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Breast cancer patient delay in Fukushima, Japan following the 2011 triple disaster: a long-term retrospective study

  • Akihiko Ozaki1, 12Email author,
  • Shuhei Nomura2, 13,
  • Claire Leppold3,
  • Masaharu Tsubokura4,
  • Tetsuya Tanimoto5,
  • Takeru Yokota1,
  • Shigehira Saji6,
  • Toyoaki Sawano1,
  • Manabu Tsukada1,
  • Tomohiro Morita7,
  • Sae Ochi7,
  • Shigeaki Kato8,
  • Masahiro Kami9,
  • Tsuyoshi Nemoto10,
  • Yukio Kanazawa11 and
  • Hiromichi Ohira1
BMC Cancer201717:423

https://doi.org/10.1186/s12885-017-3412-4

Received: 28 August 2016

Accepted: 8 June 2017

Published: 19 June 2017

Abstract

Background

Little information is available concerning how patient delay may be affected by mass disasters. The main objectives of the present study are to identify whether there was a post-disaster increase in the risk of experiencing patient delay among breast cancer patients in an area affected by the 2011 triple disaster in Fukushima, Japan, and to elucidate factors associated with post-disaster patient delay. Sociodemographic factors (age, employment status, cohabitant status and evacuation status), health characteristics, and health access- and disaster-related factors were specifically considered.

Methods

Records of symptomatic breast cancer patients diagnosed from 2005 to 2016 were retrospectively reviewed to calculate risk ratios (RRs) for patient delay in every year post-disaster compared with the pre-disaster baseline. Total and excessive patient delays were respectively defined as three months or more and twelve months or more from symptom recognition to first medical consultation. Logistic regression analysis was conducted for pre- and post-disaster patient delay in order to reveal any factors potentially associated with patient delay, and changes after the disaster.

Results

Two hundred nineteen breast cancer patients (122 pre-disaster and 97 post-disaster) were included. After adjustments for age, significant post-disaster increases in RRs of experiencing both total (RR: 1.66, 95% Confidence Interval (CI): 1.02–2.70, p < 0.05) and excessive patient delay (RR: 4.49, 95% CI: 1.73–11.65, p < 0.01) were observed. The RRs for total patient delay peaked in the fourth year post-disaster, and significant increases in the risk of excessive patient delay were observed in the second, fourth, and fifth years post-disaster, with more than five times the risk observed pre-disaster. A family history of any cancer was the only factor significantly associated with total patient delay post-disaster (odds ratio: 0.38, 95% CI: 0.15–0.95, p < 0.05), while there were no variables associated with delay pre-disaster.

Conclusions

The triple disaster in Fukushima appears to have led to an increased risk of patient delay among breast cancer patients, and this trend has continued for five years following the disaster.

Keywords

Breast cancer Patient delay Social support Psychosocial stress Health service Fukushima Minamisoma Nuclear power plant Disaster

Background

Breast cancer is the most common cancer and cause of cancer death for females, making it a significant part of the global cancer burden [1]. A considerable proportion of patients seek medical consultation only after they notice symptoms, such as a breast lump [2]. Among these symptomatic patients, patient delay, generally defined as a delay in first medical consultation of three months or longer following the first recognition of symptoms [3, 4], is a problem that may lead to a late stage diagnosis and worsened prognosis of breast cancer [5, 6]. Associations have been found between clinical, sociodemographic, and psychosocial factors and patient delay [4, 79]. Yet, in contrast to a predominant research focus on associations between individual characteristics and patient delay, there is still limited understanding of the broader societal factors that may influence patient delay.

Recently, studies have underlined the importance of the context in which patients discover their symptoms and seek help. For instance, patients who are socially isolated or feel overwhelming responsibilities and stress concerning work or care of family members may be at higher risk of delaying medical consultation [2, 4, 7, 8]. In addition, poor access to medical services may contribute to the delays [4, 8]. The above literature highlights the importance of accounting for the social contexts patients inhabit, in order to fully understand the causes of patient delay. However, this is a difficult point to accomplish given that social contexts can change over time; previous studies have not addressed potential relationships between these changes and patient delay [4, 5, 8, 9]. Mass disasters provide a unique opportunity to assess how rapidly-changing social contexts may impact patient delay, as they can simultaneously disintegrate social connections of victims and access to medical institutions, while exposing disaster victims to high levels of stress [1013].

On 11 March 2011, Japan experienced the Great East Japan Earthquake, ensuing tsunami, and the Fukushima Daiichi Nuclear Power Plant (FDNPP) disaster, referred to as Japan’s triple disaster [14, 15]. So-so District is located in the coastal area of Fukushima Prefecture, Japan, housing the FDNPP (Fig. 1), and was struck by all three disasters [1619]. The central government issued mandatory evacuation orders for the 20 km radius around the nuclear power plant, and voluntary evacuation orders for the 20–30 km radius [11, 16, 19]. As a result, over 80,000 people in the mandatory evacuation zone were forced to evacuate [20], and more than 70,000 of them have continued evacuation as of 5 September 2015 [21]. In Minamisoma City, the largest municipality in So-so District, the original population of 72,000 drastically decreased to approximately 10,000 in April 2011, slowly recovering to 57,000 in October 2015 [16, 22]. As evacuation occurred primarily among young to middle-aged generations, the city has experienced rapid aging, with the proportion of elderly residents (≥ 65 years old) increasing from 26.5% in 2010 to 32.0% in 2015 [22]. Furthermore, the mean number of people per households has decreased from 3.00 in 2010 to 2.23 in 2015 [22]. Medical care services in the city were additionally affected, with the closure of 56% of medical institutions, and approximately a 15–20% decrease in health care providers in the two years post-disaster [16]. Moreover, fear of radiation exposure has persisted among local residents in Minamisoma compared to other areas of Fukushima [23], indicating the potential of long-lasting psychosocial stress amongst this population [24, 25]. These large-scale changes, including sociodemographic makeup of the city, access to healthcare, and psychosocial effects of the disaster are likely to have complicated cancer management and social support indispensable for cancer patients in the area, yet there is little information available on how breast cancer patients may have been affected to date.
Fig. 1

Map of Minamisoma City and its location within So-so District, Fukushima, with air dose rate. Minamisoma Municipal General Hospital and Watanabe Hospital are located 23 km and 25 km north of Fukushima Daiichi Nuclear Power Plant, respectively, both of which are within the voluntary evacuation zone. The air dose rate of radiation as of April 2011 is also described in this map. Approval for re-use of the image has been granted from ESRI Japan Corporation

The objectives of the present study are to identify 1) whether there was a post-disaster increase in the risk of experiencing patient delay among breast cancer patients, and 2) whether any of the following factors were associated with post-disaster patient delay: sociodemographic factors (age, cohabitant status and evacuation status), access to medical institutions, and psychosocial stress. The results of these inquiries will provide new information on the influence of mass disasters on patient delay.

Methods

Study settings and participants

This study took place at Minamisoma Municipal General Hospital (MMGH) and Watanabe Hospital (WH), located in Minamisoma City (Fig. 1). MMGH and WH stopped outpatient services immediately after the disaster, yet both restarted these services in June 2011. WH was the only medical institution in So-so District with an attending physician specialized in breast cancer care before the disaster. However, it lost inpatient functioning post-disaster, and stopped surgical therapy and chemotherapy for breast cancer patients. In August 2011, the WH breast cancer specialist moved to MMGH to restart breast cancer care for local residents, and since then, MMGH has been the only medical institution with a breast cancer specialist in So-so District (as of July 2016). These two hospitals are therefore considered to be the core breast cancer centers of So-so District.

