthermore, (Walker & Flowers, 2011) report that

black people are significantly less likely than white people to find suicide acceptable and more

likely to classify deaths as not suicide. White people were more likely to respond that suicide is

acceptable compared to black people and this might underrepresent the suicide risk for other

races (Walker & Flowers, 2011). Another study which compared Non-Hispanic Whites and

Black college students found that black students said they had more hope, were more goal

oriented and scored higher on motivation to reach their goals which all served as protective

factors for suicide (Davidson & Wingate, 2011). In addition, White women reported higher

prevalence rates of self-harm behaviors than African-American women and are more likely to

utilize self-harm behaviors (Sansone, Sellbom, Chang, & Jewell, 2012). Some possible reasons

as to why there might be a difference is that African- American women are less open about their

self-harm behavior and may also are less likely to engage in mental health treatment (Connor et

al. 2010).

However, this current study showed that while race was significant at predicting the

history of suicide; ethnicity was not a significant predictor. One possible reason might be that

both Non-Hispanic White (NHW) and racial-ethnic minorities (REM) appeared to have similar

34

suicide ideation rates when entering into college despite having the highest rates in high school

(De Luca, Yan, Lytle, & Brownson, 2014).

The current study’s findings regarding model 1 to 3 adjusting for race, gender and age

generally moved the association away from the null (1) suggesting that race gender and age were

negative confounders in the association between substance abuse for tobacco, marijuana and

alcohol. However, when adjusting for depression, the association moved back toward the null

suggesting that depression is a positive confounder in the relationship between substance abuse

and history of suicide (lifetime risk of suicide) in our sample population (Table 6). Tobacco use

was associated with screening positive for depression (Mackenzie et al., 2011). Depression has

shown to lead to increased risk for self-injury, other risky behaviors and also attempting or

committing suicide among college students (Gollust, Eisenberg & Golberstein, 2008).

Limitations of the Study

Despite the fact that this study has produced significant findings, several limitations are

worthy to mention. Despite the dataset being already collected and cleaned, there may be still

some errors in the data which might limit the analysis. The BHS survey was a self-administered

online survey; therefore there might be bias due to self-report. Surveys have the ability to

provide diverse information freely especially due to the availability of data collection possible

through online tools (Miller, 2012). In addition random measurement error, self-reports may

also have systematic bias if the participants in the survey have inaccurate recall or intentionally

provide incorrect answers (Bauhoff, 2011). Furthermore, since many sensitive and personal

questions were asked social desirability may have played a role in some of the responses from

the youth. Social desirability is an important problem in drug and alcohol research (Zemore,

35

2012). Few studies have shown that social desirability has an influence on the answers given for

question asked about drug and alcohol consumption (Zemore, 2012). Pressure from providers

giving treatment could influence people high on social desirability to overemphasize their

willingness to change and also stigma associated with alcohol and drug addiction could cause the

same set of participants to underplay their problem (Zemore, 2012). Another limitation is the

generalizability or external validity of the sample population. The sample population consisted of

youth within the age groups of ages 14-24 who visited primary care offices in North-Eastern

Pennsylvania and surrounding counties, primarily white participants and female participants

approximately made 2/3 of the sample population. Therefore, the results of the study may be

applicable to the population within Pennsylvania, but may not be valid and generalized to the

total general population regarding suicidal behavior and risk. A final limitation is that the BHS

was created to allow medical personnel to investigate quickly a large variety of psychological,

social and environmental domains and as a result, the data used was not from a planned research

battery of measure to come to a conclusion for specific diagnoses or to accurately assess NSSI or

STB. The BHS is more of a tool for the primary care physician to use during a clinical

assessment (Diamond et al., 2010).

Strengths of the Study

Despite the several limitations, this study has several strengths. The BHS tool could be

administered online and anywhere with a computer, or an I-pad, or other mobile technology, the

36

survey could be administered at numerous different sites such as emergency departments,

primary care physician offices, schools and other locations. As a result, a large sample size of

5466 participants was used for this study which provided enough power and internal validity.

Self-reported surveys are often commonly used in public health because they offer a good

balance between costs and reliability (Bauhoff, 2011). Furthermore, because the BHS tool was

an online survey tool, interviewers did not have to be trained in conducting interviews which

saved time, money and resources. In addition, since interviewers were not conducting the

interviews, there was no interviewer bias.

Conclusions and implications of the Study

The present study confirmed that substance abuse is a very significant predictor for

history of suicide among the youth. Substance abuse score was significant at predicting the

lifetime suicide risk. The use of alcohol, tobacco, marijuana and all other drugs were significant

predictors at predicting a history of suicide in the participants. Race played a significant role also

in predicting a history of suicide in participants who used drugs while ethnicity was not a

significant predictor of history of suicide in our sample population. Depression was a positive

confounder in the association between substance abuse and history of suicide, but even when it

was controlled for substance abuse was a major and significant predictor for history of suicide.

While this current study found several meaningful results and supported a lot of existing

literature about substance abuse and suicidal risk among the youth, there is still need for future

studies since there is still limited data in this area.

