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Health inequalities in Israel: Explanatory factors of socio-economic inequalities in self-rated health and limiting longstanding illness
ARTICLE IN PRESS Health & Place 16 (2010) 242–251
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Health inequalities in Israel: Explanatory factors of socio-economic inequalities in self-rated health and limiting longstanding illness Varda Soskolne a,n, Orly Manor b a b
The Louis and Gabi Weisfeld School of Social Work, Bar-Ilan University, Ramat-Gan 52900, Israel. Braun School of Public Health, The Hebrew University of Jerusalem, Israel
a r t i c l e in f o
a b s t r a c t
Article history: Received 12 July 2009 Received in revised form 3 October 2009 Accepted 6 October 2009
We examined an integrated multi-level model of psychosocial, community and behavioral factors as explanatory pathways to socio-economic inequalities in health in Israel. Using a random national sample of 1328 individuals aged 30–70 and measurements of socio-economic position (education, number of cars), health outcomes—self-rated health, limiting longstanding illness (LLI), we evaluated the contribution of psychosocial factors (stressors and psychosocial resources), community factors (individual and aggregate-level social participation and social capital) and health behaviors, to the explanation of health inequalities. Community factors contributed more than psychosocial factors or health behaviors. The integrative model provided an explanation of social inequalities in both health outcomes and a full explanation for the education-LLI association. & 2009 Elsevier Ltd. All rights reserved.
Keywords: Health inequalities Self-rated health Limiting longstanding illness Explanatory factors Israel
1. Introduction Studies on socio-economic position (SEP) inequalities in health have generated consistent evidence about the scope of the inequalities (Dunn et al., 2006; Lantz et al., 2005). In the last decade, these inequalities have widened (Mackenbach et al., 2003; Jaffe et al., 2008) or remained stable (Lahelma et al., 2002). Yet, the mechanisms responsible for these inequalities are not well understood (Adler, 2006). Identifying the specific factors in each country is thus important for adaptation of interventions to reduce health inequalities (Mackenbach et al., 2002). In Israel, prior studies have already highlighted inequalities in health by socio-economic position (SEP) (e.g., Manor et al., 1999; Israel Ministry of Health, 2006), but have not considered factors explaining these inequalities. The present study examined a model of individual and community-level explanatory factors of socio-economic inequalities in health in Israel. One of the major explanatory approaches to health inequalities (Bartley, 2004), the ‘‘psychosocial environment’’ explanation suggests that material factors alone are not sufficient for explaining disparities in health, and views individual psychosocial or community factors as linking SEP to health via mediating pathways to health-related behaviors and to biological response (Brunner and Marmot, 1999; Marmot and Wilkinson, 2001). The differential psychosocial vulnerability—a higher exposure to
n
Corresponding author. Tel.: +972 3 5317806; fax: + 972 3 7384042. E-mail address: [email protected] (V. Soskolne).
1353-8292/$ - see front matter & 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.healthplace.2009.10.005
stress, limited personal control and social support (Siegrist and Marmot, 2004), and more severe cognitive perception of stress of those in lower socio-economic positions may produce negative emotions leading to poorer health (Taylor and Seeman, 1999). Wilkinson (1997) argues that adverse health effects are not produced by low absolute income but by the stress of perceived income relative to others. Community factors, such as neighborhood characteristics, community socio-economic level and social capital have also been advocated as linking individual SEP to health (Kawachi et al., 1997; Poortinga, 2006a). In Israel, all permanent residents are insured for basic medical services under the National Health Insurance Law (1994). Yet, health inequalities persist between the major social divisions in Israeli society—between Jews and Arabs, and between ethnic and social groups within the Jewish population (Shuval and Anson, 2000). The issue of health differences between Jews and Arabs is important. Nevertheless, to avoid the effects of cultural differences, the current study focused on the Jewish population, and a separate report on inequalities within the Arabs is forthcoming. Studies within the Jewish population have demonstrated a clear social gradient in mortality rates (Manor et al., 1999, 2000), and in self-reported health (Israel Ministry of Health, 2006; Tamir et al., 1998). However, no study has examined the factors that contribute to these inequalities. While there may be disagreement about the specific explanations, there is consensus that social position is linked to health via complex multilevel pathways (Singh-Manoux, 2003). Only a handful of studies have attempted to examine these explanations in an integrated manner. One study found that material variables, alone or in combination with behavioral or psychosocial variables,
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explained the association of education with mortality (van Oort et al., 2005). In contrast, a cross-sectional study of inequalities in self-rated health, psychosocial variables and health behaviors, but not material factors, explained the association of education with health (Molarius et al., 2007). Another controversy surrounds the measurement of social capital as a community or an individuallevel variable. Recent studies have attempted to overcome the disagreements by using both individual and aggregate community-level variables (Kim and Kawachi, 2006; Poortinga, 2006b). Drawing upon Stress and Coping Theory (Lazarus and Folkman, 1984), and upon comprehensive psychosocial environment conceptual frameworks (e.g., Brunner and Marmot, 1999; Robert, 1999), an integrated model of individual and community-level explanatory factors of health inequalities was outlined for the present study in order to examine their relative explanatory contribution. The conceptual framework (Fig. 1) delineates the groups of factors and their integration as explanatory factors of the association of SEP variables with health: (a) Individual-level psychosocial factors; (b) community social environment characteristics, measured as both individual and aggregate, community-level variables; (c) the combined multi-level associations—the direct link of SEP and health together with psychosocial and community variables; (d) Healthrelated behaviors; (e) finally, the integrated model is represented by the combined multi-level associations: the direct link of SEP with health together with psychosocial, social environment and behavioral factors and with the community-level socioeconomic score.
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It is important to note, however, that although the model outlines the associations from SEP to individual health, some may go in both directions. Additionally, this framework does not attempt to encompass psychobiological stress processes and the inter-correlations between psychosocial, community and behavioral variables, which are beyond the scope of the present study. Access to health care was omitted after preliminary findings showed that almost all interviewees reported easy access. Based on the above model of individual and community level factors, we aimed to examine the associations between SEP measures and physical health status, and to assess the degree to which the associations are mediated by psychosocial, community or behavioral factors and their combinations. We opted to employ several SEP measures, as they represent different dimensions and forces associated with health (Bartley, 2004), and to tap their associations with two different health outcomes.
2. Methods 2.1. Sample and sampling A national representative random sample of Jewish Israeli citizens aged 30–70 living in urban areas was selected in stages. First, using stratified sampling, only urban local authorities were selected from the Israeli Central Bureau of Statistics (CBS) list,
Demographic control variables (a)
(b)
Community environment
SEP Individual psychosocial variables
- Neighborhood conditions - Social capital, individual and community levels: trust, reciprocity, help; social participation
- Stressors (acute and chronic) - Cognitive perception of stressors - Psychosocial resources (mastery, social support, coping efficacy)
(d)
(a)
(b)
Community-level socioeconomic score
Health behaviors
(c) (e)
(e)
(d) (e)
(c) (e)
INDIVIDUAL HEALTH
Note: Some of the associations may go in both directions and could be presented by bidirectional arrows. Dashed lines represent associations not examined in the present study. Pathways of explanatory factors are labeled with letters (a) to (e). See text for details. Fig. 1. . Outline of the conceptual model of the study.
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based on the size and socio-economic rank (CBS, 1999), yielding 54 local authorities. Second, within these authorities, statistical areas in which Jewish residents comprised more than 50% of residents were selected (1190 statistical areas). Third, the statistical areas were stratified according to three factors: geographical region of the country (four regions), religiosity (Orthodox religious or not Orthodox) and socio-economic score (a range of 20 ranks). Random sampling of statistical areas was conducted in each stratum, based on its size and the size of the population, resulting in 49 statistical areas. In the final stage, 50 addresses were randomly sampled from each statistical area. Of these, 1958 households were eligible for the study (the others were non-residential, incorrect addresses or households with persons ineligible for inclusion). One person in each household was interviewed, alternating between men and women. A total of 1328 (68%) persons completed the interview, 521 (27%) refused to be interviewed and the rest were not located after four visits. 2.2. Data collection procedures The study was approved by the Hebrew University—Hadassah Review Board. Data were collected in late 2003 by means of faceto-face interviews at home in Hebrew or in Russian (due to the large influx of immigrants to Israel from the former Soviet Union in the 1990s). Community socio-economic score was retrieved from the CBS (1999). 2.3. Participants The mean age of the participants was 48.2 (SD= 11.9) and 45% were male. The majority (76%) were married; 54% were born in Israel, 28% immigrated before 1990 and 18% since 1990 (almost all from the former Soviet Union); 47% defined themselves as secular, 35% as traditional (observing the main religious traditions) and 17% as religious or ultra-Orthodox. These main demographic characteristics of the final sample were very similar to those of the general Jewish population in this age range (CBS, 2004), thereby assuring the representativeness of our sample. 2.4. Measures 2.4.1. SEP measurements Two widely used measures were selected. Level of education was measured as years of schooling, and further collapsed into four levels: 0–8 years, 9–12 years, 13–15 years and 16+ years. Financial assets were measured as the number of cars in the household’s possession and categorized into three levels: none, one, two or more. 2.4.2. Health measurements Two global indicators of morbidity, which are related but not overlapping and have been shown to be valid indicators of health status were selected. Self-rated health (SRH) was measured using a 5-level ordinal single item of an overall assessment of health status (Idler and Benyamini, 1997). For the analyses, this indicator was dichotomized into ‘‘0’’ good (very good and good) vs. ‘‘1’’ poor (fair, poor and very poor). Limiting longstanding illness (LLI), a ‘‘no’’ vs. ‘‘yes’’ measure of functional limitation, intended to focus on chronic conditions that impair an individual’s daily activities (Manor et al., 2001). 2.4.3. Individual psychosocial variables Brief questionnaires that have satisfactory psychometric properties and are suitable for health surveys (Karlsson et al., 1995) were preferred over lengthy questionnaires in order to
reduce respondent burden. Several psychosocial variables which followed a highly skewed distribution prior or after transformation were dichotomized by choosing up to the lowest third to denote low levels of each measure vs. the highest two thirds. (a) Stressors: Two abbreviated scales, adapted from a study on health differences (McDonough and Walters, 2001) were used. (1) Recent life events measured positive responses to nine events that happened to the respondent or someone close to the respondent in the past year, including an item unique to the situation in Israel at the time of the study (exposure to terror). (2) Chronic stressors measured positive responses to an 8-item scale reflecting enduring financial, social life, relationship and work stress. The two scales were further dichotomized into ‘‘0’’ low (no event or 0–3 chronic stressors), or ‘‘1’’ high levels (1+ events or 4+ chronic stressors). (b) Cognitive appraisal of the stressors was assessed by asking the respondent to rate the level of personal stressfulness in relation to each of the life events experienced, ranging from ‘‘1’’—not at all, to ‘‘5’’—to a large extent. The sum score was dichotomized into ‘‘0’’ low stress (mean scores of 0–6), or ‘‘1’’ high stress (score of 7 or more). (c) Psychosocial resources were measured by three items: (1) Mastery: A 7-item scale measuring the ability to deal with or exert control over issues as they arise in people’s lives was used (Pearlin and Schooler, 1978). The total mean score ranges from ‘‘0’’ to ‘‘4’’ (high levels of mastery). Cronbach’s alpha was 0.84. (2) Coping efficacy: two face-valid items assessed efficacy in coping with daily disruptions and emotional stress caused by the most severe recent event (Manne and Glassman, 2000). The sum of the responses is used, (range 2–10), with higher scores representing higher coping efficacy. (3) Social support was measured by a brief 6-item scale tapping emotional, material and informational support, adapted from large-scale social surveys (Karlsson et al., 1995). Cronbach’s alpha was 0.89. The mean score was dichotomized into ‘‘0’’ high support, vs. ‘‘1’’ low support.
