The research, published in BMC Cancer, was conducted by a team based at Shahid Sadoughi University of Medical Sciences in Yazd and collaborating institutions, using records from the Iranian National Cancer Registry. The team analyzed 4,928 cases of Hodgkin lymphoma and 13,600 cases of non-Hodgkin lymphoma diagnosed over the four-year study window. To make the numbers comparable across years and populations, the researchers calculated age-standardized incidence rates, a statistical adjustment that accounts for differences in the age structure of the population, using the World Health Organization’s standard population as the reference. This adjustment matters because a country with a young population can show fewer raw cancer counts simply because fewer people have reached the ages at which cancer risk climbs steeply.

For Hodgkin lymphoma, a disease that typically originates in B lymphocytes and is strongly associated with Epstein-Barr virus infection in many regions, the age-standardized incidence rate rose from 1.4 per 100,000 people in 2014 to 1.59 per 100,000 in 2017. That may sound like a small shift, but in epidemiological terms a steady climb of this magnitude over just four years signals a genuine trend rather than random year-to-year fluctuation, particularly when the same direction of change appears across demographic subgroups. The rates were not distributed evenly across the country. Yazd province recorded the highest Hodgkin lymphoma rate in 2014 at 2.74 per 100,000, while Lorestan topped the national figures in 2017 at 2.50 per 100,000, both well above the national average.

Sex-based differences emerged clearly in the data. Among Iranian men, the age-standardized incidence of Hodgkin lymphoma climbed from 1.66 per 100,000 in 2014 to 1.89 per 100,000 in 2017, while among women the corresponding figures rose from 1.13 to 1.29 per 100,000. In other words, men carried roughly a fifty percent higher burden of the disease than women throughout the study period, a gap consistent with patterns reported in many other populations and one that researchers attribute to a complex interplay of hormonal, immunological, and environmental factors. The parallel rise in both sexes suggests that whatever is driving the overall increase operates broadly rather than within one group alone.

Non-Hodgkin lymphoma, which encompasses a large and heterogeneous family of B-cell and T-cell malignancies, showed both a higher baseline and a similar upward trajectory. The age-standardized incidence rate increased from 4.26 per 100,000 in 2014 to 4.54 per 100,000 in 2017, meaning that non-Hodgkin lymphoma struck Iranians at roughly three times the rate of its Hodgkin counterpart. The provincial extremes were striking: Yazd again recorded the highest rate in 2014 at 7.13 per 100,000, and Ilam province reached 8.13 per 100,000 in 2017, figures that approach levels seen in some high-income countries. As with Hodgkin lymphoma, men were disproportionately affected, with rates rising from 5.29 to 5.52 per 100,000 over the study period, compared with a range of 3.26 to 3.58 per 100,000 among women.

What sets this study apart from routine registry reports is its use of spatial analysis, a set of techniques borrowed from geography that ask whether disease cases are scattered randomly across a map or whether they cluster in ways that hint at shared environmental or social causes. The researchers employed geographic information system tools and computed Moran’s Index, a formal statistical measure of spatial autocorrelation in which values near zero indicate randomness and positive values indicate that neighboring areas tend to share similar disease rates. For Hodgkin lymphoma, the analysis detected statistically significant geographic clustering in 2014, with a Moran’s Index of 0.12 and a p-value of 0.05, and again in 2015, with an index of 0.14 and a p-value of 0.03. Intriguingly, this clustering dissolved in 2016 and 2017, suggesting that the geographic pattern of the disease was itself shifting during the study years.

For non-Hodgkin lymphoma, the spatial story was different. In most years the cases showed no significant clustering, but 2016 stood out with clear and highly significant geographic aggregation, marked by a Moran’s Index of 0.18 and a p-value below 0.001. A transient clustering pattern like this is exactly the kind of signal that motivates further investigation: it could reflect a localized environmental exposure, an outbreak of an infection known to trigger lymphoproliferative disease, changes in diagnostic practice concentrated in certain provinces, or simply variation in how completely the registry captured cases from different regions. The authors are careful not to overinterpret the pattern, but they emphasize that such geographic heterogeneity deserves systematic follow-up.

