Statistical Unit Research Articles

Biofuel Pellets as a Sustainable Energy Solution: Exploring the Role of Biomass and Regional Waste Management in Turkey's TR63 Region

In recent years, the increasing demand for sustainable and renewable energy sources has led to significant interest in biofuels. Bifuel pellets, which combine biomass materials with fossil fuels, have emerged as an innovative and environmentally friendly energy solution. Typically made from agricultural residues, wood waste, or energy crops, blended with fossil fuels such as coal or oil derivatives, bifuel pellets offer improved energy efficiency and combustion properties. By bridging the gap between traditional fossil fuels and renewable energy alternatives, bifuel pellets provide a transitional solution that enhances both calorific value and combustion efficiency. The dual composition of these pellets allows for better adaptation to existing energy infrastructures while supporting reductions in greenhouse gas emissions. In Turkey, regional development and waste management strategies have been shaped by the adoption of the Nomenclature of Territorial Units for Statistics (NUTS) system since 2002. The TR63 NUTS Level 2 Region, which includes the provinces of Hatay, Kahramanmaraş, and Osmaniye, plays a critical role in agriculture and livestock production. Due to these activities, the region contributes significantly to waste generation. This research highlights the potential of bifuel pellets as a viable energy option for the TR63 Region, where agricultural and livestock residues could serve as essential feedstock for sustainable energy production. The integration of bifuel pellets into regional energy policies could help address environmental goals while reducing dependence on conventional fossil fuels.

Regional agglomeration index in the spatial location of creative industries: a case study in Türkiye

ABSTRACT The aim of this study is to reveal the spatial location choice of creative industries (CI) in Türkiye by developing a regional agglomeration index (RAI) based on entrepreneurial numbers. To achieve this, analysis and evaluation were conducted through spatial modeling based on ARAS-GIS integration, focusing on the numbers of entrepreneurs in the CI sectors. Within this scope, the regional agglomeration of CI in the Nomenclature of Territorial Units for Statistics (NUTS) Level-2 regions is illustrated through a spatial map based on index values. Results show Istanbul has the highest agglomeration index, followed by Ankara and Izmir, highlighting regional disparities. The east-west divide indicates unequal CI distribution nationwide. Findings suggest that a vision should be adopted that spreads innovative strategies for different branches of the CI across all NUTS Level-2 regions, emphasizing the role and importance of the CI in local economic development strategies.

Role of turnover, downsizing, overtime and night shifts on workplace violence against healthcare workers: a seven-year ecological study

BackgroundAbout one-third of workers identify organisational factors as contributors to workplace violence (WPV), but the associations between these factors and WPV have primarily been explored retrospectively and with measures of perceived organisational constraints, hence providing limited information for prevention. Therefore, we assessed whether objectively measured ward-level indicators of turnover, downsizing, overtime, and night shifts are associated with the occurrence of WPV and whether these associations vary by ward type.MethodsWe conducted an ecological study at a university hospital in northern Italy from 2016 to 2022, using wards as statistical unit (average: 230 wards per year). Active surveillance of WPV was based on an in-hospital incident reporting procedure, updated in November 2021 based on Health Regional Administration guidelines; 2021 was therefore excluded as a transition year. Individual-level administrative data were used to compute ward-level yearly indicators of turnover, downsizing, overtime and night shifts per active worker. Using generalised linear models, we estimated rate ratios (RRs) for yearly WPV counts per 1 SD increase in the indicators, controlling for study period, ward type (emergency department [ED], psychiatric ward, other) and ward sociodemographic composition.ResultsA total of 337 WPV episodes occurred in the 1381 ward-year observations. The WPV rates per 100 active workers per year increased from 1.40 (95%CI: 1.23–1.60) during 2016–2020 to 3.48 (2.90–4.17) in 2022. Higher turnover (RR, 95%CI: 1.47, 1.23–1.75) and downsizing (1.12, 1.00-1.24) were associated with a greater occurrence of WPV across all wards; these associations were consistent across the study periods. In wards with night shift scheduling, turnover (1.64, 1.40–1.92), downsizing (1.21, 1.04–1.40) and the mean number of night shifts (2.50, 1.37–4.56) were associated with WPV. The association between night shifts and WPV was greater in psychiatric wards (RR = 8.73; interaction p-value = 0.02), whereas the role of downsizing was greater in EDs (RR = 1.42, interaction p-value = 0.09) and the role of turnover was greater in the other wards (RR = 1.34, interaction p-value = 0.16).ConclusionWork organisational factors are associated with the occurrence of WPV episodes against healthcare workers. Ward type-tailored priorities should be given to minimising turnover and downsizing and promoting a fairer allocation of night shifts to decrease WPV occurrence.

