The health status of refugees and asylum seekers varies significantly across the European region. Differences are attributed to the political nature of the legal categories of “asylum seeker” and “refugee”; the wide disparities in national health services; and the diversity in individual characteristics of this population including age, gender, socioeconomic background, country of origin, ethnicity, language proficiency, migration trajectory, and legal status. Refugees are considered to be at risk of being or becoming relatively “unhealthy migrants” compared to those migrating on the basis of economic motives, who are characterized by the “healthy migrant effect.” Refugees and asylum seekers are at risk to the drivers of declining health associated with settlement such as poor diet and housing. Restricted access to health care whether from legal, economic, cultural, or language barriers is another likely cause of declining health status. There is also evidence to suggest that the “embodiment” of the experience of exclusion and marginalization that refugee and asylum seekers face in countries of resettlement significantly drives decrements in the health status of this population.
Article
Health Status of Refugees and Asylum Seekers in Europe
Rachel Humphris and Hannah Bradby
Article
Health and Health Care Access Among Diverse Groups of Elders in the United States: An Intersectionality Approach
Sadaf Arefi Milani and Kyriakos S. Markides
Great interest has been shown in recent years about the influence of diversity on access to health care and health status, especially over the life course. Substantial interest has been shown in diversity by race/ethnicity, gender, socioeconomic status, and also sexual orientation and rurality. A life course perspective whereby life conditions earlier in life influence health care access and health status later in life, with increasing application of an intersectionality perspective, is crucial to understanding how statuses delineated by social class, race/ethnicity, gender, sexual orientation, and age interact to influence later life outcomes. Application of intersectionality to the study of aging and health is relatively recent, in conjunction with the increasingly popular cumulative advantage/disadvantage life course perspective, promises to lead to significant advances in the field of diversity, aging, and health in the United States and elsewhere.
Article
Using Large Data Sets to Measure Health Status and Service Use of Older Adults
Kimberly E. Lind and Magdalena Z. Raban
Commonly used data sources for measuring health status and service use of older adults include national surveys and secondary data analysis of electronic data sources including healthcare claims data and electronic health records (EHRs). Depending on how the data are generated in EHRs and medical claims, and depending on how long people are observed for, the ability to measure prevalence or incidence of chronic conditions and the ability to measure incidence or a history of acute conditions will vary. Various data types spanning standardized data (diagnostic codes, procedure codes), medication administered or prescribed, unstructured free text such as clinical notes, and clinical assessment data can all be used to measure health status and service use. Different data sources and types of variables have different benefits and limitations depending on how data are generated and the incentives for those recording data (i.e., healthcare providers and billing staff) to be complete. Testing assumptions and exploring the validity of measures can be accomplished by approaches such as comparing agreement of measures (e.g., disease prevalence) across data tables within a data source, comparing agreement with linked data sources, and comparing rates of disease or service use to rates in data sources that have similar populations. Future directions for administrative data such as data linkage and natural language processing will improve the utility of administrative data. The information and concepts are broadly applicable, but for illustrative purposes, examples of how these approaches have been applied to electronic data from administrative records including EHRs and claims data to fill important knowledge gaps and measure health status and quality of care from Australia and the United States are presented.