To evaluate the impact of the 3.11 triple disaster on patient delay among breast cancer patients in So-so District, we retrospectively assessed the records of symptomatic breast cancer patients, newly diagnosed based on pathological findings, with first presentation to either of MMGH or WH from 1 January 2005 to 10 March 2016. The study period of 2005–2016 was chosen in order to assess any long-term influence of the disasters. Because this study focused on the period before a confirmatory diagnosis, we included patients who were later referred to other academic institutions or cancer centers after a pathological diagnosis of breast cancer in the two study hospitals. Patients with recurrent breast cancer, male patients, and those from outside So-so District were excluded. We also excluded those who were referred to MMGH or WH in the study period after first medical consultation to other medical providers during the non-study period. In the post-disaster period, we excluded those who had moved into the area following the disaster. Through the above process, patients were identified and categorized into two groups: pre- (from 1 January 2005 to 10 March 2011) or post-disaster (from 11 March 2011 to 10 March 2016).

Analytical data

First, in order to assess the existence of patient delay, the date of first symptom discovery by patients or their families, and the date of the first medical consultation were collected from patient records. When a patient was referred from other medical providers, the first visit at the initial medical institution was considered as the first medical consultation.

Second, factors potentially associated with patient delay were extracted from the records. Sociodemographic factors at the first presentation were considered as follows: age [7, 8], cohabitant status (number of cohabitant family members, living with children or not, and living with a partner or not) [4, 8] and employment status (full-time job or not) [4, 8, 9]. Clinical characteristics were additionally included as follows; major symptoms (lump or non-lump) [9], hormone receptor (HR) status (positive or negative), stage at the diagnosis [5], American Society of Anesthesiologists (ASA) physical classification system (normal healthy patient, patient with mild systemic disease, and patient with severe systemic disease) [5, 26], body mass index (BMI) (<25 kg/m2, 25–30 kg/m2, or ≥30 kg/m2) [5], history of benign breast disease (yes or no) [8, 9], history of breast cancer (yes or no), mammography screening within two years (yes or no) [5], and family history of any kind of cancer (yes or no) [27, 28]. It has been suggested that HR-positive breast cancer progresses more slowly compared to HR-negative cancer [29]. Additionally considering that slow-developing cancers can lead to delayed medical consultation [30], HR status may influence length of patient interval. HR status was defined to be positive if either estrogen receptor status or progesterone receptor status was positive according to a cut-off value of no less than 1% in immunohistochemical (IHC) analysis [31]. As a population-based breast cancer screening is conducted through biennial mammography in Japan [32], previous experience of screening was classified according to whether there was attendance of mammography within the past two years. The details of family history of cancer, including which relatives had cancer and what kind of cancer they had, could not be examined because of data limitations. Four variables related to health care access were taken into account [3, 4]: hospital of the first medical consultation (MMGH or WH) and its linear distance to each patient’s residence, a referral to MMGH or WH from other medical providers (yes or no), and the interval from first medical consultation to first breast cancer specific examination (i.e. mammography, ultrasonography, or biopsy) [33].

Lastly, factors possibly associated with patient delay in disaster settings were gathered from patient records. In general settings, psychosocial distress can lead to a longer delay in first medical consultation among breast cancer patients [34]. A recent study suggested that evacuation status and residential air dose rates may be associated with psychosocial stress of local residents in the aftermath of Fukushima nuclear disaster [35]. We therefore considered it valuable to examine residential address of each patient and the air dose rate of radiation at their post-disaster residence in exploring the potential influence of psychosocial stress on post-disaster patient delay. The residential address of each patient was categorized into mandatory evacuation zone, voluntary evacuation zone, or non-evacuation zone of So-so District. For pre-disaster patients, residential addresses at the time of medical consultation were extracted, and for post-disaster patients, residential address at the time of the disaster was extracted. Methods of identifying the air dose rate for each participant’s residence are summarized in the next section. Further, medical costs of post-disaster patients were classified as free or not, according to the original addresses of participants; although Japan has universal health care [36], those who originally lived in mandatory evacuation zone or voluntary evacuation zone of Fukushima are now completely exempt from paying any medical fees as a disaster relief measure by the central government [37]. Hypothesized relationships between patient delay and factors studied are summarized in Table 1.
Table 1

Factors possibly associated with patient delay

Characteristics

Contributing factors / Protective factors

1. Sociodemographic factors

 Age

Contributing

 Cohabitance with family

Protective

  – Living with a partner

 

  – Living with children

 

 Full-time job

Contributing

2. Clinical factors

 Symptom of lump

Protective

 Past history of benign breast disease

Protective

 Past history of breast cancer

Protective

 Family history of breast cancer

Protective

 Underlying diseases

Contributing

 Obesity

Contributing

 Slowly developing cancer

Contributing

  – Positive HR status

 

 Recent experience of mammography

Contributing

3. Factors representing health care access

 Adequate access to health service

Protective

  – Short distance

 

  – Little or no cost

 

  – Sufficient medical providers

 

  – Short consultation interval

 

4. Disaster-related factors

 Psychosocial stress

Contributing

  – Evacuation status

 

  – Air dose rate

 

Measure of patient interval and patient delay

Patient interval was defined as the period from first recognition of a breast cancer-related symptom to the first medical consultation. According to the most generally used definition of patient delay [3, 6], total patient delay was defined as an interval of three months or longer. In order to assess patient delay comprehensively, we also introduced an additional category; excessive patient delay, defined as an interval of twelve months or longer [4, 38, 39].

Air dose rate at home

After the FDNPP disaster, the Japanese Ministry of Education, Culture, Sports, Science, and Technology (MEXT) has conducted airborne radiation monitoring inside the 80 km radius of the FDNPP. The methods of these measurements have been documented in detail in a previous study [40]. All monitored results are publicly available [41].

In the present study, the post-disaster air dose rate at each participant’s residence was calculated using the following approach. We considered the results of the fourth MEXT monitoring performed between 22 October and 5 November 2011; sixth monitoring between 31 October and 16 November 2012; eighth monitoring between 2 and 19 November 2013; ninth between 1 September and 7 November 2014; and tenth between 12 September and 4 November 2015, for the radiation levels of the first (2011–2012), second (2012–2013), third (2013–2014), forth (2014–2015), and fifth (2015–2016) year post-disaster, respectively [41]. These values were averaged by a 500-m2 mesh on the basis of the Japan Profile for Geographical Information Standards (elevation and slope angle fourth mesh data) [42], and each participant’s house was assigned to a mesh area. This approach enabled estimation of the air dose rate at the home of each participant. In cases where the patients had experienced evacuation, post-evacuation addresses were used.

Data analysis

We conducted two primary analyses. First, changes in the risk of experiencing total and excessive delay pre- and post-disaster were evaluated in the following manner. The overall and annual post-disaster incidence rates were respectively calculated by dividing the number of those with delays by the total number of breast cancer patients during each period from the date of the Great East Japan Earthquake, 11 March 2011 to 10 March 2016. For comparison, we calculated the overall incidence during pre-disaster period (from 1 January 2005 to 10 March 2011) by the same method, and utilized this value as a pre-disaster baseline. Based on these data, changes in risk in the overall post-disaster period (2011–2016) and each year post-disaster (2011–2012; 2012–2013; 2013–2014; 2014–2015; 2015–2016) compared to the pre-disaster baseline were identified as a risk ratio (RR), adjusted for age. Second, to identify factors associated with post-disaster patient delay, we constructed logistic regression models for total and excessive patient delay in post-disaster patients. Regression models for pre-disaster patient delay were also constructed, in order to address potential changes in the associations of factors with the patient delay pre- and post-disaster. All factors mentioned in the analytical data section were considered for this analysis. A P value of <0.05 was regarded as significant. All analyses were conducted by STATA/MP 14.1.

