Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2210.10930

Inequities in Breast Cancer Outcomes in Chile: An Analysis of Case Fatality and Survival Rates (2007-2018)

Benjamín Madariaga·Susana Mondschein·Soledad Torres
View original source ↗Read source document ↗Export metadata ↓

Abstract

Introduction: The goal of this paper is to study inequities in breast cancer (BC) health care outcomes for Chilean women, including case fatality (FR) and survival rates (SR), stratified by type of health care provider and geographical area. A secondary goal is to estimate BC incidence (IR) and mortality (MR) rates by health care providers and region. Methods: We used two public anonymized databases provided by the Ministry of Health: the national death and hospital discharges datasets. For survival analysis, we used the Kaplan Meier product-limit estimator (KM) with a 95% ci and the Cox proportional hazards model (CM) with null-hypothesis significance testing of p>0.001. Results: We considered a cohort of 58,254 and 16,615 BC hospital discharges and deaths for the period 2007-2018. New cases and deaths due to BC increased by 43.6% and 33.6% respectively. Avg age-adjusted IR and MR were 44.0 and 10.5, respectively. Women affiliated to a private provider (ISAPRE) have an avg age adjusted IR of 60.6 compared to 38.8 for women affiliated with the public provider (FONASA). The national FR has remained constant over time, with a mean of 26.8. Women affiliated with ISAPRE had a considerably lower FR during the period under study, with an avg of 15.7 compared to 27.5 for women in FONASA. The avg 5-year SR were 0.81 and 0.90 for FONASA and ISAPRE. Women from the Metropolitan area have higher SRs than women from other regions. SRs obtained using the CM have a similar behavior to those obtained by the KM. Discussion: Despite the inclusion of BC in the GES plan in 2005 to provide greater, there are still significant differences in FR and SR for patients affiliated to ISAPRE compared to those in FONASA, a choice that is directly associated with socioeconomic level, and for patients in the Metropolitan and other regions. Further studies are required to determine the causes of these disparities.

Explore related subjects

stat.AP

Keep this discovery

BibTeXRIS

Benjamín Madariaga, Susana Mondschein, Soledad Torres. 2022-10-19. Inequities in Breast Cancer Outcomes in Chile: An Analysis of Case Fatality and Survival Rates (2007-2018). https://arxiv.org/abs/2210.10930

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Privacy-Preserving Causal Meta-Mediation Analysis with Survival Outcomes

Privacy and data-governance constraints often prevent pooling individual-level data across studies, limiting the use of conventional approaches for causal media- tion analysis in multicenter settings. We propose a federated causal meta-mediation framework for right-censored time-to-event outcomes that enables collaborative es- timation without sharing individual-level data. Our framework targets natural indirect effects in a prespecified population by combining information on mediator and outcome mechanisms across distributed data sources. A site-by-site identifi- cation strategy further allows heterogeneity across data sources to be character- ized, with a variance decomposition separating outcome-related, mediator-related, and interaction components. We develop federated one-step and targeted maxi- mum likelihood estimators that accommodate data-adaptive and machine-learning methods for nuisance-function estimation. The finite-sample performance of the proposed estimators is evaluated through numerical simulations. To illustrate the practical utility of the framework, we apply it on data from the French National Health Data System to evaluate the role of methotrexate coprescription in explain- ing the effect of TNFi versus IL-12/23 inhibitor therapy on treatment persistence among psoriatic patients.

stat.AP↗
arXiv2026

Geospatial Foundation Models Capture Health-Relevant Dimensions of Place Beyond Conventional Social Risk Indices

Area-based social risk indices summarize residents' socioeconomic conditions but incompletely capture physical features of place that may affect health. We evaluated whether numerical representations of physical place produced by four geospatial foundation model families from 2022 satellite data explained residual variance in tract-level associations between the Area Deprivation Index, Social Deprivation Index, and Social Vulnerability Index with health outcomes. We used LightGBM to predict variables from the American Community Survey and 40 chronic disease and health-behavior outcomes from CDC PLACES across 82,646 census tracts in the contiguous United States, evaluating performance across 10 held-out states. Among survey variables, models were moderately predictive of some variables including housing type (R-squared up to 0.54) but weak for disability, unemployment, and income disparity. For health outcomes, models explained up to 54% of variance left unexplained by social risk indices, with the largest gains for annual checkups, arthritis, and high blood pressure. Mean total variance explained by geospatial foundation models across the 40 health-related outcomes increased from 0.31 in the smallest tract-size decile to 0.39 in the largest. Geospatial foundation models capture health-relevant features of place not represented by conventional social risk indices and may usefully augment them in epidemiological analyses.

stat.AP↗
arXiv2026

A spatiotemporal negative binomial model with dynamic dispersion: An application to Tuberculosis infections

Tuberculosis (TB) remains a critical public health concern in Brazil, characterized by pronounced spatial heterogeneity and fluctuating temporal volatility. In this paper, we study monthly TB notifications across 61 microregions of Sao Paulo state from 2001 to 2024. To do this, we introduce a negative binomial spatial integer-valued generalized autoregressive conditional heteroskedastic (INGARCH) model featuring jointly dynamic conditional means and time-varying dispersion. To capture inter-regional spillovers, we incorporate both discrete adjacency structures and a novel continuous distance-based formulation leveraging the Matern correlation function. Parameter estimation via conditional maximum likelihood employs a two-step profile-likelihood iterative scheme, demonstrating solid finite-sample performance in simulation studies. Applied to the Sao Paulo TB surveillance data, the framework substantially outperforms standard Poisson and fixed-dispersion spatiotemporal baselines in empirical fit and uncertainty quantification, maintaining nominal 95% predictive coverage across both dense metropolitan centers and rural microregions. Our results reveal marked spatial heterogeneity in baseline incidence, dynamic overdispersion driven by localized outbreaks, and short-range spatial interaction decay. By accurately modeling spatiotemporal volatility, the proposed methodology provides a robust statistical tool to support public health surveillance, policy-making, and resource allocation.

stat.AP↗