Sex/Gender and Socioeconomic Differences in thePredictive Ability of Self-Rated Health for Mortality
Akihiro Nishi1,2,3*, Ichiro Kawachi1, Kokoro Shirai1,4,5, Hiroshi Hirai6, Seungwon Jeong7, Katsunori
Kondo7
1 Department of Society, Human Development, and Health, Harvard School of Public Health, Boston, Massachusetts, United States of America, 2 Institute for Quantitative
Social Science, Harvard University, Boston, Massachusetts, United States of America, 3 Department of Public Health, Graduate School of Medicine, The University of Tokyo,
Tokyo, Japan, 4 Department of Global Health and Population, Harvard School of Public Health, Boston, Massachusetts, United States of America, 5 Department of Human
Sciences, School of Law and Letters, University of the Ryukyus, Okinawa, Japan, 6 Iwate University Faculty of Engineering, Iwate University, Morioka, Japan, 7 Center for
Well-being and Society, Nihon Fukushi University, Nagoya, Japan

Abstract
Background: Studies have reported that the predictive ability of self-rated health (SRH) for mortality varies by sex/genderand socioeconomic group. The purpose of this study is to evaluate this relationship in Japan and explore the potential
reasons for differences between the groups.
Methodology/Principal Findings: The analyses in the study were based on the Aichi Gerontological Evaluation Study’s(AGES) 2003 Cohort Study in Chita Peninsula, Japan, which followed the four-year survival status of 14,668 communitydwelling people who were at least 65 years old at the start of the study. We first examined sex/gender and education-level
differences in association with fair/poor SRH. We then estimated the sex/gender- and education-specific hazard ratios (HRs)of mortality associated with lower SRH using Cox models. Control variables, including health behaviors (smoking anddrinking), symptoms of depression, and chronic co-morbid conditions, were added to sequential regression models. The
results showed men and women reported a similar prevalence of lower SRH. However, lower SRH was a stronger predictor
of mortality in men (HR = 2.44 [95% confidence interval (CI): 2.14–2.80]) than in women (HR = 1.88 [95% CI: 1.44–2.47]; p forsex/gender interaction = 0.018). The sex/gender difference in the predictive ability of SRH was progressively attenuated withthe additional introduction of other co-morbid conditions. The predictive ability among individuals with high school
education (HR = 2.39 [95% CI: 1.74–3.30]) was similar to that among individuals with less than a high school education(HR = 2.14 [95% CI: 1.83–2.50]; p for education interaction = 0.549).
Conclusions: The sex/gender difference in the predictive ability of SRH for mortality among this elderly Japanese population
may be explained by male/female differences in what goes into an individual’s assessment of their SRH, with males
apparently weighting depressive symptoms more than females.
Citation: Nishi A, Kawachi I, Shirai K, Hirai H, Jeong S, et al. (2012) Sex/Gender and Socioeconomic Differences in the Predictive Ability of Self-Rated Health for
Mortality. PLoS ONE 7(1): e30179. doi:10.1371/journal.pone.0030179
Editor: Antje Timmer, Bremen Institute of Preventive Research and Social Medicine, Germany
Received June 8, 2011; Accepted December 14, 2011; Published January 19, 2012
Copyright: ß 2012 Nishi et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The AGES project is supported by the Ministry of Education, Culture, Sports, Science and Technology of Japan (the 21st Century COE Program ‘‘Asian
COE toward new policy science for social well-being and development’’ in Nihon Fukushi University, ‘‘Academic Frontier’’ Project, the Japan Society for the
Promotion of Science (Grants-in-Aid for Scientific Research: 14310105, 17730347, 18390200, 19122901), and Ministry of Health, Labour, and Welfare (Health
Labour Sciences Research Grant: H22-Choju-Shitei-008). The funders had no role in study design, data collection and analysis, decision to publish, or preparationof the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: anishi@hsph.harvard.edu

some studies have found that the ability of SRH to predict
subsequent mortality is higher among more educated individuals
compared to those with fewer years of schooling [10,18], although
not all studies have reported this result. In addition, some studies
have found the predictive ability of SRH for mortality to be higher
among men than women [9,10,14–16,19].
There are alternative explanations for the differential performance of SRH by population subgroups [18,20]. First, somegroups may be more attuned to their health status and, thus, able
to provide a more accurate, condensed assessment of their bodily
conditions. For example, if less educated individuals inaccurately
assess their health status (i.e., either subjectively over- or underrating their ‘‘true’’ health status), then such non-differential

