Trends in care needs for older people in Albania and the potential role of socio-economic changes
Bibliographic record
Abstract
Abstract Background The youngest in Europe thirty years ago, Albanians have aged more quickly than other people in the region. National census 2023 showed that one in five residents in Albania is 65 years old or older. That proportion was 11,3% in 2011 and 7,5% in 2001. For the first time, we examine the trends in older Albanians’ rates of disability and morbidity and talk about the impact of socioeconomic factors. Methods All potential sources of quantitative data about older people health in Albania are reviewed, including two last national censuses (2011,2023) and two cross-sectional population surveys (MOSHA 2008,2018). We also use data from a multicenter cohort study (IMIAS 2012-2016), that allows for international comparisons. Results In 2023, 19% people over 65 years old had at least one of disabilities standardized by Washington Group. Disabilities increase with age reaching 44% for those over 85 years old, and are consistently higher among women. Disability rate is 65% higher in rural areas compared to cities. Interestingly, the 2023 disability rates were significantly lower than those of 2011 census, when they were measured for the first time. In MOSHA studies prevalence of bed-bound elderly decreased from 8% to 5%. Chronic diseases and pain were also on decrease along with the poverty self-reporting. Conversely, 11% of older people were living alone in Census 2023, compared to only 8% in 2011, with similar trends observed in MOSHA surveys. Older people from Tirana in IMIAS survey, showed lower mobility compared to participants from Canadian samples. IMIAS and MOSHA studies consistently showed that older people of lower socio-economic categories have poorer health and decreased mobility. Conclusions With a rapidly ageing population and increasing loneliness, Albania faces imminent challenges in supporting its elderly. Improving socioeconomic conditions of older people would be a key policy to alleviate the burden of disabilities and poor health. Key messages • Ageing, coupled with a shortage of human resources, is a major challenge for middle income countries’ healthcare. • Improving elderly's socioeconomic conditions could reduce their need for care.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".