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Keywords:

  • affective disorders;
  • mental health economics;
  • public policy and psychiatry

Abstract

  1. Top of page
  2. Abstract
  3. METHODS
  4. RESULTS
  5. DISCUSSION
  6. ACKNOWLEDGMENTS
  7. REFERENCES

Aim:  Major depression is expected to become the leading contributor to disease burden worldwide by 2020. Previous studies have shown that the societal cost of depression is not less than that of other major illnesses, such as cardiovascular diseases or AIDS. Nevertheless, the cost of depression in Japan has never been examined. The goal of the present study was to estimate the total cost of depression in Japan and to clarify the characteristics of this burden.

Methods:  A prevalence-based approach was adopted to measure the total cost of depression. The total cost of depression was regarded as being comprised of the direct cost, morbidity cost and mortality cost. Diagnoses included in this study were depressive episodes and recurrent depressive disorder according to the ICD-10 or major depressive disorder according to the DSM-IV. Data were collected from publicly available statistics and the World Mental Health Japan Survey database.

Results:  The total cost of depression among adults in Japan in 2005 was estimated to be ¥2.0 trillion. The direct cost was ¥0.18 trillion. The morbidity cost was ¥0.92 trillion, while the mortality cost was ¥0.88 trillion.

Conclusion:  The societal costs caused by depression in Japan are enormous, as in other developed countries. Low morbidity costs and extremely high mortality costs are characteristic in Japan. Effective interventions for preventing suicide could reduce the societal costs of depression.

MAJOR DEPRESSION IS expected to become the second leading cause of disease burden by 2020.1 However, compared to its actual impact, the budget allocated to mental health care is limited in many countries, accounting for only 2–3% of the total health care budget in developing countries and approximately 7% in developed countries,2 despite the fact that the disease burden as a result of mental disorders accounts for 20–25% of the total burden. Factors such as stigma, a high morbidity rate rather than a high mortality rate, and the large number of untreated patients might further contribute to an underestimation of the impact of these diseases. Thus, an accurate estimate of the burden generated by this disorder is crucial.

Previous studies have shown that the cost of depression was as much as $83.1bn in 2000 in the USA3 and £9.1bn in 2000 in the UK.4 These results indicate that the cost of depression is not less than that of other major illnesses, such as cardiovascular diseases or AIDS.5 Research in developing countries has also clarified the huge social impact caused by this illness.6,7 Nevertheless, the societal cost of depression in Japan has never been examined. The extremely high rate of suicide in Japan suggests that depression might have a much heavier societal burden in this country than in the above-mentioned countries. On the other hand, a previous epidemiological study8 reported that the prevalence of depression in Japan was much lower than that in the USA or the UK. These contradictory reports make the total impact of depression in Japan uncertain.

The goal of the present study was to estimate the total cost of depression in Japan in 2005 so as to understand the extent of the societal burden caused by depression.

METHODS

  1. Top of page
  2. Abstract
  3. METHODS
  4. RESULTS
  5. DISCUSSION
  6. ACKNOWLEDGMENTS
  7. REFERENCES

A prevalence-based approach was adopted to measure the total cost of depression among adults (20 years of age or more) in Japan in 2005. The total cost of depression was regarded as being comprised of the direct costs (including outpatient and inpatient treatment costs), the morbidity costs, and the mortality costs. Depressive episodes and recurrent depressive disorder diagnosed according to the ICD-10 criteria were defined as depression when evaluating the direct costs, while depression was defined as a major depressive disorder according to the DSM-IV criteria when estimating the morbidity and mortality costs. The nature of the available data was responsible for this discrepancy in the definitions used for the diagnosis of depression when assessing different cost aspects. According to both the ICD-10 and DSM-IV criteria for the diagnosis of depression, other mood disorders, such as dysthymia or a depressive state associated with bipolar disorder, were excluded so as to represent the precise burden of depression. This study was conducted from a societal perspective. Data were collected from publically available statistics and literature, including the World Mental Health Survey Japan (WMH-J) database.

