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Associations of chronic conditions, APOE4 allele, stress factors, and health behaviors with self-rated health

BMC Geriatrics201515:137

https://doi.org/10.1186/s12877-015-0132-y

Received: 12 January 2014

Accepted: 14 October 2015

Published: 26 October 2015

Abstract

Background

Self-rated health (SRH) has been widely used to measure the overall health status of older adults. Research has shown that SRH is determined by a large array of factors, such as chronic disease conditions, genetic markers (e.g., Apolipoprotein E, APOE, NM_000041), stress factors, and health behaviors. However, few studies have incorporated these factors simultaneously in the analytic framework of SRH. The aim of this study is to examine the associations of these four sets of factors with SRH.

Methods

Using a dataset from a population-based, random-cluster survey of 1,005 elderly respondents aged 54–91 conducted in Taiwan in 2000, we use logistic regressions to examine associations of chronic health conditions, the APOE4 allele stress factors, and health behaviors with SRH. The four disease conditions include diabetes, heart diseases, gastric ulcers, and chronic obstructive pulmonary disease. Stress factors are measured by traumatic events (having an earthquake-damaged house) and chronic life stress (financial difficulty). Health behaviors include smoking, drinking alcohol, vegetable and fruit intake, daily milk intake, and physical exercise.

Results

Diabetes, heart diseases, gastric ulcers, and chronic obstructive pulmonary disease are found to be associated with 2.63 (95 % CI: 1.75–3.95), 1.72 (95 % CI: 1.15–2.58), 1.94 (95 % CI: 1.35–2.80), and 2.54 (95 % CI: 1.66–3.92) odds ratios of poor SRH. The APOE4 allele is found to be significantly associated with poor SRH with odd ratio of 1.58 (95 % CI: 1.02–2.41). Financial difficulty is associated with increased likelihood of poor SRH, with odds ratios of 1.76 (95 % CI: 1.22–2.54) Doing exercise more than 5 times per week are associated with reduced likelihood of poor SRH by 44 % (odds ratio is 0.56, 95 % CI: 0.39–1.82). The interaction term between gender and gastric ulcer showed that the impact of gastric ulcer on SRH is more pronounced in women than in men, with an odds ratio of 2.63 (95 % CI: 1.24–5.58).

Conclusions

Chronic conditions and the APOE4 allele are significantly associated with increased likelihood of reporting poor health, and the associations appear differently among women and men. To better understand the mechanism of how people self-assess their overall health, chronic conditions and genetic components should be considered together with conventional factors such as life stress and health behaviors.

Keywords

Self-rated health Chronic diseases APOE4 Stress factors Health behaviors

Background

Self-rated health (SRH) is one of the most widely used indicators in population health research [1, 2] and is assumed to be a valid measure of the overall global assessment of a person’s current well-being [3]. Traditionally, medical examination data are considered the “gold standard” to measure health or predict disability [4]. However, it has been pointed out that SRH can sometimes be a better predictor of well-being than biological markers and SRH data can be readily collected for large numbers of individuals at minimal cost [5]. The predictive strength of SRH is its multidimensional measurement of healthy aging that incorporates a wide variety of factors [6].

Previous research has found that both chronic life stress and stress from a traumatic shock are strongly associated with poor SRH [7]. In early studies before the 1980s, the majority of research in this area focused on acute stressful life events – for example, natural disasters, combat, and physical assaults [811]. However, as modern life fills with increasing pressures, research has drawn attention to the influence of chronic stressors on health outcomes [1214]. Measures of chronic life stress generally assess the impact of stressors that last for prolonged but often unspecified periods – for example, low socioeconomic status (SES), or poor working condition or long hours [15, 16].

In addition to stress factors, health behaviors have a significant impact on health and are typically measured by factors such as one’s physical activity, diet, alcohol intake, and use of tobacco products. Specifically, eating fruits and vegetables is positively linked to good health [17], while smoking and heavy use of alcohol have a negative impact on health [18]. Physical exercise promotes health by improving physical and cognitive function [19, 20] and provides a means of socializing in an environment charged with positive emotional content [21].

