The research team set out to examine whether socioeconomic disparities influence admission to two distinct care models that both operate under the umbrella of Comprehensive Geriatric Assessment, a structured, multidisciplinary approach to evaluating the medical, functional, and psychosocial needs of older people. The first model was Hospital at Home, abbreviated HaH, which brings physician-led, nurse-supported acute care into the patient’s residence. The second was a bed-based intermediate care unit, or BBU, where patients receive comparable geriatric care but within a physical facility. Both services serve the same catchment population and are grounded in the same assessment philosophy, which makes them an ideal natural comparison: if admission decisions were driven purely by clinical criteria, the socioeconomic profiles of patients in the two pathways should look broadly similar. They did not.

To conduct the analysis, the researchers assembled a retrospective cohort of adults aged 65 and older who had been admitted to either the Hospital at Home service or the bed-based intermediate care unit at Parc Sanitari Pere Virgili between 2018 and 2023. Rather than relying solely on clinical records, the team linked these data to administrative health records that contained individual socioeconomic variables as well as small area-level indicators describing the neighborhoods in which patients lived. This dual-level design is technically important. Many studies of health inequality capture only individual characteristics such as income or education, but a person’s opportunity to receive care at home also depends on contextual factors — housing quality, caregiver availability, and the social fabric of the surrounding area — that individual variables cannot fully capture.

The statistical approach reflected this multi-layered structure. The researchers used mixed models, a class of regression techniques that can simultaneously estimate the effect of individual and area-level predictors while accounting for the clustering of patients within small geographic units. These models produced odds ratios quantifying how socioeconomic variables influenced the probability of being admitted to Hospital at Home rather than to the bed-based unit, adjusted for demographic and clinical characteristics. They also generated measures of clustering and heterogeneity, such as the intraclass correlation and interval-based odds ratio statistics, that reveal how much of the variation in admission decisions is attributable to where a patient lives rather than to who the patient is.

The headline finding was stark. After adjustment for demographic and clinical factors, patients with higher income had significantly greater odds of being admitted to Hospital at Home: the adjusted odds ratio was 1.68, with a 95 percent confidence interval of 1.45 to 1.96. In practical terms, this means that wealthier older adults were substantially more likely to receive their acute geriatric care at home, while economically disadvantaged patients with comparable clinical profiles were more often directed to the bed-based intermediate care unit. The unadjusted comparisons reinforced the pattern: Hospital at Home patients were, on average, younger, less burdened by multimorbidity, and more socioeconomically advantaged than their counterparts in the bed-based unit. Some of that difference reflects genuine clinical selection criteria, since home-based acute care requires a suitable home environment and a degree of stability, but the persistence of the income effect after adjustment indicates that clinical factors do not tell the whole story.

Equally revealing was the geography of the disparity. The researchers found that area-level socioeconomic context explained roughly 20 percent of the variability in Hospital at Home admission, and that the strength of socioeconomic effects varied widely across small areas within the city. In some neighborhoods, socioeconomic position appeared to exert a powerful influence on whether a patient reached the home-based pathway; in others, the effect was muted. This heterogeneity, quantified through the mixed-model framework using measures such as the median odds ratio and the proportion of opposed odds ratios, suggests that access to hospital care at home is not uniformly patterned across Barcelona but is locally mediated — by differences in housing stock, social support networks, and possibly by the way services are organized and delivered in different primary care service areas.

The implications reach far beyond one Catalan health district. Hospital at Home models are expanding rapidly across Europe, North America, and beyond, propelled by aging populations, hospital capacity pressures, and evidence that many older patients fare better at home. Governments and health systems have embraced the model as a cornerstone of future acute care. Yet the Barcelona findings highlight a structural risk: if eligibility and referral processes implicitly favor patients with higher incomes, better housing, or stronger family support networks, the model could widen rather than narrow existing health inequalities. Patients who stand to benefit most from avoiding a hospital admission — those living in deprived neighborhoods, with fewer resources and more fragmented support — may be the very ones least likely to be offered home-based care, defaulting instead to institutional settings that carry higher risks of functional decline for frail older people.

The authors frame their conclusions as a call to action for health systems designing and scaling home hospital services. Ensuring equitable delivery, they argue, requires explicitly addressing the social barriers that shape access: identifying health-related social needs during the Comprehensive Geriatric Assessment, adapting services to patients whose homes or support networks are less favorable, and monitoring socioeconomic equity in referral patterns as rigorously as clinical outcomes are monitored. The study’s granularity matters here. By using small area-level indicators rather than crude citywide measures, the researchers demonstrated that disadvantage operates at a neighborhood scale, meaning that equity interventions may need to be locally targeted rather than uniformly applied.

