Including migrant populations in health impact assessments
Bibliographic record
Abstract
In 2010, there were 214 million international migrants worldwide, a number that is projected to double by 2050. (1) Migrants' motives for leaving their countries of origin include employment and education opportunities, escape from conflict and discrimination and the desire to raise families in economically and politically stable environments. New migrants are often healthier than the general population on arrival, but their health may deteriorate after settlement, (2) due to unfamiliar social conditions, infectious diseases, or restricted access to health services. Cultural and linguistic barriers may contribute to poor delivery of health services. The 61st World Health Assembly called on all Member States to promote migrant-sensitive health (3) Some subgroups--especially refugees--have a greater burden of infectious diseases and mental disorders than the indigenous population. (4) Guidelines have been developed to assist health workers in the clinical management of migrating populations. (4) However, there are no explicit decision-support tools for policy-makers to ensure health equity for migrants. Here we discuss how health impact assessment can account for the needs of migrant populations. Health impact assessment Health impact assessment can improve health equity by mitigating unintended harms and maximizing the benefits of programmes or policies. This approach supports decision-makers by suggesting actionable recommendations for emerging policies and programmes. (5) A number of health impact assessment tools have been developed (6) several of which have emphasized the need to consider disadvantaged population groups. However, a recent review found that only 14% of health impact assessments mentioned migrants in the evaluation and only 2% included them in their recommendations. (7) A recent consultation held at the WHO Collaborating Centre for Knowledge Translation and Health Technology Assessment in Health Equity identified four challenges to the inclusion of migrants in health impact assessment: (i) including migrants in the scope of the assessment; (ii) obtaining data on migrants; (iii) engaging migrant communities; and (iv) successfully appealing to decision-makers. Addressing the challenges Based on our own experience from developing migrant-focused guidelines in Canada, we discuss how these four challenges can be addressed. The first challenge is to include migrants in the scope of the assessment. Ideally, scoping should be deliberate and involve health researchers and community experts. In practice, however, the scope of health impact assessment is limited by timelines, political agendas and resources. Those who perform health impact assessments may only have the capacity to consider accessible populations and this may leave out migrants and other vulnerable populations. When appropriate, irregular migrants and/or refugee populations should be included in health impact assessments to maximize the value of the assessment and its recommendations. Appropriate scenarios for inclusion would include existing or future risk for inequities in migrant populations. Equity considerations should be consistent and well-defined. For example, some studies may erroneously consider ethnicity and migrant status as interchangeable. Migration, in itself, may be a unique social determinant of health distinct from ethnicity, language, culture or religion. Vulnerable subgroups of the population should be integrated within health system policies and programmes, with consideration given to historical contexts and local capacity, such as universal access to basic health care. Without explicit mention of migrants during the scoping stage, migrant populations may be forgotten in the assessment and recommendations. The second challenge is to source migrant-specific data. Migrants are heterogeneous, in terms of countries of origin, migration experiences, status, needs and abilities. …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".