HANZE v2.1: an improved database of flood impacts in Europe from 1870 to 2020

Abstract. Assessing long-term trends in flood losses and attributing them to climatic and socioeconomic changes requires comprehensive and systematic collection of historical information. Here, we present flood impact data for Europe that are part of the HANZE (Historical Analysis of Natural HaZards) database. The dataset covers riverine, pluvial, coastal, and compound floods that have occurred in 42 European countries between 1870 and 2020. The data were obtained by extensive data collection from more than 800 sources ranging from news reports through government databases to scientific papers. The dataset includes 2521 events characterized by at least one impact statistic: area inundated, fatalities, persons affected. or economic loss. Economic losses are presented both in the original currencies and price levels and with the inflation and exchange rate adjusted to the 2020 value of the euro. The spatial footprint of affected areas is consistently recorded using more than 1400 subnational units corresponding, with minor exceptions, to the European Union's Nomenclature of Territorial Units for Statistics (NUTS) level 3. Daily start and end dates, information on causes of the events, notes on data quality issues or associated non-flood impacts, and full bibliography of each record supplement the dataset. Apart from the possibility of downloading the data (https://doi.org/10.5281/zenodo.8410025; Paprotny, 2023a), the database can be viewed, filtered, and visualized online at https://naturalhazards.eu (last access: 4 November 2024). The dataset is designed to be complementary to HANZE-Exposure, a high-resolution model of historical exposure changes (such as population and asset values) and be easily usable in statistical and spatial analyses, including multi-hazard studies.

Diabetes mellitus EU comorbidity index: using data from EHIS 2019 and GBD disability weights

Abstract Background Individuals with diabetes mellitus (DM) exhibit several of comorbidity burdens, with lower perceived quality of life also being a factor. Literature on the impact of DM is available, including studies on the index devised by (A. Andreella et al., 2023) for the assessment of comorbidities of health conditions. This study aims to adapt and validate this index for use as a standardized DM comorbidity index within the EU. Methods A cross-sectional study was carried out using microdata from the European Health Interview Survey (EHIS) 2019. The study included adults aged 25 years or older who reported having DM (n = 20,042) from 27 EU member states. Comorbidity index was calculated to assess the comorbidity of having 9 non-communicable diseases using disability weights obtained from the Global Burden of Disease 2019 and self-perceived general health using multiplicative approach. Beta regression analysis was used to evaluate the association between comorbidity index and several demographic, socioeconomic and lifestyle variables. Comorbidity index was calculated for each nomenclature of territorial units for statistics (NUTS 2). Results The comorbidity index ranged from 0.049 to 0.993. The results indicated that having a higher education and residing in urban areas were less likely to have a high comorbidity index. Being unemployed or in other employment statuses demonstrated a significant, positive association with the index. Income exhibited a gradual, positive association with each quintile, while consuming fruits and vegetables less than once a week was found to be associated with an increased index. The highest comorbidity index was observed in Calabria (Italy), followed by Lubuskie (Poland), and the lowest was observed in Antwerp (Belgium), followed by Övre Norrland (Sweden). Conclusions By identifying comorbidity burden, targeted interventions and regional-specific strategies can be implemented to improve the quality of life for individuals with DM. Key messages • Quantifying the burden of comorbidities allows understanding of health status of DM patients. • Comorbidity index shows geographic variation in the EU which contributes to tailored policy interventions.

Sense of community, life satisfaction and psychological distress among seniors – HUNT4 study, Norway

