Cross-sectional equity analysis of accessibility by automobile to tertiary care emergency services in Cali, Colombia in 2020
Bibliographic record
Abstract
Abstract This study provides data on equity in accessibility to tertiary care emergency services in Cali, accounting for traffic congestion, in two separate weeks in 2020. This cross-sectional study builds on a proof-of-concept, the AMORE Project (1) and provides a baseline assessment of accessibility to urgent tertiary care at peak and free flow traffic times in Cali. 1 It makes the case for assessing travel time over distance, and accounting for traffic congestion. This study indicates that people in vulnerable situations have to travel longer and therefore invest more of their personal direct and indirect resources to access tertiary care emergency departments than the average population. This study emphasizes the added value of integrating new data sources that can inform health services and urban planning. These new data sources merit future testing by concerned stakeholders. 1 This study used the digital AMORE Platform to show the effects of traffic congestion on equitable access to tertiary care emergency departments. The data shows which populations take longer to reach a facility within a time threshold under different traffic congestion levels. The broader proof-of-concept assesses the value of new data obtained by integrating secondary data from publicly available sources. These sources combine geospatial analysis with census microdata, health services location data, and bigdata for travel times. The analysis covered the city of Cali, which has 2.258 million residents and is the third-largest city in Colombia. The analysis shows the projected accessibility assessments for two weeks during the COVID-19 pandemic, 6 – 12 July 2020, and 23 – 29 November 2020. Restrictions on car travel had been lifted before the July assessment, but stay-at-home orders were in place during the November assessment, which showed substantially less traffic. This assessment found that traffic congestion sharply reduces accessibility to tertiary emergency care. Reduced access has the greatest impact on people with less education, those living in low-income households or on the periphery of Cali, and specific ethnic groups (e.g., nomadic people like the Rrom, and Afro-descendants). This assessment also identifies the concentration of tertiary care emergency departments in areas of lower population density, leaving large swaths of the population with poor accessibility. Data was reported in dashboards that used simple univariate and bivariate analyses. In July 2020, the estimated overall accessibility at peak traffic hours was 37% and in November 2020 it increased to 57% due to reduced traffic congestion. These results illustrate the value of the proposed tools in monitoring and adjusting to changing conditions.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".