Methodologically, the study has the strengths and limitations typical of retrospective administrative data research. Its cohort design captures real-world admission patterns over six years, and the linkage of individual records to area-level socioeconomic data provides a nuanced picture that single-level analyses would miss. Ethics approval was granted by the Vall d’Hebron Research Institute Ethics Committee under protocol PR(AG)107/2020, with informed consent waived because the analysis used anonymized retrospective datasets and was conducted in accordance with the Declaration of Helsinki. The work was funded by the Instituto de Salud Carlos III through project PI22/00845, co-funded by the European Union, and the authors declare no competing interests. Because the data reflect a single provider in Barcelona, the exact magnitudes of the disparities may differ elsewhere, but the analytical strategy — and the warning it carries — is readily transferable to any health system rolling out home-based acute care.

The article, published open access in BMC Geriatrics on 23 September 2026, arrives at a moment when the future shape of hospital care is being actively negotiated in legislatures, insurers’ boardrooms, and clinical guidelines. As Hospital at Home shifts from pilot projects to mainstream infrastructure, the Barcelona study offers a clear technical lesson: admission algorithms and referral pathways are not socially neutral. Without deliberate design, the comfort and clinical advantages of being treated at home will flow preferentially to those who already hold the most resources, while older adults with the greatest socioeconomic vulnerability remain on the ward. The research suggests that the measure of a successful hospital at home program should therefore be twofold — not only whether patients recover well at home, but also whether the patients admitted to those beds at home are there because of their clinical need, and not because of their income or their postcode.

Subject of Research: Socioeconomic inequalities in access to hospital at home care for older adults

Article Title: Beyond clinical need: socioeconomic inequalities in access to hospital at home among older adults

Article References: Belfiori, M., Pérez, L. M., Villa-García, L., Planesas-Pérez, O., Salucci, C., Inzitari, M., & Ribera, A. (2026). Beyond clinical need: socioeconomic inequalities in access to hospital at home among older adults. BMC Geriatrics. https://doi.org/10.1186/s12877-026-08310-z

Image Credits: AI Generated

DOI: 10.1186/s12877-026-08310-z

Keywords: Hospital at Home, socioeconomic inequality, older adults, geriatrics, Comprehensive Geriatric Assessment, health equity, Barcelona, intermediate care, income disparities, health services research, mixed models, BMC Geriatrics

The central obstacle is sugarcane’s extraordinary genome. Modern cultivars are highly polyploid and frequently aneuploid, typically carrying on the order of 100 to 130 chromosomes, with local copy number varying between roughly six and fourteen copies per homoeologous group. They descend from interspecific hybridisation between the domesticated Saccharum officinarum and the wild S. spontaneum, a history of so-called nobilisation that delivered landmark disease-resistant varieties but left modern germplasm with a narrow founder base, extensive linkage disequilibrium and strong non-additive genetic effects. Recent work, including the highly contiguous polyploid reference genome of the cultivar R570 published in Nature in 2024, has resolved individual haplotypes across approximately 12 chromosome copies, opening the door to far more precise marker development and, potentially, to attributing epistatic interactions to specific genomic regions rather than anonymous marker pairs.

These biological realities collide with an unusually long and structured breeding pipeline. In the Australian system operated by Sugar Research Australia, controlled crosses produce true seed that enters progeny assessment trials, where entire families are ranked and parents selected backwards on the basis of family plot means. Surviving individuals advance to clonal assessment trials and then to multi-environment final assessment trials spanning plant and repeated ratoon crops. The whole journey from cross to commercial release spans a decade or more. Crucially, the review notes, information currently passes between these stages almost entirely through selection decisions: the same parents appear as contributors to family means, as clonal entries and as check clones, yet their breeding values are rarely refined by a model that considers all of these contributions simultaneously.

The statistical challenges of the early stages are substantial and, the authors argue, often underappreciated. Family plots in progeny assessment trials contain multiple unique seedlings from the same full-sib family, so repeated plots are samples from the same cross population rather than true biological replicates. Treating them as replicates of a single genetic entity distorts variance partitioning and inflates error variance, undermining heritability estimates and selection accuracy. Appropriate models must scale Mendelian sampling variance for family plot means. Beyond this, single-row plots are highly sensitive to spatial heterogeneity and interplot competition, which has a heritable component that can be modelled as indirect genetic effects; ignoring it risks selecting genotypes that are aggressive competitors in trials but underperform in commercial single-variety stands. Crop cycle, year and environment are also frequently confounded, complicating genotype-by-environment modelling that relies heavily on factor analytic mixed models.

