Perfil de óbitos e causas da mortalidade de migrantes internacionais no Brasil, 2011–2022
Bibliographic record
Abstract
OBJECTIVE: To describe the mortality profile and analyze the causes of death among international migrants residing in Brazil between 2011 and 2022. METHODS: This is a cross-sectional, descriptive, and ecological study based on secondary data. The sociodemographic profile of deaths reported from 2011 to 2022 was analyzed. Absolute and relative frequencies, as well as mortality rates per 100,000 inhabitants, were calculated by country of birth, macroregion, and federative unit of residence. The underlying causes of death were also analyzed. RESULTS: A total of 173,807 deaths among international migrants were recorded between 2011 and 2022, with the highest number in 2021 (17,779; 10.2%). The predominant mortality profile was male (97,053; 55.8%), aged ≥ 81 years (104,308; 60.0%), White (136,835; 78.7%), and widowed (72,156; 41.5%). Most deceased migrants were born in Portugal (64,909; 37.3%), Japan (22,748; 13.1%), Italy (16,178; 9.3%), and Spain (12,835; 7.3%). The highest mortality rates were observed among migrants born in Lithuania (190,079/100,000), Serbia (146,794/100,000), and Hungary (96,395/100,000). The Southeast (137,457; 79.1%) and South (18,603; 10.7%) macroregions accounted for the majority of deaths, with the highest mortality rates observed in the states of Ri o de Janeiro (25,349/100,000), São Paulo (23,574/100,000), and Mato Grosso do Sul (18,683/100,000). T he covid-19 pandemic led to an increase in infectious and parasitic diseases from 2020 onward, peaking in 2021 (4,460; 25.0% of deaths that year). The most frequent underlying cause of death during the study period was unspecified acute myocardial infarction (12,141; 7.0%). CONCLUSION: This study highlights the need for targeted health actions addressing international migrant profiles at higher risk of death and their specific causes of mortality. However, the general invisibility of this population in public health indicators hinders the implementation of effective strategies. Policies ensuring equitable access to healthcare services, medications, and vaccines are essential to improve long-term health outcomes of this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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; both teacher heads agree on what is shown here.
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".