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Article

The Associations between Evacuation Status and Lifestyle-Related Diseases in Fukushima after the Great East Japan Earthquake: The Fukushima Health Management Survey

by
Zhichao Sun
1,2,
Hironori Imano
1,3,
Eri Eguchi
4,
Fumikazu Hayashi
4,5,
Tetsuya Ohira
4,5,
Renzhe Cui
6,
Seiji Yasumura
5,7,
Akira Sakai
5,8,
Michio Shimabukuro
5,9,
Hitoshi Ohto
5,
Kenji Kamiya
5,10 and
Hiroyasu Iso
1,11,*
1
Public Health, Department of Social Medicine, Osaka University Graduate School of Medicine, Osaka 565-0871, Japan
2
Health Town Development Science Center, Yao City Health Center, Osaka 581-0006, Japan
3
Department of Public Health, Kindai University Faculty of Medicine, Osakasayama 589-8511, Japan
4
Department of Epidemiology, Fukushima Medical University School of Medicine, Fukushima 960-1295, Japan
5
Radiation Medical Science Center for the Fukushima Health Management Survey, Fukushima Medical University, Fukushima 960-1295, Japan
6
Department of Internal Medicine, Okanami General Hospital, Iga 518-0842, Japan
7
Department of Public Health, Fukushima Medical University School of Medicine, Fukushima 960-1295, Japan
8
Department of Radiation Life Sciences, Fukushima Medical University School of Medicine, Fukushima 960-1295, Japan
9
Department of Diabetes, Endocrinology and Metabolism, Fukushima Medical University School of Medicine, Fukushima 960-1295, Japan
10
Research Institute for Radiation Biology and Medicine, Hiroshima University, Hiroshima 734-8553, Japan
11
Institute for Global Health Policy Research, National Center for Global Health and Medicine, Tokyo 162-8655, Japan
 
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*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2022, 19(9), 5661; https://doi.org/10.3390/ijerph19095661
Submission received: 28 March 2022 / Revised: 3 May 2022 / Accepted: 5 May 2022 / Published: 6 May 2022
(This article belongs to the Topic Multiagency Approach to Disaster Management, Focusing on Triage, Treatment and Transport)
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Abstract

:
Background: This study aimed to investigate the association between evacuation status and lifestyle-related disease risks among Fukushima residents following the Great East Japan earthquake. Methods: Fukushima health management survey respondents were classified into non-evacuees, returnees, evacuees in lifted areas, and evacuees in banned areas. During a seven-year follow-up, 22,234 men and 31,158 women were included. Those with a history of diabetes, hypertension, or dyslipidemia at baseline were excluded. The odds ratios of risk factors (ORs) and 95% confidence intervals (CIs) for diabetes, hypertension, and dyslipidemia were calculated using a logistic regression model. Spatial autocorrelation of the prevalence of these diseases in the Fukushima area in 2017, was calculated to detect the disease prevalence status. Results: The risks of diabetes, hypertension, and dyslipidemia were higher in evacuees in banned areas than in non-evacuees; the multivariable ORs were 1.32 (95% CI: 1.19–1.46), 1.15 (1.06–1.25), and 1.20 (1.11–1.30) for diabetes, hypertension, and dyslipidemia, respectively. Returnees and evacuees in lifted areas had no increased risk of diseases. The area analyzed had a non-uniform spatial distribution of diabetes, hypertension, and hyperlipidemia, with clusters around Fukushima and Koriyama. Conclusion: Our findings imply the need for continuous support for evacuees in banned areas.

1. Introduction

The Great East Japan earthquake occurred on 11 March 2011, causing a large tsunami [1] and a severe accident at the Fukushima Dai-ichi Nuclear Power Plant [2]. These serious disasters resulted in extensive damage to the coastal area adjacent to the east of Fukushima, infrastructure destruction, and potential ultra-low-dose level radioactive pollution. Thus, many residents needed to evacuate, as implemented by the national and Fukushima Prefecture governments [3].
Evacuation affects lifestyle and has been associated with increased alcohol consumption [4], high smoking prevalence [5], and impaired sleep quality [6]. Lifestyle changes, such as those mentioned above, have a strong effect on lifestyle-related diseases. Moreover, changes in the living environment and socio-economic factors [7,8] could affect the mental health of the evacuees. People who were forced to leave their homes were more likely to develop post-traumatic stress disorder [9,10], and approximately 4.7% of the residents in the Fukushima Prefecture lost or changed their job [11]. Previous studies have also shown that evacuees had higher risks of diabetes, heart disease, and sudden cardiac death [12,13] than non-evacuees.
To date, restrictions have been lifted in 67.8% of the previously restricted areas [14], and the national and prefectural governments have encouraged the evacuees to return to their homes. However, some people remained reluctant to return, although the areas were cleaned and declared safe [15]. Therefore, people who continued to evacuate have been forced to live in temporary houses and face new interpersonal relationships.
Evacuation status may impact lifestyle and cardiovascular risk factors, such as diabetes, hypertension, and dyslipidemia. In this study, we hypothesized that the evacuees forced to live outside their original houses in banned areas may have a higher risk of diabetes, hypertension, and dyslipidemia, and that returnees and evacuees in lifted areas do not have these increased risks. We used the database affiliated with the Fukushima health management survey to test this hypothesis.

2. Materials and Methods

2.1. Participants

We used the following three databases from the Fukushima health management survey (FHMS) in 2017 [16]: comprehensive health checks, mental health and lifestyle survey, and basic survey. Comprehensive health checks included two sets of respondents as follows: (1) people in the evacuation zone specified by the government and (2) people outside of the evacuation zone in the Fukushima Prefecture. The evacuation zone comprised Iitate Village (mura), Kawauchi Village, Katsurao Village, Hirono Town (machi), Naraha Town, Tomioka Town, Okuma Town, Futaba Town, Namie Town, Minamisoma City, and Tamura City. The mental health and lifestyle survey included these 13 areas.
Figure 1a presents a flow chart of the longitudinal analysis used in this study, with a follow-up for up to 7 years. Among the 89,571 participants of the comprehensive health check database, we excluded 27,334 who were aged <20 years and 12,321 who did not participate in the mental health and lifestyle survey. A total of 49,916 participants were included in the analysis. Subsequently, we excluded participants with a history of diabetes (n = 5224), hypertension (n = 21,754), or dyslipidemia (n = 25,522) at baseline. At follow-up, there were 11,693 participants with diabetes, 8234 with hypertension, and 7021 with dyslipidemia who never responded. Finally, we analyzed 32,999 participants with diabetes, 19,928 with hypertension, and 17,373 with hyperlipidemia.
For the spatial analysis, 53,094 individuals were included in the 2017 total comprehensive health check database. We excluded 4204 individuals aged <20 years. Finally, 48,890 individuals were included in the analysis (Figure 1b).

