Healthy ageing in a multi-ethnic population: A descriptive cross-sectional analysis from the HELIUS study.

OBJECTIVE: We investigated ethnic health disparities in the Healthy Life in an Urban Setting multi-ethnic cohort using the multidimensional Healthy Ageing Score. STUDY DESIGN: We conducted a cross-sectional analysis of the study baseline data (2011-2015) collected through questionnaires/physical examinations for 17,091 participants (54.8 % women, mean (SD) age = 44.5 (12.8) years) from South-Asian Surinamese (14.8 %), African Surinamese (20.5 %), Dutch (24.3 %), Moroccan (15.5 %), Turkish (14.9 %), and Ghanaian (10.1 %) origins, living in Amsterdam, the Netherlands. MAIN OUTCOME MEASURES: We computed the Healthy Ageing Score developed in the Rotterdam Study, which has seven biopsychosocial domains: chronic diseases, mental health, cognitive function, physical function, pain, social support, and quality of life. That score was used to discern between healthy, moderate, and poor ageing. We explored differences in healthy ageing by ethnicity, sex, and age group using multinomial logistic regression. RESULTS: The Healthy Ageing Score [overall: poor (69.0 %), moderate (24.8 %), and healthy (6.2 %)] differed between ethnicities and was poorer in women and after midlife (cut-off 45 years) across ethnicities (all p < 0.001). In the fully adjusted models in men and women, poor ageing (vs. healthy ageing) was highest in the South-Asian Surinamese [adjusted odds ratios (95 % confidence intervals)] [2.96 (2.24-3.90) and 6.88 (3.29-14.40), respectively] and Turkish [2.80 (2.11-3.73) and 7.10 (3.31-15.24), respectively] vs. Dutch, in the oldest [5.89 (3.62-9.60) and 13.17 (1.77-98.01), respectively] vs. youngest, and in the divorced [1.48 (1.10-2.01) and 2.83 (1.39-5.77), respectively] vs. married. Poor ageing was inversely associated with educational and occupational levels, mainly in men. CONCLUSIONS: Compared with those of Dutch ethnic origin, ethnic minorities displayed less healthy ageing, which was more pronounced in women, before and after midlife, and was associated with sociodemographic factors.

Socioeconomic Inequalities in Type 2 Diabetes: Mediation Through Status Anxiety?

Objectives: While status anxiety has received attention as a potential mechanism generating health inequalities, empirical evidence is still limited. Studies have been ecological and have largely focused on mental and not physical health outcomes. Methods: We conducted individual-level analyses to assess status anxiety (feelings of inferiority resulting from social comparisons) and resources (financial difficulties) as mediators of the relationship between socioeconomic status (SES) (education/occupation/employment status) and type 2 diabetes (T2D). We used cross-sectional data of 21,150 participants (aged 18-70 years) from the Amsterdam-based HELIUS study. We estimated associations using logistic regression models and estimated mediated proportions using natural effect modelling. Results: Odds of status anxiety were higher among participants with a low SES [e.g., OR = 2.66 (95% CI: 2.06-3.45) for elementary versus academic occupation]. Odds of T2D were 1.49 (95% CI: 1.12-1.97) times higher among participants experiencing status anxiety. Proportion of the SES-T2D relationship mediated was 3.2% (95% CI: 1.5%-7.0%) through status anxiety and 10.9% (95% CI: 6.6%-18.0%) through financial difficulties. Conclusion: Status anxiety and financial difficulties played small but consistent mediating roles. These individual-level analyses underline status anxiety's importance and imply that status anxiety requires attention in efforts to reduce health inequalities.

Higher prevalence of depressed mood in immigrants' offspring reflects their social conditions in the host country: The HELIUS study.

