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Psychosomatic Medicine 68:801-807 (2006)
© 2006 American Psychosomatic Society

ORIGINAL ARTICLES

Allostatic Load and Clinical Risk as Related to Sense of Coherence in Middle-Aged Women

Petra Lindfors, PhD, Olle Lundberg, PhD and Ulf Lundberg, PhD

From the Department of Psychology, Stockholm University and Centre for Health Equity Studies, Stockholm University/Karolinska Institutet, Stockholm, Sweden (P.L., U.L.); Centre for Health Equity Studies, Stockholm University/Karolinska Institutet, Stockholm, Sweden (O.L.).

Address correspondence and reprint requests to Petra Lindfors, PhD, Department of Psychology, Stockholm University and Centre for Health Equity Studies, Stockholm University/Karolinska Institutet, SE 106 91 Stockholm, Sweden. E-mail: pls{at}psychology.su.se.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 NOTES
 REFERENCES
 
Objective: To investigate how physiologic dysregulation, in terms of allostatic load and clinical risk, respectively, relates to sense of coherence (SOC) in women with no previously diagnosed pathology.

Methods: At baseline, 200 43-year-old women took part in a standardized medical health examination and completed a 3-item measure of SOC, which they completed again 6 years later. According to data from the medical examination, two different measures of physiologic dysregulation were calculated: a) a measure of allostatic load based on empirically derived cut points and b) a measure of clinical risk based on clinically significant cut points.

Results: In line with the initial hypotheses, allostatic load was found to predict future SOC, whereas clinical risk did not. In addition to baseline SOC and nicotine consumption, allostatic load was strongly associated with a weak SOC at the follow-up.

Conclusions: The better predictive value of allostatic load to clinical risk indicates that focusing solely on clinical risk obscures patterns of physiologic dysregulation that influence future SOC.

Key Words: allostatic load • clinical risk • medical examination • salutogenesis • sense of coherence • women

Abbreviations: ANOVA = analysis of variance; HbA1c = glycosylated hemoglobin; HDL = high-density lipoproteins; PEF = peak expiratory flow; SOC = sense of coherence; TC = total cholesterol; WHR = waist/hip ratio.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
METHODS
 RESULTS
 DISCUSSION
 NOTES
 REFERENCES
 
According to Antonovsky (1–3), an individual's sense of coherence (SOC) is essential to long-term health. The SOC is a global orientation that includes three theoretically related components: comprehensibility, manageability, and meaningfulness. These components describe the extent to which an individual perceives and interprets stimuli from different aspects of the environment as structured, predictable, and understandable. As a global orientation SOC involves the individual's taking into consideration a) whether these everyday life stimuli involve demands, b) whether these demands need to be dealt with, and c) whether there are resources available for dealing with these demands. The environmental stimuli can be more or less contradictory, and so dealing with them may lead to tension. Antonovsky (1–3) argues that this type of tension and stress has to be tackled to avoid negative stress. To deal successfully with the complexities of everyday life and remain healthy, all three components of SOC are necessary: an individual has to understand how it is possible to deal with an event and subsequently be able to deal with that event, as well as understand the implications of the actions taken. In keeping with Antonovsky (1–3), an individual's SOC may modify a stress reaction in various ways at various stages. For example, individuals with a strong SOC are less likely to experience stimuli as stressful, and consequently they do not suffer from the tension and stress that burden individuals with a weak SOC. Thus, the constantly ongoing and lifelong interplay between SOC and stress influences long-term health.

Although several studies have described the relationships between SOC and various health-related outcomes (4–7) and mortality (8), there have been few attempts to describe the physiologic mechanisms underlying these relationships. Considering that a strong and weak SOC are associated with different health-related outcomes (4–7), physiologic changes (i.e., risk factors) ought to be possible to detect in healthy individuals before disease onset. Previous studies have shown that women with a weak SOC have higher systolic blood pressure (9) and poorer lipid profiles (9,10) than do women with an intermediate or strong SOC. However, these studies have only examined the role of individual physiologic parameters as related to clinical risk for future ill health.