Introduction
The single-item measure of self-rated health (SRH) (‘‘In general,
how do you rate your overall health? Excellent, good, fair, or
poor?’’) is perhaps the most widely adopted health-statusassessment approach in studies around the world [1–3]. Among
the reasons for its popularity are its brevity, test-retest reliability,
and criterion validity (i.e., its ability to predict subsequentmortality from the view that mortality is the most ‘‘objective’’measure of ‘‘true’’ health) [4–8].
More recently, however, studies have begun to focus on the
differential performance of the predictive ability of SRH for
mortality across population subgroups [3,6,9–19]. For example,
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overall health at the present time?’’ Four response options were
provided: excellent, good, fair, or poor. On the questionnaire, thisitem was the third question (following inquiries about the subject’s
sex/gender and age) to avoid anchoring or priming the questionby other questions related to health conditions (which were asked
about in later sections of the survey).
Co-morbidities: The same baseline self-administered questionnaire was used to measure other health conditions by asking
whether individuals were currently receiving medical attention fora variety of specific life-threatening physical conditions, including
cancer, heart disease, stroke, hypertension, diabetes mellitus,
hyperlipidemia, respiratory disease, or liver disease (respondents
answered yes or no to each). We additionally assessed daily living
activities (walking, bathing, and hygiene), as well as 10 non-lifethreatening physical conditions (obesity, osteoporosis, joint
disease/neurological pain, trauma/fracture, gastrointestinal disease, visual impairment, hearing impairment, urinary disorder,
sleep disorder, and other). These distinctions were based on aprevious study conducted in the Netherlands on the same issue[10]. Subjects’ mental-health status was assessed by the GeriatricDepression Scale (GDS-15) [23]. Health-related behaviors (smoking [never, before, currently] and alcohol consumption [no, notfrequently, frequently]) were also measured.
Socio-demographic characteristics: We included thefollowing socio-demographic characteristics in the study: age,sex/gender, education (,high school [,10 years of education],high school [10–12 years of education], and .high school [.12years of education]), and marital status (ever married or nevermarried).
Statistical analysis: We created logistic regression models to
explore the associations between SRH and sex/gender andeducation. Education (,high school or not) and SRH (fair/poor
or not) were dichotomized to allow us to apply a concise
interpretation of the interaction (product term). We conducted
the following sequence of regression analyses (Models 1A to 5A): in
Model 1A, we controlled for age only. In Model 2A, we controlled
for marital status and whether the subject smoke or drank, in
addition to age. In Model 3A, we added depression symptoms. In
Model 4A, we added 13 non-life-threatening health conditions.
Finally, in Model 5A, we added eight major life-threatening healthconditions.
To explore the SRH-mortality association, Cox models wereused to estimate hazard ratios (HRs) for mortality in the different
sex/gender and education-level subgroups. The proportionalityof all predictors was examined graphically beforehand using
log-minus-log survival plots. We then created Models 1B to 4B,
in which we added SRH as an exposure variable to Models 1A
to 4A.
In both the logistic regression and Cox models, we addedinteraction terms (male6,high school, fair/poor SRH6male, andfair/poor SRH6,high school) to the sets of explanatory
covariates to statistically test the marginal difference in the effectsize in the different sex/gender and education-level subgroups.
The relative risk reduction (i.e., explainable excessive risk)
resulting from the successive addition of covariates from HR
Model xB to HR Model (x+1)B was calculated as follows, wherex = 1, 2, 3, or 4 [24,25]:

