Variables associated with suicide ideation and plans in a Japanese population

doi:10.4236/health.2011.35049

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Vol.3, No.5, 276-287 (2011)

doi:10.4236/health.2011.35049

Health

Va riables associated with suicide ideation an d plans in a

Japanese population

Takuya Hasegaw a1*, Chiyoe Murata1, Tatsuya Noda1, Tomoko Takabayashi2,

Takashi Ninomiya2, Shin ya Hayasaka1, Toshiyuki Ojima1

1Department of Community Health and Preventive Medicine, Hamamatsu University School of Medicine, Hamamatsu, Japan;

*Corresponding Author: hasetaku07172000@gmail.com

2Hamamatsu City Mental Health and Welfare Center, Hamamatsu, Japan.

Received 22 March 2011; revised 20 April 2011; accepted 26 April 2011.

ABSTRACT

The purpose of our study was to clarify vari-

ables associated with suicide ideat ion and p lan s

in a Japanese population. We conducted a ran-

dom-sampling survey on mental health and sui-

cide using a self-administered questionnaire for

Hamamatsu City residents aged 15 - 79 years

between May and June, 2008. This included

questions about gender, age, outpatient treat-

ment, alcohol problems, depression, living ar-

rangements, marital status, annual family in-

come, industry types as well as suicide ideation

and plans. The correlation between these vari-

ables and suicide ideation or plans was then

analyzed with multiple logistic regression an aly-

sis by gender. A total of 1051 responded to this

questionnaire (response rate, 53.9%). Variables

statistically associated with suicide ideation in

males included alcohol problems, depression,

lower annual family income, and accommoda-

tions/eating/drinking services, while in females,

the variables were younger age, outpatient

treatment, depression, living alone, being single,

being separated, lower annual family income,

accommodations/eating/drinking services and

unemployment. On the other hand, variables

statistically associated with suicide plans in

males were younger age, alcohol problems, de-

pression, and lower annual family income, while

in females they were younger age, alcohol

problems, depression, being separated, lower

annual family income, manufacturing, and ac-

commodations/eating/drinking services. Except

for industry types, variables associated with

suicide ideation or plans were consistent with

previous studies. The reason why workers en-

gaging in manufacturing, or accommodations/

eating/drinking services were more likely to

have suicide ideation or plans may be attributed

to the structures and/or stresses unique to

those industries.

Keywords: Suicide Ideation; Suicide Plans;

Variables

1. INTRODUCTION

Suicide is a serious public health problem not only in

Japan but also internationally. Every year, approximately

one million people die of suicide; the suicide rate in the

world is 16.0 per 100,000 as of 2000 [1]. In Japanese, it

was less than 20.0 per year until 1997, but it increased

drastically, exceeding 25.0 in 1998. Since then, it is re-

portedly around 25.0 [2]. In light of this situation, “the

Basic Act on Suicide Prevention” was approved in June

in Japan, 2006, and “the General Policies of Compre-

hensive Measures against Suicide” were formulated in

June, 2007 to urge local governments to adopt measures

for suicide prevention [2]. However, despite these meas-

ures, the Japanese suicide rate has not decreased. In

2008, suicide was the seventh leading cause of death in

Japan [3]. According to data from the World Health Or-

ganization (WHO) in 2009, Japan has one of the world’s

highest suicide rates [4].

Suicide involves multiple and interacting causes among

genetic, psychological, marital, environmental, sociocul-

tural and economical factors. Risk factors for completed

suicide have been reported to involve males, older age,

serotonin dysfunction, mental disorders such as depres-

sion or schizophrenia, alcohol problems, drug abuse, low

social class, poor employment status, low income, low

education, separation as in those widowed or divorced,

lack of social support or certain religious beliefs [5-13].

In addition, types of industries with a high suicide rate

have been reported in agriculture/forestry, mining, con-

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277277

struction, business/repair services, and transport/pro-

duction [14-21]. Suicide-correlated behaviors include

suicide ideation (thoughts of harming or killing oneself),

suicide plans (specific plans to do so) and suicide at-

tempts (non-fatal self-inflicted destructive acts with an

explicit or implied intent to die). Though suicide-corre-

lated behaviors do not necessarily end in completed sui-

cide, it is regarded as an important precursor [22,23].

Because, as with schizophrenia, a stress-vulnerability

model can be applied to the suicide process, the multiple

factors mentioned above precede this process, whereas

suicide plans are preceded by suicide ideation and the

suicide acts in turn are preceded by suicide plans [24].

