This paper introduces the concept of close-enough in the context of facility location. It is assumed that customers are willing to move from their homes to close-enough pickup locations. Given that the number of pickup locations is expanding every day, it is assumed that pickup locations can be placed everywhere. Conversely, the set of potential location for opening facilities is discrete as well as the set of customers. Opening facilities and pickup points entails an installation budget and a distribution cost to transport goods from facilities to customers and pickup locations. The $(p,t)$-Close-Enough Facility Location Problem is the problem of deciding where to locate p facilities among the finite set of candidates, where to locate t pickup points in the plane and how to allocate customers to facilities or to pickup points so that all the demand is satisfied and the total cost is minimized. In this paper, it is proved that the set of initial infinite number of pickup locations is finite in practice. Two mixed-integer linear programming models are proposed for the discrete problem. The models are enhanced with valid inequalities and a branch and price algorithm is designed for the most promising model. The findings of a comprehensive computational study reveal the performance of the different models and the branch and price algorithm and illustrate the value of pickup locations.

Moya-Martínez, A., Landete, M., & Monge, J. F. (2026). Una aplicación al problema de localización de servicios en el sistema de salud de Elche. Boletín de Estadística e Investigación Operativa, 42(1), 24-32. https://doi.org/10.63552/beio.2026.42.1.03

Background: First-line chemoimmunotherapy has improved outcomes in extensive-stage small-cell lung cancer (ES-SCLC), but survival remains heterogeneous and routine-care prognostic tools are limited. Patients and Methods: We retrospectively analyzed consecutive adults with stage IV ES-SCLC treated with platinum-etoposide plus a programmed death-ligand 1 inhibitor at three Spanish hospitals from August 2021 to December 2024. Outcomes included overall survival (OS), progression-free survival (PFS), objective response, and immune-related toxicity. Baseline Gustave Roussy Immune Score (GRIm) was defined as high-risk when at least two of albumin 35 g/L, lactate dehydrogenase above the upper limit of normal, and neutrophil-to-lymphocyte ratio > 6 were present. Baseline Cox models included high-risk GRIm, platelet-to-lymphocyte ratio > 180, and Eastern Cooperative Oncology Group performance status ≥ 2; maintenance exposure was assessed in a 3-month OS landmark analysis. Analyses were exploratory. Results: Among 51 patients, mean age was 64.2 years, 72.5% were men, and 82.3% had ECOG performance status 0– 1. During follow-up, 43 deaths and 50 PFS events occurred. Objective response rate was 70.6%, median OS was 11.9 months, and median PFS was 5.7 months. Median OS was 14.2 months with low-risk GRIm and 5.4 months with high-risk GRIm. High-risk GRIm was independently associated with worse OS (hazard ratio [HR] 5.59) and PFS (HR 3.50), while PLR > 180 was associated with worse OS (HR 2.37). In the 3-month landmark cohort (n=47), maintenance exposure remained associated with longer OS (HR 0.21), although this analysis remained vulnerable to residual selection bias. Conclusion: Baseline GRIm was the most consistent candidate prognostic marker in this real-world ES-SCLC cohort. The study did not assess prediction of immunotherapy benefit, and prospective validation is warranted before treatment decisions are guided by GRIm.

Background: In Spain, universal coverage is delivered through 17 decentralised Autonomous Communities. We quantified territorial inequality in oncology clinical trial-participation density and examined its ecological association with age-standardised cancer mortality.Methods: We used a longitudinal ecological design with regions as the unit of analysis. Trial-participation density (participants per 100,000 population, 2016-2020) and annual authorised oncology trial counts (2015-2022) came from the Spanish Registry of Clinical Studies. Annual age-standardised cancer mortality came from the Spanish National Statistics Institute. Inequality was described with means, standard deviations, ranges, coefficients of variation, and maximum/minimum ratios. The main analysis used repeated-measures ecological models with calendar-year indicators and region-clustered standard errors, with sensitivity analyses for ageing, population weighting, GDP, post-exposure years, influence, HC3 errors, and wild cluster bootstrap inference.Results: Trial-participation density varied from 0.2 to 41.9 per 100,000 population (mean 15.0, SD 10.5; coefficient of variation 70.2%), a 209-fold regional difference. Mean cancer mortality excluding 2020-2021 varied less (mean 231.5, SD 11.7 per 100,000; coefficient of variation 5.1%). Mean mortality declined from 245.5 per 100,000 in 2015 to 216.5 in 2024. In the year-adjusted repeated-measures model, a 10-unit increase in trial-participation density was associated with -6.6 fewer cancer deaths per 100,000 (95% CI -12.0 to -1.3), equivalent to moving from the lowest to the median regional participation level. After ageing adjustment, the estimate was -5.5 (95% CI -10.3 to -0.7) per 10-unit increase. The post-exposure 2022-2024 analysis preserved the direction and similar magnitude of the association. Population weighting preserved the association, and GDP total attenuated it.Conclusions: In Spain, oncology trial participation is geographically concentrated. Regions with higher trial-participation density had persistently lower ecological cancer mortality, although the association is closely linked with metropolitan scale and oncology-system capacity. Trial infrastructure should be treated as a cancer-equity issue; causal interpretation requires finer geographic and patient-level data.