2.4.4. Community social environment variables (a) Two individual-level variables were included: (1) Neighborhood living conditions—an adapted 11-item scale measuring socio-environmental problems such as noise, litter and rubbish, vandalism and burglaries (Steptoe and Feldman, 2001). Each item ranged from ‘‘1’’—not a problem, to ‘‘3’’— a severe problem. The items were summed, with higher scores representing severe problems; (2) Social capital: we adopted the structural/cognitive distinction that differentiates between the concepts of civic engagement—the extent to which citizens involve themselves in their communities, and the cognitive perception of mutual trust and solidarity among community members (Kawachi et al., 1997). Since data on civic engagement (as measured at the community level by per capita group membership) is not available in Israel, we relied on the respondents’ reports. Social participation was measured by nine items adapted from a scale of membership in formal or informal groups (Lindstrom et al., 2001), and by a civic engagement measure used in the United States (Kawachi et al., 1997), ranging from ‘‘0’’ not at all/rarely, to ‘‘2’’ very often. Cronbach’s alpha was 0.68. Social trust was assessed by three ‘‘yes’’ vs. ‘‘no’’ items that measured ‘‘perceived lack of fairness’’, ‘‘social trust’’ and ‘‘perceived helpfulness’’ (Kawachi et al., 1997), all categorized as ‘‘1’’ negative vs. ‘‘0’’ positive response. (b) Two community-level variables were employed: (1) Social capital: similar to methods used in previous research
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(e.g., Kim and Kawachi, 2006), the individual-level variables were aggregated by computing the mean of each variable among respondents within each statistical area. This approach produces a risk of same source bias. However, we could not avoid it, as there was no available data for each statistical area. 2) Socio-economic community score of the statistical area of residence (CBS, 1999). 2.4.5. Health behaviors Three items measuring smoking, physical activity and protective practices against the sun were taken from previous surveys in Israel (Tamir et al., 1998) and an additional question measuring eating a balanced diet of daily portions of fruits, vegetables, proteins, grains and dairy products (Pandey et al., 2003) were used. The response categories to all the items were recorded on a 4-level ordinal scale. (1) Smoking—never smoked, past smoker, current light smoker and current heavy smoker. (2) Physical activity lasting at least 20 min—every day, 1–2 times/week, seldom and never. 3. Use of sun protection—always, sometimes, seldom and never. 4. Diet—very balanced, balanced, somewhat unbalanced, not at all balanced. For the analysis, a scale of health behaviors was calculated as the mean of these four behaviors, ranging from ‘‘1’’—healthy behaviors, to ‘‘4’’—unhealthy behaviors. 2.4.6. Demographic control variables The respondents were asked about their age, gender, marital status, religiosity (secular, traditional, religious), country of birth and year of immigration. 2.5. Statistical analyses Analyses were carried out separately for each health measure. In the preliminary stage, the socio-demographic control variables were examined, and showed that adjustment for marital status, household size, religiosity, country of birth and year of immigration did not affect the association between SEP and health. Furthermore, this association did not vary by age or gender; hence, age and gender were controlled for in all bivariate or multivariate analyses. The next step involved the examination of the bivariate associations between SEP and each of the explanatory variables, and between the latter and SRH or LLI, using logistic regression models or ANOVA. Only variables significantly associated with both the socio-economic and health measures (potential explanatory variables) were included in the multivariate analyses. In addition, the correlations within each group of explanatory variables were examined in order to avoid multicolinearity in the regression models, but no variable had to be omitted. Finally, multilevel multivariate logistic analysis was carried out with individuals as level-one units, and statistical areas as level-two units (Rasbash et al., 2005). No cross-level interactions were examined. Modeling was conducted in stages according to six models: Model 1 included the main association between SEP and health (age and gender adjusted). In the following stages, groups of variables were added to Model 1. Model 2 included psychosocial variables only. Model 3 included community social environment variables only, and—to allow for the full potential contribution of the social capital variables—they were added at both the individual and the area levels (Kim and Kawachi, 2006). Model 4 included both the psychosocial and the community social environment variables. Model 5 included health behaviors only. Finally, Model 6 examined the final integrated model, which included the area-level socioeconomic score, in addition to all the other variables. An interim model (data are not shown but the results may be obtained from the authors)
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examined an integrated model that included psychosocial, community and health behavior variables. SEP disparities were evaluated by the odds of poor health in each SEP category, and, as a summary measure, the odds ratio between the extreme SEP categories was used (Manor et al., 1997). The contribution of each group of variables was assessed by calculating the percent of decline in the ln (OR) between the extreme categories in each of the Models, 2 through 6, compared to Model 1 (Lin et al., 1997).
3. Results 3.1. Distribution of SEP, health measures and explanatory variables The distribution of education level (0–8 years—11.6%, 9–12 years—41.7%, 13–15 years—23.6%, 16+ years—23.1%) and of the number of cars (none—38.8%, one—47%, two or more—14.2%) in the study sample was similar to that in the general Israeli population (CBS, 2004). A third (33.2%) of the respondents rated their health as poor, and 25% reported having a limiting, chronic disease. The distribution of the explanatory variables is presented in Table 1a–c, first rows. Significant proportions reported low levels of the community measures, and medium levels of social participation and neighborhood problems. 3.2. Bivariate associations between SEP and explanatory variables Both SEP measures were significantly associated with most of the psychosocial variables, but not with chronic stress (Table 1a). Additionally, only car ownership was associated with social support, yet we opted to include it in the multivariate analyses in order to obtain equivalent models for both outcomes. Of the community variables (Table 1b), SEP measures were associated only with social trust and social participation, but not with neighborhood living conditions. There was a clear indication that those with lower education levels and fewer cars reported higher exposure to stressors, lower levels of psychosocial resources and lower social trust and participation, although not significant in all categories. Similarly, every decrease in education level or in the number of cars was associated with more unhealthy behaviors (Table 1c). 3.3. Bivariate associations between SEP and health The associations of education and number of cars with SRH and LLI reveal a significant and consistent gradient of health inequalities (Tables 2–4, Model 1). Compared with the highest level of education or number of cars, the likelihood of reporting poor SRH or having an LLI increased gradually in almost all other levels. The gaps between the education levels or number of cars in SRH were greater than in LLI. 3.4. Bivariate associations between explanatory variables and health All those psychosocial, community and health behavior variables significantly associated with SEP were also associated with SRH and LLI: lower levels of all these variables were associated with higher odds of poor SRH and an LLI (p o0.001, data not shown). Additionally, multi-level analysis adjusting for confounders, yielded a significant association of area socioeconomic score with SRH (OR for middle and low socio-economic tertiles compared to the high tertitle were 1.80 and 1.88, respectively, p o0.001). The association of area socio-economic score with LLI reached statistical significance only for the middle
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Table 1 Prevalence of psychosocial and community factors and health behaviors and associations with SEPa. (a) Individual psychosocial variables Stressful life events Percentb Mean (SD), rangec
Education (years) 0–8 9–12 13–15 Z 16 Cars (number) 0 1 Z2
High= 37.9%
Education (years) 0–8 9–12 13–15 Z 16 Cars (number) 0 1 Z2
High= 23.5%
d
Appraisal of stress
Social support
High= 30.6%
Low= 23.7%
e
Mastery
Coping efficacy
3.06 (0.63), 1–4
7.11 (1.72), 2–10
Difference in B
Difference in B
OR (95% CI)
OR (95% CI)
OR (95% CI)
OR (95% CI)
1.36 (0.90, 2.06) 1.56 (1.16, 2.09) 0.98 (0.70, 1.37) 1
1.23 (0.75, 2.03) 1.15 (0.82, 1.61) 1.16 (0.79, 1.69) 1
1.33 (0.85, 2.09) 1.90 (1.39, 2.61) 1.09 (0.76, 1.58) 1
1.46 (0.91, 2.35) 1.36 (0.96, 1.95) 1.44 (0.97, 2.14) 1
1.58 (1.10, 2.26) 1.34 (0.94, 1.90) 1
1.28 (0.85, 1.93) 1.14 (0.76, 1.70) 1
1.92 (1.30, 2.83) 1.440 (0.98, 2.12) 1
2.14 (1.37, 3.35) 1.38 (0.88, 2.16) 1
0
Trust
Unhelpfulnessd
Social participation
Neighborhood problemsd
Low= 60.5%
High= 52.3% 0.83 (0.37), 0–2
18.52 (4.76), 7–33
Difference in B
Difference in B
(b) Community variables Lack of fairnessd Percent b Mean (SD), range
Chronic stressors
High= 29.3% c.
OR (95% CI)
OR (95% CI)
OR (95% CI)
1.33 (0.87, 2.04) 1.10 (0.81, 1.50) 0.84 (0.58, 1.20) 1
2.21 (1.45, 3.39) 1.86 (1.39, 2.48) 1.09 (0.79, 1.50) 1
1.17 (0.78, 1.76) 1.21 (0.91, 1.61) 0.82 (0.60, 1.13) 1
1.40 (0.95, 2.06) 1.35 (0.92, 1.97) 1
1.57 (1.12, 2.20) 1.40 (1.01, 1.95) 1
0.91 (0.65, 1.28) 0.92 (0.66, 1.28) 1
0
1.06 0.65 0.30 0
0.40 0.13
0.30 0.20 0.08
0.35 0.66 0.14 0
0.34 0.14
0.51 0.00 0
0
0
0.24 0.13 0.05
0.84 0.28 0
(c) Health behaviors: Unhealthy behavior scale 2.15 (0.69), 1–4 Mean (SD), rangec Difference in B Education (years) 0–8 9–12 13–15 Z 16 Cars (number) 0 1 Z2
0.45 0.23 0.11 0 0.25 0.09 0
a Logistic regressions or Anova, all adjusted for age and gender. The associations of education or number of cars with the explanatory variables are significant at p o0.001 unless otherwise stated. b Dichotomous variables: the rates are presented for category coded as ‘‘1’’ (vs. ‘‘0’’). c Continuous variables: higher scores indicate a higher level of the measure. d Non-significant associations with education and the number of cars. e Non-significant associations with education; significant association (po 0.001) with the number of cars.
tertile compared to the high tertile (OR= 1.45, p = 0.03), but not for the lowest tertile (OR=1.35, p = 0.089). 3.5. Multilevel analyses of explanatory pathways Examining the contribution of each set of variables (Tables 2– 5, Models 2–6) to the associations between SEP measures and the two health outcomes (Tables 2–5, Model 1) shows certain differences and similarities. (a) Models for SRH: the strong associations of education or car ownership with SRH were attenuated, but remained statisti-
cally significant in all the models (Tables 2 and 3), suggesting only a partial explanation of the associations. The inclusion of the community variables alone contributed to larger decreases in the strength of the associations of education or car ownership with SRH than the inclusion of psychosocial or behavioral variables. For example, the community variables contributed a 45% reduction in the inequalities by education, compared with 24% and 23% for the psychosocial variables or health behaviors, respectively. Compared to Model 1, the associations were further reduced after the combined inclusion of the psychosocial together with the community environment variables (Model 4, Tables 2 and 3) but only 6– 9% above the attenuation of the OR by community variables
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Table 2 Odds ratios for self-rated health from multi-level models: education as SEP measure (n= 1258)a.
Education, years
Psychosocial variables Stressful life events
Appraisal of stressors Social support
0–8 9–12 13–15 16 +
Model 1 OR (95% CI)
Model 2 OR (95% CI)
Model 3 OR (95% CI)
Model 4 OR (95% CI)
Model 5 OR (95% CI)
Model 6 OR (95% CI)
4.38 (2.72, 7.05) 2.38 (1.65, 3.43) 1.93 (1.29, 2.88) 1.00
3.33 (2.02, 5.50) 1.93 (1.32, 2.84) 1.78 (1.17, 2.71) 1.00
2.38 (1.45, 3.89) 1.59 (1.09, 2.33) 1.71 (1.12, 2.59) 1.00
2.15 (1.28, 3.61) 1.42 (0.95, 2.11) 1.64 (1.07, 2.51) 1.00
3.63 (2.23, 5.91) 2.17 (1.50, 3.15) 1.85 (1.23, 2.77) 1.00
2.06 (1.22, 3.49) 1.39 (0.93, 2.08) 1.61 (1.05, 2.48) 1.00
Z2 0–1
0.74 (0.41, 1.34) 1.00
0.75 (0.41, 1.36) 1.00
0.73 (0.40, 1.33) 1.00
High Low Low High
1.65 1.00 1.33 1.00 0.55 0.78
1.65 1.00 1.13 1.00 0.64 0.81
1.70 1.00 1.16 1.00 0.65 0.82
Masteryb Coping efficacyb Community variables Trust—individual
(0.88, 3.07) (0.96, 1.84) (0.43, 0.70) (0.71, 0.86)
Low High
Trust—aggregateb Social participation Individual Aggregateb Health behaviorsb Area socio-economic score
b c
(0.81, 1.58) (0.50, 0.82) (0.74, 0.89)
(0.91, 3.18) (0.83, 1.62) (0.50, 0.83) (0.75, 0.90)
1.83 (1.36, 2.46) 1.00 0.91 (0.32, 2.54)
1.69 (1.25, 2.30) 1.00 0.83 (0.26, 2.64)
1.70 (1.25, 2.31) 1.00 0.91 (0.29, 2.90)
0.17 (0.11, 0.27) 0.36 (0.13, 1.02)
0.25 (0.16, 0.39) 0.44 (0.14, 1.40)
0.27 (0.17, 0.42) 0.32 (0.10, 1.01) 1.21 (0.97, 1.51)
1.60 (1.31, 1.95) Tertile III (low) Tertile II (medium) Tertile I (high)
Changec a
(0.89, 3.07)
0.73 (0.40, 1.36) 1.02 (0.66, 1.58) 1.00 24%
45%
51%
23%
54%
All models are adjusted for age and gender. Higher scores represent higher levels of mastery, coping efficacy, social participation, and unhealthy behaviors and lower levels of aggregate social trust. Change in ln(OR) between Model 1 and each of the following models.