2.2. Changes in Evacuation Status

Figure 2 shows the changes in the evacuation areas in the Fukushima Prefecture in 2017, based on the information provided by the national and local governments [14,17]. As of 2017, areas that still have restrictions were labeled as Area1 (red color); those that have lifted restrictions, Area 2 (orange color); those with a history of voluntary refuge [17], Area 3 (yellow color); and those outside of the Fukushima Prefecture, Area 4 (green color).
Evacuees were defined as follows: those who had lived in Area 2 or 3 before the earthquake and evacuated from lifted areas until 2017 were defined as evacuees from lifted areas, and those who lived in Area 1 before the earthquake were defined as evacuees from banned areas. Non-evacuees were defined as all individuals living in Areas 3 and 4 who never changed their residences. Returnees were defined as individuals who lived in Area 2 before the earthquake, evacuated to Area 3 or 4 after the earthquake, and returned to their homes in Area 2 before 2017.

2.3. Lifestyle Behaviors and Social Factors

Smoking and drinking behaviors, sleep, physical activity, job change, and education level were obtained from the mental health & lifestyle survey data. We assessed the smoking status of the participants using the question, “Do you smoke?” with the following options: “non-smoker”, “ex-smoker”, and “current smoker”. Those who selected “current smoker” were considered as current smokers. Participants’ alcohol intake was assessed using the question, “Do you consume alcohol?” with the following options: “non-drinker (less than once per month)”, “ex-drinker”, and “drinker (once or more per month)”. Those who selected “drinker (once or more per month)” were considered as current drinkers. Sleep quality was evaluated using the question, “Are you satisfied with the length of sleep for the past month?” with the following options: “satisfied” and “not satisfied”. Physical activity level was assessed using the question, “Do you exercise regularly?” with the following options: “≥daily”, “2–4 times/week”, “weekly”, and “almost never”. Those who selected “≥daily”, “2–4 times/week”, or “weekly” were considered to have a physical activity frequency of at least once a week. Education level was assessed by the question, “What is your last educational level?” with the following options: “elementary or junior high school”, “high school”, “vocational school or junior college”, and “university or graduate school”. Those who selected “university or graduate school” were considered to have received college or higher education. Change of job was assessed by the question, “Did you experience a change in work situation since the disaster?” with the following options: “yes” and “no”. Psychological distress was evaluated using Kessler Psychological Distress (K6), and participants with a score of ≥13 were considered to have psychological distress.
Weight was measured in light indoor clothing without shoes, and height was recorded barefoot by well-trained staff. Weight and height measurements were obtained from comprehensive health check data. Body mass index (BMI) was calculated as weight (kg)/[height] (m)2.

2.4. Onset of Diabetes, Hypertension, and Dyslipidemia

The onset of diabetes mellitus, hypertension, and dyslipidemia was acquired from the comprehensive health check data. Hypertension was defined as systolic blood pressure (SBP) ≥ 140 mmHg, diastolic blood pressure (DBP) ≥ 90 mmHg [18], and/or the use of antihypertensive medication. Diabetes was defined as a fasting plasma glucose (FPG) level ≥ 126 mg/dL (7.0 mmol/L), random blood glucose (RBG) level ≥ 200 (11.1 mmol/L), HbA1c ≥ 6.5% [19], and/or the use of insulin injection or hypoglycemic drugs. Dyslipidemia was defined as plasma triglyceride (TG) level ≥ 150 mg/dL (fasting time), high-density lipoprotein cholesterol (HDL-C) level ≤ 40 mg/dL, low-density lipoprotein cholesterol (LDL-C) level ≥ 140 mg/dL [20], and/or the use of lipid-lowering agents.

2.5. Addresses and Standardized Prevalence Ratios in the Fukushima Prefecture

We used the current postal code from the basic survey data for the spatial analysis to ensure reliability. Diabetes, hypertension, and hyperlipidemia were defined based on the comprehensive health check database of the whole prefecture. The standardized prevalence ratios (SPRs) for diabetes, hypertension, and dyslipidemia were used to avoid distortion due to inappropriate age adjustment. The SPRs for diabetes, hypertension, and hyperlipidemia in each municipality in the Fukushima Prefecture were calculated compared to the 1985 Japanese standard population model. Municipality SPRs were calculated by dividing the municipality observed cases by the municipality expected cases [21,22].

2.6. Statistical Analysis

First, we calculated the age-adjusted mean values and prevalence of risk factors using analysis of covariance. Multiple linear regression was performed to compare the returnees, evacuees in lifted areas, and evacuees in banned areas with the non-evacuees.
Using the logistic regression model, age- and multivariable-adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for diabetes, hypertension, and hyperlipidemia among the returnees, evacuees in lifted areas, and evacuees in banned areas, compared with the non-evacuees were calculated. The adjustment variables included age (continuous), BMI (quintiles), cigarette smoking status (never-smoker, ex-smoker, current smoker), alcohol consumption (non-drinker, ex-drinker, current drinker), physical activity (≥once weekly or <once weekly), sleep satisfaction (satisfied or not satisfied), change of job (yes or no), and educational status (elementary or junior high school, high school, vocational school or junior college, university or graduate school). Statistical analyses were conducted using SAS version 9.4 (SAS Institute, Inc., Cary, NC, USA). Two-tailed p values < 0.05 were considered statistically significant.
The global Moran’s index [23] was used to analyze regional spatial autocorrelation to identify geographic clustering. Hotspot analysis (Getis-Ord Gi*) [24] was used to determine the clusters. Hot spots represent a high-value spatial cluster of diabetes, hypertension, or dyslipidemia, whereas cold spots represent a low-value spatial cluster in the Fukushima Prefecture. Statistical significance was set at p < 0.05, and 90% CIs were dependent on the z < −1.65 or z > +1.65, whereas 95% CIs were dependent on the z < −1.96 or z > +1.96. All spatial analyses were conducted in ArcGis10.8.1 (Esri, Inc., Redlands, CA, USA).