Direct costs

Outpatient costs

Outpatient cost data were collected from the Patient Survey9 and a Survey of Medical Care Activities in Public Health Insurance.10 Briefly, the Patient Survey estimated the number of patients in each diagnostic category for all physical and mental diseases, while the Survey of Medical Care Activities in Public Health Insurance showed the treatment expenses covered by public health insurance in each diagnostic category. Regarding depression, the Survey of Medical Care Activities in Public Health Insurance10 showed the total combined outpatient costs for all mood disorders, including depression, bipolar disorder, dysthymia and so on; thus, the costs specifically associated with depression were not indicated. Then, the proportion of patients with depression among all the patients with mood disorders was calculated so as to examine the costs associated only with depression, assuming that the average outpatient cost among patients with different mood disorders was the same.

Inpatient costs

Data on the cost of inpatient treatment was also obtained from the Patient Survey9 and the Survey of Medical Care Activities in Public Health Insurance.10 Similar to the situation for the outpatient costs, only the total cost for all mood disorders was available; consequently, the rate of depression among all the mood disorders in the Patient Survey was determined9 and used to calculate the inpatient treatment cost for depression. It was assumed that no difference in the average treatment costs existed among the different mood disorders.

Medication costs

Previous studies3,4 only included antidepressants when calculating the medication costs for the treatment of depression. However, antidepressants are also likely to be prescribed for patients with other disorders, such as panic disorder, obsessive–compulsive disorder, post-traumatic stress disorder, and so on. On the other hand, a certain proportion of depressed patients are not treated with antidepressants. Thus, we estimated the medication costs for depression as the cost of all prescribed drugs for patients diagnosed as having depression, including antidepressants, mood stabilizers, anti-anxiety drugs, and so on.

The Survey of Medical Care Activities in Public Health Insurance10 provides the total medication costs for mood disorders (i.e. depression, bipolar disorder, dysthymia and all other mood disorders), without providing the specific total costs for depression. Thus, the total medication cost for depression was calculated by multiplying the net costs for mood disorders by the rate of patients with depression among those with all mood disorders.

Indirect costs

Indirect costs are comprised of morbidity and mortality costs. Morbidity costs occur when patients are not able to function normally as a result of their illness,11 while mortality costs arise when patients die as a result of suicide at an age earlier than the average life expectancy.

Morbidity costs

Morbidity costs reflect the productivity loss caused by absence from work (i.e. absenteeism) and productivity loss while at work (i.e. presenteeism).

Absenteeism

Absenteeism was calculated by multiplying the total working days lost because of depression by the average daily earning in 2005. To estimate the total number of days lost, first the yearly number of cases of depression was calculated using the prevalence data obtained from the WMH-J (Table 1). As previously noted, the DSM-IV criteria for major depressive disorder were used for this calculation because of the data availability. Theoretically, the cost of absenteeism can be calculated by multiplying the number of cases by the labor participation rate, the average working days lost, and the daily earning. Data regarding the number of working days lost per year per depressed employee were also collected from the WMH-J. The average daily earning was calculated based on the Basic Survey on Wage Structure12 and the Monthly Labour Survey.13