Table 3 Odds ratios for self-rated health from multi-level models: the number of cars as SEP measure (n= 1256)a.
Car ownership
Psychosocial variables Stressful life events Appraisal of stressors Social support
0 1 2+
Model 1 OR (95% CI)
Model 2 OR (95% CI)
Model 3 OR (95% CI)
Model 4 OR (95% CI)
Model 5 OR (95% CI)
Model 6 OR (95% CI)
5.24 (3.23, 8.51) 2.09 (1.31, 3.36) 1.00
3.77 (2.27, 6.25) 1.90 (1.17, 3.10) 1.00
2.80 (1.68, 4.64) 1.62 (1.00, 2.65) 1.00
2.40 (1.42, 4.07) 1.55 (0.94, 2.56) 1.00
4.97 (3.03, 8.15) 2.08 (1.28, 3.36) 1.00
2.50 (1.47, 4.27) 1.57 (0.95, 2.59) 1.00
Z2 0–1 High Low Low High
Masteryb Coping efficacyb Community variables Trust—individual
0.74 1.00 1.60 1.00 1.22 1.00 0.61 0.77
Low High
Trust—aggregateb Social participation Individual Aggregateb Health behaviorsb Area socio-economic score
Changec a b c
(0.41, 1.35)
0.72 (0.40, 1.32) 1.00 1.64 (0.87, 3.07) 1.07 (0.76, 1.50) 1.00 0.68 (0.53, 0.88) 0.81 (0.74, 0.89)
0.71 1.00 1.70 1.00 1.10 1.00 0.69 0.82
1.81 (1.35, 2.44) 1.00 1.13 (0.38, 3.35)
1.67 (1.23, 2.27) 1.00 1.01 (0.31, 3.32)
1.67 (1.23, 2.26) 1.00 1.14 (0.35, 3.78)
0.18 (0.11, 0.28) 0.61 (0.20, 1.90)
0.25 (0.16, 0.39) 0.67 (0.20, 2.30)
0.28 (0.17, 0.44) 0.46 (0.34, 1.62)
(0.85, 3.01) (0.88, 1.70) (0.47, 0.78) (0.71, 0.85)
1.65 (1.35, 2.01) Tertile III (low) Tertile II (medium) Tertile I (high) 20%
38%
47%
4%
(0.38, 1.29) (0.90, 3.20) (0.78, 1.55) (0.53, 0.89) (0.75, 0.90)
1.26 (1.02, 1.57) 0.68 (0.36, 1.30) 1.01 (0.64, 1.60) 1.00 45%
All models are adjusted for age and gender. Higher scores represent higher levels of mastery, coping efficacy, social participation, and unhealthy behaviors and lower levels of aggregate social trust. Change in ln(OR) between Model 1 and each of the following models.
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Table 4 Odds ratios for limiting longstanding illness from multi-level models: education as SEP measure (n= 1257)a.
Education, years
0–8 9–12 13–15 16+
Psychosocial variables Stressful life events
Appraisal of stressors Social support
Model 1 OR (95% CI)
Model 2 OR (95% CI)
Model 3 OR (95% CI)
Model 4 OR (95% CI)
Model 5 OR (95% CI)
Model 6 OR (95% CI)
1.98 (1.24, 3.17) 1.71 (1.18, 2.48) 1.00 (0.65, 1.54) 1.00
1.55 (0.95, 2.53) 1.43 (0.98, 2.10) 0.92 (0.59, 1.42) 1.00
1.36 (0.84, 2.22) 1.33 (0.91, 1.96) 0.91 (0.59, 1.40) 1.00
1.23 (0.75, 2.04) 1.22 (0.82, 1.81) 0.87 (0.56, 1.35) 1.00
1.78 (1.10, 2.87) 1.62 (1.12, 2.36) 0.97 (0.63, 1.49) 1.00
1.21 (0.72, 2.01) 1.22 (0.82, 1.81) 0.86 (0.55, 1.35) 1.00
Z2 0–1
1.13 (0.63, 2.01) 1.00
1.12 (0.62, 1.99) 1.00
1.09 (0.61, 1.95) 1.00
High Low Low High
1.25 1.00 1.01 1.00 0.65 0.88
1.26 1.00 0.92 1.00 0.70 0.90
1.31 1.00 0.95 1.00 0.70 0.91
Masteryb Coping efficacyb Community variables Trust—individual Trust—aggregate
(0.68, 2.31) (0.72, 1.41) (0.51, 0.83) (0.81, 0.97)
Low High
Health behaviorsb Area socio-economic score
c
(0.55, 0.90) (0.82, 0.99)
(0.67, 1.34) (0.55, 0.90) (0.83, 1.00)
1.31 (0.97, 1.78) 1.00
1.23 (0.90, 1.67) 1.00
1.23 (0.90, 1.68) 1.00
1.21 (0.43, 3.46)
1.11 (0.35, 3.52)
1.20 (0.39, 3.71)
0.35 (0.23, 0.55) 0.96 (0.33, 2.74)
0.46 (0.29, 0.72) 1.10 (0.35, 3.47)
0.47 (0.30, 0.74) 0.70 (0.17, 2.91) 1.27 (1.03, 1.56)
Tertile III (low) Tertile II (medium) Tertile I (high)
Changec
b
(0.65, 1.30)
(0.71, 2.42)
b
Social participation Individual Aggregateb
a
(0.69, 2.31)
35%
54%
69%
15%
1.05 (0.84, 1.31) 0.68 (0.37, 1.25) 1.06 (0.69, 1.63) 1.00 71%
All models are adjusted for age and gender. Higher scores represent higher levels of mastery, coping efficacy, social participation, and unhealthy behaviors and lower levels of aggregate social trust. Change in ln(OR) between Model 1 and each of the following models.
Table 5 Odds ratios for limiting longstanding illness from multi-level models: number of cars as SEP measure (n= 1255)a.
Car ownership
Psychosocial variables Stressful life events Appraisal of stressors Social support
0 1 2+
Model 1 OR (95% CI)
Model 2 OR (95% CI)
Model 3 OR (95% CI)
Model 4 OR (95% CI)
Model 5 OR (95% CI)
Model 6 OR (95% CI)
3.13 (1.91, 5.14) 1.80 (1.10, 2.95) 1.00
2.38 (1.42, 3.99) 1.63 (0.98, 2.70) 1.00
2.27 (1.34, 3.83) 1.57 (0.95, 2.59) 1.00
1.97 (1.15, 3.38) 1.49 (0.90, 2.48) 1.00
3.08 (1.86, 5.11) 1.84 (1.11, 3.03) 1.00
2.03 (1.22, 3.60) 1.50 (0.90, 2.49) 1.00
Z2 0–1 High Low Low High
Masteryb Coping efficacyb Community variables Trust—individual
1.18 1.00 1.23 1.00 0.94 1.00 0.69 0.88
1.16 1.00 1.24 1.00 0.88 1.00 0.74 0.90
(0.67, 2.27) (0.67, 1.32) (0.54, 0.89) (0.80, 0.96)
(0.65, 2.09)
1.12 1.00 1.31 1.00 0.90 1.00 0.75 0.91
(0.67, 2.29) (0.62, 1.24) (0.58, 0.95) (0.82, 0.99)
(0.62, 2.02) (0.71, 2.42) (0.64, 1.28) (0.58, 0.97) (0.83, 0.99)
Trust—aggregateb
1.36 (1.01, 1.85) 1.00 1.53 (0.53, 4.43)
1.27 (0.93, 1.73) 1.00 1.36 (0.43, 4.32)
1.26 (0.93, 1.72) 1.00 1.53 (0.50, 4.67)
Social participation Individual Aggregateb
0.38 (0.25, 0.59) 1.49 (0.49, 4.55)
0.47 (0.30, 0.75) 1.52 (0.46, 5.09)
0.50 (0.31, 0.79) 0.75 (0.18, 3.16)
Health behaviorsb Area socio-economic score
Changec a b c
Low High
(0.66, 2.12)
1.26 (1.02, 1.54) Tertile III (low) Tertile II (medium) Tertile I (high) 24%
28%
40%
1%
1.06 (0.85, 1.32) 0.55 (0.30, 1.02) 0.96 (0.62, 1.47) 1.00 38%
All models are adjusted for age and gender. Higher scores represent higher levels of mastery, coping efficacy, social participation, and unhealthy behaviors and lower levels of aggregate social trust. Change in ln(OR) between Model 1 and each of the following models.
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only (Model 3). Finally, after the inclusion of all the variables (Model 6, Tables 2 and 3), the associations were further reduced. (b) Models for LLI: whereas the association of education with LLI was fully explained (became non-significant, Table 4), the association of car ownership with LLI was attenuated but remained significant in all the models (Table 5). The inclusion of either individual psychosocial or community variables, but not that of health behaviors alone, was sufficient to explain the association of education with LLI. The combined inclusion of the psychosocial and community variables (Model 4, Table 4) contributed to a further decrease in the non-significant association, while there was no further contribution of the final integrated model (Model 6, Table 4). In contrast, when the number of cars was the SEP measure (Table 5), individual psychosocial or community variables contributed somewhat similarly to the reduction in the strength of the associations, while health behaviors hardly attenuated it. The inclusion of both psychosocial and community variables reduced the strength of the association even further, but it remained significant (Model 4); a negligible increase was observed in the final integrated model (Model 6).
In all the analyses, the final integrated model (Model 6, Tables 2–5) showed that the addition of the community socioeconomic score was negligible to further attenuation of SES-SRH or LLI associations beyond the reduction by the combined psychosocial and community variables (Model 4). Moreover, the attenuation in these models was similar or identical to that in a model combining psychosocial, community and health behavior variables (data not shown).
4. Discussion and conclusions The integrative multilevel model showed a considerable explanation of social inequalities in both health outcomes; however, it did not fully elucidate the SEP-health associations, except for that between education and LLI. Additionally, of the explanatory variables, the contribution of community factors to the explanation of inequalities in SRH was substantial, whereas that of psychosocial factors was more modest and that of health behaviors was smaller or negligible. In contrast, community factors had a larger effect on inequalities in LLI compared with the psychosocial or behavioral factors only for educational inequalities, while their contribution to inequalities by LLI by number of cars was comparable to that of the psychosocial factors and much larger than that of health behaviors. Thus, by examining two health measures and both education and an asset-based measure of SEP, namely the number of cars, our results extend previous research on the social gradient in health in Israel (Jaffe et al., 2008) and support evidence from European countries (Mackenbach et al., 2002). The study identified the potential mediators, corroborating findings that low SEP is related to psychosocial factors, such as stressful life conditions (Lantz et al., 2005), mastery/control (Bosma et al., 1999), social support (Taylor and Seeman, 1999) and risky health behaviors (Link and Phelan, 1995). The results showed that social participation and social trust are the two elements associated with both SEP and health. The lack of associations of the other elements of social capital (lack of fairness and helpfulness) or of neighborhood problems with SEP, may be related to the heterogeneous conditions of Israeli neighborhoods, where some better-off areas may suffer from several of the measured problems.
