The scale of the underlying dataset is what sets this analysis apart. The investigators assembled a cohort of every individual registered with the Lombardy Regional Health Service as of January 2020, aggregating monthly data by sex, age, country of origin, municipality or ZIP code area, and pre-existing comorbidities. The region’s eight local public health authorities, which administer surveillance and prevention across distinct catchment areas, contributed records that allowed the researchers to track three separate outcomes: confirmed COVID-19 incidence, hospital admissions, and mortality. Crucially, the team also had access to SARS-CoV-2 lineage data from the regional genomic repository, which enabled them to divide the study period into four sub-periods defined by the predominant viral variants circulating at the time. This variant-based segmentation meant the researchers could ask not only how the epidemic changed over time, but how the relationship between risk factors and outcomes itself evolved as the virus mutated.

The demographic profile of the population under study helps explain why Lombardy was hit so hard. Of the more than ten million residents included in the cohort, 45 percent were aged 50 or older, and 22 percent carried a history of cardiac disease. These figures describe a region with a substantial reservoir of older adults and cardiovascular vulnerability, precisely the characteristics epidemiologists associate with severe COVID-19 outcomes. The study’s multivariable regression models, run separately for each of the four variant-defined sub-periods, confirmed that advancing age remained the dominant risk factor for both infection and death throughout the entire pandemic. Neurologic and renal comorbidities also emerged as consistent predictors of mortality, alongside cardiac conditions, underscoring that the burden of severe disease fell disproportionately on people with chronic organ dysfunction.

One of the most striking findings concerns the geography of the early epidemic. Contrary to the intuitive expectation that a respiratory virus would ignite first in the largest urban centers, the outbreak in Lombardy was initially concentrated in a handful of Local Health Districts, none of which contained large cities. This observation aligns with the now well-understood story of the virus’s silent spread through smaller provincial towns in the Po Valley before the first cases were even recognized. The geographic heterogeneity persisted throughout the study period: incidence, hospitalization rates, and mortality varied substantially across districts, and the researchers found that geographical location itself remained an independent predictor of outcomes even after accounting for age, sex, comorbidities, and other individual-level characteristics.

That persistent geographic signal carries an important interpretive message. Because the models adjusted for measurable individual risk factors, the residual effect of location hints at the influence of local epidemic dynamics, such as the timing and intensity of transmission waves in each community, and of health system factors, including differences in testing capacity, care pathways, and hospital load between districts. The authors suggest that areas impacted early in the pandemic experienced distinct trajectories compared with those hit later, and that these local dynamics shaped outcomes in ways that individual-level data alone cannot capture. In practical terms, the same person’s risk of severe outcomes depended not only on their age and health status but also on where and when they lived through the pandemic, a finding with clear implications for how regional health systems should allocate resources during future epidemic emergencies.

The temporal trends documented in the study reveal a pattern that public health officials observed qualitatively but had rarely been able to quantify so systematically. As overall SARS-CoV-2 transmission increased in the population, driven by successive waves of more transmissible variants and eventually by widespread immunity from infection and vaccination, hospital admissions and mortality declined over time. The decline was especially pronounced in areas that had been impacted early, such as the districts at the epicenter of the first wave, where the combination of prior infection, vaccination coverage, and adapted clinical management appears to have progressively decoupled case numbers from severe outcomes. The researchers also tracked test positivity rates and vaccination coverage across the local public health authorities and districts, providing a parallel account of how diagnostic intensity and immunization campaigns varied geographically and interacted with the epidemic’s course.

Methodologically, the study exemplifies the power of routinely collected administrative health data when linked at population scale. Rather than relying on sampled surveys or voluntary reporting, the team exploited the complete registry of Regional Health Service beneficiaries, pseudonymized according to the ISO 25237:2017 standard by each health protection agency before analysis. Only fully anonymized, aggregated data were made available to the study group, a design that satisfied Italian data protection regulations and the EU General Data Protection Regulation while preserving the statistical power of a true total-population cohort. The multivariable regression framework, applied within each variant-defined sub-period, allowed the investigators to estimate how the hazard of infection and death associated with each risk factor changed as the virus and the population’s immunity evolved, a level of temporal resolution that few previous studies have achieved.