3. Results

During a seven-year follow-up, 1822 participants had diabetes, 3609 had hypertension, and 4361 had dyslipidemia.

3.1. Characteristics of Participants at Baseline

Table 1 shows the age-adjusted mean values and characteristics at baseline according to the evacuation status. We found that 47.7% of the participants had been evacuated or were still evacuees. Compared with the non-evacuees, both evacuees in lifted areas and those in banned areas were younger and had a higher proportion of current smokers, current alcohol drinkers, dissatisfaction with sleep, change in their job, and university or graduate school education. Compared with the non-evacuees, the returnees were likely to have a lower average age and BMI and a higher proportion of dissatisfaction with sleep, change in their job, and university or graduate school education. Additionally, 11.6% of evacuees in banned areas had a K6 score of ≥13, which accounted for the highest proportion of individuals who had psychological distress.

3.2. Associations between Evacuate Status and Diabetes, Hypertension, and Dyslipidemia

Table 2 presents the age- and multivariable-adjusted odds ratios (ORs) and 95% confidence intervals (95% CIs) for diabetes, hypertension, and dyslipidemia for the returnees, evacuees in lifted areas, and evacuees in the banned areas. The ORs for diabetes, hypertension, and dyslipidemia for evacuees in the banned areas were significantly higher than those for non-evacuees, and these associations remained statistically significant even after adjusting for confounders. The multivariable ORs (95% CIs) were 1.35 (1.22–1.51) for diabetes, 1.14 (1.05–1.24) for hypertension, and 1.22 (1.13–1.32) for dyslipidemia. The ORs for diabetes, hypertension, and dyslipidemia were higher in returnees than that in non-evacuees, albeit not statistically significantly. There was no statistically significant association between the evacuees in lifted areas and the non-evacuees. With additional adjustment for psychological distress, the results still showed the same associations. Multivariable ORs (95% CIs) were 1.35 (1.21–1.50) for diabetes, 1.14 (1.05–1.24) for hypertension, and 1.22 (1.13–1.32) for dyslipidemia.
Gender-specific analyses (Table 3) showed similar associations, except for hypertension in men. The multivariable ORs (95% CI) for diabetes, hypertension, dyslipidemia were 1.33 (1.15–1.55), 1.08 (0.95–1.23), and 1.31 (1.16–1.48) among male evacuees in banned area and 1.38 (1.19–1.61), 1.20 (1.08–1.35), and 1.21 (1.09–1.34) among female evacuees. Additional adjustment for psychological distress also showed the same associations. Multivariable ORs (95% CIs) for diabetes, hypertension, and dyslipidemia were 1.33 (1.15–1.54), 1.08 (0.94–1.23), and 1.31 (1.16–1.48), respectively, among male evacuees in banned areas and 1.38 (1.18–1.60), 1.20 (1.08–1.35), and 1.20 (1.09–1.33) among female evacuees in banned areas.

3.3. Spatial Distribution Characteristics

The global spatial autocorrelation showed that the prevalence of diabetes, hypertension, and hyperlipidemia was positively spatially autocorrelated in Fukushima (Supplementary Table S1). The global Moran’s indexes for diabetes, hypertension, and dyslipidemia were 0.17, 0.16, and 0.34, respectively. The administrative region around the Fukushima and Koriyama cities were determined as clusters (Figure 3). However, Iwaki City is in the lower right corner of Fukushima Prefecture, so the spatial pattern may lack of significance.