About 12% of those with suicide ideation attempted sui-

cide [25]. Furthermore, they committed suicide attempts

6.09 times more frequently than those without suicide

ideation [25]. Such attempts are one of the most impor-

tant predictors of completed suicide, since 10% - 15% of

those attempting suicide have reportedly gone through

with it [26,27].

Several studies have mentioned risk factors for sui-

cide-correlated behaviors, some of which reported that

most of them were the same as the factors for completed

suicide. For example, mental problems including de-

pression or drug abuse, separation, poor employment

status, low income or low education have been reported

as risk factors for suicide-correlated behaviors [7,13,

28-35]. However, some factors such as gender or age

have been reported to exert various effects on specific

suicide-correlated behaviors. For example, such behav-

iors are more common worldwide among females or the

young, while completed suicide is more common among

males and the elderly, though these tendencies do not

necessarily apply to all countries or regions [36].

Some local governments have developed various

measures for suicide prevention, one of which is to make

appropriate intervention for those with suicide-correlated

behaviors. Using a self-administered questionnaire, we

conducted a random-sampling survey of suicide-corre-

lated behaviors, particularly suicide ideation or plans,

among Hamamatsu City residents. The aim of our study

was to clarify risk factors associated with suicide idea-

tion and plans in a Japanese population.

2. METHODS

2.1. Subjects

We had to establish measures for a City-wide suicide

preventive strategy. That was followed by the General

Policies of Comprehensive Measures against Suicide. To

grasp the actual situation of suicidality in Hamamatsu

City, a cross-sectional investigation was conducted by

the City. We cooperated in sampling the subjects and

preparing the survey questionnaire, the data from which

were used in our study. Between May and June, 2008,

self-administered questionnaires were mailed to a sam-

ple of 1950 Hamamatsu City residents aged 15 - 79 years,

selected by stratified random sampling based on their

gender, age and ward. Among them, a total of 1051 re-

sponded (response rate, 53.9%). After excluding 142

subjects for incomplete data, the remaining 909 (401

males and 508 females) were analyzed. The study objec-

tives were explained to the subjects via a form distrib-

uted at the time of the survey; only subjects who agreed

to participate in this survey responded. The survey data

were anonymously collected by Hamamatsu City and

kept confidential, making individual identification im-

possible.

2.2. Measures

2.2.1. Suicide Ideation or Plans

The relevant responses in this category were elicited

by a questionnaire used in the Multisite Intervention

Study on Suicidal Behavior (SUPRE-MISS) that WHO

was conducted as part of a global suicide prevention

program in eight countries [37]. The following question

was used to assess suicide ideation: “Have you ever

thought about committing suicide in the last twelve

months?”, followed by two choices: “yes” or “no”. The

following question was used to assess suicide plans:

“Have you ever made plans for committing suicide?”,

followed by two choices: “yes” or “no”.

2.2.2. Alcohol Problems

Alcohol problems were assessed using a Japanese-

translated CAGE (C, Cut down; A, Annoyed; G, Guilty;

E, Eye-opener) questionnaire, followed by 2 choices:

“yes” or “no” [38,39]. Those who responded in the af-

firmative two or more times were considered positive

(having alcohol problems).

2.2.3. Depression

Depression was assessed with a Japanese-translated

CES-D (Center for Epidemiologic Studies Depression

Scale), followed by 4 choices: “0”, “1”, “2” or “3” [40].

Those with a total of sixteen or more points were con-

sidered positive (depressed).

2.2.4. Other Variables

Furthermore, this questionnaire items included gender,

age, outpatient treatment, living arrangements, marital

status, annual family income and industry types. Age

was categorized into 3 variables: “39 yrs or less,” “40 - 59

years,” or “60 years or more.” Outpatient treatment was

denoted by a single item question “Do you receive

T. Hasegawa et al. / Health 3 (2011) 276-287

278

Table 1. Prevalence of suicide ideation and plans (N = 909).

Males Females

Variables

N Suicide IdeationSuicide PlansN Suicide Ideation Suicide Plans

Total 401 7.5 11.0 508 9.1 19.1

Age

≤ 39 133 9.0 17.3 188 12.2 25.0

40 - 59 141 8.5 9.2 193 9.8 21.2

60 ≤ 127 4.7 6.3 127 3.1 7.1

Outpatient Treatment

No 229 7.0 11.8 280 5.7 19.6

Yes 172 8.1 9.9 228 13.2 18.4

Alcohol Problems

Negative 337 6.2 9.8 487 8.6 17.5

Positive 64 14.1 17.2 21 19.0 57.1

Depression

Negative 296 2.4 7.4 347 3.2 13.5

Positive 105 21.9 21.0 161 21.7 31.1

Living Arrangements

Living with Someone Else 371 8.1 11.6 474 7.8 18.4

Living Alone 30 0.0 3.3 34 26.5 29.4

Marital Status

Married 291 7.2 10.3 362 5.2 16.0

Single 92 8.7 13.0 100 17.0 22.0

Separated 18 5.6 11.1 46

21.7 37.0

Annual Family Income (Yen/Year)