The findings also carry lessons that extend well beyond Lombardy. The confirmation that neurologic and renal comorbidities predict mortality, in addition to the more widely recognized cardiac and pulmonary conditions, refines the clinical risk stratification that guided vaccination prioritization and protective measures during the pandemic. The demonstration that geographic location independently shapes outcomes even in a region with a single, relatively uniform health system suggests that epidemic management cannot rely on one-size-fits-all policies; sub-regional surveillance and locally tailored interventions matter. And the observation that severity declined as transmission rose, reflecting the transition from a naive population facing a lethal novel virus to an immunized population facing endemic circulation, provides a quantitative template for anticipating how future respiratory pathogens might behave as they make the same transition.

Lombardy’s experience was in many respects the harshest test any European health system faced in 2020, when hospitals in Bergamo, Brescia, and Cremona became global symbols of the pandemic’s toll. This new analysis converts that traumatic experience into structured evidence, mapping exactly where and when the virus struck, who was most vulnerable, and how the determinants of severe disease shifted across the ancestral, Alpha, Delta, and Omicron eras. The work was funded by Fondazione Cariplo as part of a broader initiative on post-COVID syndrome research, and it forms part of the PASCNET network’s effort to build lasting analytical capacity from the pandemic’s data legacy. As the authors conclude, the findings contribute essential insights into the dynamics of COVID-19 in one of Europe’s earliest and most severely affected regions, evidence that will inform pandemic preparedness, health system planning, and epidemiological modeling for years to come. For a region that served as the pandemic’s European ground zero, the three-year map now drawn represents both a record of what was endured and a guide for what may come.

Subject of Research: Population-based epidemiological mapping of COVID-19 incidence, hospitalization, and mortality in Lombardy, Italy, from 2020 to 2022

Article Title: Mapping the COVID-19 epidemic in Lombardy, Italy, years 2020–2022: a population-based study of temporal trends, geographic distribution, and risk factors for incidence, hospitalization, and mortality

Article References: Villa, S., Magnoni, P., Mazzali, C., Zucchi, A., Maifredi, G., Cavalieri d’Oro, L., Gambino, M. L., Fanetti, A. C., Perotti, P. G., Villa, M., Valsecchi, M. G., Vigani, D., Lucifora, C., Pregliasco, F. E., Cereda, D., Leoni, O., Russo, A. G., the PASCNET study group, Franzoni, F., … Zuccaro, V. (2026). Mapping the COVID-19 epidemic in Lombardy, Italy, years 2020–2022: a population-based study of temporal trends, geographic distribution, and risk factors for incidence, hospitalization, and mortality. BMC Public Health. https://doi.org/10.1186/s12889-026-29682-2

Image Credits: AI Generated

DOI: 10.1186/s12889-026-29682-2

Keywords: COVID-19, Lombardy, SARS-CoV-2, epidemiology, spatial analysis, risk factors, hospitalization, mortality, variants of concern, vaccination coverage, public health, population-based study

A new study by Eleonora Trappolini, Elisa Barbiano di Belgiojoso and Stefania M.L. Rimoldi, published in SSM – Population Health, tackles this question with an unusually direct research design. Drawing on the 2025 MIGHTY Survey of migrants in Lombardy, Italy, the authors asked respondents to rate their health twice: once as it was before they left their country of origin, and once as it stood at the time of interview. This retrospective pre-migration measure, rare in European datasets, allowed the researchers to compare each individual with their own past self, rather than comparing different groups of people who may differ in countless unobserved ways. The analytical sample comprised 2,331 foreign-born residents of Lombardy, roughly half of them women, including undocumented migrants and naturalised citizens who are typically invisible to residence-based statistics.

The methodological core of the study is a correlated random-effects ordinal logistic regression, a statistical framework that separates within-individual change from between-individual differences. Each respondent contributed two observations, one pre-migration and one post-migration, with age treated as a time-varying covariate and supplemented by Mundlak terms, which capture the individual-level means of time-varying variables and allow the model to distinguish ageing from the effect of migration itself. Because the outcome, self-rated health, is measured on a five-point scale from very good to very bad, the authors used ordinal models and, when diagnostic tests revealed that the proportional-odds assumption was violated, they relaxed it, presenting category-specific marginal effects rather than forcing a single summary coefficient onto a process that operates differently at different points of the health distribution.