Extending the medical focus on individual physiologic parameters, clinically significant values, and the associated risk for future ill health, the concept of allostatic load has been proposed as a multisystems approach describing how daily stress relates to health and disease (11–13). In contrast to the notion of clinical risk, this model focuses on the individual's experience of challenging events and associated physiologic responses of the body. This model also takes into account the ability of bodily systems to reach stability through change and distinguishes between the effects of acute and chronic stress responses. Acute stress responses necessary to adapt to current demands posed by the environment have a protective effect when followed by periods of rest and recovery, whereas recurrent stress responses and a prolonged activation of different bodily systems increase wear and tear of bodily resources. Similar to the notion of clinical risk, the allostatic load model takes into account that well-functioning and healthy physiologic systems should respond with activity within a given, optimal range. But, in contrast to clinical risk, the allostatic load model acknowledges that, over the life course after years of challenges associated with the stress of dealing with everyday life, bodily systems may start to wear down. The consequences of such processes typically involve dysregulation in multiple bodily systems, which are characterized by physiologic responses deviating from the optimal range or by increased difficulties in returning to baseline levels or resting levels after various bodily challenges. For a detailed description on the physiologic processes underlying allostatic load, see McEwen (12) and McEwen and Seeman (13). This cumulative dysregulation may result in an allostatic load that, in turn, increases the risk for future ill health and disease. Importantly, the onset and consequences of the physiologic changes associated with allostatic load may have health-related effects before being considered clinically significant. Such effects have been found in research showing that allostatic load is a better predictor of health than is a doctor's diagnosis of a disease (14).

To reflect the multisystems approach, allostatic load has been operationalized as a summary indicator of physiologic challenge in multiple bodily systems (15). This summary indicator has recently been found to predict different health outcomes, including cardiovascular disease, cognitive decline, and mortality (14–17). With few exceptions, research on allostatic load has included negative health outcomes only (14–17). On the positive side, most studies have examined the associations between allostatic load and social relationships and provided support for the hypothesis that positive social experiences are associated with lower allostatic load (18,19).

Applying a multisystems approach, the present study set out to investigate how physiologic dysregulation, in terms of allostatic load and clinical risk, are related to SOC and each of its components in a cohort of healthy middle-aged women, representative of the general population. Drawing on previous research (1–3,12,14), allostatic load was hypothesized to be a better predictor of SOC and its components than was clinical risk.


   METHODS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
RESULTS
 DISCUSSION
 NOTES
 REFERENCES
 
Participants
Data came from the Individual Development and Adaptation research program (20,21) that includes individuals living in Örebro, a middle-sized Swedish town. In 1998, questionnaires were administered to all eligible 43-year-old women (N = 639) participating in the study, and a smaller but representative subsample (n = 369) was invited to participate in a routine medical examination (20). Six years later, a follow-up questionnaire was mailed to all the then 49-year-old women who remained in the study. Of the 629 eligible women, 514 volunteered to take part in the follow-up (22). Complete data from the health checkup and the two questionnaire studies were available for 311 participants. Initial analyses (not shown) showed no significant differences in terms of demographic factors and health-related measures between those who participated in the follow-up and the others (22). For the purpose of the present study, women with chronic diseases and/or on medication, known to affect physiologic parameters, were excluded, leaving 200 women with no previously diagnosed pathology for the statistical analyses. The research was approved by a local ethics committee.

Medical Examination
All medical examinations were performed by a district nurse at a local county health-care center. These involved the measurement of blood pressure, lung function, and blood sampling. In addition to these measurements, a subsequent health examination was performed by either of two local general practitioners (both women). Finally, all women were asked questions on nicotine consumption and their menstrual status. Nicotine consumption (i.e., smoking and using snuff) was assessed using single-item questions with dichotomous response alternatives (yes/no). The questions on menstrual status involved the time and regularity of periods, whether they had had a hysterectomy or received hormone replacement therapy or experienced any kind of menopausal symptoms such as irregular periods or hot flashes. The procedure was analogous to those regularly performed at the community health-care centers and was highly representative for examinations in general practice. A TriCuff and a HELP heart level pillow (AJ Medical, Lidingö, Sweden) were used to assess systolic blood pressure (mm Hg) and diastolic blood pressure (mm Hg) twice after 5 minutes of rest. A Spirometer Microlab 3300 (Meddela Medical AB, Täby, Sweden) was used to assess peak expiratory flow (PEF; a measure of lung function). Waist/hip ratio (WHR; a measure reflecting chronic metabolism) was calculated based on waist circumference (cm) measured at the narrowest point between the rib and iliac crest, and hip circumference (cm) at the maximal buttocks. Blood samples were drawn in fasting state to determine total cholesterol (TC), high-density lipoproteins (HDL), and glycosylated hemoglobin (HbA1c; an integrated measure of glucose metabolism during the previous 30–90 days). HbA1c was analyzed in blood, whereas TC and HDL were analyzed in plasma using standardized laboratory methods (23,24).