≤ 1 999 999 29 17.2 13.8 39 25.6 25.6

2 000 000 - 6 999 999 231 7.4 12.1 282 5.3 17.0

7 000 000 ≤ 112 4.5 6.3 117 11.1 16.2

Unknown 29 10.3 17.2 70 11.4 28.6

Industry Types

Construction 25 12.0 8.0 16 12.5 18.8

Manufacturing 113 6.2 11.5 65 9.2 33.8

Wholesale/Retail Trade 20 10.0 20.0 37 2.7 10.8

Accommodations/Eating/Drinking Services 3 66.7 33.3 20 20.0 35.0

Medical/Health Care and Welfare 8 0.0 12.5 55 9.1 23.6

Government 23 4.3 13.0 11 18.2 27.3

Other Services 38 7.9 5.3 42 9.5 26.2

Others 78 3.8 7.7 51 9.8 15.7

Housewife/Househusband 1 0.0 0.0 132 3.8 10.6

Student 19 21.1 15.8 31 9.7 16.1

Unemployment 73 6.8 12.3 48 18.8 14.6

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279279

treatment at a hospital, clinic, or acupuncture/acupres-

sure clinic due to disease?”, followed by two choices:

“yes” or “no”. Living arrangements were categorized

into 2 variables: “living with someone else” or “living

alone”. Marital status was categorized into 3 variables:

“married”, “single” or “separated (widow/widower or

divorced)”. Annual family income in yen was catego-

rized into 4 variables: “1,999,999 or less”, “2,000,000 to

6,999,999”, “7,000,000 or more” or “unknown”. Indus-

try types were categorized into the following 20 vari-

ables: “agriculture/forestry,” “fisheries,” “mining and

quarrying of stone and gravel”, “construction”, “manu-

facturing”, “electricity/gas/heat supply/ water”, “infor-

mation/communications”, “transport/postal activities”,

“wholesale/retail trade”, “finance/insurance”, “real es-

tate/rental and leasing goods”, “accommodations/ eat-

ing/drinking services”, “medical/health care and wel-

fare”, “education/learning support”, “government”, “other

services”, “other”, “housewife/househusband”, “student”

or “unemployment”. At analysis, because the total num-

ber of male or female subjects were 15 or less, the fol-

lowing industries were included in “others”; “agricul-

ture/forestry”, “fisheries”, “mining and quarrying of

stone and gravel”, “electricity/gas/heat supply/water”,

“information/communications”, “transport/postal activi-

ties”, “finance/insurance”, “real estate/rental and leasing

goods”, “education/learning support” and “other”.

2.3. Data Analysis

Descriptive epidemiological statistics were used to

compute the number and percentage of subjects with

suicide ideation or plans by each variable. The correla-

tion between each variable and suicide ideation or plans

was analyzed with logistic regression models by gender

after adjustments only for age in years (Model 1), and

age in years with outpatient treatment, alcohol problems,

depression, living arrangements, marital status, annual

family income and industry types (Model 2). Odds ratio

(OR), 95% confidence interval (95% CI) and P value

were calculated. Used as reference groups were subjects

aged 60 years or more with no outpatient treatment, no

alcohol problems, no depression, lived with someone else,

were married, engaged in wholesale/retail trade, or whose

annual family income was 7,000,000 or more. P values

of 0.05 or less were considered to be statistically sig-

nificant. All statistical calculations were performed with

SPSS for Windows, version 17.0 (SPSS Inc., Chicago).

3. RESULTS

Table 1 shows the descriptive epidemiological char-

acteristics of our study subjects with suicide ideation or

plans. Among them, 8.4% (7.5% of males and 9.1% of

females) or 15.5% (11.0% of males and 19.1% of fe-

males) had suicide ideation or plans, respectively. The

younger both males and females were, the more likely

they were to have suicide ideation or plans. Females who

had outpatient treatment were more likely to have sui-

cide ideation than those who did not. Both males and

females with alcohol problems were more likely to have

suicide ideation or plans than those without such prob-

lems. Depression showed the same tendency. A lower

percentage of males who were living alone had suicide

ideation or plans than those living with someone else,

though a higher percentage of females living alone had

suicide ideation or plans than those living with someone

else. Females who were separated were more likely to

have suicide ideation or plans. The lower the income

males had, the more they were likely to have suicide

ideation or plans while the lower the income females had,

the more they were likely to only have suicide plans.