exposure misclassifications will tend to attenuate the association of
SRH with the health outcome (mortality). Alternatively, different
population subgroups may be systematically biased in theirsubjective assessments. For example, if women are overly sensitive
to their somatic symptoms and exaggerate their health problems— while, conversely, men deny and downplay their problems ifthey are not severe or life-threatening — then one would expect tosee a stronger association between SRH and mortality amongmen. Spiers and her colleagues suggest that the sex/genderdifference in the SRH-mortality relationship is due to ‘‘variation in
the definitions that individuals call upon when rating their health,’’rather than to actual differences in physical conditions [14].
In short, researchers need to have a better understanding of thesources of the differential performance of SRH in tapping theunderlying health status that they are trying to capture. In thepresent study, we specifically focus on examining sex/gender andeducation-level differences in the relationship between SRH andmortality and on testing whether the differences in theseassociations could be explained by underlying variations in(objectively assessed) health conditions.
Based on previous reports in Western settings [18,19], we
hypothesized that we would find (1) a weaker association between
SRH and mortality among women compared to men and that thisfinding could be explained by a stronger link between SRH andnon-life-threatening physical conditions among women, and (2) astronger association between SRH and mortality among individuals with higher education levels.

Methods
Participants
The analyses in the study were based on an observational
prospective study, ‘‘AGES 2003 Cohort Study,’’ which is part of thelarger Aichi Gerontological Evaluation Study (AGES). Details of thisproject have been described elsewhere [21,22]. Briefly, we firstobtained approval for our study in six municipalities in Chita
Peninsula (south of Nagoya, the fourth largest city in Japan), Aichi
Prefecture. Using a cluster random sampling approach, we sampledpeople from the municipalities who were at least 65 years old and did
not need nursing care in 2003. A self-administered questionnaire wasmailed to eligible individuals in late 2003, and 14,804 individuals
returned the questionnaire. The enrollment rate was 50.4%, which isquite favorable compared to other cohort studies (for example, theNurses’ Health Study II had a baseline participation rate of around24%) (see official website of Nurses’ Health Study – accessed at
http://www.channing.harvard.edu/nhs/?page_id = 70). According
to the limited information available on non-respondents, which wasprovided from several of the six municipalities, there were no large
differences between respondents and non-respondents in terms ofage and gender, while people with a higher socioeconomic status
were more likely to respond (65.1% for the highest socioeconomic
status group; 49.5% for the lowest socioeconomic status group) [22].
The study protocol and informed consent procedure were approvedby the ethics committee in Research of Human Subjects at Nihon
Fukushi University. The vital status of subjects enrolled in the AGES
2003 cohort was obtained by matching them to the residential basic
database in local governments. We recorded subjects’ date of death
and any moves out of the residential area. After eliminating thesepeople, we followed up on the remaining 14,668 subjects for the next
1,461 days (48 months).

½ðHR Model xB{HR Model ðxz1ÞBÞ=ðHR Model 1B ðUnadjustedÞ{1Þ|100 ð%Þ

Measures
Self-rated health: Using the 2003 baseline questionnaire, we
assessed SRH using the question, ‘‘How would you rate your
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The assumption is that the risk reduction calculated from HR
Model xB to HR Model (x+1)B can be interpreted as the2

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Self-Rated Health and Mortality Association

contribution of the added covariates to the excess risk for mortality
associated with fair/poor SRH.
Clustered standard error was calculated in all of the analyses toaccount for clustering of respondents in the six sampled
municipalities [26]. A two-tailed p value of ,0.05 was considered
statistically significant. We used Stata/IC version 12.0 (StataCorp,
College Station, TX, USA).

completed schooling (95% CI: 1.30–1.47). However, after
adjusting for differences in chronic health conditions, this excess
risk was attenuated. This suggests that individuals with lesseducation have more co-morbid chronic conditions, which is
identical to the observation in Table 1.