The headline finding is stark: once ageing is accounted for, post-migration health deterioration is concentrated among women. Among female respondents, the odds of reporting poorer self-rated health after migration were roughly four times higher than before, an association that remained stable across every model specification. Among men, the corresponding coefficient was smaller and not statistically significant. Before migration, men and women reported almost identical probabilities of being in very good health, 38.5 percent and 38.0 percent respectively. After migration, those probabilities fell to 29.1 percent among men and 24.6 percent among women, opening a gender gap where none had existed. The decline was driven primarily by the loss of very good health and its redistribution into the fair category, with women gaining seven percentage points in the fair category compared with four points among men. Movement into the worst health categories remained modest for both groups.

This gendered pattern is consistent with the double burden hypothesis. Migrant women in Italy are disproportionately employed in domestic and care work, sectors characterised by social isolation, emotional labour, irregular schedules, blurred boundaries between work and rest, and weak labour protections. On top of paid employment, many carry disproportionate unpaid and transnational caregiving responsibilities, juggling the roles of worker, mother, partner and breadwinner simultaneously. The authors caution that their survey does not directly measure time use or caregiving, so these mechanisms remain plausible interpretations rather than demonstrated pathways. But the fact that the gender gap emerges only after migration, from a baseline of near-identical health, strongly suggests that it reflects post-migration experiences rather than differential selection into migration.

Occupational status told a more complicated story, and one that differed sharply by gender. Among men, the decline in very good health varied significantly across occupational positions, ranging from under five percentage points among employees in non-physically demanding jobs to nearly twenty points among those in marginal or non-standard work arrangements. Self-employed men in non-physically demanding occupations also showed pronounced deterioration, a finding the authors interpret in light of the Italian context, where self-employment often functions as a fallback for those shut out of standard employment and can entail economic insecurity, long hours and weak social protections. Among women, by contrast, the decline was both larger and remarkably uniform across occupational categories; the interaction between migration timing and occupational status was not statistically significant in this group. This asymmetry, confirmed by a significant three-way interaction between migration timing, gender and occupation, complicates any simple double-burden narrative. One reading is that the burdens migrant women face, combining paid work with unpaid care, cut across occupational positions, elevating deterioration everywhere and leaving little room for occupation to differentiate the decline. For men, health consequences appear more tightly bound to specific working conditions.

The authors are careful about what their design can and cannot establish. Occupational status was measured at interview, after the health change had occurred, so the associations cannot be read causally; migrants whose health has deteriorated may also drift into self-employment or out of employment altogether, a selection process that could partly generate the observed gradients. Unemployed and inactive respondents showed smaller losses in the best health categories but larger increases in poor health, a pattern consistent with reverse causation, whereby illness pushes people out of work. The authors also acknowledge that their occupational classification, which distinguishes physically demanding from non-physically demanding jobs, may understate the physical and psychosocial strain of domestic and care work, the very sectors where migrant women are concentrated, potentially masking heterogeneity among women.

Retrospective self-reports carry their own hazards, and the study confronts them head-on. Recall bias may lead some respondents to idealise their pre-migration health, a phenomenon known as rosy retrospection, while response shift, a change in the internal standards against which people judge their health, may alter how the same objective condition is reported before and after years of life in a new society. Robustness analyses suggest these mechanisms may inflate the magnitude of the estimated decline but do not manufacture it: deterioration is present even among the most recently arrived migrants, the gender difference survives fixed-effects specifications, and a bounding analysis indicates that only a large, gender-specific recall bias could overturn the result. Other limitations include the restriction to Lombardy, the observational equivalence of cumulative exposure and ageing in a two-observation design, and the possibility of selective return migration, the so-called salmon bias, which could bias estimates in either direction.