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Community factors made a substantial contribution in explaining inequalities in SRH, and in explaining educational inequalities in LLI. These results provide preliminary evidence that social capital plays a major role in linking individual SEP and health in Israel, and are compatible with studies conducted in other countries. Social participation may link SEP with health because it may represent SEP differences in other dimensions contributing to health, such as traditional family orientation in Israeli society (Lewin-Epstein et al., 2000), or feelings of empowerment (Itzhaki and York, 2000). Further exploration needs to refine this association and examine which types of group memberships are more important. It should be noted, however, that associations between social participation and health could be bi-directional, as existing illness can act as a barrier for active participation. Our findings that individual-level rather than aggregate-level social capital variables were associated with the health outcomes, strengthen the need for additional research on this issue to further understand the meaning of social capital in different cultures (Forbes and Wainwright, 2001). Only a modest portion of the inequalities in SRH was explained by the individual psychosocial factors, indicating that these explanatory variables do not play an important role in the SEP– SRH association in Israel. While these findings resemble those showing that work-related psychosocial factors made no contribution in explaining inequalities in MI in England (Ferrie et al., 2005), they contrast findings on mortality in The Netherlands (van Oort et al., 2005). This may stem from the different outcome measures but also from cultural variations. In a European study, psychosocial factors had a substantial explanatory power for inequalities in SRH in a French cohort but a very modest one in a British cohort (Fuhrer et al., 2002). Also relevant to our results is the unique terror-related exposure to stress experienced by all segments of the Israeli population during the last decade, which may lead to different stress-processing mechanisms. The modest or marginal contribution of health behaviors to the explanation of inequalities supports previous results (Mulatu and Schooler, 2002; Lantz et al., 2001) and suggests that changing health behaviors cannot be an exclusive solution for reducing SEP disparities in health. Finally, the lack of independent effect of area-level socioeconomic score in explaining health inequalities beyond that of the other individual and area-level factors calls for further studies. While other research (Steptoe and Feldman, 2001), including Israeli studies (Jaffe et al., 2005), showed that living in poorer areas is associated with increased risk of mortality or morbidity, sparse information is available on the additional contribution of area-level SEP in explaining health inequalities. Some limitations of the findings need to be addressed. The cross-sectional design of the current study limits the ability to assess causality. Our analysis focused on an additive model, did not include the evaluation of interactive effects, and is limited by our choice of variables, their measurement and the order of their entry into the regression models. Despite these limitations, the findings provide additional data to the few empirical studies that examined the relative contribution of potential pathways to health inequalities, enabling the testing of current theories. The findings contribute to the understanding of factors that explain health inequalities rather than determinants of health (Graham, 2004), and serve as an important step in research on health inequalities in Israel. Examining a comprehensive conceptual framework in a sizeable and representative sample, the findings have several implications. They show that use of two dimensions of health outcomes—self-rated health and chronic illness—highlight similarities and differences in explanatory factors of inequalities. The integrative multi-level model of psychosocial, community and behavioral factors
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presented here provided a full explanation only for the education– LLI association. For other associations the explanation was substantial, yet only partial. Additional explanatory factors, for example, more specific material circumstances (van Oort et al., 2005), or early life factors (Ferrie et al., 2005) should be expanded in future, prospective studies. Our research might not only increase awareness among policy makers as to the scope of social inequalities in health, but also outline the type of interventions required to reduce them. Interventions targeting both community and individual psychosocial factors will be most beneficial in reducing inequalities in SRH and in illness. Promotion of health behaviors alone is not sufficient to reduce these inequalities. The findings clearly support the claim that reduction of social inequalities in health should be a target of a national health policy in Israel in order to realize the principles of justice, equality and reciprocity of the National Health Insurance Law.
Acknowledgements This study was supported by Grant no. 2001/7 from the Israel National Center for Health Policy and Health Services Research. The authors thank Ms. Hagit Hochner for her assistance in data analysis.
References Adler, N., 2006. When one’s main effect is another’s error: Material vs. psychosocial explanations of health disparities. A commentary on Macleod et al. Social Science and Medicine 63, 846–850. Bartley, M., 2004. Health Inequality. An Introduction to Theories, Concepts and Methods. Polity, Cambridge. Bosma, H., Schrijvers, C.T.M., Mackenbach, J.P., 1999. Socioeconomic inequalities in mortality and importance of perceived control: cohort study. British Medical Journal 319, 1469–1470. Brunner, E., Marmot, M., 1999. Social organization, stress, and health. In: Marmot, M., Wilkinson, R.G. (Eds.), Social Determinants of Health. Oxford University Press, New York. CBS, 1999. In: Characteristics and Ranking of Local Authorities According to the Population’s Socio-Economic Level in 1995. Publication 1039. Central Bureau of Statistics, Jerusalem. CBS, 2004. In: Statistical Abstracts. Israel 2004. Central Bureau of Statistics, Jerusalem. Dunn, J.R., Veenstra, G., Ross, N., 2006. Psychosocial and neo-material dimensions of SES and health revisited: predictors of self-rated health in a Canadian national survey. Social Science and Medicine 62, 1465–1473. Ferrie, J.E., Martikainen, P., Shipley, M.J., Marmot, M.G., 2005. Self-reported economic difficulties and coronary events in men: evidence from the Whitehall II study. International Journal of Epidemiology 34, 640–648. Forbes, A. Wainwright, S.P., 2001. On the methodological, theoretical and philosophical context of health inequalities research: A critique. Social Science & Medicine 53, 801–816. Fuhrer, R., Shipley, M.J., Chastang, J.F., Schmaus, A., Niedhammer, J., Stansfeld, S.A., Goldberg, M., Marmot, M.G., 2002. Socioeconomic position, health, and possible explanations; A tale of two cohorts. American Journal of Public Health 92, 1290–1294. Graham, H., 2004. Social determinants and their unequal distribution: clarifying policy understandings. Milbank Quarterly 82, 101–124. Idler, E.L., Benyamini, Y., 1997. Self rated health and mortality: a review of twenty-seven community studies. Journal of Health and Social Behavior 38, 21–37. Israel Ministry of Health, 2006. National Survey of health 2003/2004. Updated 20.11.2006. http://www.health.gov.il/pages/default.asp?maincat=2catId=688 PageId=3885. Accessed 15.6.2007. Itzhaki, H., York, A., 2000. Empowerment and community participation: does gender make a difference?. Social Work Research 24, 225–234. Jaffe, D.H., Eisenbach, Z., Newmark, Y.D., Manor, O., 2005. Individual household and nationhood socioeconomic status and mortality: a study of absolute and relative deprivation. Social Science and Medicine 60, 989–997. Jaffe, D.H., Neumark, Y.D., Eisenbach, Z., Manor, O., 2008. Educational inequalities in mortality among Israeli Jews: changes over time in a dynamic population. Health and Place 14, 287–298. Karlsson, J., Sjostrom, L., Sullivan, M., 1995. Swedish obese subjects (SOS)—an intervention study of obesity. Measuring psychosocial factors and health by means of short-form questionnaires. Results from a method study. Journal of Clinical Epidemiology 48, 817–823.
Kawachi, I., Kennedy, B.P., Lochner, K., Protow-Stith, D., 1997. Social capital, income inequality, and mortality. American Journal of Public Health 87, 1492–1498. Kim, D., Kawachi, I., 2006. A multilevel analysis of key forms of community- and individual-level social capital as predictors of self-rated health in the United States. Journal of Urban Health 83, 813–826. ¨ Lahelma, E., Keskimaki, I., Rahkonen, O., 2002. Income maintenance policies: The example of Finland. In: Mackenbach, J.P., Bakker, M. (Eds.), Reducing Inequalities in Health: a European Perspective. Routledge, London and New York. Lantz, P.M., House, J.S., Mero, R.P., Williams, D.R., 2005. Stress, life events, and socioeconomic disparities in health: results from the Americans’ changing life study. Journal of Health and Social Behavior 46, 274–288. Lantz, P.M., Lynch, J.W., House, J.S., Lepkowski, J.M., Mero, R.P., Musick, M.A., Williams, D.R., 2001. Socioeconomic disparities in health change in a longitudinal study of US adults: the role of health-risk behaviors. Social Science and Medicine 53, 29–40. Lazarus, R.S., Folkman, S., 1984. Stress, Appraisal and Coping. Springer Publishing Company, New York. Lewin-Epstein, N., Stier, H., Braun, M., Langfeldt, B., 2000. Family policy and public attitudes in Germany and Israel. European Sociological Review 16, 385–401. Lin, D.Y., Fleming, T.R., De Gruttola, V., 1997. Estimating the proportion of treatment effect explained by a surrogate marker. Statistics in Medicine 16, 1515–1527. Link, B.G., Phelan, J., 1995. Social conditions as fundamental causes of disease. Journal of Health and Social Behavior. Lindstrom, M., Hanson, B.S., Ostergren, P.O., 2001. Socioeconomic differences in leisure-time physical activity: the role of social participation and social capital in shaping health related behavior. Social Science and Medicine 52, 441–451. Mackenbach, J.P., Bakker, M.J., Kunst, A.E., Diderichsen, F., 2002. Socioeconomic inequalities in health in Europe. An overview. In: Mackenbach, J.P., Bakker, M. (Eds.), Reducing Inequalities in Health: A European Perspective. Routledge, London and New York. Mackenbach, J.P., Bos, V., Andersen, O., Cardano, M., Costa, G., Harding, S., Reid, A., ¨ ., Valkonen, T., Kunst, A.E., 2003. Widening socioeconomic ¨ Hemstrom, O inequalities in mortality in six Western European countries. International Journal of Epidemiology 32, 830–837. Manne, S, Glassman, M., 2000. Perceived control, coping efficacy, and avoidance coping as mediators between spouses unsupportive behaviors and cancer patients; psychological distress. Health Psychology 19, 155–164. Manor, O., Eisenbach, Z., Israeli, A., Friedlander, Y., 2000. Mortality differentials among Israeli Women: The Israel longitudinal mortality study. Social Science and Medicine 51, 1175–1188. Manor, O., Eisenbach, Z., Peritz, E., Friedlander, Y., 1999. Mortality differentials among Israeli men. American Journal of Public Health 89, 1807–1813. Manor, O., Matthews, S., Power, C., 1997. Comparing measures of health inequality. Social Science and Medicine 45, 761–771. Manor, O., Power, C., Matthews, S., 2001. Self rated health and limiting longstanding illness: inter-relationships with morbidity in early adulthood. International Journal of Epidemiology 30, 600–607. Marmot, M.G., Wilkinson, R.G., 2001. Psychosocial and material pathways in the relation between income and health: a response to Lynch et al. British Medical Journal 322 (7296), 1233–1236. McDonough, P., Walters, V., 2001. Gender and health: reassessing patterns and explanations. Social Science and Medicine 52, 547–559. Molarius, A., Berglund, K., Eriksson, C., Lambe, M., Nordstrom, E., Eriksson, H.G., Feldman, I., 2007. Socioeconomic conditions, lifestyle factors, and self-rated health among men and women in Sweden. European Journal of Public Health 17, 125–133. Mulatu, M.S, Schooler, C., 2002. Casual connections between socio-economic status and health: reciprocal effects and mediating mechanisms. Journal of Health and Social Behavior 43, 22–41. National Health Insurance Law, 1994. /http://www.health.gov.il/Download/pages/ law_bituch.pdfS (Hebrew). Pandey, S.K., Hart, J.J., Tiwary, S., 2003. Women’s health and the Internet: understanding emerging trends and implications. Social Science and Medicine 56, 179–191. Pearlin, L.I., Schooler, C., 1978. The structure of coping. Journal of Health and Social Behavior 19, 2–21. Poortinga, W., 2006aa. Social relations or social capital? Individual and community health effects of bonding social capital. Social Science and Medicine 63, 255–270. Poortinga, W., 2006bb. Social capital: an individual or collective resource for health?. Social Science and Medicine 62, 292–302. Rasbash, J., Steele, F., Browne, J.W., Prosser, B., 2005. A user’s guide to MLwiN version 2.0. University of Bristol. Robert, S.A., 1999. Socioeconomic position and health: the independent contribution of community socioeconomic context. Annual Review of Sociology 25, 489–516. Shuval, J. T., Anson, O., 2000. Social Structure and Health in Israel. Jerusalem: The Hebrew University: Magness Press., Hebrew. Siegrist, J., Marmot, M., 2004. Health inequalities and the psychosocial environment—two scientific challenges. Social Science and Medicine 58, 1463–1473. Singh-Manoux, A., 2003. Psychosocial factors and public health. Journal of Epidemiology and Community Health 57, 553–556.
ARTICLE IN PRESS V. Soskolne, O. Manor / Health & Place 16 (2010) 242–251
Steptoe, A., Feldman, P.J., 2001. Neighborhood problems as sources of chronic stress: development of a measure of neighborhood problems, and associations with socioeconomic status and health. Annals of Behavioral Medicine 23, 177–185. Tamir, D., Dayan, I., Weinstein, R., Arin, R., 1998. In: Knowledge, Attitudes and Health Behaviors In Israel-1998. Israel Ministry of Health., Hebrew, Jerusalem.
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Taylor, S.E., Seeman, T., 1999. Psychosocial resources and the SES—health relationship. Annals of the New York Academy of Sciences 896, 210–225. van Oort, F.V.A., van Lenthe, F.J., Mackenbach, J.P., 2005. Material, psychosocial, and behavioural factors in the explanation of educational inequalities in mortality in the Netherlands. Journal of Epidemiology and Community Health 59, 214–220. Wilkinson, R., 1997. Socio-economic determinants of health: health inequalities— relative or absolute material standards. British Medical Journal 314, 591–595.