Table 1.  Parameters and their standard error (SE) for calculating morbidity cost
Men
Age range (years)Population12-moth prevalenceLabor participation rate§ (%)Working days lost due to absenteeismRelative ratio of days equivalent of presenteeism against that of absenteeism††Daily earning‡‡ (¥)
n% (SE)Days (SE)ratio (SE)
20–243 688 9072.1 (0.9)6211.3 (3.7)2.3 (0.8)12 272
25–294 118 8342.1 (0.9)8811.3 (3.7)2.3 (0.8)15 924
30–344 866 0212.1 (0.9)9211.3 (3.7)2.3 (0.8)19 379
35–394 346 9680.8 (0.5)9311.3 (3.7)2.3 (0.8)23 343
40–444 020 7930.8 (0.5)9411.3 (3.7)2.3 (0.8)26 327
45–493 837 6491.1 (0.5)9411.3 (3.7)2.3 (0.8)27 486
50–544 361 5431.1 (0.5)9211.3 (3.7)2.3 (0.8)27 401
55–595 064 5822.0 (0.7)9011.3 (3.7)2.3 (0.8)25 897
60–654 148 5252.0 (0.7)6611.3 (3.7)2.3 (0.8)17 357
65≤10 872 7290.2 (0.4)2911.3 (3.7)2.3 (0.8)15 134
Women
Age range (years)Population12-moth prevalenceLabor participation rate§ (%)Working days lost due to absenteeismRelative ratio of days equivalent of presenteeism against that of absenteeism††Daily earning‡‡ (¥)
n% (SE)Days (SE)ratio (SE)
  • Derived from Vital statistics in 2005. Distribution: deterministic.

  • Derived from World Mental Health Survey Japan database. Distribution: beta.

  • §

    Calculated based on Labor Force Survey in 2005. Distribution: deterministic.

  • Derived from World Mental Health Survey Japan database. Distribution: gamma.

  • ††

    Calculated from Kessler.15 Distribution: gamma.

  • ‡‡

    Derived from Basic Survey on Wage Structure in 2005.12 Distribution: deterministic.

20–243 504 0816.0 (1.2)65.011.3 (3.7)2.3 (0.8)11 254
25–293 979 0006.0 (1.2)70.311.3 (3.7)2.3 (0.8)13 630
30–344 726 3346.0 (1.2)58.811.3 (3.7)2.3 (0.8)14 878
35–394 245 8751.7 (0.7)60.011.3 (3.7)2.3 (0.8)15 991
40–443 947 8671.7 (0.7)68.311.3 (3.7)2.3 (0.8)15 898
45–493 812 5504.1 (1.0)71.811.3 (3.7)2.3 (0.8)15 456
50–544 382 2754.1 (1.0)67.011.3 (3.7)2.3 (0.8)14 717
55–595 159 2771.8 (0.6)58.411.3 (3.7)2.3 (0.8)14 215
60–654 378 2471.8 (0.6)39.011.3 (3.7)2.3 (0.8)11 517
65≤14 781 6221.1 (0.4)12.711.3 (3.7)2.3 (0.8)11 629
Presenteeism

Presenteeism is defined as productivity loss arising from employees who go to work when they are ill. However, we could not find any reliable data regarding presenteeism caused by depression in a Japanese setting. Therefore, we decided to conduct a literature review to determine the relative ratio of days of presenteeism versus those of absenteeism.

Reports were included in the results of the literature review if they met the following conditions:

  • • 
    an observational study performed in a large, representative, community sample taken from the general population,
  • • 
    the rates of absenteeism and presenteeism were measured directly from the samples, and depression was defined using a recent psychiatric diagnostic classification system, such as the ICD, or DSM, to distinguish from ill-defined psychological distress or stress as an outcome.

We excluded studies using workplace samples because such studies were unlikely to represent a diversity of vocations. The evidence was further restricted to peer-reviewed, published, English-language reports.

We performed the literature review using PubMed and the following search terms: depression, absenteeism, presenteeism, and productivity loss. Twenty-four articles were discovered. However, only two articles14,15 met the above-mentioned criteria for inclusion in the search results. Integrating the results from the two articles resulted in a remarkable degree of heterogeneity (I2 = 100%). Therefore, identifying a relative ratio by performing a meta-analysis based on these two articles was judged as being an inappropriate methodology. Therefore, the authors decided to adopt the results of the study by Kessler15 because it used the Health Performance Questionnaire, a validated scale for measuring presenteeism, while the results reported by Steward were based on subject interviews.

The number of equivalent days of presenteeism was thus calculated by multiplying the number of work days lost because of absenteeism by the relative ratio of days lost as a result of presenteeism versus days lost because of absenteeism, as calculated above.