4. Discussion

This study revealed that evacuees in banned areas had a higher risk of diabetes, hypertension, and dyslipidemia than non-evacuees, whereas returnees and evacuees in lifted areas did not have increased risks. These associations remained significant even after adjustment for selected lifestyles, education level, and change of job. Poor lifestyle factors including smoking, heavy alcohol consumption, physical inactivity, and inadequate sleep have been proven to enhance the incidence the lifestyle-related diseases [25,26]. Factors related to socioeconomic status such as low education level and change of job have also been confirmed as risk factors for the incidence of cardiovascular and metabolic diseases [7,27]. In addition, a high-high cluster of diabetes, hypertension, and dyslipidemia around the cities of Fukushima and Koriyama was noted. This study is the first to evaluate the risk of lifestyle-related diseases among returnees and evacuees in the lifted areas, and evacuees in the banned areas.
We attempted to explain why the evacuees in the banned areas had a higher risk of diabetes, hypertension, and hyperlipidemia than the other groups and the causes of spatial clustering in the discussion below.
First, in our study, the excess risks of diabetes, hypertension, and dyslipidemia among evacuees in banned areas were not altered after adjustment for psychological distress. However, this result in 2017 did not negate the possibility that that psychological distress confounded or mediated the excess risks probably because mental stress may temper over time [28].
Mental stress has been associated with an increased risks of diabetes [29], hypertension [30], and dyslipidemia [31,32]. Moreover, the incidence of diabetes increased [33,34] among evacuees immediately following the disaster. The hypothalamic–pituitary–adrenal axis [35,36] increases circulating cortisol levels, and under chronic stress conditions, the pituitary gland secretes vasopressin [35], which could affect glucose and lipid metabolism, leading to diabetes, hypertension, and dyslipidemia.
Second, diverse socio-economic factors may have influenced the incidence of lifestyle-related diseases. Evacuees in banned areas were closer to the center of the accident, were more vulnerable to the negative impact of the accident, and had no choice but to evacuate. Sugimoto et al. showed that long-term evacuation could lead to a poor perceived health status [35]. In addition, a recent report reported that evacuees in the banned area had less communication with others regarding their daily lives than those in the lifted areas [36]. These factors may have increased the risk of lifestyle-related disease onset.
Furthermore, the evacuees in the banned areas needed to leave their own houses and lose their material possessions and jobs, leading to a loss of purpose in life. Unemployment has been considered as a common factor that could increase the risk of delayed mental illness [37,38,39]. In addition, house damage, tsunami experience, nuclear power plant accident experience, and loss of family, realty, and close friends were associated with increased mental stress [40]. Moreover, we assumed that evacuees in the banned areas who were eager to return to their home but were unable to do so have a greater burden; thus, their risk of developing lifestyle-related diseases may be higher.
According to our findings, the prevalence clusters of hypertension, diabetes, and dyslipidemia were mainly located around the cities of Fukushima and Koriyama. Fukushima City is the provincial capital, whereas Koriyama City is one of the most populous commercial cities in the Fukushima province. Therefore, collective infrastructural resources are concentrated in Fukushima and Koriyama [41]. Additionally, after the disaster, these two cities, and the surrounding areas closest to the disaster site, quickly established emergency-relevant infrastructure and accepted many evacuees [42]. Therefore, this could partially explain why the spatial pattern of diabetes, hypertension, and dyslipidemia prevalence in the Fukushima and Koriyama cities were different from other cities.
Compared with other similar studies [13,43,44,45], this study has the following salient features. First, it analyzed a large population-based cohort, which not only included the residents in the affected areas of the Great East Japan earthquake, but also those throughout the entire Fukushima Prefecture. Second, over 70% of participants were followed-up for seven years from 2011–2017. Third, we adjusted for several potential confounders, including lifestyle and socioeconomic factors.
However, this study had some limitations. First, each participant may not have taken the comprehensive health checks and mental health and lifestyle surveys conducted annually. Therefore, we could not assess the impact of lifestyle changes on the incidence of diabetes, hypertension, and dyslipidemia. Second, we did not have data on the proportion of people who evacuated outside the Fukushima Prefecture and the prevalence of diseases in cities, towns, and villages in other prefectures around the Fukushima Prefecture. Third, regarding the spatial analysis, we only examined the prevalence of diabetes, hypertension, and dyslipidemia in 2017. Therefore, we could not examine the dynamic clustering process of each region. Lastly, the lifestyle parameters were based on a self-reported questionnaire and liable to misclassification.
Nevertheless, this is the first study to describe the prevalence and incidence of diabetes, hypertension, and dyslipidemia in the Fukushima area using both a cross-sectional design for the spatial dimension and a longitudinal design for the temporal dimension.

5. Conclusions

During a 7-year follow-up after the Great East Japan earthquake, evacuees in the banned areas had a higher incidence of diabetes, hypertension, and dyslipidemia than non-evacuees. Our findings imply the importance of continuous support for the prevention of lifestyle-related diseases for the evacuees in banned areas.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijerph19095661/s1, Table S1: Global Moran index of the spatial distribution of the prevalence of lifestyle-related diseases by administrative division among examinees.

Author Contributions

Z.S., R.C., H.I. (Hiroyasu Iso) and T.O. contributed to the study design; Z.S., E.E., F.H., T.O., S.Y., M.S., A.S., H.O. and K.K. were responsible for the data collection and overseeing the study procedures; The analysis was conducted by Z.S. and F.H.; The manuscript was written by Z.S.; H.I. (Hironori Imano), E.E., F.H., T.O., R.C., S.Y., A.S., M.S., H.O., K.K. and H.I. (Hiroyasu Iso) made significant contributions to the critically interpreted the results and provided intellectual content. All authors have read and agreed to the published version of the manuscript.

Funding

This survey was supported by the Japan National Health Fund for Children and Adults Affected by the Nuclear Incident; the Institute for Transdisciplinary Graduate Degree Programs of Osaka University, the Projects for Leading Graduate Schools on Interdisciplinary Program for Biomedical Science; the Network-type Joint Usage/Research Center for Radiation Disaster Medical Science, the Projects for Research on risk communication regarding radiation disasters; the Japan’s Science and Technology Agency, Projects for Support for Pioneering Research Initiated by the Next Generation (grant number JPMJSP2138); and Research Project on Health Effects of Radiation organized by the Ministry of the Environment, Japan.

Institutional Review Board Statement

The study protocol was approved by the ethics committees of the Fukushima Medical University (IRB, approval number: 20018) and the Osaka University (IRB, approval number: 1319 and 2148). The target of this observational study was residents in the Fukushima Prefecture at the time of the disaster, and no intervention was implemented during the observation process. The study was conducted in accordance with the Declaration of Helsinki.

Informed Consent Statement

Informed consent was obtained from the community representatives to conduct an epidemiological study based on the guidelines of the Council for International Organizations of Medical Science.

Data Availability Statement

The datasets analyzed during the present study are not publicly avail-able because the data from the Fukushima Health Management Survey belongs to the government of Fukushima Prefecture and can only be used within the organization.