Contents lists available at ScienceDirect
Health & Place journal homepage: www.elsevier.com/locate/healthplace
Health inequalities in Israel: Explanatory factors of socio-economic inequalities in self-rated health and limiting longstanding illness Varda Soskolne a,n, Orly Manor b a b
The Louis and Gabi Weisfeld School of Social Work, Bar-Ilan University, Ramat-Gan 52900, Israel. Braun School of Public Health, The Hebrew University of Jerusalem, Israel
a r t i c l e in f o
a b s t r a c t
Article history: Received 12 July 2009 Received in revised form 3 October 2009 Accepted 6 October 2009
We examined an integrated multi-level model of psychosocial, community and behavioral factors as explanatory pathways to socio-economic inequalities in health in Israel. Using a random national sample of 1328 individuals aged 30–70 and measurements of socio-economic position (education, number of cars), health outcomes—self-rated health, limiting longstanding illness (LLI), we evaluated the contribution of psychosocial factors (stressors and psychosocial resources), community factors (individual and aggregate-level social participation and social capital) and health behaviors, to the explanation of health inequalities. Community factors contributed more than psychosocial factors or health behaviors. The integrative model provided an explanation of social inequalities in both health outcomes and a full explanation for the education-LLI association. & 2009 Elsevier Ltd. All rights reserved.
Keywords: Health inequalities Self-rated health Limiting longstanding illness Explanatory factors Israel
1. Introduction Studies on socio-economic position (SEP) inequalities in health have generated consistent evidence about the scope of the inequalities (Dunn et al., 2006; Lantz et al., 2005). In the last decade, these inequalities have widened (Mackenbach et al., 2003; Jaffe et al., 2008) or remained stable (Lahelma et al., 2002). Yet, the mechanisms responsible for these inequalities are not well understood (Adler, 2006). Identifying the specific factors in each country is thus important for adaptation of interventions to reduce health inequalities (Mackenbach et al., 2002). In Israel, prior studies have already highlighted inequalities in health by socio-economic position (SEP) (e.g., Manor et al., 1999; Israel Ministry of Health, 2006), but have not considered factors explaining these inequalities. The present study examined a model of individual and community-level explanatory factors of socio-economic inequalities in health in Israel. One of the major explanatory approaches to health inequalities (Bartley, 2004), the ‘‘psychosocial environment’’ explanation suggests that material factors alone are not sufficient for explaining disparities in health, and views individual psychosocial or community factors as linking SEP to health via mediating pathways to health-related behaviors and to biological response (Brunner and Marmot, 1999; Marmot and Wilkinson, 2001). The differential psychosocial vulnerability—a higher exposure to
n
Corresponding author. Tel.: +972 3 5317806; fax: + 972 3 7384042. E-mail address: [email protected] (V. Soskolne).
1353-8292/$ - see front matter & 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.healthplace.2009.10.005
stress, limited personal control and social support (Siegrist and Marmot, 2004), and more severe cognitive perception of stress of those in lower socio-economic positions may produce negative emotions leading to poorer health (Taylor and Seeman, 1999). Wilkinson (1997) argues that adverse health effects are not produced by low absolute income but by the stress of perceived income relative to others. Community factors, such as neighborhood characteristics, community socio-economic level and social capital have also been advocated as linking individual SEP to health (Kawachi et al., 1997; Poortinga, 2006a). In Israel, all permanent residents are insured for basic medical services under the National Health Insurance Law (1994). Yet, health inequalities persist between the major social divisions in Israeli society—between Jews and Arabs, and between ethnic and social groups within the Jewish population (Shuval and Anson, 2000). The issue of health differences between Jews and Arabs is important. Nevertheless, to avoid the effects of cultural differences, the current study focused on the Jewish population, and a separate report on inequalities within the Arabs is forthcoming. Studies within the Jewish population have demonstrated a clear social gradient in mortality rates (Manor et al., 1999, 2000), and in self-reported health (Israel Ministry of Health, 2006; Tamir et al., 1998). However, no study has examined the factors that contribute to these inequalities. While there may be disagreement about the specific explanations, there is consensus that social position is linked to health via complex multilevel pathways (Singh-Manoux, 2003). Only a handful of studies have attempted to examine these explanations in an integrated manner. One study found that material variables, alone or in combination with behavioral or psychosocial variables,
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explained the association of education with mortality (van Oort et al., 2005). In contrast, a cross-sectional study of inequalities in self-rated health, psychosocial variables and health behaviors, but not material factors, explained the association of education with health (Molarius et al., 2007). Another controversy surrounds the measurement of social capital as a community or an individuallevel variable. Recent studies have attempted to overcome the disagreements by using both individual and aggregate community-level variables (Kim and Kawachi, 2006; Poortinga, 2006b). Drawing upon Stress and Coping Theory (Lazarus and Folkman, 1984), and upon comprehensive psychosocial environment conceptual frameworks (e.g., Brunner and Marmot, 1999; Robert, 1999), an integrated model of individual and community-level explanatory factors of health inequalities was outlined for the present study in order to examine their relative explanatory contribution. The conceptual framework (Fig. 1) delineates the groups of factors and their integration as explanatory factors of the association of SEP variables with health: (a) Individual-level psychosocial factors; (b) community social environment characteristics, measured as both individual and aggregate, community-level variables; (c) the combined multi-level associations—the direct link of SEP and health together with psychosocial and community variables; (d) Healthrelated behaviors; (e) finally, the integrated model is represented by the combined multi-level associations: the direct link of SEP with health together with psychosocial, social environment and behavioral factors and with the community-level socioeconomic score.
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It is important to note, however, that although the model outlines the associations from SEP to individual health, some may go in both directions. Additionally, this framework does not attempt to encompass psychobiological stress processes and the inter-correlations between psychosocial, community and behavioral variables, which are beyond the scope of the present study. Access to health care was omitted after preliminary findings showed that almost all interviewees reported easy access. Based on the above model of individual and community level factors, we aimed to examine the associations between SEP measures and physical health status, and to assess the degree to which the associations are mediated by psychosocial, community or behavioral factors and their combinations. We opted to employ several SEP measures, as they represent different dimensions and forces associated with health (Bartley, 2004), and to tap their associations with two different health outcomes.
2. Methods 2.1. Sample and sampling A national representative random sample of Jewish Israeli citizens aged 30–70 living in urban areas was selected in stages. First, using stratified sampling, only urban local authorities were selected from the Israeli Central Bureau of Statistics (CBS) list,
Demographic control variables (a)
(b)
Community environment
SEP Individual psychosocial variables
- Neighborhood conditions - Social capital, individual and community levels: trust, reciprocity, help; social participation
- Stressors (acute and chronic) - Cognitive perception of stressors - Psychosocial resources (mastery, social support, coping efficacy)
(d)
(a)
(b)
Community-level socioeconomic score
Health behaviors
(c) (e)
(e)
(d) (e)
(c) (e)
INDIVIDUAL HEALTH
Note: Some of the associations may go in both directions and could be presented by bidirectional arrows. Dashed lines represent associations not examined in the present study. Pathways of explanatory factors are labeled with letters (a) to (e). See text for details. Fig. 1. . Outline of the conceptual model of the study.
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based on the size and socio-economic rank (CBS, 1999), yielding 54 local authorities. Second, within these authorities, statistical areas in which Jewish residents comprised more than 50% of residents were selected (1190 statistical areas). Third, the statistical areas were stratified according to three factors: geographical region of the country (four regions), religiosity (Orthodox religious or not Orthodox) and socio-economic score (a range of 20 ranks). Random sampling of statistical areas was conducted in each stratum, based on its size and the size of the population, resulting in 49 statistical areas. In the final stage, 50 addresses were randomly sampled from each statistical area. Of these, 1958 households were eligible for the study (the others were non-residential, incorrect addresses or households with persons ineligible for inclusion). One person in each household was interviewed, alternating between men and women. A total of 1328 (68%) persons completed the interview, 521 (27%) refused to be interviewed and the rest were not located after four visits. 2.2. Data collection procedures The study was approved by the Hebrew University—Hadassah Review Board. Data were collected in late 2003 by means of faceto-face interviews at home in Hebrew or in Russian (due to the large influx of immigrants to Israel from the former Soviet Union in the 1990s). Community socio-economic score was retrieved from the CBS (1999). 2.3. Participants The mean age of the participants was 48.2 (SD= 11.9) and 45% were male. The majority (76%) were married; 54% were born in Israel, 28% immigrated before 1990 and 18% since 1990 (almost all from the former Soviet Union); 47% defined themselves as secular, 35% as traditional (observing the main religious traditions) and 17% as religious or ultra-Orthodox. These main demographic characteristics of the final sample were very similar to those of the general Jewish population in this age range (CBS, 2004), thereby assuring the representativeness of our sample. 2.4. Measures 2.4.1. SEP measurements Two widely used measures were selected. Level of education was measured as years of schooling, and further collapsed into four levels: 0–8 years, 9–12 years, 13–15 years and 16+ years. Financial assets were measured as the number of cars in the household’s possession and categorized into three levels: none, one, two or more. 2.4.2. Health measurements Two global indicators of morbidity, which are related but not overlapping and have been shown to be valid indicators of health status were selected. Self-rated health (SRH) was measured using a 5-level ordinal single item of an overall assessment of health status (Idler and Benyamini, 1997). For the analyses, this indicator was dichotomized into ‘‘0’’ good (very good and good) vs. ‘‘1’’ poor (fair, poor and very poor). Limiting longstanding illness (LLI), a ‘‘no’’ vs. ‘‘yes’’ measure of functional limitation, intended to focus on chronic conditions that impair an individual’s daily activities (Manor et al., 2001). 2.4.3. Individual psychosocial variables Brief questionnaires that have satisfactory psychometric properties and are suitable for health surveys (Karlsson et al., 1995) were preferred over lengthy questionnaires in order to
reduce respondent burden. Several psychosocial variables which followed a highly skewed distribution prior or after transformation were dichotomized by choosing up to the lowest third to denote low levels of each measure vs. the highest two thirds. (a) Stressors: Two abbreviated scales, adapted from a study on health differences (McDonough and Walters, 2001) were used. (1) Recent life events measured positive responses to nine events that happened to the respondent or someone close to the respondent in the past year, including an item unique to the situation in Israel at the time of the study (exposure to terror). (2) Chronic stressors measured positive responses to an 8-item scale reflecting enduring financial, social life, relationship and work stress. The two scales were further dichotomized into ‘‘0’’ low (no event or 0–3 chronic stressors), or ‘‘1’’ high levels (1+ events or 4+ chronic stressors). (b) Cognitive appraisal of the stressors was assessed by asking the respondent to rate the level of personal stressfulness in relation to each of the life events experienced, ranging from ‘‘1’’—not at all, to ‘‘5’’—to a large extent. The sum score was dichotomized into ‘‘0’’ low stress (mean scores of 0–6), or ‘‘1’’ high stress (score of 7 or more). (c) Psychosocial resources were measured by three items: (1) Mastery: A 7-item scale measuring the ability to deal with or exert control over issues as they arise in people’s lives was used (Pearlin and Schooler, 1978). The total mean score ranges from ‘‘0’’ to ‘‘4’’ (high levels of mastery). Cronbach’s alpha was 0.84. (2) Coping efficacy: two face-valid items assessed efficacy in coping with daily disruptions and emotional stress caused by the most severe recent event (Manne and Glassman, 2000). The sum of the responses is used, (range 2–10), with higher scores representing higher coping efficacy. (3) Social support was measured by a brief 6-item scale tapping emotional, material and informational support, adapted from large-scale social surveys (Karlsson et al., 1995). Cronbach’s alpha was 0.89. The mean score was dichotomized into ‘‘0’’ high support, vs. ‘‘1’’ low support.