Ethical approval

The ethics committee of MMGH assessed and granted approval for this study (approval number: 27–03). The ethics committee agreed that written consent from the participants was not required as this study was a retrospective analysis of patient records. All data were anonymized prior to analysis.

Results

Patient characteristics

In the study period, 298 patients were newly diagnosed with breast cancer; 167 pre-disaster patients and 131 post-disaster patients. All non-symptomatic patients, comprised of 44 pre-disaster patients and 34 post-disaster patients, whose cancers were identified by a breast cancer examination (i.e. physical examination, mammography or ultrasonography) or incidentally, were excluded. Resultantly, there were 220 symptomatic patients in total; 123 pre-disaster patients and 97 post-disaster patients. Pre-determined exclusion criteria were then applied. One patient with a first consultation before the study period was excluded from the pre-disaster population. No patients with recurrent cancer, male patients, or those from outside So-so District were observed pre- or post-disaster. Additionally, no post-disaster patients had moved into the area following the disaster. There were 4 patients with a history of breast cancer defined as metachronal contralateral breast cancer cases, rather than recurrent cases. In total, 219 female breast cancer patients (73.5% of all cases, 99.5% of all symptomatic cases) were included, with 122 pre-disaster patients (73.1% of all pre-disaster cases, 99.2% of all pre-disaster symptomatic cases) and 97 post-disaster patients (74.0% of all post-disaster cases, 100.0% of all post-disaster symptomatic cases). The process of patient selection is displayed in Fig. 2.
Fig. 2

Process of participant inclusion

Table 2 shows characteristics of the patients included in the analysis. There was no significant difference in the age distribution between pre- and post-disaster patients (Chi-squared test, p = 0.74). Additionally, distributions of breast cancer stage were not significantly different pre- and post-disaster (Fisher’s exact test, p = 0.22). However, a significantly smaller median number of cohabitant family members (1 vs. 2, p < 0.05), and a significantly smaller proportion of those living with children (47.4% vs. 59.0%, p < 0.05) were observed among post-disaster patients compared to pre-disaster patients. As number of cohabitant family member did not follow a normal distribution, we reported a median for this variable instead of its mean. With respect to clinical characteristics, there was no significant difference in the proportions of patients presenting with a lump between pre- and post-disaster patients (86.9% vs. 93.8%, p = 0.09). Regarding access to health service, there was no significant difference in the median distance between patient residence and study institutions at the first presentation (3.6 km vs. 3.3 km, p = 0.68) and the median days from first medical consultation to first breast cancer specific examination (0 vs. 0, p = 0.71) pre- and post-disaster. In the post-disaster period, there was a smaller proportion of patients who had resided in areas which became classified as parts of the mandatory evacuation zone, compared to the proportions of pre-disaster patients living in these areas (12.4% vs. 31.2%, p < 0.01). Also, in the post-disaster group, 84.5% of the patients were exempt from paying medical fees, and a mean air dose rate at patient’s residence was 0.31 μSv/h.
Table 2

Participants’ characteristics

Characteristic

Pre-disaster (N = 122)

Post-disaster (N = 97)

P-value

Age (N, %)

  

0.74

 –50]

25 (20.5)

16 (16.5)

 

 (50–65]

41 (33.6)

33 (34.0)

 

 (65–

56 (45.9)

48 (49.5)

 

Engaged in a full-time job (N, %)

24 (19.7)

22 (22.7)

0.59

Number of cohabitant family membersa (median, range)

2 (0–7)

1 (0–6)

<0.05

Living with a partner (N, %)

  

0.23

 Yes

66 (54.1)

63 (65.0)

 

 No

50 (41.0)

34 (35.1)

 

 Missing

6 (4.9)

0 (0.0)

 

Living with children (N, %)

  

<0.05

 Yes

72 (59.0)

46 (47.4)

 

 No

44 (36.1)

51 (52.6)

 

 Missing

6 (4.9)

0 (0.0)

 

Presence of lump (N, %)

106 (86.9)

91 (93.8)

0.09

Hormone receptor (N, %)

  

<0.001

 Positive

80 (65.6)

89 (91.8)

 

 Negative

37 (30.3)

8 (8.3)

 

 Missing

5 (4.1)

0 (0.0)

 

Stage (N, %)

  

0.22

 0

11 (9.0)

5 (5.2)

 

 I

33 (27.1)

29 (29.9)

 

 II

48 (39.3)

42 (43.3)

 

 III

27 (22.1)

14 (14.4)

 

 IV

3 (2.5)

7 (7.2)

 

ASA Physical classification system (N, %)

  

0.52

 Normal healthy patient

59 (48.4)

40 (41.2)

 

 Patient with mild systemic disease

50 (41.0)

47 (48.5)

 

 Patient with severe systemic disease

13 (10.7)

10 (10.3)

 

Body mass index (kg/m2, N, %)

  

0.33

 –25]

75 (61.5)

62 (63.9)

 

 (25–30]

29 (23.8)

28 (28.9)

 

 (30–

14 (11.5)

6 (6.2)

 

 Missing

4 (3.3)

1 (1.0)

 

History of benign breast disease (N, %)

7 (5.7)

6 (6.2)

0.89

History of breast cancer (N, %)

2 (1.6)

2 (2.1)

1.00

Undertook mammography screening within past two years (N, %)

15 (12.3)

12 (12.4)

0.99

Family history of any cancer (N, %)

  

0.46

 Yes

50 (41.0)

46 (47.4)

 

 No

68 (55.7)

51 (52.6)

 

 Missing

4 (3.3)

0 (0.0)

 

Hospital (N, %)

  

<0.001

 Minamisoma Municipal General Hospital

14 (11.5)

97 (100)

 

 Watanabe Hospital

108 (88.5)

0 (0.0)

 

Distance from hospital (median (km), range)

3.6 (0.2–47.0)

3.3 (0.2–22.8)

0.68

Referral from other medical providers (N, %)

55 (45.1)

33 (34.0)

0.10

Days from first medical consultation to first examinationb (median, range)

0 (0–1096)

0 (0–676)

0.71

Residential areac (N, %)

  

<0.01

 Non-evacuation zone of So-so District

13 (10.7)

15 (15.5)

 

 Voluntary evacuation zone

71 (58.2)

70 (72.2)

 

 Mandatory evacuation zone

38 (31.2)

12 (12.4)

 

Exempt from medical feesd (N, %)

N/A

82 (84.5)

 

Air dose ratede (average (μSv/h), age)

N/A

0.31 (0.17)

 

aData is missing in five pre-disaster patients

bData is missing in one post-disaster patient

cFor pre-disaster patients, this indicates place of residence at the time of first medical consultation, and for post-disaster patients, indicates place of residence at the time of Japan’s triple disaster

dData is available only among post-disaster patients

eThis indicates approximate air dose rate at home at the time of first medical consultation

Delay in the first medical consultation

RRs of experiencing patient delay post- vs. pre-disaster are shown in Table 3. When we compared the overall post-disaster population with the pre-disaster baseline, there was a significant increase in the age-adjusted RR for both total patient delay (RR: 1.66, 95% Confidence Interval (CI): 1.02–2.70, p < 0.05) and excessive patient delay (RR: 4.49, 95% CI: 1.73–11.65, p < 0.01). According to the analysis of respective year, the RR of experiencing total patient delay peaked in the fourth year post-disaster (RR: 2.05, 95% CI: 1.10–3.81, p < 0.05), and that of excessive patient delay exceeded 5.0 in the second (RR: 5.58, 95% CI: 1.77–17.56, p < 0.01), fourth (RR: 5.27, 95% CI: 1.73–16.03, p < 0.01), and fifth year (RR: 5.24, 95% CI: 1.64–16.78, p < 0.01) post-disaster.
Table 3

Proportions and crude and age-adjusted risk ratios of patient delay post- versus pre-disaster (95% CI)

Characteristics

Proportion (%)

Crude risk ratio

Age-adjusted risk ratio

Total delay (≥3 months)

 Pre-disaster

18.0% (22/122)

Ref.