Acknowledgments

We thank all the member who belongs to the Fukushima Health Management Survey for their support. The findings and conclusions of this article are solely the author’s responsibility and do not represent the official views of the Fukushima Prefecture Government or the Japanese Government.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Fujiwara, T.; Kodaira, S.; No, T.; Kaiho, Y.; Takahashi, N.; Kaneda, Y. The 2011 Tohoku-Oki Earthquake: Displacement Reaching the Trench Axis. Science 2011, 334, 1240. [Google Scholar] [CrossRef] [PubMed]
  2. Hirose, K. 2011 Fukushima Dai-ichi nuclear power plant accident: Summary of regional radioactive deposition monitoring results. J. Environ. Radioact. 2012, 111, 13–17. [Google Scholar] [CrossRef] [PubMed]
  3. Ubaura, M. Changes in Land Use after the Great East Japan Earthquake and Related Issues of Urban Form. In 2011 Japan Earthquake and Tsunami: Reconstruction and Restoration: Insights and Assessment after 5 Years; Springer: Berlin/Heidelberg, Germany, 2018; Volume 47, pp. 183–203. [Google Scholar] [CrossRef]
  4. Ueda, Y.; Murakami, M.; Maeda, M.; Yabe, H.; Suzuki, Y.; Orui, M.; Yasumura, S.; Ohira, T.; Fukushima Hlth Management, S. Risk Factors for Problem Drinking among Evacuees in Fukushima following the Great East Japan Earthquake: The Fukushima Health Management Survey. Tohoku J. Exp. Med. 2019, 248, 239–252. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Osaki, Y.; Maesato, H.; Minobe, R.; Kinjo, A.; Kuwabara, Y.; Imamoto, A.; Myoga, Y.; Matsushita, S.; Higuchi, S. Changes in smoking behavior among victims after the great East Japan earthquake and tsunami. Environ. Health Prev. Med. 2020, 25, 19. [Google Scholar] [CrossRef]
  6. Zhang, W.; Ohira, T.; Maeda, M.; Nakano, H.; Iwasa, H.; Yasumura, S.; Ohtsuru, A.; Harigane, M.; Suzuki, Y.; Horikoshi, N.; et al. The association between self-reported sleep dissatisfaction after the Great East Japan Earthquake, and a deteriorated socioeconomic status in the evacuation area: The Fukushima Health Management Survey. Sleep Med. 2020, 68, 63–70. [Google Scholar] [CrossRef]
  7. Zhang, W.; Ohira, T.; Yasumura, S.; Maeda, M.; Otsuru, A.; Harigane, M.; Horikoshi, N.; Suzuki, Y.; Yabe, H.; Nagai, M.; et al. Effects of socioeconomic factors on cardiovascular-related symptoms among residents in Fukushima after the Great East Japan Earthquake: A cross-sectional study using data from the Fukushima Health Management Survey. BMJ Open 2017, 7, e014077. [Google Scholar] [CrossRef]
  8. Hagiwara, Y.; Yabe, Y.; Sugawara, Y.; Sato, M.; Watanabe, T.; Kanazawa, K.; Sonofuchi, K.; Koide, M.; Sekiguchi, T.; Tsuchiya, M.; et al. Influence of living environments and working status on low back pain for survivors of the Great East Japan Earthquake. J. Orthop. Sci. 2016, 21, 138–142. [Google Scholar] [CrossRef]
  9. Li, X.Y.; Aida, J.; Hikichi, H.; Kondo, K.; Kawachi, I. Association of Postdisaster Depression and Posttraumatic Stress Disorder With Mortality Among Older Disaster Survivors of the 2011 Great East Japan Earthquake and Tsunami. JAMA Netw. Open 2019, 2, e1917550. [Google Scholar] [CrossRef]
  10. Nagamine, M.; Giltay, E.J.; Shigemura, J.; van der Wee, N.J.; Yamamoto, T.; Takahashi, Y.; Saito, T.; Tanichi, M.; Koga, M.; Toda, H.; et al. Assessment of Factors Associated With Long-term Posttraumatic Stress Symptoms Among 56 388 First Responders After the 2011 Great East Japan Earthquake. JAMA Netw. Open 2020, 3, e2018339. [Google Scholar] [CrossRef]
  11. Statistics Bureau. Labour Force Survey. 2011. Available online: https://www.stat.go.jp/data/roudou/rireki/gaiyou.html#ft_4hanki (accessed on 28 March 2022).
  12. Takiguchi, M.; Ohira, T.; Nakano, H.; Yumiya, Y.; Yamaki, T.; Yoshihisa, A.; Nakazato, K.; Suzuki, H.; Ishikawa, T.; Yasumura, S.; et al. Trends in the Incidence of Sudden Deaths and Heart Diseases in Fukushima After the Great East Japan Earthquake. Int. Heart J. 2019, 60, 1253–1258. [Google Scholar] [CrossRef] [Green Version]
  13. Satoh, H.; Ohira, T.; Nagai, M.; Hosoya, M.; Sakai, A.; Yasumura, S.; Ohtsuru, A.; Kawasaki, Y.; Suzuki, H.; Takahashi, A.; et al. Evacuation is a risk factor for diabetes development among evacuees of the Great East Japan earthquake: A 4-year follow-up of the Fukushima Health Management Survey. Diabetes Metab. 2019, 45, 312–315. [Google Scholar] [CrossRef] [PubMed]
  14. Life Support Team for Nuclear Survivors. Situation in the Evacuation Zone. 2018. Available online: https://www.mext.go.jp/b_menu/shingi/chousa/kaihatu/016/shiryo/__icsFiles/afieldfile/2018/08/10/1408009_03_1.pdf (accessed on 28 March 2022).
  15. Editorial Committee for the Paper on Decontamination Projects. Decontamination Projects for Radioactive Contamination Discharged by Tokyo Electric Power Company Fukushima Daiichi Nuclear Power Station Accident. 2018. Available online: http://josen.env.go.jp/en/policy_document/pdf/decontamination_report1807_01.pdf (accessed on 28 March 2022).