Measures of Physiologic Dysregulation
Both our measures of physiologic dysregulation (i.e., allostatic load and clinical risk) were based on the same set of physiologic parameters. Table 1 includes these parameters and the cut points used to determine allostatic load and clinical risk, respectively.


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  		TABLE 1. Criterion Cut Points, Percentage of Individuals With High Clinical Risk, and Descriptive Statistics for Parameters Included in the Measures of Physiologic Dysregulation for Healthy Women (N = 200) in the Study of Individual Development and Adaptation

 

Allostatic Load
Allostatic load has been operationalized as a summary indicator of cumulative physiologic dysregulation across different bodily systems (15). Following previous research, our index of allostatic load was determined empirically by adding the number of parameters for which the individual had a value in the highest load quartile, that is, the top quartile for all parameters except HDL and PEF for which values within the lowest quartile equal high load. High scores indicate high allostatic load. The quartile distribution of physiologic parameters is one way of identifying individuals with a higher activity in their physiologic systems and an increased risk for future ill health. Previous studies comparing this method with other ways of summarizing the data have yielded similar findings for various health-related outcomes (15).

Clinical Risk
An index of clinical risk was determined by established guidelines on clinical risk and intervals for normal function used in medical practice for blood pressure (25,26), for all parameters assessed in blood or serum (23,24), PEF (27), and WHR (28). Reference intervals for PEF were adjusted for age, gender, and height (actual versus expected value, that is, percentage of expected PEF), and individuals performing below expectation were coded as clinical risk. The index of clinical risk was calculated by adding the number of parameters for which the individual had a clinically significant value, that is, values outside the normal range or below specific cut points defined in Table 1. High scores indicate high clinical risk.

Questionnaires
In addition to questions on education, marital status, number of children, and menstrual status (i.e., menstruating or not), the questionnaires distributed in 1998 and 2004 included a measure of SOC.

SOC
All participants answered the three-item version of SOC that has been evaluated in a representative sample of the Swedish population aged 25 to 75 years (29). Based on the complex theoretical reasoning underlying Antonovsky's (1) original instrument, this brief measure consists of three questions, each corresponding to one of the components (i.e., manageability, meaningfulness, and comprehensibility) covered by the original instrument: a) Do you usually see solutions to problems and difficulties that other people find hopeless? (manageability), b) Do you usually feel that your daily life is a source of personal satisfaction? (meaningfulness), and c) Do you usually feel that the things that happen to you in your daily life are hard to understand? (comprehensibility). Answers are indicated in a 3-point response format including "yes, usually," scored as 0, "yes sometimes," scored as 1, and "no," scored as 2. After reversing of the scores on the third question, an additive index was calculated, with high scores indicating a weak SOC. Previous studies of the three-item measure have shown satisfactory test-retest reliability, and factor analyses have demonstrated that the items constitute a single factor similar to that of the original SOC measure (4,29). Also, a strong association (r = 0.66) has been reported between the three-item measure and the original scale. In addition, the relationships between the three-item measure and other variables have been found to be similar to those reported in research using the original 29-item SOC measure (4,8,29).

Statistical Analyses
Pearson's product-moment correlation coefficients were computed to examine relationships between total SOC scores at different points in time (i.e., item-to-item associations for each of the three components and item-to-scale associations for the SOC measure) and relationships between the measures of physiologic dysregulation. Such correlation coefficients were also computed to examine how individual physiologic parameters were related to the different measures of physiologic dysregulation. A t test for dependent measures was performed to examine potential time-related changes in SOC. An analysis of variance (ANOVA) was performed to investigate cross-sectional effects of SOC and education on physiologic dysregulation.