What the study ultimately delivers is a reframing of migrant health inequality as a dynamic process rather than a static comparison. The probability of reporting bad or very bad health remained low both before and after migration, confirming that migrants are a positively selected population, but the erosion of that advantage is neither uniform nor random. It is steeper for women than for men, and for men it tracks occupational position, with the sharpest declines among those at the margins of the labour market. The authors argue that this stratification calls for differentiated policy: for men, stronger labour protections and occupational health services extended to non-standard and undeclared work; for women, interventions that reach beyond the workplace to address the combined weight of paid and unpaid care, including extending health surveillance into private households and improving physicians’ capacity to identify work-related illness among migrant home-care workers. As Europe’s workforce ages and its dependence on migrant labour deepens, the finding that the price of migration is paid unevenly, and most heavily by women, is one that policymakers cannot afford to ignore.

Subject of Research: Gender and occupational differences in post-migration health decline among migrants in Italy

Article Title: Do all migrants decline equally? Gender and occupational status differences in post-migration health in Italy

Article References: Trappolini, E., Barbiano di Belgiojoso, E., & Rimoldi, S. M. (2026). Do all migrants decline equally? Gender and occupational status differences in post-migration health in Italy. SSM – Population Health, Article 101971. https://doi.org/10.1016/j.ssmph.2026.101971

Image Credits: AI Generated

DOI: 10.1016/j.ssmph.2026.101971

Keywords: migrant health, healthy immigrant effect, exhausted migrant effect, self-rated health, gender inequality, occupational status, Italy, Lombardy, longitudinal analysis, cumulative disadvantage, care work, labour market segmentation

The research team collected RSV-positive upper respiratory tract samples from patients who tested positive between October 2023 and March 2025, spanning two consecutive seasons that straddled the deployment of nirsevimab. Whole viral genomes were recovered using an amplicon-based sequencing protocol carried out on Illumina platforms, with assemblies generated through a consensus-building pipeline known as vcf-consensus-builder. This approach allowed the investigators to capture near-complete genomes from a substantial fraction of positive samples, providing the resolution needed to place each virus within a fine-grained phylogenetic framework and to examine amino acid variation in the proteins most relevant to prevention and treatment.

In total, the study retrieved 302 RSV genome sequences, with 129 derived from the 2023–2024 season and 173 from the 2024–2025 season. The clinical composition of the cohort reflects the burden of RSV disease in the region: most infections occurred in infants aged one year or younger, who accounted for 56.0 percent of cases, and males made up 57.3 percent of the sampled population. Lower respiratory tract infections were documented in 72.7 percent of cases, 67.2 percent of patients required hospitalization, and 15.0 percent were admitted to an intensive care unit. Notably, the dataset included 17 nirsevimab breakthrough infections, cases in which infants who had received the monoclonal antibody nonetheless developed symptomatic RSV disease.

Phylogenetic analysis using maximum-likelihood methods implemented in IQ-TREE, with a GTR plus F, invariant sites, and gamma model of substitution, assigned the circulating viruses to established clades within the two major RSV subgroups. The most frequently detected clades were A.D.3, representing 26.8 percent of sequences, and B.D.E.1, representing 20.2 percent, followed by A.D.1.2 at 17.5 percent and A.D.5.2 at 14.2 percent. This distribution confirms that multiple lineages of both RSV-A and RSV-B co-circulated in Lombardy throughout the study period, with no single clade achieving complete dominance across the two seasons.

Beyond clade assignment, the researchers applied Bayesian coalescent methods under a constant population size prior and a strict molecular clock to define what they termed large phylogenetic clusters, or LPCs. These were groups of more than ten sequences supported by a posterior probability of one, indicating tightly linked transmission chains or rapidly expanding lineages. Four such clusters were identified, and two of them proved particularly striking: a cluster of 56 A.D.3 strains and another of 17 A.D.1.2 strains were detected exclusively in the 2024–2025 season, the first season of routine nirsevimab use. The emergence of season-specific clusters suggests that new viral lineages established themselves in the population after the prophylaxis program began.

A central concern of the study was the monitoring of mutations associated with resistance to the preventive and therapeutic agents now deployed against RSV. The fusion protein, or F, is the target of the monoclonal antibodies palivizumab, nirsevimab, and clesrovimab, while the nucleoprotein, or N, is the target of the small-molecule inhibitor zelicapavir. Resistance-associated mutations were identified in two samples from the 2023–2024 season and two from the 2024–2025 season. These included K272E in an A.D.5.3 strain and N262Y in an A.D.3 strain, both associated with reduced susceptibility to palivizumab, and N208D in a B.D.E.1 strain as well as the combination of K68I and L204S in a B.D.E.1 nirsevimab breakthrough infection, mutations linked to nirsevimab resistance.

