Multivariate Estimation Research Articles

Social capital as an element of impact on public health: a gender gap analysis for Spain

Abstract The recent COVID-19 pandemic highlighted the importance of health, not only for physical but also mental well-being. The isolation to which the population was subjected in many countries drastically reduced social interaction at that time and emphasized the relevance of social capital. The aim of this article is to study the importance of social capital in health, and its impact in gender inequalities, in order to provide guidance on social policy that contributes to the reduction of the gender health gap. We used the Spanish data from the European Survey on Living Conditions (EU-SILC). Spain is a representative country of Southern Europe, where there are strong gender inequalities in health, especially among the elderly. The selected sample includes the population aged 25 years and over. In total, 25,054 individuals were considered, of whom 47% were men and 53% were women. Self-rated health was used as a general indicator of health status. Different sociodemographic variables and various social capital variables, such as satisfaction with social relationships or meeting with family and friends, were included in the analysis. In order to carry out this analysis, several multivariate logistic estimations were performed. In our results, social capital variables were highly significant and positively associated with perceived health. In the case of women, there is a strong positive association, especially relevant for older women (over 75) and those with lower levels of education (no education or primary education). In both cases, these are the most vulnerable women, who report a poorer health status, with a gender gap in health particularly intense in our estimations for older women. In conclusion, social policies supporting elderly women, promoting participation in group activities and facilitating relationships with others, constitute a strategy of great impact on their perception of health. Key messages • Social capital variables were highly significant and positively associated with perceived health, especially relevant for older women and those with lower levels of education. • Social policies supporting elderly women, promoting participation in group activities and facilitating relationships with others, constitute a strategy of great impact on their perception of health.

Racial/Ethnic Disparities in Misidentification of Dementia in Medicare Claims: Results from the Washington Heights-Inwood Columbia Aging Project.

Misidentification of dementia in Medicare claims is quite common. We examined potential race/ethnic disparities in misidentification of dementia in Medicare claims in a diverse cohort of older adults who underwent careful clinical assessment. Participants were enrolled in the Washington Heights-Inwood Columbia Aging Project (WHICAP), a multiethnic, population-based, prospective study of cognitive aging in which dementia status was assessed using a rigorous clinical protocol. ICD-9-CM and ICD-10-CM diagnosis codes in all available Medicare claims (1999-2019) were compared to clinical dementia diagnosis and categorized into three mutually exclusive groups: 1) congruent-, 2) over-, and 3) under- identification during the study period. Multinomial logistic regression model was used to examine the relationship between race (White, African American/Black, other) and ethnicity (Hispanic/Latinx, non-Hispanic/Latinx) and congruency of dementia identification after controlling for clinical (cognition, function, comorbidities) and demographic characteristics (age, sex, education), and inpatient and outpatient utilization. Across all person-years, 88.4% had congruent identification of dementia compared to clinical diagnosis, in 4.1% of the times participants were over-identified with dementia, and 7.5% of the times the participants were under-identified. Rates of misidentification was higher in minority participants than in White, non-Hispanic participants. Multivariable estimation results showed that the probability of over-identification with dementia was 2.2% higher for African American/Black than White (p = 0.05) and 2.7% higher for Hispanic participants than non-Hispanics (p = 0.03) participants. Differences in under-identification by race/ethnicity were not statistically significant. African American/Black and Hispanic participants were more likely over-identified with dementia in Medicare claims.

Advancing Sampling Techniques: Multivariate Ratio Estimation for Variance Vector in Two-Phase Sampling

The problem of variance vector estimation using multi-auxiliary variables is not quite often considered in the literature. In the present study, we propose a multivariate ratio estimation approach for estimating a vector of variances in two-phase sampling using a vector of variables, following the situation if the desired parameters are available only for some of the auxiliary variables. Some special cases of the proposed generalized multivariate variance estimator have also been discussed. Expressions of the bias, and generalized variance has been derived. With the help of real-life data, the applicability of the proposed multivariate variance estimator has been given, and it is shown that the proposed multivariate estimator is more efficient than the modified versions of multivariate variance estimators. A simulation study has also been carried out to show that the proposed estimator surpasses the modified version of multivariate estimators.