SRH-mortality associations
During the four-year follow-up (from 2003 to 2007), 788 men
died in 26,482.9 person-years, and 430 women died in 29,723.6
person-years. Additionally, 58 men and 111 women moved out of
the study area. Older individuals, men, and individuals with alower education level had higher mortality rates (Table 1).
Cox models for the SRH-mortality association (Table 3-I)
showed that individuals who reported fair/poor SRH wereapproximately two times more likely to die during the follow-up
period (age-adjusted HR = 2.20 [95% CI: 1.87–2.58], without
including interaction terms). In the same model, the interaction forsex/gender was statistically significant (p = 0.018), suggesting men
exhibit a stronger association between SRH and mortality than
women. The interaction for education was not significant(p = 0.549).
The stratified analyses by sex/gender (Table 3-II) showed that
the SRH-mortality association among men (age-adjusted
HR = 2.44 [95% CI: 2.14–2.80]) was stronger than the corresponding association among women (age-adjusted HR = 1.88
[95% CI: 1.44–2.47]). The stratified analyses by education level(Table 3-III) also showed that the SRH-mortality association
among people with a higher education (age-adjusted HR = 2.39
[95% CI: 1.74–3.30]) seemed to be stronger than among thosewith a lower education (age-adjusted HR = 2.14 [95% CI: 1.83–
2.50]). (The interaction terms shown were not significant).
When we examined the regression models with the successive
introduction of control variables, the sex/gender difference in the

Results
Sex/gender-SRH and education-SRH associations atbaseline
As expected, increasing age was associated with higher levels ofdepression, an increase in non-life-threatening health conditions,and a higher prevalence of fair/poor SRH among both men andwomen (Table 1). Higher levels of depression and non-lifethreatening health conditions were more prevalent among womenthan men. Lower educational attainment (,high school) was also
associated with a higher prevalence of fair/poor SRH (p,0.001
among both sexes/genders; the p value was obtained using the
age-adjusted logistic regression model for the education-SRH
association) (Table 2 [Model 1A]).
In the age-adjusted model (Table 2), men were no more likely
to report fair/poor health than women. Subsequent models that
adjusted for sex/gender differences in pre-existing co-morbid
conditions resulted in a higher odds ratio for poor SRH among
men compared to women. For example, after controlling for 13
co-morbid conditions, men were 1.27 times more likely to reportpoor SRH than women (95% CI: 1.09–1.48) (Table 2 [Model
4A]).
In the age-adjusted model, individuals who had completed lessthan a high school education were 1.38 times more likely to reportfair/poor health compared to individuals with higher levels ofTable 1. Self-rated health at baseline and four-year mortality in AGES 2003 Cohort Study in Japan.

Depression[GDS15]

Men

Women

Non-life-threatening Life-threatening
physical conditions physical conditions

Baseline self-rated healthFour-year Mortality

A Age

n

Score

SE

NumberSE

NumberSE

Excellent /good

Fair/poor

Alive

Dead

65–69

2525

3.28

0.13

0.61

0.03

0.76

0.02

76.2%

23.8%

94.1%

5.9%

70–74

2085

3.55

0.08

0.74

0.03

0.82

0.01

73.2%

26.8%

91.4%

8.6%

75–79

1368

3.83

0.17

0.90

0.03

0.88

0.01

64.7%

35.3%

85.0%

15.0%

80–84

580

3.94

0.21

1.02

0.02

0.83

0.03

67.6%

32.4%

77.3%

22.7%

85–

251

4.25

0.37

1.28

0.09

0.71

0.03

66.9%

33.1%

61.4%

38.6%

65–69

2374

3.38

0.09

0.69

0.02

0.65

0.02

78.0%

22.0%

97.8%

2.2%

70–74

2089

3.81

0.06

0.94

0.03

0.76

0.02

70.9%

29.1%

96.4%

3.6%

75–79

1658

4.13

0.14

1.17

0.01

0.80

0.01

64.2%

35.8%

94.4%

5.6%

80–84

824

4.27

0.20

1.19

0.08

0.79

0.03

66.3%

33.7%

90.8%

9.2%

85–

424

4.79

0.13

1.25

0.05

0.71

0.03

68.9%

31.1%

73.1%

26.9%

B Education
Men

Women

,High school

3801

3.97

0.09

0.83

0.03

0.78

0.01

68.7%

31.3%

87.5%

12.5%

High school

1822

2.97

0.10

0.66

0.03

0.83

0.02

76.7%

23.3%

90.7%

9.3%

.High school

907

3.00

0.12

0.71

0.03

0.88

0.02

75.5%

24.5%

91.3%

8.7%

,High school

4633

4.11

0.08

0.99

0.01

0.72

0.01

69.1%

30.9%

94.0%

6.0%

High school

2120

3.31

0.09

0.92

0.03

0.77

0.02

75.2%

24.8%

95.1%

5.0%

.High school

370

3.17

0.12

0.83

0.03

0.75

0.04

77.5%

22.5%

96.0%

4.1%

doi:10.1371/journal.pone.0030179.t001

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Table 2. Odds ratios of sex/gender and education for fair/poor self-rated health at baseline in logistic regression modelsa.
Model 1Ab