The estimated number of equivalent days of presenteeism was then combined with the number of days lost because of absenteeism. The morbidity cost was then estimated by multiplying the total equivalent days of both absenteeism and presenteeism by the average daily earning for each age range. During the morbidity cost calculation, a variety of uncertain parameters were used. To reflect the uncertainty of the results, a probabilistic sensitivity analysis (PSA) was performed to estimate the mean cost and its 95% confidential interval (CI). The details of this method are described in the ‘Uncertainty’ section below. All the parameters and their distributions that were included in the model to calculate morbidity cost are shown in Table 1.

Mortality costs

Mortality cost was defined as the expected lifetime earning lost caused by suicides due to depression. It was calculated by multiplying the estimated number of suicides as a result of depression by the expected lifetime earning. The total number of suicides was obtained from Statistics of Suicide.16 The ratio of suicide caused by depression was derived from a report by Kaga17 published in 2009 (Table 2). The reason why we chose this ratio was that although the sample size was relatively small (n = 76), a psychological autopsy had been conducted for 76 of the suicides and the demographic data of the samples were quite representative of the data for all suicide cases in Japan.

Table 2.  Parameters and their standard error (SE) for calculating mortality cost
Men
Age range (years)SuicidesRate of suicides related to depressionExpected lifetime earning§
n% (SE)¥: thousand
20–29235752.7 (5.8)120 924
30–39338952.7 (5.8)118 614
40–49412052.7 (5.8)93 296
50–59601652.7 (5.8)51 256
60≤706052.7 (5.8)9 929
Unknown21652.7 (5.8)63 196
Women
Age range (years)SuicidesRate of suicides related to depressionExpected lifetime earning§
n% (SE)¥: thousand
  • Derived from Statistics of Suicide in 2005.16 Distribution: deterministic.

  • Derived from Kaga.17 Distribution: beta.

  • §

    Derived from Basic Survey on Wage Structure in 2005.12 Distribution: deterministic.

20–29105252.7 (5.8)55 616
30–39121752.7 (5.8)48 962
40–49108852.7 (5.8)36 897
50–59157052.7 (5.8)19 578
60≤383452.7 (5.8)3 715
Unknown2552.7 (5.8)23 196

The lifetime earning was calculated based on the Basic Survey on Wage Structure12 and the Labour Force Survey.18 Three percent was adopted as the discounting rate, as this rate has been used in recent international studies.19 Similar to the morbidity cost calculations, an uncertain parameter (i.e. rate of suicides related to depression) was included in the mortality cost calculation; thus, a PSA was performed to estimate the mean mortality cost and its 95%CI. The details of this analysis are shown in the following section. All the parameters and their distributions included in the model to calculate mortality cost are shown in Table 2.

Uncertainty

To determine the costs related to depression, we assembled the best evidence available. Nevertheless, many of the parameters used in the morbidity and mortality cost calculations are somewhat uncertain. Consequently, we performed a PSA to estimate the average cost of depression and its 95%CI.

The details of the parameters and their distributions for the uncertainty assumptions that were used are shown in Tables 1 and 2. The probability distributions around the input variables are based on the standard errors (SE) quoted in, or calculated from previous literature reports. We used @excel 2007 software, which is basically a macro to allow multiple recalculations of Microsoft-excel spreadsheet data using a different value from the uncertainty distribution defined for the input variables. We calculated the mean values and their SE for the output variables from amongst 1000 values that were generated in this manner. The results were then presented to two significant digits together with their SE or 95%CI.

RESULTS

  1. Top of page
  2. Abstract
  3. METHODS
  4. RESULTS
  5. DISCUSSION
  6. ACKNOWLEDGMENTS
  7. REFERENCES

Direct costs

The direct cost of depression in Japan in 2005 was estimated as ¥0.18 trillion. This figure includes outpatient care, inpatient treatment costs, and medication costs. The details of the calculation are shown in Table 3.