Because the women in the study group were likely to enter menopause during the time from baseline to follow-up, initial analyses were performed to investigate whether menstrual status was associated with other study variables at the different points in time. In 1998, 83.5% of the women reported that they were premenopausal. About 16.5% had stopped menstruating on account of their having had a previous hysterectomy and/or entered menopause. In 2004, the percentage of women who had stopped menstruating had increased to 27.7%. However, there were no significant relationships between menstrual status and the study variables for either time point, and, consequently, menstrual status was not included in subsequent analyses.

Hierarchic multiple regression analyses were performed to examine how a set of a priori defined variables predicts SOC scores in 2004. In contrast to the automated stepwise regression procedure, this approach is guided by theory or logic, meaning that the researcher controls the analysis (30,31). The predictors included marital status (0 = married or living with a partner; 1 = single), education (0 = high education, studies at university; 1 = low education, upper secondary school or lower), number of children, baseline full summary SOC scores (1998), nicotine consumption (0 = no; 1 = yes), and physiologic dysregulation. The order of entering predictors was based on theoretic (1–3,9) and logical assumptions of how these predictors can relate to SOC from a lifespan developmental perspective, with predictors presumed causally before others being entered early. Demographic characteristics were controlled for and entered in the first step. To investigate their relative importance, baseline SOC was entered in the second step, whereas nicotine consumption was entered in the third step. The fourth and final step included entering a measure of physiologic dysregulation. Hierarchic multiple regression analyses were performed using the total SOC score and each of the SOC components as outcomes. Such analyses were performed for both measures of physiologic dysregulation. In all analyses, the significance level was set to p < .05.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
DISCUSSION
 NOTES
 REFERENCES
 
Sample Characteristics
Most women (77.5%) were married or living with a partner. The vast majority (94.5%) had children, and most women (47.5%) had two children. A majority of the women (51.5%) had a lower education, and most of these (79.6%) had completed upper secondary school. Of the highly educated women, 81.4% had a university degree.

SOC
For each SOC component, the item-to-item correlation between scores in 1998 and 2004 were significant (manageability: rp [200] = 0.42, <0.0001; meaningfulness: rp [200] = 0.29, <0.0001; comprehensibility: rp [200] = 0.24, <0.001).

Scores on the total SOC measure ranged from 0 to 5 in 1998 and from 0 to 6 in 2004. The correlation between total SOC scores in 1998 and 2004 was significant (rp [200] = 0.46, p < .0001) as were the item-to-scale correlations (1998: rp ranging from 0.53 to 0.74, p < .0001; 2004: rp ranging from 0.49 to 0.72, p < .0001), with the lowest correlations emerging for the comprehensibility item. Analysis of total scores showed no significant differences (t [199] = –1.65, p > .05) in SOC between 1998 (M = 1.43, SD = 1.02) and 2004 (M = 1.55, SD = 1.03).

Allostatic Load and Clinical Risk
Descriptive statistics and cut points for the two measures of physiologic dysregulation are shown in Table 1. Scores on the allostatic load measure ranged from 0 to 7 (M = 1.55, SD = 1.38), and scores on the clinical risk measure ranged from 0 to 4 (M = 1.09, SD = 0.70). There was a significant association between the measures of allostatic load and clinical risk (r (200) = 0.45, p < .0001). Correlations between allostatic load, clinical risk, and the physiologic parameters are shown in Table 2. All physiologic parameters were significantly associated with the allostatic load index. For clinical risk, however, no significant associations emerged for HbA1c and HDL.


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  		TABLE 2. Correlations Between Individual Physiologic Parameters and Measures of Physiologic Dysregulation (N = 200)

 

With respect to cross-sectional relationships, there were no significant relationships between the total SOC scores and the two load measures. Similarly, no significant relationships were found for the three separate SOC items and the two load measures. Furthermore, there were no significant cross-sectional associations between SOC and education and allostatic load and clinical risk, respectively.