Model 2Ab

Model 3Ab

Model 4Ab

Model 5Ab

(Adjusted for age)

(Model 1A+marital
status, smoking, anddrinking)(Model 2A+
depression [GDS15])(Model 3A+13 nonlife-threatening
physical conditions)(Model 4A+8 lifethreatening physicalconditions)

ORs

95%CI

ORs

95%CI

ORs

95%CI

ORs

95%CI

ORs

95%CI

Sex/gender (Malevs female)

0.99

0.86

1.14

1.09

0.89

1.35

1.17

0.97

1.41

1.27

1.09

1.48

1.14

0.99

1.31

Education (,highschool vs high/.
high school)

1.38

1.30

1.47

1.36

1.26

1.46

1.11

1.00

1.23

1.08

0.98

1.19

1.16

1.07

1.26

Interaction term
(male6,high school)p = 0.613

p = 0.819

p = 0.861

p = 0.940

p = 0.818aInteraction terms were excluded in the models when we reported the ORs and 95% CIs on the table (effect-only models).
OR of sex/gender and that of education were calculated by different two models in Model 1A. In contrast, OR of sex/gender and that of education were calculated
simultaneously by a single model in Model 2A, 3A, 4A, and 5A.
doi:10.1371/journal.pone.0030179.t002
b

Table 3. Hazard ratios of self-rated health (SRH), sex/gender, and education for mortality during four-year follow-up in Cox
modelsa.
Model 1Bb

Model 2Bb

(Adjusted forage)(Model 1B+
marital status,
smoking, anddrinking)

Model 3Bb

Model 4Bb

Model 5Bb

(Model2B+depression[GDS15])(Model 3B+13
non-lifethreatening
physicalconditions)(Model 4B+8 lifethreatening
physicalconditions)

I Both sex/genders
Both sex/genders with all education levels

HRs

95% CI

HRs

95% CI

HRs

95% CI

HRs

95% CI

HRs

95% CISRH (fair/poor vs excellent/good)

2.20

1.87

2.58

2.23

1.90

2.61

1.97

1.70

2.28

1.97

1.67

2.31

1.67

1.35

2.07

Sex/gender (Male vs female)

2.06

1.78

2.39

2.06

1.57

2.71

2.06

1.55

2.74

1.99

1.48

2.67

1.90

1.38

2.62

0.87

1.27

Education (,high school vs high/.high school) 1.12

0.90

1.38

1.05

0.96

0.79

1.16

0.95

0.78

1.15

0.93

0.79

1.11

Depression (GDS15)c

1.08

1.07

1.11

-

1.05

1.03

1.07

1.04

1.02

1.06

1.04

1.02

1.06

Interaction term (fair/poor SRH6male)p = 0.018

p = 0.090

p = 0.465

p = 0.449

p = 0.924

Interaction term (fair/poor SRH6,high school)p = 0.549

p = 0.364

p = 0.221

p = 0.216

p = 0.258

II Stratified by Sex/gender
Men with all education levels
SRH (fair/poor vs excellent/good)

2.44

2.14

2.80

2.36

2.00

2.78

2.00

1.71

2.34

2.05

1.69

2.48

1.70

1.35

2.15

Education (,high school vs high/.high school) 1.19

0.90

1.56

1.05

0.84

1.32

0.93

0.73

1.19

0.93

0.71

1.23

0.91

0.72

1.15

1.88

1.44

2.47

2.01

1.66

2.43

1.93

1.55

2.40

1.85

1.40

2.43

1.67

1.16

2.42

Education (,high school vs high/.high school) 1.11

0.87

1.42

1.06

0.75

1.48

1.02

0.75

1.39

0.97

0.71

1.33

0.97

0.71

1.33

Women with all education levels
SRH (fair/poor vs excellent/good)

