Cumulative Distribution Function Research Articles

Pharmacometric and statistical considerations for dose optimization.

The probability of target attainment (PTA) is a common metric in drug dose optimization, but it requires a specific known target concentration threshold. Such target thresholds are not always available for some treatments, and patient and disease groups, particularly when treating children. This study performed pharmacokinetic and pharmacokinetic-pharmacodynamic (PKPD) simulations to explore different statistical approaches for determining the optimal dose for unknown PK and PKPD targets. To determine an optimal dose, PK and PKPD outcomes in typical patients with a standard adult dosing regimen were simulated and set as the reference profile, and compared to simulated outcomes for different dosing regimens in the population of interest. Statistical distances between the empirical cumulative distribution functions of the outcomes from all possible dosing regimens were calculated and compared to the reference profile. An optimal dose for known PK and PKPD target outcomes was selected to maintain the outcome above the assigned target, while optimal dosing in a population of interest with an unknown target was selected to generate equivalent PK and PKPD outcomes as the typical population. All of the dose optimization methods with commonly used PK and PKPD models and covariates were implemented as an open source freely available Shiny web-application. The developed pharmacometric method for dose optimization in populations with known and unknown target levels were robust and reproducible, and the implementation of a freely accessible Shiny web-application ensures widespread use and could be a useful tool for dose optimization in populations of interest.

Dust mass in protoplanetary disks with porous dust opacities

Atacama Large Millimeter/submillimeter Array surveys have suggested that protoplanetary disks are not massive enough to form the known exoplanet population, based on the assumption that the millimeter continuum emission is optically thin. In this work, we investigate how the mass determination is influenced when the porosity of dust grains is considered in radiative transfer models. The results show that disks with porous dust opacities yield similar dust temperatures, but systematically lower millimeter fluxes, as compared to disks that incorporate compact dust grains. Moreover, we have recalibrated the relation between dust temperature and stellar luminosity for a wide range of stellar parameters. We also calculated the dust masses of a large sample of disks using the traditionally analytic approach. The median dust mass from our calculation is about six times higher than the literature result, and this is mostly driven by the different opacities of porous and compact grains. A comparison of the cumulative distribution function between disk dust masses and exoplanet masses shows that the median exoplanet mass is about two times lower than the median dust mass when grains are assumed to be porous and there are no exoplanetary systems with masses higher than the most massive disks. Our analysis suggests that adopting porous dust opacities may alleviate the mass budget problem for planet formation. As an example illustrating the combined effects of optical depth and porous dust opacities on the mass estimation, we conducted new IRAM/NIKA-2 observations toward the IRAS\,04370+2559 disk and performed a detailed radiative transfer modeling of the spectral energy distribution (SED). The best-fit dust mass is roughly 100 times higher than the value given by a traditionally analytic calculation. Future spatially resolved observations at various wavelengths are required to better constrain the dust mass.

Digitized fuel load survey in commercial and university office buildings for fire safety assessment

Fuel load significantly affects fire development in compartments, and its design value derives from the statistical results of numerous surveys. This work enhances the fuel load database by employing a digitized survey method to assess 27 university and commercial offices in Hong Kong and Mainland, China. The results revealed significant differences in fuel load densities: university offices averaged 382 MJ/m2 (SD: 297 MJ/m2) and commercial offices averaged 1804 MJ/m2 (SD: 1319 MJ/m2). Moreover, it proposed an online questionnaire method to overcome onsite access limitations. Furthermore, it identified higher fuel load densities in commercial offices due to higher paper-made content and greater occupancy density than university offices. The fuel load of university offices was lower than that in previous surveys and design codes, while commercial fuel load was higher. Notably, there is an increasing tendency of fuel load density and plastic combustible composition over the years. Additionally, it considers that Gompertz distribution better fits cumulative probabilities of fuel load density data with a small sample size. Overall, it provides a valuable database for future fire scenario design, fire codes edition, and fire safety assessment and discusses future collaboration with AI applications.