  16. Yasumura, S.; Hosoya, M.; Yamashita, S.; Kamiya, K.; Abe, M.; Akashi, M.; Kodama, K.; Ozasa, K.; Fukushima Hlth Management Survey, G. Study Protocol for the Fukushima Health Management Survey. J. Epidemiol. 2012, 22, 375–383. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. Research Collaborators Conference. Voluntary Evacuation Related Data. 2011. Available online: https://www.mext.go.jp/b_menu/shingi/chousa/kaihatu/016/shiryo/__icsFiles/afieldfile/2011/11/25/1313502_3.pdf (accessed on 28 March 2022).
  18. Unger, T.; Borghi, C.; Charchar, F.; Khan, N.A.; Poulter, N.R.; Prabhakaran, D.; Ramirez, A.; Schlaich, M.; Stergiou, G.S.; Tomaszewski, M.; et al. 2020 International Society of Hypertension global hypertension practice guidelines. J. Hypertens. 2020, 38, 982–1004. [Google Scholar] [CrossRef] [PubMed]
  19. Araki, E.; Goto, A.; Kondo, T.; Noda, M.; Noto, H.; Origasa, H.; Osawa, H.; Taguchi, A.; Tanizawa, Y.; Tobe, K.; et al. Japanese Clinical Practice Guideline for Diabetes 2019. J. Diabetes Investig. 2020, 11, 1020–1076. [Google Scholar] [CrossRef] [PubMed]
  20. Kinoshita, M.; Yokote, K.; Arai, H.; Iida, M.; Ishigaki, Y.; Ishibashi, S.; Umemoto, S.; Egusa, G.; Ohmura, H.; Okamura, T.; et al. Japan Atherosclerosis Society (JAS) Guidelines for Prevention of Atherosclerotic Cardiovascular Diseases 2017. J. Atheroscler. Thromb. 2018, 25, 846–984. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  21. Chan, C.K.; Feinstein, A.R.; Jekel, J.F.; Wells, C.K. The Value and Hazards of Standardization in Clinical Epidemiologic Research. J. Clin. Epidemiol. 1988, 41, 1125–1134. [Google Scholar] [CrossRef]
  22. Tripepi, G.; Jager, K.J.; Dekker, F.W.; Zoccali, C. Stratification for Confounding—Part 2: Direct and Indirect Standardization. Nephron Clin. Pract. 2010, 116, C322–C325. [Google Scholar] [CrossRef]
  23. Moran, P.A.P. Notes on Continuous Stochastic Phenomena. Biometrika 1950, 37, 17–23. [Google Scholar] [CrossRef]
  24. Getis, A.; Ord, J.K. The Analysis of Spatial Association by Use of Distance Statistics. Geogr. Anal. 1992, 24, 189–206. [Google Scholar] [CrossRef]
  25. Ketola, E.; Sipila, R.; Makela, M. Effectiveness of individual lifestyle interventions in reducing cardiovascular disease and risk factors. Ann. Med. 2000, 32, 239–251. [Google Scholar] [CrossRef]
  26. Deng, X.R.; Wang, P.X.; Yuan, H.J. Epidemiology, risk factors across the spectrum of age-related metabolic diseases. J. Trace Elem. Med. Biol. 2020, 61, 126497. [Google Scholar] [CrossRef] [PubMed]
  27. Nagai, M.; Ohira, T.; Zhang, W.; Nakano, H.; Maeda, M.; Yasumura, S.; Abe, M.; Fukushima Health Management Survey. Lifestyle-related factors that explain disaster-induced changes in socioeconomic status and poor subjective health: A cross-sectional study from the Fukushima health management survey. BMC Public Health 2017, 17, 340. [Google Scholar] [CrossRef] [PubMed]
  28. Matsumoto, K.; Sakuma, A.; Ueda, I.; Nagao, A.; Takahashi, Y. Psychological trauma after the Great East Japan Earthquake. Psychiatry Clin. Neurosci. 2016, 70, 318–331. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  29. Hackett, R.A.; Steptoe, A. Type 2 diabetes mellitus and psychological stress—A modifiable risk factor. Nat. Rev. Endocrinol. 2017, 13, 547–560. [Google Scholar] [CrossRef] [PubMed]
  30. Ushakov, A.V.; Ivanchenko, V.S.; Gagarina, A.A. Psychological Stress in Pathogenesis of Essential Hypertension. Curr. Hypertens. Rev. 2016, 12, 203–214. [Google Scholar] [CrossRef]
  31. Devaki, M.; Nirupama, R.; Yajurvedi, H.N. Chronic stress-induced oxidative damage and hyperlipidemia are accompanied by atherosclerotic development in rats. Stress Int. J. Biol. Stress 2013, 16, 233–243. [Google Scholar] [CrossRef]
  32. McCann, B.S.; Magee, M.S.; Broyles, F.C.; Vaughan, M.; Albers, J.J.; Knopp, R.H. Acute Psychological Stress and Epinephrine Infusion in Normolipidemic and Hyperlipidemic Men—Effects on Plasma-Lipid and Apoprotein Concentrations. Psychosom. Med. 1995, 57, 165–176. [Google Scholar] [CrossRef]
  33. Satoh, H.; Ohira, T.; Hosoya, M.; Sakai, A.; Watanabe, T.; Ohtsuru, A.; Kawasaki, Y.; Suzuki, H.; Takahashi, A.; Kobashi, G.; et al. Evacuation after the Fukushima Daiichi Nuclear Power Plant Accident Is a Cause of Diabetes: Results from the Fukushima Health Management Survey. J. Diabetes Res. 2015, 2015, 627390. [Google Scholar] [CrossRef]
  34. Satoh, H.; Ohira, T.; Nagai, M.; Hosoya, M.; Sakai, A.; Watanabe, T.; Ohtsuru, A.; Kawasaki, Y.; Suzuki, H.; Takahashi, A.; et al. Hypo-high-density Lipoprotein Cholesterolemia Caused by Evacuation after the Fukushima Daiichi Nuclear Power Plant Accident: Results from the Fukushima Health Management Survey. Intern. Med. 2016, 55, 1967–1976. [Google Scholar] [CrossRef] [Green Version]
  35. Sugimoto, T.; Shinozaki, T.; Miyamoto, Y. Aftershocks associated with impaired health caused by the great East Japan disaster among youth across Japan: A national cross-sectional survey. Interact. J. Med. Res. 2013, 2, e31. [Google Scholar] [CrossRef] [Green Version]