Digital Financial Inclusion and Remittances: An Empirical Study on Bangladeshi Migrant Households

Globally, large numbers of adults remain unbanked, and most of them live in rural areas of the Third World. The recent outbreak of the COVID-19 pandemic has shown us how inequalities in accessing financial services continue to affect us. However, digital financial inclusion has emerged as an effective tool used to tackle socioeconomic ills and drive economic development. In fact, due to these modern technological developments, the number of studies in this area is very limited, especially in the context of developing economies. This study examines the impacts of migrant remittances on digital financial inclusion within households in Bangladesh by using the Migration and Remittance Household Survey. To meet the research objectives of this study, a household survey was conducted and 2165 households interviewed in 2022–2023 in Bangladesh. The survey data collected was tested using univariate and multivariate estimations. This study finds that the coefficient of remittance has positive relationships with the probability of e-bank accounts and the use of mobile banking for a household’s financial transactions. However, the use of ATM cards by households for financial transactions has not been significantly affected. The article concludes that remittance flows may enhance access to and use of means of digital financial inclusion by reducing some of the barriers and costs in Bangladesh, which could greatly contribute to the country’s economic growth by creating and increasing a strong fund for investment. The findings of this study can help in taking various steps to facilitate the most powerful financial sector of Bangladesh, namely, remittance management.

Independent prognostic value of high-risk ventricular premature complexes during exercise or recovery in asymptomatic patients: A meta-analysis of observational studies

IntroductionVentricular premature contractions (VPCs) are a common finding during cardiac stress tests. The independent prognostic value of these findings in patients in asymptomatic patients is unclear. MethodsWe conducted a systematic review and meta-analysis of observational studies exploring the independent prognostic value of VPCs to predict all-cause mortality. The secondary outcome was cardiovascular (CV) mortality. We excluded studies that did not report outcomes after adjusting for ≥1 confounder. Random effect meta-analyses were used to predict cumulative hazard ratios. We stratified results based on VPC during exercise or recovery. ResultsWe found 7 studies with 24,518 patients that met our inclusion criteria. Two studies reported all-cause mortality only, 1 study reported CV mortality only, rest 4 reported both. There was significant heterogeneity in the baseline population, definition of high-risk VPCs, and variables used in adjusted models. Using multivariable summary estimates from individual studies, only VPCs during exercise were associated with a higher risk of all-cause mortality (HR 1.27, 95 % CI 1.07, 1.48). Both VPCs during exercise and recovery were associated with a higher risk CV mortality (HR 1.69, 95 % CI 1.19, 2.20, I2 = 17.6 % and 1.62, 95 % CI 1.25, 2.00, p < 0.001 for both). ConclusionHigh-risk VPCs during exercise is associated with increased risk of all-cause and CV mortality, while those during recovery are associated with an increased risk of CV mortality only.

The Many Forms of Co-kriging: A Diversity of Multivariate Spatial Estimators

AbstractIn this expository review paper, we show that co-kriging, a widely used geostatistical multivariate optimal linear estimator, has a diverse range of extensions that we have collected and illustrated to show the potential of this spatial interpolator. In the context of spatial stochastic processes, this paper covers scenarios including increasing the spatial resolution of a spatial variable (downscaling), solving inverse problems, estimating directional derivatives, and spatial interpolation taking boundary conditions into account. All these spatial interpolators are optimal linear estimators in the sense of being unbiased and minimising the variance of the estimation error.

Tidal harmonics retrieval using GNSS-R dual-frequency complex observations

Tidal analysis and methods for estimation and prediction of ocean tidal constitutes are essential in a large area of scientific disciplines, for example, navigation, onshore and offshore engineering, and production of green energy. Ground-based Global Navigation Satellite System-Reflectometry (GNSS-R) has been proposed as an alternative method for measuring sea surface height. We use 6 years of GNSS-R observations at In-phase and Quadrature levels from July 2015 to May 2021 obtained from a dedicated receiver and sea-looking left hand circular polarization antenna for estimating sea level (SL). In the first step, the multivariate least-square harmonic estimation (LS-HE) method is applied for SL estimation. Then, final SL time series are generated by combining estimated SL from all satellites at L1 and L2 frequencies in the averaging step. The 6-year root-mean-square error between GNSS-R L12 sea surface heights and a collocated tide gauge (TG) is 5.8 cm with a correlation of 0.948 for a high temporal resolution of 5 min with 15 min averaging window. Afterward, using the univariate LS-HE, we detect tidal harmonics with periods between 30 min to 1 year. The detection results highlight a good match between GNSS-R and TG. Higher harmonics, i.e., the periods shorter than 3 h, show stronger signatures in GNSS-R data. Finally, we estimate the amplitude and phase of standard tidal harmonics from the two datasets. The results show an overall good agreement between the datasets with a few exceptions.