Evaluation of burning detection using modified VGG19 for LULC classification changes

Abstract. This study aims to evaluate the effectiveness of a modified architecture of convolutional neural network (CNN) VGG19 for detecting fires and changes in land use and land cover classification (LULC). Remote sensing data from the Landsat 8 Operational Land Imagery (OLI) satellite was used to collect images from two distinct regions, one of which was used to obtain a dataset containing 1000 labeled images, and the other region was used to perform inference and verify the generalization of the model in an area with a high annual occurrence of fires. Analyses were conducted using a time series of normalized difference vegetation index (NDVI) and complementary cumulative distribution function (CCDF), to determine the potential for analysis in that area and define the periods of burning, pre-burning and post-burning. The VGG19 architecture was modified to maintain the input sizes of the images, resulting in a significant increase of 20.90 percentage points in the F1 score compared to the original architecture, as well as a 68.76% reduction in convergence time. In addition, the Gradient-weighted Class Activation Mapping (Grad-CAM) technique was used to improve the interpretability of the model at the moment of inference. The proposed methodology offers an approach for detecting burns by altering the LULC classification, and the modified VGG19 showed superior results.

Quantification of contaminant mass discharge and uncertainties: Method and challenges in application at contaminated sites

Contaminant mass discharge (CMD) estimation involves combining multilevel concentration and flow measurements to quantify the contaminant mass passing through a control plane downgradient of a point source. However, geological heterogeneities and limited data introduce uncertainties that complicate CMD estimation and risk assessment. Although CMD is increasingly used in groundwater management, methods for quantifying and handling these uncertainties are still needed. This study develops and tests a CMD estimation method based on Bayesian geostatistics to quantify CMD uncertainties using data from a control plane perpendicular to the contaminant plume.By combining geostatistical conditional simulations of the spatial concentration distribution with the flow, an ensemble of CMD realizations is generated, from which a cumulative distribution function is derived. A key element of this approach is the use of a macrodispersive transport model to simulate the spatial concentration trend. This ensures that the estimated concentration reflects the expected physical behavior of the contaminant plume while also allowing the integration of site-specific conceptual information.The method is applicable to plumes with dissolved contaminants, such as chlorinated solvents, petroleum hydrocarbons, Per- and polyfluoroalkyl substances (PFAS) and pesticides. Site-specific conceptual understanding is used to inform the prior probability density functions of the structural model parameters and to define acceptable simulated concentration limits. We applied the method at three sites contaminated with chlorinated ethenes, demonstrating its robustness across varying information levels and data availability.Our results shows that strong site-specific conceptual knowledge and high sampling density constrain the CMD uncertainty (CV = 21 %) and results in estimated model parameters and a spatial concentration distribution that agrees well with the conceptual model. For a site with less data and limited conceptual knowledge, CMD and concentration distribution estimates are still feasible, though with higher uncertainty (CV = 41 %). Extending the method to account for multiple source zones and complex plume migration improved parameter identification and reduced the 95 % CMD confidence interval by 11 % ([4950–8750] to [5090–8480] g yr−1), while also providing a spatial concentration distribution in better agreement with the plume conceptualization.This study highlights the importance of integrating site-specific conceptual knowledge in CMD estimation, particularly for less-sampled sites. The method can furthermore assist in identifying remediation targets, evaluating remedial effectiveness, and optimizing sampling strategies.

An augmented integral method for probability distribution evaluation of performance functions

The paper proposes an efficient augmented integral method for probability distribution evaluation of performance functions. In the proposed method, the performance function is augmented by adding an auxiliary random variable, whose probability density function (PDF) and cumulative distribution function (CDF) are formulated as the integrations of the original performance function with respect to basic random variables. The optimal auxiliary random variable is determined to provide an accurate estimation of the integrations by investigating the geometric properties of integrands and a distribution parameter optimization approach based on moment analysis. According to the convolution formula, the relationship between the PDFs of the augmented performance function and the original performance function is clarified. Then, the PDF of the original performance function is calculated by solving an unconstrained optimization problem that is established using the convolution formula. Finally, three numerical examples are investigated to demonstrate the efficiency and accuracy of the proposed method for structural reliability analysis. The results indicate that the proposed method can evaluate the probability distribution of performance functions accurately and efficiently, even when the performance functions are strongly nonlinear and implicit.

