Cardiovascular diseases (CVDs) are the leading cause of disability and death worldwide (WHO, 2016). The mortality rate has been declining in some western-European countries over the past decade, partly due to successful implementation of prevention strategies (Roth et al. 2015). Use of primary prevention strategies can identify high-risk individuals for prevention and treatment of CVD risk factors, for example by estimating the risk of CVD (Piepoli et al. 2016; Stewart, 2017).
Some ethnic minority groups experience disparities in multiple CVD risk aspects, such as incidence, prevalence, mortality rate and estimated CVD risk. Although improved cardiovascular management causes the decline in morbidity and mortality, the overall progress is retained by an unequal decline for ethnic minority populations. In the UK, there were significantly smaller declines in coronary heart disease mortality rates for migrants from Jamaica, Pakistan, Bangladesh and Poland, compared with men from England and Wales (Harding et al. 2008). Also in CVD risk assessment, migrants from South Asia show increased risk of CVDs compared to European host population (Dalton, 2014). The underlying cause of these ethnic disparities in CVD risk, have not yet been fully explained.
Ethnic minority groups experience CVD risk factor disparities compared to the host population, in for example socioeconomic status (SES) (Ski et al. 2014) and traditional CVD risk factors, such as hypertension and diabetes (Rabanal et al. 2017). However, only part of the CVD risk disparities can be explained by these risk factors (Cooper et al. 2000).
Part of the ethnic disparities in CVD risk, may be explained by ethnic differences in the occurrence of stressful life events. The presence of stressful life events has been associated with several negative physical health outcomes, such as CVD mortality (Rutters et al. 2014), type 2 diabetes (Maksimovic et al. 2014) and metabolic syndrome (Rutters et al. 2015). Ethnic minority groups, as well as groups with low SES, have a higher risk of experiencing stressful life events (Hatch & Dohrenwend, 2007). It remains unknown whether estimated CVD risk disparities among ethnic minorities are attributable to disparities in occurrence of stressful life events.
The aim of this study was to investigate the occurrence of stressful life events between ethnic groups, and its effect on the association between ethnicity and estimated CVD risk.
The data has been obtained during the multi-ethnic Healthy Life in an Urban Setting (HELIUS) study. HELIUS is a large-scale prospective cohort study carried out in Amsterdam, The Netherlands. The aims of the HELIUS study have been described by Stronks et al. (2013). In brief, the primary aim of the HELIUS study is to unravel the unequal burden of disease between ethnic groups. Participants aged 18-70, were randomly sampled and stratified by ethnicity via the municipality register. Via questionnaire and physical examination, data of participants with Dutch, Turkish, Moroccan, Surinamese and Ghanaian origin, living in Amsterdam had been obtained.
The ethnicity of participants had been defined according to the country of birth of the participant and that of his/her parents. Participants were defined as of non-Dutch ethnic origin if he/she fulfilled one of two criteria: (1) he/she was born outside the Netherlands and has at least one parent born outside the Netherlands (first generation), or (2) he/she was born in the Netherlands but both parents were born outside the Netherlands (second generation). Participants with Surinamese background, were further classified according to self-reported ethnic origin into ‘African’, ‘South-Asian’ or ‘other’. Participants were defined as of Dutch origin if the person and both parents were born in the Netherlands.
Estimated cardiovascular risk
Cardiovascular risk was estimated using the CVD risk algorithm currently used in Dutch primary care. This algorithm is based on the SCORE algorithm for low-risk countries, and estimates the 10-year risk of CVD mortality and morbidity based on age, sex, blood pressure, total cholesterol/high density lipoprotein cholesterol ratio (TC/HDL), smoking status and, in addition, diabetes. In participants with a diabetes diagnosis, 15 years is added to the age (NHG-standaard, 2012). The algorithm is applicable to participants without prior CVD, who between 40 and 70 years of age, or in case of a diabetes diagnosis, 25 and 55 years of age. Smoking status was obtained via questionnaire. Blood pressure was measured twice, using a validated automated digital blood pressure device (WatchBP HOME; Microlife AG) on the left arm in a seated position after the person had been seated for at least five minutes. Fasting blood samples were drawn, from which fasting glucose, total and HDL cholesterol were determined. Diabetes status is defined by self-reported diagnosis of diabetes, use of glucose-lowering medication or in case of a fasting glucose ≥7.0 mmol/L.
Stressful life events
The presence of stressful life events is defined according to an adapted version of the list used in NEMESIS-II questionnaire. Participants were asked whether, in the past twelve months one or more of the following negative life events occurred: (1) suffered from a serious illness or injury, (2) a close relative had a serious illness or injury, (3) a parent, child, brother, sister or spouse died, (4) another relative or close friend died, (5) a steady relationship ended, (6) a long term-friendship with friend or family member was broken off, (7) had a serious problem with good friend, family member or neighbor, (8) were sacked or became unemployed, (9) had a major financial crisis, (10) had other important negative life events. Presence of stressful life events in the past twelve months is classified as ‘yes’, if one or more of the questions above had been answered with ‘yes’. Participants who answered ‘no’ to all questions, were classified as not having experienced any stressful life events in the past twelve months.
Indicators of SES
SES was estimated by self-reported educational level and occupational level. Educational level is defined via questionnaire and based on the highest qualification attained, either in the Netherlands or in the country of origin. Educational level was categorized into four groups: (1) no or elementary schooling, (2) lower vocational or lower secondary schooling, (3) intermediate vocational or intermediate or higher secondary schooling, and (4) higher vocational schooling or university. Occupational level was based on job title and job description, including a question on fulfilling an executive function, and was classified according to Dutch Standard Occupational Classification system for 2010. This classification provides an extensive systematic list of all professions in the Dutch system. Based on this document, occupational level was classified into: (1) elementary, (2) lower, (3) intermediate and (4) higher or academic.
Baseline data collected from both questionnaire and physical examination was determined among 22,165 participants. Participants with Javanese Surinamese (n=233), other/unknown Surinamese (n=267), and other/unknown (n=48) ethnic background were excluded from this study. Furthermore, participants based on missing data regarding stressful life events (n=119), cardiovascular risk (i.e. blood pressure, smoking status and/or total cholesterol) were excluded. In addition, participants not eligible for CVD risk estimation based on age (younger than 40 years, or 25 years among those with diabetes), or based on prior CVD, were excluded (n=11,715). This resulted in a study population of 10,096 participants.
Baseline characteristics are described and compared between ethnic groups using chi-square analyses or linear regression analyses. All analyses are stratified by sex and adjusted for age. Ethnicity was used as the determinant in a linear regression analysis to examine the association with estimated CVD risk. To understand the role of stressful life events within the different ethnic groups, a logistic regression analysis was used. Furthermore, the effect of stressful life events on estimated CVD risk was analyzed by a linear regression analysis. Finally, a multiple logistic regression analysis was used for the association between the dependent variable estimated CVD risk, and independent variables ethnicity and stressful life events. Additionally, the analysis was adjusted for both educational and occupational level, because both SES indicators influence the effect and occurrence of stressful life events (McLeod & Kessler, 1990).
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