Among industry types, both males and females engaging

in accommodations/eating/drinking services showed the

highest percentage of suicide ideators or planners. Re-

garding gender difference, there was no statistical dif-

ference in suicide ideation (Model 1, OR: 1.18, 95% CI:

0.73 - 1.91, Model 2, OR: 0.99, 95% CI: 0.54 - 1.83),

though statistically more females than males had suicide

plans (Model 1, OR: 1.83, 95% CI: 1.24 - 2.69, Model 2,

OR: 2.16, 95% CI: 1.36 - 3.46) (data not shown).

Table 2 shows the results of logistic regression analy-

sis for variables associated with suicide ideation in males.

Statistically significant variables associated with suicide

ideation in both Models were depression and an annual

family income of 1,999,999 or less. Alcohol problems

and accommodations/eating/drinking services showed

statistical significance only in Model 1.

Table 3 shows the results of logistic regression analy-

sis for variables associated with suicide ideation in fe-

males. Statistically significant variables associated with

suicide ideation in both Models were 39 yrs or less, 40 -

59 yrs, outpatient treatment, depression and being sepa-

rated. Statistically significant variables associated with

suicide ideation only in Model 1 were living alone, being

single, an annual family income of 1,999,999 or less,

accommodations/eating/drinking services and unemploy-

ment.

Table 4 shows the results of logistic regression analy-

sis for variables associated with suicide plans in males.

Statistically significant variables associated with suicide

plans in both Models were 39 years or less, alcohol

problems and depression. A statistically significant vari-

able associated with suicide plans only in Model 1 was

an annual family income of 1,999,999 or less.

Table 5 shows the results of logistic regression analy-

sis for variables associated wi h suicide plans in females. t

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Table 2. Logistic regression analysis for variables associated with suicide ideation in males (N = 401).

Variables Model 1 P value OR(95%CI)*Model 2 P value

Age

≤ 39 2.00 (0.73 - 5.50) 0.179 3.16 (0.57 - 17.55) 0.187

40 - 59 1.88 (0.68 - 5.16) 0.223 2.77 (0.62 - 12.30) 0.180

60 ≤ 1 (reference) 1 (reference)

Outpatient Treatment

No 1 (reference) 1 (reference)

Yes 1.54 (0.69-3.40) 0.291 1.50 (0.57-3.96) 0.416

Alcohol Problems

Negative 1 (reference) 1(reference)

Positive 2.62 (1.13 - 6.08) 0.025 2.79 (0.93 - 8.39) 0.067

Depression

Negative 1 (reference) 1 (reference)

Positive 11.39 (4.70 - 27.62) < 0.001 11.54 (4.31 - 30.92) < 0.001

Living Arrangements

Living with Someone else 1 (reference) 1 (reference)

Living Alone −** −**

Marital Status

Married 1 (reference) 1 (reference)

Single 0.97 (0.36 - 2.59) 0.948 0.69 (0.15 - 3.08) 0.624

Separated 0.76 (0.10 - 6.00) 0.790 2.44 (0.19 - 31.39) 0.495

Annual Family Income (Yen/year)

≤ 1 999 999 8.22 (1.94 - 34.95) 0.004 6.56 (1.02 - 42.17) 0.047

2 000 000 - 6 999 999 1.92 (0.68 - 5.40) 0.216 1.08 (0.31 - 3.75) 0.908

7 000 000 ≤ 1 (reference) 1 (reference)

Unknown 2.21 (0.47 - 10.40) 0.317 1.37 (0.21 - 9.01) 0.741

Industry Types

Construction 1.31 (0.19 - 8.86) 0.781 1.40 (0.14 - 13.90) 0.774

Manufacturing 0.60 (0.11 - 3.14) 0.544 0.84 (0.11 - 6.32) 0.865

Wholesale/Retail Trade 1 (reference) 1 (reference)

Accommodations/Eating/Drinking Services 20.82 (1.15 - 375.73)0.040 19.65 (0.84 - 461.06) 0.064

Medical/Health Care and Welfare −** −**

Government 0.40 (0.03 - 4.89) 0.475 0.78 (0.05 - 13.24) 0.863

Other Services 0.78 (0.12 - 5.14) 0.797 0.92 (0.10 - 8.72) 0.945

Others 0.39 (0.06 - 2.55) 0.327 0.45 (0.05 - 4.22) 0.487

Housewife/Househusband −** −**

Student 2.30 (0.35 - 15.14) 0.387 3.64 (0.28 - 47.49) 0.324

Unemployment 1.03 (0.16 - 6.53) 0.973 0.63 (0.06 - 6.93) 0.704

Model 1: Adjusted for age in years; Model 2: Adjusted for age in years, outpatient treatment, alcohol problems, depression, living arrangements, marital status,

annual family income and industry types; *OR: odds ratio; CI: confidence interval; **There were no such subjects.