Chronic diseases (i.e., coronary heart disease, diabetes, and depression) tend to be prevalent among older adults and are evidenced to be closely linked with poor SRH among the elderly [2224]. People with chronic diseases can experience pain and disability that leads to poor SRH. For example, Research has reported that the presence of chronic diseases is one of core predictors of SRH [25, 26].

SRH also has a genetic basis. Nearly a third of the variability in SRH can be attributed to genetic factors [27]. As noted by Zhang et. al (p1), “Integrating genetic markers into population health research will contribute to a better understanding of the mechanisms through which social and behavioral factors affect population health” [7]. Some studies have examined the association of genetic markers such as the Apolipoprotein E4 (APOE4) allele with health outcomes. For example, research has shown that carriers of the APOE4 allele have an increased risk for earlier age of onset of Alzheimer’s disease and Parkinson’s disease [28, 29], as well as decline in cognitive function and functional status among older adults without dementia [30]. These findings suggest that the APOE4 allele plays an important role in SRH.

However, one shortcoming of the existing literature is that few studies have incorporated chronic diseases, genetic markers, social environmental factors, and health behaviors simultaneously in their analytic framework of SRH. This study attempts to incorporate the APOE4 allele and chronic disease information together with stress factors and health behaviors into research on SRH. We aim to provide a deeper examination of self-rated health among an aging population. The data are from a population-based survey of older adults in Taiwan with a broad range of information including not only self-reports of physical, psychological, and social well-being, but also extensive clinical data based on medical examination and laboratory analyses.

Methods

Dataset

The data used in the study were from the Social Environment and Biomarkers of Aging Study (SEBAS) in Taiwan, collected from a representative subsample randomly selected from the 1999 Taiwan Longitudinal Study of Aging (TLSA). The Bureau of Health Promotion of the Department of Health in Taiwan granted approvals for the protection of human subjects for SEBAS. Data were collected through home visits by interviewers. A written consent was obtained from each of all participants with the rare exceptions when a participant who could not read or write. In that case, a consent form was read by the interviewer and signed by a witness.

The 2000 wave of the SEBAS (hereafter the SEBAS 2000) was conducted from July through December 2000 using 27 original primary survey units (PSU) from the TLSA and 10 new townships [31]. All respondents residing in a given PSU were selected for interviews. Data were collected through face-to-face interviews using a structured questionnaire, physical examination, and bio-specimen collection. This human subjects research was approved by the institutional review boards at Princeton University, RAND, Georgetown University, and the Bureau of Health Promotion in Taiwan.

The SEBAS 2000 in-home interview was conducted by a public health nurse who was well-known and highly-respected locally. The questionnaire covered chronic conditions, physical functioning, psychological well-being, cognitive capacity, utilization of health services, and social networks/support. The interviewer then evaluated each respondent’s health. Among the 1,497 respondents, 1,023 received physical exams at a nearby hospital several weeks later (111 were not eligible for health examination and 363 refused to participate). After list-wise deletion, of the 1,023 individuals who completed the survey, 1,005 subjects without any missing data were included in statistical analyses. The age range of participants was 54–91, with 628 persons aged 65 or older and 421 women.

Biospecimens were collected by survey staff during the in-home interview and related the hospital visit. Survey staff collected the 12-hour urine specimen at the participant’s home and accompanied the participant to the hospital on the morning of the scheduled appointment. During the hospital visit, participants were asked about their health history, family disease history, health-related behaviors, and current long-term medications. Blood pressure and anthropometric measurements (i.e., respondent’s height, weight, waist and hip circumference) were performed and a blood specimen was taken to measure biomarkers.

Union Clinical Laboratories (UCL) took responsibility for immediate shipment of the specimens to their headquarters in Taipei, followed standard laboratory protocols for conducting assays, and provided the results to the Bureau of Health Promotion (BHP, in the Department of Health of Taiwan) within two weeks. One genetic marker, the APOE gene, was also obtained by blood specimen using the polymerase chain reaction amplification refractory mutation system (PCR-ARMS) and polymerase chain reaction restriction fragment length polymorphism (PCR-RFLP) analysis. Data quality evaluations conducted during and after the fieldwork by BHP indicate that the SEBAS 2000 rendered reliable data [32].