Limited Preservation of Strike‐Slip Surface Displacement in the Geomorphic Record

AbstractOffset geomorphic markers are commonly used to interpret slip history of strike‐slip faults and have played an important role in forming earthquake recurrence models. These data sets are typically analyzed using cumulative probability methods to interpret average amounts of slip in past earthquakes. However, interpretation of the geomorphic record to infer surface slip history is complicated by slip variability, measurement uncertainty, and modification of offset features in the landscape. To investigate how well geomorphic data record surface slip, we use offset measurements from recent strike‐slip surface ruptures (n = 39), faults with geomorphic evidence of multiple strike‐slip earthquakes (n = 29), and synthetic slip distributions with added noise (n10,000) to examine the constraints of the geomorphic record and the underlying assumptions of the cumulative offset probability distribution analysis method. We find that the geomorphic record is unlikely to resolve more than two paleo‐slip distributions, except in specific cases with low slip variability, high slip‐per‐event, and semiarid climate. In cases where site‐specific conditions allow for interpretation of more than two earthquakes, lateral extrapolation along a fault is not straightforward because on‐fault displacement and distributed deformation may be spatially variable in each earthquake. We also find that average slip in modern earthquakes is adequately recovered by probability methods, but the reported prevalence of strike‐slip faults with characteristic slip history is not supported by geomorphic data. We also propose updated methods to interpret slip history and construct uncertainty bounds for paleo‐slip distributions.

Non-destructive measurement of the dispersion of High-density polyethylene/Polystyrene blends using near-infrared diffuse reflectance spectroscopy based on deep learning

The dispersion of polymer blends significantly influences their overall performance. However, current methods for characterizing dispersion are destructive and time-consuming. In this study, a near-infrared (NIR) regression model was first developed for the measurement of cumulative particle size distribution function (CDF) of dispersed particles in polymer blends, thus indirectly and quantitively characterizing dispersion. High-density Polyethylene/Polystyrene (HDPE/PS) with varying dispersed particle sizes were prepared by adjusting the content of PS, flow field intensity during processing, and viscosity ratio between HDPE and PS. Scanning electron microscopy (SEM) was used to provide reference particle size values for calibrating the NIR data. Deep-learning methods were used for the model building. Compared to traditional partial least-square regression, deep learning methods demonstrated superior fitting ability, The best-performing model, an MLP with three hidden layers, achieved an coefficient of determination (R2) of approximately 0.93 on the independent test set. The predicted CDFs closely matched the actual values, with no significant deviations observed. This study provides a non-destructive and time-saving new method for characterizing the dispersion of polymer blends.

On the representation and computational aspects of the distribution of a linear combination of independent noncentral chi-squared random variables

Thiss article presents two new results relevant to a linear combination of χ2 random variables. The first result expresses the cumulative distribution function as a simple multiple of a certain tilted density. The second result shows that in important cases the inversion integral for the density may be expressed as a sum of relatively simple real integrals which, using suitable numerical methods, are straightforward to compute quickly and accurately.

Resultados clínicos y radiológicos del vástago SL-Plus con un seguimiento mínimo de 10 años: un estudio retrospectivo

Purpose: The SL-Plus cementless stem was introduced in 1993 as an evolution of the Alloclassic stem with some modifications. The aim of this study was to analyse if these modifications have any influence in clinical and radiological outcomes at minimum 10-year follow-up.Methods: Sixty-eight consecutive implants (64 patients) were retrospective evaluated. The mean duration of clinical and radiographic follow-up was 12.2 years. There were 41 female and 23 male patients with a median age of 73.5 years. All complications and reinterventions were collected. Functional outcomes were assessed using the modified Harris Hip Score (mHHS). Radiographs were evaluated immediately after the operation, after a minimum 2-year follow-up and by the end of follow-up (time A, B and C, respectively).Results: Two stems were revised, one due to periprosthetic fracture and one due to aseptic loosening with a cumulative probability of not having a stem revision for any reason of 97.2% at 12.2 years. Mean mHHS was 76.7 points at the latest follow-up. Significant differences were found in distal migration (time A: 5.9mm±6.7, B: 6.9mm±7.1 and C: 8.2mm±6.3; p=0.000) and varus angulation (time A: 0.0° ±2, B: 0.0° ±2 and C: 1.0° ±3; p<0.001), although these stems showed radiographic signs of osseointegration. Thirty-three hips (48.5%) showed any radiolucent line around the stem, most of them located the proximal femur (Gruen 1 and 7). Multivariate regression analysis showed lower mHHS scores in older patients (p=0.004) and female (p=0.00).Conclusions: The modifications of the SL-Plus stem influence the long-term outcome of the implant regarding radiological results, particularly in progressive varus angulation and distal migration. However, our study has not been able to demonstrate any clinical repercussions: functional scores and survival free of all cause revision were favorable and comparable to previous reports.Level of evidence: Therapeutic Level IV (Case Series)