T. Hasegawa et al. / Health 3 (2011) 276-287

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Table 3. Logistic regression analysis for variables associated with suicide ideation in females (N = 508).

Variables Model 1 P value OR(95%CI)*Model 2 P value

Age

≤ 39 4.29 (1.45 - 12.71) 0.009 12.31 (2.45 - 61.81) 0.002

40 - 59 3.36 (1.12 - 10.11) 0.031 10.01 (2.28 - 43.86) 0.002

60 ≤ 1 (reference) 1 (reference)

Outpatient Treatment

No 1 (reference) 1 (reference)

Yes 3.44 (1.78 - 6.65) < 0.001 2.62 (1.21 - 5.67) 0.014

Alcohol problems

Negative 1 (reference) 1 (reference)

Positive 2.05 (0.66 - 6.44) 0.217 0.77 (0.18 - 3.33) 0.722

Depression

Negative 1 (reference) 1 (reference)

Positive 8.51 (4.13 - 17.55) < 0.001 6.38 (2.85 - 14.28) < 0.001

Living Arrangements

Living with Someone else 1 (reference) 1 (reference)

Living Alone 4.48 (1.91 - 10.52) 0.001 1.22 (0.36 - 4.19) 0.752

Marital Status

Married 1 (reference) 1 (reference)

Single 2.94 (1.30 - 6.64) 0.009 2.39 (0.79 - 7.26) 0.124

Separated 6.95 (2.83 - 17.03) < 0.001 3.56 (1.03 - 12.25) 0.045

Annual family income (Yen/year)

≤ 1 999 999 4.23 (1.59 - 11.28) 0.004 1.50 (0.40 - 5.59) 0.545

2 000 000 - 6 999 999 0.54 (0.24 - 1.18) 0.122 0.42 (0.17 - 1.05) 0.064

7 000 000 ≤ 1 (reference) 1 (reference)

Unknown 0.88 (0.33 - 2.38) 0.807 0.34 (0.10 - 1.15) 0.082

Industry Types

Construction 4.58 (0.38 - 55.30) 0.231 3.07 (0.22 - 43.55) 0.408

Manufacturing 3.48 (0.40 - 30.72) 0.261 2.37 (0.24 - 23.43) 0.462

Wholesale/Retail Trade 1 (reference) 1 (reference)

Accommodations/Eating/Drinking Services 10.26 (1.04 - 101.47)0.046 4.74 (0.40 - 56.55) 0.218

Medical/Health Care and Welfare 3.10 (0.35 - 27.94) 0.312 1.60 (0.15 - 16.71) 0.696

Government 6.25 (0.50 - 77.50) 0.154 2.31 (0.16 - 33.46) 0.539

Other Services 3.50 (0.37 - 33.11) 0.274 2.42 (0.22 - 26.70) 0.470

Others 3.99 (0.44 - 36.12) 0.218 3.12 (0.30 - 31.96) 0.339

Housewife/Househusband 1.52 (0.17 - 13.58) 0.708 2.42 (0.24 - 24.66) 0.458

Student 2.65 (0.25 - 28.31) 0.420 0.96 (0.08 - 11.94) 0.973

Unemployment 15.76 (1.80 - 138.36)0.013 7.58 (0.73 - 78.66) 0.090

Model 1: Adjusted for age in years; Model 2: Adjusted for age in years, outpatient treatment, alcohol problems, depression, living arrangements, marital status,

nnual family income and industry types; *OR: odds ratio; CI: confidence interval. a

T. Hasegawa et al. / Health 3 (2011) 276-287

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Statistically significant variables associated with suicide

plans in both Models were 39 years or less, 40 - 59 years,

alcohol problems, depression, being separated, and

manufacturing. Statistically significant variables associ-

ated with suicide plans only in Model 1 were an annual

family income of 1,999,999 or less and accommoda-

tions/eating/drinking services.

4. DISCUSSION

To the best of knowledge, this study is the first to in-

vestigate the factors associated with suicide ideation and

plans in a Japanese population after consideration of

multiple factors.