Ref.

 Post-disaster

  Overall population

29.9% (29/97)

1.66 (1.02–2.69)*

1.66 (1.02–2.70)*

  2011–2012a

20.0% (2/10)

1.11 (0.30–4.05)

1.11 (0.30–4.04)

  2012–2013a

27.3% (6/22)

1.51 (0.69–3.30)

1.51 (0.69–3.30)

  2013–2014a

26.7% (4/15)

1.48 (0.59–3.71)

1.49 (0.59–3.74)

  2014–2015a

37.0% (10/27)

2.05 (1.10–3.82)*

2.05 (1.10–3.81)*

  2015–2016a

30.4% (7/23)

1.69 (0.82–3.48)

1.75 (0.84–3.63)

Excessive delay (≥12 months)

 Pre-disaster

4.1% (5/122)

Ref.

Ref.

 Post-disaster

  Overall population

18.6% (18/97)

4.53 (1.74–11.76)**

4.49 (1.73–11.65)**

  2011–2012a

10.0% (1/10)

2.44 (0.31–18.91)

2.44 (0.31–18.85)

  2012–2013a

22.7% (5/22)

5.55 (1.75–17.57)**

5.58 (1.77–17.56)**

  2013–2014a

6.7% (1/15)

1.63 (0.20–13.01)

1.63 (0.20–12.94)

  2014–2015a

22.2% (6/27)

5.42 (1.78–16.47)**

5.27 (1.73–16.03)**

  2015–2016a

21.7% (5/23)

5.30 (1.67–16.87)**

5.24 (1.64–16.78)**

aIn each period, the starting date is 11 March

*<0.05, **<0.01

Patient interval, instead of date of first symptom recognition, was reported in the majority of patient records. In such cases, the reported interval was directly collected from the patient records. We summarize sociodemographic factors, patient interval and date of first presentation for patients with excessive patient delay for both pre- and post-disaster in Table 4. Among the 18 patients with excessive patient delay in the post-disaster group, 27.8% (5/18) discovered their symptoms before the disaster.
Table 4

Profiles of patients with excessive patient delay pre- and post-disaster

Patient

Age

First presentation

Patient interval

Residential areaa

Full-time job

Cohabitant children

Pre-disaster

 1

(50–65]

Apr.-Jun. 2007

120 months

Voluntary evacuation zone

Yes

No

 2

(65-

Apr.-Jun. 2008

48 months

Voluntary evacuation zone

No

No

 3

(50–65]

Sep.-Nov. 2008

18 months

Voluntary evacuation zone

No

No

 4

(65-

Dec. 2008-Feb. 2009

120 months

Non-evacuation zone

No

Yes

 5

−50]

Sep.-Nov. 2009

36 months

Mandatory evacuation zone

Yes

Yes

Post-disaster

 1

(50–65]

Dec. 2011-Feb. 2012

24 months

Non-evacuation zone

No

Yes

 2

(65-

Jun.-Aug. 2012

18 months

Non-evacuation zone

No

No

 3

(50–65]

Sep.-Nov. 2012

17 months

Voluntary evacuation zone

Yes

No

 4

(65-

Sep.-Nov. 2012

12 months

Voluntary evacuation zone

No

No

 5

−50]

Dec. 2012-Feb. 2013

12 months

Voluntary evacuation zone

No

No

 6

(65-

Dec. 2012-Feb. 2013

25 months

Voluntary evacuation zone

Yes

No

 7

(65-

Mar.-May 2013

24 months

Voluntary evacuation zone

No

No

 8

(65-

Mar.-May 2014

12 months

Non-evacuation zone

No

No

 9

−50]

Jun.-Aug. 2014

13 months

Non-evacuation zone

Yes

Yes

 10

(65-

Sep.-Nov. 2014

24 months

Voluntary evacuation zone

No

No

 11

(65-

Sep.-Nov. 2014

84 months

Voluntary evacuation zone

No

Yes

 12

−50]

Sep.-Nov. 2014

24 months

Voluntary evacuation zone

No

No

 13

(65-

Dec. 2014-Feb. 2015

24 months

Voluntary evacuation zone

No

No

 14

(50–65]

Jun.-Aug. 2015

24 months

Mandatory evacuation zone

No

No

 15

(65-

Sep.-Nov. 2015

24 months

Voluntary evacuation zone

No

No

 16

(65-

Sep.-Nov. 2015

120 months

Voluntary evacuation zone

No

No

 17

(50–65]

Sep.-Nov. 2015

13 months

Mandatory evacuation zone

No

Yes

 18

(50–65]

Dec. 2015-Feb. 2016

12 months

Voluntary evacuation zone

Yes

No

aFor pre-disaster patients, this indicates place of residence at the time of first medical consultation, and for post-disaster patients, indicates place of residence at the time of Japan’s triple disaster

Factors related to post-disaster patient delay

The results of univariate logistic regression analysis for pre- and post-disaster total patient delay are shown in Tables 5 and 6, respectively. In the pre-disaster period, no associations between total patient delay and access-related factors, sociodemographic factors, or other clinical characteristics studied were observed. In the post-disaster period, none of access- and disaster-related factors and sociodemographic factors were significantly associated with experiencing total patient delay, however a significant association was observed with having a family history of any cancer (odds ratio: 0.38, 95% CI: 0.15–0.95, p < 0.50). Due to lack of significant variables, we did not conduct multivariate logistic regression analysis for total patient delay.
Table 5

Pre-disaster odds ratios for patient delay (univariate regressions)

Variable

Odds Ratio (95% CI)

Patients analyzed (No.)

Hospital

 

122

 Watanabe Hospital

Ref.

108

 Minamisoma Municipal General Hospital

1.28 (0.32–5.02)

14

Distance from hospital

1.02 (0.96–1.08)

122

Referral from other medical providers

 

122

 No

Ref.

67

 Yes

1.99 (0.78–5.10)

55

Days from first medical consultation to first examination

1.00 (0.98–1.02)

122

Residential areaa

 

122

 Non-evacuation zone of So-so District

Ref.

13

 Voluntary evacuation zone

2.69 (0.32–22.56)

71

 Mandatory evacuation zone

3.20 (0.36–28.42)

38

Age

 

122

 –50]

Ref.

25

 (50–65]

0.82 (0.23–2.94)

41

 (65–

0.87 (0.26–2.87)

56

Engaged in a full-time job

 

122

 No

Ref.

98

 Yes

1.25 (0.41–3.82)

24

Number of cohabitant family members

 

117

 0–1

Ref.

44

 2–3

1.95 (0.58–6.55)

40

 More than 4

2.50 (0.73–8.50)

33

Living with a partner

 

116

 No

Ref.

50

 Yes

1.01 (0.39–2.63)

66

Living with children

 

116

 No

Ref.

44

 Yes

0.99 (0.37–2.62)

72

Lump

 

122

 No

Ref.

16

 Yes

0.95 (0.25–3.65)

106

Hormone receptor

 

117

 Negative

Ref.

37

 Positive

1.19 (0.42–3.37)

80

Stage

 

122

 0

Ref.

11

 1

2.22 (0.24–20.83)

33

 2

1.71 (0.19–15.51)

48

 3 or 4

3.64 (0.40–33.12)

30

ASA physical classification system

 

122

 Normal healthy patient

Ref.

59

 Patient with mild systemic disease

1.39 (0.52–3.74)

50

 Patient with severe systemic disease

1.67 (0.38–7.27)

13

Body mass index (kg/m2)

 

118

 –25]

Ref.