Bayesian multivariate genetic analysis improves translational insights

While lipid traits are known essential mediators of cardiovascular disease, few approaches have taken advantage of their shared genetic effects. We apply a Bayesian multivariate size estimator, mash, to GWAS of four lipid traits in the Million Veterans Program (MVP) and provide posterior mean and local false sign rates for all effects. These estimates borrow information across traits to improve effect size accuracy. We show that controlling local false sign rates accurately and powerfully identifies replicable genetic associations and that multivariate control furthers the ability to explain complex diseases. Our application yields high concordance between independent datasets, more accurately prioritizes causal genes, and significantly improves polygenic prediction beyond state-of-the-art methods by up to 59% for lipid traits. The use of Bayesian multivariate genetic shrinkage has yet to be applied to human quantitative trait GWAS results, and we present a staged approach to prediction on a polygenic scale.

JS09.6.A ASSESSMENT OF RESPONSE TO REGORAFENIB IN RECURRENT GLIOMA PATIENTS USING FET PET AND MRI INCLUDING ADC VALUES

Abstract BACKGROUND The phase 2 REGOMA trial suggested that glioblastoma patients treated with the multikinase inhibitor regorafenib at first progression have significantly longer overall survival than patients treated with lomustine. Here, we evaluated the value of O-(2-[18F]fluoroethyl)-L-tyrosine (FET) PET and MRI for the evaluation of response to regorafenib in recurrent glioma patients. MATERIAL AND METHODS Twenty patients (age range, 30-72 years) with CNS WHO grade 3 or 4 gliomas (glioblastoma, 85%) at recurrence were treated according to the REGOMA trial with regorafenib at 160 mg/d for the first three weeks of each 4-week cycle, with individual dose adjustment according to toxicity. FET PET and MRI were performed at baseline and follow-up after the second cycle. Static FET PET parameters comprised tumor-to-brain ratios (TBR) and metabolic tumor volumes (MTV). Parameters derived from dynamic FET PET acquisition were time-to-peak and slope. MRI response assessment was based on RANO criteria. Additionally, mean and minimum apparent diffusion coefficients (ADC) were obtained from diffusion-weighted MRI. FET PET parameters and ADC thresholds were defined using ROC analyses to predict an overall survival of ≥ 6 months. The predictive values of FET PET parameters, ADC values, and RANO criteria were subsequently evaluated using univariate and multivariate survival estimates. RESULTS Patients received a median of three regorafenib cycles (range, 2-10 cycles). After treatment initiation, the median follow-up was 8.6 months (range, 3.2-27.3 months). The median number of previous treatment lines was two (range, 1-4 lines). At the time of data analysis, 17 patients (85%) had died. After two cycles of regorafenib, a regorafenib-induced reduction of mean TBR values by ≥ 10% predicted a longer overall survival (9.9 vs. 5.3 months; P=0.023). Interestingly, absolute mean TBR values ≤ 2.0 at follow-up were prognostic and associated with longer OS (10.6 vs. 4.5 months; P=0.007). In contrast, MTV, dynamic PET parameters, RANO criteria, and ADC values were not predictive for response or prognostic (all P&amp;gt;0.05). Multivariate survival analyses revealed that relative mean TBR changes were most potent in predicting response to regorafenib (P=0.038; HR, 0.246) and absolute mean TBR values for prognostication (P&amp;lt;0.001; HR, 0.005). CONCLUSION Data suggest that FET PET parameters are clinically valuable for identifying responders to regorafenib early after treatment initiation and helpful for prognostication. MRI parameters, including ADC values, do not appear to add valuable information in this context.