Abstract Background Strong sense of community with one’s neighbors may benefit psychosocial well-being. Moreover, local communities with high level of belonging and social cohesion can have a positive impact on public mental health. This study aims to investigate associations between sense of community and life satisfaction - at both an individual and local community level in a Norwegian county. Methods Data from Trøndelag Health Study 4 (HUNT4: 2017-2019) on 22 939 participants were linked to geographic data of residence from Statistics Norway. The basic statistical unit in Norwegian, known as “grunnkrets,” serves to identify participants’ local communities. These units are subdivisions of municipalities, covering geographically contiguous areas that typically include a few hundred inhabitants. This population-based study enables investigation of variation in sense of community, trust, life satisfaction and psychological distress on individual and community level. Results Seniors (median age 70.3 years) with strong sense of community with their neighbors had more than three times higher odds (95%CI 3.08-3.93) of high level of life satisfaction and less than 0.3 times lower odds (95%CI 0.26-0.33) of psychological distress symptoms vs those with low sense of community, adjusted to sociodemographic variables. The average sense of community varied between local communities (N = 284). At this local geographical unit, strong sense of community was associated with high level of trust, enjoying the neighborhood and high life satisfaction. Conclusions The study can inform public health policy to consider development of social infrastructures that bolster sense of community among seniors within neighborhoods. Policy and action plans targeting to increase the overall sense of community in neighborhoods may contribute to create mental health-promoting and age-friendly local communities in Norway. Key messages • At individual level, sense of community with neighbors are associated with life satisfaction and psychological distress among Norwegian seniors. • The average sense of community varies between geographic local areas and are associated with level of trust, enjoying the neighborhood, average life satisfaction, and psychological distress.

Trends and socio-demographic determinants of suicide-related mortality in Türkiye: An epidemiological study from 2009 to 2022.

Suicide is a leading cause of death, particularly among the young age group, in Türkiye, a country with a high population of young people. The aim of this study is determine the suicide related mortality trend and some associated socio-demographic factors in Türkiye between 2009 and 2022. The study includes an epidemiological trend on suicide. The Turkish Statistical Institute's data portal was used. The study population is 46,691 people (Male/female ratios = 74.6%/ 25.4%). Changes in mortality trends were evaluated using the Joinpoint Regression method with Annual Percentage Change (APC) and Average Percentage Change (AAPC) analysis. Over the past 14 years in Türkiye, there has been a statistically significant increase in the standardized mortality rate among males (2009-2022 APC = .76, p = .012) and a statistically non-significant decrease among females (2009-2022 APC = -1.62, p = .083). According to the Nomenclature of Territorial Units for Statistics (NUTS-1) classification, the highest increase is observed in the Central Anatolia region followed by the Western Black Sea region (APC = 3.0 and 2.7, respectively; p < .005). While the percentage of primary school and below has decreasing trends (2009-2022 APC = -7.11, p < .005), there is a significant increase in the percentage of university graduates (2009-2016 APC = 10.87, p < .001). Regarding the marital status, there is a significant decrease in the percentage of those who are married (2009-2013 APC = -3.36, p < .001; 2013-2016 APC = 2.42, p = .037; 2016-2022 APC = -2.31, p = .003) and a significant increase in those who have never been married (2009-2013 APC = 2.63, p < .001; 2013-2016 APC = -1.36, p = .002). Although suicide related mortality is below the global average, there has been an increasing trend in turkey in the last 14 years. Analytical and intervention studies on the socio-demographic determinants supporting the increasing trend are needed.

Fusion regression methods with repeated functional data

Linear regression and classification methods with repeated functional data are considered. For each statistical unit in the sample, a real-valued parameter is observed over time under different conditions related by some neighborhood structure (spatial, group, etc.). Two regression methods based on fusion penalties are proposed to consider the dependence induced by this structure. These methods aim to obtain parsimonious coefficient regression functions, by determining if close conditions are associated with common regression coefficient functions. The first method is a generalization to functional data of the variable fusion methodology based on the 1-nearest neighbor. The second one relies on the group fusion lasso penalty which assumes some grouping structure of conditions and allows for homogeneity among the regression coefficient functions within groups. Numerical simulations and an application of electroencephalography data are presented.

Tests for equality of several mean vector functions for multivariate functional data with applications

In various scientific and technological fields, it is common to encounter multivariate functional data, where continuous vector functions for statistical units are observed over a specific interval. This paper aims to investigate the equality of mean vector functions in multi-sample multivariate functional data. To accomplish this, two novel global testing statistics are introduced by integrating or maximising the pointwise Lawley-Hotelling trace test statistic. The paper also provides asymptotic expressions for these proposed testing statistics under the null hypothesis and establishes their consistency with respect to the square root of the sample size. The performance of the two new testing procedures is demonstrated through simulation studies and the analysis of a real-world data example, highlighting their advantages over existing methods.

Accessibility and Older and Foreign Populations: Exploring Local Spatial Heterogeneities across Italy

The interplay between accessibility and population change is a relatively new subject in Italian academic research. Along with social and economic factors such as regional economic prosperity, the ease of movement inside and outside an area can play a pivotal role in shaping population dynamics. This study seeks to explore the spatial distribution and spatial relationships of three indicators, including one related to real accessibility (RAI) and two others related, respectively, to the shares of the older population (SOP) and of the foreign population (SFP). An exploratory spatial data analysis is, therefore, conducted at the local level using Italian municipalities as the statistical units for the empirical analysis. Local univariate spatial autocorrelation analysis is used together with a regression analysis based on ordinary least squares (OLS) and geographically weighted regression (GWR) models. The results provide valuable insights into the local heterogeneity that characterizes the distribution of each indicator and the local relationship between them, highlighting the importance of thinking locally in quantitative social sciences.