Dependent variable

The measure of self-rated health status was based on a simple question: “Regarding your current state of health, do you feel it is excellent, good, average, not so good, or poor?” A binary measure of poor SRH was coded for logistic regression analyses (1 = not so good or poor SRH, 0 = excellent/good/average SRH). Other ordinal categorizations were also tested and the conclusions were very similar.

Independent variables

The SEBAS 2000 measured fourteen diseases or conditions: high blood pressure, diabetes, heart diseases, stroke, cancer/malignant tumor, bronchitis/emphysema/pneumonia/lung disease/asthma and other lower respiratory tract diseases, arthritis/rheumatism, gastric ulcer/stomach ailment, liver/gallbladder disease, hip fracture, cataract, kidney disease, gout, and spinal/vertebrae spur. All the chronic diseases were self-reported in response to the question “Have you ever had this disease?” Each condition was coded 1 if an individual said yes and 0 otherwise. For this analysis we only included chronic diseases with a prevalence rate of 10 % or higher and with a significant bivariate association with SRH. While hypertension was frequent, it was found to be underreported through evaluation of the SEBAS 2000 with other survey datasets and validation with laboratory results [33]. Eventually, only four diseases, diabetes, heart diseases, gastric ulcer, and chronic obstructive pulmonary disease (COPD) were used to measure physical health in this study.

All respondents who reported having one of these conditions at the time of the interview indicated that a physician delivered the diagnosis (94.7 % for heart diseases, 97.3 % for diabetes, 88.5 % for COPD, and 87.7 % for ulcer). The prevalence rates of these four diseases in SEBAS 2000 were similar to those found in the National Health Interview Survey in Taiwan in 2001 [34]. Moreover, the evaluation of the SEBAS 2000 revealed that, with the exception of hypertension, the accuracy of self-reported chronic disease information in the Taiwan study was similar to that in the United States [33].

The information of the APOE gene, which was obtained from blood specimens analyzed for allele variant, had three alleles: E2, E3 and E4. A binary measure of the APOE4 allele was coded as 1 if the individual carries one or two copies of the E4 allele (carrier), and 0 otherwise (non-carrier).

Stress was measured by traumatic events and chronic life stress. Experiencing housing damage during the 1999 earthquake, which occurred a year before the survey was conducted, was used to measure life stress due to a traumatic event. Environmental stress such as financial difficulty was a major element of psychological stress and was used to measure chronic stress.

The 1999 earthquake was the greatest disaster in late 21st century in Taiwan. It occurred in Jiji, Nantou County, Taiwan on Sept. 21, 1999. Some 2,415 people were killed and 11,305 were injured. The “Quake of the Century” had a profound effect on the whole island, and even on some mainland provinces. The Richter magnitude scale of the 1999 earthquake ranged from 4 in the south (Kaohsiung) to 5 in the north (Taibei) and east (Hualian) and 7 in the west (Yunlin and Jiayiin). Survey participants were asked “Was there any damage or loss to the house in which you usually lived prior to the earthquake?” Response categories were yes (1) and no (0).

Financial condition was measured by the question “Do you (and your spouse) have enough money or any difficulty meeting monthly living expenses or other expenditures?” Possible responses were: “1 = enough money, with some left over; 2 = just enough money, no difficulty; 3 = some difficulty; and 4 = much difficulty.” Financial difficulty was coded 1 if the individual selected the third or fourth categories, and 0 otherwise.

Health behaviors included smoking, drinking alcohol, physical exercise, and diet. Four health behaviors were recoded into binary variables (1 = yes, 0 = no) measured by the following questions: “In the past six months, did you smoke?” “In the past six months, did you drink alcohol?” “Do you drink milk every day?” and “Do you eat at least three servings of vegetables and two servings of fruit every day?” For physical exercise, a three-category option of frequency (<=1, 2–5, and 6+) was designed in the questionnaire, and we directly used its categorization without any modification.

To obtain robust results, we controlled for socio-demographic factors in the statistical analyses. Socio-demographic variables included gender, age, marital status, ethnicity, urban or rural residence, education, and occupation. We also included obesity as a measure of physical condition. In accordance with WHO’s criterion of body-mass index (BMI) for Asian populations, we defined obesity as a BMI greater than 23, weight/height (kg/m) [35].