Predictors of SOC and Its Components
Table 3 shows results from hierarchic multiple regression analyses that were aimed to investigate the predictors of SOC and its components. Models 1 to 3 are similar for both allostatic load and clinical risk, and ANOVAs for these models were significant at p < .05. Of the demographic control variables entered in step 1, education and marital status were significant predictors: low education was associated with a weaker SOC at the follow-up (adjusted R2 = 0.04, p < .01), and being single was associated with less meaningfulness (adjusted R2 = 0.04, p < .05). When baseline scores of SOC or its components were added in step 2, the amount of variance explained increased sharply for SOC (R2 change = 0.18, p < .001) and manageability (R2 change = 0.17, p < .001). A similar increase was found for meaningfulness (R2 change = 0.07, p < .001) and comprehensibility (R2 change = 0.05, p < .001), respectively, but the amount of explained variance was more modest. In sum, these associations confirm that baseline scores are strongly associated with future scores. In step 3, nicotine consumption was found a significant predictor of SOC (adjusted R2 = 0.24, p < .05), with smoking being associated with a weaker SOC. Moreover, adding nicotine consumption rendered nonsignificant the effect of education. In contrast, nicotine consumption was not a significant predictor of any of the three components. In the final step, either measure of physiologic dysregulation was added: whereas clinical risk had no impact on future SOC (B = 0.16, SE B = 0.08; ß = 0.11, p > .05) and produced no change in the variance explained (adjusted R2 = 0.24; R2 change = 0.00, p > .05), allostatic load was significantly associated with future SOC (Table 3). This final model, including nicotine consumption, baseline SOC, and allostatic load as significant predictors, accounted for 26% of the variation (the ANOVA was significant at p < .0001). Separate analyses of the three components showed no significant relationships between the different measures of physiologic dysregulation and manageability or comprehensibility at follow-up. However, together with marital status and baseline meaningfulness, both clinical risk (B = 0.11, SE B = 0.05; ß = 0.14, p < .05) and allostatic load (B = 0.10, SE B = 0.03; ß = 0.22, p < .01) were significantly related to future meaningfulness. Specifically, in addition to being single and having less meaningfulness at baseline, a high clinical risk or a high allostatic load was associated with less meaningfulness at the follow-up. Comparing the results for clinical risk and allostatic load showed that the ANOVAs for both these predictors were significant (p < .0001). However, a slightly higher F value (7.42 versus 6.26) emerged for allostatic load. Also, adding clinical risk to the model produced a minor change in the variance explained (adjusted R2 = 0.14; R2 change = 0.02, p < .05), whereas adding allostatic load resulted in a slightly bigger change (adjusted R2 = 0.16; R2 change = 0.04, p < .01). To summarize, in the follow-up, clinical risk was associated with less meaningfulness only, whereas allostatic load was associated with both a weak SOC and less meaningfulness.


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  		TABLE 3. Predictors of Sense of Coherence and Its Components in Healthy Middle-Aged Women (N = 200)

 


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
NOTES
 REFERENCES
 
The present study showed that an integrated measure of physiologic dysregulation, that is, allostatic load, is a significant predictor of future SOC in middle-aged women with no previously diagnosed pathology. More important, allostatic load was a better predictor of future SOC than was clinical risk. Additional analyses of each SOC-component revealed that these relationships were significant for meaningfulness only. This time, both allostatic load and clinical risk were found significant predictors, but again, the results were somewhat clearer for allostatic load. These results are in line with previous cross-sectional research (32,33) showing relationships between physiology and purpose in life. However, those studies (32,33) also show linkages between physiology and other salutogenic components, which is in line with Antonovsky's (1–3) emphasis on combining different components. So even though meaningfulness emerged as a key component, associations were also found between allostatic load and total SOC scores.

To some extent, the present findings confirm our initial hypothesis, but they also replicate previous research (14,17) demonstrating the value of using a multisystems measure of allostatic load instead of focusing on solely clinical risk or individual physiologic parameters. Moreover, relating allostatic load to SOC and showing that it influences SOC over a fairly long follow-up period, the present research adds to previous research by demonstrating linkages between preclinical pathology and salutogenic aspects of health. In keeping with Antonovsky (1–3), such a weakened SOC may trigger a vicious circle: a weak SOC will even further reduce an individual's capacity to deal successfully with everyday life, which increases tension and stress and simultaneously adds to the wear and tear of bodily resources and increases the risk for future ill health. Conversely, a strong SOC triggers a positive process, which increases the chances for staying healthy.