  36. Institute of Disaster Area Revitalization, Regrowth and Governance. National Survey on People Evacuated Due to the Nuclear Power Plant Accident. Institute of Disaster Area Revitalization, Regrowth and Governance, Kwansei Gakuin University. Available online: https://www.kwansei.ac.jp/fukkou/research/survey/detail/20210120.html (accessed on 28 March 2022).
  37. Maehlisen, M.H.; Pasgaard, A.A.; Mortensen, R.N.; Vardinghus-Nielsen, H.; Torp-Pedersen, C.; Boggild, H. Perceived stress as a risk factor of unemployment: A register-based cohort study. BMC Public Health 2018, 18, 728. [Google Scholar] [CrossRef] [PubMed]
  38. Morishima, R.; Ando, S.; Araki, T.; Usami, S.; Kanehara, A.; Tanaka, S.; Kasai, K. The course of chronic and delayed onset of mental illness and the risk for suicidal ideation after the Great East Japan Earthquake of 2011: A community-based longitudinal study. Psychiatry Res. 2019, 273, 171–177. [Google Scholar] [CrossRef] [PubMed]
  39. Orpana, H.M.; Lemyre, L.; Gravel, R. Income and psychological distress: The role of the social environment. Health Rep. 2009, 20, 21–28. [Google Scholar] [PubMed]
  40. Shiga, T.; Zhang, W.; Ohira, T.; Suzuki, Y.; Maeda, M.; Mashiko, H.; Yabe, H.; Iwasa, H.; Nakano, H.; Yasumura, S.; et al. Socioeconomic status, damage-related conditions, and PTSD following the Fukushima-daiichi nuclear power plant accident: The Fukushima Health Management Survey. Fukushima J. Med. Sci. 2021, 67, 71–82. [Google Scholar] [CrossRef] [PubMed]
  41. Fukushima Prefectural Government. Overview of Fukushima Prefecture. 2021. Available online: https://www.pref.fukushima.lg.jp/sec/11045b/r3youran.html (accessed on 28 March 2022).
  42. Disaster Countermeasures Office. Progress of Emergency Temporary Housing, Rented Housing, and Public Housing. 2022. Available online: https://www.pref.fukushima.lg.jp/site/portal/ps-nyuukyojoukyou.html (accessed on 28 March 2022).
  43. Ohira, T.; Hosoya, M.; Yasumura, S.; Satoh, H.; Suzuki, H.; Sakai, A.; Ohtsuru, A.; Kawasaki, Y.; Takahashi, A.; Ozasa, K.; et al. Evacuation and Risk of Hypertension After the Great East Japan Earthquake The Fukushima Health Management Survey. Hypertension 2016, 68, 558–564. [Google Scholar] [CrossRef]
  44. Shiba, K.; Hikichi, H.; Aida, J.; Kondo, K.; Kawachi, I. Long-Term Associations Between Disaster Experiences and Cardiometabolic Risk: A Natural Experiment From the 2011 Great East Japan Earthquake and Tsunami. Am. J. Epidemiol. 2019, 188, 1109–1119. [Google Scholar] [CrossRef] [PubMed]
  45. Takahashi, S.; Yonekura, Y.; Tanno, K.; Shimoda, H.; Sakata, K.; Ogawa, A.; Kobayashi, S.; Kawachi, I. Increased incidence of metabolic syndrome among older survivors relocated to temporary housing after the 2011 Great East Japan earthquake & tsunami. Metab. Open 2020, 7, 100042. [Google Scholar] [CrossRef]
Ijerph 19 05661 g001 550
Figure 1. Flow diagram of the participant selection process: (a) longitudinal analysis; (b) spatial analysis.
Figure 1. Flow diagram of the participant selection process: (a) longitudinal analysis; (b) spatial analysis.
Ijerph 19 05661 g001
Ijerph 19 05661 g002 550
Figure 2. Group design based on the history of the Fukushima evacuation area and caution area. Area 1: still difficult to return at the time of the deadline; Area 2: where the evacuation alerts have been lifted at the time of the deadline; Area 3: near the evacuation area or with a history of voluntary evacuation; and Area 4: all other areas.
Figure 2. Group design based on the history of the Fukushima evacuation area and caution area. Area 1: still difficult to return at the time of the deadline; Area 2: where the evacuation alerts have been lifted at the time of the deadline; Area 3: near the evacuation area or with a history of voluntary evacuation; and Area 4: all other areas.
Ijerph 19 05661 g002
Ijerph 19 05661 g003 550
Figure 3. Hot spot analysis of spatial prevalence of lifestyle-related diseases among survey participants: (a) Spatial pattern of diabetes; (b) Spatial pattern of hypertension; (c) Spatial pattern of dyslipidemia.
Figure 3. Hot spot analysis of spatial prevalence of lifestyle-related diseases among survey participants: (a) Spatial pattern of diabetes; (b) Spatial pattern of hypertension; (c) Spatial pattern of dyslipidemia.
Ijerph 19 05661 g003
Table
Table 1. Characteristics of participants at baseline according to evacuation status (N = 49,916).
Table 1. Characteristics of participants at baseline according to evacuation status (N = 49,916).
Non-Evacuees
(n = 26,115)		Returnees
(n = 1573)		Evacuees in
Lifted Areas
(n = 5559)		Evacuees in
Banned Areas
(n = 16,669)
Age (years) Mean ± SD57.4 ± 14.555.4 ± 14.1 ***46.1 ± 16.5 ***54.1 ± 15.2 ***
BMI (kg/m2) Mean ± SD23.8 ± 3.7723.7 ± 3.5223.2 ± 3.89 ***23.9 ± 3.92 ***
Current alcohol drinker (%)34.938.2 **38.5 **37.9 ***
Current smoker (%)13.112.816.7 **16.6 ***
Sleep, inadequate (%)26.433.4 ***33.2 ***32.8 ***
Physical activity, ≥ once/week (%)40.840.232.7 **39.7 ***
Change of job, yes (%)33.956.5 ***48.7 ***53.5 ***
Education attainment, i.e., university or graduate school (%)4.86.7 ***11.1 ***7.1 ***
Psychological distress (K6 score of ≥13) (%)6.710.9 ***9.4 ***11.6 ***
Difference from non-evacuees: ** p < 0.01; *** p < 0.001.