2.4.4. Community social environment variables (a) Two individual-level variables were included: (1) Neighborhood living conditions—an adapted 11-item scale measuring socio-environmental problems such as noise, litter and rubbish, vandalism and burglaries (Steptoe and Feldman, 2001). Each item ranged from ‘‘1’’—not a problem, to ‘‘3’’— a severe problem. The items were summed, with higher scores representing severe problems; (2) Social capital: we adopted the structural/cognitive distinction that differentiates between the concepts of civic engagement—the extent to which citizens involve themselves in their communities, and the cognitive perception of mutual trust and solidarity among community members (Kawachi et al., 1997). Since data on civic engagement (as measured at the community level by per capita group membership) is not available in Israel, we relied on the respondents’ reports. Social participation was measured by nine items adapted from a scale of membership in formal or informal groups (Lindstrom et al., 2001), and by a civic engagement measure used in the United States (Kawachi et al., 1997), ranging from ‘‘0’’ not at all/rarely, to ‘‘2’’ very often. Cronbach’s alpha was 0.68. Social trust was assessed by three ‘‘yes’’ vs. ‘‘no’’ items that measured ‘‘perceived lack of fairness’’, ‘‘social trust’’ and ‘‘perceived helpfulness’’ (Kawachi et al., 1997), all categorized as ‘‘1’’ negative vs. ‘‘0’’ positive response. (b) Two community-level variables were employed: (1) Social capital: similar to methods used in previous research
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(e.g., Kim and Kawachi, 2006), the individual-level variables were aggregated by computing the mean of each variable among respondents within each statistical area. This approach produces a risk of same source bias. However, we could not avoid it, as there was no available data for each statistical area. 2) Socio-economic community score of the statistical area of residence (CBS, 1999). 2.4.5. Health behaviors Three items measuring smoking, physical activity and protective practices against the sun were taken from previous surveys in Israel (Tamir et al., 1998) and an additional question measuring eating a balanced diet of daily portions of fruits, vegetables, proteins, grains and dairy products (Pandey et al., 2003) were used. The response categories to all the items were recorded on a 4-level ordinal scale. (1) Smoking—never smoked, past smoker, current light smoker and current heavy smoker. (2) Physical activity lasting at least 20 min—every day, 1–2 times/week, seldom and never. 3. Use of sun protection—always, sometimes, seldom and never. 4. Diet—very balanced, balanced, somewhat unbalanced, not at all balanced. For the analysis, a scale of health behaviors was calculated as the mean of these four behaviors, ranging from ‘‘1’’—healthy behaviors, to ‘‘4’’—unhealthy behaviors. 2.4.6. Demographic control variables The respondents were asked about their age, gender, marital status, religiosity (secular, traditional, religious), country of birth and year of immigration. 2.5. Statistical analyses Analyses were carried out separately for each health measure. In the preliminary stage, the socio-demographic control variables were examined, and showed that adjustment for marital status, household size, religiosity, country of birth and year of immigration did not affect the association between SEP and health. Furthermore, this association did not vary by age or gender; hence, age and gender were controlled for in all bivariate or multivariate analyses. The next step involved the examination of the bivariate associations between SEP and each of the explanatory variables, and between the latter and SRH or LLI, using logistic regression models or ANOVA. Only variables significantly associated with both the socio-economic and health measures (potential explanatory variables) were included in the multivariate analyses. In addition, the correlations within each group of explanatory variables were examined in order to avoid multicolinearity in the regression models, but no variable had to be omitted. Finally, multilevel multivariate logistic analysis was carried out with individuals as level-one units, and statistical areas as level-two units (Rasbash et al., 2005). No cross-level interactions were examined. Modeling was conducted in stages according to six models: Model 1 included the main association between SEP and health (age and gender adjusted). In the following stages, groups of variables were added to Model 1. Model 2 included psychosocial variables only. Model 3 included community social environment variables only, and—to allow for the full potential contribution of the social capital variables—they were added at both the individual and the area levels (Kim and Kawachi, 2006). Model 4 included both the psychosocial and the community social environment variables. Model 5 included health behaviors only. Finally, Model 6 examined the final integrated model, which included the area-level socioeconomic score, in addition to all the other variables. An interim model (data are not shown but the results may be obtained from the authors)
245
examined an integrated model that included psychosocial, community and health behavior variables. SEP disparities were evaluated by the odds of poor health in each SEP category, and, as a summary measure, the odds ratio between the extreme SEP categories was used (Manor et al., 1997). The contribution of each group of variables was assessed by calculating the percent of decline in the ln (OR) between the extreme categories in each of the Models, 2 through 6, compared to Model 1 (Lin et al., 1997).
3. Results 3.1. Distribution of SEP, health measures and explanatory variables The distribution of education level (0–8 years—11.6%, 9–12 years—41.7%, 13–15 years—23.6%, 16+ years—23.1%) and of the number of cars (none—38.8%, one—47%, two or more—14.2%) in the study sample was similar to that in the general Israeli population (CBS, 2004). A third (33.2%) of the respondents rated their health as poor, and 25% reported having a limiting, chronic disease. The distribution of the explanatory variables is presented in Table 1a–c, first rows. Significant proportions reported low levels of the community measures, and medium levels of social participation and neighborhood problems. 3.2. Bivariate associations between SEP and explanatory variables Both SEP measures were significantly associated with most of the psychosocial variables, but not with chronic stress (Table 1a). Additionally, only car ownership was associated with social support, yet we opted to include it in the multivariate analyses in order to obtain equivalent models for both outcomes. Of the community variables (Table 1b), SEP measures were associated only with social trust and social participation, but not with neighborhood living conditions. There was a clear indication that those with lower education levels and fewer cars reported higher exposure to stressors, lower levels of psychosocial resources and lower social trust and participation, although not significant in all categories. Similarly, every decrease in education level or in the number of cars was associated with more unhealthy behaviors (Table 1c). 3.3. Bivariate associations between SEP and health The associations of education and number of cars with SRH and LLI reveal a significant and consistent gradient of health inequalities (Tables 2–4, Model 1). Compared with the highest level of education or number of cars, the likelihood of reporting poor SRH or having an LLI increased gradually in almost all other levels. The gaps between the education levels or number of cars in SRH were greater than in LLI. 3.4. Bivariate associations between explanatory variables and health All those psychosocial, community and health behavior variables significantly associated with SEP were also associated with SRH and LLI: lower levels of all these variables were associated with higher odds of poor SRH and an LLI (p o0.001, data not shown). Additionally, multi-level analysis adjusting for confounders, yielded a significant association of area socioeconomic score with SRH (OR for middle and low socio-economic tertiles compared to the high tertitle were 1.80 and 1.88, respectively, p o0.001). The association of area socio-economic score with LLI reached statistical significance only for the middle
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Table 1 Prevalence of psychosocial and community factors and health behaviors and associations with SEPa. (a) Individual psychosocial variables Stressful life events Percentb Mean (SD), rangec
Education (years) 0–8 9–12 13–15 Z 16 Cars (number) 0 1 Z2
High= 37.9%
Education (years) 0–8 9–12 13–15 Z 16 Cars (number) 0 1 Z2
High= 23.5%
d
Appraisal of stress
Social support
High= 30.6%
Low= 23.7%
e
Mastery
Coping efficacy
3.06 (0.63), 1–4
7.11 (1.72), 2–10
Difference in B
Difference in B
OR (95% CI)
OR (95% CI)
OR (95% CI)
OR (95% CI)
1.36 (0.90, 2.06) 1.56 (1.16, 2.09) 0.98 (0.70, 1.37) 1
1.23 (0.75, 2.03) 1.15 (0.82, 1.61) 1.16 (0.79, 1.69) 1
1.33 (0.85, 2.09) 1.90 (1.39, 2.61) 1.09 (0.76, 1.58) 1
1.46 (0.91, 2.35) 1.36 (0.96, 1.95) 1.44 (0.97, 2.14) 1
1.58 (1.10, 2.26) 1.34 (0.94, 1.90) 1
1.28 (0.85, 1.93) 1.14 (0.76, 1.70) 1
1.92 (1.30, 2.83) 1.440 (0.98, 2.12) 1
2.14 (1.37, 3.35) 1.38 (0.88, 2.16) 1
0
Trust
Unhelpfulnessd
Social participation
Neighborhood problemsd
Low= 60.5%
High= 52.3% 0.83 (0.37), 0–2
18.52 (4.76), 7–33
Difference in B
Difference in B
(b) Community variables Lack of fairnessd Percent b Mean (SD), range
Chronic stressors
High= 29.3% c.
OR (95% CI)
OR (95% CI)
OR (95% CI)
1.33 (0.87, 2.04) 1.10 (0.81, 1.50) 0.84 (0.58, 1.20) 1
2.21 (1.45, 3.39) 1.86 (1.39, 2.48) 1.09 (0.79, 1.50) 1
1.17 (0.78, 1.76) 1.21 (0.91, 1.61) 0.82 (0.60, 1.13) 1
1.40 (0.95, 2.06) 1.35 (0.92, 1.97) 1
1.57 (1.12, 2.20) 1.40 (1.01, 1.95) 1
0.91 (0.65, 1.28) 0.92 (0.66, 1.28) 1
0
1.06 0.65 0.30 0
0.40 0.13
0.30 0.20 0.08
0.35 0.66 0.14 0
0.34 0.14
0.51 0.00 0
0
0
0.24 0.13 0.05
0.84 0.28 0
(c) Health behaviors: Unhealthy behavior scale 2.15 (0.69), 1–4 Mean (SD), rangec Difference in B Education (years) 0–8 9–12 13–15 Z 16 Cars (number) 0 1 Z2
0.45 0.23 0.11 0 0.25 0.09 0
a Logistic regressions or Anova, all adjusted for age and gender. The associations of education or number of cars with the explanatory variables are significant at p o0.001 unless otherwise stated. b Dichotomous variables: the rates are presented for category coded as ‘‘1’’ (vs. ‘‘0’’). c Continuous variables: higher scores indicate a higher level of the measure. d Non-significant associations with education and the number of cars. e Non-significant associations with education; significant association (po 0.001) with the number of cars.
tertile compared to the high tertile (OR= 1.45, p = 0.03), but not for the lowest tertile (OR=1.35, p = 0.089). 3.5. Multilevel analyses of explanatory pathways Examining the contribution of each set of variables (Tables 2– 5, Models 2–6) to the associations between SEP measures and the two health outcomes (Tables 2–5, Model 1) shows certain differences and similarities. (a) Models for SRH: the strong associations of education or car ownership with SRH were attenuated, but remained statisti-
cally significant in all the models (Tables 2 and 3), suggesting only a partial explanation of the associations. The inclusion of the community variables alone contributed to larger decreases in the strength of the associations of education or car ownership with SRH than the inclusion of psychosocial or behavioral variables. For example, the community variables contributed a 45% reduction in the inequalities by education, compared with 24% and 23% for the psychosocial variables or health behaviors, respectively. Compared to Model 1, the associations were further reduced after the combined inclusion of the psychosocial together with the community environment variables (Model 4, Tables 2 and 3) but only 6– 9% above the attenuation of the OR by community variables
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Table 2 Odds ratios for self-rated health from multi-level models: education as SEP measure (n= 1258)a.
Education, years
Psychosocial variables Stressful life events
Appraisal of stressors Social support
0–8 9–12 13–15 16 +
Model 1 OR (95% CI)
Model 2 OR (95% CI)
Model 3 OR (95% CI)
Model 4 OR (95% CI)
Model 5 OR (95% CI)
Model 6 OR (95% CI)
4.38 (2.72, 7.05) 2.38 (1.65, 3.43) 1.93 (1.29, 2.88) 1.00
3.33 (2.02, 5.50) 1.93 (1.32, 2.84) 1.78 (1.17, 2.71) 1.00
2.38 (1.45, 3.89) 1.59 (1.09, 2.33) 1.71 (1.12, 2.59) 1.00
2.15 (1.28, 3.61) 1.42 (0.95, 2.11) 1.64 (1.07, 2.51) 1.00
3.63 (2.23, 5.91) 2.17 (1.50, 3.15) 1.85 (1.23, 2.77) 1.00
2.06 (1.22, 3.49) 1.39 (0.93, 2.08) 1.61 (1.05, 2.48) 1.00
Z2 0–1
0.74 (0.41, 1.34) 1.00
0.75 (0.41, 1.36) 1.00
0.73 (0.40, 1.33) 1.00
High Low Low High
1.65 1.00 1.33 1.00 0.55 0.78
1.65 1.00 1.13 1.00 0.64 0.81
1.70 1.00 1.16 1.00 0.65 0.82
Masteryb Coping efficacyb Community variables Trust—individual
(0.88, 3.07) (0.96, 1.84) (0.43, 0.70) (0.71, 0.86)
Low High
Trust—aggregateb Social participation Individual Aggregateb Health behaviorsb Area socio-economic score
b c
(0.81, 1.58) (0.50, 0.82) (0.74, 0.89)
(0.91, 3.18) (0.83, 1.62) (0.50, 0.83) (0.75, 0.90)
1.83 (1.36, 2.46) 1.00 0.91 (0.32, 2.54)
1.69 (1.25, 2.30) 1.00 0.83 (0.26, 2.64)
1.70 (1.25, 2.31) 1.00 0.91 (0.29, 2.90)
0.17 (0.11, 0.27) 0.36 (0.13, 1.02)
0.25 (0.16, 0.39) 0.44 (0.14, 1.40)
0.27 (0.17, 0.42) 0.32 (0.10, 1.01) 1.21 (0.97, 1.51)
1.60 (1.31, 1.95) Tertile III (low) Tertile II (medium) Tertile I (high)
Changec a
(0.89, 3.07)
0.73 (0.40, 1.36) 1.02 (0.66, 1.58) 1.00 24%
45%
51%
23%
54%
All models are adjusted for age and gender. Higher scores represent higher levels of mastery, coping efficacy, social participation, and unhealthy behaviors and lower levels of aggregate social trust. Change in ln(OR) between Model 1 and each of the following models.