Motivation through indirect compensation: Evaluating employee performance in Enugu State Civil Service Commission

Purpose: This study evaluates the impact of indirect compensation on employee job performance by the Enugu State Civil Service Commission. Research Methodology: This study employed a cross-sectional survey research design with a population of 280 participants, comprising staff from the Administration, Planning and Budgeting, and Research &amp; Statistics Units, spanning grade levels 2-16 within the Enugu State Civil Service Commission. Data were collected using a questionnaire as the primary tool and rigorously analyzed using the Pearson correlation coefficient in the Statistical Package for Social Sciences (SPSS) version 23. Results: The findings revealed a significant positive impact of indirect compensation metrics on employee performance of the Enugu State Civil Service Commission. Limitations: The study specifically targeted personnel within grade levels 2-16 of the Enugu State Civil Service Commission. Contribution: This research significantly advances our understanding of indirect compensation and its impact on employee performance by pioneering new concepts, refining existing theories, and conducting comprehensive empirical studies. Funding: This research did not receive any funding from any Agency or Organization.

Analysis of Population Change of the 0-14 Year Age Group in Türkiye (2000-2022)

Age groups with wide and narrow ranges provide the opportunity to make detailed evaluations for planning studies in demographic analyses. Particularly, population pyramids are prepared using age group data by sex. In the bottom three rows of the pyramids, 0-4, 5-9, and 10-14 age group data can be used to evaluate population growth and fertility rates. In addition, age groups provide an indication of the amount of dependent and active population in a society, thus facilitating future population forecasts. Broad age ranges categorize the 0-14 age group as the young dependant population, the 15-64 age group as the active population, and the 65+ age group as the elderly dependent population. The proportional change of these age groups in certain date intervals and the reasons for which age groups are in an upward or downward trend also give an idea about the general welfare levels of countries. The current population's demographic trends guide the planning of employment, education, health, and many other socio-economic investments. Population policies are based on the proportional distribution of age groups and population projections. In this study, the change in the population of the 0-14 age group in Türkiye has been analyzed according to the Classification of Territorial Units for Statistics Level-1 (İBBS Düzey-1) since 2000. The population amount, population density, age, and gender structure of this age group are emphasized. In the research, State Institute of Statistics (DIE) 2000 General Population Census and TURKSTAT (TÜİK) Regional statistics data for 2022 were utilized. In 2000, since the Classification of Territorial Units for Statistics had not yet been established, province-based data from 2000 were evaluated and new data were created at the “İBBS Düzey-1” (CUTS Level-1) scale in order to ensure integrity in tables and graphs.

Mixtures of Probit Regression Models with Overlapping Clusters

Studies with binary outcomes on a heterogeneous population are quite common. Typically, the heterogeneity is modelled through varying effect coefficients within some binary regression setting combined with a clustering procedure. Most of the existing methods assign statistical units to distinct and non-overlapping clusters. However, there are scenarios where units exhibit a more complex organization and the clusters can be thought as partially overlapping. In this case, the standard approach does not work. In this paper, we define a mixture of regression models that allows overlapping clusters. This approach involves an overlap function that maps the regression coefficients, either at the unit or response level, of the parent clusters into the coefficients of the multiple allocation clusters. In order to deal with this intrinsic heterogeneity, regression analyses have to be stratified for different groups of observations or clusters. We present a computationally efficient Monte Carlo Markov Chain (MCMC) scheme for the case of a mixture of probit regressions. A simulation study shows the overall performance of the method. We conclude with two illustrative examples of modelling voting behavior, involving United States (US) Supreme Court justices over a number of topics and members of the United Kingdom (UK) parliament over divisions related to Brexit. These applications provide insights on the usefulness of the method in real applications. The method described can be extended to the case of a generic mixture of multivariate generalized linear models under overlapping clusters.