III Stratified by Education
Lower education (both sex/genders)
SRH (fair/poor vs excellent/good)

2.14

1.83

2.50

2.15

1.83

2.52

1.86

1.50

2.30

1.85

1.53

2.23

1.61

1.21

2.14

Sex/gender (Male vs female)

2.40

2.01

2.89

1.96

1.36

2.82

1.89

1.29

2.75

1.83

1.27

2.65

1.77

1.18

2.65

SRH (fair/poor vs excellent/good)

2.39

1.74

3.30

2.43

1.85

3.19

2.24

1.59

3.16

2.25

1.56

3.24

1.74

1.20

2.52

Sex/gender (Male vs female)

2.28

1.79

2.90

2.27

1.44

3.58

2.40

1.52

3.79

2.36

1.41

3.95

2.20

1.34

3.63

Higher education (both sex/genders)aInteraction terms were excluded in the models when we reported the HRs and 95% CIs on the table (effect-only models).
HR of each cell in Model 1B was calculated by each different model. In contrast, HR of sex/gender and that of education (and that of depression after Model 3B) were
calculated simultaneously by a single model in Model 2B, 3B, 4B, and 5B.
c
As depression was included as the control variable in Model 3B in addition to the variables in Model 2B, HR of depression in Model 2B was not reported.
doi:10.1371/journal.pone.0030179.t003
b

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roughly the same prevalence of fair/poor SRH. This is consistent
with the findings of some previous studies in Western settings,
where women live longer than men but also tend to report poorer
SRH [9,19]. In our study, older Japanese men were more likely toreport fair/poor SRH than women after we controlled for sex/
gender differences in the prevalence of additional self-reported comorbid health conditions. Our second noteworthy finding is thatthe association between fair/poor SRH and subsequent mortality
was stronger for men than women. This finding is consistent withother studies from the Netherlands [19] and the United Kingdom[14]. Third, the sex/gender difference in the association of fair/poor SRH and mortality was progressively attenuated (and almost
eliminated) after controlling for additional self-reported healthconditions. Our findings in this regard are inconsistent withprevious studies from the Netherlands [19] and the United
Kingdom [14], both of which used almost the same statisticalprocedures and reported that the sex/gender differential in thepredictive ability of SRH remained even after controlling for arange of health conditions.
In terms of differences associated with educational attainment,
our study made four noteworthy findings. First, people with lower
educational attainment were more likely to report poor SRH,
which was attenuated (or explained) when we added co-morbid
conditions to the regression models. This implies that differencesin SRH across education-level groups reflect the real underlying
variation in ‘‘objective’’ health. Second, we found that the SRHmortality association tended to be similar across education levels.
This finding is consistent with previous studies reported in Sweden[17] and the United Kingdom [12] but inconsistent with studies
from the United States [18] and the Netherlands [10]. We usededucational attainment as the proxy measure of relative socioeconomic status, and differences in the predictive ability of SRH
according to socioeconomic status could be related to howegalitarian a country’s society is (see the GINI coefficient of eachcountry among 1992–2007, Japan: 0.25, Sweden: 0.25, United
Kingdom: 0.36, United States: 0.41, and Netherland: 0.31) [27].
Our interpretation of this difference based on education level isthat although people with less education have more healthassociation of fair/poor SRH and mortality was equalized after wecontrolled for the complete set of co-morbid conditions (Model 5B
male HR = 1.70; Model 5B female HR = 1.67).
We next examined the contributions of different clusters ofcovariates to the excess risk linking fair/poor SRH to mortality.
We illustrate this for the overall sample, as well as for men versus
women and low versus high education levels (Figure 1). Amongmen, 48.6% of the excess risk could not be explained by thevariables in our models, whereas 76.0% of the excess risk among
women could not be explained.
Depression symptoms explained 24.7% of the excess risk of fair/poor SRH for mortality for men, as compared to 8.7% for women.
Depression symptoms also explained 25.5% of the excess risk offair/poor SRH for mortality among people with lower educationlevels, as compared to 13.6% among those with a higher
education.