Table 3 Odds ratios for self-rated health from multi-level models: the number of cars as SEP measure (n= 1256)a.
Car ownership
Psychosocial variables Stressful life events Appraisal of stressors Social support
0 1 2+
Model 1 OR (95% CI)
Model 2 OR (95% CI)
Model 3 OR (95% CI)
Model 4 OR (95% CI)
Model 5 OR (95% CI)
Model 6 OR (95% CI)
5.24 (3.23, 8.51) 2.09 (1.31, 3.36) 1.00
3.77 (2.27, 6.25) 1.90 (1.17, 3.10) 1.00
2.80 (1.68, 4.64) 1.62 (1.00, 2.65) 1.00
2.40 (1.42, 4.07) 1.55 (0.94, 2.56) 1.00
4.97 (3.03, 8.15) 2.08 (1.28, 3.36) 1.00
2.50 (1.47, 4.27) 1.57 (0.95, 2.59) 1.00
Z2 0–1 High Low Low High
Masteryb Coping efficacyb Community variables Trust—individual
0.74 1.00 1.60 1.00 1.22 1.00 0.61 0.77
Low High
Trust—aggregateb Social participation Individual Aggregateb Health behaviorsb Area socio-economic score
Changec a b c
(0.41, 1.35)
0.72 (0.40, 1.32) 1.00 1.64 (0.87, 3.07) 1.07 (0.76, 1.50) 1.00 0.68 (0.53, 0.88) 0.81 (0.74, 0.89)
0.71 1.00 1.70 1.00 1.10 1.00 0.69 0.82
1.81 (1.35, 2.44) 1.00 1.13 (0.38, 3.35)
1.67 (1.23, 2.27) 1.00 1.01 (0.31, 3.32)
1.67 (1.23, 2.26) 1.00 1.14 (0.35, 3.78)
0.18 (0.11, 0.28) 0.61 (0.20, 1.90)
0.25 (0.16, 0.39) 0.67 (0.20, 2.30)
0.28 (0.17, 0.44) 0.46 (0.34, 1.62)
(0.85, 3.01) (0.88, 1.70) (0.47, 0.78) (0.71, 0.85)
1.65 (1.35, 2.01) Tertile III (low) Tertile II (medium) Tertile I (high) 20%
38%
47%
4%
(0.38, 1.29) (0.90, 3.20) (0.78, 1.55) (0.53, 0.89) (0.75, 0.90)
1.26 (1.02, 1.57) 0.68 (0.36, 1.30) 1.01 (0.64, 1.60) 1.00 45%
All models are adjusted for age and gender. Higher scores represent higher levels of mastery, coping efficacy, social participation, and unhealthy behaviors and lower levels of aggregate social trust. Change in ln(OR) between Model 1 and each of the following models.
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Table 4 Odds ratios for limiting longstanding illness from multi-level models: education as SEP measure (n= 1257)a.
Education, years
0–8 9–12 13–15 16+
Psychosocial variables Stressful life events
Appraisal of stressors Social support
Model 1 OR (95% CI)
Model 2 OR (95% CI)
Model 3 OR (95% CI)
Model 4 OR (95% CI)
Model 5 OR (95% CI)
Model 6 OR (95% CI)
1.98 (1.24, 3.17) 1.71 (1.18, 2.48) 1.00 (0.65, 1.54) 1.00
1.55 (0.95, 2.53) 1.43 (0.98, 2.10) 0.92 (0.59, 1.42) 1.00
1.36 (0.84, 2.22) 1.33 (0.91, 1.96) 0.91 (0.59, 1.40) 1.00
1.23 (0.75, 2.04) 1.22 (0.82, 1.81) 0.87 (0.56, 1.35) 1.00
1.78 (1.10, 2.87) 1.62 (1.12, 2.36) 0.97 (0.63, 1.49) 1.00
1.21 (0.72, 2.01) 1.22 (0.82, 1.81) 0.86 (0.55, 1.35) 1.00
Z2 0–1
1.13 (0.63, 2.01) 1.00
1.12 (0.62, 1.99) 1.00
1.09 (0.61, 1.95) 1.00
High Low Low High
1.25 1.00 1.01 1.00 0.65 0.88
1.26 1.00 0.92 1.00 0.70 0.90
1.31 1.00 0.95 1.00 0.70 0.91
Masteryb Coping efficacyb Community variables Trust—individual Trust—aggregate
(0.68, 2.31) (0.72, 1.41) (0.51, 0.83) (0.81, 0.97)
Low High
Health behaviorsb Area socio-economic score
c
(0.55, 0.90) (0.82, 0.99)
(0.67, 1.34) (0.55, 0.90) (0.83, 1.00)
1.31 (0.97, 1.78) 1.00
1.23 (0.90, 1.67) 1.00
1.23 (0.90, 1.68) 1.00
1.21 (0.43, 3.46)
1.11 (0.35, 3.52)
1.20 (0.39, 3.71)
0.35 (0.23, 0.55) 0.96 (0.33, 2.74)
0.46 (0.29, 0.72) 1.10 (0.35, 3.47)
0.47 (0.30, 0.74) 0.70 (0.17, 2.91) 1.27 (1.03, 1.56)
Tertile III (low) Tertile II (medium) Tertile I (high)
Changec
b
(0.65, 1.30)
(0.71, 2.42)
b
Social participation Individual Aggregateb
a
(0.69, 2.31)
35%
54%
69%
15%
1.05 (0.84, 1.31) 0.68 (0.37, 1.25) 1.06 (0.69, 1.63) 1.00 71%
All models are adjusted for age and gender. Higher scores represent higher levels of mastery, coping efficacy, social participation, and unhealthy behaviors and lower levels of aggregate social trust. Change in ln(OR) between Model 1 and each of the following models.
Table 5 Odds ratios for limiting longstanding illness from multi-level models: number of cars as SEP measure (n= 1255)a.
Car ownership
Psychosocial variables Stressful life events Appraisal of stressors Social support
0 1 2+
Model 1 OR (95% CI)
Model 2 OR (95% CI)
Model 3 OR (95% CI)
Model 4 OR (95% CI)
Model 5 OR (95% CI)
Model 6 OR (95% CI)
3.13 (1.91, 5.14) 1.80 (1.10, 2.95) 1.00
2.38 (1.42, 3.99) 1.63 (0.98, 2.70) 1.00
2.27 (1.34, 3.83) 1.57 (0.95, 2.59) 1.00
1.97 (1.15, 3.38) 1.49 (0.90, 2.48) 1.00
3.08 (1.86, 5.11) 1.84 (1.11, 3.03) 1.00
2.03 (1.22, 3.60) 1.50 (0.90, 2.49) 1.00
Z2 0–1 High Low Low High
Masteryb Coping efficacyb Community variables Trust—individual
1.18 1.00 1.23 1.00 0.94 1.00 0.69 0.88
1.16 1.00 1.24 1.00 0.88 1.00 0.74 0.90
(0.67, 2.27) (0.67, 1.32) (0.54, 0.89) (0.80, 0.96)
(0.65, 2.09)
1.12 1.00 1.31 1.00 0.90 1.00 0.75 0.91
(0.67, 2.29) (0.62, 1.24) (0.58, 0.95) (0.82, 0.99)
(0.62, 2.02) (0.71, 2.42) (0.64, 1.28) (0.58, 0.97) (0.83, 0.99)
Trust—aggregateb
1.36 (1.01, 1.85) 1.00 1.53 (0.53, 4.43)
1.27 (0.93, 1.73) 1.00 1.36 (0.43, 4.32)
1.26 (0.93, 1.72) 1.00 1.53 (0.50, 4.67)
Social participation Individual Aggregateb
0.38 (0.25, 0.59) 1.49 (0.49, 4.55)
0.47 (0.30, 0.75) 1.52 (0.46, 5.09)
0.50 (0.31, 0.79) 0.75 (0.18, 3.16)
Health behaviorsb Area socio-economic score
Changec a b c
Low High
(0.66, 2.12)
1.26 (1.02, 1.54) Tertile III (low) Tertile II (medium) Tertile I (high) 24%
28%
40%
1%
1.06 (0.85, 1.32) 0.55 (0.30, 1.02) 0.96 (0.62, 1.47) 1.00 38%
All models are adjusted for age and gender. Higher scores represent higher levels of mastery, coping efficacy, social participation, and unhealthy behaviors and lower levels of aggregate social trust. Change in ln(OR) between Model 1 and each of the following models.
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only (Model 3). Finally, after the inclusion of all the variables (Model 6, Tables 2 and 3), the associations were further reduced. (b) Models for LLI: whereas the association of education with LLI was fully explained (became non-significant, Table 4), the association of car ownership with LLI was attenuated but remained significant in all the models (Table 5). The inclusion of either individual psychosocial or community variables, but not that of health behaviors alone, was sufficient to explain the association of education with LLI. The combined inclusion of the psychosocial and community variables (Model 4, Table 4) contributed to a further decrease in the non-significant association, while there was no further contribution of the final integrated model (Model 6, Table 4). In contrast, when the number of cars was the SEP measure (Table 5), individual psychosocial or community variables contributed somewhat similarly to the reduction in the strength of the associations, while health behaviors hardly attenuated it. The inclusion of both psychosocial and community variables reduced the strength of the association even further, but it remained significant (Model 4); a negligible increase was observed in the final integrated model (Model 6).
In all the analyses, the final integrated model (Model 6, Tables 2–5) showed that the addition of the community socioeconomic score was negligible to further attenuation of SES-SRH or LLI associations beyond the reduction by the combined psychosocial and community variables (Model 4). Moreover, the attenuation in these models was similar or identical to that in a model combining psychosocial, community and health behavior variables (data not shown).
4. Discussion and conclusions The integrative multilevel model showed a considerable explanation of social inequalities in both health outcomes; however, it did not fully elucidate the SEP-health associations, except for that between education and LLI. Additionally, of the explanatory variables, the contribution of community factors to the explanation of inequalities in SRH was substantial, whereas that of psychosocial factors was more modest and that of health behaviors was smaller or negligible. In contrast, community factors had a larger effect on inequalities in LLI compared with the psychosocial or behavioral factors only for educational inequalities, while their contribution to inequalities by LLI by number of cars was comparable to that of the psychosocial factors and much larger than that of health behaviors. Thus, by examining two health measures and both education and an asset-based measure of SEP, namely the number of cars, our results extend previous research on the social gradient in health in Israel (Jaffe et al., 2008) and support evidence from European countries (Mackenbach et al., 2002). The study identified the potential mediators, corroborating findings that low SEP is related to psychosocial factors, such as stressful life conditions (Lantz et al., 2005), mastery/control (Bosma et al., 1999), social support (Taylor and Seeman, 1999) and risky health behaviors (Link and Phelan, 1995). The results showed that social participation and social trust are the two elements associated with both SEP and health. The lack of associations of the other elements of social capital (lack of fairness and helpfulness) or of neighborhood problems with SEP, may be related to the heterogeneous conditions of Israeli neighborhoods, where some better-off areas may suffer from several of the measured problems.