The response rate in this study was 53.9%. This was

almost the same rate as in the previous Japanese study

(response rate, 58.4%) [41]. The present study showed

that the prevalence of suicide ideation and plans was 8.4

and 15.5%, respectively. Bertolote et al. reported that

with the SUPRE-MISS protocol the prevalence of sui-

cide-correlated behaviors varied widely among different

countries [37]. They reported that the prevalence of sui-

cide ideation and plans in a general population was 2.6%

- 25.4% and 1.1% - 15.6%, respectively [37]. In contrast,

Scocco et al. reported that the prevalence of suicide

ideation and plans in an Italian population was 3.0% and

0.7%, respectively [13]. In Japanese studies, Ono et al.

reported that the prevalence of suicide ideation and plans

in Japan was 10.9% and 2.1%, respectively [41]. The

prevalence of suicide plans in our study was higher than

in previous studies. In general, the prevalence of suicide

ideation was higher than that of suicide plans, whereas

our study showed that the prevalence of suicide ideation

was lower.

The possible reason for that discrepancy may be that

subjects were supposed to first answer a question about

suicide ideation in the last twelve months, followed by a

question about lifetime suicide plans in our study, sug-

gesting that there might be some subjects who misun-

derstood the second question as one about lifetime sui-

cide ideation. The questionnaire used in the SUPRE-

MISS protocol included several questions about lifetime

suicide ideation or plans, the first time he or she seri-

ously entertained suicide ideation or plans, suicide idea-

tion or plans in the last twelve months, and the last time

when he or she seriously considered suicide ideation or

plans; all of them were supposed to be asked in the order

above. In addition, in the studies mentioned above, sub-

jects were directly interviewed, but not asked to respond

to a self-administered questionnaire, and responses in

regard to suicide ideation or plans differed slightly from

our questions. Thus, it may not be appropriate to com-

pare our study to their protocol, though the prevalence of

suicide ideation in our study was generally consistent

with that in previous studies.

Though there was no statistical gender difference with

suicide ideation in our study, females were more likely

to have made suicide plans than males. Most studies

reported that the prevalence of suicide ideation in fe-

males was higher than in males, though that might vary

by countries or regions [36]. Scocco et al. reported that

in Italy the prevalence of suicide plans among females

was higher than in males [13]. In Japanese studies, Ono

et al. found no gender difference in the prevalence of

suicide ideation or plans [41]. This discrepancy may be

due to the difference of subjects and investigation year

between our study and previous studies.

The factors associated with suicide ideation were al-

cohol problems, depression and lower annual family

income in males, and younger age, outpatient treatment,

depression, living alone, being single, being separated

and lower annual family income in females. These re-

sults are consistent with those in previous studies show-

ing risk factors associated with suicide ideation [13,29,

30,32-35,41-43]. Variables associated with suicide plans

showed the same tendency. Although few studies men-

tioned risk factors associated with suicide plans, Scocco

et al. reported that in an Italian population risk factors

associated with suicide plans were female, alcohol prob-

lems and depression, and that the younger the subjects

were, the more likely they were to have suicide plans,

though not statistically significant [13]. Suicide and sui-

cide-correlated behaviors are mainly brought out by de-

pression. Depression results from multiple factors in-

cluding biological, psychosocial and existential, eco-

nomical, and marital factors. Alcohol problems, lower

income, having diseases, loneliness from such as living

alone, being single or being separated are main causes of

depression, bringing out suicidal thoughts.

Since work is one of the most important human activi-

ties, it has a major impact on human suicidality. Numer-

ous studies have mentioned the correlation between oc-

cupation or industry and completed suicide [14-21,

44-61]. Thus, suicide-correlated behaviors may be re-

lated to them. In our study, industry types statistically

associated with suicide ideation were accommodations/

eating/drinking services in males, and accommodations/

eating/drinking services and unemployment in females.

On the other hand, while there were no industry types

statistically associated with suicide plans in males,

manufacturing and accommodations/eating/drinking

services were associated with suicide plans in females.

Only a few studies have addressed the correlation between

unemployment and suicide ideation [31,33]. However, to

our knowledge, no previous studies have examined the

correlation between industry types and suicide-correlated

behaviors. The reasons why orkers engaged in manu- w

T. Hasegawa et al. / Health 3 (2011) 276-287

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Table 4. Logistic regression analysis for variables associated with suicide plans in males (N = 401).