Outcomes Following Acute Kidney Injury Requiring Dialysis: A Cohort Study

ObjectiveRetrospective study to examine the outcomes of acute kidney injury requiring dialysis (AKI-D) patients that received outpatient hemodialysis as part of continued AKI-D care and explore factors associated with recovery of kidney function and discontinuation of dialysis. MethodsRecords of all admissions to Emory Dialysis centers between January 2010 to December 2021 were reviewed to include patients with confirmed diagnosis of AKI-D. Basic demographics, comorbidities, duration of hospitalization and cause of AKI were extracted from hospital records and cross-referenced with the dialysis center electronic health record. Patients were followed starting from the day of the first outpatient hemodialysis up to 180 days. All hemodialysis sessions and laboratory data were analyzed. Logistic regression models were used to examine factors associated with recovery of kidney function, defined as survival free of dialysis. Results132 patients were analyzed, corresponding to 12,662 patient-day of outpatient AKI-D care. Among those, 19 (14%) patients died during the observation period. 54% of patients were male and 70% were Black. Median Age was 64 years (IQR 49-79). 42 patients (32%) recovered enough kidney function to discontinue dialysis. Median time to kidney recovery was 31 days (IQR: 19-75), and the cumulative probability of kidney recovery at 6 months was 39%. Patients who discontinued dialysis were younger (58 vs 66 years), had higher estimated glomerular filtration rate (eGFR) at time of admission (69 vs 46 ml/min/1.73m2) to the hospital, and were less likely to have a history of hypertension (61% vs 82%). Intra-dialytic hypotension was more common in patients who did not recover kidney function. Conclusions39% of patients with AKI-D recovered kidney function within 180 days of outpatient HD start. The median time to recovery was 31 days. Younger age, higher e-GFR at time of hospital admission, and absence of hypertension were predictors of kidney recovery. Patients who recover kidney function experienced episodes of intradialytic hypotension less frequently.

Association between dietary patterns and the prognosis of amyotrophic lateral sclerosis in China: a cross-sectional study.

Recently, a growing number of studies have specifically examined the impact of dietary variables on the development and progression of amyotrophic lateral sclerosis (ALS). The purpose of this study was to investigate the correlation between different dietary patterns and Chinese ALS patients' prognosis. A retrospective study was conducted by recruiting 590 patients with ALS who attended and were regularly followed at hospitals in Nanjing from 2016 to 2023. Nutrient intake was calculated using dietary information collected through the food frequency questionnaire (FFQ), and patients were divided into a control group and special diet groups, including a high-calorie group (HC), a high-protein group (HP), and a ketogenic diet group (KD), based on their specific intake. And used the Kaplan-Meier product limiting distribution to compare the time required to transition between phases of different dietary patterns and to estimate cumulative survival probabilities. Patients in the HP had a better nutritional status. And the disease progression rate (ΔFS) was significantly associated with dietary patterns, with the KD group having the lowest ΔFS. Meanwhile, special diets extended the survival time of stage 4 patients but had no effect on the overall survival of the disease. A special diet can be one of effective options for patients with advanced ALS. Patients with poor nutritional status may choose the HP diet, whereas those with underlying conditions should consider the ketogenic diet with caution.

Statistical Lifetime of Composites Subjected to Random and Ordered Block Loadings

ABSTRACTThis study presents a procedure based on constant amplitude (CA) fatigue data to predict the statistical fatigue lifetime of glass/orthopolyester composites subjected to repeated ordered and random two, three, and six sequences of block loadings. A numerical routine was developed to detect cycle‐by‐cycle the statistical strength degradation progression until failure, assuming that the strength at the end of a block cycle equals the strength at the start of the successive one and that the individual samples' static strength, the amount of degraded strength, and fatigue life share the same rank in their respective cumulative distribution function. Predictions conform to the statistically undetectable loading sequence effects and lightly overestimate the lifetimes of random and ordered high‐to‐low (1/100 cycles) repeated two‐block loadings. The vanishing effect of the loading sequence when the block extents remain fixed, the block extent effects for a given three‐block sequence, and the lifetimes of three‐block loadings were fully predicted. The six‐block sequence's experimental lifetimes with different block loading orders and block extent fell within the predicted lifetimes' cumulative distribution function. A reliable damage rule based on residual strength was proposed and compared to the Miner's rule.