Due to a documented relationship with SRH, disability in instrumental activities of daily livings (IADL) was also included in the analysis [36]. IADL disability involved limitations on buying personal items, managing money/paying bills, riding bus or train by oneself, doing physical work at home, doing light tasks at home, and making phone calls. Respondents were coded as IADL disabled (1) if the individual had at least some difficulty with one or more items, and 0 otherwise.

Statistical analysis

After examining descriptive statistics (Table 1), we estimated a logistic regression model of the associations of stress factors and health behaviors with odds of poor SRH while controlling for socio-demographic factors (Model I in Table 2). We then incorporated chronic diseases and the APOE4 allele to examine how measures of physical health and genetic information may alter the associations in Model I (Model II, Table 2). Finally, because of significant gender differences in the relative importance of factors associated with SRH [37], we examined all interactions between gender and chronic diseases, the APOE4 allele, stress factors, and health behaviors. However, only one interaction (between gender and ulcer) was significant. We thus analyzed a model that included this interaction in addition to all factors in Model II (Model III, Table 2).
Table 1

Proportion of poor self-reported health by study factors

Variable

Code

N

Poor SRH%

p

Overall

--

1,005

26.97

 

Number of chronic diseases

0

491

17.11

***

 

1

379

28.50

 
 

2

112

57.14

 
 

3+

23

60.87

 

 Diabetes

No

848

23.47

***

 

Yes

157

45.86

 

 Heart diseases

No

840

24.17

***

 

Yes

164

40.85

 

 Gastric ulcer

No

780

24.23

***

 

Yes

225

36.44

 

 COPD

No

875

24.00

***

 

Yes

130

46.92

 

APOE4 allele carriers

No

862

25.99

 
 

Yes

143

32.87

 

Financial difficulty

No

753

23.64

***

 

Yes

249

37.35

 

Having an earthquake-damaged house

No

882

25.74

*

 

Yes

123

35.77

 

Smokers

No

783

28.99

*

 

Yes

221

19.91

 

Daily alcohol use

No

937

27.43

 
 

Yes

68

20.59

 

Daily milk intake

No

608

27.47

 
 

Yes

395

26.33

 

Vegetable and fruit diet

No

474

32.28

***

 

Yes

529

22.31

 

# of times doing exercise/week

<=1

392

35.04

***

 

2–5

198

25.25

 
 

6+

415

20.00

 

Controls

    

 Gender

Men

584

23.29

**

 

Women

421

32.07

 

 Age

<70

433

29.56

 
 

> = 70

572

25.00

 

 Marital status

Others

291

31.96

*

 

Currently

714

24.93

 

 Ethnicity

Fujian

712

27.39

 
 

Hakka

122

24.59

 
 

Mainlander

171

26.90

 

 Rural residence

No

697

28.41

 
 

Yes

308

23.70

 

 Education

None

330

36.97

***

 

Primary

407

25.06

 
 

Secondary

197

17.26

 
 

College+

71

18.31

 

 Occupation

Professionals

58

15.52

 
 

Clericals

215

22.79

 
 

Blue-collar workers

348

28.45

 
 

Farmers

384

29.69

 

 IADL disabled

No

701

18.69

***

 

Yes

304

46.05

 

 BMI scores

<=23

939

26.30

 
 

>23

66

36.36

 

(1) COPD = chronic obstructive pulmonary disease. (2) P-value reports significance of chi-square test of difference in proportion between reference categories. (3) *p < 0.05; **p < 0.01; ***p < 0.001

Table 2

Odds ratios of poor SRH for chronic diseases, APOE4 allele, stress factors and health behaviors

 

Model I

Model II

Model III

 

OR

95 % CI

 

OR

95 % CI

 

OR

95 % CI

 

Chronic Diseases

         

 Diabetes (no)

   

2.63

1.75–3.95

***

2.63

1.75–3.95

***

 Heart diseases (no)

   

1.71

1.14–2.56

**

1.72

1.15–2.58

**

 Gastric ulcer (no)

   