BACKGROUND: Immigrants from low- and middle-income countries who have settled in high-income countries show higher risks of depression in comparison with host populations. The risks are associated with adverse social conditions. Indecisive results have been reported on the depression risks of the offspring of immigrant populations. OBJECTIVE: To assess the prevalence of depressed mood in immigrant offspring relative to the host population and to analyse whether that risk is explained by social conditions. METHODS: Cross-sectional data from the Dutch HELIUS study were analysed, involving 19,904 men and women of Dutch, South-Asian Surinamese, African Surinamese, Turkish or Moroccan ethnic descent aged 18 to 70. Depressive symptomatology was assessed using the Patient Health Questionnaire-9 (PHQ-9). Indicators of social conditions were socioeconomic position (educational level, occupational level, employment status), perceived ethnic discrimination and sociocultural integration (ethnic identity, cultural orientation, social network). We used logistic regression to assess the risk of depressed mood (PHQ-9 sum score ≥10) in immigrants' offspring, as well as in first generation immigrants, relative to the risk in the host population. Social indicators were stepwise added to the model. RESULTS: The prevalence of depressed mood was 13% to 20% among immigrant offspring, with the lowest level for those of African Surinamese descent; prevalence in the Dutch origin population was 7%. Relative risk of depressed mood, expressed as average marginal effects (AMEs), decreased substantially in all offspring groups after adjustment for socioeconomic indicators and discrimination. E.g. the AME of Turkish vs. Dutch decreased from 0.11 (0.08-0.13) to 0.05 (0.03-0.08). Patterns resembled those in first generation immigrants. CONCLUSIONS: Results suggest that the observed higher prevalence of depressed mood in immigrants' offspring will decline to the level of the host population as the various populations grow closer in terms of socioeconomic position and as immigrant offspring cease to experience discrimination.

To what extent do socioeconomic inequalities in SRH reflect inequalities in burden of disease? The HELIUS study.

BACKGROUND: Self-rated health (SRH), an attractive measure for health monitoring, shows persistent inequalities with regard to socioeconomic status (SES). However, knowledge on the extent to which inequalities in SRH reflect inequalities in disease burden is lacking. METHODS: Data come from the multi-ethnic HEalthy LIfe in an Urban Setting study (Dutch, South-Asian Surinamese, African Surinamese, Ghanaian, Turkish or Moroccan origin, N = 19 379, aged 18-70). SES was defined by educational and occupational level. Disease burden was operationalized as chronic diseases, physical and mental functioning (measured with SF-12) and depressive symptoms (measured with PHQ-9). We applied logistic regression analyses and reported average marginal effects (AME). RESULTS: Dutch origin participants with low educational or low occupational level had higher probabilities of reporting fair/poor SRH, compared to the highest levels (AME = 0.20 95% CI: 0.13;0.27; and 0.12 (0.09;0.15), respectively). Associations were attenuated after adjusting for all disease burden indicators, to AME = 0.03 (0.01;0.04) and AME = 0.02 (-0.00;0.04). In all the non-Dutch origin groups, a larger part of the inequalities remained after adjustment. CONCLUSION: Socioeconomic inequalities in SRH are for a large part explained by higher disease burden in lower socioeconomic groups, but less so in those with non-Dutch origin. Future research should examine if our conclusions also hold for trend data on inequalities in SRH.

Do ethnic inequalities in multimorbidity reflect ethnic differences in socioeconomic status? The HELIUS study.

BACKGROUND: The burden of multimorbidity is likely higher in ethnic minority populations, as most individual diseases are more prevalent in minority groups. However, information is scarce. We examined ethnic inequalities in multimorbidity, and investigated to what extent they reflect differences in socioeconomic status (SES). METHODS: We included Healthy Life in an Urban Setting study participants of Dutch (N = 4582), South-Asian Surinamese (N = 3258), African Surinamese (N = 4267), Ghanaian (N = 2282), Turkish (N = 3879) and Moroccan (N = 4094) origin (aged 18-70 years). Educational level, employment status, income situation and multimorbidity were defined based on questionnaires. We described the prevalence and examined age-adjusted ethnic inequalities in multimorbidity with logistic regression analyses. To assess the contribution of SES, we added SES indicators to the age-adjusted model. RESULTS: The prevalence of multimorbidity ranged from 27.1 to 53.4% in men and from 38.5 to 69.6% in women. The prevalence of multimorbidity in most ethnic minority groups was comparable to the prevalence among Dutch participants who were 1-3 decades older. After adjustment for SES, the odds of multimorbidity remained significantly higher in ethnic minority groups. For instance, age-adjusted OR for multimorbidity for the Turkish compared to the Dutch changed from 4.43 (3.84-5.13) to 2.34 (1.99-2.75) in men and from 5.35 (4.69-6.10) to 2.94 (2.54-3.41) in women after simultaneous adjustment for all SES indicators. CONCLUSIONS: We found a significantly higher prevalence of multimorbidity in ethnic minority men and women compared to Dutch, and results pointed to an earlier onset of multimorbidity in ethnic minority groups. These inequalities in multimorbidity were not fully accounted for by differences in SES.