Future studies should aim at generalizability of the results. A population-based study that has an

equal race distribution and equal number of males and females will mean the results of the study

37

may be more applicable to the general population between the ages of 14-24. In addition, future

research with a different study design such as cohort or case-control study designs to measure the

suicidal risk might be needed.

In summary and conclusion, this current study showed that suicide is a major public

health problem and is responsible for the third largest number of deaths for the population

between the ages of 14-24 years old. Substance abuse is a significant predictor for suicide

ideation and risk in this population even after controlling for depression. Therefore, it is

recommended that understanding the cultural context of suicidal behavior among the youth is

essential for effective prevention. In addition, screening interventions and survey tools that detect

substance abuse problems in the young and adolescent population are extremely important and

might help prevent loss of young lives through aiming to detect suicidal ideation and behavior in

this population.

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Appendix A: Literature review table

Reference #

First author, country and year off publication

Type of study

Sample size and response/participation rate

Sampling method

Occ group/physical demands required

Exposure description

Odds Ratio (95% CI)

Author's conclusions Representative sample statistical analysis yes + no -

2 Bagge, USA, 2008

Cross-Secional

Not reported Sampled adolescents at hospital after suicide attempt

Adolescent group. No job.

Alcohol is exposure

Not reported Evidence for both situational factors and trait factors that influence Alcohol involvement and suicide.

+

42

6 Dube, USA, 2003

Cohort

4665 women and 3948 men. 70% response

Questionnaire mailed to subjects

No info Illicit drug use

Lifetime drug use with ACE score of > 5 had an OR of 4.3 (3.5-5.4).

The number of ACE’s a person is exposed to had a strong graded relationship to the risk of drug initiation.

+

7 Greydanus , USA, 2010

Cross-esctional

Several different studies with different samples. 121 adolescents

Conducted surveys

Adolescents no job

Chronic illness

Not reported Children with chronic illness are more likely to be depressed and depression is a major factor in suicide attempts

+

16 Makikyro, USA, 2004

Cohort

157 patients/ 187

Interviews No info Smoking

Ocassional smoking 3.32 (1.09-10.10).

Frequent smoking 3.00 (1.08-10.10).

Daily smoking was significantly related to suicide attempts and self-mutilation.

+

18 O’Mara, USA, 2012

Cross-sectional

299/451 adolescents and 305 parents

Questionnaires

No info Screening

Not Reported Positive support for screening for suicide risk and other mental health problems

+

19 Peebles, USA, 2010

Retrospective cohort

1432 adolescents

Intake evaluations

Adolescents no occupation

Eating Disorder

Not Reported SIB is common in the population and associated with eating disorder, substance abuse and etc.

+

22 Schilling, USA, 2009

Cross-sectional

31,953 participants

SOS program student screening form

Students and adolescents

Alcohol use

38.2 (29.6-48.3) Use of alcohol while sad or depressed was a marker for suicidal behavior in adolescents

+

25 Whitlock, USA, 2013

Longitudinal study

1,466 students/ 14,372 students

Questionnaire

Students Non-suicidal self injury

Lifetime NSSI incidents Adjusted OR 3.8 (1.4-10.3).

NSSI prior to suicide behavior serves as a gateway behavior for suicide and may reduce inhibition through self-injury.

+

26 Wintersteen, USA, 2010

Cross-sectional

No number given

Standardized screening at 3 primary care practices

No info No exposure testing for suicide risk

Clinic A OR 2.04(1.56-2.51)

Clinic B OR 3.20 (2.69-3.71)

Clinic C OR 1.85 (1.38-2.31)

Standardized screening for suicide risk in primary care can detect youth with suicide ideation and prompt a referral to a behavioral health care center before a fatal or serious suicide is made

+

8 Hawton, USA, 2012

Several studies. Cross sectional and prospective school studies.

No number N/A N/A Self-harm

Not reported Self-harm and suicide are major public health issues and there are many challenges to their management and prevention.

+

43

Appendix B: Timeline of Project Activities:

Project Activity Mo 1

2 3 4 5 6 7 8 9 10

11

12

Project development X X X

IRB Submission X X

Recruitment N/A

Data collection N/A

Development of computer files/ data entry

X X

Data Analysis X X X

Report writing X X X X

Dissemination of findings X X

Appendix C. Socio-Demographics Graphs

44

Figure 1: Gender Distribution

66%

33%0% 0%

Gender Percent Distribution

Female MaleTransgender (Female to Male)Transgender (Male to Female)

Figure 2: Percent distribution of race of the participants

45

0

10

20

30

40

50

60

70

8075.05

7.260.8 2.89 1.53

12.47

Race DistributionPe

rcen

tage

46

Figure 3 Percentage age distributions of the participants

14-17 18-20 21-240

1020304050607080

71.99

17.610.41

Age Distribution

Age groups

Perc

enta

ge %

47

Figure 4 Percentage distributions of participants currently attending school or planning to

return to school compared to those who are not in school.

90

10

School attendance

YesNo

48

Figure 5 Percentage distributions of the participants who currently have a job compared to

those who do not have a job.

Yes

No

0 10 20 30 40 50 60 70 80

28.1

71.9

Employment status

Percentage %