Health conditions of older adults in complex humanitarian settings in low- and middle-income countries: a retrospective analysis of 2019–2025 data from Médecins Sans Frontières-supported inpatient departments
Bibliographic record
Abstract
BACKGROUND: Inpatient admissions of older adults in humanitarian settings in low-income and middle-income countries remain poorly documented, likely leading to gaps in the delivery of age-appropriate health services. This analysis aims to contribute to age-adapted and gender-adapted healthcare strategies in humanitarian settings. METHODS: This multicountry study includes adults who were admitted at Médecins Sans Frontières-supported inpatient departments in humanitarian settings across four regions between July 2019 and April 2025. Diagnoses of diseases and syndromes were compared between younger adults (20-49 years old) and older adults (50 years or older), stratified by sex, using regression analyses. RESULTS: Data of 149 483 adults were included. Most adults were admitted to inpatient departments for non-communicable diseases (NCDs) (40.7%), followed by communicable diseases (23.3%) and trauma or injury (20.4%). Compared with younger adults, older adults had higher odds of admission being for chronic non-infectious respiratory diseases (OR=2.32; 95% CI 2.27 to 2.38), acute cerebrovascular events (OR=2.17; 95% CI 2.09 to 2.26), acute cardiogenic events (OR=1.93; 95% CI 1.90 to 1.97), lower respiratory tract infections (LRTIs) (OR=1.42; 95% CI 1.41 to 1.44) and acute watery diarrhoea (AWD) (OR=1.20; 95% CI 1.17 to 1.22). Across age groups, women had higher odds of admission being for malaria, AWD, LRTIs, chronic non-infectious respiratory diseases and acute hypertensive crises than men. Older women had higher odds of admission being for complications of diabetes than older men. LRTIs were the leading cause of hospitalisation for older adults in three out of four regions. CONCLUSIONS: Older adults in humanitarian settings face intersecting vulnerabilities related to age, gender and geography, with a dual burden of infectious and NCDs. Gender disparities were evident, as older women were more frequently admitted. Including older adults in preventive interventions, while addressing care gaps such as trauma, multimorbidity and palliative needs, is essential to deliver more equitable, inclusive and effective health responses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".