Table 3.  Direct cost
(¥: billion)
 Treatment costMedication costTotal
  1. Total may not equal the sum of all costs due to rounding.

  2. Results are presented to two significant digits only.

Inpatient462.048
Outpatient7058130
Total12060180

Indirect costs

Morbidity costs

As a result of the PSA, the mean days lost because of absenteeism arising from depression per year per depressed employee was calculated to be 11 days (SE, 3.7). The number of equivalent days lost because of presenteeism appeared to be 26 days (SE, 13). Thus, the total number of days lost because of depression per year per depressed employee was 38 days (SE, 16). Using this result, the morbidity cost was calculated as ¥0.92 trillion (Table 4).

Table 4.  Total costs
 ¥: trillion95% Confidence interval
  1. Total may not equal the sum of all costs due to rounding.

  2. Results are presented to two significant digits only.

  3. Direct cost is deterministic.

Direct cost0.18
Morbidity cost0.920.077–1.8
Mortality cost0.880.69–1.1
Total costs2.01.1–2.8
Mortality costs

The total number of suicides in Japan in 2005 was 31 944. A PSA indicated that 53% of these suicides were related to depression. Consequently, the total mortality cost because of depression was ¥0.88 trillion (Table 4).

Total cost of depression in Japan in 2005

Using the above-mentioned figures, the total cost of depression among adults in Japan in 2005 was estimated as ¥2.0 trillion. The direct cost was ¥0.18 trillion (outpatient cost of ¥0.13 trillion and inpatient cost of ¥0.050 trillion). The morbidity cost was ¥0.92 trillion, while the mortality cost was ¥0.88 trillion. The percentages of the total cost for each component were 9% for direct cost, 47% for morbidity, and 45% for mortality (Fig. 1).

image

Figure 1. Proportion of (inline image) direct, (inline image) morbidity and (inline image) mortality costs of depression in Japan. Total may not equal the sum of all percentages due to rounding.

Download figure to PowerPoint

Robustness of the estimated costs

The uncertainty of the input parameter values was a key factor in evaluating the cost of depression. The effects of such uncertainties were measured using a PSA for morbidity, mortality, and the total costs.

The PSA showed the mean morbidity cost to be ¥0.92 trillion, with a 95%CI between ¥0.077 trillion and ¥1.8 trillion. In the same way, the mean mortality cost and 95%CI for this parameter were ¥0.88 trillion and from ¥0.69 trillion to ¥1.1 trillion, respectively. As a result, the total cost was ¥2.0 trillion, with a 95%CI of ¥1.1 trillion–¥2.8 trillion.

DISCUSSION

  1. Top of page
  2. Abstract
  3. METHODS
  4. RESULTS
  5. DISCUSSION
  6. ACKNOWLEDGMENTS
  7. REFERENCES

As indicated in previous studies,3,4 the societal burden caused by depression is enormous. However, the cost of depression in Japan has never been estimated. To our knowledge, this report represents the first estimation of the societal cost of depression in Japan.

First, we would like to explore the knowledge gained from this study, such as the clarification of points related to access to treatment and morbidity and mortality costs. Later, the implications for policies and limitations of the study will be discussed.

Access to treatment

Regarding access to the treatment of depression, this study revealed a discrepancy between the number of patients suffering from depression and the number of patients who actually receive treatment. According to the WMH-J, approximately 2 million people are thought to have suffered from depression in 2005. However, the Patient Survey9 showed that only 900 000 patients received treatment in 2005, implying that less than half of all patients with depression actually received treatment in 2005. Thus, a policy to encourage individuals to seek appropriate care is needed.