The current landscape of academic statistical and data science collaboration units with examples

The current landscape of academic statistical and data science collaboration units with examples

Evaluation of Breast Cancer Screening Program in Al-Warith Hospital

Introduction: Breast cancer is a significant threat to Iraqi women's health. Early diagnosis of breast cancer increases the chance of recovery and life expectancy. Screening is the primary tool for early diagnosis and timely treatment of breast cancer in the early stages. Objectives: To evaluate the screening program in detecting breast cancer in the Al-Warith screening center, Karbalaa, Iraq, from 1/8/2023 to 1/5/2024. Methods: A cross-sectional descriptive study was conducted among 510 Iraqi women who came to the outpatient breast clinic. Their demographic information related to the current research was selected randomly; it was taken from the medical records in the statistics unit in the hospital. The collected data were coded and entered into SPSS 16.0 (Statistical Package for the Social Sciences (SPSS) 22.0 by IBM) (SPSS for Windows, Rel. 22.0., 2016, SPSS Inc., Chicago, IL, USA). The categorical data were analyzed by frequency and percentage. Results: 167 were screened, representing 32.75% of the total mean age (54.3±13.20) years old. Clinical screening identified normal findings in 87.50% of participants, while 4.17% had benign findings, and 8.33% were clinically screened and advised to review breast clinical recurrently. Ultrasound screening showed normal findings in 32.00% of patients and benign results in 60.00%, with 8.00% having BIRAD [4, 5] results. Mammography screening revealed normal findings in 40.43% of patients and benign results in 53.19%, with 6.38% having BIRAD [4, 5] results. Biopsies were performed on eight patients, resulting in 75.00% undergoing FNA and 25.00% undergoing True Cut Biopsy. Ultimately, breast cancer was detected in 1.20% of the asymptomatic patients screened. Conclusion: The data highlights the crucial role of regular screening for both asymptomatic and symptomatic women in the early detection of breast conditions. Raising public awareness is a vital strategy for healthcare providers. These efforts can greatly enhance the early detection and treatment of breast cancer and other related conditions. By following these recommendations, we can achieve better patient outcomes and improve overall public health.

A multidimensional framework for assessing cultural heritage vulnerability to flood hazards

ABSTRACT This study proposes a multidimensional framework for the assessment of cultural heritage vulnerability to flood hazards. In this framework, cultural heritage is framed within a landscape approach drawing on the concept of the historic urban landscape as a physical entity, a lived space and a layered socio-economic environment. In such manner, this study emphasises that climate change disturbs both tangible and intangible aspects of cultural heritage – such as social systems and daily life practices – by modifying the material conditions that sustain them. Thus, the proposed framework integrates built and natural heritage with area-based vulnerability and socio-economic deprivation, considering households’ exposure to flood risk. The analysis follows a spatial and quantitative approach using open geospatial data available at the statistical unit level. The data are processed and analysed employing QGIS software and statistical analysis. Results confirm that cultural heritage vulnerability is highly associated with socio-economic deprivation and area-based vulnerability. The understanding of this interrelation is key to the identification of historic urban landscape qualities and priority areas for reconstruction and social support.

Redefining family structures: Births out of wedlock in 21st century Greece

AbstractThis study explores Greece's unique position as the country with the lowest nonmarital childbearing rate in Europe. The socio‐demographic profile of mothers is being analysed in three categories of marital status: married, unmarried, and in civil partnership. Unpublished data, custom‐made exclusively for this study from the Hellenic Statistical Authority (ELSTAT), have been used, spanning from 2019 to 2021. Key focus areas include maternal age at childbirth, educational attainment, and nationality (Greek, EU27 except for Greek, and non‐EU). Geographical analysis is conducted at the Regional Unit level (Nomenclature of territorial units for statistics [NUTS] 3 classification). Findings reveal that Greek teenage and early twenties mothers, as well as those with low educational level, are more likely to be unwed, implying that maternity might have come because of an unplanned pregnancy. On the other hand, those in civil partnerships resemble married mothers in age and educational attainment. Non‐Greek nationals show a higher prevalence of nonmarital births, and a sociodemographic profile that implies that unwed maternity constitutes a conscious choice for them. This study not only illuminates the characteristics of unwed mothers in Greece but also provides empirical insights for evaluating prevailing theoretical frameworks regarding family dynamics in countries facing low fertility rates.

Outlier identification and adjustment for time series

Identification and replacement of erroneous data is of fundamental importance for the quality of statistical surveys. If statistical units are continuously sampled over an extended period, time series methods can facilitate this task. Numerous outlier identification and replacement procedures are accessible for this particular purpose, like RegArima Approaches within the seasonal adjustment procedures in X13-Arima or Tramo/Seats. These algorithms can be used to identify different types of outliers, like additive outliers, level shifts or transitory changes. In this paper an alternative outlier identification procedure is proposed which is based on a nonlinear model estimated with support vector regressions. The focus of this procedure is on the identification of additive outliers and on the applicability for short time series with less than 3 years of observations.