The rising trend itself invites several possible explanations, none of which can be confirmed from registry data alone. Improvements in diagnostic imaging, pathology services, and cancer registration could account for part of the increase, as better detection inevitably raises recorded incidence. Yet the consistency of the trend across both lymphoma types and both sexes over a short period keeps open the possibility of true changes in disease frequency. Internationally, non-Hodgkin lymphoma incidence has been linked to factors ranging from viral infections such as Epstein-Barr virus and HIV to immunosuppression, autoimmune conditions, pesticide exposure, and aging populations. Iran’s distinct regional patterns, with provinces such as Yazd, Lorestan, and Ilam repeatedly exceeding national averages, provide natural starting points for studies that could test environmental and infectious hypotheses directly.

The study’s authors describe lymphoma in Iran as a silent public health concern, a phrase that captures the way these malignancies accumulate burden without attracting the attention devoted to more common cancers. Their conclusion is a call for targeted public health interventions, particularly in the provinces where incidence runs highest, and for further research to clarify the risk factors underlying the geographic and demographic disparities the registry data reveal. Because the analysis rests on de-identified registry records and was approved by an institutional ethics committee, it demonstrates how routinely collected national health data, when combined with spatial statistics, can generate actionable intelligence at relatively low cost.

For the international cancer research community, the Iranian findings add a valuable data point from a Middle Eastern population whose lymphoma epidemiology has been less thoroughly documented than that of Europe or North America. The combination of rising age-standardized rates, a persistent male excess, and province-level hotspots paints a picture of a disease landscape in transition. Whether the upward slope of 2014 through 2017 has continued in the years since, and whether the fleeting clusters of 2015 and 2016 reappear in later data, are questions that only sustained registry investment can answer. What is already clear is that lymphoma in Iran is neither rare nor randomly distributed, and that the tools now exist to find it wherever it concentrates.

Subject of Research: Epidemiology and geographic clustering of Hodgkin and non-Hodgkin lymphoma in Iran based on national cancer registry data

Article Title: Epidemiology of hodgkin and non-Hodgkin lymphomas in Iran: shedding light on a silent public health concern

Article References: Ghafouri, A. H., Rahmanian, V., Hosseini, S., Hosseini, S. M. H., & Hazar, N. (2026). Epidemiology of hodgkin and non-Hodgkin lymphomas in Iran: shedding light on a silent public health concern. BMC Cancer. https://doi.org/10.1186/s12885-026-17101-z

Image Credits: AI Generated

DOI: 10.1186/s12885-026-17101-z

Keywords: Hodgkin lymphoma, non-Hodgkin lymphoma, Iran, cancer epidemiology, age-standardized incidence rate, National Cancer Registry, spatial analysis, Moran's Index, GIS, public health, Epstein-Barr virus, geographic clustering

The research, conducted by Juarez Antônio da Silva Júnior of the Federal University of Pernambuco, examined landslide occurrences recorded by Recife’s Civil Defense between 2015 and 2024. Rather than treating each neighborhood as an isolated unit, as conventional frequency counts and ordinary statistical models tend to do, the study measured whether landslides in one neighborhood were related to landslides in adjacent ones. The central tool was the Global Moran’s Index, a measure of spatial autocorrelation that quantifies whether similar values—here, landslide counts—sit closer together in space than chance would predict. Positive, significant values indicate clustering; values near zero indicate randomness.

The results were unambiguous. In most years of the historical series, the Global Moran’s Index was positive and statistically significant, ranging from moderate values of roughly 0.17 to 0.27 between 2015 and 2017, dipping during an attenuation period from 2018 to 2020, and then strengthening again to a peak of about 0.33 in 2023. In other words, throughout nearly the entire decade, neighborhoods with many landslides tended to be surrounded by other neighborhoods with many landslides, while quiet zones stayed quiet together. The risk, the analysis shows, is not diffuse across the city but concentrated in stable geographic poles.

To locate those poles precisely, the study turned to Local Indicators of Spatial Association, or LISA, a technique proposed by geographer Luc Anselin in 1995 that decomposes global autocorrelation into neighborhood-level clusters. LISA classifies each area into categories such as High-High, where a high-incidence neighborhood is surrounded by similarly high neighbors, or Low-Low, where low values cluster together, along with outlier categories where a unit diverges from its surroundings. The spatial weighting was built using Queen-type contiguity, meaning neighborhoods were considered neighbors if they shared a boundary or a vertex—an approach well suited to the irregular geometries of urban districts.