The cumulative breakage probability by repeated stressing of γ-Al2O3 granules

The cumulative breakage probability by repeated stressing of γ-Al2O3 granules, was described using two models. The first model, where breakage probability by single stressing of granules remains constant, was not suitable for repeated stressing. The model was inappropriate to determine the change in the particle’s microstructure during stressing. Whereas in the second model, the hardening effect to be taken into account during stressing and was defined by a reduction factor of breakage probability. The deformation behavior of granules was used to indicate the change of microstructure of particles during stressing. In this case, the stiffness of the granules increased by means of plastic yielding and consolidation granules. In the compression test, the stiffness increased rapidly through fixed treatment. Meanwhile, in the case of rotated treatment the stiffness was significantly unaffected, as it was randomly distributed throughout the particle surface. Consequently, the cumulative breakage probability of rotated granules was lower compared to the fixed one.

Discrete Joint Random Variables in Fréchet-Weibull Distribution: A Comprehensive Mathematical Framework with Simulations, Goodness-of-Fit Analysis, and Informed Decision-Making

This paper introduces a novel four-parameter discrete bivariate distribution, termed the bivariate discretized Fréchet–Weibull distribution (BDFWD), with marginals derived from the discretized Fréchet–Weibull distribution. Several statistical and reliability properties are thoroughly examined, including the joint cumulative distribution function, joint probability mass function, joint survival function, bivariate hazard rate function, and bivariate reversed hazard rate function, all presented in straightforward forms. Additionally, properties such as moments and their related concepts, the stress–strength model, total positivity of order 2, positive quadrant dependence, and the median are examined. The BDFWD is capable of modeling asymmetric dispersion data across various forms of hazard rate shapes and kurtosis. Following the introduction of the mathematical and statistical frameworks of the BDFWD, the maximum likelihood estimation approach is employed to estimate the model parameters. A simulation study is also conducted to investigate the behavior of the generated estimators. To demonstrate the capability and flexibility of the BDFWD, three distinct datasets are analyzed from various fields, including football score records, recurrence times to infection for kidney dialysis patients, and student marks from two internal examination statistical papers. The study confirms that the BDFWD outperforms competitive distributions in terms of efficiency across various discrete data applications.

Patent Keyword Analysis Using Regression Modeling Based on Quantile Cumulative Distribution Function

Patents contain detailed information of researched and developed technologies. We analyzed patent documents to understand the technology in a given domain. For the patent data analysis, we extracted the keywords from the patent documents using text mining techniques. Next, we built a patent document–keyword matrix using the patent keywords and analyzed the matrix data using statistical methods. Each element of the matrix represents the frequency of a keyword that occurs in a patent document. In general, most of the elements were zero because the keyword becomes a column of the matrix even if it occurs in only one document. Due to this zero-inflated problem, we experienced difficulty in analyzing patent keywords using existing statistical methods such as linear regression analysis. The purpose of this paper is to build a statistical model to solve the zero-inflated problem. In this paper, we propose a regression model based on quantile cumulative distribution function to solve this problem that occurs in patent keyword analysis. We perform experiments to show the performance of our proposed method using patent documents related to blockchain technology. We compare regression modeling based on a quantile cumulative distribution function with convenient models such as linear regression modeling. We expect that this paper will contribute to overcoming the zero-inflated problem in patent keyword analysis performed in various technology fields.

Semi-supervised distribution learning

Abstract This study addresses the challenge of distribution estimation and inference in a semi-supervised setting. In contrast to prior research focusing on parameter inference, this work explores the complexities of semi-supervised distribution estimation, particularly the uniformity problem inherent in functional processes. To tackle this issue, we introduce a versatile framework designed to extract valuable information from unlabelled data by approximating a conditional distribution on covariates. The proposed estimator is derived from the K-fold cross-fitting strategy, exhibiting both consistency and asymptotic Gaussian process properties. Under mild conditions, the proposed estimator outperforms the empirical cumulative distribution function in terms of asymptotic efficiency. Several applications of the methodology are given, including parameter inference and goodness-of-fit tests.