75

 (25–30]

1.37 (0.46–4.07)

29

 (30–

2.10 (0.56–7.81)

14

History of benign breast disease

 

122

 No

Ref.

115

 Yes

0.75 (0.09–6.53)

7

Undertook mammography screening within past two years

 

122

 No

Ref.

107

 Yes

2.65 (0.80–8.72)

15

Family history of any cancer

 

118

 No

Ref.

68

 Yes

1.64 (0.63–4.22)

50

aThis indicates place of residence at the time of first medical consultation

Table 6

Post-disaster odds ratios for patient delay (univariate regressions)

Variable

Odds Ratio (95% CI)

Patients analyzed (No.)

Hospitala

 

97

 Watanabe Hospital

N/A

0

 Minamisoma Municipal General Hospital

N/A

97

Distance from hospital

0.96 (0.89–1.05)

97

Referral from other medical providers

 

97

 No

Ref.

61

 Yes

1.58 (0.64–3.89)

33

Days from first medical consultation to first examination

1.00 (1.00–1.01)

96

Residential areab

 

97

 Non-evacuation zone of So-so District

Ref.

15

 Voluntary evacuation zone

1.26 (0.36–4.40)

70

 Mandatory evacuation zone

0.92 (0.16–5.21)

12

Age

 

97

 –50]

Ref.

16

 (50–65]

1.50 (0.39–5.75)

33

 (65–

1.24 (0.34–4.49)

48

Engaged in a full-time job

 

97

 No

Ref.

75

 Yes

1.47 (0.54–4.01)

22

Number of cohabitant family members

 

97

 0–1

Ref.

49

 2–3

0.68 (0.26–1.77)

34

 More than 4

0.51 (0.13–2.09)

14

Living with a partner

 

97

 No

Ref.

34

 Yes

1.29 (0.51–3.27)

63

Living with children

 

97

 No

Ref.

51

 Yes

0.58 (0.24–1.40)

46

Lump

 

97

 No

Ref.

6

 Yes

0.19 (0.03–1.10)

91

Hormone receptor

 

97

 Positive

Ref.

89

 Negative

0.69 (0.15–3.09)

8

Stage

 

92

 0

N/A

5

 1

N/A

29

 2

N/A

42

 3 or 4

N/A

21

ASA physical classification system

 

97

 Normal healthy patient

Ref.

40

 Patient with mild systemic disease

1.27 (0.49–3.29)

47

 Patient with severe systemic disease

3.00 (0.72–12.55)

10

Body mass index (kg/m2)

 

96

 –25]

Ref.

62

 (25–30]

1.71 (0.67–4.39)

28

 (30–

0.53 (0.06–4.87)

6

History of benign breast disease

 

97

 No

Ref.

91

 Yes

1.19 (0.20–6.86)

6

Undertook mammography screening within past two years

 

97

 No

Ref.

85

 Yes

0.76 (0.19–3.02)

12

Family history of any cancer

 

97

 No

Ref.

51

 Yes

0.38 (0.15–0.95)*

46

Air dose rate [μSv/h]

0.71 (0.05–10.03)

97

aOdds ratio was not calculated because Watanabe Hospital stopped inpatient oncology services post-disaster

bThis indicates place of residence at the time of Japan’s triple disaster

*<0.05

We attempted to conduct logistic regression analyses for excessive patient delay in respective pre- and post-disaster periods. However, it was difficult to establish a stable model of pre-disaster excessive patient delay owing to limited numbers of participants with this delay. We present characteristics of pre- and post-disaster patients stratified by with or without excessive and total patient delay in Additional files 1 and 2. Only 22.2% (4/18) of post-disaster patients with excessive patient delay lived with their children, compared to 53.2% (42/79) of those without excessive patient delay.

Discussion

In this long-term retrospective study of 219 patients with symptomatic breast cancer in an area severely damaged by the 2011 triple disaster, we found an increased risk of patient delay among post-disaster patients compared to those pre-disaster, a trend which has continued for five years after the disaster. Additionally, a smaller proportion of the patients living with children and lower median number of cohabitant family members were observed post-disaster, compared with the pre-disaster period. However, we could not elucidate contributing or protective factors for post-disaster patient delay, aside from a family history of cancer (Tables 5 and 6).

The extent of increased risk in both total and excessive patient delay after the disaster was prominent. Although the proportion of those with total patient delay was 18.0% pre-disaster, a similar range as that observed in routine clinical settings of high-income countries (HICs) [5, 43], it reached 29.9% post-disaster, a level compatible to that observed in low- and middle-income countries (LMICs) [3]. Furthermore, 18.6% of all post-disaster patients experienced excessive patient delay, compared to only 4.1% pre-disaster. Generally, the definition of excessive patient delay is adopted in studies conducted in LMICs [4, 6, 39], as this type of prolonged delay occurs more frequently in LMICs compared to HICs [3]. However, we hypothesize that irregular circumstances, such as disasters, could cause excessive patient delay even in HICs, as suggested by the results of this study. It is to be noted that possible reasons for patient delay in post-disaster Fukushima could be different from those in LMICs, where limited access to health care and poor knowledge or awareness of breast cancer are regarded as the primary factors contributing to patient delay [3, 4]. The potential contributing factors to patient delay in our study are discussed in the following sections, including health care access, social support from family members, and psychosocial stress.

The present findings are in line with previous studies which showed a devastating impact of mass disasters on general aspects of cancer management [12, 13, 4446]. Yet, while these reports primarily focused on the immediate aftermath of disasters [12, 13, 44, 45], our study revealed a long-lasting effect of the triple disaster on those with breast cancer in affected areas. Moreover, although immediate deterioration in healthcare access has been underlined in previous studies [13, 44, 46], breast cancer oncology services were essentially recovered and maintained in So-so District from three months post-disaster. Indeed, healthcare access, measured as linear distance between one’s residence and hospital of first medical consultation (MMGH or WH), referral from other medical providers, or interval from first medical consultation to first breast cancer specific examination, did not differ significantly pre- and post-disaster despite closure of oncology service in WH. Furthermore, no associations were found between these variables and total patient delay. It is additionally to be noted that Minamisoma City has continuously provided mammography screening to local residents throughout the post-disaster period. It can therefore be argued that, rather than changes in healthcare access, alternative mechanisms may have contributed to patient delay among post-disaster breast cancer patients in the present study.

It is notable that there was a significant decrease in proportion of those living with children (47.4% vs. 59.0%, p < 0.05) and the median number of cohabitant family members (1 vs. 2, p < 0.05) post-disaster compared with pre-disaster. There findings are compatible with demographic changes that Minamisoma City has experienced post-disaster [22]. It is true that all regression results regarding factors related to cohabitant status were null (Tables 5 and 6). However, it can be speculated that these post-disaster demographic changes may have contributed to decreased social support patients could acquire from family members, increasing the risk of experiencing patient delay among this population. In fact, previous studies have demonstrated the importance of social support to the process of seeking medical attention in disaster settings [13, 47, 48]. In particular, social support from children, rather than partners, may lessen the risk of patient delay and improve the prognosis of breast cancer patients [7, 49]. In the post-disaster period, only 22.2% (4/18) of patients with excessive patient delay lived with their children, compared to 53.2% (42/79) of patients without excessive patient delay (Additional file 1), further supporting our hypothesized relationship between poor social support and patient delay after the disaster.