1.97

1.37–2.84

***

1.94

1.35–2.80

***

 COPD (no)

   

2.57

1.67–3.95

***

2.54

1.66–3.92

***

APOE4allele (no)

   

1.55

1.01–2.39

*

1.58

1.02–2.41

*

Stress

         

 Financial difficulty (no)

1.69

1.19–2.40

**

1.77

1.23–2.56

**

1.76

1.22–2.54

**

 Having an earthquake-damaged house (no)

1.56

1.01–2.43

*

1.50

0.94–2.39

 

1.50

0.94–2.39

 

Behaviors

         

 Smoking (no)

0.65

0.41–1.01

 

0.65

0.41–1.03

 

0.65

0.41–1.03

 

 Daily alcohol use (no)

1.00

0.51–1.97

 

1.05

0.53–2.09

 

1.01

0.51–2.01

 

 Vegetable and fruit diet (no)

0.78

0.57–1.07

 

0.75

0.54–1.05

 

0.76

0.54–1.06

 

 Daily milk intake (no)

1.12

0.80–1.57

 

1.13

0.80–1.61

 

1.15

0.81–1.63

 

 Doing exercise 2–5 times/week (<=1 time)

0.78

0.51–1.18

 

0.70

0.46–1.08

 

0.72

0.47–1.11

 

 Doing exercise 5+ times/week (<1 time)

0.56

0.39–0.79

**

0.55

0.38–0.80

**

0.56

0.39–0.82

**

Controls

         

 Women (men)

0.95

0.64–1.41

 

0.93

0.62–1.41

 

0.95

0.63–1.43

 

 Ages > =70 (<70)

1.06

0.74–1.51

 

1.11

0.76–1.62

 

1.10

0.76–1.60

 

 Currently married (no)

0.94

0.66–1.33

 

0.95

0.66–1.39

 

0.97

0.67–1.40

 

 Ethnicity Hakka (Fujian)

1.09

0.67–1.78

 

1.21

0.72–2.01

 

1.22

0.73–2.04

 

 Ethnicity Mainlander (Fujian)

1.40

0.87–2.24

 

1.23

0.75–2.03

 

1.27

0.77–2.08

 

 Rural (urban)

0.95

0.67–1.36

 

0.96

0.67–1.40

 

0.96

0.67–1.40

 

 Primary schooling (none)

0.93

0.64–1.36

 

0.92

0.62–1.37

 

0.92

0.62–1.37

 

 Secondary schooling (none)

0.59

0.34–1.03

 

0.58

0.33–1.05

 

0.58

0.33–1.04

 

 College degrees or higher (none)

0.87

0.38–2.01

 

0.87

0.36–2.06

 

0.86

0.36–2.05

 

 Clerical (white collar workers)

1.58

0.67–3.72

 

1.60

0.65–3.90

 

1.66

0.68–4.05

 

 Blue-collar workers (white collar workers)

1.61

0.67–3.89

 

1.59

0.63–4.00

 

1.62

0.64–4.09

 

 Farmers (white collar workers)

1.25

0.51–3.06

 

1.30

0.51–3.32

 

1.35

0.53–3.46

 

 IADL disabled (no)

3.13

2.20–4.47

***

2.65

1.83–3.84

***

2.67

1.85–3.87

***

 BMI >23 (<=23)

1.27

0.72–2.52

 

1.47

0.81–2.66

 

1.45

0.80–2.61

 

Interaction Effect

         

 Women*Gastric ulcer

      

2.63

1.24–5.58

*

- Log likelihood

521.2***

488.0***

481.1***

(1) Odds ratios and 95 % confidence intervals are obtained from binary logistic regressions. The category in the parentheses is the reference group of each variable. n2) *p < 0.05; **p < 0.01; ***p < 0.001