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Community factors made a substantial contribution in explaining inequalities in SRH, and in explaining educational inequalities in LLI. These results provide preliminary evidence that social capital plays a major role in linking individual SEP and health in Israel, and are compatible with studies conducted in other countries. Social participation may link SEP with health because it may represent SEP differences in other dimensions contributing to health, such as traditional family orientation in Israeli society (Lewin-Epstein et al., 2000), or feelings of empowerment (Itzhaki and York, 2000). Further exploration needs to refine this association and examine which types of group memberships are more important. It should be noted, however, that associations between social participation and health could be bi-directional, as existing illness can act as a barrier for active participation. Our findings that individual-level rather than aggregate-level social capital variables were associated with the health outcomes, strengthen the need for additional research on this issue to further understand the meaning of social capital in different cultures (Forbes and Wainwright, 2001). Only a modest portion of the inequalities in SRH was explained by the individual psychosocial factors, indicating that these explanatory variables do not play an important role in the SEP– SRH association in Israel. While these findings resemble those showing that work-related psychosocial factors made no contribution in explaining inequalities in MI in England (Ferrie et al., 2005), they contrast findings on mortality in The Netherlands (van Oort et al., 2005). This may stem from the different outcome measures but also from cultural variations. In a European study, psychosocial factors had a substantial explanatory power for inequalities in SRH in a French cohort but a very modest one in a British cohort (Fuhrer et al., 2002). Also relevant to our results is the unique terror-related exposure to stress experienced by all segments of the Israeli population during the last decade, which may lead to different stress-processing mechanisms. The modest or marginal contribution of health behaviors to the explanation of inequalities supports previous results (Mulatu and Schooler, 2002; Lantz et al., 2001) and suggests that changing health behaviors cannot be an exclusive solution for reducing SEP disparities in health. Finally, the lack of independent effect of area-level socioeconomic score in explaining health inequalities beyond that of the other individual and area-level factors calls for further studies. While other research (Steptoe and Feldman, 2001), including Israeli studies (Jaffe et al., 2005), showed that living in poorer areas is associated with increased risk of mortality or morbidity, sparse information is available on the additional contribution of area-level SEP in explaining health inequalities. Some limitations of the findings need to be addressed. The cross-sectional design of the current study limits the ability to assess causality. Our analysis focused on an additive model, did not include the evaluation of interactive effects, and is limited by our choice of variables, their measurement and the order of their entry into the regression models. Despite these limitations, the findings provide additional data to the few empirical studies that examined the relative contribution of potential pathways to health inequalities, enabling the testing of current theories. The findings contribute to the understanding of factors that explain health inequalities rather than determinants of health (Graham, 2004), and serve as an important step in research on health inequalities in Israel. Examining a comprehensive conceptual framework in a sizeable and representative sample, the findings have several implications. They show that use of two dimensions of health outcomes—self-rated health and chronic illness—highlight similarities and differences in explanatory factors of inequalities. The integrative multi-level model of psychosocial, community and behavioral factors
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presented here provided a full explanation only for the education– LLI association. For other associations the explanation was substantial, yet only partial. Additional explanatory factors, for example, more specific material circumstances (van Oort et al., 2005), or early life factors (Ferrie et al., 2005) should be expanded in future, prospective studies. Our research might not only increase awareness among policy makers as to the scope of social inequalities in health, but also outline the type of interventions required to reduce them. Interventions targeting both community and individual psychosocial factors will be most beneficial in reducing inequalities in SRH and in illness. Promotion of health behaviors alone is not sufficient to reduce these inequalities. The findings clearly support the claim that reduction of social inequalities in health should be a target of a national health policy in Israel in order to realize the principles of justice, equality and reciprocity of the National Health Insurance Law.
Acknowledgements This study was supported by Grant no. 2001/7 from the Israel National Center for Health Policy and Health Services Research. The authors thank Ms. Hagit Hochner for her assistance in data analysis.
References Adler, N., 2006. When one’s main effect is another’s error: Material vs. psychosocial explanations of health disparities. A commentary on Macleod et al. Social Science and Medicine 63, 846–850. Bartley, M., 2004. Health Inequality. An Introduction to Theories, Concepts and Methods. Polity, Cambridge. Bosma, H., Schrijvers, C.T.M., Mackenbach, J.P., 1999. Socioeconomic inequalities in mortality and importance of perceived control: cohort study. British Medical Journal 319, 1469–1470. Brunner, E., Marmot, M., 1999. Social organization, stress, and health. In: Marmot, M., Wilkinson, R.G. (Eds.), Social Determinants of Health. Oxford University Press, New York. CBS, 1999. In: Characteristics and Ranking of Local Authorities According to the Population’s Socio-Economic Level in 1995. Publication 1039. Central Bureau of Statistics, Jerusalem. CBS, 2004. In: Statistical Abstracts. Israel 2004. Central Bureau of Statistics, Jerusalem. Dunn, J.R., Veenstra, G., Ross, N., 2006. Psychosocial and neo-material dimensions of SES and health revisited: predictors of self-rated health in a Canadian national survey. Social Science and Medicine 62, 1465–1473. Ferrie, J.E., Martikainen, P., Shipley, M.J., Marmot, M.G., 2005. Self-reported economic difficulties and coronary events in men: evidence from the Whitehall II study. International Journal of Epidemiology 34, 640–648. Forbes, A. Wainwright, S.P., 2001. On the methodological, theoretical and philosophical context of health inequalities research: A critique. Social Science & Medicine 53, 801–816. Fuhrer, R., Shipley, M.J., Chastang, J.F., Schmaus, A., Niedhammer, J., Stansfeld, S.A., Goldberg, M., Marmot, M.G., 2002. Socioeconomic position, health, and possible explanations; A tale of two cohorts. American Journal of Public Health 92, 1290–1294. Graham, H., 2004. Social determinants and their unequal distribution: clarifying policy understandings. Milbank Quarterly 82, 101–124. Idler, E.L., Benyamini, Y., 1997. Self rated health and mortality: a review of twenty-seven community studies. Journal of Health and Social Behavior 38, 21–37. Israel Ministry of Health, 2006. National Survey of health 2003/2004. Updated 20.11.2006. http://www.health.gov.il/pages/default.asp?maincat=2catId=688 PageId=3885. Accessed 15.6.2007. Itzhaki, H., York, A., 2000. Empowerment and community participation: does gender make a difference?. Social Work Research 24, 225–234. Jaffe, D.H., Eisenbach, Z., Newmark, Y.D., Manor, O., 2005. Individual household and nationhood socioeconomic status and mortality: a study of absolute and relative deprivation. Social Science and Medicine 60, 989–997. Jaffe, D.H., Neumark, Y.D., Eisenbach, Z., Manor, O., 2008. Educational inequalities in mortality among Israeli Jews: changes over time in a dynamic population. Health and Place 14, 287–298. Karlsson, J., Sjostrom, L., Sullivan, M., 1995. Swedish obese subjects (SOS)—an intervention study of obesity. Measuring psychosocial factors and health by means of short-form questionnaires. Results from a method study. Journal of Clinical Epidemiology 48, 817–823.
Kawachi, I., Kennedy, B.P., Lochner, K., Protow-Stith, D., 1997. Social capital, income inequality, and mortality. American Journal of Public Health 87, 1492–1498. Kim, D., Kawachi, I., 2006. A multilevel analysis of key forms of community- and individual-level social capital as predictors of self-rated health in the United States. Journal of Urban Health 83, 813–826. ¨ Lahelma, E., Keskimaki, I., Rahkonen, O., 2002. Income maintenance policies: The example of Finland. In: Mackenbach, J.P., Bakker, M. (Eds.), Reducing Inequalities in Health: a European Perspective. Routledge, London and New York. Lantz, P.M., House, J.S., Mero, R.P., Williams, D.R., 2005. Stress, life events, and socioeconomic disparities in health: results from the Americans’ changing life study. Journal of Health and Social Behavior 46, 274–288. Lantz, P.M., Lynch, J.W., House, J.S., Lepkowski, J.M., Mero, R.P., Musick, M.A., Williams, D.R., 2001. Socioeconomic disparities in health change in a longitudinal study of US adults: the role of health-risk behaviors. Social Science and Medicine 53, 29–40. Lazarus, R.S., Folkman, S., 1984. Stress, Appraisal and Coping. Springer Publishing Company, New York. Lewin-Epstein, N., Stier, H., Braun, M., Langfeldt, B., 2000. Family policy and public attitudes in Germany and Israel. European Sociological Review 16, 385–401. Lin, D.Y., Fleming, T.R., De Gruttola, V., 1997. Estimating the proportion of treatment effect explained by a surrogate marker. Statistics in Medicine 16, 1515–1527. Link, B.G., Phelan, J., 1995. Social conditions as fundamental causes of disease. Journal of Health and Social Behavior. Lindstrom, M., Hanson, B.S., Ostergren, P.O., 2001. Socioeconomic differences in leisure-time physical activity: the role of social participation and social capital in shaping health related behavior. Social Science and Medicine 52, 441–451. Mackenbach, J.P., Bakker, M.J., Kunst, A.E., Diderichsen, F., 2002. Socioeconomic inequalities in health in Europe. An overview. In: Mackenbach, J.P., Bakker, M. (Eds.), Reducing Inequalities in Health: A European Perspective. Routledge, London and New York. Mackenbach, J.P., Bos, V., Andersen, O., Cardano, M., Costa, G., Harding, S., Reid, A., ¨ ., Valkonen, T., Kunst, A.E., 2003. Widening socioeconomic ¨ Hemstrom, O inequalities in mortality in six Western European countries. International Journal of Epidemiology 32, 830–837. Manne, S, Glassman, M., 2000. Perceived control, coping efficacy, and avoidance coping as mediators between spouses unsupportive behaviors and cancer patients; psychological distress. Health Psychology 19, 155–164. Manor, O., Eisenbach, Z., Israeli, A., Friedlander, Y., 2000. Mortality differentials among Israeli Women: The Israel longitudinal mortality study. Social Science and Medicine 51, 1175–1188. Manor, O., Eisenbach, Z., Peritz, E., Friedlander, Y., 1999. Mortality differentials among Israeli men. American Journal of Public Health 89, 1807–1813. Manor, O., Matthews, S., Power, C., 1997. Comparing measures of health inequality. Social Science and Medicine 45, 761–771. Manor, O., Power, C., Matthews, S., 2001. Self rated health and limiting longstanding illness: inter-relationships with morbidity in early adulthood. International Journal of Epidemiology 30, 600–607. Marmot, M.G., Wilkinson, R.G., 2001. Psychosocial and material pathways in the relation between income and health: a response to Lynch et al. British Medical Journal 322 (7296), 1233–1236. McDonough, P., Walters, V., 2001. Gender and health: reassessing patterns and explanations. Social Science and Medicine 52, 547–559. Molarius, A., Berglund, K., Eriksson, C., Lambe, M., Nordstrom, E., Eriksson, H.G., Feldman, I., 2007. Socioeconomic conditions, lifestyle factors, and self-rated health among men and women in Sweden. European Journal of Public Health 17, 125–133. Mulatu, M.S, Schooler, C., 2002. Casual connections between socio-economic status and health: reciprocal effects and mediating mechanisms. Journal of Health and Social Behavior 43, 22–41. National Health Insurance Law, 1994. /http://www.health.gov.il/Download/pages/ law_bituch.pdfS (Hebrew). Pandey, S.K., Hart, J.J., Tiwary, S., 2003. Women’s health and the Internet: understanding emerging trends and implications. Social Science and Medicine 56, 179–191. Pearlin, L.I., Schooler, C., 1978. The structure of coping. Journal of Health and Social Behavior 19, 2–21. Poortinga, W., 2006aa. Social relations or social capital? Individual and community health effects of bonding social capital. Social Science and Medicine 63, 255–270. Poortinga, W., 2006bb. Social capital: an individual or collective resource for health?. Social Science and Medicine 62, 292–302. Rasbash, J., Steele, F., Browne, J.W., Prosser, B., 2005. A user’s guide to MLwiN version 2.0. University of Bristol. Robert, S.A., 1999. Socioeconomic position and health: the independent contribution of community socioeconomic context. Annual Review of Sociology 25, 489–516. Shuval, J. T., Anson, O., 2000. Social Structure and Health in Israel. Jerusalem: The Hebrew University: Magness Press., Hebrew. Siegrist, J., Marmot, M., 2004. Health inequalities and the psychosocial environment—two scientific challenges. Social Science and Medicine 58, 1463–1473. Singh-Manoux, A., 2003. Psychosocial factors and public health. Journal of Epidemiology and Community Health 57, 553–556.
ARTICLE IN PRESS V. Soskolne, O. Manor / Health & Place 16 (2010) 242–251
Steptoe, A., Feldman, P.J., 2001. Neighborhood problems as sources of chronic stress: development of a measure of neighborhood problems, and associations with socioeconomic status and health. Annals of Behavioral Medicine 23, 177–185. Tamir, D., Dayan, I., Weinstein, R., Arin, R., 1998. In: Knowledge, Attitudes and Health Behaviors In Israel-1998. Israel Ministry of Health., Hebrew, Jerusalem.
251
Taylor, S.E., Seeman, T., 1999. Psychosocial resources and the SES—health relationship. Annals of the New York Academy of Sciences 896, 210–225. van Oort, F.V.A., van Lenthe, F.J., Mackenbach, J.P., 2005. Material, psychosocial, and behavioural factors in the explanation of educational inequalities in mortality in the Netherlands. Journal of Epidemiology and Community Health 59, 214–220. Wilkinson, R., 1997. Socio-economic determinants of health: health inequalities— relative or absolute material standards. British Medical Journal 314, 591–595.