Variables Model 1 P Value OR (95%CI)*Model 2 P Value

Age

≤ 39 3.11 (1.34 - 7.24) 0.009 6.19 (1.77 - 21.63) 0.004

40 - 59 1.51 (0.61 - 3.77) 0.377 2.38 (0.75 - 7.49) 0.139

60 ≤ 1 (reference) 1 (reference)

Outpatient Treatment

No 1 (reference) 1 (reference)

Yes 1.12 (0.56 - 2.24) 0.745 1.01 (0.48 - 2.14) 0.976

Alcohol Problems

Negative 1 (reference) 1 (reference)

Positive 2.19 (1.02 - 4.68) 0.044 2.67 (1.11 - 6.46) 0.029

Depression

Negative 1 (reference) 1 (reference)

Positive 3.06 (1.60 - 5.85) 0.001 2.98 (1.47 - 6.00) 0.002

Living Arrangements

Living with Someone Else 1 (reference) 1 (reference)

Living Alone 0.23 (0.03 - 1.78) 0.160 0.20 (0.02 - 1.83) 0.156

Marital Status

Married 1 (reference) 1 (reference)

Single 0.70 (0.31 - 1.58) 0.387 0.78 (0.28 - 2.22) 0.648

Separated 1.21 (0.26 - 5.65) 0.806 2.47 (0.39 - 15.61) 0.338

Annual Family Income (Yen/Year)

≤ 1 999 999 4.21 (1.04 - 17.06) 0.044 2.64 (0.55 - 12.73) 0.227

2 000 000 - 6 999 999 2.21 (0.92 - 5.30) 0.075 1.85 (0.73 - 4.72) 0.197

7 000 000 ≤ 1 (reference) 1 (reference)

Unknown 2.17 (0.61 - 7.71) 0.233 1.83 (0.44 - 7.59) 0.408

Industry Types

Construction 0.44 (0.07 - 2.80) 0.386 0.41 (0.06 - 2.94) 0.372

Manufacturing 0.59 (0.17 - 2.08) 0.409 0.82 (0.19 - 3.45) 0.782

Wholesale/Retail Trade 1 (reference) 1 (reference)

Accommodations/Eating/Drinking Services 3.82 (0.25 - 57.84) 0.334 3.43 (0.20 - 58.52) 0.395

Medical/Health Care and Welfare 0.75 (0.07 - 8.40) 0.816 1.53 (0.12 - 19.66) 0.746

Government 0.79 (0.15 - 4.22) 0.779 1.17 (0.18 - 7.65) 0.873

Other Services 0.23 (0.04 - 1.43) 0.116 0.26 (0.04 - 1.85) 0.178

Others 0.40 (0.10 - 1.62) 0.199 0.48 (0.10 - 2.27) 0.358

Housewife/Househusband −** −**

Student 0.48 (0.09 - 2.60) 0.397 0.77 (0.10 - 5.80) 0.802

Unemployment 1.30 (0.31 - 5.51) 0.721 1.50 (0.27 - 8.23) 0.641

Model 1: Adjusted for age in years; Model 2: Adjusted for age in years, outpatient treatment, alcohol problems, depression, living arrangements, marital status,

annual family income and industry types; *OR: odds ratio; CI: confidence interval; **There were no such subjects.

T. Hasegawa et al. / Health 3 (2011) 276-287

284

Table 5. Logistic regression analysis for variables associated with suicide plans in females (N = 508).

Variables Model 1 P value OR(95%CI)*Model 2 P Value

Age

≤ 39 4.37 (2.06 - 9.29) < 0.001 5.89 (2.26 - 15.31) < 0.001

40 - 59 3.54 (1.65 - 7.57) 0.001 4.60 (1.90 - 11.16) 0.001

60 ≤ 1 (reference) 1 (reference)

Outpatient Treatment

No 1 (reference) 1 (reference)

Yes 1.20 (0.75 - 1.91) 0.453 1.01 (0.60 - 1.72) 0.963

Alcohol Problems

Negative 1 (reference) 1 (reference)

Positive 5.24 (2.12 - 12.97) < 0.001 5.07 (1.74 - 14.83) 0.003

Depression

Negative 1 (reference) 1 (reference)

Positive 2.79 (1.75 - 4.46) <0.001 2.35 (1.39 - 3.99) 0.002

Living Arrangements

Living with Someone else 1 (reference) 1 (reference)

Living Alone 1.93 (0.87 - 4.27) 0.105 0.79 (0.27 - 2.33) 0.673

Marital Status

Married 1 (reference) 1 (reference)

Single 0.91 (0.49 - 1.69) 0.767 0.64 (0.29 - 1.44) 0.282

Separated 4.96 (2.35 - 10.47) < 0.001 3.74 (1.47 - 9.56) 0.006

Annual Family Income (Yen/Year)