The local analysis revealed two large, statistically significant High-High hotspots at opposite ends of the municipality. In the North Zone, the neighborhoods of Guabiraba, Passarinho, Dois Unidos, Nova Descoberta, and Linha do Tiro emerged as chronic hotspots, classified as High-High in four to six of the ten years analyzed. In the South Zone, COHAB, Ibura, Jordão, and Barro showed recurring clusters, particularly in 2017, 2018, and above all 2022—a year of exceptional rainfall that produced the highest landslide count in the entire series, with some neighborhoods exceeding 50 occurrences and LISA values greater than 1 across broad contiguous areas. By contrast, central and coastal districts such as Boa Viagem, Pina, Graças, and Derby were dominated by Low-Low patterns, confirming consistently low risk in the flat, highly urbanized heart of the city.

Beyond mapping clusters in individual years, the study introduced two temporal indicators derived from the LISA results: the frequency of High-High classification and a persistence ranking that distinguishes chronic hotspots, significant in at least five of ten years, from episodic and intermittent ones. This persistence analysis showed that the northern hotspot is not a product of any single disaster but a structural condition—vulnerability that is rooted in time as well as space. Descriptive statistics reinforced the picture: neighborhoods such as Dois Unidos displayed extreme interannual swings, recording no cases in 2018, 104 cases in 2019, and only 2 in 2020, while Sen’s slope estimates suggested a modest overall downward trend in several historically critical areas, possibly reflecting municipal interventions.

The spatial patterns aligned closely with independent evidence. When the identified clusters were overlaid on landslide susceptibility maps prepared by the Geological Survey of Brazil (SGB/CPRM) and on the city’s continuous landslide monitoring map, the correspondence was strong. The CPRM maps classify the hilly terrain of the North and South zones as highly susceptible to mass movements, and it is precisely there that the chronic hotspots sit. The city’s own monitoring points, concentrated in the North, Northeast, and South administrative regions, coincide with the High-High clusters. This convergence between empirical occurrence data and model-based susceptibility mapping strengthens confidence that the statistical signals reflect genuine geotechnical and social conditions rather than artifacts of reporting.

The underlying drivers are as much social as geological. Recife, home to nearly 1.5 million people at a density of more than 6,800 per square kilometer, combines a humid tropical climate with steep slopes occupied by self-built housing. Studies cited in the paper document chaotic occupation, narrow streets, inadequate drainage, vegetation loss, and construction in permanent preservation areas in neighborhoods such as Passarinho, where urban growth reached 41 percent between 1975 and 2022 while vegetation cover fell by 30 percent. An estimated 207,000 residents live in at-risk areas, and Recife was ranked the fifth most impacted city by flash floods and landslides in terms of population in CEMADEN studies following the catastrophic May 2022 rains, which affected more than 200,000 inhabitants and caused dozens of deaths.

The methodological lesson extends beyond Recife. Spatial autocorrelation analysis has proven its value in fields from dengue epidemiology in Nepal to hepatitis A mapping in Indonesia and vegetation fragmentation studies in Zimbabwe, and recent work has integrated LISA with machine learning models to improve landslide prediction in China. By demonstrating that a decade of municipal civil defense records, freely available under an open data license and processed with open-source Python libraries such as GeoPandas and PySAL, can yield actionable risk intelligence, the study offers a replicable template for other cities with rugged terrain in the Brazilian Northeast and beyond.

The practical implications are direct. The author recommends prioritizing slope containment works, expanded drainage, vegetation recovery, control of irregular occupation, and relocation of families in imminent danger in the chronic northern hotspots, while reinforcing containment structures, permanent geotechnical monitoring, and contingency planning in the southern cluster. As climate change intensifies extreme rainfall events across the region, distinguishing chronic from episodic risk areas becomes essential for allocating scarce public resources. The decade of data from Recife delivers a clear message: landslide risk has an address, and prevention efforts should go precisely there.

Subject of Research: Spatiotemporal autocorrelation of landslide occurrences across neighborhoods of Recife, Brazil, from 2015 to 2024 using the Global Moran's Index and LISA.

Article Title: Spatiotemporal autocorrelation of landslides in neighborhoods of Recife, Brazil (2015–2024) based on the Moran and Lisa index

Article References: da Silva Júnior, J. A. (2026). Spatiotemporal autocorrelation of landslides in neighborhoods of Recife, Brazil (2015–2024) based on the Moran and Lisa index. Discover Geoscience, 4(1), Article 354. https://doi.org/10.1007/s44288-026-00722-z

Image Credits: AI Generated

DOI: 10.1007/s44288-026-00722-z

Keywords: landslides, spatial autocorrelation, Moran's Index, LISA, Recife, Brazil, spatial hotspots, urban risk, geotechnical instability, disaster risk management, GIS, susceptibility mapping