We could not find any associations between post-disaster patient delay and variables, such as evacuation status and air dose rate at residence. We speculated that these variables could reflect post-disaster psychosocial stress among breast cancer patients, because they were found to be associated with psychosocial stress in Fukushima residents in a previous study [35]. However, it is possible that psychosocial stress may not have been captured accurately by these markers in the present study. Obtaining reliable information on radiation risks, such as environmental radiation level at one’s residence, may have been challenging for local residents, as repeatedly suggested in previous studies [11, 25, 50]. Resultantly, It is possible that local residents of Minamisoma City may feel anxiety over potential radiation exposure, regardless of individual measurement results in their area [23, 51]. Additionally, there have been several hypotheses made for sources of psychosocial stressors after the Fukushima nuclear incident, apart from radiation exposure, as follows; decades of work expected to be necessary for decommissioning the FNDPP reactor [52], discordant perceptions of radiation risks among families and communities, and community tensions which have occurred as a result of disparities in governmental compensations and restrictions [25, 53]. In order to comprehensively evaluate any relationships between psychosocial stress and patient delay after this disaster, a qualitative method may be useful, as its efficacy has been demonstrated in multiple previous studies on breast cancer patient delay in general settings [2, 4, 8].

There was a significantly higher proportion of HR-positive breast cancer after the disaster, compared with the pre-disaster period (91.8% vs. 65.6%, p < 0.001). Breast cancer can be affected by radiation exposure [54], and its incidence may increase 10–15 years following serious radiation exposure [54], as seen in the areas seriously contaminated after the Chernobyl nuclear power plant accident [55]. However, the World Health Organization and the United Nations Scientific Committee on the Effects of Atomic Radiation have recently concluded that lifetime risk of developing cancer is marginal among adults in the general public exposed to the Fukushima nuclear disaster [56, 57]. Therefore, it appears unlikely that radiation exposure would have caused the increased proportions of HR positivity in the present study. Notably, while 88.5% of pre-disaster patients first visited WH, all of the post-disaster patients visited MMGH. Taking into account the changes local breast cancer oncology service has experienced, discordance in interpreting and reproducing HR positivity among antibodies utilized in IHC analysis and pathologists may have contributed to the present findings [58, 59].

We find it possible that our present findings on patient delay may be applicable to other types of cancer in disaster settings, as social disruption and psychosocial stress among cancer patients may not be limited to breast cancer in such situations [13, 47]. Notably, studies performed in non-disaster settings have suggested that patients with breast cancer were least likely to delay their presentation compared to those with colorectal, urological, gynecological or hematological cancer [60], and that that social support and emotional health of cancer patients can widely influence how soon they make first medical consultations, regardless of cancer type [6163]. It may be therefore reasonable to hypothesize that patients with other types of cancer may delay their first medical consultation to the same extent or more than breast cancer patients in disaster settings. We suggest that further studies should be conducted to assess applicability of the present results to other cancers.

Limitations

This study has several limitations. First, it is possible that selection bias may have influenced our results. A potential effect of post-disaster demographic changes, primarily due to mass-evacuation among young- and middle-aged generations, should be acknowledged as it may have resulted in differences between the pre- and post-disaster populations of So-So district. For example, if younger residents evacuated and did not return, an older population may naturally have led to larger proportions of patient delay [7, 8], which could have led to an overestimation of our results. However, aside from differences in areas of residential address and cohabitant status, we could find no significant differences in demographics of pre- and post-disaster participants, and it is particularly notable that there were no changes in the age distribution of patients in pre- and post-disaster periods. Second, although we included as many breast cancer patients as possible in the affected areas based on the data from MMGH and WH, there was still a limited sample size that provided low power for analyses and the observed results may have been affected by random error. Third, in most cases, patient intervals were reported instead of date of symptom recognition. As patient intervals lengthened, these variables may be more easily affected by recall biases. Finally, we could not evaluate some factors known to be associated with patient delay but not reported in patient records, including breast cancer knowledge, personalities, and sources of social support apart from family members [4, 8, 49].

Conclusions

This is the first study to assess the impact of the 2011 triple disaster on cancer patients in a severely affected area of Fukushima. The risk of experiencing patient delay was significantly higher post-disaster, compared to the pre-disaster period, and this trend has continued for five years following the disaster. This study presents unprecedented information on breast cancer patient delay in the long-term aftermath of a disaster.

Abbreviations

ASA: 

American Society of Anesthesiologists

BMI: 

Body mass index

CI: 

Confidence Interval

FDNPP: 

Fukushima Daiichi Nuclear Power Plant

HICs: 

High-income counties

LMICs: 

Low- and middle-income countries

HR: 

Hormone receptor

IHC: 

Immunohistochemical

MEXT: 

Ministry of Education, Culture, Sports, Science, and Technology

MMGH: 

Minamisoma Municipal General Hospital

RR: 

Risk ratio

WH: 

Watanabe Hospital

Declarations

Acknowledgements

We do not have any funding. We express our gratitude to Mr. Kiyotaka Sawa, Mr. Yasuhito Shirose, Ms. Hiromi Suenaga, Ms. Yumiko Kohata, and the staff of Watanabe Hospital and Minamisoma Municipal General Hospital for data collection and management. We also would like to thank Dr. Michio Murakami and Mr. Masatsugu Tanaki, for their constructive opinions on this study.

Funding

Not applicable.

Availability of data and materials

The datasets analysed during the current study are available from the corresponding author on reasonable request.

Authors’ contributions

AO, MasT, MK, and HO contributed to conception and design of the study. AO, TY, TS, ManT, TN, and YK contributed to data collection and assembly. SN analyzed the data. CL TT, SS, TM, SO, and SK contributed to interpretation of the analyzed data. AO wrote the manuscript. All the authors contributed to critical revision of the paper. All authors read and approved the final manuscript.

Competing interests

None declared.

Consent for publication

Not applicable.

Ethics approval and consent to participate

The study was conducted according to the principles described in the Declaration of Helsinki, and the Minamisoma Municipal General Hospital Institutional Review Board provided ethical approval for this study, on 21 May 2015 (Approval number: 27–03). Written informed consent from participants was waived by the committee, as this was a retrospective study of existing records. All data were anonymised prior to analysis.

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Authors’ Affiliations

(1)
Department of Surgery, Minamisoma Municipal General Hospital
(2)
Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London
(3)
Department of Research, Minamisoma Municipal General Hospital
(4)
Department of Radiation Protection, Minamisoma Municipal General Hospital
(5)
Department of Internal Medicine, Jyoban Hospital of Tokiwa Foundation
(6)
Department of Medical Oncology, Fukushima Medical University
(7)
Department of Internal Medicine, Soma Central Hospital
(8)
Research Institute of Innovative Medicine, Jyoban Hospital of Tokiwakai Group
(9)
Medical Governance Research Institute
(10)
Department of Home Medical Care, Minamisoma Municipal General Hospital
(11)
Department of Gastroenterology, Minamisoma Municipal General Hospital
(12)
Department of Epidemiology and Biostatistics, Teikyo University Graduate School of Public Health
(13)
Department of Global Health Policy, Graduate School of Medicine, The University of Tokyo

