Disability types, determinants and healthcare utilisation amongst Afghan adults: a secondary analysis of the Model Disability Survey of Afghanistan
Bibliographic record
Abstract
OBJECTIVES: The needs of people with disability in Afghanistan are not well understood. We describe the characteristics, healthcare utilisation patterns, and experience of care among Afghan adults with moderate or severe disability (MSD) by disability type. DESIGN: We mapped 47 questions related to functional disability in the cross-sectional Model Disability Survey of Afghanistan (MDSA) 2019 into 7 disability domains based on the WHO Disability Assessment Schedule 2.0. We conducted multivariable hierarchical logistic regression to identify drivers of high disability burden. SETTING: The MDSA primary sampling unit were villages in rural areas and neighbourhoods in urban areas, and the secondary sample units were the settlements within districts. PARTICIPANTS: The MDSA collected data for 14 520 households across all 34 provinces. The adult tool of the survey was administered to a randomly selected household member aged 18 years or older. MAIN OUTCOME MEASURES: The main outcome measured was moderate or severe disability (MSD), which was estimated using a Rasch composite score. RESULTS: MSD prevalence was upwards of 35% in 6/7 domains. Across most disability types, being a woman, older age, residing in rural areas, being uneducated, non-Pashtun ethnicity, being unmarried, living in a household in the low-income tertiles and a non-working household had the highest levels of MSD (p<0.05). Determinants of MSD varied by domain; however, variables including better access to health facilities and better experience of care (higher satisfaction with time spent and respect during visits) were generally protective. People with MSD in the self-care and life activities domains had the highest and lowest healthcare utilisation, respectively. CONCLUSIONS: Disability in Afghanistan is at public health crisis levels, with vulnerable populations being impacted most severely. To ensure progress towards Afghanistan's 2030 Sustainable Development Goals, targeted interventions for disability types based on population risk factors should be implemented.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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; 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".