Discussion
Several noteworthy findings emerged regarding sex/gender andsocioeconomic differences in the SRH-mortality association
among this Japanese older population, as compared with previous
studies from other countries. Our first hypothesis (stronger
association among men) was partially supported by the analysis.
While a stronger association was found among men, we found thiswas because the presence of depressive symptoms had a stronger
influence on men’s ratings of SRH than women’s ratings. This wascontrary to our prior hypothesis that the presence of physicalhealth conditions would explain the stronger association of SRH tomortality among men.
Our second hypothesis (stronger association between SRH andmortality among more educated individuals) was not supported by
our analyses. The predictive ability of SRH for mortality was
similar among individuals with different levels of schooling.

Comparisons with previous studies
We found three noteworthy results with respect to the sex/
gender difference in SRH. First, Japanese women and men report

Figure 1. Percentages of explainable excessive risk of self-rated health for age-adjusted mortality by other self-reported measures
added in Model 1B to 5Ba. a As some values of the explainable excessive risk by other self-reported measures (relative risk reduction) were
negative (i.e., 2X.X%), the sum of the percentages in each bar was not 100% among several subgroups.
doi:10.1371/journal.pone.0030179.g001

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problems (and hence report poorer overall SRH), their assessmentsof their health are less accurate than those of people with moreeducation in terms of rating the impact of potentially lifethreatening conditions on their SRH levels. Third, we found thatthe non-significant educational difference in the predictive abilityof poor SRH was more attenuated when we added life-threatening
physical conditions as control variables. Lastly, we found that
depression symptoms explained about 26% of the excess risk ofpoor SRH on mortality among individuals with a lower educationlevel, while life-threatening physical conditions played a greaterrole (36%) in explaining excess risk among individuals with ahigher education level.
How do people assess their own health? A recent
landmark review article on SRH provided a theoretical frameworkfor how people assess their own health [3,28]. According to Jylha’s
theory, people go through three stages in the process of assessing
their health: (1) ‘‘recognizing the meaning of health and identifying
the components that should be included as components of self
health,’’ (2) ‘‘considering the way in which those components
should be taken into account,’’ and (3) ‘‘deciding which of thelevels in the presented scale best summarizes these components.’’
Thus the theory posits that individuals move through a logical andsequential series of mental steps when asked to rate their healthstatus. . However, the logical flow of Jylha’s theory has been
criticized because (1) an individual’s decision-making process is notthat logical and is influenced by psychological filters and (2) eachindividual does not have access to their complete healthinformation for the decision-making process [28]. Therefore,
critics argue that Jylha’s assumption (i.e., that individuals arerational enough to thoroughly and carefully think through thethree stages one by one) does not fit with reality and that
individuals are more heuristic [28,29].
Our research findings can contribute to the theoretical debate
on at least three points. First, people seem to rate their own healthin a heuristic manner, as proposed by Huisman and Deeg [28],because the study participants in every subgroup exhibited an
excess risk for mortality which could not be completely explainedby a set of diagnosed illnesses and socio-demographic factors.
Second, the percentages of explainable excessive risk varied
according to socio-demographic factors and depression symptoms,
and moreover, the set of explanatory factors differed for eachsubgroup. This suggests that people in different subgroups may bedifferentially utilizing information in order to rate their health.
Third, when we focus on the sex/gender difference, thepercentage of unexplainable excessive risk was greater among
women (76%) than men (48.6%), implying that Japanese womenare more heuristic than Japanese men in the process of assessing
their own health (assuming that added control variables constitutethe available information for assessing their own health). Overall,
our findings support the argument that a framework of
psychological factors should be added to Jylha’s theoreticalframework, as Huisman and Deeg suggest [3,28].
What is SRH? Beyond the debate on how people assess theirown health, a more fundamental set of questions raised by thisresearch include: What is SRH? Subjective health? Objective
health? True health? Although these questions have not yet beenanswered, several scholars have suggested measuring true/
objective health using the SRH question in self-administeredquestionnaires via several methods, including Jylha’s theoreticalframework [3], described above; an anchoring vignette [2]; andvalidity evaluation with bio-markers [3,30], where the focus in
relation to SRH is the distance between latent true/objective
health and self-rated subjective health [20,30]. On the other hand,several scholars discuss how SRH is ‘‘a measure of people’s
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perception of their health rather than a measure of true health,’’and therefore, it can be ‘‘the most informative from the holistic
point of view’’ [28].
Although our findings cannot provide us with a direct clue
about what SRH is, the stepwise inclusion of control variables inadjusted models is suggestive of what ‘‘goes into’’ an individual’s
assessment of SRH. However, two important hypotheticalexplanations are missing from Jylha’s theory and the foregoing
discussion, which should be discussed here to understand our
findings more deeply [3]. First, bio-physiological changes inside
the body, which can be detected by slight changes in the level of abio-marker (inflammatory, immunological, endocrinological, etc.)
through a blood test and which the host (the individual) has not yet
perceived in their mind, can contribute to SRH. Several biomarkers (e.g., hemoglobin, albumin, interleukin-1 b, tumor
necrosis factor a) have been associated with SRH, and suchfactors can contribute to the host’s SRH without their knowledge[3,30]. Thus, the single-item SRH question and its answer may bepartly based on the rich information provided by ongoing biophysiological changes in the body, which can be useful in thecontext of promoting activities and preventive medicine.
Second, the distance or discrepancy between subjective healthand objective health cannot be merely a measurement error; it is acausal factor (beyond a predictive factor) for mortality. SRH is aform of self-fulfilling prophecy [31]. This idea is captured in a
traditional Japanese proverb, ‘‘yamai wa ki kara,’’ which translatesas ‘‘illness springs from one’s spirit’’ (‘‘ki’’) and refers to theJapanese conception that physical illness can result from a person’sframe of mind toward body and physical illness itself [32]. It is
possible that individuals who report lower SRH feel defeated insome way and that this state of mind has an adverse effect on their
physical health. To date, no study has been able to tease this out.
Therefore, future studies need to work on testing the ‘‘selffulfilling prophecy’’ hypothesis for the SRH-mortality association,
as well as establishing the theory of assessing one’s health to see thebio-physiological mechanism behind the SRH-mortality association. To establish the model, numerous broad scientific studies —from molecular to social — are required now. Overall, the theoryof assessing one’s health could incorporate psychological filters,
not-yet-perceived bio-physiological changes, and self-fulfilling
prophecy into one explanatory model.

