Multimorbidity Among Migrants in Europe: Associations with Country of Birth and Country of Residence Público
Woody, Meaghan (Spring 2021)
Published
Abstract
Background: Migrants are a growing population in Europe; in 2016, foreign-born individuals accounted for 10.7% of the European Union population. To explain disease patterns in migrant populations, the contextual circumstances of life experiences pre- and post-migration should be understood. Multimorbidity, defined as the occurrence of two or more chronic diseases existing concurrently in the same individual, is a holistic approach to studying chronic disease to understand disease patterns that occur due to environmental, social, and personal risk factors.
Objective: This study aims to investigate if multimorbidity is associated with country of origin among migrants residing in Europe, and if this association is modified by country of residence.
Methods: We used the Survey of Health, Aging, and Retirement in Europe (SHARE), a cross-national, multidisciplinary panel survey representative of individuals aged 50 and older living in Europe. Examining 112,612 native-born and 11,266 migrants sampled in 2002-2017, we investigated cross-sectional associations between country of birth and multimorbidity. Self-reported chronic conditions used to define multimorbidity were: heart attack, high blood pressure, high blood cholesterol, stroke, diabetes, chronic lung disease, cancer, stomach ulcer, Parkinson’s disease, cataracts, and hip fracture. We examined whether associations differed by migrant’s country of residence, compared to native-born individuals in the same residence. Multinomial logistic regression models were used to assess multimorbidity and adjust for potential confounders.
Results: 37.65% of migrants and 35.10% of native-born individuals reported having multimorbidity. Compared to the native-born population, multimorbidity was significantly higher among migrants born in Eastern Europe (OR: 1.41, 95% CI: 1.31, 1.52) and Central and West Asia (OR: 1.16, 95% CI: 0.96, 1.40), and lower among migrants born in Southeast, South, and East Asia (OR: 0.66, 95% CI: 0.51, 0.87). Results remained significant after adjusting for socioeconomic factors. In the overall association between country of birth and multimorbidity, significant interaction was observed between country of birth and country of residence.
Conclusion: Overall, country of birth and country of residence are each associated with multimorbidity. These results underscore the importance of monitoring migrant health in national and regional health surveys to better understand the needs of the population and inform migrant-inclusive policies.
Table of Contents
Table of Contents
I. Introduction......1
Background......1
Objective and Research Question......2
II. Literature Review......3
Defining Multimorbidity......3
Multimorbidity in Europe......3
Multimorbidity Risk Factors......6
Age......6
Female Sex......6
Low Socioeconomic Status and Low Education......7
Migration and Chronic Disease......8
Conceptual Framework......10
III. Data and Methods......12
Data Source......12
Sample Population......13
Data Collection......14
Data Preparation......17
Sample Creation......17
Data Cleaning and Coding......18
Missing Data......18
Statistical Analysis......19
Descriptive Analyses......20
Modeling......20
IV. Results......22
Descriptive Results of Study Population......22
Multivariate Logistic Regression Models for Multimorbidity......23
V. Discussion......26
Strengths and Limitations......27
Conclusion......29
VI. Data Acknowledgements......31
VII. References......32
VIII. Tables and Figures......39
Table 1. Sample Characteristics of Survey of Health of Aging and Retirement in Europe (SHARE) by Migrant Status as characterized by Country of Birth Geographic Region (N=123,878)......39
Table 2. Sample Characteristics of Survey of Health of Aging and Retirement in Europe (SHARE)a (N=123,878)......41
Table 3. Multinomial logistic regression models for predicting multimorbidity among migrants in Europe, controlling for selected characteristics......43
Table 4. Multinomial Logistic Regression Adjusted Beta Estimates and Standard Errors for predicting multimorbidity by country of birth modified by country of residence, controlling for selected characteristicsa......45
IX. Appendices......46
Table 1. International Organization for Standardization (ISO) Geographic Region Classifications......46
Table 2. Descriptive Summary Statistics of Variables and Missingness for Survey of Health of Aging and Retirement in Europe (SHARE) 2002-2017 (N = 131,219)......48
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