Results

Univariate analysis

Table 1 provided the distribution of poor SRH by independent variables: chronic diseases, APOE4 allele status, stress factors, and health behaviors, as well as controls. More than 80 % respondents had at least one chronic disease. Overall, 26.97 % of individuals reported poor SRH, and poor SRH was prevalent in individuals who reported diabetes (45.86 %, p < 0.001), heart diseases (40.58 %, p < 0.001), ulcer (36.44 %, p < 0.001) and COPD (46.92 %, p < 0.001). Chi square tests showed that individuals who experienced financial difficulty (37.35 %, p < 0.001) and earthquake losses (35.77 %, p = 0.019) were more likely to report poor SRH than those without such experiences. Individuals who exercised less than once a week (35.04 %, p < 0.001) or had poor dietary habits (32.28 %, p < 0.001) appeared to have a higher rate of poor SRH compared to those with more regular physical activity. Interestingly, cigarette smokers were less likely to report poor SRH (28.99 %, p = 0.012) than non-smokers, even though cigarette smoking has been linked to poor health. Next, these bivariate associations with poor SRH need to be tested simultaneously in one model while controlling for potential confounding factors in multiple regression analyses.

Multiple logistic regression analysis

We first fitted a logistic regression for odds of poor SRH including stress factors and health behaviors while controlling for sociodemographics (Model I in Table 2). The two stress factors, having financial difficulty (OR = 1.69, 95 % CI: 1.19–2.40) and having an earthquake-damaged house (OR = 1.56, 95 % CI: 1.01–2.43), were significantly associated with increased odds of poor SRH. The health behavior of engaging in physical activity 6 times per week or more was significantly associated with decreased odds of poor SRH compared to exercising once a week or less than (OR = 0.56, 95 % CI: 0.39–0.79).

When chronic diseases and the APOE4 allele were added to the regression analysis (Model II in Table 2), financial difficulty and frequent exercise remained significantly associated with SRH, indicating that these associations are independent of chronic disease conditions and APOE4 allele. With regard to association between chronic diseases and SRH, we observed strong associations of all four major chronic diseases with increased odds of poor SRH: diabetes (OR = 2.63, 95 % CI: 1.75–3.95), heart diseases (OR = 1.71, 95 % CI: 1.14–2.56), ulcer (OR = 1.97, 95 % CI: 1.37–2.84) and COPD (OR = 2.57, 95 % CI: 1.67–3.95). The association between APOE4 allele and SRH was also significant, with 55 % higher odds of poor SRH (95 % CI: 1.01–2.39) for carriers compared to non-carriers.

Model III in Table 2 provided results from multiple logistic regressions including an interaction between gender and ulcer. The results showed an odds ratio of 2.63 (95 % CI: 1.24–5.58) for the interaction, suggesting that impact of ulcer on SRH is more pronounced in women than men.

Discussion

This study provides a deeper examination of self-rated health (SRH) among an aging population by applying a model of genetic, physical, stress, and behavioral factors to a population-based survey of older adults in Taiwan with a broad range of self-reported and clinical data. We find that diabetes, heart diseases, gastric ulcers, and chronic obstructive pulmonary disease are associated with poor SRH as reported in the literature [3840]. Chronic disease could affect health outcomes in several ways. Chronic disease as a measure of physical health may directly affect individuals’ assessment of overall health [41]. Moreover, chronic disease may result in physiological impairment which limits physical or emotional abilities [42], indirectly increasing the likelihood of poor SRH.

One interesting finding is that the association between gastric ulcer and SRH is stronger in women than in men. This finding may be due to gender-based differences in attitudes toward this chronic health condition [43]. Literature has shown that men seem to worry less about long-term complications compared to women [44]. It is possible that women have a stronger sense to worry about gastric ulcer, which may explain why this disease is associated with poor SRH among women. Such sense could results in gender difference in medical care seeking behaviors [45]. Consequently, women will give more weight to the presence of the disease when they assess their overall health. Nevertheless, more studies are need to verify this finding from different populations to shed the lights to the underlying mechanism.

We further find that the APOE4 allele is significantly associated with increased likelihood of reporting poor health. The APOE4 allele is associated with an increased risk of reporting poor SRH in many ways related to cognitive and functional status. Previous research suggests that APOE might play a neurotrophic function in the central nervous system and that altered functioning of this molecule could result in neurodegeneration [46]. Recent studies have found that the APOE4 allele is significantly and uniquely related to lower cognitive scores, significantly increases odds of cognitive decline, and exacerbates the process of cognitive impairment [28, 29]. Moreover, the presence of the APOE4 allele is also associated with functional deficit, apart from the effects of neuropsychological performance. Therefore, the APOE4 allele is associated with poor SRH directly through cognitive decline and indirectly through functional status.