Among the 17 nirsevimab breakthrough infections, the investigators also detected the S213R substitution at antigenic site Ø of the F protein in an A.D.1.2 sample. Site Ø is the principal binding site for nirsevimab, and changes at this region can diminish antibody binding. By contrast, no mutations conferring resistance to clesrovimab, a newer monoclonal antibody that binds a different region of F, were detected during the study. However, four substitutions were observed at the clesrovimab binding site: K433R in a B.D.4.1.1 strain, C439R in an A.D.1.2 strain, N437S in an A.D.5.2 strain, and N444S in an A.D.3 strain, with three of the four appearing during the 2024–2025 season. These variants were not classified as resistance mutations, but their presence at a pharmacologically critical epitope warrants continued attention.

The nucleoprotein analysis likewise revealed signals relevant to antiviral therapy. The I129M substitution in the N protein, which is associated with resistance to zelicapavir, was detected in a B.D.E.1 sample from the 2024–2025 season at an intrapatient relative abundance of 0.05, indicating that the variant existed as a minor species within the quasispecies of that infection rather than as the dominant viral population. Such low-frequency variants are typically invisible to conventional diagnostic assays but can be resolved by deep sequencing, and their detection underscores the value of high-resolution genomic surveillance in identifying resistance determinants before they become established at the population level.

Multivariable analysis comparing the two seasons revealed several statistically significant differences associated with the 2024–2025 period. That season was characterized by an increased number of sequences belonging to large phylogenetic clusters, greater amino acid variability at antigenic site Ø of the F protein, and higher Ct values on diagnostic testing, which correspond to lower viral loads in respiratory samples. Hospital intensive care unit admissions were also fewer in the second season. While the observational design of the study precludes drawing direct causal links between nirsevimab deployment and these shifts, the concurrence of prophylaxis introduction with reduced disease severity, altered viral population structure, and expanded F protein variability provides a compelling rationale for sustained monitoring.

The overall message from the Lombardy surveillance program is one of cautious reassurance combined with vigilance. Nirsevimab breakthrough infections remained rare relative to the scale of the prophylaxis campaign, and bona fide resistance mutations were detected in only a handful of samples across two seasons. At the same time, the virus is demonstrably diversifying, with season-specific phylogenetic clusters and accumulating variation at antibody binding sites on the F protein. As monoclonal antibodies and vaccines become mainstays of RSV prevention worldwide, the Lombardy experience illustrates how whole-genome sequencing, phylogenetic clustering, and mutation-level analysis can be woven into routine public health practice, ensuring that shifts in viral evolution are detected early enough to inform and adjust prophylaxis strategies before they compromise protection of the most vulnerable patients.

Subject of Research: Genomic evolution and monoclonal antibody resistance of respiratory syncytial virus in Lombardy before and after nirsevimab introduction

Article Title: Genomic characterization of circulating respiratory syncytial virus strains in Lombardy before and after the introduction of nirsevimab prophylaxis

Article References: Uceda Renteria, S., Galli, C., Ferrari, G., Cossu, M. V., Lombardi, A., Tagliabue, C., Galli, G., Parisi, A., Pellegrinelli, L., Romano, G., Bono, P., Pitrolo, A. M. G., Salpini, R., Salvini, F., Svicher, V., Bosis, S., Callegaro, A., Gori, A., Baldanti, F., … Alteri, C. (2026). Genomic characterization of circulating respiratory syncytial virus strains in Lombardy before and after the introduction of nirsevimab prophylaxis. Genome Medicine. https://doi.org/10.1186/s13073-026-01768-x

Image Credits: AI Generated

DOI: 10.1186/s13073-026-01768-x

Keywords: respiratory syncytial virus, nirsevimab, genomic surveillance, viral evolution, monoclonal antibodies, fusion protein, phylogenetics, whole-genome sequencing, antiviral resistance, breakthrough infections, Lombardy, public health