Morbidity costs

The morbidity cost in this study was much lower than those reported in other countries. Previous studies in Western countries have reported that morbidity costs comprise 60–90% of the total cost, while the morbidity cost in the present report was only 47%. This difference can be explained in three ways. First, the mortality cost in Japan comprised an extraordinarily large percentage of the total costs. Second, the morbidity cost might have been underestimated. The number of lost working days as a result of absenteeism was calculated from the WMH-J data, which consisted of self-reported information. Thus, a recall bias might have existed. Furthermore, because of the lack of available data on presenteeism in Japan, it was estimated based on data reported abroad.15 Thus, this data might also have been underestimated. And third, patients with depression in Japan may be unlikely to take sickness leave while suffering from depression. The total number of working days lost because of depression per capita in Japan was one-fifth and one-seventh of the values reported in the UK and USA, respectively. Thus, the number of lost working days in Japan was much smaller than that reported in the UK or the USA, partly because the prevalence rate was lower in Japan. However, the difference in total days lost between Japan and the UK or the USA was much larger than the difference in the prevalence rates, implying that depressed patients in Japan are unlikely to take a leave of absence from work even when rest is needed. As well as an increase in access to appropriate healthcare services, the provision of an environment in which patients can rest when needed is also important.

Mortality costs

The mortality cost of depression in Japan was ¥0.88 trillion (47% of the total). This high figure was mainly due to the extraordinarily high suicide rate in this country. The number of annual suicides in Japan surpassed 30 000 in the late 1990s and remains at the same level at present. Although no single factor can explain this result, the economic condition is thought to be one of the biggest factors contributing to this result.

One possible criticism of this result is that the human capital approach, which is defined as the lost future gross income arising from premature death, was used in this analysis to estimate the mortality cost. Criticisms, such as the possibility that this approach may discriminate against the unemployed or that this approach may overestimate the mortality costs, have been previously explored.20

The rationale behind the latter assertion is based on the fact that, in reality, workers are replaced if they die prematurely. This reasoning might make sense from the perspective of the labor force. However, when considering the burden of diseases on society, we are afraid that this approach may underestimate the effect of premature death on the patients, their families, and society. Therefore, we concluded that the human capital approach was a reasonable means of evaluating the mortality cost.

Implications for policies

The results of this study indicate that the economic burden of depression is huge in Japan, as it is in other developed countries. Some political implications can be drawn from these results. First, as in other countries, only a limited number of individuals with depression seek medical care. Thus, a policy to encourage individuals to seek appropriate care is needed. Second, the possibility that patients who should concentrate on their treatment tend to continue working should be a concern. This situation is not reasonable, even from the prospective of companies, because the productivity loss of patients with depression who continue to work (i.e. presenteeism) is enormous, as previously described. Thus, early interventions, such as effective screening in conjunction with assistance for access to appropriate treatment,21 should be considered.

Finally, efforts to prevent suicide are urgently needed. The suicide rate is two and four times higher in Japan than that in the USA and UK, respectively. Considering that approximately half of the total costs of depression in Japan can be attributed to mortality costs, effective interventions to prevent suicides should be implemented. Of course, increasing the number of patients with access to healthcare is likely to be one solution. But, when we consider the fact that the number of suicides has not decreased even though access to treatment has increased over the last few decades,9,16 merely providing treatment may not be sufficient, and more effective interventions, in addition to conventional treatment, should be considered.

Limitations

Although this research revealed important information regarding the societal burden caused by depression in Japan, it also had some limitations. Because uncertain parameters were used to calculate the total cost, a PSA, rather than a point estimate, was adopted to clarify the expected mean cost and its 95%CI. The results showed that the mean total cost was ¥2.0 trillion, with a 95%CI of ¥1.1 trillion–2.8 trillion. This range is considerable. The substantial uncertainty of the morbidity cost, especially the uncertainty of the presenteeism cost, is thought to have played a major role in this result. Specifically, the uncertainty in the cost of presenteeism was derived from the fact that no data for this parameter exist in a Japanese setting. This forced us to combine presenteeism data from another country with data for absenteeism in Japan to estimate the cost arising from presenteeism. Although this process is technically appropriate, it led to considerable uncertainty in the morbidity costs. Obtaining more precise data related to productivity loss while at work in a Japanese setting would help to improve the accuracy of the estimated cost in this analysis. The fact that this study does not reflect all the costs caused by depression could be another limitation. Patients were included in the morbidity cost analysis if they were employed or self-employed. The morbidity costs associated with other patients, such as housewives or unemployed individuals, were not calculated because it was technically impossible to convert their productivity loss into a monetary reward. To improve the accuracy of the present estimate, an investigation that includes unemployed and self-employed individuals is needed.