Genomic selection, which predicts genetic merit from genome-wide markers, has matured rapidly in sugarcane since early proof-of-concept studies in 2013. GBLUP and RR-BLUP models remain robust baselines for the highly polygenic traits that dominate commercial value, including tonnes of cane per hectare, commercial cane sugar and fibre percentage. More complex approaches, including Bayesian variable-selection models, kernel methods and machine learning, have delivered incremental and trait-specific gains. Modelling non-additive effects through dominance and epistatic relationship matrices has improved clonal prediction in some settings, but the review cautions that in populations with strong relatedness and extended linkage disequilibrium, additive relationship matrices can partially absorb non-additive variation, blurring the distinction between the total genetic value relevant to clonal selection and the additive breeding value that drives long-term response in parents.

Allele dosage is a critical unresolved complication. Most operational genomic prediction in sugarcane has relied on pseudo-diploid encoding of single-dose markers, which discards copy-number information. Dosage-aware relationship matrices, formalised for autotetraploid potato and extended to higher ploidies, can dramatically outperform diploidised encodings in simulations with many multi-dose heterozygotes and strong dominance, yet show little advantage in populations dominated by simplex markers, which is precisely the situation in many current sugarcane panels. Continuous genotype representations based on normalised array intensities have delivered modest accuracy gains of roughly five to seven percent for commercial cane sugar and fibre, though not for cane yield. Aneuploidy adds a further layer, because nominal ploidy does not define local ploidy at any given marker, meaning that standard dosage models may be misspecified across a substantial fraction of loci.

The review’s most forward-looking contribution is its treatment of genomic cross prediction and mate allocation. Because crossing capacity, field space and flowering synchrony all limit how many parental combinations can be tested, the choice of crosses shapes the genetic variance entering the pipeline. Sugarcane breeders have long relied on empirically proven crosses, implicitly capturing parental merit and favourable non-additive interactions, but at the cost of reduced exploration of new combinations and heightened inbreeding risk. Predicting the expected mean and within-family genetic variance of untested crosses, and allocating matings under constraints on relatedness, flowering compatibility and seed inventory, could allow programmes to balance short-term gain against long-term diversity. Tools such as AlphaMate and SimpleMating already exist, but the authors stress that full polyploid-aware implementation remains a research priority, hindered by unresolved phasing at approximately twelvefold dosage and by variance predictions that are consistently less accurate than mean predictions.

Lessons from maize, wheat, cassava and potato transfer only partially. Hybrid crops with defined heterotic groups, fixed ploidy and inbred parents present a fundamentally different prediction problem from a clonally propagated, outbred polyploid in which individual-level non-additive effects are not fixed in the product. The usefulness criterion combining cross mean and within-cross variance, validated in diploid and tetraploid systems, may underperform in sugarcane without polyploid-specific adaptation. Nonetheless, training-population design principles hold: representing key founders broadly across many families outperforms deep sampling of a few, and validation schemes should mirror how prediction will actually be used, whether ranking related candidates within a cycle or forecasting performance in future environments.

The authors conclude with a practical research agenda. Family plots should be modelled as means of sampled full sibs rather than replicated genotypes. Environment definitions must distinguish region and year to capture stability under climate variability. Competition should be modelled explicitly in multi-entrial frameworks. Most ambitiously, progeny, clonal and final assessment trial records should be integrated within stage-integrated mixed-model analyses that combine pedigree and genomic relationships, allowing uncertainty to propagate coherently from family evaluation through clonal testing to parent recycling and cross design. Such integration, the review argues, is not merely a statistical refinement but a fundamental shift in breeding strategy, one that could deliver faster, more robust and more sustainable genetic improvement for a crop on which global food and energy systems increasingly depend.

Subject of Research: Statistical and genomic strategies to accelerate sugarcane genetic improvement, from family trials to genomic mate allocation

Article Title: From family trials to genomic mate allocation: statistical and genomic strategies to accelerate sugarcane genetic improvement

Article References: Rigby, A., Atkin, F., Hayes, B., Hickey, L., & Yadav, S. (2026). From family trials to genomic mate allocation: statistical and genomic strategies to accelerate sugarcane genetic improvement. Theoretical and Applied Genetics, 139(10), Article 266. https://doi.org/10.1007/s00122-026-05373-9

Image Credits: AI Generated

DOI: 10.1007/s00122-026-05373-9

Keywords: sugarcane, genomic selection, polyploidy, allele dosage, mate allocation, cross prediction, genetic gain, mixed models, genotype-by-environment interaction, Saccharum, breeding pipeline, non-additive effects