A novel adaptive multi-scale Rényi transfer entropy based on kernel density estimation

The utilization of Rényi Transfer Entropy (RTE) as a powerful model for analyzing causal relationships among variables has become pervasive in the field of complex systems time series causality detection. However, there are two major challenges in using RTE: the inherent multilevel structure of causal associations in the systems, and the precision in RTE estimation. To address these challenges, this paper proposes an adaptive multi-scale Rényi transfer entropy based on kernel density estimation. The framework of causal detection based on the novel RTE consists of two parts: adaptive discrete wavelet transform (ADWT)-based time series decomposition and multivariate kernel density estimation (MKDE)-based causality network generation. In the ADWT-based time series decomposition, the original series are decomposed into different frequency bands by optimal wavelet coefficients, which is generated adaptively by an auto-encoder. In the MKDE-based causality network generation, the causal network between the variables is represented by an adjacency matrix composed of their decomposition components in each layer, and the values of the matrix are the RTE values between the variables. In order to accurate estimation of RTE values an evaluation criterion for KDE under a uniform measure in both univariate and multivariate cases and the optimal bandwidth selection is provided in this part. To validate the effectiveness of the novel causal measure in this paper, the proposed method is tested on the synthetic and real data, and the results show that it can effectively detect causal relationships among variables at different levels in non-stationary time series of both bidirectional and undirectional complex systems. Compared to the other RTE estimators, the proposed method can detect the causality accurately and avoid the spurious causality.

Influenza Vaccine Effectiveness against Hospitalization, Season 2021/22: A Test-Negative Design Study in Barcelona.

Vaccination is considered the most effective measure for preventing influenza and its complications. The influenza vaccine effectiveness (IVE) varies annually due to the evolution of influenza viruses and the update of vaccine composition. Assessing the IVE is crucial to facilitate decision making in public health policies. to estimate the IVE against hospitalization and its determinants in the 2021/22 season in a Spanish tertiary hospital. We conducted a prospective observational test-negative design study within the Development of Robust and Innovative Vaccine Effectiveness (DRIVE) project. Hospitalized patients with severe acute respiratory infection (SARI) and an available influenza reverse transcription polymerase chain reaction (RT-PCR) were selected and classified as cases (positive influenza RT-PCR) or controls (negative influenza RT-PCR). Vaccine information was obtained from electronic clinical records shared by public healthcare providers. Information about potential confounders was obtained from hospital clinical registries. The IVE was calculated by subtracting the ratio of the odds of vaccination in cases and controls from one, as a percentage (IVE = (1 - odds ratio (OR)) × 100). Multivariate IVE estimates were calculated using logistic regression. In total, 260 severe acute respiratory infections (SARI) were identified, of which 34 were positive for influenza, and all were subtype A(H3N2). Fifty-three percent were vaccinated. Adjusted IVE against hospitalization was 26.4% (95% CI -69% to 112%). IVE determinants could not be explored due to sample size limitations. Our data revealed non-significant moderate vaccine effectiveness against hospitalization for the 2021/2022 season.

EMA Health Indicator Extraction Based on Improved Multivariate State Estimation Technique With a Composite Operator

EMA Health Indicator Extraction Based on Improved Multivariate State Estimation Technique With a Composite Operator

Machine learning approach to the background reduction in singly charged cosmic-ray isotope measurements with AMS-02

Studying the isotopic composition of single-charge cosmic rays (CRs) provides essential data to investigate the CR propagation processes in our Galaxy. While current measurements are rare above 4 GeV/nucleon, the Alpha Magnetic Spectrometer (AMS-02) is able to measure the isotopic fluxes up to 10 GeV/n by combining the momentum measured by the silicon tracker with the precise measurements of the velocity provided by its Ring Imaging Cherenkov Detector (RICH). The correct measurement of the particles’ velocity is essential for identifying isotopes through their mass. This is particularly challenging for single-charge particles due to the low number of photons they produce in their Cherenkov rings, which makes the reconstruction easily disrupted by noise. Hence, identifying the sources and cleaning the sample from the background is essential for ensuring the quality of the rings. In this paper, we propose a novel approach to track the events whose mass is misidentified due to interactions inside the AMS-02 detector. Based on the actual location of these interactions, we propose a novel strategy to mitigate the background effectively and with high efficiency, which includes using cut-based selection criteria and a multivariate estimator based on the signals detected by the RICH.

