The Effects of Distance, Time, and Nonspatial Factors on Hemodialysis Access in Qatar
Bibliographic record
Abstract
Background A long distance and time spent traveling to a hemodialysis (HD) center and other factors, such as comorbidities, can significantly impact HD patient compliance, satisfaction, and cost. Uncertainty about HD-dependent patients’ geographical location may lead to inappropriate distribution of HD centers. The present study investigates travel time, distance, and nonspatial factors affecting HD center accessibility within a 30-km radius in the State of Qatar. Materials and methods The study included all HD-dependent patients residing in Qatar between March 1, 2020, and December 31, 2021. There were 921 patients dialyzed in six HD centers across Qatar. Our methodology incorporated descriptive and analytical cross-sectional designs to accurately identify the shortest routes and quickest travel times. We used two applications (Maptive {Vancouver, WA: BatchGeo LLC} and Google Maps {Mountain View, CA: Google LLC}) and marked a driving distance of 30 km as the main assessment scale and measurement standard, allowing optimum spatial accessibility determination. Results On average, patients traveled approximately 19±4.2 km, requiring almost 17.6±3.4 minutes to reach the assigned HD center three times per week. Based on geographic-spatial accessibility analysis, patients living in Umm Salal drove 31.4±3.5 km in 32.4±4.7 minutes, Al Daayen patients drove 30.2 km in 25.3 minutes, and others even drove more than 70 km to access HD sessions. Approximately 37.8% of Qatar’s municipalities had no HD centers within their boundaries, but nearly 47% of HD-dependent patients lived in those municipalities. Additionally, some municipalities had HD centers; however, their general population density was less than 100 inhabitants/km2, and they had relatively few patients requiring regular HD. We noted a statistically significant correlation between the patients’ residences and the locations of HD centers, whether they were located within or outside municipalities. Also, nonspatial factors may have affected the likelihood of reaching a hemodialysis center within a 30-km distance, including two or more comorbid conditions, having HD for at least five years, living in a municipality with more than 1,000 inhabitants/km2, being female, and attending dialysis centers that are more than 30 km away. Conclusion Although the available HD centers were sufficient for the present number of patients requiring HD, HD center locations did not match the patients’ distribution, leading to difficulties for some patients. Understanding the impact of this geographic mismatch, population density, and other spatial factors helps significantly improve patient care and satisfaction at minimal cost. Furthermore, considering all these factors is crucial when planning new centers to achieve higher satisfaction and compliance as well as better health 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".