≤ 1 999 999 2.67 (1.07 - 6.64) 0.035 1.55 (0.51 - 4.74) 0.442

2 000 000 - 6 999 999 1.32 (0.73 - 2.40) 0.357 1.40 (0.72 - 2.73) 0.316

7 000 000 ≤ 1 (reference) 1 (reference)

Unknown 1.99 (0.93 - 4.24) 0.075 1.92 (0.80 - 4.63) 0.144

Industry types

Construction 1.70 (0.33 - 8.81) 0.527 0.98 (0.15 - 6.38) 0.986

Manufacturing 4.17 (1.28 - 13.61) 0.018 4.92 (1.33 - 18.15) 0.017

Wholesale/Retail Trade 1 (reference) 1 (reference)

Accommodations/Eating/Drinking Services 5.08 (1.23 - 20.98) 0.025 3.17 (0.65 - 15.50) 0.154

Medical/Health Care and Welfare 2.23 (0.66 - 7.57) 0.197 3.04 (0.79 - 11.61) 0.105

Government 2.45 (0.45 - 13.33) 0.300 1.89 (0.31 - 11.34) 0.488

Other Services 2.75 (0.78 - 9.67) 0.115 3.30 (0.82 - 13.20) 0.092

Others 1.55 (0.42 - 5.68) 0.509 1.90 (0.46 - 7.79) 0.375

Housewife/Househusband 1.04 (0.32 - 3.42) 0.952 1.32 (0.35 - 4.94) 0.678

Student 1.10 (0.26 - 4.75) 0.897 1.28 (0.25 - 6.48) 0.767

Unemployment 2.23 (0.57 - 8.69) 0.247 1.60 (0.35 - 7.38) 0.545

Model 1: Adjusted for age in years; Model 2: Adjusted for age in years, outpatient treatment, alcohol problems, depression, living arrangements, marital status,

nnual family income and industry types; *OR: odds ratio; CI: confidence interval. a

T. Hasegawa et al. / Health 3 (2011) 276-287

285285

facturing or accommodations/eating/drinking services

were that they were more likely to have suicidal ideation

or plans due to the structures and/or physical and mental

stresses specific to such industries. The major stressors

on manufacturing workers are reported to be offensive

physical, chemical and ergonomic factors in the working

environment, low job control, high job demands, bur-

densome patterns such as shift work, unstable working

hours and overtime work, income pay inadequate for

hours worked, sudden changes in work schedules, few

opportunities to develop their abilities and techniques,

monotonous operations, role ambiguity, lack of social

support from their boss or colleagues, unstable status

such as irregular employment, restructuring, sudden

transfer or relocation, and few opportunities to partici-

pate in their companies’ administration and management

[62]. On the other hand, stressors on workers in accom-

modations/eating/drinking services are also reported to

be various work patterns, shift work, low income, hard

and long working hours, long standing time, exposure to

unpleasant food odors, and increased opportunities to

reward for various demands from customers with dif-

ferent values [63]. These stresses may be related to de-

pression, bringing out suicidal thoughts.

Openly accessible at

Several limitations should be considered when inter-

preting these results. First, a cross-sectional design used

in our study does not necessarily demonstrate that the

variables associated with suicide ideation or plans are

causal factors. Second, a small sample size calls for cau-

tious interpretation of the results, since a type 2 error

cannot be excluded. Third, this questionnaire does not

include the onset age of suicide ideation or plans and

time since the onset of suicide ideation or plans. Fourth,

our study did not assess several important mental condi-

tions such as schizophrenia or borderline personality

disorder because it proved very difficult to judge these

diseases only from a self-administered questionnaire.

Despite these limitations, our study shed light on multi-

ple risk factors associated with suicide ideation/plans

among a Japanese population.

In conclusion, alcohol problems, depression, inade-

quate annual family income and accommodations/eating/

drinking services in males, and younger age, outpatient

treatment, depression, living alone, being single, being

separated, insufficient annual family income, accommo-

dations/eating/drinking services and unemployment in

females may be factors statistically associated with sui-

cide ideation. On the other hand, younger age, alcohol

problems, depression and inadequate annual family in-

come in males, and younger age, alcohol problems, de-

pression, being separated, lower annual family income,

manufacturing and accommodations/eating/drinking ser-

vices in females may be factors associated with suicide

plans. It is important to consider these factors in devel-

oping anti-suicidal measures to prevent suicide ideation

or plans from escalating into suicidal attempts or com-

pletions.

5. ACKNOWLEDGEMENTS

The authors would like to express their sincere appreciation to all

the participants in our study.

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