Stress factors are generally thought to influence health outcomes through negative emotional responses [47]. Our study indicates that stress from traumatic shock (earthquake in 1999) is significantly associated with increased odds of poor SRH among older adults. However, this relationship is explained by the inclusion of chronic diseases. Studies have shown that people who are exposed to traumatic events are at increased risk for somatic symptoms and physical illnesses, as well as for major depression, panic disorder, and generalized anxiety disorder, which lowers reserve capacity of dealing with life stress and shocks from the environment, and eventually suffer from some diseases [47]. Consistent with previous research [7], chronic life stress like financial difficulty is associated with SRH. Social contextual factors, especially socioeconomic status, are sufficiently stable over time for SRH [48]. Not only do environmental conditions operate directly in the causation of stress reactions, but the environmental setting and related socioeconomic status also provide and withhold the resources necessary to draw upon in coping with stress [49].

Negative health behaviors, such as lack of exercise, have been considered major factors in determining health outcomes [17, 19]. Physical activity is related to better functioning and overall health status compared with inactivity. Both cross-sectional and longitudinal studies have reported that physical activity delays the deterioration in health status or is related to a better health status compared with unhealthy behaviors [50, 51]. Some studies argue that people do take their health behaviors into consideration when they rate their overall health condition [52].

In interpreting our results, following limitations should take into account. One is that the data on chronic diseases is self-reported based on a person's own understanding of his or her health, which may not be in accordance with the appraisal of medical experts. Although the two evaluations can certainly be combined, major tension often exists between the two perspectives [53]. Another limitation of the study is the lack of neuropathologic conformation of relationships between the APOE4 allele and SRH and between the four chronic diseases and SRH. Although population-based study has sufficient capability to adjust for risk factors, such as genetic, physical, stress, and behavioral factors, general associations need to be followed up with smaller-scale basic science research. Thus, epidemiological and molecular studies are required to define the precise pathway by which the APOE4 allele and chronic disease confers risk of poor SRH and to determine how and why the relationships differ between women and men. Third, the sample size is not large, which prohibited us from examining SRH and its associated factors by subpopulation. Moreover, our analyses have a cross-sectional nature, which are less robust than those based on longitudinal data in studying SRH. Research using longitudinal datasets is thus preferable.

Conclusions

Chronic conditions and the APOE4 allele are associated with significantly increased likelihood of reporting poor health, and the associations appear differently among women and men. To better understand the mechanism on how the respondents self-assess their overall health, models of SRH need to consider chronic conditions and genetic components together with conventional factors such as life stresses or behaviors. Examining the relative associations of those factors with health and well-being could help to optimize and target resources and activities. According to our study, intervention programs should focus principally on older people who suffer from chronic diseases. Further research is also needed to examine older adults’ needs stemming from poor SRH in more depth, with a focus on discrepancies between different groups based on social, genetic, or chronic disease information.

Abbreviations

SRH: 

Self-rated health

APOE: 

Apo lipoprotein E

SES: 

Socioeconomic status

TLSA: 

Taiwan Longitudinal Study of Aging

SEBAS: 

Social Environment and Biomarkers of Aging Study

PSU: 

Primary survey units

UCL: 

Union Clinical Laboratories

BHP: 

Bureau of Health Promotion

PCR-ARMS: 

Polymerase chain reaction amplification refractory mutation system

PCR-RFLP: 

Polymerase chain reaction restriction fragment length polymorphism

COPD: 

Chronic obstructive pulmonary

BMI: 

Body-mass index

IADL: 

Instrumental activities of daily livings

Declarations

Acknowledgments

This study is jointly supported by National Natural Sciences Foundation of China grants (71110107025, 71233001, 71490732), UNFPA and the NIH (R01AG023627).

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Social Work, Zhou Enlai School of Government, Nankai University
(2)
Department of Sociology, the University of North Carolina
(3)
Department of Sociology, Peking University

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© Hu and Lu. 2015

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