Measurement invariance of the SF-12 among different demographic groups: The HELIUS study.

AIM: To investigate whether items of the SF-12, widely used to assess health outcome in clinical practice and public health research, provide unbiased measurements of underlying constructs in different demographic groups regarding gender, age, educational level and ethnicity. METHODS: We included 23,146 men and women aged 18-70 of Dutch, South-Asian Surinamese, African Surinamese, Ghanaian, Turkish, or Moroccan origin from the HELIUS study. Both multiple group confirmatory factor analyses (MGCFA), with increasingly stringent model constraints (i.e. assessing Configural, Metric, Strong and Strict measurement invariance (MI)), and regression analysis were conducted to establish comparability of SF-12 items across demographic groups. RESULTS: MI regarding gender, age and education was tested in the ethnic Dutch group (N = 4,615). In each subsequent step of testing MI, change in goodness-of-fit measures did not exceed 0.010 (RMSEA) or 0.004 (CFI). Moreover, goodness-of-fit indices showed good fit for strict invariance models: RMSEA<0.055; CFI>0.97. Regarding ethnicity, RMSEA values of metric and subsequent models fell above 0.055, indicating violation of measurement invariance in factor loadings, thresholds and residual variances. Regression analysis revealed possible age-, education- and ethnicity-related DIF. Adjustment for this DIF had little impact on the magnitude of age and educational differences in physical and mental health, but ethnic inequalities in physical health-and to a lesser extent mental health-were reduced after DIF adjustment. CONCLUSIONS: We found no evidence of violation of measurement invariance of the SF-12 regarding gender, age and educational level. If minor DIF would remain undetected in our MGCFA analyses, we showed that this would have negligible effect on the magnitude of demographic health inequalities. Regarding ethnicity, the SF-12 was not measurement invariant. After accounting for DIF, we observed a reduction of ethnic inequalities in health, in particular in physical health. Caution is warranted when comparing SF-12 scores across population groups with various ethnic backgrounds.

Do conceptualisations of health differ across social strata? A concept mapping study among lay people.

OBJECTIVES: The legitimacy of policies that aim at tackling socioeconomic inequalities in health can be challenged if they do not reflect the conceptualisations of health that are valued in all strata. Therefore, this study analyses how different socioeconomic groups formulate their own answers regarding: what does health mean to you? DESIGN: Concept mapping procedures were performed in three groups that differ in educational level. All procedures followed exactly the same design. SETTING: Area of the city of Utrecht, the Netherlands. PARTICIPANTS: Lay persons with a lower, intermediate and higher educational level (±15/group). RESULTS: The concept maps for the three groups consisted of nine, eight and seven clusters each, respectively. Four clusters occurred in all groups: absence of disease/disabilities, health-related behaviours, social life, attitude towards life. The content of some of these differed between groups, for example, behaviours were interpreted as having opportunities to behave healthily in the lower education group, and in terms of their impact on health in the higher education group. Other clusters appeared to be specific for particular groups, such as autonomy (intermediate/higher education group). Finally, ranking ranged from a higher ranking of the positively formulated aspects in the higher education group (eg, lust for life) to that of the negatively formulated aspects in the lower education group (eg, having no chronic disease). CONCLUSION: Our results provide indications to suggest that people in lower socioeconomic groups are more likely to show a conceptualisation of health that refers to (1) the absence of health threats (vs positive aspects), (2) a person within his/her circumstances (vs quality of own body/mind), (3) the value of functional (vs hedonistic) notions and (4) an accepting (vs active) attitude towards life.

Cohort profile: the Healthy Life in an Urban Setting (HELIUS) study in Amsterdam, The Netherlands.

