Effects of social inequalities on survival from cervical vs breast cancer: Quito, Ecuador, 2000–2019
Abstract
Background: Social position is associated with cancer survival, yet single-indicator analysis may not capture its multidimensional nature. We evaluated social phenotypes, all-cause mortality, and their temporal stability in breast vs cervical cancer to identify high-risk subgroups in middle-income settings.
Methods: We analyzed 14,962 women (2000–2019) in Quito, Ecuador using data from the Quito cancer registry. Six phenotypes (C1–C6) were derived using PAM/Gower distance based on age, education, and occupation. Bayesian Cox models were used to estimate hazard ratios (HR) and 95%CI (adjusted for age, stage, and health sector), incorporating three-way interactions (phenotype/cancer-type/year) to assess survival stability over time (breast as reference). Death-certificate-only (DCO) cases were excluded.
Results: Survival differed across phenotypes (p < 0.0001). The professional/higher-education cluster (C6) had longest median survival (113.7 months). The socio-data exclusion cluster (C1; 97.9% unknown education) showed extremely short survival (median: 0; IQR: 0, 8.9), likely reflecting late clinical entry despite DCO exclusion. In multivariable models, cervical cancer amplified survival disparities vs breast cancer, with higher HRs in C1 (4.48; CI: 2.64–7.92) and unpaid/primary education (3.19; CI:2.12–5.11). Public sector diagnosis was independently associated with mortality (HR: 1.88; CI:1.71–2.06). These survival inequalities remained persistent over the study period (interaction-CIs:0.37–1.46).
Conclusions: Social inequities and health sector fragmentation were associated with mortality independent of cancer stage, with cervical cancer amplifying persistent survival gaps. These findings highlight the value of multidimensional approaches to identify high-risk groups using routine data and support interventions targeting delays in diagnosis and treatment pathways.
Key messages: • Social inequalities in cervical cancer multiply the survival penalties of marginalization, maintaining a mortality gap that remained stable over the 20-year study period (2000–2019).
• Health sector fragmentation reflects structural barriers; public sector diagnosis is associated with a near-doubling of mortality independent of stage, marking entry-point inequities.
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BibTeX
@article{davila2026effects,
title = {{Effects of social inequalities on survival from cervical vs breast cancer: Quito, Ecuador, 2000–2019}},
author = {RA Ayala Davila and M Gualan and CT Albacura-Campues and PJ Cooper and NC Romero-Sandoval},
journal = {The European Journal of Public Health},
year = {2026},
month = oct,
doi = {10.1093/eurpub/ckag169.074},
url = {https://doi.org/10.1093/eurpub/ckag169.074},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - Effects of social inequalities on survival from cervical vs breast cancer: Quito, Ecuador, 2000–2019
AU - RA Ayala Davila
AU - M Gualan
AU - CT Albacura-Campues
AU - PJ Cooper
AU - NC Romero-Sandoval
PY - 2026
DA - 2026/10/01
JO - The European Journal of Public Health
DO - 10.1093/eurpub/ckag169.074
UR - https://doi.org/10.1093/eurpub/ckag169.074
AB - Background: Social position is associated with cancer survival, yet single-indicator analysis may not capture its multidimensional nature. We evaluated social phenotypes, all-cause mortality, and their temporal stability in breast vs cervical cancer to identify high-risk subgroups in middle-income settings. Methods: We analyzed 14,962 women (2000–2019) in Quito, Ecuador using data from the Quito cancer registry. Six phenotypes (C1–C6) were derived using PAM/Gower distance based on age, education, and occupation. Bayesian Cox models were used to estimate hazard ratios (HR) and 95%CI (adjusted for age, stage, and health sector), incorporating three-way interactions (phenotype/cancer-type/year) to assess survival stability over time (breast as reference). Death-certificate-only (DCO) cases were excluded. Results: Survival differed across phenotypes (p < 0.0001). The professional/higher-education cluster (C6) had longest median survival (113.7 months). The socio-data exclusion cluster (C1; 97.9% unknown education) showed extremely short survival (median: 0; IQR: 0, 8.9), likely reflecting late clinical entry despite DCO exclusion. In multivariable models, cervical cancer amplified survival disparities vs breast cancer, with higher HRs in C1 (4.48; CI: 2.64–7.92) and unpaid/primary education (3.19; CI:2.12–5.11). Public sector diagnosis was independently associated with mortality (HR: 1.88; CI:1.71–2.06). These survival inequalities remained persistent over the study period (interaction-CIs:0.37–1.46). Conclusions: Social inequities and health sector fragmentation were associated with mortality independent of cancer stage, with cervical cancer amplifying persistent survival gaps. These findings highlight the value of multidimensional approaches to identify high-risk groups using routine data and support interventions targeting delays in diagnosis and treatment pathways. Key messages: • Social inequalities in cervical cancer multiply the survival penalties of marginalization, maintaining a mortality gap that remained stable over the 20-year study period (2000–2019). • Health sector fragmentation reflects structural barriers; public sector diagnosis is associated with a near-doubling of mortality independent of stage, marking entry-point inequities.
SN - 1101-1262
SN - 1464-360X
ER -