Table
Table 2. Age-adjusted and multivariable odds ratios of diabetes, hypertension, and dyslipidemia according to evacuation status.
Table 2. Age-adjusted and multivariable odds ratios of diabetes, hypertension, and dyslipidemia according to evacuation status.
Total
Non-EvacueesReturneesEvacuees in
Lifted Areas		Evacuees in
Banned Areas
No. at risk, n16,7841284320711,724
Diabetes, n87563118766
Age-adjusted OR (95% CI)Ref.1.04 (0.80–1.35)0.96 (0.79–1.17)1.45 (1.30–1.59) ***
Multivariable OR (95% CI) §Ref.1.04 (0.79–1.35)1.00 (0.82–1.22)1.35 (1.22–1.51) ***
Multivariable OR (95% CI) §§Ref.1.03 (0.79–1.35)0.99 (0.81–1.21)1.35 (1.21–1.50) ***
No. at risk, n936780824177336
Hypertension, n18281462671368
Age-adjusted OR (95% CI)Ref.1.07 (0.88–1.29)0.84 (0.73–0.97) *1.17 (1.08–1.27) ***
Multivariable OR (95% CI) §Ref.1.06 (0.87–1.29)0.87 (0.75–1.00)1.14 (1.05–1.24) **
Multivariable OR (95% CI) §§Ref.1.06 (0.87–1.29)0.87 (0.75–1.00)1.14 (1.05–1.24) **
No. at risk, n862861720316097
Dyslipidemia, n21001524211688
Age-adjusted OR (95% CI)Ref.1.10 (0.91–1.33)0.97 (0.86–1.10)1.28 (1.18–1.38) ***
Multivariable OR (95% CI) §Ref.1.07 (0.88–1.30)0.97 (0.86–1.10)1.22 (1.13–1.32) ***
Multivariable OR (95% CI) §§Ref.1.07 (0.88–1.29)0.97 (0.86–1.10)1.22 (1.13–1.32) ***
CI, confidence interval; OR, odds ratio. * p < 0.05; ** p < 0.01;*** p < 0.001. § Adjust for age, body mass index, smoking status, alcohol consumption, sports time, sleep quality, education level, and change of job. §§ Adjusted further for psychological distress.
Table
Table 3. Gender-specific age-adjusted and multivariable odds ratios of diabetes, hypertension, and dyslipidemia according to evacuation status.
Table 3. Gender-specific age-adjusted and multivariable odds ratios of diabetes, hypertension, and dyslipidemia according to evacuation status.
MenWomen
Non-
Evacuees		ReturneesEvacuees in Lifted AreasEvacuees in
Banned Areas		Non-
Evacuees		ReturneeEvacuees in
Lifted Areas		Evacuees in
Banned Areas
No. at risk, n65054481062449810,27983621457226
Diabetes, n45030594024253359364
Age-adjusted OR (95% CI)Ref.1.06 (0.72–1.55)1.01 (0.76–1.34)1.46 (1.27–1.68) ***Ref.1.06 (0.74–1.52)0.94 (0.71–1.24)1.41 (1.22–1.63) ***
Multivariable OR (95% CI) §Ref.1.03 (0.70–1.52)1.03 (0.77–1.37)1.33 (1.15–1.55) ***Ref.1.06 (0.73–1.52)0.97 (0.73–1.28)1.38 (1.19–1.61) ***
Multivariable OR (95% CI) §§Ref.1.02 (0.69–1.51)1.02 (0.77–1.37)1.33 (1.15–1.54) ***Ref.1.05 (0.73–1.52)0.96 (0.72–1.28)1.38 (1.18–1.60) ***
No. at risk, n32592457182473610856316994863
Hypertension, n78951122589103995145779
Age-adjusted OR (95% CI)Ref.0.95 (0.69–1.33)0.90 (0.72–1.12)1.13 (0.99–1.28) *Ref.1.16 (0.91–1.47)0.81 (0.67–0.99)1.21 (1.08–1.34) **
Multivariable OR (95% CI) §Ref.0.92 (0.66–1.29)0.93 (0.74–1.16)1.08 (0.95–1.23)Ref.1.13 (0.89–1.45)0.84 (0.69–1.02)1.20 (1.08–1.35) **
Multivariable OR (95% CI) §§Ref.0.92 (0.66–1.28)0.93 (0.74–1.16)1.08 (0.94–1.23)Ref.1.13 (0.89–1.45)0.84 (0.69–1.02)1.20 (1.08–1.35) **
No. at risk, n36122256272258501639214043839
Dyslipidemia, n890501576981210102264990
Age-adjusted OR (95% CI)Ref.0.87 (0.63–1.20)1.01 (0.83–1.23)1.36 (1.21–1.53) ***Ref.1.28 (1.01–1.62)0.97 (0.83–1.14)1.24 (1.12–1.37) ***
Multivariable OR (95% CI) §Ref.0.85 (0.61–1.18)1.01 (0.82–1.23)1.31 (1.16–1.48) ***Ref.1.24 (0.98–1.58)0.96 (0.82–1.13)1.21 (1.09–1.34) ***
Multivariable OR (95% CI) §§Ref.0.85 (0.61–1.18)1.01 (0.83–1.24)1.31 (1.16–1.48) ***Ref.1.24 (0.97–1.57)0.96 (0.82–1.23)1.20 (1.09–1.33) ***
* p < 0.05; ** p < 0.01; *** p < 0.001. § Adjust for age, body mass index, smoking status, alcohol consumption, sports time, sleep quality, education level, and change of job. §§ Adjusted further for psychological distress.
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Sun, Z.; Imano, H.; Eguchi, E.; Hayashi, F.; Ohira, T.; Cui, R.; Yasumura, S.; Sakai, A.; Shimabukuro, M.; Ohto, H.; et al. The Associations between Evacuation Status and Lifestyle-Related Diseases in Fukushima after the Great East Japan Earthquake: The Fukushima Health Management Survey. Int. J. Environ. Res. Public Health 2022, 19, 5661. https://doi.org/10.3390/ijerph19095661

AMA Style

Sun Z, Imano H, Eguchi E, Hayashi F, Ohira T, Cui R, Yasumura S, Sakai A, Shimabukuro M, Ohto H, et al. The Associations between Evacuation Status and Lifestyle-Related Diseases in Fukushima after the Great East Japan Earthquake: The Fukushima Health Management Survey. International Journal of Environmental Research and Public Health. 2022; 19(9):5661. https://doi.org/10.3390/ijerph19095661

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Sun, Zhichao, Hironori Imano, Eri Eguchi, Fumikazu Hayashi, Tetsuya Ohira, Renzhe Cui, Seiji Yasumura, Akira Sakai, Michio Shimabukuro, Hitoshi Ohto, and et al. 2022. "The Associations between Evacuation Status and Lifestyle-Related Diseases in Fukushima after the Great East Japan Earthquake: The Fukushima Health Management Survey" International Journal of Environmental Research and Public Health 19, no. 9: 5661. https://doi.org/10.3390/ijerph19095661

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