PURPOSE: Ethnic minority groups usually have a more unfavourable disease risk profile than the host population. In Europe, ethnic inequalities in health have been observed in relatively small studies, with limited possibilities to explore underlying causes. The aim of the Healthy Life in an Urban Setting (HELIUS) study is to investigate the causes of (the unequal burden of) diseases across ethnic groups, focusing on three disease categories: cardiovascular diseases, mental health and infectious diseases. PARTICIPANTS: The HELIUS study is a prospective cohort study among six large ethnic groups living in Amsterdam, the Netherlands. Between 2011 and 2015, a total 24 789 participants (aged 18-70 years) were included at baseline. Similar-sized samples of individuals of Dutch, African Surinamese, South-Asian Surinamese, Ghanaian, Turkish and Moroccan origin were included. Participants filled in an extensive questionnaire and underwent a physical examination that included the collection of biological samples (biobank). FINDINGS TO DATE: Data on physical, behavioural, psychosocial and biological risk factors, and also ethnicity-specific characteristics (eg, culture, migration history, ethnic identity, socioeconomic factors and discrimination) were collected, as were measures of health outcomes (cardiovascular, mental health and infections). The first results have confirmed large inequalities in health between ethnic groups, such as diabetes and depressive symptoms, and also early markers of disease such as arterial wave reflection and chronic kidney disease, which can only just partially be explained by inequalities in traditional risk factors, such as obesity and socioeconomic status. In addition, the first results provided important clues for targeting prevention and healthcare. FUTURE PLANS: HELIUS will be used for further research on the underlying causes of ethnic differences in health. Follow-up data will be obtained by repeated measurements and by linkages with existing registries (eg, hospital data, pharmacy data and insurance data).

Measurement invariance testing of the PHQ-9 in a multi-ethnic population in Europe: the HELIUS study.

BACKGROUND: In Western European countries, the prevalence of depressive symptoms is higher among ethnic minority groups, compared to the host population. We explored whether these inequalities reflect variance in the way depressive symptoms are measured, by investigating whether items of the PHQ-9 measure the same underlying construct in six ethnic groups in the Netherlands. METHODS: A total of 23,182 men and women aged 18-70 of Dutch, South-Asian Surinamese, African Surinamese, Ghanaian, Turkish or Moroccan origin were included in the HELIUS study and had answered to at least one of the PHQ-9 items. We conducted multiple group confirmatory factor analyses (MGCFA), with increasingly stringent model constraints (i.e. assessing Configural, Metric, Strong and Strict measurement invariance (MI)), and regression analysis, to confirm comparability of PHQ-9 items across ethnic groups. RESULTS: A one-factor model, where all nine items reflect a single underlying construct, showed acceptable model fit and was used for MI testing. In each subsequent step, change in goodness-of-fit measures did not exceed 0.015 (RMSEA) or 0.01 (CFI). Moreover, strict invariance models showed good or acceptable model fit (Men: RMSEA = 0.050; CFI = 0.985; Women: RMSEA = 0.058; CFI = 0.979), indicating between-group equality of item clusters, factor loadings, item thresholds and residual variances. Finally, regression analysis did not indicate potential ethnicity-related differential item functioning (DIF) of the PHQ-9. CONCLUSIONS: This study provides evidence of measurement invariance of the PHQ-9 regarding ethnicity, implying that the observed inequalities in depressive symptoms cannot be attributed to DIF.

Seventeen-year time trend in poor self-rated health in older adults: changing contributions of chronic diseases and disability.

BACKGROUND: Studies on trends in the self-rated health (SRH) of older people have shown conflicting results, which might partly be explained by changing associations between SRH and indicators of other health dimensions over time. Therefore, this study investigates 17-year time trends in older adults' poor SRH, in the context of trends in chronic diseases and disability, between 1992 and 2009. METHODS: Data originate from six measurement waves of the Longitudinal Aging Study Amsterdam (N = 4009, ages 60-85 years). SRH was assessed with the question 'How is your health in general?' The presence of lung disease, cardiac disease, peripheral arterial disease, diabetes mellitus, stroke, arthritis and cancer was assessed by self-report. Two severity levels of disability were assessed with six questions on physical functioning. Generalized Estimating Equations (GEE) analysis was applied to assess statistical significance in each time trend. RESULTS: There was a stable trend in the prevalence of poor SRH and severe disability, while the mean number of chronic diseases (1.3-1.8) and the prevalence of mild disability (20.5-32.1%) increased between 1992 and 2009. The association between poor SRH and chronic diseases became weaker, whereas the association between poor SRH and severe disability became stronger over time. Most unfavourable trends were observed in the older old and the lower educated. CONCLUSION: Our results suggest that the seeming stability of poor SRH hides underlying increases in chronic diseases and disability: over time, people may attach importance to different aspects of health when rating their overall health.