In contrast to most previous studies of allostatic load that include older individuals and thereby maximize levels of allostatic load (34), this study investigated a group of middle-aged women with no previously diagnosed pathology. Thus, variations in physiologic parameters associated with age, gender, and poor health were minimized. Although this reduces the generalizability of the findings, investigating such a homogeneous sample yields a valuable description of the linkages between SOC and allostatic load, which is not confounded by physiologic changes associated with ill health and chronic disease. Then again, the focus on healthy women may have reduced the predictive power of the measure of clinical risk. Yet, even within a homogeneous group, individuals are likely to exhibit differences in levels of physiologic dysregulation, which partly reflect differences in life experiences and health behaviors, including menstrual status and nicotine consumption. Of these factors, menstrual status was not related to future SOC, which may be explained by the fact that the vast majority of the women were premenopausal. However, nicotine consumption (24% were smokers) remained a significant predictor of future SOC. Smoking and the fact that some of the women might have been unfamiliar with the spirometry procedure may explain why 43.5% of them performed below expectation in the assessment of lung function. Among potential unmeasured confounders not included in the predictive models are, for instance, psychosocial factors such as self-esteem, adaptive coping, and depression. However, recent research (8) has shown strong associations between SOC and mortality that are independent of other psychosocial factors.

The parameters included in the operationalization of physiologic dysregulation used in this study do not cover as many systems as would be desirable. For instance, other operationalizations of allostatic load have included stress hormones, reflecting activity within the HPA axis or the sympathoadrenomedullary system, and immunological parameters (14,17). But, similar to previous studies that do not include such parameters (34), the present research was restricted to the parameters available in a specific dataset. Apart from a measure of lung function (PEF), the parameters included in our operationalization of allostatic load mainly cover risk factors for cardiovascular and metabolic diseases. The use of such a proxy measure of allostatic load probably underestimates the linkages to SOC; conversely, this is less likely for clinical risk. This is because clinical cut points for stress hormones allow for high variations (23,24) and values above cut points are less likely to be found among healthy individuals. Also, two of our physiologic parameters (HbA1c and HDL) made no significant contribution to clinical risk, whereas all parameters contribute to the total sum of allostatic load. This means that the measure of allostatic load was less restricted in range and variation than that of clinical risk, something that is likely to have increased the sensitivity of the allostatic load measure. However, analyses (not shown) including markers of inflammation (albumin, white blood cells) and liver function (alanine aminotransferase and glutamyl transferase) produced similar findings.

Ideally, the study of the lifelong and reciprocal interplay between SOC and measures of cumulative physiologic dysregulation should involve the concurrent longitudinal assessments of both psychological and physiologic processes. Lacking such concurrent assessments, the present study design restricts conclusions concerning causality. Moreover, the data should preferably be analyzed using statistical techniques specifically designed to tease apart the different components and directions of causality involved in this lifespan developmental process. However, the present sample is too small to allow such an analysis (30,31), and, consequently, the findings need to be reproduced in larger and more diversified samples that permit cross-validation and the concurrent analysis of the complex interplay between physiologic and psychological processes.

To conclude, this study suggests that preclinical pathology manifested as allostatic load, together with previous levels of SOC, are important for predicting future SOC, particularly meaningfulness, in women. Because few studies have examined linkages between measures of physiologic dysregulation, this research extends previous findings by indicating linkages between allostatic load and salutogenic aspects of health.

We are grateful to the women who volunteered to participate in this study, which was part of the longitudinal research program Individual Development and Adaptation, led by Prof. Lars R. Bergman at the Department of Psychology, Stockholm University. Thanks must also go to the IDA II research team and to Ola Andersson for managing the data. Financial support for the data collection was provided by the Swedish Committee for the Planning and Coordination of Research, the Swedish Social Science Research Council, and the Örebro City Council.


   NOTES
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 NOTES
REFERENCES
 
Received for publication September 23, 2005; revision received March 15, 2006.

This study was supported by grants to Prof. Ulf Lundberg from the Bank of Sweden Tercentenary Foundation and the Swedish Research Council and to Petra Lindfors from the Anna Ahlström and Ellen Terserus Foundation.

DOI:10.1097/01.psy.0000232267.56605.22


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 NOTES
 REFERENCES
 

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D. Hasson, U. Von Thiele Schwarz, and P. Lindfors
Self-rated Health and Allostatic Load in Women Working in Two Occupational Sectors
J Health Psychol, May 1, 2009; 14(4): 568 - 577.
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