Decomposition of socioeconomic inequalities in arboviral diseases in Brazil and Colombia (2007–2017)
Bibliographic record
Abstract
BACKGROUND: We used surveillance data from Brazil and Colombia during 2007-2017 to assess the presence of socioeconomic inequalities on dengue, chikungunya and Zika at the neighborhood level in two Latin American cities. METHODS: To quantify the inequality, we estimated and decomposed the relative concentration index of inequality (RCI) accounting for the spatiotemporal distribution of the diseases. RESULTS: There were 281 426 arboviral cases notified in Fortaleza, Brazil, and 40 889 in Medellin, Colombia. The RCI indicated greater concentration of dengue cases among people living in low socioeconomic settings in both sites. The RCIs for chikungunya in Fortaleza covered the line of equality during their introduction in 2014, while the RCIs for Zika and chikungunya in Medellin indicated the presence of a small inequality. The RCI decomposition showed that year of notification and age were the main contributors to this inequality. In Medellin, the RCI decomposition showed that age and access to waste management accounted for 75.5%, 72.2% and 54.5% of the overall inequality towards the poor for dengue, chikungunya and Zika, respectively. CONCLUSIONS: Our study presents estimates of the socioeconomic inequality of arboviruses and its decomposition in two Latin American cities. We corroborate the concentration of arboviral diseases in low socioeconomic neighborhoods and identify that year of occurrence, age, presence of healthcare facilities and waste management are key determinants of the heterogenous distribution of endemic arboviruses across the socioeconomic spectrum.
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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 | 0.002 | 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) | 0.002 | 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".