References

  1. Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. Global cancer statistics, 2012. CA Cancer J Clin. 2015;65:87–108.View ArticlePubMedGoogle Scholar
  2. Khakbazan Z, Taghipour A, Latifnejad Roudsari R, Mohammadi E. Help seeking behavior of women with self-discovered breast cancer symptoms: a meta-ethnographic synthesis of patient delay. PLoS One. 2014;9:e110262.View ArticlePubMedPubMed CentralGoogle Scholar
  3. Unger-Saldana K. Challenges to the early diagnosis and treatment of breast cancer in developing countries. World J Clin Oncol. 2014;5:465–77.View ArticlePubMedPubMed CentralGoogle Scholar
  4. Unger-Saldana K, Infante-Castaneda CB. Breast cancer delay: a grounded model of help-seeking behaviour. Soc Sci Med. 2011;72:1096–104.View ArticlePubMedGoogle Scholar
  5. Arndt V, Sturmer T, Stegmaier C, Ziegler H, Dhom G, Brenner H. Patient delay and stage of diagnosis among breast cancer patients in Germany -- a population based study. Br J Cancer. 2002;86:1034–40.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Richards MA, Westcombe AM, Love SB, Littlejohns P, Ramirez AJ. Influence of delay on survival in patients with breast cancer: a systematic review. Lancet. 1999;353:1119–26.View ArticlePubMedGoogle Scholar
  7. Ramirez AJ, Westcombe AM, Burgess CC, Sutton S, Littlejohns P, Richards MA. Factors predicting delayed presentation of symptomatic breast cancer: a systematic review. Lancet. 1999;353:1127–31.View ArticlePubMedGoogle Scholar
  8. Lam WW, Tsuchiya M, Chan M, Chan SW, Or A, Fielding R. Help-seeking patterns in Chinese women with symptoms of breast disease: a qualitative study. J Public Health (Oxf). 2009;31:59–68.View ArticleGoogle Scholar
  9. Burgess C, Hunter MS, Ramirez AJ. A qualitative study of delay among women reporting symptoms of breast cancer. Br J Gen Pract. 2001;51:967–71.PubMedPubMed CentralGoogle Scholar
  10. Arrieta MI, Foreman RD, Crook ED, Icenogle ML. Providing continuity of care for chronic diseases in the aftermath of Katrina: from field experience to policy recommendations. Disaster Med Public Health Prep. 2009;3:174–82.View ArticlePubMedPubMed CentralGoogle Scholar
  11. Brumfiel G. Fukushima: fallout of fear. Nature. 2013;493:290–3.View ArticlePubMedGoogle Scholar
  12. Advisory HKC, Kessler RC. Hurricane Katrina's impact on the care of survivors with chronic medical conditions. J Gen Intern Med. 2007;22:1225–30.View ArticleGoogle Scholar
  13. Twombly R. Cancer Community offers unprecedented support after hurricanes slam U.S. Gulf Coast. J Natl Cancer Inst. 2005;97:1716–8.View ArticlePubMedGoogle Scholar
  14. Shibahara S. The 2011 Tohoku earthquake and devastating tsunami. Tohoku J Exp Med. 2011;223:305–7.View ArticlePubMedGoogle Scholar
  15. Brumfiel G, Cyranoski D. Quake sparks nuclear crisis. Nature. 2011;471:273–5.View ArticlePubMedGoogle Scholar
  16. Zhang H, Yan W, Oba A, Zhang W. Radiation-driven migration: the case of Minamisoma City, Fukushima, Japan, after the Fukushima nuclear accident. Int J Environ Res Public Health. 2014;11:9286–305.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Imanaka T, Endo S, Sugai M, Ozawa S, Shizuma K, Yamamoto M. Early radiation survey of Iitate village, which was heavily contaminated by the Fukushima Daiichi accident, conducted on 28 and 29 March 2011. Health Phys. 2012;102:680–6.View ArticlePubMedGoogle Scholar
  18. Kamada N, Saito O, Endo S, Kimura A, Shizuma K. Radiation doses among residents living 37 km northwest of the Fukushima Dai-ichi nuclear power plant. J Environ Radioact. 2012;110:84–9.View ArticlePubMedGoogle Scholar
  19. Nomura S, Blangiardo M, Tsubokura M, Ozaki A, Morita T, Hodgson S. Postnuclear disaster evacuation and chronic health in adults in Fukushima. Japan: a long-term retrospective analysis BMJ Open. 2016;6:e010080.PubMedGoogle Scholar
  20. Nuclear Regulation Authority. Conceptual diagram of evacuation-directed zones and the number of popluation and households of each region (as of the end of December 2013). 2013. http://www.meti.go.jp/earthquake/nuclear/pdf/131231a.pdf. Accessed 27 August 2016.
  21. Nuclear Regulation Authority. Conceptual diagram of evacuation-directed zones and the number of population and households of each region (as of 5 September 2015). 2015. http://www.meti.go.jp/earthquake/nuclear/kinkyu/hinanshiji/2015/pdf/0905_02gainenzu.pdf. Accessed 27 August 2016.
  22. Minamisoma City Office. Minamisoma Statistical Yearbook (In Japanese). 2016. http://www.city.minamisoma.lg.jp/index.cfm/8,4226,c,html/4226/20160328-153234.pdf. Accessed 10 August 2016.
  23. Hayano RS, Tsubokura M, Miyazaki M, Ozaki A, Shimada Y, Kambe T, et al. Whole-body counter surveys of over 2700 babies and small children in and around Fukushima prefecture 33 to 49 months after the Fukushima Daiichi NPP accident. Proc Jpn Acad Ser B Phys Biol Sci. 2015;91:440–6.View ArticlePubMedPubMed CentralGoogle Scholar
  24. Suzuki Y, Yabe H, Yasumura S, Ohira T, Niwa S, Ohtsuru A, et al. Psychological distress and the perception of radiation risks: the Fukushima health management survey. Bull World Health Organ. 2015;93:598–605.View ArticlePubMedPubMed CentralGoogle Scholar
  25. Hasegawa A, Tanigawa K, Ohtsuru A, Yabe H, Maeda M, Shigemura J, et al. Health effects of radiation and other health problems in the aftermath of nuclear accidents, with an emphasis on Fukushima. Lancet. 2015;386:479–88.View ArticlePubMedGoogle Scholar
  26. American Society of Anesthesiologists. ASA Physical Status Classification System. 2014. https://www.asahq.org/resources/clinical-information/asa-physical-status-classification-system. Accessed 20 July 2016.
  27. Taib NA, Yip CH, Low WY. Recognising symptoms of breast cancer as a reason for delayed presentation in Asian women--the psycho-socio-cultural model for breast symptom appraisal: opportunities for intervention. Asian Pac J Cancer Prev. 2011;12:1601–8.PubMedGoogle Scholar
  28. O'Mahony M, Hegarty J, McCarthy G. Women's help seeking behaviour for self discovered breast cancer symptoms. Eur J Oncol Nurs. 2011;15:410–8.View ArticlePubMedGoogle Scholar
  29. Chu KC, Anderson WF, Fritz A, Ries LA, Brawley OW. Frequency distributions of breast cancer characteristics classified by estrogen receptor and progesterone receptor status for eight racial/ethnic groups. Cancer. 2001;92:37–45.View ArticlePubMedGoogle Scholar
  30. Aitken-Swan J, Paterson R. The cancer patient: delay in seeking advice. BMJ. 1955;1:623–7.View ArticlePubMedPubMed CentralGoogle Scholar
  31. Hammond ME, Hayes DF, Dowsett M, Allred DC, Hagerty KL, Badve S, et al. American Society of Clinical Oncology/college of American pathologists guideline recommendations for immunohistochemical testing of estrogen and progesterone receptors in breast cancer. J Clin Oncol. 2010;28:2784–95.View ArticlePubMedPubMed CentralGoogle Scholar
  32. Sauvaget C, Nishino Y, Konno R, Tase T, Morimoto T, Hisamichi S. Challenges in breast and cervical cancer control in Japan. Lancet Oncol. 2016;17:e305–12.View ArticlePubMedGoogle Scholar
  33. Arndt V, Sturmer T, Stegmaier C, Ziegler H, Becker A, Brenner H. Provider delay among patients with breast cancer in Germany: a population-based study. J Clin Oncol. 2003;21:1440–6.View ArticlePubMedGoogle Scholar