ACKNOWLEDGMENTS

  1. Top of page
  2. Abstract
  3. METHODS
  4. RESULTS
  5. DISCUSSION
  6. ACKNOWLEDGMENTS
  7. REFERENCES

We would like to thank Professor Kawakami and the other staff members of the WMH-J for permitting us to use the WMH-J database for part of this research. WMH-J is supported by the Grant for Research on Psychiatric and Neurological Diseases and Mental Health (H13-SHOGAI-023, H14-TOKUBETSU-026, H16-KOKORO-013) from the Japan Ministry of Health, Labour, and Welfare. We would also like to thank the staff members, field coordinators, and interviewers of the WMH Japan 2002–2004 Survey. The WMH Japan 2002–2004 Survey was performed in conjunction with the World Health Organization World Mental Health (WMH) Survey Initiative. We also thank the WMH staff for assistance with instrumentation, fieldwork, and data analysis. These activities were supported by the US National Institute of Mental Health (R01MH070884), the John D. and Catherine T. MacArthur Foundation, the Pfizer Foundation, the US Public Health Service (R13-MH066849, R01-MH069864, and R01 DA016558), the Fogarty International Center (FIRCA R01-TW006481), the Pan American Health Organization, Eli Lilly and Company, Ortho-McNeil Pharmaceutical, Inc., GlaxoSmithKline, and Bristol-Myers Squibb. A complete list of WMH publications can be found at http://www.hcp.med.harvard.edu/wmh/.

Other Authors of WMH-J 2002–2006 Survey Group

Yoshibumi Nakane (Division of Human Sociology, Nagasaki International University Graduate School), Yoshikazu Nakamura (Department of Public Health, Jichi Medical School), Akira Fukao (Department of Public Health, Yamagata University, Graduate School of Medical Science), Itsuko Horiguchi (Department of Public Health, Juntendo University Graduate School of Medicine), Hisateru Tachimori (National Institute of Mental Health, National Center of Neurology and Psychiatry), Noboru Iwata (Department of Clinical Psychology, Hiroshima International University), Hidenori Uda (Director General of the Health, Social Welfare, and Environmental Department, Osumi Regional Promotion Bureau, Kagoshima Prefecture), Hideyuki Nakane (Division of Neuropsychiatry, Department of Translational Medical Sciences, Nagasaki University Graduate School of Biomedical Sciences), Makoto Watanabe (Department of Preventive Cardiology, National Cardiovascular Center), Masashi Oorui (Department of Public Health, Yamagata University, Graduate School of Medical Science), Kazushi Funayama (Yokohama City Turumi Public Health and Welfare Center), Yoichi Naganuma (National Institute of Mental Health, National Center of Neurology and Psychiatry), Yukihiro Hata (Department of Psychiatry, Field of Social and Behavioral Medicine, Kagoshima University Graduate School of Medical and Dental Sciences), Masayo Kobayashi (Department of Public Health, Jichi Medical School), Tadayuki Ahiko (Murayama Public Health Center, Yamagata Prefecture), Yuko Yamamoto (Department of Public Health, Juntendo University Graduate School of Medicine), Tadashi Takeshima (National Institute of Mental Health, National Center of Neurology and Psychiatry), Takehiko Kikkawa (President, Seisen Jogakuin College).

REFERENCES

  1. Top of page
  2. Abstract
  3. METHODS
  4. RESULTS
  5. DISCUSSION
  6. ACKNOWLEDGMENTS
  7. REFERENCES
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