Understanding inequalities in spatial accessibility to multi-tier healthcare for older adults in rapidly aging Bangladesh
Bibliographic record
Abstract
This research examines inequalities in spatial accessibility to multi-tier public healthcare services for older adults in Bangladesh, a low- and middle-income country (LMIC) in the Global South that is experiencing rapid demographic shifts. Ensuring that older people have effective and equitable access to healthcare is a critical objective in public health and transportation policy as inadequate access can lead to adverse health outcomes. However, inequalities in accessibility to multi-tier public healthcare services have not been extensively studied in South Asian LMICs of the Global South, particularly in the context of aging societies. This research evaluates spatial accessibility to primary, secondary, and tertiary public healthcare facilities for older adults in Bangladesh using the enhanced two-step floating catchment area (E2SFCA) method. Additionally, we use Gini coefficients to examine inequalities in geographical accessibility to multi-tier public healthcare services. The results show that healthcare accessibility in Bangladesh is unequally distributed both spatially and across different tiers of healthcare services. Accessibility differs greatly across space, with areas of poor access predominantly located in rural and remote areas. Inequalities in accessibility are also evident within divisions (the highest-level administrative units in Bangladesh), generally increasing from urban to rural and remote areas. Furthermore, inequality in healthcare accessibility increases from primary to tertiary care, with tertiary care showing the highest levels of inequality across all divisions, except for Dhaka and Chattogram, the two major administrative divisions in Bangladesh. This study is among the first to explore inequalities in geographic accessibility to multi-tier public healthcare for older people in an under-examined LMIC in South Asia such as Bangladesh. By highlighting the challenges faced in accessing these essential services, our research contributes to the broader understanding of healthcare accessibility in response to the rapidly aging societies in the Global South. • Investigates inequalities in spatial accessibility to multi-tier public healthcare services for older adults in Bangladesh. • Reveals significant disparities in healthcare accessibility, with older adults in rural areas facing the greatest barriers. • Recommends targeted policy interventions to improve healthcare access for underserved older populations. • One of the first studies to examine healthcare accessibility inequalities for older adults in rapidly aging Bangladesh.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".