  34. Nosarti C, Crayford T, Roberts JV, Elias E, McKenzie K, David AS. Delay in presentation of symptomatic referrals to a breast clinic: patient and system factors. Br J Cancer. 2000;82:742–8.View ArticlePubMedPubMed CentralGoogle Scholar
  35. Kunii Y, Suzuki Y, Shiga T, Yabe H, Yasumura S, Maeda M, et al. Severe psychological distress of evacuees in evacuation zone caused by the Fukushima Daiichi nuclear power plant accident: the Fukushima health management survey. PLoS One. 2016;11:e0158821.View ArticlePubMedPubMed CentralGoogle Scholar
  36. Ikegami N, Yoo BK, Hashimoto H, Matsumoto M, Ogata H, Babazono A, et al. Japanese universal health coverage: evolution, achievements, and challenges. Lancet. 2011;378:1106–15.View ArticlePubMedGoogle Scholar
  37. Ministry of Healh, Labour and Welfare. Information for the Greast East Japan Earthquake (In Japanese). http://www.mhlw.go.jp/shinsai_jouhou/iryouhoken.html. Accessed 27 August 2016.
  38. Pater JL, Loeb M, Siu TO. A multivariate analysis of the contribution of "auxometry" to prognosis in breast cancer. J Chronic Dis. 1979;32:375–84.View ArticlePubMedGoogle Scholar
  39. Dye TD, Bogale S, Hobden C, Tilahun Y, Deressa T, Reeler A. Experience of initial symptoms of breast cancer and triggers for action in ethiopia. Int J Breast Cancer. 2012;2012:908547.View ArticlePubMedPubMed CentralGoogle Scholar
  40. Nomura S, Tsubokura M, Furutani T, Hayano RS, Kami M, Kanazawa Y, et al. Dependence of radiation dose on the behavioral patterns among school children: a retrospective analysis 18 to 20 months following the 2011 Fukushima nuclear incident in Japan. J Radiat Res. 2016;57:1–8.View ArticlePubMedGoogle Scholar
  41. Ministry of Education, Culture, Sports, Science and Technology. Results of airborne monitoring survey (In Japanese). http://radioactivity.nsr.go.jp/ja/list/362/list-1.html. Accessed 27 August 2016.
  42. Ministry of Land, Infrastructure and Transport. Japan Profile for Geographic Information Standards Tokyo. http://www.gsi.go.jp/ENGLISH/page_e30210.html. Accessed 27 August 2016.
  43. Montella M, Crispo A, D'Aiuto G, De Marco M, de Bellis G, Fabbrocini G, et al. Determinant factors for diagnostic delay in operable breast cancer patients. Eur J Cancer Prev. 2001;10:53–9.View ArticlePubMedGoogle Scholar
  44. Imamura CK, Ueno NT. Health services: how can we address cancer care after a natural disaster? Nat Rev Clin Oncol. 2011;8:387–8.View ArticlePubMedGoogle Scholar
  45. Harmon RE, Boulmay BC. Restoration of medical oncology services at LSU interim public Hospital in new Orleans after Hurricane Katrina: a two-year experience of LSUHSC. J La State Med Soc. 2011;163:144–7.PubMedGoogle Scholar
  46. Loehn B, Pou AM, Nuss DW, Tenney J, McWhorter A, DiLeo M, et al. Factors affecting access to head and neck cancer care after a natural disaster: a post-Hurricane Katrina survey. Head Neck. 2011;33:37–44.View ArticlePubMedGoogle Scholar
  47. van Meerveld J. The continuing anguish of a lucky evacuee. Health Aff (Millwood). 2006;25:489–90.View ArticleGoogle Scholar
  48. Ozaki A, Leppold C, Tsubokura M, Tanimoto T, Saji S, Kato S et al. Social isolation and cancer management after the 2011 triple disaster in Fukushima, Japan: A case report of breast cancer with patient and provider delay. Medicine (Baltimore). 2016;95:e4027.Google Scholar
  49. Kroenke CH, Kubzansky LD, Schernhammer ES, Holmes MD, Kawachi I. Social networks, social support, and survival after breast cancer diagnosis. J Clin Oncol. 2006;24:1105–11.View ArticlePubMedGoogle Scholar
  50. Gouweloos J, Duckers M, te Brake H, Kleber R, Drogendijk A. Psychosocial care to affected citizens and communities in case of CBRN incidents: a systematic review. Environ Int. 2014;72:46–65.View ArticlePubMedGoogle Scholar
  51. Tsubokura M, Kato S, Morita T, Nomura S, Kami M, Sakaihara K, et al. Assessment of the annual additional effective doses amongst Minamisoma children during the second year after the Fukushima Daiichi nuclear power plant disaster. PLoS One. 2015;10:e0129114.View ArticlePubMedPubMed CentralGoogle Scholar
  52. Secretariat for Contaminated Water and Decommissioning Issues Team, Cabinet Office, Government of Japan. Revision of the Mid-and-Long-Term Roadmap. 2015. http://www.meti.go.jp/english/earthquake/nuclear/decommissioning/pdf/20150725_01a.pdf. Accessed 27 August 2016.
  53. Bromet EJ, Havenaar JM, Guey LT. A 25 year retrospective review of the psychological consequences of the Chernobyl accident. Clin Oncol (R Coll Radiol). 2011;23:297–305.View ArticleGoogle Scholar
  54. Kamiya K, Ozasa K, Akiba S, Niwa O, Kodama K, Takamura N, et al. Long-term effects of radiation exposure on health. Lancet. 2015;386:469–78.View ArticlePubMedGoogle Scholar
  55. Pukkala E, Kesminiene A, Poliakov S, Ryzhov A, Drozdovitch V, Kovgan L, et al. Breast cancer in Belarus and Ukraine after the Chernobyl accident. Int J Cancer. 2006;119:651–8.View ArticlePubMedGoogle Scholar
  56. World Health Organization. Health risk assessment from the nuclear accident after the 2011 great East Japan earthquake and tsunami, based on a preliminary dose estimation. Geneva: World Health Organization; 2013.Google Scholar
  57. United Nations Scientific Committee on the Effects of Atomic Radiation. Annex a: levels and effects of radiation exposure due to the nuclear accident after the 2011 great east-Japan earthquake and tsunami. New York: United Nations Scientific Committee on the Effects of Atomic Radiation; 2014.Google Scholar
  58. Cheang MC, Treaba DO, Speers CH, Olivotto IA, Bajdik CD, Chia SK, et al. Immunohistochemical detection using the new rabbit monoclonal antibody SP1 of estrogen receptor in breast cancer is superior to mouse monoclonal antibody 1D5 in predicting survival. J Clin Oncol. 2006;24:5637–44.View ArticlePubMedGoogle Scholar
  59. Chebil G, Bendahl PO, Ferno M. South Sweden breast cancer group. North Sweden Breast Cancer Group Estrogen and progesterone receptor assay in paraffin-embedded breast cancer--reproducibility of assessment Acta Oncol. 2003;42:43–7.PubMedGoogle Scholar
  60. Forbes LJ, Warburton F, Richards MA, Ramirez AJ. Risk factors for delay in symptomatic presentation: a survey of cancer patients. Br J Cancer. 2014;111:581–8.View ArticlePubMedPubMed CentralGoogle Scholar
  61. McCutchan GM, Wood F, Edwards A, Richards R, Brain KE. Influences of cancer symptom knowledge, beliefs and barriers on cancer symptom presentation in relation to socioeconomic deprivation: a systematic review. BMC Cancer. 2015;15:1000.View ArticlePubMedPubMed CentralGoogle Scholar
  62. Hubbard G, Macmillan I, Canny A, Forbat L, Neal RD, O'Carroll RE, et al. Cancer symptom awareness and barriers to medical help seeking in Scottish adolescents: a cross-sectional study. BMC Public Health. 2014;14:1117.View ArticlePubMedPubMed CentralGoogle Scholar
  63. Pinquart M, Duberstein PR. Associations of social networks with cancer mortality: a meta-analysis. Crit Rev Oncol Hematol. 2010;75:122–37.View ArticlePubMedGoogle Scholar

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