Contributions of causes of death to differentials in life expectancy by internal migrant status in the Netherlands. A population register based study, 2015–2019

Important health differences exist in the context of international migration and residential mobility. Less is known about health differences regarding the medium-distance level of internal migration. This study examines life expectancy gaps between internal movers and stayers in the Netherlands and their underlying processes by assessing the contribution of different causes of death by age and sex. It uses individually-linked death counts and population exposures extracted from population registers, covering the native Dutch population aged 10+ from 2015 to 2019. The pooled data were disaggregated by causes-of-death group (neurodegenerative diseases, cardiovascular diseases, lifestyle-related mortality, external causes, and other causes), internal migrant status (movers and stayers, based on past 10-year residence in the 40 NUTS-3 [Nomenclature of Territorial Units for Statistics, level 3] regions), age, and sex. Comparing movers and stayers, we computed life expectancy at age 10 (e10), age- and cause-specific mortality risks, and applied decomposition methods to assess contributions of causes of death to e10 gaps. In the Netherlands in 2015–2019, e10 was lower for movers between NUTS-3 regions than stayers (males: 2.49 years; females: 3.51 years), due to excess mortality for movers at most ages. Movers only had a lower mortality than stayers at younger working ages (males: ages 20–44; females: ages 20–34). Mortality from neurodegenerative diseases and cardiovascular diseases were the largest contributors to the e10 gap, especially at ages 75+ and for females. Mortality from lifestyle-related and external causes of death contributed less, with the largest contributions for females aged 75–89 and males aged 45–69. The lower e10 of movers in the Netherlands is likely explained by health selection effects–in particular care-related moves as coping behaviour–rather than by causal effects through risk accumulation. Research focusing on regional or spatial heterogeneity of the mover-stayer health gap would be insightful to further understand these processes.

Immune response to SARS-CoV-2 vaccine administered to patients with solid cancers under systemic treatment: A prospective, monocentric study (Immunocovid-19).

e23093 Background: Despite vaccination, COVID-19 could still cause severe disease in immunocompromised patients (pts). While subsequent vaccine doses increase immune response in the general population, few data concerning cancer pts under systemic treatment are reported. Methods: This study will be conducted on blood samples from 1000 pts admitted to Reggio Emilia AUSL-IRCCS Cancer Center and undergoing systemic therapies who received ≥ 2 vaccine doses. The samples will be analyzed at Clinical Microbiology and Chemistry Laboratories of Reggio Emilia AUSL-IRCCS. We will perform antibody IgM-IgG serology by means of COVID-19 VIRCLIA® Monotest and anti-spike antibodies evaluation using LIAISON® SARS CoV-2 Trimerics IgG. We will also investigate cell immune response (B and T lymphocyte counts) and analyze specific lymphocyte subsets on the first 200 pts treated with chemo- (CT) or immunotherapy (IO). Then, we will evaluate the T-cell response on the first 50 pts and those with an inadequate anti-spike serologic response through the T-SPOT.COVID test. Statistical analyses will be conducted by the Clinical Studies and Statistical Unit Staff. Results: Here, we report preliminary data on the first 240 pts enrolled and treated with CT (55.4%), IO (26.3%), or other therapies (18.3%). Most of the pts were male (58%), had metastatic cancer (82%), and no previous COVID-19 infection (60%). 84% of pts were affected by gastrointestinal, lung, urogenital and breast cancer. We stratified pts in three groups according to the time from the last vaccine dose (0-6/6-12/ &gt; 12 months). We showed no significant decrease in cellular and humoral immune response over time from the last vaccine dose, independent of the specific treatment received, also if CT (Table 1). Furthermore, clinical characteristics as gender, treatment, and primary tumor site appear not to influence immune response, even if a trend to a higher response in pts previously affected by COVID-19 has been observed. The T-SPOT.COVID test performed on the first 27 pts showed that 19% had no T-cell activation, even with good IgG response. Conclusions: This feasible and cost-effective study has a potential impact on public health. Although these are preliminary data, the immune response to COVID-19 vaccines remains good after 12 months from the last dose administered in cancer pts. The enrollment is ongoing, and we could provide relevant information on the influence of clinical factors on the immune response and identify specific subgroups that could benefit from different vaccine approaches. [Table: see text]