Study limitations
There are several limitations of and points of discussions
regarding the present study. First, the duration of follow-up (fouryears) was relatively short compared to some previous studies (upto 10 years) [6]. Although this limitation could be overcome withadditional follow-up with the same cohort, the trade-off is changes
in SRH (and hence exposure to misclassification) over time.
Second, the relatively low response rate to the baseline
questionnaire (50.4%) could yield a risk of selection bias (e.g.,biased estimate of HR of SRH for mortality). However, thedifferences in the socioeconomic status (see the Introduction to thispaper) and health condition characteristics (unknown and notreported, but possible) between respondents and non-respondents
were arguably not likely to make the estimate biased because suchdifferences could not directly affect the SRH-mortality association
itself, at least not after the adjustment for education and healthconditions (life-threatening and non-life-threatening diseases). If
more information on non-respondents was available, we could test
the explanation above using statistical analysis or perform theregression analysis with multiple imputation technique. Third,
educational attainment levels among the older study population
could differ from that of the entire Japanese population, given the
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Self-Rated Health and Mortality Association

rapid economic development of Japan over the last 50 years,
which has led to an increase in educational attainment with eachbirth cohort.

Acknowledgments
We thank Dustin Duncan (Harvard School of Public Health) and themembers of Aichi Gerontological Evaluation Study (AGES) for useful
discussion. A.N. acknowledges support from the Nakajima Foundation in
Japan for his academic endeavors at Harvard University.

Conclusions
In summary, we did not find a socioeconomic difference in the
SRH-mortality association, but we did find a sex/genderdifference, which was attenuated after adjusting for several socialand medical factors. Although the theoretical framework provided
by Jylha [3] hinted at this sex/gender difference, biological andpsychological factors may need to be incorporated into the model.

Author Contributions
Conceived and designed the experiments: AN. Performed the experiments:
HH SJ KK. Analyzed the data: AN IK KS. Contributed reagents/
materials/analysis tools: AN IK. Wrote the paper: AN IK KS HH SJ KK.

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