Estimation of the genetic parameters for temperament in Haflinger horses based on the results of a character test using a pedigree-based relationship matrix

Literature provides indications that the personality and temperament of a horse are sufficiently genetically determined. This background was the impetus to continue collecting phenotypic information on the temperament in the South Tyrolean Haflinger horses, not only to have marketing tools but also to be able to estimate genetic parameters for a future genetic evaluation. The latter should open the possibility to include temperament traits in the breeding goal. Therefore, the aim of the present study was to consider for the very first-time phenotypic information from character testing for estimating genetic parameters for temperament in Haflinger horses. For the present study, data from routine character testing of 210 South Tyrolean Haflinger mares between 2019 and 2021 were considered. A multivariate estimation of heritability (h2) and genetic correlations (r) was carried out for the criteria ‘Interest’, ‘Activity’ and ‘Excitement’. In addition, univariate and bivariate estimation runs were also carried out. The pedigree-based heritability for the behavioural trait Interest was lowest with 0.07 and a standard error of ±0.04. For the other two criteria, Activity and Excitement, h2 was 0.19 ± 0.05 and 0.21 ± 0.04, respectively. The genetic correlation between Interest and Activity and between Interest and Excitement was in both cases negative with −0.52 and −0.61, respectively while the genetic correlation between Excitement and Activity was positive (0.99). Results of the present study highlight the potential of considering temperament traits in breeding of Haflinger horses.

Tác động xã hội, lòng tin và quyết định chọn nơi mua thực phẩm tươi sống của người dân tại Thành phố Hồ Chí Minh

The purpose of the article is to examine the influence of factors on consumers’ decision to choose where to buy fresh food in Ho Chi Minh City. Two factors that are focused in the study are trust in supply chain actors, food management and social impact. The study uses the Multivariate Probit estimation model with a sample size of 329 observations. The results show that there are many factors affecting the purchasing decisions of households such as: gender, income, freshness, etc. In particular, the factors of trust for actors in the supply chain, Food management such as: government, manufacturer, retailer, social impact gives mixed results. From the research results, the article proposes a number of solutions for the parties involved in improving product quality in order to improve consumer confidence in the food supply chain as well as build Good relationship with customers.

On the use of robust estimators of multivariate location for heterogeneous data

The properties of orthogonal equivariance and componentwise increasingness are often discussed for estimators of multivariate location. The former property is linked to a coordinate-free nature of the data (as in spatial data), whereas the latter property is linked to an ordered nature of the data (typically formed by several univariate variables). Since both properties do not get along well with each other, if robustness in the presence of outliers is to be pursued, one must choose between them. Admittedly, most of the literature on multivariate estimation of location, in which affine equivariance is pursued, typically abandons the latter property. Unfortunately, in the more and more common presence of heterogeneous data (as in spatio-temporal data), both orthogonal equivariance (therefore, affine equivariance) and componentwise increasingness might bring very disappointing results. For this very reason, both properties should be forfeited and only required for some of the components.

Multivariate accelerate shelf life test (MASLT) based on hyperspectral imaging to estimate shelf life of cape gooseberry (Physalis peruviana L.)

For first time, it was proposed a multivariate shelf life estimation of cape gooseberry (CG) based on Multivariate Accelerating Shelf Life Testing (MASLT) and Near Infrared Hyperspectral Imaging (NIR-HSI). CG samples were stored at 6, 10 and 20 °C for 0, 3, 6, 9 and 12 days. Each day samples were imaged, evaluated by a sensory panel and characterized in physicochemical composition. The best region of interest in hyperspectral images was selected, then a Principal Component Analysis (PCA) was performed. The time-related PC2 scores were extracted, used to build kinetic charts and fit to zero-order reaction (R2 > 0.82). From the sensory evaluation, we defined cut-off value of -0.3576, corresponding to the sample stored at 20 °C for 6 days. Finally, the shelf life was estimated at 7.5 days, 15.4 days and 20.5 days for cape gooseberry stored at 20, 10 and 6 °C, respectively. In conclusion, we developed a non-contact method based on NIR-HSI to estimate the shelf life of cape gooseberry, which is related to global degradation, being more realistic.