Dealing with regression models’ endogeneity by means of an adjusted estimator for the Gaussian copula approach

AbstractEndogeneity in regression models is a key marketing research concern. The Gaussian copula approach offers an instrumental variable (IV)-free technique to mitigate endogeneity bias in regression models. Previous research revealed substantial finite sample bias when applying this method to regression models with an intercept. This is particularly problematic as models in marketing studies almost always require an intercept. To resolve this limitation, our research determines the bias’s sources, making several methodological advances in the process. First, we show that the cumulative distribution function estimation’s quality strongly affects the Gaussian copula approach’s performance. Second, we use this insight to develop an adjusted estimator that improves the Gaussian copula approach’s finite sample performance in regression models with (and without) an intercept. Third, as a broader contribution, we extend the framework for copula estimation to models with multiple endogenous variables on continuous scales and exogenous variables on discrete and continuous scales, and non-linearities such as interaction terms. Fourth, simulation studies confirm that the new adjusted estimator outperforms the established ones. Further simulations also underscore that our extended framework allows researchers to validly deal with multiple endogenous and exogenous regressors, and the interactions between them. Fifth, we demonstrate the adjusted estimator and the general framework’s systematic application, using an empirical marketing example with real-world data. These contributions enable researchers in marketing and other disciplines to effectively address endogeneity problems in their models by using the improved Gaussian copula approach.

Probabilistic Approach for Assessing the Occupational Risk of Olfactometric Examiners: Methodology Description and Application to Real Exposure Scenario

Human examiners, known as panelists, are exposed to an unknown occupational exposure risk while determining odor concentration (Cod) using dynamic olfactometry. In the literature, a few papers, based on a deterministic approach, have been proposed to establish this occupational risk. As a result, the purpose of this study is to develop and apply a probabilistic approach, based on the randomization of exposure parameters, for assessing and evaluating the occupational exposure risk among olfactometric examiners. In this methodology, the risk is assessed by computing the hazard index (HI) and inhalation risk (IR) to determine the non-carcinogenic and carcinogenic risks. To randomize the exposure parameters, a Monte Carlo simulation was described and then applied in real exposure scenario to establish the exposure risk in terms of probability. Therefore, a one-year survey of the working activity of olfactometric examiners of Laboratorio Olfattometrico of Politecnico di Milano university was conducted. Based on this data collection (exposure parameters and chemical data, divided according to sample categories), a randomized exposure scenario was constructed to estimate the probability and cumulative distribution function of risk parameters. Different distributions were obtained for different industrial samples categories and were compared with respect to acceptability criteria (the value of HI and IR at 95th percentile of distribution). The elaboration provided evidence that negligible non-carcinogenic and carcinogenic risks are associated with the panelists’ activity, according to an entire annual dataset. The application of probabilistic risk assessment provides a more comprehensive and effective characterization of the general exposure scenario for olfactometric examiners, surpassing the limitations of a deterministic approach. This method can be extended to future exposure scenarios and enables the selection of the most effective risk management strategies to protect the health of olfactometric examiners.

Agricultural activities reduce the risk of requiring long-term care among older Japanese

Abstract Introduction Controlling the risks that lead to requiring long-term care for the older adults is a crucial medical and socio-economic issue. This study investigated the impact of agricultural activities (AAs) on the risk of requiring long-term care. Methods A 5-year follow-up was conducted on 1,009 healthy individuals aged 65-80 years (mean age 72 years; 44% men) living in a rural area (Wakayama, Japan). Requiring long-term care was assessed as requiring support or nursing care (RSNC) according to the Japanese criteria. Health status including frailty and metabolic syndrome were also assessed using Japanese criteria. Work status and lifestyle habits were assessed using self-administered questionnaires. The main outcome was the probability of RSNC. COX proportional hazards regressions were performed, stratified by occupation (agricultural worker or other) or frequency of AAs (none, less than or &amp;gt;150 days/year), adjusted for sex, age, frailty, metabolic syndrome and household as possible confounding factors. Results Approximately 35% of the participants were in agriculture, forestry, and fisheries as occupation, and 78% engaged in AAs. A total of 85 participants RSNC and 39 deaths were observed during the follow-up. The cumulative survival probability did not differ according to occupation or frequency of AAs, whereas the probability of RSNC was significantly lower in participants engaged in AAs (hazard ratio 0.37 [95% CI 0.22, 0.62]; &amp;lt;150 days/year, 0.29 [95% CI 0.17, 0.50]; &amp;gt;150 days/year). Participants engaged in AAs were significantly more likely to engage in low- and vigorous-intensity physical activity, and to participate in industry and community organizations and relating events. Conclusions These findings suggest that AAs may promote a physically and socially active lifestyle, independent of frailty or metabolic syndrome, and reduce the risk of RSNC. Key messages • Prevention of long-term care is important in the older population. • A lifestyle associated with agricultural activities may contribute to healthy aging.