Multi-scale adaptive multivariate state estimation fault detection enhancement for time-varying industrial system based on multi-output Gaussian process autoregression

Most chemical processes are time-varying and nonlinear due to load changes, product demand transitions or other causes, causing unsatisfactory performance of conventional non-parametric multivariate statistical fault detection method. In this work, a probabilistic multivariate state estimation fault detection method based on multi-output Gaussian process autoregression is first developed to overcome the linear limitation and noise information ignorance of multivariate state estimation technology-based fault detection methods. In multi-output Gaussian process regression framework, the Leave-One-Sample-Out strategy is developed to construct the dataset by leaving each historical sample from the memory matrix out as one training output in turn while regarding the corresponding remaining historical samples as one training input. Then, we design the multi-output Gaussian process autoregression model to estimate the current state of the system. Utilizing the estimation covariance information, a probabilistic index based on multivariate Kullback-Leibler divergence is developed for fault detection. Moreover, a multi-scale adaptive fault detection enhancement based on discrete multi-objective Archimedes optimization and just-in-time learning is further proposed to handle the time-varying problem. In this scheme, the memory matrix is batch-adaptively updated at monthly intervals based on discrete multi-objective Archimedes optimization to handle the slow change of system characteristics caused by equipment degradation. On the other hand, to cope with the rapid change of system characteristics caused by operation transition, the just-in-time learning is employed to select samples from the memory matrix that best match to the current system state for sample-adaptive updating. Finally, two industrial cases are used to verify the performance of the proposed method.

An evaluation of the Gilbert-McKern pubic symphyseal age estimation method using Bayesian statistics and principal component analysis: A computed tomographic exploration in an Indian population.

Age of an individual constitutes a primary facet of human identification. In cases where skeletal remains present for examination, bony markers distributed throughout the skeletal framework are employed for age estimation. Amongst these markers, the pubic symphysis constitutes one of the more commonly utilized structures. Gilbert-McKern's pubic symphyseal age estimation method was derived to complement the original three component method, and permit accurate age estimation in females. However, subsequent investigations with the Gilbert-McKern method are limited, and completely lacking for an Indian population. In the present study, CT scans of 380 consenting individuals (190 males and 190 females) undergoing CT examinations for therapeutic purposes, aged 10 years and above, were scored according to Gilbert-McKern's three component method. A significant sexual dimorphism was observed with the scoring of the ventral rampart and symphyseal rim. An overall accuracy of 29.50% was obtained in females, indicating that the method lacks forensic utility in its primal form. Highest posterior density and highest posterior density region values were computed for each component using Bayesian analysis in both sexes, to enable age estimation from individual components, whilst overcoming issues of age mimicry. Amongst the three components, symphyseal rim furnished the most accurate and precise estimates of age, whereas the ventral rampart garnered highest error computations, in both sexes. Principal component analysis was utilized for multivariate age estimation by taking into consideration this differential contribution of individual components. Weighted summary age models, derived using principal component analysis, furnished inaccuracy values of 12.19 years and 12.30 years in females and males, respectively. Bayesian error computations obtained with the symphyseal rim in both sexes were even lower than those obtained with weighted summary age models, demonstrating its suitability as an independent age marker. Despite the use of statistical modalities of Bayesian inference and principal component analysis for age estimation, the method did not yield significantly reduced error rates in females, demonstrating its limited forensic applicability. While, statistically significant sex differences were observed with the scoring of Gilbert-McKern's components, concordant correlations, comparable accuracy and absolute error values were obtained for both sexes, indicating that the Gilbert-McKern method can be utilized to age either sex. However, inaccuracy and bias values obtained with different statistical modalities, as well as broad age intervals furnished with Bayesian analysis demonstrate the overall limited applicability of the Gilbert-McKern method in aging males and females of an Indian population.

Flood Vulnerability Models and Household Flood Damage Mitigation Measures: An Econometric Analysis of Survey Data

AbstractFlood events are expected to increase in their frequency and severity, which results in higher flood risk without additional adaptation measures. The information gained from flood risk models is essential in effective disaster risk management. However, vulnerability estimations are often a large driver of uncertainty, and flood damage is rarely estimated due to a lack of empirical damage data from flood events. This study uses a unique data set with experienced damages and the implementation of flood damage mitigation (FDM) measures on the household level, collected after the flood event in the Netherlands in 2021. Flood damage models that control for several hazard, exposure, and vulnerability indicators are estimated and allow for additional input in flood risk models. Previous estimates of the effectiveness of FDM measures are prone to a selection bias, as households that do, and do not implement FDM measures systematically differ in their risk profiles. By using an instrumental variable‐estimation, this study overcomes this selection bias and finds significant reductions in flood damage due to FDM measures. These reductions can be incorporated in multivariate flood vulnerability estimations, which indicate that FDM measures significantly reduce flood damage. Providing information on flood hazard, as well as implementing early warning systems, is crucial for ensuring effective flood risk management.